From bdfe2b6ddd1bc67654b2c3c7731b2e55605fe8c7 Mon Sep 17 00:00:00 2001 From: Ed Date: Wed, 6 Mar 2024 11:25:58 -0800 Subject: [PATCH] Revisions to align with new hydraulics package release --- 01-units.Rmd | 2 +- 02-water-properties.Rmd | 40 +- 04-pipeflow.Rmd | 14 +- 05-open_channel.Rmd | 28 +- .../designing-for-floods-flood-hydrology.html | 147 +- docs/fate-of-precipitation.html | 145 +- docs/flow-in-open-channels.html | 548 +- docs/groundwater.html | 3 +- .../figure-html/sp-openrect-1.png | Bin 0 -> 15247 bytes .../figure-html/sp-openrect2-1.png | Bin 0 -> 16327 bytes ...tics---forces-exerted-by-water-bodies.html | 3 +- docs/index.html | 7 +- ...management-of-water-resources-systems.html | 223 +- docs/momentum-in-water-flow.html | 39 +- docs/properties-of-water.html | 455 +- ...ow-they-operate-in-a-hydraulic-system.html | 41 +- docs/reference-keys.txt | 3 + docs/references.html | 12 +- docs/search_index.json | 2 +- ...ability-in-design-planning-for-change.html | 291 +- ...he-hydrologic-cycle-and-precipitation.html | 257 +- docs/units-in-fluid-mechanics.html | 5 +- .../water-flowing-in-pipes-energy-losses.html | 179 +- ed-bookdown.bib | 13831 +--------------- .../figure-html/sp-openrect-1.png | Bin 0 -> 15247 bytes .../figure-html/sp-openrect2-1.png | Bin 0 -> 16327 bytes index.Rmd | 2 +- 27 files changed, 1357 insertions(+), 14920 deletions(-) create mode 100644 docs/hydr-watres_main_files/figure-html/sp-openrect-1.png create mode 100644 docs/hydr-watres_main_files/figure-html/sp-openrect2-1.png create mode 100644 hydr-watres_main_files/figure-html/sp-openrect-1.png create mode 100644 hydr-watres_main_files/figure-html/sp-openrect2-1.png diff --git a/01-units.Rmd b/01-units.Rmd index 6ac24b5..2ffc29e 100644 --- a/01-units.Rmd +++ b/01-units.Rmd @@ -59,7 +59,7 @@ $${1*10^{-6}~m^2/s}*\frac{1 ~ft^2/s}{0.0929~m^2/s}=1.076*10^{-5} ~ft^2/s$$ Another example converts between two quantities in the US system: 100 gallons per minute to cfs: $${100 ~gpm}*\frac{1 ~cfs}{448.8 ~gpm}=0.223 ~cfs$$ -The _units_ package in R can do these conversions and more, and also checks that conversions are permissible (producing an error if incompatible units are used). +The [units](https://cran.r-project.org/package=units) package in R can do these conversions and more, and also checks that conversions are permissible (producing an error if incompatible units are used). ```{r units-ex1, echo=TRUE, collapse=TRUE} units::units_options(set_units_mode = "symbols") diff --git a/02-water-properties.Rmd b/02-water-properties.Rmd index 98e37f2..d35f891 100644 --- a/02-water-properties.Rmd +++ b/02-water-properties.Rmd @@ -7,7 +7,7 @@ knitr::opts_chunk$set( ) ``` -Fundamental properties of water allow the description of the forces it exerts and how it behaves while in motion. Many were listed in Chapter . +Fundamental properties of water allow the description of the forces it exerts and how it behaves while in motion. A table of these properties can be generated with the [hydraulics](https://cran.r-project.org/package=hydraulics) package using a command like `water_table(units = "SI")`. A summary of basic water properties, which vary with temperature, is shown in Table \@ref(tab:water-props-SI) for SI units and Table \@ref(tab:water-props-Eng) for US (or Eng) units. @@ -16,7 +16,25 @@ suppressWarnings(suppressMessages(library(dplyr))) tbl <- hydraulics::water_table(units = "SI") unitlist <- sapply(unname(tbl),units::deparse_unit) colnames <- names(tbl) -tbl <- docxtools::format_engr(units::drop_units(tbl), sigdig = c(0, 4, 4, 4, 4, 4, 3, 3)) +tbl <- units::drop_units(tbl) +# Format as integer +cols_we_want = c("Temp") +tbl[, cols_we_want] <- lapply(tbl[, cols_we_want], function (x) {formatdown::format_decimal(x, digits = 0)} +) +# Format with one decimal +cols_we_want = c("Density") +tbl[, cols_we_want] <- lapply(tbl[, cols_we_want], function (x) {formatdown::format_decimal(x, digits = 1)} +) +# Format to 4 digits, omit power of ten notation +#tbl$Density <- formatdown::format_power(tbl$Density, digits = 4, omit_power = c(0, 3)) +# Format using power-of-ten notation to 4 significant digits +cols_we_want = c("Spec_Weight", "Viscosity", "Kinem_Visc", "Sat_VP") +tbl[, cols_we_want] <- lapply(tbl[, cols_we_want], function (x) {formatdown::format_power(x, digits = 4, format = "sci", omit_power = c(-1, 4))} +) +# Format using power-of-ten notation to 3 significant digits +cols_we_want = c("Surf_Tens", "Bulk_Mod") +tbl[, cols_we_want] <- lapply(tbl[, cols_we_want], function (x) {formatdown::format_power(x, digits = 3, format = "sci", omit_power = c(-1, 4))} +) knitr::kable(tbl, col.names = unitlist, align = "c", format = "html", booktabs = TRUE, caption = "Water properties in SI units") %>% kableExtra::add_header_above(header = colnames, line = F, align = "c") %>% @@ -27,7 +45,23 @@ knitr::kable(tbl, col.names = unitlist, align = "c", format = "html", tbl <- hydraulics::water_table(units = "Eng") unitlist <- sapply(unname(tbl),units::deparse_unit) colnames <- names(tbl) -tbl <- docxtools::format_engr(units::drop_units(tbl), sigdig = c(0, 4, 4, 4, 4, 4, 3, 3)) +tbl <- units::drop_units(tbl) +# Format as integer +cols_we_want = c("Temp") +tbl[, cols_we_want] <- lapply(tbl[, cols_we_want], function (x) {formatdown::format_decimal(x, digits = 0)} +) +# Format with one decimal +cols_we_want = c("Density") +tbl[, cols_we_want] <- lapply(tbl[, cols_we_want], function (x) {formatdown::format_decimal(x, digits = 1)} +) +# Format using power-of-ten notation to 4 significant digits +cols_we_want = c("Spec_Weight", "Viscosity", "Kinem_Visc", "Sat_VP") +tbl[, cols_we_want] <- lapply(tbl[, cols_we_want], function (x) {formatdown::format_power(x, digits = 4, format = "sci", omit_power = c(-1, 4))} +) +# Format using power-of-ten notation to 3 significant digits +cols_we_want = c("Surf_Tens", "Bulk_Mod") +tbl[, cols_we_want] <- lapply(tbl[, cols_we_want], function (x) {formatdown::format_power(x, digits = 3, format = "sci", omit_power = c(-1, 4))} +) knitr::kable(tbl, col.names = unitlist, align = "c", format = "html", booktabs = TRUE, caption = "Water properties in US units") %>% kableExtra::add_header_above(header = colnames, line = F, align = "c") %>% diff --git a/04-pipeflow.Rmd b/04-pipeflow.Rmd index 6888162..c31d10c 100644 --- a/04-pipeflow.Rmd +++ b/04-pipeflow.Rmd @@ -71,7 +71,7 @@ hydraulics::moody() Because of the form of the equations, they can sometimes be a challenge to solve, especially by hand. It can help to classify the types of problems based on what variable is unknown. These are summarized in Table \@ref(tab:prob-types). ```{r prob-types, echo=FALSE} -knitr::kable(data.frame(Type=c("1","2","3"), Known=c("Q (or V), D, ks, L","hL, D, ks, L","hL, Q (or V), ks, L"), Unknown=c("hL","Q (or V)","D")), format="pipe", padding=0, escape = F, booktabs = TRUE, caption = "Types of Energy Loss Problems in Pipe Flow") +knitr::kable(data.frame(Type=c("1","2","3"), Known=c("Q (or V), D, ks, L","h~L~, D, k~s~, L","h~L~, Q (or V), ks, L"), Unknown=c("h~L~","Q (or V)","D")), format="pipe", padding=0, escape = F, booktabs = TRUE, caption = "Types of Energy Loss Problems in Pipe Flow") ``` When solving by hand the types in Table \@ref(tab:prob-types) become progressively more difficult, but when using solvers the difference in complexity is subtle. @@ -260,17 +260,25 @@ The root is seen to lie very close to D=0.5 m. Repeated trials can home in on th ### Solving for D using an equation solver An equation solver automatically accomplishes the manual steps of the prior demonstration. The equation from 1.6 can be written as a function that can then be solved for the root, again using _R_ software for the demonstration: -```{r qfcn1, message=FALSE, warning=FALSE} +```{r dfcn1, message=FALSE, warning=FALSE} q_fcn <- function(D, Q, hf, L, ks, nu, g) { -2.221 * D^2 * sqrt(( g * D * hf)/L) * log10((ks/D)/3.7 + (1.784 * nu/D) * sqrt(L/(g * D * hf))) - Q } ``` The _uniroot_ function can solve the equation in R (or use a comparable approach in other software) for a reasonable range of D values -```{r qfcn2, message=FALSE, warning=FALSE} +```{r dfcn2, message=FALSE, warning=FALSE} ans <- uniroot(q_fcn, interval=c(0.01,4.0),Q=0.416, hf=0.6, L=100, ks=0.000046, nu=1.023053e-06, g=9.81)$root cat(sprintf("D = %.3f m\n", ans)) ``` +### Solving for D using an R package + +The _hydraulics_ R package implements an equation solving technique like that above to allow the direct solution of Type 3 problems. the prior example is solved using that package as shown beliow. +```{r dfcn3, message=FALSE, warning=FALSE} +ans <- hydraulics::darcyweisbach(Q=0.416, hf=0.6, L=100, ks=0.000046, nu=1.023e-06, ret_units = TRUE, units = c('SI')) +knitr::kable(format(as.data.frame(ans), digits = 3), format = "pipe") +``` + ## Parallel pipes: solving a system of equations In the examples above the challenge was often to solve a single implicit equation. The manual iteration approach can work to solve two equations, but as the number of equations increases, especially when using implicit equations, using an equation solver is needed. diff --git a/05-open_channel.Rmd b/05-open_channel.Rmd index 074fb79..3e39e31 100644 --- a/05-open_channel.Rmd +++ b/05-open_channel.Rmd @@ -269,19 +269,23 @@ where C=20.16 for SI units and C=13.53 for US Customary (English) units. ### Solving the Manning equation for a circular pipe in R -As was demonstrated with pipe flow, a function could be written with Equation \@ref(eq:man-c) and a solver applied to find the value of $\theta$ for the given flow conditions with a known D, S, n and Q. The value for $\theta$ could then be used with Equations \@ref(eq:circg1), \@ref(eq:circg2) and \@ref(eq:circg3) to recover geometric values. +As was demonstrated with pipe flow, a function could be written with Equation \@ref(eq:man-c) and a solver applied to find the value of $\theta$ for the given flow conditions with a known _D_, _S_, _n_ and _Q_. The value for $\theta$ could then be used with Equations \@ref(eq:circg1), \@ref(eq:circg2) and \@ref(eq:circg3) to recover geometric values. + +Hydraulic analysis of circular pipes flowing partially full often account for the value of Manning's _n_ varying with depth [@camp_design_1946]; some standards recommend fixed _n_ values, and others require the use of a depth-varying _n_. The _R_ package _hydraulics_ has implemented those routines to enable these calculations, including using a fixed _n_ (the default) or a depth-varing _n_. -The _R_ package _hydraulics_ has implemented those routines to enable these calculations. For an existing pipe, a common problem is the determination of the depth, _y_ that a given flow _Q_, will have given a pipe diameter _d_, slope _S_ and roughness _n_. Example \@ref(exm:circx1) demonstrates this. ::: {.example #circx1} -Find the uniform (normal) flow depth, y, for a trapezoidal channel with Q=225 ft^3^/s, n=0.016, m=2, b=10 _ft_, S=0.0006. +Find the uniform (normal) flow depth, y, for a trapezoidal channel with Q=225 ft^3^/s, n=0.016, m=2, b=10 _ft_, S=0.0006. Do this assuming both that Manning _n_ is constant with depth and that it varies with depth. ::: The function _manningc_ from the _hydraulics_ package is used. Any one of the variables in the Manning equation, and related geometric variables, may be treated as an unknown. ```{r manningc-1, message=FALSE, warning=FALSE} ans <- hydraulics::manningc(Q=0.01, n=0.013, Sf=0.001, d = 0.2, units="SI", ret_units = TRUE) -knitr::kable(format(as.data.frame(ans), digits = 2), format = "pipe", padding=0) +ans2 <- hydraulics::manningc(Q=0.01, n=0.013, Sf=0.001, d = 0.2, n_var = TRUE, units="SI", ret_units = TRUE) +df <- data.frame(Constant_n = unlist(ans), Variable_n = unlist(ans2)) +knitr::kable(df, format = "html", digits=3, padding = 0, col.names = c("Constant n","Variable n")) |> +kableExtra::kable_styling(full_width = F) ``` It is also sometimes convenient to see a cross-section diagram. @@ -365,9 +369,9 @@ knitr::include_graphics("images/rectangular_channel.png") A specific energy diagram is very helpful for establishing upstream conditions and estimating $y_2$. -(ref:figopenrect-2) A specific energy diagram for the conditions of Example \@ref(exm:open-rectx). +(ref:figorect-2) A specific energy diagram for the conditions of Example \@ref(exm:open-rectx). -```{r sp_openrect, fig.width = 4, fig.asp = 1.0, fig.align="center", fig.cap='(ref:figopenrect-2)'} +```{r sp-openrect, fig.width = 4, fig.asp = 1.0, fig.align="center", fig.cap='(ref:figorect-2)'} p1 <- hydraulics::spec_energy_trap( Q = 2.2, b = 0.5, m = 0, y = 2, scale = 2.5, units = "SI" ) p1 ``` @@ -396,16 +400,16 @@ Re(polyroot(c(0.9667, 0, -1.997, 1))) The negative root is meaningless, the lower positive root is the supercritical depth for $E_2 = 1.997 ~ m$, and the larger positive root is the subcritical depth. Thus the correct solution is $y_2 = 1.64 ~ m$ when the channel bottom rises by 0.25 m. -The specific energy diagram shows that if $\Delta z > E_1 - E_{min}$, the downstream specific energy, $E_2$ would be to the left of the curve, so no feasible solution would exist. At that point damming would occur, raising the upstream depth, $y_1$, and thus increasing $E_1$ until $E_2 = E_{min}$. The largest rise in channel bottom height that will not cause damming is called the critical hump height: $\Delta z_{c} = E_1 - E_{min}$. +A vertical line or other annotation can be added to the specific energy diagram to indicate $E_2$ using _ggplot2_ with a command like `p1 + ggplot2::geom_vline(xintercept = 1.997, linetype=3)`. The _hydraulics_ R package can also add lines to a specific energy diagram for up to two depths: -A vertical can be added to the specific energy diagram to indicate $E_2$: +(ref:figorect-3) A specific energy diagram for the conditions of Example \@ref(exm:open-rectx) with added annotation for when the bottom elecation rises. -```{r spec_energy_diag_2, fig.width = 4, fig.asp = 1.0, fig.align="center", message=FALSE, warning=FALSE} -p1 + - ggplot2::geom_vline(xintercept = 1.997, linetype=3) + - ggplot2::annotate("text", x=1.9, y=2.5, label=expression(E[2]), angle=90) +```{r sp-openrect2, fig.width = 4, fig.asp = 1.0, fig.align="center", fig.cap='(ref:figorect-3)'} +p2 <- hydraulics::spec_energy_trap(Q = 2.2, b = 0.5, m = 0, y = c(2, 1.64), scale = 2.5, units = "SI") +p2 ``` +The specific energy diagram shows that if $\Delta z > E_1 - E_{min}$, the downstream specific energy, $E_2$ would be to the left of the curve, so no feasible solution would exist. At that point damming would occur, raising the upstream depth, $y_1$, and thus increasing $E_1$ until $E_2 = E_{min}$. The largest rise in channel bottom height that will not cause damming is called the critical hump height: $\Delta z_{c} = E_1 - E_{min}$. ## Gradually varied steady flow {#gvf-section} diff --git a/docs/designing-for-floods-flood-hydrology.html b/docs/designing-for-floods-flood-hydrology.html index 81b1f71..d747b65 100644 --- a/docs/designing-for-floods-flood-hydrology.html +++ b/docs/designing-for-floods-flood-hydrology.html @@ -25,7 +25,7 @@ - + @@ -210,6 +210,7 @@
  • 4.7 Parallel pipes: solving a system of equations
  • 4.8 Simple pipe networks: the Hardy-Cross method
    • @@ -366,39 +367,39 @@

    10.1 Engineering design requires

    Example 10.1 A temporary dam is constructed while a repair is built. It will be in place 5 years and is designed to protect against floods up to a 20-year recurrence interval (i.e., there is a \(p=\frac{1}{20}=0.05\), or 5% chance, that it will be exceeded in any one year). What is the probability of (a) no failure in the 5 year period, and (b) at least two failures in 5 years.

    -
    # (a) 
-ans1 <- dbinom(0, 5, 0.05)
-cat(sprintf("Probability of exactly zero occurrences in 5 years = %.4f %%",100*ans1))
-#> Probability of exactly zero occurrences in 5 years = 77.3781 %
-# (b)
-ans2 <- 1 - pbinom(1,5,.05) # or pbinom(1,5,.05, lower.tail=FALSE)
-cat(sprintf("Probability of 2 or more failures in 5 years = %.2f %%",100*ans2))
-#> Probability of 2 or more failures in 5 years = 2.26 %
    +
    # (a) 
+ans1 <- dbinom(0, 5, 0.05)
+cat(sprintf("Probability of exactly zero occurrences in 5 years = %.4f %%",100*ans1))
+#> Probability of exactly zero occurrences in 5 years = 77.3781 %
+# (b)
+ans2 <- 1 - pbinom(1,5,.05) # or pbinom(1,5,.05, lower.tail=FALSE)
+cat(sprintf("Probability of 2 or more failures in 5 years = %.2f %%",100*ans2))
+#> Probability of 2 or more failures in 5 years = 2.26 %

    While the next example uses normally distributed data, most data in hydrology are better described by other distributions.

    Example 10.2 Annual average streamflows in some location are normally distributed with a mean annual flow of 20 m\(^3\)/s and a standard deviation of 6 m\(^3\)/s. Find (a) the probability of experiencing a year with less than (or equal to) 10 m\(^3\)/s, (b) greater than 32 m\(^3\)/s, and (c) the annual average flow that would be expected to be exceeded 10% of the time.

    -
    # (a)
-ans1 <- pnorm(10, mean=20, sd=6)
-cat(sprintf("Probability of less than 10 = %.2f %%",100*ans1))
-#> Probability of less than 10 = 4.78 %
-# (b)
-ans2 <- pnorm(32, mean=20, sd=6, lower.tail = FALSE) #or 1 - pnorm(32, mean=20, sd=6)
-cat(sprintf("Probability of greater than or equal to 30 = %.2f %%",100*ans2))
-#> Probability of greater than or equal to 30 = 2.28 %
-# (c)
-ans3 <- qnorm(.1, mean=20, sd=6, lower.tail=FALSE)
-cat(sprintf("flow exceeded 10%% of the time = %.2f m^3/s",ans3))
-#> flow exceeded 10% of the time = 27.69 m^3/s
-# plot to visualize answers
-x <- seq(0,40,0.1)
-y<- pnorm(x,mean=20,sd=6)
-xlbl <- expression(paste(Flow, ",", ~ m^"3"/s))
-plot(x ,y ,type="l",lwd=2, xlab = xlbl, ylab= "Prob. of non-exceedance")
-abline(v=10,col="black", lwd=2, lty=2)
-abline(v=32,col="blue", lwd=2, lty=2)
-abline(h=0.9,col="green", lwd=2, lty=2)
-legend("bottomright",legend=c("(a)","(b)","(c)"),col=c("black","blue","green"), cex=0.8, lty=2)
    +
    # (a)
+ans1 <- pnorm(10, mean=20, sd=6)
+cat(sprintf("Probability of less than 10 = %.2f %%",100*ans1))
+#> Probability of less than 10 = 4.78 %
+# (b)
+ans2 <- pnorm(32, mean=20, sd=6, lower.tail = FALSE) #or 1 - pnorm(32, mean=20, sd=6)
+cat(sprintf("Probability of greater than or equal to 30 = %.2f %%",100*ans2))
+#> Probability of greater than or equal to 30 = 2.28 %
+# (c)
+ans3 <- qnorm(.1, mean=20, sd=6, lower.tail=FALSE)
+cat(sprintf("flow exceeded 10%% of the time = %.2f m^3/s",ans3))
+#> flow exceeded 10% of the time = 27.69 m^3/s
+# plot to visualize answers
+x <- seq(0,40,0.1)
+y<- pnorm(x,mean=20,sd=6)
+xlbl <- expression(paste(Flow, ",", ~ m^"3"/s))
+plot(x ,y ,type="l",lwd=2, xlab = xlbl, ylab= "Prob. of non-exceedance")
+abline(v=10,col="black", lwd=2, lty=2)
+abline(v=32,col="blue", lwd=2, lty=2)
+abline(h=0.9,col="green", lwd=2, lty=2)
+legend("bottomright",legend=c("(a)","(b)","(c)"),col=c("black","blue","green"), cex=0.8, lty=2)
    Illustration of three solutions.

    @@ -412,12 +413,12 @@

    10.2 Estimating floods when you h

    10.2.1 Installing helpful packages

    The USGS has developed many R packages, including one for retrieval of data, dataRetrieval. Since this resides on CRAN, the package can be installed with (the use of ‘!requireNamespace’ skips the installation if it already is installed):

    -
    if (!requireNamespace("dataRetrieval", quietly = TRUE)) install.packages("dataRetrieval")
    +
    if (!requireNamespace("dataRetrieval", quietly = TRUE)) install.packages("dataRetrieval")

    Other USGS packages that are very helpful for peak flow analysis are not on CRAN, but rather housed in a USGS repository. The easiest way to install packages from that archive is using the install.load package. Then the install_load command will first search the standard CRAN archive for the package, and if it is not found there the USGS archive is searched. Packages are also loaded (equivalent to using the library command). install_load also installs dependencies of packages, so here installing smwrGraphs also installs smwrBase. The prefix smwr refers to their use in support of the excellent reference Statistical Methods in Water Resources.

    -
    if (!requireNamespace("install.load", quietly = TRUE)) install.packages("install.load")
-install.load::install_load("smwrGraphs")  #this command also installs smwrBase
    +
    if (!requireNamespace("install.load", quietly = TRUE)) install.packages("install.load")
+install.load::install_load("smwrGraphs")  #this command also installs smwrBase

    Lastly, the lmomco package has extensive capabilities to work with many forms of probability distributions, and has functions for calculating distribution parameters (like skew) that we will use.

    -
    if (!requireNamespace("lmomco", quietly = TRUE)) install.packages("lmomco")
    +
    if (!requireNamespace("lmomco", quietly = TRUE)) install.packages("lmomco")

    10.2.2 Download, manipulate, and plot the data for a site

    @@ -429,19 +430,19 @@

    10.2.2 Download, manipulate, and

    While the data could be downloaded and saved locally through that link, it is convenient here to use the dataRetrieval command.

    -
    Qpeak_download <- dataRetrieval::readNWISpeak(siteNumbers="11169000")
    +
    Qpeak_download <- dataRetrieval::readNWISpeak(siteNumbers="11169000")

    The data used here are also available as part of the hydromisc package, and may be obtained by typing hydromisc::Qpeak_download.

    It is always helpful to look at the downloaded data frame before doing anything with it. There are many columns that are not needed or that have repeated information. There are also some rows that have no data (‘NA’ values). It is also useful to change some column names to something more intuitive. We will need to define the water year (a water year begins October 1 and ends September 30).

    -
    Qpeak <- Qpeak_download[!is.na(Qpeak_download$peak_dt),c('peak_dt','peak_va')]
-colnames(Qpeak)[colnames(Qpeak)=="peak_dt"] <- "Date"
-colnames(Qpeak)[colnames(Qpeak)=="peak_va"] <- "Peak"
-Qpeak$wy <- smwrBase::waterYear(Qpeak$Date)
    +
    Qpeak <- Qpeak_download[!is.na(Qpeak_download$peak_dt),c('peak_dt','peak_va')]
+colnames(Qpeak)[colnames(Qpeak)=="peak_dt"] <- "Date"
+colnames(Qpeak)[colnames(Qpeak)=="peak_va"] <- "Peak"
+Qpeak$wy <- smwrBase::waterYear(Qpeak$Date)

    The data have now been simplified so that can be used more easily in the subsequent flood frequency analysis. Data should always be plotted, which can be done many ways. As a demonstration of highlighting specific years in a barplot, the strongest El Niño years (in 1930-2002) from NOAA Physical Sciences Lab can be highlighted in red.

    -
    xlbl <- "Water Year"
-ylbl <- expression("Peak Flow, " ~ ft^{3}/s)
-nino_years <- c(1983,1998,1992,1931,1973,1987,1941,1958,1966, 1995)
-cols <- c("blue", "red")[(Qpeak$wy %in% nino_years) + 1]
-barplot(Qpeak$Peak, names.arg = Qpeak$wy, xlab = xlbl, ylab=ylbl, col=cols)
    +
    xlbl <- "Water Year"
+ylbl <- expression("Peak Flow, " ~ ft^{3}/s)
+nino_years <- c(1983,1998,1992,1931,1973,1987,1941,1958,1966, 1995)
+cols <- c("blue", "red")[(Qpeak$wy %in% nino_years) + 1]
+barplot(Qpeak$Peak, names.arg = Qpeak$wy, xlab = xlbl, ylab=ylbl, col=cols)
    Annual peak flows for USGS gauge 11169000, highlighting strong El Niño years in red.

    @@ -457,18 +458,18 @@

    10.2.3 Flood frequency analysis where y is the flow at the designated return period, \(\overline{y}\) is the mean of all \(y\) values and \(s_y\) is the standard deviation. In most instances, \(y\) is a log-transformed flow; in the US a base-10 logarithm is generally used. \(K\) is a frequency factor, which is a function of the return period, the parent distribution, and often the skew of the y values. The guidance of the USGS (as in Guidelines for Determining Flood Flow Frequency, Bulletin 17C) (England, J.F. et al., 2019) is to use the log-Pearson Type III (LP-III) distribution for flood frequency data, though in different settings other distributions can perform comparably. For using the LP-III distribution, we will need several statistical properties of the data: mean, standard deviation, and skew, all of the log-transformed data, calculated as follows.

    -
    mn <- mean(log10(Qpeak$Peak))
-std <- sd(log10(Qpeak$Peak))
-g <- lmomco::pmoms(log10(Qpeak$Peak))$skew
    +
    mn <- mean(log10(Qpeak$Peak))
+std <- sd(log10(Qpeak$Peak))
+g <- lmomco::pmoms(log10(Qpeak$Peak))$skew

    With those calculated, a defined return period can be chosen and the flood frequency factors, from Equation (10.1), calculated for that return period (the example here is for a 50-year return period). The qnorm function from base R and the qpearsonIII function from the smwrBase package make this straightforward, and K values for Equation (10.1) are obtained for a lognormal, Knorm, and LP-III, Klp3.

    -
    RP <- 50 
-Knorm <- qnorm(1 - 1/RP) 
-Klp3 <- smwrBase::qpearsonIII(1-1/RP, skew = g)
    +
    RP <- 50 
+Knorm <- qnorm(1 - 1/RP) 
+Klp3 <- smwrBase::qpearsonIII(1-1/RP, skew = g)

    Now the flood frequency equation (10.1) can be applied to calculate the flows associated with the 50-year return period for each of the distributions. Remember to take the anti-log of your answer to return to standard units.

    -
    Qpk_LN <- mn + Knorm * std
-Qpk_LP3 <- mn + Klp3 * std
-sprintf("RP = %d years, Qpeak LN = %.0f cfs, Qpeak LP3 = %.0f",RP,10^Qpk_LN,10^Qpk_LP3)
-#> [1] "RP = 50 years, Qpeak LN = 18362 cfs, Qpeak LP3 = 12396"
    +
    Qpk_LN <- mn + Knorm * std
+Qpk_LP3 <- mn + Klp3 * std
+sprintf("RP = %d years, Qpeak LN = %.0f cfs, Qpeak LP3 = %.0f",RP,10^Qpk_LN,10^Qpk_LP3)
+#> [1] "RP = 50 years, Qpeak LN = 18362 cfs, Qpeak LP3 = 12396"

    10.2.4 Creating a flood frequency plot

    @@ -484,29 +485,29 @@

    10.2.4 Creating a flood frequency \tag{10.3} \end{equation}\]

    While not necessary, to add probabilities to the annual flow sequence we will create a new data frame consisting of the observed peak flows, sorted in descending order.

    -
    df_pp <- as.data.frame(list('Obs_peak'=sort(Qpeak$Peak,decreasing = TRUE)))
    +
    df_pp <- as.data.frame(list('Obs_peak'=sort(Qpeak$Peak,decreasing = TRUE)))

    This can be done with fewer commands, but here is an example where first a rank column is created (1=highest peak in the record of N years), followed by adding columns for the exceedance and non-exceedence probabilities:

    -
    df_pp$rank <- as.integer(seq(1:length(df_pp$Obs_peak)))
-df_pp$exc_prob <- (df_pp$rank/(1+length(df_pp$Obs_peak)))
-df_pp$ne_prob <- 1-df_pp$exc_prob
    +
    df_pp$rank <- as.integer(seq(1:length(df_pp$Obs_peak)))
+df_pp$exc_prob <- (df_pp$rank/(1+length(df_pp$Obs_peak)))
+df_pp$ne_prob <- 1-df_pp$exc_prob

    For each of the non-exceedance probabilities calculated for the observed peak flows, use the flood frequency equation (10.1) to estimate the peak flow that would be predicted by a lognormal or LP-III distribution. This is the same thing that was done above for a specified return period, but now it will be “applied” to an entire column.

    -
    df_pp$LN_peak <- mapply(function(x) {10^(mn+std*qnorm(x))}, df_pp$ne_prob)
-df_pp$LP3_peak <- mapply(function(x) {10^(mn+std*smwrBase::qpearsonIII(x, skew=g))},df_pp$ne_prob)
    +
    df_pp$LN_peak <- mapply(function(x) {10^(mn+std*qnorm(x))}, df_pp$ne_prob)
+df_pp$LP3_peak <- mapply(function(x) {10^(mn+std*smwrBase::qpearsonIII(x, skew=g))},df_pp$ne_prob)

    There are many packages that create probability plots (see, for example, the versatile scales package for ggplot2). For this example the USGS smwrGraphs package is used. First, for aesthetics, create x- and y- axis labels.

    -
    ylbl <- expression("Peak Flow, " ~ ft^{3}/s)
-xlbl <- "Non-exceedence Probability"
    +
    ylbl <- expression("Peak Flow, " ~ ft^{3}/s)
+xlbl <- "Non-exceedence Probability"

    The smwrGraphs package works most easily if it writes output directly to a file, a PNG file in this case, using the setPNG command; the file name and its dimensions in inches are given as arguments, and the PNG device is opened for writing. This is followed by commands to plot the data on a graph. Technically, the data are plotted to an object here is called prob.pl. The probPlot command plots the observed peaks as points, where the alpha argument is the a in Equation (10.2). Additional points or lines are added with the addXY command, used here to add the LN and LP3 data as lines (one solid, one dashed). Finally, a legend is added (the USGS refers to that as an “Explanation”), and the output PNG file is closed with the dev.off() command.

    -
    smwrGraphs::setPNG("probplot_smwr.png",6.5, 3.5)
-#>  width height 
-#>    6.5    3.5 
-#> [1] "Setting up markdown graphics device:  probplot_smwr.png"
-prob.pl <- smwrGraphs::probPlot(df_pp$Obs_peak, alpha = 0.0, Plot=list(what="points",size=0.05,name="Obs"), xtitle=xlbl, ytitle=ylbl)
-prob.pl <- smwrGraphs::addXY(df_pp$ne_prob,df_pp$LN_peak,Plot=list(what="lines",name="LN"),current=prob.pl)
-prob.pl <- smwrGraphs::addXY(df_pp$ne_prob,df_pp$LP3_peak,Plot=list(what="lines",type="dashed",name="LP3"),current=prob.pl)
-smwrGraphs::addExplanation(prob.pl,"ul",title="")
-dev.off()
-#> png 
-#>   2
    +
    smwrGraphs::setPNG("probplot_smwr.png",6.5, 3.5)
+#>  width height 
+#>    6.5    3.5 
+#> [1] "Setting up markdown graphics device:  probplot_smwr.png"
+prob.pl <- smwrGraphs::probPlot(df_pp$Obs_peak, alpha = 0.0, Plot=list(what="points",size=0.05,name="Obs"), xtitle=xlbl, ytitle=ylbl)
+prob.pl <- smwrGraphs::addXY(df_pp$ne_prob,df_pp$LN_peak,Plot=list(what="lines",name="LN"),current=prob.pl)
+prob.pl <- smwrGraphs::addXY(df_pp$ne_prob,df_pp$LP3_peak,Plot=list(what="lines",type="dashed",name="LP3"),current=prob.pl)
+smwrGraphs::addExplanation(prob.pl,"ul",title="")
+dev.off()
+#> png 
+#>   2
    The output won’t be immediately visible in RStudio – navigate to the file and click on it to view it. Figure 10.5 shows the output from the above commands.
    Probability plot for USGS gauge 11169000 for years 1930-2002. diff --git a/docs/fate-of-precipitation.html b/docs/fate-of-precipitation.html index b2bb065..d9fdc56 100644 --- a/docs/fate-of-precipitation.html +++ b/docs/fate-of-precipitation.html @@ -25,7 +25,7 @@ - + @@ -210,6 +210,7 @@
  • 4.7 Parallel pipes: solving a system of equations
  • 4.8 Simple pipe networks: the Hardy-Cross method
    • @@ -460,11 +461,11 @@

    9.4.2 Basic snowmelt theory and s

    Example 9.1 Manually calibrate an index snowmelt model for a SNOTEL site using one year of data.

    Visit the SNOTEL to select a site. In this example site 1050, Horse Meadow, located in California, is used. Next download the data using the snotelr package (install the package first, if needed).

    -
    sta <- "1050"
-snow_data <- snotelr::snotel_download(site_id = sta, internal = TRUE)
    +
    sta <- "1050"
+snow_data <- snotelr::snotel_download(site_id = sta, internal = TRUE)

    Plot the data to assess the period available and how complete it is.

    -
    plot(as.Date(snow_data$date), snow_data$snow_water_equivalent,
-     type = "l", xlab = "Date", ylab = "SWE (mm)")
    +
    plot(as.Date(snow_data$date), snow_data$snow_water_equivalent,
+     type = "l", xlab = "Date", ylab = "SWE (mm)")
    Snow water equivalent at SNOTEL site 1050.

    @@ -473,12 +474,12 @@

    9.4.2 Basic snowmelt theory and s

    Note the units are SI. If you download data directly from the SNOTEL web site the data would be in conventional US units. snotelr converts the data to SI units as it imports. The package includes a function snotel_metric that could be used to convert raw data downloaded from the SNOTEL website to SI units.

    For this exercise, extract a single (water) year, meaning from 1-Oct to 30-Sep, so an entire winter is in one year. In addition, create a data frame that only includes columns that are needed.

    -
    snow_data_subset <- subset(snow_data, as.Date(date) > as.Date("2008-10-01") &
- as.Date(date) < as.Date("2009-09-30"))
-snow_data_sel <- subset(snow_data_subset, select=c("date", "snow_water_equivalent", "precipitation", "temperature_mean", "temperature_min", "temperature_max"))
-plot(as.Date(snow_data_sel$date),snow_data_sel$snow_water_equivalent,
-     type = "l",xlab = "Date", ylab = "SWE (mm)")
-grid()
    +
    snow_data_subset <- subset(snow_data, as.Date(date) > as.Date("2008-10-01") &
+ as.Date(date) < as.Date("2009-09-30"))
+snow_data_sel <- subset(snow_data_subset, select=c("date", "snow_water_equivalent", "precipitation", "temperature_mean", "temperature_min", "temperature_max"))
+plot(as.Date(snow_data_sel$date),snow_data_sel$snow_water_equivalent,
+     type = "l",xlab = "Date", ylab = "SWE (mm)")
+grid()
    Snow water equivalent at SNOTEL site 1050 for water year 2009.

    @@ -498,13 +499,13 @@

    9.4.2 Basic snowmelt theory and s
  • Snow water retention factor, rcap. When snow melts some of it can be retained via capillarity in the snow pack. It can re-freeze or drain out. This is expressed as a fraction of the snow pack that is frozen. The default is 2.5% (rcap = 0.025).

    • Start with some assumed values and run the snow model.

    -
    Tmax_snow <- 3  
-Tmin_rain <- 2
-kd <- 1
-snow_estim <- hydromisc::snow.sim(DATA=snow_data_sel, 
-                                  Tmax=Tmax_snow, 
-                                  Tmin=Tmin_rain,
-                                  kd=kd)
    +
    Tmax_snow <- 3  
+Tmin_rain <- 2
+kd <- 1
+snow_estim <- hydromisc::snow.sim(DATA=snow_data_sel, 
+                                  Tmax=Tmax_snow, 
+                                  Tmin=Tmin_rain,
+                                  kd=kd)

    Now the simulated values can be compared to the observations. If not installed already, install the hydroGOF package, which has some useful functions for evaluating how well modeled output fits observations. In the plot that follows we specify three measures of goodness-of-fit:

    • Mean Absolute Error (MAE)
      • @@ -512,12 +513,12 @@

      9.4.2 Basic snowmelt theory and s
    • Root Mean Square Error divided by the Standard Deviation (RSR) These are discussed in detail in other references, but the aim is to calibrate (change the input parameters) until these values are low.

    -
    obs <- snow_data_sel$snow_water_equivalent
-sim <- snow_estim$swe_simulated
-hydroGOF::ggof(sim, obs, na.rm = TRUE, dates=snow_data_sel$date,
-               gofs=c("MAE", "RMSE", "PBIAS"),
-               xlab = "", ylab="SWE, mm", 
-               tick.tstep="months", cex=c(0,0),lwd=c(2,2))
    +
    obs <- snow_data_sel$snow_water_equivalent
+sim <- snow_estim$swe_simulated
+hydroGOF::ggof(sim, obs, na.rm = TRUE, dates=snow_data_sel$date,
+               gofs=c("MAE", "RMSE", "PBIAS"),
+               xlab = "", ylab="SWE, mm", 
+               tick.tstep="months", cex=c(0,0),lwd=c(2,2))
    Simulated and Observed SWE at SNOTEL site 1050 for water year 2009.

    @@ -530,39 +531,39 @@

    9.4.2 Basic snowmelt theory and s

    9.4.3 Snow model calibration

    While manual model calibration can improve the fit, a more complete calibration involves optimization methods that search the parameter space for the optimal combination of parameter values. A useful tool for doing that is the optim function, part of the stats package installed with base R.

    Using the optimization package requires establishing a function that should be minimized, where the parameters to be included in the optimization are the first argument. The optim function requires you to explicitly give ranges over which parameters can be varied, via the upper and lower arguments. An example of this follows, where the four main model parameters noted above are used, and the MAE is minimized.

    -
    fcn_to_minimize <- function(par,datain, obs){
-  snow_estim <- hydromisc::snow.sim(DATA=datain, Tmax=par[1], Tmin=par[2], kd=par[3], Tmelt=par[4])
-  calib.stats <- hydroGOF::gof(snow_estim$swe_simulated,obs,na.rm=TRUE)
-  objective_stat <- as.numeric(calib.stats['MAE',])
-  return(objective_stat)
-}
-opt_res <- optim(par=c(0.5,1,1,0),fn=fcn_to_minimize,
-                 lower=c(-1,-1,0.5,-2),
-                 upper=c(3,1,5,3),
-                 method="L-BFGS-B",
-                 datain=snow_data_sel,
-                 obs=obs)
-#print out optimal parameters - note Tmax and Tmin can be reversed during optimization
-cat(sprintf("Optimal parameters:\nTmax=%.1f\nTmin=%.1f\nkd=%.2f\nTmelt=%.1f\n",
-            max(opt_res$par[1],opt_res$par[2]),min(opt_res$par[1],opt_res$par[2]),
-            opt_res$par[3],opt_res$par[4]))
-#> Optimal parameters:
-#> Tmax=1.0
-#> Tmin=0.5
-#> kd=1.05
-#> Tmelt=-0.0
    +
    fcn_to_minimize <- function(par,datain, obs){
+  snow_estim <- hydromisc::snow.sim(DATA=datain, Tmax=par[1], Tmin=par[2], kd=par[3], Tmelt=par[4])
+  calib.stats <- hydroGOF::gof(snow_estim$swe_simulated,obs,na.rm=TRUE)
+  objective_stat <- as.numeric(calib.stats['MAE',])
+  return(objective_stat)
+}
+opt_res <- optim(par=c(0.5,1,1,0),fn=fcn_to_minimize,
+                 lower=c(-1,-1,0.5,-2),
+                 upper=c(3,1,5,3),
+                 method="L-BFGS-B",
+                 datain=snow_data_sel,
+                 obs=obs)
+#print out optimal parameters - note Tmax and Tmin can be reversed during optimization
+cat(sprintf("Optimal parameters:\nTmax=%.1f\nTmin=%.1f\nkd=%.2f\nTmelt=%.1f\n",
+            max(opt_res$par[1],opt_res$par[2]),min(opt_res$par[1],opt_res$par[2]),
+            opt_res$par[3],opt_res$par[4]))
+#> Optimal parameters:
+#> Tmax=1.0
+#> Tmin=0.5
+#> kd=1.05
+#> Tmelt=-0.0

    The results using the optimal parameters can be plotted to visualize the simulation.

    -
    snow_estim_opt <- hydromisc::snow.sim(DATA=snow_data_sel,
-                      Tmax=max(opt_res$par[1],opt_res$par[2]),
-                      Tmin=min(opt_res$par[1],opt_res$par[2]),
-                      kd=opt_res$par[3],
-                      Tmelt=opt_res$par[4])
-obs <- snow_data_sel$snow_water_equivalent
-sim <- snow_estim_opt$swe_simulated
-hydroGOF::ggof(sim, obs, na.rm = TRUE, dates=snow_data_sel$date,
-               gofs=c("MAE", "RMSE", "PBIAS"),
-               xlab = "", ylab="SWE, mm", 
-               tick.tstep="months", cex=c(0,0),lwd=c(2,2))
    +
    snow_estim_opt <- hydromisc::snow.sim(DATA=snow_data_sel,
+                      Tmax=max(opt_res$par[1],opt_res$par[2]),
+                      Tmin=min(opt_res$par[1],opt_res$par[2]),
+                      kd=opt_res$par[3],
+                      Tmelt=opt_res$par[4])
+obs <- snow_data_sel$snow_water_equivalent
+sim <- snow_estim_opt$swe_simulated
+hydroGOF::ggof(sim, obs, na.rm = TRUE, dates=snow_data_sel$date,
+               gofs=c("MAE", "RMSE", "PBIAS"),
+               xlab = "", ylab="SWE, mm", 
+               tick.tstep="months", cex=c(0,0),lwd=c(2,2))
    Optimal simulation of SWE at SNOTEL site 1050 for water year 2009.

    @@ -574,22 +575,22 @@

    9.4.3 Snow model calibration

    9.4.4 Estimating climate change impacts on snow

    Once a reasonable calibration is obtained, the effect of increasing temperatures on SWE can be simulated by including the deltaT argument in the hydromisc::snow.sim command. Here a 3\(^\circ\)C uniform temperature increase is imposed on the optimal parameterization above.

    -
    dT <- 3.0 
-snow_plus3 <- hydromisc::snow.sim(DATA=snow_data_sel,
-                      Tmax=max(opt_res$par[1],opt_res$par[2]),
-                      Tmin=min(opt_res$par[1],opt_res$par[2]),
-                      kd=opt_res$par[3],
-                      Tmelt=opt_res$par[4],
-                      deltaT = dT)
-simplusdT <- snow_plus3$swe_simulated
-# plot the results
-dTlegend  <- expression("Simulated"*+3~degree*C)
-plot(as.Date(snow_data_sel$date),obs,type = "l",xlab = "", ylab = "SWE (mm)")
-lines(as.Date(snow_estim$date),sim,lty=2,col="blue")
-lines(as.Date(snow_estim$date),simplusdT,lty=3,col="red")
-legend("topright", legend = c("Observed", "Simulated",dTlegend),
-       lty = c(1,2,3), col=c("black","blue","red"))
-grid()
    +
    dT <- 3.0 
+snow_plus3 <- hydromisc::snow.sim(DATA=snow_data_sel,
+                      Tmax=max(opt_res$par[1],opt_res$par[2]),
+                      Tmin=min(opt_res$par[1],opt_res$par[2]),
+                      kd=opt_res$par[3],
+                      Tmelt=opt_res$par[4],
+                      deltaT = dT)
+simplusdT <- snow_plus3$swe_simulated
+# plot the results
+dTlegend  <- expression("Simulated"*+3~degree*C)
+plot(as.Date(snow_data_sel$date),obs,type = "l",xlab = "", ylab = "SWE (mm)")
+lines(as.Date(snow_estim$date),sim,lty=2,col="blue")
+lines(as.Date(snow_estim$date),simplusdT,lty=3,col="red")
+legend("topright", legend = c("Observed", "Simulated",dTlegend),
+       lty = c(1,2,3), col=c("black","blue","red"))
+grid()
    Observed SWE and simulated with observed meteorology and increased temperatures.

    diff --git a/docs/flow-in-open-channels.html b/docs/flow-in-open-channels.html index 5585931..fb0ce01 100644 --- a/docs/flow-in-open-channels.html +++ b/docs/flow-in-open-channels.html @@ -25,7 +25,7 @@ - + @@ -210,6 +210,7 @@

  • 4.7 Parallel pipes: solving a system of equations
  • 4.8 Simple pipe networks: the Hardy-Cross method
    • @@ -466,26 +467,26 @@

    5.3.1 Solving the Manning equatio

    Example 5.1 Find the flow depth, y, for a trapezoidal channel with Q=225 ft3/s, n=0.016, m=2, b=10 ft, S=0.0006.

    The Manning equation can be set up as a function in terms of a missing variable, here using normal depth, y as the missing variable.

    -
    yfun <- function(y) {
-  Q - (((y * (b + m * y)) ^ (5 / 3) * sqrt(S)) * (C / n) / ((b + 2 * y * sqrt(1 + m ^ 2)) ^ (2 / 3)))
-  }
    +
    yfun <- function(y) {
+  Q - (((y * (b + m * y)) ^ (5 / 3) * sqrt(S)) * (C / n) / ((b + 2 * y * sqrt(1 + m ^ 2)) ^ (2 / 3)))
+  }

    Because these use US Customary (or English) units, C=1.486. Define all of the needed input variables for the function.

    -
    Q <- 225.
-n <- 0.016
-m <- 2
-b <- 10.0
-S <- 0.0006
-C <- 1.486
    +
    Q <- 225.
+n <- 0.016
+m <- 2
+b <- 10.0
+S <- 0.0006
+C <- 1.486

    Use the R function uniroot to find a single root within a defined interval. Set the interval (the range of possible y values in which to search for a root) to cover all plausible values, here from 0.0001 mm to 200 m.

    -
    ans <- uniroot(yfun, interval = c(0.0000001, 200), extendInt = "yes")
-cat(sprintf("Normal Depth: %.3f ft\n", ans$root))
-#> Normal Depth: 3.406 ft
    +
    ans <- uniroot(yfun, interval = c(0.0000001, 200), extendInt = "yes")
+cat(sprintf("Normal Depth: %.3f ft\n", ans$root))
+#> Normal Depth: 3.406 ft

    Functions can usually be given multiple values as input, returning the corresponding values of output. this allows plots to be created to show, for example, how the left side of Equation (5.10) varies with different values of depth, y.

    -
    ys <- seq(0.1, 5, 0.1)
-plot(ys,yfun(ys), type='l', xlab = "y, ft", ylab = "Function to solve for zero")
-abline(h=0)
-grid()
    +
    ys <- seq(0.1, 5, 0.1)
+plot(ys,yfun(ys), type='l', xlab = "y, ft", ylab = "Function to solve for zero")
+abline(h=0)
+grid()
    Variation of the left side of Equation (5.10) with y for Example 5.1.

    @@ -501,12 +502,12 @@

    5.3.2 Solving the Manning equatio

    Example 5.2 Find the uniform (normal) flow depth, y, for a trapezoidal channel with Q=225 ft3/s, n=0.016, m=2, b=10 ft, S=0.0006.

    Specifying “Eng” units ensures the correct C value is used. Sf is the same as S in Equations (5.2) and (5.9) since flow is uniform.

    -
    ans <- hydraulics::manningt(Q = 225., n = 0.016, m = 2, b = 10., Sf = 0.0006, units = "Eng")
-cat(sprintf("Normal Depth: %.3f ft\n", ans$y))
-#> Normal Depth: 3.406 ft
-#critical depth is also returned, along with other variables.
-cat(sprintf("Critical Depth: %.3f ft\n", ans$yc))
-#> Critical Depth: 2.154 ft
    +
    ans <- hydraulics::manningt(Q = 225., n = 0.016, m = 2, b = 10., Sf = 0.0006, units = "Eng")
+cat(sprintf("Normal Depth: %.3f ft\n", ans$y))
+#> Normal Depth: 3.406 ft
+#critical depth is also returned, along with other variables.
+cat(sprintf("Critical Depth: %.3f ft\n", ans$yc))
+#> Critical Depth: 2.154 ft

    5.3.3 Solving the Manning equation using a spreadsheet like Excel

    @@ -535,8 +536,8 @@

    5.3.4 Optimal trapezoidal geometr

    Example 5.3 Find the optimal channel width to transmit 360 ft3/s at a depth of 3 ft with n=0.015, m=1, S=0.0006.

    -
    ans <- hydraulics::manningt(Q = 360., n = 0.015, m = 1, y = 3.0, Sf = 0.00088, units = "Eng")
-knitr::kable(format(as.data.frame(ans), digits = 2), format = "pipe", padding=0)
    +
    ans <- hydraulics::manningt(Q = 360., n = 0.015, m = 1, y = 3.0, Sf = 0.00088, units = "Eng")
+knitr::kable(format(as.data.frame(ans), digits = 2), format = "pipe", padding=0)
    @@ -594,12 +595,12 @@

    5.3.4 Optimal trapezoidal geometr

    -
    cat(sprintf("Optimal bottom width: %.5f ft\n", ans$bopt))
-#> Optimal bottom width: 4.76753 ft
    +
    cat(sprintf("Optimal bottom width: %.5f ft\n", ans$bopt))
+#> Optimal bottom width: 4.76753 ft

    The results show that, aside from the rounding, the required width is approximately 20 ft, and the optimal bottom width for optimal hydraulic efficiency would be 4.76 ft. To check the depth that would be associated with a channel of the optimal width, substitute the optimal width for b and solve for y:

    -
    ans <- hydraulics::manningt(Q = 360., n = 0.015, m = 1, b = 4.767534, Sf = 0.00088, units = "Eng")
-cat(sprintf("Optimal depth: %.5f ft\n", ans$yopt))
-#> Optimal depth: 5.75492 ft
    +
    ans <- hydraulics::manningt(Q = 360., n = 0.015, m = 1, b = 4.767534, Sf = 0.00088, units = "Eng")
+cat(sprintf("Optimal depth: %.5f ft\n", ans$yopt))
+#> Optimal depth: 5.75492 ft

    @@ -642,79 +643,179 @@

    5.4 Circular Channels (flowing pa where C=20.16 for SI units and C=13.53 for US Customary (English) units.

    5.4.1 Solving the Manning equation for a circular pipe in R

    -

    As was demonstrated with pipe flow, a function could be written with Equation (5.17) and a solver applied to find the value of \(\theta\) for the given flow conditions with a known D, S, n and Q. The value for \(\theta\) could then be used with Equations (5.13), (5.14) and (5.15) to recover geometric values.

    -

    The R package hydraulics has implemented those routines to enable these calculations. -For an existing pipe, a common problem is the determination of the depth, y that a given flow Q, will have given a pipe diameter d, slope S and roughness n. Example 5.4 demonstrates this.

    +

    As was demonstrated with pipe flow, a function could be written with Equation (5.17) and a solver applied to find the value of \(\theta\) for the given flow conditions with a known D, S, n and Q. The value for \(\theta\) could then be used with Equations (5.13), (5.14) and (5.15) to recover geometric values.

    +

    Hydraulic analysis of circular pipes flowing partially full often account for the value of Manning’s n varying with depth (Camp, 1946); some standards recommend fixed n values, and others require the use of a depth-varying n. The R package hydraulics has implemented those routines to enable these calculations, including using a fixed n (the default) or a depth-varing n.

    +

    For an existing pipe, a common problem is the determination of the depth, y that a given flow Q, will have given a pipe diameter d, slope S and roughness n. Example 5.4 demonstrates this.

    -

    Example 5.4 Find the uniform (normal) flow depth, y, for a trapezoidal channel with Q=225 ft3/s, n=0.016, m=2, b=10 ft, S=0.0006.

    +

    Example 5.4 Find the uniform (normal) flow depth, y, for a trapezoidal channel with Q=225 ft3/s, n=0.016, m=2, b=10 ft, S=0.0006. Do this assuming both that Manning n is constant with depth and that it varies with depth.

    The function manningc from the hydraulics package is used. Any one of the variables in the Manning equation, and related geometric variables, may be treated as an unknown.

    -
    ans <- hydraulics::manningc(Q=0.01, n=0.013, Sf=0.001, d = 0.2, units="SI", ret_units = TRUE)
-knitr::kable(format(as.data.frame(ans), digits = 2), format = "pipe", padding=0)
    - +
    ans <- hydraulics::manningc(Q=0.01, n=0.013, Sf=0.001, d = 0.2, units="SI", ret_units = TRUE)
+ans2 <- hydraulics::manningc(Q=0.01, n=0.013, Sf=0.001, d = 0.2, n_var = TRUE, units="SI", ret_units = TRUE)
+df <- data.frame(Constant_n = unlist(ans), Variable_n = unlist(ans2))
+knitr::kable(df, format = "html", digits=3, padding = 0, col.names = c("Constant n","Variable n")) |> 
+kableExtra::kable_styling(full_width = F)
    +
    - - - + + + + - - - + + + + - - - + + + + - - - + + + + - - - + + + + - - - + + + + - - - + + + + - - - + + + + - - - + + + + - - - + + + + - - - + + + + - - - + + + + - - - + + + + - - - + + + +
    ans
    + +Constant n + +Variable n +
    Q0.01 [m^3/s]
    +Q + +0.010 + +0.010 +
    V0.38 [m/s]
    +V + +0.376 + +0.344 +
    A0.027 [m^2]
    +A + +0.027 + +0.029 +
    P0.44 [m]
    +P + +0.437 + +0.482 +
    R0.061 [m]
    +R + +0.061 + +0.060 +
    y0.16 [m]
    +y + +0.158 + +0.174 +
    d0.2 [m]
    +d + +0.200 + +0.200 +
    Sf0.001 [1]
    +Sf + +0.001 + +0.001 +
    n0.013 [1]
    +n + +0.013 + +0.014 +
    yc0.085 [m]
    +yc + +0.085 + +0.085 +
    Fr0.3 [1]
    +Fr + +0.297 + +0.235 +
    Re22343 [1]
    +Re + +22342.979 + +20270.210 +
    Qf0.01 [m^3/s]
    +Qf + +0.010 + +0.010 +

    It is also sometimes convenient to see a cross-section diagram.

    -
    hydraulics::xc_circle(y = ans$y, d=ans$d, units = "SI")
    +
    hydraulics::xc_circle(y = ans$y, d=ans$d, units = "SI")

    @@ -735,7 +836,7 @@

    5.5 Critical flow\(y_c\) indicates critical flow depth.

    This is important for understanding what may happen to the water surface when flow encounters an obstacle or transition. For the channel of Example 5.3, the diagram is shown in Figure 5.4.

    -
    hydraulics::spec_energy_trap( Q = 360, b = 20, m = 1, scale = 4, units = "Eng" )
    +
    hydraulics::spec_energy_trap( Q = 360, b = 20, m = 1, scale = 4, units = "Eng" )
    A specific energy diagram for the conditions of Example 5.3.

    @@ -747,7 +848,7 @@

    5.5 Critical flow\(E-E_c=3.42-3.03=0.39 ft\) critical depth would occur over the hump. A rise of anything greater than that would cause damming to occur. Once flow over a hump is critical, downstream of the hump the flow will be in supercritical conditions, flowing at the alternate depth.

    The specific energy for a given depth y and alternate depth can be added to the plot by including an argument for depth, y, as in Figure 5.5.

    -
    hydraulics::spec_energy_trap( Q = 360, b = 20, m = 1, scale = 4, y=3.0, units = "Eng" )
    +
    hydraulics::spec_energy_trap( Q = 360, b = 20, m = 1, scale = 4, y=3.0, units = "Eng" )
    A specific energy diagram for the conditions of Example 5.3 with an additional y value added.

    @@ -785,12 +886,12 @@

    5.6 Flow in Rectangular Channels<

    A specific energy diagram is very helpful for establishing upstream conditions and estimating \(y_2\).

    -
    p1 <- hydraulics::spec_energy_trap( Q = 2.2, b = 0.5, m = 0, y = 2, scale = 2.5, units = "SI" )
-p1
    -
    -A specific energy diagram for the conditions of Example 5.5. +
    p1 <- hydraulics::spec_energy_trap( Q = 2.2, b = 0.5, m = 0, y = 2, scale = 2.5, units = "SI" )
+p1
    +
    +A specific energy diagram for the conditions of Example 5.5.

    -(#fig:sp_openrect)A specific energy diagram for the conditions of Example 5.5. +Figure 5.7: A specific energy diagram for the conditions of Example 5.5.

    The values of \(y_c\) and \(E_{min}\) shown in the plot can be verified using Equations (5.21) and (5.22). This should always be checked to describe the incoming flow and what will happen as flow passes over a hump. Since \(y_1\) > \(y_c\) the upstream flow is subcritical, and flow can be expected to drop as it passes over the hump. Upstream and downstream specific energy are related by Equation (5.23):

    @@ -808,15 +909,20 @@

    5.6 Flow in Rectangular Channels< From the specific energy diagram, for \(E_2=1.997 ~ m\) a depth of about \(y_2 \approx 1.6 ~ m\) would be expected, and the flow would continue in subcritical conditions. The value of \(y_2\) can be calculated using Equation (5.20): \[1.997 = y_2 + \frac{4.4^2}{2(9.81)(y_2^2)}\] which can be rearranged to \[0.9967 - 1.997 y_2^2 + y_2^3= 0\] Solving a polynomial in R is straightforward using the polyroot function and using Re to extract the real portion of the solution.

    -
    Re(polyroot(c(0.9667, 0, -1.997, 1)))
-#> [1]  0.9703764 -0.6090519  1.6356755
    +
    Re(polyroot(c(0.9667, 0, -1.997, 1)))
+#> [1]  0.9703764 -0.6090519  1.6356755

    The negative root is meaningless, the lower positive root is the supercritical depth for \(E_2 = 1.997 ~ m\), and the larger positive root is the subcritical depth. Thus the correct solution is \(y_2 = 1.64 ~ m\) when the channel bottom rises by 0.25 m.

    +

    A vertical line or other annotation can be added to the specific energy diagram to indicate \(E_2\) using ggplot2 with a command like p1 + ggplot2::geom_vline(xintercept = 1.997, linetype=3). The hydraulics R package can also add lines to a specific energy diagram for up to two depths:

    + +
    p2 <- hydraulics::spec_energy_trap(Q = 2.2, b = 0.5, m = 0, y = c(2, 1.64), scale = 2.5, units = "SI")
+p2
    +
    +A specific energy diagram for the conditions of Example 5.5 with added annotation for when the bottom elecation rises. +

    +Figure 5.8: A specific energy diagram for the conditions of Example 5.5 with added annotation for when the bottom elecation rises. +

    +

    The specific energy diagram shows that if \(\Delta z > E_1 - E_{min}\), the downstream specific energy, \(E_2\) would be to the left of the curve, so no feasible solution would exist. At that point damming would occur, raising the upstream depth, \(y_1\), and thus increasing \(E_1\) until \(E_2 = E_{min}\). The largest rise in channel bottom height that will not cause damming is called the critical hump height: \(\Delta z_{c} = E_1 - E_{min}\).

    -

    A vertical can be added to the specific energy diagram to indicate \(E_2\):

    -
    p1 + 
-  ggplot2::geom_vline(xintercept = 1.997, linetype=3) +
-  ggplot2::annotate("text", x=1.9, y=2.5, label=expression(E[2]), angle=90)
    -

    5.7 Gradually varied steady flow

    @@ -825,18 +931,18 @@

    5.7 Gradually varied steady flow< \(y_c\), critical depth, found using Equation (5.4) \(y_0\), normal depth, found using Equation (5.2) \(y\), flow depth, found using Equation (5.5)

    -If \(y_n < y_c\) flow is supercritical (for example, flowing down a steep slope); if \(y_n > y_c\) flow is subcritical. Variations in the water surface are classified by profile types based on to whether the normal flow is subcritical (or mild sloped, M) or supercritical (steep, S), as in Figure 5.7 (Davidian, Jacob, 1984). +If \(y_n < y_c\) flow is supercritical (for example, flowing down a steep slope); if \(y_n > y_c\) flow is subcritical. Variations in the water surface are classified by profile types based on to whether the normal flow is subcritical (or mild sloped, M) or supercritical (steep, S), as in Figure 5.9 (Davidian, Jacob, 1984).
    Types of flow profiles on mild and steep slopes

    -Figure 5.7: Types of flow profiles on mild and steep slopes +Figure 5.9: Types of flow profiles on mild and steep slopes

    -In addition to channel transitions, changes in channel slow of roughness (Manning n) will cause the flow surface to vary. Some of these conditions are illustrated in Figure 5.8 (Davidian, Jacob, 1984). +In addition to channel transitions, changes in channel slow of roughness (Manning n) will cause the flow surface to vary. Some of these conditions are illustrated in Figure 5.10 (Davidian, Jacob, 1984).
    Types of flow profiles with changes in slope or roughness

    -Figure 5.8: Types of flow profiles with changes in slope or roughness +Figure 5.10: Types of flow profiles with changes in slope or roughness

    Typically, for supercritical flow the calculations start at an upstream cross section and move downstream. For subcritical flow calculations proceed upstream. However, for the direct step method, a negative result will indicate upstream, and a positive result indicates downstream.

    @@ -847,7 +953,7 @@

    5.7.1 The direct step method A gradually varied flow example.

    -Figure 5.9: A gradually varied flow example. +Figure 5.11: A gradually varied flow example.

    The distance between these two cross-sections, \({\Delta}X\), is calculated using Equation (5.25) @@ -865,88 +971,88 @@

    5.7.1 The direct step methodExample 5.6 Water flows at 10 m3/s in a trapezoidal channel with n=0.015, bottom width 3 m, side slope of 2:1 (H:V) and longitudinal slope 0.0009 (0.09%). At the location of a USGS stream gage the flow depth is 1.4 m. Use the direct step method to find the distance to the point where the depth is 1.2 m and determine whether it is upstream or downstream.

    Begin by setting up a function to calculate the Manning slope and setting up the input data.

    -
    #function to calculate Manning slope
-slope_f <- function(V,n,R,C) {
-  return(V^2*n^2/(C^2*R^(4./3.)))
-}
-#Now set up input data ##################################
-#input Flow
-Q=10.0
-#input depths:
-y1 <- 1.4 #starting depth
-y2 <- 1.2 #final depth
-#Define the number of steps into which the difference in y will be broken
-nsteps <- 2
-
-#channel geometry:
-bottom_width <- 3
-side_slope <- 2         #side slope is H:V. Use zero for rectangular
-manning_n <- 0.015
-long_slope <- 0.0009
-units <- "SI"           #"SI" or "Eng"
-if (units == "SI") {
-  C <- 1                #Manning constant: 1 for SI, 1.49 for US units
-  g <- 9.81             
-} else {                #"Eng" means English, or US system
-  C <- 1.49
-  g <- 32.2
-}
-
-#find depth increment for each step, depths at which to solve
-depth_incr <- (y2 - y1) / nsteps
-depths <- seq(from=y1, to=y2, by=depth_incr)
    +
    #function to calculate Manning slope
+slope_f <- function(V,n,R,C) {
+  return(V^2*n^2/(C^2*R^(4./3.)))
+}
+#Now set up input data ##################################
+#input Flow
+Q=10.0
+#input depths:
+y1 <- 1.4 #starting depth
+y2 <- 1.2 #final depth
+#Define the number of steps into which the difference in y will be broken
+nsteps <- 2
+
+#channel geometry:
+bottom_width <- 3
+side_slope <- 2         #side slope is H:V. Use zero for rectangular
+manning_n <- 0.015
+long_slope <- 0.0009
+units <- "SI"           #"SI" or "Eng"
+if (units == "SI") {
+  C <- 1                #Manning constant: 1 for SI, 1.49 for US units
+  g <- 9.81             
+} else {                #"Eng" means English, or US system
+  C <- 1.49
+  g <- 32.2
+}
+
+#find depth increment for each step, depths at which to solve
+depth_incr <- (y2 - y1) / nsteps
+depths <- seq(from=y1, to=y2, by=depth_incr)

    First check to see if the flow is subcritical or supercritical and find the normal depth. Critical and normal depths can be calculated using the manningt function in the hydraulics package, as in Example 5.2. However, because other functionality of the rivr package is used, these will be calculated using functions from the rivr package.

    -
    rivr::critical_depth(Q = Q, yopt = y1, g = g, B = bottom_width , SS = side_slope)
-#> [1] 0.8555011
-#note using either depth for yopt produces the same answer
-rivr::normal_depth(So = long_slope, n = manning_n, Q = Q, yopt = y1, Cm = C, B = bottom_width , SS = side_slope)
-#> [1] 1.147137
    -

    The normal depth is greater than the critical depth, so the channel has a mild slope. The beginning and ending depths are above normal depth. This indicates the profile type, following Figure 5.7, is M-1, so the flow depth should decrease in depth going upstream. This also verifies that the flow depth between these two points does not pass through critical flow, so is a valid gradually varied flow problem.

    +
    rivr::critical_depth(Q = Q, yopt = y1, g = g, B = bottom_width , SS = side_slope)
+#> [1] 0.8555011
+#note using either depth for yopt produces the same answer
+rivr::normal_depth(So = long_slope, n = manning_n, Q = Q, yopt = y1, Cm = C, B = bottom_width , SS = side_slope)
+#> [1] 1.147137
    +

    The normal depth is greater than the critical depth, so the channel has a mild slope. The beginning and ending depths are above normal depth. This indicates the profile type, following Figure 5.9, is M-1, so the flow depth should decrease in depth going upstream. This also verifies that the flow depth between these two points does not pass through critical flow, so is a valid gradually varied flow problem.

    For each increment the \({\Delta}X\) value needs to be calculated, and they need to be accumulated to find the total length, L, between the two defined depths.

    -
    #loop through each channel segment (step), calculating the length for each segment. 
-#The channel_geom function from the rivr package is helpful
-L <- 0
-for ( i in 1:nsteps ) {
-  #find hydraulic geometry, E and Sf at first depth
-  xc1 <- rivr::channel_geom(y=depths[i], B=bottom_width, SS=side_slope)
-  V1 <- Q/xc1[['A']]
-  R1 <- xc1[['R']]
-  E1 <- depths[i] + V1^2/(2*g)
-  Sf1 <- slope_f(V1,manning_n,R1,C)
-  
-  #find hydraulic geometry, E and Sf at second depth
-  xc2 <- rivr::channel_geom(y=depths[i+1], B=bottom_width, SS=side_slope)
-  V2 <- Q/xc2[['A']]
-  R2 <- xc2[['R']]
-  E2 <- depths[i+1] + V2^2/(2*g)
-  Sf2 <- slope_f(V2,manning_n,R2,C)
-  
-  Sf_avg <- (Sf1 + Sf2) / 2.0
-  dX <- (E1 - E2) / (Sf_avg - long_slope)
-  L <- L + dX
-}
-cat(sprintf("Using %d steps, total distance from depth %.2f to %.2f = %.2f m\n", nsteps, y1, y2, L))
-#> Using 2 steps, total distance from depth 1.40 to 1.20 = -491.75 m
    -The result is negative, verifying that the location of depth y2 is upstream of y1. Of course, the result will become more precise as more incremental steps are included, as shown in Figure 5.10 +
    #loop through each channel segment (step), calculating the length for each segment. 
+#The channel_geom function from the rivr package is helpful
+L <- 0
+for ( i in 1:nsteps ) {
+  #find hydraulic geometry, E and Sf at first depth
+  xc1 <- rivr::channel_geom(y=depths[i], B=bottom_width, SS=side_slope)
+  V1 <- Q/xc1[['A']]
+  R1 <- xc1[['R']]
+  E1 <- depths[i] + V1^2/(2*g)
+  Sf1 <- slope_f(V1,manning_n,R1,C)
+  
+  #find hydraulic geometry, E and Sf at second depth
+  xc2 <- rivr::channel_geom(y=depths[i+1], B=bottom_width, SS=side_slope)
+  V2 <- Q/xc2[['A']]
+  R2 <- xc2[['R']]
+  E2 <- depths[i+1] + V2^2/(2*g)
+  Sf2 <- slope_f(V2,manning_n,R2,C)
+  
+  Sf_avg <- (Sf1 + Sf2) / 2.0
+  dX <- (E1 - E2) / (Sf_avg - long_slope)
+  L <- L + dX
+}
+cat(sprintf("Using %d steps, total distance from depth %.2f to %.2f = %.2f m\n", nsteps, y1, y2, L))
+#> Using 2 steps, total distance from depth 1.40 to 1.20 = -491.75 m
    +The result is negative, verifying that the location of depth y2 is upstream of y1. Of course, the result will become more precise as more incremental steps are included, as shown in Figure 5.12
    Variation of number of calculation steps to final calculated distance.

    -Figure 5.10: Variation of number of calculation steps to final calculated distance. +Figure 5.12: Variation of number of calculation steps to final calculated distance.

    The direct step method is also implemented in the hydraulics package, and can be applied to the same problem as above, as illustrated in Example 5.7.

    Example 5.7 Water flows at 10 m3/s in a trapezoidal channel with n=0.015, bottom width 3 m, side slope of 2:1 (H:V) and longitudinal slope 0.0009 (0.09%). At the location of a USGS stream gage the flow depth is 1.4 m. Use the direct step method to find the distance to the point where the depth is 1.2 m and determine whether it is upstream or downstream.

    -
    hydraulics::direct_step(So=0.0009, n=0.015, Q=10, y1=1.4, y2=1.2, b=3, m=2, nsteps=2, units="SI")
-#> y1=1.400, y2=1.200, yn=1.147, yc=0.855585
-#> Profile type  = M1
-#> # A tibble: 3 × 7
-#>       x     z     y     A       Sf     E    Fr
-#>   <dbl> <dbl> <dbl> <dbl>    <dbl> <dbl> <dbl>
-#> 1    0  0       1.4  8.12 0.000407  1.48 0.405
-#> 2 -192. 0.173   1.3  7.28 0.000548  1.40 0.466
-#> 3 -492. 0.443   1.2  6.48 0.000753  1.32 0.541
    +
    hydraulics::direct_step(So=0.0009, n=0.015, Q=10, y1=1.4, y2=1.2, b=3, m=2, nsteps=2, units="SI")
+#> y1=1.400, y2=1.200, yn=1.147, yc=0.855585
+#> Profile type  = M1
+#> # A tibble: 3 × 7
+#>       x     z     y     A       Sf     E    Fr
+#>   <dbl> <dbl> <dbl> <dbl>    <dbl> <dbl> <dbl>
+#> 1    0  0       1.4  8.12 0.000407  1.48 0.405
+#> 2 -192. 0.173   1.3  7.28 0.000548  1.40 0.466
+#> 3 -492. 0.443   1.2  6.48 0.000753  1.32 0.541

    This produces the same result, and verifies that the water surface profile is type M-1.

    @@ -957,14 +1063,14 @@

    5.7.2 Standard step methodExample 5.8 For the same channel and flow rate as Example 5.6, determine the depth of water at the distance L determined above.

    The function requires the distance to be positive, so apply the absolute value to the L value.

    -
    dist = abs(L)
-ans <- rivr::compute_profile(So = long_slope, n = manning_n, Q = Q, y0 = y1, Cm = C, g = g, B = bottom_width, SS = side_slope, stepdist = dist/nsteps, totaldist = dist)
-#Distances along the channel where depths were determined
-ans$x
-#> [1]    0.0000 -245.8742 -491.7483
-#Depths at each distance
-ans$y
-#> [1] 1.400000 1.277009 1.200592
    +
    dist = abs(L)
+ans <- rivr::compute_profile(So = long_slope, n = manning_n, Q = Q, y0 = y1, Cm = C, g = g, B = bottom_width, SS = side_slope, stepdist = dist/nsteps, totaldist = dist)
+#Distances along the channel where depths were determined
+ans$x
+#> [1]    0.0000 -245.8742 -491.7483
+#Depths at each distance
+ans$y
+#> [1] 1.400000 1.277009 1.200592

    This shows the distances and depths at each of the steps defined. Consistent with the above, the distances are negative, showing that they are progressing upstream. The results are identical for \(y_2\) using the direct step method.

    @@ -973,14 +1079,14 @@

    5.8 Rapidly varied flow (the hydr
    A hydraulic jump at St. Anthony Falls, Minnesota.

    -Figure 5.11: A hydraulic jump at St. Anthony Falls, Minnesota. +Figure 5.13: A hydraulic jump at St. Anthony Falls, Minnesota.

    -

    In the discussion of critical flow in Section 5.5, the concept of alternate depths was introduced, where a given flow rate in a channel with known geometry typically may assume two possible values, one subcritical and one supercritical. For the case of supercritical flow transitioning to subcritical flow, a smooth transition is impossible, so a hydraulic jump occurs. A hydraulic jump always dissipates some of the incoming energy. A hydraulic jump is depicted in Figure 5.12 (Peterka, Alvin J., 1978).

    +

    In the discussion of critical flow in Section 5.5, the concept of alternate depths was introduced, where a given flow rate in a channel with known geometry typically may assume two possible values, one subcritical and one supercritical. For the case of supercritical flow transitioning to subcritical flow, a smooth transition is impossible, so a hydraulic jump occurs. A hydraulic jump always dissipates some of the incoming energy. A hydraulic jump is depicted in Figure 5.14 (Peterka, Alvin J., 1978).

    A typical hydraulic jump.

    -Figure 5.12: A typical hydraulic jump. +Figure 5.14: A typical hydraulic jump.

    @@ -1012,32 +1118,32 @@

    5.8.1 Sequent (or conjugate) dept

    Example 5.9 A trapezoidal channel with a bottom width of 0.5 m and a side slope of 1:1 carries a flow of 0.2 m3/s. The depth on one side of a hydraulic jump is 0.1 m. Find the sequent depth, the energy head loss, and the power dissipation in Watts.

    -
    flow <- 0.2
-ans <- hydraulics::sequent_depth(Q=flow,b=0.5,y=0.1,m=1,units = "SI", ret_units = TRUE)
-#print output of function
-as.data.frame(ans)
-#>                  ans
-#> y            0.1 [m]
-#> y_seq  0.3941009 [m]
-#> yc      0.217704 [m]
-#> Fr      3.635731 [1]
-#> Fr_seq 0.3465538 [1]
-#> E       0.666509 [m]
-#> E_seq  0.4105265 [m]
-#Find energy head loss
-hl <- abs(ans$E - ans$E_seq)
-hl
-#> 0.2559825 [m]
-#Express this as a power loss
-gamma <- hydraulics::specwt(units = "SI")
-P <- gamma*flow*hl
-cat(sprintf("Power loss = %.1f Watts\n",P))
-#> Power loss = 501.4 Watts
    -

    The energy loss across hydraulic jumps varies with the Froude number of the incoming flow, as shown in depicted in Figure 5.13 (Peterka, Alvin J., 1978).

    +
    flow <- 0.2
+ans <- hydraulics::sequent_depth(Q=flow,b=0.5,y=0.1,m=1,units = "SI", ret_units = TRUE)
+#print output of function
+as.data.frame(ans)
+#>                  ans
+#> y            0.1 [m]
+#> y_seq  0.3941009 [m]
+#> yc      0.217704 [m]
+#> Fr      3.635731 [1]
+#> Fr_seq 0.3465538 [1]
+#> E       0.666509 [m]
+#> E_seq  0.4105265 [m]
+#Find energy head loss
+hl <- abs(ans$E - ans$E_seq)
+hl
+#> 0.2559825 [m]
+#Express this as a power loss
+gamma <- hydraulics::specwt(units = "SI")
+P <- gamma*flow*hl
+cat(sprintf("Power loss = %.1f Watts\n",P))
+#> Power loss = 501.4 Watts
    +

    The energy loss across hydraulic jumps varies with the Froude number of the incoming flow, as shown in depicted in Figure 5.15 (Peterka, Alvin J., 1978).

    Types of hydraulic jumps.

    -Figure 5.13: Types of hydraulic jumps. +Figure 5.15: Types of hydraulic jumps.

    @@ -1049,25 +1155,25 @@

    5.8.2 Location of a hydraulic jum

    Example 5.10 A rectangular (a trapezoid with side slope, m=0) concrete channel with a bottom width of 3 m carries a flow of 8 m3/s. The upstream channel slopes steeply at So=0.018 and discharges onto a mild slope of So=0.0015. Determine the height of the jump and its location.

    First find the normal depth on each slope, and the critical depth for the channel.

    -
    yn1 <- hydraulics::manningt(Q = 8, n = 0.013, m = 0, Sf = 0.018, b = 3, units = "SI")$y
-yn2 <- hydraulics::manningt(Q = 8, n = 0.013, m = 0, Sf = 0.0015, b = 3, units = "SI")$y
-yc <- hydraulics::manningt(Q = 8, n = 0.013, m = 0, Sf = 0.0015, b = 3, units = "SI")$yc
-cat(sprintf("yn1 = %.3f m, yn2 = %.3f m, yc = %.3f m\n", yn1, yn2, yc))
-#> yn1 = 0.498 m, yn2 = 1.180 m, yc = 0.898 m
    +
    yn1 <- hydraulics::manningt(Q = 8, n = 0.013, m = 0, Sf = 0.018, b = 3, units = "SI")$y
+yn2 <- hydraulics::manningt(Q = 8, n = 0.013, m = 0, Sf = 0.0015, b = 3, units = "SI")$y
+yc <- hydraulics::manningt(Q = 8, n = 0.013, m = 0, Sf = 0.0015, b = 3, units = "SI")$yc
+cat(sprintf("yn1 = %.3f m, yn2 = %.3f m, yc = %.3f m\n", yn1, yn2, yc))
+#> yn1 = 0.498 m, yn2 = 1.180 m, yc = 0.898 m

    Recall that the calculation of yc only depends on flow and channel geometry (Q, b, m), so the values of n and Sf can be arbitrary for that command. These results confirm that flow is supercritical upstream and subcritical downstream, so a hydraulic jump will occur.

    -

    The hydraulic jump will either begin at yn1 (and jump to the sequent depth for yn1) or end at yn2 (beginning at the sequent depth for yn2). The possibilities are shown in Figure 5.7 in the lower right panel. First check the two sequent depths.

    -
    yn1_seq <- hydraulics::sequent_depth(Q = 8, b = 3, y=yn1, m = 0, units = "SI")$y_seq
-yn2_seq <- hydraulics::sequent_depth(Q = 8, b = 3, y=yn2, m = 0, units = "SI")$y_seq
-cat(sprintf("yn1_seq = %.3f m, yn2_seq = %.3f m\n", yn1_seq, yn2_seq))
-#> yn1_seq = 1.476 m, yn2_seq = 0.666 m
    +

    The hydraulic jump will either begin at yn1 (and jump to the sequent depth for yn1) or end at yn2 (beginning at the sequent depth for yn2). The possibilities are shown in Figure 5.9 in the lower right panel. First check the two sequent depths.

    +
    yn1_seq <- hydraulics::sequent_depth(Q = 8, b = 3, y=yn1, m = 0, units = "SI")$y_seq
+yn2_seq <- hydraulics::sequent_depth(Q = 8, b = 3, y=yn2, m = 0, units = "SI")$y_seq
+cat(sprintf("yn1_seq = %.3f m, yn2_seq = %.3f m\n", yn1_seq, yn2_seq))
+#> yn1_seq = 1.476 m, yn2_seq = 0.666 m

    This confirms that if the jump began at yn1 (on the steep slope) it would need to jump a level below yn2, with an S-1 curve providing the gradual increase in depth to yn2. Since yn1_seq exceeds yn2, this is not possible. That can be verified using the direct_step function to show the distance from yn1_seq to yn2 would need to be upstream (negative x values in the result), which cannot occur for this case. This means the alternate case must exist, with an M-3 profile raising yn1 to yn2_seq at which point the jump occurs. The direct step method can find this distance along the channel.

    -
    hydraulics::direct_step(So=0.0015, n=0.013, Q=8, y1=yn1, y2=yn2_seq, b=3, m=0, nsteps=2, units="SI")
-#> # A tibble: 3 × 7
-#>       x       z     y     A      Sf     E    Fr
-#>   <dbl>   <dbl> <dbl> <dbl>   <dbl> <dbl> <dbl>
-#> 1   0    0      0.498  1.49 0.0180   1.96  2.42
-#> 2  23.4 -0.0350 0.582  1.75 0.0113   1.65  1.92
-#> 3  44.6 -0.0669 0.666  2.00 0.00761  1.48  1.57
    +
    hydraulics::direct_step(So=0.0015, n=0.013, Q=8, y1=yn1, y2=yn2_seq, b=3, m=0, nsteps=2, units="SI")
+#> # A tibble: 3 × 7
+#>       x       z     y     A      Sf     E    Fr
+#>   <dbl>   <dbl> <dbl> <dbl>   <dbl> <dbl> <dbl>
+#> 1   0    0      0.498  1.49 0.0180   1.96  2.42
+#> 2  23.4 -0.0350 0.582  1.75 0.0113   1.65  1.92
+#> 3  44.6 -0.0669 0.666  2.00 0.00761  1.48  1.57

    The number of calculation steps (nsteps) can be increased for greater precision, but 2 steps is adequate here.

    diff --git a/docs/groundwater.html b/docs/groundwater.html index e02116d..5e0f6c8 100644 --- a/docs/groundwater.html +++ b/docs/groundwater.html @@ -25,7 +25,7 @@ - + @@ -210,6 +210,7 @@
  • 4.7 Parallel pipes: solving a system of equations
  • 4.8 Simple pipe networks: the Hardy-Cross method
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zk5uVj=;~(ivtWBmN3ncC0`!e@zpgq{9hh_CQ|EN{@k10%y8qkj(f@~QC0v^#VHCp@ W&2&yW!Gae67tU#&%~UnN_x}L + @@ -210,6 +210,7 @@
  • 4.7 Parallel pipes: solving a system of equations
  • 4.8 Simple pipe networks: the Hardy-Cross method
    • diff --git a/docs/index.html b/docs/index.html index ad0da44..681b910 100644 --- a/docs/index.html +++ b/docs/index.html @@ -25,7 +25,7 @@ - + @@ -210,6 +210,7 @@
  • 4.7 Parallel pipes: solving a system of equations
  • 4.8 Simple pipe networks: the Hardy-Cross method
    • @@ -349,12 +350,12 @@

    Hydraulics and Water Resources: Examples Using R

    Professor, Civil, Environmental, and Sustainable Engineering Department, Santa Clara University
    -

    2023-12-28

    +

    2024-03-06

    Preface

    This is a compilation of various R exercises and examples created over many years. They have been used mostly in undergraduate civil engineering classes including fluid mechanics, hydraulics, and water resources. This is a dynamic work, and will be regularly updated as errors are identified, improved presentation is developed, or new topics or examples are introduced. I welcome any suggestions or comments.

    -

    In what follows, text will be intentionally brief. More extensive discussion and description can be found in any fluid mechanics, applied hydraulics, or water resources engineering text. Symbology for hydraulics in this reference generally follows that of Finnemore and Maurer (2023). Fundamental equations will be introduced though the emphasis will be on applications to solve common problems. Also, since this is written by a civil engineer, the only fluids included are water and air, since that accounts for nearly all problems encountered in the field.

    +

    In what follows, text will be intentionally brief. More extensive discussion and description can be found in any fluid mechanics, applied hydraulics, or water resources engineering text. Symbology for hydraulics in this reference generally follows that of Finnemore and Maurer (2024). Fundamental equations will be introduced though the emphasis will be on applications to solve common problems. Also, since this is written by a civil engineer, the only fluids included are water and air, since that accounts for nearly all problems encountered in the field.

    Solving water problems is rarely done by hand calculations, though the importance of performing order of magnitude ‘back of the envelope’ calculations cannot be overstated. Whether using a hand calculator, spreadsheet, or a programming language to produce a solution, having a sense of when an answer is an outlier will help catch errors.

    Scripting languages are powerful tools for performing calculations, providing a fully traceable and reproducible path from your input to a solution. Open source languages have the benefit of being free to use, and invite users to be part of a community helping improve the language and its capabilities. The language of choice for this book is R (R Core Team, 2022), chosen for its straightforward syntax, powerful graphical capabilities, wide use in engineering and in many other disciplines, and by using the RStudio interface, it can look and feel a lot like Matlab® with which most engineering students have some experience.

    diff --git a/docs/management-of-water-resources-systems.html b/docs/management-of-water-resources-systems.html index afe8df4..f9eb2f3 100644 --- a/docs/management-of-water-resources-systems.html +++ b/docs/management-of-water-resources-systems.html @@ -25,7 +25,7 @@ - + @@ -210,6 +210,7 @@
  • 4.7 Parallel pipes: solving a system of equations
  • 4.8 Simple pipe networks: the Hardy-Cross method
    • @@ -406,14 +407,14 @@

    12.1.3 Graphing the solution spac

    12.1.4 Setting up the problem in R

    An R package useful for solving linear programming problems is the lpSolveAPI package. Install that if necessary, and also install the knitr and kableExtra packages, since thet are very useful for printing the many tables that linear programming involves.

    Begin by creating an empty linear model. The (0,2) means zero constraints (they’ll be added later) and 2 decision variables. The next two lines just assign names to the decision variables. Because we will use many functions of the lpSolveAPI package, load the library first. Load the kableExtra package too.

    -
    library(lpSolveAPI)
-library(kableExtra)
-example.lp <- lpSolveAPI::make.lp(0,2)     # 0 constraints and 2 decision variables
-ColNames <- c("X1","X2")                   
-colnames(example.lp) <- ColNames           # Set the names of the decision variables
    +
    library(lpSolveAPI)
+library(kableExtra)
+example.lp <- lpSolveAPI::make.lp(0,2)     # 0 constraints and 2 decision variables
+ColNames <- c("X1","X2")                   
+colnames(example.lp) <- ColNames           # Set the names of the decision variables

    Now set up the objective function. Minimization is the default goal of this R function, but we’ll set it anyway to be clear. The second argument is the vector of coefficients for the decision variables, meaning X2 is minimized.

    -
    set.objfn(example.lp,c(0,1)) 
-x <- lp.control(example.lp, sense="min") #save output to a dummy variable
    +
    set.objfn(example.lp,c(0,1)) 
+x <- lp.control(example.lp, sense="min") #save output to a dummy variable

    The next step is to define the constraints. Four constraints were listed above. Additional constraints could be added that \(X1\ge 0\) and \(X2\ge 0\), however, variable ranges in this LP solver are [0,infinity] by default, so for this example and we do not need to include constraints for positive results. If necessary, decision variable valid ranges can be set using set.bounds().

    Constraints are defined with the add.constraint command. Figure 12.4 provides an annotated example of the use of an add.constraint command.
    @@ -424,41 +425,41 @@

    12.1.4 Setting up the problem in

    Type ?add.constraint in the console for additional details.

    The four constraints for this example are added with:

    -
    add.constraint(example.lp, xt=c(400,-300), type="<=", rhs=0, indices=c(1,2))
-add.constraint(example.lp, xt=c(1,1), type=">=", rhs=7500)
-add.constraint(example.lp, xt=c(1,0), type="<=", rhs=4000)
-add.constraint(example.lp, xt=c(0,1), type="<=", rhs=7500)
    +
    add.constraint(example.lp, xt=c(400,-300), type="<=", rhs=0, indices=c(1,2))
+add.constraint(example.lp, xt=c(1,1), type=">=", rhs=7500)
+add.constraint(example.lp, xt=c(1,0), type="<=", rhs=4000)
+add.constraint(example.lp, xt=c(0,1), type="<=", rhs=7500)

    That completes the setup of the linear model. You can view the model to verify the values you entered by typing the name of the model.

    -
    example.lp
-#> Model name: 
-#>             X1    X2          
-#> Minimize     0     1          
-#> R1         400  -300  <=     0
-#> R2           1     1  >=  7500
-#> R3           1     0  <=  4000
-#> R4           0     1  <=  7500
-#> Kind       Std   Std          
-#> Type      Real  Real          
-#> Upper      Inf   Inf          
-#> Lower        0     0
    +
    example.lp
+#> Model name: 
+#>             X1    X2          
+#> Minimize     0     1          
+#> R1         400  -300  <=     0
+#> R2           1     1  >=  7500
+#> R3           1     0  <=  4000
+#> R4           0     1  <=  7500
+#> Kind       Std   Std          
+#> Type      Real  Real          
+#> Upper      Inf   Inf          
+#> Lower        0     0

    If it has a large number of decision variables it only prints a summary, but in that case you can use write.lp(example.lp, "example_lp.txt", "lp") to create a viewable file with the model.

    Now the model can be solved.

    -
    solve(example.lp)
-#> [1] 0
    +
    solve(example.lp)
+#> [1] 0

    If the solver finds an optimal solution it will return a zero.

    12.1.5 Interpreting the optimal results

    View the final value of the objective function by retrieving it and printing it:

    -
    optimal_solution <- get.objective(example.lp)
-print(paste0("Optimal Solution = ",round(optimal_solution,2),sep=""))
-#> [1] "Optimal Solution = 4285.71"
    +
    optimal_solution <- get.objective(example.lp)
+print(paste0("Optimal Solution = ",round(optimal_solution,2),sep=""))
+#> [1] "Optimal Solution = 4285.71"

    For more detail, recover the values of each of the decision variables.

    -
    vars <- get.variables(example.lp)
    +
    vars <- get.variables(example.lp)

    Next you can print the sensitivity report – a vector of M constraints followed by N decision variables. It helps to create a data frame for viewing and printing the results. Nicer printing is achieved using the kable and kableExtra functions.

    -
    sens <- get.sensitivity.obj(example.lp)$objfrom
-results1 <- data.frame(variable=ColNames,value=vars,gradient=as.integer(sens))
-kbl(results1, booktabs = TRUE) %>% kable_styling(full_width = F)
    +
    sens <- get.sensitivity.obj(example.lp)$objfrom
+results1 <- data.frame(variable=ColNames,value=vars,gradient=as.integer(sens))
+kbl(results1, booktabs = TRUE) %>% kable_styling(full_width = F)
    @@ -500,10 +501,10 @@

    12.1.5 Interpreting the optimal r

    The above shows decision variable values for the optimal solution. The Gradient is the change in the objective function for a unit increase in the decision variable. Here a negative gradient for decision variable \(X1\), the groundwater withdrawal, means that increasing the groundwater withdrawal will have a negative effect on the objective function, (to minimize \(X2\)): that is intuitive, since increasing groundwater withdrawal can reduce reservoir supply on a one-to-one basis.

    To look at which constraints are binding, retrieve the $duals part of the output.

    -
    m <- length(get.constraints(example.lp)) #number of constraints
-duals <- get.sensitivity.rhs(example.lp)$duals[1:m]
-results2 <- data.frame(constraint=c(seq(1:m)),multiplier=duals)
-kbl(results2, booktabs = TRUE) %>% kable_styling(full_width = F)
    +
    m <- length(get.constraints(example.lp)) #number of constraints
+duals <- get.sensitivity.rhs(example.lp)$duals[1:m]
+results2 <- data.frame(constraint=c(seq(1:m)),multiplier=duals)
+kbl(results2, booktabs = TRUE) %>% kable_styling(full_width = F)
    @@ -686,22 +687,22 @@

    12.2.1 Problem summary

    12.2.2 Setting up the problem in R

    Create variables for the known or assumed initial values for the system.

    -
    penstock_cap <- 160        #penstock capacity in million m3/month
-res_cap <- 550             #reservoir capacity in million m3
-res_init_vol <- res_cap/2  #set initial reservoir capacity equal to half of capacity
-irrig_dem <- c(0,0,0,0,40,130,230,250,180,110,0,0)
-revenue_water <- 800       #revenue for delivered irrigation water, $/million m3
-revenue_power <- 350       #revenue for power generated, $/million m3
    +
    penstock_cap <- 160        #penstock capacity in million m3/month
+res_cap <- 550             #reservoir capacity in million m3
+res_init_vol <- res_cap/2  #set initial reservoir capacity equal to half of capacity
+irrig_dem <- c(0,0,0,0,40,130,230,250,180,110,0,0)
+revenue_water <- 800       #revenue for delivered irrigation water, $/million m3
+revenue_power <- 350       #revenue for power generated, $/million m3

    A time series of 20 years (January 2000 through December 2019) of monthly flows for this exercise is included with the hydromisc package. Load that and extract the first 12 months to use in this example.

    -
    inflows_20years <- hydromisc::inflows_20years
-inflows <- as.numeric(window(inflows_20years, start = c(2000, 1), end = c(2000, 12)))
    +
    inflows_20years <- hydromisc::inflows_20years
+inflows <- as.numeric(window(inflows_20years, start = c(2000, 1), end = c(2000, 12)))

    It helps to illustrate how the irrigation demands and inflows vary, and therefore why storage might be useful in regulating flow to provide more reliable irrigation deliveries.

    -
    par(mgp=c(2,1,0))
-ylbl <- expression(10 ^6 ~ m ^3/month)
-plot(inflows, type="l", col="blue", xlab="Month", ylab=ylbl)
-lines(irrig_dem, col="darkgreen", lty=2)
-legend("topright",c("Inflows","Irrigation Demand"),lty = c(1,2), col=c("blue","darkgreen"))
-grid()
    +
    par(mgp=c(2,1,0))
+ylbl <- expression(10 ^6 ~ m ^3/month)
+plot(inflows, type="l", col="blue", xlab="Month", ylab=ylbl)
+lines(irrig_dem, col="darkgreen", lty=2)
+legend("topright",c("Inflows","Irrigation Demand"),lty = c(1,2), col=c("blue","darkgreen"))
+grid()
    Inflows and irrigation demand.

    @@ -712,9 +713,9 @@

    12.2.2 Setting up the problem in

    12.2.3 Building the linear model

    Following the same steps as for a simple 2-variable problem, begin by setting up a linear model. Because there are so many decision variables, it helps to add names to them.

    -
    reser.lp <- make.lp(0,48)
-DecisionVarNames <- c(paste0("s",1:12),paste0("r",1:12),paste0("h",1:12),paste0("d",1:12))
-colnames(reser.lp) <- DecisionVarNames
    +
    reser.lp <- make.lp(0,48)
+DecisionVarNames <- c(paste0("s",1:12),paste0("r",1:12),paste0("h",1:12),paste0("d",1:12))
+colnames(reser.lp) <- DecisionVarNames
    From this point on, the decision variables will be addressed by their indices, that is, their numeric position in this sequence of 48 values. To summarize their positions:

    @@ -763,53 +764,53 @@

    12.2.3 Building the linear model<

    Using these indices as a guide, set up the objective function and initialize the linear model. While not necessary, redirecting the output of the lp.control to a variable prevents a lot of output to the console. The following takes the revenue from hydropower and irrigation (in $ per 10\(^6\)m\(^3\)/month), multiplies them by the 12 monthly values for the hydropower flows and the irrigation deliveries, and sets the objective to maximize their sum, as described by Equation (12.1).

    -
    set.objfn(reser.lp,c(rep(revenue_power,12),rep(revenue_water,12)),indices = c(25:48))
-x <- lp.control(reser.lp, sense="max")
    +
    set.objfn(reser.lp,c(rep(revenue_power,12),rep(revenue_water,12)),indices = c(25:48))
+x <- lp.control(reser.lp, sense="max")

    With the LP setup, the constraints need to be applied. Negative releases, storage, or river flows don’t make sense, so they all need to be positive, so \(s_t\ge0\), \(r_t\ge0\), \(h_t\ge0\) for all 12 months, but because the lpSolveAPI package assumes all decision variables have a range of \(0\le x\le \infty\) these do not need to be explicitly added as constraints. When using other software packages these may need to be included.

    12.2.3.1 Constraints 1-12: Maximum storage

    The maximum capacity of the reservoir cannot be exceeded in any month, or \(s_t\le 600\) for all 12 months. This can be added in a simple loop:

    -
    for (i in seq(1,12)) {
-  add.constraint(reser.lp, xt=c(1), type="<=", rhs=res_cap, indices=c(i))
-}
    +
    for (i in seq(1,12)) {
+  add.constraint(reser.lp, xt=c(1), type="<=", rhs=res_cap, indices=c(i))
+}

    12.2.3.2 Constraints 13-24: Irrigation diversions

    The irrigation diversions should never exceed the demand. While for some months they are set to zero, since decision variables are all assumed non-negative, we can just assign all irrigation deliveries using the \(\le\) operator.

    -
    for (i in seq(1,12)) {
-  add.constraint(reser.lp, xt=c(1), type="<=", rhs=irrig_dem[i], indices=c(i+36))
-}
    +
    for (i in seq(1,12)) {
+  add.constraint(reser.lp, xt=c(1), type="<=", rhs=irrig_dem[i], indices=c(i+36))
+}

    12.2.3.3 Constraints 25-36: Hydropower

    Hydropower release cannot exceed the penstock capacity in any month: \(h_t\le 180\) for all 12 months. This can be done following the example above for the maximum storage constraint

    -
    for (i in seq(1,12)) {
-  add.constraint(reser.lp, xt=c(1), type="<=", rhs=penstock_cap, indices=c(i+24))
-}
    +
    for (i in seq(1,12)) {
+  add.constraint(reser.lp, xt=c(1), type="<=", rhs=penstock_cap, indices=c(i+24))
+}

    12.2.3.4 Constraints 37-48: Reservoir release

    Reservoir release must equal or exceed irrigation deliveries, which is another way of saying that the water remaining in the river after the diversion cannot be negative. In other words \(r_1-d_1\ge 0\), \(r_2-d_2\ge 0\), … for all 12 months. For constraints involving more than one decision variable the constraint equations look a little different, and keeping track of the indices associated with each decision variable is essential.

    -
    for (i in seq(1,12)) {
-  add.constraint(reser.lp, xt=c(1,-1), type=">=", rhs=0, indices=c(i+12,i+36))
-}
    +
    for (i in seq(1,12)) {
+  add.constraint(reser.lp, xt=c(1,-1), type=">=", rhs=0, indices=c(i+12,i+36))
+}

    12.2.3.5 Constraints 49-60: Hydropower

    Hydropower generation will be less than or equal to reservoir release in every month, or \(r_1-h_1\ge 0\), \(r_2-h_2\ge 0\), … for all 12 months.

    -
    for (i in seq(1,12)) {
-  add.constraint(reser.lp, xt=c(1,-1), type=">=", rhs=0, indices=c(i+12,i+24))
-}
    +
    for (i in seq(1,12)) {
+  add.constraint(reser.lp, xt=c(1,-1), type=">=", rhs=0, indices=c(i+12,i+24))
+}

    12.2.3.6 Constraints 61-72: Conservation of mass

    Finally, considering the reservoir, the inflow minus the outflow in any month must equal the change in storage over that month. That can be expressed in an equation with decision variables on the left side as: \[s_t-s_{t-1}+r_t=inflow_t\] where \(t\) is a month from 1-12 and \(s_t\) is the storage at the end of month \(t\). We need to use the initial reservoir volume, \(s_0\) (given above in the problem statement) for the first month’s mass balance, so the above would become \(s_1-s_0+r_1=inflow_1\), or \(s_1+r_1=inflow_1+s_0\). All subsequent months can be assigned in a loop,

    -
    add.constraint(reser.lp, xt=c(1,1), type="=", rhs=inflows[1]+res_init_vol, indices=c(1,13))
-for (i in seq(2,12)) {
-  add.constraint(reser.lp, xt=c(1,-1,1), type="=", rhs=inflows[i], indices=c(i,i-1,i+12))
-}
    +
    add.constraint(reser.lp, xt=c(1,1), type="=", rhs=inflows[1]+res_init_vol, indices=c(1,13))
+for (i in seq(2,12)) {
+  add.constraint(reser.lp, xt=c(1,-1,1), type="=", rhs=inflows[i], indices=c(i,i-1,i+12))
+}
    This completes the LP model setup. Especially for larger models, it is helpful to save the model. You can use something like write.lp(reser.lp, "reservoir_LP.txt", "lp") to create a file (readable using any text file viewer, like Notepad++) with all of the model details. It can also be read into R with the read.lp command to load the complete LP. The beginning of the file for this LP looks like:
    The top of the linear model file produced by write.lp(). @@ -821,18 +822,18 @@

    12.2.3.6 Constraints 61-72: Conse

    12.2.3.7 Solving the model and interpreting output

    Solve the LP and retrieve the value of the objective function.

    -
    solve(reser.lp)
-#> [1] 0
-get.objective(reser.lp)
-#> [1] 1230930
    +
    solve(reser.lp)
+#> [1] 0
+get.objective(reser.lp)
+#> [1] 1230930

    To look at the hydropower generation, and to see how often spill occurs, it helps to view the associated decision variables (as noted above, these are indices 12-24 and 25-36).

    -
    vars <- get.variables(reser.lp)       # retrieve decision variable values
-results0 <- data.frame(variable=DecisionVarNames,value=vars)
-r0 <- cbind(results0[13:24, ], results0[25:36, ])
-rownames(r0) <- c()
-names(r0) <- c("Decision Variable","Value","Decision Variable","Value")
-kbl(r0, booktabs = TRUE) %>% 
-  kable_styling(bootstrap_options = c("striped","condensed"),full_width = F)
    +
    vars <- get.variables(reser.lp)       # retrieve decision variable values
+results0 <- data.frame(variable=DecisionVarNames,value=vars)
+r0 <- cbind(results0[13:24, ], results0[25:36, ])
+rownames(r0) <- c()
+names(r0) <- c("Decision Variable","Value","Decision Variable","Value")
+kbl(r0, booktabs = TRUE) %>% 
+  kable_styling(bootstrap_options = c("striped","condensed"),full_width = F)
    @@ -1180,11 +1181,11 @@

    12.2.3.7 Solving the model and in

    August and September see a shortfall in irrigation deliveries where full demand is not met.

    Finally, finding which constraints are binding can provide insights into how a system might be modified to improve the optimal solution. This is done similarly to the simpler problem above, by retrieving the duals portion of the sensitivity results. To address the question of whether the size of the reservoir is a binding constraint, that is, whether increasing reservoir size would improve the optimal results, only the first 12 constraints are printed.

    -
    m <- length(get.constraints(reser.lp))             # retrieve the number of constraints
-duals <- get.sensitivity.rhs(reser.lp)$duals[1:m]
-results2 <- data.frame(Constraint=c(seq(1:m)),Multiplier=duals)
-kbl(results2[1:12,], booktabs = TRUE) %>% 
-  kable_styling(bootstrap_options = c("striped","condensed"),full_width = F)
    +
    m <- length(get.constraints(reser.lp))             # retrieve the number of constraints
+duals <- get.sensitivity.rhs(reser.lp)$duals[1:m]
+results2 <- data.frame(Constraint=c(seq(1:m)),Multiplier=duals)
+kbl(results2[1:12,], booktabs = TRUE) %>% 
+  kable_styling(bootstrap_options = c("striped","condensed"),full_width = F)
    @@ -1322,33 +1323,33 @@

    12.3 More Realistic Reservoir Ope

    12.3.1 Reservoir operation

    While SDP is a topic that is far more advanced than what will be covered here, one R package will be introduced. For reservoir optimization, the R package reservoir can use SDP to derive an optimal operating rule for a reservoir given a sequence of inflows using a single or multiple constraints. The package can also take any derived rule curve and operate a reservoir using it, which is what will be demonstrated here.

    First, place the optimal releases, according to the LP above, into a new vector to be used as a set of target releases for the reservoir operation.

    -
    target_release <- results0[13:24, ]$value
    +
    target_release <- results0[13:24, ]$value

    The reservoir can be operated (for the same 12-month period, with the same 12 inflows as above) with a single command.

    -
    x <- reservoir::simRes(inflows, target_release, res_cap, plot = F)
    +
    x <- reservoir::simRes(inflows, target_release, res_cap, plot = F)

    The total revenue from hydropower generation and irrigation deliveries is computed as follows.

    -
    irrig_releases <- pmin(x$releases,irrig_dem)
-irrig_benefits <- sum(irrig_releases*revenue_water)
-hydro_releases <- pmin(x$releases,penstock_cap)
-hydro_benefits <- hydro_releases*revenue_power
-sum(irrig_benefits,hydro_benefits)
-#> [1] 1230930
    +
    irrig_releases <- pmin(x$releases,irrig_dem)
+irrig_benefits <- sum(irrig_releases*revenue_water)
+hydro_releases <- pmin(x$releases,penstock_cap)
+hydro_benefits <- hydro_releases*revenue_power
+sum(irrig_benefits,hydro_benefits)
+#> [1] 1230930

    Unsurprisingly, this produces the same result as with the LP example.

    12.3.2 Performing stochastic dynamic programming

    The optimal releases, or target releases, were established based on a single year. the SDP in the reservoir package can be used to determine optimal releases based on a time series of inflows. Here the entire 20-year inflow sequence is used to generate a multiobjective optimal solution for the system. A weighting must be applied to describe the importance of meeting different parts of the objective function. The target release(s) cannot be zero, so a small constant is added.

    -
    weight_water <- revenue_water/(revenue_water + revenue_power)
-weight_power <- revenue_power/(revenue_water + revenue_power)
-z <- reservoir::sdp_multi(inflows_20years, cap=res_cap, target = irrig_dem+0.01, 
-                          R_max = penstock_cap, spill_targ = 0.95, 
-                          weights = c(weight_water, weight_power, 0.00), 
-                          loss_exp = c(1, 1, 1), tol=0.99, S_initial=0.5, plot=FALSE)
    -
    irrig_releases2 <- pmin(z$releases,irrig_dem)
-irrig_benefits2 <- sum(irrig_releases2*revenue_water)
-hydro_releases2 <- pmin(z$releases,penstock_cap)
-hydro_benefits2 <- hydro_releases2*revenue_power
-sum(irrig_benefits2,hydro_benefits2)/20
-#> [1] 911240
    +
    weight_water <- revenue_water/(revenue_water + revenue_power)
+weight_power <- revenue_power/(revenue_water + revenue_power)
+z <- reservoir::sdp_multi(inflows_20years, cap=res_cap, target = irrig_dem+0.01, 
+                          R_max = penstock_cap, spill_targ = 0.95, 
+                          weights = c(weight_water, weight_power, 0.00), 
+                          loss_exp = c(1, 1, 1), tol=0.99, S_initial=0.5, plot=FALSE)
    +
    irrig_releases2 <- pmin(z$releases,irrig_dem)
+irrig_benefits2 <- sum(irrig_releases2*revenue_water)
+hydro_releases2 <- pmin(z$releases,penstock_cap)
+hydro_benefits2 <- hydro_releases2*revenue_power
+sum(irrig_benefits2,hydro_benefits2)/20
+#> [1] 911240

    For a 20-year period, the average annual revenue will always be less than that for a single year where the optimal releases are designed based on that same year.

    diff --git a/docs/momentum-in-water-flow.html b/docs/momentum-in-water-flow.html index e55be43..3d29529 100644 --- a/docs/momentum-in-water-flow.html +++ b/docs/momentum-in-water-flow.html @@ -25,7 +25,7 @@ - + @@ -210,6 +210,7 @@
  • 4.7 Parallel pipes: solving a system of equations
  • 4.8 Simple pipe networks: the Hardy-Cross method
    • @@ -400,24 +401,24 @@

    6.2 The momentum equation in pipe \tag{6.4} \end{equation}\]

    This can be set up in R in many ways, such as the following.

    -
    #Input Data -- ensure units are consistent in ft, lbf (pound force), sec
-D1 <- units::set_units(18/12, ft)
-D2 <- units::set_units(18/12, ft)
-P1 <- units::set_units(60*144, lbf/ft^2) #convert psi to lbf/ft^2
-P2 <- units::set_units(60*144, lbf/ft^2)
-theta <- 30*(pi/180) #convert to radians for sin, cos functions
-rho <- hydraulics::dens(T=68, units="Eng", ret_units = TRUE)
-
-# calculations - vary flow from 0 to 20 ft^3/s
-Q <- units::set_units(seq(0,20,1), ft^3/s)
-A1 <- pi/4*D1^2
-A2 <- pi/4*D2^2
-V1 <- Q/A1
-V2 <- Q/A2
-Rx <- P1*A1-P2*A2*cos(theta)-rho*Q*(V2*cos(theta)-V1)
-Ry <- P2*A2*sin(theta)-rho*Q*(-V2*sin(theta))
-R <- sqrt(Rx^2 + Ry^2)
-plot(Q,R)
    +
    #Input Data -- ensure units are consistent in ft, lbf (pound force), sec
+D1 <- units::set_units(18/12, ft)
+D2 <- units::set_units(18/12, ft)
+P1 <- units::set_units(60*144, lbf/ft^2) #convert psi to lbf/ft^2
+P2 <- units::set_units(60*144, lbf/ft^2)
+theta <- 30*(pi/180) #convert to radians for sin, cos functions
+rho <- hydraulics::dens(T=68, units="Eng", ret_units = TRUE)
+
+# calculations - vary flow from 0 to 20 ft^3/s
+Q <- units::set_units(seq(0,20,1), ft^3/s)
+A1 <- pi/4*D1^2
+A2 <- pi/4*D2^2
+V1 <- Q/A1
+V2 <- Q/A2
+Rx <- P1*A1-P2*A2*cos(theta)-rho*Q*(V2*cos(theta)-V1)
+Ry <- P2*A2*sin(theta)-rho*Q*(-V2*sin(theta))
+R <- sqrt(Rx^2 + Ry^2)
+plot(Q,R)

    When Q=0, only the pressure terms contribute to R. This plot shows that for typical water main conditions the change in direction of the velocity vectors adds a small amount (less than 3% in this example) to the calculated R value. This is why design guidelines for water mains often neglect the velocity term in Equation (6.2). In other industrial or laboratory conditions it may not be valid to neglect that term.

    diff --git a/docs/properties-of-water.html b/docs/properties-of-water.html index 9c0dc38..6f7f9ed 100644 --- a/docs/properties-of-water.html +++ b/docs/properties-of-water.html @@ -25,7 +25,7 @@ - + @@ -210,6 +210,7 @@
  • 4.7 Parallel pipes: solving a system of equations
  • 4.8 Simple pipe networks: the Hardy-Cross method
    • @@ -345,7 +346,7 @@

    Chapter 2 Properties of water (and air)

    -

    Fundamental properties of water allow the description of the forces it exerts and how it behaves while in motion. Many were listed in Chapter .

    +

    Fundamental properties of water allow the description of the forces it exerts and how it behaves while in motion. A table of these properties can be generated with the hydraulics package using a command like water_table(units = "SI").

    A summary of basic water properties, which vary with temperature, is shown in Table 2.1 for SI units and Table 2.2 for US (or Eng) units.

    @@ -453,25 +454,25 @@

    Chapter 2 Properties of water (an \(5\)

    @@ -482,22 +483,22 @@

    Chapter 2 Properties of water (an \(999.7\)

    @@ -508,22 +509,22 @@

    Chapter 2 Properties of water (an \(999.1\)

    @@ -534,22 +535,22 @@

    Chapter 2 Properties of water (an \(998.2\)

    @@ -560,22 +561,22 @@

    Chapter 2 Properties of water (an \(997.1\)

    @@ -586,22 +587,22 @@

    Chapter 2 Properties of water (an \(995.7\)

    @@ -612,22 +613,22 @@

    Chapter 2 Properties of water (an \(994.1\)

    @@ -638,22 +639,22 @@

    Chapter 2 Properties of water (an \(992.2\)

    @@ -664,22 +665,22 @@

    Chapter 2 Properties of water (an \(990.2\)

    @@ -690,22 +691,22 @@

    Chapter 2 Properties of water (an \(988.1\)

    @@ -716,22 +717,22 @@

    Chapter 2 Properties of water (an \(985.7\)

    @@ -742,22 +743,22 @@

    Chapter 2 Properties of water (an \(983.2\)

    @@ -768,22 +769,22 @@

    Chapter 2 Properties of water (an \(980.6\)

    @@ -794,22 +795,22 @@

    Chapter 2 Properties of water (an \(977.7\)

    @@ -820,22 +821,22 @@

    Chapter 2 Properties of water (an \(974.8\)

    @@ -846,22 +847,22 @@

    Chapter 2 Properties of water (an \(971.7\)

    @@ -872,22 +873,22 @@

    Chapter 2 Properties of water (an \(968.5\)

    @@ -898,22 +899,22 @@

    Chapter 2 Properties of water (an \(965.2\)

    @@ -924,22 +925,22 @@

    Chapter 2 Properties of water (an \(961.7\)

    @@ -950,22 +951,22 @@

    Chapter 2 Properties of water (an \(958.1\)

    @@ -1050,25 +1051,25 @@

    Chapter 2 Properties of water (an \(32\)

    @@ -1076,25 +1077,25 @@

    Chapter 2 Properties of water (an \(42\)

    @@ -1102,25 +1103,25 @@

    Chapter 2 Properties of water (an \(52\)

    @@ -1128,25 +1129,25 @@

    Chapter 2 Properties of water (an \(62\)

    @@ -1154,25 +1155,25 @@

    Chapter 2 Properties of water (an \(72\)

    @@ -1180,25 +1181,25 @@

    Chapter 2 Properties of water (an \(82\)

    @@ -1206,25 +1207,25 @@

    Chapter 2 Properties of water (an \(92\)

    @@ -1232,25 +1233,25 @@

    Chapter 2 Properties of water (an \(102\)

    @@ -1258,25 +1259,25 @@

    Chapter 2 Properties of water (an \(112\)

    @@ -1284,25 +1285,25 @@

    Chapter 2 Properties of water (an \(122\)

    @@ -1310,25 +1311,25 @@

    Chapter 2 Properties of water (an \(132\)

    @@ -1336,25 +1337,25 @@

    Chapter 2 Properties of water (an \(142\)

    @@ -1362,25 +1363,25 @@

    Chapter 2 Properties of water (an \(152\)

    @@ -1388,25 +1389,25 @@

    Chapter 2 Properties of water (an \(162\)

    @@ -1414,25 +1415,25 @@

    Chapter 2 Properties of water (an \(172\)

    @@ -1440,25 +1441,25 @@

    Chapter 2 Properties of water (an \(182\)

    @@ -1466,25 +1467,25 @@

    Chapter 2 Properties of water (an \(192\)

    @@ -1492,25 +1493,25 @@

    Chapter 2 Properties of water (an \(202\)

    @@ -1518,25 +1519,25 @@

    Chapter 2 Properties of water (an \(212\)

    diff --git a/docs/pumps-and-how-they-operate-in-a-hydraulic-system.html b/docs/pumps-and-how-they-operate-in-a-hydraulic-system.html index b646d2a..8027ed8 100644 --- a/docs/pumps-and-how-they-operate-in-a-hydraulic-system.html +++ b/docs/pumps-and-how-they-operate-in-a-hydraulic-system.html @@ -25,7 +25,7 @@ - + @@ -210,6 +210,7 @@
  • 4.7 Parallel pipes: solving a system of equations
  • 4.8 Simple pipe networks: the Hardy-Cross method
    • @@ -373,12 +374,12 @@

    7.1 Defining the system curve

    Example 7.1 Develop a system curve for a pipe with a diameter of 20 inches, length of 3884 ft, and absolute roughness of 0.0005 ft. Use kinematic viscocity, \(\nu\) = 1.23 x 10-5 ft2/s. Assume a static head, z2 - z1 = 30 ft.

    -
    ans <- hydraulics::darcyweisbach(Q = 1,D = 20/12, L = 3884, ks = 0.0005, nu = 1.23e-5, units = "Eng")
-cat(sprintf("Coefficient K: %.3f\n", ans$hf))
-#> Coefficient K: 0.160
-scurve <- hydraulics::systemcurve(hs = 30, K = ans$hf, units = "Eng")
-print(scurve$eqn)
-#> [1] "h == 30 + 0.16*Q^2"
    +
    ans <- hydraulics::darcyweisbach(Q = 1,D = 20/12, L = 3884, ks = 0.0005, nu = 1.23e-5, units = "Eng")
+cat(sprintf("Coefficient K: %.3f\n", ans$hf))
+#> Coefficient K: 0.160
+scurve <- hydraulics::systemcurve(hs = 30, K = ans$hf, units = "Eng")
+print(scurve$eqn)
+#> [1] "h == 30 + 0.16*Q^2"

    For this function of the hydraulics package, Q is either in ft\(^3\)/s or m\(^3\)/s, depending on whether Eng or SI is specified for units. Often data for flows in pumping systems are in other units such as gpm or liters/s, so unit conversions would need to be applied.

    @@ -420,16 +421,16 @@

    7.2 Defining the pump characteris

    Example 7.2 Develop a pump characteristic curve for the pump in Figure 7.2, using the three points marked in red. Use the poly2 form from Table 7.1.

    -
    qgpm <- units::set_units(c(0, 5000, 7850), gallons/minute)
-#Convert units to those needed for package, and consistent with system curve
-qcfs <- units::set_units(qgpm, ft^3/s)
-#Head units, read from the plot, are already in ft so setting units is not needed
-hft <- c(81, 60, 20) 
-pcurve <- hydraulics::pumpcurve(Q = qcfs, h = hft, eq = "poly2", units = "Eng")
-print(pcurve$eqn)
-#> [1] "h == 82.5 - 0.201*Q^2"
    +
    qgpm <- units::set_units(c(0, 5000, 7850), gallons/minute)
+#Convert units to those needed for package, and consistent with system curve
+qcfs <- units::set_units(qgpm, ft^3/s)
+#Head units, read from the plot, are already in ft so setting units is not needed
+hft <- c(81, 60, 20) 
+pcurve <- hydraulics::pumpcurve(Q = qcfs, h = hft, eq = "poly2", units = "Eng")
+print(pcurve$eqn)
+#> [1] "h == 82.5 - 0.201*Q^2"

    The function pumpcurve returns a pumpcurve object that includes the polynomial fit equation and a simple plot to check the fit. This can be plotted as in Figure 7.3

    -
    pcurve$p
    +
    pcurve$p
    A pump characteristic curve

    @@ -443,11 +444,11 @@

    7.3 Finding the operating point

    Example 7.3 Find the operating point for the pump and system curves developed in Examples 7.1 and 7.2.

    -
    oppt <- hydraulics::operpoint(pcurve = pcurve, scurve = scurve)
-cat(sprintf("Operating Point: Q = %.3f, h = %.3f\n", oppt$Qop, oppt$hop))
-#> Operating Point: Q = 12.051, h = 53.285
    +
    oppt <- hydraulics::operpoint(pcurve = pcurve, scurve = scurve)
+cat(sprintf("Operating Point: Q = %.3f, h = %.3f\n", oppt$Qop, oppt$hop))
+#> Operating Point: Q = 12.051, h = 53.285

    The function operpoint function returns an operpoint object that includes the a plot of both curves. This can be plotted as in Figure 7.4

    -
    oppt$p
    +
    oppt$p
    The pump operating point

    diff --git a/docs/reference-keys.txt b/docs/reference-keys.txt index c66b103..e1f0605 100644 --- a/docs/reference-keys.txt +++ b/docs/reference-keys.txt @@ -93,8 +93,10 @@ eq:open-rect2 eq:open-rect3 exm:open-rectx fig:rectchannelfig +fig:sp-openrect eq:open-rect-sp1 eq:open-rect-sp2 +fig:sp-openrect2 fig:gvfcurves fig:gvfcurves2 fig:gvffig1 @@ -239,6 +241,7 @@ solving-for-q-using-an-r-package solving-for-pipe-diameter-d-type-3-problems solving-for-d-using-manual-iterations solving-for-d-using-an-equation-solver +solving-for-d-using-an-r-package parallel-pipes-solving-a-system-of-equations simple-pipe-networks-the-hardy-cross-method flow-in-open-channels diff --git a/docs/references.html b/docs/references.html index 1697978..799d5fb 100644 --- a/docs/references.html +++ b/docs/references.html @@ -25,7 +25,7 @@ - + @@ -210,6 +210,7 @@

  • 4.7 Parallel pipes: solving a system of equations
  • 4.8 Simple pipe networks: the Hardy-Cross method
    • @@ -353,6 +354,9 @@

    References Astagneau, P. C., Thirel, G., Delaigue, O., Guillaume, J. H. A., Parajka, J., Brauer, C. C., et al. (2021). Technical note: Hydrology modelling R packages – a unified analysis of models and practicalities from a user perspective. Hydrology and Earth System Sciences, 25(7), 3937–3973. https://doi.org/10.5194/hess-25-3937-2021

    +
    +Camp, T. R. (1946). Design of sewers to facilitate flow. Sewage Works Journal, 18, 3–16. +
    Davidian, Jacob. (1984). Computation of water-surface profiles in open channels (No. Techniques of Water-Resources Investigations, Book 3, Chapter A15). https://doi.org/10.3133/twri03A15
    @@ -362,8 +366,8 @@

    References England, J.F., Cohn, T.A., Faber, B.A., Stedinger, J.R., Thomas, W.O., Veilleux, A.G., et al. (2019). Guidelines for Determining Flood Flow Frequency Bulletin 17C (Techniques and {Methods}) (p. 148). Reston, Virginia: U.S. Department of the Interior, U.S. Geological Survey. Retrieved from https://doi.org/10.3133/tm4B5

    -
    -Finnemore, E. J., & Maurer, E. (Eds.). (2023). Fluid mechanics with civil engineering applications (11th ed.). New York: McGraw Hill. +
    +Finnemore, E. J., & Maurer, E. (2024). Fluid mechanics with civil engineering applications (Eleventh edition). New York: McGraw-Hill.
    Fox-Kemper, B., Hewitt, H., Xiao, C., Aðalgeirsdóttir, G., Drijfhout, S., Edwards, T., et al. (2021). Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The physical science basis. Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change. Masson-, V. Delmotte, P. Zhai, A. Pirani, SL Connors, C. Péan, S. Berger, et …. @@ -411,7 +415,7 @@

    ReferencesEquations for Pipe-Flow Problems. Journal of the Hydraulics Division, 102(5), 657–664. https://doi.org/10.1061/JYCEAJ.0004542

    -Tebaldi, C., Strauss, B. H., & Zervas, C. E. (2012). Modelling sea level rise impacts on storm surges along US coasts. Environmental Research Letters, 7(1), 014032. +Tebaldi, C., Strauss, B. H., & Zervas, C. E. (2012). Modelling sea level rise impacts on storm surges along US coasts. Environmental Research Letters, 7(1), 014032. https://doi.org/10.1088/1748-9326/7/1/014032
    Wurbs, R. A., & James, W. P. (2002). Water resources engineering. Upper Saddle River, NJ: Prentice Hall. diff --git a/docs/search_index.json b/docs/search_index.json index 94e12b3..fd68da4 100644 --- a/docs/search_index.json +++ b/docs/search_index.json @@ -1 +1 @@ -[["index.html", "Hydraulics and Water Resources: Examples Using R Preface Introduction to R and RStudio Citing this reference Copyright", " Hydraulics and Water Resources: Examples Using R Ed Maurer Professor, Civil, Environmental, and Sustainable Engineering Department, Santa Clara University 2023-12-28 Preface This is a compilation of various R exercises and examples created over many years. They have been used mostly in undergraduate civil engineering classes including fluid mechanics, hydraulics, and water resources. This is a dynamic work, and will be regularly updated as errors are identified, improved presentation is developed, or new topics or examples are introduced. I welcome any suggestions or comments. In what follows, text will be intentionally brief. More extensive discussion and description can be found in any fluid mechanics, applied hydraulics, or water resources engineering text. Symbology for hydraulics in this reference generally follows that of Finnemore and Maurer (2023). Fundamental equations will be introduced though the emphasis will be on applications to solve common problems. Also, since this is written by a civil engineer, the only fluids included are water and air, since that accounts for nearly all problems encountered in the field. Solving water problems is rarely done by hand calculations, though the importance of performing order of magnitude ‘back of the envelope’ calculations cannot be overstated. Whether using a hand calculator, spreadsheet, or a programming language to produce a solution, having a sense of when an answer is an outlier will help catch errors. Scripting languages are powerful tools for performing calculations, providing a fully traceable and reproducible path from your input to a solution. Open source languages have the benefit of being free to use, and invite users to be part of a community helping improve the language and its capabilities. The language of choice for this book is R (R Core Team, 2022), chosen for its straightforward syntax, powerful graphical capabilities, wide use in engineering and in many other disciplines, and by using the RStudio interface, it can look and feel a lot like Matlab® with which most engineering students have some experience. Introduction to R and RStudio No introduction to R or RStudio is provided here. It is assumed that the reader has installed R (and RStudio), is comfortable installing and updating packages, and understands the basics of R scripting. Some resources that can provide an introduction to R include: A brief overview, aimed at students at Santa Clara University. An Introduction to R, a comprehensive reference by the R Core Team. Introduction to Programming with R by Stauffer et al., materials for a university course, including interactive exercises. R for Water Resources Data Science, with both introductory and intermediate level courses online. As I developed these exercises and text, I learned R through the work of many others, and the excellent help offered by skilled people sharing their knowledge on stackoverflow. The methods shown here are not the only ways to solve these problems, and users are invited to share alternative or better solutions. Citing this reference Maurer, Ed, 2023. Hydraulics and Water Resources: Examples Using R, doi:10.5281/zenodo.7576843 https://edm44.github.io/hydr-watres-book/. Copyright This work is provided under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). As a summary, this license allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator. This is a summary of (and not a substitute for) the license. "],["units-in-fluid-mechanics.html", "Chapter 1 Units in Fluid Mechanics", " Chapter 1 Units in Fluid Mechanics Before beginning with problem solving methods it helps to recall some important quantities in fluid mechanics and their associated units. While the world has generally moved forward into standardizing the use of the SI unit system, the U.S. stubbornly holds onto the antiquated US (sometimes called the British Gravitational, BG) system. This means practicing engineers must be familiar with both systems, and be able to convert between the two systems. These important quantities are shown in Table 1.1. Table 1.1: Dimensions and units for common quantities. Quantity Symbol Dimensions US (or BG) Units SI Units US to SI multiply by Length L \\(L\\) \\(ft\\) \\(m\\) 0.3048 Acceleration a \\(LT^{-2}\\) \\(ft/s^2\\) \\(m/s^{2}\\) 0.3048 Mass m \\(M\\) \\(slug\\) \\(kg\\) 14.59 Force F \\(F\\) \\(lb\\) \\(N\\) 4.448 Density \\(\\rho\\) \\(ML^{-3}\\) \\(slug/ft^3\\) \\(kg/m^3\\) 515.4 Energy/Work FL \\({ft}\\cdot{lb}\\) \\({N}\\cdot{m}=joule (J)\\) 1.356 Flowrate Q \\(L^{3}/T\\) \\(ft^{3}/s\\)=cfs \\(m^{3}/s\\) 0.02832 Kinematic viscocity \\(\\nu\\) \\(L^{2}/T\\) \\(ft^{2}/s\\) \\(m^{2}/s\\) 0.0929 Power \\(FLT^{-1}\\) \\({ft}\\cdot{lb/s}\\) \\({N}\\cdot{m/s}=watt (W)\\) 1.356 Pressure p \\(FL^{-2}\\) \\(lb/in^2=psi\\) \\(N/m^2=Pa\\) 6895 Specific Weight \\(\\gamma\\) \\(FL^{-3}\\) \\(lb/ft^3\\) \\(N/m^3\\) 157.1 Velocity V \\(LT^{-1}\\) \\(ft/s\\) \\(m/s\\) 0.3048 (Dynamic) Viscocity \\(\\mu\\) \\(FTL^{-2}\\) \\({lb}\\cdot{s/ft^2}\\) \\({N}\\cdot{s/m^2}={Pa}\\cdot{s}\\) 47.88 Volume \\(\\forall\\) \\(L^3\\) \\(ft^3\\) \\(m^3\\) 0.02832 There are many other units that must be accommodated. For example, one may encounter the poise to describe (dynamic) viscosity (\\(1~Pa*s = 10~poise\\)), or the stoke for kinematic viscocity (\\(1~m^2/s=10^4~stokes\\)). Many hydraulic systems use gallons per minute (gpm) as a unit of flow (\\(1~ft^3/s=448.8~gpm\\)), and larger water systems often use millions of gallons per day (mgd) (\\(1~mgd = 1.547~ft^3/s\\)). For volume, the SI system often uses liters (\\(l\\)) instead of \\(m^3\\) (\\(1~m^3=1000~l\\)). One regular conversion that needs to occur is the translation between mass (m) and weight (W), where \\(W=mg\\), where \\(g\\) is gravitational acceleration on the earth’s surface: \\(g=9.81~m/s^2=32.2~ft/s^2\\). When working with forces (such as with momentum problems or hydrostatic forces) be sure to work with weights/forces, not mass. It is straightforward to use conversion factors in the table to manipulate values between the systems, multiplying by the factor shown to go from US to SI units, or dividing to do the \\[{1*10^{-6}~m^2/s}*\\frac{1 ~ft^2/s}{0.0929~m^2/s}=1.076*10^{-5} ~ft^2/s\\] Another example converts between two quantities in the US system: 100 gallons per minute to cfs: \\[{100 ~gpm}*\\frac{1 ~cfs}{448.8 ~gpm}=0.223 ~cfs\\] The units package in R can do these conversions and more, and also checks that conversions are permissible (producing an error if incompatible units are used). units::units_options(set_units_mode = "symbols") Q_gpm <- units::set_units(100, gallon/min) Q_gpm #> 100 [gallon/min] Q_cfs <- units::set_units(Q_gpm, ft^3/s) Q_cfs #> 0.2228009 [ft^3/s] Repeating the unit conversion of viscosity using the units package: Example 1.1 Convert kinematic viscosity from SI to Eng units. nu <- units::set_units(1e-6, m^2/s) nu #> 1e-06 [m^2/s] units::set_units(nu, ft^2/s) #> 1.076391e-05 [ft^2/s] The units package also produces correct units during operations. For example, multiplying mass by g should produce weight. Example 1.2 Using the units package to produce correct units during mathematical operations. #If you travel at 88 ft/sec for 1 hour, how many km would you travel? v <- units::set_units(88, ft/s) t <- units::set_units(1, hour) d <- v*t d #> 316800 [ft] units::set_units(d, km) #> 96.56064 [km] #What is the weight of a 4 slug mass, in pounds and Newtons? m <- units::set_units(4, slug) g <- units::set_units(32.2, ft/s^2) w <- m*g #Notice the units are technically correct, but have not been simplified in this case w #> 128.8 [ft*slug/s^2] #These can be set manually to verify that lbf (pound-force) is a valid equivalent units::set_units(w, lbf) #> 128.8 [lbf] units::set_units(w, N) #> 572.9308 [N] "],["properties-of-water.html", "Chapter 2 Properties of water (and air) 2.1 Properties important for water standing still 2.2 Properties important for moving water 2.3 Atmosperic Properties", " Chapter 2 Properties of water (and air) Fundamental properties of water allow the description of the forces it exerts and how it behaves while in motion. Many were listed in Chapter . A summary of basic water properties, which vary with temperature, is shown in Table 2.1 for SI units and Table 2.2 for US (or Eng) units. Table 2.1: Water properties in SI units Temp Density Spec_Weight Viscosity Kinem_Visc Sat_VP Surf_Tens Bulk_Mod C kg m-3 N m-3 N s m-2 m2 s-1 Pa N m-1 Pa \\(0\\) \\(999.9\\) \\({9.809}\\times 10^{3}\\) \\({1.734}\\times 10^{-3}\\) \\({1.734}\\times 10^{-6}\\) \\(611.2\\) \\({75.7}\\times 10^{-3}\\) \\({2.02}\\times 10^{9}\\) \\(5\\) \\(1000\\) \\({9.810}\\times 10^{3}\\) \\({1.501}\\times 10^{-3}\\) \\({1.501}\\times 10^{-6}\\) \\(872.6\\) \\({74.9}\\times 10^{-3}\\) \\({2.06}\\times 10^{9}\\) \\(10\\) \\(999.7\\) \\({9.807}\\times 10^{3}\\) \\({1.310}\\times 10^{-3}\\) \\({1.311}\\times 10^{-6}\\) \\({1.228}\\times 10^{3}\\) \\({74.2}\\times 10^{-3}\\) \\({2.10}\\times 10^{9}\\) \\(15\\) \\(999.1\\) \\({9.801}\\times 10^{3}\\) \\({1.153}\\times 10^{-3}\\) \\({1.154}\\times 10^{-6}\\) \\({1.706}\\times 10^{3}\\) \\({73.5}\\times 10^{-3}\\) \\({2.14}\\times 10^{9}\\) \\(20\\) \\(998.2\\) \\({9.793}\\times 10^{3}\\) \\({1.021}\\times 10^{-3}\\) \\({1.023}\\times 10^{-6}\\) \\({2.339}\\times 10^{3}\\) \\({72.7}\\times 10^{-3}\\) \\({2.18}\\times 10^{9}\\) \\(25\\) \\(997.1\\) \\({9.781}\\times 10^{3}\\) \\({910.8}\\times 10^{-6}\\) \\({913.5}\\times 10^{-9}\\) \\({3.170}\\times 10^{3}\\) \\({72.0}\\times 10^{-3}\\) \\({2.22}\\times 10^{9}\\) \\(30\\) \\(995.7\\) \\({9.768}\\times 10^{3}\\) \\({817.4}\\times 10^{-6}\\) \\({821.0}\\times 10^{-9}\\) \\({4.247}\\times 10^{3}\\) \\({71.2}\\times 10^{-3}\\) \\({2.25}\\times 10^{9}\\) \\(35\\) \\(994.1\\) \\({9.752}\\times 10^{3}\\) \\({738.0}\\times 10^{-6}\\) \\({742.4}\\times 10^{-9}\\) \\({5.629}\\times 10^{3}\\) \\({70.4}\\times 10^{-3}\\) \\({2.26}\\times 10^{9}\\) \\(40\\) \\(992.2\\) \\({9.734}\\times 10^{3}\\) \\({669.9}\\times 10^{-6}\\) \\({675.1}\\times 10^{-9}\\) \\({7.385}\\times 10^{3}\\) \\({69.6}\\times 10^{-3}\\) \\({2.28}\\times 10^{9}\\) \\(45\\) \\(990.2\\) \\({9.714}\\times 10^{3}\\) \\({611.2}\\times 10^{-6}\\) \\({617.3}\\times 10^{-9}\\) \\({9.595}\\times 10^{3}\\) \\({68.8}\\times 10^{-3}\\) \\({2.28}\\times 10^{9}\\) \\(50\\) \\(988.1\\) \\({9.693}\\times 10^{3}\\) \\({560.5}\\times 10^{-6}\\) \\({567.2}\\times 10^{-9}\\) \\({12.35}\\times 10^{3}\\) \\({67.9}\\times 10^{-3}\\) \\({2.29}\\times 10^{9}\\) \\(55\\) \\(985.7\\) \\({9.670}\\times 10^{3}\\) \\({516.2}\\times 10^{-6}\\) \\({523.7}\\times 10^{-9}\\) \\({15.76}\\times 10^{3}\\) \\({67.1}\\times 10^{-3}\\) \\({2.28}\\times 10^{9}\\) \\(60\\) \\(983.2\\) \\({9.645}\\times 10^{3}\\) \\({477.6}\\times 10^{-6}\\) \\({485.7}\\times 10^{-9}\\) \\({19.95}\\times 10^{3}\\) \\({66.2}\\times 10^{-3}\\) \\({2.28}\\times 10^{9}\\) \\(65\\) \\(980.6\\) \\({9.619}\\times 10^{3}\\) \\({443.5}\\times 10^{-6}\\) \\({452.3}\\times 10^{-9}\\) \\({25.04}\\times 10^{3}\\) \\({65.4}\\times 10^{-3}\\) \\({2.26}\\times 10^{9}\\) \\(70\\) \\(977.7\\) \\({9.592}\\times 10^{3}\\) \\({413.5}\\times 10^{-6}\\) \\({422.9}\\times 10^{-9}\\) \\({31.20}\\times 10^{3}\\) \\({64.5}\\times 10^{-3}\\) \\({2.25}\\times 10^{9}\\) \\(75\\) \\(974.8\\) \\({9.563}\\times 10^{3}\\) \\({386.9}\\times 10^{-6}\\) \\({396.9}\\times 10^{-9}\\) \\({38.60}\\times 10^{3}\\) \\({63.6}\\times 10^{-3}\\) \\({2.22}\\times 10^{9}\\) \\(80\\) \\(971.7\\) \\({9.533}\\times 10^{3}\\) \\({363.1}\\times 10^{-6}\\) \\({373.7}\\times 10^{-9}\\) \\({47.42}\\times 10^{3}\\) \\({62.7}\\times 10^{-3}\\) \\({2.20}\\times 10^{9}\\) \\(85\\) \\(968.5\\) \\({9.501}\\times 10^{3}\\) \\({341.9}\\times 10^{-6}\\) \\({353.0}\\times 10^{-9}\\) \\({57.87}\\times 10^{3}\\) \\({61.8}\\times 10^{-3}\\) \\({2.17}\\times 10^{9}\\) \\(90\\) \\(965.2\\) \\({9.468}\\times 10^{3}\\) \\({322.9}\\times 10^{-6}\\) \\({334.5}\\times 10^{-9}\\) \\({70.18}\\times 10^{3}\\) \\({60.8}\\times 10^{-3}\\) \\({2.14}\\times 10^{9}\\) \\(95\\) \\(961.7\\) \\({9.434}\\times 10^{3}\\) \\({305.7}\\times 10^{-6}\\) \\({317.9}\\times 10^{-9}\\) \\({84.61}\\times 10^{3}\\) \\({59.9}\\times 10^{-3}\\) \\({2.10}\\times 10^{9}\\) \\(100\\) \\(958.1\\) \\({9.399}\\times 10^{3}\\) \\({290.2}\\times 10^{-6}\\) \\({302.9}\\times 10^{-9}\\) \\({101.4}\\times 10^{3}\\) \\({58.9}\\times 10^{-3}\\) \\({2.07}\\times 10^{9}\\) Table 2.2: Water properties in US units Temp Density Spec_Weight Viscosity Kinem_Visc Sat_VP Surf_Tens Bulk_Mod F slug ft-3 lbf ft-3 lbf s ft-2 ft2 s-1 lbf ft-2 lbf ft-1 lbf ft-2 \\(32\\) \\(1.938\\) \\(62.42\\) \\({36.21}\\times 10^{-6}\\) \\({18.73}\\times 10^{-6}\\) \\(12.77\\) \\({5.18}\\times 10^{-3}\\) \\({42.2}\\times 10^{6}\\) \\(42\\) \\(1.939\\) \\(62.43\\) \\({30.87}\\times 10^{-6}\\) \\({15.96}\\times 10^{-6}\\) \\(18.94\\) \\({5.13}\\times 10^{-3}\\) \\({43.1}\\times 10^{6}\\) \\(52\\) \\(1.938\\) \\(62.40\\) \\({26.58}\\times 10^{-6}\\) \\({13.75}\\times 10^{-6}\\) \\(27.62\\) \\({5.07}\\times 10^{-3}\\) \\({44.0}\\times 10^{6}\\) \\(62\\) \\(1.937\\) \\(62.36\\) \\({23.11}\\times 10^{-6}\\) \\({11.96}\\times 10^{-6}\\) \\(39.64\\) \\({5.02}\\times 10^{-3}\\) \\({45.0}\\times 10^{6}\\) \\(72\\) \\(1.934\\) \\(62.29\\) \\({20.26}\\times 10^{-6}\\) \\({10.50}\\times 10^{-6}\\) \\(56.00\\) \\({4.96}\\times 10^{-3}\\) \\({45.9}\\times 10^{6}\\) \\(82\\) \\(1.932\\) \\(62.20\\) \\({17.90}\\times 10^{-6}\\) \\({9.290}\\times 10^{-6}\\) \\(77.99\\) \\({4.90}\\times 10^{-3}\\) \\({46.7}\\times 10^{6}\\) \\(92\\) \\(1.928\\) \\(62.09\\) \\({15.94}\\times 10^{-6}\\) \\({8.286}\\times 10^{-6}\\) \\(107.2\\) \\({4.84}\\times 10^{-3}\\) \\({47.2}\\times 10^{6}\\) \\(102\\) \\(1.925\\) \\(61.97\\) \\({14.29}\\times 10^{-6}\\) \\({7.443}\\times 10^{-6}\\) \\(145.3\\) \\({4.78}\\times 10^{-3}\\) \\({47.5}\\times 10^{6}\\) \\(112\\) \\(1.920\\) \\(61.83\\) \\({12.89}\\times 10^{-6}\\) \\({6.732}\\times 10^{-6}\\) \\(194.7\\) \\({4.72}\\times 10^{-3}\\) \\({47.7}\\times 10^{6}\\) \\(122\\) \\(1.916\\) \\(61.68\\) \\({11.71}\\times 10^{-6}\\) \\({6.126}\\times 10^{-6}\\) \\(258.0\\) \\({4.66}\\times 10^{-3}\\) \\({47.8}\\times 10^{6}\\) \\(132\\) \\(1.911\\) \\(61.52\\) \\({10.69}\\times 10^{-6}\\) \\({5.608}\\times 10^{-6}\\) \\(338.1\\) \\({4.59}\\times 10^{-3}\\) \\({47.7}\\times 10^{6}\\) \\(142\\) \\(1.905\\) \\(61.34\\) \\({9.808}\\times 10^{-6}\\) \\({5.162}\\times 10^{-6}\\) \\(438.5\\) \\({4.53}\\times 10^{-3}\\) \\({47.5}\\times 10^{6}\\) \\(152\\) \\(1.899\\) \\(61.16\\) \\({9.046}\\times 10^{-6}\\) \\({4.775}\\times 10^{-6}\\) \\(563.2\\) \\({4.46}\\times 10^{-3}\\) \\({47.2}\\times 10^{6}\\) \\(162\\) \\(1.893\\) \\(60.96\\) \\({8.381}\\times 10^{-6}\\) \\({4.438}\\times 10^{-6}\\) \\(716.9\\) \\({4.39}\\times 10^{-3}\\) \\({46.8}\\times 10^{6}\\) \\(172\\) \\(1.887\\) \\(60.75\\) \\({7.797}\\times 10^{-6}\\) \\({4.144}\\times 10^{-6}\\) \\(904.5\\) \\({4.32}\\times 10^{-3}\\) \\({46.2}\\times 10^{6}\\) \\(182\\) \\(1.880\\) \\(60.53\\) \\({7.283}\\times 10^{-6}\\) \\({3.884}\\times 10^{-6}\\) \\({1.132}\\times 10^{3}\\) \\({4.25}\\times 10^{-3}\\) \\({45.5}\\times 10^{6}\\) \\(192\\) \\(1.873\\) \\(60.30\\) \\({6.828}\\times 10^{-6}\\) \\({3.655}\\times 10^{-6}\\) \\({1.405}\\times 10^{3}\\) \\({4.18}\\times 10^{-3}\\) \\({44.8}\\times 10^{6}\\) \\(202\\) \\(1.865\\) \\(60.06\\) \\({6.423}\\times 10^{-6}\\) \\({3.452}\\times 10^{-6}\\) \\({1.731}\\times 10^{3}\\) \\({4.11}\\times 10^{-3}\\) \\({44.0}\\times 10^{6}\\) \\(212\\) \\(1.858\\) \\(59.81\\) \\({6.061}\\times 10^{-6}\\) \\({3.271}\\times 10^{-6}\\) \\({2.118}\\times 10^{3}\\) \\({4.04}\\times 10^{-3}\\) \\({43.2}\\times 10^{6}\\) What follows is a brief discussion of some of these properties, and how they can be applied in R. All of the properties shown in the tables above are produced using the hydraulics R package. The documentation for that package provides details on its use. The water property functions in the hydraulics package can be called using the ret_units input to allow it to return an object of class units, as designated by the package units. This enables capabilities for new units to be deduced as operations are performed on the values. Concise examples are in the vignettes for the ‘units’ package. 2.1 Properties important for water standing still An intrinsic property of water is its mass. In the presence of gravity, it exerts a weight on its surroundings. Forces caused by the weight of water enter design in many ways. Example 2.1 uses water mass and weight in a calculation. Example 2.1 Determine the tension in the 8 mm diameter rope holding a bucket containing 12 liters of water. Ignore the weight of the bucket. Assume a water temperature of 20 \\(^\\circ\\)C. rho = hydraulics::dens(T = 20, units = 'SI', ret_units = TRUE) #Water density: rho #> 998.2336 [kg/m^3] #Find mass by multiplying by volume vol <- units::set_units(12, liter) m <- rho * vol #Convert mass to weight in Newtons g <- units::set_units(9.81, m/s^2) w <- units::set_units(m*g, "N") #Divide by cross-sectional area of the rope to obtain tension area <- units::set_units(pi/4 * 8^2, mm^2) tension <- w/area #Express the result in Pascals units::set_units(tension, Pa) #> 2337828 [Pa] #For demonstration, convert to psi units::set_units(tension, psi) #> 339.0733 [psi] For example 2.1 units could have been tracked manually throughout, as if done by hand. The convenience of using the units package allows conversions that can be used to check hand calculations. Water expands as it is heated, which is part of what is driving sea-level rise globally. Approximately 90% of excess energy caused by global warming pollution is absorbed by oceans, with most of that occurring in the upper ocean: 0-700 m of depth (Fox-Kemper et al., 2021). Example 2.2 uses water mass and weight in a calculation. Example 2.2 Assume the ocean is made of fresh water (the change in density of sea water with temperature is close enough to fresh water for this illustration). Assume a 700 m thick upper layer of the ocean. Assuming this upper layer has an initial temperature of 15 \\(^\\circ\\)C and calculate the change in mean sea level due to a 2 \\(^\\circ\\)C rise in temperature of this upper layer. It may help to consider a single 1m x 1m column of water with h=700 m high under original conditions. Since mass is conserved, and mass = volume x density, this is simple: \\[LWh_1\\cdot\\rho_1=LWh_2\\cdot\\rho_2\\] or \\[h_2=h_1\\frac{\\rho_1}{\\rho_2}\\] rho1 = hydraulics::dens(T = 15, units = 'SI') rho2 = hydraulics::dens(T = 17, units = 'SI') h2 = 700 * (rho1/rho2) cat(sprintf("Change in sea level = %.3f m\\n", h2-700)) #> Change in sea level = 0.227 m The bulk modulus, Ev, relates the change in specific volume to the change in pressure, and defined as in Equation (2.1). \\[\\begin{equation} E_v=-v\\frac{dp}{dv} \\tag{2.1} \\end{equation}\\] which can be discretized: \\[\\begin{equation} \\frac{v_2-v_1}{v_1}=-\\frac{p_2-p_1}{E_v} \\tag{2.2} \\end{equation}\\] where \\(v\\) is the specific volume (\\(v=\\frac{1}{\\rho}\\)) and \\(p\\) is pressure. Example 2.3 shows one application of this. Example 2.3 A barrel of water has an initial temperature of 15 \\(^\\circ\\)C at atmospheric pressure (p=0 Pa gage). Plot the pressure the barrel must exert to have no change in volume as the water warms to 20 \\(^\\circ\\)C. Here essentially the larger specific volume (at a higher temperature) is then compressed by \\({\\Delta}P\\) to return the volume to its original value. Thus, subscript 1 indicates the warmer condition, and subscript 2 the original at 15 \\(^\\circ\\)C. dp <- function(tmp) { rho2 <- hydraulics::dens(T = 15, units = 'SI') rho1 <- hydraulics::dens(T = tmp, units = 'SI') Ev <- hydraulics::Ev(T = tmp, units = 'SI') return((-((1/rho2) - (1/rho1))/(1/rho1))*Ev) } temps <- seq(from=15, to=20, by=1) plot(temps,dp(temps), xlab="Temperature, C", ylab="Pressure increase, Pa", type="b") Figure 2.1: Approximate change in pressure as water temperature increases. These very high pressures required to compress water, even by a small fraction, validates the ordinary assumption that water can be considered incompressible in most applications. It should be noted that the Ev values produced by the hydraulics package only vary with temperature, and assume standard atmospheric pressure; in reality, Ev values increase with increasing pressure so the values plotted here serve only as a demonstration and underestimate the pressure increase. 2.2 Properties important for moving water When describing the behavior of moving water in civil engineering infrastructure like pipes and channels there are three primary water properties used used in calculations, all of which vary with water temperature: density (\\(\\rho\\)), dynamic viscosity(\\(\\mu\\)), and kinematic viscosity(\\(\\nu\\)), which are related by Equation (2.3). \\[\\begin{equation} \\nu=\\frac{\\mu}{\\rho} \\tag{2.3} \\end{equation}\\] Viscosity is caused by interaction of the fluid molecules as they are subjected to a shearing force. This is often illustrated by a conceptual sketch of two parallel plates, one fixed and one moving at a constant speed, with a fluid in between. Perhaps more intuitively, a smore can be used. If the velocity of the marshmallow filling varies linearly, it will be stationary (V=0) at the bottom and moving at the same velocity as the upper cracker at the top (V=U). The force needed to move the upper cracker can be calculated using Equation (2.4) \\[\\begin{equation} F=A{\\mu}\\frac{dV}{dy} \\tag{2.4} \\end{equation}\\] where y is the distance between the crackers and A is the cross-sectional area of a cracker. Equation (2.4) is often written in terms of shear stress \\({\\tau}\\) as in Equation (2.5) \\[\\begin{equation} \\frac{F}{A}={\\tau}={\\mu}\\frac{dV}{dy} \\tag{2.5} \\end{equation}\\] The following demonstrates a use of these relationships. Example 2.4 Determine force required to slide the top cracker at 1 cm/s with a thickness of marshmallow of 0.5 cm. The cross-sectional area of the crackers is 10 cm\\(^2\\). The viscosity (dynamic viscosity, as can be discerned by the units) of marshmallow is about 0.1 Pa\\(\\cdot\\)s. #Assign variables A <- units::set_units(10, cm^2) U <- units::set_units(1, cm/s) y <- units::set_units(0.5, cm) mu <- units::set_units(0.1, Pa*s) #Find shear stress tau <- mu * U / y tau #> 0.2 [Pa] #Since stress is F/A, multiply tau by A to find F, convert to Newtons and pounds units::set_units(tau*A, N) #> 2e-04 [N] units::set_units(tau*A, lbf) #> 4.496179e-05 [lbf] Water is less viscous than marshmallow, so viscosity for water has much lower values than in the example. Values for water can be obtained using the hydraulics R package for calculations, using the dens, dvisc, and kvisc. All of the water property functions can accept a list of input temperature values, enabling visualization of a property with varying water temperature, as shown in Figure 2.2. Ts <- seq(0, 100, 10) nus <- hydraulics::kvisc(T = Ts, units = 'SI') xlbl <- expression("Temperature, " (degree*C)) ylbl <- expression("Kinematic viscosity," ~nu~ (m^{2}/s)) par(cex=0.8, mgp = c(2,0.7,0)) plot(Ts, nus, xlab = xlbl, ylab = ylbl, type="l") Figure 2.2: Variation of kinematic viscosity with temperature. 2.3 Atmosperic Properties Since water interacts with the atmosphere, through processes like evaporation and condensation, some basic properties of air are helpful. Selected characteristics of the standard atmosphere, as determined by the International Civil Aviation Organization (ICAO), are included in the hydraulics package. Three functions atmpres, atmdens, and atmtemp return different properties of the standard atmosphere, which vary with altitude. These are summarized in Table 2.3 for SI units and Table 2.4 for US (or Eng) units. Table 2.3: ICAO standard atmospheric properties in SI units Altitude Temp Pressure Density m C Pa kg m-3 0 15.00 101325.0 1.22500 1000 8.50 89876.3 1.11166 2000 2.00 79501.4 1.00655 3000 -4.49 70121.1 0.90925 4000 -10.98 61660.4 0.81935 5000 -17.47 54048.2 0.73643 6000 -23.96 47217.6 0.66011 7000 -30.45 41105.2 0.59002 8000 -36.93 35651.5 0.52579 9000 -43.42 30800.6 0.46706 10000 -49.90 26499.8 0.41351 11000 -56.38 22699.8 0.36480 12000 -62.85 19354.6 0.32062 13000 -69.33 16421.2 0.28067 14000 -75.80 13859.4 0.24465 15000 -82.27 11631.9 0.21229 Table 2.4: ICAO standard atmospheric properties in US units Altitude Temp Pressure Density ft F lbf ft-2 slug ft-3 0 59.00 2116.2 0.00237 5000 41.17 1760.9 0.00205 10000 23.36 1455.6 0.00175 15000 5.55 1194.8 0.00149 20000 -12.25 973.3 0.00127 25000 -30.05 786.3 0.00107 30000 -47.83 629.7 0.00089 35000 -65.61 499.3 0.00074 40000 -83.37 391.8 0.00061 45000 -101.13 303.9 0.00049 50000 -118.88 232.7 0.00040 As with water property functions, the data in the table can be extracted using individual commands for use in calculations. All atmospheric functions have input arguments of altitude (ft or m), unit system (SI or Eng), and whether or not units should be returned. hydraulics::atmpres(alt = 3000, units = "SI", ret_units = TRUE) #> 70121.14 [Pa] 2.3.1 Ideal gas law Because air is compressible, its density changes with pressure, and the temperature responds to compression. These are related through the ideal gas law, Equation (2.6) \\[\\begin{equation} \\begin{split} p={\\rho}RT\\\\ p{\\forall}=mRT \\end{split} \\tag{2.6} \\end{equation}\\] where \\(p\\) is absolute pressure, \\(\\forall\\) is the volume, \\(R\\) is the gas constant, \\(T\\) is absolute temperature, and \\(m\\) is the mass of the gas. When air changes its condition between two states, the ideal gas law can be restated as Equation (2.7) \\[\\begin{equation} \\frac{p_1{\\forall_1}}{T_1}=\\frac{p_2{\\forall_2}}{T_2} \\tag{2.7} \\end{equation}\\] Two convenient forms of Equation (2.7) apply for specific conditions. If mass is conserved, and conditions are isothermal, m, R, T are constant (isothermal conditions): \\[\\begin{equation} p_1{\\forall_1}=p_2{\\forall_2} \\tag{2.8} \\end{equation}\\] If mass is conserved and temperature changes adiabatically (meaning no heat is exchanged with surroundings) and reversibly, these are isentropic conditions, governed by Equations (2.9). \\[\\begin{equation} \\begin{split} p_1{\\forall_1}^k=p_2{\\forall_2}^k\\\\ \\frac{T_2}{T_1}=\\left(\\frac{p_2}{p_1}\\right)^{\\frac{k-1}{k}} \\end{split} \\tag{2.9} \\end{equation}\\] Properties of air used in these formulations of the ideal gas law are shown in Table 2.5. Table 2.5: Air properties at standard sea-level atmospheric pressure Gas Constant, R Sp. Heat, cp Sp. Heat, cv Sp. Heat Ratio, k 1715 ft lbf degR-1 slug-1 6000 ft lbf degR-1 slug-1 4285 ft lbf degR-1 slug-1 1.4 287 m N K-1 kg-1 1003 m N K-1 kg-1 717 m N K-1 kg-1 1.4 "],["hydrostatics---forces-exerted-by-water-bodies.html", "Chapter 3 Hydrostatics - forces exerted by water bodies 3.1 Pressure and force 3.2 Force on a plane area 3.3 Forces on curved surfaces", " Chapter 3 Hydrostatics - forces exerted by water bodies When water is motionless its weight exerts a pressure on surfaces with which it is in contact. The force is function of the density of the fluid and the depth. Figure 3.1: The Clywedog dam by Nigel Brown, CC BY-SA 2.0, via Wikimedia Commons 3.1 Pressure and force A consideration of all of the forces acting on a particle in a fluid in equilibrium produces Equation (3.1). \\[\\begin{equation} \\frac{dp}{dz}=-{\\gamma} \\tag{3.1} \\end{equation}\\] where \\(p\\) is pressure (\\(p=F/A\\)), \\(z\\) is height measured upward from a datum, and \\({\\gamma}\\) is the specific weight of the fluid (\\(\\gamma={\\rho}g\\)). Rewritten using depth (downward from the water surface), \\(h\\), produces Equation (3.2). \\[\\begin{equation} h=\\frac{p}{\\gamma} \\tag{3.2} \\end{equation}\\] Example 3.1 Find the force on the bottom of a 0.4 m diameter barrel filled with (20 \\(^\\circ\\)C) water for barrel heights from 0.5 m to 1.5 m. area <- pi/4*0.4^2 gamma <- hydraulics::specwt(T = 20, units = 'SI') heights <- seq(from=0.5, to=1.5, by=0.05) pressures <- gamma * heights forces <- pressures * area plot(forces,heights, xlab="Total force on barrel bottom, N", ylab="Depth of water, m", type="l") grid() Figure 3.2: Force on barrel bottom. The linear relationship is what was expected. 3.2 Force on a plane area For a submerged flat surface, the magnitude of the hydrostatic force can be found using Equation (3.3). \\[\\begin{equation} F={\\gamma}y_c\\sin{\\theta}A={\\gamma}h_cA \\tag{3.3} \\end{equation}\\] The force is located as defined by Equation (3.4). \\[\\begin{equation} y_p=y_c+\\frac{I_c}{y_cA} \\tag{3.4} \\end{equation}\\] The variables correspond to the definitions in Figure 3.3. Figure 3.3: Forces on a plane area, by Ertunc, CC BY-SA 4.0, via Wikimedia Commons The location of the centroid and the moment of inertia, \\(I_c\\) for some common shapes are shown in Figure 3.4 (Moore, J. et al., 2022). The variables correspond to the definitions in Figure 3.4. Figure 3.4: Centroids and moments of inertia for common shapes Example 3.2 A 6 m long hinged gate with a width of 1 m (into the paper) is at an angle of 60o and is held in place by a horizontal cable. Plot the tension in the cable, \\(T\\), as the water depth, \\(h\\), varies from 0.1 to 4 m in depth. Ignore the weight of the gate. Figure 3.5: Reservoir with hinged gate (Olivier Cleyne, CC0 license, via Wikimedia Commons) The surface area of the gate that is wetted is \\(A=L{\\cdot}w=\\frac{h{\\cdot}w}{\\sin(60)}\\). The wetted area is rectangular, so \\(h_c=\\frac{h}{2}\\). The magnitude of the force uses (3.3): \\[F={\\gamma}h_cA={\\gamma}\\frac{h}{2}\\frac{h{\\cdot}w}{\\sin(60)}\\] The distance along the plane from the water surface to the centroid of the wetted area is \\(y_c=\\frac{1}{2}\\frac{h}{\\sin(60)}\\). The moment of inertia for the rectangular wetted area is \\(I_c=\\frac{1}{12}w\\left(\\frac{h}{\\sin(60)}\\right)^3\\). Taking moments about the hinge at the bottom of the gate yields \\(T{\\cdot}6\\sin(60)-F{\\cdot}\\left(\\frac{h}{\\sin(60)}-y_p\\right)=0\\) or \\(T=\\frac{F}{6\\cdot\\sin(60)}\\left(\\frac{h}{\\sin(60)}-y_p\\right)\\) These equations can be used in R to create the desired plot. gate_length <- 6.0 w <- 1.0 theta <- 60*pi/180 #convert angle to radians h <- seq(from=0.1, to=4.1, by=0.25) gamma <- hydraulics::specwt(T = 20, units = 'SI') area <- h*w/sin(theta) hc <- h/2 Force <- gamma*hc*area yc <- (1/2)*h/(sin(theta)) Ic <- (1/12)*w*(h/sin(theta))^3 yp <- yc + (Ic/(yc*area)) Tension <- Force/(gate_length*sin(theta)) * (h/sin(theta) - yp) plot(Tension,h, xlab="Cable tension, N", ylab="Depth of water, m", type="l") grid() 3.3 Forces on curved surfaces For forces on curved surfaces, the procedure is often to calculate the vertical, \\(F_V\\), and horizontal, \\(F_H\\), hydrostatic forces separately. \\(F_H\\) is simpler, since it is the horizontal force on a (plane) vertical projection of the submerged surface, so the methods of Section 3.2 apply. The vertical component, \\(F_V\\), for a submerged surface with water above has a magnitude of the weight of the water above it, which acts through the center of volume. For a curved surface with water below it the magnitude of \\(F_V\\) is the volume of the ‘mising’ water that would be above it, and the force acts upward. Figure 3.6: Forces on curved surfaces, by Ertunc, CC BY-SA 4.0, via Wikimedia Commons A classic example of a curved surface in civil engineering hydraulics is a radial (or Tainter) gate, as in Figure 3.7. Figure 3.7: Radial gates on the Rogue River, OR. To simplify the geometry, a problem is presented in Example 3.3 where the gate meets the base at a horizontal angle. Example 3.3 A radial gate with radius R=6 m and a width of 1 m (into the paper) controls water. Find the horizontal and vertical hydrostatic forces for depths, \\(h\\), from 0 to 6 m. The horizontal hydrostatic force is that acting on a rectangle of height \\(h\\) and width \\(w\\): \\[F_H=\\frac{1}{2}{\\gamma}h^2w\\] which acts at a height of \\(y_c=\\frac{h}{3}\\) from the bottom of the gate. The vertical component has a magnitude equal to the weight of the ‘missing’ water indicated on the sketch. The calculation of its volume requires the area of a circular sector minus the area of a triangle above it. The angle, \\(\\theta\\) is found using geometry to be \\({\\theta}=cos^{-1}\\left(\\frac{R-h}{R}\\right)\\). Using the equations for areas of these two components as in Figure 3.4, the following is obtained: \\[F_V={\\gamma}w\\left(\\frac{R^2\\theta}{2}-\\frac{R-h}{2}R\\sin{\\theta}\\right)\\] The line of action of \\(F_V\\) can be determined by combining the components for centroids of the composite shapes, again following Figure 3.4. Because the line of action of the resultant force on a curcular gate must pass through the center of the circle (since hydrostatic forces always act normal to the gate), the moments about the hinge of \\(F_H\\) and \\(F_V\\) must equal zero. \\[\\sum{M}_{hinge}=0=F_H\\left(R-h/3\\right)-F_V{\\cdot}x_c\\] This produces the equation: \\[x_c=\\frac{F_H\\left(R-h/3\\right)}{F_V}\\] These equations can be solved in many ways, such as the following. R <- units::set_units(6.0, m) w <- units::set_units(1.0, m) gamma <- hydraulics::specwt(T = 20, units = 'SI', ret_units = TRUE) h <- units::set_units(seq(from=0, to=6, by=1), m) #angle in radians throughout, units not needed theta <- units::drop_units(acos((R-h)/R)) area <- h*w/sin(theta) Fh <- (1/2)*gamma*h^2*w yc <- h/3 Fv <- gamma*w*((R^2*theta)/2 - ((R-h)/2) * R*sin(theta)) xc <- Fh*(R-h/3)/Fv Ftotal <- sqrt(Fh^2+Fv^2) tibble::tibble(h=h, Fh=Fh, yc=yc, Fv=Fv, xc=xc, Ftotal=Ftotal) #> # A tibble: 7 × 6 #> h Fh yc Fv xc Ftotal #> [m] [N] [m] [N] [m] [N] #> 1 0 0 0 0 NaN 0 #> 2 1 4896. 0.333 22041. 1.26 22578. #> 3 2 19585. 0.667 60665. 1.72 63748. #> 4 3 44067. 1 108261. 2.04 116886. #> 5 4 78341. 1.33 161583. 2.26 179573. #> 6 5 122408. 1.67 218398. 2.43 250363. #> 7 6 176268. 2 276881. 2.55 328228. "],["water-flowing-in-pipes-energy-losses.html", "Chapter 4 Water flowing in pipes: energy losses 4.1 Important dimensionless quantity 4.2 Friction Loss in Circular Pipes 4.3 Solving Pipe friction problems 4.4 Solving for head loss (Type 1 problems) 4.5 Solving for Flow or Velocity (Type 2 problems) 4.6 Solving for pipe diameter, D (Type 3 problems) 4.7 Parallel pipes: solving a system of equations 4.8 Simple pipe networks: the Hardy-Cross method", " Chapter 4 Water flowing in pipes: energy losses Flow in civil engineering infrastructure is usually either in pipes, where it is not exposed to the atmosphere and flows under pressure, or open channels (canals, rivers, etc.). this chapter is concerned only with water flow in pipes. Once water begins to move engineering problems often need to relate the flow rate to the energy dissipated. To accomplish this, the flow needs to be classified using dimensionless constants since energy dissipation varies with the flow conditions. 4.1 Important dimensionless quantity As water begins to move, the characteristics are described by two quantities in engineering hydraulics: the Reynolds number, Re and the Froude number Fr. The latter is more important for open channel flow and will be discussed in that chapter. Reynolds number describes the turbulence of the flow, defined by the ratio of kinematic forces, expressed by velocity V and a characteristic length such as pipe diameter, D, to viscous forces as expressed by the kinematic viscosity \\(\\nu\\), as in Equation (4.1) \\[\\begin{equation} Re=\\frac{VD}{\\nu} \\tag{4.1} \\end{equation}\\] For open channels the characteristic length is the hydraulic depth, the area of flow divided by the top width. For adequately turbulent conditions to exists, Reynolds numbers should exceed 4000 for full pipes, and 2000 for open channels. 4.2 Friction Loss in Circular Pipes The energy at any point along a pipe containing flowing water is often described by the energy per unit weight, or energy head, E, as in Equation (4.2) \\[\\begin{equation} E = z+\\frac{P}{\\gamma}+\\alpha\\frac{V^2}{2g} \\tag{4.2} \\end{equation}\\] where P is the pressure, \\(\\gamma=\\rho g\\) is the specific weight of water, z is the elevation of the point, V is the average velocity, and each term has units of length. \\(\\alpha\\) is a kinetic energy adjustment factor to account for non-uniform velocity distribution across the cross-section. \\(\\alpha\\) is typically assumed to be 1.0 for turbulent flow in circular pipes because the value is close to 1.0 and \\(\\frac{V^2}{2g}\\) (the velocity head) tends to be small in relation to other terms in the equation. Some applications where velocity varies widely across a cross-section, such as a river channel with flow in a main channel and a flood plain, will need to account for values of \\(\\alpha\\) other than one. As water flows through a pipe energy is lost due to friction with the pipe walls and local disturbances (minor losses). The energy loss between two sections is expressed as \\({E_1} - {h_l} = {E_2}\\). When pipes are long, with \\(\\frac{L}{D}>1000\\), friction losses dominate the energy loss on the system, and the head loss, \\(h_l\\), is calculated as the head loss due to friction, \\(h_f\\). This energy head loss due to friction with the walls of the pipe is described by the Darcy-Weisbach equation, which estimates the energy loss per unit weight, or head loss \\({h_f}\\), which has units of length. For circular pipes it is expressed by Equation (4.3) \\[\\begin{equation} h_f = \\frac{fL}{D}\\frac{V^2}{2g} = \\frac{8fL}{\\pi^{2}gD^{5}}Q^{2} \\tag{4.3} \\end{equation}\\] In equation (4.3) f is the friction factor, typically calculated with the Colebrook equation (Equation (4.4)). \\[\\begin{equation} \\frac{1}{\\sqrt{f}} = -2\\log\\left(\\frac{\\frac{k_s}{D}}{3.7} + \\frac{2.51}{Re\\sqrt{f}}\\right) \\tag{4.4} \\end{equation}\\] In Equation (4.4) \\(k_s\\) is the absolute roughness of the pipe wall. There are close approximations to the Colebrook equation that have an explicit form to facilitate hand-calculations, but when using R or other computational tools there is no need to use approximations. 4.3 Solving Pipe friction problems As water flows through a pipe energy is lost due to friction with the pipe walls and local disturbances (minor losses). For this example assume minor losses are negligible. The energy head loss due to friction with the walls of the pipe is described by the Darcy-Weisbach equation (Equation ((4.3))), which estimates the energy loss per unit weight, or head loss hf, which has units of length. The Colebrook equation (Equation (4.4)) is commonly plotted as a Moody diagram to illustrate the relationships between the variables, in Figure 4.1. hydraulics::moody() Figure 4.1: Moody Diagram Because of the form of the equations, they can sometimes be a challenge to solve, especially by hand. It can help to classify the types of problems based on what variable is unknown. These are summarized in Table 4.1. Table 4.1: Types of Energy Loss Problems in Pipe Flow Type Known Unknown 1 Q (or V), D, ks, L hL 2 hL, D, ks, L Q (or V) 3 hL, Q (or V), ks, L D When solving by hand the types in Table 4.1 become progressively more difficult, but when using solvers the difference in complexity is subtle. 4.4 Solving for head loss (Type 1 problems) The simplest pipe flow problem to solve is when the unknown is head loss, hf (equivalent to hL in the absence of minor losses), since all variables on the right side of the Darcy-Weisbach equation are known, except f. 4.4.1 Solving for head loss by manual iteration While all unknowns are on the right side of Equation (4.3), iteration is still required because the Colebrook equation, Equation (4.4), cannot be solved explicitly for f. An illustration of solving this type of problem is shown in Example 4.1. Example 4.1 Find the head loss (due to friction) of 20\\(^\\circ\\)C water in a pipe with the following characteristics: Q=0.416 m\\(^3\\)/s, L=100m, D=0.5m, ks=0.046mm. Since the water temperature is known, first find the kinematic viscocity of water, \\({\\nu}\\), since it is needed for the Reynolds number. This can be obtained from a table in a reference or using software. Here we will use the hydraulics R package. nu <- hydraulics::kvisc(T=20, units="SI") cat(sprintf("Kinematic viscosity = %.3e m2/s\\n", nu)) #> Kinematic viscosity = 1.023e-06 m2/s We will need the Reynolds Number to use the Colebrook equation, and that can be calculated since Q is known. This can be accomplished with a calculator, or using other software (R is used here): Q <- 0.416 D <- 0.5 A <- (3.14/4)*D^2 V <- Q/A Re <- V*D/nu cat(sprintf("Velocity = %.3f m/s, Re = %.3e\\n", V, Re)) #> Velocity = 2.120 m/s, Re = 1.036e+06 Now the only unknown in the Colebrook equation is f, but unfortunately f appears on both sides of the equation. To begin the iterative process, a first guess at f is needed. A reasonable value to use is the minimum f value, fmin, given the known \\(\\frac{k_s}{D}=\\frac{0.046}{500}=0.000092=9.2\\cdot 10^{-5}\\). Reading horizontally from the right vertical axis to the left on the Moody diagram provides a value for \\(f_{min}\\approx 0.012\\). Numerically, it can be seen that f is independent of Re for large values of Re. When Re is large the second term of the Colebrook equation becomes small and the equation approaches Equation (4.5). \\[\\begin{equation} \\frac{1}{\\sqrt{f}} = -2\\log\\left(\\frac{\\frac{k_s}{D}}{3.7}\\right) \\tag{4.5} \\end{equation}\\] This independence of f with varying Re values is visible in the Moody Diagram, Figure 4.1, toward the right, where the lines become horizontal. Using Equation (4.5) the same value of fmin=0.012 is obtained, since the colebrook equation defines the Moody diagram. Iteration 1: Using f=0.012 the right side of the Colebrook equation is 8.656. the next estimate for f is then obtained by \\(\\frac{1}{\\sqrt{f}}=8.656\\) so f=0.0133. Iteration 2: Using the new value of f=0.0133 in the right side of the Colebrook equation produces 8.677. A new value for f is obtained by \\(\\frac{1}{\\sqrt{f}}=8.677\\) so f=0.0133. The solution has converged! Using the new value of f, the value for hf is calculated: \\[h_f = \\frac{8fL}{\\pi^{2}gD^{5}}Q^{2}=\\frac{8(0.0133)(100)}{\\pi^{2}(9.81)(0.5)^{5}}(0.416)^{2}=0.061 m\\] 4.4.2 Solving for headloss using an empirical approximation A shortcut that can be used to avoid iterating to find the friction factor is to use an approximation to the Colebrook equation that can be solved explicitly. One example is the Haaland equation (4.6) (Haaland, 1983). \\[\\begin{equation} \\frac{1}{\\sqrt{f}} = -1.8\\log\\left(\\left(\\frac{\\frac{k_s}{D}}{3.7}\\right)^{1.11}+\\frac{6.9}{Re}\\right) \\tag{4.6} \\end{equation}\\] For ordinary pipe flow conditions in water pipes, Equation (4.6) is accurate to within 1.5% of the Colebrook equation. There are many other empirical equations, one common one being that of Swamee and Jain (Swamee & Jain, 1976), shown in Equation (4.7). \\[\\begin{equation} \\frac{1}{\\sqrt{f}} = -2\\log\\left(\\frac{\\frac{k_s}{D}}{3.7}+\\frac{5.74}{Re^{0.9}}\\right) \\tag{4.7} \\end{equation}\\] These approximations are useful for solving problems by hand or in spreadsheets, and their accuracy is generally within the uncertainty of other input variables like the absolute roughness. 4.4.3 Solving for head loss using an equation solver Rather than use an empirical approximation (as in Section 4.4.2) to the Colebrook equation, it is straightforward to apply an equation solver to use the Colebrook equation directly. This is demonstrated in Example 4.2. Example 4.2 Find the friction factor for the same conditions as Example 4.1: D=0.5m, ks=0.046mm, and Re=1.036e+06. First, rearrange the Colebrook equation so all terms are on one side of the equation, as in Equation (4.8). \\[\\begin{equation} -2\\log\\left(\\frac{\\frac{k_s}{D}}{3.7} + \\frac{2.51}{Re\\sqrt{f}}\\right) - \\frac{1}{\\sqrt{f}}=0 \\tag{4.8} \\end{equation}\\] Create a function using whatever equation solving platform you prefer. Here the R software is used: colebrk <- function(f,ks,D,Re) -2.0*log10((ks/D)/3.7 + 2.51/(Re*(f^0.5)))-1/(f^0.5) Find the root of the function (where it equals zero), specifying a reasonable range for f values using the interval argument: f <- uniroot(colebrk, interval = c(0.008,0.1), ks=0.000046, D=0.5, Re=1.036e+06)$root cat(sprintf("f = %.4f\\n", f)) #> f = 0.0133 The same value for hf as above results. 4.4.4 Solving for head loss using an R package Equation solvers for implicit equations, like in Section 4.4.3, are built into the R package hydraulics. that can be applied directly, without writing a separate function. Example 4.3 Using the hydraulics R package, find the friction factor and head loss for the same conditions as Example 4.2: Q=0.416 m3/s, L=100 m, D=0.5m, ks=0.046mm, and nu = 1.023053e-06 m2/s. ans <- hydraulics::darcyweisbach(Q = 0.416,D = 0.5, L = 100, ks = 0.000046, nu = 1.023053e-06, units = c("SI")) #> hf missing: solving a Type 1 problem cat(sprintf("Reynolds no: %.0f\\nFriction Fact: %.4f\\nHead Loss: %.2f ft\\n", ans$Re, ans$f, ans$hf)) #> Reynolds no: 1035465 #> Friction Fact: 0.0133 #> Head Loss: 0.61 ft If only the f value is needed, the colebrook function can be used. f <- hydraulics::colebrook(ks=0.000046, V= 2.120, D=0.5, nu=1.023e-06) cat(sprintf("f = %.4f\\n", f)) #> f = 0.0133 Notice that the colebrook function needs input in dimensionally consistent units. Because it is dimensionally homogeneous and the input dimensions are consistent, the unit system does not need to be defined like with many other functions in the hydraulics package. 4.5 Solving for Flow or Velocity (Type 2 problems) When flow (Q) or velocity (V) is unknown, the Reynolds number cannot be determined, complicating the solution of the Colebrook equation. As with Secion 4.4 there are several strategies to solving these, ranging from iterative manual calculations to using software packages. For Type 2 problems, since D is known, once either V or Q is known, the other is known, since \\(Q=V{\\cdot}A=V\\frac{\\pi}{4}D^2\\). 4.5.1 Solving for Q (or V) using manual iteration Solving a Type 2 problem can be done with manual iterations, as demonstrated in Example 4.4. Example 4.4 find the flow rate, Q of 20oC water in a pipe with the following characteristics: hf=0.6m, L=100m, D=0.5m, ks=0.046mm. First rearrange the Darcy-Weisbach equation to express V as a function of f, substituting all of the known quantities: \\[V = \\sqrt{\\frac{h_f}{L}\\frac{2gD}{f}}=\\frac{0.243}{\\sqrt{f}}\\] That provides one equation relating V and f. The second equation relating V and f is one of the friction factor equations, such as the Colebrook equation or its graphic representation in the Moody diagram. An initial guess at a value for f is obtained using fmin=0.012 as was done in Example 4.1. Iteration 1: \\(V=\\frac{0.243}{\\sqrt{0.012}}=2.218\\); \\(Re=\\frac{2.218\\cdot 0.5}{1.023e-06}=1.084 \\cdot 10^6\\). A new f value is obtained from the Moody diagram or an equation using the new Re value: \\(f \\approx 0.0131\\) Iteration 2: \\(V=\\frac{0.243}{\\sqrt{0.0131}}=2.123\\); \\(Re=\\frac{2.123\\cdot 0.5}{1.023e-06}=1.038 \\cdot 10^6\\). A new f estimate: \\(f \\approx 0.0132\\) The function converges very quickly if a reasonable first guess is made. Using V=2.12 m/s, \\(Q = AV = \\left(\\frac{\\pi}{4}\\right)D^2V=0.416 m^3/s\\) 4.5.2 Solving for Q Using an Explicit Equation Solving Type 2 problems using iteration is not necessary, since an explicit equation based on the Colebrook equation can be derived. Solving the Darcy Weisbach equation for \\(\\frac{1}{\\sqrt{f}}\\) and substituting that into the Colebrook equation produces Equation (4.9). \\[\\begin{equation} Q=-2.221D^2\\sqrt{\\frac{gDh_f}{L}} \\log\\left(\\frac{\\frac{k_s}{D}}{3.7} + \\frac{1.784\\nu}{D}\\sqrt{\\frac{L}{gDh_f}}\\right) \\tag{4.9} \\end{equation}\\] This can be solved explicitly for Q=0.413 m3/s. 4.5.3 Solving for Q Using an R package Using software to solve the problem allows the use of the Colebrook equation in a straightforward format. The hydraulics package in R is applied to the same problem as above. ans <- hydraulics::darcyweisbach(D=0.5, hf=0.6, L=100, ks=0.000046, nu=1.023e-06, units = c('SI')) knitr::kable(format(as.data.frame(ans), digits = 3), format = "pipe") Q V L D hf f ks Re 0.406 2.07 100 0.5 0.6 0.0133 4.6e-05 1010392 The answer differs from the manual iteration by just over 2%, showing remarkable consistency. 4.6 Solving for pipe diameter, D (Type 3 problems) When D is unknown, neither Re nor relative roughness \\(\\frac{ks}{D}\\) are known. Referring to the Moody diagram, Figure 4.1, the difficulty in estimating a value for f (on the left axis) is evident since the positions on either the right axis (\\(\\frac{ks}{D}\\)) or x-axis (Re) are known. 4.6.1 Solving for D using manual iterations Solving for D using manual iterations is done by first rearranging Equation (4.9) to allow it to be solved for zero, as in Equation (4.10). \\[\\begin{equation} -2.221D^2\\sqrt{\\frac{gDh_f}{L}} \\log\\left(\\frac{\\frac{k_s}{D}}{3.7} + \\frac{1.784\\nu}{D}\\sqrt{\\frac{L}{gDh_f}}\\right)-Q=0 \\tag{4.10} \\end{equation}\\] Using this with manual iterations is demonstrated in Example 4.5. Example 4.5 For a similar problem to 4.4 use Q=0.416m3/s and solve for the required pipe diameter, D. This can be solved manually by guessing values and repeating the calculation in a spreadsheet or with a tool like R. Iteration 1: Guess an arbitrary value of D=0.3m. Solve the left side of Equation (4.10) to obtain a value of -0.31 Iteration 2: Guess another value for D=1.0m. The left side of Equation (4.10) produces a value for the function of 2.11 The root, when the function equals zero, lies between the two values, so the correct D is between 0.3 and 1.0. Repeated values can home in on a solution. Plotting the results from many trials can help guide toward the solution. The root is seen to lie very close to D=0.5 m. Repeated trials can home in on the result. 4.6.2 Solving for D using an equation solver An equation solver automatically accomplishes the manual steps of the prior demonstration. The equation from 1.6 can be written as a function that can then be solved for the root, again using R software for the demonstration: q_fcn <- function(D, Q, hf, L, ks, nu, g) { -2.221 * D^2 * sqrt(( g * D * hf)/L) * log10((ks/D)/3.7 + (1.784 * nu/D) * sqrt(L/(g * D * hf))) - Q } The uniroot function can solve the equation in R (or use a comparable approach in other software) for a reasonable range of D values ans <- uniroot(q_fcn, interval=c(0.01,4.0),Q=0.416, hf=0.6, L=100, ks=0.000046, nu=1.023053e-06, g=9.81)$root cat(sprintf("D = %.3f m\\n", ans)) #> D = 0.501 m 4.7 Parallel pipes: solving a system of equations In the examples above the challenge was often to solve a single implicit equation. The manual iteration approach can work to solve two equations, but as the number of equations increases, especially when using implicit equations, using an equation solver is needed. For the case of a simple pipe loop manual iterations are impractical. for this reason often fixed values of f are assumed, or an empirical energy loss equation is used. However, a single loop, identical to a parallel pipe problem, can be used to demonstrate how systems of equations can be solved simultaneously for systems of pipes. Example 4.6 demonstrates the process of assembling the equations for a solver for a parallel pipe problem. Example 4.6 Two pipes carry a flow of Q=0.5 m3/s, as depicted in Figure 4.2 Figure 4.2: Parallel Pipe Example The fundamental equations needed are the Darcy-Weisbach equation, the Colebrook equation, and continuity (conservation of mass). For the illustrated system, this means: The flows through each pipe must add to the total flow The head loss through Pipe 1 must equal that of Pipe 2 This could be set up as a system of anywhere from 2 to 10 equations to solve simultaneously. In this example four equations are used: \\[\\begin{equation} Q_1+Q_2-Q_{total}=V_1\\frac{\\pi}{4}D_1^2+V_2\\frac{\\pi}{4}D_2^2-0.5m^3/s=0 \\tag{4.11} \\end{equation}\\] and \\[\\begin{equation} Qh_{f1}-h_{f2} = \\frac{f_1L_1}{D_1}\\frac{V_1^2}{2g} -\\frac{f_2L_2}{D_2}\\frac{V_2^2}{2g}=0 \\tag{4.12} \\end{equation}\\] The other two equations are the Colebrook equation (4.8) for solving for the friction factor for each pipe. These four equations can be solved simultaneously using an equation solver, such as the fsolve function in the R package pracma. #assign known inputs - SI units Qsum <- 0.5 D1 <- 0.2 D2 <- 0.3 L1 <- 400 L2 <- 600 ks <- 0.000025 g <- 9.81 nu <- hydraulics::kvisc(T=100, units='SI') #Set up the function that sets up 4 unknowns (x) and 4 equations (y) F_trial <- function(x) { V1 <- x[1] V2 <- x[2] f1 <- x[3] f2 <- x[4] Re1 <- V1*D1/nu Re2 <- V2*D2/nu y <- numeric(length(x)) #Continuity - flows in each branch must add to total y[1] <- V1*pi/4*D1^2 + V2*pi/4*D2^2 - Qsum #Darcy-Weisbach equation for head loss - must be equal in each branch y[2] <- f1*L1*V1^2/(D1*2*g) - f2*L2*V2^2/(D2*2*g) #Colebrook equation for friction factors y[3] <- -2.0*log10((ks/D1)/3.7 + 2.51/(Re1*(f1^0.5)))-1/(f1^0.5) y[4] <- -2.0*log10((ks/D2)/3.7 + 2.51/(Re2*(f2^0.5)))-1/(f2^0.5) return(y) } #provide initial guesses for unknowns and run the fsolve command xstart <- c(2.0, 2.0, 0.01, 0.01) z <- pracma::fsolve(F_trial, xstart) #prepare some results to print Q1 <- z$x[1]*pi/4*D1^2 Q2 <- z$x[2]*pi/4*D2^2 hf1 <- z$x[3]*L1*z$x[1]^2/(D1*2*g) hf2 <- z$x[4]*L2*z$x[2]^2/(D2*2*g) cat(sprintf("Q1=%.2f, Q2=%.2f, V1=%.1f, V2=%.1f, hf1=%.1f, hf2=%.1f, f1=%.3f, f2=%.3f\\n", Q1,Q2,z$x[1],z$x[2],hf1,hf2,z$x[3],z$x[4])) #> Q1=0.15, Q2=0.35, V1=4.8, V2=5.0, hf1=30.0, hf2=30.0, f1=0.013, f2=0.012 If the fsolve command fails, a simple solution is sometimes to revise your initial guesses and try again. There are other solvers in R and every other scripting language that can be similarly implemented. If the simplification were applied for fixed f values, then Equations (4.11) and (4.12) can be solved simultaneously for V1 and V2. 4.8 Simple pipe networks: the Hardy-Cross method For water pipe networks containing multiple loops, manually setting up systems of equations is impractical. In addition, hand calculations always assume fixed f values or use an empirical friction loss equation to simplify calculations. A typical method to solve for the flow in each pipe segment in a small network uses the Hardy-Cross method. This consists of setting up an initial guess of flow (magnitude and direction) for each pipe segment, ensuring conservation of mass is preserved at each node (or vertex) in the network. Then calculations are performed for each loop, ensuring energy is conserved. When using the Darcy-Weisbach equation, Equation (4.3), for friction loss, the head loss in each pipe segment is usually expressed in a condensed form as \\({h_f = KQ^{2}}\\) where K is defined as in Equation (4.13). \\[\\begin{equation} K = \\frac{8fL}{\\pi^{2}gD^{5}} \\tag{4.13} \\end{equation}\\] When doing calculations by hand fixed f values are assumed, but when using a computational tool like R any of the methods for estimating f and hf may be applied. The Hardy-Cross method begins by assuming flows in each segment of a loop. These initial flows are then adjusted in a series of iterations. The flow adjustment in each loop is calculated at each iteration in Equation Equation (4.14). \\[\\begin{equation} \\Delta{Q_i} = -\\frac{\\sum_{j=1}^{p_i} K_{ij}Q_j|Q_j|}{\\sum_{j=1}^{p_i} 2K_{ij}Q_j^2} \\tag{4.14} \\end{equation}\\] For calculations for small systems with two or three loops can be done manually with fixed f and K values. Using the hydraulics R package to solve a small pipe network is demonstrated in Example 4.7. Example 4.7 Find the flows in each pipe in teh system shown in Figure 4.3. Input consists of pipe characteristics, pipe order and initial flows for each loop, as shown non the diagram. Figure 4.3: A sample pipe network with pipe numbers indicated in black Input for this system, assuming fixed f values, would look like the following. (If fixed K values are provided, f, L and D are not needed). These f values were estimated using \\(ks=0.00025 m\\) in the form of the Colebrook equation for fully rough flows, Equation (4.5). dfpipes <- data.frame( ID = c(1,2,3,4,5,6,7,8,9,10), #pipe ID D = c(0.3,0.2,0.2,0.2,0.2,0.15,0.25,0.15,0.15,0.25), #diameter in m L = c(250,100,125,125,100,100,125,100,100,125), #length in m f = c(.01879,.02075,.02075,.02075,.02075,.02233,.01964,.02233,.02233,.01964) ) loops <- list(c(1,2,3,4,5),c(4,6,7,8),c(3,9,10,6)) Qs <- list(c(.040,.040,.02,-.02,-.04),c(.02,0,0,-.02),c(-.02,.02,0,0)) Running the hardycross function and looking at the output after three iterations (defined by n_iter): ans <- hydraulics::hardycross(dfpipes = dfpipes, loops = loops, Qs = Qs, n_iter = 3, units = "SI") knitr::kable(ans$dfloops, digits = 4, format = "pipe", padding=0) loop pipe flow 1 1 0.0383 1 2 0.0383 1 3 0.0232 1 4 -0.0258 1 5 -0.0417 2 4 0.0258 2 6 0.0090 2 7 0.0041 2 8 -0.0159 3 3 -0.0232 3 9 0.0151 3 10 -0.0049 3 6 -0.0090 The output pipe data frame has added columns, including the flow (where direction is that for the first loop containing the segment). knitr::kable(ans$dfpipes, digits = 4, format = "pipe", padding=0) ID D L f Q K 1 0.30 250 0.0188 0.0383 159.7828 2 0.20 100 0.0208 0.0383 535.9666 3 0.20 125 0.0208 0.0232 669.9582 4 0.20 125 0.0208 -0.0258 669.9582 5 0.20 100 0.0208 -0.0417 535.9666 6 0.15 100 0.0223 0.0090 2430.5356 7 0.25 125 0.0196 0.0041 207.7883 8 0.15 100 0.0223 -0.0159 2430.5356 9 0.15 100 0.0223 0.0151 2430.5356 10 0.25 125 0.0196 -0.0049 207.7883 While the Hardy-Cross method is often used with fixed f (or K) values when it is used in exercises performed by hand, the use of the Colebrook equation allows friction losses to vary with Reynolds number. To use this approach the input data must include absolute roughness. Example values are included here: dfpipes <- data.frame( ID = c(1,2,3,4,5,6,7,8,9,10), #pipe ID D = c(0.3,0.2,0.2,0.2,0.2,0.15,0.25,0.15,0.15,0.25), #diameter in m L = c(250,100,125,125,100,100,125,100,100,125), #length in m ks = rep(0.00025,10) #absolute roughness, m ) loops <- list(c(1,2,3,4,5),c(4,6,7,8),c(3,9,10,6)) Qs <- list(c(.040,.040,.02,-.02,-.04),c(.02,0,0,-.02),c(-.02,.02,0,0)) The effect of allowing the calculation of f to be (correctly) dependent on velocity (via the Reynolds number) can be seen, though the effect on final flow values is small. ans <- hydraulics::hardycross(dfpipes = dfpipes, loops = loops, Qs = Qs, n_iter = 3, units = "SI") knitr::kable(ans$dfpipes, digits = 4, format = "pipe", padding=0) ID D L ks Q f K 1 0.30 250 3e-04 0.0382 0.0207 176.1877 2 0.20 100 3e-04 0.0382 0.0218 562.9732 3 0.20 125 3e-04 0.0230 0.0224 723.1119 4 0.20 125 3e-04 -0.0258 0.0222 718.1439 5 0.20 100 3e-04 -0.0418 0.0217 560.8321 6 0.15 100 3e-04 0.0088 0.0248 2700.4710 7 0.25 125 3e-04 0.0040 0.0280 296.3990 8 0.15 100 3e-04 -0.0160 0.0238 2590.2795 9 0.15 100 3e-04 0.0152 0.0239 2598.5553 10 0.25 125 3e-04 -0.0048 0.0270 285.4983 "],["flow-in-open-channels.html", "Chapter 5 Flow in open channels 5.1 An important dimensionless quantity 5.2 Equations for open channel flow 5.3 Trapezoidal channels 5.4 Circular Channels (flowing partially full) 5.5 Critical flow 5.6 Flow in Rectangular Channels 5.7 Gradually varied steady flow 5.8 Rapidly varied flow (the hydraulic jump)", " Chapter 5 Flow in open channels Where flowing water water is exposed to the atmosphere, and thus not under pressure, its condition is called open channel flow. Typical design challenges can be: Determining how deep water will flow in a channel Finding the bottom slope required to carry a defined flow in a channel Comparing different cross-sectional shapes and dimensions to carry flow In pipe flow the cross-sectional area does not change with flow rate, which simplifies some aspects of calculations. By contrast, in open channel flow conditions including flow depth, area, and roughness can all vary with flow rate, which tends to make the equations more cumbersome. In civil engineering applications, roughness characteristics are not usually considered as variable with flow rate. In what follows, three conditions for flow are considered: Uniform flow, where flow characteristics do not vary along the length of a channel Gradually varied flow, where flow responds to an obstruction or change in channel conditions with a gradual adjustment in flow depth Rapidly varied flow, where an abrupt channel transition results in a rapid change in water surface, the most important case of which is the hydraulic jump 5.1 An important dimensionless quantity For open channel flow, given a channel shape and flow rate, flow can usually exist at two different depths, termed subcritical (slow, deep) and supercritical (shallow, fast). The exception is at critical flow conditions, where only one depth exists, the critical depth. Which of these depths is exhibited by the flow is determined by the slope and roughness of the channel. The Froude number characterizes whether flow is critical, supercritical or subcritical, and is defined by Equation (5.1) \\[\\begin{equation} Fr=\\frac{V}{\\sqrt{gD}} \\tag{5.1} \\end{equation}\\] The Froude number characterizes flow as: Fr Condition Description <1.0 subcritical slow, deep =1.0 critical undulating, transitional >1.0 supercritical fast, shallow Critical flow is important in open-channel flow applications and is discussed further below. 5.2 Equations for open channel flow Flow conditions in an open channel under uniform flow conditions are often related by the Manning equation (5.2). \\[\\begin{equation} Q=A\\frac{C}{n}{R}^{\\frac{2}{3}}{S}^{\\frac{1}{2}} \\tag{5.2} \\end{equation}\\] In Equation (5.2), C is 1.0 for SI units and 1.49 for Eng (British Gravitational, English., or U.S. Customary) units. Q is the flow rate, A is the cross-sectional flow area, n is the Manning roughness coefficient, S is the longitudinal channel slope, and R is the hydraulic radius, defined by equation (5.3) \\[\\begin{equation} R=\\frac{A}{P} \\tag{5.3} \\end{equation}\\] where P is the wetted perimeter. Critical depth is defined by the relation (at critical conditions) in Equation (5.4) \\[\\begin{equation} \\frac{Q^{2}B}{g\\,A^{3}}=1 \\tag{5.4} \\end{equation}\\] where B is the width of the water surface (top width). Because of the channel geometry being included in A and R, it helps to work with specific shapes in adapting these equations. The two most common are trapezoidal and circular, included in Sections 5.3 and 5.4 below. As with pipe flow, the energy equation applies for one dimensional open channel flow as well, Equation (5.5): \\[\\begin{equation} \\frac{V_1^2}{2g}+y_1+z_1=\\frac{V_2^2}{2g}+y_2+z_2+h_L \\tag{5.5} \\end{equation}\\] where point 1 is upstream of point 2, V is the flow velocity, y is the flow depth, and z is the elevation of the channel bottom. \\(h_L\\) is the energy head loss from point 1 to point 2. For uniform flow, \\(h_L\\) is the drop in elevation between the two points due to the channel slope. 5.3 Trapezoidal channels In engineering applications one of the most common channel shapes is trapezoidal. Figure 5.1: Typical symmetrical trapezoidal cross section The geometrical relationships for a trapezoid are: \\[\\begin{equation} A=(b+my)y \\tag{5.6} \\end{equation}\\] \\[\\begin{equation} P=b+2y\\sqrt{1+m^2} \\tag{5.7} \\end{equation}\\] Combining Equations (5.6) and (5.7) yields: \\[\\begin{equation} R=\\frac{A}{P}=\\frac{\\left(b+my\\right)y}{b+2y\\sqrt{1+m^2}} \\tag{5.8} \\end{equation}\\] Top width: \\(B=b+2\\,m\\,y\\). Substituting Equations (5.6) and (5.8) into the Manning equation produces Equation (5.9). \\[\\begin{equation} Q=\\frac{C}{n}{\\frac{\\left(by+my^2\\right)^{\\frac{5}{3}}}{\\left(b+2y\\sqrt{1+m^2}\\right)^\\frac{2}{3}}}{S}^{\\frac{1}{2}} \\tag{5.9} \\end{equation}\\] 5.3.1 Solving the Manning equation in R To solve Equation (5.9) when any variable other than Q is unknown, it is straightforward to rearrange it to a form of y(x) = 0. \\[\\begin{equation} Q-\\frac{C}{n}{\\frac{\\left(by+my^2\\right)^{\\frac{5}{3}}}{\\left(b+2y\\sqrt{1+m^2}\\right)^\\frac{2}{3}}}{S}^{\\frac{1}{2}}=0 \\tag{5.10} \\end{equation}\\] This allows the use of a standard solver to find the root(s). If solving it by hand, trial and error can be employed as well. Example 5.1 demonstrates the solution of Equation (5.10) for the flow depth, y. A trial-and-error approach can be applied, and with careful selection of guesses a solution can be obtained relatively quickly. Using solvers makes the process much quicker and less prone to error. Example 5.1 Find the flow depth, y, for a trapezoidal channel with Q=225 ft3/s, n=0.016, m=2, b=10 ft, S=0.0006. The Manning equation can be set up as a function in terms of a missing variable, here using normal depth, y as the missing variable. yfun <- function(y) { Q - (((y * (b + m * y)) ^ (5 / 3) * sqrt(S)) * (C / n) / ((b + 2 * y * sqrt(1 + m ^ 2)) ^ (2 / 3))) } Because these use US Customary (or English) units, C=1.486. Define all of the needed input variables for the function. Q <- 225. n <- 0.016 m <- 2 b <- 10.0 S <- 0.0006 C <- 1.486 Use the R function uniroot to find a single root within a defined interval. Set the interval (the range of possible y values in which to search for a root) to cover all plausible values, here from 0.0001 mm to 200 m. ans <- uniroot(yfun, interval = c(0.0000001, 200), extendInt = "yes") cat(sprintf("Normal Depth: %.3f ft\\n", ans$root)) #> Normal Depth: 3.406 ft Functions can usually be given multiple values as input, returning the corresponding values of output. this allows plots to be created to show, for example, how the left side of Equation (5.10) varies with different values of depth, y. ys <- seq(0.1, 5, 0.1) plot(ys,yfun(ys), type='l', xlab = "y, ft", ylab = "Function to solve for zero") abline(h=0) grid() Figure 5.2: Variation of the left side of Equation (5.10) with y for Example 5.1. This validates the result in the example, showing the root of Equation (5.10), when the function has a value of 0, occurs for a depth, y of a little less than 3.5. 5.3.2 Solving the Manning equation with the hydraulics R package The hydraulics package has a manningt (the ‘t’ is for ‘trapezoid’) function for trapezoidal channels. Example 5.2 demonstrates its usage. Example 5.2 Find the uniform (normal) flow depth, y, for a trapezoidal channel with Q=225 ft3/s, n=0.016, m=2, b=10 ft, S=0.0006. Specifying “Eng” units ensures the correct C value is used. Sf is the same as S in Equations (5.2) and (5.9) since flow is uniform. ans <- hydraulics::manningt(Q = 225., n = 0.016, m = 2, b = 10., Sf = 0.0006, units = "Eng") cat(sprintf("Normal Depth: %.3f ft\\n", ans$y)) #> Normal Depth: 3.406 ft #critical depth is also returned, along with other variables. cat(sprintf("Critical Depth: %.3f ft\\n", ans$yc)) #> Critical Depth: 2.154 ft 5.3.3 Solving the Manning equation using a spreadsheet like Excel Spreadsheet software is very popular and has been modified to be able to accomplish many technical tasks such as solving equations. This example uses Excel with its solver add-in activated, though other spreadsheet software has similar solver add-ins that can be used. The first step is to enter the input data, for the same example as above, along with an initial guess for the variable you wish to solve for. The equation for which a root will be determined is typed in using the initial guess for y in this case. At this point you could use a trial-and-error approach and simply try different values for y until the equation produces something close to 0. A more efficient method is to use a solver. Check that the solver add-in is activated (in Options) and open it. Set the values appropriately. Click Solve and the y value that produces a zero for the equation will appear. If you need to solve for multiple roots, you will need to start from different initial guesses. 5.3.4 Optimal trapezoidal geometry Most fluid mechanics texts that include open channel flow include a derivation of optimal geometry for a trapezoidal channel. This is also called the most efficient cross section. What this means is for a given A and m, there is an optimal flow depth and bottom width for the channel, defined by Equations (5.11) and (5.12). \\[\\begin{equation} b_{opt}=2y\\left(\\sqrt{1+m^2}-m\\right) \\tag{5.11} \\end{equation}\\] \\[\\begin{equation} y_{opt}=\\sqrt{\\frac{A}{2\\sqrt{1+m^2}-m}} \\tag{5.12} \\end{equation}\\] These may be calculated manually, but they are also returned by the manningt function of the hydraulics package in R. Example 5.3 demonstrates this. Example 5.3 Find the optimal channel width to transmit 360 ft3/s at a depth of 3 ft with n=0.015, m=1, S=0.0006. ans <- hydraulics::manningt(Q = 360., n = 0.015, m = 1, y = 3.0, Sf = 0.00088, units = "Eng") knitr::kable(format(as.data.frame(ans), digits = 2), format = "pipe", padding=0) Q V A P R y b m Sf B n yc Fr Re bopt 360 5.3 68 28 2.4 3 20 1 0.00088 26 0.015 2.1 0.57 1159705 4.8 cat(sprintf("Optimal bottom width: %.5f ft\\n", ans$bopt)) #> Optimal bottom width: 4.76753 ft The results show that, aside from the rounding, the required width is approximately 20 ft, and the optimal bottom width for optimal hydraulic efficiency would be 4.76 ft. To check the depth that would be associated with a channel of the optimal width, substitute the optimal width for b and solve for y: ans <- hydraulics::manningt(Q = 360., n = 0.015, m = 1, b = 4.767534, Sf = 0.00088, units = "Eng") cat(sprintf("Optimal depth: %.5f ft\\n", ans$yopt)) #> Optimal depth: 5.75492 ft 5.4 Circular Channels (flowing partially full) Civil engineers encounter many situations with circular pipes that are flowing only partially full, such as storm and sanitary sewers. Figure 5.3: Typical circular cross section The relationships between the depth of water and the values needed in the Manning equation are: Depth (or fractional depth as written here) is described by Equation (5.13) \\[\\begin{equation} \\frac{y}{D}=\\frac{1}{2}\\left(1-\\cos{\\frac{\\theta}{2}}\\right) \\tag{5.13} \\end{equation}\\] Area is described by Equation (5.14) \\[\\begin{equation} A=\\left(\\frac{\\theta-\\sin{\\theta}}{8}\\right)D^2 \\tag{5.14} \\end{equation}\\] (Be sure to use theta in radians.) Wetted perimeter is described by Equation (5.15) \\[\\begin{equation} P=\\frac{D\\theta}{2} \\tag{5.15} \\end{equation}\\] Combining Equations (5.14) and (5.15): \\[\\begin{equation} R=\\frac{D}{4}\\left(1-\\frac{\\sin{\\theta}}{\\theta}\\right) \\tag{5.16} \\end{equation}\\] Top width: \\(B=D\\,sin{\\frac{\\theta}{2}}\\) Substituting Equations (5.14) and (5.16) into the Manning equation, Equation (5.2), produces (5.17). \\[\\begin{equation} \\theta^{-\\frac{2}{3}}\\left(\\theta-\\sin{\\theta}\\right)^\\frac{5}{3}-CnQD^{-\\frac{8}{3}}S^{-\\frac{1}{2}}=0 \\tag{5.17} \\end{equation}\\] where C=20.16 for SI units and C=13.53 for US Customary (English) units. 5.4.1 Solving the Manning equation for a circular pipe in R As was demonstrated with pipe flow, a function could be written with Equation (5.17) and a solver applied to find the value of \\(\\theta\\) for the given flow conditions with a known D, S, n and Q. The value for \\(\\theta\\) could then be used with Equations (5.13), (5.14) and (5.15) to recover geometric values. The R package hydraulics has implemented those routines to enable these calculations. For an existing pipe, a common problem is the determination of the depth, y that a given flow Q, will have given a pipe diameter d, slope S and roughness n. Example 5.4 demonstrates this. Example 5.4 Find the uniform (normal) flow depth, y, for a trapezoidal channel with Q=225 ft3/s, n=0.016, m=2, b=10 ft, S=0.0006. The function manningc from the hydraulics package is used. Any one of the variables in the Manning equation, and related geometric variables, may be treated as an unknown. ans <- hydraulics::manningc(Q=0.01, n=0.013, Sf=0.001, d = 0.2, units="SI", ret_units = TRUE) knitr::kable(format(as.data.frame(ans), digits = 2), format = "pipe", padding=0) ans Q 0.01 [m^3/s] V 0.38 [m/s] A 0.027 [m^2] P 0.44 [m] R 0.061 [m] y 0.16 [m] d 0.2 [m] Sf 0.001 [1] n 0.013 [1] yc 0.085 [m] Fr 0.3 [1] Re 22343 [1] Qf 0.01 [m^3/s] It is also sometimes convenient to see a cross-section diagram. hydraulics::xc_circle(y = ans$y, d=ans$d, units = "SI") 5.5 Critical flow Critical flow in open channel flow is described in general by Equation (5.4). For any channel geometry and flow rate a convenient plot is a specific energy diagram, which illustrates the different flow depths that can occur for any given specific energy. Specific energy is defined by Equation (5.18). \\[\\begin{equation} E=y+\\frac{V^2}{2g} \\tag{5.18} \\end{equation}\\] It can be interpreted as the total energy head, or energy per unit weight, relative to the channel bottom. For a trapezoidal channel, critical flow conditions occur as described by Equation (5.4). Combining that with trapezoidal geometry produces Equation (5.19) \\[\\begin{equation} \\frac{Q^2}{g}=\\frac{\\left(by_c+m{y_c}^2\\right)^3}{b+2my_c} \\tag{5.19} \\end{equation}\\] where \\(y_c\\) indicates critical flow depth. This is important for understanding what may happen to the water surface when flow encounters an obstacle or transition. For the channel of Example 5.3, the diagram is shown in Figure 5.4. hydraulics::spec_energy_trap( Q = 360, b = 20, m = 1, scale = 4, units = "Eng" ) Figure 5.4: A specific energy diagram for the conditions of Example 5.3. This provides an illustration that for y=3 ft the flow is subcritical (above the critical depth). Specific energy for the conditions of the prior example is \\[E=y+\\frac{V^2}{2g}=3.0+\\frac{5.22^2}{2*32.2}=3.42 ft\\] If the channel bottom had an abrupt rise of \\(E-E_c=3.42-3.03=0.39 ft\\) critical depth would occur over the hump. A rise of anything greater than that would cause damming to occur. Once flow over a hump is critical, downstream of the hump the flow will be in supercritical conditions, flowing at the alternate depth. The specific energy for a given depth y and alternate depth can be added to the plot by including an argument for depth, y, as in Figure 5.5. hydraulics::spec_energy_trap( Q = 360, b = 20, m = 1, scale = 4, y=3.0, units = "Eng" ) Figure 5.5: A specific energy diagram for the conditions of Example 5.3 with an additional y value added. 5.6 Flow in Rectangular Channels When working with rectangular channels the open channel equations simplify, because flow, \\(Q\\), can be expressed as flow per unit width, \\(q = Q/b\\), where \\(b\\) is the channel width. Since \\(Q/A=V\\) and \\(A=by\\), Equation (5.18) can be written as Equation (5.20): \\[\\begin{equation} E=y+\\frac{Q^2}{2gA^2}=y+\\frac{q^2}{2gy^2} \\tag{5.20} \\end{equation}\\] Equation (5.19) for critical depth, \\(y_c\\), also is simplified for rectangular channels to Equation (5.21): \\[\\begin{equation} y_c = \\left({\\frac{q^2}{g}}\\right)^{1/3} \\tag{5.21} \\end{equation}\\] Combining Equation (5.20) and Equation (5.21) shows that at critical conditions, the minimum specific energy is: \\[\\begin{equation} E_{min} = \\frac{3}{2} y_c \\tag{5.22} \\end{equation}\\] Example 5.5, based on an exercise from the open-channel flow text by Sturm (Sturm, 2021), demonstrates how to solve for the depth through a rectangular section when the bottom height changes. Example 5.5 A 0.5 m wide rectangular channel carries a flow of 2.2 m\\(^3\\)/s at a depth of 2 m (\\(y_1\\)=2m). If the channel bottom rises 0.25 m (\\(\\Delta z=0.25~ m\\)), and head loss, \\(h_L\\) over the transition is negligible, what is the depth, \\(y_2\\) after the rise in channel bottom? Figure 5.6: The rectangular channel of Example 5.5 with an increase in channel bottom height downstream. A specific energy diagram is very helpful for establishing upstream conditions and estimating \\(y_2\\). p1 <- hydraulics::spec_energy_trap( Q = 2.2, b = 0.5, m = 0, y = 2, scale = 2.5, units = "SI" ) p1 (#fig:sp_openrect)A specific energy diagram for the conditions of Example 5.5. The values of \\(y_c\\) and \\(E_{min}\\) shown in the plot can be verified using Equations (5.21) and (5.22). This should always be checked to describe the incoming flow and what will happen as flow passes over a hump. Since \\(y_1\\) > \\(y_c\\) the upstream flow is subcritical, and flow can be expected to drop as it passes over the hump. Upstream and downstream specific energy are related by Equation (5.23): \\[\\begin{equation} E_1-E_2=\\Delta z + h_L \\tag{5.23} \\end{equation}\\] Since \\(h_L\\) is negligible in this example, the downstream specific energy, \\(E_2\\) is lower that the upper \\(E_1\\) by an amount \\(\\Delta z\\), or \\[\\begin{equation} E_2 = E_1 - \\Delta z \\tag{5.24} \\end{equation}\\] For a 0.25 m rise, and using \\(q = Q/b = 2.2/0.5 = 4.4\\), combining Equation (5.24) and Equation (5.20): \\[E_2 = E_1 - 0.25 = 2 + \\frac{4.4^2}{2(9.81)(2^2)} - 0.25 = 2.247 - 0.25 = 1.997 ~m\\] From the specific energy diagram, for \\(E_2=1.997 ~ m\\) a depth of about \\(y_2 \\approx 1.6 ~ m\\) would be expected, and the flow would continue in subcritical conditions. The value of \\(y_2\\) can be calculated using Equation (5.20): \\[1.997 = y_2 + \\frac{4.4^2}{2(9.81)(y_2^2)}\\] which can be rearranged to \\[0.9967 - 1.997 y_2^2 + y_2^3= 0\\] Solving a polynomial in R is straightforward using the polyroot function and using Re to extract the real portion of the solution. Re(polyroot(c(0.9667, 0, -1.997, 1))) #> [1] 0.9703764 -0.6090519 1.6356755 The negative root is meaningless, the lower positive root is the supercritical depth for \\(E_2 = 1.997 ~ m\\), and the larger positive root is the subcritical depth. Thus the correct solution is \\(y_2 = 1.64 ~ m\\) when the channel bottom rises by 0.25 m. The specific energy diagram shows that if \\(\\Delta z > E_1 - E_{min}\\), the downstream specific energy, \\(E_2\\) would be to the left of the curve, so no feasible solution would exist. At that point damming would occur, raising the upstream depth, \\(y_1\\), and thus increasing \\(E_1\\) until \\(E_2 = E_{min}\\). The largest rise in channel bottom height that will not cause damming is called the critical hump height: \\(\\Delta z_{c} = E_1 - E_{min}\\). A vertical can be added to the specific energy diagram to indicate \\(E_2\\): p1 + ggplot2::geom_vline(xintercept = 1.997, linetype=3) + ggplot2::annotate("text", x=1.9, y=2.5, label=expression(E[2]), angle=90) 5.7 Gradually varied steady flow When water approaches an obstacle, it can back up, with its depth increasing. The effect can be observed well upstream. Similarly, as water approaches a drop, such as with a waterfall, the water level declines, and that effect can also be seen upstream. In general, any change in slope or roughness will produce changes in depth along a channel length. There are three depths that are important to define for a channel: \\(y_c\\), critical depth, found using Equation (5.4) \\(y_0\\), normal depth, found using Equation (5.2) \\(y\\), flow depth, found using Equation (5.5) If \\(y_n < y_c\\) flow is supercritical (for example, flowing down a steep slope); if \\(y_n > y_c\\) flow is subcritical. Variations in the water surface are classified by profile types based on to whether the normal flow is subcritical (or mild sloped, M) or supercritical (steep, S), as in Figure 5.7 (Davidian, Jacob, 1984). Figure 5.7: Types of flow profiles on mild and steep slopes In addition to channel transitions, changes in channel slow of roughness (Manning n) will cause the flow surface to vary. Some of these conditions are illustrated in Figure 5.8 (Davidian, Jacob, 1984). Figure 5.8: Types of flow profiles with changes in slope or roughness Typically, for supercritical flow the calculations start at an upstream cross section and move downstream. For subcritical flow calculations proceed upstream. However, for the direct step method, a negative result will indicate upstream, and a positive result indicates downstream. If the water surface passes through critical depth (from supercritical to subcritical or the reverse) it is no longer gradually varied flow and the methods in this section do not apply. This can happen at abrupt changes in channel slope or roughness, or channel transitions. 5.7.1 The direct step method The direct step method looks at two cross sections in a channel where depths, \\(y_1\\) and \\(y_2\\) are defined. Figure 5.9: A gradually varied flow example. The distance between these two cross-sections, \\({\\Delta}X\\), is calculated using Equation (5.25) \\[\\begin{equation} {\\Delta}X=\\frac{E_1-E_2}{\\overline{S}-S_0} \\tag{5.25} \\end{equation}\\] Where E is the specific energy from Equation (5.18), \\(S_0\\) is the slope of the channel bed, and \\(S\\) is the slope of the energy grade line. \\(\\overline{S}\\) is the average of the S values at each cross section calculated using the Manning equation, Equation (5.2) solved for slope, as in Equation (5.26). \\[\\begin{equation} S=\\frac{n^2\\,V^2}{C^2\\,R^{\\frac{4}{3}}} \\tag{5.26} \\end{equation}\\] Example 5.6 demonstrates this. Example 5.6 Water flows at 10 m3/s in a trapezoidal channel with n=0.015, bottom width 3 m, side slope of 2:1 (H:V) and longitudinal slope 0.0009 (0.09%). At the location of a USGS stream gage the flow depth is 1.4 m. Use the direct step method to find the distance to the point where the depth is 1.2 m and determine whether it is upstream or downstream. Begin by setting up a function to calculate the Manning slope and setting up the input data. #function to calculate Manning slope slope_f <- function(V,n,R,C) { return(V^2*n^2/(C^2*R^(4./3.))) } #Now set up input data ################################## #input Flow Q=10.0 #input depths: y1 <- 1.4 #starting depth y2 <- 1.2 #final depth #Define the number of steps into which the difference in y will be broken nsteps <- 2 #channel geometry: bottom_width <- 3 side_slope <- 2 #side slope is H:V. Use zero for rectangular manning_n <- 0.015 long_slope <- 0.0009 units <- "SI" #"SI" or "Eng" if (units == "SI") { C <- 1 #Manning constant: 1 for SI, 1.49 for US units g <- 9.81 } else { #"Eng" means English, or US system C <- 1.49 g <- 32.2 } #find depth increment for each step, depths at which to solve depth_incr <- (y2 - y1) / nsteps depths <- seq(from=y1, to=y2, by=depth_incr) First check to see if the flow is subcritical or supercritical and find the normal depth. Critical and normal depths can be calculated using the manningt function in the hydraulics package, as in Example 5.2. However, because other functionality of the rivr package is used, these will be calculated using functions from the rivr package. rivr::critical_depth(Q = Q, yopt = y1, g = g, B = bottom_width , SS = side_slope) #> [1] 0.8555011 #note using either depth for yopt produces the same answer rivr::normal_depth(So = long_slope, n = manning_n, Q = Q, yopt = y1, Cm = C, B = bottom_width , SS = side_slope) #> [1] 1.147137 The normal depth is greater than the critical depth, so the channel has a mild slope. The beginning and ending depths are above normal depth. This indicates the profile type, following Figure 5.7, is M-1, so the flow depth should decrease in depth going upstream. This also verifies that the flow depth between these two points does not pass through critical flow, so is a valid gradually varied flow problem. For each increment the \\({\\Delta}X\\) value needs to be calculated, and they need to be accumulated to find the total length, L, between the two defined depths. #loop through each channel segment (step), calculating the length for each segment. #The channel_geom function from the rivr package is helpful L <- 0 for ( i in 1:nsteps ) { #find hydraulic geometry, E and Sf at first depth xc1 <- rivr::channel_geom(y=depths[i], B=bottom_width, SS=side_slope) V1 <- Q/xc1[['A']] R1 <- xc1[['R']] E1 <- depths[i] + V1^2/(2*g) Sf1 <- slope_f(V1,manning_n,R1,C) #find hydraulic geometry, E and Sf at second depth xc2 <- rivr::channel_geom(y=depths[i+1], B=bottom_width, SS=side_slope) V2 <- Q/xc2[['A']] R2 <- xc2[['R']] E2 <- depths[i+1] + V2^2/(2*g) Sf2 <- slope_f(V2,manning_n,R2,C) Sf_avg <- (Sf1 + Sf2) / 2.0 dX <- (E1 - E2) / (Sf_avg - long_slope) L <- L + dX } cat(sprintf("Using %d steps, total distance from depth %.2f to %.2f = %.2f m\\n", nsteps, y1, y2, L)) #> Using 2 steps, total distance from depth 1.40 to 1.20 = -491.75 m The result is negative, verifying that the location of depth y2 is upstream of y1. Of course, the result will become more precise as more incremental steps are included, as shown in Figure 5.10 Figure 5.10: Variation of number of calculation steps to final calculated distance. The direct step method is also implemented in the hydraulics package, and can be applied to the same problem as above, as illustrated in Example 5.7. Example 5.7 Water flows at 10 m3/s in a trapezoidal channel with n=0.015, bottom width 3 m, side slope of 2:1 (H:V) and longitudinal slope 0.0009 (0.09%). At the location of a USGS stream gage the flow depth is 1.4 m. Use the direct step method to find the distance to the point where the depth is 1.2 m and determine whether it is upstream or downstream. hydraulics::direct_step(So=0.0009, n=0.015, Q=10, y1=1.4, y2=1.2, b=3, m=2, nsteps=2, units="SI") #> y1=1.400, y2=1.200, yn=1.147, yc=0.855585 #> Profile type = M1 #> # A tibble: 3 × 7 #> x z y A Sf E Fr #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 0 0 1.4 8.12 0.000407 1.48 0.405 #> 2 -192. 0.173 1.3 7.28 0.000548 1.40 0.466 #> 3 -492. 0.443 1.2 6.48 0.000753 1.32 0.541 This produces the same result, and verifies that the water surface profile is type M-1. 5.7.2 Standard step method The standard step method works similarly to the direct step method, except from one known depth the second depth is determined at a known distance, L. This is a preferred method when the depth at a critical location, such as a bridge, is needed. The rivr package implements the standard step method in its compute_profile function. To compare it to the direct step method, check the depth at \\(y_2\\) given the total distance from Example 5.6. Example 5.8 For the same channel and flow rate as Example 5.6, determine the depth of water at the distance L determined above. The function requires the distance to be positive, so apply the absolute value to the L value. dist = abs(L) ans <- rivr::compute_profile(So = long_slope, n = manning_n, Q = Q, y0 = y1, Cm = C, g = g, B = bottom_width, SS = side_slope, stepdist = dist/nsteps, totaldist = dist) #Distances along the channel where depths were determined ans$x #> [1] 0.0000 -245.8742 -491.7483 #Depths at each distance ans$y #> [1] 1.400000 1.277009 1.200592 This shows the distances and depths at each of the steps defined. Consistent with the above, the distances are negative, showing that they are progressing upstream. The results are identical for \\(y_2\\) using the direct step method. 5.8 Rapidly varied flow (the hydraulic jump) Figure 5.11: A hydraulic jump at St. Anthony Falls, Minnesota. In the discussion of critical flow in Section 5.5, the concept of alternate depths was introduced, where a given flow rate in a channel with known geometry typically may assume two possible values, one subcritical and one supercritical. For the case of supercritical flow transitioning to subcritical flow, a smooth transition is impossible, so a hydraulic jump occurs. A hydraulic jump always dissipates some of the incoming energy. A hydraulic jump is depicted in Figure 5.12 (Peterka, Alvin J., 1978). Figure 5.12: A typical hydraulic jump. 5.8.1 Sequent (or conjugate) depths The two depths on either side of a hydraulic jump are called sequent depths or conjugate depths. The relationship between them can be established using the momentum equation to develop an general expression (for any open channel) for the momentum function, M, as in Equation (5.27). \\[\\begin{equation} M=Ah_c+\\frac{Q^2}{gA} \\tag{5.27} \\end{equation}\\] where \\(h_c\\) is the distance from the water surface to the centroid of the channel cross-section. For a trapezoidal channel, the momentum equation becomes that described by Equation (5.28). \\[\\begin{equation} M=\\frac{by^2}{2}+\\frac{my^3}{3}+\\frac{Q^2}{gy\\left(b+my\\right)} \\tag{5.28} \\end{equation}\\] For the case of a rectangular channel, setting m=0 and setting the Momentum function for two sequent depths, y1 ans y2 equal, produces the relationship in Equation (5.29). \\[\\begin{equation} \\frac{y_2}{y_1}=\\frac{1}{2}\\left(-1+\\sqrt{1+8Fr_1^2}\\right) or \\frac{y_1}{y_2}=\\frac{1}{2}\\left(-1+\\sqrt{1+8Fr_2^2}\\right) \\tag{5.29} \\end{equation}\\] where Frn is the Froude Number [Equation (5.1)] at section n. Again, for the case of a rectangular channel, the energy head loss through a hydraulic jump simplifies to Equation (5.30). \\[\\begin{equation} h_l=\\frac{\\left(y_2-y_1\\right)^3}{4y_1y_2} \\tag{5.30} \\end{equation}\\] Given that the momentum function must be conserved on either side of a hydraulic jump, finding the sequent depth for any known depth becomes straightforward for trapezoidal shapes. Setting M1 = M2 in Equation (5.28) allows the use of a solver, as in Example 5.9. Example 5.9 A trapezoidal channel with a bottom width of 0.5 m and a side slope of 1:1 carries a flow of 0.2 m3/s. The depth on one side of a hydraulic jump is 0.1 m. Find the sequent depth, the energy head loss, and the power dissipation in Watts. flow <- 0.2 ans <- hydraulics::sequent_depth(Q=flow,b=0.5,y=0.1,m=1,units = "SI", ret_units = TRUE) #print output of function as.data.frame(ans) #> ans #> y 0.1 [m] #> y_seq 0.3941009 [m] #> yc 0.217704 [m] #> Fr 3.635731 [1] #> Fr_seq 0.3465538 [1] #> E 0.666509 [m] #> E_seq 0.4105265 [m] #Find energy head loss hl <- abs(ans$E - ans$E_seq) hl #> 0.2559825 [m] #Express this as a power loss gamma <- hydraulics::specwt(units = "SI") P <- gamma*flow*hl cat(sprintf("Power loss = %.1f Watts\\n",P)) #> Power loss = 501.4 Watts The energy loss across hydraulic jumps varies with the Froude number of the incoming flow, as shown in depicted in Figure 5.13 (Peterka, Alvin J., 1978). Figure 5.13: Types of hydraulic jumps. 5.8.2 Location of a hydraulic jump In hydraulic infrastructure where hydraulic jumps will occur there are usually engineered features, such as baffles or basins, to force a hydraulic jump to occur in specific locations, to protect downstream waterways from the turbulent effects of an uncontrolled hydraulic jump. In the absence of engineered features to cause a jump, the location of a hydraulic jump can be determined using the concepts of Sections 5.7 and 5.8. Example 5.10 demonstrates the determination of the location of a hydraulic jump when normal flow conditions exist at some distance upstream and downstream of the jump. Example 5.10 A rectangular (a trapezoid with side slope, m=0) concrete channel with a bottom width of 3 m carries a flow of 8 m3/s. The upstream channel slopes steeply at So=0.018 and discharges onto a mild slope of So=0.0015. Determine the height of the jump and its location. First find the normal depth on each slope, and the critical depth for the channel. yn1 <- hydraulics::manningt(Q = 8, n = 0.013, m = 0, Sf = 0.018, b = 3, units = "SI")$y yn2 <- hydraulics::manningt(Q = 8, n = 0.013, m = 0, Sf = 0.0015, b = 3, units = "SI")$y yc <- hydraulics::manningt(Q = 8, n = 0.013, m = 0, Sf = 0.0015, b = 3, units = "SI")$yc cat(sprintf("yn1 = %.3f m, yn2 = %.3f m, yc = %.3f m\\n", yn1, yn2, yc)) #> yn1 = 0.498 m, yn2 = 1.180 m, yc = 0.898 m Recall that the calculation of yc only depends on flow and channel geometry (Q, b, m), so the values of n and Sf can be arbitrary for that command. These results confirm that flow is supercritical upstream and subcritical downstream, so a hydraulic jump will occur. The hydraulic jump will either begin at yn1 (and jump to the sequent depth for yn1) or end at yn2 (beginning at the sequent depth for yn2). The possibilities are shown in Figure 5.7 in the lower right panel. First check the two sequent depths. yn1_seq <- hydraulics::sequent_depth(Q = 8, b = 3, y=yn1, m = 0, units = "SI")$y_seq yn2_seq <- hydraulics::sequent_depth(Q = 8, b = 3, y=yn2, m = 0, units = "SI")$y_seq cat(sprintf("yn1_seq = %.3f m, yn2_seq = %.3f m\\n", yn1_seq, yn2_seq)) #> yn1_seq = 1.476 m, yn2_seq = 0.666 m This confirms that if the jump began at yn1 (on the steep slope) it would need to jump a level below yn2, with an S-1 curve providing the gradual increase in depth to yn2. Since yn1_seq exceeds yn2, this is not possible. That can be verified using the direct_step function to show the distance from yn1_seq to yn2 would need to be upstream (negative x values in the result), which cannot occur for this case. This means the alternate case must exist, with an M-3 profile raising yn1 to yn2_seq at which point the jump occurs. The direct step method can find this distance along the channel. hydraulics::direct_step(So=0.0015, n=0.013, Q=8, y1=yn1, y2=yn2_seq, b=3, m=0, nsteps=2, units="SI") #> # A tibble: 3 × 7 #> x z y A Sf E Fr #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 0 0 0.498 1.49 0.0180 1.96 2.42 #> 2 23.4 -0.0350 0.582 1.75 0.0113 1.65 1.92 #> 3 44.6 -0.0669 0.666 2.00 0.00761 1.48 1.57 The number of calculation steps (nsteps) can be increased for greater precision, but 2 steps is adequate here. "],["momentum-in-water-flow.html", "Chapter 6 Momentum in water flow 6.1 Equations of linear momentum 6.2 The momentum equation in pipe design", " Chapter 6 Momentum in water flow When moving water changes direction or velocity, an external force must be associated with the change. In civil engineering infrastructure this is ubiquitous and the forces associated with this must be accounted for in design. Figure 6.1: Water pipe on Capitol Hill, Seattle. 6.1 Equations of linear momentum Newton’s law relates the forces applied to a body to the rate of change of linear momentum, as in Equation (6.1) \\[\\begin{equation} \\sum{\\overrightarrow{F}}=\\frac{d\\left(m\\overrightarrow{V}\\right)}{dt} \\tag{6.1} \\end{equation}\\] For fluid flow in a hydraulic system carrying a flow Q, the equation can be written in any linear direction (x-direction in this example) as in Equation (6.2). \\[\\begin{equation} \\sum{F_x}=\\rho{Q}\\left(V_{2x}-V_{1x}\\right) \\tag{6.2} \\end{equation}\\] where \\(\\rho{Q}\\) is the mass flux through the system, \\(V_{1x}\\) is the velocity in the x-direction where flow enters the system, and \\(V_{2x}\\) is the velocity in the x-direction where flow leaves the system. \\(\\sum{F_x}\\) is the vector sum of all external forces acting on the system in the x-direction. It should be noted that the values of V are the average cross-sectional velocity. A momentum correction factor (\\(\\beta\\)), can be applied when the velocity is highly non-uniform across the cross-section. In nearly all civil engineering applications the adjustment factor is close enough to 1 where it is ignored in the calculations. 6.2 The momentum equation in pipe design One of the most common civil engineering applications of the momentum equation is providing the lateral restraint where a pipe bend occurs. One approach to provide the external force to keep the pipe in equilibrium is to use a thrust block, as illustrated in Figure 6.2 (Ductile Iron Pipe Research Association, 2016). Figure 6.2: A sketch of a pipe bend with a thrust block. Example 6.1 A horizontal 18-inch diameter pipe carries flow Q of water at 68\\(^\\circ\\)F with a pressure of 60 psi and encounters a bend of angle \\(\\theta=30^\\circ\\). Show how the reaction force, R varies with the flow rate through the bend for flows up to 20 ft3/s. Ignore head loss through the bend. Taking the control volume to be the bend, the external forces acting on the bend are shown in Figure 6.3. Figure 6.3: External forces on the pipe. Note that if the pipe were not horizontal, the weight of the water in the pipe would also need to be included. Including all of the external forces in the x-direction on left side of Equation (6.2) and recognizing that V1x=V1 and V2x=V2cos\\(\\theta\\) produces: \\[P_1A_1-P_2A_2cos\\theta-R_x=\\rho{Q}\\left(V_{2}cos\\theta-V_{1}\\right)\\] Rearranging to solve for Rx gives Equation (6.3). \\[\\begin{equation} R_x=P_1A_1-P_2A_2cos\\theta-\\rho{Q}\\left(V_{2}cos\\theta-V_{1}\\right) \\tag{6.3} \\end{equation}\\] Similarly in the y-direction Equation (6.4) can be assembled, noting that V1y=0 and V2y=\\(-\\)V2sin\\(\\theta\\) . \\[\\begin{equation} R_y=P_2A_2sin\\theta-\\rho{Q}\\left(-V_{2}sin\\theta\\right) \\tag{6.4} \\end{equation}\\] This can be set up in R in many ways, such as the following. #Input Data -- ensure units are consistent in ft, lbf (pound force), sec D1 <- units::set_units(18/12, ft) D2 <- units::set_units(18/12, ft) P1 <- units::set_units(60*144, lbf/ft^2) #convert psi to lbf/ft^2 P2 <- units::set_units(60*144, lbf/ft^2) theta <- 30*(pi/180) #convert to radians for sin, cos functions rho <- hydraulics::dens(T=68, units="Eng", ret_units = TRUE) # calculations - vary flow from 0 to 20 ft^3/s Q <- units::set_units(seq(0,20,1), ft^3/s) A1 <- pi/4*D1^2 A2 <- pi/4*D2^2 V1 <- Q/A1 V2 <- Q/A2 Rx <- P1*A1-P2*A2*cos(theta)-rho*Q*(V2*cos(theta)-V1) Ry <- P2*A2*sin(theta)-rho*Q*(-V2*sin(theta)) R <- sqrt(Rx^2 + Ry^2) plot(Q,R) When Q=0, only the pressure terms contribute to R. This plot shows that for typical water main conditions the change in direction of the velocity vectors adds a small amount (less than 3% in this example) to the calculated R value. This is why design guidelines for water mains often neglect the velocity term in Equation (6.2). In other industrial or laboratory conditions it may not be valid to neglect that term. "],["pumps-and-how-they-operate-in-a-hydraulic-system.html", "Chapter 7 Pumps and how they operate in a hydraulic system 7.1 Defining the system curve 7.2 Defining the pump characteristic curve 7.3 Finding the operating point", " Chapter 7 Pumps and how they operate in a hydraulic system For any system delivering water through circular pipes with the assistance of a pump, the selection of the pump requires a consideration of both the pump characteristics and the energy required to deliver different flow rates through the system. These are described by the system and pump characteristic curves. Where they intersect defines the operating point, the flow and (energy) head at which the pump would operate in that system. 7.1 Defining the system curve Figure 7.1: A simple hydraulic system (from https://www.castlepumps.com) For a simple system the loss of head (energy per unit weight) due to friction, \\(h_f\\), is described by the Darcy-Weisbach equation, which can be simplified as in Equation (7.1). \\[\\begin{equation} h_f = \\frac{fL}{D}\\frac{V^2}{2g} = \\frac{8fL}{\\pi^{2}gD^{5}}Q^{2} = KQ{^2} \\tag{7.1} \\end{equation}\\] The total dynamic head the system requires a pump to provide, \\(h_p\\), is found by solving the energy equation from the upstream reservoir (point 1) to the downstream reservoir (point 2), as in Equation (7.2). \\[\\begin{equation} h_p = \\left(z+\\frac{P}{\\gamma}+\\frac{V^2}{2g}\\right)_2 - \\left(z+\\frac{P}{\\gamma}+\\frac{V^2}{2g}\\right)_1+h_f \\tag{7.2} \\end{equation}\\] For the simple system in Figure 7.1, the velocity can be considered negligible in both reservoirs 1 and 2, and the pressures at both reservoirs is atmospheric, so the Equation (7.2) can be simplified to (7.3). \\[\\begin{equation} h_p = \\left(z_2 - z_1\\right) + h_f=h_s+h_f=h_s+KQ^2 \\tag{7.3} \\end{equation}\\] Using the hydraulics package, the coefficient, K, can be calculated manually or using other package functions for friction loss in a pipe system using \\(Q=1\\). Using this to develop a system curve is demonstrated in Example 7.1. Example 7.1 Develop a system curve for a pipe with a diameter of 20 inches, length of 3884 ft, and absolute roughness of 0.0005 ft. Use kinematic viscocity, \\(\\nu\\) = 1.23 x 10-5 ft2/s. Assume a static head, z2 - z1 = 30 ft. ans <- hydraulics::darcyweisbach(Q = 1,D = 20/12, L = 3884, ks = 0.0005, nu = 1.23e-5, units = "Eng") cat(sprintf("Coefficient K: %.3f\\n", ans$hf)) #> Coefficient K: 0.160 scurve <- hydraulics::systemcurve(hs = 30, K = ans$hf, units = "Eng") print(scurve$eqn) #> [1] "h == 30 + 0.16*Q^2" For this function of the hydraulics package, Q is either in ft\\(^3\\)/s or m\\(^3\\)/s, depending on whether Eng or SI is specified for units. Often data for flows in pumping systems are in other units such as gpm or liters/s, so unit conversions would need to be applied. 7.2 Defining the pump characteristic curve The pump characteristic curve is based on data or graphs obtained from a pump manufacturer, such as that depicted in Figure 7.2. Figure 7.2: A sample set of pump curves (from https://www.gouldspumps.com). The three red dots are points selected to approximate the curve The three selected points, selected manually across the range of the curve, are used to generate a polynomial fit to the curve. There are many forms of equations that could be used to fit these three points to a smooth, continuous curve. Three common ones are implemented in the hydraulics package, shown in Table 7.1. Table 7.1: Common equation forms for pump characteristic curves. type Equation poly1 \\(h=a+{b}{Q}+{c}{Q}^2\\) poly2 \\(h=a+{c}{Q}^2\\) poly3 \\(h_{shutoff}+{c}{Q}^2\\) The \\(h_{shutoff}\\) value is the pump head at \\(Q={0}\\). Many methods can be used to fit a polynomial to a set of points. The hydraulics package includes the pumpcurve function for this purpose. The coordinates of the points can be input as numeric vectors, being careful to use correct units, consistent with those used for the system curve. Manufacturer’s pump curves often use units for flow that are not what the hydraulics package needs, and the units package provides a convenient way to convert them as needed. Developing the pump characteristic curve using the hydraulics package is demonstrated in Example 7.2. Example 7.2 Develop a pump characteristic curve for the pump in Figure 7.2, using the three points marked in red. Use the poly2 form from Table 7.1. qgpm <- units::set_units(c(0, 5000, 7850), gallons/minute) #Convert units to those needed for package, and consistent with system curve qcfs <- units::set_units(qgpm, ft^3/s) #Head units, read from the plot, are already in ft so setting units is not needed hft <- c(81, 60, 20) pcurve <- hydraulics::pumpcurve(Q = qcfs, h = hft, eq = "poly2", units = "Eng") print(pcurve$eqn) #> [1] "h == 82.5 - 0.201*Q^2" The function pumpcurve returns a pumpcurve object that includes the polynomial fit equation and a simple plot to check the fit. This can be plotted as in Figure 7.3 pcurve$p Figure 7.3: A pump characteristic curve 7.3 Finding the operating point The two curves can be combined to find the operating point of the selected pump in the defined system. this can be done by plotting them manually, solving the equations simultaneously, or by using software. The hydraulics package finds the operating point using the system and pump curves defined earlier. Example 7.3 shown how this is done. Example 7.3 Find the operating point for the pump and system curves developed in Examples 7.1 and 7.2. oppt <- hydraulics::operpoint(pcurve = pcurve, scurve = scurve) cat(sprintf("Operating Point: Q = %.3f, h = %.3f\\n", oppt$Qop, oppt$hop)) #> Operating Point: Q = 12.051, h = 53.285 The function operpoint function returns an operpoint object that includes the a plot of both curves. This can be plotted as in Figure 7.4 oppt$p Figure 7.4: The pump operating point "],["the-hydrologic-cycle-and-precipitation.html", "Chapter 8 The hydrologic cycle and precipitation 8.1 Precipitation observations 8.2 Precipitation frequency 8.3 Precipitation gauge consistency – double mass curves 8.4 Precipitation interpolation and areal averaging", " Chapter 8 The hydrologic cycle and precipitation All of the earlier chapters of this book dealt with the behavior of water in different hydraulic systems, such as canals or pipes. Now we consider the bigger picture of where the water originates, and ultimately how we can estimate how much water is available for different uses, and how much excess (flood) water systems will need to be designed and built to accommodate. A fundamental concept is the hydrologic cycle, depicted in Figure 8.1. Figure 8.1: The hydrologic cycle, from the USGS The primary variable in the hydrologic cycle from an engineering perspective is precipitation, since that is the source of the water used and managed in engineered systems. The hydromisc package will need to be installed to access some of the data used below. If it is not installed, do so following the instructions on the github site for the package. 8.1 Precipitation observations Direct measurement of precipitation is done with precipitation gauges, such as shown in Figure 8.2. Figure 8.2: National Weather Service standard 8-inch gauge (source: NWS). Precipitation can vary dramatically over short distances, so point measurements are challenging to work with when characterizing rainfall over a larger area. An image from an atmospheric river event over California is shown in Figure 8.3. Reflectivity values are converted to precipitation rates based on calibration with rain gauge observations. Figure 8.3: A raw radar image showing reflectivity values. Red squared indicated weather radar locations (source: NOAA). There are additional data sets that merge many sources of data to create continuous (spatially and temporally) datasets of precipitation. While these provide excellent resources for large scale studies, we will initially focus on point observations. Obtaining precipitation data can be done in many ways. Example 8.1 demonstrates one method using R using the FedData package. Example 8.1 Characterize the rainfall in the city of San Jose, in Santa Clara County. For the U.S., a good starting point is to use the mapping tools at the NOAA Climate Data Online (CDO) website. From the mapping tools page, select Observations: Daily ensure GHCN Daily is checked so you’ll look for stations that are part of the Global Historical Climatology Network and search for San Jose, CA. Figure 8.4 shows the three stations that lie within the rectangle sketched on the map, and the one that was selected. Figure 8.4: Selection results for a portion of San Jose, CA (source: CDO). The data can be downloaded directly from the CDO site as a csv file, a sample of which is included with the hydromisc package (the sample also includes air temperature data). Note the units that you specify for the data since they will not appear in the csv file. Note that this initial station search and data download can be automated in R using other packages: Using the FedData package, following a method similar to this. Using the rnoaa package, referring to the vignettes. While formats will vary depending on the source of the data, in this example we can import the csv file directly. Since units were left as ‘standard’ on the CDO website, precipitation is in inches and temperatures in oF. datafile <- system.file("extdata", "cdo_data_ghcn_23293.csv", package="hydromisc") ghcn_data <- read.csv(datafile,header=TRUE) A little cleanup of the data needs to be done to ensure the DATE column is in date format, and change any missing values (often denoted as 9999 or -9999) to NA. With missing values flagged as NA, R can ignore them, set them to zero, or fill them in with functions like the zoo::na.approx() or na.spline() functions, or using the more sophisticated imputeTS package. finally, add a ‘water year’ column (a water year begins on October 1 and ends September 30). ghcn_data$DATE <- as.Date(ghcn_data$DATE, format="%Y-%m-%d") ghcn_data$PRCP[ghcn_data$PRCP <= -999 | ghcn_data$PRCP >= 999 ] = NA wateryr <- function(d) { if (as.numeric(format(d, "%m")) >= 10) { wy = as.numeric(format(d, "%Y")) + 1 } else { wy = as.numeric(format(d, "%Y")) } } ghcn_data$wy <- sapply(ghcn_data$DATE, wateryr) A convenient package for characterizing precipitation is hydroTSM, the output of which is shown in Figure 8.5 library(hydroTSM) #create a simple data frame for plotting ghcn_prcp <- data.frame(date = ghcn_data$DATE, prcp = ghcn_data$PRCP ) #convert it to a zoo object x <- zoo::read.zoo(ghcn_prcp) hydroTSM::hydroplot(x, var.type="Precipitation", main="", var.unit="inch", pfreq = "ma", from="1999-01-01", to="2022-12-31") Figure 8.5: Monthly and annual precipitation summary for San Jose, CA for 1999-2022 This presentation shows the seasonality of rainfall in San Jose, with most falling between October and May. The mean is about 12 inches per year, with most years experiencing between 10-15 inches of precipitation. There are functions to produce many statistics such as monthly means. #calculate monthly sums monsums <- hydroTSM::daily2monthly(x, sum, na.rm = TRUE) monavg <- as.data.frame(hydroTSM::monthlyfunction(monsums, mean, na.rm = TRUE)) #if record begins in a month other than January, need to reorder monavg <- monavg[order(factor(row.names(monavg), levels = month.abb)),,drop=FALSE] colnames(monavg)[1] <- "Avg monthly precip, in" knitr::kable(monavg, digits = 2) |> kableExtra::kable_paper(bootstrap_options = "striped", full_width = F) Avg monthly precip, in Jan 2.23 Feb 2.26 Mar 1.75 Apr 1.03 May 0.26 Jun 0.10 Jul 0.00 Aug 0.00 Sep 0.10 Oct 0.60 Nov 1.21 Dec 2.31 The winter of 2016-2017 (water year 2017) was a record wet year for much of California. Figure 8.6 shows a hyetograph the daily values for that year. library(ggplot2) ghcn_prcp2 <- data.frame(date = ghcn_data$DATE, wy = ghcn_data$wy, prcp = ghcn_data$PRCP ) ggplot(subset(ghcn_prcp2, wy==2017), aes(x=date, y=prcp)) + geom_bar(stat="identity",color="red") + labs(x="", y="precipitation, inch/day") + scale_x_date(date_breaks = "1 month", date_labels = "%b %d") Figure 8.6: Daily Precipitation for San Jose, CA for water year 2017 While many other statistics could be calculated to characterize precipitation, only a handful more will be shown here. One will use a convenient function of the seas package. This is used in Figure 8.7. library(tidyverse) #The average precipitation rate for rainy days (with more then 0.01 inch) avgrainrate <- ghcn_prcp2[ghcn_prcp2$prcp > 0.01,] |> group_by(wy) |> summarise(prcp = mean(prcp)) #the number of rainy days per year nraindays <- ghcn_prcp2[ghcn_prcp2$prcp > 0.01,] |> group_by(wy) |> summarise(nraindays = length(prcp)) #Find length of consecutive dry and wet spells for the record days.dry.wet <- seas::interarrival(ghcn_prcp, var = "prcp", p.cut = 0.01, inv = FALSE) #add a water year column to the result days.dry.wet$wy <- sapply(days.dry.wet$date, wateryr) res <- days.dry.wet |> group_by(wy) |> summarise(cdd = median(dry, na.rm=TRUE), cwd = median(wet, na.rm=TRUE)) res_long <- pivot_longer(res, -wy, names_to="statistic", values_to="consecutive_days") ggplot(res_long, aes(x = wy, y = consecutive_days)) + geom_bar(aes(fill = statistic),stat = "identity", position = "dodge")+ xlab("") + ylab("Median consecutive days") Figure 8.7: Median concecutive dry days (cdd) and wet days (cwd) for each water year. 8.2 Precipitation frequency For engineering design, the uncertainty in predicting extreme rainfall, floods, or droughts is expressed as risk, typically the probability that a certain event will be equalled or exceeded in any year. The return period, T, is the inverse of the probability of exceedence, so that a storm with a 10% chance of being exceeded in any year (\\(p_{exceed}~=0.10\\)) is a \\(T=\\frac{1}{0.10}=10\\) year storm. A 10-year storm can be experienced in multiple consecutive years, so it only means that, on average over very long periods (in a stationary climate) one would expect to see one event every T years. In the U.S., precipitation frequency statistics are available at the NOAA Precipitation Frequency Data Server (PFDS). An example of the graphical data available there is shown in Figure 8.8. Figure 8.8: Intensity-duration-frequency (IDF) curves from the NOAA PFDS. The calculations performed to produce the IDF curves use decades of daily data, because many years are needed to estimate the frequency with which an event might occur. As a demonstration, however, a single year can be used to illustrate the relationship between intensity and duration, which for durations longer than about 2 hours (McCuen, 2016) can be expressed as in Equation (8.1). \\[\\begin{equation} i = aD^b \\tag{8.1} \\end{equation}\\] As a power curve, Equation (8.1) should be a straight line on a log-log plot. This is shown in Example 8.2. Example 8.2 Use the 2017 water year of rainfall data for the city of San Jose, to plot the relationship between intensity and duration for the 1, 3, 7, and 30-day events. Begin by calculating the necessary intensity and duration values. #First extract one water year of data df.one.year <- subset(ghcn_prcp, date>=as.Date("2016-10-01") & date<=as.Date("2017-09-30")) #Calculate the running mean value for the defined durations dur <- c(1,3,7,30) px <- numeric(length(dur)) for (i in 1:4) { px[i] <- max(zoo::rollmean(df.one.year$prcp,dur[i])) } #create the intensity-duration data frame df.id <- data.frame(duration=dur,intensity=px) Fit the theoretical curve (Equation (8.1)) using the nonlinear least squares function of the stats package (included with a base R installation), and plot the results. #fit a power curve to the data fit <- stats::nls(intensity ~ a*duration^b, data=df.id, start=list(a=1,b=-0.5)) print(signif(coef(fit),3)) #> a b #> 1.850 -0.751 #find estimated y-values using the fit df.id$intensity_est <- predict(fit, list(x = df.id$duration)) #duration-intensity plot with base graphics plot(x=df.id$duration,y=df.id$intensity,log='xy', pch=1, xaxt="n", xlab="Duration, day" , ylab="Intensity, inches/day") lines(x=df.id$duration,y=df.id$intensity_est,lty=2) abline( h = c(seq( 0.1,1,0.1),2.0), lty = 3, col = "lightgray") abline( v = c(1,2,3,4,5,7,10,15,20,30), lty = 3, col = "lightgray") axis(side = 1, at =c(1,2,3,4,5,7,10,15,20,30) ,labels = T) axis(side = 2, at =c(seq( 0.1,1,0.1),2.0) ,labels = T) Figure 8.9: Intensity-duration relationship for water year 2017. Calculated values are based on daily data; theoretical is the power curve fit. If this were done for many years, the results for any one duration could be combined (one value per year) and sorted in decreasing order. That means the rank assigned to the highest value would be 1, and the lowest value would be the number of years, n. The return period, T, for any event would then be found using Equation (8.2) based on the Weibull plotting position formula. \\[\\begin{equation} T=\\frac{n+1}{rank} \\tag{8.2} \\end{equation}\\] That would allow the creation of IDF curves for a point. 8.3 Precipitation gauge consistency – double mass curves The method of using double mass curves to identify changes in an obervation method (such as new instrumentation or a change of location) can be applied to precipitation gauges or any other type of measurement. This method is demonstrated with an example from the U.S. Geological survey (Searcy & Hardison, 1960). The first step is to compile data for a gauge (or better, a set of gauges) that are known to be unperturbed (Station A in the sample data set), and for a suspect gauge though to have experienced a change (Station X is this case). annual_data <- hydromisc::precip_double_mass knitr::kable(annual_data, digits = 2) |> kableExtra::kable_paper(bootstrap_options = "striped", full_width = F) Year Station_A Station_X 1926 39.75 32.85 1927 29.57 28.08 1928 42.01 33.51 1929 41.39 29.58 1930 31.55 23.76 1931 55.54 58.39 1932 48.11 46.24 1933 39.85 30.34 1934 45.40 46.78 1935 44.89 38.06 1936 32.64 42.82 1937 45.87 37.93 1938 46.05 50.67 1939 49.76 46.85 1940 47.26 50.52 1941 37.07 34.38 1942 45.89 47.60 Accumulate the (annual) precipitation (measured in inches) and plot the values for the suspect station against the reference station(s), as in Figure 8.10 . annual_sum <- data.frame(year = annual_data$Year, sum_A = cumsum(annual_data$Station_A), sum_X = cumsum(annual_data$Station_X)) #create scatterplot with a label on every point library(ggplot2) library(ggrepel) #> Warning: package 'ggrepel' was built under R version 4.2.3 ggplot(annual_sum, aes(sum_X,sum_A, label = year)) + geom_point() + geom_text_repel(size=3, direction = "y") + labs(x="Cumulative precipitation at Station A, in", y="Cumulative precipitation at Station X, in") + theme_bw() Figure 8.10: A double mass curve. The break in slope between 1930 and 1931 appears clear. This should checked with records for the station to verify whether changes did occur at that time. If the data from Station X are to be used to fill other records or estimate long-term averages, the inconsistency needs to be corrected. One method to highlight the year at which the break occurs is to plot the residuals from a best fit line to the cumulative data from the two stations, as illustrated by the Food and Agriculture Orgainization FAO. (Allen & United Nations, 1998) linfit = lm(sum_X ~ sum_A, data = annual_sum) plot(x=annual_sum$year,linfit$residuals, xlab = "Year",ylab = "Residual of regression") Figure 8.11: Residuals of the linear fit to the double-mass curve. This verifies that after 1930 the steep decline ends, so it may represent a change in location or equipment. Adusting the earlier record to be consistent with the later period is done by applying Equation (8.3). \\[\\begin{equation} y^{'}_i~=\\frac{b_2}{b_1}y_i \\tag{8.3} \\end{equation}\\] where b2 and b1 are the slopes after and before the break in slope, respectively, yi is original precipitation data, and y’i is the adjusted precipitation. This can be applied as follows. b1 <- lm(sum_X ~ sum_A, data = subset(annual_sum, year <= 1930))$coefficients[['sum_A']] b2 <- lm(sum_X ~ sum_A, data = subset(annual_sum, year > 1930))$coefficients[['sum_A']] #Adjust early values and concatenate to later values for Station X adjusted_X <- c(annual_data$Station_X[annual_data$Year <= 1930]*b2/b1, annual_data$Station_X[annual_data$Year > 1930]) annual_sum_adj <- data.frame(year = annual_data$Year, sum_A = cumsum(annual_data$Station_A), sum_X = cumsum(adjusted_X)) #Check that slope now appears more consistent ggplot(annual_sum_adj, aes(sum_X,sum_A, label = year)) + geom_point() + geom_text_repel(size=3, direction = "y") + labs(x="Cumulative precipitation at Station A, in", y="Cumulative adjusted precipitation at Station X, in") + theme_bw() Figure 8.12: A double mass curve using adjusted data at Station X. The plot shows a more consistent slope, as expected. Another plot of residuals could also validate the effect of the adjustment. 8.4 Precipitation interpolation and areal averaging It is rare that there are precipitation observations exactly where one needs data, which means existing observations must be interpolated to a point of interest. This is also used to fill in missing data in a record using surrounding observations. Interpolation is also used to use sparse observations, or observations from a variety of sources, to produce a spatially continuous grid. This is an essential step to estimating the precipitation averaged across an area that contributes streamflow to some location of concern. Estimating areal average precipitation using some simple, manual methods, has been outlined by the U.S. National Weather Service, illustrated in Figure 8.13 (source: National Weather Service). Figure 8.13: Some basic precipitation interpolation methods, from the U.S. National Weather Service. With the advent of geographical information system (GIS) software, manual interpolation is not used. Rather, more advanced spatial analysis is performed to interpolate precipitation onto a continuous grid, where the uncertainty (or skill) of different methods can be assessed. Spatial analysis methods to do this are outlined in many other references, such as Spatial Data Science and the related book Spatial Data Science with applications in R, or the reference Geocomputation with R. (Lovelace et al., 2019; Pebesma & Bivand, 2023) There are also many sources of precipitation data already interpolated to a regular grid. the geodata package provides access to many data sets, including the Worldclim biophysical data. Another source of global precipitation data, available at daily to monthly scales, is the CHIRPS data set, which has been widely used in many studies. An example of obtaining and plotting average annual precipitation over Santa Clara County is illustrated below. #Load precipitation in mm, already cropped to cover most of California datafile <- system.file("extdata", "prcp_cropped.tif", package="hydromisc") prcp <- terra::rast(datafile) scc_bound <- terra::vect(hydromisc::scc_county) scc_precip <- terra::crop(prcp, scc_bound) terra::plot(scc_precip, plg=list(title="Precip\\n(mm)", title.cex=0.7)) terra::plot(scc_bound, add=TRUE) Figure 8.14: Annual Average Precipitation over Santa Clara County, mm Spatial statistics are easily obtained using terra, a versatile package for spatial analysis. terra::summary(scc_precip) #> chirps.v2.0.1981.2020.40yrs #> Min. : 197.1 #> 1st Qu.: 354.9 #> Median : 447.9 #> Mean : 542.3 #> 3rd Qu.: 652.3 #> Max. :1297.2 #> NA's :5 "],["fate-of-precipitation.html", "Chapter 9 Fate of precipitation 9.1 Interception 9.2 Infiltration 9.3 Evaporation 9.4 Snow 9.5 Watershed analysis", " Chapter 9 Fate of precipitation As precipitation falls and can be caught on vegetation (interception), percolate into the ground (infiltration), return to the atmosphere (evaporation), or become available as runoff (if accumulating as rain or snow). The landscape (land cover and topography) and the time scale of study determine what processes are important. For example, for estimating runoff from an individual storm, interception is likely to be small, as is evaporation. On an annual average over large areas, evaporation will often be the largest component. Comprehensive hydrology models will estimate abstractions due to infiltration and interception, either by simulating the physics of the phenomenon or by using a lumped parameter that accounts for the effects of abstractions on runoff. The hydromisc package will need to be installed to access some of the code and data used below. If it is not installed, do so following the instructions on the github site for the package. 9.1 Interception Figure 9.1: Rain interception by John Robert McPherson, CC BY-SA 4, via Wikimedia Commons Interception of rainfall is generally small during individual storms (0.5-2 mm), so it is often ignored, or lumped in with other abstractions, for analyses of flood hydrology. For areas characterized by low intensity rainfall and heavy vegetation, interception can account for a larger portion of the rainfall (for example, up to 25% of annual rainfall in the Pacific Northwest) (McCuen, 2016). 9.2 Infiltration An early empirical equation describing infiltration rate into soils was developed by Horton in 1939, which takes the form of Equation (9.1). \\[\\begin{equation} f_p~=~ f_c + \\left(f_0 - f_c\\right)e^{-kt} \\tag{9.1} \\end{equation}\\] This describes a potential infiltration rate, \\(f_p\\), beginning at a maximum \\(f_0\\) and decreasing with time toward a minimum value \\(f_c\\) at a rate described by the decay constant \\(k\\). \\(f_c\\) is also equal to the saturated hydraulic conductivity, \\(K_s\\), of the soil. If rainfall rate exceeds \\(f_c\\) then this equation describes the actual infiltration rate with time. If periods of time have rainfall less intense than \\(f_c\\) it is convenient to integrate this to relate the total cumulative depth of water infiltrated, \\(F\\), and the actual infiltration rate, \\(f_p\\), as in Equation (9.2). \\[\\begin{equation} F~=~\\left[\\frac{f_c}{k}ln\\left(f_0-f_c\\right)+\\frac{f_0}{k}\\right]-\\frac{f_c}{k}ln\\left(f_p-f_c\\right)-\\frac{f_p}{k} \\tag{9.2} \\end{equation}\\] A more physically based relationship to describe infiltration rate is the Green-Ampt model. It is based on the physical laws describing the propogation of a wetting front downward through a soil column under a ponded water surface. The Green-Ampt relationship is in Equation (9.3). \\[\\begin{equation} K_st~=~F-\\left(n-\\theta_i\\right)\\Phi_f~ln\\left[1+\\frac{F}{\\left(n-\\theta_i\\right)\\Phi_f}\\right] \\tag{9.3} \\end{equation}\\] Equation (9.3) assumes ponding begins at t=0, meaning rainfall rate exceeds \\(K_s\\). When rainfall rates are less than that, adjustments to the method are used. Parameters are shown in the table below. Figure 9.2: Green-Ampt Parameter Estimates and Ranges based on Soil Texture USACE While not demonstrated here, parameters for the Horton and Green-Ampt methods can be derived from observed infiltration data using the R package vadose. The most widely used method for estimating infiltration is the NRCS method, described in detail in the NRCS document Estimating Runoff Volume and Peak Discharge.This method describes the direct runoff (as a depth), \\(Q\\), resulting from a precipitation event, \\(P\\), as in Equation (9.4). \\[\\begin{equation} Q~=~\\frac{\\left(P-I_a\\right)^2}{\\left(P-I_a\\right)+S} \\tag{9.4} \\end{equation}\\] \\(S\\) is the maximum retention of water by the soil column and \\(I_a\\) is the initial abstraction, commonly estimated as \\(I_a=0.2S\\). Substituting this into Equation (9.4) produces Equation (9.5). \\[\\begin{equation} Q~=~\\frac{\\left(P-0.2~S\\right)^2}{\\left(P+0.8~S\\right)} \\tag{9.5} \\end{equation}\\] This relationship applies as long as \\(P>0.2~S\\); Q=0 otherwise. Values for S are derived from a Curve Number (CN), which summarizes the land cover, soil type and condition: \\[CN=\\frac{1000}{10+S}\\], where \\(S\\), and subsequently \\(Q\\), are in inches. Equation (9.5) can be rearranged to a form similar to those for the Horton and Green-Ampt equations for cumulative infiltration, \\(F\\). \\[F~=~\\frac{\\left(P-0.2~S\\right)S}{P+0.8~S}\\]. 9.3 Evaporation Evaporation is simply the change of water from liquid to vapor state. Because it is difficult to separate evaporation from the soil from transpiration from vegetation, it is usually combined into Evapotranspiration, or ET; see Figure 9.3. Figure 9.3: Schematic of ET, from CIMIS ET can be estimated in a variety of ways, but it is important first to define three types of ET: - Potential ET, \\(ET_p\\) or \\(PET\\): essentially the same as the rate that water would evaporate from a free water surface. - Reference crop ET, \\(ET_{ref}\\) or \\(ET_0\\): the rate water evaporates from a well-watered reference crop, usually grass of a standard height. - Actual ET, \\(ET\\): this is the water used by a crop or other vegetation, usually calculated by adjusting the \\(ET_0\\) term by a crop coefficient that accounts for factors such as the plant height, growth stage, and soil exposure. Estimating \\(ET_0\\) can be as uncomplicated as using the Thornthwaite equation, which depends only on mean monthly temperatures, to the Penman-Monteith equation, which includes solar and longwave radiation, wind and humidity effects, and reference crop (grass) characteristics. Inclusion of more complexity, especially where observations can supply the needed input, produces more reliable estimates of \\(ET_0\\).One of the most common implementations of the Penman-Monteith equation is the version of the FAO (FAO Irrigation and drainage paper 56, or FAO56) (Allen & United Nations, 1998). Refer to FAO56 for step-by-step instructions on determining each term in the Penman-Monteith equation, Equation (9.6). \\[\\begin{equation} \\lambda~ET~=~\\frac{\\Delta\\left(R_n-G\\right)+\\rho_ac_p\\frac{\\left(e_s-e_a\\right)}{r_a}}{\\Delta+\\gamma\\left(1+\\frac{r_s}{r_a}\\right)} \\tag{9.6} \\end{equation}\\] Open water evaporation can be calculated using the original Penman equation (1948): \\[\\lambda~E_p~=~\\frac{\\Delta~R_n+\\gamma~E_a}{\\Delta~+~\\gamma}\\] where \\(R_n\\) is the net radiation available to evaporate water and \\(E_a\\) is a mass transfer function usually including humidity (or vapor pressure deficit) and wind speed. \\(\\lambda\\) is the latent heat of vaporization of water. A common implementation of the Penman equation is \\[\\begin{equation} \\lambda~E_p~=~\\frac{\\Delta~R_n+\\gamma~6.43\\left(1+0.536~U_2\\right)\\left(e_s-e\\right)}{\\Delta~+~\\gamma} \\tag{9.7} \\end{equation}\\] Here \\(E_p\\) is in mm/d, \\(\\Delta\\) and \\(\\gamma\\) are in \\(kPa~K^{-1}\\), \\(R_n\\) is in \\(MJ~m^{−2}~d^{−1}\\), \\(U_2\\) is in m/s, and \\(e_s\\) and \\(e\\) are in kPa. Variables are as defined in FAO56. Open water evaporation can also be calculated using a modified version of the Penman-Monteith equation (9.6). In this latter case, vegetation coefficients are not needed, so Equation (9.6) can be used with \\(r_s=0\\) and \\(r_a=251/(1+0.536~u_2)\\), following Thom & Oliver, 1977. The R package Evaporation has functions to calculate \\(ET_0\\) using this and many other functions. This is especially useful when calculating PET over many points or through a long time series. 9.4 Snow 9.4.1 Observations In mountainous areas a substantial portion of the precipitation may fall as snow, where it can be stored for months before melting and becoming runoff. Any hydrologic analysis in an area affected by snow must account for the dynamics of this natural reservoir and how it affects water supply. In the Western U.S., the most comprehensive observations of snow are part of the SNOTEL (SNOw TELemetry) network. Figure 9.4: The SNOTEL network. 9.4.2 Basic snowmelt theory and simple models For snow to melt, heat must be added to first bring the snowpack to the melting point; it takes about 2 kJ/kg to increase snowpack temperature 1\\(^\\circ\\)C. Additional heat is required for the phase change from ice to water (the latent heat of fusion), about 335 kJ/kg. Heat can be provided by absorbing solar radiation, longwave radiation, ground heat, warm air, warm rain falling on the snowpack or water vapor condensing on the snow. Once snow melts, it can percolate through the snowpack and be retained, similar to water retained by soil, and may re-freeze (releasing the latent heat of fusion, which can then cause more melt). As with any other hydrologic process, there are many ways it can be modeled, from simplified empirical relationships to complex physics-based representations. While accounting for all of the many processes involved would be a robust approach, often there are not adequate observations to support their use so simpler parameterization are used. Here only the simplest index-based snow model is discussed, as in Equation (9.8). \\[\\begin{equation} M~=~K_d\\left(T_a~-~T_b\\right) \\tag{9.8} \\end{equation}\\] M is the melt rate in mm/d (or in/day), \\(T_a\\) is air temperature (sometimes a daily mean, sometimes a daily maximum), \\(T_b\\) is a base temperature, usually 0\\(^\\circ\\)C (or 32\\(^\\circ\\)F), and \\(K_d\\) is a degree-day melt factor in mm/d/\\(^\\circ\\)C (or in/d/\\(^\\circ\\)F). The melt factor, \\(K_d\\), is highly dependent on local conditions and on the time of year (as an indicator of the snow pack condition); different \\(K_d\\) factors can be used for different months for example. Refreezing of melted snow, when temperatures are below \\(T_b\\), can also be estimated using an index model, such as Equation (9.9). \\[\\begin{equation} Fr~=~K_f\\left(T_b~-~T_a\\right) \\tag{9.9} \\end{equation}\\] Importantly, temperature-index snowmelt relations have been developed primarily for describing snowmelt at the end of season, after the peak of snow accumulation (typically April-May in the mountainous western U.S.), and their use during the snow accumulation season may overestimate melt. Different degree-day factors are often used, with the factors increasing later in the melt season. From a hydrologic perspective, the most important snow quality is the snow water equivalent (SWE), which is the depth of water obtained by melting the snow. An example of using a snowmelt index model follows. Example 9.1 Manually calibrate an index snowmelt model for a SNOTEL site using one year of data. Visit the SNOTEL to select a site. In this example site 1050, Horse Meadow, located in California, is used. Next download the data using the snotelr package (install the package first, if needed). sta <- "1050" snow_data <- snotelr::snotel_download(site_id = sta, internal = TRUE) Plot the data to assess the period available and how complete it is. plot(as.Date(snow_data$date), snow_data$snow_water_equivalent, type = "l", xlab = "Date", ylab = "SWE (mm)") Figure 9.5: Snow water equivalent at SNOTEL site 1050. Note the units are SI. If you download data directly from the SNOTEL web site the data would be in conventional US units. snotelr converts the data to SI units as it imports. The package includes a function snotel_metric that could be used to convert raw data downloaded from the SNOTEL website to SI units. For this exercise, extract a single (water) year, meaning from 1-Oct to 30-Sep, so an entire winter is in one year. In addition, create a data frame that only includes columns that are needed. snow_data_subset <- subset(snow_data, as.Date(date) > as.Date("2008-10-01") & as.Date(date) < as.Date("2009-09-30")) snow_data_sel <- subset(snow_data_subset, select=c("date", "snow_water_equivalent", "precipitation", "temperature_mean", "temperature_min", "temperature_max")) plot(as.Date(snow_data_sel$date),snow_data_sel$snow_water_equivalent, type = "l",xlab = "Date", ylab = "SWE (mm)") grid() Figure 9.6: Snow water equivalent at SNOTEL site 1050 for water year 2009. Now use a snow index model to simulate the SWE based on temperature and precipitation. The model used here is a modified version of that used in the hydromad package. The snow.sim command is used to run a snow index model; type ?hydromisc::snow.sim for details on its use. As a summary, the four main parameters you can adjust in the calibration of the model are: The maximum air temperature for snow, Tmax. Snow can fall at air temperatures above as high as about 3\\(^\\circ\\)C, but Tmax is usually lower. The minimum air temperature for rain, Tmin. Rain can fall when near surface air temperatures are below freezing. This may be as low as -1\\(^\\circ\\)C or maybe just a little lower, and as high as 1\\(^\\circ\\)C. Base temperature, Tmelt, the temperature at which melt begins. Usually the default of 0\\(^\\circ\\)C is used, but some adjustment (generally between -2 and 2\\(^\\circ\\)C) can be applied to improve model calibration. Snow Melt (Degree-Day) Factor, kd, which describes the melting of the snow when temperatures are above freezing. Be careful using values from different references as these are dependent on units. Typical values are between 1 and 5 mm/d/\\(^\\circ\\)C. Two additional parameters are optional; their effects are typically small. Degree-Day Factor for freezing, kf, of liquid water in the snow pack when temperatures are below freezing. By default it is set to 1\\(^\\circ\\)C/mm/day, and may vary from 0 to 2 \\(^\\circ\\)C/mm/day. Snow water retention factor, rcap. When snow melts some of it can be retained via capillarity in the snow pack. It can re-freeze or drain out. This is expressed as a fraction of the snow pack that is frozen. The default is 2.5% (rcap = 0.025). Start with some assumed values and run the snow model. Tmax_snow <- 3 Tmin_rain <- 2 kd <- 1 snow_estim <- hydromisc::snow.sim(DATA=snow_data_sel, Tmax=Tmax_snow, Tmin=Tmin_rain, kd=kd) Now the simulated values can be compared to the observations. If not installed already, install the hydroGOF package, which has some useful functions for evaluating how well modeled output fits observations. In the plot that follows we specify three measures of goodness-of-fit: Mean Absolute Error (MAE) Percent Bias (PBIAS) Root Mean Square Error divided by the Standard Deviation (RSR) These are discussed in detail in other references, but the aim is to calibrate (change the input parameters) until these values are low. obs <- snow_data_sel$snow_water_equivalent sim <- snow_estim$swe_simulated hydroGOF::ggof(sim, obs, na.rm = TRUE, dates=snow_data_sel$date, gofs=c("MAE", "RMSE", "PBIAS"), xlab = "", ylab="SWE, mm", tick.tstep="months", cex=c(0,0),lwd=c(2,2)) Figure 9.7: Simulated and Observed SWE at SNOTEL site 1050 for water year 2009. Melt is overestimated in the early part of the year and underestimated during the melt season, showing why a single index is not a very robust model. Applying two kd values, one for early to mid snow season and another for later snowmelt could improve the model, but it would make it less useful for using the model in other situations such as increased temperatures. 9.4.3 Snow model calibration While manual model calibration can improve the fit, a more complete calibration involves optimization methods that search the parameter space for the optimal combination of parameter values. A useful tool for doing that is the optim function, part of the stats package installed with base R. Using the optimization package requires establishing a function that should be minimized, where the parameters to be included in the optimization are the first argument. The optim function requires you to explicitly give ranges over which parameters can be varied, via the upper and lower arguments. An example of this follows, where the four main model parameters noted above are used, and the MAE is minimized. fcn_to_minimize <- function(par,datain, obs){ snow_estim <- hydromisc::snow.sim(DATA=datain, Tmax=par[1], Tmin=par[2], kd=par[3], Tmelt=par[4]) calib.stats <- hydroGOF::gof(snow_estim$swe_simulated,obs,na.rm=TRUE) objective_stat <- as.numeric(calib.stats['MAE',]) return(objective_stat) } opt_res <- optim(par=c(0.5,1,1,0),fn=fcn_to_minimize, lower=c(-1,-1,0.5,-2), upper=c(3,1,5,3), method="L-BFGS-B", datain=snow_data_sel, obs=obs) #print out optimal parameters - note Tmax and Tmin can be reversed during optimization cat(sprintf("Optimal parameters:\\nTmax=%.1f\\nTmin=%.1f\\nkd=%.2f\\nTmelt=%.1f\\n", max(opt_res$par[1],opt_res$par[2]),min(opt_res$par[1],opt_res$par[2]), opt_res$par[3],opt_res$par[4])) #> Optimal parameters: #> Tmax=1.0 #> Tmin=0.5 #> kd=1.05 #> Tmelt=-0.0 The results using the optimal parameters can be plotted to visualize the simulation. snow_estim_opt <- hydromisc::snow.sim(DATA=snow_data_sel, Tmax=max(opt_res$par[1],opt_res$par[2]), Tmin=min(opt_res$par[1],opt_res$par[2]), kd=opt_res$par[3], Tmelt=opt_res$par[4]) obs <- snow_data_sel$snow_water_equivalent sim <- snow_estim_opt$swe_simulated hydroGOF::ggof(sim, obs, na.rm = TRUE, dates=snow_data_sel$date, gofs=c("MAE", "RMSE", "PBIAS"), xlab = "", ylab="SWE, mm", tick.tstep="months", cex=c(0,0),lwd=c(2,2)) Figure 9.8: Optimal simulation of SWE at SNOTEL site 1050 for water year 2009. It is clear that a simple temperature index model cannot capture the snow dynamics at this location, especially during the winter when melt is significantly overestimated. 9.4.4 Estimating climate change impacts on snow Once a reasonable calibration is obtained, the effect of increasing temperatures on SWE can be simulated by including the deltaT argument in the hydromisc::snow.sim command. Here a 3\\(^\\circ\\)C uniform temperature increase is imposed on the optimal parameterization above. dT <- 3.0 snow_plus3 <- hydromisc::snow.sim(DATA=snow_data_sel, Tmax=max(opt_res$par[1],opt_res$par[2]), Tmin=min(opt_res$par[1],opt_res$par[2]), kd=opt_res$par[3], Tmelt=opt_res$par[4], deltaT = dT) simplusdT <- snow_plus3$swe_simulated # plot the results dTlegend <- expression("Simulated"*+3~degree*C) plot(as.Date(snow_data_sel$date),obs,type = "l",xlab = "", ylab = "SWE (mm)") lines(as.Date(snow_estim$date),sim,lty=2,col="blue") lines(as.Date(snow_estim$date),simplusdT,lty=3,col="red") legend("topright", legend = c("Observed", "Simulated",dTlegend), lty = c(1,2,3), col=c("black","blue","red")) grid() Figure 9.9: Observed SWE and simulated with observed meteorology and increased temperatures. 9.5 Watershed analysis Whether precipitation falls as rain or snow, how much is absorbed by plants, consumed by evapotranspiration, and what is left to become runoff, is all determined by watershed characteristics. This can include: Watershed area Slope of terrain Elevation variability (a hypsometric curve) Soil types Land cover Collecting this information begins with obtaining a digital elevation model for an area, identifying any key point or points on a stream (a watershed outlet), and then delineating the area that drains to that point. This process of watershed delineation is often done with GIS software like ArcGIS or QGIS. The R package WhiteboxTools provides capabilities for advanced terrain analysis in R. Demonstrations of the use of these tools for a watershed are in the online book Hydroinformatics at VT by JP Gannon. In particular, the chapters on mapping a stream network and delineating a watershed are excellent resources for exploring these capabilities in R. For watersheds in the U.S., watersheds, stream networks, and attributes of both can be obtained and viewed using nhdplusTools. Land cover and soil information can be obtained using the FedData package. "],["designing-for-floods-flood-hydrology.html", "Chapter 10 Designing for floods: flood hydrology 10.1 Engineering design requires probability and statistics 10.2 Estimating floods when you have peak flow observations - flood frequency analysis 10.3 Estimating floods from precipitation", " Chapter 10 Designing for floods: flood hydrology Figure 10.1: The international bridge between Fort Kent, Maine and Clair, New Brunswick during a flood (source: NOAA) Flood hydrology is generally the description of how frequently a flood of a certain level will be exceeded in a specified period. This was discussed briefly in the section on precipitation frequency, Section 8.2. The hydromisc package will need to be installed to access some of the data used below. If it is not installed, do so following the instructions on the github site for the package. 10.1 Engineering design requires probability and statistics Before diving into peak flow analysis, it helps to refresh your background in basic probability and statistics. Some excellent resources for this using R as the primary tool are: A very brief tutorial, by Prof. W.B. King A more thorough text, by Prof. G. Jay Kerns. This has a companion R package. An online flood hydrology reference based in R, by Prof. Helen Fairweather Rather than repeat what is in those references, a couple of short demonstrations here will show some of the skills needed for flood hydrology. The first example illustrates binomial probabilities, which are useful for events with only two possible outcomes (e.g., a flood happens or it doesn’t), where each outcome is independent and probabilities of each are constant. R functions for distributions use a first letter to designate what it returns: d is the density, p is the (cumulative) distribution, q is the quantile, r is a random sequence. In R the defaults for probabilities are to define them as \\(P[X~\\le~x]\\), or a probability of non-exceedance. Recall that a probability of exceedance is simply 1 - (probability of non-exceedance), or \\(P[X~\\gt~x] ~=~ 1-P[X~\\le~x]\\). In R, for quantiles or probabilities (using functions beginning with q or p like pnorm or qlnorm) setting the argument lower.tail to FALSE uses a probability of exceedance instead of non-exceedance. Example 10.1 A temporary dam is constructed while a repair is built. It will be in place 5 years and is designed to protect against floods up to a 20-year recurrence interval (i.e., there is a \\(p=\\frac{1}{20}=0.05\\), or 5% chance, that it will be exceeded in any one year). What is the probability of (a) no failure in the 5 year period, and (b) at least two failures in 5 years. # (a) ans1 <- dbinom(0, 5, 0.05) cat(sprintf("Probability of exactly zero occurrences in 5 years = %.4f %%",100*ans1)) #> Probability of exactly zero occurrences in 5 years = 77.3781 % # (b) ans2 <- 1 - pbinom(1,5,.05) # or pbinom(1,5,.05, lower.tail=FALSE) cat(sprintf("Probability of 2 or more failures in 5 years = %.2f %%",100*ans2)) #> Probability of 2 or more failures in 5 years = 2.26 % While the next example uses normally distributed data, most data in hydrology are better described by other distributions. Example 10.2 Annual average streamflows in some location are normally distributed with a mean annual flow of 20 m\\(^3\\)/s and a standard deviation of 6 m\\(^3\\)/s. Find (a) the probability of experiencing a year with less than (or equal to) 10 m\\(^3\\)/s, (b) greater than 32 m\\(^3\\)/s, and (c) the annual average flow that would be expected to be exceeded 10% of the time. # (a) ans1 <- pnorm(10, mean=20, sd=6) cat(sprintf("Probability of less than 10 = %.2f %%",100*ans1)) #> Probability of less than 10 = 4.78 % # (b) ans2 <- pnorm(32, mean=20, sd=6, lower.tail = FALSE) #or 1 - pnorm(32, mean=20, sd=6) cat(sprintf("Probability of greater than or equal to 30 = %.2f %%",100*ans2)) #> Probability of greater than or equal to 30 = 2.28 % # (c) ans3 <- qnorm(.1, mean=20, sd=6, lower.tail=FALSE) cat(sprintf("flow exceeded 10%% of the time = %.2f m^3/s",ans3)) #> flow exceeded 10% of the time = 27.69 m^3/s # plot to visualize answers x <- seq(0,40,0.1) y<- pnorm(x,mean=20,sd=6) xlbl <- expression(paste(Flow, ",", ~ m^"3"/s)) plot(x ,y ,type="l",lwd=2, xlab = xlbl, ylab= "Prob. of non-exceedance") abline(v=10,col="black", lwd=2, lty=2) abline(v=32,col="blue", lwd=2, lty=2) abline(h=0.9,col="green", lwd=2, lty=2) legend("bottomright",legend=c("(a)","(b)","(c)"),col=c("black","blue","green"), cex=0.8, lty=2) Figure 10.2: Illustration of three solutions. 10.2 Estimating floods when you have peak flow observations - flood frequency analysis For an area fortunate enough to have a long record (i.e., several decades or more) of observations, estimating flood risk is a matter of statistical data analysis. In the U.S., data, collected by the U.S. Geological Survey (USGS), can be accessed through the National Water Dashboard. Sometimes for discontinued stations it is easier to locate data through the older USGS map interface. For any site, data may be downloaded to a file, and the peakfq (watstore) format, designed to be imported into the PeakFQ software, is easy to work with in R. 10.2.1 Installing helpful packages The USGS has developed many R packages, including one for retrieval of data, dataRetrieval. Since this resides on CRAN, the package can be installed with (the use of ‘!requireNamespace’ skips the installation if it already is installed): if (!requireNamespace("dataRetrieval", quietly = TRUE)) install.packages("dataRetrieval") Other USGS packages that are very helpful for peak flow analysis are not on CRAN, but rather housed in a USGS repository. The easiest way to install packages from that archive is using the install.load package. Then the install_load command will first search the standard CRAN archive for the package, and if it is not found there the USGS archive is searched. Packages are also loaded (equivalent to using the library command). install_load also installs dependencies of packages, so here installing smwrGraphs also installs smwrBase. The prefix smwr refers to their use in support of the excellent reference Statistical Methods in Water Resources. if (!requireNamespace("install.load", quietly = TRUE)) install.packages("install.load") install.load::install_load("smwrGraphs") #this command also installs smwrBase Lastly, the lmomco package has extensive capabilities to work with many forms of probability distributions, and has functions for calculating distribution parameters (like skew) that we will use. if (!requireNamespace("lmomco", quietly = TRUE)) install.packages("lmomco") 10.2.2 Download, manipulate, and plot the data for a site Using the older USGS site mapper, and specifying that inactive stations should also be included, many stations in the south Bay Area in California are shown in Figure 10.3. Figure 10.3: Active and Inactive USGS sites recording peak flows. While the data could be downloaded and saved locally through that link, it is convenient here to use the dataRetrieval command. Qpeak_download <- dataRetrieval::readNWISpeak(siteNumbers="11169000") The data used here are also available as part of the hydromisc package, and may be obtained by typing hydromisc::Qpeak_download. It is always helpful to look at the downloaded data frame before doing anything with it. There are many columns that are not needed or that have repeated information. There are also some rows that have no data (‘NA’ values). It is also useful to change some column names to something more intuitive. We will need to define the water year (a water year begins October 1 and ends September 30). Qpeak <- Qpeak_download[!is.na(Qpeak_download$peak_dt),c('peak_dt','peak_va')] colnames(Qpeak)[colnames(Qpeak)=="peak_dt"] <- "Date" colnames(Qpeak)[colnames(Qpeak)=="peak_va"] <- "Peak" Qpeak$wy <- smwrBase::waterYear(Qpeak$Date) The data have now been simplified so that can be used more easily in the subsequent flood frequency analysis. Data should always be plotted, which can be done many ways. As a demonstration of highlighting specific years in a barplot, the strongest El Niño years (in 1930-2002) from NOAA Physical Sciences Lab can be highlighted in red. xlbl <- "Water Year" ylbl <- expression("Peak Flow, " ~ ft^{3}/s) nino_years <- c(1983,1998,1992,1931,1973,1987,1941,1958,1966, 1995) cols <- c("blue", "red")[(Qpeak$wy %in% nino_years) + 1] barplot(Qpeak$Peak, names.arg = Qpeak$wy, xlab = xlbl, ylab=ylbl, col=cols) Figure 10.4: Annual peak flows for USGS gauge 11169000, highlighting strong El Niño years in red. 10.2.3 Flood frequency analysis The general formula used for flood frequency analysis is Equation (10.1). \\[\\begin{equation} y=\\overline{y}+Ks_y \\tag{10.1} \\end{equation}\\] where y is the flow at the designated return period, \\(\\overline{y}\\) is the mean of all \\(y\\) values and \\(s_y\\) is the standard deviation. In most instances, \\(y\\) is a log-transformed flow; in the US a base-10 logarithm is generally used. \\(K\\) is a frequency factor, which is a function of the return period, the parent distribution, and often the skew of the y values. The guidance of the USGS (as in Guidelines for Determining Flood Flow Frequency, Bulletin 17C) (England, J.F. et al., 2019) is to use the log-Pearson Type III (LP-III) distribution for flood frequency data, though in different settings other distributions can perform comparably. For using the LP-III distribution, we will need several statistical properties of the data: mean, standard deviation, and skew, all of the log-transformed data, calculated as follows. mn <- mean(log10(Qpeak$Peak)) std <- sd(log10(Qpeak$Peak)) g <- lmomco::pmoms(log10(Qpeak$Peak))$skew With those calculated, a defined return period can be chosen and the flood frequency factors, from Equation (10.1), calculated for that return period (the example here is for a 50-year return period). The qnorm function from base R and the qpearsonIII function from the smwrBase package make this straightforward, and K values for Equation (10.1) are obtained for a lognormal, Knorm, and LP-III, Klp3. RP <- 50 Knorm <- qnorm(1 - 1/RP) Klp3 <- smwrBase::qpearsonIII(1-1/RP, skew = g) Now the flood frequency equation (10.1) can be applied to calculate the flows associated with the 50-year return period for each of the distributions. Remember to take the anti-log of your answer to return to standard units. Qpk_LN <- mn + Knorm * std Qpk_LP3 <- mn + Klp3 * std sprintf("RP = %d years, Qpeak LN = %.0f cfs, Qpeak LP3 = %.0f",RP,10^Qpk_LN,10^Qpk_LP3) #> [1] "RP = 50 years, Qpeak LN = 18362 cfs, Qpeak LP3 = 12396" 10.2.4 Creating a flood frequency plot Different probability distributions can produce very different results for a design flood flow. Plotting the historical observations along with the distributions, the lognormal and LP-III in this case, can help explain why they differ, and provide indications of which fits the data better. We cannot say exactly what the probability of seeing any observed flood exceeded would be. However, given a long record, the probability can be described using the general “plotting position” equation from Bulletin 17C, as in Equation (10.2). \\[\\begin{equation} p_i=\\frac{i-a}{n+1-2a} \\tag{10.2} \\end{equation}\\] where n is the total number of data points (annual peak flows in this case), \\(p_i\\) is the exceedance probability of flood observation i, where flows are ranked in descending order (so the largest observed flood has \\(i=1\\) ad the smallest is \\(i=n\\)). The parameter a is between 0 and 0.5. For simplicity, the following will use \\(a=0\\), so the plotting Equation (10.2) becomes the Weibull formula, Equation (10.3). \\[\\begin{equation} p_i=\\frac{i}{n+1} \\tag{10.3} \\end{equation}\\] While not necessary, to add probabilities to the annual flow sequence we will create a new data frame consisting of the observed peak flows, sorted in descending order. df_pp <- as.data.frame(list('Obs_peak'=sort(Qpeak$Peak,decreasing = TRUE))) This can be done with fewer commands, but here is an example where first a rank column is created (1=highest peak in the record of N years), followed by adding columns for the exceedance and non-exceedence probabilities: df_pp$rank <- as.integer(seq(1:length(df_pp$Obs_peak))) df_pp$exc_prob <- (df_pp$rank/(1+length(df_pp$Obs_peak))) df_pp$ne_prob <- 1-df_pp$exc_prob For each of the non-exceedance probabilities calculated for the observed peak flows, use the flood frequency equation (10.1) to estimate the peak flow that would be predicted by a lognormal or LP-III distribution. This is the same thing that was done above for a specified return period, but now it will be “applied” to an entire column. df_pp$LN_peak <- mapply(function(x) {10^(mn+std*qnorm(x))}, df_pp$ne_prob) df_pp$LP3_peak <- mapply(function(x) {10^(mn+std*smwrBase::qpearsonIII(x, skew=g))},df_pp$ne_prob) There are many packages that create probability plots (see, for example, the versatile scales package for ggplot2). For this example the USGS smwrGraphs package is used. First, for aesthetics, create x- and y- axis labels. ylbl <- expression("Peak Flow, " ~ ft^{3}/s) xlbl <- "Non-exceedence Probability" The smwrGraphs package works most easily if it writes output directly to a file, a PNG file in this case, using the setPNG command; the file name and its dimensions in inches are given as arguments, and the PNG device is opened for writing. This is followed by commands to plot the data on a graph. Technically, the data are plotted to an object here is called prob.pl. The probPlot command plots the observed peaks as points, where the alpha argument is the a in Equation (10.2). Additional points or lines are added with the addXY command, used here to add the LN and LP3 data as lines (one solid, one dashed). Finally, a legend is added (the USGS refers to that as an “Explanation”), and the output PNG file is closed with the dev.off() command. smwrGraphs::setPNG("probplot_smwr.png",6.5, 3.5) #> width height #> 6.5 3.5 #> [1] "Setting up markdown graphics device: probplot_smwr.png" prob.pl <- smwrGraphs::probPlot(df_pp$Obs_peak, alpha = 0.0, Plot=list(what="points",size=0.05,name="Obs"), xtitle=xlbl, ytitle=ylbl) prob.pl <- smwrGraphs::addXY(df_pp$ne_prob,df_pp$LN_peak,Plot=list(what="lines",name="LN"),current=prob.pl) prob.pl <- smwrGraphs::addXY(df_pp$ne_prob,df_pp$LP3_peak,Plot=list(what="lines",type="dashed",name="LP3"),current=prob.pl) smwrGraphs::addExplanation(prob.pl,"ul",title="") dev.off() #> png #> 2 The output won’t be immediately visible in RStudio – navigate to the file and click on it to view it. Figure 10.5 shows the output from the above commands. Figure 10.5: Probability plot for USGS gauge 11169000 for years 1930-2002. 10.2.5 Other software for peak flow analysis Much of the analysis above can be achieved using the PeakFQ software developed by the USGS. It incorporates the methods in Bulletin 17C via a graphical interface and can import data in the watstore format as discussed above in Section 10.2. The USGS has also produced the MGBT R package to perform many of the statistical calculations involved in the Bulletin 17C procedures. 10.3 Estimating floods from precipitation When extensive streamflow data are not available, flood risk can be estimated from precipitation and the characteristics of the area contributing flow to a point. While not covered here (or not yet…), there has been extensive development of hydrological modeling using R, summarized in recent papers (Astagneau et al., 2021; Slater et al., 2019). Straightforward application of methods to estimate peak flows or hydrographs resulting from design storms can by writing code to apply the Rational Formula (included in the VFS and hydRopUrban packages, for example) or the NRCS peak flow method. For more sophisticated analysis of water supply and drought, continuous modeling is required. A very good introduction to hydrological modeling in R, including model calibration and assessment, is included in the Hydroinformatics at VT reference by JP Gannon. "],["sustainability-in-design-planning-for-change.html", "Chapter 11 Sustainability in design: planning for change 11.1 Perturbing a system 11.2 Detecting changes in hydrologic data 11.3 Detecting changes in extreme events", " Chapter 11 Sustainability in design: planning for change Figure 11.1: Yearly surface temperature compared to the 20th-century average from 1880–2022, from Climate.gov All systems engineered to last more than a decade or two, so everything civil engineers work on, will need to be designed to be resilient to dramatic environmental changes. As societies respond to the impacts of a disrupted climate, demands for water, energy, housing, food, and other essential services will change. This will result in economic disruption as well. This chapter presents a few ways long-term sustainability can be considered, looking at sensitivity of systems, detection of shifts or trends, and how economic and management may respond. This is much more brief than this rich topic deserves. The hydromisc package will need to be installed to access some of the data used below. If it is not installed, do so following the instructions on the github site for the package. 11.1 Perturbing a system when a system is perturbed it can respond in many ways. A useful classification of these was developed by (Marshall & Toffel, 2005). Figure 11.2 is an adaptation of Figure 2 from that paper. Figure 11.2: Pathways of recovery or degradation a system may take after initial perturbation. In essence, after a system is degraded, it can eventually rebound to its original condition (Type 1), rebound to some other state that is degraded from its original (Types 2 and 3), or completely collapse (Type 4). Which path it taken depends on the degree of the initial disruption and the ability of the system to recover. While originally cast with time as the x-axis, Figure 11.2 is equally applicable when looking at a system that travels over a distance, such as a flowing river. The form of the curves in Figure 11.2 appear similar to a classic dissolved oxygen sag curve, as in Figure 11.3. Figure 11.3: Dissolved oxygen levels in a steam following an input of waste (source: EPA). The Streeter-Phelps equation describes the response of the dissolved oxygen (DO) levels in a water body to a perturbation, such as the discharge of wastewater with a high oxygen demand. Some important assumptions are that steady-state conditions exist, and the flow moves as plug flow, progressing downstream along a one-dimensional path. Following is Streeter-Phelps Equation (11.1). \\[\\begin{equation} D=C_s-C=\\frac{K_1^\\prime L_0}{K_2^\\prime - K_1^\\prime}\\left(e^{-K_1^\\prime t}-e^{-K_2^\\prime t}\\right)+D_0e^{-K_2^\\prime t} \\tag{11.1} \\end{equation}\\] where \\(D\\) is the DO deficit, \\(C_s\\) is the saturation DO concentration, \\(C\\) is the DO concentration, \\(D_0\\) is the initial DO deficit, \\(L_0\\) is the ultimate (first-stage) BOD at the discharge, calculated by Equation (11.2). \\[\\begin{equation} L_0=\\frac{BOD_5}{1-e^{-K_1^\\prime t}} \\tag{11.2} \\end{equation}\\] \\(K_1^\\prime\\) and \\(K_2^\\prime\\) are the deoxygenation and reaeration coefficients, both adjusted for temperature. Usually the coefficients \\(K_1\\) and \\(K_2\\) are defined at 20\\(^\\circ\\)C, and then adjusted by empirical relationships for the actual water temperature using Equation (11.3). \\[\\begin{equation} K^\\prime = K\\theta ^{T-20} \\tag{11.3} \\end{equation}\\] where \\(\\theta\\) is set to typical values of 1.135 for \\(K_1\\) for \\(T\\le20^\\circ C\\) (and 1.056 otherwise) and 1.024 for \\(K_2\\). As a demonstration, functions (only available for SI units) in the hydromisc package can be used to explore the recovery of an aquatic system from a perturbation, as in Example 11.1. Example 11.1 A river with a flow of 7 \\(m^3/s\\) and a velocity of 1.4 m/s has effluent discharged into it at a rate of 1.5 \\(m^3/s\\). The river upstream of the discharge has a temperature of 15\\(^\\circ\\)C, a \\(BOD_5\\) of 1 mg/L, and a dissolved oxygen saturation of 90 percent. The effluent is 21\\(^\\circ\\)C with a \\(BOD_5\\) of 180 mg/L and a dissolved oxygen saturation of 0 percent. The deoxygenation rate constant (at 20\\(^\\circ\\)C) is 0.4 \\(d^{-1}\\), and the reaeration rate constant is 0.8 \\(d^{-1}\\). Create a plot of DO as a percent of saturation (y-axis) vs. distance in km (x-axis). First set up the model parameters. Q <- 7 # flow of stream, m3/s V <- 1.4 # velocity of stream, m/s Qeff <- 1.5 # flow rate of effluent, m3/s DOsatupstr <- 90 # DO saturation upstream of effluent discharge, % DOsateff <- 0 # DO saturation of effluent discharge, % Triv <- 15 # temperature of receiving water, C Teff <- 21 # temperature of effluent, C BOD5riv <- 1 # 5-day BOD of receiving water, mg/L BOD5eff <- 180 # 5-day BOD of effluent, mg/L K1 <- 0.4 # deoxygenation rate constant at 20C, 1/day K2 <- 0.8 # reaeration rate constant at 20C, 1/day Calculate some of the variables needed for the Streeter-Phelps model. Type ?hydromisc::DO_functions for more information on the DO-related functions in the hydromisc package. Tmix <- hydromisc::Mixture(Q, Triv, Qeff, Teff) K1adj <- hydromisc::Kadj_deox(K1=K1, T=Tmix) K2adj <- hydromisc::Kadj_reox(K2=K2, T=Tmix) BOD5mix <- hydromisc::Mixture(Q, BOD5riv, Qeff, BOD5eff) L0 <- BOD5mix/(1-exp(-K1adj*5)) #BOD5 - ultimate Find the dissolved oxygen for 100 percent saturation (assuming no salinity) and the initial DO deficit at the point of discharge. Cs <- hydromisc::O2sat(Tmix) #DO saturation, mg/l C0 <- hydromisc::Mixture(Q, DOsatupstr/100.*Cs, Qeff, DOsateff/100.*Cs) #DO init, mg/l D0 <- Cs - C0 #initial deficit Determine a set of distances where the DO deficit will be calculated, and the corresponding times for the flow to travel that distance. xs <- seq(from=0.5, to=800, by=5) ts <- xs*1000/(V*86400) Finally, calculate the DO (as a percent of saturation) and plot the results. DO_def <- hydromisc::DOdeficit(t=ts, K1=K1adj, K2=K2adj, L0=L0, D0=D0) DO_mgl <- Cs - DO_def DO_pct <- 100*DO_mgl/Cs plot(xs,DO_pct,xlim=c(0,800),ylim=c(0,100),type="l",xlab="Distance, km",ylab="DO, %") grid() Figure 11.4: Dissolved oxygen for this example. For this example, the saturation DO concentration is 9.9 mg/L, meaning the minimum value of the curve corresponds to about 4 mg/L. The EPA notes that values this low are below that recommended for the protection of aquatic life in freshwater. This shows that while the ecosystem has not collapsed, (i.e., following a Type 4 curve in Figure 11.2), effective ecosystem functions may be lost. 11.2 Detecting changes in hydrologic data Planning for decades or more requires the ability to determine whether changes are occurring or have already occurred. Two types of changes will be considered here: step changes, caused by an abrupt change such as deforestation or a new pollutant source, and monotonic (either always increasing or decreasing) trends, caused by more gradual shifts. these are illustrated in Figures 11.5 and 11.6. Figure 11.5: A shift in phosphorus concentrations (source: USGS Scientific Investigations Report 2017-5006, App. 4, https://doi.org/10.3133/sir20175006). Figure 11.6: A trend in annual peak streamflow. (source: USGS Professional Paper 1869, https://doi.org/10.3133/pp1869). Before performing calculations related to trend significance, refer to Chapter 4 of Statistical Methods in Water Resources (Helsel, D.R. et al., 2020) to review the relationship between hypothesis testing and statistical significance. Figure 11.7 from that reference illustrates this. Figure 11.7: Four possible results of hypothesis testing. (source: Helsel et al., 2020). In the context of the example that follows, the null hypothesis, H0, is usually a statement that no trend exists. The \\(\\alpha\\)-value (the significance level) is the probability of incorrectly rejecting the null hypothesis, that is rejecting H0 when it is in fact true. The significance level that is acceptable is a decision that must be made – a common value of \\(\\alpha\\)=0.05 (5 percent) significance, also referred to as \\(1-\\alpha=0.95\\) (95 percent) confidence. A statistical test will produce a p-value, which is essentially the likelihood that the null hypothesis is true, or more technically, the probability of obtaining the calculated test statistic (on one more extreme) when the null hypothesis is true. Again, in the context of trend detection, small p-values (less than \\(\\alpha\\)) indicate greater confidence for rejecting the null hypothesis and thus supporting the existence of a “statistically significant” trend. One of the most robust impacts of a warming climate is the impact on snow. In California, historically the peak of snow accumulation tended to occur roughly on April 1 on average. To demonstrate methods for detecting changes data from the Four Trees Cooperative Snow Sensor site in California, obtained from the USDA National Water and Climate Center. These data are available as part of the hydromisc package. swe <- hydromisc::four_trees_swe plot(swe$Year, swe$April_1_SWE_in, xlab="Year", ylab="April 1 Snow Water Equivalent, in") lines(zoo::rollmean(swe, k=5), col="blue", lty=3, cex=1.4) Figure 11.8: April 1 snow water equivalent at Four Trees station, CA. The dashed line is a 5-year moving average. A plot is always useful – here a 5-year moving average, or rolling mean, is added (using the zoo package), to make any trends more observable. 11.2.1 Detecting a step change When there is a step change in a record, you need to test that the difference between the “before” and “after” conditions is large enough relative to natural variability that is can be confidently described as a change. In other words, whether the change is significant must be determined. This is done by breaking the data into two-samples and applying a statistical test, such as a t-test or the nonparametric rank-sum (or Mann-Whitney U) test. While for this example there is no obvious reason to break this data at any particular year, we’ll just look at the first and second halves. Separate the two subsets of years into two arrays of (y) values (not data frames in this case) and then create a boxplot of the two periods. yvalues1 <- swe$April_1_SWE_in[(swe$Year >= 1980) & (swe$Year <= 2001)] yvalues2 <- swe$April_1_SWE_in[(swe$Year >= 2002) & (swe$Year <= 2023)] boxplot(yvalues1,yvalues2,names=c("1980-2001","2002-2023"),boxwex=0.2,ylab="swe, in") Figure 11.9: Comparison of two records of SWE at Four Trees station, CA. Calculate the means and medians of the two periods, just for illustration. mean(yvalues1) #> [1] 19.76364 mean(yvalues2) #> [1] 15.44545 median(yvalues1) #> [1] 17.8 median(yvalues2) #> [1] 7.9 The mean for the later period is lower, as is the median. the question to pose is whether these differences are statistically significant. The following tests allow that determination. 11.2.1.1 Method 1: Using a t-test. A t-test determines the significance of a difference in the mean between two samples under a number of assumptions. These include independence of each data point (in this example, that any year’s April 1 SWE is uncorrelated with prior years) and that the data are normally distributed. This is performed with the t.test function. The alternative argument is included that the test is “two sided”; a one-sided test would test for one group being only greater than or less than the other, but here we only want to test whether they are different. The paired argument is set to FALSE since there is no correspondence between the order of values in each subset of years. t.test(yvalues1, yvalues2, var.equal = FALSE, alternative = "two.sided", paired = FALSE) #> #> Welch Two Sample t-test #> #> data: yvalues1 and yvalues2 #> t = 0.91863, df = 40.084, p-value = 0.3638 #> alternative hypothesis: true difference in means is not equal to 0 #> 95 percent confidence interval: #> -5.181634 13.817998 #> sample estimates: #> mean of x mean of y #> 19.76364 15.44545 Here the p-value is 0.36, a value much greater than \\(\\alpha = 0.05\\), so the null hypothesis cannot be rejected. The difference in the means is not significant based on this test. 11.2.1.2 Method 2: Wilcoxon rank-sum (or Mann-Whitney U) test. Like the t-test, the rank-sum test produces a p-value, but it measures a more generic measure of “central tendency” (such as a median) rather than a mean. Assumptions about independence of data are still necessary, but there is no requirement of normality of the distribution of data. It is less affected by outliers or a few extreme values than the t-test. This is performed with a standard R function. Other arguments are set as with the t-test. wilcox.test(yvalues1, yvalues2, alternative = "two.sided", paired=FALSE) #> Warning in wilcox.test.default(yvalues1, yvalues2, alternative = "two.sided", : #> cannot compute exact p-value with ties #> #> Wilcoxon rank sum test with continuity correction #> #> data: yvalues1 and yvalues2 #> W = 297, p-value = 0.1999 #> alternative hypothesis: true location shift is not equal to 0 The p-value is much lower than with the t-test, showing less influence of the two very high SWE values in the second half of the record. 11.2.2 Detecting a monotonic trend In a similar way to the step change, a monotonic trend can be tested using parameteric or non-parametric methods. Here we use the entire record to detect trends over the entire period. Linear regression may be used as a parametric method, which makes assumptions similar to the t-test (that residuals of the data are normally distributed). If the data do not conform to a normal distribution, the Mann-Kendall test can be applied, which is a non-parametric test. 11.2.2.1 Method 1: Regression To perform a linear regression in R, build a linear regression model (lm). This can take the swe data frame as input data, specifying the columns to relate linearly. m <- lm(April_1_SWE_in ~ Year, data = swe) summary(m)$coefficients #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 347.3231078 370.6915171 0.9369600 0.3541359 #> Year -0.1647357 0.1852031 -0.8894868 0.3788081 The row for “Year” provides the data on the slope. The slope shows SWE declines by 0.16 inches/year based on regression. The p-value for the slope is 0.379, much larger than the typical \\(\\alpha\\), meaning we cannot claim that a significant slope exists based on this test. So while a declining April 1 snowpack is observed at this location, it is not outside of the natural variability of the data based on a regression analysis. 11.2.2.2 Method 2: Mann-Kendall To conduct a Mann-Kendall trend test, additional packages need to be installed. There are a number available; what is shown below is one method. A non-parametric trend test (and plot) requires a few extra packages, which are installed like this: if (!require('Kendall', quietly = TRUE)) install.packages('Kendall') #> Warning: package 'Kendall' was built under R version 4.2.3 if (!require('zyp', quietly = TRUE)) install.packages('zyp') #> Warning: package 'zyp' was built under R version 4.2.3 Now the significance of the trend can be calculated. The slope associated with this test, the “Thiel-Sen slope”, is calculated using the zyp package. mk <- Kendall::MannKendall(swe$April_1_SWE_in) summary(mk) #> Score = -99 , Var(Score) = 9729 #> denominator = 934.4292 #> tau = -0.106, 2-sided pvalue =0.32044 ss <- zyp::zyp.sen(April_1_SWE_in ~ Year, data=swe) ss$coefficients #> Intercept Year #> 291.1637542 -0.1385452 The non-parametric slope shows April 1 SWE declining by 0.14 inches per year over the period. Again, however, the p-value is greater than the typical \\(\\alpha\\), so based on this method the trend is not significantly different from zero. As with the tests for a step change, the p-value is lower for the nonparametric test. A summary plot of the slopes of both methods is helpful. plot(swe$Year,swe$April_1_SWE_in, xlab = "Year",ylab = "Snow water equivalent, in") lines(swe$Year,m$fitted.values, lty=1, col="black") abline(a = ss$coefficients["Intercept"], b = ss$coefficients["Year"], col="red", lty=2) legend("topright", legend=c("Observations","Regression","Thiel-Sen"), col=c("black","black","red"),lty = c(NA,1,2), pch = c(1,NA,NA), cex=0.8) Figure 11.10: Trends of SWE at Four Trees station, CA. 11.2.3 Choosing whether to use parametric or non-parametric tests Using the parameteric tests above (t-test, regression) requires making an assumption about the underlying distribution of the data, which non-parametric tests do not require. When using a parametric test, the assumption of normality can be tested. For example, for the regression residuals can be tested with the following, where the null hypothesis is that the data are nomally distributed. shapiro.test(m$residuals)$p.value #> [1] 0.003647395 This produces a very small p-value (p < 0.01), meaning the null hypothesis that the residuals are normally distributed is rejected with >99% confidence. This means non-parametric test is more appropriate. In general, non-parametric tests are preferred in hydrologic work because data (and residuals) are rarely normally distributed. 11.3 Detecting changes in extreme events when looking at extreme events like the 100-year high tide, the methods are similar to those used in flood frequency analysis. One distinction is that flood frequency often uses a Gumbel or Log-Pearson type 3 distribution. For sea-level rise (and many other extreme events) other distributions are employed, with one common one being the Generalized Extreme Value (GEV), the cumulative distribution of which is described by Equation (11.4). \\[\\begin{equation} F\\left(x;\\mu,\\sigma,\\xi\\right)=exp\\left[-\\left(1+\\xi\\left(\\frac{x-\\mu}{\\sigma}\\right)\\right)^{-1/\\xi}\\right] \\tag{11.4} \\end{equation}\\] The three parameters \\(\\xi\\), \\(\\mu\\), and \\(\\sigma\\) represent a shape, location, and scale of the distribution function. These distribution parameter can be determined using observations of extremes over a long period or over different periods of record, much as the mean, standard, deviation, and skew are used in flood frequency calculations. The distribution can then be used to estimate the probability associated with a specific magnitude event, or conversely the event magnitude associated with a defined risk level. An excellent example of that is from Tebaldi et al. (2012) who analyzed projected extreme sea level changes through the 21st century. Figure 11.11: Projected return periods by 2050 for floods that are 100 yr events during 1959–2008, Tebaldi et al., 2012 An example using the GEV with sea level data is illustrated below. The Tebaldi et al. (2012) paper uses the R package extRemes, which we will use here. The same package has been used to study extreme wind, precipitation, temperature, streamflow, and other events, so it is a very versatile and widely-used package. Install the package if it is not already installed. if (!require('extRemes', quietly = TRUE)) install.packages('extRemes') #> Warning: package 'extRemes' was built under R version 4.2.3 #> Warning: package 'Lmoments' was built under R version 4.2.2 11.3.1 Obtaining and preparing sea-level data Sea-level data can be downloaded directly into R using the rnoaa package. However, NOAA also has a very intuitive interface that allows geographical searching and preliminary viewing of data. From the NOAA Tines & Currents site one can search an area of interest and find a tide gauge with a long record. Figure 11.12. Figure 11.12: Identification of a sea-level gauge on the NOAA Tides & Currents site. By exploring the data inventory for this station, on its home page, the gauge has a very long record, being established in 1854, with measurement of extremes for over a century. Avoid selecting a partial month, or you may not have the ability to download monthly data. Monthly data were downloaded and saved as a csv file, which is available with the hydromisc package. datafile <- system.file("extdata", "sealevel_9414290_wl.csv", package="hydromisc") dat <- read.csv(datafile,header=TRUE) These data were saved in metric units, so all levels are in meters above the selected tidal datum. there are dates indicating the month associated with each value (and day 1 is in there as a placeholder). If there are any missing data they may be labeled as “NaN”. If you see that, a clean way to address it is to first change the missing data to NA (which R recognizes) with a command such as dat[dat == "NaN"] <- NA For this example we are looking at extreme tide levels, so only retain the “Highest” and “Date” columns. peak_sl <- subset(dat, select=c("Date", "Highest")) A final data preparation is to create an annual time series with the the maximum tide level in any year. One way to facilitate this is to add a column of “year.” Then the data can be aggregated by year, creating a new data frame, taking the maximum value for each year (many other functions, like mean, median, etc. can also be used). In this example the column names are changed to make it easier to work with the data. Also, the year column is converted to an integer for plotting purposes. Any rows with NA values are removed. peak_sl$year <- as.integer(strftime(peak_sl$Date, "%Y")) peak_sl_ann <- aggregate(peak_sl$Highest,by=list(peak_sl$year),FUN=max, na.rm=TRUE) colnames(peak_sl_ann) <- c("year","peak_m") peak_sl_ann <- na.exclude(peak_sl_ann) A plot is always helpful. plot(peak_sl_ann$year,peak_sl_ann$peak_m,xlab="Year",ylab="Annual Peak Sea Level, m") Figure 11.13: Annual highest sea-levels relative to MLLW at gauge 9414290. 11.3.2 Conducting the extreme event analysis The question we will attempt to address is whether the 100-year peak tide level (the level exceeded with a 1 percent probability) has increased between the 1900-1930 and 1990-2020 periods. Extract a subset of the data for one period and fit a GEV distribution to the values. peak_sl_sub1 <- subset(peak_sl_ann, year >= 1900 & year <= 1930) gevfit1 <- extRemes::fevd(peak_sl_sub1$peak_m) gevfit1$results$par #> location scale shape #> 2.0747606 0.1004844 -0.2480902 A plot of return periods for the fit distribution is available as well. extRemes::plot.fevd(gevfit1, type="rl") Figure 11.14: Return periods based on the fit GEV distribution for 1900-1930. Points are observations; dashed lines enclose the 95% confidence interval. As is usually the case, a statistical model does well in the area with observations, but the uncertainty increases for extreme values (like estimating a 500-year event from a 30-year record). A longer record produces better (less uncertain) estimates at higher return periods. Based on the GEV fit, the 100-year recurrence interval extreme tide is determined using: extRemes::return.level(gevfit1, return.period = 100, do.ci = TRUE, verbose = TRUE) #> #> Preparing to calculate 95 % CI for 100-year return level #> #> Model is fixed #> #> Using Normal Approximation Method. #> extRemes::fevd(x = peak_sl_sub1$peak_m) #> #> [1] "Normal Approx." #> #> [1] "100-year return level: 2.35" #> #> [1] "95% Confidence Interval: (2.2579, 2.4429)" A check can be done using the reverse calculation, estimating the return period associated with a specified value of highest water level. This can be done by extracting the three GEV parameters, then running the pevd command. loc <- gevfit1$results$par[["location"]] sca <- gevfit1$results$par[["scale"]] shp <- gevfit1$results$par[["shape"]] extRemes::pevd(2.35, loc = loc, scale = sca , shape = shp, type = c("GEV")) #> [1] 0.9898699 This returns a value of 0.99 (this is the CDF value, or the probability of non-exceedence, F). Recalling that return period, \\(T=1/P=1/(1-F)\\), where P=prob. of exceedence; F=prob. of non-exceedence, the result that 2.35 meters is the 100-year highest water level is validated. Repeating the calculation for a more recent period: peak_sl_sub2 <- subset(peak_sl_ann, year >= 1990 & year <= 2020) gevfit2 <- extRemes::fevd(peak_sl_sub2$peak_m) extRemes::return.level(gevfit2, return.period = 100, do.ci = TRUE, verbose = TRUE) #> #> Preparing to calculate 95 % CI for 100-year return level #> #> Model is fixed #> #> Using Normal Approximation Method. #> extRemes::fevd(x = peak_sl_sub2$peak_m) #> #> [1] "Normal Approx." #> #> [1] "100-year return level: 2.597" #> #> [1] "95% Confidence Interval: (2.3983, 2.7957)" This returns a 100-year high tide of 2.6 m for 1990-2020, a 10.6 % increase over 1900-1930. Another way to look at this is to find out how the frequency of the past (in this case, 1900-1930) 100-year event has changed with rising sea levels. Repeating the calculations from before to capture the GEV parameters for the later period, and then plugging in the 100-year high tide from the early period: loc2 <- gevfit2$results$par[["location"]] sca2 <- gevfit2$results$par[["scale"]] shp2 <- gevfit2$results$par[["shape"]] extRemes::pevd(2.35, loc = loc2, scale = sca2 , shape = shp2, type = c("GEV")) #> [1] 0.7220968 This returns a value of 0.72 (72% non-exceedence, or 28% exceedance, in other words we expect to see an annual high tide of 2.35 m or higher in 28% of the years). The return period of this is calculated as above: T = 1/(1-0.72) = 3.6 years. So, what was the 100-year event in 1900-1930 is about a 4-year event now. "],["management-of-water-resources-systems.html", "Chapter 12 Management of water resources systems 12.1 A simple linear system with two decision variables 12.2 More complex linear programming: reservoir operation 12.3 More Realistic Reservoir Operation: non-linear programming", " Chapter 12 Management of water resources systems Figure 12.1: Lookout Point Dam on the Middle Fork Willamette River source: U.S. Army Corps of Engineers Water resources systems tend to provide a variety of benefits, such as flood control, hydroelectric power, recreation, navigation, and irrigation. Each of these provides a benefit that can quantified, and there are also associated costs that can be quantified. A challenge engineers face is how to manage a system to balance the different uses. Mathematical optimization, which can take many forms, is employed to do this. Introductions to linear programming and other forms of optimization are plentiful. For a background on the concepts and theories, refer to other references. An excellent, comprehensive reference is Water Resource Systems Planning and Management (Loucks & Van Beek, 2017), freely available online. What follows is a demonstration of using some of these optimization methods, but no recap of the theory is provided. The examples here use linear systems, where the objective function and constraints are all linear functions of the decision variables. The hydromisc package will need to be installed to access some of the data used below. If it is not installed, do so following the instructions on the github site for the package. 12.1 A simple linear system with two decision variables 12.1.1 Overview of problem formulation One of the simplest systems to optimize is a linear system of two variables, which means a graphical solution in 2-d is possible. This first demonstration is one example, a reworking of a textbook example (Wurbs & James, 2002). To set up a solution, three things must be described: the decision variables – the variables for which optimal values are sought the constraints – physical or other limitations on the decision variables (or combinations of them) the objective function – an expression, using the decision variables, of what is to be minimized or maximized. 12.1.2 Setting up an example Example 12.1 To supply water to a community, there are two sources of water available with different levels of total dissolved solids (TDS): groundwater (TDS=980 mg/l) and surface water from a reservoir (TDS=100 mg/l). The first two constraints are that a total demand of water of 7,500 m\\(^3\\)must be met, and the delivered water (mixed groundwater and reservoir supplies) can have a maximum TDS of 500 mg/l. This is illustrated in Figure 12.2. Figure 12.2: A schematic of the system for this example. Two additional constraints are that groundwater withdrawal cannot exceed 4,000 m\\(^3\\) and reservoir withdrawals cannot exceed 7,500 m\\(^3\\). There are two decision variables: X1=groundwater and X2=reservoir supply. The objective is to minimize the reservoir withdrawal while meeting the constraints. The TDS constraint is reorganized as: \\[\\frac{800~X1+100~X2}{X1+X2}\\le 400~~~or~~~ 400~X1-300~X2\\le 0\\] Rewriting the other three constraints as functions of the decision variables: \\[\\begin{align*} X1+X2 \\ge 7500 \\\\ X1 \\le 4000 \\\\ X2 \\le 7500 \\end{align*}\\] Notice that the contraints are all expressed as linear functions of the decision variables (left side of the equations) and a value on the right. 12.1.3 Graphing the solution space While this can only be done easily for systems with only two decision variables, a plot of the solution space can be done here by graphing all of the constrains and shading the region where all constraints are satisfied. Figure 12.3: The solution space, shown as the cross-hatched area. In the feasible region, it is clear that the minimum reservoir supply, X2, would be a little larger than 4,000 m\\(^3\\). 12.1.4 Setting up the problem in R An R package useful for solving linear programming problems is the lpSolveAPI package. Install that if necessary, and also install the knitr and kableExtra packages, since thet are very useful for printing the many tables that linear programming involves. Begin by creating an empty linear model. The (0,2) means zero constraints (they’ll be added later) and 2 decision variables. The next two lines just assign names to the decision variables. Because we will use many functions of the lpSolveAPI package, load the library first. Load the kableExtra package too. library(lpSolveAPI) library(kableExtra) example.lp <- lpSolveAPI::make.lp(0,2) # 0 constraints and 2 decision variables ColNames <- c("X1","X2") colnames(example.lp) <- ColNames # Set the names of the decision variables Now set up the objective function. Minimization is the default goal of this R function, but we’ll set it anyway to be clear. The second argument is the vector of coefficients for the decision variables, meaning X2 is minimized. set.objfn(example.lp,c(0,1)) x <- lp.control(example.lp, sense="min") #save output to a dummy variable The next step is to define the constraints. Four constraints were listed above. Additional constraints could be added that \\(X1\\ge 0\\) and \\(X2\\ge 0\\), however, variable ranges in this LP solver are [0,infinity] by default, so for this example and we do not need to include constraints for positive results. If necessary, decision variable valid ranges can be set using set.bounds(). Constraints are defined with the add.constraint command. Figure 12.4 provides an annotated example of the use of an add.constraint command. Figure 12.4: Annotated example of an add.constraint command. Type ?add.constraint in the console for additional details. The four constraints for this example are added with: add.constraint(example.lp, xt=c(400,-300), type="<=", rhs=0, indices=c(1,2)) add.constraint(example.lp, xt=c(1,1), type=">=", rhs=7500) add.constraint(example.lp, xt=c(1,0), type="<=", rhs=4000) add.constraint(example.lp, xt=c(0,1), type="<=", rhs=7500) That completes the setup of the linear model. You can view the model to verify the values you entered by typing the name of the model. example.lp #> Model name: #> X1 X2 #> Minimize 0 1 #> R1 400 -300 <= 0 #> R2 1 1 >= 7500 #> R3 1 0 <= 4000 #> R4 0 1 <= 7500 #> Kind Std Std #> Type Real Real #> Upper Inf Inf #> Lower 0 0 If it has a large number of decision variables it only prints a summary, but in that case you can use write.lp(example.lp, \"example_lp.txt\", \"lp\") to create a viewable file with the model. Now the model can be solved. solve(example.lp) #> [1] 0 If the solver finds an optimal solution it will return a zero. 12.1.5 Interpreting the optimal results View the final value of the objective function by retrieving it and printing it: optimal_solution <- get.objective(example.lp) print(paste0("Optimal Solution = ",round(optimal_solution,2),sep="")) #> [1] "Optimal Solution = 4285.71" For more detail, recover the values of each of the decision variables. vars <- get.variables(example.lp) Next you can print the sensitivity report – a vector of M constraints followed by N decision variables. It helps to create a data frame for viewing and printing the results. Nicer printing is achieved using the kable and kableExtra functions. sens <- get.sensitivity.obj(example.lp)$objfrom results1 <- data.frame(variable=ColNames,value=vars,gradient=as.integer(sens)) kbl(results1, booktabs = TRUE) %>% kable_styling(full_width = F) variable value gradient X1 3214.286 -1 X2 4285.714 0 The above shows decision variable values for the optimal solution. The Gradient is the change in the objective function for a unit increase in the decision variable. Here a negative gradient for decision variable \\(X1\\), the groundwater withdrawal, means that increasing the groundwater withdrawal will have a negative effect on the objective function, (to minimize \\(X2\\)): that is intuitive, since increasing groundwater withdrawal can reduce reservoir supply on a one-to-one basis. To look at which constraints are binding, retrieve the $duals part of the output. m <- length(get.constraints(example.lp)) #number of constraints duals <- get.sensitivity.rhs(example.lp)$duals[1:m] results2 <- data.frame(constraint=c(seq(1:m)),multiplier=duals) kbl(results2, booktabs = TRUE) %>% kable_styling(full_width = F) constraint multiplier 1 -0.0014286 2 0.5714286 3 0.0000000 4 0.0000000 The multipliers for each constraint are referred to as Lagrange multipliers (or shadow prices). Non-zero values of the multiplier indicate a binding capability of that constraint, and the change in the objective function that would result from a unit change in that value. Zero values are non-binding, since a unit change in their value has no effect on the optimal result. For example, constraint 3, that \\(X1 \\le 4000\\), with a multiplier of zero, could be changed (at least a small amount – there can be a limit after which it can become binding) with no effect on the optimal solution. Similarly, if constraint 2, \\(X1+X2 \\ge 7500\\), were were increased, the objective function (the optimal reservoir supply) would also increase. 12.2 More complex linear programming: reservoir operation Water resources systems are far too complicated to be summarized by two decision variables and only a few constraints, as above. Example 12.2 demonstrate how the same procedure can be applied to a slightly more complex system. This is a reformulation of an example from the same text as referenced above (Wurbs & James, 2002). Example 12.2 A river flows into a storage reservoir where the operator must decide how much water to release each month. For simplicity, inflows will by described by a fixed sequence of 12 monthly flows. There are two downstream needs to satisfy: hydropower generation and irrigation diversions. Benefits are derived from these two uses: revenues are $ 800 per 10\\(^6\\)m\\(^3\\) of water diverted for irrigation, and $ 350 per 10\\(^6\\)m\\(^3\\) for hydropower generation. The objective is to determine the releases that will maximize the total revenue. There are physical characteristics of the system that provide some constraints, and others are derived from basic physics, such as the conservation of mass. A schematic of the system is shown in Figure 12.5. Figure 12.5: A schematic of the water resources system for this example. Diversions through the penstock to the hydropower facility are limited to its capacity of 160 10\\(^6\\)m\\(^3\\)/month. For reservoir releases less than that, all of the released water can generate hydropower; flows above that capacity will spill without generating hydropower benefits. The reservoir has a volume of 550 10\\(^6\\)m\\(^3\\), so anything above that will have to be released. Assume the reservoir is at half capacity initially. The irrigation demand varies by month, and diversions up to the demand will produce benefits. These are: Month Demand, 10\\(^6\\)m\\(^3\\) Month Demand, 10\\(^6\\)m\\(^3\\) Month Demand, 10\\(^6\\)m\\(^3\\) Jan (1) 0 May (5) 40 Sep (9) 180 Feb (2) 0 Jun (6) 130 Oct (10) 110 Mar (3) 0 Jul (7) 230 Nov (11) 0 Apr (4) 0 Aug (8) 250 Dec (12) 0 12.2.1 Problem summary There are 48 decision variables in this problem, 12 monthly values for reservoir storage (s\\(_1\\)-s\\(_{12}\\)), release (r\\(_1\\)-r\\(_{12}\\)), hydropower generation (h\\(_1\\)-h\\(_{12}\\)), and agricultural diversion (d\\(_1\\)-d\\(_{12}\\)). The objective function is to maximize the revenue, which is expressed by Equation (12.1). \\[\\begin{equation} Maximize~ x_0=\\sum_{i=1}^{12}\\left(350h_i+800d_i\\right) \\tag{12.1} \\end{equation}\\] Constraints will need to be described to apply the limits to hydropower diversion and storage capacity, and to limit agricultural diversions to no more than the demand. 12.2.2 Setting up the problem in R Create variables for the known or assumed initial values for the system. penstock_cap <- 160 #penstock capacity in million m3/month res_cap <- 550 #reservoir capacity in million m3 res_init_vol <- res_cap/2 #set initial reservoir capacity equal to half of capacity irrig_dem <- c(0,0,0,0,40,130,230,250,180,110,0,0) revenue_water <- 800 #revenue for delivered irrigation water, $/million m3 revenue_power <- 350 #revenue for power generated, $/million m3 A time series of 20 years (January 2000 through December 2019) of monthly flows for this exercise is included with the hydromisc package. Load that and extract the first 12 months to use in this example. inflows_20years <- hydromisc::inflows_20years inflows <- as.numeric(window(inflows_20years, start = c(2000, 1), end = c(2000, 12))) It helps to illustrate how the irrigation demands and inflows vary, and therefore why storage might be useful in regulating flow to provide more reliable irrigation deliveries. par(mgp=c(2,1,0)) ylbl <- expression(10 ^6 ~ m ^3/month) plot(inflows, type="l", col="blue", xlab="Month", ylab=ylbl) lines(irrig_dem, col="darkgreen", lty=2) legend("topright",c("Inflows","Irrigation Demand"),lty = c(1,2), col=c("blue","darkgreen")) grid() Figure 12.6: Inflows and irrigation demand. 12.2.3 Building the linear model Following the same steps as for a simple 2-variable problem, begin by setting up a linear model. Because there are so many decision variables, it helps to add names to them. reser.lp <- make.lp(0,48) DecisionVarNames <- c(paste0("s",1:12),paste0("r",1:12),paste0("h",1:12),paste0("d",1:12)) colnames(reser.lp) <- DecisionVarNames From this point on, the decision variables will be addressed by their indices, that is, their numeric position in this sequence of 48 values. To summarize their positions: Decision Variables Indices (columns) Storage (s1-s12) 1-12 Release (r1-r12) 13-24 Hydropower (h1-h12) 25-36 Irrigation diversion (d1-d12) 37-48 Using these indices as a guide, set up the objective function and initialize the linear model. While not necessary, redirecting the output of the lp.control to a variable prevents a lot of output to the console. The following takes the revenue from hydropower and irrigation (in $ per 10\\(^6\\)m\\(^3\\)/month), multiplies them by the 12 monthly values for the hydropower flows and the irrigation deliveries, and sets the objective to maximize their sum, as described by Equation (12.1). set.objfn(reser.lp,c(rep(revenue_power,12),rep(revenue_water,12)),indices = c(25:48)) x <- lp.control(reser.lp, sense="max") With the LP setup, the constraints need to be applied. Negative releases, storage, or river flows don’t make sense, so they all need to be positive, so \\(s_t\\ge0\\), \\(r_t\\ge0\\), \\(h_t\\ge0\\) for all 12 months, but because the lpSolveAPI package assumes all decision variables have a range of \\(0\\le x\\le \\infty\\) these do not need to be explicitly added as constraints. When using other software packages these may need to be included. 12.2.3.1 Constraints 1-12: Maximum storage The maximum capacity of the reservoir cannot be exceeded in any month, or \\(s_t\\le 600\\) for all 12 months. This can be added in a simple loop: for (i in seq(1,12)) { add.constraint(reser.lp, xt=c(1), type="<=", rhs=res_cap, indices=c(i)) } 12.2.3.2 Constraints 13-24: Irrigation diversions The irrigation diversions should never exceed the demand. While for some months they are set to zero, since decision variables are all assumed non-negative, we can just assign all irrigation deliveries using the \\(\\le\\) operator. for (i in seq(1,12)) { add.constraint(reser.lp, xt=c(1), type="<=", rhs=irrig_dem[i], indices=c(i+36)) } 12.2.3.3 Constraints 25-36: Hydropower Hydropower release cannot exceed the penstock capacity in any month: \\(h_t\\le 180\\) for all 12 months. This can be done following the example above for the maximum storage constraint for (i in seq(1,12)) { add.constraint(reser.lp, xt=c(1), type="<=", rhs=penstock_cap, indices=c(i+24)) } 12.2.3.4 Constraints 37-48: Reservoir release Reservoir release must equal or exceed irrigation deliveries, which is another way of saying that the water remaining in the river after the diversion cannot be negative. In other words \\(r_1-d_1\\ge 0\\), \\(r_2-d_2\\ge 0\\), … for all 12 months. For constraints involving more than one decision variable the constraint equations look a little different, and keeping track of the indices associated with each decision variable is essential. for (i in seq(1,12)) { add.constraint(reser.lp, xt=c(1,-1), type=">=", rhs=0, indices=c(i+12,i+36)) } 12.2.3.5 Constraints 49-60: Hydropower Hydropower generation will be less than or equal to reservoir release in every month, or \\(r_1-h_1\\ge 0\\), \\(r_2-h_2\\ge 0\\), … for all 12 months. for (i in seq(1,12)) { add.constraint(reser.lp, xt=c(1,-1), type=">=", rhs=0, indices=c(i+12,i+24)) } 12.2.3.6 Constraints 61-72: Conservation of mass Finally, considering the reservoir, the inflow minus the outflow in any month must equal the change in storage over that month. That can be expressed in an equation with decision variables on the left side as: \\[s_t-s_{t-1}+r_t=inflow_t\\] where \\(t\\) is a month from 1-12 and \\(s_t\\) is the storage at the end of month \\(t\\). We need to use the initial reservoir volume, \\(s_0\\) (given above in the problem statement) for the first month’s mass balance, so the above would become \\(s_1-s_0+r_1=inflow_1\\), or \\(s_1+r_1=inflow_1+s_0\\). All subsequent months can be assigned in a loop, add.constraint(reser.lp, xt=c(1,1), type="=", rhs=inflows[1]+res_init_vol, indices=c(1,13)) for (i in seq(2,12)) { add.constraint(reser.lp, xt=c(1,-1,1), type="=", rhs=inflows[i], indices=c(i,i-1,i+12)) } This completes the LP model setup. Especially for larger models, it is helpful to save the model. You can use something like write.lp(reser.lp, \"reservoir_LP.txt\", \"lp\") to create a file (readable using any text file viewer, like Notepad++) with all of the model details. It can also be read into R with the read.lp command to load the complete LP. The beginning of the file for this LP looks like: Figure 12.7: The top of the linear model file produced by write.lp(). 12.2.3.7 Solving the model and interpreting output Solve the LP and retrieve the value of the objective function. solve(reser.lp) #> [1] 0 get.objective(reser.lp) #> [1] 1230930 To look at the hydropower generation, and to see how often spill occurs, it helps to view the associated decision variables (as noted above, these are indices 12-24 and 25-36). vars <- get.variables(reser.lp) # retrieve decision variable values results0 <- data.frame(variable=DecisionVarNames,value=vars) r0 <- cbind(results0[13:24, ], results0[25:36, ]) rownames(r0) <- c() names(r0) <- c("Decision Variable","Value","Decision Variable","Value") kbl(r0, booktabs = TRUE) %>% kable_styling(bootstrap_options = c("striped","condensed"),full_width = F) Decision Variable Value Decision Variable Value r1 160.00000 h1 160.00000 r2 160.00000 h2 160.00000 r3 160.00000 h3 160.00000 r4 89.44193 h4 89.44193 r5 40.00000 h5 40.00000 r6 130.00000 h6 130.00000 r7 230.00000 h7 160.00000 r8 197.58616 h8 160.00000 r9 160.00000 h9 160.00000 r10 112.03054 h10 112.03054 r11 96.96217 h11 96.96217 r12 105.45502 h12 105.45502 Figure 12.8: Reservoir releases and hydropower water use for optimal solution. For this optimal solution, the releases exceed the capacity of the penstock supplying the hydropower plant in July and August, meaning there would be reservoir spill during those months. Another part of the output that is important is to what degree irrigation demand is met. the irrigation delivery is associated with decision variables with indices 37-48. Decision Variable Value Irrigation Demand, 10\\(^6\\)m\\(^3\\) d1 0.0000 0 d2 0.0000 0 d3 0.0000 0 d4 0.0000 0 d5 40.0000 40 d6 130.0000 130 d7 230.0000 230 d8 197.5862 250 d9 160.0000 180 d10 110.0000 110 d11 0.0000 0 d12 0.0000 0 August and September see a shortfall in irrigation deliveries where full demand is not met. Finally, finding which constraints are binding can provide insights into how a system might be modified to improve the optimal solution. This is done similarly to the simpler problem above, by retrieving the duals portion of the sensitivity results. To address the question of whether the size of the reservoir is a binding constraint, that is, whether increasing reservoir size would improve the optimal results, only the first 12 constraints are printed. m <- length(get.constraints(reser.lp)) # retrieve the number of constraints duals <- get.sensitivity.rhs(reser.lp)$duals[1:m] results2 <- data.frame(Constraint=c(seq(1:m)),Multiplier=duals) kbl(results2[1:12,], booktabs = TRUE) %>% kable_styling(bootstrap_options = c("striped","condensed"),full_width = F) Constraint Multiplier 1 0 2 0 3 0 4 0 5 450 6 0 7 0 8 0 9 0 10 0 11 0 12 0 For this example, in only one month would a larger reservoir have a positive impact on the maximum revenue. 12.3 More Realistic Reservoir Operation: non-linear programming While the simple examples above illustrate how an optimal solution can be determined for a linear (and deterministic) reservoir system, in reality reservoirs are much more complex. Most reservoir operation studies use sophisticated software to develop and apply Rule Curves for reservoirs, aiming to optimally store and release water, preserving the storage pools as needed. Figure 12.9 shows how reservoir volumes are managed. Figure 12.9: Sample reservoir operating goals U.S. Army Corps of Engineers Many rule curves depend on the condition of the system at some prior time. Figure 12.10 shows a rule curve used to operate Folsom Reservoir on the American River in California, where the target storage depends on the total upstream storage available. Figure 12.10: Multiple rule corves based on upstream storage U.S. Army Corps of Engineers Report RD-48 One method for deriving an optimal solution for the nonlinear and random processes in a water resources system is stochastic dynamic programming (SDP). Like LP, SDP uses algorithms that optimize an objective function under specified constraints. However, SDP can accommodate non-linear, dynamic outcomes, such as those associated with floods risks or other stochastic events. SDP can combine the stochastic information with reservoir management actions, where the outcome of decisions can be dependent on the state of the system (as in Figure 12.10). Constraints can be set to be met a certain percentage of the time, rather than always. 12.3.1 Reservoir operation While SDP is a topic that is far more advanced than what will be covered here, one R package will be introduced. For reservoir optimization, the R package reservoir can use SDP to derive an optimal operating rule for a reservoir given a sequence of inflows using a single or multiple constraints. The package can also take any derived rule curve and operate a reservoir using it, which is what will be demonstrated here. First, place the optimal releases, according to the LP above, into a new vector to be used as a set of target releases for the reservoir operation. target_release <- results0[13:24, ]$value The reservoir can be operated (for the same 12-month period, with the same 12 inflows as above) with a single command. x <- reservoir::simRes(inflows, target_release, res_cap, plot = F) The total revenue from hydropower generation and irrigation deliveries is computed as follows. irrig_releases <- pmin(x$releases,irrig_dem) irrig_benefits <- sum(irrig_releases*revenue_water) hydro_releases <- pmin(x$releases,penstock_cap) hydro_benefits <- hydro_releases*revenue_power sum(irrig_benefits,hydro_benefits) #> [1] 1230930 Unsurprisingly, this produces the same result as with the LP example. 12.3.2 Performing stochastic dynamic programming The optimal releases, or target releases, were established based on a single year. the SDP in the reservoir package can be used to determine optimal releases based on a time series of inflows. Here the entire 20-year inflow sequence is used to generate a multiobjective optimal solution for the system. A weighting must be applied to describe the importance of meeting different parts of the objective function. The target release(s) cannot be zero, so a small constant is added. weight_water <- revenue_water/(revenue_water + revenue_power) weight_power <- revenue_power/(revenue_water + revenue_power) z <- reservoir::sdp_multi(inflows_20years, cap=res_cap, target = irrig_dem+0.01, R_max = penstock_cap, spill_targ = 0.95, weights = c(weight_water, weight_power, 0.00), loss_exp = c(1, 1, 1), tol=0.99, S_initial=0.5, plot=FALSE) irrig_releases2 <- pmin(z$releases,irrig_dem) irrig_benefits2 <- sum(irrig_releases2*revenue_water) hydro_releases2 <- pmin(z$releases,penstock_cap) hydro_benefits2 <- hydro_releases2*revenue_power sum(irrig_benefits2,hydro_benefits2)/20 #> [1] 911240 For a 20-year period, the average annual revenue will always be less than that for a single year where the optimal releases are designed based on that same year. "],["groundwater.html", "Chapter 13 Groundwater", " Chapter 13 Groundwater Figure 13.1: A conceptual aquifer with a pumping well U.S. Geological survey Groundwater content is forthcoming… "],["references.html", "References", " References Allen, R. G., & United Nations, F. and A. O. of the (Eds.). (1998). Crop evapotranspiration: Guidelines for computing crop water requirements. Rome: Food; Agriculture Organization of the United Nations. Astagneau, P. C., Thirel, G., Delaigue, O., Guillaume, J. H. A., Parajka, J., Brauer, C. C., et al. (2021). Technical note: Hydrology modelling R packages – a unified analysis of models and practicalities from a user perspective. Hydrology and Earth System Sciences, 25(7), 3937–3973. https://doi.org/10.5194/hess-25-3937-2021 Davidian, Jacob. (1984). Computation of water-surface profiles in open channels (No. Techniques of Water-Resources Investigations, Book 3, Chapter A15). https://doi.org/10.3133/twri03A15 Ductile Iron Pipe Research Association. (2016). Thrust Restraint Design for Ductile Iron Pipe, Seventh Edition. Retrieved from https://dipra.org/technical-resources England, J.F., Cohn, T.A., Faber, B.A., Stedinger, J.R., Thomas, W.O., Veilleux, A.G., et al. (2019). Guidelines for Determining Flood Flow Frequency Bulletin 17C (Techniques and {Methods}) (p. 148). Reston, Virginia: U.S. Department of the Interior, U.S. Geological Survey. Retrieved from https://doi.org/10.3133/tm4B5 Finnemore, E. J., & Maurer, E. (Eds.). (2023). Fluid mechanics with civil engineering applications (11th ed.). New York: McGraw Hill. Fox-Kemper, B., Hewitt, H., Xiao, C., Aðalgeirsdóttir, G., Drijfhout, S., Edwards, T., et al. (2021). Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The physical science basis. Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change. Masson-, V. Delmotte, P. Zhai, A. Pirani, SL Connors, C. Péan, S. Berger, et …. Haaland, S. E. (1983). Simple and Explicit Formulas for the Friction Factor in Turbulent Pipe Flow. Journal of Fluids Engineering, 105(1), 89–90. https://doi.org/10.1115/1.3240948 Helsel, D.R., Hirsch, R.M., Ryberg, K.R., Archfield, S.A., & Gilroy, E.J. (2020). Statistical methods in water resources: U.S. Geological Survey Techniques and Methods, book 4, chap. A3 (p. 458). U.S. Geological Survey. Retrieved from https://doi.org/10.3133/tm4a3 Loucks, D. P., & Van Beek, E. (2017). Water Resource Systems Planning and Management. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-44234-1 Lovelace, R., Nowosad, J., & Münchow, J. (2019). Geocomputation with R. Boca Raton: CRC Press, Taylor; Francis Group, CRC Press is an imprint of theTaylor; Francis Group, an informa Buisness, A Chapman & Hall Book. Marshall, J. D., & Toffel, M. W. (2005). Framing the Elusive Concept of Sustainability: A Sustainability Hierarchy. Environmental Science & Technology, 39(3), 673–682. https://doi.org/10.1021/es040394k McCuen, R. (2016). Hydrologic analysis and design, 4th. Pearson Education. Moore, J., Chatsaz, M., d’Entremont, A., Kowalski, J., & Miller, D. (2022). Mechanics Map Open Textbook Project: Engineering Mechanics. Retrieved from https://eng.libretexts.org/Bookshelves/Mechanical_Engineering/Mechanics_Map_(Moore_et_al.) Pebesma, E. J., & Bivand, R. (2023). Spatial data science: With applications in R (First edition). Boca Raton, FL: CRC Press. Peterka, Alvin J. (1978). Hydraulic design of stilling basins and energy dissipators. Department of the Interior, Bureau of Reclamation. R Core Team. (2022). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/ Searcy, J. K., & Hardison, C. H. (1960). Double-mass curves. US Government Printing Office. Slater, L. J., Thirel, G., Harrigan, S., Delaigue, O., Hurley, A., Khouakhi, A., et al. (2019). Using R in hydrology: A review of recent developments and future directions. Hydrology and Earth System Sciences, 23(7), 2939–2963. https://doi.org/10.5194/hess-23-2939-2019 Sturm, T. W. (2021). Open Channel Hydraulics (3rd Edition). New York: McGraw-Hill Education. Retrieved from https://www.accessengineeringlibrary.com/content/book/9781260469707 Swamee, P. K., & Jain, A. K. (1976). Explicit Equations for Pipe-Flow Problems. Journal of the Hydraulics Division, 102(5), 657–664. https://doi.org/10.1061/JYCEAJ.0004542 Tebaldi, C., Strauss, B. H., & Zervas, C. E. (2012). Modelling sea level rise impacts on storm surges along US coasts. Environmental Research Letters, 7(1), 014032. Wurbs, R. A., & James, W. P. (2002). Water resources engineering. Upper Saddle River, NJ: Prentice Hall. "]] +[["index.html", "Hydraulics and Water Resources: Examples Using R Preface Introduction to R and RStudio Citing this reference Copyright", " Hydraulics and Water Resources: Examples Using R Ed Maurer Professor, Civil, Environmental, and Sustainable Engineering Department, Santa Clara University 2024-03-06 Preface This is a compilation of various R exercises and examples created over many years. They have been used mostly in undergraduate civil engineering classes including fluid mechanics, hydraulics, and water resources. This is a dynamic work, and will be regularly updated as errors are identified, improved presentation is developed, or new topics or examples are introduced. I welcome any suggestions or comments. In what follows, text will be intentionally brief. More extensive discussion and description can be found in any fluid mechanics, applied hydraulics, or water resources engineering text. Symbology for hydraulics in this reference generally follows that of Finnemore and Maurer (2024). Fundamental equations will be introduced though the emphasis will be on applications to solve common problems. Also, since this is written by a civil engineer, the only fluids included are water and air, since that accounts for nearly all problems encountered in the field. Solving water problems is rarely done by hand calculations, though the importance of performing order of magnitude ‘back of the envelope’ calculations cannot be overstated. Whether using a hand calculator, spreadsheet, or a programming language to produce a solution, having a sense of when an answer is an outlier will help catch errors. Scripting languages are powerful tools for performing calculations, providing a fully traceable and reproducible path from your input to a solution. Open source languages have the benefit of being free to use, and invite users to be part of a community helping improve the language and its capabilities. The language of choice for this book is R (R Core Team, 2022), chosen for its straightforward syntax, powerful graphical capabilities, wide use in engineering and in many other disciplines, and by using the RStudio interface, it can look and feel a lot like Matlab® with which most engineering students have some experience. Introduction to R and RStudio No introduction to R or RStudio is provided here. It is assumed that the reader has installed R (and RStudio), is comfortable installing and updating packages, and understands the basics of R scripting. Some resources that can provide an introduction to R include: A brief overview, aimed at students at Santa Clara University. An Introduction to R, a comprehensive reference by the R Core Team. Introduction to Programming with R by Stauffer et al., materials for a university course, including interactive exercises. R for Water Resources Data Science, with both introductory and intermediate level courses online. As I developed these exercises and text, I learned R through the work of many others, and the excellent help offered by skilled people sharing their knowledge on stackoverflow. The methods shown here are not the only ways to solve these problems, and users are invited to share alternative or better solutions. Citing this reference Maurer, Ed, 2023. Hydraulics and Water Resources: Examples Using R, doi:10.5281/zenodo.7576843 https://edm44.github.io/hydr-watres-book/. Copyright This work is provided under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). As a summary, this license allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator. This is a summary of (and not a substitute for) the license. "],["units-in-fluid-mechanics.html", "Chapter 1 Units in Fluid Mechanics", " Chapter 1 Units in Fluid Mechanics Before beginning with problem solving methods it helps to recall some important quantities in fluid mechanics and their associated units. While the world has generally moved forward into standardizing the use of the SI unit system, the U.S. stubbornly holds onto the antiquated US (sometimes called the British Gravitational, BG) system. This means practicing engineers must be familiar with both systems, and be able to convert between the two systems. These important quantities are shown in Table 1.1. Table 1.1: Dimensions and units for common quantities. Quantity Symbol Dimensions US (or BG) Units SI Units US to SI multiply by Length L \\(L\\) \\(ft\\) \\(m\\) 0.3048 Acceleration a \\(LT^{-2}\\) \\(ft/s^2\\) \\(m/s^{2}\\) 0.3048 Mass m \\(M\\) \\(slug\\) \\(kg\\) 14.59 Force F \\(F\\) \\(lb\\) \\(N\\) 4.448 Density \\(\\rho\\) \\(ML^{-3}\\) \\(slug/ft^3\\) \\(kg/m^3\\) 515.4 Energy/Work FL \\({ft}\\cdot{lb}\\) \\({N}\\cdot{m}=joule (J)\\) 1.356 Flowrate Q \\(L^{3}/T\\) \\(ft^{3}/s\\)=cfs \\(m^{3}/s\\) 0.02832 Kinematic viscocity \\(\\nu\\) \\(L^{2}/T\\) \\(ft^{2}/s\\) \\(m^{2}/s\\) 0.0929 Power \\(FLT^{-1}\\) \\({ft}\\cdot{lb/s}\\) \\({N}\\cdot{m/s}=watt (W)\\) 1.356 Pressure p \\(FL^{-2}\\) \\(lb/in^2=psi\\) \\(N/m^2=Pa\\) 6895 Specific Weight \\(\\gamma\\) \\(FL^{-3}\\) \\(lb/ft^3\\) \\(N/m^3\\) 157.1 Velocity V \\(LT^{-1}\\) \\(ft/s\\) \\(m/s\\) 0.3048 (Dynamic) Viscocity \\(\\mu\\) \\(FTL^{-2}\\) \\({lb}\\cdot{s/ft^2}\\) \\({N}\\cdot{s/m^2}={Pa}\\cdot{s}\\) 47.88 Volume \\(\\forall\\) \\(L^3\\) \\(ft^3\\) \\(m^3\\) 0.02832 There are many other units that must be accommodated. For example, one may encounter the poise to describe (dynamic) viscosity (\\(1~Pa*s = 10~poise\\)), or the stoke for kinematic viscocity (\\(1~m^2/s=10^4~stokes\\)). Many hydraulic systems use gallons per minute (gpm) as a unit of flow (\\(1~ft^3/s=448.8~gpm\\)), and larger water systems often use millions of gallons per day (mgd) (\\(1~mgd = 1.547~ft^3/s\\)). For volume, the SI system often uses liters (\\(l\\)) instead of \\(m^3\\) (\\(1~m^3=1000~l\\)). One regular conversion that needs to occur is the translation between mass (m) and weight (W), where \\(W=mg\\), where \\(g\\) is gravitational acceleration on the earth’s surface: \\(g=9.81~m/s^2=32.2~ft/s^2\\). When working with forces (such as with momentum problems or hydrostatic forces) be sure to work with weights/forces, not mass. It is straightforward to use conversion factors in the table to manipulate values between the systems, multiplying by the factor shown to go from US to SI units, or dividing to do the \\[{1*10^{-6}~m^2/s}*\\frac{1 ~ft^2/s}{0.0929~m^2/s}=1.076*10^{-5} ~ft^2/s\\] Another example converts between two quantities in the US system: 100 gallons per minute to cfs: \\[{100 ~gpm}*\\frac{1 ~cfs}{448.8 ~gpm}=0.223 ~cfs\\] The units package in R can do these conversions and more, and also checks that conversions are permissible (producing an error if incompatible units are used). units::units_options(set_units_mode = "symbols") Q_gpm <- units::set_units(100, gallon/min) Q_gpm #> 100 [gallon/min] Q_cfs <- units::set_units(Q_gpm, ft^3/s) Q_cfs #> 0.2228009 [ft^3/s] Repeating the unit conversion of viscosity using the units package: Example 1.1 Convert kinematic viscosity from SI to Eng units. nu <- units::set_units(1e-6, m^2/s) nu #> 1e-06 [m^2/s] units::set_units(nu, ft^2/s) #> 1.076391e-05 [ft^2/s] The units package also produces correct units during operations. For example, multiplying mass by g should produce weight. Example 1.2 Using the units package to produce correct units during mathematical operations. #If you travel at 88 ft/sec for 1 hour, how many km would you travel? v <- units::set_units(88, ft/s) t <- units::set_units(1, hour) d <- v*t d #> 316800 [ft] units::set_units(d, km) #> 96.56064 [km] #What is the weight of a 4 slug mass, in pounds and Newtons? m <- units::set_units(4, slug) g <- units::set_units(32.2, ft/s^2) w <- m*g #Notice the units are technically correct, but have not been simplified in this case w #> 128.8 [ft*slug/s^2] #These can be set manually to verify that lbf (pound-force) is a valid equivalent units::set_units(w, lbf) #> 128.8 [lbf] units::set_units(w, N) #> 572.9308 [N] "],["properties-of-water.html", "Chapter 2 Properties of water (and air) 2.1 Properties important for water standing still 2.2 Properties important for moving water 2.3 Atmosperic Properties", " Chapter 2 Properties of water (and air) Fundamental properties of water allow the description of the forces it exerts and how it behaves while in motion. A table of these properties can be generated with the hydraulics package using a command like water_table(units = \"SI\"). A summary of basic water properties, which vary with temperature, is shown in Table 2.1 for SI units and Table 2.2 for US (or Eng) units. Table 2.1: Water properties in SI units Temp Density Spec_Weight Viscosity Kinem_Visc Sat_VP Surf_Tens Bulk_Mod C kg m-3 N m-3 N s m-2 m2 s-1 Pa N m-1 Pa \\(0\\) \\(999.9\\) \\(9809\\) \\(1.734 \\times 10^{-3}\\) \\(1.734 \\times 10^{-6}\\) \\(611.2\\) \\(7.57 \\times 10^{-2}\\) \\(2.02 \\times 10^{9}\\) \\(5\\) \\(1000.0\\) \\(9810\\) \\(1.501 \\times 10^{-3}\\) \\(1.501 \\times 10^{-6}\\) \\(872.6\\) \\(7.49 \\times 10^{-2}\\) \\(2.06 \\times 10^{9}\\) \\(10\\) \\(999.7\\) \\(9807\\) \\(1.310 \\times 10^{-3}\\) \\(1.311 \\times 10^{-6}\\) \\(1228\\) \\(7.42 \\times 10^{-2}\\) \\(2.10 \\times 10^{9}\\) \\(15\\) \\(999.1\\) \\(9801\\) \\(1.153 \\times 10^{-3}\\) \\(1.154 \\times 10^{-6}\\) \\(1706\\) \\(7.35 \\times 10^{-2}\\) \\(2.14 \\times 10^{9}\\) \\(20\\) \\(998.2\\) \\(9793\\) \\(1.021 \\times 10^{-3}\\) \\(1.023 \\times 10^{-6}\\) \\(2339\\) \\(7.27 \\times 10^{-2}\\) \\(2.18 \\times 10^{9}\\) \\(25\\) \\(997.1\\) \\(9781\\) \\(9.108 \\times 10^{-4}\\) \\(9.135 \\times 10^{-7}\\) \\(3170\\) \\(7.20 \\times 10^{-2}\\) \\(2.22 \\times 10^{9}\\) \\(30\\) \\(995.7\\) \\(9768\\) \\(8.174 \\times 10^{-4}\\) \\(8.210 \\times 10^{-7}\\) \\(4247\\) \\(7.12 \\times 10^{-2}\\) \\(2.25 \\times 10^{9}\\) \\(35\\) \\(994.1\\) \\(9752\\) \\(7.380 \\times 10^{-4}\\) \\(7.424 \\times 10^{-7}\\) \\(5629\\) \\(7.04 \\times 10^{-2}\\) \\(2.26 \\times 10^{9}\\) \\(40\\) \\(992.2\\) \\(9734\\) \\(6.699 \\times 10^{-4}\\) \\(6.751 \\times 10^{-7}\\) \\(7385\\) \\(6.96 \\times 10^{-2}\\) \\(2.28 \\times 10^{9}\\) \\(45\\) \\(990.2\\) \\(9714\\) \\(6.112 \\times 10^{-4}\\) \\(6.173 \\times 10^{-7}\\) \\(9595\\) \\(6.88 \\times 10^{-2}\\) \\(2.29 \\times 10^{9}\\) \\(50\\) \\(988.1\\) \\(9693\\) \\(5.605 \\times 10^{-4}\\) \\(5.672 \\times 10^{-7}\\) \\(1.235 \\times 10^{4}\\) \\(6.79 \\times 10^{-2}\\) \\(2.29 \\times 10^{9}\\) \\(55\\) \\(985.7\\) \\(9670\\) \\(5.162 \\times 10^{-4}\\) \\(5.237 \\times 10^{-7}\\) \\(1.576 \\times 10^{4}\\) \\(6.71 \\times 10^{-2}\\) \\(2.29 \\times 10^{9}\\) \\(60\\) \\(983.2\\) \\(9645\\) \\(4.776 \\times 10^{-4}\\) \\(4.857 \\times 10^{-7}\\) \\(1.995 \\times 10^{4}\\) \\(6.62 \\times 10^{-2}\\) \\(2.28 \\times 10^{9}\\) \\(65\\) \\(980.6\\) \\(9619\\) \\(4.435 \\times 10^{-4}\\) \\(4.523 \\times 10^{-7}\\) \\(2.504 \\times 10^{4}\\) \\(6.54 \\times 10^{-2}\\) \\(2.26 \\times 10^{9}\\) \\(70\\) \\(977.7\\) \\(9592\\) \\(4.135 \\times 10^{-4}\\) \\(4.229 \\times 10^{-7}\\) \\(3.120 \\times 10^{4}\\) \\(6.45 \\times 10^{-2}\\) \\(2.25 \\times 10^{9}\\) \\(75\\) \\(974.8\\) \\(9563\\) \\(3.869 \\times 10^{-4}\\) \\(3.969 \\times 10^{-7}\\) \\(3.860 \\times 10^{4}\\) \\(6.36 \\times 10^{-2}\\) \\(2.23 \\times 10^{9}\\) \\(80\\) \\(971.7\\) \\(9533\\) \\(3.631 \\times 10^{-4}\\) \\(3.737 \\times 10^{-7}\\) \\(4.742 \\times 10^{4}\\) \\(6.27 \\times 10^{-2}\\) \\(2.20 \\times 10^{9}\\) \\(85\\) \\(968.5\\) \\(9501\\) \\(3.419 \\times 10^{-4}\\) \\(3.530 \\times 10^{-7}\\) \\(5.787 \\times 10^{4}\\) \\(6.18 \\times 10^{-2}\\) \\(2.17 \\times 10^{9}\\) \\(90\\) \\(965.2\\) \\(9468\\) \\(3.229 \\times 10^{-4}\\) \\(3.345 \\times 10^{-7}\\) \\(7.018 \\times 10^{4}\\) \\(6.08 \\times 10^{-2}\\) \\(2.14 \\times 10^{9}\\) \\(95\\) \\(961.7\\) \\(9434\\) \\(3.057 \\times 10^{-4}\\) \\(3.179 \\times 10^{-7}\\) \\(8.461 \\times 10^{4}\\) \\(5.99 \\times 10^{-2}\\) \\(2.10 \\times 10^{9}\\) \\(100\\) \\(958.1\\) \\(9399\\) \\(2.902 \\times 10^{-4}\\) \\(3.029 \\times 10^{-7}\\) \\(1.014 \\times 10^{5}\\) \\(5.89 \\times 10^{-2}\\) \\(2.07 \\times 10^{9}\\) Table 2.2: Water properties in US units Temp Density Spec_Weight Viscosity Kinem_Visc Sat_VP Surf_Tens Bulk_Mod F slug ft-3 lbf ft-3 lbf s ft-2 ft2 s-1 lbf ft-2 lbf ft-1 lbf ft-2 \\(32\\) \\(1.9\\) \\(62.42\\) \\(3.621 \\times 10^{-5}\\) \\(1.873 \\times 10^{-5}\\) \\(12.77\\) \\(5.18 \\times 10^{-3}\\) \\(4.22 \\times 10^{7}\\) \\(42\\) \\(1.9\\) \\(62.43\\) \\(3.087 \\times 10^{-5}\\) \\(1.596 \\times 10^{-5}\\) \\(18.94\\) \\(5.13 \\times 10^{-3}\\) \\(4.31 \\times 10^{7}\\) \\(52\\) \\(1.9\\) \\(62.40\\) \\(2.658 \\times 10^{-5}\\) \\(1.375 \\times 10^{-5}\\) \\(27.62\\) \\(5.07 \\times 10^{-3}\\) \\(4.40 \\times 10^{7}\\) \\(62\\) \\(1.9\\) \\(62.36\\) \\(2.311 \\times 10^{-5}\\) \\(1.196 \\times 10^{-5}\\) \\(39.64\\) \\(5.02 \\times 10^{-3}\\) \\(4.50 \\times 10^{7}\\) \\(72\\) \\(1.9\\) \\(62.29\\) \\(2.026 \\times 10^{-5}\\) \\(1.050 \\times 10^{-5}\\) \\(56.00\\) \\(4.96 \\times 10^{-3}\\) \\(4.59 \\times 10^{7}\\) \\(82\\) \\(1.9\\) \\(62.20\\) \\(1.790 \\times 10^{-5}\\) \\(9.290 \\times 10^{-6}\\) \\(77.99\\) \\(4.90 \\times 10^{-3}\\) \\(4.67 \\times 10^{7}\\) \\(92\\) \\(1.9\\) \\(62.09\\) \\(1.594 \\times 10^{-5}\\) \\(8.286 \\times 10^{-6}\\) \\(107.2\\) \\(4.84 \\times 10^{-3}\\) \\(4.72 \\times 10^{7}\\) \\(102\\) \\(1.9\\) \\(61.97\\) \\(1.429 \\times 10^{-5}\\) \\(7.443 \\times 10^{-6}\\) \\(145.3\\) \\(4.78 \\times 10^{-3}\\) \\(4.75 \\times 10^{7}\\) \\(112\\) \\(1.9\\) \\(61.83\\) \\(1.289 \\times 10^{-5}\\) \\(6.732 \\times 10^{-6}\\) \\(194.7\\) \\(4.72 \\times 10^{-3}\\) \\(4.77 \\times 10^{7}\\) \\(122\\) \\(1.9\\) \\(61.68\\) \\(1.171 \\times 10^{-5}\\) \\(6.126 \\times 10^{-6}\\) \\(258\\) \\(4.66 \\times 10^{-3}\\) \\(4.78 \\times 10^{7}\\) \\(132\\) \\(1.9\\) \\(61.52\\) \\(1.069 \\times 10^{-5}\\) \\(5.608 \\times 10^{-6}\\) \\(338.1\\) \\(4.59 \\times 10^{-3}\\) \\(4.77 \\times 10^{7}\\) \\(142\\) \\(1.9\\) \\(61.34\\) \\(9.808 \\times 10^{-6}\\) \\(5.162 \\times 10^{-6}\\) \\(438.5\\) \\(4.53 \\times 10^{-3}\\) \\(4.75 \\times 10^{7}\\) \\(152\\) \\(1.9\\) \\(61.16\\) \\(9.046 \\times 10^{-6}\\) \\(4.775 \\times 10^{-6}\\) \\(563.2\\) \\(4.46 \\times 10^{-3}\\) \\(4.72 \\times 10^{7}\\) \\(162\\) \\(1.9\\) \\(60.96\\) \\(8.381 \\times 10^{-6}\\) \\(4.438 \\times 10^{-6}\\) \\(716.9\\) \\(4.39 \\times 10^{-3}\\) \\(4.68 \\times 10^{7}\\) \\(172\\) \\(1.9\\) \\(60.75\\) \\(7.797 \\times 10^{-6}\\) \\(4.144 \\times 10^{-6}\\) \\(904.5\\) \\(4.32 \\times 10^{-3}\\) \\(4.62 \\times 10^{7}\\) \\(182\\) \\(1.9\\) \\(60.53\\) \\(7.283 \\times 10^{-6}\\) \\(3.884 \\times 10^{-6}\\) \\(1132\\) \\(4.25 \\times 10^{-3}\\) \\(4.55 \\times 10^{7}\\) \\(192\\) \\(1.9\\) \\(60.30\\) \\(6.828 \\times 10^{-6}\\) \\(3.655 \\times 10^{-6}\\) \\(1405\\) \\(4.18 \\times 10^{-3}\\) \\(4.48 \\times 10^{7}\\) \\(202\\) \\(1.9\\) \\(60.06\\) \\(6.423 \\times 10^{-6}\\) \\(3.452 \\times 10^{-6}\\) \\(1731\\) \\(4.11 \\times 10^{-3}\\) \\(4.40 \\times 10^{7}\\) \\(212\\) \\(1.9\\) \\(59.81\\) \\(6.061 \\times 10^{-6}\\) \\(3.271 \\times 10^{-6}\\) \\(2118\\) \\(4.04 \\times 10^{-3}\\) \\(4.32 \\times 10^{7}\\) What follows is a brief discussion of some of these properties, and how they can be applied in R. All of the properties shown in the tables above are produced using the hydraulics R package. The documentation for that package provides details on its use. The water property functions in the hydraulics package can be called using the ret_units input to allow it to return an object of class units, as designated by the package units. This enables capabilities for new units to be deduced as operations are performed on the values. Concise examples are in the vignettes for the ‘units’ package. 2.1 Properties important for water standing still An intrinsic property of water is its mass. In the presence of gravity, it exerts a weight on its surroundings. Forces caused by the weight of water enter design in many ways. Example 2.1 uses water mass and weight in a calculation. Example 2.1 Determine the tension in the 8 mm diameter rope holding a bucket containing 12 liters of water. Ignore the weight of the bucket. Assume a water temperature of 20 \\(^\\circ\\)C. rho = hydraulics::dens(T = 20, units = 'SI', ret_units = TRUE) #Water density: rho #> 998.2336 [kg/m^3] #Find mass by multiplying by volume vol <- units::set_units(12, liter) m <- rho * vol #Convert mass to weight in Newtons g <- units::set_units(9.81, m/s^2) w <- units::set_units(m*g, "N") #Divide by cross-sectional area of the rope to obtain tension area <- units::set_units(pi/4 * 8^2, mm^2) tension <- w/area #Express the result in Pascals units::set_units(tension, Pa) #> 2337828 [Pa] #For demonstration, convert to psi units::set_units(tension, psi) #> 339.0733 [psi] For example 2.1 units could have been tracked manually throughout, as if done by hand. The convenience of using the units package allows conversions that can be used to check hand calculations. Water expands as it is heated, which is part of what is driving sea-level rise globally. Approximately 90% of excess energy caused by global warming pollution is absorbed by oceans, with most of that occurring in the upper ocean: 0-700 m of depth (Fox-Kemper et al., 2021). Example 2.2 uses water mass and weight in a calculation. Example 2.2 Assume the ocean is made of fresh water (the change in density of sea water with temperature is close enough to fresh water for this illustration). Assume a 700 m thick upper layer of the ocean. Assuming this upper layer has an initial temperature of 15 \\(^\\circ\\)C and calculate the change in mean sea level due to a 2 \\(^\\circ\\)C rise in temperature of this upper layer. It may help to consider a single 1m x 1m column of water with h=700 m high under original conditions. Since mass is conserved, and mass = volume x density, this is simple: \\[LWh_1\\cdot\\rho_1=LWh_2\\cdot\\rho_2\\] or \\[h_2=h_1\\frac{\\rho_1}{\\rho_2}\\] rho1 = hydraulics::dens(T = 15, units = 'SI') rho2 = hydraulics::dens(T = 17, units = 'SI') h2 = 700 * (rho1/rho2) cat(sprintf("Change in sea level = %.3f m\\n", h2-700)) #> Change in sea level = 0.227 m The bulk modulus, Ev, relates the change in specific volume to the change in pressure, and defined as in Equation (2.1). \\[\\begin{equation} E_v=-v\\frac{dp}{dv} \\tag{2.1} \\end{equation}\\] which can be discretized: \\[\\begin{equation} \\frac{v_2-v_1}{v_1}=-\\frac{p_2-p_1}{E_v} \\tag{2.2} \\end{equation}\\] where \\(v\\) is the specific volume (\\(v=\\frac{1}{\\rho}\\)) and \\(p\\) is pressure. Example 2.3 shows one application of this. Example 2.3 A barrel of water has an initial temperature of 15 \\(^\\circ\\)C at atmospheric pressure (p=0 Pa gage). Plot the pressure the barrel must exert to have no change in volume as the water warms to 20 \\(^\\circ\\)C. Here essentially the larger specific volume (at a higher temperature) is then compressed by \\({\\Delta}P\\) to return the volume to its original value. Thus, subscript 1 indicates the warmer condition, and subscript 2 the original at 15 \\(^\\circ\\)C. dp <- function(tmp) { rho2 <- hydraulics::dens(T = 15, units = 'SI') rho1 <- hydraulics::dens(T = tmp, units = 'SI') Ev <- hydraulics::Ev(T = tmp, units = 'SI') return((-((1/rho2) - (1/rho1))/(1/rho1))*Ev) } temps <- seq(from=15, to=20, by=1) plot(temps,dp(temps), xlab="Temperature, C", ylab="Pressure increase, Pa", type="b") Figure 2.1: Approximate change in pressure as water temperature increases. These very high pressures required to compress water, even by a small fraction, validates the ordinary assumption that water can be considered incompressible in most applications. It should be noted that the Ev values produced by the hydraulics package only vary with temperature, and assume standard atmospheric pressure; in reality, Ev values increase with increasing pressure so the values plotted here serve only as a demonstration and underestimate the pressure increase. 2.2 Properties important for moving water When describing the behavior of moving water in civil engineering infrastructure like pipes and channels there are three primary water properties used used in calculations, all of which vary with water temperature: density (\\(\\rho\\)), dynamic viscosity(\\(\\mu\\)), and kinematic viscosity(\\(\\nu\\)), which are related by Equation (2.3). \\[\\begin{equation} \\nu=\\frac{\\mu}{\\rho} \\tag{2.3} \\end{equation}\\] Viscosity is caused by interaction of the fluid molecules as they are subjected to a shearing force. This is often illustrated by a conceptual sketch of two parallel plates, one fixed and one moving at a constant speed, with a fluid in between. Perhaps more intuitively, a smore can be used. If the velocity of the marshmallow filling varies linearly, it will be stationary (V=0) at the bottom and moving at the same velocity as the upper cracker at the top (V=U). The force needed to move the upper cracker can be calculated using Equation (2.4) \\[\\begin{equation} F=A{\\mu}\\frac{dV}{dy} \\tag{2.4} \\end{equation}\\] where y is the distance between the crackers and A is the cross-sectional area of a cracker. Equation (2.4) is often written in terms of shear stress \\({\\tau}\\) as in Equation (2.5) \\[\\begin{equation} \\frac{F}{A}={\\tau}={\\mu}\\frac{dV}{dy} \\tag{2.5} \\end{equation}\\] The following demonstrates a use of these relationships. Example 2.4 Determine force required to slide the top cracker at 1 cm/s with a thickness of marshmallow of 0.5 cm. The cross-sectional area of the crackers is 10 cm\\(^2\\). The viscosity (dynamic viscosity, as can be discerned by the units) of marshmallow is about 0.1 Pa\\(\\cdot\\)s. #Assign variables A <- units::set_units(10, cm^2) U <- units::set_units(1, cm/s) y <- units::set_units(0.5, cm) mu <- units::set_units(0.1, Pa*s) #Find shear stress tau <- mu * U / y tau #> 0.2 [Pa] #Since stress is F/A, multiply tau by A to find F, convert to Newtons and pounds units::set_units(tau*A, N) #> 2e-04 [N] units::set_units(tau*A, lbf) #> 4.496179e-05 [lbf] Water is less viscous than marshmallow, so viscosity for water has much lower values than in the example. Values for water can be obtained using the hydraulics R package for calculations, using the dens, dvisc, and kvisc. All of the water property functions can accept a list of input temperature values, enabling visualization of a property with varying water temperature, as shown in Figure 2.2. Ts <- seq(0, 100, 10) nus <- hydraulics::kvisc(T = Ts, units = 'SI') xlbl <- expression("Temperature, " (degree*C)) ylbl <- expression("Kinematic viscosity," ~nu~ (m^{2}/s)) par(cex=0.8, mgp = c(2,0.7,0)) plot(Ts, nus, xlab = xlbl, ylab = ylbl, type="l") Figure 2.2: Variation of kinematic viscosity with temperature. 2.3 Atmosperic Properties Since water interacts with the atmosphere, through processes like evaporation and condensation, some basic properties of air are helpful. Selected characteristics of the standard atmosphere, as determined by the International Civil Aviation Organization (ICAO), are included in the hydraulics package. Three functions atmpres, atmdens, and atmtemp return different properties of the standard atmosphere, which vary with altitude. These are summarized in Table 2.3 for SI units and Table 2.4 for US (or Eng) units. Table 2.3: ICAO standard atmospheric properties in SI units Altitude Temp Pressure Density m C Pa kg m-3 0 15.00 101325.0 1.22500 1000 8.50 89876.3 1.11166 2000 2.00 79501.4 1.00655 3000 -4.49 70121.1 0.90925 4000 -10.98 61660.4 0.81935 5000 -17.47 54048.2 0.73643 6000 -23.96 47217.6 0.66011 7000 -30.45 41105.2 0.59002 8000 -36.93 35651.5 0.52579 9000 -43.42 30800.6 0.46706 10000 -49.90 26499.8 0.41351 11000 -56.38 22699.8 0.36480 12000 -62.85 19354.6 0.32062 13000 -69.33 16421.2 0.28067 14000 -75.80 13859.4 0.24465 15000 -82.27 11631.9 0.21229 Table 2.4: ICAO standard atmospheric properties in US units Altitude Temp Pressure Density ft F lbf ft-2 slug ft-3 0 59.00 2116.2 0.00237 5000 41.17 1760.9 0.00205 10000 23.36 1455.6 0.00175 15000 5.55 1194.8 0.00149 20000 -12.25 973.3 0.00127 25000 -30.05 786.3 0.00107 30000 -47.83 629.7 0.00089 35000 -65.61 499.3 0.00074 40000 -83.37 391.8 0.00061 45000 -101.13 303.9 0.00049 50000 -118.88 232.7 0.00040 As with water property functions, the data in the table can be extracted using individual commands for use in calculations. All atmospheric functions have input arguments of altitude (ft or m), unit system (SI or Eng), and whether or not units should be returned. hydraulics::atmpres(alt = 3000, units = "SI", ret_units = TRUE) #> 70121.14 [Pa] 2.3.1 Ideal gas law Because air is compressible, its density changes with pressure, and the temperature responds to compression. These are related through the ideal gas law, Equation (2.6) \\[\\begin{equation} \\begin{split} p={\\rho}RT\\\\ p{\\forall}=mRT \\end{split} \\tag{2.6} \\end{equation}\\] where \\(p\\) is absolute pressure, \\(\\forall\\) is the volume, \\(R\\) is the gas constant, \\(T\\) is absolute temperature, and \\(m\\) is the mass of the gas. When air changes its condition between two states, the ideal gas law can be restated as Equation (2.7) \\[\\begin{equation} \\frac{p_1{\\forall_1}}{T_1}=\\frac{p_2{\\forall_2}}{T_2} \\tag{2.7} \\end{equation}\\] Two convenient forms of Equation (2.7) apply for specific conditions. If mass is conserved, and conditions are isothermal, m, R, T are constant (isothermal conditions): \\[\\begin{equation} p_1{\\forall_1}=p_2{\\forall_2} \\tag{2.8} \\end{equation}\\] If mass is conserved and temperature changes adiabatically (meaning no heat is exchanged with surroundings) and reversibly, these are isentropic conditions, governed by Equations (2.9). \\[\\begin{equation} \\begin{split} p_1{\\forall_1}^k=p_2{\\forall_2}^k\\\\ \\frac{T_2}{T_1}=\\left(\\frac{p_2}{p_1}\\right)^{\\frac{k-1}{k}} \\end{split} \\tag{2.9} \\end{equation}\\] Properties of air used in these formulations of the ideal gas law are shown in Table 2.5. Table 2.5: Air properties at standard sea-level atmospheric pressure Gas Constant, R Sp. Heat, cp Sp. Heat, cv Sp. Heat Ratio, k 1715 ft lbf degR-1 slug-1 6000 ft lbf degR-1 slug-1 4285 ft lbf degR-1 slug-1 1.4 287 m N K-1 kg-1 1003 m N K-1 kg-1 717 m N K-1 kg-1 1.4 "],["hydrostatics---forces-exerted-by-water-bodies.html", "Chapter 3 Hydrostatics - forces exerted by water bodies 3.1 Pressure and force 3.2 Force on a plane area 3.3 Forces on curved surfaces", " Chapter 3 Hydrostatics - forces exerted by water bodies When water is motionless its weight exerts a pressure on surfaces with which it is in contact. The force is function of the density of the fluid and the depth. Figure 3.1: The Clywedog dam by Nigel Brown, CC BY-SA 2.0, via Wikimedia Commons 3.1 Pressure and force A consideration of all of the forces acting on a particle in a fluid in equilibrium produces Equation (3.1). \\[\\begin{equation} \\frac{dp}{dz}=-{\\gamma} \\tag{3.1} \\end{equation}\\] where \\(p\\) is pressure (\\(p=F/A\\)), \\(z\\) is height measured upward from a datum, and \\({\\gamma}\\) is the specific weight of the fluid (\\(\\gamma={\\rho}g\\)). Rewritten using depth (downward from the water surface), \\(h\\), produces Equation (3.2). \\[\\begin{equation} h=\\frac{p}{\\gamma} \\tag{3.2} \\end{equation}\\] Example 3.1 Find the force on the bottom of a 0.4 m diameter barrel filled with (20 \\(^\\circ\\)C) water for barrel heights from 0.5 m to 1.5 m. area <- pi/4*0.4^2 gamma <- hydraulics::specwt(T = 20, units = 'SI') heights <- seq(from=0.5, to=1.5, by=0.05) pressures <- gamma * heights forces <- pressures * area plot(forces,heights, xlab="Total force on barrel bottom, N", ylab="Depth of water, m", type="l") grid() Figure 3.2: Force on barrel bottom. The linear relationship is what was expected. 3.2 Force on a plane area For a submerged flat surface, the magnitude of the hydrostatic force can be found using Equation (3.3). \\[\\begin{equation} F={\\gamma}y_c\\sin{\\theta}A={\\gamma}h_cA \\tag{3.3} \\end{equation}\\] The force is located as defined by Equation (3.4). \\[\\begin{equation} y_p=y_c+\\frac{I_c}{y_cA} \\tag{3.4} \\end{equation}\\] The variables correspond to the definitions in Figure 3.3. Figure 3.3: Forces on a plane area, by Ertunc, CC BY-SA 4.0, via Wikimedia Commons The location of the centroid and the moment of inertia, \\(I_c\\) for some common shapes are shown in Figure 3.4 (Moore, J. et al., 2022). The variables correspond to the definitions in Figure 3.4. Figure 3.4: Centroids and moments of inertia for common shapes Example 3.2 A 6 m long hinged gate with a width of 1 m (into the paper) is at an angle of 60o and is held in place by a horizontal cable. Plot the tension in the cable, \\(T\\), as the water depth, \\(h\\), varies from 0.1 to 4 m in depth. Ignore the weight of the gate. Figure 3.5: Reservoir with hinged gate (Olivier Cleyne, CC0 license, via Wikimedia Commons) The surface area of the gate that is wetted is \\(A=L{\\cdot}w=\\frac{h{\\cdot}w}{\\sin(60)}\\). The wetted area is rectangular, so \\(h_c=\\frac{h}{2}\\). The magnitude of the force uses (3.3): \\[F={\\gamma}h_cA={\\gamma}\\frac{h}{2}\\frac{h{\\cdot}w}{\\sin(60)}\\] The distance along the plane from the water surface to the centroid of the wetted area is \\(y_c=\\frac{1}{2}\\frac{h}{\\sin(60)}\\). The moment of inertia for the rectangular wetted area is \\(I_c=\\frac{1}{12}w\\left(\\frac{h}{\\sin(60)}\\right)^3\\). Taking moments about the hinge at the bottom of the gate yields \\(T{\\cdot}6\\sin(60)-F{\\cdot}\\left(\\frac{h}{\\sin(60)}-y_p\\right)=0\\) or \\(T=\\frac{F}{6\\cdot\\sin(60)}\\left(\\frac{h}{\\sin(60)}-y_p\\right)\\) These equations can be used in R to create the desired plot. gate_length <- 6.0 w <- 1.0 theta <- 60*pi/180 #convert angle to radians h <- seq(from=0.1, to=4.1, by=0.25) gamma <- hydraulics::specwt(T = 20, units = 'SI') area <- h*w/sin(theta) hc <- h/2 Force <- gamma*hc*area yc <- (1/2)*h/(sin(theta)) Ic <- (1/12)*w*(h/sin(theta))^3 yp <- yc + (Ic/(yc*area)) Tension <- Force/(gate_length*sin(theta)) * (h/sin(theta) - yp) plot(Tension,h, xlab="Cable tension, N", ylab="Depth of water, m", type="l") grid() 3.3 Forces on curved surfaces For forces on curved surfaces, the procedure is often to calculate the vertical, \\(F_V\\), and horizontal, \\(F_H\\), hydrostatic forces separately. \\(F_H\\) is simpler, since it is the horizontal force on a (plane) vertical projection of the submerged surface, so the methods of Section 3.2 apply. The vertical component, \\(F_V\\), for a submerged surface with water above has a magnitude of the weight of the water above it, which acts through the center of volume. For a curved surface with water below it the magnitude of \\(F_V\\) is the volume of the ‘mising’ water that would be above it, and the force acts upward. Figure 3.6: Forces on curved surfaces, by Ertunc, CC BY-SA 4.0, via Wikimedia Commons A classic example of a curved surface in civil engineering hydraulics is a radial (or Tainter) gate, as in Figure 3.7. Figure 3.7: Radial gates on the Rogue River, OR. To simplify the geometry, a problem is presented in Example 3.3 where the gate meets the base at a horizontal angle. Example 3.3 A radial gate with radius R=6 m and a width of 1 m (into the paper) controls water. Find the horizontal and vertical hydrostatic forces for depths, \\(h\\), from 0 to 6 m. The horizontal hydrostatic force is that acting on a rectangle of height \\(h\\) and width \\(w\\): \\[F_H=\\frac{1}{2}{\\gamma}h^2w\\] which acts at a height of \\(y_c=\\frac{h}{3}\\) from the bottom of the gate. The vertical component has a magnitude equal to the weight of the ‘missing’ water indicated on the sketch. The calculation of its volume requires the area of a circular sector minus the area of a triangle above it. The angle, \\(\\theta\\) is found using geometry to be \\({\\theta}=cos^{-1}\\left(\\frac{R-h}{R}\\right)\\). Using the equations for areas of these two components as in Figure 3.4, the following is obtained: \\[F_V={\\gamma}w\\left(\\frac{R^2\\theta}{2}-\\frac{R-h}{2}R\\sin{\\theta}\\right)\\] The line of action of \\(F_V\\) can be determined by combining the components for centroids of the composite shapes, again following Figure 3.4. Because the line of action of the resultant force on a curcular gate must pass through the center of the circle (since hydrostatic forces always act normal to the gate), the moments about the hinge of \\(F_H\\) and \\(F_V\\) must equal zero. \\[\\sum{M}_{hinge}=0=F_H\\left(R-h/3\\right)-F_V{\\cdot}x_c\\] This produces the equation: \\[x_c=\\frac{F_H\\left(R-h/3\\right)}{F_V}\\] These equations can be solved in many ways, such as the following. R <- units::set_units(6.0, m) w <- units::set_units(1.0, m) gamma <- hydraulics::specwt(T = 20, units = 'SI', ret_units = TRUE) h <- units::set_units(seq(from=0, to=6, by=1), m) #angle in radians throughout, units not needed theta <- units::drop_units(acos((R-h)/R)) area <- h*w/sin(theta) Fh <- (1/2)*gamma*h^2*w yc <- h/3 Fv <- gamma*w*((R^2*theta)/2 - ((R-h)/2) * R*sin(theta)) xc <- Fh*(R-h/3)/Fv Ftotal <- sqrt(Fh^2+Fv^2) tibble::tibble(h=h, Fh=Fh, yc=yc, Fv=Fv, xc=xc, Ftotal=Ftotal) #> # A tibble: 7 × 6 #> h Fh yc Fv xc Ftotal #> [m] [N] [m] [N] [m] [N] #> 1 0 0 0 0 NaN 0 #> 2 1 4896. 0.333 22041. 1.26 22578. #> 3 2 19585. 0.667 60665. 1.72 63748. #> 4 3 44067. 1 108261. 2.04 116886. #> 5 4 78341. 1.33 161583. 2.26 179573. #> 6 5 122408. 1.67 218398. 2.43 250363. #> 7 6 176268. 2 276881. 2.55 328228. "],["water-flowing-in-pipes-energy-losses.html", "Chapter 4 Water flowing in pipes: energy losses 4.1 Important dimensionless quantity 4.2 Friction Loss in Circular Pipes 4.3 Solving Pipe friction problems 4.4 Solving for head loss (Type 1 problems) 4.5 Solving for Flow or Velocity (Type 2 problems) 4.6 Solving for pipe diameter, D (Type 3 problems) 4.7 Parallel pipes: solving a system of equations 4.8 Simple pipe networks: the Hardy-Cross method", " Chapter 4 Water flowing in pipes: energy losses Flow in civil engineering infrastructure is usually either in pipes, where it is not exposed to the atmosphere and flows under pressure, or open channels (canals, rivers, etc.). this chapter is concerned only with water flow in pipes. Once water begins to move engineering problems often need to relate the flow rate to the energy dissipated. To accomplish this, the flow needs to be classified using dimensionless constants since energy dissipation varies with the flow conditions. 4.1 Important dimensionless quantity As water begins to move, the characteristics are described by two quantities in engineering hydraulics: the Reynolds number, Re and the Froude number Fr. The latter is more important for open channel flow and will be discussed in that chapter. Reynolds number describes the turbulence of the flow, defined by the ratio of kinematic forces, expressed by velocity V and a characteristic length such as pipe diameter, D, to viscous forces as expressed by the kinematic viscosity \\(\\nu\\), as in Equation (4.1) \\[\\begin{equation} Re=\\frac{VD}{\\nu} \\tag{4.1} \\end{equation}\\] For open channels the characteristic length is the hydraulic depth, the area of flow divided by the top width. For adequately turbulent conditions to exists, Reynolds numbers should exceed 4000 for full pipes, and 2000 for open channels. 4.2 Friction Loss in Circular Pipes The energy at any point along a pipe containing flowing water is often described by the energy per unit weight, or energy head, E, as in Equation (4.2) \\[\\begin{equation} E = z+\\frac{P}{\\gamma}+\\alpha\\frac{V^2}{2g} \\tag{4.2} \\end{equation}\\] where P is the pressure, \\(\\gamma=\\rho g\\) is the specific weight of water, z is the elevation of the point, V is the average velocity, and each term has units of length. \\(\\alpha\\) is a kinetic energy adjustment factor to account for non-uniform velocity distribution across the cross-section. \\(\\alpha\\) is typically assumed to be 1.0 for turbulent flow in circular pipes because the value is close to 1.0 and \\(\\frac{V^2}{2g}\\) (the velocity head) tends to be small in relation to other terms in the equation. Some applications where velocity varies widely across a cross-section, such as a river channel with flow in a main channel and a flood plain, will need to account for values of \\(\\alpha\\) other than one. As water flows through a pipe energy is lost due to friction with the pipe walls and local disturbances (minor losses). The energy loss between two sections is expressed as \\({E_1} - {h_l} = {E_2}\\). When pipes are long, with \\(\\frac{L}{D}>1000\\), friction losses dominate the energy loss on the system, and the head loss, \\(h_l\\), is calculated as the head loss due to friction, \\(h_f\\). This energy head loss due to friction with the walls of the pipe is described by the Darcy-Weisbach equation, which estimates the energy loss per unit weight, or head loss \\({h_f}\\), which has units of length. For circular pipes it is expressed by Equation (4.3) \\[\\begin{equation} h_f = \\frac{fL}{D}\\frac{V^2}{2g} = \\frac{8fL}{\\pi^{2}gD^{5}}Q^{2} \\tag{4.3} \\end{equation}\\] In equation (4.3) f is the friction factor, typically calculated with the Colebrook equation (Equation (4.4)). \\[\\begin{equation} \\frac{1}{\\sqrt{f}} = -2\\log\\left(\\frac{\\frac{k_s}{D}}{3.7} + \\frac{2.51}{Re\\sqrt{f}}\\right) \\tag{4.4} \\end{equation}\\] In Equation (4.4) \\(k_s\\) is the absolute roughness of the pipe wall. There are close approximations to the Colebrook equation that have an explicit form to facilitate hand-calculations, but when using R or other computational tools there is no need to use approximations. 4.3 Solving Pipe friction problems As water flows through a pipe energy is lost due to friction with the pipe walls and local disturbances (minor losses). For this example assume minor losses are negligible. The energy head loss due to friction with the walls of the pipe is described by the Darcy-Weisbach equation (Equation ((4.3))), which estimates the energy loss per unit weight, or head loss hf, which has units of length. The Colebrook equation (Equation (4.4)) is commonly plotted as a Moody diagram to illustrate the relationships between the variables, in Figure 4.1. hydraulics::moody() Figure 4.1: Moody Diagram Because of the form of the equations, they can sometimes be a challenge to solve, especially by hand. It can help to classify the types of problems based on what variable is unknown. These are summarized in Table 4.1. Table 4.1: Types of Energy Loss Problems in Pipe Flow Type Known Unknown 1 Q (or V), D, ks, L hL 2 hL, D, ks, L Q (or V) 3 hL, Q (or V), ks, L D When solving by hand the types in Table 4.1 become progressively more difficult, but when using solvers the difference in complexity is subtle. 4.4 Solving for head loss (Type 1 problems) The simplest pipe flow problem to solve is when the unknown is head loss, hf (equivalent to hL in the absence of minor losses), since all variables on the right side of the Darcy-Weisbach equation are known, except f. 4.4.1 Solving for head loss by manual iteration While all unknowns are on the right side of Equation (4.3), iteration is still required because the Colebrook equation, Equation (4.4), cannot be solved explicitly for f. An illustration of solving this type of problem is shown in Example 4.1. Example 4.1 Find the head loss (due to friction) of 20\\(^\\circ\\)C water in a pipe with the following characteristics: Q=0.416 m\\(^3\\)/s, L=100m, D=0.5m, ks=0.046mm. Since the water temperature is known, first find the kinematic viscocity of water, \\({\\nu}\\), since it is needed for the Reynolds number. This can be obtained from a table in a reference or using software. Here we will use the hydraulics R package. nu <- hydraulics::kvisc(T=20, units="SI") cat(sprintf("Kinematic viscosity = %.3e m2/s\\n", nu)) #> Kinematic viscosity = 1.023e-06 m2/s We will need the Reynolds Number to use the Colebrook equation, and that can be calculated since Q is known. This can be accomplished with a calculator, or using other software (R is used here): Q <- 0.416 D <- 0.5 A <- (3.14/4)*D^2 V <- Q/A Re <- V*D/nu cat(sprintf("Velocity = %.3f m/s, Re = %.3e\\n", V, Re)) #> Velocity = 2.120 m/s, Re = 1.036e+06 Now the only unknown in the Colebrook equation is f, but unfortunately f appears on both sides of the equation. To begin the iterative process, a first guess at f is needed. A reasonable value to use is the minimum f value, fmin, given the known \\(\\frac{k_s}{D}=\\frac{0.046}{500}=0.000092=9.2\\cdot 10^{-5}\\). Reading horizontally from the right vertical axis to the left on the Moody diagram provides a value for \\(f_{min}\\approx 0.012\\). Numerically, it can be seen that f is independent of Re for large values of Re. When Re is large the second term of the Colebrook equation becomes small and the equation approaches Equation (4.5). \\[\\begin{equation} \\frac{1}{\\sqrt{f}} = -2\\log\\left(\\frac{\\frac{k_s}{D}}{3.7}\\right) \\tag{4.5} \\end{equation}\\] This independence of f with varying Re values is visible in the Moody Diagram, Figure 4.1, toward the right, where the lines become horizontal. Using Equation (4.5) the same value of fmin=0.012 is obtained, since the colebrook equation defines the Moody diagram. Iteration 1: Using f=0.012 the right side of the Colebrook equation is 8.656. the next estimate for f is then obtained by \\(\\frac{1}{\\sqrt{f}}=8.656\\) so f=0.0133. Iteration 2: Using the new value of f=0.0133 in the right side of the Colebrook equation produces 8.677. A new value for f is obtained by \\(\\frac{1}{\\sqrt{f}}=8.677\\) so f=0.0133. The solution has converged! Using the new value of f, the value for hf is calculated: \\[h_f = \\frac{8fL}{\\pi^{2}gD^{5}}Q^{2}=\\frac{8(0.0133)(100)}{\\pi^{2}(9.81)(0.5)^{5}}(0.416)^{2}=0.061 m\\] 4.4.2 Solving for headloss using an empirical approximation A shortcut that can be used to avoid iterating to find the friction factor is to use an approximation to the Colebrook equation that can be solved explicitly. One example is the Haaland equation (4.6) (Haaland, 1983). \\[\\begin{equation} \\frac{1}{\\sqrt{f}} = -1.8\\log\\left(\\left(\\frac{\\frac{k_s}{D}}{3.7}\\right)^{1.11}+\\frac{6.9}{Re}\\right) \\tag{4.6} \\end{equation}\\] For ordinary pipe flow conditions in water pipes, Equation (4.6) is accurate to within 1.5% of the Colebrook equation. There are many other empirical equations, one common one being that of Swamee and Jain (Swamee & Jain, 1976), shown in Equation (4.7). \\[\\begin{equation} \\frac{1}{\\sqrt{f}} = -2\\log\\left(\\frac{\\frac{k_s}{D}}{3.7}+\\frac{5.74}{Re^{0.9}}\\right) \\tag{4.7} \\end{equation}\\] These approximations are useful for solving problems by hand or in spreadsheets, and their accuracy is generally within the uncertainty of other input variables like the absolute roughness. 4.4.3 Solving for head loss using an equation solver Rather than use an empirical approximation (as in Section 4.4.2) to the Colebrook equation, it is straightforward to apply an equation solver to use the Colebrook equation directly. This is demonstrated in Example 4.2. Example 4.2 Find the friction factor for the same conditions as Example 4.1: D=0.5m, ks=0.046mm, and Re=1.036e+06. First, rearrange the Colebrook equation so all terms are on one side of the equation, as in Equation (4.8). \\[\\begin{equation} -2\\log\\left(\\frac{\\frac{k_s}{D}}{3.7} + \\frac{2.51}{Re\\sqrt{f}}\\right) - \\frac{1}{\\sqrt{f}}=0 \\tag{4.8} \\end{equation}\\] Create a function using whatever equation solving platform you prefer. Here the R software is used: colebrk <- function(f,ks,D,Re) -2.0*log10((ks/D)/3.7 + 2.51/(Re*(f^0.5)))-1/(f^0.5) Find the root of the function (where it equals zero), specifying a reasonable range for f values using the interval argument: f <- uniroot(colebrk, interval = c(0.008,0.1), ks=0.000046, D=0.5, Re=1.036e+06)$root cat(sprintf("f = %.4f\\n", f)) #> f = 0.0133 The same value for hf as above results. 4.4.4 Solving for head loss using an R package Equation solvers for implicit equations, like in Section 4.4.3, are built into the R package hydraulics. that can be applied directly, without writing a separate function. Example 4.3 Using the hydraulics R package, find the friction factor and head loss for the same conditions as Example 4.2: Q=0.416 m3/s, L=100 m, D=0.5m, ks=0.046mm, and nu = 1.023053e-06 m2/s. ans <- hydraulics::darcyweisbach(Q = 0.416,D = 0.5, L = 100, ks = 0.000046, nu = 1.023053e-06, units = c("SI")) #> hf missing: solving a Type 1 problem cat(sprintf("Reynolds no: %.0f\\nFriction Fact: %.4f\\nHead Loss: %.2f ft\\n", ans$Re, ans$f, ans$hf)) #> Reynolds no: 1035465 #> Friction Fact: 0.0133 #> Head Loss: 0.61 ft If only the f value is needed, the colebrook function can be used. f <- hydraulics::colebrook(ks=0.000046, V= 2.120, D=0.5, nu=1.023e-06) cat(sprintf("f = %.4f\\n", f)) #> f = 0.0133 Notice that the colebrook function needs input in dimensionally consistent units. Because it is dimensionally homogeneous and the input dimensions are consistent, the unit system does not need to be defined like with many other functions in the hydraulics package. 4.5 Solving for Flow or Velocity (Type 2 problems) When flow (Q) or velocity (V) is unknown, the Reynolds number cannot be determined, complicating the solution of the Colebrook equation. As with Secion 4.4 there are several strategies to solving these, ranging from iterative manual calculations to using software packages. For Type 2 problems, since D is known, once either V or Q is known, the other is known, since \\(Q=V{\\cdot}A=V\\frac{\\pi}{4}D^2\\). 4.5.1 Solving for Q (or V) using manual iteration Solving a Type 2 problem can be done with manual iterations, as demonstrated in Example 4.4. Example 4.4 find the flow rate, Q of 20oC water in a pipe with the following characteristics: hf=0.6m, L=100m, D=0.5m, ks=0.046mm. First rearrange the Darcy-Weisbach equation to express V as a function of f, substituting all of the known quantities: \\[V = \\sqrt{\\frac{h_f}{L}\\frac{2gD}{f}}=\\frac{0.243}{\\sqrt{f}}\\] That provides one equation relating V and f. The second equation relating V and f is one of the friction factor equations, such as the Colebrook equation or its graphic representation in the Moody diagram. An initial guess at a value for f is obtained using fmin=0.012 as was done in Example 4.1. Iteration 1: \\(V=\\frac{0.243}{\\sqrt{0.012}}=2.218\\); \\(Re=\\frac{2.218\\cdot 0.5}{1.023e-06}=1.084 \\cdot 10^6\\). A new f value is obtained from the Moody diagram or an equation using the new Re value: \\(f \\approx 0.0131\\) Iteration 2: \\(V=\\frac{0.243}{\\sqrt{0.0131}}=2.123\\); \\(Re=\\frac{2.123\\cdot 0.5}{1.023e-06}=1.038 \\cdot 10^6\\). A new f estimate: \\(f \\approx 0.0132\\) The function converges very quickly if a reasonable first guess is made. Using V=2.12 m/s, \\(Q = AV = \\left(\\frac{\\pi}{4}\\right)D^2V=0.416 m^3/s\\) 4.5.2 Solving for Q Using an Explicit Equation Solving Type 2 problems using iteration is not necessary, since an explicit equation based on the Colebrook equation can be derived. Solving the Darcy Weisbach equation for \\(\\frac{1}{\\sqrt{f}}\\) and substituting that into the Colebrook equation produces Equation (4.9). \\[\\begin{equation} Q=-2.221D^2\\sqrt{\\frac{gDh_f}{L}} \\log\\left(\\frac{\\frac{k_s}{D}}{3.7} + \\frac{1.784\\nu}{D}\\sqrt{\\frac{L}{gDh_f}}\\right) \\tag{4.9} \\end{equation}\\] This can be solved explicitly for Q=0.413 m3/s. 4.5.3 Solving for Q Using an R package Using software to solve the problem allows the use of the Colebrook equation in a straightforward format. The hydraulics package in R is applied to the same problem as above. ans <- hydraulics::darcyweisbach(D=0.5, hf=0.6, L=100, ks=0.000046, nu=1.023e-06, units = c('SI')) knitr::kable(format(as.data.frame(ans), digits = 3), format = "pipe") Q V L D hf f ks Re 0.406 2.07 100 0.5 0.6 0.0133 4.6e-05 1010392 The answer differs from the manual iteration by just over 2%, showing remarkable consistency. 4.6 Solving for pipe diameter, D (Type 3 problems) When D is unknown, neither Re nor relative roughness \\(\\frac{ks}{D}\\) are known. Referring to the Moody diagram, Figure 4.1, the difficulty in estimating a value for f (on the left axis) is evident since the positions on either the right axis (\\(\\frac{ks}{D}\\)) or x-axis (Re) are known. 4.6.1 Solving for D using manual iterations Solving for D using manual iterations is done by first rearranging Equation (4.9) to allow it to be solved for zero, as in Equation (4.10). \\[\\begin{equation} -2.221D^2\\sqrt{\\frac{gDh_f}{L}} \\log\\left(\\frac{\\frac{k_s}{D}}{3.7} + \\frac{1.784\\nu}{D}\\sqrt{\\frac{L}{gDh_f}}\\right)-Q=0 \\tag{4.10} \\end{equation}\\] Using this with manual iterations is demonstrated in Example 4.5. Example 4.5 For a similar problem to 4.4 use Q=0.416m3/s and solve for the required pipe diameter, D. This can be solved manually by guessing values and repeating the calculation in a spreadsheet or with a tool like R. Iteration 1: Guess an arbitrary value of D=0.3m. Solve the left side of Equation (4.10) to obtain a value of -0.31 Iteration 2: Guess another value for D=1.0m. The left side of Equation (4.10) produces a value for the function of 2.11 The root, when the function equals zero, lies between the two values, so the correct D is between 0.3 and 1.0. Repeated values can home in on a solution. Plotting the results from many trials can help guide toward the solution. The root is seen to lie very close to D=0.5 m. Repeated trials can home in on the result. 4.6.2 Solving for D using an equation solver An equation solver automatically accomplishes the manual steps of the prior demonstration. The equation from 1.6 can be written as a function that can then be solved for the root, again using R software for the demonstration: q_fcn <- function(D, Q, hf, L, ks, nu, g) { -2.221 * D^2 * sqrt(( g * D * hf)/L) * log10((ks/D)/3.7 + (1.784 * nu/D) * sqrt(L/(g * D * hf))) - Q } The uniroot function can solve the equation in R (or use a comparable approach in other software) for a reasonable range of D values ans <- uniroot(q_fcn, interval=c(0.01,4.0),Q=0.416, hf=0.6, L=100, ks=0.000046, nu=1.023053e-06, g=9.81)$root cat(sprintf("D = %.3f m\\n", ans)) #> D = 0.501 m 4.6.3 Solving for D using an R package The hydraulics R package implements an equation solving technique like that above to allow the direct solution of Type 3 problems. the prior example is solved using that package as shown beliow. ans <- hydraulics::darcyweisbach(Q=0.416, hf=0.6, L=100, ks=0.000046, nu=1.023e-06, ret_units = TRUE, units = c('SI')) knitr::kable(format(as.data.frame(ans), digits = 3), format = "pipe") ans Q 0.416 [m^3/s] V 2.11 [m/s] L 100 [m] D 0.501 [m] hf 0.6 [m] f 0.0133 [1] ks 4.6e-05 [m] Re 1032785 [1] 4.7 Parallel pipes: solving a system of equations In the examples above the challenge was often to solve a single implicit equation. The manual iteration approach can work to solve two equations, but as the number of equations increases, especially when using implicit equations, using an equation solver is needed. For the case of a simple pipe loop manual iterations are impractical. for this reason often fixed values of f are assumed, or an empirical energy loss equation is used. However, a single loop, identical to a parallel pipe problem, can be used to demonstrate how systems of equations can be solved simultaneously for systems of pipes. Example 4.6 demonstrates the process of assembling the equations for a solver for a parallel pipe problem. Example 4.6 Two pipes carry a flow of Q=0.5 m3/s, as depicted in Figure 4.2 Figure 4.2: Parallel Pipe Example The fundamental equations needed are the Darcy-Weisbach equation, the Colebrook equation, and continuity (conservation of mass). For the illustrated system, this means: The flows through each pipe must add to the total flow The head loss through Pipe 1 must equal that of Pipe 2 This could be set up as a system of anywhere from 2 to 10 equations to solve simultaneously. In this example four equations are used: \\[\\begin{equation} Q_1+Q_2-Q_{total}=V_1\\frac{\\pi}{4}D_1^2+V_2\\frac{\\pi}{4}D_2^2-0.5m^3/s=0 \\tag{4.11} \\end{equation}\\] and \\[\\begin{equation} Qh_{f1}-h_{f2} = \\frac{f_1L_1}{D_1}\\frac{V_1^2}{2g} -\\frac{f_2L_2}{D_2}\\frac{V_2^2}{2g}=0 \\tag{4.12} \\end{equation}\\] The other two equations are the Colebrook equation (4.8) for solving for the friction factor for each pipe. These four equations can be solved simultaneously using an equation solver, such as the fsolve function in the R package pracma. #assign known inputs - SI units Qsum <- 0.5 D1 <- 0.2 D2 <- 0.3 L1 <- 400 L2 <- 600 ks <- 0.000025 g <- 9.81 nu <- hydraulics::kvisc(T=100, units='SI') #Set up the function that sets up 4 unknowns (x) and 4 equations (y) F_trial <- function(x) { V1 <- x[1] V2 <- x[2] f1 <- x[3] f2 <- x[4] Re1 <- V1*D1/nu Re2 <- V2*D2/nu y <- numeric(length(x)) #Continuity - flows in each branch must add to total y[1] <- V1*pi/4*D1^2 + V2*pi/4*D2^2 - Qsum #Darcy-Weisbach equation for head loss - must be equal in each branch y[2] <- f1*L1*V1^2/(D1*2*g) - f2*L2*V2^2/(D2*2*g) #Colebrook equation for friction factors y[3] <- -2.0*log10((ks/D1)/3.7 + 2.51/(Re1*(f1^0.5)))-1/(f1^0.5) y[4] <- -2.0*log10((ks/D2)/3.7 + 2.51/(Re2*(f2^0.5)))-1/(f2^0.5) return(y) } #provide initial guesses for unknowns and run the fsolve command xstart <- c(2.0, 2.0, 0.01, 0.01) z <- pracma::fsolve(F_trial, xstart) #prepare some results to print Q1 <- z$x[1]*pi/4*D1^2 Q2 <- z$x[2]*pi/4*D2^2 hf1 <- z$x[3]*L1*z$x[1]^2/(D1*2*g) hf2 <- z$x[4]*L2*z$x[2]^2/(D2*2*g) cat(sprintf("Q1=%.2f, Q2=%.2f, V1=%.1f, V2=%.1f, hf1=%.1f, hf2=%.1f, f1=%.3f, f2=%.3f\\n", Q1,Q2,z$x[1],z$x[2],hf1,hf2,z$x[3],z$x[4])) #> Q1=0.15, Q2=0.35, V1=4.8, V2=5.0, hf1=30.0, hf2=30.0, f1=0.013, f2=0.012 If the fsolve command fails, a simple solution is sometimes to revise your initial guesses and try again. There are other solvers in R and every other scripting language that can be similarly implemented. If the simplification were applied for fixed f values, then Equations (4.11) and (4.12) can be solved simultaneously for V1 and V2. 4.8 Simple pipe networks: the Hardy-Cross method For water pipe networks containing multiple loops, manually setting up systems of equations is impractical. In addition, hand calculations always assume fixed f values or use an empirical friction loss equation to simplify calculations. A typical method to solve for the flow in each pipe segment in a small network uses the Hardy-Cross method. This consists of setting up an initial guess of flow (magnitude and direction) for each pipe segment, ensuring conservation of mass is preserved at each node (or vertex) in the network. Then calculations are performed for each loop, ensuring energy is conserved. When using the Darcy-Weisbach equation, Equation (4.3), for friction loss, the head loss in each pipe segment is usually expressed in a condensed form as \\({h_f = KQ^{2}}\\) where K is defined as in Equation (4.13). \\[\\begin{equation} K = \\frac{8fL}{\\pi^{2}gD^{5}} \\tag{4.13} \\end{equation}\\] When doing calculations by hand fixed f values are assumed, but when using a computational tool like R any of the methods for estimating f and hf may be applied. The Hardy-Cross method begins by assuming flows in each segment of a loop. These initial flows are then adjusted in a series of iterations. The flow adjustment in each loop is calculated at each iteration in Equation Equation (4.14). \\[\\begin{equation} \\Delta{Q_i} = -\\frac{\\sum_{j=1}^{p_i} K_{ij}Q_j|Q_j|}{\\sum_{j=1}^{p_i} 2K_{ij}Q_j^2} \\tag{4.14} \\end{equation}\\] For calculations for small systems with two or three loops can be done manually with fixed f and K values. Using the hydraulics R package to solve a small pipe network is demonstrated in Example 4.7. Example 4.7 Find the flows in each pipe in teh system shown in Figure 4.3. Input consists of pipe characteristics, pipe order and initial flows for each loop, as shown non the diagram. Figure 4.3: A sample pipe network with pipe numbers indicated in black Input for this system, assuming fixed f values, would look like the following. (If fixed K values are provided, f, L and D are not needed). These f values were estimated using \\(ks=0.00025 m\\) in the form of the Colebrook equation for fully rough flows, Equation (4.5). dfpipes <- data.frame( ID = c(1,2,3,4,5,6,7,8,9,10), #pipe ID D = c(0.3,0.2,0.2,0.2,0.2,0.15,0.25,0.15,0.15,0.25), #diameter in m L = c(250,100,125,125,100,100,125,100,100,125), #length in m f = c(.01879,.02075,.02075,.02075,.02075,.02233,.01964,.02233,.02233,.01964) ) loops <- list(c(1,2,3,4,5),c(4,6,7,8),c(3,9,10,6)) Qs <- list(c(.040,.040,.02,-.02,-.04),c(.02,0,0,-.02),c(-.02,.02,0,0)) Running the hardycross function and looking at the output after three iterations (defined by n_iter): ans <- hydraulics::hardycross(dfpipes = dfpipes, loops = loops, Qs = Qs, n_iter = 3, units = "SI") knitr::kable(ans$dfloops, digits = 4, format = "pipe", padding=0) loop pipe flow 1 1 0.0383 1 2 0.0383 1 3 0.0232 1 4 -0.0258 1 5 -0.0417 2 4 0.0258 2 6 0.0090 2 7 0.0041 2 8 -0.0159 3 3 -0.0232 3 9 0.0151 3 10 -0.0049 3 6 -0.0090 The output pipe data frame has added columns, including the flow (where direction is that for the first loop containing the segment). knitr::kable(ans$dfpipes, digits = 4, format = "pipe", padding=0) ID D L f Q K 1 0.30 250 0.0188 0.0383 159.7828 2 0.20 100 0.0208 0.0383 535.9666 3 0.20 125 0.0208 0.0232 669.9582 4 0.20 125 0.0208 -0.0258 669.9582 5 0.20 100 0.0208 -0.0417 535.9666 6 0.15 100 0.0223 0.0090 2430.5356 7 0.25 125 0.0196 0.0041 207.7883 8 0.15 100 0.0223 -0.0159 2430.5356 9 0.15 100 0.0223 0.0151 2430.5356 10 0.25 125 0.0196 -0.0049 207.7883 While the Hardy-Cross method is often used with fixed f (or K) values when it is used in exercises performed by hand, the use of the Colebrook equation allows friction losses to vary with Reynolds number. To use this approach the input data must include absolute roughness. Example values are included here: dfpipes <- data.frame( ID = c(1,2,3,4,5,6,7,8,9,10), #pipe ID D = c(0.3,0.2,0.2,0.2,0.2,0.15,0.25,0.15,0.15,0.25), #diameter in m L = c(250,100,125,125,100,100,125,100,100,125), #length in m ks = rep(0.00025,10) #absolute roughness, m ) loops <- list(c(1,2,3,4,5),c(4,6,7,8),c(3,9,10,6)) Qs <- list(c(.040,.040,.02,-.02,-.04),c(.02,0,0,-.02),c(-.02,.02,0,0)) The effect of allowing the calculation of f to be (correctly) dependent on velocity (via the Reynolds number) can be seen, though the effect on final flow values is small. ans <- hydraulics::hardycross(dfpipes = dfpipes, loops = loops, Qs = Qs, n_iter = 3, units = "SI") knitr::kable(ans$dfpipes, digits = 4, format = "pipe", padding=0) ID D L ks Q f K 1 0.30 250 3e-04 0.0382 0.0207 176.1877 2 0.20 100 3e-04 0.0382 0.0218 562.9732 3 0.20 125 3e-04 0.0230 0.0224 723.1119 4 0.20 125 3e-04 -0.0258 0.0222 718.1439 5 0.20 100 3e-04 -0.0418 0.0217 560.8321 6 0.15 100 3e-04 0.0088 0.0248 2700.4710 7 0.25 125 3e-04 0.0040 0.0280 296.3990 8 0.15 100 3e-04 -0.0160 0.0238 2590.2795 9 0.15 100 3e-04 0.0152 0.0239 2598.5553 10 0.25 125 3e-04 -0.0048 0.0270 285.4983 "],["flow-in-open-channels.html", "Chapter 5 Flow in open channels 5.1 An important dimensionless quantity 5.2 Equations for open channel flow 5.3 Trapezoidal channels 5.4 Circular Channels (flowing partially full) 5.5 Critical flow 5.6 Flow in Rectangular Channels 5.7 Gradually varied steady flow 5.8 Rapidly varied flow (the hydraulic jump)", " Chapter 5 Flow in open channels Where flowing water water is exposed to the atmosphere, and thus not under pressure, its condition is called open channel flow. Typical design challenges can be: Determining how deep water will flow in a channel Finding the bottom slope required to carry a defined flow in a channel Comparing different cross-sectional shapes and dimensions to carry flow In pipe flow the cross-sectional area does not change with flow rate, which simplifies some aspects of calculations. By contrast, in open channel flow conditions including flow depth, area, and roughness can all vary with flow rate, which tends to make the equations more cumbersome. In civil engineering applications, roughness characteristics are not usually considered as variable with flow rate. In what follows, three conditions for flow are considered: Uniform flow, where flow characteristics do not vary along the length of a channel Gradually varied flow, where flow responds to an obstruction or change in channel conditions with a gradual adjustment in flow depth Rapidly varied flow, where an abrupt channel transition results in a rapid change in water surface, the most important case of which is the hydraulic jump 5.1 An important dimensionless quantity For open channel flow, given a channel shape and flow rate, flow can usually exist at two different depths, termed subcritical (slow, deep) and supercritical (shallow, fast). The exception is at critical flow conditions, where only one depth exists, the critical depth. Which of these depths is exhibited by the flow is determined by the slope and roughness of the channel. The Froude number characterizes whether flow is critical, supercritical or subcritical, and is defined by Equation (5.1) \\[\\begin{equation} Fr=\\frac{V}{\\sqrt{gD}} \\tag{5.1} \\end{equation}\\] The Froude number characterizes flow as: Fr Condition Description <1.0 subcritical slow, deep =1.0 critical undulating, transitional >1.0 supercritical fast, shallow Critical flow is important in open-channel flow applications and is discussed further below. 5.2 Equations for open channel flow Flow conditions in an open channel under uniform flow conditions are often related by the Manning equation (5.2). \\[\\begin{equation} Q=A\\frac{C}{n}{R}^{\\frac{2}{3}}{S}^{\\frac{1}{2}} \\tag{5.2} \\end{equation}\\] In Equation (5.2), C is 1.0 for SI units and 1.49 for Eng (British Gravitational, English., or U.S. Customary) units. Q is the flow rate, A is the cross-sectional flow area, n is the Manning roughness coefficient, S is the longitudinal channel slope, and R is the hydraulic radius, defined by equation (5.3) \\[\\begin{equation} R=\\frac{A}{P} \\tag{5.3} \\end{equation}\\] where P is the wetted perimeter. Critical depth is defined by the relation (at critical conditions) in Equation (5.4) \\[\\begin{equation} \\frac{Q^{2}B}{g\\,A^{3}}=1 \\tag{5.4} \\end{equation}\\] where B is the width of the water surface (top width). Because of the channel geometry being included in A and R, it helps to work with specific shapes in adapting these equations. The two most common are trapezoidal and circular, included in Sections 5.3 and 5.4 below. As with pipe flow, the energy equation applies for one dimensional open channel flow as well, Equation (5.5): \\[\\begin{equation} \\frac{V_1^2}{2g}+y_1+z_1=\\frac{V_2^2}{2g}+y_2+z_2+h_L \\tag{5.5} \\end{equation}\\] where point 1 is upstream of point 2, V is the flow velocity, y is the flow depth, and z is the elevation of the channel bottom. \\(h_L\\) is the energy head loss from point 1 to point 2. For uniform flow, \\(h_L\\) is the drop in elevation between the two points due to the channel slope. 5.3 Trapezoidal channels In engineering applications one of the most common channel shapes is trapezoidal. Figure 5.1: Typical symmetrical trapezoidal cross section The geometrical relationships for a trapezoid are: \\[\\begin{equation} A=(b+my)y \\tag{5.6} \\end{equation}\\] \\[\\begin{equation} P=b+2y\\sqrt{1+m^2} \\tag{5.7} \\end{equation}\\] Combining Equations (5.6) and (5.7) yields: \\[\\begin{equation} R=\\frac{A}{P}=\\frac{\\left(b+my\\right)y}{b+2y\\sqrt{1+m^2}} \\tag{5.8} \\end{equation}\\] Top width: \\(B=b+2\\,m\\,y\\). Substituting Equations (5.6) and (5.8) into the Manning equation produces Equation (5.9). \\[\\begin{equation} Q=\\frac{C}{n}{\\frac{\\left(by+my^2\\right)^{\\frac{5}{3}}}{\\left(b+2y\\sqrt{1+m^2}\\right)^\\frac{2}{3}}}{S}^{\\frac{1}{2}} \\tag{5.9} \\end{equation}\\] 5.3.1 Solving the Manning equation in R To solve Equation (5.9) when any variable other than Q is unknown, it is straightforward to rearrange it to a form of y(x) = 0. \\[\\begin{equation} Q-\\frac{C}{n}{\\frac{\\left(by+my^2\\right)^{\\frac{5}{3}}}{\\left(b+2y\\sqrt{1+m^2}\\right)^\\frac{2}{3}}}{S}^{\\frac{1}{2}}=0 \\tag{5.10} \\end{equation}\\] This allows the use of a standard solver to find the root(s). If solving it by hand, trial and error can be employed as well. Example 5.1 demonstrates the solution of Equation (5.10) for the flow depth, y. A trial-and-error approach can be applied, and with careful selection of guesses a solution can be obtained relatively quickly. Using solvers makes the process much quicker and less prone to error. Example 5.1 Find the flow depth, y, for a trapezoidal channel with Q=225 ft3/s, n=0.016, m=2, b=10 ft, S=0.0006. The Manning equation can be set up as a function in terms of a missing variable, here using normal depth, y as the missing variable. yfun <- function(y) { Q - (((y * (b + m * y)) ^ (5 / 3) * sqrt(S)) * (C / n) / ((b + 2 * y * sqrt(1 + m ^ 2)) ^ (2 / 3))) } Because these use US Customary (or English) units, C=1.486. Define all of the needed input variables for the function. Q <- 225. n <- 0.016 m <- 2 b <- 10.0 S <- 0.0006 C <- 1.486 Use the R function uniroot to find a single root within a defined interval. Set the interval (the range of possible y values in which to search for a root) to cover all plausible values, here from 0.0001 mm to 200 m. ans <- uniroot(yfun, interval = c(0.0000001, 200), extendInt = "yes") cat(sprintf("Normal Depth: %.3f ft\\n", ans$root)) #> Normal Depth: 3.406 ft Functions can usually be given multiple values as input, returning the corresponding values of output. this allows plots to be created to show, for example, how the left side of Equation (5.10) varies with different values of depth, y. ys <- seq(0.1, 5, 0.1) plot(ys,yfun(ys), type='l', xlab = "y, ft", ylab = "Function to solve for zero") abline(h=0) grid() Figure 5.2: Variation of the left side of Equation (5.10) with y for Example 5.1. This validates the result in the example, showing the root of Equation (5.10), when the function has a value of 0, occurs for a depth, y of a little less than 3.5. 5.3.2 Solving the Manning equation with the hydraulics R package The hydraulics package has a manningt (the ‘t’ is for ‘trapezoid’) function for trapezoidal channels. Example 5.2 demonstrates its usage. Example 5.2 Find the uniform (normal) flow depth, y, for a trapezoidal channel with Q=225 ft3/s, n=0.016, m=2, b=10 ft, S=0.0006. Specifying “Eng” units ensures the correct C value is used. Sf is the same as S in Equations (5.2) and (5.9) since flow is uniform. ans <- hydraulics::manningt(Q = 225., n = 0.016, m = 2, b = 10., Sf = 0.0006, units = "Eng") cat(sprintf("Normal Depth: %.3f ft\\n", ans$y)) #> Normal Depth: 3.406 ft #critical depth is also returned, along with other variables. cat(sprintf("Critical Depth: %.3f ft\\n", ans$yc)) #> Critical Depth: 2.154 ft 5.3.3 Solving the Manning equation using a spreadsheet like Excel Spreadsheet software is very popular and has been modified to be able to accomplish many technical tasks such as solving equations. This example uses Excel with its solver add-in activated, though other spreadsheet software has similar solver add-ins that can be used. The first step is to enter the input data, for the same example as above, along with an initial guess for the variable you wish to solve for. The equation for which a root will be determined is typed in using the initial guess for y in this case. At this point you could use a trial-and-error approach and simply try different values for y until the equation produces something close to 0. A more efficient method is to use a solver. Check that the solver add-in is activated (in Options) and open it. Set the values appropriately. Click Solve and the y value that produces a zero for the equation will appear. If you need to solve for multiple roots, you will need to start from different initial guesses. 5.3.4 Optimal trapezoidal geometry Most fluid mechanics texts that include open channel flow include a derivation of optimal geometry for a trapezoidal channel. This is also called the most efficient cross section. What this means is for a given A and m, there is an optimal flow depth and bottom width for the channel, defined by Equations (5.11) and (5.12). \\[\\begin{equation} b_{opt}=2y\\left(\\sqrt{1+m^2}-m\\right) \\tag{5.11} \\end{equation}\\] \\[\\begin{equation} y_{opt}=\\sqrt{\\frac{A}{2\\sqrt{1+m^2}-m}} \\tag{5.12} \\end{equation}\\] These may be calculated manually, but they are also returned by the manningt function of the hydraulics package in R. Example 5.3 demonstrates this. Example 5.3 Find the optimal channel width to transmit 360 ft3/s at a depth of 3 ft with n=0.015, m=1, S=0.0006. ans <- hydraulics::manningt(Q = 360., n = 0.015, m = 1, y = 3.0, Sf = 0.00088, units = "Eng") knitr::kable(format(as.data.frame(ans), digits = 2), format = "pipe", padding=0) Q V A P R y b m Sf B n yc Fr Re bopt 360 5.3 68 28 2.4 3 20 1 0.00088 26 0.015 2.1 0.57 1159705 4.8 cat(sprintf("Optimal bottom width: %.5f ft\\n", ans$bopt)) #> Optimal bottom width: 4.76753 ft The results show that, aside from the rounding, the required width is approximately 20 ft, and the optimal bottom width for optimal hydraulic efficiency would be 4.76 ft. To check the depth that would be associated with a channel of the optimal width, substitute the optimal width for b and solve for y: ans <- hydraulics::manningt(Q = 360., n = 0.015, m = 1, b = 4.767534, Sf = 0.00088, units = "Eng") cat(sprintf("Optimal depth: %.5f ft\\n", ans$yopt)) #> Optimal depth: 5.75492 ft 5.4 Circular Channels (flowing partially full) Civil engineers encounter many situations with circular pipes that are flowing only partially full, such as storm and sanitary sewers. Figure 5.3: Typical circular cross section The relationships between the depth of water and the values needed in the Manning equation are: Depth (or fractional depth as written here) is described by Equation (5.13) \\[\\begin{equation} \\frac{y}{D}=\\frac{1}{2}\\left(1-\\cos{\\frac{\\theta}{2}}\\right) \\tag{5.13} \\end{equation}\\] Area is described by Equation (5.14) \\[\\begin{equation} A=\\left(\\frac{\\theta-\\sin{\\theta}}{8}\\right)D^2 \\tag{5.14} \\end{equation}\\] (Be sure to use theta in radians.) Wetted perimeter is described by Equation (5.15) \\[\\begin{equation} P=\\frac{D\\theta}{2} \\tag{5.15} \\end{equation}\\] Combining Equations (5.14) and (5.15): \\[\\begin{equation} R=\\frac{D}{4}\\left(1-\\frac{\\sin{\\theta}}{\\theta}\\right) \\tag{5.16} \\end{equation}\\] Top width: \\(B=D\\,sin{\\frac{\\theta}{2}}\\) Substituting Equations (5.14) and (5.16) into the Manning equation, Equation (5.2), produces (5.17). \\[\\begin{equation} \\theta^{-\\frac{2}{3}}\\left(\\theta-\\sin{\\theta}\\right)^\\frac{5}{3}-CnQD^{-\\frac{8}{3}}S^{-\\frac{1}{2}}=0 \\tag{5.17} \\end{equation}\\] where C=20.16 for SI units and C=13.53 for US Customary (English) units. 5.4.1 Solving the Manning equation for a circular pipe in R As was demonstrated with pipe flow, a function could be written with Equation (5.17) and a solver applied to find the value of \\(\\theta\\) for the given flow conditions with a known D, S, n and Q. The value for \\(\\theta\\) could then be used with Equations (5.13), (5.14) and (5.15) to recover geometric values. Hydraulic analysis of circular pipes flowing partially full often account for the value of Manning’s n varying with depth (Camp, 1946); some standards recommend fixed n values, and others require the use of a depth-varying n. The R package hydraulics has implemented those routines to enable these calculations, including using a fixed n (the default) or a depth-varing n. For an existing pipe, a common problem is the determination of the depth, y that a given flow Q, will have given a pipe diameter d, slope S and roughness n. Example 5.4 demonstrates this. Example 5.4 Find the uniform (normal) flow depth, y, for a trapezoidal channel with Q=225 ft3/s, n=0.016, m=2, b=10 ft, S=0.0006. Do this assuming both that Manning n is constant with depth and that it varies with depth. The function manningc from the hydraulics package is used. Any one of the variables in the Manning equation, and related geometric variables, may be treated as an unknown. ans <- hydraulics::manningc(Q=0.01, n=0.013, Sf=0.001, d = 0.2, units="SI", ret_units = TRUE) ans2 <- hydraulics::manningc(Q=0.01, n=0.013, Sf=0.001, d = 0.2, n_var = TRUE, units="SI", ret_units = TRUE) df <- data.frame(Constant_n = unlist(ans), Variable_n = unlist(ans2)) knitr::kable(df, format = "html", digits=3, padding = 0, col.names = c("Constant n","Variable n")) |> kableExtra::kable_styling(full_width = F) Constant n Variable n Q 0.010 0.010 V 0.376 0.344 A 0.027 0.029 P 0.437 0.482 R 0.061 0.060 y 0.158 0.174 d 0.200 0.200 Sf 0.001 0.001 n 0.013 0.014 yc 0.085 0.085 Fr 0.297 0.235 Re 22342.979 20270.210 Qf 0.010 0.010 It is also sometimes convenient to see a cross-section diagram. hydraulics::xc_circle(y = ans$y, d=ans$d, units = "SI") 5.5 Critical flow Critical flow in open channel flow is described in general by Equation (5.4). For any channel geometry and flow rate a convenient plot is a specific energy diagram, which illustrates the different flow depths that can occur for any given specific energy. Specific energy is defined by Equation (5.18). \\[\\begin{equation} E=y+\\frac{V^2}{2g} \\tag{5.18} \\end{equation}\\] It can be interpreted as the total energy head, or energy per unit weight, relative to the channel bottom. For a trapezoidal channel, critical flow conditions occur as described by Equation (5.4). Combining that with trapezoidal geometry produces Equation (5.19) \\[\\begin{equation} \\frac{Q^2}{g}=\\frac{\\left(by_c+m{y_c}^2\\right)^3}{b+2my_c} \\tag{5.19} \\end{equation}\\] where \\(y_c\\) indicates critical flow depth. This is important for understanding what may happen to the water surface when flow encounters an obstacle or transition. For the channel of Example 5.3, the diagram is shown in Figure 5.4. hydraulics::spec_energy_trap( Q = 360, b = 20, m = 1, scale = 4, units = "Eng" ) Figure 5.4: A specific energy diagram for the conditions of Example 5.3. This provides an illustration that for y=3 ft the flow is subcritical (above the critical depth). Specific energy for the conditions of the prior example is \\[E=y+\\frac{V^2}{2g}=3.0+\\frac{5.22^2}{2*32.2}=3.42 ft\\] If the channel bottom had an abrupt rise of \\(E-E_c=3.42-3.03=0.39 ft\\) critical depth would occur over the hump. A rise of anything greater than that would cause damming to occur. Once flow over a hump is critical, downstream of the hump the flow will be in supercritical conditions, flowing at the alternate depth. The specific energy for a given depth y and alternate depth can be added to the plot by including an argument for depth, y, as in Figure 5.5. hydraulics::spec_energy_trap( Q = 360, b = 20, m = 1, scale = 4, y=3.0, units = "Eng" ) Figure 5.5: A specific energy diagram for the conditions of Example 5.3 with an additional y value added. 5.6 Flow in Rectangular Channels When working with rectangular channels the open channel equations simplify, because flow, \\(Q\\), can be expressed as flow per unit width, \\(q = Q/b\\), where \\(b\\) is the channel width. Since \\(Q/A=V\\) and \\(A=by\\), Equation (5.18) can be written as Equation (5.20): \\[\\begin{equation} E=y+\\frac{Q^2}{2gA^2}=y+\\frac{q^2}{2gy^2} \\tag{5.20} \\end{equation}\\] Equation (5.19) for critical depth, \\(y_c\\), also is simplified for rectangular channels to Equation (5.21): \\[\\begin{equation} y_c = \\left({\\frac{q^2}{g}}\\right)^{1/3} \\tag{5.21} \\end{equation}\\] Combining Equation (5.20) and Equation (5.21) shows that at critical conditions, the minimum specific energy is: \\[\\begin{equation} E_{min} = \\frac{3}{2} y_c \\tag{5.22} \\end{equation}\\] Example 5.5, based on an exercise from the open-channel flow text by Sturm (Sturm, 2021), demonstrates how to solve for the depth through a rectangular section when the bottom height changes. Example 5.5 A 0.5 m wide rectangular channel carries a flow of 2.2 m\\(^3\\)/s at a depth of 2 m (\\(y_1\\)=2m). If the channel bottom rises 0.25 m (\\(\\Delta z=0.25~ m\\)), and head loss, \\(h_L\\) over the transition is negligible, what is the depth, \\(y_2\\) after the rise in channel bottom? Figure 5.6: The rectangular channel of Example 5.5 with an increase in channel bottom height downstream. A specific energy diagram is very helpful for establishing upstream conditions and estimating \\(y_2\\). p1 <- hydraulics::spec_energy_trap( Q = 2.2, b = 0.5, m = 0, y = 2, scale = 2.5, units = "SI" ) p1 Figure 5.7: A specific energy diagram for the conditions of Example 5.5. The values of \\(y_c\\) and \\(E_{min}\\) shown in the plot can be verified using Equations (5.21) and (5.22). This should always be checked to describe the incoming flow and what will happen as flow passes over a hump. Since \\(y_1\\) > \\(y_c\\) the upstream flow is subcritical, and flow can be expected to drop as it passes over the hump. Upstream and downstream specific energy are related by Equation (5.23): \\[\\begin{equation} E_1-E_2=\\Delta z + h_L \\tag{5.23} \\end{equation}\\] Since \\(h_L\\) is negligible in this example, the downstream specific energy, \\(E_2\\) is lower that the upper \\(E_1\\) by an amount \\(\\Delta z\\), or \\[\\begin{equation} E_2 = E_1 - \\Delta z \\tag{5.24} \\end{equation}\\] For a 0.25 m rise, and using \\(q = Q/b = 2.2/0.5 = 4.4\\), combining Equation (5.24) and Equation (5.20): \\[E_2 = E_1 - 0.25 = 2 + \\frac{4.4^2}{2(9.81)(2^2)} - 0.25 = 2.247 - 0.25 = 1.997 ~m\\] From the specific energy diagram, for \\(E_2=1.997 ~ m\\) a depth of about \\(y_2 \\approx 1.6 ~ m\\) would be expected, and the flow would continue in subcritical conditions. The value of \\(y_2\\) can be calculated using Equation (5.20): \\[1.997 = y_2 + \\frac{4.4^2}{2(9.81)(y_2^2)}\\] which can be rearranged to \\[0.9967 - 1.997 y_2^2 + y_2^3= 0\\] Solving a polynomial in R is straightforward using the polyroot function and using Re to extract the real portion of the solution. Re(polyroot(c(0.9667, 0, -1.997, 1))) #> [1] 0.9703764 -0.6090519 1.6356755 The negative root is meaningless, the lower positive root is the supercritical depth for \\(E_2 = 1.997 ~ m\\), and the larger positive root is the subcritical depth. Thus the correct solution is \\(y_2 = 1.64 ~ m\\) when the channel bottom rises by 0.25 m. A vertical line or other annotation can be added to the specific energy diagram to indicate \\(E_2\\) using ggplot2 with a command like p1 + ggplot2::geom_vline(xintercept = 1.997, linetype=3). The hydraulics R package can also add lines to a specific energy diagram for up to two depths: p2 <- hydraulics::spec_energy_trap(Q = 2.2, b = 0.5, m = 0, y = c(2, 1.64), scale = 2.5, units = "SI") p2 Figure 5.8: A specific energy diagram for the conditions of Example 5.5 with added annotation for when the bottom elecation rises. The specific energy diagram shows that if \\(\\Delta z > E_1 - E_{min}\\), the downstream specific energy, \\(E_2\\) would be to the left of the curve, so no feasible solution would exist. At that point damming would occur, raising the upstream depth, \\(y_1\\), and thus increasing \\(E_1\\) until \\(E_2 = E_{min}\\). The largest rise in channel bottom height that will not cause damming is called the critical hump height: \\(\\Delta z_{c} = E_1 - E_{min}\\). 5.7 Gradually varied steady flow When water approaches an obstacle, it can back up, with its depth increasing. The effect can be observed well upstream. Similarly, as water approaches a drop, such as with a waterfall, the water level declines, and that effect can also be seen upstream. In general, any change in slope or roughness will produce changes in depth along a channel length. There are three depths that are important to define for a channel: \\(y_c\\), critical depth, found using Equation (5.4) \\(y_0\\), normal depth, found using Equation (5.2) \\(y\\), flow depth, found using Equation (5.5) If \\(y_n < y_c\\) flow is supercritical (for example, flowing down a steep slope); if \\(y_n > y_c\\) flow is subcritical. Variations in the water surface are classified by profile types based on to whether the normal flow is subcritical (or mild sloped, M) or supercritical (steep, S), as in Figure 5.9 (Davidian, Jacob, 1984). Figure 5.9: Types of flow profiles on mild and steep slopes In addition to channel transitions, changes in channel slow of roughness (Manning n) will cause the flow surface to vary. Some of these conditions are illustrated in Figure 5.10 (Davidian, Jacob, 1984). Figure 5.10: Types of flow profiles with changes in slope or roughness Typically, for supercritical flow the calculations start at an upstream cross section and move downstream. For subcritical flow calculations proceed upstream. However, for the direct step method, a negative result will indicate upstream, and a positive result indicates downstream. If the water surface passes through critical depth (from supercritical to subcritical or the reverse) it is no longer gradually varied flow and the methods in this section do not apply. This can happen at abrupt changes in channel slope or roughness, or channel transitions. 5.7.1 The direct step method The direct step method looks at two cross sections in a channel where depths, \\(y_1\\) and \\(y_2\\) are defined. Figure 5.11: A gradually varied flow example. The distance between these two cross-sections, \\({\\Delta}X\\), is calculated using Equation (5.25) \\[\\begin{equation} {\\Delta}X=\\frac{E_1-E_2}{\\overline{S}-S_0} \\tag{5.25} \\end{equation}\\] Where E is the specific energy from Equation (5.18), \\(S_0\\) is the slope of the channel bed, and \\(S\\) is the slope of the energy grade line. \\(\\overline{S}\\) is the average of the S values at each cross section calculated using the Manning equation, Equation (5.2) solved for slope, as in Equation (5.26). \\[\\begin{equation} S=\\frac{n^2\\,V^2}{C^2\\,R^{\\frac{4}{3}}} \\tag{5.26} \\end{equation}\\] Example 5.6 demonstrates this. Example 5.6 Water flows at 10 m3/s in a trapezoidal channel with n=0.015, bottom width 3 m, side slope of 2:1 (H:V) and longitudinal slope 0.0009 (0.09%). At the location of a USGS stream gage the flow depth is 1.4 m. Use the direct step method to find the distance to the point where the depth is 1.2 m and determine whether it is upstream or downstream. Begin by setting up a function to calculate the Manning slope and setting up the input data. #function to calculate Manning slope slope_f <- function(V,n,R,C) { return(V^2*n^2/(C^2*R^(4./3.))) } #Now set up input data ################################## #input Flow Q=10.0 #input depths: y1 <- 1.4 #starting depth y2 <- 1.2 #final depth #Define the number of steps into which the difference in y will be broken nsteps <- 2 #channel geometry: bottom_width <- 3 side_slope <- 2 #side slope is H:V. Use zero for rectangular manning_n <- 0.015 long_slope <- 0.0009 units <- "SI" #"SI" or "Eng" if (units == "SI") { C <- 1 #Manning constant: 1 for SI, 1.49 for US units g <- 9.81 } else { #"Eng" means English, or US system C <- 1.49 g <- 32.2 } #find depth increment for each step, depths at which to solve depth_incr <- (y2 - y1) / nsteps depths <- seq(from=y1, to=y2, by=depth_incr) First check to see if the flow is subcritical or supercritical and find the normal depth. Critical and normal depths can be calculated using the manningt function in the hydraulics package, as in Example 5.2. However, because other functionality of the rivr package is used, these will be calculated using functions from the rivr package. rivr::critical_depth(Q = Q, yopt = y1, g = g, B = bottom_width , SS = side_slope) #> [1] 0.8555011 #note using either depth for yopt produces the same answer rivr::normal_depth(So = long_slope, n = manning_n, Q = Q, yopt = y1, Cm = C, B = bottom_width , SS = side_slope) #> [1] 1.147137 The normal depth is greater than the critical depth, so the channel has a mild slope. The beginning and ending depths are above normal depth. This indicates the profile type, following Figure 5.9, is M-1, so the flow depth should decrease in depth going upstream. This also verifies that the flow depth between these two points does not pass through critical flow, so is a valid gradually varied flow problem. For each increment the \\({\\Delta}X\\) value needs to be calculated, and they need to be accumulated to find the total length, L, between the two defined depths. #loop through each channel segment (step), calculating the length for each segment. #The channel_geom function from the rivr package is helpful L <- 0 for ( i in 1:nsteps ) { #find hydraulic geometry, E and Sf at first depth xc1 <- rivr::channel_geom(y=depths[i], B=bottom_width, SS=side_slope) V1 <- Q/xc1[['A']] R1 <- xc1[['R']] E1 <- depths[i] + V1^2/(2*g) Sf1 <- slope_f(V1,manning_n,R1,C) #find hydraulic geometry, E and Sf at second depth xc2 <- rivr::channel_geom(y=depths[i+1], B=bottom_width, SS=side_slope) V2 <- Q/xc2[['A']] R2 <- xc2[['R']] E2 <- depths[i+1] + V2^2/(2*g) Sf2 <- slope_f(V2,manning_n,R2,C) Sf_avg <- (Sf1 + Sf2) / 2.0 dX <- (E1 - E2) / (Sf_avg - long_slope) L <- L + dX } cat(sprintf("Using %d steps, total distance from depth %.2f to %.2f = %.2f m\\n", nsteps, y1, y2, L)) #> Using 2 steps, total distance from depth 1.40 to 1.20 = -491.75 m The result is negative, verifying that the location of depth y2 is upstream of y1. Of course, the result will become more precise as more incremental steps are included, as shown in Figure 5.12 Figure 5.12: Variation of number of calculation steps to final calculated distance. The direct step method is also implemented in the hydraulics package, and can be applied to the same problem as above, as illustrated in Example 5.7. Example 5.7 Water flows at 10 m3/s in a trapezoidal channel with n=0.015, bottom width 3 m, side slope of 2:1 (H:V) and longitudinal slope 0.0009 (0.09%). At the location of a USGS stream gage the flow depth is 1.4 m. Use the direct step method to find the distance to the point where the depth is 1.2 m and determine whether it is upstream or downstream. hydraulics::direct_step(So=0.0009, n=0.015, Q=10, y1=1.4, y2=1.2, b=3, m=2, nsteps=2, units="SI") #> y1=1.400, y2=1.200, yn=1.147, yc=0.855585 #> Profile type = M1 #> # A tibble: 3 × 7 #> x z y A Sf E Fr #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 0 0 1.4 8.12 0.000407 1.48 0.405 #> 2 -192. 0.173 1.3 7.28 0.000548 1.40 0.466 #> 3 -492. 0.443 1.2 6.48 0.000753 1.32 0.541 This produces the same result, and verifies that the water surface profile is type M-1. 5.7.2 Standard step method The standard step method works similarly to the direct step method, except from one known depth the second depth is determined at a known distance, L. This is a preferred method when the depth at a critical location, such as a bridge, is needed. The rivr package implements the standard step method in its compute_profile function. To compare it to the direct step method, check the depth at \\(y_2\\) given the total distance from Example 5.6. Example 5.8 For the same channel and flow rate as Example 5.6, determine the depth of water at the distance L determined above. The function requires the distance to be positive, so apply the absolute value to the L value. dist = abs(L) ans <- rivr::compute_profile(So = long_slope, n = manning_n, Q = Q, y0 = y1, Cm = C, g = g, B = bottom_width, SS = side_slope, stepdist = dist/nsteps, totaldist = dist) #Distances along the channel where depths were determined ans$x #> [1] 0.0000 -245.8742 -491.7483 #Depths at each distance ans$y #> [1] 1.400000 1.277009 1.200592 This shows the distances and depths at each of the steps defined. Consistent with the above, the distances are negative, showing that they are progressing upstream. The results are identical for \\(y_2\\) using the direct step method. 5.8 Rapidly varied flow (the hydraulic jump) Figure 5.13: A hydraulic jump at St. Anthony Falls, Minnesota. In the discussion of critical flow in Section 5.5, the concept of alternate depths was introduced, where a given flow rate in a channel with known geometry typically may assume two possible values, one subcritical and one supercritical. For the case of supercritical flow transitioning to subcritical flow, a smooth transition is impossible, so a hydraulic jump occurs. A hydraulic jump always dissipates some of the incoming energy. A hydraulic jump is depicted in Figure 5.14 (Peterka, Alvin J., 1978). Figure 5.14: A typical hydraulic jump. 5.8.1 Sequent (or conjugate) depths The two depths on either side of a hydraulic jump are called sequent depths or conjugate depths. The relationship between them can be established using the momentum equation to develop an general expression (for any open channel) for the momentum function, M, as in Equation (5.27). \\[\\begin{equation} M=Ah_c+\\frac{Q^2}{gA} \\tag{5.27} \\end{equation}\\] where \\(h_c\\) is the distance from the water surface to the centroid of the channel cross-section. For a trapezoidal channel, the momentum equation becomes that described by Equation (5.28). \\[\\begin{equation} M=\\frac{by^2}{2}+\\frac{my^3}{3}+\\frac{Q^2}{gy\\left(b+my\\right)} \\tag{5.28} \\end{equation}\\] For the case of a rectangular channel, setting m=0 and setting the Momentum function for two sequent depths, y1 ans y2 equal, produces the relationship in Equation (5.29). \\[\\begin{equation} \\frac{y_2}{y_1}=\\frac{1}{2}\\left(-1+\\sqrt{1+8Fr_1^2}\\right) or \\frac{y_1}{y_2}=\\frac{1}{2}\\left(-1+\\sqrt{1+8Fr_2^2}\\right) \\tag{5.29} \\end{equation}\\] where Frn is the Froude Number [Equation (5.1)] at section n. Again, for the case of a rectangular channel, the energy head loss through a hydraulic jump simplifies to Equation (5.30). \\[\\begin{equation} h_l=\\frac{\\left(y_2-y_1\\right)^3}{4y_1y_2} \\tag{5.30} \\end{equation}\\] Given that the momentum function must be conserved on either side of a hydraulic jump, finding the sequent depth for any known depth becomes straightforward for trapezoidal shapes. Setting M1 = M2 in Equation (5.28) allows the use of a solver, as in Example 5.9. Example 5.9 A trapezoidal channel with a bottom width of 0.5 m and a side slope of 1:1 carries a flow of 0.2 m3/s. The depth on one side of a hydraulic jump is 0.1 m. Find the sequent depth, the energy head loss, and the power dissipation in Watts. flow <- 0.2 ans <- hydraulics::sequent_depth(Q=flow,b=0.5,y=0.1,m=1,units = "SI", ret_units = TRUE) #print output of function as.data.frame(ans) #> ans #> y 0.1 [m] #> y_seq 0.3941009 [m] #> yc 0.217704 [m] #> Fr 3.635731 [1] #> Fr_seq 0.3465538 [1] #> E 0.666509 [m] #> E_seq 0.4105265 [m] #Find energy head loss hl <- abs(ans$E - ans$E_seq) hl #> 0.2559825 [m] #Express this as a power loss gamma <- hydraulics::specwt(units = "SI") P <- gamma*flow*hl cat(sprintf("Power loss = %.1f Watts\\n",P)) #> Power loss = 501.4 Watts The energy loss across hydraulic jumps varies with the Froude number of the incoming flow, as shown in depicted in Figure 5.15 (Peterka, Alvin J., 1978). Figure 5.15: Types of hydraulic jumps. 5.8.2 Location of a hydraulic jump In hydraulic infrastructure where hydraulic jumps will occur there are usually engineered features, such as baffles or basins, to force a hydraulic jump to occur in specific locations, to protect downstream waterways from the turbulent effects of an uncontrolled hydraulic jump. In the absence of engineered features to cause a jump, the location of a hydraulic jump can be determined using the concepts of Sections 5.7 and 5.8. Example 5.10 demonstrates the determination of the location of a hydraulic jump when normal flow conditions exist at some distance upstream and downstream of the jump. Example 5.10 A rectangular (a trapezoid with side slope, m=0) concrete channel with a bottom width of 3 m carries a flow of 8 m3/s. The upstream channel slopes steeply at So=0.018 and discharges onto a mild slope of So=0.0015. Determine the height of the jump and its location. First find the normal depth on each slope, and the critical depth for the channel. yn1 <- hydraulics::manningt(Q = 8, n = 0.013, m = 0, Sf = 0.018, b = 3, units = "SI")$y yn2 <- hydraulics::manningt(Q = 8, n = 0.013, m = 0, Sf = 0.0015, b = 3, units = "SI")$y yc <- hydraulics::manningt(Q = 8, n = 0.013, m = 0, Sf = 0.0015, b = 3, units = "SI")$yc cat(sprintf("yn1 = %.3f m, yn2 = %.3f m, yc = %.3f m\\n", yn1, yn2, yc)) #> yn1 = 0.498 m, yn2 = 1.180 m, yc = 0.898 m Recall that the calculation of yc only depends on flow and channel geometry (Q, b, m), so the values of n and Sf can be arbitrary for that command. These results confirm that flow is supercritical upstream and subcritical downstream, so a hydraulic jump will occur. The hydraulic jump will either begin at yn1 (and jump to the sequent depth for yn1) or end at yn2 (beginning at the sequent depth for yn2). The possibilities are shown in Figure 5.9 in the lower right panel. First check the two sequent depths. yn1_seq <- hydraulics::sequent_depth(Q = 8, b = 3, y=yn1, m = 0, units = "SI")$y_seq yn2_seq <- hydraulics::sequent_depth(Q = 8, b = 3, y=yn2, m = 0, units = "SI")$y_seq cat(sprintf("yn1_seq = %.3f m, yn2_seq = %.3f m\\n", yn1_seq, yn2_seq)) #> yn1_seq = 1.476 m, yn2_seq = 0.666 m This confirms that if the jump began at yn1 (on the steep slope) it would need to jump a level below yn2, with an S-1 curve providing the gradual increase in depth to yn2. Since yn1_seq exceeds yn2, this is not possible. That can be verified using the direct_step function to show the distance from yn1_seq to yn2 would need to be upstream (negative x values in the result), which cannot occur for this case. This means the alternate case must exist, with an M-3 profile raising yn1 to yn2_seq at which point the jump occurs. The direct step method can find this distance along the channel. hydraulics::direct_step(So=0.0015, n=0.013, Q=8, y1=yn1, y2=yn2_seq, b=3, m=0, nsteps=2, units="SI") #> # A tibble: 3 × 7 #> x z y A Sf E Fr #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 0 0 0.498 1.49 0.0180 1.96 2.42 #> 2 23.4 -0.0350 0.582 1.75 0.0113 1.65 1.92 #> 3 44.6 -0.0669 0.666 2.00 0.00761 1.48 1.57 The number of calculation steps (nsteps) can be increased for greater precision, but 2 steps is adequate here. "],["momentum-in-water-flow.html", "Chapter 6 Momentum in water flow 6.1 Equations of linear momentum 6.2 The momentum equation in pipe design", " Chapter 6 Momentum in water flow When moving water changes direction or velocity, an external force must be associated with the change. In civil engineering infrastructure this is ubiquitous and the forces associated with this must be accounted for in design. Figure 6.1: Water pipe on Capitol Hill, Seattle. 6.1 Equations of linear momentum Newton’s law relates the forces applied to a body to the rate of change of linear momentum, as in Equation (6.1) \\[\\begin{equation} \\sum{\\overrightarrow{F}}=\\frac{d\\left(m\\overrightarrow{V}\\right)}{dt} \\tag{6.1} \\end{equation}\\] For fluid flow in a hydraulic system carrying a flow Q, the equation can be written in any linear direction (x-direction in this example) as in Equation (6.2). \\[\\begin{equation} \\sum{F_x}=\\rho{Q}\\left(V_{2x}-V_{1x}\\right) \\tag{6.2} \\end{equation}\\] where \\(\\rho{Q}\\) is the mass flux through the system, \\(V_{1x}\\) is the velocity in the x-direction where flow enters the system, and \\(V_{2x}\\) is the velocity in the x-direction where flow leaves the system. \\(\\sum{F_x}\\) is the vector sum of all external forces acting on the system in the x-direction. It should be noted that the values of V are the average cross-sectional velocity. A momentum correction factor (\\(\\beta\\)), can be applied when the velocity is highly non-uniform across the cross-section. In nearly all civil engineering applications the adjustment factor is close enough to 1 where it is ignored in the calculations. 6.2 The momentum equation in pipe design One of the most common civil engineering applications of the momentum equation is providing the lateral restraint where a pipe bend occurs. One approach to provide the external force to keep the pipe in equilibrium is to use a thrust block, as illustrated in Figure 6.2 (Ductile Iron Pipe Research Association, 2016). Figure 6.2: A sketch of a pipe bend with a thrust block. Example 6.1 A horizontal 18-inch diameter pipe carries flow Q of water at 68\\(^\\circ\\)F with a pressure of 60 psi and encounters a bend of angle \\(\\theta=30^\\circ\\). Show how the reaction force, R varies with the flow rate through the bend for flows up to 20 ft3/s. Ignore head loss through the bend. Taking the control volume to be the bend, the external forces acting on the bend are shown in Figure 6.3. Figure 6.3: External forces on the pipe. Note that if the pipe were not horizontal, the weight of the water in the pipe would also need to be included. Including all of the external forces in the x-direction on left side of Equation (6.2) and recognizing that V1x=V1 and V2x=V2cos\\(\\theta\\) produces: \\[P_1A_1-P_2A_2cos\\theta-R_x=\\rho{Q}\\left(V_{2}cos\\theta-V_{1}\\right)\\] Rearranging to solve for Rx gives Equation (6.3). \\[\\begin{equation} R_x=P_1A_1-P_2A_2cos\\theta-\\rho{Q}\\left(V_{2}cos\\theta-V_{1}\\right) \\tag{6.3} \\end{equation}\\] Similarly in the y-direction Equation (6.4) can be assembled, noting that V1y=0 and V2y=\\(-\\)V2sin\\(\\theta\\) . \\[\\begin{equation} R_y=P_2A_2sin\\theta-\\rho{Q}\\left(-V_{2}sin\\theta\\right) \\tag{6.4} \\end{equation}\\] This can be set up in R in many ways, such as the following. #Input Data -- ensure units are consistent in ft, lbf (pound force), sec D1 <- units::set_units(18/12, ft) D2 <- units::set_units(18/12, ft) P1 <- units::set_units(60*144, lbf/ft^2) #convert psi to lbf/ft^2 P2 <- units::set_units(60*144, lbf/ft^2) theta <- 30*(pi/180) #convert to radians for sin, cos functions rho <- hydraulics::dens(T=68, units="Eng", ret_units = TRUE) # calculations - vary flow from 0 to 20 ft^3/s Q <- units::set_units(seq(0,20,1), ft^3/s) A1 <- pi/4*D1^2 A2 <- pi/4*D2^2 V1 <- Q/A1 V2 <- Q/A2 Rx <- P1*A1-P2*A2*cos(theta)-rho*Q*(V2*cos(theta)-V1) Ry <- P2*A2*sin(theta)-rho*Q*(-V2*sin(theta)) R <- sqrt(Rx^2 + Ry^2) plot(Q,R) When Q=0, only the pressure terms contribute to R. This plot shows that for typical water main conditions the change in direction of the velocity vectors adds a small amount (less than 3% in this example) to the calculated R value. This is why design guidelines for water mains often neglect the velocity term in Equation (6.2). In other industrial or laboratory conditions it may not be valid to neglect that term. "],["pumps-and-how-they-operate-in-a-hydraulic-system.html", "Chapter 7 Pumps and how they operate in a hydraulic system 7.1 Defining the system curve 7.2 Defining the pump characteristic curve 7.3 Finding the operating point", " Chapter 7 Pumps and how they operate in a hydraulic system For any system delivering water through circular pipes with the assistance of a pump, the selection of the pump requires a consideration of both the pump characteristics and the energy required to deliver different flow rates through the system. These are described by the system and pump characteristic curves. Where they intersect defines the operating point, the flow and (energy) head at which the pump would operate in that system. 7.1 Defining the system curve Figure 7.1: A simple hydraulic system (from https://www.castlepumps.com) For a simple system the loss of head (energy per unit weight) due to friction, \\(h_f\\), is described by the Darcy-Weisbach equation, which can be simplified as in Equation (7.1). \\[\\begin{equation} h_f = \\frac{fL}{D}\\frac{V^2}{2g} = \\frac{8fL}{\\pi^{2}gD^{5}}Q^{2} = KQ{^2} \\tag{7.1} \\end{equation}\\] The total dynamic head the system requires a pump to provide, \\(h_p\\), is found by solving the energy equation from the upstream reservoir (point 1) to the downstream reservoir (point 2), as in Equation (7.2). \\[\\begin{equation} h_p = \\left(z+\\frac{P}{\\gamma}+\\frac{V^2}{2g}\\right)_2 - \\left(z+\\frac{P}{\\gamma}+\\frac{V^2}{2g}\\right)_1+h_f \\tag{7.2} \\end{equation}\\] For the simple system in Figure 7.1, the velocity can be considered negligible in both reservoirs 1 and 2, and the pressures at both reservoirs is atmospheric, so the Equation (7.2) can be simplified to (7.3). \\[\\begin{equation} h_p = \\left(z_2 - z_1\\right) + h_f=h_s+h_f=h_s+KQ^2 \\tag{7.3} \\end{equation}\\] Using the hydraulics package, the coefficient, K, can be calculated manually or using other package functions for friction loss in a pipe system using \\(Q=1\\). Using this to develop a system curve is demonstrated in Example 7.1. Example 7.1 Develop a system curve for a pipe with a diameter of 20 inches, length of 3884 ft, and absolute roughness of 0.0005 ft. Use kinematic viscocity, \\(\\nu\\) = 1.23 x 10-5 ft2/s. Assume a static head, z2 - z1 = 30 ft. ans <- hydraulics::darcyweisbach(Q = 1,D = 20/12, L = 3884, ks = 0.0005, nu = 1.23e-5, units = "Eng") cat(sprintf("Coefficient K: %.3f\\n", ans$hf)) #> Coefficient K: 0.160 scurve <- hydraulics::systemcurve(hs = 30, K = ans$hf, units = "Eng") print(scurve$eqn) #> [1] "h == 30 + 0.16*Q^2" For this function of the hydraulics package, Q is either in ft\\(^3\\)/s or m\\(^3\\)/s, depending on whether Eng or SI is specified for units. Often data for flows in pumping systems are in other units such as gpm or liters/s, so unit conversions would need to be applied. 7.2 Defining the pump characteristic curve The pump characteristic curve is based on data or graphs obtained from a pump manufacturer, such as that depicted in Figure 7.2. Figure 7.2: A sample set of pump curves (from https://www.gouldspumps.com). The three red dots are points selected to approximate the curve The three selected points, selected manually across the range of the curve, are used to generate a polynomial fit to the curve. There are many forms of equations that could be used to fit these three points to a smooth, continuous curve. Three common ones are implemented in the hydraulics package, shown in Table 7.1. Table 7.1: Common equation forms for pump characteristic curves. type Equation poly1 \\(h=a+{b}{Q}+{c}{Q}^2\\) poly2 \\(h=a+{c}{Q}^2\\) poly3 \\(h_{shutoff}+{c}{Q}^2\\) The \\(h_{shutoff}\\) value is the pump head at \\(Q={0}\\). Many methods can be used to fit a polynomial to a set of points. The hydraulics package includes the pumpcurve function for this purpose. The coordinates of the points can be input as numeric vectors, being careful to use correct units, consistent with those used for the system curve. Manufacturer’s pump curves often use units for flow that are not what the hydraulics package needs, and the units package provides a convenient way to convert them as needed. Developing the pump characteristic curve using the hydraulics package is demonstrated in Example 7.2. Example 7.2 Develop a pump characteristic curve for the pump in Figure 7.2, using the three points marked in red. Use the poly2 form from Table 7.1. qgpm <- units::set_units(c(0, 5000, 7850), gallons/minute) #Convert units to those needed for package, and consistent with system curve qcfs <- units::set_units(qgpm, ft^3/s) #Head units, read from the plot, are already in ft so setting units is not needed hft <- c(81, 60, 20) pcurve <- hydraulics::pumpcurve(Q = qcfs, h = hft, eq = "poly2", units = "Eng") print(pcurve$eqn) #> [1] "h == 82.5 - 0.201*Q^2" The function pumpcurve returns a pumpcurve object that includes the polynomial fit equation and a simple plot to check the fit. This can be plotted as in Figure 7.3 pcurve$p Figure 7.3: A pump characteristic curve 7.3 Finding the operating point The two curves can be combined to find the operating point of the selected pump in the defined system. this can be done by plotting them manually, solving the equations simultaneously, or by using software. The hydraulics package finds the operating point using the system and pump curves defined earlier. Example 7.3 shown how this is done. Example 7.3 Find the operating point for the pump and system curves developed in Examples 7.1 and 7.2. oppt <- hydraulics::operpoint(pcurve = pcurve, scurve = scurve) cat(sprintf("Operating Point: Q = %.3f, h = %.3f\\n", oppt$Qop, oppt$hop)) #> Operating Point: Q = 12.051, h = 53.285 The function operpoint function returns an operpoint object that includes the a plot of both curves. This can be plotted as in Figure 7.4 oppt$p Figure 7.4: The pump operating point "],["the-hydrologic-cycle-and-precipitation.html", "Chapter 8 The hydrologic cycle and precipitation 8.1 Precipitation observations 8.2 Precipitation frequency 8.3 Precipitation gauge consistency – double mass curves 8.4 Precipitation interpolation and areal averaging", " Chapter 8 The hydrologic cycle and precipitation All of the earlier chapters of this book dealt with the behavior of water in different hydraulic systems, such as canals or pipes. Now we consider the bigger picture of where the water originates, and ultimately how we can estimate how much water is available for different uses, and how much excess (flood) water systems will need to be designed and built to accommodate. A fundamental concept is the hydrologic cycle, depicted in Figure 8.1. Figure 8.1: The hydrologic cycle, from the USGS The primary variable in the hydrologic cycle from an engineering perspective is precipitation, since that is the source of the water used and managed in engineered systems. The hydromisc package will need to be installed to access some of the data used below. If it is not installed, do so following the instructions on the github site for the package. 8.1 Precipitation observations Direct measurement of precipitation is done with precipitation gauges, such as shown in Figure 8.2. Figure 8.2: National Weather Service standard 8-inch gauge (source: NWS). Precipitation can vary dramatically over short distances, so point measurements are challenging to work with when characterizing rainfall over a larger area. An image from an atmospheric river event over California is shown in Figure 8.3. Reflectivity values are converted to precipitation rates based on calibration with rain gauge observations. Figure 8.3: A raw radar image showing reflectivity values. Red squared indicated weather radar locations (source: NOAA). There are additional data sets that merge many sources of data to create continuous (spatially and temporally) datasets of precipitation. While these provide excellent resources for large scale studies, we will initially focus on point observations. Obtaining precipitation data can be done in many ways. Example 8.1 demonstrates one method using R using the FedData package. Example 8.1 Characterize the rainfall in the city of San Jose, in Santa Clara County. For the U.S., a good starting point is to use the mapping tools at the NOAA Climate Data Online (CDO) website. From the mapping tools page, select Observations: Daily ensure GHCN Daily is checked so you’ll look for stations that are part of the Global Historical Climatology Network and search for San Jose, CA. Figure 8.4 shows the three stations that lie within the rectangle sketched on the map, and the one that was selected. Figure 8.4: Selection results for a portion of San Jose, CA (source: CDO). The data can be downloaded directly from the CDO site as a csv file, a sample of which is included with the hydromisc package (the sample also includes air temperature data). Note the units that you specify for the data since they will not appear in the csv file. Note that this initial station search and data download can be automated in R using other packages: Using the FedData package, following a method similar to this. Using the rnoaa package, referring to the vignettes. While formats will vary depending on the source of the data, in this example we can import the csv file directly. Since units were left as ‘standard’ on the CDO website, precipitation is in inches and temperatures in oF. datafile <- system.file("extdata", "cdo_data_ghcn_23293.csv", package="hydromisc") ghcn_data <- read.csv(datafile,header=TRUE) A little cleanup of the data needs to be done to ensure the DATE column is in date format, and change any missing values (often denoted as 9999 or -9999) to NA. With missing values flagged as NA, R can ignore them, set them to zero, or fill them in with functions like the zoo::na.approx() or na.spline() functions, or using the more sophisticated imputeTS package. finally, add a ‘water year’ column (a water year begins on October 1 and ends September 30). ghcn_data$DATE <- as.Date(ghcn_data$DATE, format="%Y-%m-%d") ghcn_data$PRCP[ghcn_data$PRCP <= -999 | ghcn_data$PRCP >= 999 ] = NA wateryr <- function(d) { if (as.numeric(format(d, "%m")) >= 10) { wy = as.numeric(format(d, "%Y")) + 1 } else { wy = as.numeric(format(d, "%Y")) } } ghcn_data$wy <- sapply(ghcn_data$DATE, wateryr) A convenient package for characterizing precipitation is hydroTSM, the output of which is shown in Figure 8.5 library(hydroTSM) #create a simple data frame for plotting ghcn_prcp <- data.frame(date = ghcn_data$DATE, prcp = ghcn_data$PRCP ) #convert it to a zoo object x <- zoo::read.zoo(ghcn_prcp) hydroTSM::hydroplot(x, var.type="Precipitation", main="", var.unit="inch", pfreq = "ma", from="1999-01-01", to="2022-12-31") Figure 8.5: Monthly and annual precipitation summary for San Jose, CA for 1999-2022 This presentation shows the seasonality of rainfall in San Jose, with most falling between October and May. The mean is about 12 inches per year, with most years experiencing between 10-15 inches of precipitation. There are functions to produce many statistics such as monthly means. #calculate monthly sums monsums <- hydroTSM::daily2monthly(x, sum, na.rm = TRUE) monavg <- as.data.frame(hydroTSM::monthlyfunction(monsums, mean, na.rm = TRUE)) #if record begins in a month other than January, need to reorder monavg <- monavg[order(factor(row.names(monavg), levels = month.abb)),,drop=FALSE] colnames(monavg)[1] <- "Avg monthly precip, in" knitr::kable(monavg, digits = 2) |> kableExtra::kable_paper(bootstrap_options = "striped", full_width = F) Avg monthly precip, in Jan 2.23 Feb 2.26 Mar 1.75 Apr 1.03 May 0.26 Jun 0.10 Jul 0.00 Aug 0.00 Sep 0.10 Oct 0.60 Nov 1.21 Dec 2.31 The winter of 2016-2017 (water year 2017) was a record wet year for much of California. Figure 8.6 shows a hyetograph the daily values for that year. library(ggplot2) ghcn_prcp2 <- data.frame(date = ghcn_data$DATE, wy = ghcn_data$wy, prcp = ghcn_data$PRCP ) ggplot(subset(ghcn_prcp2, wy==2017), aes(x=date, y=prcp)) + geom_bar(stat="identity",color="red") + labs(x="", y="precipitation, inch/day") + scale_x_date(date_breaks = "1 month", date_labels = "%b %d") Figure 8.6: Daily Precipitation for San Jose, CA for water year 2017 While many other statistics could be calculated to characterize precipitation, only a handful more will be shown here. One will use a convenient function of the seas package. This is used in Figure 8.7. library(tidyverse) #The average precipitation rate for rainy days (with more then 0.01 inch) avgrainrate <- ghcn_prcp2[ghcn_prcp2$prcp > 0.01,] |> group_by(wy) |> summarise(prcp = mean(prcp)) #the number of rainy days per year nraindays <- ghcn_prcp2[ghcn_prcp2$prcp > 0.01,] |> group_by(wy) |> summarise(nraindays = length(prcp)) #Find length of consecutive dry and wet spells for the record days.dry.wet <- seas::interarrival(ghcn_prcp, var = "prcp", p.cut = 0.01, inv = FALSE) #add a water year column to the result days.dry.wet$wy <- sapply(days.dry.wet$date, wateryr) res <- days.dry.wet |> group_by(wy) |> summarise(cdd = median(dry, na.rm=TRUE), cwd = median(wet, na.rm=TRUE)) res_long <- pivot_longer(res, -wy, names_to="statistic", values_to="consecutive_days") ggplot(res_long, aes(x = wy, y = consecutive_days)) + geom_bar(aes(fill = statistic),stat = "identity", position = "dodge")+ xlab("") + ylab("Median consecutive days") Figure 8.7: Median concecutive dry days (cdd) and wet days (cwd) for each water year. 8.2 Precipitation frequency For engineering design, the uncertainty in predicting extreme rainfall, floods, or droughts is expressed as risk, typically the probability that a certain event will be equalled or exceeded in any year. The return period, T, is the inverse of the probability of exceedence, so that a storm with a 10% chance of being exceeded in any year (\\(p_{exceed}~=0.10\\)) is a \\(T=\\frac{1}{0.10}=10\\) year storm. A 10-year storm can be experienced in multiple consecutive years, so it only means that, on average over very long periods (in a stationary climate) one would expect to see one event every T years. In the U.S., precipitation frequency statistics are available at the NOAA Precipitation Frequency Data Server (PFDS). An example of the graphical data available there is shown in Figure 8.8. Figure 8.8: Intensity-duration-frequency (IDF) curves from the NOAA PFDS. The calculations performed to produce the IDF curves use decades of daily data, because many years are needed to estimate the frequency with which an event might occur. As a demonstration, however, a single year can be used to illustrate the relationship between intensity and duration, which for durations longer than about 2 hours (McCuen, 2016) can be expressed as in Equation (8.1). \\[\\begin{equation} i = aD^b \\tag{8.1} \\end{equation}\\] As a power curve, Equation (8.1) should be a straight line on a log-log plot. This is shown in Example 8.2. Example 8.2 Use the 2017 water year of rainfall data for the city of San Jose, to plot the relationship between intensity and duration for the 1, 3, 7, and 30-day events. Begin by calculating the necessary intensity and duration values. #First extract one water year of data df.one.year <- subset(ghcn_prcp, date>=as.Date("2016-10-01") & date<=as.Date("2017-09-30")) #Calculate the running mean value for the defined durations dur <- c(1,3,7,30) px <- numeric(length(dur)) for (i in 1:4) { px[i] <- max(zoo::rollmean(df.one.year$prcp,dur[i])) } #create the intensity-duration data frame df.id <- data.frame(duration=dur,intensity=px) Fit the theoretical curve (Equation (8.1)) using the nonlinear least squares function of the stats package (included with a base R installation), and plot the results. #fit a power curve to the data fit <- stats::nls(intensity ~ a*duration^b, data=df.id, start=list(a=1,b=-0.5)) print(signif(coef(fit),3)) #> a b #> 1.850 -0.751 #find estimated y-values using the fit df.id$intensity_est <- predict(fit, list(x = df.id$duration)) #duration-intensity plot with base graphics plot(x=df.id$duration,y=df.id$intensity,log='xy', pch=1, xaxt="n", xlab="Duration, day" , ylab="Intensity, inches/day") lines(x=df.id$duration,y=df.id$intensity_est,lty=2) abline( h = c(seq( 0.1,1,0.1),2.0), lty = 3, col = "lightgray") abline( v = c(1,2,3,4,5,7,10,15,20,30), lty = 3, col = "lightgray") axis(side = 1, at =c(1,2,3,4,5,7,10,15,20,30) ,labels = T) axis(side = 2, at =c(seq( 0.1,1,0.1),2.0) ,labels = T) Figure 8.9: Intensity-duration relationship for water year 2017. Calculated values are based on daily data; theoretical is the power curve fit. If this were done for many years, the results for any one duration could be combined (one value per year) and sorted in decreasing order. That means the rank assigned to the highest value would be 1, and the lowest value would be the number of years, n. The return period, T, for any event would then be found using Equation (8.2) based on the Weibull plotting position formula. \\[\\begin{equation} T=\\frac{n+1}{rank} \\tag{8.2} \\end{equation}\\] That would allow the creation of IDF curves for a point. 8.3 Precipitation gauge consistency – double mass curves The method of using double mass curves to identify changes in an obervation method (such as new instrumentation or a change of location) can be applied to precipitation gauges or any other type of measurement. This method is demonstrated with an example from the U.S. Geological survey (Searcy & Hardison, 1960). The first step is to compile data for a gauge (or better, a set of gauges) that are known to be unperturbed (Station A in the sample data set), and for a suspect gauge though to have experienced a change (Station X is this case). annual_data <- hydromisc::precip_double_mass knitr::kable(annual_data, digits = 2) |> kableExtra::kable_paper(bootstrap_options = "striped", full_width = F) Year Station_A Station_X 1926 39.75 32.85 1927 29.57 28.08 1928 42.01 33.51 1929 41.39 29.58 1930 31.55 23.76 1931 55.54 58.39 1932 48.11 46.24 1933 39.85 30.34 1934 45.40 46.78 1935 44.89 38.06 1936 32.64 42.82 1937 45.87 37.93 1938 46.05 50.67 1939 49.76 46.85 1940 47.26 50.52 1941 37.07 34.38 1942 45.89 47.60 Accumulate the (annual) precipitation (measured in inches) and plot the values for the suspect station against the reference station(s), as in Figure 8.10 . annual_sum <- data.frame(year = annual_data$Year, sum_A = cumsum(annual_data$Station_A), sum_X = cumsum(annual_data$Station_X)) #create scatterplot with a label on every point library(ggplot2) library(ggrepel) #> Warning: package 'ggrepel' was built under R version 4.2.3 ggplot(annual_sum, aes(sum_X,sum_A, label = year)) + geom_point() + geom_text_repel(size=3, direction = "y") + labs(x="Cumulative precipitation at Station A, in", y="Cumulative precipitation at Station X, in") + theme_bw() Figure 8.10: A double mass curve. The break in slope between 1930 and 1931 appears clear. This should checked with records for the station to verify whether changes did occur at that time. If the data from Station X are to be used to fill other records or estimate long-term averages, the inconsistency needs to be corrected. One method to highlight the year at which the break occurs is to plot the residuals from a best fit line to the cumulative data from the two stations, as illustrated by the Food and Agriculture Orgainization FAO. (Allen & United Nations, 1998) linfit = lm(sum_X ~ sum_A, data = annual_sum) plot(x=annual_sum$year,linfit$residuals, xlab = "Year",ylab = "Residual of regression") Figure 8.11: Residuals of the linear fit to the double-mass curve. This verifies that after 1930 the steep decline ends, so it may represent a change in location or equipment. Adusting the earlier record to be consistent with the later period is done by applying Equation (8.3). \\[\\begin{equation} y^{'}_i~=\\frac{b_2}{b_1}y_i \\tag{8.3} \\end{equation}\\] where b2 and b1 are the slopes after and before the break in slope, respectively, yi is original precipitation data, and y’i is the adjusted precipitation. This can be applied as follows. b1 <- lm(sum_X ~ sum_A, data = subset(annual_sum, year <= 1930))$coefficients[['sum_A']] b2 <- lm(sum_X ~ sum_A, data = subset(annual_sum, year > 1930))$coefficients[['sum_A']] #Adjust early values and concatenate to later values for Station X adjusted_X <- c(annual_data$Station_X[annual_data$Year <= 1930]*b2/b1, annual_data$Station_X[annual_data$Year > 1930]) annual_sum_adj <- data.frame(year = annual_data$Year, sum_A = cumsum(annual_data$Station_A), sum_X = cumsum(adjusted_X)) #Check that slope now appears more consistent ggplot(annual_sum_adj, aes(sum_X,sum_A, label = year)) + geom_point() + geom_text_repel(size=3, direction = "y") + labs(x="Cumulative precipitation at Station A, in", y="Cumulative adjusted precipitation at Station X, in") + theme_bw() Figure 8.12: A double mass curve using adjusted data at Station X. The plot shows a more consistent slope, as expected. Another plot of residuals could also validate the effect of the adjustment. 8.4 Precipitation interpolation and areal averaging It is rare that there are precipitation observations exactly where one needs data, which means existing observations must be interpolated to a point of interest. This is also used to fill in missing data in a record using surrounding observations. Interpolation is also used to use sparse observations, or observations from a variety of sources, to produce a spatially continuous grid. This is an essential step to estimating the precipitation averaged across an area that contributes streamflow to some location of concern. Estimating areal average precipitation using some simple, manual methods, has been outlined by the U.S. National Weather Service, illustrated in Figure 8.13 (source: National Weather Service). Figure 8.13: Some basic precipitation interpolation methods, from the U.S. National Weather Service. With the advent of geographical information system (GIS) software, manual interpolation is not used. Rather, more advanced spatial analysis is performed to interpolate precipitation onto a continuous grid, where the uncertainty (or skill) of different methods can be assessed. Spatial analysis methods to do this are outlined in many other references, such as Spatial Data Science and the related book Spatial Data Science with applications in R, or the reference Geocomputation with R. (Lovelace et al., 2019; Pebesma & Bivand, 2023) There are also many sources of precipitation data already interpolated to a regular grid. the geodata package provides access to many data sets, including the Worldclim biophysical data. Another source of global precipitation data, available at daily to monthly scales, is the CHIRPS data set, which has been widely used in many studies. An example of obtaining and plotting average annual precipitation over Santa Clara County is illustrated below. #Load precipitation in mm, already cropped to cover most of California datafile <- system.file("extdata", "prcp_cropped.tif", package="hydromisc") prcp <- terra::rast(datafile) scc_bound <- terra::vect(hydromisc::scc_county) scc_precip <- terra::crop(prcp, scc_bound) terra::plot(scc_precip, plg=list(title="Precip\\n(mm)", title.cex=0.7)) terra::plot(scc_bound, add=TRUE) Figure 8.14: Annual Average Precipitation over Santa Clara County, mm Spatial statistics are easily obtained using terra, a versatile package for spatial analysis. terra::summary(scc_precip) #> chirps.v2.0.1981.2020.40yrs #> Min. : 197.1 #> 1st Qu.: 354.9 #> Median : 447.9 #> Mean : 542.3 #> 3rd Qu.: 652.3 #> Max. :1297.2 #> NA's :5 "],["fate-of-precipitation.html", "Chapter 9 Fate of precipitation 9.1 Interception 9.2 Infiltration 9.3 Evaporation 9.4 Snow 9.5 Watershed analysis", " Chapter 9 Fate of precipitation As precipitation falls and can be caught on vegetation (interception), percolate into the ground (infiltration), return to the atmosphere (evaporation), or become available as runoff (if accumulating as rain or snow). The landscape (land cover and topography) and the time scale of study determine what processes are important. For example, for estimating runoff from an individual storm, interception is likely to be small, as is evaporation. On an annual average over large areas, evaporation will often be the largest component. Comprehensive hydrology models will estimate abstractions due to infiltration and interception, either by simulating the physics of the phenomenon or by using a lumped parameter that accounts for the effects of abstractions on runoff. The hydromisc package will need to be installed to access some of the code and data used below. If it is not installed, do so following the instructions on the github site for the package. 9.1 Interception Figure 9.1: Rain interception by John Robert McPherson, CC BY-SA 4, via Wikimedia Commons Interception of rainfall is generally small during individual storms (0.5-2 mm), so it is often ignored, or lumped in with other abstractions, for analyses of flood hydrology. For areas characterized by low intensity rainfall and heavy vegetation, interception can account for a larger portion of the rainfall (for example, up to 25% of annual rainfall in the Pacific Northwest) (McCuen, 2016). 9.2 Infiltration An early empirical equation describing infiltration rate into soils was developed by Horton in 1939, which takes the form of Equation (9.1). \\[\\begin{equation} f_p~=~ f_c + \\left(f_0 - f_c\\right)e^{-kt} \\tag{9.1} \\end{equation}\\] This describes a potential infiltration rate, \\(f_p\\), beginning at a maximum \\(f_0\\) and decreasing with time toward a minimum value \\(f_c\\) at a rate described by the decay constant \\(k\\). \\(f_c\\) is also equal to the saturated hydraulic conductivity, \\(K_s\\), of the soil. If rainfall rate exceeds \\(f_c\\) then this equation describes the actual infiltration rate with time. If periods of time have rainfall less intense than \\(f_c\\) it is convenient to integrate this to relate the total cumulative depth of water infiltrated, \\(F\\), and the actual infiltration rate, \\(f_p\\), as in Equation (9.2). \\[\\begin{equation} F~=~\\left[\\frac{f_c}{k}ln\\left(f_0-f_c\\right)+\\frac{f_0}{k}\\right]-\\frac{f_c}{k}ln\\left(f_p-f_c\\right)-\\frac{f_p}{k} \\tag{9.2} \\end{equation}\\] A more physically based relationship to describe infiltration rate is the Green-Ampt model. It is based on the physical laws describing the propogation of a wetting front downward through a soil column under a ponded water surface. The Green-Ampt relationship is in Equation (9.3). \\[\\begin{equation} K_st~=~F-\\left(n-\\theta_i\\right)\\Phi_f~ln\\left[1+\\frac{F}{\\left(n-\\theta_i\\right)\\Phi_f}\\right] \\tag{9.3} \\end{equation}\\] Equation (9.3) assumes ponding begins at t=0, meaning rainfall rate exceeds \\(K_s\\). When rainfall rates are less than that, adjustments to the method are used. Parameters are shown in the table below. Figure 9.2: Green-Ampt Parameter Estimates and Ranges based on Soil Texture USACE While not demonstrated here, parameters for the Horton and Green-Ampt methods can be derived from observed infiltration data using the R package vadose. The most widely used method for estimating infiltration is the NRCS method, described in detail in the NRCS document Estimating Runoff Volume and Peak Discharge.This method describes the direct runoff (as a depth), \\(Q\\), resulting from a precipitation event, \\(P\\), as in Equation (9.4). \\[\\begin{equation} Q~=~\\frac{\\left(P-I_a\\right)^2}{\\left(P-I_a\\right)+S} \\tag{9.4} \\end{equation}\\] \\(S\\) is the maximum retention of water by the soil column and \\(I_a\\) is the initial abstraction, commonly estimated as \\(I_a=0.2S\\). Substituting this into Equation (9.4) produces Equation (9.5). \\[\\begin{equation} Q~=~\\frac{\\left(P-0.2~S\\right)^2}{\\left(P+0.8~S\\right)} \\tag{9.5} \\end{equation}\\] This relationship applies as long as \\(P>0.2~S\\); Q=0 otherwise. Values for S are derived from a Curve Number (CN), which summarizes the land cover, soil type and condition: \\[CN=\\frac{1000}{10+S}\\], where \\(S\\), and subsequently \\(Q\\), are in inches. Equation (9.5) can be rearranged to a form similar to those for the Horton and Green-Ampt equations for cumulative infiltration, \\(F\\). \\[F~=~\\frac{\\left(P-0.2~S\\right)S}{P+0.8~S}\\]. 9.3 Evaporation Evaporation is simply the change of water from liquid to vapor state. Because it is difficult to separate evaporation from the soil from transpiration from vegetation, it is usually combined into Evapotranspiration, or ET; see Figure 9.3. Figure 9.3: Schematic of ET, from CIMIS ET can be estimated in a variety of ways, but it is important first to define three types of ET: - Potential ET, \\(ET_p\\) or \\(PET\\): essentially the same as the rate that water would evaporate from a free water surface. - Reference crop ET, \\(ET_{ref}\\) or \\(ET_0\\): the rate water evaporates from a well-watered reference crop, usually grass of a standard height. - Actual ET, \\(ET\\): this is the water used by a crop or other vegetation, usually calculated by adjusting the \\(ET_0\\) term by a crop coefficient that accounts for factors such as the plant height, growth stage, and soil exposure. Estimating \\(ET_0\\) can be as uncomplicated as using the Thornthwaite equation, which depends only on mean monthly temperatures, to the Penman-Monteith equation, which includes solar and longwave radiation, wind and humidity effects, and reference crop (grass) characteristics. Inclusion of more complexity, especially where observations can supply the needed input, produces more reliable estimates of \\(ET_0\\).One of the most common implementations of the Penman-Monteith equation is the version of the FAO (FAO Irrigation and drainage paper 56, or FAO56) (Allen & United Nations, 1998). Refer to FAO56 for step-by-step instructions on determining each term in the Penman-Monteith equation, Equation (9.6). \\[\\begin{equation} \\lambda~ET~=~\\frac{\\Delta\\left(R_n-G\\right)+\\rho_ac_p\\frac{\\left(e_s-e_a\\right)}{r_a}}{\\Delta+\\gamma\\left(1+\\frac{r_s}{r_a}\\right)} \\tag{9.6} \\end{equation}\\] Open water evaporation can be calculated using the original Penman equation (1948): \\[\\lambda~E_p~=~\\frac{\\Delta~R_n+\\gamma~E_a}{\\Delta~+~\\gamma}\\] where \\(R_n\\) is the net radiation available to evaporate water and \\(E_a\\) is a mass transfer function usually including humidity (or vapor pressure deficit) and wind speed. \\(\\lambda\\) is the latent heat of vaporization of water. A common implementation of the Penman equation is \\[\\begin{equation} \\lambda~E_p~=~\\frac{\\Delta~R_n+\\gamma~6.43\\left(1+0.536~U_2\\right)\\left(e_s-e\\right)}{\\Delta~+~\\gamma} \\tag{9.7} \\end{equation}\\] Here \\(E_p\\) is in mm/d, \\(\\Delta\\) and \\(\\gamma\\) are in \\(kPa~K^{-1}\\), \\(R_n\\) is in \\(MJ~m^{−2}~d^{−1}\\), \\(U_2\\) is in m/s, and \\(e_s\\) and \\(e\\) are in kPa. Variables are as defined in FAO56. Open water evaporation can also be calculated using a modified version of the Penman-Monteith equation (9.6). In this latter case, vegetation coefficients are not needed, so Equation (9.6) can be used with \\(r_s=0\\) and \\(r_a=251/(1+0.536~u_2)\\), following Thom & Oliver, 1977. The R package Evaporation has functions to calculate \\(ET_0\\) using this and many other functions. This is especially useful when calculating PET over many points or through a long time series. 9.4 Snow 9.4.1 Observations In mountainous areas a substantial portion of the precipitation may fall as snow, where it can be stored for months before melting and becoming runoff. Any hydrologic analysis in an area affected by snow must account for the dynamics of this natural reservoir and how it affects water supply. In the Western U.S., the most comprehensive observations of snow are part of the SNOTEL (SNOw TELemetry) network. Figure 9.4: The SNOTEL network. 9.4.2 Basic snowmelt theory and simple models For snow to melt, heat must be added to first bring the snowpack to the melting point; it takes about 2 kJ/kg to increase snowpack temperature 1\\(^\\circ\\)C. Additional heat is required for the phase change from ice to water (the latent heat of fusion), about 335 kJ/kg. Heat can be provided by absorbing solar radiation, longwave radiation, ground heat, warm air, warm rain falling on the snowpack or water vapor condensing on the snow. Once snow melts, it can percolate through the snowpack and be retained, similar to water retained by soil, and may re-freeze (releasing the latent heat of fusion, which can then cause more melt). As with any other hydrologic process, there are many ways it can be modeled, from simplified empirical relationships to complex physics-based representations. While accounting for all of the many processes involved would be a robust approach, often there are not adequate observations to support their use so simpler parameterization are used. Here only the simplest index-based snow model is discussed, as in Equation (9.8). \\[\\begin{equation} M~=~K_d\\left(T_a~-~T_b\\right) \\tag{9.8} \\end{equation}\\] M is the melt rate in mm/d (or in/day), \\(T_a\\) is air temperature (sometimes a daily mean, sometimes a daily maximum), \\(T_b\\) is a base temperature, usually 0\\(^\\circ\\)C (or 32\\(^\\circ\\)F), and \\(K_d\\) is a degree-day melt factor in mm/d/\\(^\\circ\\)C (or in/d/\\(^\\circ\\)F). The melt factor, \\(K_d\\), is highly dependent on local conditions and on the time of year (as an indicator of the snow pack condition); different \\(K_d\\) factors can be used for different months for example. Refreezing of melted snow, when temperatures are below \\(T_b\\), can also be estimated using an index model, such as Equation (9.9). \\[\\begin{equation} Fr~=~K_f\\left(T_b~-~T_a\\right) \\tag{9.9} \\end{equation}\\] Importantly, temperature-index snowmelt relations have been developed primarily for describing snowmelt at the end of season, after the peak of snow accumulation (typically April-May in the mountainous western U.S.), and their use during the snow accumulation season may overestimate melt. Different degree-day factors are often used, with the factors increasing later in the melt season. From a hydrologic perspective, the most important snow quality is the snow water equivalent (SWE), which is the depth of water obtained by melting the snow. An example of using a snowmelt index model follows. Example 9.1 Manually calibrate an index snowmelt model for a SNOTEL site using one year of data. Visit the SNOTEL to select a site. In this example site 1050, Horse Meadow, located in California, is used. Next download the data using the snotelr package (install the package first, if needed). sta <- "1050" snow_data <- snotelr::snotel_download(site_id = sta, internal = TRUE) Plot the data to assess the period available and how complete it is. plot(as.Date(snow_data$date), snow_data$snow_water_equivalent, type = "l", xlab = "Date", ylab = "SWE (mm)") Figure 9.5: Snow water equivalent at SNOTEL site 1050. Note the units are SI. If you download data directly from the SNOTEL web site the data would be in conventional US units. snotelr converts the data to SI units as it imports. The package includes a function snotel_metric that could be used to convert raw data downloaded from the SNOTEL website to SI units. For this exercise, extract a single (water) year, meaning from 1-Oct to 30-Sep, so an entire winter is in one year. In addition, create a data frame that only includes columns that are needed. snow_data_subset <- subset(snow_data, as.Date(date) > as.Date("2008-10-01") & as.Date(date) < as.Date("2009-09-30")) snow_data_sel <- subset(snow_data_subset, select=c("date", "snow_water_equivalent", "precipitation", "temperature_mean", "temperature_min", "temperature_max")) plot(as.Date(snow_data_sel$date),snow_data_sel$snow_water_equivalent, type = "l",xlab = "Date", ylab = "SWE (mm)") grid() Figure 9.6: Snow water equivalent at SNOTEL site 1050 for water year 2009. Now use a snow index model to simulate the SWE based on temperature and precipitation. The model used here is a modified version of that used in the hydromad package. The snow.sim command is used to run a snow index model; type ?hydromisc::snow.sim for details on its use. As a summary, the four main parameters you can adjust in the calibration of the model are: The maximum air temperature for snow, Tmax. Snow can fall at air temperatures above as high as about 3\\(^\\circ\\)C, but Tmax is usually lower. The minimum air temperature for rain, Tmin. Rain can fall when near surface air temperatures are below freezing. This may be as low as -1\\(^\\circ\\)C or maybe just a little lower, and as high as 1\\(^\\circ\\)C. Base temperature, Tmelt, the temperature at which melt begins. Usually the default of 0\\(^\\circ\\)C is used, but some adjustment (generally between -2 and 2\\(^\\circ\\)C) can be applied to improve model calibration. Snow Melt (Degree-Day) Factor, kd, which describes the melting of the snow when temperatures are above freezing. Be careful using values from different references as these are dependent on units. Typical values are between 1 and 5 mm/d/\\(^\\circ\\)C. Two additional parameters are optional; their effects are typically small. Degree-Day Factor for freezing, kf, of liquid water in the snow pack when temperatures are below freezing. By default it is set to 1\\(^\\circ\\)C/mm/day, and may vary from 0 to 2 \\(^\\circ\\)C/mm/day. Snow water retention factor, rcap. When snow melts some of it can be retained via capillarity in the snow pack. It can re-freeze or drain out. This is expressed as a fraction of the snow pack that is frozen. The default is 2.5% (rcap = 0.025). Start with some assumed values and run the snow model. Tmax_snow <- 3 Tmin_rain <- 2 kd <- 1 snow_estim <- hydromisc::snow.sim(DATA=snow_data_sel, Tmax=Tmax_snow, Tmin=Tmin_rain, kd=kd) Now the simulated values can be compared to the observations. If not installed already, install the hydroGOF package, which has some useful functions for evaluating how well modeled output fits observations. In the plot that follows we specify three measures of goodness-of-fit: Mean Absolute Error (MAE) Percent Bias (PBIAS) Root Mean Square Error divided by the Standard Deviation (RSR) These are discussed in detail in other references, but the aim is to calibrate (change the input parameters) until these values are low. obs <- snow_data_sel$snow_water_equivalent sim <- snow_estim$swe_simulated hydroGOF::ggof(sim, obs, na.rm = TRUE, dates=snow_data_sel$date, gofs=c("MAE", "RMSE", "PBIAS"), xlab = "", ylab="SWE, mm", tick.tstep="months", cex=c(0,0),lwd=c(2,2)) Figure 9.7: Simulated and Observed SWE at SNOTEL site 1050 for water year 2009. Melt is overestimated in the early part of the year and underestimated during the melt season, showing why a single index is not a very robust model. Applying two kd values, one for early to mid snow season and another for later snowmelt could improve the model, but it would make it less useful for using the model in other situations such as increased temperatures. 9.4.3 Snow model calibration While manual model calibration can improve the fit, a more complete calibration involves optimization methods that search the parameter space for the optimal combination of parameter values. A useful tool for doing that is the optim function, part of the stats package installed with base R. Using the optimization package requires establishing a function that should be minimized, where the parameters to be included in the optimization are the first argument. The optim function requires you to explicitly give ranges over which parameters can be varied, via the upper and lower arguments. An example of this follows, where the four main model parameters noted above are used, and the MAE is minimized. fcn_to_minimize <- function(par,datain, obs){ snow_estim <- hydromisc::snow.sim(DATA=datain, Tmax=par[1], Tmin=par[2], kd=par[3], Tmelt=par[4]) calib.stats <- hydroGOF::gof(snow_estim$swe_simulated,obs,na.rm=TRUE) objective_stat <- as.numeric(calib.stats['MAE',]) return(objective_stat) } opt_res <- optim(par=c(0.5,1,1,0),fn=fcn_to_minimize, lower=c(-1,-1,0.5,-2), upper=c(3,1,5,3), method="L-BFGS-B", datain=snow_data_sel, obs=obs) #print out optimal parameters - note Tmax and Tmin can be reversed during optimization cat(sprintf("Optimal parameters:\\nTmax=%.1f\\nTmin=%.1f\\nkd=%.2f\\nTmelt=%.1f\\n", max(opt_res$par[1],opt_res$par[2]),min(opt_res$par[1],opt_res$par[2]), opt_res$par[3],opt_res$par[4])) #> Optimal parameters: #> Tmax=1.0 #> Tmin=0.5 #> kd=1.05 #> Tmelt=-0.0 The results using the optimal parameters can be plotted to visualize the simulation. snow_estim_opt <- hydromisc::snow.sim(DATA=snow_data_sel, Tmax=max(opt_res$par[1],opt_res$par[2]), Tmin=min(opt_res$par[1],opt_res$par[2]), kd=opt_res$par[3], Tmelt=opt_res$par[4]) obs <- snow_data_sel$snow_water_equivalent sim <- snow_estim_opt$swe_simulated hydroGOF::ggof(sim, obs, na.rm = TRUE, dates=snow_data_sel$date, gofs=c("MAE", "RMSE", "PBIAS"), xlab = "", ylab="SWE, mm", tick.tstep="months", cex=c(0,0),lwd=c(2,2)) Figure 9.8: Optimal simulation of SWE at SNOTEL site 1050 for water year 2009. It is clear that a simple temperature index model cannot capture the snow dynamics at this location, especially during the winter when melt is significantly overestimated. 9.4.4 Estimating climate change impacts on snow Once a reasonable calibration is obtained, the effect of increasing temperatures on SWE can be simulated by including the deltaT argument in the hydromisc::snow.sim command. Here a 3\\(^\\circ\\)C uniform temperature increase is imposed on the optimal parameterization above. dT <- 3.0 snow_plus3 <- hydromisc::snow.sim(DATA=snow_data_sel, Tmax=max(opt_res$par[1],opt_res$par[2]), Tmin=min(opt_res$par[1],opt_res$par[2]), kd=opt_res$par[3], Tmelt=opt_res$par[4], deltaT = dT) simplusdT <- snow_plus3$swe_simulated # plot the results dTlegend <- expression("Simulated"*+3~degree*C) plot(as.Date(snow_data_sel$date),obs,type = "l",xlab = "", ylab = "SWE (mm)") lines(as.Date(snow_estim$date),sim,lty=2,col="blue") lines(as.Date(snow_estim$date),simplusdT,lty=3,col="red") legend("topright", legend = c("Observed", "Simulated",dTlegend), lty = c(1,2,3), col=c("black","blue","red")) grid() Figure 9.9: Observed SWE and simulated with observed meteorology and increased temperatures. 9.5 Watershed analysis Whether precipitation falls as rain or snow, how much is absorbed by plants, consumed by evapotranspiration, and what is left to become runoff, is all determined by watershed characteristics. This can include: Watershed area Slope of terrain Elevation variability (a hypsometric curve) Soil types Land cover Collecting this information begins with obtaining a digital elevation model for an area, identifying any key point or points on a stream (a watershed outlet), and then delineating the area that drains to that point. This process of watershed delineation is often done with GIS software like ArcGIS or QGIS. The R package WhiteboxTools provides capabilities for advanced terrain analysis in R. Demonstrations of the use of these tools for a watershed are in the online book Hydroinformatics at VT by JP Gannon. In particular, the chapters on mapping a stream network and delineating a watershed are excellent resources for exploring these capabilities in R. For watersheds in the U.S., watersheds, stream networks, and attributes of both can be obtained and viewed using nhdplusTools. Land cover and soil information can be obtained using the FedData package. "],["designing-for-floods-flood-hydrology.html", "Chapter 10 Designing for floods: flood hydrology 10.1 Engineering design requires probability and statistics 10.2 Estimating floods when you have peak flow observations - flood frequency analysis 10.3 Estimating floods from precipitation", " Chapter 10 Designing for floods: flood hydrology Figure 10.1: The international bridge between Fort Kent, Maine and Clair, New Brunswick during a flood (source: NOAA) Flood hydrology is generally the description of how frequently a flood of a certain level will be exceeded in a specified period. This was discussed briefly in the section on precipitation frequency, Section 8.2. The hydromisc package will need to be installed to access some of the data used below. If it is not installed, do so following the instructions on the github site for the package. 10.1 Engineering design requires probability and statistics Before diving into peak flow analysis, it helps to refresh your background in basic probability and statistics. Some excellent resources for this using R as the primary tool are: A very brief tutorial, by Prof. W.B. King A more thorough text, by Prof. G. Jay Kerns. This has a companion R package. An online flood hydrology reference based in R, by Prof. Helen Fairweather Rather than repeat what is in those references, a couple of short demonstrations here will show some of the skills needed for flood hydrology. The first example illustrates binomial probabilities, which are useful for events with only two possible outcomes (e.g., a flood happens or it doesn’t), where each outcome is independent and probabilities of each are constant. R functions for distributions use a first letter to designate what it returns: d is the density, p is the (cumulative) distribution, q is the quantile, r is a random sequence. In R the defaults for probabilities are to define them as \\(P[X~\\le~x]\\), or a probability of non-exceedance. Recall that a probability of exceedance is simply 1 - (probability of non-exceedance), or \\(P[X~\\gt~x] ~=~ 1-P[X~\\le~x]\\). In R, for quantiles or probabilities (using functions beginning with q or p like pnorm or qlnorm) setting the argument lower.tail to FALSE uses a probability of exceedance instead of non-exceedance. Example 10.1 A temporary dam is constructed while a repair is built. It will be in place 5 years and is designed to protect against floods up to a 20-year recurrence interval (i.e., there is a \\(p=\\frac{1}{20}=0.05\\), or 5% chance, that it will be exceeded in any one year). What is the probability of (a) no failure in the 5 year period, and (b) at least two failures in 5 years. # (a) ans1 <- dbinom(0, 5, 0.05) cat(sprintf("Probability of exactly zero occurrences in 5 years = %.4f %%",100*ans1)) #> Probability of exactly zero occurrences in 5 years = 77.3781 % # (b) ans2 <- 1 - pbinom(1,5,.05) # or pbinom(1,5,.05, lower.tail=FALSE) cat(sprintf("Probability of 2 or more failures in 5 years = %.2f %%",100*ans2)) #> Probability of 2 or more failures in 5 years = 2.26 % While the next example uses normally distributed data, most data in hydrology are better described by other distributions. Example 10.2 Annual average streamflows in some location are normally distributed with a mean annual flow of 20 m\\(^3\\)/s and a standard deviation of 6 m\\(^3\\)/s. Find (a) the probability of experiencing a year with less than (or equal to) 10 m\\(^3\\)/s, (b) greater than 32 m\\(^3\\)/s, and (c) the annual average flow that would be expected to be exceeded 10% of the time. # (a) ans1 <- pnorm(10, mean=20, sd=6) cat(sprintf("Probability of less than 10 = %.2f %%",100*ans1)) #> Probability of less than 10 = 4.78 % # (b) ans2 <- pnorm(32, mean=20, sd=6, lower.tail = FALSE) #or 1 - pnorm(32, mean=20, sd=6) cat(sprintf("Probability of greater than or equal to 30 = %.2f %%",100*ans2)) #> Probability of greater than or equal to 30 = 2.28 % # (c) ans3 <- qnorm(.1, mean=20, sd=6, lower.tail=FALSE) cat(sprintf("flow exceeded 10%% of the time = %.2f m^3/s",ans3)) #> flow exceeded 10% of the time = 27.69 m^3/s # plot to visualize answers x <- seq(0,40,0.1) y<- pnorm(x,mean=20,sd=6) xlbl <- expression(paste(Flow, ",", ~ m^"3"/s)) plot(x ,y ,type="l",lwd=2, xlab = xlbl, ylab= "Prob. of non-exceedance") abline(v=10,col="black", lwd=2, lty=2) abline(v=32,col="blue", lwd=2, lty=2) abline(h=0.9,col="green", lwd=2, lty=2) legend("bottomright",legend=c("(a)","(b)","(c)"),col=c("black","blue","green"), cex=0.8, lty=2) Figure 10.2: Illustration of three solutions. 10.2 Estimating floods when you have peak flow observations - flood frequency analysis For an area fortunate enough to have a long record (i.e., several decades or more) of observations, estimating flood risk is a matter of statistical data analysis. In the U.S., data, collected by the U.S. Geological Survey (USGS), can be accessed through the National Water Dashboard. Sometimes for discontinued stations it is easier to locate data through the older USGS map interface. For any site, data may be downloaded to a file, and the peakfq (watstore) format, designed to be imported into the PeakFQ software, is easy to work with in R. 10.2.1 Installing helpful packages The USGS has developed many R packages, including one for retrieval of data, dataRetrieval. Since this resides on CRAN, the package can be installed with (the use of ‘!requireNamespace’ skips the installation if it already is installed): if (!requireNamespace("dataRetrieval", quietly = TRUE)) install.packages("dataRetrieval") Other USGS packages that are very helpful for peak flow analysis are not on CRAN, but rather housed in a USGS repository. The easiest way to install packages from that archive is using the install.load package. Then the install_load command will first search the standard CRAN archive for the package, and if it is not found there the USGS archive is searched. Packages are also loaded (equivalent to using the library command). install_load also installs dependencies of packages, so here installing smwrGraphs also installs smwrBase. The prefix smwr refers to their use in support of the excellent reference Statistical Methods in Water Resources. if (!requireNamespace("install.load", quietly = TRUE)) install.packages("install.load") install.load::install_load("smwrGraphs") #this command also installs smwrBase Lastly, the lmomco package has extensive capabilities to work with many forms of probability distributions, and has functions for calculating distribution parameters (like skew) that we will use. if (!requireNamespace("lmomco", quietly = TRUE)) install.packages("lmomco") 10.2.2 Download, manipulate, and plot the data for a site Using the older USGS site mapper, and specifying that inactive stations should also be included, many stations in the south Bay Area in California are shown in Figure 10.3. Figure 10.3: Active and Inactive USGS sites recording peak flows. While the data could be downloaded and saved locally through that link, it is convenient here to use the dataRetrieval command. Qpeak_download <- dataRetrieval::readNWISpeak(siteNumbers="11169000") The data used here are also available as part of the hydromisc package, and may be obtained by typing hydromisc::Qpeak_download. It is always helpful to look at the downloaded data frame before doing anything with it. There are many columns that are not needed or that have repeated information. There are also some rows that have no data (‘NA’ values). It is also useful to change some column names to something more intuitive. We will need to define the water year (a water year begins October 1 and ends September 30). Qpeak <- Qpeak_download[!is.na(Qpeak_download$peak_dt),c('peak_dt','peak_va')] colnames(Qpeak)[colnames(Qpeak)=="peak_dt"] <- "Date" colnames(Qpeak)[colnames(Qpeak)=="peak_va"] <- "Peak" Qpeak$wy <- smwrBase::waterYear(Qpeak$Date) The data have now been simplified so that can be used more easily in the subsequent flood frequency analysis. Data should always be plotted, which can be done many ways. As a demonstration of highlighting specific years in a barplot, the strongest El Niño years (in 1930-2002) from NOAA Physical Sciences Lab can be highlighted in red. xlbl <- "Water Year" ylbl <- expression("Peak Flow, " ~ ft^{3}/s) nino_years <- c(1983,1998,1992,1931,1973,1987,1941,1958,1966, 1995) cols <- c("blue", "red")[(Qpeak$wy %in% nino_years) + 1] barplot(Qpeak$Peak, names.arg = Qpeak$wy, xlab = xlbl, ylab=ylbl, col=cols) Figure 10.4: Annual peak flows for USGS gauge 11169000, highlighting strong El Niño years in red. 10.2.3 Flood frequency analysis The general formula used for flood frequency analysis is Equation (10.1). \\[\\begin{equation} y=\\overline{y}+Ks_y \\tag{10.1} \\end{equation}\\] where y is the flow at the designated return period, \\(\\overline{y}\\) is the mean of all \\(y\\) values and \\(s_y\\) is the standard deviation. In most instances, \\(y\\) is a log-transformed flow; in the US a base-10 logarithm is generally used. \\(K\\) is a frequency factor, which is a function of the return period, the parent distribution, and often the skew of the y values. The guidance of the USGS (as in Guidelines for Determining Flood Flow Frequency, Bulletin 17C) (England, J.F. et al., 2019) is to use the log-Pearson Type III (LP-III) distribution for flood frequency data, though in different settings other distributions can perform comparably. For using the LP-III distribution, we will need several statistical properties of the data: mean, standard deviation, and skew, all of the log-transformed data, calculated as follows. mn <- mean(log10(Qpeak$Peak)) std <- sd(log10(Qpeak$Peak)) g <- lmomco::pmoms(log10(Qpeak$Peak))$skew With those calculated, a defined return period can be chosen and the flood frequency factors, from Equation (10.1), calculated for that return period (the example here is for a 50-year return period). The qnorm function from base R and the qpearsonIII function from the smwrBase package make this straightforward, and K values for Equation (10.1) are obtained for a lognormal, Knorm, and LP-III, Klp3. RP <- 50 Knorm <- qnorm(1 - 1/RP) Klp3 <- smwrBase::qpearsonIII(1-1/RP, skew = g) Now the flood frequency equation (10.1) can be applied to calculate the flows associated with the 50-year return period for each of the distributions. Remember to take the anti-log of your answer to return to standard units. Qpk_LN <- mn + Knorm * std Qpk_LP3 <- mn + Klp3 * std sprintf("RP = %d years, Qpeak LN = %.0f cfs, Qpeak LP3 = %.0f",RP,10^Qpk_LN,10^Qpk_LP3) #> [1] "RP = 50 years, Qpeak LN = 18362 cfs, Qpeak LP3 = 12396" 10.2.4 Creating a flood frequency plot Different probability distributions can produce very different results for a design flood flow. Plotting the historical observations along with the distributions, the lognormal and LP-III in this case, can help explain why they differ, and provide indications of which fits the data better. We cannot say exactly what the probability of seeing any observed flood exceeded would be. However, given a long record, the probability can be described using the general “plotting position” equation from Bulletin 17C, as in Equation (10.2). \\[\\begin{equation} p_i=\\frac{i-a}{n+1-2a} \\tag{10.2} \\end{equation}\\] where n is the total number of data points (annual peak flows in this case), \\(p_i\\) is the exceedance probability of flood observation i, where flows are ranked in descending order (so the largest observed flood has \\(i=1\\) ad the smallest is \\(i=n\\)). The parameter a is between 0 and 0.5. For simplicity, the following will use \\(a=0\\), so the plotting Equation (10.2) becomes the Weibull formula, Equation (10.3). \\[\\begin{equation} p_i=\\frac{i}{n+1} \\tag{10.3} \\end{equation}\\] While not necessary, to add probabilities to the annual flow sequence we will create a new data frame consisting of the observed peak flows, sorted in descending order. df_pp <- as.data.frame(list('Obs_peak'=sort(Qpeak$Peak,decreasing = TRUE))) This can be done with fewer commands, but here is an example where first a rank column is created (1=highest peak in the record of N years), followed by adding columns for the exceedance and non-exceedence probabilities: df_pp$rank <- as.integer(seq(1:length(df_pp$Obs_peak))) df_pp$exc_prob <- (df_pp$rank/(1+length(df_pp$Obs_peak))) df_pp$ne_prob <- 1-df_pp$exc_prob For each of the non-exceedance probabilities calculated for the observed peak flows, use the flood frequency equation (10.1) to estimate the peak flow that would be predicted by a lognormal or LP-III distribution. This is the same thing that was done above for a specified return period, but now it will be “applied” to an entire column. df_pp$LN_peak <- mapply(function(x) {10^(mn+std*qnorm(x))}, df_pp$ne_prob) df_pp$LP3_peak <- mapply(function(x) {10^(mn+std*smwrBase::qpearsonIII(x, skew=g))},df_pp$ne_prob) There are many packages that create probability plots (see, for example, the versatile scales package for ggplot2). For this example the USGS smwrGraphs package is used. First, for aesthetics, create x- and y- axis labels. ylbl <- expression("Peak Flow, " ~ ft^{3}/s) xlbl <- "Non-exceedence Probability" The smwrGraphs package works most easily if it writes output directly to a file, a PNG file in this case, using the setPNG command; the file name and its dimensions in inches are given as arguments, and the PNG device is opened for writing. This is followed by commands to plot the data on a graph. Technically, the data are plotted to an object here is called prob.pl. The probPlot command plots the observed peaks as points, where the alpha argument is the a in Equation (10.2). Additional points or lines are added with the addXY command, used here to add the LN and LP3 data as lines (one solid, one dashed). Finally, a legend is added (the USGS refers to that as an “Explanation”), and the output PNG file is closed with the dev.off() command. smwrGraphs::setPNG("probplot_smwr.png",6.5, 3.5) #> width height #> 6.5 3.5 #> [1] "Setting up markdown graphics device: probplot_smwr.png" prob.pl <- smwrGraphs::probPlot(df_pp$Obs_peak, alpha = 0.0, Plot=list(what="points",size=0.05,name="Obs"), xtitle=xlbl, ytitle=ylbl) prob.pl <- smwrGraphs::addXY(df_pp$ne_prob,df_pp$LN_peak,Plot=list(what="lines",name="LN"),current=prob.pl) prob.pl <- smwrGraphs::addXY(df_pp$ne_prob,df_pp$LP3_peak,Plot=list(what="lines",type="dashed",name="LP3"),current=prob.pl) smwrGraphs::addExplanation(prob.pl,"ul",title="") dev.off() #> png #> 2 The output won’t be immediately visible in RStudio – navigate to the file and click on it to view it. Figure 10.5 shows the output from the above commands. Figure 10.5: Probability plot for USGS gauge 11169000 for years 1930-2002. 10.2.5 Other software for peak flow analysis Much of the analysis above can be achieved using the PeakFQ software developed by the USGS. It incorporates the methods in Bulletin 17C via a graphical interface and can import data in the watstore format as discussed above in Section 10.2. The USGS has also produced the MGBT R package to perform many of the statistical calculations involved in the Bulletin 17C procedures. 10.3 Estimating floods from precipitation When extensive streamflow data are not available, flood risk can be estimated from precipitation and the characteristics of the area contributing flow to a point. While not covered here (or not yet…), there has been extensive development of hydrological modeling using R, summarized in recent papers (Astagneau et al., 2021; Slater et al., 2019). Straightforward application of methods to estimate peak flows or hydrographs resulting from design storms can by writing code to apply the Rational Formula (included in the VFS and hydRopUrban packages, for example) or the NRCS peak flow method. For more sophisticated analysis of water supply and drought, continuous modeling is required. A very good introduction to hydrological modeling in R, including model calibration and assessment, is included in the Hydroinformatics at VT reference by JP Gannon. "],["sustainability-in-design-planning-for-change.html", "Chapter 11 Sustainability in design: planning for change 11.1 Perturbing a system 11.2 Detecting changes in hydrologic data 11.3 Detecting changes in extreme events", " Chapter 11 Sustainability in design: planning for change Figure 11.1: Yearly surface temperature compared to the 20th-century average from 1880–2022, from Climate.gov All systems engineered to last more than a decade or two, so everything civil engineers work on, will need to be designed to be resilient to dramatic environmental changes. As societies respond to the impacts of a disrupted climate, demands for water, energy, housing, food, and other essential services will change. This will result in economic disruption as well. This chapter presents a few ways long-term sustainability can be considered, looking at sensitivity of systems, detection of shifts or trends, and how economic and management may respond. This is much more brief than this rich topic deserves. The hydromisc package will need to be installed to access some of the data used below. If it is not installed, do so following the instructions on the github site for the package. 11.1 Perturbing a system when a system is perturbed it can respond in many ways. A useful classification of these was developed by (Marshall & Toffel, 2005). Figure 11.2 is an adaptation of Figure 2 from that paper. Figure 11.2: Pathways of recovery or degradation a system may take after initial perturbation. In essence, after a system is degraded, it can eventually rebound to its original condition (Type 1), rebound to some other state that is degraded from its original (Types 2 and 3), or completely collapse (Type 4). Which path it taken depends on the degree of the initial disruption and the ability of the system to recover. While originally cast with time as the x-axis, Figure 11.2 is equally applicable when looking at a system that travels over a distance, such as a flowing river. The form of the curves in Figure 11.2 appear similar to a classic dissolved oxygen sag curve, as in Figure 11.3. Figure 11.3: Dissolved oxygen levels in a steam following an input of waste (source: EPA). The Streeter-Phelps equation describes the response of the dissolved oxygen (DO) levels in a water body to a perturbation, such as the discharge of wastewater with a high oxygen demand. Some important assumptions are that steady-state conditions exist, and the flow moves as plug flow, progressing downstream along a one-dimensional path. Following is Streeter-Phelps Equation (11.1). \\[\\begin{equation} D=C_s-C=\\frac{K_1^\\prime L_0}{K_2^\\prime - K_1^\\prime}\\left(e^{-K_1^\\prime t}-e^{-K_2^\\prime t}\\right)+D_0e^{-K_2^\\prime t} \\tag{11.1} \\end{equation}\\] where \\(D\\) is the DO deficit, \\(C_s\\) is the saturation DO concentration, \\(C\\) is the DO concentration, \\(D_0\\) is the initial DO deficit, \\(L_0\\) is the ultimate (first-stage) BOD at the discharge, calculated by Equation (11.2). \\[\\begin{equation} L_0=\\frac{BOD_5}{1-e^{-K_1^\\prime t}} \\tag{11.2} \\end{equation}\\] \\(K_1^\\prime\\) and \\(K_2^\\prime\\) are the deoxygenation and reaeration coefficients, both adjusted for temperature. Usually the coefficients \\(K_1\\) and \\(K_2\\) are defined at 20\\(^\\circ\\)C, and then adjusted by empirical relationships for the actual water temperature using Equation (11.3). \\[\\begin{equation} K^\\prime = K\\theta ^{T-20} \\tag{11.3} \\end{equation}\\] where \\(\\theta\\) is set to typical values of 1.135 for \\(K_1\\) for \\(T\\le20^\\circ C\\) (and 1.056 otherwise) and 1.024 for \\(K_2\\). As a demonstration, functions (only available for SI units) in the hydromisc package can be used to explore the recovery of an aquatic system from a perturbation, as in Example 11.1. Example 11.1 A river with a flow of 7 \\(m^3/s\\) and a velocity of 1.4 m/s has effluent discharged into it at a rate of 1.5 \\(m^3/s\\). The river upstream of the discharge has a temperature of 15\\(^\\circ\\)C, a \\(BOD_5\\) of 1 mg/L, and a dissolved oxygen saturation of 90 percent. The effluent is 21\\(^\\circ\\)C with a \\(BOD_5\\) of 180 mg/L and a dissolved oxygen saturation of 0 percent. The deoxygenation rate constant (at 20\\(^\\circ\\)C) is 0.4 \\(d^{-1}\\), and the reaeration rate constant is 0.8 \\(d^{-1}\\). Create a plot of DO as a percent of saturation (y-axis) vs. distance in km (x-axis). First set up the model parameters. Q <- 7 # flow of stream, m3/s V <- 1.4 # velocity of stream, m/s Qeff <- 1.5 # flow rate of effluent, m3/s DOsatupstr <- 90 # DO saturation upstream of effluent discharge, % DOsateff <- 0 # DO saturation of effluent discharge, % Triv <- 15 # temperature of receiving water, C Teff <- 21 # temperature of effluent, C BOD5riv <- 1 # 5-day BOD of receiving water, mg/L BOD5eff <- 180 # 5-day BOD of effluent, mg/L K1 <- 0.4 # deoxygenation rate constant at 20C, 1/day K2 <- 0.8 # reaeration rate constant at 20C, 1/day Calculate some of the variables needed for the Streeter-Phelps model. Type ?hydromisc::DO_functions for more information on the DO-related functions in the hydromisc package. Tmix <- hydromisc::Mixture(Q, Triv, Qeff, Teff) K1adj <- hydromisc::Kadj_deox(K1=K1, T=Tmix) K2adj <- hydromisc::Kadj_reox(K2=K2, T=Tmix) BOD5mix <- hydromisc::Mixture(Q, BOD5riv, Qeff, BOD5eff) L0 <- BOD5mix/(1-exp(-K1adj*5)) #BOD5 - ultimate Find the dissolved oxygen for 100 percent saturation (assuming no salinity) and the initial DO deficit at the point of discharge. Cs <- hydromisc::O2sat(Tmix) #DO saturation, mg/l C0 <- hydromisc::Mixture(Q, DOsatupstr/100.*Cs, Qeff, DOsateff/100.*Cs) #DO init, mg/l D0 <- Cs - C0 #initial deficit Determine a set of distances where the DO deficit will be calculated, and the corresponding times for the flow to travel that distance. xs <- seq(from=0.5, to=800, by=5) ts <- xs*1000/(V*86400) Finally, calculate the DO (as a percent of saturation) and plot the results. DO_def <- hydromisc::DOdeficit(t=ts, K1=K1adj, K2=K2adj, L0=L0, D0=D0) DO_mgl <- Cs - DO_def DO_pct <- 100*DO_mgl/Cs plot(xs,DO_pct,xlim=c(0,800),ylim=c(0,100),type="l",xlab="Distance, km",ylab="DO, %") grid() Figure 11.4: Dissolved oxygen for this example. For this example, the saturation DO concentration is 9.9 mg/L, meaning the minimum value of the curve corresponds to about 4 mg/L. The EPA notes that values this low are below that recommended for the protection of aquatic life in freshwater. This shows that while the ecosystem has not collapsed, (i.e., following a Type 4 curve in Figure 11.2), effective ecosystem functions may be lost. 11.2 Detecting changes in hydrologic data Planning for decades or more requires the ability to determine whether changes are occurring or have already occurred. Two types of changes will be considered here: step changes, caused by an abrupt change such as deforestation or a new pollutant source, and monotonic (either always increasing or decreasing) trends, caused by more gradual shifts. these are illustrated in Figures 11.5 and 11.6. Figure 11.5: A shift in phosphorus concentrations (source: USGS Scientific Investigations Report 2017-5006, App. 4, https://doi.org/10.3133/sir20175006). Figure 11.6: A trend in annual peak streamflow. (source: USGS Professional Paper 1869, https://doi.org/10.3133/pp1869). Before performing calculations related to trend significance, refer to Chapter 4 of Statistical Methods in Water Resources (Helsel, D.R. et al., 2020) to review the relationship between hypothesis testing and statistical significance. Figure 11.7 from that reference illustrates this. Figure 11.7: Four possible results of hypothesis testing. (source: Helsel et al., 2020). In the context of the example that follows, the null hypothesis, H0, is usually a statement that no trend exists. The \\(\\alpha\\)-value (the significance level) is the probability of incorrectly rejecting the null hypothesis, that is rejecting H0 when it is in fact true. The significance level that is acceptable is a decision that must be made – a common value of \\(\\alpha\\)=0.05 (5 percent) significance, also referred to as \\(1-\\alpha=0.95\\) (95 percent) confidence. A statistical test will produce a p-value, which is essentially the likelihood that the null hypothesis is true, or more technically, the probability of obtaining the calculated test statistic (on one more extreme) when the null hypothesis is true. Again, in the context of trend detection, small p-values (less than \\(\\alpha\\)) indicate greater confidence for rejecting the null hypothesis and thus supporting the existence of a “statistically significant” trend. One of the most robust impacts of a warming climate is the impact on snow. In California, historically the peak of snow accumulation tended to occur roughly on April 1 on average. To demonstrate methods for detecting changes data from the Four Trees Cooperative Snow Sensor site in California, obtained from the USDA National Water and Climate Center. These data are available as part of the hydromisc package. swe <- hydromisc::four_trees_swe plot(swe$Year, swe$April_1_SWE_in, xlab="Year", ylab="April 1 Snow Water Equivalent, in") lines(zoo::rollmean(swe, k=5), col="blue", lty=3, cex=1.4) Figure 11.8: April 1 snow water equivalent at Four Trees station, CA. The dashed line is a 5-year moving average. A plot is always useful – here a 5-year moving average, or rolling mean, is added (using the zoo package), to make any trends more observable. 11.2.1 Detecting a step change When there is a step change in a record, you need to test that the difference between the “before” and “after” conditions is large enough relative to natural variability that is can be confidently described as a change. In other words, whether the change is significant must be determined. This is done by breaking the data into two-samples and applying a statistical test, such as a t-test or the nonparametric rank-sum (or Mann-Whitney U) test. While for this example there is no obvious reason to break this data at any particular year, we’ll just look at the first and second halves. Separate the two subsets of years into two arrays of (y) values (not data frames in this case) and then create a boxplot of the two periods. yvalues1 <- swe$April_1_SWE_in[(swe$Year >= 1980) & (swe$Year <= 2001)] yvalues2 <- swe$April_1_SWE_in[(swe$Year >= 2002) & (swe$Year <= 2023)] boxplot(yvalues1,yvalues2,names=c("1980-2001","2002-2023"),boxwex=0.2,ylab="swe, in") Figure 11.9: Comparison of two records of SWE at Four Trees station, CA. Calculate the means and medians of the two periods, just for illustration. mean(yvalues1) #> [1] 19.76364 mean(yvalues2) #> [1] 15.44545 median(yvalues1) #> [1] 17.8 median(yvalues2) #> [1] 7.9 The mean for the later period is lower, as is the median. the question to pose is whether these differences are statistically significant. The following tests allow that determination. 11.2.1.1 Method 1: Using a t-test. A t-test determines the significance of a difference in the mean between two samples under a number of assumptions. These include independence of each data point (in this example, that any year’s April 1 SWE is uncorrelated with prior years) and that the data are normally distributed. This is performed with the t.test function. The alternative argument is included that the test is “two sided”; a one-sided test would test for one group being only greater than or less than the other, but here we only want to test whether they are different. The paired argument is set to FALSE since there is no correspondence between the order of values in each subset of years. t.test(yvalues1, yvalues2, var.equal = FALSE, alternative = "two.sided", paired = FALSE) #> #> Welch Two Sample t-test #> #> data: yvalues1 and yvalues2 #> t = 0.91863, df = 40.084, p-value = 0.3638 #> alternative hypothesis: true difference in means is not equal to 0 #> 95 percent confidence interval: #> -5.181634 13.817998 #> sample estimates: #> mean of x mean of y #> 19.76364 15.44545 Here the p-value is 0.36, a value much greater than \\(\\alpha = 0.05\\), so the null hypothesis cannot be rejected. The difference in the means is not significant based on this test. 11.2.1.2 Method 2: Wilcoxon rank-sum (or Mann-Whitney U) test. Like the t-test, the rank-sum test produces a p-value, but it measures a more generic measure of “central tendency” (such as a median) rather than a mean. Assumptions about independence of data are still necessary, but there is no requirement of normality of the distribution of data. It is less affected by outliers or a few extreme values than the t-test. This is performed with a standard R function. Other arguments are set as with the t-test. wilcox.test(yvalues1, yvalues2, alternative = "two.sided", paired=FALSE) #> Warning in wilcox.test.default(yvalues1, yvalues2, alternative = "two.sided", : #> cannot compute exact p-value with ties #> #> Wilcoxon rank sum test with continuity correction #> #> data: yvalues1 and yvalues2 #> W = 297, p-value = 0.1999 #> alternative hypothesis: true location shift is not equal to 0 The p-value is much lower than with the t-test, showing less influence of the two very high SWE values in the second half of the record. 11.2.2 Detecting a monotonic trend In a similar way to the step change, a monotonic trend can be tested using parameteric or non-parametric methods. Here we use the entire record to detect trends over the entire period. Linear regression may be used as a parametric method, which makes assumptions similar to the t-test (that residuals of the data are normally distributed). If the data do not conform to a normal distribution, the Mann-Kendall test can be applied, which is a non-parametric test. 11.2.2.1 Method 1: Regression To perform a linear regression in R, build a linear regression model (lm). This can take the swe data frame as input data, specifying the columns to relate linearly. m <- lm(April_1_SWE_in ~ Year, data = swe) summary(m)$coefficients #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 347.3231078 370.6915171 0.9369600 0.3541359 #> Year -0.1647357 0.1852031 -0.8894868 0.3788081 The row for “Year” provides the data on the slope. The slope shows SWE declines by 0.16 inches/year based on regression. The p-value for the slope is 0.379, much larger than the typical \\(\\alpha\\), meaning we cannot claim that a significant slope exists based on this test. So while a declining April 1 snowpack is observed at this location, it is not outside of the natural variability of the data based on a regression analysis. 11.2.2.2 Method 2: Mann-Kendall To conduct a Mann-Kendall trend test, additional packages need to be installed. There are a number available; what is shown below is one method. A non-parametric trend test (and plot) requires a few extra packages, which are installed like this: if (!require('Kendall', quietly = TRUE)) install.packages('Kendall') #> Warning: package 'Kendall' was built under R version 4.2.3 if (!require('zyp', quietly = TRUE)) install.packages('zyp') #> Warning: package 'zyp' was built under R version 4.2.3 Now the significance of the trend can be calculated. The slope associated with this test, the “Thiel-Sen slope”, is calculated using the zyp package. mk <- Kendall::MannKendall(swe$April_1_SWE_in) summary(mk) #> Score = -99 , Var(Score) = 9729 #> denominator = 934.4292 #> tau = -0.106, 2-sided pvalue =0.32044 ss <- zyp::zyp.sen(April_1_SWE_in ~ Year, data=swe) ss$coefficients #> Intercept Year #> 291.1637542 -0.1385452 The non-parametric slope shows April 1 SWE declining by 0.14 inches per year over the period. Again, however, the p-value is greater than the typical \\(\\alpha\\), so based on this method the trend is not significantly different from zero. As with the tests for a step change, the p-value is lower for the nonparametric test. A summary plot of the slopes of both methods is helpful. plot(swe$Year,swe$April_1_SWE_in, xlab = "Year",ylab = "Snow water equivalent, in") lines(swe$Year,m$fitted.values, lty=1, col="black") abline(a = ss$coefficients["Intercept"], b = ss$coefficients["Year"], col="red", lty=2) legend("topright", legend=c("Observations","Regression","Thiel-Sen"), col=c("black","black","red"),lty = c(NA,1,2), pch = c(1,NA,NA), cex=0.8) Figure 11.10: Trends of SWE at Four Trees station, CA. 11.2.3 Choosing whether to use parametric or non-parametric tests Using the parameteric tests above (t-test, regression) requires making an assumption about the underlying distribution of the data, which non-parametric tests do not require. When using a parametric test, the assumption of normality can be tested. For example, for the regression residuals can be tested with the following, where the null hypothesis is that the data are nomally distributed. shapiro.test(m$residuals)$p.value #> [1] 0.003647395 This produces a very small p-value (p < 0.01), meaning the null hypothesis that the residuals are normally distributed is rejected with >99% confidence. This means non-parametric test is more appropriate. In general, non-parametric tests are preferred in hydrologic work because data (and residuals) are rarely normally distributed. 11.3 Detecting changes in extreme events when looking at extreme events like the 100-year high tide, the methods are similar to those used in flood frequency analysis. One distinction is that flood frequency often uses a Gumbel or Log-Pearson type 3 distribution. For sea-level rise (and many other extreme events) other distributions are employed, with one common one being the Generalized Extreme Value (GEV), the cumulative distribution of which is described by Equation (11.4). \\[\\begin{equation} F\\left(x;\\mu,\\sigma,\\xi\\right)=exp\\left[-\\left(1+\\xi\\left(\\frac{x-\\mu}{\\sigma}\\right)\\right)^{-1/\\xi}\\right] \\tag{11.4} \\end{equation}\\] The three parameters \\(\\xi\\), \\(\\mu\\), and \\(\\sigma\\) represent a shape, location, and scale of the distribution function. These distribution parameter can be determined using observations of extremes over a long period or over different periods of record, much as the mean, standard, deviation, and skew are used in flood frequency calculations. The distribution can then be used to estimate the probability associated with a specific magnitude event, or conversely the event magnitude associated with a defined risk level. An excellent example of that is from Tebaldi et al. (2012) who analyzed projected extreme sea level changes through the 21st century. Figure 11.11: Projected return periods by 2050 for floods that are 100 yr events during 1959–2008, Tebaldi et al., 2012 An example using the GEV with sea level data is illustrated below. The Tebaldi et al. (2012) paper uses the R package extRemes, which we will use here. The same package has been used to study extreme wind, precipitation, temperature, streamflow, and other events, so it is a very versatile and widely-used package. Install the package if it is not already installed. if (!require('extRemes', quietly = TRUE)) install.packages('extRemes') #> Warning: package 'extRemes' was built under R version 4.2.3 #> Warning: package 'Lmoments' was built under R version 4.2.2 11.3.1 Obtaining and preparing sea-level data Sea-level data can be downloaded directly into R using the rnoaa package. However, NOAA also has a very intuitive interface that allows geographical searching and preliminary viewing of data. From the NOAA Tines & Currents site one can search an area of interest and find a tide gauge with a long record. Figure 11.12. Figure 11.12: Identification of a sea-level gauge on the NOAA Tides & Currents site. By exploring the data inventory for this station, on its home page, the gauge has a very long record, being established in 1854, with measurement of extremes for over a century. Avoid selecting a partial month, or you may not have the ability to download monthly data. Monthly data were downloaded and saved as a csv file, which is available with the hydromisc package. datafile <- system.file("extdata", "sealevel_9414290_wl.csv", package="hydromisc") dat <- read.csv(datafile,header=TRUE) These data were saved in metric units, so all levels are in meters above the selected tidal datum. there are dates indicating the month associated with each value (and day 1 is in there as a placeholder). If there are any missing data they may be labeled as “NaN”. If you see that, a clean way to address it is to first change the missing data to NA (which R recognizes) with a command such as dat[dat == "NaN"] <- NA For this example we are looking at extreme tide levels, so only retain the “Highest” and “Date” columns. peak_sl <- subset(dat, select=c("Date", "Highest")) A final data preparation is to create an annual time series with the the maximum tide level in any year. One way to facilitate this is to add a column of “year.” Then the data can be aggregated by year, creating a new data frame, taking the maximum value for each year (many other functions, like mean, median, etc. can also be used). In this example the column names are changed to make it easier to work with the data. Also, the year column is converted to an integer for plotting purposes. Any rows with NA values are removed. peak_sl$year <- as.integer(strftime(peak_sl$Date, "%Y")) peak_sl_ann <- aggregate(peak_sl$Highest,by=list(peak_sl$year),FUN=max, na.rm=TRUE) colnames(peak_sl_ann) <- c("year","peak_m") peak_sl_ann <- na.exclude(peak_sl_ann) A plot is always helpful. plot(peak_sl_ann$year,peak_sl_ann$peak_m,xlab="Year",ylab="Annual Peak Sea Level, m") Figure 11.13: Annual highest sea-levels relative to MLLW at gauge 9414290. 11.3.2 Conducting the extreme event analysis The question we will attempt to address is whether the 100-year peak tide level (the level exceeded with a 1 percent probability) has increased between the 1900-1930 and 1990-2020 periods. Extract a subset of the data for one period and fit a GEV distribution to the values. peak_sl_sub1 <- subset(peak_sl_ann, year >= 1900 & year <= 1930) gevfit1 <- extRemes::fevd(peak_sl_sub1$peak_m) gevfit1$results$par #> location scale shape #> 2.0747606 0.1004844 -0.2480902 A plot of return periods for the fit distribution is available as well. extRemes::plot.fevd(gevfit1, type="rl") Figure 11.14: Return periods based on the fit GEV distribution for 1900-1930. Points are observations; dashed lines enclose the 95% confidence interval. As is usually the case, a statistical model does well in the area with observations, but the uncertainty increases for extreme values (like estimating a 500-year event from a 30-year record). A longer record produces better (less uncertain) estimates at higher return periods. Based on the GEV fit, the 100-year recurrence interval extreme tide is determined using: extRemes::return.level(gevfit1, return.period = 100, do.ci = TRUE, verbose = TRUE) #> #> Preparing to calculate 95 % CI for 100-year return level #> #> Model is fixed #> #> Using Normal Approximation Method. #> extRemes::fevd(x = peak_sl_sub1$peak_m) #> #> [1] "Normal Approx." #> #> [1] "100-year return level: 2.35" #> #> [1] "95% Confidence Interval: (2.2579, 2.4429)" A check can be done using the reverse calculation, estimating the return period associated with a specified value of highest water level. This can be done by extracting the three GEV parameters, then running the pevd command. loc <- gevfit1$results$par[["location"]] sca <- gevfit1$results$par[["scale"]] shp <- gevfit1$results$par[["shape"]] extRemes::pevd(2.35, loc = loc, scale = sca , shape = shp, type = c("GEV")) #> [1] 0.9898699 This returns a value of 0.99 (this is the CDF value, or the probability of non-exceedence, F). Recalling that return period, \\(T=1/P=1/(1-F)\\), where P=prob. of exceedence; F=prob. of non-exceedence, the result that 2.35 meters is the 100-year highest water level is validated. Repeating the calculation for a more recent period: peak_sl_sub2 <- subset(peak_sl_ann, year >= 1990 & year <= 2020) gevfit2 <- extRemes::fevd(peak_sl_sub2$peak_m) extRemes::return.level(gevfit2, return.period = 100, do.ci = TRUE, verbose = TRUE) #> #> Preparing to calculate 95 % CI for 100-year return level #> #> Model is fixed #> #> Using Normal Approximation Method. #> extRemes::fevd(x = peak_sl_sub2$peak_m) #> #> [1] "Normal Approx." #> #> [1] "100-year return level: 2.597" #> #> [1] "95% Confidence Interval: (2.3983, 2.7957)" This returns a 100-year high tide of 2.6 m for 1990-2020, a 10.6 % increase over 1900-1930. Another way to look at this is to find out how the frequency of the past (in this case, 1900-1930) 100-year event has changed with rising sea levels. Repeating the calculations from before to capture the GEV parameters for the later period, and then plugging in the 100-year high tide from the early period: loc2 <- gevfit2$results$par[["location"]] sca2 <- gevfit2$results$par[["scale"]] shp2 <- gevfit2$results$par[["shape"]] extRemes::pevd(2.35, loc = loc2, scale = sca2 , shape = shp2, type = c("GEV")) #> [1] 0.7220968 This returns a value of 0.72 (72% non-exceedence, or 28% exceedance, in other words we expect to see an annual high tide of 2.35 m or higher in 28% of the years). The return period of this is calculated as above: T = 1/(1-0.72) = 3.6 years. So, what was the 100-year event in 1900-1930 is about a 4-year event now. "],["management-of-water-resources-systems.html", "Chapter 12 Management of water resources systems 12.1 A simple linear system with two decision variables 12.2 More complex linear programming: reservoir operation 12.3 More Realistic Reservoir Operation: non-linear programming", " Chapter 12 Management of water resources systems Figure 12.1: Lookout Point Dam on the Middle Fork Willamette River source: U.S. Army Corps of Engineers Water resources systems tend to provide a variety of benefits, such as flood control, hydroelectric power, recreation, navigation, and irrigation. Each of these provides a benefit that can quantified, and there are also associated costs that can be quantified. A challenge engineers face is how to manage a system to balance the different uses. Mathematical optimization, which can take many forms, is employed to do this. Introductions to linear programming and other forms of optimization are plentiful. For a background on the concepts and theories, refer to other references. An excellent, comprehensive reference is Water Resource Systems Planning and Management (Loucks & Van Beek, 2017), freely available online. What follows is a demonstration of using some of these optimization methods, but no recap of the theory is provided. The examples here use linear systems, where the objective function and constraints are all linear functions of the decision variables. The hydromisc package will need to be installed to access some of the data used below. If it is not installed, do so following the instructions on the github site for the package. 12.1 A simple linear system with two decision variables 12.1.1 Overview of problem formulation One of the simplest systems to optimize is a linear system of two variables, which means a graphical solution in 2-d is possible. This first demonstration is one example, a reworking of a textbook example (Wurbs & James, 2002). To set up a solution, three things must be described: the decision variables – the variables for which optimal values are sought the constraints – physical or other limitations on the decision variables (or combinations of them) the objective function – an expression, using the decision variables, of what is to be minimized or maximized. 12.1.2 Setting up an example Example 12.1 To supply water to a community, there are two sources of water available with different levels of total dissolved solids (TDS): groundwater (TDS=980 mg/l) and surface water from a reservoir (TDS=100 mg/l). The first two constraints are that a total demand of water of 7,500 m\\(^3\\)must be met, and the delivered water (mixed groundwater and reservoir supplies) can have a maximum TDS of 500 mg/l. This is illustrated in Figure 12.2. Figure 12.2: A schematic of the system for this example. Two additional constraints are that groundwater withdrawal cannot exceed 4,000 m\\(^3\\) and reservoir withdrawals cannot exceed 7,500 m\\(^3\\). There are two decision variables: X1=groundwater and X2=reservoir supply. The objective is to minimize the reservoir withdrawal while meeting the constraints. The TDS constraint is reorganized as: \\[\\frac{800~X1+100~X2}{X1+X2}\\le 400~~~or~~~ 400~X1-300~X2\\le 0\\] Rewriting the other three constraints as functions of the decision variables: \\[\\begin{align*} X1+X2 \\ge 7500 \\\\ X1 \\le 4000 \\\\ X2 \\le 7500 \\end{align*}\\] Notice that the contraints are all expressed as linear functions of the decision variables (left side of the equations) and a value on the right. 12.1.3 Graphing the solution space While this can only be done easily for systems with only two decision variables, a plot of the solution space can be done here by graphing all of the constrains and shading the region where all constraints are satisfied. Figure 12.3: The solution space, shown as the cross-hatched area. In the feasible region, it is clear that the minimum reservoir supply, X2, would be a little larger than 4,000 m\\(^3\\). 12.1.4 Setting up the problem in R An R package useful for solving linear programming problems is the lpSolveAPI package. Install that if necessary, and also install the knitr and kableExtra packages, since thet are very useful for printing the many tables that linear programming involves. Begin by creating an empty linear model. The (0,2) means zero constraints (they’ll be added later) and 2 decision variables. The next two lines just assign names to the decision variables. Because we will use many functions of the lpSolveAPI package, load the library first. Load the kableExtra package too. library(lpSolveAPI) library(kableExtra) example.lp <- lpSolveAPI::make.lp(0,2) # 0 constraints and 2 decision variables ColNames <- c("X1","X2") colnames(example.lp) <- ColNames # Set the names of the decision variables Now set up the objective function. Minimization is the default goal of this R function, but we’ll set it anyway to be clear. The second argument is the vector of coefficients for the decision variables, meaning X2 is minimized. set.objfn(example.lp,c(0,1)) x <- lp.control(example.lp, sense="min") #save output to a dummy variable The next step is to define the constraints. Four constraints were listed above. Additional constraints could be added that \\(X1\\ge 0\\) and \\(X2\\ge 0\\), however, variable ranges in this LP solver are [0,infinity] by default, so for this example and we do not need to include constraints for positive results. If necessary, decision variable valid ranges can be set using set.bounds(). Constraints are defined with the add.constraint command. Figure 12.4 provides an annotated example of the use of an add.constraint command. Figure 12.4: Annotated example of an add.constraint command. Type ?add.constraint in the console for additional details. The four constraints for this example are added with: add.constraint(example.lp, xt=c(400,-300), type="<=", rhs=0, indices=c(1,2)) add.constraint(example.lp, xt=c(1,1), type=">=", rhs=7500) add.constraint(example.lp, xt=c(1,0), type="<=", rhs=4000) add.constraint(example.lp, xt=c(0,1), type="<=", rhs=7500) That completes the setup of the linear model. You can view the model to verify the values you entered by typing the name of the model. example.lp #> Model name: #> X1 X2 #> Minimize 0 1 #> R1 400 -300 <= 0 #> R2 1 1 >= 7500 #> R3 1 0 <= 4000 #> R4 0 1 <= 7500 #> Kind Std Std #> Type Real Real #> Upper Inf Inf #> Lower 0 0 If it has a large number of decision variables it only prints a summary, but in that case you can use write.lp(example.lp, \"example_lp.txt\", \"lp\") to create a viewable file with the model. Now the model can be solved. solve(example.lp) #> [1] 0 If the solver finds an optimal solution it will return a zero. 12.1.5 Interpreting the optimal results View the final value of the objective function by retrieving it and printing it: optimal_solution <- get.objective(example.lp) print(paste0("Optimal Solution = ",round(optimal_solution,2),sep="")) #> [1] "Optimal Solution = 4285.71" For more detail, recover the values of each of the decision variables. vars <- get.variables(example.lp) Next you can print the sensitivity report – a vector of M constraints followed by N decision variables. It helps to create a data frame for viewing and printing the results. Nicer printing is achieved using the kable and kableExtra functions. sens <- get.sensitivity.obj(example.lp)$objfrom results1 <- data.frame(variable=ColNames,value=vars,gradient=as.integer(sens)) kbl(results1, booktabs = TRUE) %>% kable_styling(full_width = F) variable value gradient X1 3214.286 -1 X2 4285.714 0 The above shows decision variable values for the optimal solution. The Gradient is the change in the objective function for a unit increase in the decision variable. Here a negative gradient for decision variable \\(X1\\), the groundwater withdrawal, means that increasing the groundwater withdrawal will have a negative effect on the objective function, (to minimize \\(X2\\)): that is intuitive, since increasing groundwater withdrawal can reduce reservoir supply on a one-to-one basis. To look at which constraints are binding, retrieve the $duals part of the output. m <- length(get.constraints(example.lp)) #number of constraints duals <- get.sensitivity.rhs(example.lp)$duals[1:m] results2 <- data.frame(constraint=c(seq(1:m)),multiplier=duals) kbl(results2, booktabs = TRUE) %>% kable_styling(full_width = F) constraint multiplier 1 -0.0014286 2 0.5714286 3 0.0000000 4 0.0000000 The multipliers for each constraint are referred to as Lagrange multipliers (or shadow prices). Non-zero values of the multiplier indicate a binding capability of that constraint, and the change in the objective function that would result from a unit change in that value. Zero values are non-binding, since a unit change in their value has no effect on the optimal result. For example, constraint 3, that \\(X1 \\le 4000\\), with a multiplier of zero, could be changed (at least a small amount – there can be a limit after which it can become binding) with no effect on the optimal solution. Similarly, if constraint 2, \\(X1+X2 \\ge 7500\\), were were increased, the objective function (the optimal reservoir supply) would also increase. 12.2 More complex linear programming: reservoir operation Water resources systems are far too complicated to be summarized by two decision variables and only a few constraints, as above. Example 12.2 demonstrate how the same procedure can be applied to a slightly more complex system. This is a reformulation of an example from the same text as referenced above (Wurbs & James, 2002). Example 12.2 A river flows into a storage reservoir where the operator must decide how much water to release each month. For simplicity, inflows will by described by a fixed sequence of 12 monthly flows. There are two downstream needs to satisfy: hydropower generation and irrigation diversions. Benefits are derived from these two uses: revenues are $ 800 per 10\\(^6\\)m\\(^3\\) of water diverted for irrigation, and $ 350 per 10\\(^6\\)m\\(^3\\) for hydropower generation. The objective is to determine the releases that will maximize the total revenue. There are physical characteristics of the system that provide some constraints, and others are derived from basic physics, such as the conservation of mass. A schematic of the system is shown in Figure 12.5. Figure 12.5: A schematic of the water resources system for this example. Diversions through the penstock to the hydropower facility are limited to its capacity of 160 10\\(^6\\)m\\(^3\\)/month. For reservoir releases less than that, all of the released water can generate hydropower; flows above that capacity will spill without generating hydropower benefits. The reservoir has a volume of 550 10\\(^6\\)m\\(^3\\), so anything above that will have to be released. Assume the reservoir is at half capacity initially. The irrigation demand varies by month, and diversions up to the demand will produce benefits. These are: Month Demand, 10\\(^6\\)m\\(^3\\) Month Demand, 10\\(^6\\)m\\(^3\\) Month Demand, 10\\(^6\\)m\\(^3\\) Jan (1) 0 May (5) 40 Sep (9) 180 Feb (2) 0 Jun (6) 130 Oct (10) 110 Mar (3) 0 Jul (7) 230 Nov (11) 0 Apr (4) 0 Aug (8) 250 Dec (12) 0 12.2.1 Problem summary There are 48 decision variables in this problem, 12 monthly values for reservoir storage (s\\(_1\\)-s\\(_{12}\\)), release (r\\(_1\\)-r\\(_{12}\\)), hydropower generation (h\\(_1\\)-h\\(_{12}\\)), and agricultural diversion (d\\(_1\\)-d\\(_{12}\\)). The objective function is to maximize the revenue, which is expressed by Equation (12.1). \\[\\begin{equation} Maximize~ x_0=\\sum_{i=1}^{12}\\left(350h_i+800d_i\\right) \\tag{12.1} \\end{equation}\\] Constraints will need to be described to apply the limits to hydropower diversion and storage capacity, and to limit agricultural diversions to no more than the demand. 12.2.2 Setting up the problem in R Create variables for the known or assumed initial values for the system. penstock_cap <- 160 #penstock capacity in million m3/month res_cap <- 550 #reservoir capacity in million m3 res_init_vol <- res_cap/2 #set initial reservoir capacity equal to half of capacity irrig_dem <- c(0,0,0,0,40,130,230,250,180,110,0,0) revenue_water <- 800 #revenue for delivered irrigation water, $/million m3 revenue_power <- 350 #revenue for power generated, $/million m3 A time series of 20 years (January 2000 through December 2019) of monthly flows for this exercise is included with the hydromisc package. Load that and extract the first 12 months to use in this example. inflows_20years <- hydromisc::inflows_20years inflows <- as.numeric(window(inflows_20years, start = c(2000, 1), end = c(2000, 12))) It helps to illustrate how the irrigation demands and inflows vary, and therefore why storage might be useful in regulating flow to provide more reliable irrigation deliveries. par(mgp=c(2,1,0)) ylbl <- expression(10 ^6 ~ m ^3/month) plot(inflows, type="l", col="blue", xlab="Month", ylab=ylbl) lines(irrig_dem, col="darkgreen", lty=2) legend("topright",c("Inflows","Irrigation Demand"),lty = c(1,2), col=c("blue","darkgreen")) grid() Figure 12.6: Inflows and irrigation demand. 12.2.3 Building the linear model Following the same steps as for a simple 2-variable problem, begin by setting up a linear model. Because there are so many decision variables, it helps to add names to them. reser.lp <- make.lp(0,48) DecisionVarNames <- c(paste0("s",1:12),paste0("r",1:12),paste0("h",1:12),paste0("d",1:12)) colnames(reser.lp) <- DecisionVarNames From this point on, the decision variables will be addressed by their indices, that is, their numeric position in this sequence of 48 values. To summarize their positions: Decision Variables Indices (columns) Storage (s1-s12) 1-12 Release (r1-r12) 13-24 Hydropower (h1-h12) 25-36 Irrigation diversion (d1-d12) 37-48 Using these indices as a guide, set up the objective function and initialize the linear model. While not necessary, redirecting the output of the lp.control to a variable prevents a lot of output to the console. The following takes the revenue from hydropower and irrigation (in $ per 10\\(^6\\)m\\(^3\\)/month), multiplies them by the 12 monthly values for the hydropower flows and the irrigation deliveries, and sets the objective to maximize their sum, as described by Equation (12.1). set.objfn(reser.lp,c(rep(revenue_power,12),rep(revenue_water,12)),indices = c(25:48)) x <- lp.control(reser.lp, sense="max") With the LP setup, the constraints need to be applied. Negative releases, storage, or river flows don’t make sense, so they all need to be positive, so \\(s_t\\ge0\\), \\(r_t\\ge0\\), \\(h_t\\ge0\\) for all 12 months, but because the lpSolveAPI package assumes all decision variables have a range of \\(0\\le x\\le \\infty\\) these do not need to be explicitly added as constraints. When using other software packages these may need to be included. 12.2.3.1 Constraints 1-12: Maximum storage The maximum capacity of the reservoir cannot be exceeded in any month, or \\(s_t\\le 600\\) for all 12 months. This can be added in a simple loop: for (i in seq(1,12)) { add.constraint(reser.lp, xt=c(1), type="<=", rhs=res_cap, indices=c(i)) } 12.2.3.2 Constraints 13-24: Irrigation diversions The irrigation diversions should never exceed the demand. While for some months they are set to zero, since decision variables are all assumed non-negative, we can just assign all irrigation deliveries using the \\(\\le\\) operator. for (i in seq(1,12)) { add.constraint(reser.lp, xt=c(1), type="<=", rhs=irrig_dem[i], indices=c(i+36)) } 12.2.3.3 Constraints 25-36: Hydropower Hydropower release cannot exceed the penstock capacity in any month: \\(h_t\\le 180\\) for all 12 months. This can be done following the example above for the maximum storage constraint for (i in seq(1,12)) { add.constraint(reser.lp, xt=c(1), type="<=", rhs=penstock_cap, indices=c(i+24)) } 12.2.3.4 Constraints 37-48: Reservoir release Reservoir release must equal or exceed irrigation deliveries, which is another way of saying that the water remaining in the river after the diversion cannot be negative. In other words \\(r_1-d_1\\ge 0\\), \\(r_2-d_2\\ge 0\\), … for all 12 months. For constraints involving more than one decision variable the constraint equations look a little different, and keeping track of the indices associated with each decision variable is essential. for (i in seq(1,12)) { add.constraint(reser.lp, xt=c(1,-1), type=">=", rhs=0, indices=c(i+12,i+36)) } 12.2.3.5 Constraints 49-60: Hydropower Hydropower generation will be less than or equal to reservoir release in every month, or \\(r_1-h_1\\ge 0\\), \\(r_2-h_2\\ge 0\\), … for all 12 months. for (i in seq(1,12)) { add.constraint(reser.lp, xt=c(1,-1), type=">=", rhs=0, indices=c(i+12,i+24)) } 12.2.3.6 Constraints 61-72: Conservation of mass Finally, considering the reservoir, the inflow minus the outflow in any month must equal the change in storage over that month. That can be expressed in an equation with decision variables on the left side as: \\[s_t-s_{t-1}+r_t=inflow_t\\] where \\(t\\) is a month from 1-12 and \\(s_t\\) is the storage at the end of month \\(t\\). We need to use the initial reservoir volume, \\(s_0\\) (given above in the problem statement) for the first month’s mass balance, so the above would become \\(s_1-s_0+r_1=inflow_1\\), or \\(s_1+r_1=inflow_1+s_0\\). All subsequent months can be assigned in a loop, add.constraint(reser.lp, xt=c(1,1), type="=", rhs=inflows[1]+res_init_vol, indices=c(1,13)) for (i in seq(2,12)) { add.constraint(reser.lp, xt=c(1,-1,1), type="=", rhs=inflows[i], indices=c(i,i-1,i+12)) } This completes the LP model setup. Especially for larger models, it is helpful to save the model. You can use something like write.lp(reser.lp, \"reservoir_LP.txt\", \"lp\") to create a file (readable using any text file viewer, like Notepad++) with all of the model details. It can also be read into R with the read.lp command to load the complete LP. The beginning of the file for this LP looks like: Figure 12.7: The top of the linear model file produced by write.lp(). 12.2.3.7 Solving the model and interpreting output Solve the LP and retrieve the value of the objective function. solve(reser.lp) #> [1] 0 get.objective(reser.lp) #> [1] 1230930 To look at the hydropower generation, and to see how often spill occurs, it helps to view the associated decision variables (as noted above, these are indices 12-24 and 25-36). vars <- get.variables(reser.lp) # retrieve decision variable values results0 <- data.frame(variable=DecisionVarNames,value=vars) r0 <- cbind(results0[13:24, ], results0[25:36, ]) rownames(r0) <- c() names(r0) <- c("Decision Variable","Value","Decision Variable","Value") kbl(r0, booktabs = TRUE) %>% kable_styling(bootstrap_options = c("striped","condensed"),full_width = F) Decision Variable Value Decision Variable Value r1 160.00000 h1 160.00000 r2 160.00000 h2 160.00000 r3 160.00000 h3 160.00000 r4 89.44193 h4 89.44193 r5 40.00000 h5 40.00000 r6 130.00000 h6 130.00000 r7 230.00000 h7 160.00000 r8 197.58616 h8 160.00000 r9 160.00000 h9 160.00000 r10 112.03054 h10 112.03054 r11 96.96217 h11 96.96217 r12 105.45502 h12 105.45502 Figure 12.8: Reservoir releases and hydropower water use for optimal solution. For this optimal solution, the releases exceed the capacity of the penstock supplying the hydropower plant in July and August, meaning there would be reservoir spill during those months. Another part of the output that is important is to what degree irrigation demand is met. the irrigation delivery is associated with decision variables with indices 37-48. Decision Variable Value Irrigation Demand, 10\\(^6\\)m\\(^3\\) d1 0.0000 0 d2 0.0000 0 d3 0.0000 0 d4 0.0000 0 d5 40.0000 40 d6 130.0000 130 d7 230.0000 230 d8 197.5862 250 d9 160.0000 180 d10 110.0000 110 d11 0.0000 0 d12 0.0000 0 August and September see a shortfall in irrigation deliveries where full demand is not met. Finally, finding which constraints are binding can provide insights into how a system might be modified to improve the optimal solution. This is done similarly to the simpler problem above, by retrieving the duals portion of the sensitivity results. To address the question of whether the size of the reservoir is a binding constraint, that is, whether increasing reservoir size would improve the optimal results, only the first 12 constraints are printed. m <- length(get.constraints(reser.lp)) # retrieve the number of constraints duals <- get.sensitivity.rhs(reser.lp)$duals[1:m] results2 <- data.frame(Constraint=c(seq(1:m)),Multiplier=duals) kbl(results2[1:12,], booktabs = TRUE) %>% kable_styling(bootstrap_options = c("striped","condensed"),full_width = F) Constraint Multiplier 1 0 2 0 3 0 4 0 5 450 6 0 7 0 8 0 9 0 10 0 11 0 12 0 For this example, in only one month would a larger reservoir have a positive impact on the maximum revenue. 12.3 More Realistic Reservoir Operation: non-linear programming While the simple examples above illustrate how an optimal solution can be determined for a linear (and deterministic) reservoir system, in reality reservoirs are much more complex. Most reservoir operation studies use sophisticated software to develop and apply Rule Curves for reservoirs, aiming to optimally store and release water, preserving the storage pools as needed. Figure 12.9 shows how reservoir volumes are managed. Figure 12.9: Sample reservoir operating goals U.S. Army Corps of Engineers Many rule curves depend on the condition of the system at some prior time. Figure 12.10 shows a rule curve used to operate Folsom Reservoir on the American River in California, where the target storage depends on the total upstream storage available. Figure 12.10: Multiple rule corves based on upstream storage U.S. Army Corps of Engineers Report RD-48 One method for deriving an optimal solution for the nonlinear and random processes in a water resources system is stochastic dynamic programming (SDP). Like LP, SDP uses algorithms that optimize an objective function under specified constraints. However, SDP can accommodate non-linear, dynamic outcomes, such as those associated with floods risks or other stochastic events. SDP can combine the stochastic information with reservoir management actions, where the outcome of decisions can be dependent on the state of the system (as in Figure 12.10). Constraints can be set to be met a certain percentage of the time, rather than always. 12.3.1 Reservoir operation While SDP is a topic that is far more advanced than what will be covered here, one R package will be introduced. For reservoir optimization, the R package reservoir can use SDP to derive an optimal operating rule for a reservoir given a sequence of inflows using a single or multiple constraints. The package can also take any derived rule curve and operate a reservoir using it, which is what will be demonstrated here. First, place the optimal releases, according to the LP above, into a new vector to be used as a set of target releases for the reservoir operation. target_release <- results0[13:24, ]$value The reservoir can be operated (for the same 12-month period, with the same 12 inflows as above) with a single command. x <- reservoir::simRes(inflows, target_release, res_cap, plot = F) The total revenue from hydropower generation and irrigation deliveries is computed as follows. irrig_releases <- pmin(x$releases,irrig_dem) irrig_benefits <- sum(irrig_releases*revenue_water) hydro_releases <- pmin(x$releases,penstock_cap) hydro_benefits <- hydro_releases*revenue_power sum(irrig_benefits,hydro_benefits) #> [1] 1230930 Unsurprisingly, this produces the same result as with the LP example. 12.3.2 Performing stochastic dynamic programming The optimal releases, or target releases, were established based on a single year. the SDP in the reservoir package can be used to determine optimal releases based on a time series of inflows. Here the entire 20-year inflow sequence is used to generate a multiobjective optimal solution for the system. A weighting must be applied to describe the importance of meeting different parts of the objective function. The target release(s) cannot be zero, so a small constant is added. weight_water <- revenue_water/(revenue_water + revenue_power) weight_power <- revenue_power/(revenue_water + revenue_power) z <- reservoir::sdp_multi(inflows_20years, cap=res_cap, target = irrig_dem+0.01, R_max = penstock_cap, spill_targ = 0.95, weights = c(weight_water, weight_power, 0.00), loss_exp = c(1, 1, 1), tol=0.99, S_initial=0.5, plot=FALSE) irrig_releases2 <- pmin(z$releases,irrig_dem) irrig_benefits2 <- sum(irrig_releases2*revenue_water) hydro_releases2 <- pmin(z$releases,penstock_cap) hydro_benefits2 <- hydro_releases2*revenue_power sum(irrig_benefits2,hydro_benefits2)/20 #> [1] 911240 For a 20-year period, the average annual revenue will always be less than that for a single year where the optimal releases are designed based on that same year. "],["groundwater.html", "Chapter 13 Groundwater", " Chapter 13 Groundwater Figure 13.1: A conceptual aquifer with a pumping well U.S. Geological survey Groundwater content is forthcoming… "],["references.html", "References", " References Allen, R. G., & United Nations, F. and A. O. of the (Eds.). (1998). Crop evapotranspiration: Guidelines for computing crop water requirements. Rome: Food; Agriculture Organization of the United Nations. Astagneau, P. C., Thirel, G., Delaigue, O., Guillaume, J. H. A., Parajka, J., Brauer, C. C., et al. (2021). Technical note: Hydrology modelling R packages – a unified analysis of models and practicalities from a user perspective. Hydrology and Earth System Sciences, 25(7), 3937–3973. https://doi.org/10.5194/hess-25-3937-2021 Camp, T. R. (1946). Design of sewers to facilitate flow. Sewage Works Journal, 18, 3–16. Davidian, Jacob. (1984). Computation of water-surface profiles in open channels (No. Techniques of Water-Resources Investigations, Book 3, Chapter A15). https://doi.org/10.3133/twri03A15 Ductile Iron Pipe Research Association. (2016). Thrust Restraint Design for Ductile Iron Pipe, Seventh Edition. Retrieved from https://dipra.org/technical-resources England, J.F., Cohn, T.A., Faber, B.A., Stedinger, J.R., Thomas, W.O., Veilleux, A.G., et al. (2019). Guidelines for Determining Flood Flow Frequency Bulletin 17C (Techniques and {Methods}) (p. 148). Reston, Virginia: U.S. Department of the Interior, U.S. Geological Survey. Retrieved from https://doi.org/10.3133/tm4B5 Finnemore, E. J., & Maurer, E. (2024). Fluid mechanics with civil engineering applications (Eleventh edition). New York: McGraw-Hill. Fox-Kemper, B., Hewitt, H., Xiao, C., Aðalgeirsdóttir, G., Drijfhout, S., Edwards, T., et al. (2021). Ocean, Cryosphere and Sea Level Change. In Climate Change 2021: The physical science basis. Contribution of working group I to the sixth assessment report of the intergovernmental panel on climate change. Masson-, V. Delmotte, P. Zhai, A. Pirani, SL Connors, C. Péan, S. Berger, et …. Haaland, S. E. (1983). Simple and Explicit Formulas for the Friction Factor in Turbulent Pipe Flow. Journal of Fluids Engineering, 105(1), 89–90. https://doi.org/10.1115/1.3240948 Helsel, D.R., Hirsch, R.M., Ryberg, K.R., Archfield, S.A., & Gilroy, E.J. (2020). Statistical methods in water resources: U.S. Geological Survey Techniques and Methods, book 4, chap. A3 (p. 458). U.S. Geological Survey. Retrieved from https://doi.org/10.3133/tm4a3 Loucks, D. P., & Van Beek, E. (2017). Water Resource Systems Planning and Management. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-44234-1 Lovelace, R., Nowosad, J., & Münchow, J. (2019). Geocomputation with R. Boca Raton: CRC Press, Taylor; Francis Group, CRC Press is an imprint of theTaylor; Francis Group, an informa Buisness, A Chapman & Hall Book. Marshall, J. D., & Toffel, M. W. (2005). Framing the Elusive Concept of Sustainability: A Sustainability Hierarchy. Environmental Science & Technology, 39(3), 673–682. https://doi.org/10.1021/es040394k McCuen, R. (2016). Hydrologic analysis and design, 4th. Pearson Education. Moore, J., Chatsaz, M., d’Entremont, A., Kowalski, J., & Miller, D. (2022). Mechanics Map Open Textbook Project: Engineering Mechanics. Retrieved from https://eng.libretexts.org/Bookshelves/Mechanical_Engineering/Mechanics_Map_(Moore_et_al.) Pebesma, E. J., & Bivand, R. (2023). Spatial data science: With applications in R (First edition). Boca Raton, FL: CRC Press. Peterka, Alvin J. (1978). Hydraulic design of stilling basins and energy dissipators. Department of the Interior, Bureau of Reclamation. R Core Team. (2022). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/ Searcy, J. K., & Hardison, C. H. (1960). Double-mass curves. US Government Printing Office. Slater, L. J., Thirel, G., Harrigan, S., Delaigue, O., Hurley, A., Khouakhi, A., et al. (2019). Using R in hydrology: A review of recent developments and future directions. Hydrology and Earth System Sciences, 23(7), 2939–2963. https://doi.org/10.5194/hess-23-2939-2019 Sturm, T. W. (2021). Open Channel Hydraulics (3rd Edition). New York: McGraw-Hill Education. Retrieved from https://www.accessengineeringlibrary.com/content/book/9781260469707 Swamee, P. K., & Jain, A. K. (1976). Explicit Equations for Pipe-Flow Problems. Journal of the Hydraulics Division, 102(5), 657–664. https://doi.org/10.1061/JYCEAJ.0004542 Tebaldi, C., Strauss, B. H., & Zervas, C. E. (2012). Modelling sea level rise impacts on storm surges along US coasts. Environmental Research Letters, 7(1), 014032. https://doi.org/10.1088/1748-9326/7/1/014032 Wurbs, R. A., & James, W. P. (2002). Water resources engineering. Upper Saddle River, NJ: Prentice Hall. "]] diff --git a/docs/sustainability-in-design-planning-for-change.html b/docs/sustainability-in-design-planning-for-change.html index 7941872..dad94df 100644 --- a/docs/sustainability-in-design-planning-for-change.html +++ b/docs/sustainability-in-design-planning-for-change.html @@ -25,7 +25,7 @@ - + @@ -210,6 +210,7 @@
  • 4.7 Parallel pipes: solving a system of equations
  • 4.8 Simple pipe networks: the Hardy-Cross method
    • @@ -392,36 +393,36 @@

    11.1 Perturbing a systemExample 11.1 A river with a flow of 7 \(m^3/s\) and a velocity of 1.4 m/s has effluent discharged into it at a rate of 1.5 \(m^3/s\). The river upstream of the discharge has a temperature of 15\(^\circ\)C, a \(BOD_5\) of 1 mg/L, and a dissolved oxygen saturation of 90 percent. The effluent is 21\(^\circ\)C with a \(BOD_5\) of 180 mg/L and a dissolved oxygen saturation of 0 percent. The deoxygenation rate constant (at 20\(^\circ\)C) is 0.4 \(d^{-1}\), and the reaeration rate constant is 0.8 \(d^{-1}\). Create a plot of DO as a percent of saturation (y-axis) vs. distance in km (x-axis).

    First set up the model parameters.

    -
    Q <- 7             # flow of stream, m3/s
-V <- 1.4           # velocity of stream, m/s
-Qeff <- 1.5        # flow rate of effluent, m3/s
-DOsatupstr <- 90   # DO saturation upstream of effluent discharge, %
-DOsateff <- 0      # DO saturation of effluent discharge, %
-Triv <- 15         # temperature of receiving water, C
-Teff <- 21         # temperature of effluent, C
-BOD5riv <- 1       # 5-day BOD of receiving water, mg/L
-BOD5eff <- 180     # 5-day BOD of effluent, mg/L
-K1 <- 0.4          # deoxygenation rate constant at 20C, 1/day
-K2 <- 0.8          # reaeration rate constant at 20C, 1/day
    +
    Q <- 7             # flow of stream, m3/s
+V <- 1.4           # velocity of stream, m/s
+Qeff <- 1.5        # flow rate of effluent, m3/s
+DOsatupstr <- 90   # DO saturation upstream of effluent discharge, %
+DOsateff <- 0      # DO saturation of effluent discharge, %
+Triv <- 15         # temperature of receiving water, C
+Teff <- 21         # temperature of effluent, C
+BOD5riv <- 1       # 5-day BOD of receiving water, mg/L
+BOD5eff <- 180     # 5-day BOD of effluent, mg/L
+K1 <- 0.4          # deoxygenation rate constant at 20C, 1/day
+K2 <- 0.8          # reaeration rate constant at 20C, 1/day

    Calculate some of the variables needed for the Streeter-Phelps model. Type ?hydromisc::DO_functions for more information on the DO-related functions in the hydromisc package.

    -
    Tmix <- hydromisc::Mixture(Q, Triv, Qeff, Teff)
-K1adj <- hydromisc::Kadj_deox(K1=K1, T=Tmix)
-K2adj <- hydromisc::Kadj_reox(K2=K2, T=Tmix)
-BOD5mix <- hydromisc::Mixture(Q, BOD5riv, Qeff, BOD5eff)
-L0 <- BOD5mix/(1-exp(-K1adj*5)) #BOD5 - ultimate
    +
    Tmix <- hydromisc::Mixture(Q, Triv, Qeff, Teff)
+K1adj <- hydromisc::Kadj_deox(K1=K1, T=Tmix)
+K2adj <- hydromisc::Kadj_reox(K2=K2, T=Tmix)
+BOD5mix <- hydromisc::Mixture(Q, BOD5riv, Qeff, BOD5eff)
+L0 <- BOD5mix/(1-exp(-K1adj*5)) #BOD5 - ultimate

    Find the dissolved oxygen for 100 percent saturation (assuming no salinity) and the initial DO deficit at the point of discharge.

    -
    Cs <- hydromisc::O2sat(Tmix) #DO saturation, mg/l
-C0 <- hydromisc::Mixture(Q, DOsatupstr/100.*Cs, Qeff, DOsateff/100.*Cs) #DO init, mg/l
-D0 <- Cs - C0 #initial deficit
    +
    Cs <- hydromisc::O2sat(Tmix) #DO saturation, mg/l
+C0 <- hydromisc::Mixture(Q, DOsatupstr/100.*Cs, Qeff, DOsateff/100.*Cs) #DO init, mg/l
+D0 <- Cs - C0 #initial deficit

    Determine a set of distances where the DO deficit will be calculated, and the corresponding times for the flow to travel that distance.

    -
    xs <- seq(from=0.5, to=800, by=5)
-ts <- xs*1000/(V*86400)
    +
    xs <- seq(from=0.5, to=800, by=5)
+ts <- xs*1000/(V*86400)

    Finally, calculate the DO (as a percent of saturation) and plot the results.

    -
    DO_def <- hydromisc::DOdeficit(t=ts, K1=K1adj, K2=K2adj, L0=L0, D0=D0)
-DO_mgl <- Cs - DO_def
-DO_pct <- 100*DO_mgl/Cs
-plot(xs,DO_pct,xlim=c(0,800),ylim=c(0,100),type="l",xlab="Distance, km",ylab="DO, %")
-grid()
    +
    DO_def <- hydromisc::DOdeficit(t=ts, K1=K1adj, K2=K2adj, L0=L0, D0=D0)
+DO_mgl <- Cs - DO_def
+DO_pct <- 100*DO_mgl/Cs
+plot(xs,DO_pct,xlim=c(0,800),ylim=c(0,100),type="l",xlab="Distance, km",ylab="DO, %")
+grid()
    Dissolved oxygen for this example.

    @@ -455,9 +456,9 @@

    11.2 Detecting changes in hydrolo

    In the context of the example that follows, the null hypothesis, H0, is usually a statement that no trend exists. The \(\alpha\)-value (the significance level) is the probability of incorrectly rejecting the null hypothesis, that is rejecting H0 when it is in fact true. The significance level that is acceptable is a decision that must be made – a common value of \(\alpha\)=0.05 (5 percent) significance, also referred to as \(1-\alpha=0.95\) (95 percent) confidence.

    A statistical test will produce a p-value, which is essentially the likelihood that the null hypothesis is true, or more technically, the probability of obtaining the calculated test statistic (on one more extreme) when the null hypothesis is true. Again, in the context of trend detection, small p-values (less than \(\alpha\)) indicate greater confidence for rejecting the null hypothesis and thus supporting the existence of a “statistically significant” trend.

    One of the most robust impacts of a warming climate is the impact on snow. In California, historically the peak of snow accumulation tended to occur roughly on April 1 on average. To demonstrate methods for detecting changes data from the Four Trees Cooperative Snow Sensor site in California, obtained from the USDA National Water and Climate Center. These data are available as part of the hydromisc package.

    -
    swe <- hydromisc::four_trees_swe
-plot(swe$Year, swe$April_1_SWE_in, xlab="Year", ylab="April 1 Snow Water Equivalent, in")
-lines(zoo::rollmean(swe, k=5), col="blue", lty=3, cex=1.4)
    +
    swe <- hydromisc::four_trees_swe
+plot(swe$Year, swe$April_1_SWE_in, xlab="Year", ylab="April 1 Snow Water Equivalent, in")
+lines(zoo::rollmean(swe, k=5), col="blue", lty=3, cex=1.4)
    April 1 snow water equivalent at Four Trees station, CA. The dashed line is a 5-year moving average.

    @@ -470,9 +471,9 @@

    11.2.1 Detecting a step changeWhen there is a step change in a record, you need to test that the difference between the “before” and “after” conditions is large enough relative to natural variability that is can be confidently described as a change. In other words, whether the change is significant must be determined. This is done by breaking the data into two-samples and applying a statistical test, such as a t-test or the nonparametric rank-sum (or Mann-Whitney U) test.

    While for this example there is no obvious reason to break this data at any particular year, we’ll just look at the first and second halves. Separate the two subsets of years into two arrays of (y) values (not data frames in this case) and then create a boxplot of the two periods.

    -
    yvalues1 <- swe$April_1_SWE_in[(swe$Year >= 1980) & (swe$Year <= 2001)]
-yvalues2 <- swe$April_1_SWE_in[(swe$Year >= 2002) & (swe$Year <= 2023)]
-boxplot(yvalues1,yvalues2,names=c("1980-2001","2002-2023"),boxwex=0.2,ylab="swe, in")
    +
    yvalues1 <- swe$April_1_SWE_in[(swe$Year >= 1980) & (swe$Year <= 2001)]
+yvalues2 <- swe$April_1_SWE_in[(swe$Year >= 2002) & (swe$Year <= 2023)]
+boxplot(yvalues1,yvalues2,names=c("1980-2001","2002-2023"),boxwex=0.2,ylab="swe, in")
    Comparison of two records of SWE at Four Trees station, CA.

    @@ -480,45 +481,45 @@

    11.2.1 Detecting a step change

    Calculate the means and medians of the two periods, just for illustration.

    -
    mean(yvalues1)
-#> [1] 19.76364
-mean(yvalues2)
-#> [1] 15.44545
-median(yvalues1)
-#> [1] 17.8
-median(yvalues2)
-#> [1] 7.9
    +
    mean(yvalues1)
+#> [1] 19.76364
+mean(yvalues2)
+#> [1] 15.44545
+median(yvalues1)
+#> [1] 17.8
+median(yvalues2)
+#> [1] 7.9

    The mean for the later period is lower, as is the median. the question to pose is whether these differences are statistically significant. The following tests allow that determination.

    11.2.1.1 Method 1: Using a t-test.

    A t-test determines the significance of a difference in the mean between two samples under a number of assumptions. These include independence of each data point (in this example, that any year’s April 1 SWE is uncorrelated with prior years) and that the data are normally distributed. This is performed with the t.test function. The alternative argument is included that the test is “two sided”; a one-sided test would test for one group being only greater than or less than the other, but here we only want to test whether they are different. The paired argument is set to FALSE since there is no correspondence between the order of values in each subset of years.

    -
    t.test(yvalues1, yvalues2, var.equal = FALSE, alternative = "two.sided", paired = FALSE)
-#> 
-#>  Welch Two Sample t-test
-#> 
-#> data:  yvalues1 and yvalues2
-#> t = 0.91863, df = 40.084, p-value = 0.3638
-#> alternative hypothesis: true difference in means is not equal to 0
-#> 95 percent confidence interval:
-#>  -5.181634 13.817998
-#> sample estimates:
-#> mean of x mean of y 
-#>  19.76364  15.44545
    +
    t.test(yvalues1, yvalues2, var.equal = FALSE, alternative = "two.sided", paired = FALSE)
+#> 
+#>  Welch Two Sample t-test
+#> 
+#> data:  yvalues1 and yvalues2
+#> t = 0.91863, df = 40.084, p-value = 0.3638
+#> alternative hypothesis: true difference in means is not equal to 0
+#> 95 percent confidence interval:
+#>  -5.181634 13.817998
+#> sample estimates:
+#> mean of x mean of y 
+#>  19.76364  15.44545

    Here the p-value is 0.36, a value much greater than \(\alpha = 0.05\), so the null hypothesis cannot be rejected. The difference in the means is not significant based on this test.

    11.2.1.2 Method 2: Wilcoxon rank-sum (or Mann-Whitney U) test.

    Like the t-test, the rank-sum test produces a p-value, but it measures a more generic measure of “central tendency” (such as a median) rather than a mean. Assumptions about independence of data are still necessary, but there is no requirement of normality of the distribution of data. It is less affected by outliers or a few extreme values than the t-test.

    This is performed with a standard R function. Other arguments are set as with the t-test.

    -
    wilcox.test(yvalues1, yvalues2, alternative = "two.sided", paired=FALSE) 
-#> Warning in wilcox.test.default(yvalues1, yvalues2, alternative = "two.sided", :
-#> cannot compute exact p-value with ties
-#> 
-#>  Wilcoxon rank sum test with continuity correction
-#> 
-#> data:  yvalues1 and yvalues2
-#> W = 297, p-value = 0.1999
-#> alternative hypothesis: true location shift is not equal to 0
    +
    wilcox.test(yvalues1, yvalues2, alternative = "two.sided", paired=FALSE) 
+#> Warning in wilcox.test.default(yvalues1, yvalues2, alternative = "two.sided", :
+#> cannot compute exact p-value with ties
+#> 
+#>  Wilcoxon rank sum test with continuity correction
+#> 
+#> data:  yvalues1 and yvalues2
+#> W = 297, p-value = 0.1999
+#> alternative hypothesis: true location shift is not equal to 0

    The p-value is much lower than with the t-test, showing less influence of the two very high SWE values in the second half of the record.

    @@ -528,38 +529,38 @@

    11.2.2 Detecting a monotonic tren

    11.2.2.1 Method 1: Regression

    To perform a linear regression in R, build a linear regression model (lm). This can take the swe data frame as input data, specifying the columns to relate linearly.

    -
    m <- lm(April_1_SWE_in ~ Year, data = swe) 
-summary(m)$coefficients
-#>                Estimate  Std. Error    t value  Pr(>|t|)
-#> (Intercept) 347.3231078 370.6915171  0.9369600 0.3541359
-#> Year         -0.1647357   0.1852031 -0.8894868 0.3788081
    +
    m <- lm(April_1_SWE_in ~ Year, data = swe) 
+summary(m)$coefficients
+#>                Estimate  Std. Error    t value  Pr(>|t|)
+#> (Intercept) 347.3231078 370.6915171  0.9369600 0.3541359
+#> Year         -0.1647357   0.1852031 -0.8894868 0.3788081

    The row for “Year” provides the data on the slope. The slope shows SWE declines by 0.16 inches/year based on regression. The p-value for the slope is 0.379, much larger than the typical \(\alpha\), meaning we cannot claim that a significant slope exists based on this test. So while a declining April 1 snowpack is observed at this location, it is not outside of the natural variability of the data based on a regression analysis.

    11.2.2.2 Method 2: Mann-Kendall

    To conduct a Mann-Kendall trend test, additional packages need to be installed. There are a number available; what is shown below is one method.

    A non-parametric trend test (and plot) requires a few extra packages, which are installed like this:

    -
    if (!require('Kendall', quietly = TRUE)) install.packages('Kendall')
-#> Warning: package 'Kendall' was built under R version 4.2.3
-if (!require('zyp', quietly = TRUE)) install.packages('zyp')
-#> Warning: package 'zyp' was built under R version 4.2.3
    +
    if (!require('Kendall', quietly = TRUE)) install.packages('Kendall')
+#> Warning: package 'Kendall' was built under R version 4.2.3
+if (!require('zyp', quietly = TRUE)) install.packages('zyp')
+#> Warning: package 'zyp' was built under R version 4.2.3

    Now the significance of the trend can be calculated. The slope associated with this test, the “Thiel-Sen slope”, is calculated using the zyp package.

    -
    mk <- Kendall::MannKendall(swe$April_1_SWE_in)
-summary(mk)
-#> Score =  -99 , Var(Score) = 9729
-#> denominator =  934.4292
-#> tau = -0.106, 2-sided pvalue =0.32044
-ss <- zyp::zyp.sen(April_1_SWE_in ~ Year, data=swe) 
-ss$coefficients
-#>   Intercept        Year 
-#> 291.1637542  -0.1385452
    +
    mk <- Kendall::MannKendall(swe$April_1_SWE_in)
+summary(mk)
+#> Score =  -99 , Var(Score) = 9729
+#> denominator =  934.4292
+#> tau = -0.106, 2-sided pvalue =0.32044
+ss <- zyp::zyp.sen(April_1_SWE_in ~ Year, data=swe) 
+ss$coefficients
+#>   Intercept        Year 
+#> 291.1637542  -0.1385452

    The non-parametric slope shows April 1 SWE declining by 0.14 inches per year over the period. Again, however, the p-value is greater than the typical \(\alpha\), so based on this method the trend is not significantly different from zero. As with the tests for a step change, the p-value is lower for the nonparametric test.

    A summary plot of the slopes of both methods is helpful.

    -
    plot(swe$Year,swe$April_1_SWE_in, xlab = "Year",ylab = "Snow water equivalent, in")
-lines(swe$Year,m$fitted.values, lty=1, col="black")
-abline(a = ss$coefficients["Intercept"], b = ss$coefficients["Year"], col="red", lty=2)
-legend("topright", legend=c("Observations","Regression","Thiel-Sen"),  
-  col=c("black","black","red"),lty = c(NA,1,2), pch = c(1,NA,NA), cex=0.8)
    +
    plot(swe$Year,swe$April_1_SWE_in, xlab = "Year",ylab = "Snow water equivalent, in")
+lines(swe$Year,m$fitted.values, lty=1, col="black")
+abline(a = ss$coefficients["Intercept"], b = ss$coefficients["Year"], col="red", lty=2)
+legend("topright", legend=c("Observations","Regression","Thiel-Sen"),  
+  col=c("black","black","red"),lty = c(NA,1,2), pch = c(1,NA,NA), cex=0.8)
    Trends of SWE at Four Trees station, CA.

    @@ -571,8 +572,8 @@

    11.2.2.2 Method 2: Mann-Kendall

    11.2.3 Choosing whether to use parametric or non-parametric tests

    Using the parameteric tests above (t-test, regression) requires making an assumption about the underlying distribution of the data, which non-parametric tests do not require. When using a parametric test, the assumption of normality can be tested. For example, for the regression residuals can be tested with the following, where the null hypothesis is that the data are nomally distributed.

    -
    shapiro.test(m$residuals)$p.value
-#> [1] 0.003647395
    +
    shapiro.test(m$residuals)$p.value
+#> [1] 0.003647395

    This produces a very small p-value (p < 0.01), meaning the null hypothesis that the residuals are normally distributed is rejected with >99% confidence. This means non-parametric test is more appropriate. In general, non-parametric tests are preferred in hydrologic work because data (and residuals) are rarely normally distributed.

    @@ -592,9 +593,9 @@

    11.3 Detecting changes in extreme

    An example using the GEV with sea level data is illustrated below. The Tebaldi et al. (2012) paper uses the R package extRemes, which we will use here. The same package has been used to study extreme wind, precipitation, temperature, streamflow, and other events, so it is a very versatile and widely-used package. Install the package if it is not already installed.

    -
    if (!require('extRemes', quietly = TRUE)) install.packages('extRemes')
-#> Warning: package 'extRemes' was built under R version 4.2.3
-#> Warning: package 'Lmoments' was built under R version 4.2.2
    +
    if (!require('extRemes', quietly = TRUE)) install.packages('extRemes')
+#> Warning: package 'extRemes' was built under R version 4.2.3
+#> Warning: package 'Lmoments' was built under R version 4.2.2

    11.3.1 Obtaining and preparing sea-level data

    Sea-level data can be downloaded directly into R using the rnoaa package. However, NOAA also has a very intuitive interface that allows geographical searching and preliminary viewing of data. From the NOAA Tines & Currents site one can search an area of interest and find a tide gauge with a long record. @@ -606,20 +607,20 @@

    11.3.1 Obtaining and preparing se

    By exploring the data inventory for this station, on its home page, the gauge has a very long record, being established in 1854, with measurement of extremes for over a century. Avoid selecting a partial month, or you may not have the ability to download monthly data. Monthly data were downloaded and saved as a csv file, which is available with the hydromisc package.

    -
    datafile <- system.file("extdata", "sealevel_9414290_wl.csv", package="hydromisc")
-dat <- read.csv(datafile,header=TRUE)
    +
    datafile <- system.file("extdata", "sealevel_9414290_wl.csv", package="hydromisc")
+dat <- read.csv(datafile,header=TRUE)

    These data were saved in metric units, so all levels are in meters above the selected tidal datum. there are dates indicating the month associated with each value (and day 1 is in there as a placeholder).

    If there are any missing data they may be labeled as “NaN”. If you see that, a clean way to address it is to first change the missing data to NA (which R recognizes) with a command such as

    -
    dat[dat == "NaN"] <- NA
    +
    dat[dat == "NaN"] <- NA

    For this example we are looking at extreme tide levels, so only retain the “Highest” and “Date” columns.

    -
    peak_sl <- subset(dat, select=c("Date", "Highest"))
    +
    peak_sl <- subset(dat, select=c("Date", "Highest"))

    A final data preparation is to create an annual time series with the the maximum tide level in any year. One way to facilitate this is to add a column of “year.” Then the data can be aggregated by year, creating a new data frame, taking the maximum value for each year (many other functions, like mean, median, etc. can also be used). In this example the column names are changed to make it easier to work with the data. Also, the year column is converted to an integer for plotting purposes. Any rows with NA values are removed.

    -
    peak_sl$year <- as.integer(strftime(peak_sl$Date, "%Y"))
-peak_sl_ann <- aggregate(peak_sl$Highest,by=list(peak_sl$year),FUN=max, na.rm=TRUE)
-colnames(peak_sl_ann) <- c("year","peak_m")
-peak_sl_ann <- na.exclude(peak_sl_ann)
    +
    peak_sl$year <- as.integer(strftime(peak_sl$Date, "%Y"))
+peak_sl_ann <- aggregate(peak_sl$Highest,by=list(peak_sl$year),FUN=max, na.rm=TRUE)
+colnames(peak_sl_ann) <- c("year","peak_m")
+peak_sl_ann <- na.exclude(peak_sl_ann)

    A plot is always helpful.

    -
    plot(peak_sl_ann$year,peak_sl_ann$peak_m,xlab="Year",ylab="Annual Peak Sea Level, m")
    +
    plot(peak_sl_ann$year,peak_sl_ann$peak_m,xlab="Year",ylab="Annual Peak Sea Level, m")
    Annual highest sea-levels relative to MLLW at gauge 9414290.

    @@ -630,13 +631,13 @@

    11.3.1 Obtaining and preparing se

    11.3.2 Conducting the extreme event analysis

    The question we will attempt to address is whether the 100-year peak tide level (the level exceeded with a 1 percent probability) has increased between the 1900-1930 and 1990-2020 periods. Extract a subset of the data for one period and fit a GEV distribution to the values.

    -
    peak_sl_sub1 <- subset(peak_sl_ann, year >= 1900 & year <= 1930)
-gevfit1 <- extRemes::fevd(peak_sl_sub1$peak_m)
-gevfit1$results$par
-#>   location      scale      shape 
-#>  2.0747606  0.1004844 -0.2480902
    +
    peak_sl_sub1 <- subset(peak_sl_ann, year >= 1900 & year <= 1930)
+gevfit1 <- extRemes::fevd(peak_sl_sub1$peak_m)
+gevfit1$results$par
+#>   location      scale      shape 
+#>  2.0747606  0.1004844 -0.2480902

    A plot of return periods for the fit distribution is available as well.

    -
    extRemes::plot.fevd(gevfit1, type="rl")
    +
    extRemes::plot.fevd(gevfit1, type="rl")
    Return periods based on the fit GEV distribution for 1900-1930. Points are observations; dashed lines enclose the 95% confidence interval.

    @@ -645,50 +646,50 @@

    11.3.2 Conducting the extreme eve

    As is usually the case, a statistical model does well in the area with observations, but the uncertainty increases for extreme values (like estimating a 500-year event from a 30-year record). A longer record produces better (less uncertain) estimates at higher return periods.

    Based on the GEV fit, the 100-year recurrence interval extreme tide is determined using:

    -
    extRemes::return.level(gevfit1, return.period = 100, do.ci = TRUE, verbose = TRUE)
-#> 
-#>  Preparing to calculate  95 % CI for  100-year return level 
-#> 
-#>  Model is   fixed 
-#> 
-#>  Using Normal Approximation Method.
-#> extRemes::fevd(x = peak_sl_sub1$peak_m)
-#> 
-#> [1] "Normal Approx."
-#> 
-#> [1] "100-year return level: 2.35"
-#> 
-#> [1] "95% Confidence Interval: (2.2579, 2.4429)"
    +
    extRemes::return.level(gevfit1, return.period = 100, do.ci = TRUE, verbose = TRUE)
+#> 
+#>  Preparing to calculate  95 % CI for  100-year return level 
+#> 
+#>  Model is   fixed 
+#> 
+#>  Using Normal Approximation Method.
+#> extRemes::fevd(x = peak_sl_sub1$peak_m)
+#> 
+#> [1] "Normal Approx."
+#> 
+#> [1] "100-year return level: 2.35"
+#> 
+#> [1] "95% Confidence Interval: (2.2579, 2.4429)"

    A check can be done using the reverse calculation, estimating the return period associated with a specified value of highest water level. This can be done by extracting the three GEV parameters, then running the pevd command.

    -
    loc <- gevfit1$results$par[["location"]]
-sca <- gevfit1$results$par[["scale"]]
-shp <- gevfit1$results$par[["shape"]]
-extRemes::pevd(2.35, loc = loc, scale = sca , shape = shp, type = c("GEV"))
-#> [1] 0.9898699
    +
    loc <- gevfit1$results$par[["location"]]
+sca <- gevfit1$results$par[["scale"]]
+shp <- gevfit1$results$par[["shape"]]
+extRemes::pevd(2.35, loc = loc, scale = sca , shape = shp, type = c("GEV"))
+#> [1] 0.9898699

    This returns a value of 0.99 (this is the CDF value, or the probability of non-exceedence, F). Recalling that return period, \(T=1/P=1/(1-F)\), where P=prob. of exceedence; F=prob. of non-exceedence, the result that 2.35 meters is the 100-year highest water level is validated.

    Repeating the calculation for a more recent period:

    -
    peak_sl_sub2 <- subset(peak_sl_ann, year >= 1990 & year <= 2020)
-gevfit2 <- extRemes::fevd(peak_sl_sub2$peak_m)
-extRemes::return.level(gevfit2, return.period = 100, do.ci = TRUE, verbose = TRUE)
-#> 
-#>  Preparing to calculate  95 % CI for  100-year return level 
-#> 
-#>  Model is   fixed 
-#> 
-#>  Using Normal Approximation Method.
-#> extRemes::fevd(x = peak_sl_sub2$peak_m)
-#> 
-#> [1] "Normal Approx."
-#> 
-#> [1] "100-year return level: 2.597"
-#> 
-#> [1] "95% Confidence Interval: (2.3983, 2.7957)"
    +
    peak_sl_sub2 <- subset(peak_sl_ann, year >= 1990 & year <= 2020)
+gevfit2 <- extRemes::fevd(peak_sl_sub2$peak_m)
+extRemes::return.level(gevfit2, return.period = 100, do.ci = TRUE, verbose = TRUE)
+#> 
+#>  Preparing to calculate  95 % CI for  100-year return level 
+#> 
+#>  Model is   fixed 
+#> 
+#>  Using Normal Approximation Method.
+#> extRemes::fevd(x = peak_sl_sub2$peak_m)
+#> 
+#> [1] "Normal Approx."
+#> 
+#> [1] "100-year return level: 2.597"
+#> 
+#> [1] "95% Confidence Interval: (2.3983, 2.7957)"

    This returns a 100-year high tide of 2.6 m for 1990-2020, a 10.6 % increase over 1900-1930. Another way to look at this is to find out how the frequency of the past (in this case, 1900-1930) 100-year event has changed with rising sea levels. Repeating the calculations from before to capture the GEV parameters for the later period, and then plugging in the 100-year high tide from the early period:

    -
    loc2 <- gevfit2$results$par[["location"]]
-sca2 <- gevfit2$results$par[["scale"]]
-shp2 <- gevfit2$results$par[["shape"]]
-extRemes::pevd(2.35, loc = loc2, scale = sca2 , shape = shp2, type = c("GEV"))
-#> [1] 0.7220968
    +
    loc2 <- gevfit2$results$par[["location"]]
+sca2 <- gevfit2$results$par[["scale"]]
+shp2 <- gevfit2$results$par[["shape"]]
+extRemes::pevd(2.35, loc = loc2, scale = sca2 , shape = shp2, type = c("GEV"))
+#> [1] 0.7220968

    This returns a value of 0.72 (72% non-exceedence, or 28% exceedance, in other words we expect to see an annual high tide of 2.35 m or higher in 28% of the years). The return period of this is calculated as above: T = 1/(1-0.72) = 3.6 years. So, what was the 100-year event in 1900-1930 is about a 4-year event now.

    diff --git a/docs/the-hydrologic-cycle-and-precipitation.html b/docs/the-hydrologic-cycle-and-precipitation.html index b6b546a..918b519 100644 --- a/docs/the-hydrologic-cycle-and-precipitation.html +++ b/docs/the-hydrologic-cycle-and-precipitation.html @@ -25,7 +25,7 @@ - + @@ -210,6 +210,7 @@
  • 4.7 Parallel pipes: solving a system of equations
  • 4.8 Simple pipe networks: the Hardy-Cross method
    • @@ -393,27 +394,27 @@

    8.1 Precipitation observations

    While formats will vary depending on the source of the data, in this example we can import the csv file directly. Since units were left as ‘standard’ on the CDO website, precipitation is in inches and temperatures in oF.

    -
    datafile <- system.file("extdata", "cdo_data_ghcn_23293.csv", package="hydromisc")
-ghcn_data <- read.csv(datafile,header=TRUE)
    +
    datafile <- system.file("extdata", "cdo_data_ghcn_23293.csv", package="hydromisc")
+ghcn_data <- read.csv(datafile,header=TRUE)

    A little cleanup of the data needs to be done to ensure the DATE column is in date format, and change any missing values (often denoted as 9999 or -9999) to NA. With missing values flagged as NA, R can ignore them, set them to zero, or fill them in with functions like the zoo::na.approx() or na.spline() functions, or using the more sophisticated imputeTS package. finally, add a ‘water year’ column (a water year begins on October 1 and ends September 30).

    -
    ghcn_data$DATE <- as.Date(ghcn_data$DATE, format="%Y-%m-%d")
-ghcn_data$PRCP[ghcn_data$PRCP <= -999 | ghcn_data$PRCP >= 999 ] = NA
-wateryr <- function(d) {
-  if (as.numeric(format(d, "%m")) >= 10) {
-    wy = as.numeric(format(d, "%Y")) + 1
-  } else {
-    wy = as.numeric(format(d, "%Y"))
-  }
-}
-ghcn_data$wy <- sapply(ghcn_data$DATE, wateryr)
    +
    ghcn_data$DATE <- as.Date(ghcn_data$DATE, format="%Y-%m-%d")
+ghcn_data$PRCP[ghcn_data$PRCP <= -999 | ghcn_data$PRCP >= 999 ] = NA
+wateryr <- function(d) {
+  if (as.numeric(format(d, "%m")) >= 10) {
+    wy = as.numeric(format(d, "%Y")) + 1
+  } else {
+    wy = as.numeric(format(d, "%Y"))
+  }
+}
+ghcn_data$wy <- sapply(ghcn_data$DATE, wateryr)

    A convenient package for characterizing precipitation is hydroTSM, the output of which is shown in Figure 8.5

    -
    library(hydroTSM)
-#create a simple data frame for plotting
-ghcn_prcp <- data.frame(date = ghcn_data$DATE, prcp = ghcn_data$PRCP )
-#convert it to a zoo object
-x <-  zoo::read.zoo(ghcn_prcp)
-hydroTSM::hydroplot(x, var.type="Precipitation", main="", var.unit="inch",
-pfreq = "ma", from="1999-01-01", to="2022-12-31")
    +
    library(hydroTSM)
+#create a simple data frame for plotting
+ghcn_prcp <- data.frame(date = ghcn_data$DATE, prcp = ghcn_data$PRCP )
+#convert it to a zoo object
+x <-  zoo::read.zoo(ghcn_prcp)
+hydroTSM::hydroplot(x, var.type="Precipitation", main="", var.unit="inch",
+pfreq = "ma", from="1999-01-01", to="2022-12-31")
    Monthly and annual precipitation summary for San Jose, CA for 1999-2022

    @@ -421,14 +422,14 @@

    8.1 Precipitation observations

    This presentation shows the seasonality of rainfall in San Jose, with most falling between October and May. The mean is about 12 inches per year, with most years experiencing between 10-15 inches of precipitation. There are functions to produce many statistics such as monthly means.

    -
    #calculate monthly sums
-monsums <- hydroTSM::daily2monthly(x, sum, na.rm = TRUE)
-monavg <- as.data.frame(hydroTSM::monthlyfunction(monsums, mean, na.rm = TRUE))
-#if record begins in a month other than January, need to reorder
-monavg <- monavg[order(factor(row.names(monavg), levels = month.abb)),,drop=FALSE]
-colnames(monavg)[1]  <- "Avg monthly precip, in"
-knitr::kable(monavg, digits = 2) |> 
-  kableExtra::kable_paper(bootstrap_options = "striped", full_width = F)
    +
    #calculate monthly sums
+monsums <- hydroTSM::daily2monthly(x, sum, na.rm = TRUE)
+monavg <- as.data.frame(hydroTSM::monthlyfunction(monsums, mean, na.rm = TRUE))
+#if record begins in a month other than January, need to reorder
+monavg <- monavg[order(factor(row.names(monavg), levels = month.abb)),,drop=FALSE]
+colnames(monavg)[1]  <- "Avg monthly precip, in"
+knitr::kable(monavg, digits = 2) |> 
+  kableExtra::kable_paper(bootstrap_options = "striped", full_width = F)

    @@ -430,22 +431,22 @@

    Chapter 2 Properties of water (an \(999.9\)

    -\({9.809}\times 10^{3}\) +\(9809\) -\({1.734}\times 10^{-3}\) +\(1.734 \times 10^{-3}\) -\({1.734}\times 10^{-6}\) +\(1.734 \times 10^{-6}\) \(611.2\) -\({75.7}\times 10^{-3}\) +\(7.57 \times 10^{-2}\) -\({2.02}\times 10^{9}\) +\(2.02 \times 10^{9}\)
    -\(1000\) +\(1000.0\) -\({9.810}\times 10^{3}\) +\(9810\) -\({1.501}\times 10^{-3}\) +\(1.501 \times 10^{-3}\) -\({1.501}\times 10^{-6}\) +\(1.501 \times 10^{-6}\) \(872.6\) -\({74.9}\times 10^{-3}\) +\(7.49 \times 10^{-2}\) -\({2.06}\times 10^{9}\) +\(2.06 \times 10^{9}\)
    -\({9.807}\times 10^{3}\) +\(9807\) -\({1.310}\times 10^{-3}\) +\(1.310 \times 10^{-3}\) -\({1.311}\times 10^{-6}\) +\(1.311 \times 10^{-6}\) -\({1.228}\times 10^{3}\) +\(1228\) -\({74.2}\times 10^{-3}\) +\(7.42 \times 10^{-2}\) -\({2.10}\times 10^{9}\) +\(2.10 \times 10^{9}\)
    -\({9.801}\times 10^{3}\) +\(9801\) -\({1.153}\times 10^{-3}\) +\(1.153 \times 10^{-3}\) -\({1.154}\times 10^{-6}\) +\(1.154 \times 10^{-6}\) -\({1.706}\times 10^{3}\) +\(1706\) -\({73.5}\times 10^{-3}\) +\(7.35 \times 10^{-2}\) -\({2.14}\times 10^{9}\) +\(2.14 \times 10^{9}\)
    -\({9.793}\times 10^{3}\) +\(9793\) -\({1.021}\times 10^{-3}\) +\(1.021 \times 10^{-3}\) -\({1.023}\times 10^{-6}\) +\(1.023 \times 10^{-6}\) -\({2.339}\times 10^{3}\) +\(2339\) -\({72.7}\times 10^{-3}\) +\(7.27 \times 10^{-2}\) -\({2.18}\times 10^{9}\) +\(2.18 \times 10^{9}\)
    -\({9.781}\times 10^{3}\) +\(9781\) -\({910.8}\times 10^{-6}\) +\(9.108 \times 10^{-4}\) -\({913.5}\times 10^{-9}\) +\(9.135 \times 10^{-7}\) -\({3.170}\times 10^{3}\) +\(3170\) -\({72.0}\times 10^{-3}\) +\(7.20 \times 10^{-2}\) -\({2.22}\times 10^{9}\) +\(2.22 \times 10^{9}\)
    -\({9.768}\times 10^{3}\) +\(9768\) -\({817.4}\times 10^{-6}\) +\(8.174 \times 10^{-4}\) -\({821.0}\times 10^{-9}\) +\(8.210 \times 10^{-7}\) -\({4.247}\times 10^{3}\) +\(4247\) -\({71.2}\times 10^{-3}\) +\(7.12 \times 10^{-2}\) -\({2.25}\times 10^{9}\) +\(2.25 \times 10^{9}\)
    -\({9.752}\times 10^{3}\) +\(9752\) -\({738.0}\times 10^{-6}\) +\(7.380 \times 10^{-4}\) -\({742.4}\times 10^{-9}\) +\(7.424 \times 10^{-7}\) -\({5.629}\times 10^{3}\) +\(5629\) -\({70.4}\times 10^{-3}\) +\(7.04 \times 10^{-2}\) -\({2.26}\times 10^{9}\) +\(2.26 \times 10^{9}\)
    -\({9.734}\times 10^{3}\) +\(9734\) -\({669.9}\times 10^{-6}\) +\(6.699 \times 10^{-4}\) -\({675.1}\times 10^{-9}\) +\(6.751 \times 10^{-7}\) -\({7.385}\times 10^{3}\) +\(7385\) -\({69.6}\times 10^{-3}\) +\(6.96 \times 10^{-2}\) -\({2.28}\times 10^{9}\) +\(2.28 \times 10^{9}\)
    -\({9.714}\times 10^{3}\) +\(9714\) -\({611.2}\times 10^{-6}\) +\(6.112 \times 10^{-4}\) -\({617.3}\times 10^{-9}\) +\(6.173 \times 10^{-7}\) -\({9.595}\times 10^{3}\) +\(9595\) -\({68.8}\times 10^{-3}\) +\(6.88 \times 10^{-2}\) -\({2.28}\times 10^{9}\) +\(2.29 \times 10^{9}\)
    -\({9.693}\times 10^{3}\) +\(9693\) -\({560.5}\times 10^{-6}\) +\(5.605 \times 10^{-4}\) -\({567.2}\times 10^{-9}\) +\(5.672 \times 10^{-7}\) -\({12.35}\times 10^{3}\) +\(1.235 \times 10^{4}\) -\({67.9}\times 10^{-3}\) +\(6.79 \times 10^{-2}\) -\({2.29}\times 10^{9}\) +\(2.29 \times 10^{9}\)
    -\({9.670}\times 10^{3}\) +\(9670\) -\({516.2}\times 10^{-6}\) +\(5.162 \times 10^{-4}\) -\({523.7}\times 10^{-9}\) +\(5.237 \times 10^{-7}\) -\({15.76}\times 10^{3}\) +\(1.576 \times 10^{4}\) -\({67.1}\times 10^{-3}\) +\(6.71 \times 10^{-2}\) -\({2.28}\times 10^{9}\) +\(2.29 \times 10^{9}\)
    -\({9.645}\times 10^{3}\) +\(9645\) -\({477.6}\times 10^{-6}\) +\(4.776 \times 10^{-4}\) -\({485.7}\times 10^{-9}\) +\(4.857 \times 10^{-7}\) -\({19.95}\times 10^{3}\) +\(1.995 \times 10^{4}\) -\({66.2}\times 10^{-3}\) +\(6.62 \times 10^{-2}\) -\({2.28}\times 10^{9}\) +\(2.28 \times 10^{9}\)
    -\({9.619}\times 10^{3}\) +\(9619\) -\({443.5}\times 10^{-6}\) +\(4.435 \times 10^{-4}\) -\({452.3}\times 10^{-9}\) +\(4.523 \times 10^{-7}\) -\({25.04}\times 10^{3}\) +\(2.504 \times 10^{4}\) -\({65.4}\times 10^{-3}\) +\(6.54 \times 10^{-2}\) -\({2.26}\times 10^{9}\) +\(2.26 \times 10^{9}\)
    -\({9.592}\times 10^{3}\) +\(9592\) -\({413.5}\times 10^{-6}\) +\(4.135 \times 10^{-4}\) -\({422.9}\times 10^{-9}\) +\(4.229 \times 10^{-7}\) -\({31.20}\times 10^{3}\) +\(3.120 \times 10^{4}\) -\({64.5}\times 10^{-3}\) +\(6.45 \times 10^{-2}\) -\({2.25}\times 10^{9}\) +\(2.25 \times 10^{9}\)
    -\({9.563}\times 10^{3}\) +\(9563\) -\({386.9}\times 10^{-6}\) +\(3.869 \times 10^{-4}\) -\({396.9}\times 10^{-9}\) +\(3.969 \times 10^{-7}\) -\({38.60}\times 10^{3}\) +\(3.860 \times 10^{4}\) -\({63.6}\times 10^{-3}\) +\(6.36 \times 10^{-2}\) -\({2.22}\times 10^{9}\) +\(2.23 \times 10^{9}\)
    -\({9.533}\times 10^{3}\) +\(9533\) -\({363.1}\times 10^{-6}\) +\(3.631 \times 10^{-4}\) -\({373.7}\times 10^{-9}\) +\(3.737 \times 10^{-7}\) -\({47.42}\times 10^{3}\) +\(4.742 \times 10^{4}\) -\({62.7}\times 10^{-3}\) +\(6.27 \times 10^{-2}\) -\({2.20}\times 10^{9}\) +\(2.20 \times 10^{9}\)
    -\({9.501}\times 10^{3}\) +\(9501\) -\({341.9}\times 10^{-6}\) +\(3.419 \times 10^{-4}\) -\({353.0}\times 10^{-9}\) +\(3.530 \times 10^{-7}\) -\({57.87}\times 10^{3}\) +\(5.787 \times 10^{4}\) -\({61.8}\times 10^{-3}\) +\(6.18 \times 10^{-2}\) -\({2.17}\times 10^{9}\) +\(2.17 \times 10^{9}\)
    -\({9.468}\times 10^{3}\) +\(9468\) -\({322.9}\times 10^{-6}\) +\(3.229 \times 10^{-4}\) -\({334.5}\times 10^{-9}\) +\(3.345 \times 10^{-7}\) -\({70.18}\times 10^{3}\) +\(7.018 \times 10^{4}\) -\({60.8}\times 10^{-3}\) +\(6.08 \times 10^{-2}\) -\({2.14}\times 10^{9}\) +\(2.14 \times 10^{9}\)
    -\({9.434}\times 10^{3}\) +\(9434\) -\({305.7}\times 10^{-6}\) +\(3.057 \times 10^{-4}\) -\({317.9}\times 10^{-9}\) +\(3.179 \times 10^{-7}\) -\({84.61}\times 10^{3}\) +\(8.461 \times 10^{4}\) -\({59.9}\times 10^{-3}\) +\(5.99 \times 10^{-2}\) -\({2.10}\times 10^{9}\) +\(2.10 \times 10^{9}\)
    -\({9.399}\times 10^{3}\) +\(9399\) -\({290.2}\times 10^{-6}\) +\(2.902 \times 10^{-4}\) -\({302.9}\times 10^{-9}\) +\(3.029 \times 10^{-7}\) -\({101.4}\times 10^{3}\) +\(1.014 \times 10^{5}\) -\({58.9}\times 10^{-3}\) +\(5.89 \times 10^{-2}\) -\({2.07}\times 10^{9}\) +\(2.07 \times 10^{9}\)
    -\(1.938\) +\(1.9\) \(62.42\) -\({36.21}\times 10^{-6}\) +\(3.621 \times 10^{-5}\) -\({18.73}\times 10^{-6}\) +\(1.873 \times 10^{-5}\) \(12.77\) -\({5.18}\times 10^{-3}\) +\(5.18 \times 10^{-3}\) -\({42.2}\times 10^{6}\) +\(4.22 \times 10^{7}\)
    -\(1.939\) +\(1.9\) \(62.43\) -\({30.87}\times 10^{-6}\) +\(3.087 \times 10^{-5}\) -\({15.96}\times 10^{-6}\) +\(1.596 \times 10^{-5}\) \(18.94\) -\({5.13}\times 10^{-3}\) +\(5.13 \times 10^{-3}\) -\({43.1}\times 10^{6}\) +\(4.31 \times 10^{7}\)
    -\(1.938\) +\(1.9\) \(62.40\) -\({26.58}\times 10^{-6}\) +\(2.658 \times 10^{-5}\) -\({13.75}\times 10^{-6}\) +\(1.375 \times 10^{-5}\) \(27.62\) -\({5.07}\times 10^{-3}\) +\(5.07 \times 10^{-3}\) -\({44.0}\times 10^{6}\) +\(4.40 \times 10^{7}\)
    -\(1.937\) +\(1.9\) \(62.36\) -\({23.11}\times 10^{-6}\) +\(2.311 \times 10^{-5}\) -\({11.96}\times 10^{-6}\) +\(1.196 \times 10^{-5}\) \(39.64\) -\({5.02}\times 10^{-3}\) +\(5.02 \times 10^{-3}\) -\({45.0}\times 10^{6}\) +\(4.50 \times 10^{7}\)
    -\(1.934\) +\(1.9\) \(62.29\) -\({20.26}\times 10^{-6}\) +\(2.026 \times 10^{-5}\) -\({10.50}\times 10^{-6}\) +\(1.050 \times 10^{-5}\) \(56.00\) -\({4.96}\times 10^{-3}\) +\(4.96 \times 10^{-3}\) -\({45.9}\times 10^{6}\) +\(4.59 \times 10^{7}\)
    -\(1.932\) +\(1.9\) \(62.20\) -\({17.90}\times 10^{-6}\) +\(1.790 \times 10^{-5}\) -\({9.290}\times 10^{-6}\) +\(9.290 \times 10^{-6}\) \(77.99\) -\({4.90}\times 10^{-3}\) +\(4.90 \times 10^{-3}\) -\({46.7}\times 10^{6}\) +\(4.67 \times 10^{7}\)
    -\(1.928\) +\(1.9\) \(62.09\) -\({15.94}\times 10^{-6}\) +\(1.594 \times 10^{-5}\) -\({8.286}\times 10^{-6}\) +\(8.286 \times 10^{-6}\) \(107.2\) -\({4.84}\times 10^{-3}\) +\(4.84 \times 10^{-3}\) -\({47.2}\times 10^{6}\) +\(4.72 \times 10^{7}\)
    -\(1.925\) +\(1.9\) \(61.97\) -\({14.29}\times 10^{-6}\) +\(1.429 \times 10^{-5}\) -\({7.443}\times 10^{-6}\) +\(7.443 \times 10^{-6}\) \(145.3\) -\({4.78}\times 10^{-3}\) +\(4.78 \times 10^{-3}\) -\({47.5}\times 10^{6}\) +\(4.75 \times 10^{7}\)
    -\(1.920\) +\(1.9\) \(61.83\) -\({12.89}\times 10^{-6}\) +\(1.289 \times 10^{-5}\) -\({6.732}\times 10^{-6}\) +\(6.732 \times 10^{-6}\) \(194.7\) -\({4.72}\times 10^{-3}\) +\(4.72 \times 10^{-3}\) -\({47.7}\times 10^{6}\) +\(4.77 \times 10^{7}\)
    -\(1.916\) +\(1.9\) \(61.68\) -\({11.71}\times 10^{-6}\) +\(1.171 \times 10^{-5}\) -\({6.126}\times 10^{-6}\) +\(6.126 \times 10^{-6}\) -\(258.0\) +\(258\) -\({4.66}\times 10^{-3}\) +\(4.66 \times 10^{-3}\) -\({47.8}\times 10^{6}\) +\(4.78 \times 10^{7}\)
    -\(1.911\) +\(1.9\) \(61.52\) -\({10.69}\times 10^{-6}\) +\(1.069 \times 10^{-5}\) -\({5.608}\times 10^{-6}\) +\(5.608 \times 10^{-6}\) \(338.1\) -\({4.59}\times 10^{-3}\) +\(4.59 \times 10^{-3}\) -\({47.7}\times 10^{6}\) +\(4.77 \times 10^{7}\)
    -\(1.905\) +\(1.9\) \(61.34\) -\({9.808}\times 10^{-6}\) +\(9.808 \times 10^{-6}\) -\({5.162}\times 10^{-6}\) +\(5.162 \times 10^{-6}\) \(438.5\) -\({4.53}\times 10^{-3}\) +\(4.53 \times 10^{-3}\) -\({47.5}\times 10^{6}\) +\(4.75 \times 10^{7}\)
    -\(1.899\) +\(1.9\) \(61.16\) -\({9.046}\times 10^{-6}\) +\(9.046 \times 10^{-6}\) -\({4.775}\times 10^{-6}\) +\(4.775 \times 10^{-6}\) \(563.2\) -\({4.46}\times 10^{-3}\) +\(4.46 \times 10^{-3}\) -\({47.2}\times 10^{6}\) +\(4.72 \times 10^{7}\)
    -\(1.893\) +\(1.9\) \(60.96\) -\({8.381}\times 10^{-6}\) +\(8.381 \times 10^{-6}\) -\({4.438}\times 10^{-6}\) +\(4.438 \times 10^{-6}\) \(716.9\) -\({4.39}\times 10^{-3}\) +\(4.39 \times 10^{-3}\) -\({46.8}\times 10^{6}\) +\(4.68 \times 10^{7}\)
    -\(1.887\) +\(1.9\) \(60.75\) -\({7.797}\times 10^{-6}\) +\(7.797 \times 10^{-6}\) -\({4.144}\times 10^{-6}\) +\(4.144 \times 10^{-6}\) \(904.5\) -\({4.32}\times 10^{-3}\) +\(4.32 \times 10^{-3}\) -\({46.2}\times 10^{6}\) +\(4.62 \times 10^{7}\)
    -\(1.880\) +\(1.9\) \(60.53\) -\({7.283}\times 10^{-6}\) +\(7.283 \times 10^{-6}\) -\({3.884}\times 10^{-6}\) +\(3.884 \times 10^{-6}\) -\({1.132}\times 10^{3}\) +\(1132\) -\({4.25}\times 10^{-3}\) +\(4.25 \times 10^{-3}\) -\({45.5}\times 10^{6}\) +\(4.55 \times 10^{7}\)
    -\(1.873\) +\(1.9\) \(60.30\) -\({6.828}\times 10^{-6}\) +\(6.828 \times 10^{-6}\) -\({3.655}\times 10^{-6}\) +\(3.655 \times 10^{-6}\) -\({1.405}\times 10^{3}\) +\(1405\) -\({4.18}\times 10^{-3}\) +\(4.18 \times 10^{-3}\) -\({44.8}\times 10^{6}\) +\(4.48 \times 10^{7}\)
    -\(1.865\) +\(1.9\) \(60.06\) -\({6.423}\times 10^{-6}\) +\(6.423 \times 10^{-6}\) -\({3.452}\times 10^{-6}\) +\(3.452 \times 10^{-6}\) -\({1.731}\times 10^{3}\) +\(1731\) -\({4.11}\times 10^{-3}\) +\(4.11 \times 10^{-3}\) -\({44.0}\times 10^{6}\) +\(4.40 \times 10^{7}\)
    -\(1.858\) +\(1.9\) \(59.81\) -\({6.061}\times 10^{-6}\) +\(6.061 \times 10^{-6}\) -\({3.271}\times 10^{-6}\) +\(3.271 \times 10^{-6}\) -\({2.118}\times 10^{3}\) +\(2118\) -\({4.04}\times 10^{-3}\) +\(4.04 \times 10^{-3}\) -\({43.2}\times 10^{6}\) +\(4.32 \times 10^{7}\)
    @@ -539,12 +540,12 @@

    8.1 Precipitation observations

    The winter of 2016-2017 (water year 2017) was a record wet year for much of California. Figure 8.6 shows a hyetograph the daily values for that year.

    -
    library(ggplot2)
-ghcn_prcp2 <- data.frame(date = ghcn_data$DATE, wy = ghcn_data$wy, prcp = ghcn_data$PRCP )
-ggplot(subset(ghcn_prcp2, wy==2017), aes(x=date, y=prcp)) + 
-  geom_bar(stat="identity",color="red") +
-  labs(x="", y="precipitation, inch/day") +
-  scale_x_date(date_breaks = "1 month", date_labels =  "%b %d")
    +
    library(ggplot2)
+ghcn_prcp2 <- data.frame(date = ghcn_data$DATE, wy = ghcn_data$wy, prcp = ghcn_data$PRCP )
+ggplot(subset(ghcn_prcp2, wy==2017), aes(x=date, y=prcp)) + 
+  geom_bar(stat="identity",color="red") +
+  labs(x="", y="precipitation, inch/day") +
+  scale_x_date(date_breaks = "1 month", date_labels =  "%b %d")
    Daily Precipitation for San Jose, CA for water year 2017

    @@ -552,22 +553,22 @@

    8.1 Precipitation observations

    While many other statistics could be calculated to characterize precipitation, only a handful more will be shown here. One will use a convenient function of the seas package. This is used in Figure 8.7.

    -
    library(tidyverse)
-#The average precipitation rate for rainy days (with more then 0.01 inch)
-avgrainrate <- ghcn_prcp2[ghcn_prcp2$prcp > 0.01,] |> group_by(wy) |>
-  summarise(prcp = mean(prcp))
-#the number of rainy days per year
-nraindays <- ghcn_prcp2[ghcn_prcp2$prcp > 0.01,] |> group_by(wy) |>
-  summarise(nraindays = length(prcp))
-#Find length of consecutive dry and wet spells for the record
-days.dry.wet <- seas::interarrival(ghcn_prcp, var = "prcp", p.cut = 0.01, inv = FALSE)
-#add a water year column to the result
-days.dry.wet$wy <- sapply(days.dry.wet$date, wateryr)
-res <- days.dry.wet |> group_by(wy) |> summarise(cdd = median(dry, na.rm=TRUE), cwd = median(wet, na.rm=TRUE))
-res_long <- pivot_longer(res, -wy, names_to="statistic", values_to="consecutive_days")
-ggplot(res_long, aes(x = wy, y = consecutive_days)) +
-  geom_bar(aes(fill = statistic),stat = "identity", position = "dodge")+
-  xlab("") + ylab("Median consecutive days")
    +
    library(tidyverse)
+#The average precipitation rate for rainy days (with more then 0.01 inch)
+avgrainrate <- ghcn_prcp2[ghcn_prcp2$prcp > 0.01,] |> group_by(wy) |>
+  summarise(prcp = mean(prcp))
+#the number of rainy days per year
+nraindays <- ghcn_prcp2[ghcn_prcp2$prcp > 0.01,] |> group_by(wy) |>
+  summarise(nraindays = length(prcp))
+#Find length of consecutive dry and wet spells for the record
+days.dry.wet <- seas::interarrival(ghcn_prcp, var = "prcp", p.cut = 0.01, inv = FALSE)
+#add a water year column to the result
+days.dry.wet$wy <- sapply(days.dry.wet$date, wateryr)
+res <- days.dry.wet |> group_by(wy) |> summarise(cdd = median(dry, na.rm=TRUE), cwd = median(wet, na.rm=TRUE))
+res_long <- pivot_longer(res, -wy, names_to="statistic", values_to="consecutive_days")
+ggplot(res_long, aes(x = wy, y = consecutive_days)) +
+  geom_bar(aes(fill = statistic),stat = "identity", position = "dodge")+
+  xlab("") + ylab("Median consecutive days")
    Median concecutive dry days (cdd) and wet days (cwd) for each water year.

    @@ -595,33 +596,33 @@

    8.2 Precipitation frequencyExample 8.2 Use the 2017 water year of rainfall data for the city of San Jose, to plot the relationship between intensity and duration for the 1, 3, 7, and 30-day events.

    Begin by calculating the necessary intensity and duration values.

    -
    #First extract one water year of data
-df.one.year <- subset(ghcn_prcp, date>=as.Date("2016-10-01") & 
-                        date<=as.Date("2017-09-30"))
-#Calculate the running mean value for the defined durations
-dur <- c(1,3,7,30)
-px <- numeric(length(dur))
-for (i in 1:4) {
-  px[i] <- max(zoo::rollmean(df.one.year$prcp,dur[i]))
-}
-#create the intensity-duration data frame
-df.id <- data.frame(duration=dur,intensity=px)
    +
    #First extract one water year of data
+df.one.year <- subset(ghcn_prcp, date>=as.Date("2016-10-01") & 
+                        date<=as.Date("2017-09-30"))
+#Calculate the running mean value for the defined durations
+dur <- c(1,3,7,30)
+px <- numeric(length(dur))
+for (i in 1:4) {
+  px[i] <- max(zoo::rollmean(df.one.year$prcp,dur[i]))
+}
+#create the intensity-duration data frame
+df.id <- data.frame(duration=dur,intensity=px)

    Fit the theoretical curve (Equation (8.1)) using the nonlinear least squares function of the stats package (included with a base R installation), and plot the results.

    -
    #fit a power curve to the data
-fit <- stats::nls(intensity ~ a*duration^b, data=df.id, start=list(a=1,b=-0.5))
-print(signif(coef(fit),3))
-#>      a      b 
-#>  1.850 -0.751
-#find estimated y-values using the fit
-df.id$intensity_est <- predict(fit, list(x = df.id$duration))
-#duration-intensity plot with base graphics
-plot(x=df.id$duration,y=df.id$intensity,log='xy', pch=1, xaxt="n",
-     xlab="Duration, day" , ylab="Intensity, inches/day")
-lines(x=df.id$duration,y=df.id$intensity_est,lty=2)
-abline( h = c(seq( 0.1,1,0.1),2.0), lty = 3, col = "lightgray")
-abline( v = c(1,2,3,4,5,7,10,15,20,30), lty = 3, col = "lightgray")
-axis(side = 1, at =c(1,2,3,4,5,7,10,15,20,30) ,labels = T)
-axis(side = 2, at =c(seq( 0.1,1,0.1),2.0) ,labels = T)
    +
    #fit a power curve to the data
+fit <- stats::nls(intensity ~ a*duration^b, data=df.id, start=list(a=1,b=-0.5))
+print(signif(coef(fit),3))
+#>      a      b 
+#>  1.850 -0.751
+#find estimated y-values using the fit
+df.id$intensity_est <- predict(fit, list(x = df.id$duration))
+#duration-intensity plot with base graphics
+plot(x=df.id$duration,y=df.id$intensity,log='xy', pch=1, xaxt="n",
+     xlab="Duration, day" , ylab="Intensity, inches/day")
+lines(x=df.id$duration,y=df.id$intensity_est,lty=2)
+abline( h = c(seq( 0.1,1,0.1),2.0), lty = 3, col = "lightgray")
+abline( v = c(1,2,3,4,5,7,10,15,20,30), lty = 3, col = "lightgray")
+axis(side = 1, at =c(1,2,3,4,5,7,10,15,20,30) ,labels = T)
+axis(side = 2, at =c(seq( 0.1,1,0.1),2.0) ,labels = T)
    Intensity-duration relationship for water year 2017. Calculated values are based on daily data; theoretical is the power curve fit.

    @@ -639,9 +640,9 @@

    8.2 Precipitation frequency8.3 Precipitation gauge consistency – double mass curves

    The method of using double mass curves to identify changes in an obervation method (such as new instrumentation or a change of location) can be applied to precipitation gauges or any other type of measurement. This method is demonstrated with an example from the U.S. Geological survey (Searcy & Hardison, 1960).

    The first step is to compile data for a gauge (or better, a set of gauges) that are known to be unperturbed (Station A in the sample data set), and for a suspect gauge though to have experienced a change (Station X is this case).

    -
    annual_data <- hydromisc::precip_double_mass
-knitr::kable(annual_data, digits = 2) |> 
-  kableExtra::kable_paper(bootstrap_options = "striped", full_width = F)
    +
    annual_data <- hydromisc::precip_double_mass
+knitr::kable(annual_data, digits = 2) |> 
+  kableExtra::kable_paper(bootstrap_options = "striped", full_width = F)
    @@ -847,20 +848,20 @@

    8.3 Precipitation gauge consisten

    Accumulate the (annual) precipitation (measured in inches) and plot the values for the suspect station against the reference station(s), as in Figure 8.10 .

    -
    annual_sum <- data.frame(year = annual_data$Year, 
-                         sum_A = cumsum(annual_data$Station_A),
-                         sum_X = cumsum(annual_data$Station_X))
-
-#create scatterplot with a label on every point
-library(ggplot2)
-library(ggrepel)
-#> Warning: package 'ggrepel' was built under R version 4.2.3
-ggplot(annual_sum, aes(sum_X,sum_A, label = year)) +
-  geom_point() +
-  geom_text_repel(size=3, direction = "y") + 
-  labs(x="Cumulative precipitation at Station A, in", 
-       y="Cumulative precipitation at Station X, in") +
-  theme_bw()
    +
    annual_sum <- data.frame(year = annual_data$Year, 
+                         sum_A = cumsum(annual_data$Station_A),
+                         sum_X = cumsum(annual_data$Station_X))
+
+#create scatterplot with a label on every point
+library(ggplot2)
+library(ggrepel)
+#> Warning: package 'ggrepel' was built under R version 4.2.3
+ggplot(annual_sum, aes(sum_X,sum_A, label = year)) +
+  geom_point() +
+  geom_text_repel(size=3, direction = "y") + 
+  labs(x="Cumulative precipitation at Station A, in", 
+       y="Cumulative precipitation at Station X, in") +
+  theme_bw()
    A double mass curve.

    @@ -870,8 +871,8 @@

    8.3 Precipitation gauge consisten

    The break in slope between 1930 and 1931 appears clear. This should checked with records for the station to verify whether changes did occur at that time. If the data from Station X are to be used to fill other records or estimate long-term averages, the inconsistency needs to be corrected.

    One method to highlight the year at which the break occurs is to plot the residuals from a best fit line to the cumulative data from the two stations, as illustrated by the Food and Agriculture Orgainization FAO. (Allen & United Nations, 1998)

    -
    linfit = lm(sum_X ~ sum_A, data = annual_sum)
-plot(x=annual_sum$year,linfit$residuals, xlab = "Year",ylab = "Residual of regression")
    +
    linfit = lm(sum_X ~ sum_A, data = annual_sum)
+plot(x=annual_sum$year,linfit$residuals, xlab = "Year",ylab = "Residual of regression")
    Residuals of the linear fit to the double-mass curve.

    @@ -885,23 +886,23 @@

    8.3 Precipitation gauge consisten \tag{8.3} \end{equation}\] where b2 and b1 are the slopes after and before the break in slope, respectively, yi is original precipitation data, and y’i is the adjusted precipitation. This can be applied as follows.

    -
    b1 <- lm(sum_X ~ sum_A, data = subset(annual_sum, year <= 1930))$coefficients[['sum_A']]
-b2 <- lm(sum_X ~ sum_A, data = subset(annual_sum, year > 1930))$coefficients[['sum_A']]
-
-#Adjust early values and concatenate to later values for Station X
-adjusted_X <- c(annual_data$Station_X[annual_data$Year <= 1930]*b2/b1, 
-                annual_data$Station_X[annual_data$Year > 1930])
-annual_sum_adj <- data.frame(year = annual_data$Year, 
-                         sum_A = cumsum(annual_data$Station_A),
-                         sum_X = cumsum(adjusted_X))
-
-#Check that slope now appears more consistent
-ggplot(annual_sum_adj, aes(sum_X,sum_A, label = year)) +
-  geom_point() +
-  geom_text_repel(size=3, direction = "y") + 
-  labs(x="Cumulative precipitation at Station A, in", 
-       y="Cumulative adjusted precipitation at Station X, in") +
-  theme_bw()
    +
    b1 <- lm(sum_X ~ sum_A, data = subset(annual_sum, year <= 1930))$coefficients[['sum_A']]
+b2 <- lm(sum_X ~ sum_A, data = subset(annual_sum, year > 1930))$coefficients[['sum_A']]
+
+#Adjust early values and concatenate to later values for Station X
+adjusted_X <- c(annual_data$Station_X[annual_data$Year <= 1930]*b2/b1, 
+                annual_data$Station_X[annual_data$Year > 1930])
+annual_sum_adj <- data.frame(year = annual_data$Year, 
+                         sum_A = cumsum(annual_data$Station_A),
+                         sum_X = cumsum(adjusted_X))
+
+#Check that slope now appears more consistent
+ggplot(annual_sum_adj, aes(sum_X,sum_A, label = year)) +
+  geom_point() +
+  geom_text_repel(size=3, direction = "y") + 
+  labs(x="Cumulative precipitation at Station A, in", 
+       y="Cumulative adjusted precipitation at Station X, in") +
+  theme_bw()
    A double mass curve using adjusted data at Station X.

    @@ -923,13 +924,13 @@

    8.4 Precipitation interpolation a

    With the advent of geographical information system (GIS) software, manual interpolation is not used. Rather, more advanced spatial analysis is performed to interpolate precipitation onto a continuous grid, where the uncertainty (or skill) of different methods can be assessed. Spatial analysis methods to do this are outlined in many other references, such as Spatial Data Science and the related book Spatial Data Science with applications in R, or the reference Geocomputation with R. (Lovelace et al., 2019; Pebesma & Bivand, 2023)

    There are also many sources of precipitation data already interpolated to a regular grid. the geodata package provides access to many data sets, including the Worldclim biophysical data. Another source of global precipitation data, available at daily to monthly scales, is the CHIRPS data set, which has been widely used in many studies. An example of obtaining and plotting average annual precipitation over Santa Clara County is illustrated below.

    -
    #Load precipitation in mm, already cropped to cover most of California
-datafile <- system.file("extdata", "prcp_cropped.tif", package="hydromisc")
-prcp <- terra::rast(datafile)
-scc_bound <- terra::vect(hydromisc::scc_county)
-scc_precip <- terra::crop(prcp, scc_bound)
-terra::plot(scc_precip, plg=list(title="Precip\n(mm)", title.cex=0.7))
-terra::plot(scc_bound, add=TRUE)
    +
    #Load precipitation in mm, already cropped to cover most of California
+datafile <- system.file("extdata", "prcp_cropped.tif", package="hydromisc")
+prcp <- terra::rast(datafile)
+scc_bound <- terra::vect(hydromisc::scc_county)
+scc_precip <- terra::crop(prcp, scc_bound)
+terra::plot(scc_precip, plg=list(title="Precip\n(mm)", title.cex=0.7))
+terra::plot(scc_bound, add=TRUE)
    Annual Average Precipitation over Santa Clara County, mm

    @@ -937,15 +938,15 @@

    8.4 Precipitation interpolation a

    Spatial statistics are easily obtained using terra, a versatile package for spatial analysis.

    -
    terra::summary(scc_precip)
-#>  chirps.v2.0.1981.2020.40yrs
-#>  Min.   : 197.1             
-#>  1st Qu.: 354.9             
-#>  Median : 447.9             
-#>  Mean   : 542.3             
-#>  3rd Qu.: 652.3             
-#>  Max.   :1297.2             
-#>  NA's   :5
    +
    terra::summary(scc_precip)
+#>  chirps.v2.0.1981.2020.40yrs
+#>  Min.   : 197.1             
+#>  1st Qu.: 354.9             
+#>  Median : 447.9             
+#>  Mean   : 542.3             
+#>  3rd Qu.: 652.3             
+#>  Max.   :1297.2             
+#>  NA's   :5

    diff --git a/docs/units-in-fluid-mechanics.html b/docs/units-in-fluid-mechanics.html index 80d5845..2fe06b9 100644 --- a/docs/units-in-fluid-mechanics.html +++ b/docs/units-in-fluid-mechanics.html @@ -25,7 +25,7 @@ - + @@ -210,6 +210,7 @@
  • 4.7 Parallel pipes: solving a system of equations
  • 4.8 Simple pipe networks: the Hardy-Cross method
    • @@ -488,7 +489,7 @@

    Chapter 1 Units in Fluid Mechanic \[{1*10^{-6}~m^2/s}*\frac{1 ~ft^2/s}{0.0929~m^2/s}=1.076*10^{-5} ~ft^2/s\]

    Another example converts between two quantities in the US system: 100 gallons per minute to cfs: \[{100 ~gpm}*\frac{1 ~cfs}{448.8 ~gpm}=0.223 ~cfs\] -The units package in R can do these conversions and more, and also checks that conversions are permissible (producing an error if incompatible units are used).

    +The units package in R can do these conversions and more, and also checks that conversions are permissible (producing an error if incompatible units are used).

    units::units_options(set_units_mode = "symbols")
 Q_gpm <- units::set_units(100, gallon/min)
 Q_gpm
diff --git a/docs/water-flowing-in-pipes-energy-losses.html b/docs/water-flowing-in-pipes-energy-losses.html
index 4b81fc2..f5f805f 100644
--- a/docs/water-flowing-in-pipes-energy-losses.html
+++ b/docs/water-flowing-in-pipes-energy-losses.html
@@ -25,7 +25,7 @@
 
 
 
-
+
 
   
   
@@ -210,6 +210,7 @@
 
 
  • 4.7 Parallel pipes: solving a system of equations
    • 
 
  • 4.8 Simple pipe networks: the Hardy-Cross method
    • 
@@ -403,16 +404,16 @@ 
    4.3 Solving Pipe friction problem
 
 1
 Q (or V), D, ks, L
-hL
+hL
 
 
 2
-hL, D, ks, L
+hL, D, ks, L
 Q (or V)
 
 
 3
-hL, Q (or V), ks, L
+hL, Q (or V), ks, L
 D
 
 
@@ -599,6 +600,54 @@ 
    4.6.2 Solving for D using an equa
 cat(sprintf("D = %.3f m\n", ans))
 #> D = 0.501 m

    +
    +

    4.6.3 Solving for D using an R package

    +

    The hydraulics R package implements an equation solving technique like that above to allow the direct solution of Type 3 problems. the prior example is solved using that package as shown beliow.

    +
    ans <- hydraulics::darcyweisbach(Q=0.416, hf=0.6, L=100, ks=0.000046, nu=1.023e-06, ret_units = TRUE, units = c('SI'))
+knitr::kable(format(as.data.frame(ans), digits = 3), format = "pipe")
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ans
    Q0.416 [m^3/s]
    V2.11 [m/s]
    L100 [m]
    D0.501 [m]
    hf0.6 [m]
    f0.0133 [1]
    ks4.6e-05 [m]
    Re1032785 [1]
    +

    4.7 Parallel pipes: solving a system of equations

    @@ -631,46 +680,46 @@

    4.7 Parallel pipes: solving a sys \end{equation}\]

    The other two equations are the Colebrook equation (4.8) for solving for the friction factor for each pipe.

    These four equations can be solved simultaneously using an equation solver, such as the fsolve function in the R package pracma.

    -
    #assign known inputs - SI units
-Qsum <- 0.5
-D1 <- 0.2
-D2 <- 0.3
-L1 <- 400
-L2 <- 600
-ks <- 0.000025
-g <- 9.81
-nu <- hydraulics::kvisc(T=100, units='SI')
-
-#Set up the function that sets up 4 unknowns (x) and 4 equations (y)
-F_trial <- function(x) {
-  V1 <- x[1]
-  V2 <- x[2]
-  f1 <- x[3]
-  f2 <- x[4]
-  Re1 <- V1*D1/nu
-  Re2 <- V2*D2/nu
-  y <- numeric(length(x))
-  #Continuity - flows in each branch must add to total
-  y[1] <- V1*pi/4*D1^2 + V2*pi/4*D2^2 - Qsum
-  #Darcy-Weisbach equation for head loss - must be equal in each branch
-  y[2] <- f1*L1*V1^2/(D1*2*g) - f2*L2*V2^2/(D2*2*g)
-  #Colebrook equation for friction factors
-  y[3] <- -2.0*log10((ks/D1)/3.7 + 2.51/(Re1*(f1^0.5)))-1/(f1^0.5)
-  y[4] <- -2.0*log10((ks/D2)/3.7 + 2.51/(Re2*(f2^0.5)))-1/(f2^0.5)
-  return(y)
-}
-
-#provide initial guesses for unknowns and run the fsolve command
-xstart <- c(2.0, 2.0, 0.01, 0.01)
-z <- pracma::fsolve(F_trial, xstart)
-
-#prepare some results to print
-Q1 <- z$x[1]*pi/4*D1^2
-Q2 <- z$x[2]*pi/4*D2^2
-hf1 <- z$x[3]*L1*z$x[1]^2/(D1*2*g)
-hf2 <- z$x[4]*L2*z$x[2]^2/(D2*2*g)
-cat(sprintf("Q1=%.2f, Q2=%.2f, V1=%.1f, V2=%.1f, hf1=%.1f, hf2=%.1f, f1=%.3f, f2=%.3f\n", Q1,Q2,z$x[1],z$x[2],hf1,hf2,z$x[3],z$x[4]))
-#> Q1=0.15, Q2=0.35, V1=4.8, V2=5.0, hf1=30.0, hf2=30.0, f1=0.013, f2=0.012
    +
    #assign known inputs - SI units
+Qsum <- 0.5
+D1 <- 0.2
+D2 <- 0.3
+L1 <- 400
+L2 <- 600
+ks <- 0.000025
+g <- 9.81
+nu <- hydraulics::kvisc(T=100, units='SI')
+
+#Set up the function that sets up 4 unknowns (x) and 4 equations (y)
+F_trial <- function(x) {
+  V1 <- x[1]
+  V2 <- x[2]
+  f1 <- x[3]
+  f2 <- x[4]
+  Re1 <- V1*D1/nu
+  Re2 <- V2*D2/nu
+  y <- numeric(length(x))
+  #Continuity - flows in each branch must add to total
+  y[1] <- V1*pi/4*D1^2 + V2*pi/4*D2^2 - Qsum
+  #Darcy-Weisbach equation for head loss - must be equal in each branch
+  y[2] <- f1*L1*V1^2/(D1*2*g) - f2*L2*V2^2/(D2*2*g)
+  #Colebrook equation for friction factors
+  y[3] <- -2.0*log10((ks/D1)/3.7 + 2.51/(Re1*(f1^0.5)))-1/(f1^0.5)
+  y[4] <- -2.0*log10((ks/D2)/3.7 + 2.51/(Re2*(f2^0.5)))-1/(f2^0.5)
+  return(y)
+}
+
+#provide initial guesses for unknowns and run the fsolve command
+xstart <- c(2.0, 2.0, 0.01, 0.01)
+z <- pracma::fsolve(F_trial, xstart)
+
+#prepare some results to print
+Q1 <- z$x[1]*pi/4*D1^2
+Q2 <- z$x[2]*pi/4*D2^2
+hf1 <- z$x[3]*L1*z$x[1]^2/(D1*2*g)
+hf2 <- z$x[4]*L2*z$x[2]^2/(D2*2*g)
+cat(sprintf("Q1=%.2f, Q2=%.2f, V1=%.1f, V2=%.1f, hf1=%.1f, hf2=%.1f, f1=%.3f, f2=%.3f\n", Q1,Q2,z$x[1],z$x[2],hf1,hf2,z$x[3],z$x[4]))
+#> Q1=0.15, Q2=0.35, V1=4.8, V2=5.0, hf1=30.0, hf2=30.0, f1=0.013, f2=0.012

    If the fsolve command fails, a simple solution is sometimes to revise your initial guesses and try again. There are other solvers in R and every other scripting language that can be similarly implemented.

    If the simplification were applied for fixed f values, then Equations (4.11) and (4.12) can be solved simultaneously for V1 and V2.

    @@ -700,17 +749,17 @@

    4.8 Simple pipe networks: the Har

    Input for this system, assuming fixed f values, would look like the following. (If fixed K values are provided, f, L and D are not needed). These f values were estimated using \(ks=0.00025 m\) in the form of the Colebrook equation for fully rough flows, Equation (4.5).

    -
    dfpipes <- data.frame(
-  ID = c(1,2,3,4,5,6,7,8,9,10),                                #pipe ID
-  D = c(0.3,0.2,0.2,0.2,0.2,0.15,0.25,0.15,0.15,0.25),         #diameter in m
-  L = c(250,100,125,125,100,100,125,100,100,125),              #length in m
-  f = c(.01879,.02075,.02075,.02075,.02075,.02233,.01964,.02233,.02233,.01964)
-)
-loops <- list(c(1,2,3,4,5),c(4,6,7,8),c(3,9,10,6))
-Qs <- list(c(.040,.040,.02,-.02,-.04),c(.02,0,0,-.02),c(-.02,.02,0,0))
    +
    dfpipes <- data.frame(
+  ID = c(1,2,3,4,5,6,7,8,9,10),                                #pipe ID
+  D = c(0.3,0.2,0.2,0.2,0.2,0.15,0.25,0.15,0.15,0.25),         #diameter in m
+  L = c(250,100,125,125,100,100,125,100,100,125),              #length in m
+  f = c(.01879,.02075,.02075,.02075,.02075,.02233,.01964,.02233,.02233,.01964)
+)
+loops <- list(c(1,2,3,4,5),c(4,6,7,8),c(3,9,10,6))
+Qs <- list(c(.040,.040,.02,-.02,-.04),c(.02,0,0,-.02),c(-.02,.02,0,0))

    Running the hardycross function and looking at the output after three iterations (defined by n_iter):

    -
    ans <- hydraulics::hardycross(dfpipes = dfpipes, loops = loops, Qs = Qs, n_iter = 3, units = "SI")
-knitr::kable(ans$dfloops, digits = 4, format = "pipe", padding=0)
    +
    ans <- hydraulics::hardycross(dfpipes = dfpipes, loops = loops, Qs = Qs, n_iter = 3, units = "SI")
+knitr::kable(ans$dfloops, digits = 4, format = "pipe", padding=0)
    @@ -788,7 +837,7 @@

    4.8 Simple pipe networks: the Har

    The output pipe data frame has added columns, including the flow (where direction is that for the first loop containing the segment).

    -
    knitr::kable(ans$dfpipes, digits = 4, format = "pipe", padding=0)
    +
    knitr::kable(ans$dfpipes, digits = 4, format = "pipe", padding=0)
    @@ -884,17 +933,17 @@

    4.8 Simple pipe networks: the Har

    While the Hardy-Cross method is often used with fixed f (or K) values when it is used in exercises performed by hand, the use of the Colebrook equation allows friction losses to vary with Reynolds number. To use this approach the input data must include absolute roughness. Example values are included here:

    -
    dfpipes <- data.frame(
-  ID = c(1,2,3,4,5,6,7,8,9,10),                         #pipe ID
-  D = c(0.3,0.2,0.2,0.2,0.2,0.15,0.25,0.15,0.15,0.25),  #diameter in m
-  L = c(250,100,125,125,100,100,125,100,100,125),       #length in m
-  ks = rep(0.00025,10)                                  #absolute roughness, m
-)
-loops <- list(c(1,2,3,4,5),c(4,6,7,8),c(3,9,10,6))
-Qs <- list(c(.040,.040,.02,-.02,-.04),c(.02,0,0,-.02),c(-.02,.02,0,0))
    +
    dfpipes <- data.frame(
+  ID = c(1,2,3,4,5,6,7,8,9,10),                         #pipe ID
+  D = c(0.3,0.2,0.2,0.2,0.2,0.15,0.25,0.15,0.15,0.25),  #diameter in m
+  L = c(250,100,125,125,100,100,125,100,100,125),       #length in m
+  ks = rep(0.00025,10)                                  #absolute roughness, m
+)
+loops <- list(c(1,2,3,4,5),c(4,6,7,8),c(3,9,10,6))
+Qs <- list(c(.040,.040,.02,-.02,-.04),c(.02,0,0,-.02),c(-.02,.02,0,0))

    The effect of allowing the calculation of f to be (correctly) dependent on velocity (via the Reynolds number) can be seen, though the effect on final flow values is small.

    -
    ans <- hydraulics::hardycross(dfpipes = dfpipes, loops = loops, Qs = Qs, n_iter = 3, units = "SI")
-knitr::kable(ans$dfpipes, digits = 4, format = "pipe", padding=0)
    +
    ans <- hydraulics::hardycross(dfpipes = dfpipes, loops = loops, Qs = Qs, n_iter = 3, units = "SI")
+knitr::kable(ans$dfpipes, digits = 4, format = "pipe", padding=0)
    diff --git a/ed-bookdown.bib b/ed-bookdown.bib index 09bcdc0..1ef38b3 100644 --- a/ed-bookdown.bib +++ b/ed-bookdown.bib @@ -1,13779 +1,4 @@ -@misc{noauthor_zotero_nodate, - title = {Zotero {\textbar} {Your} personal research assistant}, - url = {https://www.zotero.org/start}, - urldate = {2021-10-28}, -} - -@misc{noauthor_zotero_nodate-1, - title = {Zotero {\textbar} {Your} personal research assistant}, - url = {https://www.zotero.org/start}, - urldate = {2021-10-28}, - file = {Zotero | Your personal research assistant:C\:\\Users\\EdMaurer\\Zotero\\storage\\7TJGSCBK\\start.html:text/html}, -} - -@incollection{d_g_vaughan_observations_2013, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Observations: {Cryosphere}}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {D. G. Vaughan and J. C. Comiso and I. Allison and J. Carrasco and G. Kaser and R. Kwok and P. Mote and T. Murray and F. Paul and J. Ren and E. Rignot and O. Solomina and K. Steffen and T. Zhang}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley}, - year = {2013}, - pages = {317--382}, - annote = {The following values have no corresponding Zotero field:section: 4electronic-resource-num: 10.1017/CBO9781107415324.012}, -} - -@article{vano_climate_2010, - title = {Climate change impacts on water management and irrigated agriculture in the {Yakima} {River} {Basin}, {Washington}, {USA}}, - volume = {102}, - issn = {1573-1480}, - doi = {10.1007/s10584-010-9856-z}, - abstract = {The Yakima River Reservoir system supplies water to {\textasciitilde}180,000 irrigated hectares through the operation of five reservoirs with cumulative storage of {\textasciitilde}30\% mean annual river flow. Runoff is derived mostly from winter precipitation in the Cascade Mountains, much of which is stored as snowpack. Climate change is expected to result in earlier snowmelt runoff and reduced summer flows. Effects of these changes on irrigated agriculture were simulated using a reservoir system model coupled to a hydrological model driven by downscaled scenarios from 20 climate models archived by the 2007 Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report. We find earlier snowmelt results in increased water delivery curtailments. Historically, the basin experienced substantial water shortages in 14\% of years. Without adaptations, for IPCC A1B global emission scenarios, water shortages increase to 27\% (13\% to 49\% range) in the 2020s, to 33\% in the 2040s, and 68\% in the 2080s. For IPCC B1 emissions scenarios, shortages occur in 24\% (7\% to 54\%) of years in the 2020s, 31\% in the 2040s and 43\% in the 2080s. Historically unprecedented conditions where senior water rights holders suffer shortfalls occur with increasing frequency in both A1B and B1 scenarios. Economic losses include expected annual production declines of 5\%–16\%, with greater probabilities of operating losses for junior water rights holders.}, - number = {1}, - journal = {Climatic Change}, - author = {Vano, Julie A. and Scott, Michael J. and Voisin, Nathalie and Stöckle, Claudio O. and Hamlet, Alan F. and Mickelson, Kristian E. B. and Elsner, Marketa McGuire and Lettenmaier, Dennis P.}, - year = {2010}, - pages = {287--317}, - annote = {The following values have no corresponding Zotero field:label: Vano2010work-type: journal article}, -} - -@article{vano_sensitivity-based_2014, - title = {A sensitivity-based approach to evaluating future changes in {Colorado} {River} discharge}, - volume = {122}, - issn = {0165-0009}, - doi = {10.1007/s10584-013-1023-x}, - language = {English}, - number = {4}, - journal = {Climatic Change}, - author = {Vano, JulieA and Lettenmaier, DennisP}, - month = feb, - year = {2014}, - pages = {621--634}, - annote = {The following values have no corresponding Zotero field:alt-title: Climatic Changepublisher: Springer Netherlands}, -} - -@article{van_vuuren_rcp26_2011, - title = {{RCP2}.6: exploring the possibility to keep global mean temperature increase below 2°{C}}, - volume = {109}, - issn = {0165-0009}, - doi = {10.1007/s10584-011-0152-3}, - language = {English}, - number = {1-2}, - journal = {Climatic Change}, - author = {van Vuuren, Detlef and Stehfest, Elke and Elzen, Michel and Kram, Tom and Vliet, Jasper and Deetman, Sebastiaan and Isaac, Morna and Klein Goldewijk, Kees and Hof, Andries and Mendoza Beltran, Angelica and Oostenrijk, Rineke and Ruijven, Bas}, - month = nov, - year = {2011}, - pages = {95--116}, - annote = {The following values have no corresponding Zotero field:alt-title: Climatic Changepublisher: Springer Netherlands}, -} - -@article{van_rheenen_potential_2004, - title = {Potential implications of {PCM} climate change scenarios for {Sacramento}-{San} {Joaquin} {River} {Basin} hydrology and water resources}, - volume = {62}, - issn = {0165-0009}, - abstract = {The potential effects of climate change on the hydrology and water resources of the Sacramento - San Joaquin River Basin were evaluated using ensemble climate simulations generated by the U. S. Department of Energy and National Center for Atmospheric Research Parallel Climate Model (DOE/NCAR PCM). Five PCM scenarios were employed. The first three were ensemble runs from 1995 - 2099 with a 'business as usual' global emissions scenario, each with different atmospheric initializations. The fourth was a 'control climate' scenario with greenhouse gas emissions set at 1995 levels and run through 2099. The fifth was a historical climate simulation forced with evolving greenhouse gas concentrations from 1870 - 2000, from which a 50-year portion is taken for use in bias-correction of the other runs. From these global simulations, transient monthly temperature and precipitation sequences were statistically downscaled to produce continuous daily hydrologic model forcings, which drove a macro-scale hydrology model of the Sacramento - San Joaquin River Basins at a 1/8-degree spatial resolution, and produced daily streamflow sequences for each climate scenario. Each streamflow scenario was used in a water resources system model that simulated current and predicted future performance of the system. The progressive warming of the PCM scenarios (approximately 1.2 degreesC atmidcentury, and 2.2 degreesC by the 2090s), coupled with reductions in winter and spring precipitation ( from 10 to 25\%), markedly reduced late spring snowpack ( by as much as half on average by the end of the century). Progressive reductions in winter, spring, and summer streamflow were less severe in the northern part of the study domain than in the south, where a seasonality shift was apparent. Results from the water resources system model indicate that achieving and maintaining status quo ( control scenario climate) system performance in the future would be nearly impossible, given the altered climate scenario hydrologies. The most comprehensive of the mitigation alternatives examined satisfied only 87 - 96\% of environmental targets in the Sacramento system, and less than 80\% in the San Joaquin system. It is evident that demand modification and system infrastructure improvements will be required to account for the volumetric and temporal shifts in flows predicted to occur with future climates in the Sacramento - San Joaquin River basins.}, - language = {English}, - number = {1-3}, - journal = {Climatic Change}, - author = {Van Rheenen, N. T. and Wood, A. W. and Palmer, R. N. and Lettenmaier, D. P.}, - month = feb, - year = {2004}, - keywords = {model, nino southern-oscillation, simulation, streamflow, united-states}, - pages = {257--281}, - annote = {768FATimes Cited:11Cited References Count:30}, - annote = {The following values have no corresponding Zotero field:auth-address: Lettenmaier, DP Univ Washington, Dept Civil \& Environm Engn, 164 Wilcox Hall,POB 352700, Seattle, WA 98195 USA Univ Washington, Dept Civil \& Environm Engn, Seattle, WA 98195 USAaccession-num: ISI:000188531900011}, -} - -@article{julie_a_vano_understanding_2014, - title = {Understanding {Uncertainties} in {Future} {Colorado} {River} {Streamflow}}, - volume = {95}, - doi = {10.1175/bams-d-12-00228.1}, - abstract = {The Colorado River is the primary water source for more than 30 million people in the United States and Mexico. Recent studies that project streamf low changes in the Colorado River all project annual declines, but the magnitude of the projected decreases range from less than 10\% to 45\% by the mid-twenty-first century. To understand these differences, we address the questions the management community has raised: Why is there such a wide range of projections of impacts of future climate change on Colorado River streamflow, and how should this uncertainty be interpreted? We identify four major sources of disparities among studies that arise from both methodological and model differences. In order of importance, these are differences in 1) the global climate models (GCMs) and emission scenarios used; 2) the ability of land surface and atmospheric models to simulate properly the high-elevation runoff source areas; 3) the sensitivities of land surface hydrology models to precipitation and temperature changes; and 4) the methods used to statistically downscale GCM scenarios. In accounting for these differences, there is substantial evidence across studies that future Colorado River streamflow will be reduced under the current trajectories of anthropogenic greenhouse gas emissions because of a combination of strong temperature-induced runoff curtailment and reduced annual precipitation. Reconstructions of preinstrumental streamflows provide additional insights; the greatest risk to Colorado River streamf lows is a multidecadal drought, like that observed in paleoreconstructions, exacerbated by a steady reduction in flows due to climate change. This could result in decades of sustained streamflows much lower than have been observed in the {\textasciitilde}100 years of instrumental record.}, - number = {1}, - journal = {Bulletin of the American Meteorological Society}, - author = {Julie A. Vano and Bradley Udall and Daniel R. Cayan and Jonathan T. Overpeck and Levi D. Brekke and Tapash Das and Holly C. Hartmann and Hugo G. Hidalgo and Martin Hoerling and Gregory J. McCabe and Kiyomi Morino and Robert S. Webb and Kevin Werner and Dennis P. Lettenmaier}, - year = {2014}, - pages = {59--78}, -} - -@article{van_oldenborgh_nino_2005, - title = {El {Niño} in a changing climate: a multi-model study}, - volume = {2}, - journal = {Ocean Science Discussions}, - author = {van Oldenborgh, G. J. and Philip, S. and Collins, M.}, - year = {2005}, - pages = {267--298}, -} - -@article{van_der_schrier_sensitivity_2011, - title = {The sensitivity of the {PDSI} to the {Thornthwaite} and {Penman}-{Monteith} parameterizations for potential evapotranspiration}, - volume = {116}, - issn = {0148-0227}, - doi = {10.1029/2010jd015001}, - abstract = {Potential evapotranspiration (PET) is one of the inputs to the Palmer Drought Severity Index (PDSI). A common approach to calculating PDSI is to use the Thornthwaite method for estimating PET because of its readily available input data: monthly mean temperatures. PET estimates based on Penman-type approaches are considered to be more physically realistic, but require more diverse input data. This study assesses the differences in global PDSI maps using the two estimates for PET. Annually accumulated PET estimates based on alternative Thornthwaite and Penman-Monteith, parameterizations have very different amplitudes. However, we show that PDSI values based on the two PET estimates are very similar, in terms of correlation, regional averages, trends, and in terms of identifying extremely dry or wet months. The reason for this insensitivity to the method of calculating PET relates to the calculations in the simple water balance model which is at the heart of the PDSI algorithm. It is shown that in many areas, actual evapotranspiration is limited by the availability of soil moisture and is at markedly lower levels compared to its potential value. In other areas, the water balance does change, but the quantity central to the calculation of the PDSI is, by construction, a reflection of the actual precipitation, which makes it largely insensitive to the use of the Thornthwaite PET rather than the Penman-Monteith PET. A secondary reason is that the impact of PET as input to a scaling parameter in the PDSI algorithm is very modest compared to the more dominant influence of the precipitation.}, - number = {D3}, - journal = {J. Geophys. Res.}, - author = {van der Schrier, G. and Jones, P. D. and Briffa, K. R.}, - year = {2011}, - keywords = {1812 Hydrology: Drought, 1818 Hydrology: Evapotranspiration, 3309 Atmospheric Processes: Climatology, PDSI, Penman-Monteith, potential evapotranspiration, Thornthwaite}, - pages = {D03106}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{van_den_dool_searching_1994, - title = {Searching for analogues, how long must one wait?}, - volume = {2A}, - number = {46}, - journal = {Tellus}, - author = {van den Dool, H. M.}, - year = {1994}, - pages = {314--324}, -} - -@article{uvo_statistical_2001, - title = {Statistical atmospheric downscaling for rainfall estimation in {Kyushu} {Island}, {Japan}}, - volume = {5}, - abstract = {The present paper develops linear regression models based on singular value decomposition (SVD) with the aim of downscaling atmospheric variables statistically to estimate average rainfall in the Chikugo River Basin, Kyushu Island, southern Japan, on a 12-hour basis. Models were designed to take only significantly correlated areas into account in the downscaling procedure, By using particularly precipitable water in combination with wind speeds at 850 hPa, correlation coefficients between observed and estimated precipitation exceeding 0.8 were reached. The correlations exhibited a seasonal variation with higher values during autumn and winter than during spring and summer. The SVD analysis preceding the model development highlighted three important features of the rainfall regime in southern Japan: (1) the so-called Bai-u front which is responsible for the majority of summer rainfall, (2) the strong circulation pattern associated with autumn rainfall, and (3) the strong influence of orographic lifting creating a pronounced east-west gradient across Kyushu Island. Results confirm the feasibility of establishing meaningful statistical relationships between atmospheric state and basin rainfall even at time scales of less than one day.}, - number = {2}, - journal = {Hydrol. Earth Syst. Sci.}, - author = {Uvo, C. B. and Olsson, J. and Morita, O. and Jinno, K. and Kawamura, A. and Nishiyama, K. and Koreeda, N. and Nakashima, T.}, - month = jun, - year = {2001}, - pages = {259--271}, - annote = {SI469AHHYDROL EARTH SYST SCI}, - annote = {The following values have no corresponding Zotero field:alt-title: Hydrol. Earth Syst. Sci.accession-num: ISI:000170790000012}, -} - -@book{usgcrp_climate_2017, - address = {Washington, DC, USA}, - title = {Climate {Science} {Special} {Report}: {Fourth} {National} {Climate} {Assessment}, {Volume} {I}}, - publisher = {U.S. Global Change Research Program}, - author = {USGCRP}, - editor = {Wuebbles, D. and Fahey, D. W. and Hibbard, K. A. and Dokken, D. J. and Stewart, B. C. and Maycock, T. K.}, - year = {2017}, -} - -@article{trigo_simulation_1999, - title = {Simulation of daily temperatures for climate change scenarios over {Portugal}: a neural network model approach}, - volume = {13}, - abstract = {Methods to assess the impact of global warming on the temperature regime of a single site are explored with reference to Coimbra in Portugal. The basis of the analysis is information taken from a climate change simulation performed with a state-of-the-art general circulation model (the Hadley Centre model). First, it is shown that the model is unable to reproduce accurately the statistics of daily maximum and minimum temperature at the site. Second, using a re-analysis data set, downscaling models are developed to predict site temperature from large-scale free atmosphere variables derived from the sea level pressure and 500 hPa geopotential height fields. In particular, the relative performances of linear models and non-linear artificial neural networks are compared using a set of rigorous validation techniques. It is shown that even a simple configuration of a 2-layer non-linear neural network significantly improves on the performance of a linear model. Finally, the non-linear neural network model is initialised with general circulation model output to construct scenarios of daily temperature at the present day (1970-79) and for a future decade (2090-99). These scenarios are analysed with special attention to the comparison of the frequencies of heat waves (days with maximum temperature greater than 35 degreesC) and cold spells (days with minimum temperature below 5 degreesC).}, - number = {1}, - journal = {Climate Research}, - author = {Trigo, R. M. and Palutikof, J. P.}, - month = sep, - year = {1999}, - pages = {45--59}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:000171723100004}, - annote = {V3097CLIMATE RES}, -} - -@incollection{trenberth_observations_2007, - address = {Cambridge, United Kingdom and New York, NY, USA.}, - title = {Observations: {Surface} and {Atmospheric} {Climate} {Change}}, - booktitle = {Climate {Change} 2007: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fourth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {Trenberth, K. E. and Jones, P. D. and Ambenje, P. and Bojariu, R. and Easterling, D. and Klein Tank, A. and Parker, D. and Rahimzadeh, F. and Renwick, J. A. and Rusticucci, M. and Soden, B. and Zhai, P.}, - editor = {Solomon, S. and Qin, D. and Manning, M. and Chen, Z. and Marquis, M. and Averyt, K. B. and Tignor, M. and Miller, H. L.}, - year = {2007}, -} - -@article{trenberth_changing_2003, - title = {The changing character of precipitation}, - volume = {84}, - issn = {0003-0007}, - abstract = {From a societal, weather, and climate perspective, precipitation intensity, duration, frequency, and phase are as much of concern as total amounts, as these factors determine the disposition of precipitation once it hits the ground and how much runs off. At the extremes of precipitation incidence are the events that give rise to floods and droughts, whose changes in occurrence and severity have an enormous impact on the environment and society. Hence, advancing understanding and the ability to model and predict the character of precipitation is vital but requires new approaches to examining data and models. Various mechanisms, storms and so forth, exist to bring about precipitation. Because the rate of precipitation, conditional on when it falls, greatly exceeds the rate of replenishment of moisture by surface evaporation, most precipitation comes from moisture already in the atmosphere at the time the storm begins, and transport of moisture by the storm-scale circulation into the storm is vital. Hence, the intensity of precipitation depends on available moisture, especially for heavy events. As climate warms, the amount of moisture in the atmosphere, which is governed by the Clausius-Clapeyron equation, is expected to rise much faster than the total precipitation amount, which is governed by the surface heat budget through evaporation. This implies that the main changes to be experienced are in the character of precipitation: increases in intensity must be offset by decreases in duration or frequency of events. The timing, duration, and intensity of precipitation can be systematically explored via the diurnal cycle, whose correct simulation in models remains an unsolved challenge of vital importance in global climate change. Typical problems include the premature initiation of convection, and precipitation events that are too light and too frequent. These challenges in observations, modeling, and understanding precipitation changes are being taken up in the NCAR "Water Cycle Across Scales" initiative, which will exploit the diurnal cycle as a test bed for a hierarchy of models to promote improvements in models.}, - language = {English}, - number = {9}, - journal = {Bulletin of the American Meteorological Society}, - author = {Trenberth, K. E. and Dai, A. G. and Rasmussen, R. M. and Parsons, D. B.}, - month = sep, - year = {2003}, - keywords = {united-states, area rain-rate, atmospheric moisture, climate-change, global precipitation, ice-scattering signature, north-america, southern-oscillation, thunderstorm frequencies, water-vapor}, - pages = {1205--+}, - annote = {726VBTimes Cited:23Cited References Count:71}, - annote = {The following values have no corresponding Zotero field:auth-address: Trenberth, KE Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 USA Natl Ctr Atmospher Res, Boulder, CO 80307 USAaccession-num: ISI:000185620900022}, -} - -@article{trenberth_changes_2011, - title = {Changes in precipitation with climate change}, - volume = {47}, - issn = {0936-577X}, - number = {1}, - journal = {Climate Research}, - author = {Trenberth, Kevin E.}, - year = {2011}, - pages = {123}, - annote = {The following values have no corresponding Zotero field:publisher: Citeseer}, -} - -@techreport{usaid_energy_1994, - address = {Washington, D.C.}, - title = {Energy from sugarcane cogeneration in el {Salvador}}, - institution = {United States Agency for International Development, Office of Energy, Environment, and Technology Bureau for Global Programs, Field Support, and Research}, - author = {USAID}, - year = {1994}, - pages = {79}, - annote = {The following values have no corresponding Zotero field:number: Report No. 94-03}, -} - -@techreport{usace_water_1998, - address = {Mobile, AL}, - title = {Water resources assessment of {El} {Salvador}}, - institution = {United States Army Corps of Engineers, Mobile District \& Topographic Engineering Center}, - author = {USACE}, - year = {1998}, - pages = {71}, -} - -@article{urban_projected_2012, - title = {Projected temperature changes indicate significant increase in interannual variability of {U}.{S}. maize yields}, - volume = {112}, - issn = {0165-0009}, - doi = {10.1007/s10584-012-0428-2}, - language = {English}, - number = {2}, - journal = {Climatic Change}, - author = {Urban, Daniel and Roberts, MichaelJ and Schlenker, Wolfram and Lobell, DavidB}, - month = may, - year = {2012}, - pages = {525--533}, - annote = {The following values have no corresponding Zotero field:alt-title: Climatic Changepublisher: Springer Netherlands}, -} - -@article{tryhorn_comparison_2011, - title = {A comparison of techniques for downscaling extreme precipitation over the {Northeastern} {United} {States}}, - volume = {31}, - issn = {1097-0088}, - doi = {10.1002/joc.2208}, - number = {13}, - journal = {International Journal of Climatology}, - author = {Tryhorn, Lee and DeGaetano, Art}, - year = {2011}, - keywords = {bias correction, climate change, dynamical downscaling, precipitation, regional climate, statistical downscaling}, - pages = {1975--1989}, - annote = {The following values have no corresponding Zotero field:publisher: John Wiley \& Sons, Ltd.}, -} - -@article{trenberth_rural_2004, - title = {Rural land-use change and climate}, - volume = {427}, - journal = {Nature}, - author = {Trenberth, K. E.}, - year = {2004}, - pages = {213}, -} - -@article{trenberth_conceptual_1999, - title = {Conceptual framework for changes of extremes of the hydrological cycle with climate change}, - volume = {42}, - issn = {0165-0009}, - abstract = {A physically based conceptual framework is put forward that explains why an increase in heavy precipitation events should be a primary manifestation of the climate change that accompanies increases in greenhouse gases in the atmosphere. Increased concentrations of greenhouse gases in the atmosphere increase downwelling infrared radiation, and this global heating at the surface not only acts to increase temperatures but also increases evaporation which enhances the atmospheric moisture content. Consequently all weather systems, ranging from individual clouds and thunderstorms to extratropical cyclones, which feed on the available moisture through storm-scale moisture convergence, are likely to produce correspondingly enhanced precipitation rates. Increases in heavy rainfall at the expense of more moderate rainfall are the consequence along with increased runoff and risk of flooding. However, because of constraints in the surface energy budget, there are also implications for the frequency and/or efficiency of precipitation. It follows that increased attention should be given to trends in atmospheric moisture content, and datasets on hourly precipitation rates and frequency need to be developed and analyzed as well as total accumulation.}, - language = {English}, - number = {1}, - journal = {Climatic Change}, - author = {Trenberth, K. E.}, - month = may, - year = {1999}, - keywords = {model, united-states, north-america, water-vapor, co2, daily precipitation, frequency, sensitivity, trends, variability}, - pages = {327--339}, - annote = {236JQTimes Cited:36Cited References Count:29}, - annote = {The following values have no corresponding Zotero field:auth-address: Trenberth, KE Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 USA Natl Ctr Atmospher Res, Boulder, CO 80307 USAaccession-num: ISI:000082596800018}, -} - -@article{tohver_impacts_2014, - title = {Impacts of 21st-{Century} {Climate} {Change} on {Hydrologic} {Extremes} in the {Pacific} {Northwest} {Region} of {North} {America}}, - volume = {50}, - issn = {1752-1688}, - doi = {10.1111/jawr.12199}, - number = {6}, - journal = {JAWRA Journal of the American Water Resources Association}, - author = {Tohver, Ingrid M. and Hamlet, Alan F. and Lee, Se-Yeun}, - year = {2014}, - keywords = {climate change, Columbia River basin, downscaled global climate models, flood, hydrologic extremes, hydrologic models, low flow}, - pages = {1461--1476}, -} - -@article{timbal_analogue-based_2001, - title = {An analogue-based method to downscale surface air temperature: application for {Australia}}, - volume = {17}, - abstract = {A statistical model (SM) has been developed to downscale large- scale predictors given by general circulation models (GCMs); emphasis has been put on local surface air temperature in two areas of interest: the south-west corner (SWC) of Western Australia and the Murray-Darling Basin (MDB) in southeastern Australia. This is a complementary approach to the dynamical modelling of climate change using high-resolution nested regional models. The analogue technique was chosen for this study as it has proven successful in the past for midlatitude climate and, in particular, for forecasting in Australia. Furthermore, the analogue technique is successful in reproducing spells of anomalous events. The development and validation datasets used for both predictors and predictants cover the 1970-1993 period. Predictors are extracted from a dataset of operational analyses for the Australian region. Several predictors have been assessed alone and combined. Mean sea level pressure and temperature at 850 hPa have been identified as the most useful combination. Predictants have come from quality controlled stations with daily temperature extremes for the 1970-1993 period. Twenty two stations in the SWC and 29 in the MDB have been selected. The sensitivity of the SM has been tested to several internal parameters. The number of atmospheric predictors and the geographical domain on which large-scale fields are used are key factors that maximise the skill of the SM. Several metrics have been tested taking into account the state of the predictors on the day or, in order to describe the evolution of the atmosphere, over several days. This latter has been particularly useful in improving the representation of anomalous spells as it partially incorporates the auto-correlation of surface temperature. The correlation obtained between the observed local temperature series and the reconstructed series ranges between 0.5 and 0.8. Best results are obtained in summer and for maximum temperature. The reproduction of spells is satisfactory for most stations. The SM is then applied to large-scale fields obtained from a GCM forced by observed sea surface temperature; the improvement gained when using the SM instead of relying on the surface temperature calculated by the GCM is shown.}, - number = {12}, - journal = {Climate Dynamics}, - author = {Timbal, B. and McAvaney, B. J.}, - month = sep, - year = {2001}, - pages = {947--963}, - annote = {477BNCLIM DYNAM}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Dyn.accession-num: ISI:000171263400004}, -} - -@article{tian_seasonal_2014, - title = {Seasonal {Prediction} of {Regional} {Reference} {Evapotranspiration} {Based} on {Climate} {Forecast} {System} {Version} 2}, - volume = {15}, - issn = {1525-755X}, - doi = {10.1175/jhm-d-13-087.1}, - abstract = {AbstractReference evapotranspiration (ETo) is an important hydroclimatic variable for water planning and management. This research explored the potential of using the Climate Forecast System, version 2 (CFSv2), for seasonal predictions of ETo over the states of Alabama, Georgia, and Florida. The 12-km ETo forecasts were produced by downscaling coarse-scale ETo forecasts from the CFSv2 retrospective forecast archive and by downscaling CFSv2 maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), solar radiation (Rs), and wind speed (Wind) individually and calculating ETo using those downscaled variables. All the ETo forecasts were calculated using the Penman?Monteith equation. Sensitivity coefficients were evaluated to quantify how and how much does each of the variables influence ETo. Two statistical downscaling methods were tested: 1) spatial disaggregation (SD) and 2) spatial disaggregation with quantile mapping bias correction (SDBC). The downscaled ETo from the coarse-scale ETo showed similar skill to those by first downscaling individual variables and then calculating ETo. The sensitivity coefficients showed Tmax and Rs had the greatest influence on ETo, followed by Tmin and Tmean, and Wind. The downscaled Tmax showed highest predictability, followed by Tmean, Tmin, Rs, and Wind. SDBC had slightly better performance than SD for both probabilistic and deterministic forecasts. The skill was locally and seasonally dependent. The CFSv2-based ETo forecasts showed higher predictability in cold seasons than in warm seasons. The CFSv2 model could better predict ETo in cold seasons during El Niño?Southern Oscillation (ENSO) events only when the forecast initial condition was in either the El Niño or La Niña phase of ENSO.}, - number = {3}, - journal = {Journal of Hydrometeorology}, - author = {Tian, Di and Martinez, Christopher J. and Graham, Wendy D.}, - month = jun, - year = {2014}, - pages = {1166--1188}, - annote = {doi: 10.1175/JHM-D-13-087.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/JHM-D-13-087.1}, -} - -@article{thibeault_changing_2010, - title = {Changing climate in the {Bolivian} {Altiplano}: {CMIP3} projections for temperature and precipitation extremes}, - volume = {115}, - issn = {0148-0227}, - doi = {D08103 10.1029/2009jd012718}, - abstract = {Rural agriculture in the Bolivian Altiplano is vulnerable to climate related shocks including drought, frost, and flooding. We examine multimodel, multiscenario projections of eight precipitation and temperature extreme indices for the Altiplano and compute temperature indices for La Paz/Alto, covering 1973-2007. Significant increasing trends in observed warm nights and warm spells are consistent with increasing temperatures in the tropical Andes. The increase in observed frost days is not simulated by the models in the 20th century, and projections of warm nights, frost days, and heat waves are consistent with projected annual cycle temperature increases; PDFs are outside their 20th century ranges by 2070-2099. Projected increases in precipitation extremes share the same sign as observed trends at Patacamaya and are consistent with annual cycle projections indicating a later rainy season characterized by less frequent, more intense precipitation. Patacamaya precipitation indices show shifts in observed distributions not seen in the models until 2020-2049, implying that precipitation changes may occur earlier than projected. The observed increase in frost days can be understood within the context of precipitation changes and an increase in radiative cooling. Model warm/wet biases suggest that a decrease in frost days may not occur as early or be as large as projected. Nevertheless, consistencies between simulated and observed extremes, other than frost days, suggest the directions of projected changes are reliable. These results are a first step toward providing the critical information necessary to reduce threats to food security and water resources in the Altiplano from changing climate.}, - language = {English}, - journal = {Journal of Geophysical Research-Atmospheres}, - author = {Thibeault, J. M. and Seth, A. and Garcia, M.}, - month = apr, - year = {2010}, - keywords = {20th-century, america, coupled model simulations, future, glacier, impacts, indexes, observed trends, rainfall, tropical andes}, - annote = {ISI Document Delivery No.: 587YZTimes Cited: 2Cited Reference Count: 45Thibeault, J. M. Seth, A. Garcia, M.Office of Science, U.S. Department of Energy ; USAID [LTR-4]The authors thank Xuebin Zhang and Feng Yang from ETCCDI for their assistance with the La Paz temperature data and Claudia Tebaldi for helpful comments and suggestions. The thorough and constructive comments from three anonymous reviewers have improved the quality of this manuscript substantively. The authors thank the international modeling groups for providing their data for analysis, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the model data, the JSC/CLIVAR Working Group on Coupled Modelling (WGCM) and their Coupled Model Intercomparison Project (CMIP3) and Climate Simulation Panel for organizing the model data analysis activity, and the IPCC WG1 TSU for technical support. The IPCC Data Archive at Lawrence Livermore National Laboratory is supported by the Office of Science, U.S. Department of Energy. This research was supported by a Long-Term Research award (LTR-4) in the Sustainable Agriculture and Natural Resource Management (SANREM) Collaborative Research Program (CRSP) with funding from USAID.Amer geophysical unionWashington}, - annote = {The following values have no corresponding Zotero field:auth-address: [Thibeault, J. M.; Seth, A.] Univ Connecticut, Dept Geog, Storrs, CT 06269 USA. [Garcia, M.] Univ Mayor San Andres, Inst Invest Agropecuarias \& Recursos Nat, La Paz, Bolivia. Thibeault, JM, Univ Connecticut, Dept Geog, CLAS Bldg,215 Glenbrook Rd,U-4148, Storrs, CT 06269 USA. jeanne.thibeault@uconn.edualt-title: J. Geophys. Res.-Atmos.accession-num: ISI:000277034100002work-type: Article}, -} - -@article{themesl_empirical-statistical_2011, - title = {Empirical-statistical downscaling and error correction of daily precipitation from regional climate models}, - volume = {31}, - issn = {1097-0088}, - doi = {10.1002/joc.2168}, - number = {10}, - journal = {International Journal of Climatology}, - author = {Themeßl, M. and Gobiet, Andreas and Leuprecht, Armin}, - year = {2011}, - keywords = {bias correction, Alpine region, empirical statistical downscaling, error correction, precipitation modelling, regional climate modelling}, - pages = {1530--1544}, - annote = {The following values have no corresponding Zotero field:publisher: John Wiley \& Sons, Ltd.}, -} - -@article{trenberth_definition_1997, - title = {The definition of {El} {Niño}}, - volume = {78}, - issn = {0003-0007}, - abstract = {A review is given of the meaning of the term "El Nino" and how it has changed in time, so there is no universal single definition. This needs to be recognized for scientific uses, and precision can only be achieved if the particular definition is identified in each use to reduce the possibility of misunderstanding. For quantitative purposes, possible definitions are explored that match the El Ninos identified historically after 1950, and it is suggested that an El Nino can be said to occur if 5-month running means of sea surface temperature (SST) anomalies in the Nino 3.4 region (5 degrees N-5 degrees S, 120 degrees-170 degrees W) exceed 0.4 degrees C for 6 months or more. With this definition, El Ninos occur 31\% of the time and La Ninas (with an equivalent definition) occur 23\% of the time. The histogram of Nino 3.4 SST anomalies reveals a bimodal character. An advantage of such a definition is that it allows the beginning, end, duration, and magnitude of each event to be quantified. Most El Ninos begin in the northern spring or perhaps summer and peak from November to January in sea surface temperatures.}, - language = {English}, - number = {12}, - journal = {Bulletin of the American Meteorological Society}, - author = {Trenberth, K. E.}, - month = dec, - year = {1997}, - keywords = {southern oscillation}, - pages = {2771--2777}, - annote = {The following values have no corresponding Zotero field:auth-address: Trenberth, KE Pob 3000, Boulder, Co 80307 USA Natl Ctr Atmospher Res, Boulder, CO 80307 USAaccession-num: ISI:000072304100002}, - annote = {Yz885Times Cited:290Cited References Count:12}, -} - -@article{timbal_future_2008, - title = {Future projections of winter rainfall in southeast {Australia} using a statistical downscaling technique}, - volume = {86}, - issn = {0165-0009}, - doi = {10.1007/s10584-007-9279-7}, - number = {1}, - journal = {Climatic Change}, - author = {Timbal, B. and Jones, D.}, - year = {2008}, - keywords = {Earth and Environmental Science}, - pages = {165--187}, - annote = {The following values have no corresponding Zotero field:publisher: Springer Netherlands}, -} - -@article{thrasher_technical_2012, - title = {Technical {Note}: {Bias} correcting climate model simulated daily temperature extremes with quantile mapping}, - volume = {16}, - journal = {Hydrol. Earth Syst. Sci.}, - author = {Thrasher, B. and Maurer, E. P. and McKellar, C. and Duffy, P. B.}, - year = {2012}, - pages = {3309--3314, doi:10.5194/hess--16--3309--2012}, -} - -@article{themesl_empirical-statistical_2012, - title = {Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal}, - volume = {112}, - issn = {0165-0009}, - doi = {10.1007/s10584-011-0224-4}, - number = {2}, - journal = {Climatic Change}, - author = {Themeßl, Matthias and Gobiet, Andreas and Heinrich, Georg}, - year = {2012}, - keywords = {Earth and Environmental Science}, - pages = {449--468}, - annote = {The following values have no corresponding Zotero field:publisher: Springer Netherlands}, -} - -@article{teutschbein_evaluation_2011, - title = {Evaluation of different downscaling techniques for hydrological climate-change impact studies at the catchment scale}, - volume = {37}, - issn = {0930-7575}, - doi = {10.1007/s00382-010-0979-8}, - language = {English}, - number = {9-10}, - journal = {Climate Dynamics}, - author = {Teutschbein, Claudia and Wetterhall, Fredrik and Seibert, Jan}, - month = nov, - year = {2011}, - keywords = {Climate change, GCM, HBV, Hydrological impact modeling, Precipitation, Statistical downscaling, Streamflow, Sweden, Temperature}, - pages = {2087--2105}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim Dynpublisher: Springer-Verlag}, -} - -@article{tebaldi_modelling_2012, - title = {Modelling sea level rise impacts on storm surges along {US} coasts}, - volume = {7}, - issn = {1748-9326}, - abstract = {Sound policies for protecting coastal communities and assets require good information about vulnerability to flooding. Here, we investigate the influence of sea level rise on expected storm surge-driven water levels and their frequencies along the contiguous United States. We use model output for global temperature changes, a semi-empirical model of global sea level rise, and long-term records from 55 nationally distributed tidal gauges to develop sea level rise projections at each gauge location. We employ more detailed records over the period 1979–2008 from the same gauges to elicit historic patterns of extreme high water events, and combine these statistics with anticipated relative sea level rise to project changing local extremes through 2050. We find that substantial changes in the frequency of what are now considered extreme water levels may occur even at locations with relatively slow local sea level rise, when the difference in height between presently common and rare water levels is small. We estimate that, by mid-century, some locations may experience high water levels annually that would qualify today as ‘century’ (i.e., having a chance of occurrence of 1\% annually) extremes. Today’s century levels become ‘decade’ (having a chance of 10\% annually) or more frequent events at about a third of the study gauges, and the majority of locations see substantially higher frequency of previously rare storm-driven water heights in the future. These results add support to the need for policy approaches that consider the non-stationarity of extreme events when evaluating risks of adverse climate impacts.}, - number = {1}, - journal = {Environmental Research Letters}, - author = {Tebaldi, C. and Strauss, B. H. and Zervas, C. E.}, - year = {2012}, - pages = {014032}, -} - -@article{tebaldi_delayed_2013, - title = {Delayed detection of climate mitigation benefits due to climate inertia and variability}, - doi = {10.1073/pnas.1300005110}, - abstract = {Climate change mitigation acts by reducing greenhouse gas emissions, and thus curbing, or even reversing, the increase in their atmospheric concentration. This reduces the associated anthropogenic radiative forcing, and hence the size of the warming. Because of the inertia and internal variability affecting the climate system and the global carbon cycle, it is unlikely that a reduction in warming would be immediately discernible. Here we use 21st century simulations from the latest ensemble of Earth System Model experiments to investigate and quantify when mitigation becomes clearly discernible. We use one of the scenarios as a reference for a strong mitigation strategy, Representative Concentration Pathway (RCP) 2.6 and compare its outcome with either RCP4.5 or RCP8.5, both of which are less severe mitigation pathways. We analyze global mean atmospheric CO2, and changes in annually and seasonally averaged surface temperature at global and regional scales. For global mean surface temperature, the median detection time of mitigation is about 25–30 y after RCP2.6 emissions depart from the higher emission trajectories. This translates into detection of a mitigation signal by 2035 or 2045, depending on whether the comparison is with RCP8.5 or RCP4.5, respectively. The detection of climate benefits of emission mitigation occurs later at regional scales, with a median detection time between 30 and 45 y after emission paths separate. Requiring a 95\% confidence level induces a delay of several decades, bringing detection time toward the end of the 21st century.}, - journal = {Proceedings of the National Academy of Sciences}, - author = {Tebaldi, Claudia and Friedlingstein, Pierre}, - month = oct, - year = {2013}, -} - -@article{teutschbein_is_2013, - title = {Is bias correction of regional climate model ({RCM}) simulations possible for non-stationary conditions?}, - volume = {17}, - issn = {1607-7938}, - doi = {10.5194/hess-17-5061-2013}, - number = {12}, - journal = {Hydrol. Earth Syst. Sci.}, - author = {Teutschbein, C. and Seibert, J.}, - year = {2013}, - pages = {5061--5077}, - annote = {HESS}, - annote = {The following values have no corresponding Zotero field:publisher: Copernicus Publications}, -} - -@article{tebaldi_regional_2004, - title = {Regional probabilities of precipitation change: {A} {Bayesian} analysis of multimodel simulations}, - volume = {31}, - journal = {Geophysical Research Letters}, - author = {Tebaldi, C. and Mearns, L. O. and Nychka, D. and Smith, R. L.}, - year = {2004}, - pages = {L24213, doi:10.1029/2004GL021276}, -} - -@article{tebaldi_use_2007, - title = {The use of the multi-model ensemble in probabilistic climate projections}, - volume = {365}, - issn = {1364-503X}, - doi = {10.1098/rsta.2007.2076}, - abstract = {Recent coordinated efforts, in which numerous climate models have been run for a common set of experiments, have produced large datasets of projections of future climate for various scenarios. Those multi-model ensembles sample initial condition, parameter as well as structural uncertainties in the model design, and they have prompted a variety of approaches to quantify uncertainty in future climate in a probabilistic way. This paper outlines the motivation for using multi-model ensembles, reviews the methodologies published so far and compares their results for regional temperature projections. The challenges in interpreting multi-model results, caused by the lack of veri. cation of climate projections, the problem of model dependence, bias and tuning as well as the difficulty in making sense of an 'ensemble of opportunity', are discussed in detail.}, - language = {English}, - number = {1857}, - journal = {Philosophical Transactions of the Royal Society a-Mathematical Physical and Engineering Sciences}, - author = {Tebaldi, C. and Knutti, R.}, - month = aug, - year = {2007}, - keywords = {aogcm simulations, averaging rea method, change simulations, ensembles, global climate models, multi-model, ocean circulation models, performance-based weighting, prediction, probabilistic projections, quantifying, regional climate change, reliability, seasonal forecasts, structural uncertainty, system properties, temperature, uncertainty}, - pages = {2053--2075}, - annote = {ISI Document Delivery No.: 183LCTimes Cited: 69Cited Reference Count: 68Tebaldi, Claudia Knutti, RetoRoyal societyLondon}, - annote = {The following values have no corresponding Zotero field:auth-address: Natl Ctr Atmospher Res, Inst Study Soc \& Environm, Boulder, CO 80304 USA. ETH, Inst Atmospher \& Climate Sci, CH-8092 Zurich, Switzerland. Tebaldi, C, Natl Ctr Atmospher Res, Inst Study Soc \& Environm, POB 3000, Boulder, CO 80304 USA. tebaldi@ucar.edualt-title: Philos. Trans. R. Soc. A-Math. Phys. Eng. Sci.accession-num: ISI:000247573300005work-type: Review}, -} - -@article{tebaldi_intercomparison_2006, - title = {An intercomparison of model-simulated historical and future changes in extreme events}, - volume = {79}, - number = {3-4}, - journal = {Climatic Change}, - author = {Tebaldi, C. and Hayhoe, K. and Arblaster, J. M. and Meehl, G. A.}, - year = {2006}, - pages = {185--211, doi: 10.1007/s10584--006--9051--4}, -} - -@article{tebaldi_pattern_2014, - title = {Pattern scaling: {Its} strengths and limitations, and an update on the latest model simulations}, - volume = {122}, - issn = {1573-1480}, - doi = {10.1007/s10584-013-1032-9}, - abstract = {We review the ideas behind the pattern scaling technique, and focus on its value and limitations given its use for impact assessment and within integrated assessment models. We present estimates of patterns for temperature and precipitation change from the latest transient simulations available from the Coupled Model Inter-comparison Project Phase 5 (CMIP5), focusing on multi-model mean patterns, and characterizing the sources of variability of these patterns across models and scenarios. The patterns are compared to those obtained from the previous set of experiments, under CMIP3. We estimate the significance of the emerging differences between CMIP3 and CMIP5 results through a bootstrap exercise, while also taking into account the fundamental differences in scenario and model ensemble composition. All in all, the robustness of the geographical features in patterns of temperature and precipitation, when computed as multi-model means, is confirmed by this comparison. The intensity of the change (in both the warmer and cooler areas with respect to global temperature change, and the drier and wetter regions) is overall heightened per degree of global warming in the ensemble mean of the new simulations. The presence of stabilized scenarios in the new set of simulations allows investigation of the performance of the technique once the system has gotten close to equilibrium. Overall, the well established validity of the technique in approximating the forced signal of change under increasing concentrations of greenhouse gases is confirmed.}, - number = {3}, - journal = {Climatic Change}, - author = {Tebaldi, Claudia and Arblaster, Julie M.}, - year = {2014}, - pages = {459--471}, - annote = {The following values have no corresponding Zotero field:label: Tebaldi2014work-type: journal article}, -} - -@article{taylor_why_2013, - title = {Why dry? {Investigating} the future evolution of the {Caribbean} {Low} {Level} {Jet} to explain projected {Caribbean} drying}, - volume = {33}, - issn = {1097-0088}, - doi = {10.1002/joc.3461}, - number = {3}, - journal = {International Journal of Climatology}, - author = {Taylor, Michael A. and Whyte, Felicia S. and Stephenson, Tannecia S. and Campbell, Jayaka D.}, - year = {2013}, - keywords = {precipitation, Caribbean, CLLJ, global warming, PRECIS}, - pages = {784--792}, - annote = {The following values have no corresponding Zotero field:publisher: John Wiley \& Sons, Ltd.}, -} - -@article{tang_usable_2012, - title = {Usable {Science}? {The} {U}.{K}. {Climate} {Projections} 2009 and {Decision} {Support} for {Adaptation} {Planning}}, - volume = {4}, - issn = {1948-8327}, - doi = {10.1175/wcas-d-12-00028.1}, - number = {4}, - journal = {Weather, Climate, and Society}, - author = {Tang, S. and Dessai, S.}, - month = oct, - year = {2012}, - pages = {300--313}, - annote = {doi: 10.1175/WCAS-D-12-00028.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/WCAS-D-12-00028.1}, -} - -@article{taylor_influence_2002, - title = {Influence of {Tropical} {Atlantic} versus the tropical {Pacific} on {Caribbean} rainfall}, - volume = {107}, - number = {C9}, - journal = {Journal of Geophysical Research}, - author = {Taylor, M. A. and Enfield, D. B. and Chen, A. A.}, - year = {2002}, - pages = {3127, doi:10.1029/2001JC001097}, -} - -@article{taylor_overview_2012, - title = {An {Overview} of {CMIP5} and the experiment design}, - volume = {93}, - journal = {Bulletin of the American Meteorological Society}, - author = {Taylor, K. E. and Stouffer, R. J. and Meehl, G. A.}, - year = {2012}, - pages = {485--498, doi: 10.1175/BAMS--D--11--00094.1}, -} - -@article{strauss_carbon_2015, - title = {Carbon choices determine {US} cities committed to futures below sea level}, - doi = {10.1073/pnas.1511186112}, - abstract = {Anthropogenic carbon emissions lock in long-term sea-level rise that greatly exceeds projections for this century, posing profound challenges for coastal development and cultural legacies. Analysis based on previously published relationships linking emissions to warming and warming to rise indicates that unabated carbon emissions up to the year 2100 would commit an eventual global sea-level rise of 4.3–9.9 m. Based on detailed topographic and population data, local high tide lines, and regional long-term sea-level commitment for different carbon emissions and ice sheet stability scenarios, we compute the current population living on endangered land at municipal, state, and national levels within the United States. For unabated climate change, we find that land that is home to more than 20 million people is implicated and is widely distributed among different states and coasts. The total area includes 1,185–1,825 municipalities where land that is home to more than half of the current population would be affected, among them at least 21 cities exceeding 100,000 residents. Under aggressive carbon cuts, more than half of these municipalities would avoid this commitment if the West Antarctic Ice Sheet remains stable. Similarly, more than half of the US population-weighted area under threat could be spared. We provide lists of implicated cities and state populations for different emissions scenarios and with and without a certain collapse of the West Antarctic Ice Sheet. Although past anthropogenic emissions already have caused sea-level commitment that will force coastal cities to adapt, future emissions will determine which areas we can continue to occupy or may have to abandon.}, - journal = {Proceedings of the National Academy of Sciences}, - author = {Strauss, Benjamin H. and Kulp, Scott and Levermann, Anders}, - month = oct, - year = {2015}, -} - -@article{stott_detection_2010, - title = {Detection and attribution of climate change: a regional perspective}, - volume = {1}, - issn = {1757-7799}, - doi = {10.1002/wcc.34}, - number = {2}, - journal = {Wiley Interdisciplinary Reviews: Climate Change}, - author = {Stott, Peter A. and Gillett, Nathan P. and Hegerl, Gabriele C. and Karoly, David J. and Stone, Dáithí A. and Zhang, Xuebin and Zwiers, Francis}, - year = {2010}, - pages = {192--211}, - annote = {The following values have no corresponding Zotero field:publisher: John Wiley \& Sons, Inc.}, -} - -@article{tanaka_climate_2006, - title = {Climate warming and water management adaptation for {California}}, - volume = {76}, - journal = {Climatic Change}, - author = {Tanaka, S. K. and Zhu, T. and Lund, J. R. and Howitt, R. E. and Jenkins, M. W. and Pulido, M. A. and Tauber, M. and Ritzema, R. S. and Ferreira, I. C.}, - year = {2006}, - pages = {361--387}, -} - -@article{tabor_globally_2010, - title = {Globally downscaled climate projections for assessing the conservation impacts of climate change}, - volume = {20}, - issn = {1051-0761}, - doi = {10.1890/09-0173.1}, - number = {2}, - journal = {Ecological Applications}, - author = {Tabor, Karyn and Williams, John W.}, - month = mar, - year = {2010}, - pages = {554--565}, - annote = {doi: 10.1890/09-0173.1}, - annote = {The following values have no corresponding Zotero field:publisher: Ecological Society of Americawork-type: doi: 10.1890/09-0173.1}, -} - -@article{sun_how_2006, - title = {How {Often} {Does} {It} {Rain}?}, - volume = {19}, - issn = {0894-8755}, - doi = {10.1175/jcli3672.1}, - number = {6}, - journal = {Journal of Climate}, - author = {Sun, Ying and Solomon, Susan and Dai, Aiguo and Portmann, Robert W.}, - month = mar, - year = {2006}, - pages = {916--934}, - annote = {doi: 10.1175/JCLI3672.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/JCLI3672.1}, -} - -@article{sun_hybrid_2015, - title = {A {Hybrid} {Dynamical}–{Statistical} {Downscaling} {Technique}. {Part} {II}: {End}-of-{Century} {Warming} {Projections} {Predict} a {New} {Climate} {State} in the {Los} {Angeles} {Region}}, - volume = {28}, - issn = {0894-8755}, - doi = {10.1175/JCLI-D-14-00197.1}, - abstract = {AbstractUsing the hybrid downscaling technique developed in part I of this study, temperature changes relative to a baseline period (1981?2000) in the greater Los Angeles region are downscaled for two future time slices: midcentury (2041?60) and end of century (2081?2100). Two representative concentration pathways (RCPs) are considered, corresponding to greenhouse gas emission reductions over coming decades (RCP2.6) and to continued twenty-first-century emissions increases (RCP8.5). All available global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are downscaled to provide likelihood and uncertainty estimates. By the end of century under RCP8.5, a distinctly new regional climate state emerges: average temperatures will almost certainly be outside the interannual variability range seen in the baseline. Except for the highest elevations and a narrow swath very near the coast, land locations will likely see 60?90 additional extremely hot days per year, effectively adding a new season of extreme heat. In mountainous areas, a majority of the many baseline days with freezing nighttime temperatures will most likely not occur. According to a similarity metric that measures daily temperature variability and the climate change signal, the RCP8.5 end-of-century climate will most likely be only about 50\% similar to the baseline. For midcentury under RCP2.6 and RCP8.5 and end of century under RCP2.6, these same measures also indicate a detectable though less significant climatic shift. Therefore, while measures reducing global emissions would not prevent climate change at this regional scale in the coming decades, their impact would be dramatic by the end of the twenty-first century.}, - number = {12}, - journal = {Journal of Climate}, - author = {Sun, Fengpeng and Walton, Daniel B. and Hall, Alex}, - month = jun, - year = {2015}, - pages = {4618--4636}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Society}, -} - -@article{stouffer_changes_2007, - title = {Changes of {Variability} in {Response} to {Increasing} {Greenhouse} {Gases}. {Part} {I}: {Temperature}}, - volume = {20}, - abstract = {This study documents the temperature variance change in two different versions of a coupled ocean\&\#8211;atmosphere general circulation model forced with estimates of future increases of greenhouse gas (GHG) and aerosol concentrations. The variance changes are examined using an ensemble of 8 transient integrations for the older model version and 10 transient integrations for the newer one. Monthly and annual data are used to compute the mean and variance changes. Emphasis is placed upon computing and analyzing the variance changes for the middle of the twenty-first century and compared with those found in a control integration.The large-scale variance of lower-tropospheric temperature (including surface air temperature) generally decreases in high latitudes particularly during fall due to a delayed onset of sea ice as the climate warms. Sea ice acts to insolate the atmosphere from the much larger heat capacity of the ocean. Therefore, the near-surface temperature variance tends to be larger over the sea ice\&\#8211;covered regions, than the nearby ice-free regions. The near-surface temperature variance also decreases during the winter and spring due to a general reduction in the extent of sea ice during winter and spring.Changes in storminess were also examined and were found to have relatively little effect upon the reduction of temperature variance. Generally small changes of surface air temperature variance occurred in low and midlatitudes over both land and oceanic areas year-round. An exception to this was a general reduction of variance in the equatorial Pacific Ocean for the newer model. Small increases in the surface air temperature variance occur in mid- to high latitudes during the summer months, suggesting the possibility of more frequent and longer-lasting heat waves in response to increasing GHGs.}, - number = {21}, - journal = {Journal of Climate}, - author = {Stouffer, R. J. and Wetherald, R. T.}, - month = nov, - year = {2007}, - pages = {5455--5467}, -} - -@incollection{t_f_stocker_technical_2013, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Technical {Summary}}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {T. F. Stocker and D. Qin and G. -K. Plattner and L. V. Alexander and S. K. Allen and N. L. Bindoff and F. -M. Bréon and J. A. Church and U. Cubasch and S. Emori and P. Forster and P. Friedlingstein and N. Gillett and J. M. Gregory and D. L. Hartmann and E. Jansen and B. Kirtman and R. Knutti and K. Krishna Kumar and P. Lemke and J. Marotzke and V. Masson-Delmotte and G. A. Meehl and I. I. Mokhov and S. Piao and V. Ramaswamy and D. Randall and M. Rhein and M. Rojas and C. Sabine and D. Shindell and L. D. Talley and D. G. Vaughan and S. -P. Xie}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley}, - year = {2013}, - pages = {33--115}, - annote = {The following values have no corresponding Zotero field:section: TSelectronic-resource-num: 10.1017/CBO9781107415324.005}, -} - -@article{stoner_assessing_nodate, - title = {Assessing {General} {Circulation} {Model} {Simulations} of {Atmospheric} {Teleconnection} {Patterns}}, - author = {Stoner, A. M. K. and Hayhoe, K. and Wuebbles, D. J.}, -} - -@article{stone_impacts_2001, - title = {{IMPACTS} {OF} {CLIMATE} {CHANGE} {ON} {MISSOURI} {RWER} {BASIN} {WATER} {YIELD1}}, - volume = {37}, - issn = {1752-1688}, - doi = {10.1111/j.1752-1688.2001.tb03626.x}, - number = {5}, - journal = {JAWRA Journal of the American Water Resources Association}, - author = {Stone, Mark C. and Hotchkiss, Rollin H. and Hubbard, Carter M. and Fontaine, Thomas A. and Mearns, Linda O. and Arnold, Jeff G.}, - year = {2001}, - keywords = {global climate change, meteorology/climatology, Missouri River Basin, regional circulation model, reservoir modeling, surface water hydrology, water resources}, - pages = {1119--1129}, - annote = {The following values have no corresponding Zotero field:publisher: Blackwell Publishing Ltd}, -} - -@incollection{stocker_physical_2001, - address = {Cambridge}, - title = {Physical climate processes and feedbacks}, - booktitle = {Climate {Change} 2001: {The} {Scientific} {Basis}}, - publisher = {Cambridge University Press}, - author = {Stocker, T. F. and Clarke, G. K. C. and Le Treut, H. and Lindzen, R. S. and Meleshko, V. P. and Mugara, R. K. and Palmer, T. N. and Pierrehumbert, R. T. and Sellers, P. J. and Trenberth, K. E. and Willebrand, J.}, - editor = {Houghton, J. T. and Ding, Y. and Griggs, D. J. and Noguer, M. and van der Linden, P. J. and Xiaosu, D.}, - year = {2001}, - pages = {417--470}, - annote = {The following values have no corresponding Zotero field:section: 7}, -} - -@article{stehlik_multivariate_2002, - title = {Multivariate stochastic downscaling model for generating daily precipitation series based on atmospheric circulation}, - volume = {256}, - abstract = {The goal of the paper is to present a model for generating daily precipitation time series and its applications to two climatologically different areas. The rainfall is modeled as stochastic process coupled to atmospheric circulation. Rainfall is linked to the circulation patterns using conditional model parameters. Any kind of circulation pattern classification can be used for this purpose. In this study a new fuzzy rule based method of circulation patterns classification was used, The advantage of this classification technique is the fact that in contrast to common circulation patterns classifications its objective is to explain the variability of local precipitation. It means that the circulation patterns explain the relation between large-scale atmospheric circulation and surface climate (precipitation). Therefore the circulation patterns obtained by this classification method are suitable as input for the subsequent precipitation downscaling. The model was successfully applied in two regions with different climate conditions: Central Europe (Germany) and Eastern Mediterranean (Greece). Several tests like comparison of mean seasonal cycles, comparison of mean values and deviations of yearly totals and other standard diagnostics showed that simulated values agree fairly well with historical data. (C) 2002 Elsevier Science B.V. All rights reserved.}, - number = {1-2}, - journal = {Journal of Hydrology}, - author = {Stehlik, J. and Bardossy, A.}, - month = jan, - year = {2002}, - pages = {120--141}, - annote = {508ZNJ HYDROL}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Hydrol.accession-num: ISI:000173119800008}, -} - -@article{stedinger_flood_2008, - title = {Flood {Frequency} {Analysis} in the {United} {States}: {Time} to {Update}}, - volume = {13}, - doi = {doi:10.1061/(ASCE)1084-0699(2008)13:4(199)}, - number = {4}, - journal = {Journal of Hydrologic Engineering}, - author = {Stedinger, J. and Griffis, V.}, - year = {2008}, - pages = {199--204}, -} - -@incollection{stocker_physical_2001-1, - address = {Cambridge}, - title = {Physical climate processxes and feedbacks}, - booktitle = {Climate {Change} 2001: {The} {Scientific} {Basis}}, - publisher = {Cambridge University Press}, - author = {Stocker, T. F.}, - editor = {Houghton, J. T.}, - year = {2001}, - pages = {417--470}, - annote = {The following values have no corresponding Zotero field:section: 7}, -} - -@article{stewart_changes_2005, - title = {Changes toward earlier streamflow timing across western {North} {America}}, - volume = {18}, - issn = {0894-8755}, - abstract = {The highly variable timing of streamflow in snowmelt-dominated basins across western North America is an important consequence, and indicator, of climate fluctuations. Changes in the timing of snowmelt-derived streamflow from 1948 to 2002 were investigated in a network of 302 western North America gauges by examining the center of mass for flow, spring pulse onset dates, and seasonal fractional flows through trend and principal component analyses. Statistical analysis of the streamflow timing measures with Pacific climate indicators identified local and key large-scale processes that govern the regionally coherent parts of the changes and their relative importance. -Widespread and regionally coherent trends toward earlier onsets of springtime snowmelt and streamflow have taken place across most of western North America, affecting an area that is much larger than previously recognized. These timing changes have resulted in increasing fractions of annual flow occurring earlier in the water year by 1-4 weeks. The immediate (or proximal) forcings for the spatially coherent parts of the year-to-year fluctuations and longer-term trends of streamflow timing have been higher winter and spring temperatures. Although these temperature changes are partly controlled by the decadal-scale Pacific climate mode [Pacific decadal oscillation (PDO)], a separate ani significant part of the variance is associated with a springtime warming trend that spans the PDO phases.}, - language = {English}, - number = {8}, - journal = {Journal of Climate}, - author = {Stewart, I. T. and Cayan, D. R. and Dettinger, M. D.}, - month = apr, - year = {2005}, - keywords = {united-states, climate-change, precipitation, variability, atmospheric circulation, mass-balance, pacific-northwest, river-basin, sierra-nevada, snowmelt runoff}, - pages = {1136--1155}, - annote = {924JMTimes Cited:0Cited References Count:47}, - annote = {The following values have no corresponding Zotero field:auth-address: Stewart, IT Scripps Inst Oceanog, 9500 Gilman Dr, La Jolla, CA 92093 USA Scripps Inst Oceanog, La Jolla, CA 92093 USA US Geol Survey, La Jolla, CA USAaccession-num: ISI:000228975100002}, -} - -@article{stewart_changes_2004, - title = {Changes in snowmelt runoff timing in western {North} {America} under a 'business as usual' climate change scenario}, - volume = {62}, - issn = {0165-0009}, - abstract = {Spring snowmelt is the most important contribution of many rivers in western North America. If climate changes, this contribution may change. A shift in the timing of springtime snowmelt towards earlier in the year already is observed during 1948 - 2000 in many western rivers. Streamflow timing changes for the 1995 - 2099 period are projected using regression relations between observed streamflow-timing responses in each river, measured by the temporal centroid of streamflow (CT) each year, and local temperature (TI) and precipitation ( PI) indices. Under 21st century warming trends predicted by the Parallel Climate Model (PCM) under business-as-usual greenhouse-gas emissions, streamflow timing trends across much of western North America suggest even earlier springtime snowmelt than observed to date. Projected CT changes are consistent with observed rates and directions of change during the past five decades, and are strongest in the Pacific Northwest, Sierra Nevada, and Rocky Mountains, where many rivers eventually run 30 - 40 days earlier. The modest PI changes projected by PCM yield minimal CT changes. The responses of CT to the simultaneous effects of projected TI and PI trends are dominated by the TI changes. Regression-based CT projections agree with those from physically-based simulations of rivers in the Pacific Northwest and Sierra Nevada.}, - language = {English}, - number = {1-3}, - journal = {Climatic Change}, - author = {Stewart, I. T. and Cayan, D. R. and Dettinger, M. D.}, - month = feb, - year = {2004}, - keywords = {model, united-states}, - pages = {217--232}, - annote = {768FATimes Cited:7Cited References Count:17}, - annote = {The following values have no corresponding Zotero field:auth-address: Stewart, IT Univ Calif San Diego, Scripps Inst Oceanog, 9500 Gilman Dr, La Jolla, CA 92093 USA Univ Calif San Diego, Scripps Inst Oceanog, La Jolla, CA 92093 USA US Geol Survey, La Jolla, CA USA}, -} - -@article{steger_alpine_2013, - title = {Alpine snow cover in a changing climate: a regional climate model perspective}, - volume = {41}, - issn = {1432-0894}, - doi = {10.1007/s00382-012-1545-3}, - abstract = {An analysis is presented of an ensemble of regional climate model (RCM) experiments from the ENSEMBLES project in terms of mean winter snow water equivalent (SWE), the seasonal evolution of snow cover, and the duration of the continuous snow cover season in the European Alps. Two sets of simulations are considered, one driven by GCMs assuming the SRES A1B greenhouse gas scenario for the period 1951–2099, and the other by the ERA-40 reanalysis for the recent past. The simulated SWE for Switzerland for the winters 1971–2000 is validated against an observational data set derived from daily snow depth measurements. Model validation shows that the RCMs are capable of simulating the general spatial and seasonal variability of Alpine snow cover, but generally underestimate snow at elevations below 1,000 m and overestimate snow above 1,500 m. Model biases in snow cover can partly be related to biases in the atmospheric forcing. The analysis of climate projections for the twenty first century reveals high inter-model agreement on the following points: The strongest relative reduction in winter mean SWE is found below 1,500 m, amounting to 40–80 \% by mid century relative to 1971–2000 and depending upon the model considered. At these elevations, mean winter temperatures are close to the melting point. At higher elevations the decrease of mean winter SWE is less pronounced but still a robust feature. For instance, at elevations of 2,000–2,500 m, SWE reductions amount to 10–60 \% by mid century and to 30–80 \% by the end of the century. The duration of the continuous snow cover season shows an asymmetric reduction with strongest shortening in springtime when ablation is the dominant factor for changes in SWE. We also find a substantial ensemble-mean reduction of snow reliability relevant to winter tourism at elevations below about 1,800 m by mid century, and at elevations below about 2,000 m by the end of the century.}, - number = {3}, - journal = {Climate Dynamics}, - author = {Steger, Christian and Kotlarski, Sven and Jonas, Tobias and Schär, Christoph}, - month = aug, - year = {2013}, - pages = {735--754}, - annote = {The following values have no corresponding Zotero field:label: Steger2013work-type: journal article}, -} - -@article{solman_local_1999, - title = {Local estimates of global climate change: {A} statistical downscaling approach}, - volume = {19}, - abstract = {For the purposes of estimating local changes in surface climate at selected stations in the central Argentina region, induced by an enhanced CO, concentration, projected by general circulation models (GCM), a statistical method to derive local scale monthly mean minimum, maximum and mean temperatures from large-scale atmospheric predictors is presented. Empirical relationships are derived among selected variables from the NCEP re-analyses and local data for summer and winter months, tested against an independent set of observed data and subsequently applied to the MADAM and MPI GCM control runs. Finally, the statistical approach is applied to a climate change experiment performed with the MPI model to construct a local climate change scenario. The comparison between the estimated versus the observed mean temperature fields shows good agreement and the temporal evolution of the estimated variables is well-captured, though, the estimated temperatures contain less interannual variability than the observations. For the present day climate simulation, the results from the HADAM and MPI GCMs are used. It is shown that the pattern of estimated temperatures obtained using the MPI large-scale predictors matches the observations for summer months, though minimum and mean temperatures are slightly underestimated in the southeast part of the domain. However, the differences are well within the range of the observed variability. The possible anthropogenic climate change at the local scale is assessed by applying the statistical method to the results of the perturbed run conducted with the MPI model. For summer and winter months, the local temperature increase is smaller for minimum temperature than for maximum temperature for almost all the stations, yielding an enhanced temperature amplitude in both seasons. The temperature amplitude (difference between maximum and minimum) for summer months was larger than for winter months. The estimated maximum temperature increase is found to be larger for summer months than for winter months for all the stations, while for the minimum, temperature increases for summer and winter months are similar. Copyright (C) 1999 Royal Meteorological Society.}, - number = {8}, - journal = {International Journal of Climatology}, - author = {Solman, S. A. and Nunez, M. N.}, - month = jun, - year = {1999}, - pages = {835--861}, - annote = {215FYINT J CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:000081372300003}, -} - -@techreport{state_of_california_2017_2017, - address = {Sacramento, CA}, - title = {2017 {CVFPP} {Update} – {Climate} {Change} {Analysis} {Technical} {Memorandum}}, - institution = {Department of Water Resources}, - author = {State of California}, - year = {2017}, - pages = {154}, -} - -@article{stainforth_uncertainty_2005, - title = {Uncertainty in predictions of the climate response to rising levels of greenhouse gases}, - volume = {433}, - number = {7024}, - journal = {Nature}, - author = {Stainforth, D. A. and Aina, T. and Christensen, C. and Collins, M. and Faull, N. and Frame, D. J. and Kettleborough, J. A. and Knight, S. and Martin, A. and Murphy, J. M. and Piani, C. and Sexton, D. and Smith, L. A. and Spicer, R. A. and Thorpe, A. J. and Allen, M. R.}, - year = {2005}, - pages = {403--406}, - annote = {0028-083610.1038/nature0330110.1038/nature03301}, -} - -@article{snyder_future_2003, - title = {Future climate change and upwelling in the {California} {Current}}, - volume = {30}, - issn = {0094-8276}, - doi = {10.1029/2003gl017647}, - abstract = {Observations show that wind-driven upwelling along the California coast has increased over the past 30 years. Some have postulated that the increase in wind-driven upwelling is due largely to increased greenhouse gas forcing, but such an association has been speculative. Since global and regional simulations of future wind-driven upwelling do not exist for the California coast, we used a regional climate model (RCM) to estimate changes in wind-driven upwelling under increased CO2 concentrations. Here we show in both equilibrium and transient climate experiments that there is an intensified upwelling season, with some changes in seasonality of upwelling. This intensification may lead to enhanced productivity along the coast of California and possibly ameliorate increases in sea surface temperature due to greenhouse gas forcing.}, - number = {15}, - journal = {Geophysical Research Letters}, - author = {Snyder, Mark A. and Sloan, Lisa C. and Diffenbaugh, Noah S. and Bell, Jason L.}, - year = {2003}, - keywords = {1610 Global Change: Atmosphere, 1635 Global Change: Oceans, 3309 Meteorology and Atmospheric Dynamics: Climatology}, - pages = {1823}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{snyder_climate_2002, - title = {Climate responses to a doubling of atmospheric carbon dioxide for a climatically vulnerable region}, - volume = {29}, - issn = {0094-8276}, - abstract = {[1] Global modeling studies of future climate change predict large scale climatic responses to increased atmospheric carbon dioxide (CO2). While there have been several regional climate modeling studies that produced results at spatial and temporal scales relevant for climate change impact analysis, few have employed statistical significance testing of results. In a sensitivity study that focused on mean climate states, we use a regional climate model to generate ensembles of climate scenarios under atmospheric conditions of 280 and 560 ppm CO2, for a domain centered over California. We find statistically significant responses by mean annual and monthly temperature, precipitation, and snow to CO2 doubling. Relative to the 280 ppm results, 560 ppm results show temperature increasing everywhere in the region annually (up to 3.8degreesC), and in every month, with the greatest monthly surface warming at high elevations. Snow accumulation decreased everywhere, and precipitation increased in northern regions by up to 23\%, on a mean annual basis.}, - language = {English}, - number = {11}, - journal = {Geophysical Research Letters}, - author = {Snyder, M. A. and Bell, J. L. and Sloan, L. C. and Duffy, P. B. and Govindasamy, B.}, - month = jun, - year = {2002}, - keywords = {model, united-states, perspective}, - pages = {--}, - annote = {610MNTimes Cited:10Cited References Count:15}, - annote = {The following values have no corresponding Zotero field:auth-address: Snyder, MA Univ Calif Santa Cruz, Dept Earth Sci, Santa Cruz, CA 95064 USA Univ Calif Santa Cruz, Dept Earth Sci, Santa Cruz, CA 95064 USA Lawrence Livermore Natl Lab, Climate \& Carbon Cycle Modeling Grp, Livermore, CA 94550 USAaccession-num: ISI:000178964000046}, -} - -@article{smith_bayesian_2009, - title = {Bayesian {Modeling} of {Uncertainty} in {Ensembles} of {Climate} {Models}}, - volume = {104}, - issn = {0162-1459}, - doi = {10.1198/jasa.2009.0007}, - abstract = {Projections of future climate change caused by increasing greenhouse gases depend critically on numerical climate model, coupling the ocean and atmosphere (global climate models [GCMs]). However, different models differ substantially in their projections, which raises the question of how the different models can best be combined into a probability distribution of future climate change. For this analysis, we have collected both Current and future projected mean temperatures produced by nine climate models for 22 regions of the earth. We also have estimates of current mean temperatures from actual observations, together with standard errors, that can be used to calibrate the climate models. We propose a Bayesian analysis that allows us to combine the different climate models into a posterior distribution of future temperature increase, for each of the 22 regions, while allowing for the different climate models to have different variances. Two versions of the analysis are proposed: a univariate analysis in which each region is analyzed separately, and a multivariate analysis in which the 22 regions are combined into an overall statistical model. A cross-validation approach is proposed to confirm the reasonableness of our Bayesian predictive distributions. The results, of this analysis allow for a quantification of the uncertainty of climate model projections as a Bayesian posterior distribution, substantially extending previous approaches to uncertainty in climate models.}, - language = {English}, - number = {485}, - journal = {Journal of the American Statistical Association}, - author = {Smith, R. L. and Tebaldi, C. and Nychka, D. and Mearns, L. O.}, - month = mar, - year = {2009}, - keywords = {Climate change, aogcm simulations, averaging rea method, reliability, Bayesian modeling of uncertainty, Cross-validation, forecasts, multimodel ensemble, Prediction, probability, projections, range, system}, - pages = {97--116}, - annote = {ISI Document Delivery No.: 425PITimes Cited: 7Cited Reference Count: 44Smith, Richard L. Tebaldi, Claudia Nychka, Doug Mearns, Linda O.National Science Foundation [DMS-0084375, DMS-0355474]; NOAA [NA050AR4310020]This research was partially supported by the NCAR Weather and Climate Impacts Assessment Science Program, which is funded by the National Science Foundation. Additional support was provided by the National Science Foundation (grants DMS-0084375 to Smith and DMS-0355474 to the Geophysical Statistics Project at NCAR) and NOAA (grant NA050AR4310020 to Smith).Amer statistical assocAlexandria}, - annote = {The following values have no corresponding Zotero field:auth-address: [Smith, Richard L.] Univ N Carolina, Dept Stat \& Operat Res, Chapel Hill, NC 27599 USA. [Tebaldi, Claudia] Climate Cent, Princeton, NJ 08542 USA. [Nychka, Doug; Mearns, Linda O.] Natl Ctr Atmospher Res, Boulder, CO 80307 USA. [Nychka, Doug] Inst Math Appl Geosciences IMAGe, Boulder, CO 80307 USA. [Mearns, Linda O.] Inst Study Soc \& Environm ISSE, Boulder, CO 80307 USA. Smith, RL, Univ N Carolina, Dept Stat \& Operat Res, Chapel Hill, NC 27599 USA. rls@email.unc.edu claudia.tebaldi@gmail.com nychka@ucar.edu lindam@ucar.edualt-title: J. Am. Stat. Assoc.accession-num: ISI:000264649200013work-type: Article}, -} - -@incollection{smith_hydrologic_2003, - address = {Washington, D.C.}, - series = {{AGU} {Water} and {Science} and {Applications} {Series}}, - title = {Hydrologic model calibration in the {National} {Weather} {Service}}, - volume = {6}, - booktitle = {Calibration of {Watershed} {Models}}, - publisher = {American Geophysical Union}, - author = {Smith, M. B. and Laurine, D. and Koren, V. and Reed, S. and Zhang, Z.}, - editor = {Duan, Q. and Sorooshian, S. and Gupta, H. and Rosseau, A. and Turcotte, R.}, - year = {2003}, - pages = {133--152}, -} - -@article{snyder_modeled_2004, - title = {Modeled regional climate change in the hydrologic regions of {California}: {A} {CO2} sensitivity study}, - volume = {40}, - issn = {1093-474X}, - abstract = {Using a regional climate model (RegCM2.5), the potential impacts on the climate of California of increasing atmospheric CO2 concentrations were explored from the perspective of the state's 10 hydrologic regions. Relative to preindustrial CO2 conditions (280 ppm), doubled preindustrial CO2 conditions (560 ppm) produced increased temperatures of up to VC on an annual average basis and of up to 5degreesC on a monthly basis. Temperature increases were greatest in the central and northern regions. On a monthly basis, the temperature response was greatest in February, March, and May for nearly all regions. Snow accumulation was significantly decreased in all months and regions, with the greatest reduction occurring in the Sacramento River region. Precipitation results indicate drier winters for all regions, with a large reduction in precipitation from December to April and a smaller decrease from May to November. The result is a wet season that is slightly reduced in length. Findings suggest that the total amount of water in the state will decrease, water needs will increase, and the timing of water availability will be greatly perturbed.}, - language = {English}, - number = {3}, - journal = {Journal of the American Water Resources Association}, - author = {Snyder, M. A. and Sloan, L. C. and Bell, J. L.}, - month = jun, - year = {2004}, - keywords = {simulation, precipitation, variability, system, asia, atmospheric co2, continental united-states, events, responses}, - pages = {591--601}, - annote = {835LLTimes Cited:1Cited References Count:27}, - annote = {The following values have no corresponding Zotero field:auth-address: Snyder, MA Univ Calif Santa Cruz, Dept Earth Sci, Climate Change \& Impacts Lab, 1156 High St, Santa Cruz, CA 95064 USA Univ Calif Santa Cruz, Dept Earth Sci, Climate Change \& Impacts Lab, Santa Cruz, CA 95064 USAaccession-num: ISI:000222484600003}, -} - -@article{snyder_transient_2005, - title = {Transient future climate over the western {United} {States} using a regional climate model}, - volume = {9}, - journal = {Earth Interactions}, - author = {Snyder, M. A. and Sloan, L. C.}, - year = {2005}, - pages = {Paper No. 11, 1--21}, -} - -@article{snover_climate-change_2003, - title = {Climate-{Change} {Scenarios} for {Water} {Planning} {Studies}: {Pilot} {Applications} in the {Pacific} {Northwest}}, - volume = {84}, - issn = {0003-0007}, - doi = {10.1175/BAMS-84-11-1513}, - abstract = {Abstract No Abstract Available. -No Abstract Available.}, - number = {11}, - journal = {Bulletin of the American Meteorological Society}, - author = {Snover, Amy K. and Hamlet, Alan F. and Lettenmaier, Dennis P.}, - month = nov, - year = {2003}, - pages = {1513--1518}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Society}, -} - -@article{small_central_2007, - title = {The {Central} {American} midsummer drought: regional aspects and large-scale forcing}, - volume = {20}, - journal = {Journal of Climate}, - author = {Small, R. J. O. and de Szoeke, S. P. and Xie, S.-P.}, - year = {2007}, - pages = {4853--4873, doi:101175/JCLI4261.1}, -} - -@techreport{skamarock_description_2008, - address = {Boulder, Colorado, USA}, - title = {A description of the {Advanced} {Research} {WRF} version 3. {NCAR} {Tech}. {Note} {NCAR}/{TN}-475+{STR}}, - institution = {Mesoscale and Microscale Meteorology Division, National Center for Atmospheric Research}, - author = {Skamarock, W. C. and Klemp, J. B. and Dudhia, J. and Gill, D. O. and Barker, D. M. and Duda, M. G. and Huang, X.-Y. and Wang, W. and Powers, J. G.}, - year = {2008}, - pages = {113}, - annote = {The following values have no corresponding Zotero field:number: doi:https://doi.org/10.5065/D68S4MVH}, -} - -@article{william_c_skamarock_multiscale_2012, - title = {A {Multiscale} {Nonhydrostatic} {Atmospheric} {Model} {Using} {Centroidal} {Voronoi} {Tesselations} and {C}-{Grid} {Staggering}}, - volume = {140}, - doi = {10.1175/mwr-d-11-00215.1}, - abstract = {AbstractThe formulation of a fully compressible nonhydrostatic atmospheric model called the Model for Prediction Across Scales–Atmosphere (MPAS-A) is described. The solver is discretized using centroidal Voronoi meshes and a C-grid staggering of the prognostic variables, and it incorporates a split-explicit time-integration technique used in many existing nonhydrostatic meso- and cloud-scale models. MPAS can be applied to the globe, over limited areas of the globe, and on Cartesian planes. The Voronoi meshes are unstructured grids that permit variable horizontal resolution. These meshes allow for applications beyond uniform-resolution NWP and climate prediction, in particular allowing embedded high-resolution regions to be used for regional NWP and regional climate applications. The rationales for aspects of this formulation are discussed, and results from tests for nonhydrostatic flows on Cartesian planes and for large-scale flow on the sphere are presented. The results indicate that the solver is as accurate as existing nonhydrostatic solvers for nonhydrostatic-scale flows, and has accuracy comparable to existing global models using icosahedral (hexagonal) meshes for large-scale flows in idealized tests. Preliminary full-physics forecast results indicate that the solver formulation is robust and that the variable-resolution-mesh solutions are well resolved and exhibit no obvious problems in the mesh-transition zones.}, - number = {9}, - journal = {Monthly Weather Review}, - author = {William C. Skamarock and Joseph B. Klemp and Michael G. Duda and Laura D. Fowler and Sang-Hun Park and Todd D. Ringler}, - year = {2012}, - keywords = {Numerical analysis/modeling,Grid systems,Model evaluation/performance,Nonhydrostatic models}, - pages = {3090--3105}, -} - -@article{sinha_impacts_2010, - title = {Impacts of future climate change on soil frost in the midwestern {United} {States}}, - volume = {115}, - issn = {0148-0227}, - doi = {10.1029/2009jd012188}, - abstract = {Historical observations indicate a shift toward shorter winters and increased average air temperatures in the midwestern United States. Furthermore, a rise in soil temperatures is likely to be enhanced under projections of increased air temperature; however, reduced snow cover during winter may lead to colder soil temperatures in the future. Cumulatively, these changes will affect cold season processes in the region. Therefore the impact of such changes on cold season processes were analyzed under two climate models (Geophysical Fluid Dynamics Laboratory version CM2.1.1 (GFDL) and UK Met Office Hadley Center Climate Model, version 3.1 (HadCM3)) and three scenarios (B1, A1B, and A2) by implementing the variable infiltration capacity land surface model from 1977 to 2099. Ensemble averages of the two models for the three scenarios indicated that both air temperature and precipitation would increase in the cold season (December\&\#8211;May), with the greatest increases projected under the A2 scenario by late in the 21st century (2070\&\#8211;2099). Also during this period, the median number of days when air temperature was below 0°C reduced in comparison to the base period (1977\&\#8211;2006) by 25, 35, and 38 days for the B1, A1B, and A2 scenarios, respectively. The number of freeze-thaw cycles increased in the south-central Wisconsin and the northern regions of Michigan by up to 3 cycles, while the duration of soil frost decreased by between 2 weeks and nearly 2 months during 2070\&\#8211;2099 with respect to the base period.}, - number = {D8}, - journal = {J. Geophys. Res.}, - author = {Sinha, Tushar and Cherkauer, Keith A.}, - year = {2010}, - keywords = {climate change, 1823 Hydrology: Frozen ground, 1833 Hydrology: Hydroclimatology, 1865 Hydrology: Soils, soil frost, soil temperature, VIC}, - pages = {D08105}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{simonovic_mapping_2017, - title = {Mapping {Extreme} {Rainfall} {Statistics} for {Canada} under {Climate} {Change} {Using} {Updated} {Intensity}-{Duration}-{Frequency} {Curves}}, - volume = {143}, - doi = {doi:10.1061/(ASCE)WR.1943-5452.0000725}, - number = {3}, - journal = {Journal of Water Resources Planning and Management}, - author = {Simonovic, S. P. and Schardong, A. and Sandink, D.}, - year = {2017}, -} - -@article{silva_improved_2007, - title = {An {Improved} {Gridded} {Historical} {Daily} {Precipitation} {Analysis} for {Brazil}}, - volume = {8}, - issn = {1525-755X}, - doi = {10.1175/jhm598.1}, - number = {4}, - journal = {Journal of Hydrometeorology}, - author = {Silva, Viviane B. S. and Kousky, Vernon E. and Shi, Wei and Higgins, R. Wayne}, - month = aug, - year = {2007}, - pages = {847--861}, - annote = {doi: 10.1175/JHM598.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/JHM598.1}, -} - -@article{sillmann_climate_2013, - title = {Climate extremes indices in the {CMIP5} multimodel ensemble: {Part} 1. {Model} evaluation in the present climate}, - volume = {118}, - issn = {2169-8996}, - doi = {10.1002/jgrd.50203}, - journal = {Journal of Geophysical Research: Atmospheres}, - author = {Sillmann, J. and Kharin, V. V. and Zhang, X. and Zwiers, F. W. and Bronaugh, D.}, - year = {2013}, - keywords = {precipitation, temperature, 0550 Model verification and validation, 1610 Atmosphere, 1626 Global climate models, 3354 Precipitation, 4313 Extreme events, climate model evaluation, extreme events, observations, reanalysis}, - pages = {1716--1733}, -} - -@article{shi_how_2008, - title = {How {Essential} is {Hydrologic} {Model} {Calibration} to {Seasonal} {Streamflow} {Forecasting}?}, - volume = {9}, - abstract = {Hydrologic model calibration is usually a central element of streamflow forecasting based on the ensemble streamflow prediction (ESP) method. Evaluation measures of forecast errors such as root-mean-square error (RMSE) are heavily influenced by bias, which in turn is readily reduced by calibration. On the other hand, bias can also be reduced by postprocessing (e.g., \&\#8220;training\&\#8221; bias correction schemes based on retrospective simulation error statistics). This observation invites the question: How much is forecast error reduced by calibration, beyond what can be accomplished by postprocessing to remove bias? The authors address this question through retrospective evaluation of forecast errors at eight streamflow forecast locations distributed across the western United States. Forecast periods of length ranging from 1 to 6 months are investigated, for forecasts initiated from 1 December to 1 June, which span the period when most runoff occurs from snowmelt-dominated western U.S. rivers. ESP forecast errors are evaluated both for uncalibrated forecasts to which a percentile mapping bias correction approach is applied, and for forecasts from an objectively calibrated model without explicit bias correction. Using the coefficient of prediction (Cp), which essentially is a measure of the fraction of variance explained by the forecast, the authors find that the reduction in forecast error as measured by Cp that is achieved by bias correction alone is nearly as great as that resulting from hydrologic model calibration.}, - number = {6}, - journal = {Journal of Hydrometeorology}, - author = {Shi, Xiaogang and Wood, Andrew W. and Lettenmaier, Dennis P.}, - month = dec, - year = {2008}, - pages = {1350--1363}, -} - -@incollection{shepard_computer_1984, - title = {Computer mapping: {The} {SYMAP} interpolation algorithm}, - booktitle = {Spatial {Statistics} and {Models}}, - publisher = {D. Reidel}, - author = {Shepard, D. S.}, - editor = {Gaile, G. L. and Willmott, C. J.}, - year = {1984}, - pages = {133--145}, -} - -@article{shen_projection_2008, - title = {Projection of future world water resources under {SRES} scenarios: water withdrawal}, - volume = {53}, - journal = {Hydrological Science Journal}, - author = {Shen, Y. and Oki, T. and Utsumi, N. and Kanae, S. and Hanasaki, N.}, - year = {2008}, - pages = {11--33}, - annote = {The following values have no corresponding Zotero field:publisher: International Association of Hydrological Sciences}, -} - -@article{sheffield_little_2012, - title = {Little change in global drought over the past 60 years}, - volume = {491}, - issn = {1476-4687 (Electronic) 0028-0836 (Linking)}, - doi = {10.1038/nature11575}, - abstract = {Drought is expected to increase in frequency and severity in the future as a result of climate change, mainly as a consequence of decreases in regional precipitation but also because of increasing evaporation driven by global warming. Previous assessments of historic changes in drought over the late twentieth and early twenty-first centuries indicate that this may already be happening globally. In particular, calculations of the Palmer Drought Severity Index (PDSI) show a decrease in moisture globally since the 1970s with a commensurate increase in the area in drought that is attributed, in part, to global warming. The simplicity of the PDSI, which is calculated from a simple water-balance model forced by monthly precipitation and temperature data, makes it an attractive tool in large-scale drought assessments, but may give biased results in the context of climate change. Here we show that the previously reported increase in global drought is overestimated because the PDSI uses a simplified model of potential evaporation that responds only to changes in temperature and thus responds incorrectly to global warming in recent decades. More realistic calculations, based on the underlying physical principles that take into account changes in available energy, humidity and wind speed, suggest that there has been little change in drought over the past 60 years. The results have implications for how we interpret the impact of global warming on the hydrological cycle and its extremes, and may help to explain why palaeoclimate drought reconstructions based on tree-ring data diverge from the PDSI-based drought record in recent years.}, - number = {7424}, - journal = {Nature}, - author = {Sheffield, J. and Wood, E. F. and Roderick, M. L.}, - month = nov, - year = {2012}, - keywords = {Temperature, *Global Warming, *Models, Theoretical, Droughts/*statistics \& numerical data, Time Factors}, - pages = {435--8}, - annote = {Sheffield, JustinWood, Eric FRoderick, Michael LengResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, Non-P.H.S.England2012/11/16 06:00Nature. 2012 Nov 15;491(7424):435-8. doi: 10.1038/nature11575.}, - annote = {The following values have no corresponding Zotero field:auth-address: Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544, USA. justin@princeton.edupublisher: Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.accession-num: 23151587work-type: 10.1038/nature11575}, -} - -@article{sheffield_development_2006, - title = {Development of a 50-yr high-resolution global dataset of meteorological forcings for land surface modeling}, - volume = {19}, - number = {13}, - journal = {Journal of Climate}, - author = {Sheffield, J. and Goteti, G. and Wood, E. F.}, - year = {2006}, - pages = {3088--3111}, -} - -@article{sheffield_north_2013, - title = {North {American} {Climate} in {CMIP5} {Experiments}. {Part} {II}: {Evaluation} of {Historical} {Simulations} of {Intraseasonal} to {Decadal} {Variability}}, - volume = {26}, - issn = {0894-8755}, - doi = {10.1175/JCLI-D-12-00593.1}, - abstract = {AbstractThis is the second part of a three-part paper on North American climate in phase 5 of the Coupled Model Intercomparison Project (CMIP5) that evaluates the twentieth-century simulations of intraseasonal to multidecadal variability and teleconnections with North American climate. Overall, the multimodel ensemble does reasonably well at reproducing observed variability in several aspects, but it does less well at capturing observed teleconnections, with implications for future projections examined in part three of this paper. In terms of intraseasonal variability, almost half of the models examined can reproduce observed variability in the eastern Pacific and most models capture the midsummer drought over Central America. The multimodel mean replicates the density of traveling tropical synoptic-scale disturbances but with large spread among the models. On the other hand, the coarse resolution of the models means that tropical cyclone frequencies are underpredicted in the Atlantic and eastern North Pacific. The frequency and mean amplitude of ENSO are generally well reproduced, although teleconnections with North American climate are widely varying among models and only a few models can reproduce the east and central Pacific types of ENSO and connections with U.S. winter temperatures. The models capture the spatial pattern of Pacific decadal oscillation (PDO) variability and its influence on continental temperature and West Coast precipitation but less well for the wintertime precipitation. The spatial representation of the Atlantic multidecadal oscillation (AMO) is reasonable, but the magnitude of SST anomalies and teleconnections are poorly reproduced. Multidecadal trends such as the warming hole over the central?southeastern United States and precipitation increases are not replicated by the models, suggesting that observed changes are linked to natural variability.}, - number = {23}, - journal = {Journal of Climate}, - author = {Sheffield, J. and Camargo, Suzana J. and Fu, Rong and Hu, Qi and Jiang, Xianan and Johnson, Nathaniel and Karnauskas, Kristopher B. and Kim, Seon Tae and Kinter, Jim and Kumar, Sanjiv and Langenbrunner, Baird and Maloney, Eric and Mariotti, Annarita and Meyerson, Joyce E. and Neelin, J. David and Nigam, Sumant and Pan, Zaitao and Ruiz-Barradas, Alfredo and Seager, Richard and Serra, Yolande L. and Sun, De-Zheng and Wang, Chunzai and Xie, Shang-Ping and Yu, Jin-Yi and Zhang, Tao and Zhao, Ming}, - month = dec, - year = {2013}, - pages = {9247--9290}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Society}, -} - -@article{shannon_cross-scale_1996, - title = {Cross-scale relationships regarding local temperature inversions at {Cape} {Town} and global climate change implications}, - volume = {92}, - abstract = {This study develops a downscaling methodology for establishing relationships between Cape Town's winter synoptic scale circulation and temperature inversions in the boundary layer Application lion of the method is demonstrated by deriving inversion frequencies from general circulation model (GCM) data sets. Daily midday radiosonde data at Cape Town International airport for the winter of 1989 were classified in a binary manner for the identification of temperature inversions, and an artificial neural network (ANN) was trained to relate sea-level pressure fields over Cape Town to local inversion conditions. ANNs were used to derive the relationships because of their robustness in the presence of noise and because they, do not have the constraints of linearity. These relationships were then applied to the simulations generated by two GCMs, the GISS and GENESIS models, in order to investigate their ability to represent the synoptic controls on local inversions. The two GCMs compared favourably with each other and were reasonably similar to the NMC's observational data regarding the number of temperature inversions each reflected. Following this, circulation for a simulation of doubled atmospheric CO2 was used to predict possible future changes in the frequency of inversions over Cape Town. The occurrence of temperature inversions increased by approximately 25\% using the GISS model and by 33\% using GENESIS. This putative increase in inversion frequency as a result of global climate change could have implications for the city's atmospheric pollution.}, - number = {4}, - journal = {South African Journal of Science}, - author = {Shannon, D. A. and Hewitson, B. C.}, - month = apr, - year = {1996}, - pages = {213--216}, - annote = {The following values have no corresponding Zotero field:alt-title: S. Afr. J. Sci.accession-num: ISI:A1996UP88000017}, - annote = {UP880S AFR J SCI}, -} - -@article{sheffield_characteristics_2007, - title = {Characteristics of global and regional drought, 1950-2000: {Analysis} of soil moisture data from off-line simulation of the terrestrial hydrologic cycle}, - volume = {112}, - issn = {0148-0227}, - doi = {10.1029/2006jd008288}, - abstract = {Drought occurrence is analyzed over global land areas for 1950-2000 using soil moisture data from a simulation of the terrestrial water cycle with the Variable Infiltration Capacity (VIC) land surface model, which is forced by an observation based meteorological data set. A monthly drought index based on percentile soil moisture values relative to the 50-year climatology is analyzed in terms of duration, intensity and severity at global and regional scales. Short-term droughts (\<= 6 months) are prevalent in the Tropics and midlatitudes, where inter-annual climate variability is highest. Medium term droughts (7\&\#8211;12 months) are more frequent in mid- to high-latitudes. Long term (12+ months) droughts are generally restricted to sub-Saharan Africa and higher northern latitudes. The Sahel region stands out for having experienced long-term and severe drought conditions. Severe regional drought events are systematically identified in terms of spatial coverage, based on different thresholds of duration and intensity. For example, in northern Europe, 1996 and 1975 were the years of most extensive 3- and 12-month duration drought, respectively. In northern Asia, severe drought events are characterized by persistent soil moisture anomalies over the wintertime. The drought index identifies several well-known events, including the 1988 US, 1982/83 Australian, 1983/4 Sahel and 1965/66 Indian droughts which are generally ranked as the severest and most spatially extensive in the record. Comparison with the PDSI shows general agreement at global scales and for these major events but they diverge considerably in cooler regions and seasons, and especially in latter years when the PDSI shows a larger drying trend.}, - number = {D17}, - journal = {J. Geophys. Res.}, - author = {Sheffield, J. and Wood, E. F.}, - year = {2007}, - keywords = {1812 Hydrology: Drought, 1833 Hydrology: Hydroclimatology, 1616 Global Change: Climate variability, 1817 Hydrology: Extreme events, 1866 Hydrology: Soil moisture, Global drought, land surface modeling, soil moisture}, - pages = {D17115}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{seager_model_2007, - title = {Model {Projections} of an {Imminent} {Transition} to a {More} {Arid} {Climate} in {Southwestern} {North} {America}}, - volume = {316}, - doi = {10.1126/science.1139601}, - abstract = {How anthropogenic climate change will affect hydroclimate in the arid regions of southwestern North America has implications for the allocation of water resources and the course of regional development. Here we show that there is a broad consensus among climate models that this region will dry in the 21st century and that the transition to a more arid climate should already be under way. If these models are correct, the levels of aridity of the recent multiyear drought or the Dust Bowl and the 1950s droughts will become the new climatology of the American Southwest within a time frame of years to decades.}, - number = {5828}, - journal = {Science}, - author = {Seager, Richard and Ting, Mingfang and Held, Isaac and Kushnir, Yochanan and Lu, Jian and Vecchi, Gabriel and Huang, Huei-Ping and Harnik, Nili and Leetmaa, Ants and Lau, Ngar-Cheung and Li, Cuihua and Velez, Jennifer and Naik, Naomi}, - month = may, - year = {2007}, - pages = {1181--1184}, -} - -@article{scott_climate_2003, - title = {Climate change and the skiing industry in southern {Ontario} ({Canada}): exploring the importance of snowmaking as a technical adaptation}, - volume = {23}, - abstract = {The winter tourism industry has been repeatedly identified as potentially vulnerable to global climate change. Climate change impact assessments of ski areas in Australia, Europe and North America all project negative consequences for the industry. An important limitation of earlier studies has been the incomplete consideration of snowmaking as a climate adaptation strategy. Recognising that snowmaking is an integral component of the ski industry, this study examined how current and improved snowmaking capacity affects the vulnerability of the ski industry in southern Ontario (Canada) to climate variability and change. A 17 yr record of daily snow conditions and operations from a primary ski area in the region was used to calibrate a ski season simulation model that included a snowmaking module with climatic thresholds and operational decision rules based on interviews with ski area managers. Climate change scenarios (2020s, 2050s, 2080s) were developed by downscaling climate variables from 4 general circulation models (using both IS92a and SIZES emission scenarios) with the LARS weather generator (parameterized to local climate stations) for input into a daily snow depth simulation model. In contrast to earlier studies, the results indicate that ski areas in the region could remain operational in a warmer climate, particularly within existing business planning and investment time horizons (into the 2020s), The economic impact of additional snowmaking requirements remains an important uncertainty. Under climate change scenarios and current snowmaking technology, the average ski season at the case study ski area was projected to reduce by 0-16 \% in the 2020s, 7-32 \% in the 2050s and 11-50 \% in the 2080s. Concurrent with the projected ski season losses, the estimated amount of snowmaking required increased by 36-144 \% in the scenarios for the 2020s. Required snowmaking amounts increased by 48-187 \% in the scenarios for the 2020s. The ability of individual ski areas to absorb additional snowmaking costs and remain economically viable in addition to the relative impact of climate change on other nearby ski regions (Quebec, Michigan and Vermont) remain important avenues of further research. The findings reveal the importance of examining a wide range of climate change scenarios and the necessity of including snowmaking and other adaptation strategies in future climate change vulnerability assessments of the ski industry and winter tourism in other regions of the world.}, - number = {2}, - journal = {Climate Research}, - author = {Scott, D. and McBoyle, G. and Mills, B.}, - month = jan, - year = {2003}, - pages = {171--181}, - annote = {671UDCLIMATE RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:000182481700007}, -} - -@article{marla_schwartz_significant_2017, - title = {Significant and {Inevitable} {End}-of-{Twenty}-{First}-{Century} {Advances} in {Surface} {Runoff} {Timing} in {California}’s {Sierra} {Nevada}}, - volume = {18}, - doi = {10.1175/jhm-d-16-0257.1}, - abstract = {AbstractUsing hybrid dynamical–statistical downscaling, 3-km-resolution end-of-twenty-first-century runoff timing changes over California’s Sierra Nevada for all available global climate models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are projected. All four representative concentration pathways (RCPs) adopted by the Intergovernmental Panel on Climate Change’s Fifth Assessment Report are examined. These multimodel, multiscenario projections allow for quantification of ensemble-mean runoff timing changes and an associated range of possible outcomes due to both intermodel variability and choice of forcing scenario. Under a “business as usual” forcing scenario (RCP8.5), warming leads to a shift toward much earlier snowmelt-driven surface runoff in 2091–2100 compared to 1991–2000, with advances of as much as 80 days projected in the 35-model ensemble mean. For a realistic “mitigation” scenario (RCP4.5), the ensemble-mean change is smaller but still large (up to 30 days). For all plausible forcing scenarios and all GCMs, the simulated changes are statistically significant, so that a detectable change in runoff timing is inevitable. Even for the mitigation scenario, the ensemble-mean change is approximately equivalent to one standard deviation of the natural variability at most elevations. Thus, even when greenhouse gas emissions are curtailed, the runoff change is climatically significant. For the business-as-usual scenario, the ensemble-mean change is approximately two standard deviations of the natural variability at most elevations, portending a truly dramatic change in surface hydrology by the century’s end if greenhouse gas emissions continue unabated.}, - number = {12}, - journal = {Journal of Hydrometeorology}, - author = {Marla Schwartz and Alex Hall and Fengpeng Sun and Daniel Walton and Neil Berg}, - year = {2017}, - keywords = {Runoff,Snowmelt/icemelt,Climate change,Hydrology,Land surface model,Regional models}, - pages = {3181--3197}, -} - -@article{semenov_use_2010, - title = {Use of multi-model ensembles from global climate models for assessment of climate change impacts}, - volume = {41}, - number = {1}, - journal = {Climate Research}, - author = {Semenov, M. A. and Stratonovitch, P.}, - year = {2010}, - pages = {1--14}, - annote = {10.3354/cr00836}, -} - -@article{semenov_use_1997, - title = {Use of a stochastic weather generator in the development of climate change scenarios}, - volume = {35}, - abstract = {Climate change scenarios with a high spatial and temporal resolution are required in the evaluation of the effects of climate change on agricultural potential and agricultural risk. Such scenarios should reproduce changes in mean weather characteristics as well as incorporate the changes in climate variability indicated by the global climate model (GCM) used. Recent work on the sensitivity of crop models and climatic extremes has clearly demonstrated that changes in variability can have more profound effects on crop yield and on the probability of extreme weather events than simple changes in the mean values. The construction of climate change scenarios based on spatial regression downscaling and on the use of a local stochastic weather generator is described. Regression downscaling translated the coarse resolution GCM grid-box predictions of climate change to site-specific values. These values were then used to perturb the parameters of the stochastic weather generator in order to simulate site-specific daily weather data. This approach permits the incorporation of changes in the mean and variability of climate in a consistent and computationally inexpensive way. The stochastic weather generator used in this study, LARS-WG, has been validated across Europe and has been shown to perform well in the simulation of different weather statistics, including those climatic extremes relevant to agriculture. The importance of downscaling and the incorporation of climate variability are demonstrated at two European sites where climate change scenarios were constructed using the UK Met. Office high resolution GCM equilibrium and transient experiments.}, - number = {4}, - journal = {Clim. Change}, - author = {Semenov, M. A. and Barrow, E. M.}, - month = apr, - year = {1997}, - pages = {397--414}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Changeaccession-num: ISI:A1997WY92400002}, - annote = {WY924CLIMATIC CHANGE}, -} - -@techreport{schulzweida_cdo_2011, - address = {Hamburg, Germany}, - title = {{CDO} {User}'s {Guide}, {Climate} {Data} {Operators}, {Version} 1.4.7}, - institution = {Max Planck Institute (MPI) for Meteorology}, - author = {Schulzweida, U. and Kornblueh, L. and Quast, R.}, - year = {2011}, -} - -@article{schubert_downscaling_1998, - title = {Downscaling local extreme temperature changes in south-eastern {Australia} from the {CSIRO} {Mark2} {GCM}}, - volume = {18}, - abstract = {Climate impact studies crucially rely on climate change information at high spatial and temporal resolutions. Since the most developed tools for estimating future climate change-the general circulation models (GCMs)-still operate on rather coarse spatial scales, their output has to be downscaled in order to provide the needed high resolution input for climate impact models. In this study, a perfect prognosis approach is employed to downscale daily local temperature extremes at several stations in south-eastern Australia from synoptic scale atmospheric circulation fields. The statistical model combines principal component analysis and linear multiple regression and is suitable to explain a considerable part of both short and long frequency variations of local temperature extremes. Using simulated and observed daily data the regional to local performance of the Mark2 GCM, developed by the Commonwealth Scientific and Industrial Research Organisation (CSIRO), was validated over the Australian region. While the regional temperature extremes simulated under present-day climate conditions are in general agreement with the observed climate, there are highly significant differences on the local scale. The observed daily synoptic scale atmospheric circulation, however, is well reproduced by the GCM. This supports the idea of using these reliably simulated climatic parameters to estimate the changes in local temperature extremes under altered global climate conditions. The downscaling model was applied to synoptic scale atmospheric circulation fields generated by the CSIRO Mark2 GCM under 1 x CO2 and 2 x CO2 conditions. Compared to the extreme temperature changes simulated by the GCM directly, the downscaled variations are much weaker. Several sources of uncertainty might be causing these differences. Firstly, the statistical model is stationary. Therefore, it is not capable of including changes in the relationships between circulation and local temperature which are likely to occur under 2 x CO2 climate changes. It also only captures circulation-related temperature variations. An analysis of the circulation changes under 2 x CO2, however, reveals almost no significant differences to the present day conditions. That leads to the conclusion, that the quite dramatic changes of local temperature extremes under doubled CO2 are not driven by circulation changes and might be: almost solely caused by changed radiative properties of the atmosphere. (C) 1998 Royal Meteorological Society.}, - number = {13}, - journal = {International Journal of Climatology}, - author = {Schubert, S.}, - month = nov, - year = {1998}, - pages = {1419--1438}, - annote = {138HWINT J CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:000076967300002}, -} - -@article{schoof_downscaling_2001, - title = {Downscaling temperature and precipitation: {A} comparison of regression-based methods and artificial neural networks}, - volume = {21}, - abstract = {A comparison of two statistical downscaling methods for daily maximum and minimum surface air temperature, total daily precipitation and total monthly precipitation at Indianapolis, IN, USA, is presented. The analysis is conducted for two seasons, the growing season and the non-growing season, defined based on variability of surface air temperature. The predictors used in the downscaling are indices of the synoptic scale circulation derived from rotated principal components analysis (PCA) and cluster analysis of variables extracted from an 18- year record from seven rawinsonde stations in the Midwest region of the United States. PCA yielded seven significant components for the growing season and five significant components for the non-growing season. These PCs explained 86\% and 83\% of the original rawinsonde data for the growing and non-growing seasons, respectively. Cluster analysis of the PC scores using the average linkage method resulted in eight growing season synoptic types and twelve non-growing synoptic types. The downscaling of temperature and precipitation is conducted using PC scores and cluster frequencies in regression models and artificial neural networks (ANNs). Regression models and ANNs yielded similar results, but the data for each regression model violated at least one of the assumptions of regression analysis. As expected, the accuracy of the downscaling models for temperature was superior to that for precipitation. The accuracy of all temperature models was improved by adding an autoregressive term, which also changed the relative importance of the dominant anomaly patterns as manifest in the PC scores. Application of the transfer functions to model daily maximum and minimum temperature data from an independent time series resulted in correlation coefficients of 0.34-0.89. In accord with previous studies, the precipitation models exhibited lesser predictive capabilities. The correlation coefficient for predicted versus observed daily precipitation totals was less than 0.5 for both seasons, while that for monthly total precipitation was below 0.65. The downscaling techniques are discussed in terms of model performance, comparison of techniques and possible model improvements. Copyright (C) 2001 Royal Meteorological Society.}, - number = {7}, - journal = {International Journal of Climatology}, - author = {Schoof, J. T. and Pryor, S. C.}, - month = jun, - year = {2001}, - pages = {773--790}, - annote = {447XGINT J CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:000169597300001}, -} - -@article{schnur_case_1998, - title = {A case study of statistical downscaling in {Australia} using weather classification by recursive partitioning}, - volume = {213}, - abstract = {Because of their coarse resolution General Circulation Models (GCMs) lack the ability to predict reliably surface variables that may be needed for climate effects studies on a regional scale. Stochastic downscaling methods, based on the relationship between large-scale circulation types and regional surface variables, offer one approach to bridge this scale mismatch. The method explored in this paper uses recursive partitioning (also known as Classification Tree Analysis) to identify circulation patterns (weather states) in observed sea level pressure (SLP) fields that are most closely related to certain rainfall occurrence patterns (dry/wet) at multiple stations. The joint distribution functions of rainfall amounts at a set of stations are then used to define a statistical model for rainfall conditional on these weather states and the previous day's rainfall occurrence/absence. This model is applied to SLP fields from control, 2 x CO2 equilibrium and transient 100 year GCM integrations performed at the Max- Planck-Institut fur Meteorologie, Hamburg to simulate local rainfall corresponding to the large-scale GCM climates. The weather generator is applied for several case studies in Australia. Four regions, one each in the West, the North, the Southeast and East of Australia are considered, for the summer and winter seasons, respectively. These regions correspond to different climate zones: the North has mainly summer monsoon rain, the West has dominant frontal winter rainfall, and moderate Southeast has a relatively uniform rainfall distribution throughout the year. Thus, the performance of the weather generator in different climatic regimes can be evaluated. Since rainfall amounts are simulated by sampling from historical data a statistical uncertainty is attached to the generated rainfall sequences which is due to the procedure used. This is reflected in uncertainties of the downscaled mean rainfall of generally less than +/- 10\%, with larger numerical intervals for dry stations and seasons. The effects of a 2 x CO2 signal in the equilibrium run on local rainfall are not dramatic. For the transient experiment there is an indication of more rainy days and a decrease in the length of dry periods in West Australia in winter. The strongest change is identified in the North in the monsoon season which would have decreasing rainfall. No unequivocal changes were found for the Southeast. However, consistent with earlier studies, the bias of GCM control simulations strongly affects the results, as the difference between precipitation statistics based on observed and control run pressure fields often exceeds the difference between GCM control and altered climate. Considering this deficiency in current GCM simulations, the disagreement between different climate models and the uncertainty attached to the statistical downscaling method, not too much reliance can be put on the relevance of these results for regional hydrologic impact modeling. (C) 1998 Elsevier Science B.V. All rights reserved.}, - number = {1-4}, - journal = {Journal of Hydrology}, - author = {Schnur, R. and Lettenmaier, D. P.}, - month = dec, - year = {1998}, - pages = {362--379}, - annote = {151LVJ HYDROL}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Hydrol.accession-num: ISI:000077722300024}, -} - -@article{schubert_statistical_1997, - title = {A statistical model to downscale local daily temperature extremes from synoptic-scale atmospheric circulation patterns in the {Australian} region}, - volume = {13}, - abstract = {The study seeks to describe one method of deriving information about local daily temperature extremes from larger scale atmospheric flow patterns using statistical tools. This is considered to be one step towards downscaling coarsely gridded climate data from global climate models (GCMs) to finer spatial scales. Downscaling is necessary in order to bridge the spatial mismatch between GCMs and climate impact models which need information on spatial scales that the GCMs cannot provide. The method of statistical downscaling is based on physical interaction between atmospheric processes with different spatial scales, in this case between synoptic scale mean sea level pressure (MSLP) fields and local temperature extremes at several stations in southeast Australia. In this study it was found that most of the day-to-day spatial variability of the synoptic circulation over the Australian region can be captured by six principal components. Using the scores of these PCs as multivariate indicators of the circulation a substantial part of the local daily temperature variability could be explained. The inclusion of temperature persistence noticeably improved the performance of the statistical model. The model established and tested with observations is thought to be finally applied to GCM-simulated pressure fields in order to estimate pressure- related changes in local temperature extremes under altered CO2 conditions.}, - number = {3}, - journal = {Climate Dynamics}, - author = {Schubert, S. and HendersonSellers, A.}, - month = mar, - year = {1997}, - pages = {223--234}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Dyn.accession-num: ISI:A1997WT94300005}, - annote = {WT943CLIM DYNAM}, -} - -@article{schlenker_water_2007, - title = {Water {Availability}, {Degree} {Days}, and the {Potential} {Impact} of {Climate} {Change} on {Irrigated} {Agriculture} in {California}}, - volume = {81}, - issn = {0165-0009}, - doi = {10.1007/s10584-005-9008-z}, - language = {English}, - number = {1}, - journal = {Climatic Change}, - author = {Schlenker, Wolfram and Hanemann, W. Michael and Fisher, AnthonyC}, - month = mar, - year = {2007}, - pages = {19--38}, - annote = {The following values have no corresponding Zotero field:alt-title: Climatic Changepublisher: Springer Netherlands}, -} - -@article{schlosser_assessing_2010, - title = {Assessing {Evapotranspiration} {Estimates} from the {Second} {Global} {Soil} {Wetness} {Project} ({GSWP}-2) {Simulations}}, - volume = {11}, - doi = {doi:10.1175/2010JHM1203.1}, - number = {4}, - journal = {Journal of Hydrometeorology}, - author = {Schlosser, C. Adam and Gao, Xiang}, - year = {2010}, - pages = {880--897}, -} - -@article{scherrer_snow-albedo_2012, - title = {Snow-albedo feedback and {Swiss} spring temperature trends}, - volume = {110}, - issn = {1434-4483}, - doi = {10.1007/s00704-012-0712-0}, - abstract = {We quantify the effect of the snow-albedo feedback on Swiss spring temperature trends using daily temperature and snow depth measurements from six station pairs for the period 1961–2011. We show that the daily mean 2-m temperature of a spring day without snow cover is on average 0.4 °C warmer than one with snow cover at the same location. This estimate is comparable with estimates from climate modelling studies. Caused by the decreases in snow pack, the snow-albedo feedback amplifies observed temperature trends in spring. The influence is small and confined to areas around the upward-moving snow line in spring and early summer. For the 1961–2011 period, the related temperature trend increases are in the order of 3–7 \% of the total observed trend.}, - number = {4}, - journal = {Theoretical and Applied Climatology}, - author = {Scherrer, S. C. and Ceppi, P. and Croci-Maspoli, M. and Appenzeller, C.}, - month = dec, - year = {2012}, - pages = {509--516}, - annote = {The following values have no corresponding Zotero field:label: Scherrer2012work-type: journal article}, -} - -@article{scalzitti_climate_2016, - title = {Climate change impact on the roles of temperature and precipitation in western {U}.{S}. snowpack variability}, - volume = {43}, - issn = {1944-8007}, - doi = {10.1002/2016GL068798}, - number = {10}, - journal = {Geophysical Research Letters}, - author = {Scalzitti, Jason and Strong, Courtenay and Kochanski, Adam}, - year = {2016}, - keywords = {dynamical downscaling, 1626 Global climate models, 0736 Snow, 1637 Regional climate change, 1884 Water supply, 3305 Climate change and variability, climate change-driven snowpack, pseudo global warming, snowpack variability, western United States, WRF}, - pages = {2016GL068798}, -} - -@article{sauerborn_future_1999, - title = {Future rainfall erosivity derived from large-scale climate models - methods and scenarios for a humid region}, - volume = {93}, - abstract = {The global greenhouse effect is expected not only to increase mean global temperatures, but also to influence characteristics of rainfall. Increased variability of precipitation and a higher amount of erosive rainfall in Middle Europe may also increase soil erosion, in this study the modified Fournier's index is used to derive future rainfall erosivity values by the use of predicted monthly rainfall amounts for elaborating an index in accordance with the universal soil loss equation. The fundamental idea is the significant correlation between this index and the erosivity factor R. Future monthly precipitation data which are required for those calculations were produced by a general circulation model and adjusted to finer scales by expanded downscaling. The results suggest an increase of rainfall erosivity for right stations in North Rhine-Westphalia (Germany). (C) 1999 Elsevier Science B.V. All rights reserved.}, - number = {3-4}, - journal = {Geoderma}, - author = {Sauerborn, P. and Klein, A. and Botschek, J. and Skowronek, A.}, - month = dec, - year = {1999}, - pages = {269--276}, - annote = {276HRGEODERMA}, - annote = {The following values have no corresponding Zotero field:alt-title: Geodermaaccession-num: ISI:000084872500007}, -} - -@incollection{p_ciais_carbon_2013, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Carbon and {Other} {Biogeochemical} {Cycles}}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {P. Ciais and C. Sabine and G. Bala and L. Bopp and V. Brovkin and J. Canadell and A. Chhabra and R. DeFries and J. Galloway and M. Heimann and C. Jones and C. Le Quéré and R. B. Myneni and S. Piao and P. Thornton}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley}, - year = {2013}, - pages = {465--570}, - annote = {The following values have no corresponding Zotero field:section: 6electronic-resource-num: 10.1017/CBO9781107415324.015}, -} - -@techreport{cifuentes_cambio_2008, - address = {Santiago, Chile}, - title = {Cambio climático: coonsecuencias y desafíos para {Chile}}, - institution = {Pontificia Universidad Católica de Chile}, - author = {Cifuentes, L. A. and Meza, F. J.}, - editor = {Centro Interdisciplinario de Cambio Global}, - year = {2008}, -} - -@article{christensen_multimodel_2007, - title = {A multimodel ensemble approach to assessment of climate change impacts on the hydrology and water resources of the {Colorado} {River} basin}, - volume = {in review}, - journal = {Hydrology and Earth System Sciences}, - author = {Christensen, N. and Lettenmaier, D. P.}, - year = {2007}, -} - -@incollection{christensen_regional_2007, - address = {Cambridge, United Kingdom and New York, NY, USA.}, - title = {Regional climate projections}, - booktitle = {Climate {Change} 2007: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fourth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {Christensen, J. H. and Hewitson, B. and Busuioc, A. and Chen, A. and Gao, X. and Held, I. and Jones, R. and Kolli, R. K. and Kwon, W.-T. and Laprise, R. and Magaña Rueda, V. and Mearns, L. and Menéndez, C. G. and Räisänen, J. and Rinke, A. and Sarr, A. and Whetton, P.}, - editor = {Solomon, S. and Qin, D. and Manning, M. and Chen, Z. and Marquis, M. and Averyt, K. B. and Tignor, M. and Miller, H. L.}, - year = {2007}, -} - -@article{chiew_influence_2009, - title = {Influence of global climate model selection on runoff impact assessment}, - volume = {379 (2009)}, - journal = {Journal of Hydrology}, - author = {Chiew, F. H. S. and Teng, J. and Vaze, J. and Kirono, D. G. C.}, - year = {2009}, - pages = {172--180}, -} - -@article{christensen_need_2008, - title = {On the need for bias correction of regional climate change projections of temperature and precipitation}, - volume = {35}, - issn = {0094-8276}, - doi = {10.1029/2008gl035694}, - abstract = {Within the framework of the European project ENSEMBLES (ensembles-based predictions of climate changes and their impacts) we explore the systematic bias in simulated monthly mean temperature and precipitation for an ensemble of thirteen regional climate models (RCMs). The models have been forced with the European Centre for Medium Range Weather Forecasting Reanalysis (ERA40) and are compared to a new high resolution gridded observational data set. We find that each model has a distinct systematic bias relating both temperature and precipitation bias to the observed mean. By excluding the twenty-five percent warmest and wettest months, respectively, we find that a derived second-order fit from the remaining months can be used to estimate the values of the excluded months. We demonstrate that the common assumption of bias cancellation (invariance) in climate change projections can have significant limitations when temperatures in the warmest months exceed 4\&\#8211;6 °C above present day conditions.}, - number = {20}, - journal = {Geophysical Research Letters}, - author = {Christensen, J. H. and Boberg, F. and Christensen, O. B. and Lucas-Picher, P.}, - year = {2008}, - keywords = {precipitation, regional climate change, temperature, 1610 Global Change: Atmosphere, 1637 Global Change: Regional climate change, 3333 Atmospheric Processes: Model calibration, 3354 Atmospheric Processes: Precipitation, 3399 Atmospheric Processes: General or miscellaneous}, - pages = {L20709}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{cherkauer_variable_2003, - title = {Variable infiltration capacity cold land process model updates}, - volume = {38}, - journal = {Global and Planetary Change}, - author = {Cherkauer, K. A. and Bowling, L. C. and Lettenmaier, D. P.}, - year = {2003}, - pages = {151--159}, -} - -@article{chen_stream_1998, - title = {Stream temperature simulation of forested riparian areas: {I}. {Watershed}-scale model development}, - volume = {124}, - issn = {0733-9372}, - abstract = {To simulate stream temperatures on a watershed scale, shading dynamics of topography and riparian vegetation must be computed for estimating the amount of solar radiation that is actually absorbed by water for each stream reach. A series of computational procedures identifying the geometric relationships among the sun position, stream location and orientation, and riparian shading characteristics were used to develop a computer program called SHADE. The SHADE-generated solar radiation data are used by the Hydrologic Simulation Program-FORTRAN (HSPF) to simulate hourly stream temperatures. A methodology for computing the heat flux between water and streambed was selected, evaluated, and implemented in the HSPF code. This work advances the state of the art in watershed analysis by providing a quantitative tool for relating riparian forest management to stream temperature, which is a vital component of aquatic habitat. This paper describes the modeling strategies, the SHADE program in terms of algorithms and procedures, the integration of SHADE with HSPF, and the algorithms and evaluation of the bed conduction of heat. A companion paper presents an application of the SHADE-HSPF modeling system for the Upper Grande Ronde watershed in northeast Oregon.}, - language = {English}, - number = {4}, - journal = {Journal of Environmental Engineering-Asce}, - author = {Chen, Y. D. and Carsel, R. F. and McCutcheon, S. C. and Nutter, W. L.}, - month = apr, - year = {1998}, - keywords = {diffuse, lakes}, - pages = {304--315}, - annote = {ISI Document Delivery No.: ZD551Times Cited: 40Cited Reference Count: 40Asce-amer soc civil engineersNew york}, - annote = {The following values have no corresponding Zotero field:auth-address: Chinese Univ Hong Kong, Dept Geog, Shatin, NT, Hong Kong. US EPA, Nat Exposure Res Lab, Ecosys Res Div, Athens, GA 30605 USA. Univ Georgia, Daniel B Warnell Sch Forest Resour, Athens, GA 30602 USA. Chen, YD, Chinese Univ Hong Kong, Dept Geog, Shatin, NT, Hong Kong.alt-title: J. Environ. Eng.-ASCEaccession-num: ISI:000072697900003work-type: Article}, -} - -@article{chen_low-frequency_1994, - title = {Low-frequency aspects of the large-scale circulation and {West} {Coast} {United} {States} temperature/precipitation fluctuations in a simplified general circulation model}, - volume = {7}, - number = {11}, - journal = {Journal of Climate}, - author = {Chen, S.-C. and Cayan, D.}, - year = {1994}, - pages = {1668--1683}, -} - -@article{chen_global_2002, - title = {Global {Land} {Precipitation}: {A} 50-yr {Monthly} {Analysis} {Based} on {Gauge} {Observations}}, - volume = {3}, - issn = {1525-755X}, - doi = {10.1175/1525-7541(2002)003<0249:glpaym>2.0.co;2}, - number = {3}, - journal = {Journal of Hydrometeorology}, - author = {Chen, Mingyue and Xie, Pingping and Janowiak, John E. and Arkin, Phillip A.}, - month = jun, - year = {2002}, - pages = {249--266}, - annote = {doi: 10.1175/1525-7541(2002)003{\textless}0249:GLPAYM{\textgreater}2.0.CO;2}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/1525-7541(2002)003{\textless}0249:GLPAYM{\textgreater}2.0.CO;2}, -} - -@article{jie_chen_using_2016, - title = {Using {Natural} {Variability} as a {Baseline} to {Evaluate} the {Performance} of {Bias} {Correction} {Methods} in {Hydrological} {Climate} {Change} {Impact} {Studies}}, - volume = {17}, - doi = {doi:10.1175/JHM-D-15-0099.1}, - abstract = {AbstractPostprocessing of climate model outputs is usually performed to remove biases prior to performing climate change impact studies. The evaluation of the performance of bias correction methods is routinely done by comparing postprocessed outputs to observed data. However, such an approach does not take into account the inherent uncertainty linked to natural climate variability and may end up recommending unnecessary complex postprocessing methods. This study evaluates the performance of bias correction methods using natural variability as a baseline. This baseline implies that any bias between model simulations and observations is only significant if it is larger than the natural climate variability. Four bias correction methods are evaluated with respect to reproducing a set of climatic and hydrological statistics. When using natural variability as a baseline, complex bias correction methods still outperform the simplest ones for precipitation and temperature time series, although the differences are much smaller than in all previous studies. However, after driving a hydrological model using the bias-corrected precipitation and temperature, all bias correction methods perform similarly with respect to reproducing 46 hydrological metrics over two watersheds in different climatic zones. The sophisticated distribution mapping correction methods show little advantage over the simplest scaling method. The main conclusion is that simple bias correction methods appear to be just as good as other more complex methods for hydrological climate change impact studies. While sophisticated methods may appear more theoretically sound, this additional complexity appears to be unjustified in hydrological impact studies when taking into account the uncertainty linked to natural climate variability.}, - number = {8}, - journal = {Journal of Hydrometeorology}, - author = {Jie Chen and Blaise Gauvin St-Denis and François P. Brissette and Philippe Lucas-Picher}, - year = {2016}, - keywords = {Physical Meteorology and Climatology,Climate change,Climate variability,Hydrology,Hydrometeorology}, - pages = {2155--2174}, -} - -@article{chen_contribution_2011, - title = {On the contribution of statistical bias correction to the uncertainty in the projected hydrological cycle}, - volume = {38}, - issn = {0094-8276}, - doi = {10.1029/2011gl049318}, - abstract = {Global hydrological modeling is affected by three sources of uncertainty: (i) the choice of the global climate model (GCM) used to provide meteorological forcing data; (ii) the choice of future greenhouse gas concentration scenario; and (iii) the choice of the decade used to derive the bias correction parameters. We present a comparative analysis of these uncertainties and compare them to the inter-annual variability. The analysis focuses on discharge, integrated runoff and total precipitation over ten large catchments, representative of different climatic areas of the globe. Results are similar for all catchments, all hydrological variables and throughout the year with few exceptions. We find that the choice of different decadal periods over which to derive the bias correction parameters is a source of comparatively minor uncertainty, while other sources play larger and similarly significant roles. This is true for both the means and the extremes of the studied hydrological variables.}, - number = {20}, - journal = {Geophysical Research Letters}, - author = {Chen, Cui and Haerter, Jan O. and Hagemann, Stefan and Piani, Claudio}, - year = {2011}, - keywords = {uncertainty, 1626 Global Change: Global climate models (3337, 4928), 1655 Global Change: Water cycles (1836), 1694 Global Change: Instruments and techniques, 1873 Hydrology: Uncertainty assessment (1990, 3275), climate projection, comparative analysis, hydrological cycle, statistical bias correction}, - pages = {L20403}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{charles_validation_1999, - title = {Validation of downscaling models for changed climate conditions: case study of southwestern {Australia}}, - volume = {12}, - abstract = {Statistical downscaling of general circulation models (GCMs) and limited area models (LAMs) has been promoted as a method for simulating regional- to point-scale precipitation under changed climate conditions. However, several studies have shown that downscaled precipitation is either insensitive to changes in climatic forcing, or inconsistent with the broad-scale changes indicated by the host GCM(s). This has been recently attributed to the omission of the effect that changes in atmospheric moisture content have on precipitation. We describe validation of a nonhomogeneous hidden Markov model (NHMM) for changed climate conditions and apply it to a network of 30 daily precipitation stations in southwestern Australia. NHMMs fitted to 1 x CO2 LAM data were validated by assessing their performance in predicting 2 x CO2 LAM precipitation. The inclusion of 850 hPa dew point temperature depression, a predictor reflecting relative (rather than absolute) atmospheric moisture content, was found to be crucial to successful performance of the NHMM under 2 x CO2 conditions. The NHMM validated for the LAM data was fitted to the historical 30 station network and then used to downscale the 2 x CO2 LAM atmospheric data, producing plausible predictions of station precipitation under 2 x CO2 conditions. Our results highlight that the validation of a statistical downscaling technique for present day conditions does not necessarily imply legitimacy for changed climate conditions. Thus statistical downscaling studies that have not attempted to determine the plausibility of their predictions for the changed climate conditions should be viewed with caution.}, - number = {1}, - journal = {Climate Research}, - author = {Charles, S. P. and Bates, B. C. and Whetton, P. H. and Hughes, J. P.}, - month = jun, - year = {1999}, - pages = {1--14}, - annote = {236VNCLIMATE RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:000082620100001}, -} - -@article{changnon_problems_2003, - title = {Problems in estimating impacts of future climate change on {Midwestern} corn yields}, - volume = {58}, - abstract = {Recent studies of trends in Midwestern precipitation show marked increases over the last 50 years of the 20th Century, and most climate models project that future rainfall in the Corn Belt will be increased further. During five years, 1988- 1991 and 1994, field tests were conducted on agricultural test plots in central Illinois, an area typical of the Corn Belt, to discern how corn yields reacted to varying levels of added rainfall (+10\%, +25\%, and +40\%) during the growing season. The best treatment over the five years was a 40\% rain increase, with an average yield increase of 9\%. Its yield increase was up to 34\% in a hot-dry year, but below that of natural rainfall in a wet year as were the yields of the other lesser increases. The average yield changes from the three treatments were not statistically significantly different. Major interannual yield differences were found in the yields for each rain treatment, reflecting how rain timing and temperatures also have major effects on yields. A 40\% summer rain increase has little influence if natural rains do not occur in the high stress period of mid summer. The plots results show that only small average increases in corn yields occur from growing season rain additions in the 10\% to 40\% range, except in dry years. Weather-crop yield regression models incorporating the same rain increases predict greater yield increases than found in these field tests. This suggests that future yields projected for a wetter climate using yield-weather models may be over- estimated. The plot sample size is small but conditions sampled in the five years represented 43\% of all past 97 growing seasons in central Illinois and extremely good and bad weather years, which resulted in large between-year yield differences. Hence, the experimental results provide useful information about how increased rainfall may affect future corn yields, especially since the sample included three of the five types of dry growing seasons found in the area's climate since 1900.}, - number = {1-2}, - journal = {Clim. Change}, - author = {Changnon, S. A. and Hollinger, S. E.}, - month = may, - year = {2003}, - pages = {109--118}, - annote = {668WVCLIMATIC CHANGE}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Changeaccession-num: ISI:000182318900007}, -} - -@article{cayan_enso_1999, - title = {{ENSO} and hydrologic extremes in the western {United} {States}}, - volume = {12}, - journal = {Journal of Climate}, - author = {Cayan, D. R. and Redmond, K. T. and Riddle, L. G.}, - year = {1999}, - pages = {2881--2893}, -} - -@article{cayan_climate_2007, - title = {Climate change scenarios for the {California} region}, - volume = {(in review)}, - journal = {Climatic Change}, - author = {Cayan, D. R. and Maurer, E. P. and Dettinger, M. D. and Tyree, M. and Hayhoe, K.}, - year = {2007}, -} - -@article{chen_model_2002, - title = {Model mismatch between global and regional simulations}, - volume = {29}, - abstract = {[1] A method to identify and to correct the large-scale biases in regional climate simulations is proposed. This bias is caused by the mismatch of the regional and the driving global models' physical parameterizations. It is suggested that simulations at spatial resolution similar to the driving global model should be used to estimate the possible bias in the higher resolution simulations. This correction is shown for a climate downscaling experiment over Southeast Asia. The global model is the NCAR community climate model (CCM3), and the regional model is the regional spectral model (RSM) of NCEP. The bias is estimated by taking the difference between a RSM0 (RSM run with similar CCM3 resolution) and the CCM3 solution. Once this bias is removed, the resulting seasonal-mean at higher resolution simulation shows a large-scale pattern more similar to that of the driving CCM3, but still retains additional realistic regional details.}, - number = {5}, - journal = {Geophysical Research Letters}, - author = {Chen, S. C.}, - month = mar, - year = {2002}, - pages = {art. no.--1060}, - annote = {609DCGEOPHYS RES LETT}, - annote = {The following values have no corresponding Zotero field:alt-title: Geophys. Res. Lett.accession-num: ISI:000178886500017}, -} - -@article{chen_influence_1999, - title = {The influence of the {North} {Atlantic} {Oscillation} on the regional temperature variability in {Sweden}: spatial and temporal variations}, - volume = {51}, - abstract = {A statistical analysis of the seasonal and interannual variations in the regional temperature anomalies of Sweden during 1861-1994 is performed. The study uses homogenized monthly temperatures averaged over 6 regions to minimize the non-climatic and local-scale climatic effects. It is found that the temperature variability shows a clear regional and seasonal dependency. The topography, the influence of the sea acid the synoptic climatology may have determined the dependency. The anomaly is related to variations in the North Atlantic Oscillation (NAO) expressed by an index (NAOI) and the extent to which the temperature anomaly can be explained by the NAO is investigated. The results show that the NAO has an important effect on the regional Swedish temperature on the monthly and interannual scales. The relationship between the temperature and NAOI over the period 1985-1994 are strong, implying that the NAOI may be a suitable candidate for a statistical downscaling model of the regional temperature. However, correlation analysis over different 31-year periods shows that the strength of the association varies with time and region. The further north the weaker the association. On the other hand, the temporal variations of the moving correlations for the 6 regions are similar. Part of the temporal variations may be explained by the averaged strength of NAO during different 31-year periods. This is especially evident for southern Sweden. At last, the coherency spectrums between the temperature anomalies acid the NAO index is determined, which enables an examination of the association over the frequency domain. The result supports the idea that the NAO has an important effect on the Swedish temperature, though the strength of the association varied with rime. These results have implications for statistical downscaling.}, - number = {4}, - journal = {Tellus Series a-Dynamic Meteorology and Oceanography}, - author = {Chen, D. L. and Hellstrom, C.}, - month = aug, - year = {1999}, - pages = {505--516}, - annote = {229RETELLUS A-DYN METEOROL OCEANOG}, - annote = {The following values have no corresponding Zotero field:alt-title: Tellus Ser. A-Dyn. Meteorol. Oceanol.accession-num: ISI:000082207700004}, -} - -@techreport{centella_escenarios_1998, - address = {San Salvador, El Salvador}, - title = {Escenarios climáticos de referencia para la {República} de {El} {Salvador}}, - institution = {Programa de las Naciones Unidas para el Desarrollo}, - author = {Centella, A. and Castillo, L. and Aguilar, A.}, - year = {1998}, - pages = {21}, - annote = {The following values have no corresponding Zotero field:number: PNUD ELS97G32}, -} - -@article{d_r_cayan_climate_2006, - title = {Climate change scenarios for the {California} region, {Climatic} {Change}}, - volume = {(in review)}, - journal = {Climatic Change}, - author = {D. R. Cayan and E. P. Maurer and M. D. Dettinger and M. Tyree and K. Hayhoe}, - year = {2006}, -} - -@article{cayan_changes_2001, - title = {Changes in the onset of spring in the {Western} {United} {States}}, - volume = {82}, - journal = {Bulletin of the American Meteorological Society}, - author = {Cayan, D. R. and Kammerdiener, S. A. and Dettinger, M. D. and Caprio, J. M. and Peterson, D. H.}, - year = {2001}, - pages = {399--415}, -} - -@article{cayan_interannual_1996, - title = {Interannual climate variability and snowpack in the western {United} {States}}, - volume = {9}, - number = {5}, - journal = {Journal of Climate}, - author = {Cayan, D. R.}, - year = {1996}, - pages = {928--948}, -} - -@techreport{cayan_climate_2009, - address = {Sacramento, CA}, - title = {Climate change scenarios and sea level rise estimates for the 2008 {California} climate change scenarios assessment}, - institution = {California Energy Commission, California Climate Change Center}, - author = {Cayan, D. and Tyree, M. and Dettinger, M. and Hidalgo, H. and Das, T. and Maurer, E. and Bromirski, P. and Graham, N. and Flick, R.}, - month = aug, - year = {2009}, - pages = {64}, - annote = {The following values have no corresponding Zotero field:number: CEC-500-2009-014}, -} - -@article{cayan_overview_2008, - title = {Overview of the {California} climate change scenarios project}, - volume = {87}, - issn = {0165-0009}, - doi = {10.1007/s10584-007-9352-2}, - number = {0}, - journal = {Climatic Change}, - author = {Cayan, Daniel and Luers, Amy and Franco, Guido and Hanemann, Michael and Croes, Bart and Vine, Edward}, - year = {2008}, - keywords = {Earth and Environmental Science}, - pages = {1--6}, - annote = {The following values have no corresponding Zotero field:publisher: Springer Netherlands}, -} - -@article{cavazos_large-scale_1999, - title = {Large-scale circulation anomalies conducive to extreme precipitation events and derivation of daily rainfall in northeastern {Mexico} and southeastern {Texas}}, - volume = {12}, - abstract = {The severe impacts of climate variability and climate hazards on society reveal the increasing need for improving regional and local-scale climate diagnosis. A new downscaling approach for climate diagnosis is presented here. It is based on artificial neural network (ANN) techniques that derive relationships from the large-and local-scale atmospheric controls to the local winter climate. This study documents the large-scale conditions associated with extreme precipitation events in northeastern Mexico and southeastern Texas during the 1985-93 period, and demonstrates the ability of ANN to simulate realistic relationships between circularion-humidity fields and daily precipitation at local scale. The diagnostic model employs a neural network that preclassifies the winter circulation and humidity fields into different patterns. The results from this neural network classification approach, known as a self-organizing map (SOM), indicate that negative (positive) anomalies of winter precipitation over the study area are associated with 1) a weaker (stronger) Aleutian low, 2) a stronger (weaker) North Pacific high, 3) a negative (positive) phase of the Pacific-North American pattern, and 4) cold (warm) ENSO events. The atmospheric patterns classified with the SOM technique are then used as input to another neural network (feed-forward ANN) that captures over 60\% of the daily rainfall variance over the region. This further reveals that the SOM preclassification of days with similar atmospheric conditions succeeded in emphasizing the differences of the atmospheric variance that are conducive to extreme precipitation. This resulted in a downscaling model that is highly sensitive to local- and large-scale weather anomalies associated with ENSO warm events and cold air outbreaks.}, - number = {5}, - journal = {J. Clim.}, - author = {Cavazos, T.}, - month = may, - year = {1999}, - pages = {1506--1523}, - annote = {2193PRJ CLIMATE}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Clim.accession-num: ISI:000080145700011}, -} - -@article{casola_assessing_2009, - title = {Assessing the {Impacts} of {Global} {Warming} on {Snowpack} in the {Washington} {Cascades}*}, - volume = {22}, - issn = {0894-8755}, - doi = {10.1175/2008jcli2612.1}, - number = {10}, - journal = {Journal of Climate}, - author = {Casola, Joseph H. and Cuo, Lan and Livneh, Ben and Lettenmaier, Dennis P. and Stoelinga, Mark T. and Mote, Philip W. and Wallace, John M.}, - month = may, - year = {2009}, - pages = {2758--2772}, - annote = {doi: 10.1175/2008JCLI2612.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/2008JCLI2612.1}, -} - -@incollection{carter_new_2007, - address = {Cambridge, UK}, - title = {New {Assessment} {Methods} and the {Characterisation} of {Future} {Conditions}. {Climate} {Change} 2007: {Impacts}, {Adaptation} and {Vulnerability}. {Contribution} of {Working} {Group} {II} to the {Fourth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {Carter, T. R. and Jones, R. N. and Lu, X. and Bhadwal, S. and Conde, C. and Mearns, L. O. and O’Neill, B. C. and Rounsevell, M. D. A. and Zurek, M. B.}, - editor = {M. L. Parry and Canziani, O. F. and Palutikof, J. P. and van der Linden, P. J. and Hanson, C. E.}, - year = {2007}, - pages = {133--171}, -} - -@article{cayan_decadal_1998, - title = {Decadal {Variability} of {Precipitation} over {Western} {North} {America}}, - volume = {11}, - doi = {doi:10.1175/1520-0442(1998)011<3148:DVOPOW>2.0.CO;2}, - number = {12}, - journal = {Journal of Climate}, - author = {Cayan, Daniel R. and Dettinger, Michael D. and Diaz, Henry F. and Graham, Nicholas E.}, - year = {1998}, - pages = {3148--3166}, -} - -@article{cavazos_downscaling_1997, - title = {Downscaling large-scale circulation to local winter rainfall in north-eastern {Mexico}}, - volume = {17}, - abstract = {The large-scale atmospheric controls on winter rainfall variability for north-eastern Mexico and south-eastern Texas are diagnosed based on 8 years (1985-1993) of daily data from the Goddard Space Flight Center four-dimensional data assimilation scheme. Downscaling techniques based on artificial neural nets are used to derive transfer functions from the large-scale circulation to local precipitation. Daily rainfall amounts are predicted from synoptic sea-level pressure, 500 hPa heights, and the 100-500 hPa thickness, and summed over the cool season (NDJF). The monthly rainfall totals are also predicted independently from other indices of the large-scale circulation, such as 100-500 hPa thickness, the Pacific/North American (PNA) pattern, and a standardized Southern Oscillation Index (SOI) derived from the Tahiti minus Darwin sea-level pressure difference. Time-lagged component scores from a rotated principal component analysis of sea-level pressure, 500 hPa heights, and 100-500 hPa thickness serve as input to a neural net that produces time-series of daily rainfall amounts for 20 grid-points in the study area. The correlation between the observed and predicted rainfall is greater than 0.7 in the coastal plains of the Gulf of Mexico and less than 0.7 over the Sierra Madre and the Gulf, suggesting an increase in the importance of local rainfall processes in the last two regions. The analysis shows a systematic relationship between the performance of the net and physiography, which is confirmed by the consistency of the patterns of spatial correlation, mean absolute error, and root-mean-square error at different time- scales. The net captures the phase and amplitude of most of the rainfall events, reflecting the influence of the large-scale circulation. However, interannual fluctuations in rainfall associated with persistent El Nino conditions are partly bypassed by the net, suggesting that more information is needed to predict extreme events. The PNA pattern does not seem to be associated with local rainfall in north-eastern Mexico and south-eastern Texas during the period analysed. (C) 1997 by the Royal Meteorological Society.}, - number = {10}, - journal = {International Journal of Climatology}, - author = {Cavazos, T.}, - month = aug, - year = {1997}, - pages = {1069--1082}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:A1997XU49500004}, - annote = {XU495INT J CLIMATOL}, -} - -@article{carter_developing_1996, - title = {Developing scenarios of atmosphere, weather and climate for northern regions}, - volume = {5}, - abstract = {Future changes in atmospheric composition and consequent global and regional climate change are of increasing concern to policy makers, planners and the public. However, predictions of these changes are uncertain. In the absence of single, firm predictions, the next best approach is to identify sets of plausible future conditions termed scenarios. This paper focuses on the development of climate change scenarios for northern high latitude regions. Three methods of scenario development can be identified: use of analogues having conditions similar to those expected in the study region, application of general circulation me del results, and composite methods that combine information from different sources. A composite approach has been used to produce scenarios of temperature, precipitation, carbon dioxide and sea-level change for Finland up to 2100, as part of the Finnish Research Programme on Climate Change (SILMU). Tools for applying these scenarios in impact assessment studies, including stochastic weather generators and spatial downscaling techniques, are also examined. The SILMU scenarios attempt to capture uncertainties both in future emissions of greenhouse gases and aerosols into the atmosphere and in the global climate response to these emissions. Two types of scenario were developed: (i) simple ''policy-oriented'' scenarios and (ii) detailed ''scientific'' scenarios. These are compared with new model estimates of future climate and recent observed changes in climate over certain high latitude regions.}, - number = {3}, - journal = {Agricultural and Food Science in Finland}, - author = {Carter, T. R.}, - year = {1996}, - pages = {235--249}, - annote = {The following values have no corresponding Zotero field:alt-title: Agr. Food Sci. Finlandaccession-num: ISI:A1996VV19900005}, - annote = {VV199AGR FOOD SCI FINLAND}, -} - -@article{cannon_downscaling_2002, - title = {Downscaling recent streamflow conditions in {British} {Columbia}, {Canada} using ensemble neural network models}, - volume = {259}, - abstract = {Variations in climate conditions during recent decades in British Columbia, Canada have occurred coincident to significant changes in streamflow conditions in the province. In the current study. the ability of empirical downscaling models to resolve these changes is investigated using ensemble neural networks forced with synoptic-scale atmospheric conditions. Five-day averages of streamflow data from 21 watersheds in the region are modelled using atmospheric data from the NCEP/NCAR reanalysis project as inputs. Ability of the downscaling models to predict streamflow and changes in streamflow between 19751986 and 1987-1998 is evaluated using a combination of model performance statistics, comparisons between long-term averages, and results from non-parametric statistical tests. {\textbackslash} While performance varied between systems, results suggest that empirical downscaling models for streamflow are capable of predicting changes in streamflow observed during recent decades using only large-scale atmospheric conditions as model inputs. Based on comparisons between stepwise linear regression and neural network models. the latter approach is recommended, particularly when trying to model systems with complex non-linear and interactive relationships between inputs and outputs. The use of ensemble averaging as a part of the modelling process is investigated and a number of recommendations are made with respect to this methodology. (C) 2002 Elsevier Science B.V. All rights reserved.}, - number = {1-4}, - journal = {Journal of Hydrology}, - author = {Cannon, A. J. and Whitfield, P. H.}, - month = mar, - year = {2002}, - pages = {136--151}, - annote = {523JLJ HYDROL}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Hydrol.accession-num: ISI:000173950200009}, -} - -@article{cannon_graphical_2002, - title = {A graphical sensitivity analysis for statistical climate models: {Application} to {Indian} monsoon rainfall prediction by artificial neural networks and multiple linear regression models}, - volume = {22}, - abstract = {A form of sensitivity analysis is described that illustrates the effects that inputs have on outputs of statistical models. The strength and sign of relationships, the types of nonlinearity, and the presence of interactions between inputs can be diagnosed using this technique. Intended for interpreting flexible nonlinear models, the graphical sensitivity analysis is applied to artificial neural networks (ANNs) in this study. As ANNs are increasingly being used for climate prediction, the discussion focuses on specific problems associated with their use in this context. The technique is illustrated using a real-world, long-range climate prediction example. Principal components (PCs) of circulation fields prior to the Indian summer monsoon are related to rainfall during monsoon months for the 1958-98 period. The skill of multiple linear regression and ensemble ANNs are compared using a resampling procedure. Interpretation of the models is then conducted using traditional diagnostic tools and graphical sensitivity analysis. This provides an improved investigation of precursor circulation field-summer monsoon rainfall relationships identified in a previous modelling study. The relatively stable, linear relationship identified between the May 200 hPa geopotential height field and summer monsoon rainfall is confirmed. Correlations previously identified between 850 hPa geopotential heights during January and rainfall by ANNs are shown to be the result of a weakly nonlinear, interactive relationship involving the first and second PCs of this field. An analysis of out-of-sample model predictions suggests that this relationship does not persist over the entire study period. This may result from a modulation of the strength of the circulation-rainfall relationship by El Nino-southern oscillation. Stratification of the results also reveals a relatively strong, nearly linear relationship with monsoon strength during years exhibiting positive scores of the second PC. On extending the analysis to longer lead-times, the surface pressure and 850 hPa geopotential height fields during November show relatively strong, persistent precursor relationships with summer monsoon rainfall. Sensitivity analyses suggest a mildly nonlinear relationship that is common to both fields. Copyright (C) 2002 Environment Canada. Published by John Wiley Sons, Ltd.}, - number = {13}, - journal = {International Journal of Climatology}, - author = {Cannon, A. J. and McKendry, I. G.}, - month = nov, - year = {2002}, - pages = {1687--1708}, - annote = {621FZINT J CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:000179579000006}, -} - -@article{butsic_climate_2011, - title = {Climate {Change} and {Housing} {Prices}: {Hedonic} {Estimates} for {Ski} {Resorts} in {Western} {North} {America}}, - volume = {87}, - number = {1}, - journal = {Land Economics}, - author = {Butsic, V. and Hanak, E. and Valletta, R. G.}, - year = {2011}, - pages = {75--91}, -} - -@article{busuioc_verification_1999, - title = {Verification of {GCM}-generated regional seasonal precipitation for current climate and of statistical downscaling estimates under changing climate conditions}, - volume = {12}, - abstract = {Empirical downscaling procedures relate large-scale atmospheric features with local features such as station rainfall in order to facilitate local scenarios of climate change. The purpose of the present paper is twofold: first, a downscaling technique is used as a diagnostic tool to verify the performance of climate models on the regional scale; second, a technique is proposed for verifying the validity of empirical downscaling procedures in climate change applications. The case considered is regional seasonal precipitation in Romania. The downscaling model is a regression based on canonical correlation analysis between observed station precipitation and European-scale sea level pressure (SLP). The climate models considered here are the T21 and T42 versions of the Hamburg ECHAM3 atmospheric GCM run in "time-slice" mode. The climate change scenario refers to the expected time of doubled carbon dioxide concentrations around the year 2050. The downscaling model is skillful for all seasons except spring. The general features of the large-scale SLP variability are reproduced fairly well by both GCMs in all seasons. The climate models reproduce the empirically determined precipitation-SLP link in winter, whereas the observed link is only partially captured for the other seasons. Thus, these models may be considered skillful with respect to regional precipitation during winter, and partially during the other seasons. Generally, applications of statistical downscaling to climate change scenarios have been based on the assumption that the empirical link between the large-scale and regional parameters remains valid under a changed climate. In this study, a rationale is proposed for this assumption by showing the consistency of the 2 x CO2 GCM scenarios in winter, derived directly from the gridpoint data, with the regional scenarios obtained through empirical downscaling. Since the skill of the GCMs in regional terms is already established, it is concluded that the downscaling technique is adequate for describing climatically changing regional and local conditions, at least for precipitation in Romania during winter.}, - number = {1}, - journal = {J. Clim.}, - author = {Busuioc, A. and von Storch, H. and Schnur, R.}, - month = jan, - year = {1999}, - pages = {258--272}, - annote = {165XZJ CLIMATE}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Clim.accession-num: ISI:000078548300021}, -} - -@article{busuioc_performance_2001, - title = {Performance of statistical downscaling models in {GCM} validation and regional climate change estimates: {Application} for {Swedish} precipitation}, - volume = {21}, - abstract = {This study deals with an analysis of the performance of a general circulation model (GCM) (HadCM2) in reproducing the large-scale circulation mechanisms controlling Swedish precipitation variability, and in estimating regional climate changes owing to increased CO2 concentration by using canonical correlation analysis (CCA). Seasonal precipitation amounts at 33 stations in Sweden over the period 1899-1990 are used. The large-scale circulation is represented by sea level pressure (SLP) over the Atlantic-European region. The link between seasonal Swedish precipitation and large-scale SLP variability is strong in all seasons, but especially in winter and autumn. For these two seasons, the link is a consequence of the North Atlantic Oscillation (NAO) pattern. In winter, another important mechanism is related to a cyclonic/anticyclonic structure centred over southern Scandinavia. In the past century, this connection has remained almost unchanged in time for all seasons except spring. The downscaling model that is built on the basis of this link is skilful in all seasons, but especially so in winter and autumn. This observed link is only partially reproduced by the HadCM2 model, while large-scale SLP variability is fairly well reproduced in all seasons. A concept about optimum statistical downscaling models for climate change purposes is proposed. The idea is related to the capability of the statistical downscaling model to reproduce low frequency variability, rather than having the highest skill in terms of explained variance. By using these downscaling models, it was found that grid point and downscaled climate signals are similar (increasing precipitation) in summer and autumn, while in winter, the amplitudes of the two signals are different. In spring, both signals show a slight increase in the northern and southern parts of Sweden. Copyright (C) 2001 Royal Meteorological Society.}, - number = {5}, - journal = {International Journal of Climatology}, - author = {Busuioc, A. and Chen, D. L. and Hellstrom, C.}, - month = apr, - year = {2001}, - pages = {557--578}, - annote = {429JLINT J CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:000168513400002}, -} - -@article{buser_bayesian_2009, - title = {Bayesian multi-model projection of climate: bias assumptions and interannual variability}, - volume = {33}, - issn = {0930-7575}, - doi = {10.1007/s00382-009-0588-6}, - abstract = {Current climate change projections are based on comprehensive multi-model ensembles of global and regional climate simulations. Application of this information to impact studies requires a combined probabilistic estimate taking into account the different models and their performance under current climatic conditions. Here we present a Bayesian statistical model for the distribution of seasonal mean surface temperatures for control and scenario periods. The model combines observational data for the control period with the output of regional climate models (RCMs) driven by different global climate models (GCMs). The proposed Bayesian methodology addresses seasonal mean temperatures and considers both changes in mean temperature and interannual variability. In addition, unlike previous studies, our methodology explicitly considers model biases that are allowed to be time-dependent (i.e. change between control and scenario period). More specifically, the model considers additive and multiplicative model biases for each RCM and introduces two plausible assumptions ("constant bias'' and "constant relationship'') about extrapolating the biases from the control to the scenario period. The resulting identifiability problem is resolved by using informative priors for the bias changes. A sensitivity analysis illustrates the role of the informative prior. As an example, we present results for Alpine winter and summer temperatures for control (1961 1990) and scenario periods (2071-2100) under the SRES A2 greenhouse gas scenario. For winter, both bias assumptions yield a comparable mean warming of 3.5-3.6 degrees C. For summer, the two different assumptions have a strong influence on the probabilistic prediction of mean warming, which amounts to 5.4 degrees C and 3.4 degrees C for the "constant bias'' and "constant relation'' assumptions, respectively. Analysis shows that the underlying reason for this large uncertainty is due to the overestimation of summer interannual variability in all models considered. Our results show the necessity to consider potential bias changes when projecting climate under an emission scenario. Further work is needed to determine how bias information can be exploited for this task.}, - language = {English}, - number = {6}, - journal = {Climate Dynamics}, - author = {Buser, C. M. and Kunsch, H. R. and Luthi, D. and Wild, M. and Schar, C.}, - month = nov, - year = {2009}, - keywords = {forecasts, Alpine, atmosphere, Bayesian, Bias change, ensemble, european climate, late 21st-century, Model bias, model projections, Multi-model prediction, performance, quantifying uncertainty, RCM, region, simulations, temperature variability}, - pages = {849--868}, - annote = {ISI Document Delivery No.: 505HUTimes Cited: 3Cited Reference Count: 48Buser, Christoph M. Kuensch, H. R. Luethi, D. Wild, M. Schaer, C.Swiss Center for Scientific Computing (CSCS) ; Fifth and Sixth Framework Programme of the European Union ; Swiss Ministry for Education and Research ; Swiss National Science Foundation (NCCR Climate) ; PRUDENCE [EVK2-CT2001-00132]The ETH climate simulations were conducted on the computing facilities of ETH and the Swiss Center for Scientific Computing (CSCS), and the associated research was supported by the Fifth and Sixth Framework Programme of the European Union (projects PRUDENCE and ENSEMBLES), by the Swiss Ministry for Education and Research, and by the Swiss National Science Foundation (NCCR Climate). Data have been provided through the PRUDENCE data archive, funded by the EU through contract EVK2-CT2001-00132. We thank three referees for constructive comments.SpringerNew york}, - annote = {The following values have no corresponding Zotero field:auth-address: [Buser, Christoph M.; Kuensch, H. R.] ETH, Seminar Stat, CH-8092 Zurich, Switzerland. [Luethi, D.; Wild, M.; Schaer, C.] ETH, Inst Atmospher \& Climate Sci, CH-8092 Zurich, Switzerland. Buser, CM, ETH, Seminar Stat, Ramistr 101, CH-8092 Zurich, Switzerland. buser@stat.math.ethz.chalt-title: Clim. Dyn.accession-num: ISI:000270684100009work-type: Article}, -} - -@article{busch_statistical-dynamical_2001, - title = {Statistical-dynamical extrapolation of a nested regional climate simulation}, - volume = {19}, - abstract = {A method is introduced by which the results of a regional climate model (RCM) that is temporarily nested in a general circulation model (GCM) are extrapolated in time. The procedure is based on a weather-type classification scheme using cluster analysis. It relies on statistical relationships between GCM and RCM output which are deduced from the overlapping nesting period. For a validation of the extrapolation scheme, a 30 yr GCM simulation of the Hadley Centre with a continuously nested RCM is used, The predictor is the large-scale 500 hPa geopotential height, and the predictands are the regional-scale surface temperature and precipitation. A comparison of the results of the extrapolation scheme with the direct RCM output for the winter temperature and precipitation in Central Europe demonstrates the potential of the method developed. Provided that results of a long-term GCM run are available and the statistics can be transferred from the overlapping period to the extrapolation period, the new method allows a reduction in the computational effort needed for long-term RCM simulations by shortening the integration time considerably.}, - number = {1}, - journal = {Climate Research}, - author = {Busch, U. and Heimann, D.}, - month = nov, - year = {2001}, - pages = {1--13}, - annote = {510RZCLIMATE RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:000173222200001}, -} - -@techreport{burnash_generalized_1973, - address = {Sacramento, CA}, - title = {A generalized streamflow simulation system - {Conceptual} {Modeling} for {Digital} {Computers}}, - institution = {Joint Federal and State River Forecast Center, U.S. National Weather Service and California Department of Water Resources}, - author = {Burnash, R. J. C. and Ferral, R. L. and McQuire, R. A.}, - year = {1973}, - pages = {204}, -} - -@incollection{burnash_nws_1995, - address = {Littleton, Colorado}, - title = {The {NWS} {River} {Forecast} {System} - {Catchment} {Modeling}}, - booktitle = {Computer {Models} of {Watershed} {Hydrology}}, - publisher = {Water Resources Publications}, - author = {Burnash, R. J. C.}, - editor = {Singh, V. P.}, - year = {1995}, - pages = {311--366}, -} - -@article{burger_selected_2002, - title = {Selected precipitation scenarios across {Europe}}, - volume = {262}, - abstract = {We report results of the project, European River Flood Occurrence and Total Risk Assessment System (EUROTAS), which was aimed at assessing the risk of current and future floodings for Europe, including the effects of climatic change. With respect to the latter, precipitation scenarios have been derived from a global climate model using the method of expanded downscaling (EDS), and regionalized for the catchment of the river Pinios (Greece). Jizera (Cechia), Saar (Germany), and Thames (UK). We present both the climate change information conveyed by the scenarios and an assessment about its statistical significance. For this report, all simulations were rerun using an improved EDS model version. Most notably, the inclusion of atmospheric humidity to the set of global predictor fields helped reducing the underlying uncertainty of the results. When driven by observed atmospheric fields (analyses), the EDS model performed in most cases satisfactorily, as it reproduced the larger precipitation clusters with good accuracy. To evaluate the precipitation scenarios, a statistical test was applied that compared what we defined as 'current climate' with the 'changed climate' of the scenario. To define current climate, local precipitation observations were used together with EDS regionalizations of the analyses and of a climate model control simulation. It turned out that in spite of the considerable uncertainty stemming from model errors and, more importantly, natural variability, a significant redistribution of rainfall is projected. Specifically, the scenarios show a decay of rainfall frequency for all catchments, which for Pinios leads to an overall negative water balance. This is accompanied, and for the other catchments outweighed, by a strong rainfall intensification. For the latter, we provide a more detailed extreme value analysis. We emphasize that the results should be interpreted with caution and not overemphasized quantitatively. Scenarios of precipitation are still of prototype nature and under steady development. (C) 2002 Elsevier Science B.V. All rights reserved.}, - number = {1-4}, - journal = {Journal of Hydrology}, - author = {Burger, G.}, - month = may, - year = {2002}, - pages = {99--110}, - annote = {552DCJ HYDROL}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Hydrol.accession-num: ISI:000175602400007}, -} - -@article{bunde_scaling_2003, - title = {Scaling in the atmosphere: {On} global laws of persistence and tests of climate models}, - volume = {11}, - abstract = {Characterizing the complex atmospheric variability at all pertinent temporal and spatial scales remains one of the most important challenges to scientific research today.(1-5) The main issues are to quantify, within reasonably narrow limits, the potential extent of global warming, and to downscale the global results in order to describe and quantify the regional implications of global change.}, - journal = {Fractals-Complex Geometry Patterns and Scaling in Nature and Society}, - author = {Bunde, A. and Havlin, S.}, - month = feb, - year = {2003}, - pages = {205--216}, - annote = {S663EEFRACTALS}, - annote = {The following values have no corresponding Zotero field:alt-title: Fractals-Complex Geom. Patterns Scaling Nat. Soc.accession-num: ISI:000181992600023}, -} - -@book{budyko_climate_1974, - address = {New York, NY}, - title = {Climate and {Life}}, - publisher = {Academic Press}, - author = {Budyko, M. I.}, - editor = {Miller, D. H.}, - year = {1974}, -} - -@article{burlando_effects_2002, - title = {Effects of transient climate change on basin hydrology. 1. {Precipitation} scenarios for the {Arno} {River}, central {Italy}}, - volume = {16}, - abstract = {Long-term simulations of temporal rainfall and temperature under transient global climate conditions are discussed to give an insight into potential modifications of atmospheric inputs at the basin scale in the Arno River in central Italy. The outputs from a global circulation model (GCM), simulating climate changes due to an increase in the greenhouse effect resulting from a continuous trend in the growth of CO2 atmospheric concentration and accounting for the influence of both CO2 and sulphate aerosols, are downscaled using a stochastic approach based on the observed nonstationarity of precipitation and temperature patterns. By using the historical joint variability of the internal structure of storm events, one can infer future changes in storm duration and depth from GCM trend variables, thus indicating the extent of changes in the occurrence of wet and dry periods and in the daily rates, including those of distributional properties at the monthly and annual scales. Because the changes detected mainly affect the tails of the distributions, one can conclude that modifications can occur in both low and high values of rainfall at the monthly and annual scales, with a shift of the storm patterns towards shorter and more intense convective rainfall, especially in the summer season. Stochastic simulation also shows that the distributional and scaling properties of rainfall extremes may progressively change, thus indicating that some revision of current practices to estimate extreme storms is needed to account for possible effects of non- stationary climate conditions, This approach provides local precipitation that, together with temperature scenarios, can be used for hydrological simulation of basin water fluxes in the Arno River, as reported in a companion paper (Burlando P, Rosso R. Hydrological Processes this issue). Copyright (C) 2002 John Wiley Sons, Ltd.}, - number = {6}, - journal = {Hydrological Processes}, - author = {Burlando, P. and Rosso, R.}, - month = apr, - year = {2002}, - pages = {1151--1175}, - annote = {SI535HNHYDROL PROCESS}, - annote = {The following values have no corresponding Zotero field:alt-title: Hydrol. Process.accession-num: ISI:000174639300003}, -} - -@article{burger_downscaling_2012, - title = {Downscaling {Extremes}—{An} {Intercomparison} of {Multiple} {Statistical} {Methods} for {Present} {Climate}}, - volume = {25}, - issn = {0894-8755}, - doi = {10.1175/jcli-d-11-00408.1}, - number = {12}, - journal = {Journal of Climate}, - author = {Bürger, G. and Murdock, T. Q. and Werner, A. T. and Sobie, S. R. and Cannon, A. J.}, - month = jun, - year = {2012}, - pages = {4366--4388}, - annote = {doi: 10.1175/JCLI-D-11-00408.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/JCLI-D-11-00408.1}, -} - -@article{burger_expanded_1996, - title = {Expanded downscaling for generating local weather scenarios}, - volume = {7}, - abstract = {In an attempt to reconcile the capabilities of statistical downscaling and the demands of ecosystem modeling, the technique of expanded downscaling is introduced. Aimed at use in ecosystem models, emphasis is placed on the preservation of daily variability to the extent that possible climate change permits. Generally, the expansion is possible for any statistical model which is formulated by utilizing some form of regression, but I will concentrate on linear models as they are easier to handle. Linear statistical downscaling assumes that the local climate anomalies are linearly linked to the global circulation anomalies. In expanded downscaling, in contrast, I propose that the local climate covariance is linked bilinearly to the global circulation covariance. This is done by transforming the technique of unconstrained minimization of the error cost function into a constrained minimization problem, with the preservation of local covariance forming the side condition. A general normalization routine is included on the local side in order to perform the downscaling exclusively with normally distributed variables. Application of the expanded operator to the daily, global circulation works essentially like a weather generator. Using observed geopotential height fields over the North Atlantic and Europe gave consistent results for the weather station at Potsdam with 14 measured quantities, even for moisture-related variables. For GCM (general circulation model) scenarios, satisfactory results are obtained when the original variables are normally distributed. If they are not, strong sensitivity even to small input changes cause the normalization to produce large errors. Non-normally distributed variables such as most moisture variables are therefore strongly affected by even slight deficiencies of current GCMs with respect to daily variability and climatology. This marks the limit of applicability of expanded downscaling.}, - number = {2}, - journal = {Climate Research}, - author = {Burger, G.}, - month = nov, - year = {1996}, - pages = {111--128}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:A1996WE47400003}, - annote = {WE474CLIMATE RES}, -} - -@article{buma_impact_2000, - title = {Impact of climate change on a landslide in {South} {East} {France}, simulated using different {GCM} scenarios and downscaling methods for local precipitation}, - volume = {15}, - abstract = {The aim of this paper is to assess the influence of different climate scenarios on scenarios for the impact variable 'landslide activity'. For this purpose, a site-specific model was used, relating the activity of a landslide in South East France to climate. Landslide activity was reconstructed from tree ring data. Hydrological field data indicated that the controlling climatic variable is net precipitation (precipitation minus evapotranspiration). However, this variable and hence the impact model could not explain all of the variations in landslide activity. The landslide model was fed with 1 temperature and several precipitation scenarios obtained by applying 3 different methods for downscaling 3 different general circulation model (GCM) simulations of the large-scale climate. The skill of the downscaling methods in reproducing the historical local precipitation was either limited or trivial, but fair enough to justify further application. The resulting scenarios for landslide activity were quite similar, with the exception of 2 specific combinations of GCM and downscaling method. Furthermore, short- term climatic variation, plausibly represented in one of the downscaling methods as a random noise component, caused additional variation in the resulting scenarios. The amount of variation in the climate scenarios is of the same order of magnitude as that in the landslide model. The general conclusion is not to focus on calibrating impact models while using only 1 climate scenario, but to assess the overall uncertainty of the impact scenario by considering different parameter settings of the impact model as well as different climate scenarios, as was done in the present study.}, - number = {1}, - journal = {Climate Research}, - author = {Buma, J. and Dehn, M.}, - month = may, - year = {2000}, - pages = {69--81}, - annote = {359LFCLIMATE RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:000089611800006}, -} - -@article{brutsaert_hydrologic_1998, - title = {Hydrologic cycle explains the evaporation paradox}, - volume = {396}, - issn = {0028-0836}, - language = {English}, - number = {6706}, - journal = {Nature}, - author = {Brutsaert, W. and Parlange, M. B.}, - month = nov, - year = {1998}, - pages = {30--30}, - annote = {136HETimes Cited:34Cited References Count:13}, - annote = {The following values have no corresponding Zotero field:auth-address: Brutsaert, W Cornell Univ, Sch Civil \& Environm Engn, Ithaca, NY 14853 USA Cornell Univ, Sch Civil \& Environm Engn, Ithaca, NY 14853 USA Johns Hopkins Univ, Dept Geog \& Environm Engn, Baltimore, MD 21218 USAaccession-num: ISI:000076852700035}, -} - -@article{brown_alternate_2012, - title = {An alternate approach to assessing climate risks}, - volume = {93}, - issn = {0096-3941}, - doi = {10.1029/2012eo410001}, - abstract = {U.S. federal agencies are now required to review the potential impacts of climate change on their assets and missions. Similar arrangements are also in place in the United Kingdom under reporting powers for key infrastructure providers (http://www.defra.gov.uk/environment/climate/sectors/reporting-authorities/reporting-authorities-reports/). These requirements reflect growing concern about climate resilience and the management of long-lived assets. At one level, analyzing climate risks is a matter of due diligence, given mounting scientific evidence. However, there is no consensus about the means for doing so nor about whether climate models are even ft for the purpose; in addition, several important issues are often overlooked when incorporating climate information into adaptation decisions. An alternative to the scenarioled strategy, such as an approach based on a vulnerability analysis (\&\#8220;stress test\&\#8221;), may identify practical options for resource managers.}, - number = {41}, - journal = {Eos Trans. AGU}, - author = {Brown, Casey and Wilby, Robert L.}, - year = {2012}, - keywords = {climate change, water resources, 1626 Global Change: Global climate models (3337, 4928), 4321 Natural Hazards: Climate impact (1630, 1637, 1807, 8408), 4328 Natural Hazards: Risk (8488), 6309 Policy Sciences: Decision making under uncertainty (1918, 4324, 4339), 6329 Policy Sciences: Project evaluation, decision making, risk assessment}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{brown_decision_2012, - title = {Decision scaling: {Linking} bottom-up vulnerability analysis with climate projections in the water sector}, - volume = {48}, - issn = {1944-7973}, - doi = {10.1029/2011wr011212}, - number = {9}, - journal = {Water Resources Research}, - author = {Brown, Casey and Ghile, Yonas and Laverty, Mikaela and Li, Ke}, - year = {2012}, - keywords = {climate change, decision making, risk assessment, 4328 Risk, 6309 Decision making under uncertainty, 6334 Regional planning, water planning}, - pages = {W09537}, -} - -@article{brinkmann_local_2002, - title = {Local versus remote grid points in climate downscaling}, - volume = {21}, - abstract = {The association between daily as well as monthly winter precipitation and concurrent 700 hPa heights for 3 different North American precipitation regimes was examined using correlation fields and a typing of daily 700 hPa height patterns. Wet days, regardless of circulation type, were found to be associated with negative height anomalies immediately to the west or northwest of the precipitation regions, at the 'local' grid point. These small-scale height anomalies were found to represent traveling short waves that were missed by the circulation typing scheme, The correlation fields between monthly precipitation and concurrent 700 hPa heights revealed a westward shift of the height minima to a location west of 100degreesW, to a 'remote' grid point. The nature of these remote minima was examined using the 10 wettest and 10 driest months. Decomposition of the height anomalies at the remote grid points into between-type and within-type contributions revealed that while some of the negative heights during wet months are due to an increase in the frequency of circulation patterns with below normal heights (such as troughs or weak ridges), more than two-thirds of the contribution comes from height depressions superimposed upon the larger-scale patterns and most of these are associated with positive vorticity. This leads to the conclusion that the correlation minima west of 100degreesW at the monthly time scale reflect the frequent generation of short waves during wet months over the well-known North American cyclogenetic region east of the Rocky Mountains, These results have 2 implications with respect to statistical downscaling. First, regarding techniques designed to extract information from a single grid point: the optimum grid point location may be a function of the time scale under consideration, Second, regarding techniques designed to extract information from large-scale circulation patterns: the difficulties in capturing the very important but small short waves limit their usefulness.}, - number = {1}, - journal = {Climate Research}, - author = {Brinkmann, W. A. R.}, - month = may, - year = {2002}, - pages = {27--42}, - annote = {585JJCLIMATE RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:000177521400002}, -} - -@article{bretherton_effective_1999, - title = {The {Effective} {Number} of {Spatial} {Degrees} of {Freedom} of a {Time}-{Varying} {Field}}, - volume = {12}, - doi = {doi:10.1175/1520-0442(1999)012<1990:TENOSD>2.0.CO;2}, - number = {7}, - journal = {Journal of Climate}, - author = {Bretherton, Christopher S. and Widmann, Martin and Dymnikov, Valentin P. and Wallace, John M. and Bladé, Ileana}, - year = {1999}, - pages = {1990--2009}, -} - -@article{brekke_climate_2004, - title = {Climate change impacts uncertainty for water resources in the {San} {Joaquin} {River} {Basin}, {California}}, - volume = {40}, - issn = {1093-474X}, - abstract = {A climate change impacts assessment for water resources in the San Joaquin River region of California is presented. Regional climate projections are based on a 1 percent per year CO2 increase relative to late 20th Century CO2 conditions. Two global projections of this CO2 increase scenario are considered (HadCM2 and PCM) during two future periods (2010 to 2039 and 2050 to 2079). HadCM2 projects faster warming than PCM. HadCM2 and PCM project wetter and drier conditions, respectively, relative to present climate. In the HadCM2 case, there would be increased reservoir inflows, increased storage limited by existing capacity, and increased releases for deliveries and river flows. In the PCM case, there would be decreased reservoir inflows, decreased storage and releases, and decreased deliveries. Impacts under either projection case cannot be regarded as more likely than the other. Most of the impacts uncertainty is attributable to the divergence in the precipitation projections. The range of assessed impacts is too broad to guide selection of mitigation projects. Regional planning agencies can respond by developing contingency strategies for these cases and applying the methodology herein to evaluate a broader set Of CO2 scenarios, land use projections, and operational assumptions. Improved agency access to climate projection information is necessary to support this effort.}, - language = {English}, - number = {1}, - journal = {Journal of the American Water Resources Association}, - author = {Brekke, L. D. and Miller, N. L. and Bashford, K. E. and Quinn, N. W. T. and Dracup, J. A.}, - month = feb, - year = {2004}, - pages = {149--164}, - annote = {808TGTimes Cited:0Cited References Count:17}, - annote = {The following values have no corresponding Zotero field:auth-address: Brekke, LD US Bur Reclamat, MP-710,2800 Cottage Way, Sacramento, CA 95816 USA US Bur Reclamat, Sacramento, CA 95816 USA Univ Calif Berkeley, Lawrence Berkeley Lab, Berkeley, CA 94720 USA Univ Calif Berkeley, Dept Civil \& Environm Engn, Berkeley, CA 94720 USAaccession-num: ISI:000220590800012}, -} - -@misc{brekke_climate_2009, - title = {Climate change and water resources management—{A} federal perspective: {U}.{S}. {Geological} {Survey} {Circular} 1331}, - author = {Brekke, L. D. and Kiang, J. E. and Olsen, J. R. and Pulwarty, R. S. and Raff, D. A. and Turnipseed, D. P. and Webb, R. S. and White, K. D. and U. S. Geological Survey}, - year = {2009}, - pages = {65 (https://pubs.usgs.gov/circ/1331/)}, - annote = {The following values have no corresponding Zotero field:pub-location: Reston, Virginia}, -} - -@article{brekke_significance_2008, - title = {Significance of model credibility in estimating climate projection distributions for regional hydroclimatological risk assessments}, - volume = {89}, - number = {3-4}, - journal = {Climatic Change}, - author = {Brekke, L. D. and Dettinger, M. D. and Maurer, E. P. and Anderson, M.}, - year = {2008}, - pages = {371--394, doi: 10.1007/s10584--007--9388--3}, -} - -@article{bradley_threats_2006, - title = {Threats to {Water} {Supplies} in the {Tropical} {Andes}}, - volume = {312}, - doi = {10.1126/science.1128087}, - number = {5781}, - journal = {Science}, - author = {Bradley, Raymond S. and Vuille, Mathias and Diaz, Henry F. and Vergara, Walter}, - month = jun, - year = {2006}, - pages = {1755--1756}, -} - -@article{brinkmann_modification_2000, - title = {Modification of a correlation-based circulation pattern classification to reduce within-type variability of temperature and precipitation}, - volume = {20}, - abstract = {When a correlation-based circulation pattern classification was applied to daily 700 hPa heights for 25 summer seasons, and the circulation types were related to surface temperature and precipitation for the Lake Superior basin, within-type variability was found to be high. Regarding temperature, the difference between warm and cold days of a type is a positive height anomaly centred to the east of the lake, which is very similar to the height anomaly for winter temperatures identified ill an earlier study. The difference between wet and dry days of a type is a pattern quite unlike that for temperature. It is a dipole pattern consisting of a negative anomaly to the northwest of the lake and a positive anomaly to the east and southeast. These small-scale circulation components were missed by the correlation-based classification because they are much smaller than the differences in height between circulation types. Regression models, relating monthly mean temperature anomalies to circulation type frequencies, produced results comparable to those obtained for winter temperature. For precipitation, the variance explained by the regression models is only half that obtained for temperature. Two approaches were used to reduce within-type variability and to improve the regression results: the division of the first few circulation types into warm/cold and wet/dry subtypes on the basis of differences in 700 hPa vorticity tan approach introduced in the earlier paper), and a modified correlation- based circulation pattern classification. The modification consisted of raising the correlation threshold for a few selected grid-points to capture the small-scale circulation component and also, of lowering the threshold for the comparison between maps and map sectors in order to minimize the number of unclassified days. Of the two approaches used to reduce within-type variability, the modified classification scheme produced superior results. In particular, the variance in precipitation explained by the regression models is about 30\% higher than that achieved using vorticity to create subtypes. Copyright (C) 2000 Royal Meteorological Society.}, - number = {8}, - journal = {International Journal of Climatology}, - author = {Brinkmann, W. A. R.}, - month = jun, - year = {2000}, - pages = {839--852}, - annote = {336AVINT J CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:000088280500002}, -} - -@article{brekke_assessing_2009, - title = {Assessing reservoir operations risk under climate change}, - volume = {45}, - journal = {Water Resources Research}, - author = {Brekke, L. D. and Anderson, J. D. and Maurer, E. P. and Dettinger, M. D. and Townsley, E. S. and Harrison, A. and Pruitt, T.}, - year = {2009}, - pages = {W04411, doi:10.1029/2008WR006941}, -} - -@techreport{brekke_downscaled_2013, - address = {Denver, CO}, - title = {Downscaled {CMIP3} and {CMIP5} {Climate} {Projections}: {Release} of {Downscaled} {CMIP5} {Climate} {Projections}, {Comparison} with {Preceding} {Information}, and {Summary} of {User} {Needs}}, - institution = {U.S. Dept. of Interior, Bureau of Reclamation}, - author = {Brekke, L. and Thrasher, B. and Maurer, E. P. and Pruitt, T.}, - month = may, - year = {2013}, - pages = {104}, -} - -@article{box_analysis_1964, - title = {An analysis of transformations}, - volume = {B26}, - journal = {Journal of the Royal Statistics Society}, - author = {Box, G. E. P. and Cox, D. R.}, - year = {1964}, - pages = {211--243}, -} - -@incollection{o_boucher_clouds_2013, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Clouds and {Aerosols}}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {O. Boucher and D. Randall and P. Artaxo and C. Bretherton and G. Feingold and P. Forster and V. -M. Kerminen and Y. Kondo and H. Liao and U. Lohmann and P. Rasch and S. K. Satheesh and S. Sherwood and B. Stevens and X. Y. Zhang}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley}, - year = {2013}, - pages = {571--658}, - annote = {The following values have no corresponding Zotero field:section: 7electronic-resource-num: 10.1017/CBO9781107415324.016}, -} - -@article{booth_high_2012, - title = {High sensitivity of future global warming to land carbon cycle processes}, - volume = {7}, - issn = {1748-9326}, - abstract = {Unknowns in future global warming are usually assumed to arise from uncertainties either in the amount of anthropogenic greenhouse gas emissions or in the sensitivity of the climate to changes in greenhouse gas concentrations. Characterizing the additional uncertainty in relating CO 2 emissions to atmospheric concentrations has relied on either a small number of complex models with diversity in process representations, or simple models. To date, these models indicate that the relevant carbon cycle uncertainties are smaller than the uncertainties in physical climate feedbacks and emissions. Here, for a single emissions scenario, we use a full coupled climate–carbon cycle model and a systematic method to explore uncertainties in the land carbon cycle feedback. We find a plausible range of climate–carbon cycle feedbacks significantly larger than previously estimated. Indeed the range of CO 2 concentrations arising from our single emissions scenario is greater than that previously estimated across the full range of IPCC SRES emissions scenarios with carbon cycle uncertainties ignored. The sensitivity of photosynthetic metabolism to temperature emerges as the most important uncertainty. This highlights an aspect of current land carbon modelling where there are open questions about the potential role of plant acclimation to increasing temperatures. There is an urgent need for better understanding of plant photosynthetic responses to high temperature, as these responses are shown here to be key contributors to the magnitude of future change.}, - number = {2}, - journal = {Environmental Research Letters}, - author = {Booth, B. B. and Jones, C. D. and Collins, M. and Totterdell, I. J. and Cox, P. M. and Sitch, S. and Huntingford, C. and Betts, R. A. and Harris, G. R. and Lloyd, J.}, - year = {2012}, - pages = {024002}, -} - -@article{booij_extreme_2002, - title = {Extreme daily precipitation in {Western} {Europe} with climate change at appropriate spatial scales}, - volume = {22}, - abstract = {Extreme daily precipitation for the current and changed climate at appropriate spatial scales is assessed. This is done in the context of the impact of climate change on flooding in the river Meuse in Western Europe. The objective is achieved by determining and comparing extreme precipitation from stations, reanalysis projects, global climate models and regional climate models. An extreme value reduction methodoloy based on extreme precipitation correlation structure and surface area is used to deal with the transformation between different spatial scales. It appeared that return values are simulated quite well by the regional climate models and CSIRO9, but are underestimated by the reanalyses and overestimated by CGCM1 and HadCM3. The models simulated an average increase in extreme precipitation with climate change of about 18\%, which is in the same range as the average model error and intermodel differences. The appropriate spatial scale for representing extreme precipitation was estimated at 20 km, when the bias permitted in the estimation of extreme precipitation is set at 10\%. Downscaling modelled extreme precipitation to this appropriate scale results in considerable differences between reanalysis and GCM scale and appropriate scale return values. It is therefore obvious that return values at these appropriate scales should be used instead of at their original scales. Copyright (C) 2002 Royal Meteorological Society.}, - number = {1}, - journal = {International Journal of Climatology}, - author = {Booij, M. J.}, - month = jan, - year = {2002}, - pages = {69--85}, - annote = {532ZKINT J CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:000174504800005}, -} - -@incollection{bouwer_changing_2003, - series = {Advances in {Global} {Change} {Research}}, - title = {Changing climate and increasing costs — {Implications} for liability and insurance {Climatic} {Change}: {Implications} for the {Hydrological} {Cycle} and for {Water} {Management}}, - volume = {10}, - isbn = {978-0-306-47983-0}, - publisher = {Springer Netherlands}, - author = {Bouwer, Laurens and Vellinga, Pier}, - editor = {Beniston, Martin}, - year = {2003}, - keywords = {Earth and Environmental Science}, - pages = {429--444}, - annote = {The following values have no corresponding Zotero field:electronic-resource-num: 10.1007/0-306-47983-4\_21}, -} - -@article{o_boucher_reversibility_2012, - title = {Reversibility in an {Earth} {System} model in response to {CO} 2 concentration changes}, - volume = {7}, - issn = {1748-9326}, - abstract = {We use the HadGEM2-ES Earth System model to examine the degree of reversibility of a wide range of components of the Earth System under idealized climate change scenarios where the atmospheric CO 2 concentration is gradually increased to four times the pre-industrial level and then reduced at a similar rate from several points along this trajectory. While some modelled quantities respond almost immediately to the atmospheric CO 2 concentrations, others exhibit a time lag relative to the change in CO 2 . Most quantities also exhibit a lag relative to the global-mean surface temperature change, which can be described as a hysteresis behaviour. The most surprising responses are from low-level clouds and ocean stratification in the Southern Ocean, which both exhibit hysteresis on timescales longer than expected. We see no evidence of critical thresholds in these simulations, although some of the hysteresis phenomena become more apparent above 2 × CO 2 or 3 × CO 2 . Our findings have implications for the parametrization of climate impacts in integrated assessment and simple climate models and for future climate studies of geoengineering scenarios.}, - number = {2}, - journal = {Environmental Research Letters}, - author = {O. Boucher and P. R. Halloran and E. J. Burke and M. Doutriaux-Boucher and C. D. Jones and J. Lowe and M. A. Ringer and E. Robertson and P. Wu}, - year = {2012}, - pages = {024013}, -} - -@article{bontron_analog_2002, - title = {Analog sorting of meteorological patterns: applications to operational precipitation forecast and climate evolution studies}, - abstract = {This paper summarises an adaptation method of meteorological model outputs via a pattern recognition technique based on an analog search. Its main application consists in providing medium term quantitative precipitation forecasts, both timely and spatially adapted for flood management up to 4 or 5 days ahead. During the MAP experiment, it has been applied in real time to some North Italian catchments (e.g. the Toce basin), and compared successfully, in terms of QPFs, with the outputs of the French model ARPEGE for the next 3 days. Furthermore, some applications can be found in climate research, like for the downscaling of GCM simulations. We address more particularly its diagnostic capacity to detect potential recent climatic evolutions, like changes in the frequency of occurrence of different synoptic situation.}, - number = {8}, - journal = {Houille Blanche-Revue Internationale De L Eau}, - author = {Bontron, G. and Djerboua, A. and Obled, C.}, - year = {2002}, - pages = {46--51}, - annote = {650TNHOUILLE BLANCHE}, - annote = {The following values have no corresponding Zotero field:alt-title: Houille Blanche-Rev. Int.accession-num: ISI:000181279600008}, -} - -@article{bonfils_identification_2006, - title = {Identification of external influences on temperatures in {California}}, - volume = {in review}, - journal = {Climatic Change}, - author = {Bonfils, C. and Duffy, P. B. and Santer, B. D. and Wigley, T. M. L. and Lobell, D. B. and Phillips, T. J. and Doutriaux, C.}, - year = {2006}, -} - -@article{bond_origins_1987, - title = {Origins of seawater intrusion in a coastal aquifer -- {A} case study of the {Pajaro} {Valley}, {California}}, - volume = {92}, - issn = {0022-1694}, - number = {3-4}, - journal = {Journal of Hydrology}, - author = {Bond, Linda D. and Bredehoeft, John D.}, - year = {1987}, - pages = {363--388}, - annote = {The following values have no corresponding Zotero field:work-type: doi: DOI: 10.1016/0022-1694(87)90024-2}, -} - -@article{bogardi_estimating_1996, - title = {Estimating daily wind speed under climate change}, - volume = {57}, - abstract = {A semi-empirical downscaling approach is presented to estimate spatial and temporal statistical properties of local daily mean wind speed under global climate change. The present semi- empirical downscaling method consists of two elements. Since general circulation models (GCMs) are able to reproduce the features of the present atmospheric general circulation quite correctly, the first element represents the large-scale circulation of the atmosphere. The second element is a link between local wind speed and large-scale circulation pattern (CP). The linkage is expressed by a stochastic model conditioned on CP types. Parameters of the linkage model are estimated using observed data series; then this model is utilized with GCM-generated CP type data corresponding to a 2 x CO2 scenario. Under the climate of Nebraska the lognormal distribution is the best two-parameter distribution to describe daily mean wind speed. The space-time variability of wind speed is described by a transformed multivariate autoregressive (AR) process, and the linkage between local wind and large-scale circulation is expressed as a conditional AR process, i.e. the autoregressive parameters depend on the actual daily CP type. The basic tendency of change under 2 x CO2 climate is a considerable increase of wind speed from the beginning of summer to the end of winter and a somewhat smaller wind decrease in spring. Copyright (C) 1996 Elsevier Science Ltd.}, - number = {3}, - journal = {Solar Energy}, - author = {Bogardi, I. and Matyasovszky, I.}, - month = sep, - year = {1996}, - pages = {239--248}, - annote = {The following values have no corresponding Zotero field:alt-title: Sol. Energyaccession-num: ISI:A1996WB80800009}, - annote = {WB808SOLAR ENERG}, -} - -@article{boe_statistical_2007, - title = {Statistical and dynamical downscaling of the {Seine} basin climate for hydro-meteorological studies}, - volume = {27}, - issn = {1097-0088}, - doi = {10.1002/joc.1602}, - number = {12}, - journal = {International Journal of Climatology}, - author = {Boé, J. and Terray, L. and Habets, F. and Martin, E.}, - year = {2007}, - keywords = {bias correction, climate change, dynamical downscaling, regional climate, statistical downscaling, hydrological impacts}, - pages = {1643--1655}, - annote = {The following values have no corresponding Zotero field:publisher: John Wiley \& Sons, Ltd.}, -} - -@article{boberg_overestimation_2012, - title = {Overestimation of {Mediterranean} summer temperature projections due to model deficiencies}, - volume = {2}, - issn = {1758-678X}, - doi = {10.1038/nclimate1454}, - number = {6}, - journal = {Nature Clim. Change}, - author = {Boberg, F. and Christensen, J. H.}, - year = {2012}, - pages = {433--436}, - annote = {10.1038/nclimate1454}, - annote = {The following values have no corresponding Zotero field:publisher: Nature Publishing Groupwork-type: 10.1038/nclimate1454}, -} - -@article{bischoff_500_2003, - title = {The 500 and 1000 {hPa} weather type circulations and their relationship with some extreme climatic conditions over southern {South} {America}}, - volume = {23}, - abstract = {Different circulation types for southern South America are derived from the circulation anomaly maps at 500 hPa corresponding to a 9 year period. The series of daily maps was obtained from the ECWMF reanalysis. These data are particularly useful, since real data, especially the radiosonde network, are sparse in this region. The properties to be studied are selected in such a way that they describe different flow conditions over the region, in order to obtain a statistical diagnosis useful for modelling an objective forecast. The circulation types were obtained by a correlation-map-based pattern classification technique. Lund's method is used in this paper to identify the most frequent circulation patterns. The classification method was applied to daily Z anomalies at 500 hPa over the whole record (1980-88). This allows one to analyse the evolution and presence of a particular type over different months and years. The most outstanding circulation-type structures, represented by only eight types, explain about 63\% of the total number of cases in the sample. The most frequent type, Type 1 (Z), shows an almost zonal circulation with a strong meridional gradient, associated with a trough in the west of the region. Type 2 (LCE) is represented by a low- pressure system in the centre of the region (approximately at 37degreesS, 65degreesW). In general, this type is related to the occurrence of blocking situations in the South Atlantic Ocean and the passage of cold fronts over the region. Types 3 (SW) and 4 (WNW) show a SW and WNW atmospheric circulation over the whole region with a meridional gradient lower than in Type 1. Type 5 (TNS) shows an NW-SE trough axis over the continent. Type 6 (WW) has an intense SW flow over the southern part of the continent. Type 7 (NW) shows a very deep trough to the west of the continent, located over the Pacific Ocean at 80 degreesW and 34 degreesS. The continent is affected by a NW atmospheric circulation. Type 8 (R) represents a ridge over the continent with an intense NW flow in the south. The frequency of Types 1 to 8 is about 63\%. None of the circulation types mentioned accounts for more than 12\% of the sample. There is a considerable variability in monthly circulation type distributions; the first six types only account for approximately 50\% of the variability. This suggests that the use of probability models related to circulation type occurrence in the middle troposphere should be analysed on a monthly basis. The results attempt to summarize the region's synoptic regime through a small number of circulation types at 500 hPa and the corresponding 1000 hPa maps. The frequency distribution of each type during warm, neutral and cold phases in the equatorial Pacific Ocean was analysed. These results partially explain the best dynamic conditions (cyclonic vorticity), for the Argentine humid and semi-humid region, southern Brazil, Paraguay and Uruguay, that lead to the harvest precipitation events in spring (October-December 1982) with SOI {\textless} 0. In winter these circulation types are more important during cold phases with advection of cold and dry air (July- September 1982). The circulation types at 500 hPa and the corresponding maps at 1000 hPa associated with mean maximum and minimum monthly temperature and precipitation in the region are studied. The monthly maximum and minimum precipitation recorded in stations near the Uruguay River basin in southeastern South America and the mean monthly maximum and minimum temperatures from three stations in Argentina during 1980-88 are used in the analysis. Some of the daily patterns are strongly associated with the occurrence of maximum and minimum monthly temperature and precipitation in different places of the country. In reference to the maximum and minimum precipitation records, the behaviour of the variables under study is observed to be physically consistent with the associated circulation patterns. Copyright (C) 2003 Royal Meteorological Society.}, - number = {5}, - journal = {International Journal of Climatology}, - author = {Bischoff, S. A. and Vargas, W. M.}, - month = apr, - year = {2003}, - pages = {541--556}, - annote = {671DFINT J CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:000182449500004}, -} - -@article{beyene_hydrologic_2010, - title = {Hydrologic impacts of climate change on the {Nile} {River} {Basin}: implications of the 2007 {IPCC} scenarios}, - volume = {100}, - issn = {0165-0009}, - doi = {10.1007/s10584-009-9693-0}, - number = {3}, - journal = {Climatic Change}, - author = {Beyene, Tazebe and Lettenmaier, Dennis and Kabat, Pavel}, - year = {2010}, - keywords = {Earth and Environmental Science}, - pages = {433--461}, - annote = {The following values have no corresponding Zotero field:publisher: Springer Netherlands}, -} - -@incollection{n_l_bindoff_detection_2013, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Detection and {Attribution} of {Climate} {Change}: from {Global} to {Regional}}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {N. L. Bindoff and P. A. Stott and K. M. AchutaRao and M. R. Allen and N. Gillett and D. Gutzler and K. Hansingo and G. Hegerl and Y. Hu and S. Jain and I. I. Mokhov and J. Overland and J. Perlwitz and R. Sebbari and X. Zhang}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley}, - year = {2013}, - pages = {867--952}, - annote = {The following values have no corresponding Zotero field:section: 10electronic-resource-num: 10.1017/CBO9781107415324.022}, -} - -@article{biau_estimation_1999, - title = {Estimation of precipitation by kriging in the {EOF} space of the sea level pressure field}, - volume = {12}, - abstract = {The term downscaling denotes a procedure in which local climatic information is derived from large-scale climate parameters. In this paper, the possibility of using as downscaling procedure a geostatistical interpolation technique known as kriging is explored. The authors present an example of the method by trying to reconstruct monthly winter precipitation in the Iberian Peninsula from the North Atlantic sea level pressure (SLP) field in wintertime (December- February). The main idea consists in reducing the spatial dimension of the large-scale SLP field by means of empirical orthogonal function (EOF) analysis. Each observed SLP field is represented by a point in this low-dimensional space and this point is associated with the simultaneously observed rainfall. New values of the SLP field, for instance, those simulated by a general circulation model with modified greenhouse gas concentrations, can be represented by a new point in the EOF space. The rainfall amount to be associated to this point is estimated by kriging interpolation in the EOF space. The results obtained by this geostatistical approach are compared to the ones obtained by a simpler analog method by trying to reconstruct the observed rainfall from the SLP field in an independent period. It has been found that, generally, kriging and the analog method reproduce realistically the long-term mean, that kriging is somewhat better than the analog method in reproducing the rainfall evolution, but that,contrary to the analog method, it underestimates the variance because of the well-known smoothing effect. It is argued that there exists an intrinsic incompatibility between the estimation of the mean and replication of the variability. Finally, both methods have been also applied to daily winter rainfall. The methods are also validated by downscaling winter precipitation from SLP. It is concluded that kriging yields a better estimation of daily rainfall than the analog method, but the latter better reproduces the probability distribution of rainfall amounts and of the length of dry periods.}, - number = {4}, - journal = {J. Clim.}, - author = {Biau, G. and Zorita, E. and von Storch, H. and Wackernagel, H.}, - month = apr, - year = {1999}, - pages = {1070--1085}, - annote = {182LJJ CLIMATE}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Clim.accession-num: ISI:000079500200011}, -} - -@article{betts_projected_2007, - title = {Projected increase in continental runoff due to plant responses to increasing carbon dioxide}, - volume = {448}, - number = {7157}, - journal = {Nature}, - author = {Betts, Richard A. and Boucher, Olivier and Collins, Matthew and Cox, Peter M. and Falloon, Peter D. and Gedney, Nicola and Hemming, Deborah L. and Huntingford, Chris and Jones, Chris D. and Sexton, David M. H. and Webb, Mark J.}, - year = {2007}, - pages = {1037--1041}, - annote = {0028-083610.1038/nature0604510.1038/nature06045}, - annote = {The following values have no corresponding Zotero field:publisher: Nature Publishing Group}, -} - -@article{bergant_statistical_2002, - title = {Statistical downscaling of general-circulation-model-simulated average monthly air temperature to the beginning of flowering of the dandelion ({Taraxacum} officinale) in {Slovenia}}, - volume = {46}, - abstract = {Phenological observations are a valuable source of information for investigating the relationship between climate variation and plant development. Potential climate change in the future will shift the occurrence of phenological phases. Information about future climate conditions is needed in order to estimate this shift. General circulation models (GCM) provide the best information about future climate change. They are able to simulate reliably the most important mean features on a large scale, but they fail on a regional scale because of their low spatial resolution. A common approach to bridging the scale gap is statistical downscaling, which was used to relate the beginning of flowering of Taraxacum officinale in Slovenia with the monthly mean near-surface air temperature for January, February and March in Central Europe. Statistical models were developed and tested with NCAR/NCEP Reanalysis predictor data and EARS predictand data for the period 1960-1999. Prior to developing statistical models, empirical orthogonal function (EOF) analysis was employed on the predictor data. Multiple linear regression was used to relate the beginning of flowering with expansion coefficients of the first three EOF for the Janauary, Febrauary and March air temperatures, and a strong correlation was found between them. Developed statistical models were employed on the results of two GCM (HadCM3 and ECHAM4/OPYC3) to estimate the potential shifts in the beginning of flowering for the periods 1990-2019 and 2020-2049 in comparison with the period 1960-1989. The HadCM3 model predicts, on average, 4 days earlier occurrence and ECHAM4/OPYC3 5 days earlier occurrence of flowering in the period 1990-2019. The analogous results for the period 2020- 2049 are a 10- and 11-day earlier occurrence.}, - number = {1}, - journal = {International Journal of Biometeorology}, - author = {Bergant, K. and Kajfez-Bogataj, L. and Crepinsek, Z.}, - month = feb, - year = {2002}, - pages = {22--32}, - annote = {534YNINT J BIOMETEOROL}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Biometeorol.accession-num: ISI:000174617500004}, -} - -@article{benestad_new_2011, - title = {A {New} {Global} {Set} of {Downscaled} {Temperature} {Scenarios}}, - volume = {24}, - doi = {doi:10.1175/2010JCLI3687.1}, - number = {8}, - journal = {Journal of Climate}, - author = {Benestad, Rasmus E.}, - year = {2011}, - pages = {2080--2098}, -} - -@article{betts_land_1996, - title = {The land surface-atmosphere interaction: a review based on observational and global modeling perspectives}, - volume = {101}, - number = {D3}, - journal = {Journal of Geophysical Research}, - author = {Betts, A. K. and Ball, J. H. and Beljaars, A. C. M. and Miller, M. J. and Viterbo, P. A.}, - year = {1996}, - pages = {7209--7225}, -} - -@article{beniston_snow_2003, - title = {Snow pack in the {Swiss} {Alps} under changing climatic conditions: an empirical approach for climate impacts studies}, - volume = {74}, - abstract = {In many instances, snow cover and duration are a major controlling factor on a range of environmental systems in mountain regions. When assessing the impacts of climatic change on mountain ecosystems and river basins whose origin lie in the Alps, one of the key controls on such systems will reside in changes in snow amount and duration. At present, regional climate models or statistical downscaling techniques, which are the principal methods applied to the derivation of climatic variables in a future, changing climate, do not provide adequate information at the scales required for investigations in which snow is playing a major role. A study has thus been undertaken on the behavior of snow in the Swiss Alps, in particular the duration of the seasonal snow-pack, on the basis of observational data from a number of Swiss climatological stations. It is seen that there is a distinct link between snow-cover duration and height (i.e., temperature), and that this link has a specific "signature" according to the type of winter. Milder winters are associated with higher precipitation levels than colder winters, but with more solid precipitation at elevations exceeding 1,700-2,000 m above sea-level, and more liquid precipitation below. These results can be combined within a single diagram, linking winter minimum temperature, winter precipitation, and snow-cover duration. The resulting contour surfaces can then be used to assess the manner in which the length of the snow-season may change according to specified shifts in temperature and precipitation. While the technique is clearly empirical, it can be combined with regional climate model information to provide a useful estimate of the length of the snow season with snow cover, for various climate-impacts studies.}, - number = {1-2}, - journal = {Theoretical and Applied Climatology}, - author = {Beniston, M. and Keller, F. and Goyette, S.}, - year = {2003}, - pages = {19--31}, - annote = {644LPTHEOR APPL CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Theor. Appl. Climatol.accession-num: ISI:000180919600002}, -} - -@article{benestad_can_2006, - title = {Can {We} {Expect} {More} {Extreme} {Precipitation} on the {Monthly} {Time} {Scale}?}, - volume = {19}, - abstract = {The Intergovernmental Panel on Climate Change (IPCC) Third Assessment Report states that instrumental records show an increase in precipitation by \&\#43;0.5\&\#37;\&\#8211;1\&\#37; decade\&\#8722;1 in much of the Northern Hemisphere mid- and high latitudes and a decrease of \&\#8722;0.3\&\#37; decade\&\#8722;1 over subtropical land areas. It has been postulated that these trends are associated with the enhanced levels of atmospheric CO2. In this context, it is natural to ask how continuing rising levels of CO2 may affect the climate in the future. The past IPCC reports have documented numerous studies where increased greenhouse gas concentrations have been prescribed in global climate model simulations. Now, new simulations with state-of-the-art climate models are becoming available for the next IPCC report \&\#91;the Fourth Assessment Report (AR4)\&\#93;, and results from a number of these simulations are examined in order to determine whether they indicate a change in extreme precipitation on a monthly basis. The analysis involves a simple record\&\#8211;statistics framework and shows that the upper tails of the probability distribution functions for monthly precipitation are being stretched in the mid- and high latitudes where mean-level precipitation increases have already been reported in the past. In other words, values corresponding to extreme monthly precipitation in the past are, according to these results, becoming more frequent.}, - number = {4}, - journal = {Journal of Climate}, - author = {Benestad, Rasmus E.}, - month = feb, - year = {2006}, - pages = {630--637}, -} - -@article{benestad_empirical-statistical_2004, - title = {Empirical-{Statistical} {Downscaling} in {Climate} {Modeling}}, - volume = {85}, - number = {42}, - journal = {Eos Trans. AGU}, - author = {Benestad, R. E.}, - year = {2004}, - pages = {417--422}, -} - -@article{benestad_empirically_2002, - title = {Empirically downscaled multimodel ensemble temperature and precipitation scenarios for {Norway}}, - volume = {15}, - abstract = {A number of different global climate model scenarios are used in order to infer local climate scenarios for various locations in Norway. Results from empirically downscaled multimodel ensembles of temperature and precipitation for the period 2000- 50 are presented, based on common EOFs of large-scale temperature and sea level pressure fields. Comparisons with actual records for the past show that the multimodel ensemble range tends to span the observations. All scenarios for temperature change indicate a future warming, but the sea level pressure-based scenarios for precipitation are characterized by a large scatter about zero change. The primary cause for the large spread in precipitation trend estimates is attributed to differences between the various global climate scenarios. It is also acknowledged that the sea level pressure-based empirical models may underestimate the trends as they do not take directly into account increases in the precipitation due to increased temperatures. Nevertheless, in some locations the majority of the ensemble members suggest wetter springtime conditions.}, - number = {21}, - journal = {J. Clim.}, - author = {Benestad, R. E.}, - month = nov, - year = {2002}, - pages = {3008--3027}, - annote = {604DQJ CLIMATE}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Clim.accession-num: ISI:000178601100003}, -} - -@article{bellone_hidden_2000, - title = {A hidden {Markov} model for downscaling synoptic atmospheric patterns to precipitation amounts}, - volume = {15}, - abstract = {Nonhomogeneous hidden Markov models (NHMMs) provide a relatively simple framework for simulating precipitation at multiple rain gauge stations conditional on synoptic atmospheric patterns. Building on existing NHMMs for precipitation occurrences, we propose an extension to include precipitation amounts. The model we describe assumes the existence of unobserved (or hidden) weather patterns, the weather states, which follow a Markov chain. The weather states depend on observable synoptic information and therefore serve as a link between the synoptic-scale atmospheric patterns and the local-scale precipitation. The presence of the hidden states simplifies the spatio-temporal structure of the precipitation process. We assume the temporal dependence of precipitation is completely accounted for by the Markov evolution of the weather state. The spatial dependence of precipitation can also be partially or completely accounted for by the existence of a common weather state. In the proposed model, occurrences are assumed to be conditionally spatially independent given the current weather state and, conditional on occurrences, precipitation amounts are modeled independently at each rain gauge as gamma deviates with gauge-specific parameters. We apply these methods to model precipitation at a network of 24 rain gauge stations in Washington state over the course of 17 winters. The first 12 yr are used for model fitting purposes, while the last 5 serve to evaluate the model performance. The analysis of the model results for the reserved years suggests that the characteristics of the data are captured fairly well and points to possible directions for future improvements.}, - number = {1}, - journal = {Climate Research}, - author = {Bellone, E. and Hughes, J. P. and Guttorp, P.}, - month = may, - year = {2000}, - pages = {1--12}, - annote = {359LFCLIMATE RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:000089611800001}, -} - -@article{bellenger_enso_2013, - title = {{ENSO} representation in climate models: from {CMIP3} to {CMIP5}}, - issn = {0930-7575}, - doi = {10.1007/s00382-013-1783-z}, - language = {English}, - journal = {Climate Dynamics}, - author = {Bellenger, H. and Guilyardi, E. and Leloup, J. and Lengaigne, M. and Vialard, J.}, - month = apr, - year = {2013}, - pages = {1--20}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim Dynpublisher: Springer-Verlag}, -} - -@article{bell_regional_2004, - title = {Regional changes in extreme climatic events: {A} future climate scenario}, - volume = {17}, - issn = {0894-8755}, - abstract = {In this study a regional climate model is employed to expand on modeling experiments of future climate change to address issues of 1) the timing and length of the growing season and 2) the frequency and intensity of extreme temperatures and precipitation. The study focuses on California as a climatically complex region that is vulnerable to changes in water supply and delivery. Statistically significant increases in daily minimum and maximum temperatures occur with a doubling of atmospheric carbon dioxide concentration. Increases in daily temperatures lead to increases in prolonged heat waves and length of the growing season. Changes in total and extreme precipitation vary depending upon geographic location.}, - language = {English}, - number = {1}, - journal = {Journal of Climate}, - author = {Bell, J. L. and Sloan, L. C. and Snyder, M. A.}, - month = jan, - year = {2004}, - keywords = {model, precipitation, co2, trends, variability, impacts, responses, daily temperature, diurnal range, new-zealand}, - pages = {81--87}, - annote = {759MXTimes Cited:11Cited References Count:23}, - annote = {The following values have no corresponding Zotero field:auth-address: Bell, JL Univ Calif Santa Cruz, Dept Earth Sci, Santa Cruz, CA 95064 USA Univ Calif Santa Cruz, Dept Earth Sci, Santa Cruz, CA 95064 USAaccession-num: ISI:000187742400006}, -} - -@article{benestad_comparison_2001, - title = {A comparison between two empirical downscaling strategies}, - volume = {21}, - issn = {0899-8418}, - abstract = {A new approach involving the use of common empirical orthogonal functions (EOFs) in statistical downscaling of future global climate scenarios is proposed. The advantage of this method is that it minimizes the errors associated with the downscaling of future climate scenarios. The time series from the common EOF analysis are used both for the calibration of the statistical models and the prediction of future climate scenarios. This paper presents a systematic comparison between the common EOF approach and downscaling with a more conventional method based on the 'Perfect Prog' concept. -Three different sets of experiments are carried out where the two downscaling methods are compared, and the robustness of the methods is explored taking the predictor field from different regions. The analysis indicates that climate scenarios derived using the common EOF approach are associated with smaller errors. Another important finding is that the choice of predictor domain may influence the downscaled results, and that the merit of downscaling may depend on this area as well as location and season. Copyright (C) 2001 Royal Meteorological Society.}, - language = {English}, - number = {13}, - journal = {International Journal of Climatology}, - author = {Benestad, R. E.}, - month = nov, - year = {2001}, - keywords = {model, climate-change, precipitation, variability, atmospheric circulation, simulations, analysis of robustness, climate scenarios, common principal components, empirical downscaling, gcm, global climate, local climate, solar-cycle}, - pages = {1645--1668}, - annote = {499QCTimes Cited:10Cited References Count:64}, - annote = {The following values have no corresponding Zotero field:auth-address: Benestad, RE Norway Meteorol Inst, POB 43, N-0313 Oslo, Norway Norway Meteorol Inst, N-0313 Oslo, Norwayaccession-num: ISI:000172581000005}, -} - -@misc{bedsworth_statewide_2018, - title = {Statewide {Summary} {Report}. {California}’s {Fourth} {Climate} {Change} {Assessment}. {Publication} number: {SUMCCCA4}-2018-013}, - author = {Bedsworth, L. and Cayan, D. R. and Franco, G. and Fisher, L. and Ziaja, S.}, - year = {2018}, - pages = {133}, - annote = {The following values have no corresponding Zotero field:publisher: California Governor’s Office of Planning and Research, Scripps Institution of Oceanography, California Energy Commission, California Public Utilities Commission}, -} - -@article{battin_projected_2007, - title = {Projected impacts of climate change on salmon habitat restoration}, - volume = {104}, - doi = {10.1073/pnas.0701685104}, - abstract = {Throughout the world, efforts are under way to restore watersheds, but restoration planning rarely accounts for future climate change. Using a series of linked models of climate, land cover, hydrology, and salmon population dynamics, we investigated the impacts of climate change on the effectiveness of proposed habitat restoration efforts designed to recover depleted Chinook salmon populations in a Pacific Northwest river basin. Model results indicate a large negative impact of climate change on freshwater salmon habitat. Habitat restoration and protection can help to mitigate these effects and may allow populations to increase in the face of climate change. The habitat deterioration associated with climate change will, however, make salmon recovery targets much more difficult to attain. Because the negative impacts of climate change in this basin are projected to be most pronounced in relatively pristine, high-elevation streams where little restoration is possible, climate change and habitat restoration together are likely to cause a spatial shift in salmon abundance. River basins that span the current snow line appear especially vulnerable to climate change, and salmon recovery plans that enhance lower-elevation habitats are likely to be more successful over the next 50 years than those that target the higher-elevation basins likely to experience the greatest snow–rain transition.}, - number = {16}, - journal = {Proceedings of the National Academy of Sciences}, - author = {Battin, James and Wiley, Matthew W. and Ruckelshaus, Mary H. and Palmer, Richard N. and Korb, Elizabeth and Bartz, Krista K. and Imaki, Hiroo}, - month = apr, - year = {2007}, - pages = {6720--6725}, -} - -@article{beckmann_statistical_2002, - title = {Statistical downscaling relationships for precipitation in the {Netherlands} and {North} {Germany}}, - volume = {22}, - abstract = {The statistical linkage of daily precipitation to the National Centers for Environment Prediction (NCEP) reanalysis data is described for De Bilt and Maastricht (Netherlands), and for Hamburg, Hanover and Berlin (Germany), using daily data for the period 1968-97. Two separate models were used to describe the daily precipitation at a particular site: an additive logistic model for rainfall occurrence and a generalized additive model for wet-day rainfall. Several dynamical variables and atmospheric moisture were included as predictor variables. The relative humidity at 700 hPa was considered as the moisture variable for rainfall occurrence modelling. For rainfall amount modelling, two options were compared: (i) the use of the specific humidity at 700 hPa, and (ii) the use of both the relative humidity at 700 hPa and precipitable water. An application is given with data from a time-dependent greenhouse gas forcing experiment using the coupled ECHAM4/OPYC3 atmosphere-ocean general circulation model for the periods 1968-97 and 2070-99. The fitted statistical relationships were used to estimate the changes in the mean number of wet days and the mean rainfall amounts for the winter and summer halves of the year at De Bilt, Hanover and Berlin. A decrease in the mean number of wet days was found. Despite this decrease, an increase in the mean seasonal rainfall amounts is predicted if specific humidity is used in the model for wet-day rainfall. This is caused by the larger atmospheric water content in the future climate. The effect of the increased atmospheric moisture is smaller if the alternative wet-day rainfall amount model with precipitable water and relative humidity is applied. Except for an anomalous change in mean winter rainfall at Hanover, the estimated changes from the latter model correspond quite well with those from the ECHAM4/OPYC3 model. Despite the flexibility of generalized additive models, the rainfall amount model systematically overpredicts the mean rainfall amounts in situations where extreme rainfall could be expected. Interaction between predictor effects has to be incorporated to reduce this bias. Copyright (C) 2002 Royal Meteorological Society.}, - number = {1}, - journal = {International Journal of Climatology}, - author = {Beckmann, B. R. and Buishand, T. A.}, - month = jan, - year = {2002}, - pages = {15--32}, - annote = {532ZKINT J CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:000174504800002}, -} - -@article{bates_stochastic_1998, - title = {Stochastic downscaling of numerical climate model simulations}, - volume = {13}, - abstract = {Interest in stochastic downscaling has grown from the inability of general circulation models (GCMs) and limited area models (LAMs) to reproduce observed daily precipitation statistics at local and regional scales under present climate conditons. Although GCMs and LAMs perform reasonably well in simulating synoptic atmospheric fields, they tend to over-estimate the frequency and under-estimate the intensity of daily precipitation. This paper describes the application of a nonhomogeneous hidden Markov model (NHMM) fitted to observed atmospheric and precipitation data to a LAM simulation for South-West Western Australia. The NHMM is unique amongst stochastic downscaling methods in that it determines the most distinct patterns in a multi-site, precipitation occurrence record rather than patterns in atmospheric fields. These patterns can in turn be defined as conditionally dependent on a range of daily atmospheric series. We compare the LAM simulation and the downscaled LAM simulation with observed 'winter' precipitation statistics at six stations near Perth, Western Australia. The results show that the downscaled simulations reproduce observed precipitation probabilities and wet and dry spell frequencies at each station. The LAM simulation tends to under-estimate the frequency of dry spells and over-estimate the probability of precipitation and the frequency of wet spells. (C) 1998 Elsevier Science Ltd. All rights reserved.}, - number = {3-4}, - journal = {Environmental Modelling \& Software}, - author = {Bates, B. C. and Charles, S. P. and Hughes, J. P.}, - year = {1998}, - pages = {325--331}, - annote = {149TVENVIRON MODELL SOFTW}, - annote = {The following values have no corresponding Zotero field:alt-title: Environ. Modell. Softw.accession-num: ISI:000077622600012}, -} - -@article{bass_downscaling_1997, - title = {Downscaling procedures as a tool for integration of multiple air issues}, - volume = {46}, - abstract = {In assessing the risks associated with climate change, downscaling has proven useful in linking surface changes, at scales relevant to decision making, to large-scale atmospheric circulation derived from GCM output. Stochastic downscaling is related to synoptic climatology, weather-typing approaches (classifying circulation patterns) such as the Lamb Weather Types developed for the United Kingdom (UK), the European Grosswetterlagen (Bardossy and Plate, 1992) and the Perfect Prognosis (Perfect Frog) method from numerical weather prediction. The large-scale atmospheric circulation is linked with site-specific observations of atmospheric variables, such as precipitation, wind speed or temperature, within a specified region. Classifying each day by circulation patterns is achieved by clustering algorithms, fuzzy rule bases, neural nets or decision trees. The linkages are extended to GCM output to account for climate change. Stochastic models are developed from the probability distributions for extreme events. Objective analysis can be used to interpolate values of these models to other locations. The concepts and some applications are reviewed to provide a basis for extending the downscaling approach to assessing the integrated risk of the six air issues: climate change, UV-B radiation, acid rain, transport of hazardous air pollutants, smog and suspended particulates.}, - number = {1-2}, - journal = {Environmental Monitoring and Assessment}, - author = {Bass, B. and Brook, J. R.}, - month = jun, - year = {1997}, - pages = {151--174}, - annote = {The following values have no corresponding Zotero field:alt-title: Environ. Monit. Assess.accession-num: ISI:A1997XH65200011}, - annote = {XH652ENVIRON MONIT ASSESS}, -} - -@article{barsugli_practitioners_2013, - title = {The {Practitioner}'s {Dilemma}: {How} to {Assess} the {Credibility} of {Downscaled} {Climate} {Projections}}, - volume = {94}, - issn = {2324-9250}, - doi = {10.1002/2013eo460005}, - number = {46}, - journal = {Eos, Transactions American Geophysical Union}, - author = {Barsugli, Joseph J. and Guentchev, Galina and Horton, Radley M. and Wood, Andrew and Mearns, Linda O. and Liang, Xin-Zhong and Winkler, Julie A. and Dixon, Keith and Hayhoe, Katharine and Rood, Richard B. and Goddard, Lisa and Ray, Andrea and Buja, Lawrence and Ammann, Caspar}, - year = {2013}, - keywords = {0550 Model verification and validation, 1637 Regional climate change, 6309 Decision making under uncertainty, 1630 Impacts of global change, climate variability and change, credibility, downscaled projections, quantitative evaluation}, - pages = {424--425}, -} - -@misc{barsugli_whats_2010, - address = {San Francisco, CA}, - title = {What's a billion cubic meters among friends: {The} impacts of quantile mapping bias correction on climate projections}, - author = {Barsugli, J. J.}, - month = dec, - year = {2010}, - annote = {The following values have no corresponding Zotero field:pages: Abstract GC51A-0737}, -} - -@article{barnhart_snowmelt_2016, - title = {Snowmelt rate dictates streamflow}, - volume = {43}, - doi = {doi:10.1002/2016GL069690}, - abstract = {Abstract Declining mountain snowpack and earlier snowmelt across the western United States has implications for downstream communities. We present a possible mechanism linking snowmelt rate and streamflow generation using a gridded implementation of the Budyko framework. We computed an ensemble of Budyko streamflow anomalies (BSAs) using Variable Infiltration Capacity model-simulated evapotranspiration, potential evapotranspiration, and estimated precipitation at 1/16° resolution from 1950 to 2013. BSA was correlated with simulated baseflow efficiency (r2 = 0.64) and simulated snowmelt rate (r2 = 0.42). The strong correlation between snowmelt rate and baseflow efficiency (r2 = 0.73) links these relationships and supports a possible streamflow generation mechanism wherein greater snowmelt rates increase subsurface flow. Rapid snowmelt may thus bring the soil to field capacity, facilitating below-root zone percolation, streamflow, and a positive BSA. Previous works have shown that future increases in regional air temperature may lead to earlier, slower snowmelt and hence decreased streamflow production via the mechanism proposed by this work.}, - number = {15}, - journal = {Geophysical Research Letters}, - author = {Barnhart, Theodore B. and Molotch, Noah P. and Livneh, Ben and Harpold, Adrian A. and Knowles, John F. and Schneider, Dominik}, - year = {2016}, - pages = {8006--8016}, -} - -@article{barnett_potential_2005, - title = {Potential impacts of a warming climate on water availability in snow-dominated regions}, - volume = {438}, - journal = {Nature}, - author = {Barnett, T. P. and Adam, J. C. and Lettenmaier, D. P.}, - year = {2005}, - pages = {303--309, doi:10.1038/nature04141}, -} - -@article{barnett_sustainable_2009, - title = {Sustainable water deliveries from the {Colorado} {River} in a changing climate}, - doi = {10.1073/pnas.0812762106}, - abstract = {The Colorado River supplies water to 27 million users in 7 states and 2 countries and irrigates over 3 million acres of farmland. Global climate models almost unanimously project that human-induced climate change will reduce runoff in this region by 10–30\%. This work explores whether currently scheduled future water deliveries from the Colorado River system are sustainable under different climate-change scenarios. If climate change reduces runoff by 10\%, scheduled deliveries will be missed ≈58\% of the time by 2050. If runoff reduces 20\%, they will be missed ≈88\% of the time. The mean shortfall when full deliveries cannot be met increases from ≈0.5–0.7 billion cubic meters per year (bcm/yr) in 2025 to ≈1.2–1.9 bcm/yr by 2050 out of a request of ≈17.3 bcm/yr. Such values are small enough to be manageable. The chance of a year with deliveries {\textless}14.5 bcm/yr increases to 21\% by midcentury if runoff reduces 20\%, but such low deliveries could be largely avoided by reducing scheduled deliveries. These results are computed by using estimates of Colorado River flow from the 20th century, which was unusually wet; if the river reverts to its long-term mean, shortfalls increase another 1–1.5 bcm/yr. With either climate-change or long-term mean flows, currently scheduled future water deliveries from the Colorado River are not sustainable. However, the ability of the system to mitigate droughts can be maintained if the various users of the river find a way to reduce average deliveries.}, - journal = {Proceedings of the National Academy of Sciences}, - author = {Barnett, Tim P. and Pierce, David W.}, - month = apr, - year = {2009}, -} - -@article{barnett_quantifying_2006, - title = {Quantifying uncertainty in changes in extreme event frequency in response to doubled {CO2} using a large ensemble of {GCM} simulations}, - volume = {26}, - issn = {0930-7575}, - doi = {10.1007/s00382-005-0097-1}, - abstract = {We discuss equilibrium changes in daily extreme surface air temperature and precipitation events in response to doubled atmospheric CO2, simulated in an ensemble of 53 versions of HadSM3, consisting of the HadAM3 atmospheric general circulation model (GCM) coupled to a mixed layer ocean. By virtue of its size and design, the ensemble, which samples uncertainty arising from the parameterisation of atmospheric physical processes and the effects of natural variability, provides a first opportunity to quantify the robustness of predictions of changes in extremes obtained from GCM simulations. Changes in extremes are quantified by calculating the frequency of exceedance of a fixed threshold in the 2 x CO2 simulation relative to the 1 x CO2 simulation. The ensemble-mean value of this relative frequency provides a best estimate of the expected change while the range of values across the ensemble provides a measure of the associated uncertainty. For example, when the extreme threshold is defined as the 99th percentile of the 1 x CO2 distribution, the global-mean ensemble-mean relative frequency of extremely warm days is found to be 20 in January, and 28 in July, implying that events occurring on one day per hundred under present day conditions would typically occur on 20-30 days per hundred under 2 x CO2 conditons. However the ensemble range in the relative frequency is of similar magnitude to the ensemble-mean value, indicating considerable uncertainty in the magnitude of the increase. The relative frequencies in response to doubled CO2 become smaller as the threshold used to define the extreme event is reduced. For one variable (July maximum daily temperature) we investigate this simulated variation with threshold, showing that it can be quite well reproduced by assuming the response to doubling CO2 to be characterised simply as a uniform shift of a Gaussian distribution. Nevertheless, doubling CO2 does lead to changes in the shape of the daily distributions for both temperature and precipitation, but the effect of these changes on the relative frequency of extreme events is generally larger for precipitation. For example, around one-fifth of the globe exhibits ensemble-mean decreases in time-averaged precipitation accompanied by increases in the frequency of extremely wet days. The ensemble range of changes in precipitation extremes (relative to the ensemble mean of the changes) is typically larger than for temperature extremes, indicating greater uncertainty in the precipitation changes. In the global average, extremely wet days are predicted to become twice as common under 2 x CO2 conditions. We also consider changes in extreme seasons, finding that simulated increases in the frequency of extremely warm or wet seasons under 2 x CO2 are almost everywhere greater than the corresponding increase in daily extremes. The smaller increases in the frequency of daily extremes is explained by the influence of day-to-day weather variability which inflates the variance of daily distributions compared to their seasonal counterparts.}, - language = {English}, - number = {5}, - journal = {Climate Dynamics}, - author = {Barnett, D. N. and Brown, S. J. and Murphy, J. M. and Sexton, D. M. H. and Webb, M. J.}, - month = apr, - year = {2006}, - keywords = {climate change, precipitation, sensitivity, variability, uncertainty, events, extreme events, ensemble, change projections, circulation model, climate-change simulations, future climates, general, greenhouse gases, ocean-atmosphere model, parametrisation, perturbed physics, sea-ice, temperatures}, - pages = {489--511}, - annote = {ISI Document Delivery No.: 019VWTimes Cited: 25Cited Reference Count: 72SpringerNew york}, - annote = {The following values have no corresponding Zotero field:auth-address: Hadley Ctr Climate Predict \& Res, Exeter EX1 3PB, Devon, England. Murphy, JM, Hadley Ctr Climate Predict \& Res, FitzRoy Rd, Exeter EX1 3PB, Devon, England. james.murphy@metoffice.gov.ukalt-title: Clim. Dyn.accession-num: ISI:000235865800004work-type: Article}, -} - -@article{bardossy_generating_2001, - title = {Generating of areal precipitation series in the upper {Neckar} catchment}, - volume = {26}, - abstract = {A stochastic space-time model for generating daily precipitation series has been applied in the upper Neckar catchment (Germany). The stochastic model is downscaling precipitation from the atmospheric circulation. Automated, objective and optimized classification method has been used in order to get daily series of circulation patterns. The classification was done with special emphasis of precipitation extremes. The precipitation model is conditioned on atmospheric circulation patterns and uses an multivariate autoregressive AR(1) concept to reproduce the time persistence; the annual cycle is reproduced by using Fourier series. By computing spatial covariances the spatial structure of precipitation fields is taken into account. Comparison between observed. and simulated areal precipitation computed by means of external drift kriging shows that the simulated results agree fairly well with observed data (C) 2001 Published by Elsevier Science Ltd. All rights reserved.}, - number = {9}, - journal = {Physics and Chemistry of the Earth Part B-Hydrology Oceans and Atmosphere}, - author = {Bardossy, A. and Stehlik, J. and Caspary, H. J.}, - year = {2001}, - pages = {683--687}, - annote = {469ZXPHYS CHEM EARTH P B-HYDROL OC}, - annote = {The following values have no corresponding Zotero field:alt-title: Phys. Chem. Earth Pt B-Hydrol. Oceans Atmos.accession-num: ISI:000170846900008}, -} - -@article{ballester_future_2010, - title = {Future changes in {Central} {Europe} heat waves expected to mostly follow summer mean warming}, - volume = {35}, - issn = {0930-7575}, - doi = {10.1007/s00382-009-0641-5}, - number = {7}, - journal = {Climate Dynamics}, - author = {Ballester, Joan and Rodó, Xavier and Giorgi, Filippo}, - year = {2010}, - keywords = {Earth and Environmental Science}, - pages = {1191--1205}, - annote = {The following values have no corresponding Zotero field:publisher: Springer Berlin / Heidelberg}, -} - -@article{frich_observed_2002, - title = {Observed {Coherent} changes in climatic extremes during the second half of the twentieth century}, - volume = {19}, - journal = {Climate Research}, - author = {Frich, P. and Alexander, L. V. and Della-Marta, P. and Gleason, B. and Haylock, M. and Klein Tank, A. M. G. and Peterson, T. C.}, - year = {2002}, - pages = {193--212}, -} - -@article{fowler_linking_2007, - title = {Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling}, - volume = {27}, - doi = {doi:10.1002/joc.1556}, - abstract = {Abstract There is now a large published literature on the strengths and weaknesses of downscaling methods for different climatic variables, in different regions and seasons. However, little attention is given to the choice of downscaling method when examining the impacts of climate change on hydrological systems. This review paper assesses the current downscaling literature, examining new developments in the downscaling field specifically for hydrological impacts. Sections focus on the downscaling concept; new methods; comparative methodological studies; the modelling of extremes; and the application to hydrological impacts. Consideration is then given to new developments in climate scenario construction which may offer the most potential for advancement within the ‘downscaling for hydrological impacts’ community, such as probabilistic modelling, pattern scaling and downscaling of multiple variables and suggests ways that they can be merged with downscaling techniques in a probabilistic climate change scenario framework to assess the uncertainties associated with future projections. Within hydrological impact studies there is still little consideration given to applied research; how the results can be best used to enable stakeholders and managers to make informed, robust decisions on adaptation and mitigation strategies in the face of many uncertainties about the future. It is suggested that there is a need for a move away from comparison studies into the provision of decision-making tools for planning and management that are robust to future uncertainties; with examination and understanding of uncertainties within the modelling system. Copyright © 2007 Royal Meteorological Society}, - number = {12}, - journal = {International Journal of Climatology}, - author = {Fowler, H. J. and Blenkinsop, S. and Tebaldi, C.}, - year = {2007}, - pages = {1547--1578}, -} - -@incollection{folland_observed_2001, - address = {Cambridge}, - title = {Observed {Climate} {Variability} and {Change}}, - booktitle = {Climate {Change} 2001: {The} {Scientific} {Basis}}, - publisher = {Cambridge University Press}, - author = {Folland, C. K. and Karl, T. R. and Christy, J. R. and Clarke, R. A. and Gruza, G. V. and Jouzel, J. and Mann, M. E. and Oerlemans, J. and Salinger, M. J. and Wang, S.-W.}, - editor = {Houghton, J. T. and Ding, Y. and Griggs, D. J. and Noguer, M. and van der Linden, P. J. and Xiaosu, D.}, - year = {2001}, - pages = {99--181}, - annote = {The following values have no corresponding Zotero field:section: 2}, -} - -@incollection{folland_physical_2001, - address = {Cambridge}, - title = {Physical climate processxes and feedbacks}, - booktitle = {Climate {Change} 2001: {The} {Scientific} {Basis}}, - publisher = {Cambridge University Press}, - author = {Folland, C. K. and Karl, T. R.}, - editor = {Houghton, J. T.}, - year = {2001}, - pages = {99--181}, - annote = {The following values have no corresponding Zotero field:section: 2}, -} - -@article{fowler_multi-model_2009, - title = {Multi-model ensemble estimates of climate change impacts on {UK} seasonal precipitation extremes}, - volume = {29}, - number = {3}, - journal = {International Journal of Climatology}, - author = {Fowler, H. J. and Ekström, M.}, - year = {2009}, - pages = {385--416}, -} - -@article{fowler_using_2007, - title = {Using regional climate model data to simulate historical and future river flows in northwest {England}}, - volume = {80}, - issn = {0165-0009}, - doi = {10.1007/s10584-006-9117-3}, - number = {3}, - journal = {Climatic Change}, - author = {Fowler, H. and Kilsby, C.}, - year = {2007}, - keywords = {Earth and Environmental Science}, - pages = {337--367}, - annote = {The following values have no corresponding Zotero field:publisher: Springer Netherlands}, -} - -@article{c_fortier_climate_2014, - title = {Climate change impact on combined sewer overflows}, - journal = {J. Water Resour. Plann. Manage.}, - author = {C. Fortier and A. Mailhot}, - year = {2014}, -} - -@article{heinz_dieter_fill_estimating_2003, - title = {Estimating {Instantaneous} {Peak} {Flow} from {Mean} {Daily} {Flow} {Data}}, - volume = {8}, - doi = {doi:10.1061/(ASCE)1084-0699(2003)8:6(365)}, - number = {6}, - journal = {Journal of Hydrologic Engineering}, - author = {Heinz Dieter Fill and Alexandre Arns Steiner}, - year = {2003}, - pages = {365--369}, -} - -@article{ficklin_effects_2013, - title = {Effects of projected climate change on the hydrology in the {Mono} {Lake} {Basin}, {California}}, - volume = {116}, - issn = {0165-0009}, - doi = {10.1007/s10584-012-0566-6}, - language = {English}, - number = {1}, - journal = {Climatic Change}, - author = {Ficklin, D. L. and Stewart, I. T. and Maurer, E. P.}, - month = jan, - year = {2013}, - pages = {111--131}, - annote = {The following values have no corresponding Zotero field:alt-title: Climatic Changepublisher: Springer Netherlands}, -} - -@article{ficklin_projections_2012, - title = {Projections of 21st {Century} {Sierra} {Nevada} {Local} {Hydrologic} {Flow} {Components} {Using} an {Ensemble} of {General} {Circulation} {Models1}}, - volume = {48}, - issn = {1752-1688}, - doi = {10.1111/j.1752-1688.2012.00675.x}, - number = {6}, - journal = {JAWRA Journal of the American Water Resources Association}, - author = {Ficklin, D. L. and Stewart, I. T. and Maurer, E. P.}, - year = {2012}, - keywords = {precipitation, surface water hydrology, climate variability/change, evapotranspiration, hydrologic cycle, infiltration, snow hydrology}, - pages = {1104--1125}, - annote = {The following values have no corresponding Zotero field:publisher: Blackwell Publishing Ltd}, -} - -@article{ficklin_swat_2014, - title = {{SWAT} hydrologic model parameter uncertainty and its implications for hydroclimatic projections in snowmelt-dependent watersheds}, - volume = {519, Part B}, - issn = {0022-1694}, - doi = {http://dx.doi.org/10.1016/j.jhydrol.2014.09.082}, - journal = {Journal of Hydrology}, - author = {Ficklin, Darren L. and Barnhart, Bradley L.}, - year = {2014}, - keywords = {Climate change, Equifinality, Hydrologic modeling, SWAT, Watershed, Western United States}, - pages = {2081--2090}, -} - -@article{feddema_importance_2005, - title = {The {Importance} of {Land}-{Cover} {Change} in {Simulating} {Future} {Climates}}, - volume = {310}, - doi = {10.1126/science.1118160}, - abstract = {Adding the effects of changes in land cover to the A2 and B1 transient climate simulations described in the Special Report on Emissions Scenarios (SRES) by the Intergovernmental Panel on Climate Change leads to significantly different regional climates in 2100 as compared with climates resulting from atmospheric SRES forcings alone. Agricultural expansion in the A2 scenario results in significant additional warming over the Amazon and cooling of the upper air column and nearby oceans. These and other influences on the Hadley and monsoon circulations affect extratropical climates. Agricultural expansion in the mid-latitudes produces cooling and decreases in the mean daily temperature range over many areas. The A2 scenario results in more significant change, often of opposite sign, than does the B1 scenario.}, - number = {5754}, - journal = {Science}, - author = {Feddema, Johannes J. and Oleson, Keith W. and Bonan, Gordon B. and Mearns, Linda O. and Buja, Lawrence E. and Meehl, Gerald A. and Washington, Warren M.}, - month = dec, - year = {2005}, - pages = {1674--1678}, -} - -@article{favre_north_2009, - title = {North {Pacific} cyclonic and anticyclonic transients in a global warming context: possible consequences for {Western} {North} {American} daily precipitation and temperature extremes}, - volume = {32}, - number = {7-8}, - journal = {Climate Dynamics}, - author = {Favre, A. and Gershunov, A.}, - year = {2009}, - pages = {969--987, doi:10.1007/s00382--008--0417--3}, -} - -@article{ficklin_climate_2013, - title = {Climate {Change} {Impacts} on {Streamflow} and {Subbasin}-{Scale} {Hydrology} in the {Upper} {Colorado} {River} {Basin}}, - volume = {8}, - number = {8}, - journal = {PLoS ONE}, - author = {Ficklin, D. L. and Stewart, I. T. and Maurer, E. P.}, - year = {2013}, - pages = {e71297. doi: 10.1371/journal.pone.0071297}, -} - -@article{ficklin_sensitivity_2010, - title = {Sensitivity of agricultural runoff loads to rising levels of {CO2} and climate change in the {San} {Joaquin} {Valley} watershed of {California}}, - volume = {158}, - issn = {0269-7491}, - number = {1}, - journal = {Environmental Pollution}, - author = {Ficklin, D. L. and Luo, Y. and Luedeling, E. and Gatzke, S. E. and Zhang, M.}, - year = {2010}, - keywords = {Climate change, SWAT, Agricultural pollution, California, Pesticides, Watershed modeling}, - pages = {223--234}, - annote = {The following values have no corresponding Zotero field:work-type: doi: DOI: 10.1016/j.envpol.2009.07.016}, -} - -@article{ficklin_climate_2014, - title = {Climate change and stream temperature projections in the {Columbia} {River} basin: habitat implications of spatial variation in hydrologic drivers}, - volume = {18}, - issn = {1607-7938}, - doi = {10.5194/hess-18-4897-2014}, - number = {12}, - journal = {Hydrol. Earth Syst. Sci.}, - author = {Ficklin, D. L. and Barnhart, B. L. and Knouft, J. H. and Stewart, I. T. and Maurer, E. P. and Letsinger, S. L. and Whittaker, G. W.}, - year = {2014}, - pages = {4897--4912}, - annote = {HESS}, - annote = {The following values have no corresponding Zotero field:publisher: Copernicus Publications}, -} - -@article{feddersen_reduction_1999, - title = {Reduction of model systematic error by statistical correction for dynamical seasonal predictions}, - volume = {12}, - number = {7}, - journal = {Journal of Climate}, - author = {Feddersen, H. and Navarra, A. and Ward, M. N.}, - year = {1999}, - pages = {1974--1989}, -} - -@article{feddersen_impact_2003, - title = {Impact of tropical {SST} variations on the linear predictability of the atmospheric circulation in the {Atlantic}/{European} region}, - volume = {46}, - number = {1}, - journal = {Annals of Geophysics}, - author = {Feddersen, H.}, - year = {2003}, - pages = {109--124}, -} - -@article{fasullo_less_2012, - title = {A {Less} {Cloudy} {Future}: {The} {Role} of {Subtropical} {Subsidence} in {Climate} {Sensitivity}}, - volume = {338}, - doi = {10.1126/science.1227465}, - abstract = {An observable constraint on climate sensitivity, based on variations in mid-tropospheric relative humidity (RH) and their impact on clouds, is proposed. We show that the tropics and subtropics are linked by teleconnections that induce seasonal RH variations that relate strongly to albedo (via clouds), and that this covariability is mimicked in a warming climate. A present-day analog for future trends is thus identified whereby the intensity of subtropical dry zones in models associated with the boreal monsoon is strongly linked to projected cloud trends, reflected solar radiation, and model sensitivity. Many models, particularly those with low climate sensitivity, fail to adequately resolve these teleconnections and hence are identifiably biased. Improving model fidelity in matching observed variations provides a viable path forward for better predicting future climate.}, - number = {6108}, - journal = {Science}, - author = {Fasullo, John T. and Trenberth, Kevin E.}, - month = nov, - year = {2012}, - pages = {792--794}, -} - -@article{falvey_regional_2009, - title = {Regional cooling in a warming world: {Recent} temperature trends in the southeast {Pacific} and along the west coast of subtropical {South} {America} (1979\&\#8211;2006)}, - volume = {114}, - issn = {0148-0227}, - doi = {10.1029/2008jd010519}, - abstract = {While it is widely accepted that the global mean atmospheric temperature has increased in recent decades, the spatial distribution of global warming has been complex. In this study we comprehensively characterize the spatial pattern, including vertical structure, of temperature trends along the subtropical west coast of South America (continental Chile) for the period 1979\&\#8211;2006 and examine their consistency with expectations based on the CMIP-3 ensemble of coupled ocean-atmosphere simulations for the late 20th century. In central and northern Chile (17°\&\#8211;37°S) the most notable feature is a strong contrast between surface cooling at coastal stations (\&\#8722;0.2°C/decade) and warming in the Andes (+0.25°C/decade), only 100\&\#8211;200 km further inland. Coastal radiosonde data imply that the coast-Andes variation is largely due to strong vertical stratification of temperature trends in the atmosphere west of the Andes. The coastal cooling appears to form part of a larger-scale, La Niña-like pattern and may extend below the ocean mixed layer to depths of at least 500 m. Over continental Chile the CMIP-3 GCM ensemble predicts temperature trends similar to those observed in the Andes. The cooling along the Chilean coast is not reproduced by the models, but the mean SST warming is weaker there than any other part of the world except the Southern Ocean. It is proposed that the intensification of the South Pacific Anticyclone during recent decades, which is also a simulated consequence of global warming, is likely to play a major role in maintaining cooler temperatures off the coast of Chile.}, - number = {D4}, - journal = {J. Geophys. Res.}, - author = {Falvey, Mark and Garreaud, René D.}, - year = {2009}, - keywords = {climate change, 1616 Global Change: Climate variability, 1637 Global Change: Regional climate change, 1626 Global Change: Global climate models, 3339 Atmospheric Processes: Ocean/atmosphere interactions, southeast Pacific, temperature trends}, - pages = {D04102}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{eyring_sensitivity_2010, - title = {Sensitivity of 21st century stratospheric ozone to greenhouse gas scenarios}, - volume = {37}, - issn = {0094-8276}, - doi = {10.1029/2010gl044443}, - abstract = {To understand how greenhouse gas (GHG) emissions may affect future stratospheric ozone, 21st century projections from four chemistry-climate models are examined for their dependence on six different GHG scenarios. Compared to higher GHG emissions, lower emissions result in smaller increases in tropical upwelling with resultant smaller reductions in ozone in the tropical lower stratosphere and less severe stratospheric cooling with resultant smaller increases in upper stratospheric ozone globally. Increases in reactive nitrogen and hydrogen that lead to additional chemical ozone destruction mainly play a role in scenarios with higher GHG emissions. Differences among the six GHG scenarios are found to be largest over northern midlatitudes (\&\#8764;20 DU by 2100) and in the Arctic (\&\#8764;40 DU by 2100) with divergence mainly in the second half of the 21st century. The uncertainty in the return of stratospheric column ozone to 1980 values arising from different GHG scenarios is comparable to or less than the uncertainty that arises from model differences in the larger set of 17 CCMVal-2 SRES A1B simulations. The results suggest that effects of GHG emissions on future stratospheric ozone should be considered in climate change mitigation policy and ozone projections should be assessed under more than a single GHG scenario.}, - number = {16}, - journal = {Geophysical Research Letters}, - author = {Eyring, V. and Cionni, I. and Lamarque, J. F. and Akiyoshi, H. and Bodeker, G. E. and Charlton-Perez, A. J. and Frith, S. M. and Gettelman, A. and Kinnison, D. E. and Nakamura, T. and Oman, L. D. and Pawson, S. and Yamashita, Y.}, - year = {2010}, - keywords = {climate change, 0325 Atmospheric Composition and Structure: Evolution of the atmosphere, 0340 Atmospheric Composition and Structure: Middle atmosphere: composition and chemistry, 0341 Atmospheric Composition and Structure: Middle atmosphere: constituent transport and chemistry, chemistry-climate models, stratospheric ozone projections}, - pages = {L16807}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{ehret_hess_2012, - title = {{HESS} {Opinions} "{Should} we apply bias correction to global and regional climate model data?"}, - volume = {16}, - journal = {Hydrol. Earth Syst. Sci.}, - author = {Ehret, U. and Zehe, E. and Wulfmeyer, V. and Warrach-Sagi, K. and Liebert, J.}, - year = {2012}, - pages = {3391--3404, doi:10.5194/hess--16--3391--2012}, -} - -@article{falvey_wintertime_2007, - title = {Wintertime {Precipitation} {Episodes} in {Central} {Chile}: {Associated} {Meteorological} {Conditions} and {Orographic} {Influences}}, - volume = {8}, - issn = {1525-755X}, - doi = {10.1175/jhm562.1}, - number = {2}, - journal = {Journal of Hydrometeorology}, - author = {Falvey, Mark and Garreaud, René}, - month = apr, - year = {2007}, - pages = {171--193}, - annote = {doi: 10.1175/JHM562.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/JHM562.1}, -} - -@article{eden_skill_2012, - title = {Skill, {Correction}, and {Downscaling} of {GCM}-{Simulated} {Precipitation}}, - volume = {25}, - issn = {0894-8755}, - doi = {10.1175/jcli-d-11-00254.1}, - number = {11}, - journal = {Journal of Climate}, - author = {Eden, Jonathan M. and Widmann, Martin and Grawe, David and Rast, Sebastian}, - month = jun, - year = {2012}, - pages = {3970--3984}, - annote = {doi: 10.1175/JCLI-D-11-00254.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/JCLI-D-11-00254.1}, -} - -@article{eden_downscaling_2014, - title = {Downscaling of {GCM}-{Simulated} {Precipitation} {Using} {Model} {Output} {Statistics}}, - volume = {27}, - issn = {0894-8755}, - doi = {10.1175/jcli-d-13-00063.1}, - number = {1}, - journal = {Journal of Climate}, - author = {Eden, Jonathan M. and Widmann, Martin}, - month = jan, - year = {2014}, - pages = {312--324}, - annote = {doi: 10.1175/JCLI-D-13-00063.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/JCLI-D-13-00063.1}, -} - -@article{easterling_climate_2000, - title = {Climate extremes: {Observations}, modeling, and impacts}, - volume = {289}, - abstract = {One of the major concerns with a potential change in climate is that an increase in extreme events will occur. Results of observational studies suggest that in many areas That have been analyzed, changes in total precipitation are amplified at the tails, and changes in some temperature extremes have been observed. Model output has been analyzed that shows changes in extreme events for future climates, such as increases in extreme high temperatures. decreases in extreme Low temperatures, and increases in intense precipitation events. In addition, the societal infrastructure is becoming more sensitive to weather and climate extremes, which would be exacerbated by climate change. In wild plants and animals, climate-induced extinctions, distributional and phenological changes, and species' range shifts are being documented at an increasing rate. Several apparently gradual biological changes are Linked to responses to extreme weather and climate events.}, - number = {5487}, - journal = {Science}, - author = {Easterling, D. R. and Meehl, G. A. and Parmesan, C. and Changnon, S. A. and Karl, T. R. and Mearns, L. O.}, - month = sep, - year = {2000}, - pages = {2068--2074}, - annote = {356EGSCIENCE}, - annote = {The following values have no corresponding Zotero field:alt-title: Scienceaccession-num: ISI:000089430900033}, -} - -@article{easterling_cciclivar_2003, - title = {{CCI}/{CLIVAR} {Workshop} to {Develop} {Priority} {Climate} {Indices}}, - volume = {84}, - doi = {doi:10.1175/BAMS-84-10-1403}, - number = {10}, - journal = {Bulletin of the American Meteorological Society}, - author = {Easterling, David R. and Alexander, Lisa V. and Mokssit, Abdallah and Detemmerman, Valery}, - year = {2003}, - pages = {1403--1407}, -} - -@article{easterling_development_1999, - title = {Development of regional climate scenarios using a downscaling approach}, - volume = {41}, - abstract = {As the debate on potential climate change continues, it is becoming increasingly clear that the main concerns to the general public are the potential impacts of a change in the climate on societal and biophysical systems. In order to address these concerns researchers need realistic, plausible scenarios of climate change suitable for use in impacts analysis. It is the purpose of this paper to present a downscaling method useful for developing these types of scenarios that are grounded in both General Circulation Model simulations of climate change, and in situ station data. Free atmosphere variables for four gridpoints over the Missouri, Iowa, Nebraska, Kansas (MINK) region from both control and transient simulations from the GFDL General Circulation Model were used with thirty years of nearby station data to generate surface maximum and minimum air temperatures and precipitation. The free atmosphere variables were first subject to a principal components analysis with the principal component (PC) scores used in a multiple regression to relate the upper-air variables to surface temperature and precipitation. Coefficients from the regression on station data were then used with PC scores from the model simulations to generate maximum and minimum temperature and precipitation. The statistical distributions of the downscaled temperatures and precipitation for the control run are compared with those from the observed station data. Results for the transient run are then examined. Lastly, annual time series of temperature for the downscaling results show less warming over the period of the transient simulation than the time series produced directly from the model.}, - number = {3-4}, - journal = {Clim. Change}, - author = {Easterling, D. R.}, - month = mar, - year = {1999}, - pages = {615--634}, - annote = {194JMCLIMATIC CHANGE}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Changeaccession-num: ISI:000080189100015}, -} - -@article{early_analysis_2011, - title = {Analysis of climate paths reveals potential limitations on species range shifts}, - volume = {14}, - issn = {1461-0248}, - doi = {10.1111/j.1461-0248.2011.01681.x}, - number = {11}, - journal = {Ecology Letters}, - author = {Early, Regan and Sax, Dov F.}, - year = {2011}, - keywords = {Anura, assisted migration, climate variability, dispersal, fundamental and realised niche, landscape ecology, population persistence, salamander, translocation}, - pages = {1125--1133}, - annote = {The following values have no corresponding Zotero field:publisher: Blackwell Publishing Ltd}, -} - -@article{draper_economic-engineering_2003, - title = {Economic-engineering optimization for {California} water management}, - volume = {129}, - issn = {0733-9496}, - abstract = {An economic-engineering optimization model of California's major water supply system is presented. The model's development, calibration, limitations, and results are reviewed. The major methodological conclusions are that large-scale water resources optimization models driven by economic objective functions are both possible and practical; deterministic models are useful despite their limitations; and data management, reconciliation, and documentation are important benefits of large-scale system modeling. Specific results for California indicate a great potential for water markets and conjunctive use to improve economic performance and significant economic value for expanding some conveyance facilities. Overall, economic-engineering optimization (even if deterministic) can suggest a variety of promising approaches for managing large systems. These approaches can then be refined and tested using more detailed simulation models. The process of developing large-scale models also motivates the systematic and integrated treatment of surface water, groundwater, facility, and water demand data, and identification of particularly important data problems, something of long-term value for all types of water resources analysis.}, - language = {English}, - number = {3}, - journal = {Journal of Water Resources Planning and Management-Asce}, - author = {Draper, A. J. and Jenkins, M. W. and Kirby, K. W. and Lund, J. R. and Howitt, R. E.}, - month = jun, - year = {2003}, - keywords = {system, california, economic factors, optimization, water management}, - pages = {155--164}, - annote = {669CRTimes Cited:6Cited References Count:23}, - annote = {The following values have no corresponding Zotero field:auth-address: Draper, AJ Dept Water Resources, Sacramento, CA 94236 USA Dept Water Resources, Sacramento, CA 94236 USA Univ Calif Davis, Dept Civil \& Environm Engn, Davis, CA 95616 USA Saracino Kirby Snow Water Management, Sacramento, CA 95814 USA Univ Calif Davis, Dept Agr \& Resource Econ, Davis, CA 95616 USAaccession-num: ISI:000182334800002}, -} - -@incollection{downing_chapter_2001, - address = {Cambridge, United Kingdom}, - title = {Chapter 2. {Methods} and tools}, - booktitle = {Climate {Change} 2001: {Working} {Group} {II}: {Impacts}, {Adaptation} and {Vulnerability}}, - publisher = {Cambridge University Press}, - author = {Downing, T. E. and Nishioka, S. and Parikh, K. S. and Parmesan, C. and Schneider, S. H. and Toth, F. and Yohe, G.}, - editor = {McCarthy, J. J. and Canziani, O. F. and Leary, N. A. and Dokken, D. J. and White, K. S.}, - year = {2001}, - pages = {105--143}, -} - -@article{dos_santos_trends_2011, - title = {Trends in indices for extremes in daily temperature and precipitation over {Utah}, {USA}}, - volume = {31}, - issn = {1097-0088}, - doi = {10.1002/joc.2205}, - number = {12}, - journal = {International Journal of Climatology}, - author = {dos Santos, Carlos A. C. and Neale, Christopher M. U. and Rao, Tantravahi V. R. and da Silva, Bernardo B.}, - year = {2011}, - keywords = {climate change, global warming, climatology, environmental impact, quality control, RClimdex}, - pages = {1813--1822}, - annote = {The following values have no corresponding Zotero field:publisher: John Wiley \& Sons, Ltd.}, -} - -@article{dubrovsky_creating_1997, - title = {Creating daily weather series with use of the weather generator}, - volume = {8}, - abstract = {Estimation of quantitative impacts of potential climate change on environment and various aspects of human existence requires high-resolution surface weather data. Since the direct output from general circulation models (GCMs) is unreliable at the local scale, alternative approaches - most frequently based on statistical techniques - should be used to downscale coarsely resolved GCM output patterns to finer spatial and/or temporal resolution. The downscaling techniques are briefly reviewed in the paper. Two of the approaches were followed in developing two versions of the stochastic weather generator (WG) called Met\&Roll: (i) generation of synthetic weather series by generator with parameters derived from the local observed series and then modified according to the climate change scenario that prescribes monthly increments of the individual variables; (ii) downscaling the GCM-simulated daily circulation pattern, using statistical linkage between the circulation patterns and the surface weather characteristics. Met\&Roll-1 is a four-variate surface weather generator which employs a Markov chain approach to model precipitation occurrence and an autoregressive model to simulate the solar radiation and the diurnal extreme temperatures. The validation of the generator is performed by comparison of the stochastic structure of observed and synthetic series. Uncertainties in projecting the climate change scenario into the parameters of the WG are discussed. Met\&Roll-2 is a generator which links the four surface weather variables with upper-air circulation patterns (CPs). CPs are characterized by principal components derived from 500 hPa geopotential field. The series of CPs is either generated by autoregressive model or taken from the GCM output. The first test of this generator is focused on the correlation between CPs and surface weather characteristics. (C) 1997 John Wiley \& Sons, Ltd.}, - number = {5}, - journal = {Environmetrics}, - author = {Dubrovsky, M.}, - month = oct, - year = {1997}, - pages = {409--424}, - annote = {The following values have no corresponding Zotero field:alt-title: Environmetricsaccession-num: ISI:A1997YA14500005}, - annote = {YA145ENVIRONMETRICS}, -} - -@article{doutriaux_climate_2009, - title = {Climate {Data} {Analysis} {Tools} {Facilitate} {Scientific} {Investigations}}, - volume = {90}, - issn = {2324-9250}, - doi = {10.1029/2009eo350002}, - number = {35}, - journal = {Eos, Transactions American Geophysical Union}, - author = {Doutriaux, Charles and Drach, Robert and McCoy, Renata and Mlaker, Velimir and Williams, Dean}, - year = {2009}, - keywords = {1616 Climate variability, 1694 Instruments and techniques, CDAT, Climate Data Analysis}, - pages = {297--298}, -} - -@article{domonkos_variability_2003, - title = {Variability of extreme temperature events in south–central {Europe} during the 20th century and its relationship with large-scale circulation}, - volume = {23}, - issn = {1097-0088}, - doi = {10.1002/joc.929}, - number = {9}, - journal = {International Journal of Climatology}, - author = {Domonkos, Peter and Kyselý, Jan and Piotrowicz, Katarzyna and Petrovic, Predrag and Likso, Tanja}, - year = {2003}, - keywords = {central Europe, climate change detection, extreme temperature event, southern Europe, temperature–circulation relationship}, - pages = {987--1010}, - annote = {The following values have no corresponding Zotero field:publisher: John Wiley \& Sons, Ltd.}, -} - -@article{dominguez_ipcc-ar4_2010, - title = {{IPCC}-{AR4} climate simulations for the {Southwestern} {US}: the importance of future {ENSO} projections}, - volume = {99}, - issn = {0165-0009}, - doi = {10.1007/s10584-009-9672-5}, - number = {3}, - journal = {Climatic Change}, - author = {Dominguez, Francina and Cañon, Julio and Valdes, Juan}, - year = {2010}, - keywords = {Earth and Environmental Science}, - pages = {499--514}, - annote = {The following values have no corresponding Zotero field:publisher: Springer Netherlands}, -} - -@article{dobler_quantifying_2012, - title = {Quantifying different sources of uncertainty in hydrological projections in an {Alpine} watershed}, - volume = {16}, - issn = {1607-7938}, - doi = {10.5194/hess-16-4343-2012}, - number = {11}, - journal = {Hydrol. Earth Syst. Sci.}, - author = {Dobler, C. and Hagemann, S. and Wilby, R. L. and Stötter, J.}, - year = {2012}, - pages = {4343--4360}, - annote = {HESS}, - annote = {The following values have no corresponding Zotero field:publisher: Copernicus Publications}, -} - -@article{donat_updated_2013, - title = {Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: {The} {HadEX2} dataset}, - volume = {118}, - issn = {2169-8996}, - doi = {10.1002/jgrd.50150}, - number = {5}, - journal = {Journal of Geophysical Research: Atmospheres}, - author = {Donat, M. G. and Alexander, L. V. and Yang, H. and Durre, I. and Vose, R. and Dunn, R. J. H. and Willett, K. M. and Aguilar, E. and Brunet, M. and Caesar, J. and Hewitson, B. and Jack, C. and Klein Tank, A. M. G. and Kruger, A. C. and Marengo, J. and Peterson, T. C. and Renom, M. and Oria Rojas, C. and Rusticucci, M. and Salinger, J. and Elrayah, A. S. and Sekele, S. S. and Srivastava, A. K. and Trewin, B. and Villarroel, C. and Vincent, L. A. and Zhai, P. and Zhang, X. and Kitching, S.}, - year = {2013}, - keywords = {precipitation, temperature, 1610 Atmosphere, 4313 Extreme events, observations, 3305 Climate change and variability, 1616 Climate variability, 3309 Climatology, climate extremes, global gridded dataset}, - pages = {2098--2118}, -} - -@techreport{dga_balance_1987, - address = {Santiago, Chile}, - title = {Balance hidrico de {Chile}}, - institution = {Ministerio de Obras Publicas, Direccion General de Aguas}, - author = {DGA}, - year = {1987}, -} - -@article{dettinger_simulated_2004, - title = {Simulated hydrologic responses to climate variations and change in the {Merced}, {Carson}, and {American} {River} basins, {Sierra} {Nevada}, {California}, 1900-2099}, - volume = {62}, - issn = {0165-0009}, - abstract = {Hydrologic responses of river basins in the Sierra Nevada of California to historical and future climate variations and changes are assessed by simulating daily streamflow and water-balance responses to simulated climate variations over a continuous 200-yr period. The coupled atmosphere-ocean-ice-land Parallel Climate Model provides the simulated climate histories, and existing hydrologic models of the Merced, Carson, and American Rivers are used to simulate the basin responses. The historical simulations yield stationary climate and hydrologic variations through the first part of the 20th century until about 1975 when temperatures begin to warm noticeably and when snowmelt and streamflow peaks begin to occur progressively earlier within the seasonal cycle. A future climate simulated with business-as-usual increases in greenhouse-gas and aerosol radiative forcings continues those recent trends through the 21st century with an attendant + 2.5 degreesC warming and a hastening of snowmelt and streamflow within the seasonal cycle by almost a month. The various projected trends in the business-as-usual simulations become readily visible despite realistic simulated natural climatic and hydrologic variability by about 2025. In contrast to these changes that are mostly associated with streamflow timing, long-term average totals of streamflow and other hydrologic fluxes remain similar to the historical mean in all three simulations. A control simulation in which radiative forcings are held constant at 1995 levels for the 50 years following 1995 yields climate and streamflow timing conditions much like the 1980s and 1990s throughout its duration. The availability of continuous climate-change projection outputs and careful design of initial conditions and control experiments, like those utilized here, promise to improve the quality and usability of future climate-change impact assessments.}, - language = {English}, - number = {1-3}, - journal = {Climatic Change}, - author = {Dettinger, M. D. and Cayan, D. R. and Meyer, M. and Jeton, A. E.}, - month = feb, - year = {2004}, - keywords = {model, system, impact, ncar ccm3, sacramento basin}, - pages = {283--317}, - annote = {768FATimes Cited:9Cited References Count:43}, - annote = {The following values have no corresponding Zotero field:auth-address: Dettinger, MD Univ Calif San Diego, Scripps Inst Oceanog, US Geol Survey, Dept 0224, 9500 Gilman Dr, La Jolla, CA 92093 USA Univ Calif San Diego, Scripps Inst Oceanog, US Geol Survey, Dept 0224, La Jolla, CA 92093 USA Univ Calif San Diego, Scripps Inst Oceanog, Div Climate Res, San Diego, CA 92103 USA US Geol Survey, Carson City, NV USAaccession-num: ISI:000188531900012}, -} - -@article{dettinger_climate_2016, - title = {Climate {Change} and the {Delta}}, - volume = {14}, - abstract = {doi: http://dx.doi.org/10.15447/sfews.2016v14iss3art6 Anthropogenic climate change amounts to a rapidly approaching, “new” stressor in the Sacramento–San Joaquin Delta system. In response to California’s extreme natural hydroclimatic variability, complex water-management systems have been developed, even as the Delta’s natural ecosystems have been largely devastated. Climate change is projected to challenge these management and ecological systems in different ways that are characterized by different levels of uncertainty. For example, there is high certainty that climate will warm by about 2°C more (than late-20th-century averages) by mid-century and about 4°C by end of century, if greenhouse-gas emissions continue their current rates of acceleration. Future precipitation changes are much less certain, with as many climate models projecting wetter conditions as drier. However, the same projections agree that precipitation will be more intense when storms do arrive, even as more dry days will separate storms. Warmer temperatures will likely enhance evaporative demands and raise water temperatures. Consequently, climate change is projected to yield both more extreme flood risks and greater drought risks. Sea level rise (SLR) during the 20th century was about 22 cm, and is projected to increase by at least 3-fold this century. SLR together with land subsidence threatens the Delta with greater vulnerabilities to inundation and salinity intrusion. Effects on the Delta ecosystem that are traceable to warming include SLR, reduced snowpack, earlier snowmelt and larger storm-driven streamflows, warmer and longer summers, warmer summer water temperatures, and water-quality changes. These changes and their uncertainties will challenge the operations of water projects and uses throughout the Delta’s watershed and delivery areas. Although the effects of of climate change on Delta ecosystems may be profound, the end results are difficult to predict, except that native species will fare worse than invaders. Successful preparation for the coming changes will require greater integration of monitoring, modeling, and decision making across time, variables, and space than has been historically normal.}, - number = {3}, - journal = {San Francisco Estuary and Watershed Science}, - author = {Dettinger, Michael and Anderson, Jamie and Anderson, Michael and Brown, Larry and Cayan, Daniel and Maurer, Edwin}, - year = {2016}, -} - -@article{diodato_interpolation_2005, - title = {Interpolation processes using multivariate geostatistics for mapping of climatological precipitation mean in the {Sannio} {Mountains} (southern {Italy})}, - volume = {30}, - number = {3}, - journal = {Earth Surface Processes and Landforms}, - author = {Diodato, N. and Ceccarelli, M.}, - year = {2005}, - pages = {259--268}, -} - -@article{diffenbaugh_anthropogenic_2015, - title = {Anthropogenic warming has increased drought risk in {California}}, - volume = {112}, - issn = {0027-8424}, - number = {13}, - journal = {Proceedings of the National Academy of Sciences}, - author = {Diffenbaugh, Noah S. and Swain, Daniel L. and Touma, Danielle}, - year = {2015}, - pages = {3931--3936}, -} - -@article{diffenbaugh_climate_2012, - title = {Climate change hotspots in the {CMIP5} global climate model ensemble}, - volume = {114}, - issn = {0165-0009}, - doi = {10.1007/s10584-012-0570-x}, - language = {English}, - number = {3-4}, - journal = {Climatic Change}, - author = {Diffenbaugh, NoahS and Giorgi, Filippo}, - month = oct, - year = {2012}, - pages = {813--822}, - annote = {The following values have no corresponding Zotero field:alt-title: Climatic Changepublisher: Springer Netherlands}, -} - -@article{dettinger_climate-change_2005, - title = {From climate-change spaghetti to climate-change distributions for 21st century {California}}, - volume = {3}, - number = {1}, - journal = {San Francisco Estuary \& Watershed Science}, - author = {Dettinger, M. D.}, - year = {2005}, - pages = {1--14}, -} - -@article{dettinger_winter_2004, - title = {Winter orographic precipitation ratios in the {Sierra} {Nevada} - {Large}-scale atmospheric circulations and hydrologic consequences}, - volume = {5}, - issn = {1525-755X}, - abstract = {The extent to which winter precipitation is orographically enhanced within the Sierra Nevada of California varies from storm to storm, and season to season, from occasions when precipitation rates at low and high altitudes are almost the same to instances when precipitation rates at middle elevations ( considered here) can be as much as 30 times more than at the base of the range. Analyses of large-scale conditions associated with orographic precipitation variations during storms and seasons from 1954 to 1999 show that strongly orographic storms most commonly have winds that transport water vapor across the range from a more nearly westerly direction than during less orographic storms and than during the largest overall storms, and generally the strongly orographic storms are less convectively stable. Strongly orographic conditions often follow heavy precipitation events because both of these wind conditions are present in midlatitude cyclones that form the cores of many Sierra Nevada storms. Storms during La Nina winters tend to yield larger orographic ratios (ORs) than do those during El Ninos. A simple experiment with a model of streamflows from a river basin draining the central Sierra Nevada indicates that, for a fixed overall basin-precipitation amount, a decrease in OR contributes to larger winter flood peaks and smaller springtime flows, and thus to an overall hastening of the runoff season.}, - language = {English}, - number = {6}, - journal = {Journal of Hydrometeorology}, - author = {Dettinger, M. and Redmond, K. and Cayan, D.}, - month = dec, - year = {2004}, - keywords = {model, california, caljet, climate, coast, flow, fronts, mountains, oscillation, rain}, - pages = {1102--1116}, - annote = {885VATimes Cited:0Cited References Count:33}, - annote = {The following values have no corresponding Zotero field:auth-address: Dettinger, M Univ Calif San Diego, Scripps Inst Oceanog, US Geol Survey, Dept 0224, 9500 Gilman Dr, La Jolla, CA 92093 USA Univ Calif San Diego, Scripps Inst Oceanog, US Geol Survey, Dept 0224, La Jolla, CA 92093 USA Univ Nevada, Desert Res Inst, Western Reg Climate Ctr, Reno, NV 89506 USAaccession-num: ISI:000226184000007}, -} - -@article{dettinger_component-resampling_2006, - title = {A {Component}-{Resampling} {Approach} for {Estimating} {Probability} {Distributions} from {Small} {Forecast} {Ensembles}}, - volume = {76}, - issn = {0165-0009}, - doi = {10.1007/s10584-005-9001-6}, - number = {1}, - journal = {Climatic Change}, - author = {Dettinger, Michael}, - year = {2006}, - keywords = {Earth and Environmental Science}, - pages = {149--168}, - annote = {The following values have no corresponding Zotero field:publisher: Springer Netherlands}, -} - -@article{dessai_assessing_2007, - title = {Assessing the robustness of adaptation decisions to climate change uncertainties: {A} case study on water resources management in the {East} of {England}}, - volume = {17}, - issn = {0959-3780}, - number = {1}, - journal = {Global Environmental Change}, - author = {Dessai, S. and Hulme, M.}, - year = {2007}, - keywords = {Climate change, Adaptation, East of England, Robustness, Sensitivity analysis, Uncertainty, Water resources}, - pages = {59--72}, - annote = {The following values have no corresponding Zotero field:work-type: doi: 10.1016/j.gloenvcha.2006.11.005}, -} - -@article{demaria_climate_2013, - title = {Climate change impacts on an alpine watershed in {Chile}: {Do} new model projections change the story?}, - volume = {502}, - issn = {0022-1694}, - doi = {http://dx.doi.org/10.1016/j.jhydrol.2013.08.027}, - number = {0}, - journal = {Journal of Hydrology}, - author = {Demaria, E. M. C. and Maurer, E. P. and Thrasher, B. and Vicuña, S. and Meza, F. J.}, - year = {2013}, - keywords = {Climate change, Precipitation, Hydrological modeling, Snow melting, Streamflow frequency analysis}, - pages = {128--138}, -} - -@article{demaria_using_2013, - title = {Using a {Gridded} {Global} {Dataset} to {Characterize} {Regional} {Hydroclimate} in {Central} {Chile}}, - volume = {14}, - issn = {1525-755X}, - doi = {10.1175/jhm-d-12-047.1}, - number = {1}, - journal = {Journal of Hydrometeorology}, - author = {Demaria, E. M. C. and Maurer, E. P. and Sheffield, J. and Bustos, E. and Poblete, D. and Vicuña, S. and Meza, F.}, - month = feb, - year = {2013}, - pages = {251--265}, - annote = {doi: 10.1175/JHM-D-12-047.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/JHM-D-12-047.1}, -} - -@article{delworth_gfdls_2006, - title = {{GFDL}'s {CM2} global coupled climate models - {Part} 1: {Formulation} and simulation characteristics}, - volume = {19}, - number = {5}, - journal = {Journal of Climate}, - author = {Delworth, T. L. and Broccoli, A. J. and Rosati, A. and Stouffer, R. J. and Balaji, V. and Beesley, J. A. and Cooke, W. F. and Dixon, K. W. and Dunne, J. and Dunne, K. A. and Durachta, J. W. and Findell, K. L. and Ginoux, P. and Gnanadesikan, A. and Gordon, C. T. and Griffies, S. M. and Gudgel, R. and Harrison, M. J. and Held, I. M. and Hemler, R. S. and Horowitz, L. W. and Klein, S. A. and Knutson, T. R. and Kushner, P. J. and Langenhorst, A. R. and Lee, H.-C. and Lin, S.-J. and Lu, J. and Malyshev, S. L. and Milly, P. C. D. and Ramaswamy, V. and Russell, J. and Schwarzkopf, M. D. and Shevliakova, E. and Sirutis, J. J. and Spelman, M. J. and Stern, W. F. and Winton, M. and Wittenberg, A. T. and Wyman, B. and Zeng, F. and Zhang, R.}, - year = {2006}, - pages = {643--674}, -} - -@article{deidda_multifractal_1999, - title = {Multifractal modeling of anomalous scaling laws in rainfall}, - volume = {35}, - abstract = {The coupling of hydrological distributed models to numerical weather prediction outputs is an important issue for hydrological applications such as forecasting of flood events. Downscaling meteorological predictions to the hydrological scales requires the resolution of two fundamental issues regarding precipitation, namely, (1) understanding the statistical properties and scaling laws of rainfall fields and (2) validation of downscaling models that are able to preserve statistical characteristics observed in real precipitation. In this paper we discuss the first issue by introducing a new multifractal model that appears particularly suitable for random generation of synthetic rainfall. We argue that the results presented in this paper may be also useful for the solution of the second question. Statistical behavior of rainfall in time is investigated through a high-resolution time series recorded in Geneva (Italy). The multifractal analysis shows the presence of a temporal threshold, localized around 15-20 hours, which separates two ranges of anomalous scaling laws. Synthetic time series, characterized by very similar scaling laws to the observed one, are generated with the multifractal model. The potential of the model for extreme rainfall event distributions is also discussed. The multifractal analysis of Global Atmospheric Research Program (GARP) Atlantic Tropical Experiment (GATE) radar fields have shown that statistical properties of rainfall in space depend on time durations over which precipitation is accumulated. Further analysis of some rainfall fields produced with a meteorological limited area model exhibited the same anomalous scaling as the GATE fields.}, - number = {6}, - journal = {Water Resources Research}, - author = {Deidda, R. and Benzi, R. and Siccardi, F.}, - month = jun, - year = {1999}, - pages = {1853--1867}, - annote = {198ZCWATER RESOUR RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Water Resour. Res.accession-num: ISI:000080453800015}, -} - -@article{dehn_impact_2000, - title = {Impact of climate change on slope stability using expanded downscaling}, - volume = {55}, - abstract = {Climate parameters affecting ground water and pore pressure fluctuations can, in many cases, trigger slope instability and hence landslide activity. Global warming due to the greenhouse effect and especially changes in precipitation patterns and air temperature might therefore have influences on future landslide activity. The present paper shows an assessment of climate change consequences for displacement rates of a mudslide in the Dolomites, Italy. The study is based on climate projections of a general circulation model (GCM). GCMs are able to successfully reproduce large-scale patterns of climate, while they show a poor performance on the regional scale. Therefore, GCM output is postprocessed with a statistical downscaling technique to derive local-scale climate change information from simulated atmospheric circulation patterns of the European- North Atlantic sector. The resulting precipitation and temperature series are introduced in a hydrological tank model, which calculates daily groundwater levels. Based on the groundwater data, a visco-plastic rheological model is applied to derive displacement rates of the mudslide as final output. The climate change signal is most pronounced for air temperature, while it is weaker but still significant for yearly precipitation, which is decreasing. As a consequence, yearly displacement rates show a significant reduction. The most dramatic changes, however, occur in spring with strongly lowered groundwater levels and consequently decreasing displacement rates. This is seen as an effect of reduced storage of winter precipitation as snow and hence decreasing meltwater amounts in early spring. The presented model chain with statistical downscaling, hydrological and rheological models allows the assessment of future landslide displacement affected by the greenhouse effect. The results, however, have to be taken with caution since in all parts of the model chain there are uncertainties that are difficult to address. (C) 2000 Elsevier Science B.V. All rights reserved.}, - number = {3}, - journal = {Engineering Geology}, - author = {Dehn, M. and Burger, G. and Buma, J. and Gasparetto, P.}, - month = feb, - year = {2000}, - pages = {193--204}, - annote = {300LEENG GEOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Eng. Geol.accession-num: ISI:000086253800005}, -} - -@article{deser_communication_2012, - title = {Communication of the role of natural variability in future {North} {American} climate}, - volume = {2}, - issn = {1758-678X}, - doi = {http://www.nature.com/nclimate/journal/v2/n11/abs/nclimate1562.html#supplementary-information}, - number = {11}, - journal = {Nature Clim. Change}, - author = {Deser, Clara and Knutti, Reto and Solomon, Susan and Phillips, Adam S.}, - year = {2012}, - pages = {775--779}, - annote = {10.1038/nclimate1562}, - annote = {The following values have no corresponding Zotero field:publisher: Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.work-type: 10.1038/nclimate1562}, -} - -@article{deque_global_2005, - title = {Global high resolution versus {Limited} {Area} {Model} climate change projections over {Europe}: quantifying confidence level from {PRUDENCE} results}, - volume = {25}, - issn = {0930-7575}, - doi = {10.1007/s00382-005-0052-1}, - number = {6}, - journal = {Climate Dynamics}, - author = {Déqué, M. and Jones, R. and Wild, M. and Giorgi, F. and Christensen, J. and Hassell, D. and Vidale, P. and Rockel, B. and Jacob, D. and Kjellström, E. and Castro, M. and Kucharski, F. and Hurk, B.}, - year = {2005}, - keywords = {Earth and Environmental Science}, - pages = {653--670}, - annote = {The following values have no corresponding Zotero field:publisher: Springer Berlin / Heidelberg}, -} - -@article{de_goncalves_evaluation_2006, - title = {Evaluation of model-derived and remotely sensed precipitation products for continental {South} {America}}, - volume = {111}, - issn = {0148-0227}, - doi = {10.1029/2005jd006276}, - abstract = {This paper investigates the reliability of some of the more important remotely sensed daily precipitation products available for South America as a precursor to the possible implementation of a South America Land Data Assimilation System. Precipitation data fields calculated as 6 hour predictions by the CPTEC Eta model and three different satellite-derived estimates of precipitation (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), National Environmental Satellite, Data and Information Service (NESDIS), and Tropical Rainfall Measuring Mission (TRMM)) are compared with the available observations of daily total rainfall across South America. To make this comparison, the threat score, fractional-covered area, and relative volumetric bias of the model-calculated and remotely sensed estimates are computed for the year 2000. The results show that the Eta model-calculated data and the NESDIS product capture the area without precipitation within the domain reasonably well, while the TRMM and PERSIANN products tend to underestimate the area without precipitation and to heavily overestimate the area with a small amount of precipitation. In terms of precipitation amount the NESDIS product significantly overestimates and the TRMM product significantly underestimates precipitation, while the Eta model-calculated data and PERSIANN product broadly match the domain average observations. However, both tend to bias the zonal location of precipitation more heavily toward the equator than the observations. In general, the Eta model-calculated data outperform the several remotely sensed data products currently available and evaluated in the present study.}, - number = {D16}, - journal = {J. Geophys. Res.}, - author = {de Goncalves, L. Gustavo Goncalves and Shuttleworth, W. James and Nijssen, Bart and Burke, Eleanor J. and Marengo, Jose A. and Chou, Sin Chan and Houser, Paul and Toll, David L.}, - year = {2006}, - keywords = {precipitation, 1640 Global Change: Remote sensing, 1840 Hydrology: Hydrometeorology, 3329 Atmospheric Processes: Mesoscale meteorology, remotely sensed evaluation, South America}, - pages = {D16113}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{dawson_modelling_2014, - title = {Modelling impacts of climate change on global food security}, - volume = {134}, - issn = {1573-1480}, - doi = {10.1007/s10584-014-1277-y}, - abstract = {The United Nations Food and Agriculture Organization (FAO) estimate that nearly 900 million people on the planet are suffering from chronic hunger. This state of affairs led to the making of the United Nations Millennium Development Goals in 2000, having the first goal to “Eradicate extreme poverty and hunger” with a target to halve the proportion of people who suffer from hunger. However, projections of a rapidly growing population, coupled with global climate change, is expected to have significant negative impacts on food security. To investigate this prospect, a modelling framework was developed under the QUEST-GSI programme, which we have termed FEEDME (Food Estimation and Export for Diet and Malnutrition Evaluation). The model uses country-level Food Balance Sheets (FBS) to determine mean calories on a per-capita basis, and a coefficient of variation to account for the degree of inequality in access to food across national populations. Calorific values of individual food items in the FBS of countries were modified by revision of crop yields and population changes under the SRES A1B climate change and social-economic scenarios respectively for 2050, 2085 and 2100. Under a no-climate change scenario, based upon projected changes in population and agricultural land use only, results show that 31 \% (2.5 billion people by 2050) of the global population is at risk of undernourishment if no adaptation or agricultural innovation is made in the intervening years. An additional 21 \% (1.7 billion people) is at risk of undernourishment by 2050 when climate change is taken into account. However, the model does not account for future trends in technology, improved crop varieties or agricultural trade interventions, although it is clear that all of these adaptation strategies will need to be embraced on a global scale if society is to ensure adequate food supplies for a projected global population of greater than 9 billion people.}, - number = {3}, - journal = {Climatic Change}, - author = {Dawson, Terence P. and Perryman, Anita H. and Osborne, Tom M.}, - year = {2014}, - pages = {429--440}, - annote = {The following values have no corresponding Zotero field:label: Dawson2014work-type: journal article}, -} - -@article{degaetano_time-dependent_2009, - title = {Time-{Dependent} {Changes} in {Extreme}-{Precipitation} {Return}-{Period} {Amounts} in the {Continental} {United} {States}}, - volume = {48}, - issn = {1558-8424}, - doi = {10.1175/2009jamc2179.1}, - abstract = {Abstract Partial-duration maximum precipitation series from Historical Climatology Network stations are used as a basis for assessing trends in extreme-precipitation recurrence-interval amounts. Two types of time series are analyzed: running series in which the generalized extreme-value (GEV) distribution is fit to separate overlapping 30-yr data series and lengthening series in which more recent years are iteratively added to a base series from the early part of the record. Resampling procedures are used to assess both trend and field significance. Across the United States, nearly two-thirds of the trends in the 2-, 5-, and 10-yr return-period rainfall amounts, as well as the GEV distribution location parameter, are positive. Significant positive trends in these values tend to cluster in the Northeast, western Great Lakes, and Pacific Northwest. Slopes are more pronounced in the 1960?2007 period when compared with the 1950?2007 interval. In the Northeast and western Great Lakes, the 2-yr return-period precipitation amount increases at a rate of approximately 2\% per decade, whereas the change in the 100-yr storm amount is between 4\% and 9\% per decade. These changes result primarily from an increase in the location parameter of the fitted GEV distribution. Collectively, these increases result in a median 20\% decrease in the expected recurrence interval, regardless of interval length. Thus, at stations across a large part of the eastern United States and Pacific Northwest, the 50-yr storm based on 1950?79 data can be expected to occur on average once every 40 yr, when data from the 1950?2007 period are considered.}, - number = {10}, - journal = {Journal of Applied Meteorology and Climatology}, - author = {DeGaetano, Arthur T.}, - month = oct, - year = {2009}, - pages = {2086--2099}, - annote = {doi: 10.1175/2009JAMC2179.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/2009JAMC2179.1}, -} - -@article{dawson_hydrological_2001, - title = {Hydrological modelling using artificial neural networks}, - volume = {25}, - abstract = {This review considers the application of artificial neural networks (ANNs) to rainfall-runoff modelling and flood forecasting. This is an emerging field of research, characterized by a wide variety of techniques, a diversity of geographical contexts, a general absence of intermodel comparisons, and inconsistent reporting of model skill. This article begins by outlining the basic principles of ANN modelling, common network architectures and training algorithms. The discussion then addresses related themes of the division and preprocessing of data for model calibration/validation; data standardization techniques; and methods of evaluating ANN model performance. A literature survey underlines the need for clear guidance in current modelling practice, as well as the comparison of ANN methods with more conventional statistical models. Accordingly, a template is proposed in order to assist the construction of future ANN rainfall-runoff models. Finally, it is suggested that research might focus on the extraction of hydrological 'rules' from ANN weights, and on the development of standard performance measures that penalize unnecessary model complexity.}, - number = {1}, - journal = {Progress in Physical Geography}, - author = {Dawson, C. W. and Wilby, R. L.}, - month = mar, - year = {2001}, - pages = {80--108}, - annote = {410FAPROG PHYS GEOG}, - annote = {The following values have no corresponding Zotero field:alt-title: Prog. Phys. Geogr.accession-num: ISI:000167428300004}, -} - -@article{dawson_artificial_1998, - title = {An artificial neural network approach to rainfall-runoff modelling}, - volume = {43}, - abstract = {This paper provides a discussion of the development and application of Artificial Neural Networks (ANNs) to flow forecasting in mio flood-prone UK catchments using real hydrometric data. Given relatively brief calibration data sets it was possible to construct robust models of 15-min flows with six hour lead times for the Rivers Amber and Mole. Comparisons were made between the performance of the ANN and those of conventional flood forecasting systems. The results obtained for validation forecasts were of comparable quality to those obtained from operational systems for the River Amber. The ability of the ANN to cope with missing data and to "learn" from the event currently being forecast in real time makes it an appealing alternative to conventional lumped or semi- distributed flood forecasting models. However, further research is required to determine the optimum ANN training period for a given catchment, season and hydrological contexts.}, - number = {1}, - journal = {Hydrological Sciences Journal-Journal Des Sciences Hydrologiques}, - author = {Dawson, C. W. and Wilby, R.}, - month = feb, - year = {1998}, - pages = {47--66}, - annote = {The following values have no corresponding Zotero field:alt-title: Hydrol. Sci. J.-J. Sci. Hydrol.accession-num: ISI:000073096900004}, - annote = {ZH327HYDROLOG SCI J}, -} - -@article{daud_comparison_2004, - title = {Comparison on pore development of activated carbon produced from palm shell and coconut shell}, - volume = {93}, - issn = {0960-8524}, - number = {1}, - journal = {Bioresource Technology}, - author = {Daud, Wan Mohd Ashri Wan and Ali, Wan Shabuddin Wan}, - year = {2004}, - keywords = {Activated carbon, Coconut shell, Palm shell, Pore development}, - pages = {63--69}, - annote = {The following values have no corresponding Zotero field:work-type: doi: 10.1016/j.biortech.2003.09.015}, -} - -@article{das_increases_2013, - title = {Increases in flood magnitudes in {California} under warming climates}, - volume = {501}, - issn = {0022-1694}, - doi = {http://dx.doi.org/10.1016/j.jhydrol.2013.07.042}, - number = {0}, - journal = {Journal of Hydrology}, - author = {Das, T. and Maurer, E. P. and Pierce, D. W. and Dettinger, M. D. and Cayan, D. R.}, - year = {2013}, - keywords = {Climate change, Statistical downscaling, Flood risk, Sierra Nevada}, - pages = {101--110}, -} - -@article{dankers_climate_2008, - title = {Climate change impact on flood hazard in {Europe}: {An} assessment based on high-resolution climate simulations}, - volume = {113}, - issn = {2156-2202}, - doi = {10.1029/2007JD009719}, - number = {D19}, - journal = {Journal of Geophysical Research: Atmospheres}, - author = {Dankers, Rutger and Feyen, Luc}, - year = {2008}, - keywords = {extreme events, 1637 Regional climate change, 1807 Climate impacts, 1817 Extreme events, 1821 Floods, climate impacts, floods}, - pages = {D19105}, -} - -@misc{daly_prism_1997, - address = {Reno, NV, USA}, - title = {The {PRISM} approach to mapping precipitation and temperature}, - author = {Daly, C. and Taylor, G. H. and Gibson, W. P.}, - month = oct, - year = {1997}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Society}, -} - -@article{dawson_comparison_1999, - title = {A comparison of artificial neural networks used for river flow forecasting}, - volume = {3}, - abstract = {This paper compares the performance of two artificial neural network (ANN) models-the multi layer perceptron (MLP) and the radial basis function network (RBF)-with a stepwise multiple linear regression model (SWMLR) and zero order forecasts (ZOF) of river flow. All models were trained using 15 minute rainfall-runoff data for the River Mole, a flood-prone tributary of the River Thames, UK. The models were then used to forecast river flows with a 6 hour lead time and 15 minute resolution, given only antecedent rainfall and discharge measurements. Two seasons (winter and spring) were selected for model testing using a cross-validation technique and a range of diagnostic statistics. Overall, the MLP was more skillful than the RBF, SWMLR and ZOF models. However, the RBF flow forecasts were only marginally better than those of the simpler SWMLR and ZOF models. The results compare favourably with a review of previous studies and further endorse claims that ANNs are well suited to rainfall-runoff modelling and (potentially) real-time flood forecasting.}, - number = {4}, - journal = {Hydrol. Earth Syst. Sci.}, - author = {Dawson, C. W. and Wilby, R. L.}, - month = dec, - year = {1999}, - pages = {529--540}, - annote = {288HZHYDROL EARTH SYST SCI}, - annote = {The following values have no corresponding Zotero field:alt-title: Hydrol. Earth Syst. Sci.accession-num: ISI:000085557800007}, -} - -@article{das_structure_2009, - title = {Structure and {Detectability} of {Trends} in {Hydrological} {Measures} over the {Western} {United} {States}}, - volume = {10}, - issn = {1525-755X}, - doi = {10.1175/2009jhm1095.1}, - abstract = {This study examines the geographic structure of observed trends in key hydrologically relevant variables across the western United States at (1)/(8)degrees spatial resolution during the period 1950-99. Geographical regions, latitude bands, and elevation classes where these trends are statistically significantly different from trends associated with natural climate variations are identified. Variables analyzed include late-winter and spring temperature, winter-total snowy days as a fraction of winter-total wet days, 1 April snow water equivalent (SWE) as a fraction of October-March (ONDJFM) precipitation total [precip(ONDJFM)], and seasonal [JFM] accumulated runoff as a fraction of water-year accumulated runoff. Observed changes were compared to natural internal climate variability simulated by an 850-yr control run of the finite volume version of the Community Climate System Model, version 3 (CCSM3-FV), statistically downscaled to a (1)/(8)degrees grid using the method of constructed analogs. Both observed and downscaled model temperature and precipitation data were then used to drive the Variable Infiltration Capacity (VIC) hydrological model to obtain the hydrological variables analyzed in this study. Large trends (magnitudes found less than 5\% of the time in the long control run) are common in the observations and occupy a substantial part (37\%-42\%) of the mountainous western United States. These trends are strongly related to the large-scale warming that appears over 89\% of the domain. The strongest changes in the hydrologic variables, unlikely to be associated with natural variability alone, have occurred at medium elevations [750-2500 m for JFM runoff fractions and 500-3000 m for SWE/Precip(ONDJFM)] where warming has pushed temperatures from slightly below to slightly above freezing. Further analysis using the data on selected catchments indicates that hydroclimatic variables must have changed significantly (at 95\% confidence level) over at least 45\% of the total catchment area to achieve a detectable trend in measures accumulated to the catchment scale.}, - language = {English}, - number = {4}, - journal = {Journal of Hydrometeorology}, - author = {Das, T. and Hidalgo, H. G. and Dettinger, M. D. and Cayan, D. R. and Pierce, D. W. and Bonfils, C. and Barnett, T. P. and Bala, G. and Mirin, A.}, - month = aug, - year = {2009}, - keywords = {model, climate-change, north-america, precipitation, river-basin, colorado, greenhouse-gas, mountain snowpack, snow water equivalent, soil-moisture, surface-temperature trends}, - pages = {871--892}, - annote = {ISI Document Delivery No.: 487HOTimes Cited: 0Cited Reference Count: 64Das, T. Hidalgo, H. G. Dettinger, M. D. Cayan, D. R. Pierce, D. W. Bonfils, C. Barnett, T. P. Bala, G. Mirin, A.Lawrence Livermore National Laboratory through a LDRDWe thank two anonymous reviewers and Guido Salvucci, editor for the Journal of Hydrometeorology, for their constructive suggestions. This work was supported by the Lawrence Livermore National Laboratory through a LDRD grant to the Scripps Institution of Oceanography (SIO) via the San Diego Supercomputer Center for the LUCiD project. The USGS provided salary support for MD and DC. The Scripps Institution of Oceanography and the California Energy Com-mission PIER Program, through the California Climate Change Center, provided partial salary support for DC, DP, and HH. The NOAA RISA Program provided partial salary support for DC, Mary Tyree, and Jennifer Johns at SIO. The Programfor ClimateModel Diagnosis and Intercomparison ( PCMDI) supported CB and GB ( the former via a DOE Distinguished Scientist Fellowship awarded to B. Santer). TD was partially supported by a CALFED Bay-Delta Program-funded postdoctoral fellowship grant. We thank Andrew W. Wood, Alan Hamlet, Dennis Lettenmaier at the University of Washington, and Edwin P. Maurer at Santa Clara University for sharing VIC and VIC input data. The authors thank Mary Tyree for her support in processing some of the data. We thank Jennifer Johns for her comments on the initial version of the manuscript.Amer meteorological socBoston}, - annote = {The following values have no corresponding Zotero field:auth-address: [Das, T.] UCSD, Scripps Inst Oceanog, La Jolla, CA 92093 USA. [Dettinger, M. D.; Cayan, D. R.] US Geol Survey, La Jolla, CA USA. [Bonfils, C.; Bala, G.; Mirin, A.] Lawrence Livermore Natl Lab, Livermore, CA USA. Das, T, UCSD, Scripps Inst Oceanog, 9500 Gilman Dr 0224, La Jolla, CA 92093 USA. tadas@ucsd.edualt-title: J. Hydrometeorol.accession-num: ISI:000269262800003work-type: Article}, -} - -@article{daly_statistical-topographic_1994, - title = {A statistical-topographic model for mapping climatological precipitation over mountainous terrain}, - volume = {33}, - journal = {Journal of Applied Meteorology}, - author = {Daly, C. and Neilson, R. P. and Phillips, D. L.}, - year = {1994}, - pages = {140--158}, -} - -@techreport{dalton_southeast_2010, - address = {Reston, Virginia, USA}, - title = {Southeast {Regional} {Assessment} {Project} for the {National} {Climate} {Change} and {Wildlife} {Science} {Center}: {U}.{S}. {Geological} {Survey} {Open}-{File} {Report} 2010–1213}, - institution = {U.S. Geological Survey}, - author = {Dalton, M. S. and Jones, S. A.}, - year = {2010}, - pages = {38}, -} - -@article{dai_ensemble_2001, - title = {Ensemble simulation of twenty-first century climate changes: {Business}-as-usual versus {CO2} stabilization}, - volume = {82}, - issn = {0003-0007}, - abstract = {Natural variability of the climate system imposes a large uncertainty on future climate change signals simulated by a single integration of any coupled ocean-atmosphere model. This is especially true for regional precipitation changes. Here, these uncertainties are reduced by using results from two ensembles of five integrations of a coupled ocean-atmosphere model forced by projected future greenhouse gas and sulfate aerosol changes. Under a business-as-usual scenario, the simulations show a global warming of similar to1.9 degreesC over the. twenty-first century (continuing the trend observed since the late 1970s), accompanied by a similar to3\% increase in global precipitation. Stabilizing the CO2 level at 550 ppm reduces the warming only moderately (by similar to0.4 degreesC in 2100). The patterns of seasonal-mean temperature and precipitation change in the two cases are highly correlated (r approximate to 0.99 for temperature and r approximate to 0.93 for precipitation). Over the midlatitude North Atlantic Ocean, the model produces a moderate surface cooling (1 degrees -2 degreesC, mostly in winter) over the twenty-first century. This cooling is accompanied by changes in atmospheric lapse rates over the region (i.e., larger warming in the free troposphere than at the surface), which stabilizes the surface ocean. The resultant reduction in local oceanic convection contributes to a 20\% slowdown in the thermohaline circulation.}, - language = {English}, - number = {11}, - journal = {Bulletin of the American Meteorological Society}, - author = {Dai, A. and Meehl, G. A. and Washington, W. M. and Wigley, T. M. L. and Arblaster, J. M.}, - month = nov, - year = {2001}, - keywords = {temperature, forecasts, ocean-atmosphere model, carbon-dioxide, forcings, increase, predictability}, - pages = {2377--2388}, - annote = {488HGTimes Cited:21Cited References Count:35}, - annote = {The following values have no corresponding Zotero field:auth-address: Dai, A Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 USA Natl Ctr Atmospher Res, Boulder, CO 80307 USAaccession-num: ISI:000171929700002}, -} - -@article{dai_drought_2011, - title = {Drought under global warming: a review}, - volume = {2}, - issn = {1757-7799}, - doi = {10.1002/wcc.81}, - number = {1}, - journal = {Wiley Interdisciplinary Reviews: Climate Change}, - author = {Dai, Aiguo}, - year = {2011}, - pages = {45--65}, - annote = {The following values have no corresponding Zotero field:publisher: John Wiley \& Sons, Inc.}, -} - -@article{crane_scale_2002, - title = {Scale interactions and regional climate: {Examples} from the {Susquehanna} {River} {Basin}}, - volume = {8}, - abstract = {One of the most difficult problems faced by climatologists is how to translate global climate model (GCM) output into regional- and local-scale information that health and environmental effects researchers can use. It will be decades before GCMs will be able to resolve scales small enough for most effects research, so climatologists have developed climate downscaling methods to bridge the gap between the global and local scales. There are two main streams of climate downscaling research. First, high-resolution, limited-area climate models can be embedded in the coarse-scale GCMs, producing much finer resolution climate data. Second, empirical downscaling techniques develop transfer functions linking the large-scale atmospheric circulation generated by the GCMs to surface data. Examples of both types of downscaling, aimed at improving projections of future climate in the Susquehanna River Basin (the Mid-Atlantic Region of the United States), are presented. A third case is also described in which an even higher- resolution nested atmospheric model is being developed and linked to a hydrologic model system, with the ultimate goal of simulating the environmental response to climate forcing at all time and space scales.}, - number = {1}, - journal = {Human and Ecological Risk Assessment}, - author = {Crane, R. G. and Yarnal, B. and Barron, E. J. and Hewitson, B.}, - month = jan, - year = {2002}, - pages = {147--158}, - annote = {520MZHUM ECOL RISK ASSESSMENT}, - annote = {The following values have no corresponding Zotero field:alt-title: Hum. Ecol. Risk Assess.accession-num: ISI:000173786800013}, -} - -@article{covey_overview_2003, - title = {An overview of results from the {Coupled} {Model} {Intercomparison} {Project}}, - volume = {37}, - issn = {0921-8181}, - abstract = {The Coupled Model Intercomparison Project (CMIP) collects output from global coupled ocean-atmosphere general circulation models (coupled GCMs). Among other uses, such models are employed both to detect anthropogenic effects in the climate record of the past century and to project future climatic changes due to human production of greenhouse gases and aerosols. CMIP has archived output from both constant forcing ("control run") and perturbed (1\% per year increasing atmospheric carbon dioxide) simulations. This report summarizes results form 18 CMIP models. A third of the models refrain from employing ad hoc flux adjustments at the ocean-atmosphere interface. The new generation of non-flux-adjusted control runs are nearly as stable as-and agree with observations nearly as well as-the flux-adjusted models. Both flux-adjusted and non-flux-adjusted models simulate an overall level of natural internal climate variability that is within the bounds set by observations. These developments represent significant progress in the state of the art of climate modeling since the Second (1995) Scientific Assessment Report of the Intergovernmental Panel on Climate Change (IPCC; see Gates et al. [Gates, W.L., et al., 1996. Climate models-Evaluation. Climate Climate 1995: The Science of Climate Change, Houghton, J.T., et al. (Eds.), Cambridge Univ. Press, pp. 229-284]). In the increasing-CO2 runs, differences between different models, while substantial, are not as great as one might expect from earlier assessments that relied on equilibrium climate sensitivity. (C) 2003 Elsevier Science B.V. All rights reserved.}, - language = {English}, - number = {1-2}, - journal = {Global and Planetary Change}, - author = {Covey, C. and AchutaRao, K. M. and Cubasch, U. and Jones, P. and Lambert, S. J. and Mann, M. E. and Phillips, T. J. and Taylor, K. E.}, - month = jun, - year = {2003}, - keywords = {co2, gcm, global climate, ocean-atmosphere model, sea-ice, climate, 6 centuries, cmip, flux adjustments, general-circulation model, heat transports, surface air-temperature, transient climate-change}, - pages = {103--133}, - annote = {683XBTimes Cited:23Cited References Count:70}, - annote = {The following values have no corresponding Zotero field:auth-address: Covey, C Lawrence Livermore Natl Lab, PCMDI, Mail Code L-264, Livermore, CA 94551 USA Lawrence Livermore Natl Lab, PCMDI, Livermore, CA 94551 USA Free Univ Berlin, Inst Meteorol, D-1000 Berlin, Germany Univ E Anglia, Climat Res Unit, Norwich NR4 7TJ, Norfolk, England Canadian Ctr Climate Modelling \& Anal, Victoria, BC, Canada Univ Virginia, Dept Environm Sci, Charlottesville, VA 22903 USAaccession-num: ISI:000183175400007}, -} - -@article{costa-cabral_snowpack_2013, - title = {Snowpack and runoff response to climate change in {Owens} {Valley} and {Mono} {Lake} watersheds}, - volume = {116}, - issn = {0165-0009}, - doi = {10.1007/s10584-012-0529-y}, - language = {English}, - number = {1}, - journal = {Climatic Change}, - author = {Costa-Cabral, Mariza and Roy, SujoyB and Maurer, EdwinP and Mills, WilliamB and Chen, Limin}, - month = jan, - year = {2013}, - pages = {97--109}, - annote = {The following values have no corresponding Zotero field:alt-title: Climatic Changepublisher: Springer Netherlands}, -} - -@article{dai_increasing_2012, - title = {Increasing drought under global warming in observations and models}, - volume = {advance online publication}, - issn = {1758-6798}, - doi = {http://www.nature.com/nclimate/journal/vaop/ncurrent/abs/nclimate1633.html#supplementary-information}, - journal = {Nature Clim. Change}, - author = {Dai, Aiguo}, - year = {2012}, - annote = {10.1038/nclimate1633}, - annote = {The following values have no corresponding Zotero field:publisher: Nature Publishing Groupwork-type: 10.1038/nclimate1633}, -} - -@article{curtis_interannual_2002, - title = {Interannual variability of the bimodal distribution of summertime rainfall over {Central} {America} and tropical storm activity in the far-eastern {Pacific}}, - volume = {22}, - journal = {Climate Research}, - author = {Curtis, S.}, - year = {2002}, - pages = {141--146}, -} - -@incollection{u_cubasch_introduction_2013, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Introduction}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {U. Cubasch and D. Wuebbles and D. Chen and M. C. Facchini and D. Frame and N. Mahowald and J. -G. Winther}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley}, - year = {2013}, - pages = {119--158}, - annote = {The following values have no corresponding Zotero field:section: 1electronic-resource-num: 10.1017/CBO9781107415324.007}, -} - -@incollection{cubasch_projections_2001, - address = {Cambridge}, - title = {Projections of future climate change}, - booktitle = {Climate {Change} 2001: {The} {Scientific} {Basis}}, - publisher = {Cambridge University Press}, - author = {Cubasch, U. and Meehl, G. A.}, - editor = {Houghton, J. T.}, - year = {2001}, - pages = {525--582}, -} - -@article{cosgrove_real-time_2003, - title = {Real-time and retrospective forcing in the {North} {American} {Land} {Data} {Assimilation} {System} ({NLDAS}) project}, - volume = {108}, - issn = {0148-0227}, - doi = {10.1029/2002jd003118}, - abstract = {The accuracy of forcing data greatly impacts the ability of land surface models (LSMs) to produce realistic simulations of land surface processes. With this in mind, the multi-institutional North American Land Data Assimilation System (NLDAS) project has produced retrospective (1996\&\#8211;2002) and real-time (1999\&\#8211;present) data sets to support its LSM modeling activities. Featuring 0.125° spatial resolution, hourly temporal resolution, nine primary forcing fields, and six secondary validation/model development fields, each data set is based on a backbone of Eta Data Assimilation System/Eta data and is supplemented with observation-based precipitation and radiation data. Hourly observation-based precipitation data are derived from a combination of daily National Center for Environmental Prediction Climate Prediction Center (CPC) gauge-based precipitation analyses and hourly National Weather Service Doppler radar-based (WSR-88D) precipitation analyses, wherein the hourly radar-based analyses are used to temporally disaggregate the daily CPC analyses. NLDAS observation-based shortwave values are derived from Geostationary Operational Environmental Satellite radiation data processed at the University of Maryland and at the National Environmental Satellite Data and Information Service. Extensive quality control and validation efforts have been conducted on the NLDAS forcing data sets, and favorable comparisons have taken place with Oklahoma Mesonet, Atmospheric Radiation Measurement Program/cloud and radiation test bed, and Surface Radiation observation data. The real-time forcing data set is constantly evolving to make use of the latest advances in forcing-related data sets, and all of the real-time and retrospective data are available online at http://ldas.gsfc.nasa.gov for visualization and downloading in both full and subset forms.}, - number = {D22}, - journal = {J. Geophys. Res.}, - author = {Cosgrove, Brian A. and Lohmann, Dag and Mitchell, Kenneth E. and Houser, Paul R. and Wood, Eric F. and Schaake, John C. and Robock, Alan and Marshall, Curtis and Sheffield, Justin and Duan, Qingyun and Luo, Lifeng and Higgins, R. Wayne and Pinker, Rachel T. and Tarpley, J. Dan and Meng, Jesse}, - year = {2003}, - keywords = {1866 Hydrology: Soil moisture, 1899 Hydrology: General or miscellaneous, 3322 Meteorology and Atmospheric Dynamics: Land/atmosphere interactions, 3337 Meteorology and Atmospheric Dynamics: Numerical modeling and data assimilation}, - pages = {8842}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{corte-real_circulation_1999, - title = {Circulation patterns, daily precipitation in {Portugal} and implications for climate change simulated by the second {Hadley} {Centre} {GCM}}, - volume = {15}, - abstract = {Based on principal component analysis (PCA) and a k-means clustering algorithm, daily mean sea level pressure (MSLP) fields over the northeastern Atlantic and Western Europe, simulated by the Hadley Centre's second generation coupled ocean-atmosphere GCM (HADCM2) control run (HADCM2CON), are validated by comparison with the observed daily MSLP fields. It is clear that HADCM2 reproduces daily MSLP fields and its seasonal variability over the region very well, despite suffering from some deficiencies, such as the systematic displacement of the atmospheric centres of action. Four daily circulation patterns, previously identified from the observed daily MSLP fields over the area and well related to daily precipitation in Portugal, were also well classified from the daily MSLP fields simulated by HADCM2. The model can also simulate rather successfully the relationships between the four daily circulation patterns and daily precipitation in southern Portugal. However. compared with observations, daily precipitation intensities simulated by the model are too weak in southern Portugal. Nevertheless, HADCM2 represents a considerable improvement relative to the UKTR experiment. The results described here imply that it is doubtful whether regional precipitation scenarios provided by HADCM2 can be directly applied in impact studies and that a downscaling technique, based on daily circulation patterns, might be successful in reproducing local and regional precipitation characteristics. Moreover, the four circulation patterns call also be clearly identified in the two perturbed experiments, one under greenhouse gases forcing only (HADCM2GHG) and the ether under additional forcing of sulphate aerosol (HADCM2SUL), although changes in the frequencies of occurrence of certain circulation patterns are found. Nevertheless, the observed links between regional precipitation in southern Portugal and large-scale atmospheric circulation seem likely to hold in the model's perturbed climate. It is therefore credible to use those links to downscale large-scale atmospheric circulation from GCM simulations to obtain future precipitation scenarios in southern Portugal.}, - number = {12}, - journal = {Climate Dynamics}, - author = {Corte-Real, J. and Qian, B. and Xu, H.}, - month = dec, - year = {1999}, - pages = {921--935}, - annote = {266JTCLIM DYNAM}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Dyn.accession-num: ISI:000084297400004}, -} - -@article{coquard_present_2004, - title = {Present and future surface climate in the western {USA} as simulated by 15 global climate models}, - volume = {23}, - number = {5}, - journal = {Climate Dynamics}, - author = {Coquard, J. and Duffy, P. B. and Taylor, K. E. and Iorio, J. P.}, - year = {2004}, - pages = {455--472}, -} - -@article{coppola_impact_2018, - title = {Impact of climate change on snow melt driven runoff timing over the {Alpine} region}, - volume = {51}, - issn = {1432-0894}, - doi = {10.1007/s00382-016-3331-0}, - abstract = {We investigate the climate change impact on snowmelt-driven runoff (SDR) over the Alpine region using the output from two Med-CORDEX and two EURO-CORDEX regional climate model projections (RCP8.5 scenario) at two resolutions (12, 50 km) driven by a sub-set of the CMIP5 GCMs. Comparison with the European Water Archive observed runoff dataset (242 stations) over the Alps shows a good performance by the higher resolution models in representing present day SDR, with the lower resolution simulations being less accurate in capturing the SDR timing. In the future projections all the models show a temperature increase of up to 4° by the end of the 21st century throughout the Alps and this leads to an anticipation of SDR timing throughout the year that can span from 1 to 3 months depending on the model horizontal resolution. These timing changes are associated with changes in snow cover modulated by the complex Alpine topography. In fact, model resolution plays a critical role in regulating the magnitude, timing and spatial distribution of the response of snow cover and SDR to warming. We find that the accurate simulation of changes in runoff timing requires a high resolution representation of the Alpine topography, and can be important for water storage regulations concerning energy production, agriculture and domestic use.}, - number = {3}, - journal = {Climate Dynamics}, - author = {Coppola, Erika and Raffaele, Francesca and Giorgi, Filippo}, - month = aug, - year = {2018}, - pages = {1259--1273}, - annote = {The following values have no corresponding Zotero field:label: Coppola2018work-type: journal article}, -} - -@article{conway_precipitation_1996, - title = {Precipitation and air flow indices over the {British} {Isles}}, - volume = {7}, - abstract = {The relationships between regional daily precipitation time series in the British isles and 3 indices of air flow are examined with a view to assessing their potential for use in GCM downscaling. These indices, calculated from daily grid- point sea-level pressure data, are as follows: total shear vorticity, a measure of the degree of cyclonicity; strength of the resultant flow; and angular direction of flow. The 3 indices, particularly vorticity, exert a strong control over daily precipitation characteristics such as the probability and amount of precipitation. There are significant regional differences in the relationships with precipitation, particularly between the England and Wales series and the Scotland and Northern Ireland series. Comparison of the relationships between air flow indices and regional and 2 single site precipitation series in England shows they are similar, although at the site-scale local factors may play an important role in affecting the relationships with the indices. Two models for generating daily precipitation series from vorticity are presented and evaluated by their ability to reproduce the following characteristics of precipitation over an independent validation period: annual totals and interannual variability, wet day probability and spell duration, and size of daily precipitation amounts. Model 1 is based on empirical relationships between vorticity and precipitation. Model 2 is based on user-defined categories of vorticity. The results for 2 sites (Durham and Kempsford) show that both models reproduce key characteristics of the observed daily precipitation series. Differences in model structure and number of parameters affect their accuracy in simulating the interannual variability and daily characteristics of precipitation. The air flow indices represent a significant advantage over traditional weather types because they are continuous variables. Previous downscaling techniques have relied upon classification techniques that impose artificial boundaries to define classes that may contain a wide range of conditions and no information about the intensity of development of the weather system concerned. As the 3 air flow indices are the basic determinants for describing the day's weather in many parts of the world, there is significant potential to apply this technique to other such regions. An example is shown of the relationships between daily precipitation in Switzerland and the air flow indices. The models may also be applied to the development of future daily precipitation scenarios using the coarse-scale output of GCM pressure fields.}, - number = {2}, - journal = {Climate Research}, - author = {Conway, D. and Wilby, R. L. and Jones, P. D.}, - month = nov, - year = {1996}, - pages = {169--183}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:A1996WE47400006}, - annote = {WE474CLIMATE RES}, -} - -@article{conway_use_1998, - title = {The use of weather types and air flow indices for {GCM} downscaling}, - volume = {213}, - abstract = {A variety of different methods have been proposed for downscaling large-scale General Circulation Model (GCM) output to the time and space scales required for climate impact studies. Using weather types to achieve this goal provides greater understanding of the problems that are involved compared to the many "black box" techniques that have been proposed. Analyses using Lamb weather types, counts of weather fronts and air flow indices over the British Isles show strong relationships with daily rainfall characteristics such as the probability and amount of rainfall. Three versions of a weather type method are described for generating daily rainfall series. Two methods use an objective scheme to classify daily circulation types over the British isles along the lines of Lamb's subjective classification. The third method is based on user-defined categories of vorticity. Each method categorises rainfall events according to whether the previous day was wet or dry. All three methods successfully reproduce the monthly means, persistence and interannual variability of two daily rainfall time series during a validation period. A significant advantage of using vorticity is that it is a continuous variable and is strongly related to the probability of rainfall and the magnitude of rainfall events. The paper ends with a discussion of the issues relevant to the application of this method to the development of climate scenarios from GCMs. (C) 1998 Elsevier Science B.V. All rights reserved.}, - number = {1-4}, - journal = {Journal of Hydrology}, - author = {Conway, D. and Jones, P. D.}, - month = dec, - year = {1998}, - pages = {348--361}, - annote = {151LVJ HYDROL}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Hydrol.accession-num: ISI:000077722300023}, -} - -@book{national_weather_service_modernization_committee_future_1998, - title = {Future of the {National} {Weather} {Service} {Cooperative} {Observer} {Network}}, - isbn = {978-0-309-06146-9}, - publisher = {The National Academies Press}, - author = {National Weather Service Modernization Committee and National Research Council}, - year = {1998}, -} - -@article{collins_community_2006, - title = {The {Community} {Climate} {System} {Model}: {CCSM3}}, - volume = {19}, - number = {11}, - journal = {Journal of Climate}, - author = {Collins, W. D. and Bitz, C. M. and Blackmon, M. L. and Bonan, G. B. and Bretherton, C. S. and Carton, J. A. and Chang, P. and Doney, S. C. and Hack, J. J. and Henderson, T. B. and Kiehl, J. T. and Large, W. G. and McKenna, D. S. and Santer, B. D. and Smith, R. D.}, - year = {2006}, - pages = {2122--2143}, - annote = {Special Issue of Journal of Climate}, -} - -@article{collins_challenges_2018, - title = {Challenges and opportunities for improved understanding of regional climate dynamics}, - volume = {8}, - issn = {1758-6798}, - doi = {10.1038/s41558-017-0059-8}, - abstract = {Dynamical processes in the atmosphere and ocean are central to determining the large-scale drivers of regional climate change, yet their predictive understanding is poor. Here, we identify three frontline challenges in climate dynamics where significant progress can be made to inform adaptation: response of storms, blocks and jet streams to external forcing; basin-to-basin and tropical–extratropical teleconnections; and the development of non-linear predictive theory. We highlight opportunities and techniques for making immediate progress in these areas, which critically involve the development of high-resolution coupled model simulations, partial coupling or pacemaker experiments, as well as the development and use of dynamical metrics and exploitation of hierarchies of models.}, - number = {2}, - journal = {Nature Climate Change}, - author = {Collins, Matthew and Minobe, Shoshiro and Barreiro, Marcelo and Bordoni, Simona and Kaspi, Yohai and Kuwano-Yoshida, Akira and Keenlyside, Noel and Manzini, Elisa and O’Reilly, Christopher H. and Sutton, Rowan and Xie, Shang-Ping and Zolina, Olga}, - month = feb, - year = {2018}, - pages = {101--108}, -} - -@article{collins_observational_2013, - title = {Observational challenges in evaluating climate models}, - volume = {3}, - issn = {1758-678X}, - doi = {10.1038/nclimate2012}, - number = {11}, - journal = {Nature Clim. Change}, - author = {Collins, Mat and AchutaRao, Krishna and Ashok, Karumuri and Bhandari, Satyendra and Mitra, Ashis K. and Prakash, Satya and Srivastava, Rohit and Turner, Andrew}, - year = {2013}, - pages = {940--941}, - annote = {The following values have no corresponding Zotero field:publisher: Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.work-type: Correspondence}, -} - -@incollection{m_collins_long-term_2013, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Long-term {Climate} {Change}: {Projections}, {Commitments} and {Irreversibility}}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {M. Collins and R. Knutti and J. Arblaster and J. -L. Dufresne and T. Fichefet and P. Friedlingstein and X. Gao and W. J. Gutowski and T. Johns and G. Krinner and M. Shongwe and C. Tebaldi and A. J. Weaver and M. Wehner}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley}, - year = {2013}, - pages = {1029--1136}, - annote = {The following values have no corresponding Zotero field:section: 12electronic-resource-num: 10.1017/CBO9781107415324.024}, -} - -@article{cohen_climate_2000, - title = {Climate {Change} and {Resource} {Management} in the {Columbia} {River} {Basin}}, - volume = {25}, - abstract = {Scenarios of global climate change were examined to see what impacts they might have on transboundary water management in the Columbia River basin. Scenario changes in natural streamflow were estimated using a basin hydrology model. These scenarios tended to show earlier seasonal peaks, with possible reductions in total annual flow and lower minimum flows. Impacts and adaptation responses to the natural streamflow scenarios were determined through two exercises: (a) estimations of system reliability using a reservoir model with performance measures and (b) interviews with water managers and other stakeholders in the Canadian portion of the basin. Results from the two exercises were similar, suggesting a tendency towards reduced reliability to meet objectives for power production, fisheries, and agriculture. Reliability to meet flood control objectives would be relatively unchanged in some scenarios but reduced in others. This exercise suggests that despite the high level of development and management in the Columbia, vulnerabilities would still exist, and impacts could still occur in scenarios of natural streamflow changes caused by global climate change. Many of these would be indirect, reflecting the complex relationship between the region and its climate.}, - number = {2}, - journal = {Water International}, - author = {Cohen, S. J. and Miller, K. A. and Hamlet, A. F. and Avis, W.}, - year = {2000}, - pages = {253--272}, - annote = {Water International [Water Int.]. Vol. 25, no. 2, pp. 253-272. - Jun 2000.}, -} - -@techreport{coats_effects_2010, - address = {Incline Village NV, USA}, - title = {The effects of climate change on {Lake} {Tahoe} in the 21st century: {Meteorology}, hydrology, loading and lake response}, - institution = {Pacific Southwest Research Station, Tahoe Environmental Science Center}, - author = {Coats, R. and Reuter, J. and Dettinger, M. and Riverson, J. and Sahoo, G. and Schladow, G. and Wolfe, B. and Costa-Cabral, M.}, - year = {2010}, - pages = {208}, -} - -@article{cloke_modelling_2013, - title = {Modelling climate impact on floods with ensemble climate projections}, - volume = {139}, - issn = {1477-870X}, - doi = {10.1002/qj.1998}, - number = {671}, - journal = {Quarterly Journal of the Royal Meteorological Society}, - author = {Cloke, Hannah L. and Wetterhall, Fredrik and He, Yi and Freer, Jim E. and Pappenberger, Florian}, - year = {2013}, - keywords = {GCM, RCM, perturbed physics, ENSEMBLES, hydrological model, hydrology, response surface, UKCP09}, - pages = {282--297}, - annote = {The following values have no corresponding Zotero field:publisher: John Wiley \& Sons, Ltd.}, -} - -@article{colette_regional_2012, - title = {Regional climate downscaling with prior statistical correction of the global climate forcing}, - volume = {39}, - issn = {0094-8276}, - doi = {10.1029/2012gl052258}, - abstract = {A novel climate downscaling methodology that attempts to correct climate simulation biases is proposed. By combining an advanced statistical bias correction method with a dynamical downscaling it constitutes a hybrid technique that yields nearly unbiased, high-resolution, physically consistent, three-dimensional fields that can be used for climate impact studies. The method is based on a prior statistical distribution correction of large-scale global climate model (GCM) 3-dimensional output fields to be taken as boundary forcing of a dynamical regional climate model (RCM). GCM fields are corrected using meteorological reanalyses. We evaluate this methodology over a decadal experiment. The improvement in terms of spatial and temporal variability is discussed against observations for a past period. The biases of the downscaled fields are much lower using this hybrid technique, up to a factor 4 for the mean temperature bias compared to the dynamical downscaling alone without prior bias correction. Precipitation biases are subsequently improved hence offering optimistic perspectives for climate impact studies.}, - number = {13}, - journal = {Geophysical Research Letters}, - author = {Colette, A. and Vautard, R. and Vrac, M.}, - year = {2012}, - keywords = {regional climate modelling, 1626 Global Change: Global climate models (3337, 4928), 4321 Natural Hazards: Climate impact (1630, 1637, 1807, 8408), climate impacts, 1637 Global Change: Regional climate change (4321), 3355 Atmospheric Processes: Regional modeling (4316), 4318 Natural Hazards: Statistical analysis (1984, 1986), climate downscaling, statistical correction}, - pages = {L13707}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{cogley_effective_1999, - title = {Effective {Sample} {Size} for {Glacier} {Mass} {Balance}}, - volume = {81}, - issn = {1468-0459}, - doi = {10.1111/1468-0459.00079}, - number = {4}, - journal = {Geografiska Annaler: Series A, Physical Geography}, - author = {Cogley, J. Graham}, - year = {1999}, - keywords = {Abramov Glacier., accuracy, Mass balance, sample size, statistical modelling, White Glacier}, - pages = {497--507}, - annote = {The following values have no corresponding Zotero field:publisher: Blackwell Publishers Ltd}, -} - -@article{coats_projected_2013, - title = {Projected 21st century trends in hydroclimatology of the {Tahoe} basin}, - volume = {116}, - issn = {0165-0009}, - doi = {10.1007/s10584-012-0425-5}, - number = {1}, - journal = {Climatic Change}, - author = {Coats, Robert and Costa-Cabral, Mariza and Riverson, John and Reuter, John and Sahoo, Goloka and Schladow, Geoffrey and Wolfe, Brent}, - year = {2013}, - keywords = {Earth and Environmental Science}, - pages = {51--69}, - annote = {The following values have no corresponding Zotero field:publisher: Springer Netherlands}, -} - -@article{cloern_projected_2011, - title = {Projected {Evolution} of {California}'s {San} {Francisco} {Bay}-{Delta}-{River} {System} in a {Century} of {Climate} {Change}}, - volume = {6}, - doi = {10.1371/journal.pone.0024465}, - abstract = {Background Accumulating evidence shows that the planet is warming as a response to human emissions of greenhouse gases. Strategies of adaptation to climate change will require quantitative projections of how altered regional patterns of temperature, precipitation and sea level could cascade to provoke local impacts such as modified water supplies, increasing risks of coastal flooding, and growing challenges to sustainability of native species. Methodology/Principal Findings We linked a series of models to investigate responses of California's San Francisco Estuary-Watershed (SFEW) system to two contrasting scenarios of climate change. Model outputs for scenarios of fast and moderate warming are presented as 2010–2099 projections of nine indicators of changing climate, hydrology and habitat quality. Trends of these indicators measure rates of: increasing air and water temperatures, salinity and sea level; decreasing precipitation, runoff, snowmelt contribution to runoff, and suspended sediment concentrations; and increasing frequency of extreme environmental conditions such as water temperatures and sea level beyond the ranges of historical observations. Conclusions/Significance Most of these environmental indicators change substantially over the 21st century, and many would present challenges to natural and managed systems. Adaptations to these changes will require flexible planning to cope with growing risks to humans and the challenges of meeting demands for fresh water and sustaining native biota. Programs of ecosystem rehabilitation and biodiversity conservation in coastal landscapes will be most likely to meet their objectives if they are designed from considerations that include: (1) an integrated perspective that river-estuary systems are influenced by effects of climate change operating on both watersheds and oceans; (2) varying sensitivity among environmental indicators to the uncertainty of future climates; (3) inevitability of biological community changes as responses to cumulative effects of climate change and other drivers of habitat transformations; and (4) anticipation and adaptation to the growing probability of ecosystem regime shifts.}, - number = {9}, - journal = {PLoS ONE}, - author = {Cloern, James E. and Knowles, Noah and Brown, Larry R. and Cayan, Daniel and Dettinger, Michael D. and Morgan, Tara L. and Schoellhamer, David H. and Stacey, Mark T. and van der Wegen, Mick and Wagner, R. Wayne and Jassby, Alan D.}, - year = {2011}, - pages = {e24465}, - annote = {The following values have no corresponding Zotero field:publisher: Public Library of Science}, -} - -@article{cleveland_locally_1988, - title = {Locally {Weighted} {Regression}: {An} {Approach} to {Regression} {Analysis} by {Local} {Fitting}}, - volume = {83}, - issn = {0162-1459}, - doi = {10.1080/01621459.1988.10478639}, - number = {403}, - journal = {Journal of the American Statistical Association}, - author = {Cleveland, William S. and Devlin, Susan J.}, - month = sep, - year = {1988}, - pages = {596--610}, - annote = {The following values have no corresponding Zotero field:publisher: Taylor \& Francis}, -} - -@incollection{j_a_church_sea_2013, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Sea {Level} {Change}}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {J. A. Church and P. U. Clark and A. Cazenave and J. M. Gregory and S. Jevrejeva and A. Levermann and M. A. Merrifield and G. A. Milne and R. S. Nerem and P. D. Nunn and A. J. Payne and W. T. Pfeffer and D. Stammer and A. S. Unnikrishnan}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley}, - year = {2013}, - pages = {1137--1216}, - annote = {The following values have no corresponding Zotero field:section: 13electronic-resource-num: 10.1017/CBO9781107415324.026}, -} - -@incollection{j_h_christensen_climate_2013, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Climate {Phenomena} and their {Relevance} for {Future} {Regional} {Climate} {Change}}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {J. H. Christensen and K. Krishna Kumar and E. Aldrian and S. -I. An and I. F.A. Cavalcanti and M. de Castro and W. Dong and P. Goswami and A. Hall and J. K. Kanyanga and A. Kitoh and J. Kossin and N. -C. Lau and J. Renwick and D. B. Stephenson and S. -P. Xie and T. Zhou}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley}, - year = {2013}, - pages = {1217--1308}, - annote = {The following values have no corresponding Zotero field:section: 14electronic-resource-num: 10.1017/CBO9781107415324.028}, -} - -@article{christensen_effects_2004, - title = {The effects of climate change on the hydrology and water resources of the {Colorado} {River} basin}, - volume = {62}, - issn = {0165-0009}, - abstract = {The potential effects of climate change on the hydrology and water resources of the Colorado River basin are assessed by comparing simulated hydrologic and water resources scenarios derived from downscaled climate simulations of the U. S. Department of Energy/ National Center for Atmospheric Research Parallel Climate Model (PCM) to scenarios driven by observed historical (1950 - 1999) climate. PCM climate scenarios include an ensemble of three 105-year future climate simulations based on projected 'business-as-usual' (BAU) greenhouse gas emissions and a control climate simulation based on static 1995 greenhouse gas concentrations. Downscaled transient temperature and precipitation sequences were extracted from PCM simulations, and were used to drive the Variable Infiltration Capacity ( VIC) macroscale hydrology model to produce corresponding streamflow sequences. Results for the BAU scenarios were summarized into Periods 1, 2, and 3 ( 2010 - 2039, 2040 - 2069, 2070 - 2098). Average annual temperature changes for the Colorado River basin were 0.5 degreesC warmer for control climate, and 1.0, 1.7, and 2.4 degreesC warmer for Periods 1 - 3, respectively, relative to the historical climate. Basin-average annual precipitation for the control climate was slightly (1\%) less than for observed historical climate, and 3, 6, and 3\% less for future Periods 1 - 3, respectively. Annual runoff in the control run was about 10\% lower than for simulated historical conditions, and 14, 18, and 17\% less for Periods 1 - 3, respectively. Analysis of water management operations using a water management model driven by simulated streamflows showed that streamflows associated with control and future BAU climates would significantly degrade the performance of the water resources system relative to historical conditions, with average total basin storage reduced by 7\% for the control climate and 36, 32 and 40\% for Periods 1 - 3, respectively. Releases from Glen Canyon Dam to the Lower Basin ( mandated by the Colorado River Compact) were met in 80\% of years for the control climate simulation ( versus 92\% in the historical climate simulation), and only in 59 - 75\% of years for the future climate runs. Annual hydropower output was also significantly reduced for the control and future climate simulations. The high sensitivity of reservoir system performance for future climate is a reflection of the fragile equilibrium that now exists in operation of the system, with system demands only slightly less than long-term mean annual inflow.}, - language = {English}, - number = {1-3}, - journal = {Climatic Change}, - author = {Christensen, N. S. and Wood, A. W. and Voisin, N. and Lettenmaier, D. P. and Palmer, R. N.}, - month = feb, - year = {2004}, - pages = {337--363}, - annote = {768FATimes Cited:9Cited References Count:32}, - annote = {The following values have no corresponding Zotero field:auth-address: Lettenmaier, DP Univ Washington, Dept Civil \& Environm Engn, 164 Wilcox Hall,POB 352700, Seattle, WA 98195 USA Univ Washington, Dept Civil \& Environm Engn, Seattle, WA 98195 USAaccession-num: ISI:000188531900014}, -} - -@article{bardossy_automated_2002, - title = {Automated objective classification of daily circulation patterns for precipitation and temperature downscaling based on optimized fuzzy rules}, - volume = {23}, - abstract = {A methodology for automated objective circulation pattern (CP) definition and classification is presented based on optimized fuzzy rules. The main goal of the method is to provide a basis (daily classified CPs) for downscaling of precipitation and temperature, which can be done by means of downscaling models with parameters depending on the CP. The CPs are defined using 500 or 700 hPa geopotential height anomalies. Fuzzy rules are defined by the position of high- and low-pressure anomalies. The fuzzy rules are obtained automatically using an optimization of the performance of the classification. For precipitation, the performance of the classification is measured by rainfall frequencies and rainfall amounts conditioned on the CP. So the task is to define wet or dry CPs. For temperature, the deviation from the average long-term annual cycle is used. In this way warm or cold CPs are identified. The performance of the CPs is validated using a split-sampling approach. The presented method produces physically realistic CP definitions. With the help of these definitions the observed (historical) pressure fields can be classified as shown in 2 case studies for regions with different climate conditions: Central Europe (Germany) and the Eastern Mediterranean (Greece).}, - number = {1}, - journal = {Climate Research}, - author = {Bardossy, A. and Stehlik, J. and Caspary, H. J.}, - month = dec, - year = {2002}, - pages = {11--22}, - annote = {636CFCLIMATE RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:000180436400002}, -} - -@article{bardossy_multiscale_2012, - title = {Multiscale spatial recorrelation of {RCM} precipitation to produce unbiased climate change scenarios over large areas and small}, - volume = {48}, - issn = {1944-7973}, - doi = {10.1029/2011wr011524}, - number = {9}, - journal = {Water Resources Research}, - author = {Bárdossy, András and Pegram, Geoffrey}, - year = {2012}, - keywords = {climate change, precipitation, 1637 Regional climate change, RCM, 1817 Extreme events, 1854 Precipitation, spatial structure}, - pages = {W09502}, -} - -@article{bardossy_downscaling_2011, - title = {Downscaling precipitation using regional climate models and circulation patterns toward hydrology}, - volume = {47}, - issn = {0043-1397}, - doi = {10.1029/2010wr009689}, - abstract = {The aim of this paper is to define a method for determining reasonable estimates of rainfall modeled by global circulation models (GCMs) coupled with regional climate models (RCMs). The paper describes and uses two new procedures designed to give confidence in the interpretation of such rainfall estimates. The first of these procedures is the use of circulation patterns (CPs) to define quantile-quantile (Q-Q) transforms between observed and RCM-estimated rainfall (the CPs were derived from sea level pressure (SLP) fields obtained from reanalysis of historical daily weather in a previous study). The Q-Q transforms are derived using two downscaling techniques during a 20 year calibration period and were validated during a 10 year period of observations. The second novel procedure is the use of a double Q-Q transform to estimate the rainfall patterns and amounts from GCM-RCM predictions of SLP and rainfall fields during a future period. This procedure is essential because we find that the CP-dependent rainfall frequency distributions on each block are unexpectedly different from the corresponding historical distributions. The daily rainfall fields compared are recorded on a 25 km grid over the Rhine basin in Germany; the observed daily data are averaged over the grid blocks, and the RCM values have been estimated over the same grid. Annual extremes, recorded on each block during the validation period, of (1) maximum daily rainfall and (2) the lowest 5\% of filtered rainfall were calculated to determine the ability of RCMs to capture rainfall characteristics which are important for hydrological applications. The conclusions are that (1) RCM outputs used here are good at capturing the patterns and rankings of CP-dependent rainfall; (2) CP-dependent downscaling, coupled with the double Q-Q transform, gives good estimates of the rainfall during the validation period; (3) because the RCMs offer future CP-dependent rainfall distributions that are different from the observed distributions, it is judged that these predictions, once modified by the double Q-Q transforms, are hydrologically reasonable; and (4) the climate in the Rhine basin in the future, as modeled by the RCMs, is likely to be wetter than in the past. The results suggest that such future projections may be used with cautious confidence.}, - number = {4}, - journal = {Water Resources Research}, - author = {Bárdossy, András and Pegram, Geoffrey}, - year = {2011}, - keywords = {precipitation, 1840 Hydrology: Hydrometeorology, 1854 Hydrology: Precipitation, 3305 Atmospheric Processes: Climate change and variability, 3355 Atmospheric Processes: Regional modeling, downscaling}, - pages = {W04505}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{ballester_changes_2010, - title = {Changes in {European} temperature extremes can be predicted from changes in {PDF} central statistics}, - volume = {98}, - issn = {0165-0009}, - doi = {10.1007/s10584-009-9758-0}, - abstract = {Although uncertainties are still large, many potentially dangerous effects have already been identified concerning the impacts of global warming on human societies. For example, the record-breaking 2003 summer heat wave in Europe has given a glimpse of possible future European climate conditions. Here we use an ensemble of regional climate simulations for the end of the twentieth and twenty-first centuries over Europe to show that frequency, length and intensity changes in warm and cold temperature extremes can be derived to a close approximation from the knowledge of changes in three central statistics, the mean, standard deviation and skewness of the Probability Distribution Function, for which current climate models are better suited. In particular, the effect of the skewness parameter appears to be crucial, especially in the case of cold extremes, since it mostly explains the relative warming of these events compared to the whole distribution. An application of this finding is that the future impacts of extreme heat waves and cold spells on non-climatological variables (e.g., mortality) can be estimated to a first-order approximation from observed time series of daily temperature transformed in order to account for simulated changes in these three statistics.}, - language = {English}, - number = {1-2}, - journal = {Clim. Change}, - author = {Ballester, J. and Giorgi, F. and Rodo, X.}, - month = jan, - year = {2010}, - keywords = {climate-change, future, impacts, enso, human health}, - pages = {277--284}, - annote = {ISI Document Delivery No.: 532WSTimes Cited: 2Cited Reference Count: 14Ballester, Joan Giorgi, Filippo Rodo, XavierCatalan Ministry of University and Research ; Spanish Ministry of Education and Science [CGL2007-63053]We acknowledge support from the Catalan Ministry of University and Research, and from the project PANDORA of the Spanish Ministry of Education and Science (reference number CGL2007-63053). Climate simulations have been provided through the PRUDENCE data archive, funded by the EU through contract EVK2-CT2001-00132.SpringerDordrecht}, - annote = {The following values have no corresponding Zotero field:auth-address: [Ballester, Joan; Rodo, Xavier] Inst Catala Ciencies Clima IC3, Barcelona 08005, Catalonia, Spain. [Giorgi, Filippo] Abdus Salam Int Ctr Theoret Phys, Trieste, Italy. Ballester, J, Inst Catala Ciencies Clima IC3, Carrer Dr Trueta 203, Barcelona 08005, Catalonia, Spain. joanballester@ic3.catalt-title: Clim. Changeaccession-num: ISI:000272781700013work-type: Article}, -} - -@article{bales_mountain_2006, - title = {Mountain hydrology of the western {United} {States}}, - volume = {42}, - journal = {Water Resources Research}, - author = {Bales, R. C. and Molotch, N. P. and Painter, T. H. and Dettinger, M. D. and Rice, R. and Dozier, J.}, - year = {2006}, - pages = {W08432, doi:10.1029/2005WR004387}, -} - -@article{bardossy_downscaling_1997, - title = {Downscaling from {GCMs} to local climate through stochastic linkages}, - volume = {49}, - abstract = {A methodology for estimating local climate variables such as regional precipitation and temperature using atmospheric circulation patterns is developed. A sequence of observed daily air pressure distributions is used to define circulation patterns. The classification of the circulation patterns is constructed using a fuzzy rule based approach. A multivariate stochastic model describes the link between circulation patterns and daily precipitation and daily mean temperatures at a number of selected locations. Model parameters are estimated using observed data. To assess precipitation under changed climate circulation, patterns derived from GCM output pressure values are used to condition the stochastic precipitation model. A link between the occurrence of circulation patterns and extreme precipitation and floods is also discussed. The methodology is demonstrated by the results obtained for selected European and North American locations. (C) 1997 Academic Press Limited}, - number = {1}, - journal = {Journal of Environmental Management}, - author = {Bardossy, A.}, - month = jan, - year = {1997}, - pages = {7--17}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Environ. Manage.accession-num: ISI:A1997WF16400002}, - annote = {WF164J ENVIRON MANAGE}, -} - -@article{baker_shape_2009, - title = {The {Shape} of {Things} to {Come}: {Why} {Is} {Climate} {Change} {So} {Predictable}?}, - volume = {22}, - issn = {0894-8755}, - doi = {10.1175/2009jcli2647.1}, - abstract = {The framework of feedback analysis is used to explore the controls on the shape of the probability distribution of global mean surface temperature response to climate forcing. It is shown that ocean heat uptake, which delays and damps the temperature rise, can be represented as a transient negative feedback. This transient negative feedback causes the transient climate change to have a narrower probability distribution than that of the equilibrium climate response (the climate sensitivity). In this sense, climate change is much more predictable than climate sensitivity. The width of the distribution grows gradually over time, a consequence of which is that the larger the climate change being contemplated, the greater the uncertainty is about when that change will be realized. Another consequence of this slow growth is that further efforts to constrain climate sensitivity will be of very limited value for climate projections on societally relevant time scales. Finally, it is demonstrated that the effect on climate predictability of reducing uncertainty in the atmospheric feedbacks is greater than the effect of reducing uncertainty in ocean feedbacks by the same proportion. However, at least at the global scale, the total impact of uncertainty in climate feedbacks is dwarfed by the impact of uncertainty in climate forcing, which in turn is contingent on choices made about future anthropogenic emissions.}, - language = {English}, - number = {17}, - journal = {J. Clim.}, - author = {Baker, M. B. and Roe, G. H.}, - month = aug, - year = {2009}, - keywords = {model, sensitivity, variability, temperature, projections, system, dependence, feedbacks}, - pages = {4574--4589}, - annote = {ISI Document Delivery No.: 488ULTimes Cited: 4Cited Reference Count: 37Baker, Marcia B. Roe, Gerard H.Amer meteorological socBoston}, - annote = {The following values have no corresponding Zotero field:auth-address: [Baker, Marcia B.; Roe, Gerard H.] Univ Washington, Dept Earth \& Space Sci, Seattle, WA 98195 USA. Baker, MB, Univ Washington, Dept Earth \& Space Sci, 070 Johnson Hall, Seattle, WA 98195 USA. mbbaker@u.washington.edualt-title: J. Clim.accession-num: ISI:000269375600008work-type: Article}, -} - -@article{bacon_vulnerability_2017, - title = {Vulnerability to {Cumulative} {Hazards}: {Coping} with the {Coffee} {Leaf} {Rust} {Outbreak}, {Drought}, and {Food} {Insecurity} in {Nicaragua}}, - volume = {93}, - issn = {0305-750X}, - doi = {https://doi.org/10.1016/j.worlddev.2016.12.025}, - journal = {World Development}, - author = {Bacon, Christopher M. and Sundstrom, William A. and Stewart, Iris T. and Beezer, David}, - year = {2017}, - keywords = {climate variability, agriculture, livelihoods, Nicaragua, resilience}, - pages = {136--152}, -} - -@article{bacon_food_2015, - title = {Food sovereignty, food security and fair trade: the case of an influential {Nicaraguan} smallholder cooperative}, - volume = {36}, - issn = {0143-6597}, - doi = {10.1080/01436597.2015.1002991}, - number = {3}, - journal = {Third World Quarterly}, - author = {Bacon, Christopher M.}, - month = mar, - year = {2015}, - pages = {469--488}, - annote = {The following values have no corresponding Zotero field:publisher: Routledge}, -} - -@article{ashley_flood_2008, - title = {Flood {Fatalities} in the {United} {States}}, - volume = {47}, - issn = {1558-8424}, - doi = {10.1175/2007jamc1611.1}, - abstract = {Abstract This study compiles a nationwide database of flood fatalities for the contiguous United States from 1959 to 2005. Assembled data include the location of fatalities, age and gender of victims, activity and/or setting of fatalities, and the type of flood events responsible for each fatality report. Because of uncertainties in the number of flood deaths in Louisiana from Hurricane Katrina, these data are not included in the study. Analysis of these data reveals that a majority of fatalities are caused by flash floods. People between the ages of 10 and 29 and {\textgreater}60 yr of age are found to be more vulnerable to floods. Findings reveal that human behavior contributes to flood fatality occurrences. These results also suggest that future structural modifications of flood control designs (e.g., culverts and bridges) may not reduce the number of fatalities nationwide. Spatially, flood fatalities are distributed across the United States, with high-fatality regions observed along the northeast Interstate-95 corridor, the Ohio River valley, and near the Balcones Escarpment in south-central Texas. The unique distributions found are likely driven by both physical vulnerabilities for flooding as well as the social vulnerabilities.}, - number = {3}, - journal = {Journal of Applied Meteorology and Climatology}, - author = {Ashley, Sharon T. and Ashley, Walker S.}, - month = mar, - year = {2008}, - pages = {805--818}, - annote = {doi: 10.1175/2007JAMC1611.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/2007JAMC1611.1}, -} - -@techreport{asfpm_flood_2013, - address = {Madison, WI, USA}, - title = {Flood {Mapping} for the {Nation}: {A} {Cost} {Analysis} for the {Nation}’s {Flood} {Map} {Inventory}}, - institution = {The Association of State Floodplain Managers}, - author = {ASFPM}, - year = {2013}, - pages = {15 pp.}, -} - -@article{arora_gcm-based_2002, - title = {A {GCM}-based assessment of the global moisture budget and the role of land-surface moisture reservoirs in processing precipitation}, - volume = {20}, - issn = {0930-7575}, - abstract = {The performance of the Canadian Land Surface Scheme (CLASS) when coupled to the CCCma third generation general circulation model is evaluated in an AMIP II simulation. Our primary aim is to understand how CLASS processes moisture and to compare model estimates of moisture budget components with observations. The modelled mean annual precipitation and runoff, and their latitudinal structures, compare well with observations although some discrepancies remain in the simulation of regional values of these quantities. The amplitude and phase of the first harmonic of the precipitation annual cycle also compares well with observations although less well over regions of sparse precipitation and/or high topography. In the model, the canopy plays a major role in processing moisture at the land surface indicating the importance of vegetation in climate. The canopy intercepts a large fraction of the precipitation and provides the medium for returning much moisture back to the atmosphere as evapotranspiration. Though important locally, the snow moisture reservoir plays a relatively minor role in the global moisture budget. It acts primarily as a storage and delay mechanism with winter precipitation released to the ground reservoir on melting. The ground moisture reservoir also plays a major role and processes a similar amount of moisture as the canopy, although in a different manner. The globally averaged model runoff compares well with observation-based estimates, although the model partitioning into surface runoff and drainage does not agree particularly well with the single available observation-based estimate. How moisture is processed at the land surface serves as a basis for model intercomparison and for understanding the modelled moisture budget and its variation and changes with climate change. Only the most basic quantities (precipitation, runoff, and partitioning of runoff into surface runoff and drainage) may be compared with observation-based estimates, however, and the establishment of more complete moisture budget remains an important need.}, - language = {English}, - number = {1}, - journal = {Climate Dynamics}, - author = {Arora, V. K. and Boer, G. J.}, - month = nov, - year = {2002}, - keywords = {climate variability, atmospheric models, deforestation, dynamics, parameterization, river basins, schemes pilps, soil wetness, vegetation, water-balance}, - pages = {13--29}, - annote = {627JGTimes Cited:4Cited References Count:59}, - annote = {The following values have no corresponding Zotero field:auth-address: Arora, VK Univ Victoria, Meterorol Serv Canad, Canadian Ctr Climate Modelling \& Anal, POB 1700,STN CSC, Victoria, BC V8W 2Y2, Canada Univ Victoria, Meterorol Serv Canad, Canadian Ctr Climate Modelling \& Anal, Victoria, BC V8W 2Y2, Canadaaccession-num: ISI:000179929600002}, -} - -@article{arora_effects_2001, - title = {Effects of simulated climate change on the hydrology of major river basins}, - volume = {106}, - issn = {0747-7309}, - abstract = {Changes in the climatology of precipitation, evapotranspiration, and soil moisture lead also to changes in runoff and streamflow. The potential effects of global warming on the hydrology of 23 major rivers are investigated. The runoff simulated by the Canadian Cetre for Climate Modeling and Analysis (CCCma) coupled climate model for the current climate is routed through the river system to the river mouth and compared with results for the warmer climate simulated to occur towards the end of the century. Changes in mean discharge, in the amplitude and phase of the annual streamflow cycle, in the annual maximum discharge (the flood) and its standard deviation, and in flow duration curves are all examined. Changes in flood magnitudes for different return periods are estimated using extreme value analysis. In the warmer climate, there is a general decrease in runoff and 15 out of the 23 rivers considered experience a reduction in annual mean discharge (with a median reduction of 32\%). The changes in runoff are not uniform and discharge increases for 8 rivers (with a median increase of 13\%). Middle- and high- latitude rivers typically show marked changes in the amplitude and phase of their annual cycle associated with a decrease in snowfall and an earlier spring melt in the warmer climate. Low-latitude rivers exhibit changes in mean discharge but modest changes in their annual cycle. The analysis of annual flood magnitudes show that 17 out of 23 rivers experience a reduction in mean annual flood (a median reduction of 20\%). Changes in now duration curves are used to characterize the different kinds of behavior exhibited by different groups of rivers. Differences in the regional distribution of simulated precipitation and runoff for the control simulation currently limit the application of the approach. The inferred hydrological changes are, nevertheless, plausible and consistent responses to simulated changes in precipitation and evapotranspiration and indicate the kinds of hydrological changes that could occur in a warmer climate.}, - language = {English}, - number = {D4}, - journal = {Journal of Geophysical Research-Atmospheres}, - author = {Arora, V. K. and Boer, G. J.}, - month = feb, - year = {2001}, - keywords = {co2, sensitivity, impact, greenhouse-gas, increase, general-circulation model, runoff, scale, sulfate aerosols, water-resources}, - pages = {3335--3348}, - annote = {405JETimes Cited:23Cited References Count:56}, - annote = {The following values have no corresponding Zotero field:auth-address: Arora, VK Univ Victoria, Meteorol Serv Canada, Canadian Ctr Climate Modeling \& Anal, STN CSC, POB 1700, Victoria, BC V8W 2Y2, Canada Univ Victoria, Meteorol Serv Canada, Canadian Ctr Climate Modeling \& Anal, STN CSC, Victoria, BC V8W 2Y2, Canadaaccession-num: ISI:000167155600002}, -} - -@article{arnold_large_1998, - title = {Large {Area} {Hydrologic} {Modeling} and {Assessment} {Part} {I}: {Model} {Development}}, - volume = {34}, - issn = {1752-1688}, - doi = {10.1111/j.1752-1688.1998.tb05961.x}, - number = {1}, - journal = {JAWRA Journal of the American Water Resources Association}, - author = {Arnold, J. G. and Srinivasan, R. and Muttiah, R. S. and Williams, J. R.}, - year = {1998}, - keywords = {simulation, surface water hydrology, agricultural land management, erosion, large area modeling, nonpoint source pollution, plant growth, sedimentation}, - pages = {73--89}, - annote = {The following values have no corresponding Zotero field:publisher: Blackwell Publishing Ltd}, -} - -@article{arnell_impacts_2016, - title = {The impacts of climate change on river flood risk at the global scale}, - volume = {134}, - issn = {1573-1480}, - doi = {10.1007/s10584-014-1084-5}, - abstract = {This paper presents an assessment of the implications of climate change for global river flood risk. It is based on the estimation of flood frequency relationships at a grid resolution of 0.5 × 0.5°, using a global hydrological model with climate scenarios derived from 21 climate models, together with projections of future population. Four indicators of the flood hazard are calculated; change in the magnitude and return period of flood peaks, flood-prone population and cropland exposed to substantial change in flood frequency, and a generalised measure of regional flood risk based on combining frequency curves with generic flood damage functions. Under one climate model, emissions and socioeconomic scenario (HadCM3 and SRES A1b), in 2050 the current 100-year flood would occur at least twice as frequently across 40 \% of the globe, approximately 450 million flood-prone people and 430 thousand km2 of flood-prone cropland would be exposed to a doubling of flood frequency, and global flood risk would increase by approximately 187 \% over the risk in 2050 in the absence of climate change. There is strong regional variability (most adverse impacts would be in Asia), and considerable variability between climate models. In 2050, the range in increased exposure across 21 climate models under SRES A1b is 31–450 million people and 59 to 430 thousand km2 of cropland, and the change in risk varies between −9 and +376 \%. The paper presents impacts by region, and also presents relationships between change in global mean surface temperature and impacts on the global flood hazard. There are a number of caveats with the analysis; it is based on one global hydrological model only, the climate scenarios are constructed using pattern-scaling, and the precise impacts are sensitive to some of the assumptions in the definition and application.}, - number = {3}, - journal = {Climatic Change}, - author = {Arnell, Nigel W. and Gosling, Simon N.}, - year = {2016}, - pages = {387--401}, - annote = {The following values have no corresponding Zotero field:label: Arnell2014work-type: journal article}, -} - -@article{arnell_implications_2005, - title = {Implications of climate change for freshwater inflows to the {Arctic} {Ocean}}, - volume = {110}, - journal = {Journal of Geophysical Research}, - author = {Arnell, N. W.}, - year = {2005}, - pages = {D07105, doi:10.1029/2004JD005348}, -} - -@article{archfield_fragmented_2016, - title = {Fragmented patterns of flood change across the {United} {States}}, - issn = {1944-8007}, - doi = {10.1002/2016GL070590}, - journal = {Geophysical Research Letters}, - author = {Archfield, S. A. and Hirsch, R. M. and Viglione, A. and Blöschl, G.}, - year = {2016}, - keywords = {trends, 1616 Climate variability, climate, 1807 Climate impacts, 1821 Floods, floods, 1833 Hydroclimatology, 1860 Streamflow}, - pages = {2016GL070590}, -} - -@article{annan_reliability_2010, - title = {Reliability of the {CMIP3} ensemble}, - volume = {37}, - issn = {0094-8276}, - doi = {10.1029/2009gl041994}, - abstract = {We consider paradigms for interpretation and analysis of the CMIP3 ensemble of climate model simulations. The dominant paradigm in climate science, of an ensemble sampled from a distribution centred on the truth, is contrasted with the paradigm of a statistically indistinguishable ensemble, which has been more commonly adopted in other fields. This latter interpretation (which gives rise to a natural probabilistic interpretation of ensemble output) leads to new insights about the evaluation of ensemble performance. Using the well-known rank histogram method of analysis, we find that the CMIP3 ensemble generally provides a rather good sample under the statistically indistinguishable paradigm, although it appears marginally over-dispersive and exhibits some modest biases. These results contrast strongly with the incompatibility of the ensemble with the truth-centred paradigm. Thus, our analysis provides for the first time a sound theoretical foundation, with empirical support, for the probabilistic use of multi-model ensembles in climate research.}, - number = {2}, - journal = {Geophysical Research Letters}, - author = {Annan, J. D. and Hargreaves, J. C.}, - year = {2010}, - keywords = {1626 Global Change: Global climate models, 3245 Mathematical Geophysics: Probabilistic forecasting, 3275 Mathematical Geophysics: Uncertainty quantification, IPCC, multi-model ensemble}, - pages = {L02703}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{anderson_new_2004, - title = {The new {GFDL} global atmosphere and land model {AM2}-{LM2}: {Evaluation} with prescribed {SST} simulations}, - volume = {17}, - issn = {0894-8755}, - abstract = {The configuration and performance of a new global atmosphere and land model for climate research developed at the Geophysical Fluid Dynamics Laboratory (GFDL) are presented. The atmosphere model, known as AM2, includes a new gridpoint dynamical core, a prognostic cloud scheme, and a multispecies aerosol climatology, as well as components from previous models used at GFDL. The land model, known as LM2, includes soil sensible and latent heat storage, groundwater storage, and stomatal resistance. The performance of the coupled model AM2-LM2 is evaluated with a series of prescribed sea surface temperature (SST) simulations. Particular focus is given to the model's climatology and the characteristics of interannual variability related to El Nino Southern Oscillation (ENSO). -One AM2-LM2 integration was performed according to the prescriptions of the second Atmospheric Model Intercomparison Project (AMIP II) and data were submitted to the Program for Climate Model Diagnosis and Intercomparison (PCMDI). Particular strengths of AM2-LM2, as judged by comparison to other models participating in AMIP II, include its circulation and distributions of precipitation. Prominent problems of AM2 LM2 include a cold bias to surface and tropospheric temperatures, weak tropical cyclone activity, and weak tropical intraseasonal activity associated with the Madden-Julian oscillation. -An ensemble of 10 AM2-LM2 integrations with observed SSTs for the second half of the twentieth century permits a statistically reliable assessment of the model's response to ENSO. In general, AM2-LM2 produces a realistic simulation of the anomalies in tropical precipitation and extratropical circulation that are associated with ENSO.}, - language = {English}, - number = {24}, - journal = {Journal of Climate}, - author = {Anderson, J. L. and Balaji, V. and Broccoli, A. J. and Cooke, W. F. and Delworth, T. L. and Dixon, K. W. and Donner, L. J. and Dunne, K. A. and Freidenreich, S. M. and Garner, S. T. and Gudgel, R. G. and Gordon, C. T. and Held, I. M. and Hemler, R. S. and Horowitz, L. W. and Klein, S. A. and Knutson, T. R. and Kushner, P. J. and Langenhost, A. R. and Lau, N. C. and Liang, Z. and Malyshev, S. L. and Milly, P. C. D. and Nath, M. J. and Ploshay, J. J. and Ramaswamy, V. and Schwarzkopf, M. D. and Shevliakova, E. and Sirutis, J. J. and Soden, B. J. and Stern, W. F. and Thompson, L. A. and Wilson, R. J. and Wittenberg, A. T. and Wyman, B. L. and GFDL Global Atmospheric Model Dev}, - month = dec, - year = {2004}, - keywords = {southern oscillation, climate variability, general-circulation model, cloud liquid water, cumulus parameterization, large-scale models, part i, radiation budget experiment, relaxed arakawa-schubert, step-mountain coordinate}, - pages = {4641--4673}, - annote = {884KZTimes Cited:0Cited References Count:110}, - annote = {The following values have no corresponding Zotero field:auth-address: Klein, SA Princeton Univ, NOAA, GFDL, Forrestal Campus,US Rte 1,POB 308, Princeton, NJ 08542 USA Princeton Univ, NOAA, GFDL, Princeton, NJ 08542 USA Princeton Univ, Atmospher \& Ocean Sci Program, Princeton, NJ 08544 USA RS Informat Syst, Mclean, VA USA US Geol Survey, Princeton, NJ USA Princeton Univ, Dept Ecol \& Evolutionary Biol, Princeton, NJ 08544 USAaccession-num: ISI:000226084900005}, -} - -@article{anderson_progress_2008, - title = {Progress on incorporating climate change into management of {California}’s water resources}, - volume = {87 (Suppl 1)}, - journal = {Climatic Change}, - author = {Anderson, J. and Chung, F. and Anderson, M. and Brekke, L. D. and Easton, D. and Ejeta, M. and Peterson, R. and Snyder, R.}, - year = {2008}, - pages = {S91--S108, DOI 10.1007/s10584--007--9353--1}, -} - -@techreport{anderson_calibration_2002, - address = {Silver Spring, MD}, - title = {Calibration of {Conceptual} {Hydrologic} {Models} for {Use} in {River} {Forecasting}, {Technical} {Report}, {NWS} 45}, - institution = {NOAA}, - author = {Anderson, E. A.}, - editor = {NOAA-NWS Hydrology Laboratory}, - year = {2002}, -} - -@techreport{anderson_point_1976, - address = {Silver Spring, MD}, - title = {A point energy and mass balance model of a snow cover, {NOAA} {Tech}. {Rep}. {NWS} 19}, - institution = {Office of Hydrology, NWS}, - author = {Anderson, E. A.}, - year = {1976}, - pages = {150}, -} - -@article{amador_atmospheric_2006, - title = {Atmospheric forcing of the eastern tropical {Pacific}: {A} review}, - volume = {69}, - issn = {0079-6611}, - doi = {https://doi.org/10.1016/j.pocean.2006.03.007}, - number = {2–4}, - journal = {Progress in Oceanography}, - author = {Amador, Jorge A. and Alfaro, Eric J. and Lizano, Omar G. and Magaña, Victor O.}, - year = {2006}, - keywords = {Eastern tropical Pacific climatology, Hurricanes, Intra-Americas Low-Level Jet, Low-level jets, Mid-summer drought, Seasonal cycle of precipitation, Surface ocean waves, Trans-isthmic jets, Tropical rain producing systems, Wind stress curl}, - pages = {101--142}, -} - -@article{alfaro_caracterizacion_2014, - title = {Caracterización del “veranillo” en dos cuencas de la vertiente del {Pacífico} de {Costa} {Rica}, {América} {Central}}, - volume = {62}, - doi = {http://dx.doi.org/10.15517/rbt.v62i4.20010}, - journal = {Revista de Biología Tropical/International Journal of Tropical Biology and Conservation}, - author = {Alfaro, E. J.}, - year = {2014}, - pages = {1--15}, -} - -@article{adam_adjustment_2003, - title = {Adjustment of global gridded precipitation for systematic bias}, - volume = {108}, - number = {D9}, - journal = {Journal of Geophysical Research}, - author = {Adam, J. C. and Lettenmaier, D. P.}, - year = {2003}, - pages = {1--14}, -} - -@article{amador_intra-americas_2008, - title = {The {Intra}-{Americas} {Sea} {Low}-level {Jet}}, - volume = {1146}, - issn = {1749-6632}, - doi = {10.1196/annals.1446.012}, - number = {1}, - journal = {Annals of the New York Academy of Sciences}, - author = {Amador, Jorge A.}, - year = {2008}, - keywords = {El Niño—Southern Oscillation, ENSO, Intra-Americas low-level jet, MM5 modeling, tropical climate variability}, - pages = {153--188}, - annote = {The following values have no corresponding Zotero field:publisher: Blackwell Publishing Inc}, -} - -@article{alfieri_global_2015, - title = {Global warming increases the frequency of river floods in {Europe}}, - volume = {19}, - issn = {1607-7938}, - doi = {10.5194/hess-19-2247-2015}, - number = {5}, - journal = {Hydrol. Earth Syst. Sci.}, - author = {Alfieri, L. and Burek, P. and Feyen, L. and Forzieri, G.}, - year = {2015}, - pages = {2247--2260}, - annote = {HESS}, - annote = {The following values have no corresponding Zotero field:publisher: Copernicus Publications}, -} - -@article{alexander_global_2006, - title = {Global observed changes in daily climate extremes of temperature and precipitation}, - volume = {111}, - issn = {0148-0227}, - doi = {10.1029/2005jd006290}, - abstract = {A suite of climate change indices derived from daily temperature and precipitation data, with a primary focus on extreme events, were computed and analyzed. By setting an exact formula for each index and using specially designed software, analyses done in different countries have been combined seamlessly. This has enabled the presentation of the most up-to-date and comprehensive global picture of trends in extreme temperature and precipitation indices using results from a number of workshops held in data-sparse regions and high-quality station data supplied by numerous scientists world wide. Seasonal and annual indices for the period 1951\&\#8211;2003 were gridded. Trends in the gridded fields were computed and tested for statistical significance. Results showed widespread significant changes in temperature extremes associated with warming, especially for those indices derived from daily minimum temperature. Over 70\% of the global land area sampled showed a significant decrease in the annual occurrence of cold nights and a significant increase in the annual occurrence of warm nights. Some regions experienced a more than doubling of these indices. This implies a positive shift in the distribution of daily minimum temperature throughout the globe. Daily maximum temperature indices showed similar changes but with smaller magnitudes. Precipitation changes showed a widespread and significant increase, but the changes are much less spatially coherent compared with temperature change. Probability distributions of indices derived from approximately 200 temperature and 600 precipitation stations, with near-complete data for 1901\&\#8211;2003 and covering a very large region of the Northern Hemisphere midlatitudes (and parts of Australia for precipitation) were analyzed for the periods 1901\&\#8211;1950, 1951\&\#8211;1978 and 1979\&\#8211;2003. Results indicate a significant warming throughout the 20th century. Differences in temperature indices distributions are particularly pronounced between the most recent two periods and for those indices related to minimum temperature. An analysis of those indices for which seasonal time series are available shows that these changes occur for all seasons although they are generally least pronounced for September to November. Precipitation indices show a tendency toward wetter conditions throughout the 20th century.}, - number = {D5}, - journal = {J. Geophys. Res.}, - author = {Alexander, L. V. and Zhang, X. and Peterson, T. C. and Caesar, J. and Gleason, B. and Klein Tank, A. M. G. and Haylock, M. and Collins, D. and Trewin, B. and Rahimzadeh, F. and Tagipour, A. and Rupa Kumar, K. and Revadekar, J. and Griffiths, G. and Vincent, L. and Stephenson, D. B. and Burn, J. and Aguilar, E. and Brunet, M. and Taylor, M. and New, M. and Zhai, P. and Rusticucci, M. and Vazquez-Aguirre, J. L.}, - year = {2006}, - keywords = {precipitation, trends, temperature, observations, 1616 Global Change: Climate variability, 3354 Atmospheric Processes: Precipitation, climate extremes, 3305 Atmospheric Processes: Climate change and variability}, - pages = {D05109}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{ajami_sustainable_2008, - title = {Sustainable water resource management under hydrological uncertainty}, - volume = {44}, - journal = {Water Resources Research}, - author = {Ajami, N. K. and Hornberger, G. M. and Sunding, D. L.}, - year = {2008}, - pages = {W11406, doi:10.1029/2007WR006736}, -} - -@article{ahmed_statistical_2013, - title = {Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the {U}.{S}. northeast}, - volume = {100}, - issn = {0921-8181}, - doi = {http://dx.doi.org/10.1016/j.gloplacha.2012.11.003}, - number = {0}, - journal = {Global and Planetary Change}, - author = {Ahmed, Kazi Farzan and Wang, Guiling and Silander, John and Wilson, Adam M. and Allen, Jenica M. and Horton, Radley and Anyah, Richard}, - year = {2013}, - keywords = {bias correction, statistical downscaling, climate change impact analysis, extreme climate index}, - pages = {320--332}, -} - -@article{ahrens_rainfall_2003, - title = {Rainfall downscaling in an alpine watershed applying a multiresolution approach}, - volume = {108}, - abstract = {[1] Distributed hydrological modeling in alpine watersheds needs high-resolution (similar to1 km in space, 1 h in time) rainfall input. Spatial resolution of operational numerical weather forecast or regional climate models is coarser by at least one order of magnitude. Stochastic downscaling of meteorological model output is the appropriate way to bridge this scale gap. This study examines radar data in an orographically complex area in the southern European Alps by multiplicative multiresolution decomposition. Multiresolution rainfall fluctuations exhibit simple scaling characteristics. This result motivates the implementation of a spatial downscaling model, an example of an anisotropic microcanonical multiplicative random cascade, that is applied and evaluated. Necessity and skill of downscaling is illustrated by hydrological modeling of four heavy precipitation events in an Alpine watershed ( total area of 2627 km(2)) with the grid- based model WaSiM-ETH. Downscaling of rainfall fields with 16 km grid spacing to fields with 1 km grid spacing improves the simulated event-mean runoff by 5-10\% and the peak runoff by up to 20\%.}, - number = {D8}, - journal = {Journal of Geophysical Research-Atmospheres}, - author = {Ahrens, B.}, - month = apr, - year = {2003}, - pages = {art. no.--8388}, - annote = {673RKJ GEOPHYS RES-ATMOS}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Geophys. Res.-Atmos.accession-num: ISI:000182593800001}, -} - -@article{ahmadpour_preparation_1997, - title = {The preparation of activated carbon from macadamia nutshell by chemical activation}, - volume = {35}, - issn = {0008-6223}, - number = {12}, - journal = {Carbon}, - author = {Ahmadpour, A. and Do, D. D.}, - year = {1997}, - keywords = {A. Activated carbon, B. carbonization, C. adsorption, D. porosity}, - pages = {1723--1732}, - annote = {The following values have no corresponding Zotero field:work-type: doi: 10.1016/S0008-6223(97)00127-9}, -} - -@article{abdulla_application_1996, - title = {Application of a macroscale hydrologic model to estimate the water balance of the {Arkansas}-{Red} {River} basin}, - volume = {101}, - number = {D3}, - journal = {Journal of Geophysical Research}, - author = {Abdulla, F. A. and Lettenmaier, D. P. and Wood, E. F. and Smith, J. A.}, - year = {1996}, - pages = {7449--7459}, -} - -@article{adam_correction_2006, - title = {Correction of global precipitation products for orographic effects}, - volume = {19}, - number = {1}, - journal = {Journal of Climate}, - author = {Adam, J. C. and Clark, E. A. and Lettenmaier, D. P. and Wood, E. F.}, - year = {2006}, - pages = {15--38}, -} - -@article{achutarao_enso_2006, - title = {{ENSO} {Simulation} in {Coupled} {Ocean}-{Atmosphere} {Models}: {Are} the {Current} {Models} {Better}?}, - volume = {27}, - journal = {Climate Dynamics}, - author = {AchutaRao, K. M. and Sperber, K. R.}, - year = {2006}, - pages = {1--15, doi:10.1007/s00382--006--0119--7}, -} - -@article{abatzoglou_comparison_2012, - title = {A comparison of statistical downscaling methods suited for wildfire applications}, - volume = {32}, - issn = {1097-0088}, - doi = {10.1002/joc.2312}, - number = {5}, - journal = {International Journal of Climatology}, - author = {Abatzoglou, John T. and Brown, Timothy J.}, - year = {2012}, - keywords = {climate change, downscaling, wildfire}, - pages = {772--780}, - annote = {The following values have no corresponding Zotero field:publisher: John Wiley \& Sons, Ltd.}, -} - -@article{oshima_application_2002, - title = {An application of statistical downscaling to estimate surface air temperature in {Japan}}, - volume = {107}, - abstract = {[1] In this study, a statistical downscaling model based on singular value decomposition was applied to estimate the monthly mean temperature field in Japan for January and July. The regression model estimated surface air temperature in Japan from upper air temperature in east Asia with root-mean-square errors of around 1.0degrees C for an independent verification period. The method was applied to the output of a CO2 transient run of NCAR-CSM, and the result was compared with the output of NCAR-RegCM2.5 nested in CSM. The statistical model reproduced the spatial distribution of the temperature more realistically than the CSM output. In January the results corresponded well between the models except that the climate simulated by RegCM was generally cooler than that estimated using the statistical method mainly due to the unrealistic RegCM topography. In July the results did not correspond well between the methods, since the climate is more complex and difficult to be estimated solely from the upper air temperature field. Temperature rise from 1CO(2) climate to 2CO(2) climate was larger in RegCM than in the statistical method for both months reflecting the influence of sea surface temperature rise. It was concluded that this statistical downscaling method can be applied to estimate the January mean temperature in Japan, although other predictor variables such as sea surface temperature should be included to improve the estimation in July.}, - number = {D10}, - journal = {Journal of Geophysical Research-Atmospheres}, - author = {Oshima, N. and Kato, H. and Kadokura, S.}, - month = may, - year = {2002}, - pages = {art. no.--4095}, - annote = {609HAJ GEOPHYS RES-ATMOS}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Geophys. Res.-Atmos.accession-num: ISI:000178896500011}, -} - -@book{j_rolf_olsen_adapting_2015, - address = {Reston, VA}, - title = {Adapting {Infrastructure} and {Civil} {Engineering} {Practice} to a {Changing} {Climate}}, - abstract = {Sponsored by the Committee on Technical Advancement of ASCE Adapting Infrastructure and Civil Engineering Practice to a Changing Climate presents an accurate discussion of the potential significance of climate change to engineering practice. Although considerable evidence indicates that the climate is changing, significant uncertainty exists regarding the location, timing, and magnitude of this change over the lifetime of infrastructure. Practicing engineers are faced with the dilemma of balancing future needs for engineered infrastructure with the risks posed by the effects of climate change on long-term engineering projects. The gap between climate science and engineering practice somehow must be bridged. This report identifies the technical requirements and civil engineering challenges raised by adaptation to a changing climate. Topics include: review of climate science for engineering practice; incorporating climate science into engineering practice; civil engineering sectors that might be affected by climate change; needs for research, development, and demonstration projects; and summary, conclusions, and recommendations. Three appendixes illustrate different engineering approaches to assessing or preparing for climate change. Practitioners, researchers, educators, and students of civil engineering, as well as government officials and allied professionals, will be fascinated by this discussion of the trade-offs between the expenses of increasing system reliability and the potential costs and consequences of failure to future generations.}, - publisher = {Committee on Adaptation to a Changing Climate, American Society of Civil Engineers}, - author = {J. Rolf Olsen}, - year = {2015}, - annote = {The following values have no corresponding Zotero field:electronic-resource-num: doi:10.1061/9780784479193}, -} - -@article{oki_global_2006, - title = {Global {Hydrological} {Cycles} and {World} {Water} {Resources}}, - volume = {313}, - doi = {10.1126/science.1128845}, - abstract = {Water is a naturally circulating resource that is constantly recharged. Therefore, even though the stocks of water in natural and artificial reservoirs are helpful to increase the available water resources for human society, the flow of water should be the main focus in water resources assessments. The climate system puts an upper limit on the circulation rate of available renewable freshwater resources (RFWR). Although current global withdrawals are well below the upper limit, more than two billion people live in highly water-stressed areas because of the uneven distribution of RFWR in time and space. Climate change is expected to accelerate water cycles and thereby increase the available RFWR. This would slow down the increase of people living under water stress; however, changes in seasonal patterns and increasing probability of extreme events may offset this effect. Reducing current vulnerability will be the first step to prepare for such anticipated changes.}, - number = {5790}, - journal = {Science}, - author = {Oki, Taikan and Kanae, Shinjiro}, - month = aug, - year = {2006}, - pages = {1068--1072}, -} - -@article{oelschlagel_method_1995, - title = {A {Method} for {Downscaling} {Global} {Climate} {Model}-{Calculations} by a {Statistical} {Weather} {Generator}}, - volume = {82}, - abstract = {A method is presented for deriving weather scenarios for studies of the impacts of possible climate change on the carbon-nitrogen dynamics in soil at places of interest. The future development of these soil processes is examined with a simulation model requiring weather data as input. The weather data are produced by a ''stochastic weather generator'' which is parametrised with meteorological observations. The information about possible climate change come from general circulation model (GCM) calculation results at defined grid points on the earth surface. A spatial interpolation procedure is used to combine the GCM information with the weather generation procedure by updating the generator parameters in dependence on the special moment in future.}, - number = {2}, - journal = {Ecological Modelling}, - author = {Oelschlagel, B.}, - month = oct, - year = {1995}, - pages = {199--204}, - annote = {RV454ECOL MODEL}, - annote = {The following values have no corresponding Zotero field:alt-title: Ecol. Model.accession-num: ISI:A1995RV45400008}, -} - -@article{sheila_m_olmstead_climate_2016, - title = {Climate {Change} and {Water} {Resources}: {Some} {Adaptation} {Tools} and {Their} {Limits}}, - volume = {142}, - doi = {doi:10.1061/(ASCE)WR.1943-5452.0000642}, - number = {6}, - journal = {Journal of Water Resources Planning and Management}, - author = {Sheila M. Olmstead and Karen A. Fisher-Vanden and Renata Rimsaite}, - year = {2016}, -} - -@article{oglesby_high-resolution_2016, - title = {A {High}-{Resolution} {Modeling} {Strategy} to {Assess} {Impacts} of {Climate} {Change} for {Mesoamerica} and the {Caribbean}}, - volume = {5}, - doi = {10.4236/ajcc.2016.52019}, - journal = {American Journal of Climate Change}, - author = {Oglesby, R. and Rowe, C. and Grunwaldt, A. and Ferreira, I. and Ruiz, F. and Campbell, J. and Alvarado, L. and Argenal, F. and Olmedo, B. and del Castillo, A. and Lopez, P. and Matos, E. and Nava, Y. and Perez, C. and Perez, J.}, - year = {2016}, - pages = {202--228}, -} - -@article{nykanen_orographic_2003, - title = {Orographic influences on the multiscale statistical properties of precipitation}, - volume = {108}, - abstract = {[1] A case study consisting of three consecutive orographic thunderstorms that occurred on 27 June 1995 in the Blue Ridge Mountains of Virginia is examined from the perspective of relating the surrounding meteorological forcings and underlying orography to the multiscale structure of the rainfall fields. The statistical framework for this multiscale characterization is cascade based and offers a parsimonious parameterization, which can be used in future studies for the purposes of stochastically downscaling rainfall fields. Sequences of radar- derived rainfall maps provide data with which to characterize the multiscale statistical structure and variability of the rainfall. In this case study, rainfall falling at higher topographic elevations was shown to be more intermittent and more organized than rainfall at lower elevations. This trend is contrary to previous studies analyzing the multiscale structure of orographic rainfall and is argued to be the direct result of differing meteorological factors for this type of storm such as the presence of warm rain processes and leeside orographic forcing.}, - number = {D8}, - journal = {Journal of Geophysical Research-Atmospheres}, - author = {Nykanen, D. K. and Harris, D.}, - month = mar, - year = {2003}, - pages = {art. no.--8381}, - annote = {667GRJ GEOPHYS RES-ATMOS}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Geophys. Res.-Atmos.accession-num: ISI:000182223400004}, -} - -@article{odonnell_simple_1999, - title = {A simple algorithm for generating streamflow networks for grid-based, macroscale hydrological models}, - volume = {13}, - journal = {Hydrological Processes}, - author = {O’Donnell, G. and Nijssen, B. and Lettenmaier, D. P.}, - year = {1999}, - pages = {1269--1275}, -} - -@article{niu_community_2011, - title = {The community {Noah} land surface model with multiparameterization options ({Noah}-{MP}): 1. {Model} description and evaluation with local-scale measurements}, - volume = {116}, - doi = {doi:10.1029/2010JD015139}, - abstract = {This first paper of the two-part series describes the objectives of the community efforts in improving the Noah land surface model (LSM), documents, through mathematical formulations, the augmented conceptual realism in biophysical and hydrological processes, and introduces a framework for multiple options to parameterize selected processes (Noah-MP). The Noah-MP's performance is evaluated at various local sites using high temporal frequency data sets, and results show the advantages of using multiple optional schemes to interpret the differences in modeling simulations. The second paper focuses on ensemble evaluations with long-term regional (basin) and global scale data sets. The enhanced conceptual realism includes (1) the vegetation canopy energy balance, (2) the layered snowpack, (3) frozen soil and infiltration, (4) soil moisture-groundwater interaction and related runoff production, and (5) vegetation phenology. Sample local-scale validations are conducted over the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site, the W3 catchment of Sleepers River, Vermont, and a French snow observation site. Noah-MP shows apparent improvements in reproducing surface fluxes, skin temperature over dry periods, snow water equivalent (SWE), snow depth, and runoff over Noah LSM version 3.0. Noah-MP improves the SWE simulations due to more accurate simulations of the diurnal variations of the snow skin temperature, which is critical for computing available energy for melting. Noah-MP also improves the simulation of runoff peaks and timing by introducing a more permeable frozen soil and more accurate simulation of snowmelt. We also demonstrate that Noah-MP is an effective research tool by which modeling results for a given process can be interpreted through multiple optional parameterization schemes in the same model framework.}, - number = {D12}, - journal = {Journal of Geophysical Research: Atmospheres}, - author = {Niu, Guo-Yue and Yang, Zong-Liang and Mitchell, Kenneth E. and Chen, Fei and Ek, Michael B. and Barlage, Michael and Kumar, Anil and Manning, Kevin and Niyogi, Dev and Rosero, Enrique and Tewari, Mukul and Xia, Youlong}, - year = {2011}, -} - -@article{nilsson_fragmentation_2005, - title = {Fragmentation and flow regulation of the world’s large river systems}, - volume = {308}, - journal = {Science}, - author = {Nilsson, C. and Reidy, C. A. and Dynesius, M. and Revenga, C.}, - year = {2005}, - pages = {405--408}, -} - -@article{nijssen_streamflow_1997, - title = {Streamflow simulation for continental-scale basins}, - volume = {33}, - number = {4}, - journal = {Water Resources Research}, - author = {Nijssen, B. and Lettenmaier, D. P. and Liang, X. and Wetzel, S. W. and Wood, E.}, - year = {1997}, - pages = {711--724}, -} - -@article{nemesova_weather_1992, - title = {Weather {Categorization} in {Climate} {Change} {Research} - {Preliminary}-{Results}}, - volume = {36}, - abstract = {The results of an objective weather categorization am presented. The 9 meteorological variables recorded daily during winter seasons of 1961-66 at Prague-Clementinum represent the input dataset. The principal component analysis and a few clustering procedures have been evaluated. 5 component solution and the average linkage clustering method were found optimal. The winter days have been grouped, according to their meteorological character, into 14 clusters. The warm categories represent 20\% of the time and the cold categories less than 15\% of the days. The mean maps of 1000 hPa and 500 hPa are shown for a few selected categories. Clustering techniques applied to long-time instrumental series can provide a better basis for attempting to detect temperature changes which have taken place over a long time span.}, - number = {4}, - journal = {Studia Geophysica Et Geodaetica}, - author = {Nemesova, I. and Klimperova, N. and Huth, R. and Kalvova, J.}, - year = {1992}, - pages = {370--375}, - annote = {KE554STUD GEOPHYS GEOD}, - annote = {The following values have no corresponding Zotero field:alt-title: Studia Geophys. Geod.accession-num: ISI:A1992KE55400009}, -} - -@article{neelin_tropical_2006, - title = {Tropical drying trends in global warming models and observations}, - volume = {103}, - number = {16}, - journal = {Proceedings National Academy of Sciences}, - author = {Neelin, J. D. and Münnich, M. and Su, H. and Meyerson, J. E. and Holloway, C. E.}, - year = {2006}, - pages = {6110--6115}, -} - -@article{nash_river_1970, - title = {River flow forecasting through conceptual models part {I} -- {A} discussion of principles}, - volume = {10}, - number = {3}, - journal = {Journal of Hydrology}, - author = {Nash, J. E. and Sutcliffe, J. V.}, - year = {1970}, - pages = {282--290}, -} - -@article{nijssen_global_2001, - title = {Global retrospective estimation of soil moisture using the {VIC} land surface model, 1980-1993}, - volume = {14}, - number = {8}, - journal = {Journal of Climate}, - author = {Nijssen, B. and Schnur, R. and Lettenmaier, D. P.}, - year = {2001}, - pages = {1790--1808}, -} - -@article{nijssen_predicting_2001, - title = {Predicting the discharge of global rivers}, - volume = {14}, - journal = {Journal of Climate}, - author = {Nijssen, B. and O'Donnell, G. M. and Lettenmaier, D. P. and Lohmann, D. and Wood, E. F.}, - year = {2001}, - pages = {1790--1808}, -} - -@article{nijssen_hydrologic_2001, - title = {Hydrologic sensitivity of global rivers to climate change}, - volume = {50}, - issn = {0165-0009}, - abstract = {Climate predictions from four state-of-the-art general circulation models (GCMs) were used to assess the hydrologic sensitivity to climate change of nine large, continental river basins (Amazon, Amur, Mackenzie, Mekong, Mississippi, Severnaya Dvina, Xi, Yellow, Yenisei). The four climate models (HCCPR-CM2, HCCPR-CM3, MPI-ECHAM4, and DOE-PCM3) all predicted transient climate response to changing greenhouse gas concentrations, and incorporated modern land surface parameterizations. Model-predicted monthly average precipitation and temperature changes were downscaled to the river basin level using model increments (transient minus control) to adjust for GCM bias. The variable infiltration capacity (VIC) macroscale hydrological model (MHM) was used to calculate the corresponding changes in hydrologic fluxes (especially streamflow and evapotranspiration) and moisture storages. Hydrologic model simulations were performed for decades centered on 2025 and 2045. In addition, a sensitivity study was performed in which temperature and precipitation were increased independently by 2 degreesC and 10\%, respectively, during each of four seasons. All GCMs predict a warming for all nine basins, with the greatest warming predicted to occur during the winter months in the highest latitudes. Precipitation generally increases, but the monthly precipitation signal varies more between the models than does temperature. The largest changes in the hydrological cycle are predicted for the snow-dominated basins of mid to higher latitudes. This results in part from the greater amount of warming predicted for these regions, but more importantly, because of the important role of snow in the water balance. Because the snow pack integrates the effects of climate change over a period of months, the largest changes occur in early to mid spring when snow melt occurs. The climate change responses are somewhat different for the coldest snow dominated basins than for those with more transitional snow regimes. In the coldest basins, the response to warming is an increase of the spring streamflow peak, whereas for the transitional basins spring runoff decreases. Instead, the transitional basins have large increases in winter streamflows. The hydrological response of most tropical and mid-latitude basins to the warmer and somewhat wetter conditions predicted by the GCMs is a reduction in annual streamflow, although again, considerable disagreement exists among the different GCMs. In contrast, for the high-latitude basins increases in annual flow volume are predicted in most cases.}, - language = {English}, - number = {1-2}, - journal = {Climatic Change}, - author = {Nijssen, B. and O'Donnell, G. M. and Hamlet, A. F. and Lettenmaier, D. P.}, - year = {2001}, - keywords = {united-states, pacific-northwest, ocean-atmosphere model, sea-ice, greenhouse-gas, water-resources, change simulation, coupled model, southeastern south-america, upper mississippi}, - pages = {143--175}, - annote = {441AWTimes Cited:21Cited References Count:70}, - annote = {The following values have no corresponding Zotero field:auth-address: Nijssen, B Univ Washington, Dept Civil \& Environm Engn, Box 352700, Seattle, WA 98195 USA Univ Washington, Dept Civil \& Environm Engn, Seattle, WA 98195 USAaccession-num: ISI:000169209700006}, -} - -@article{new_representing_2000, - title = {Representing twentieth-century space-time climate variability. {Part} {II}: development of 1961-90 monthly grids of terrestrial surface climate}, - volume = {12}, - journal = {Journal of Climate}, - author = {New, M. G. and Hulme, M. and Jones, P. D.}, - year = {2000}, - pages = {829--856}, -} - -@article{new_challenges_2007, - title = {Challenges in using probabilistic climate change information for impact assessments: an example from the water sector}, - volume = {365}, - doi = {10.1098/rsta.2007.2080}, - abstract = {Climate change impacts and adaptation assessments have traditionally adopted a scenario-based approach, which precludes an assessment of the relative risks of particular adaptation options. Probabilistic impact assessments, especially if based on a thorough analysis of the uncertainty in an impact forecast system, enable adoption of a risk-based assessment framework. However, probabilistic impacts information is conditional and will change over time. We explore the implications of a probabilistic end-to-end risk-based framework for climate impacts assessment, using the example of water resources in the Thames River, UK. We show that a probabilistic approach provides more informative results that enable the potential risk of impacts to be quantified, but that details of the risks are dependent on the approach used in the analysis.}, - number = {1857}, - journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences}, - author = {New, Mark and Lopez, Ana and Dessai, Suraje and Wilby, Rob}, - month = aug, - year = {2007}, - pages = {2117--2131}, -} - -@book{nakicenovic_special_2000, - address = {Cambridge, UK}, - title = {Special report on emissions scenarios}, - publisher = {Cambridge U. Press}, - author = {Nakicenovic, N.}, - year = {2000}, -} - -@incollection{g_myhre_anthropogenic_2013, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Anthropogenic and {Natural} {Radiative} {Forcing}}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {G. Myhre and D. Shindell and F. -M. Bréon and W. Collins and J. Fuglestvedt and J. Huang and D. Koch and J. -F. Lamarque and D. Lee and B. Mendoza and T. Nakajima and A. Robock and G. Stephens and T. Takemura and H. Zhang}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley}, - year = {2013}, - pages = {659--740}, - annote = {The following values have no corresponding Zotero field:section: 8electronic-resource-num: 10.1017/CBO9781107415324.018}, -} - -@article{musselman_slower_2017, - title = {Slower snowmelt in a warmer world}, - volume = {7}, - doi = {10.1038/nclimate3225}, - journal = {Nature Climate Change}, - author = {Musselman, Keith N. and Clark, Martyn P. and Liu, Changhai and Ikeda, Kyoko and Rasmussen, Roy}, - year = {2017}, - pages = {214}, - annote = {The following values have no corresponding Zotero field:publisher: Nature Publishing Groupwork-type: Article}, -} - -@article{murphy_evaluation_1999, - title = {An evaluation of statistical and dynamical techniques for downscaling local climate}, - volume = {12}, - abstract = {An assessment is made of downscaling estimates of screen temperature and precipitation observed at 976 European stations during 1983-94. A statistical downscaling technique, in which local values are inferred from observed atmospheric predictor variables, is compared against two dynamical downscaling techniques, based on the use of the screen temperature or precipitation simulated at the nearest grid point in integrations of two climate models. In one integration a global general circulation model (GCM) is constrained to reproduce the observed atmospheric circulation over the period of interest, while the second involves a high-resolution regional climate model (RCM) nested inside the GCM. The dynamical and statistical methods are compared in terms of the correlation between the estimated and observed time series of monthly anomalies. For estimates of temperature a high degree of skill is found, especially over western, central and northern Europe; for precipitation skill is lower (average correlations ranging from 0.4 in summer to 0.7 in winter). Overall, the dynamical and statistical methods show similar levels of skill, although the statistical method is better for summertime estimates of temperature while the dynamical methods give slightly better estimates of wintertime precipitation. In general, therefore, the skill with which present-day surface climate anomalies can be derived from atmospheric observations is not improved by using the sophisticated calculations of subgrid-scale processes made in climate models rather than simple empirical relationships. It does not necessarily follow that statistical and dynamical downscaling estimates of changes in surface climate will also possess equal skill. By the above measure the two dynamical techniques possess approximately equal skill; however, they art: also compared by assessing errors in the mean and variance of monthly values and errors in the simulated distributions of daily values. Such errors arise from systematic biases in the models plus the effect of unresolved local forcings. For precipitation the results show that the RCM offers clear benefits relative to the GCM: the simulated variability of both daily and monthly values, although lower than observed, is much more realistic than in the CCM because the finer grid reduces the amount of spatial smoothing implicit in the use of grid-box variables. The climatological means are also simulated better in the winter half of the year because the RCM captures some of the mesoscale detail present in observed distributions. The temperature fields contain a mesoscale orographic signal that is skillfully reproduced by the RCM; however, this is not a source of increased skill relative to the GCM since elevation biases can be largely removed using simple empirical corrections based on spatially averaged lapse rates. Nevertheless, the average skill of downscaled climatological mean temperature values is higher in the RCM in nearly all months. The additional skill arises from better resolution of local physiographical features, especially coastlines, and also from the dynamical effects of higher resolution, which generally act to reduce the large-scale systematic biases in the simulated values. Both models tend to overestimate the variability of both daily and monthly mean temperature. On average the RCM is more skillful in winter but less skillful in summer, due to excessive drying of the soil over central and southern Europe. The downscaling scores for monthly means are compared against scores obtained by using a predictor variable consisting of observations from the nearest station to the predictand station. In general the downscaling scores are significantly worse than those obtained from adjacent stations, indicating that there remains considerable scope for refining the techniques in future. In the case of dynamical downscaling progress can be made by reducing systematic errors through improvements in the representation of physical processes and increased resolution; the prospects for improving statistical downscaling will depend on the availability of the observational data needed to provide longer calibration time series and/or a wider range of predictor variables.}, - number = {8}, - journal = {J. Clim.}, - author = {Murphy, J.}, - month = aug, - year = {1999}, - pages = {2256--2284}, - annote = {1232HAJ CLIMATE}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Clim.accession-num: ISI:000082364100008}, -} - -@article{mpelasoka_new_2001, - title = {New {Zealand} climate change information derived by multivariate statistical and artificial neural networks approaches}, - volume = {21}, - abstract = {Climate change impact assessment requires data at the spatial and the temporal resolution at which impacts occur. The outputs of the current global climate models (GCMs) cannot be used directly in the development of desired climate change scenarios due to their coarse resolution. Artificial neural network (ANN) and multivariate statistics (MST) models were adapted to derive changes of site precipitation and temperature characteristics in a comparative study of their potential in downscaling GCM outputs. They were calibrated and verified using observed site temperature and precipitation over New Zealand and circulation variables from the National Center for Environment Prediction (NCEP) reanalysis. Subsequently, the models were used to derive changes of mean monthly precipitation and temperature characteristics from circulation variables projected in a transient climate change experiment performed by the Hadley Centre Global Climate Model (HadCM2). HadCM2 validated well with respect to NCEP reanalysis for its 'present climate' simulation. The predicted changes in seasonal mean sea level pressure fields over the 'New Zealand region' include intensified westerly flow particularly in winter. Therefore, the HadCM2 outputs contain information that can be used to derive localized climate change characteristics. Both downscaling models capture similar general patterns of change from the same GCM output, although they show substantial differences in localized characteristics, particularly for precipitation. The MST model suggests greater warming than the ANN model but both show larger temperature rises in summer and spring than in other seasons. Precipitation changes have marked spatial variation in their magnitude and direction; although by 2070-2099 more sites show precipitation increases than decreases, except for spring, and precipitation increases are more common in the North Island than the South Island. This intercomparison of two downscaling methods demonstrates an important source of uncertainty in regional climate change scenarios in addition to differing regional responses of GCMs. Copyright (C) 2001 Royal Meteorological Society.}, - number = {11}, - journal = {International Journal of Climatology}, - author = {Mpelasoka, F. S. and Mullan, A. B. and Heerdegen, R. G.}, - month = sep, - year = {2001}, - pages = {1415--1433}, - annote = {479CXINT J CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:000171387300008}, -} - -@article{mountain_research_initiative_elevation-dependent_2015, - title = {Elevation-dependent warming in mountain regions of the world}, - volume = {5}, - doi = {10.1038/nclimate2563 https://www.nature.com/articles/nclimate2563#supplementary-information}, - journal = {Nature Climate Change}, - author = {Mountain Research Initiative, E. D. W. Working Group and Pepin, N. and Bradley, R. S. and Diaz, H. F. and Baraer, M. and Caceres, E. B. and Forsythe, N. and Fowler, H. and Greenwood, G. and Hashmi, M. Z. and Liu, X. D. and Miller, J. R. and Ning, L. and Ohmura, A. and Palazzi, E. and Rangwala, I. and Schöner, W. and Severskiy, I. and Shahgedanova, M. and Wang, M. B. and Williamson, S. N. and Yang, D. Q.}, - year = {2015}, - pages = {424}, - annote = {The following values have no corresponding Zotero field:publisher: Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.work-type: Review Article}, -} - -@article{murphy_predictions_2000, - title = {Predictions of climate change over {Europe} using statistical and dynamical downscaling techniques}, - volume = {20}, - abstract = {Statistical and dynamical downscaling predictions of changes in surface temperature and precipitation for 2080-2100, relative to pre-industrial conditions, are compared at 976 European observing sites, for January and July. Two dynamical downscaling methods are considered, involving the use of surface temperature or precipitation simulated at the nearest grid point in a coupled ocean-atmosphere general circulation model (GCM) of resolution similar to 300 km and a 50 km regional climate model (RCM) nested inside the GCM. The statistical method (STAT) is based on observed linear regression relationships between surface temperature or precipitation and a range of atmospheric predictor variables. The three methods are equally plausible a priori, in the sense that they estimate present-day natural variations with equal skill. For temperature, differences between the RCM and GCM predictions are quite small. Larger differences occur between STAT and the dynamical predictions. For precipitation, there is a wide spread between all three methods. Differences between the RCM and GCM are increased by the meso-scale detail present in the RCM. Uncertainties in the downscaling predictions are investigated by using the STAT method to estimate the grid point changes simulated by the GCM, based on regression relationships trained using simulated rather than observed values of the predictor and the predictand variables (i.e. STAT\_SIM). In most areas the temperature changes predicted by STAT\_SIM and the GCM itself are similar, indicating that the statistical relationships trained from present climate anomalies remain valid in the perturbed climate. However, STAT\_SIM underestimates the surface warming in areas where advective predictors are important predictors of natural variability but not of climate change. For precipitation, STAT\_SIM estimates the simulated changes with lower skill, especially in January when increases in simulated precipitation related to a moister atmosphere are not captured. This occurs because moisture is rarely a strong enough predictor of natural variability to be included in the specification equation. The predictor/predictand relationships found in the GCM do not always match those found in observations. In January, the link between surface and lower tropospheric temperature is too strong. This is also true in July, when the links between precipitation and various atmospheric predictors are also too strong. These biases represent a likely source of error in both dynamical and statistical downscaling predictions. For example, simulated reductions in precipitation over southern Europe in summer may be too large. (C) British Crown Copyright 2000.}, - number = {5}, - journal = {International Journal of Climatology}, - author = {Murphy, J.}, - month = apr, - year = {2000}, - pages = {489--501}, - annote = {313GNINT J CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:000086992100003}, -} - -@incollection{mote_future_2009, - address = {Seattle, Washington, USA}, - title = {Future {Climate} in the {Pacific} {Northwest}}, - booktitle = {The {Washington} {Climate} {Change} {Impacts} {Assessment}}, - publisher = {Climate Impacts Group, Center for Science in the Earth System, Joint Institute for the Study of the Atmosphere and Oceans, University of Washington}, - author = {Mote, P. W. and Salathé, E. P.}, - editor = {McGuire Elsner, M. and Littell, J. and Whitely Binder, L.}, - year = {2009}, - pages = {414}, -} - -@article{mote_declining_2005, - title = {Declining mountain snowpack in western {North} {America}}, - volume = {86}, - issn = {0003-0007}, - abstract = {In western North America, snow provides crucial storage of winter precipitation, effectively transferring water from the relatively wet winter season to the typically dry summers. Manual and telemetered. measurements of spring snowpack, corroborated by a physically based hydrologic model, are examined here for climate-driven fluctuations and trends during the period of 1916-2002. Much of the mountain West has experienced declines in spring snowpack, especially since midcentury, despite increases in winter precipitation in many places. Analysis and modeling show that climatic trends are the dominant factor, not changes in land use, forest canopy, or other factors. The largest decreases have occurred where winter temperatures are mild, especially in the Cascade Mountains and northern California. In most mountain ranges, relative declines grow from minimal at ridgetop to substantial at snow line. Taken together, these results emphasize that the West's snow resources are already declining as earth's climate warms.}, - language = {English}, - number = {1}, - journal = {Bulletin of the American Meteorological Society}, - author = {Mote, P. W. and Hamlet, A. F. and Clark, M. P. and Lettenmaier, D. P.}, - month = jan, - year = {2005}, - keywords = {united-states, climate-change, precipitation, trends, temperature, pacific-northwest, hydrology, water-resources, columbia river-basin}, - pages = {39--49}, - annote = {896YITimes Cited:0Cited References Count:28}, - annote = {The following values have no corresponding Zotero field:auth-address: Mote, PW Univ Washington, Climate Impacts Grp, Ctr Sci Earth Syst, Box 354235, Seattle, WA 98195 USA Univ Washington, Climate Impacts Grp, Ctr Sci Earth Syst, Seattle, WA 98195 USA Univ Washington, Dept Civil \& Environm Engn, Ctr Sci Earth Syst, Seattle, WA 98195 USA Univ Colorado, CIRES, Ctr Sci \& Technol Policy Res, Boulder, CO 80309 USAaccession-num: ISI:000226970100020}, -} - -@article{mote_guidelines_2011, - title = {Guidelines for constructing climate scenarios}, - volume = {92}, - issn = {0096-3941}, - doi = {10.1029/2011eo310001}, - abstract = {Scientists and others from academia, government, and the private sector increasingly are using climate model outputs in research and decision support. For the most recent assessment report of the Intergovernmental Panel on Climate Change, 18 global modeling centers contributed outputs from hundreds of simulations, coordinated through the Coupled Model Intercomparison Project Phase 3 (CMIP3), to the archive at the Program for Climate Model Diagnostics and Intercomparison (PCMDI; http://pcmdi3.llnl.gov) [Meehl et al., 2007]. Many users of climate model outputs prefer downscaled data\&\#8212;i.e., data at higher spatial resolution\&\#8212;to direct global climate model (GCM) outputs; downscaling can be statistical [e.g., Meehl et al., 2007] or dynamical [e.g., Mearns et al., 2009]. More than 800 users have obtained downscaled CMIP3 results from one such Web site alone (see http://gdo-dcp.ucllnl.org/downscaled cmip3\_projections/, described by Meehl et al., [2007]).}, - number = {31}, - journal = {Eos Trans. AGU}, - author = {Mote, P. W. and Brekke, L. D. and Duffy, P. B. and Maurer, E. P.}, - year = {2011}, - keywords = {1626 Global Change: Global climate models (3337, 4928), 1694 Global Change: Instruments and techniques, climate, 1637 Global Change: Regional climate change (4321), downscaling, change, scenarios}, - pages = {257--258}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{mitchell_predicting_1999, - title = {Predicting regional climate change: living with uncertainty}, - volume = {23}, - abstract = {Regional climate prediction is not an insoluble problem, but it is a problem characterized by inherent uncertainty. There are two sources of this uncertainty: the unpredictability of the climatic and global systems. The climate system is rendered unpredictable by deterministic chaos; the global system renders climate prediction uncertain through the unpredictability of the external forcings imposed on the climate system. It is commonly inferred from the differences between climate models on regional scales that the models are deficient, but climate system unpredictability is such that this inference is premature; the differences are due to an unresolved combination of climate system unpredictability and model deficiencies. Since model deficiencies are discussed frequently and the two sources of inherent uncertainty are discussed only rarely, this review considers the implications of climatic and global system unpredictability for regional climate prediction. Consequently we regard regional climate prediction as a cascade of uncertainty, rather than as a single result process sullied by model deficiencies. We suggest three complementary methodological approaches: (1) the use of multiple forcing scenarios to cope with global system unpredictability; (2) the use of ensembles to cope with climate system unpredictability; and (3) the consideration of the entire response of the climate system to cope with the nature of climate change. We understand regional climate change in terms of changes in the general circulations of the atmosphere and oceans; so we illustrate the role of uncertainty in the task of regional climate prediction with the behaviour of the North Atlantic thermohaline circulation. In conclusion we discuss the implications of the uncertainties in regional climate prediction for research into the impacts of climate change, and we recognize the role of feedbacks in complicating the relatively simple cascade of uncertainties presented here.}, - number = {1}, - journal = {Progress in Physical Geography}, - author = {Mitchell, T. D. and Hulme, M.}, - month = mar, - year = {1999}, - pages = {57--78}, - annote = {176YRPROG PHYS GEOG}, - annote = {The following values have no corresponding Zotero field:alt-title: Prog. Phys. Geogr.accession-num: ISI:000079180400003}, -} - -@article{min_human_2011, - title = {Human contribution to more-intense precipitation extremes}, - volume = {470}, - issn = {0028-0836}, - doi = {http://www.nature.com/nature/journal/v470/n7334/abs/10.1038-nature09763-unlocked.html#supplementary-information}, - number = {7334}, - journal = {Nature}, - author = {Min, Seung-Ki and Zhang, Xuebin and Zwiers, Francis W. and Hegerl, Gabriele C.}, - year = {2011}, - pages = {378--381}, - annote = {10.1038/nature09763}, - annote = {The following values have no corresponding Zotero field:publisher: Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.work-type: 10.1038/nature09763}, -} - -@article{moss_next_2010, - title = {The next generation of scenarios for climate change research and assessment}, - volume = {463}, - issn = {0028-0836}, - doi = {http://www.nature.com/nature/journal/v463/n7282/suppinfo/nature08823_S1.html}, - number = {7282}, - journal = {Nature}, - author = {Moss, Richard H. and Edmonds, Jae A. and Hibbard, Kathy A. and Manning, Martin R. and Rose, Steven K. and van Vuuren, Detlef P. and Carter, Timothy R. and Emori, Seita and Kainuma, Mikiko and Kram, Tom and Meehl, Gerald A. and Mitchell, John F. B. and Nakicenovic, Nebojsa and Riahi, Keywan and Smith, Steven J. and Stouffer, Ronald J. and Thomson, Allison M. and Weyant, John P. and Wilbanks, Thomas J.}, - year = {2010}, - pages = {747--756}, - annote = {10.1038/nature08823}, - annote = {The following values have no corresponding Zotero field:publisher: Macmillan Publishers Limited. All rights reservedwork-type: 10.1038/nature08823}, -} - -@article{moriasi_model_2007, - title = {Model evaluation guidelines for systematic quantification of accuracy in watershed simulations}, - volume = {50}, - number = {3}, - journal = {Trans. of ASABE}, - author = {Moriasi, D. N. and Arnold, J. G. and Liew, M. W. V. and Bingner, R. L. and Harmel, R. D. and Veith, T. L.}, - year = {2007}, - pages = {885--900}, -} - -@article{moore_riparian_2005, - title = {Riparian microclimate and stream temperature response to forest harvesting: {A} review}, - volume = {41}, - issn = {1093-474X}, - abstract = {Forest harvesting can increase solar radiation in the riparian zone as well as wind speed and exposure to air advected from clearings, typically causing increases in summertime air, soil, and stream temperatures and decreases in relative humidity. Stream temperature increases following forest harvesting are primarily controlled by changes in insolation but also depend on stream hydrology and channel morphology Stream temperatures recovered to pre-harvest levels within 10 years in many studies but took longer in others. Leaving riparian buffers can decrease the magnitude of stream temperature increases and changes to riparian microclimate, but substantial warming has been observed for streams within both unthinned and partial retention buffers. A range of studies has demonstrated that streams may or may not cool after flowing from clearings into shaded environments, and further research is required in relation to the factors controlling downstream cooling. Further research is also required on riparian microclimate and its responses to harvesting, the influences of surface/subsurface water exchange on stream and bed temperature regimes, biological implications of temperature changes in headwater streams (both on site and downstream), and methods for quantifying shade and its influence on radiation inputs to streams and riparian zones.}, - language = {English}, - number = {4}, - journal = {J. Am. Water Resour. Assoc.}, - author = {Moore, R. D. and Spittlehouse, D. L. and Story, A.}, - month = aug, - year = {2005}, - keywords = {pacific-northwest, new-zealand, british-columbia, clear-cut, douglas-fir forest, forestry, headwater, headwater streams, hyporheic zone, microclimate, Pacific Northwest, quality, riparian, stream temperature, thermal heterogeneity, water, water temperature, watershed management, western washington}, - pages = {813--834}, - annote = {ISI Document Delivery No.: 959WLTimes Cited: 55Cited Reference Count: 135Amer water resources assocMiddleburg}, - annote = {The following values have no corresponding Zotero field:auth-address: Univ British Columbia, Dept Geog, Vancouver, BC V6T 1Z2, Canada. Univ British Columbia, Dept Forest Resources Management, Vancouver, BC V6T 1Z2, Canada. BC Minist Forests, Res Branch, Stn Provincial Govt, Victoria, BC V8W 9C2, Canada. Univ Toronto, Inst Hist \& Philosophy Sci \& Technol, Toronto, ON M5S 1K7, Canada. Moore, RD, Univ British Columbia, Dept Geog, 1984 W Mall, Vancouver, BC V6T 1Z2, Canada. rdmoore@geog.ubc.caalt-title: J. Am. Water Resour. Assoc.accession-num: ISI:000231549900004work-type: Review}, -} - -@article{mizukami_hydrologic_2014, - title = {Hydrologic {Implications} of {Different} {Large}-{Scale} {Meteorological} {Model} {Forcing} {Datasets} in {Mountainous} {Regions}}, - volume = {15}, - issn = {1525-755X}, - doi = {10.1175/jhm-d-13-036.1}, - number = {1}, - journal = {Journal of Hydrometeorology}, - author = {Mizukami, Naoki and P. Clark, Martyn and G. Slater, Andrew and D. Brekke, Levi and M. Elsner, Marketa and R. Arnold, Jeffrey and Gangopadhyay, Subhrendu}, - month = feb, - year = {2014}, - pages = {474--488}, - annote = {doi: 10.1175/JHM-D-13-036.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/JHM-D-13-036.1}, -} - -@article{naoki_mizukami_implications_2016, - title = {Implications of the {Methodological} {Choices} for {Hydrologic} {Portrayals} of {Climate} {Change} over the {Contiguous} {United} {States}: {Statistically} {Downscaled} {Forcing} {Data} and {Hydrologic} {Models}}, - volume = {17}, - doi = {doi:10.1175/JHM-D-14-0187.1}, - abstract = {AbstractContinental-domain assessments of climate change impacts on water resources typically rely on statistically downscaled climate model outputs to force hydrologic models at a finer spatial resolution. This study examines the effects of four statistical downscaling methods [bias-corrected constructed analog (BCCA), bias-corrected spatial disaggregation applied at daily (BCSDd) and monthly scales (BCSDm), and asynchronous regression (AR)] on retrospective hydrologic simulations using three hydrologic models with their default parameters (the Community Land Model, version 4.0; the Variable Infiltration Capacity model, version 4.1.2; and the Precipitation–Runoff Modeling System, version 3.0.4) over the contiguous United States (CONUS). Biases of hydrologic simulations forced by statistically downscaled climate data relative to the simulation with observation-based gridded data are presented. Each statistical downscaling method produces different meteorological portrayals including precipitation amount, wet-day frequency, and the energy input (i.e., shortwave radiation), and their interplay affects estimations of precipitation partitioning between evapotranspiration and runoff, extreme runoff, and hydrologic states (i.e., snow and soil moisture). The analyses show that BCCA underestimates annual precipitation by as much as −250 mm, leading to unreasonable hydrologic portrayals over the CONUS for all models. Although the other three statistical downscaling methods produce a comparable precipitation bias ranging from −10 to 8 mm across the CONUS, BCSDd severely overestimates the wet-day fraction by up to 0.25, leading to different precipitation partitioning compared to the simulations with other downscaled data. Overall, the choice of downscaling method contributes to less spread in runoff estimates (by a factor of 1.5–3) than the choice of hydrologic model with use of the default parameters if BCCA is excluded.}, - number = {1}, - journal = {Journal of Hydrometeorology}, - author = {Naoki Mizukami and Martyn P. Clark and Ethan D. Gutmann and Pablo A. Mendoza and Andrew J. Newman and Bart Nijssen and Ben Livneh and Lauren E. Hay and Jeffrey R. Arnold and Levi D. Brekke}, - year = {2016}, - pages = {73--98}, -} - -@article{mitchell_improved_2005, - title = {An improved method of constructing a database of monthly climate observations and associated high-resolution grids}, - volume = {25}, - issn = {1097-0088}, - doi = {10.1002/joc.1181}, - number = {6}, - journal = {International Journal of Climatology}, - author = {Mitchell, Timothy D. and Jones, Philip D.}, - year = {2005}, - keywords = {precipitation, temperature, observations, climate, cloud, grids, homogeneity, vapour}, - pages = {693--712}, - annote = {The following values have no corresponding Zotero field:publisher: John Wiley \& Sons, Ltd.}, -} - -@techreport{mitchell_comprehensive_2004, - address = {Norwich, UK}, - title = {A comprehensive set of high-resolution grids of monthly climate for {Europe} and the globe: the observed record (1901-2000) and 16 scenarios (2001-2100)}, - institution = {Tyndall Centre for Climate Change Research, University of East Anglia}, - author = {Mitchell, T. D. and Carter, T. R. and Jones, P. D. and Hulme, M. and New, M. G.}, - year = {2004}, - pages = {30}, - annote = {The following values have no corresponding Zotero field:number: Working Paper 55}, -} - -@article{milly_increasing_2002, - title = {Increasing risk of great floods in a changing climate}, - volume = {415}, - journal = {Nature}, - author = {Milly, P. C. D. and Wetherald, R. T. and Dunne, K. A. and Delworth, T. L.}, - year = {2002}, - pages = {514−517}, -} - -@article{milly_global_2002, - title = {Global {Modeling} of {Land} {Water} and {Energy} {Balances}. {Part} {I}: {The} {Land} {Dynamics} ({LaD}) {Model}}, - volume = {3}, - issn = {1525-755X}, - doi = {10.1175/1525-7541(2002)003<0283:gmolwa>2.0.co;2}, - number = {3}, - journal = {Journal of Hydrometeorology}, - author = {Milly, P. C. D. and Shmakin, A. B.}, - month = jun, - year = {2002}, - pages = {283--299}, - annote = {doi: 10.1175/1525-7541(2002)003{\textless}0283:GMOLWA{\textgreater}2.0.CO;2}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/1525-7541(2002)003{\textless}0283:GMOLWA{\textgreater}2.0.CO;2}, -} - -@article{milly_macroscale_2002, - title = {Macroscale water fluxes 2. {Water} and energy supply control of their interannual variability}, - volume = {38}, - number = {10}, - journal = {Water Resources Research}, - author = {Milly, P. C. D. and Dunne, K. A.}, - year = {2002}, - pages = {1206, doi:10.1029/2001WR00760}, -} - -@article{miller_potential_2003, - title = {Potential impacts of climate change on {California} hydrology}, - volume = {39}, - issn = {1093-474X}, - abstract = {Previous reports based on climate change scenarios have suggested that California will be subjected to increased wintertime and decreased summertime streamflow. Due to the uncertainty of projections in future climate, a new range of potential climatological future temperature shifts and precipitation ratios is applied to the Sacramento Soil Moisture Accounting Model and Anderson Snow Model in order to determine hydrologic sensitivities. Two general circulation models (GCMs) were used in this analysis: one that is warm and wet (HadCM2 run 1) and one that is cool and dry (PCM run B06.06), relative to the GCM projections for California that were part of the Third Assessment Report of the Inter-governmental Panel on Climate Change. A set of specified incremental temperature shifts from 1.5degreesC to 5.0degreesC and precipitation ratios from 0.70 to 1.30 were also used as input to the snow and soil moisture accounting models, providing for additional scenarios (e.g., warm/dry, cool/wet). Hydrologic calculations were performed for a set of California river basins that extend from the coastal mountains and Sierra Nevada northern region to the southern Sierra Nevada region; these were applied to a water allocation analysis in a companion paper. Results indicate that for all snow-producing cases, a larger proportion of the streamflow volume will occur earlier in the year. The amount and timing is dependent on the characteristics of each basin, particularly the elevation. Increased temperatures lead to a higher freezing line, therefore less snow accumulation and increased melting below the freezing height. The hydrologic response varies for each scenario, and the resulting solution set provides bounds to the range of possible change in streamflow, snowmelt, snow water equivalent, and the change in the magnitude of annual high flows. An important result that appears for all snowmelt driven runoff basins, is that late winter snow accumulation decreases by 50 percent toward the end of this century.}, - language = {English}, - number = {4}, - journal = {Journal of the American Water Resources Association}, - author = {Miller, N. L. and Bashford, K. E. and Strem, E.}, - month = aug, - year = {2003}, - keywords = {streamflow, climate change, california, basin, hydrologic impacts}, - pages = {771--784}, - annote = {715KPTimes Cited:6Cited References Count:22}, - annote = {The following values have no corresponding Zotero field:auth-address: Miller, NL Berkeley Natl Lab, 90-1116 1 Cyclotron Rd, Berkeley, CA 94720 USA Berkeley Natl Lab, Berkeley, CA 94720 USA NOAA, Natl Weather Serv, Calif Nevada River Forecast Ctr, Sacramento, CA 95821 USAaccession-num: ISI:000184971100003}, -} - -@article{michel_frequency_2007, - title = {Frequency of precipitation and temperature extremes over {France} in an anthropogenic scenario: {Model} results and statistical correction according to observed values}, - volume = {57}, - issn = {0921-8181}, - number = {1–2}, - journal = {Global and Planetary Change}, - author = {Michel, Déqué}, - year = {2007}, - keywords = {regional climate, extreme values, numerical simulation, scenario}, - pages = {16--26}, - annote = {The following values have no corresponding Zotero field:work-type: doi: 10.1016/j.gloplacha.2006.11.030}, -} - -@article{milly_global_2005, - title = {Global pattern of trends in streamflow and water availability in a changing climate}, - volume = {438}, - number = {7066}, - journal = {Nature}, - author = {Milly, P. C. D. and Dunne, K. A. and Vecchia, A. V.}, - year = {2005}, - pages = {347--350}, - annote = {0028-083610.1038/nature0431210.1038/nature04312}, -} - -@article{milly_climate_2008, - title = {{CLIMATE} {CHANGE}: {Stationarity} {Is} {Dead}: {Whither} {Water} {Management}?}, - volume = {319}, - doi = {10.1126/science.1151915}, - number = {5863}, - journal = {Science}, - author = {Milly, P. C. D. and Betancourt, Julio and Falkenmark, Malin and Hirsch, Robert M. and Kundzewicz, Zbigniew W. and Lettenmaier, Dennis P. and Stouffer, Ronald J.}, - month = feb, - year = {2008}, - pages = {573--574}, -} - -@article{milly_climate_1994, - title = {Climate, soil water storage, and the average annual water balance}, - volume = {30}, - number = {7}, - journal = {Water Resources Research}, - author = {Milly, P. C. D.}, - year = {1994}, - pages = {2143--2156}, -} - -@article{miller_projected_2002, - title = {Projected impact of climate change on the energy budget of the {Arctic} {Ocean} by a global climate model}, - volume = {15}, - issn = {0894-8755}, - abstract = {The annual energy budget of the Arctic Ocean is characterized by a net heat loss at the air-sea interface that is balanced by oceanic heat transport into the Arctic. Two 150-yr simulations (1950-2099) of a global climate model are used to examine how this balance might change if atmospheric greenhouse gases (GHGs) increase. One is a control simulation for the present climate with constant 1950 atmospheric composition, and the other is a transient experiment with observed GHGs from 1950 to 1990 and 0.5\% annual compounded increases of CO2 after 1990. For the present climate the model agrees well with observations of radiative fluxes at the top of the atmosphere, atmospheric advective energy transport into the Arctic, and surface air temperature. It also simulates the seasonal cycle and summer increase of cloud cover and the seasonal cycle of sea ice cover. In addition, the changes in high-latitude surface air temperature and sea ice cover in the GHG experiment are consistent with observed changes during the last 40 years. -Relative to the control, the last 50-yr period of the GHG experiment indicates that even though the net annual incident solar radiation at the surface decreases by 4.6 W m(-2) (because of greater cloud cover and increased cloud optical depth), the absorbed solar radiation increases by 2.8 W m(-2) (because of less sea ice). Increased cloud cover and warmer air also cause increased downward thermal radiation at the surface so that the net radiation into the ocean increases by 5.0 W m(-2). The annual increase in radiation into the ocean, however, is compensated by larger increases in sensible and latent heat fluxes out of the ocean. Although the net energy loss from the ocean surface increases by 0.8 W m(-2), this is less than the interannual variability, and the increase may not indicate a long-term trend. -The seasonal cycle of heat fluxes is significantly enhanced. The downward surface heat flux increases in summer (maximum of 19 W m(-2), or 23\% in June) while the upward heat flux increases in winter (maximum of 16 W m(-2), or 28\% in November). The increased downward flux in summer is due to a combination of increases in absorbed solar and thermal radiation and smaller losses of sensible and latent heat. The increased heat loss in winter is due to increased sensible and latent heat fluxes, which in turn are due to reduced sea ice cover. On the other hand, the seasonal cycle of surface air temperature is damped, as there is a large increase in winter temperature but little change in summer. The changes that occur in the various quantities exhibit spatial variability, with the changes being generally larger in coastal areas and at the ice margins.}, - language = {English}, - number = {21}, - journal = {Journal of Climate}, - author = {Miller, J. R. and Russell, G. L.}, - month = nov, - year = {2002}, - keywords = {co2, sensitivity, simulations, sea-ice, annual cycle, clouds, fluxes, river flow, thickness}, - pages = {3028--3042}, - annote = {604DQTimes Cited:2Cited References Count:37}, - annote = {The following values have no corresponding Zotero field:auth-address: Miller, JR Rutgers State Univ, Cook Coll, Dept Marine \& Coastal Sci, 71 Dudley Rd, New Brunswick, NJ 08901 USA Rutgers State Univ, Cook Coll, Dept Marine \& Coastal Sci, New Brunswick, NJ 08901 USA NASA Goddard, Inst Space Studies, New York, NY USAaccession-num: ISI:000178601100004}, -} - -@article{miles_pacific_2000, - title = {Pacific northwest regional assessment: {The} impacts of climate variability and climate change on the water resources of the {Columbia} {River} {Basin}}, - volume = {36}, - abstract = {The Pacific Northwest (PNW) regional assessment is an integrated examination of the consequences of natural climate variability and projected future climate change for the natural and human systems of the region. The assessment currently focuses on four sectors: hydrology/water resources, forests and forestry, aquatic ecosystems, and coastal activities. The assessment begins by identifying and elucidating the natural patterns of climate variability in the PNW on interannual to decadal timescales. The pathways through which these climate variations are manifested and the resultant impacts on the natural and human systems of the region are investigated. Knowledge of these pathways allows an analysis of the potential impacts of future climate change, as defined by IPCC climate change scenarios. In this paper, we examine the sensitivity, adaptability and vulnerability of hydrology and water resources to climate variability and change. We focus on the Columbia River Basin, which covers approximately 75 percent of the PNW and is the basis for the dominant water resources system of the PNW. The water resources system of the Columbia River is sensitive to climate variability, especially with respect to drought. Management inertia and the lack of a centralized authority coordinating all uses of the resource impede adaptability to drought and optimization of water distribution. Climate change projections suggest exacerbated conditions of conflict between users as a result of low summertime streamflow conditions. An understanding of the patterns and consequences of regional climate variability is crucial to developing an adequate response to future changes in climate.}, - number = {2}, - journal = {Journal of the American Water Resources Association}, - author = {Miles, E. L. and Snover, A. K. and Hamlet, A. F. and Callahan, B. and Fluharty, D.}, - year = {2000}, - keywords = {streamflow, Water resources, Catchment areas, Climatic changes, Distribution, Drought, Droughts, Hydrology, Regional Analysis, River basins, Stream flow, USA, Pacific Northwest, Water}, - pages = {399--420}, - annote = {Journal of the American Water Resources Association [J. Am. Water Resour. Assoc.]. Vol. 36, no. 2, pp. 399-420. Apr 2000.}, -} - -@article{merz_time_2011, - title = {Time stability of catchment model parameters: {Implications} for climate impact analyses}, - volume = {47}, - issn = {0043-1397}, - doi = {citeulike-article-id:8862842}, - number = {2}, - journal = {Water Resources Research}, - author = {Merz, Ralf and Parajka, Juraj and Blöschl, Günter}, - year = {2011}, - keywords = {catchment-studies, modelling, parameters}, -} - -@article{mengelkamp_statistical-dynamical_1997, - title = {Statistical-dynamical downscaling of wind climatologies}, - volume = {67-8}, - abstract = {A statistical-dynamical downscaling procedure is applied for an investigation into the availability of wind power over a region of 80 x 87 km which covers flat and hilly terrain. The approach is based on the statistical coupling of a regionally representative wind climate with a numerical atmospheric mesoscale model. The large-scale wind climatology is calculated by a cluster-analysis of a time series of radiosonde data over 12 years. Any of the resulting 143 clusters represents a particular combination of geostrophic wind components and vertical temperature gradient. For each cluster, a highly resolved steady-state wind field is simulated with a non- hydrostatic mesoscale model. These wind fields are statistically evaluated by weighting them with the corresponding cluster frequency. The resulting three- dimensional wind field and the frequency distributions of windspeed and direction are compared with observations at synoptic stations.}, - journal = {Journal of Wind Engineering and Industrial Aerodynamics}, - author = {Mengelkamp, H. T. and Kapitza, H. and Pfluger, U.}, - month = jun, - year = {1997}, - pages = {449--457}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Wind Eng. Ind. Aerodyn.accession-num: ISI:000071001500035}, - annote = {YL864J WIND ENG IND AERODYN}, -} - -@article{pablo_a_mendoza_effects_2015, - title = {Effects of {Hydrologic} {Model} {Choice} and {Calibration} on the {Portrayal} of {Climate} {Change} {Impacts}}, - volume = {16}, - doi = {doi:10.1175/JHM-D-14-0104.1}, - abstract = {AbstractThe assessment of climate change impacts on water resources involves several methodological decisions, including choices of global climate models (GCMs), emission scenarios, downscaling techniques, and hydrologic modeling approaches. Among these, hydrologic model structure selection and parameter calibration are particularly relevant and usually have a strong subjective component. The goal of this research is to improve understanding of the role of these decisions on the assessment of the effects of climate change on hydrologic processes. The study is conducted in three basins located in the Colorado headwaters region, using four different hydrologic model structures [PRMS, VIC, Noah LSM, and Noah LSM with multiparameterization options (Noah-MP)]. To better understand the role of parameter estimation, model performance and projected hydrologic changes (i.e., changes in the hydrology obtained from hydrologic models due to climate change) are compared before and after calibration with the University of Arizona shuffled complex evolution (SCE-UA) algorithm. Hydrologic changes are examined via a climate change scenario where the Community Climate System Model (CCSM) change signal is used to perturb the boundary conditions of the Weather Research and Forecasting (WRF) Model configured at 4-km resolution. Substantial intermodel differences (i.e., discrepancies between hydrologic models) in the portrayal of climate change impacts on water resources are demonstrated. Specifically, intermodel differences are larger than the mean signal from the CCSM–WRF climate scenario examined, even after the calibration process. Importantly, traditional single-objective calibration techniques aimed to reduce errors in runoff simulations do not necessarily improve intermodel agreement (i.e., same outputs from different hydrologic models) in projected changes of some hydrological processes such as evapotranspiration or snowpack.}, - number = {2}, - journal = {Journal of Hydrometeorology}, - author = {Pablo A. Mendoza and Martyn P. Clark and Naoki Mizukami and Andrew J. Newman and Michael Barlage and Ethan D. Gutmann and Roy M. Rasmussen and Balaji Rajagopalan and Levi D. Brekke and Jeffrey R. Arnold}, - year = {2015}, - keywords = {Watersheds,Climate change,Hydrologic cycle,Hydrologic models,Model comparison,Model evaluation/performance}, - pages = {762--780}, -} - -@article{mendoza_how_2016, - title = {How do hydrologic modeling decisions affect the portrayal of climate change impacts?}, - volume = {30}, - issn = {1099-1085}, - doi = {10.1002/hyp.10684}, - number = {7}, - journal = {Hydrological Processes}, - author = {Mendoza, Pablo A. and Clark, Martyn P. and Mizukami, Naoki and Gutmann, Ethan D. and Arnold, Jeffrey R. and Brekke, Levi D. and Rajagopalan, Balaji}, - year = {2016}, - keywords = {climate change, uncertainty, hydrologic modelling, subjectivity}, - pages = {1071--1095}, - annote = {The following values have no corresponding Zotero field:modified-date: Hyp-14-1029.r1}, -} - -@article{mendelssohn_common_2002, - title = {Common and uncommon trends in {SST} and wind stress in the {California} and {Peru}–{Chile} current systems}, - volume = {53}, - journal = {Progress in Oceanography}, - author = {Mendelssohn, R. and Schwing, F. B.}, - year = {2002}, - pages = {141--162}, -} - -@article{melillo_climate_2014, - title = {Climate change impacts in the {United} {States}}, - journal = {Third National Climate Assessment}, - author = {Melillo, Jerry M. and Richmond, Terese TC and Yohe, G. W.}, - year = {2014}, -} - -@article{meinshausen_rcp_2011, - title = {The {RCP} greenhouse gas concentrations and their extensions from 1765 to 2300}, - volume = {109}, - issn = {0165-0009}, - doi = {10.1007/s10584-011-0156-z}, - number = {1}, - journal = {Climatic Change}, - author = {Meinshausen, Malte and Smith, S. and Calvin, K. and Daniel, J. and Kainuma, M. and Lamarque, J. F. and Matsumoto, K. and Montzka, S. and Raper, S. and Riahi, K. and Thomson, A. and Velders, G. and van Vuuren, D. P.}, - year = {2011}, - keywords = {Earth and Environmental Science}, - pages = {213--241}, - annote = {The following values have no corresponding Zotero field:publisher: Springer Netherlands}, -} - -@article{fedor_mesinger_north_2006, - title = {North {American} {Regional} {Reanalysis}}, - volume = {87}, - doi = {10.1175/bams-87-3-343}, - abstract = {In 1997, during the late stages of production of NCEP–NCAR Global Reanalysis (GR), exploration of a regional reanalysis project was suggested by the GR project's Advisory Committee, “particularly if the RDAS [Regional Data Assimilation System] is significantly better than the global reanalysis at capturing the regional hydrological cycle, the diurnal cycle and other important features of weather and climate variability.” Following a 6-yr development and production effort, NCEP's North American Regional Reanalysis (NARR) project was completed in 2004, and data are now available to the scientific community. Along with the use of the NCEP Eta model and its Data Assimilation System (at 32-km–45-layer resolution with 3-hourly output), the hallmarks of the NARR are the incorporation of hourly assimilation of precipitation, which leverages a comprehensive precipitation analysis effort, the use of a recent version of the Noah land surface model, and the use of numerous other datasets that are additional or improved compared to the GR. Following the practice applied to NCEP's GR, the 25-yr NARR retrospective production period (1979–2003) is augmented by the construction and daily execution of a system for near-real-time continuation of the NARR, known as the Regional Climate Data Assimilation System (R-CDAS). Highlights of the NARR results are presented: precipitation over the continental United States (CONUS), which is seen to be very near the ingested analyzed precipitation; fits of tropospheric temperatures and winds to rawinsonde observations; and fits of 2-m temperatures and 10-m winds to surface station observations. The aforementioned fits are compared to those of the NCEP–Department of Energy (DOE) Global Reanalysis (GR2). Not only have the expectations cited above been fully met, but very substantial improvements in the accuracy of temperatures and winds compared to that of GR2 are achieved throughout the troposphere. Finally, the numerous datasets produced are outlined and information is provided on the data archiving and present data availability.}, - number = {3}, - journal = {Bulletin of the American Meteorological Society}, - author = {Fedor Mesinger and Geoff DiMego and Eugenia Kalnay and Kenneth Mitchell and Perry C. Shafran and Wesley Ebisuzaki and Dušan Jović and Jack Woollen and Eric Rogers and Ernesto H. Berbery and Michael B. Ek and Yun Fan and Robert Grumbine and Wayne Higgins and Hong Li and Ying Lin and Geoff Manikin and David Parrish and Wei Shi}, - year = {2006}, - pages = {343--360}, -} - -@article{mendez_agroecology_2013, - title = {Agroecology as a {Transdisciplinary}, {Participatory}, and {Action}-{Oriented} {Approach}}, - volume = {37}, - issn = {2168-3565}, - doi = {10.1080/10440046.2012.736926}, - number = {1}, - journal = {Agroecology and Sustainable Food Systems}, - author = {Méndez, V. Ernesto and Bacon, Christopher M. and Cohen, Roseann}, - month = jan, - year = {2013}, - pages = {3--18}, - annote = {The following values have no corresponding Zotero field:publisher: Taylor \& Francis}, -} - -@article{menabde_modeling_2000, - title = {Modeling of rainfall time series and extremes using bounded random cascades and {Levy}-stable distributions}, - volume = {36}, - abstract = {A new model for simulation of rainfall time series is proposed. It is shown that both the intensity and duration of individual rainfall events can be best modeled by a "fat-tailed" Levy- stable distribution. The temporal downscaling of individual events is produced by a new type of a bounded random cascade model. The proposed rainfall model is shown to successfully reproduce the statistical behavior of individual storms as well as, and in particular, the statistical behavior of annual maxima. In contrast, a model based on a gamma distribution for rainfall intensity substantially underestimates the absolute values of extreme events and does not correctly reproduce their scaling behavior. Similarly, a model based on self-similar random cascade (as opposed to the bounded cascade) substantially overestimates the extreme events.}, - number = {11}, - journal = {Water Resources Research}, - author = {Menabde, M. and Sivapalan, M.}, - month = nov, - year = {2000}, - pages = {3293--3300}, - annote = {368YYWATER RESOUR RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Water Resour. Res.accession-num: ISI:000090146100014}, -} - -@article{mehrotra_correcting_2015, - title = {Correcting for systematic biases in multiple raw {GCM} variables across a range of timescales}, - volume = {520}, - issn = {0022-1694}, - doi = {http://dx.doi.org/10.1016/j.jhydrol.2014.11.037}, - journal = {Journal of Hydrology}, - author = {Mehrotra, Rajeshwar and Sharma, Ashish}, - year = {2015}, - keywords = {Climate change, Correcting for biases in GCM simulations, Cross dependence, Downscaling, Multivariate Recursive Nested Bias Correction (MRNBC)}, - pages = {214--223}, -} - -@article{mehrotra_improved_2012, - title = {An improved standardization procedure to remove systematic low frequency variability biases in {GCM} simulations}, - volume = {48}, - issn = {1944-7973}, - doi = {10.1029/2012WR012446}, - number = {12}, - journal = {Water Resources Research}, - author = {Mehrotra, Rajeshwar and Sharma, Ashish}, - year = {2012}, - keywords = {climate change, 1637 Regional climate change, 1807 Climate impacts, downscaling, 1847 Modeling, 1872 Time series analysis, biases in GCM simulations of atmospheric variables, low frequency variability, recursive nested bias correction (RNBC)}, - pages = {W12601}, -} - -@article{meehl_solar_2003, - title = {Solar and greenhouse gas forcing and climate response in the twentieth century}, - volume = {16}, - issn = {0894-8755}, - abstract = {Ensemble experiments with a global coupled climate model are performed for the twentieth century with time-evolving solar, greenhouse gas, sulfate aerosol (direct effect), and ozone (tropospheric and stratospheric) forcing. Observed global warming in the twentieth century occurred in two periods, one in the early twentieth century from about the early 1900s to the 1940s, and one later in the century from, roughly, the late 1960s to the end of the century. The model's response requires the combination of solar and anthropogenic forcing to approximate the early twentieth-century warming, while the radiative forcing from increasing greenhouse gases is dominant for the response in the late twentieth century, confirming previous studies. Of particular interest here is the model's amplification of solar forcing when this acts in combination with anthropogenic forcing. This difference is traced to the fact that solar forcing is more spatially heterogeneous (i.e., acting most strongly in areas where sunlight reaches the surface) while greenhouse gas forcing is more spatially uniform. Consequently, solar forcing is subject to coupled regional feedbacks involving the combination of temperature gradients, circulation regimes, and clouds. The magnitude of these feedbacks depends on the climate's base state. Over relatively cloud-free oceanic regions in the subtropics, the enhanced solar forcing produces greater evaporation. More moisture then converges into the precipitation convergence zones, intensifying the regional monsoon and Hadley and Walker circulations, causing cloud reductions over the subtropical ocean regions, and, hence, more solar input. An additional response to solar forcing in northern summer is an enhancement of the meridional temperature gradients due to greater solar forcing over land regions that contribute to stronger West African and South Asian monsoons. Since the greenhouse gases are more spatially uniform, such regional circulation feedbacks are not as strong. These regional responses are most evident when the solar forcing occurs in concert with increased greenhouse gas forcing. The net effect of enhanced solar forcing in the early twentieth century is to produce larger solar-induced increases of tropical precipitation when calculated as a residual than for early century solar-only forcing, even though the size of the imposed solar forcing is the same. As a consequence, overall precipitation increases in the early twentieth century in the Asian monsoon regions are greater than late century increases, qualitatively consistent with observed trends in all-India rainfall. Similar effects occur in West Africa, the tropical Pacific, and the Southern Ocean tropical convergence zones.}, - language = {English}, - number = {3}, - journal = {Journal of Climate}, - author = {Meehl, G. A. and Washington, W. M. and Wigley, T. M. L. and Arblaster, J. M. and Dai, A.}, - month = feb, - year = {2003}, - keywords = {simulations, global climate, general-circulation model, sulfate aerosols, 20th-century temperature, cycle variability, increased co2, stratosphere, system model, troposphere}, - pages = {426--444}, - annote = {640JYTimes Cited:26Cited References Count:45}, - annote = {The following values have no corresponding Zotero field:auth-address: Meehl, GA Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 USA Natl Ctr Atmospher Res, Boulder, CO 80307 USAaccession-num: ISI:000180686700005}, -} - -@article{meehl_climate_2012, - title = {Climate {System} {Response} to {External} {Forcings} and {Climate} {Change} {Projections} in {CCSM4}}, - volume = {25}, - issn = {0894-8755}, - doi = {10.1175/jcli-d-11-00240.1}, - number = {11}, - journal = {Journal of Climate}, - author = {Meehl, Gerald A. and Washington, Warren M. and Arblaster, Julie M. and Hu, Aixue and Teng, Haiyan and Tebaldi, Claudia and Sanderson, Benjamin N. and Lamarque, Jean-Francois and Conley, Andrew and Strand, Warren G. and White, James B.}, - month = jun, - year = {2012}, - pages = {3661--3683}, - annote = {doi: 10.1175/JCLI-D-11-00240.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/JCLI-D-11-00240.1}, -} - -@article{meehl_current_2007, - title = {Current and future {U}.{S}. weather extremes and {El} {Niño}}, - volume = {34}, - journal = {Geophysical Research Letters}, - author = {Meehl, G. A. and Tebaldi, C. and Teng, H. and Peterson, T. C.}, - year = {2007}, - pages = {L20704, doi:10.1029/2007GL031027}, -} - -@article{meehl_trends_2000, - title = {Trends in extreme weather and climate events: {Issues} related to modeling extremes in projections of future climate change}, - volume = {81}, - abstract = {Projections of statistical aspects of weather and climate extremes can be derived from climate models representing possible future climate states. Some of the recent models have reproduced results previously reported in the Intergovernmental Panel on Climate Change (IPCC) Second Assessment Report, such as a greater frequency of extreme warm days and lower frequency of extreme cold days associated with a warmer mean climate, a decrease in diurnal temperature range associated with higher nighttime temperatures, increased precipitation intensity, midcontinent summer drying, decreasing daily variability of surface temperature in winter, and increasing variability of northern midlatitude summer surface temperatures. This reconfirmation of previous results gives an increased confidence in the credibility of the models, though agreement among models does not guarantee those changes will occur. New results since the IPCC Second Assessment Report indicate a possible increase of extreme heat stress events in a warmer climate, an increase of cooling degree days and decrease in heating degree days, an increase of precipitation extremes such that there is a decrease in return periods for 20-yr extreme precipitation events, and more detailed analyses of possible changes in 20-yr return values for extreme maximum and minimum temperatures. Additionally, recent studies are now addressing interannual and synoptic time and space scale processes that affect weather and climate extremes, such as tropical cyclones, El Nino effects, and extratropical storms. However, current climate models are not yet in agreement with respect to possible future changes in such features.}, - number = {3}, - journal = {Bulletin of the American Meteorological Society}, - author = {Meehl, G. A. and Zwiers, F. and Evans, J. and Knutson, T. and Mearns, L. and Whetton, P.}, - month = mar, - year = {2000}, - pages = {427--436}, - annote = {305EBBULL AMER METEOROL SOC}, - annote = {The following values have no corresponding Zotero field:alt-title: Bull. Amer. Meteorol. Soc.accession-num: ISI:000086525500003}, -} - -@article{meehl_factors_2004, - title = {Factors affecting climate sensitivity in global coupled models}, - volume = {17}, - issn = {0894-8755}, - abstract = {Four global coupled climate models with different combinations of atmosphere, ocean, land surface, and sea ice components are compared in idealized forcing (1\% CO2 increase) experiments. The four models are the Climate System Model (CSM), the Parallel Climate Model (PCM), the PCM/CSM Transition Model (PCTM), and the Community Climate System Model (CCSM). The hypothesis is posed that models with similar atmospheric model components should show a similar globally averaged dynamically coupled response to increasing CO2 in spite of different ocean, sea ice, and land formulations. Conversely, models with different atmospheric components should be most different in terms of the coupled globally averaged response. The two models with the same atmosphere and sea ice but different ocean components ( PCM and PCTM) have the most similar response to increasing CO2, followed closely by CSM with comparable atmosphere and different ocean and sea ice from either PCM or PCTM. The fourth model, CCSM, has a different response from the other three and, in particular, is different from PCTM in spite of having the same ocean and sea ice but different atmospheric model component. These results support the hypothesis that, to a greater degree than the other components, the atmospheric model "manages'' the relevant global feedbacks including sea ice albedo, water vapor, and clouds. The atmospheric model also affects the meridional overturning circulation in the ocean, as well as the ocean heat uptake characteristics. This is due to changes in surface fluxes of heat and freshwater that affect surface density in the ocean. For global sensitivity measures, the ocean, sea ice, and land surface play secondary roles, even though differences in these components can be important for regional climate changes.}, - language = {English}, - number = {7}, - journal = {Journal of Climate}, - author = {Meehl, G. A. and Washington, W. M. and Arblaster, J. M. and Hu, A. X.}, - month = apr, - year = {2004}, - keywords = {co2, parameterization, system model, aogcm, el-nino, land-surface climatology, ocean model}, - pages = {1584--1596}, - annote = {809ANTimes Cited:4Cited References Count:27}, - annote = {The following values have no corresponding Zotero field:auth-address: Meehl, GA Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 USA Natl Ctr Atmospher Res, Boulder, CO 80307 USAaccession-num: ISI:000220609700011}, -} - -@article{meehl_combinations_2004, - title = {Combinations of natural and anthropogenic forcings in twentieth-century climate}, - volume = {17}, - issn = {0894-8755}, - abstract = {Ensemble simulations are run with a global coupled climate model employing five forcing agents that influence the time evolution of globally averaged surface air temperature during the twentieth century. Two are natural ( volcanoes and solar) and the others are anthropogenic [e.g., greenhouse gases (GHGs), ozone (stratospheric and tropospheric), and direct effect of sulfate aerosols]. In addition to the five individual forcing experiments, an additional eight sets are performed with the forcings in various combinations. The late-twentieth-century warming can only be reproduced in the model with anthropogenic forcing ( mainly GHGs), while the early twentieth-century warming is mainly caused by natural forcing in the model ( mainly solar). However, the signature of globally averaged temperature at any time in the twentieth century is a direct consequence of the sum of the forcings. The similarity of the response to the forcings on decadal and interannual time scales is tested by performing a principal component analysis of the 13 ensemble mean globally averaged temperature time series. A significant portion of the variance of the reconstructed time series can be retained in residual calculations compared to the original single and combined forcing runs. This demonstrates that the statistics of the variances for decadal and interannual time-scale variability in the forced simulations are similar to the response from a residual calculation. That is, the variance statistics of the response of globally averaged temperatures in the forced runs are additive since they can be reproduced in the responses calculated as a residual from other combined forcing runs.}, - language = {English}, - number = {19}, - journal = {Journal of Climate}, - author = {Meehl, G. A. and Washington, W. M. and Ammann, C. M. and Arblaster, J. M. and Wigley, T. M. L. and Tebaldi, C.}, - month = oct, - year = {2004}, - keywords = {temperature, simulations, solar, uncertainties}, - pages = {3721--3727}, - annote = {860RZTimes Cited:2Cited References Count:12}, - annote = {The following values have no corresponding Zotero field:auth-address: Meehl, GA Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 USA Natl Ctr Atmospher Res, Boulder, CO 80307 USAaccession-num: ISI:000224362100005}, -} - -@incollection{meehl_global_2007, - address = {Cambridge, UK}, - title = {Global climate projections}, - booktitle = {The {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fourth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {Meehl, G. A. and Stocker, T. F. and Collins, W. D. and Friedlingstein, P. and Gaye, A. T. and Gregory, J. M. and Kitoh, A. and Knutti, R. and Murphy, J. M. and Noda, A. and Raper, S. C. B. and Watterson, I. G. and Weaver, A. J. and Zhao, Z.-C.}, - editor = {Solomon, S. and Qin, D. and Manning, M. and Chen, Z. and Marquis, M. and Averyt, K. B. and Tignor, M. and Miller, H. L.}, - year = {2007}, - pages = {747--846}, -} - -@article{meehl_introduction_2000, - title = {An introduction to trends in extreme weather and climate events: {Observations}, socioeconomic impacts, terrestrial ecological impacts, and model projections}, - volume = {81}, - abstract = {Weather and climatic extremes can have serious and damaging effects on human society and infrastructure as well as on ecosystems and wildlife. Thus, they are usually the main focus of attention of the news media in reports on climate. There are some indications from observations concerning how climatic extremes may have changed in the past. Climate models show how they could change in the future either due to natural climate fluctuations or under conditions of greenhouse gas-induced warming. These observed and modeled changes relate directly to the understanding of socioeconomic and ecological impacts related to extremes.}, - number = {3}, - journal = {Bulletin of the American Meteorological Society}, - author = {Meehl, G. A. and Karl, T. and Easterling, D. R. and Changnon, S. and Pielke, R. and Changnon, D. and Evans, J. and Groisman, P. Y. and Knutson, T. R. and Kunkel, K. E. and Mearns, L. O. and Parmesan, C. and Pulwarty, R. and Root, T. and Sylves, R. T. and Whetton, P. and Zwiers, F.}, - month = mar, - year = {2000}, - pages = {413--416}, - annote = {305EBBULL AMER METEOROL SOC}, - annote = {The following values have no corresponding Zotero field:alt-title: Bull. Amer. Meteorol. Soc.accession-num: ISI:000086525500001}, -} - -@article{meehl_overview_2005, - title = {Overview of the {Coupled} {Model} {Intercomparison} {Project}}, - volume = {86}, - issn = {0003-0007}, - abstract = {The Coupled Model Intercomparison Project (CMIP) involves study and intercomparison of multimodel simulations of present and future climate. The simulations of the future use idealized forcing in which CO, increase is compounded 1\% yr(-1) until it doubles (near year 70) with global coupled models that contain, typically, components representing atmosphere, ocean, sea ice, and land surface. Results from CMIP diagnostic sub-projects were presented at the Second CMIP Workshop held at the Max Planck Institute for Meteorology in Hamburg, Germany, in September 2003. Significant progress in diagnosing and understanding results from global coupled models has been made since the time of the First CMIP Workshop in Melbourne, Australia, in 1998. For example, the issue of flux adjustment is slowly fading as more and more models obtain stable multicentury surface climates without them. El Nino variability, usually about half the observed amplitude in the previous generation of coupled models, is now more accurately simulated in the present generation of global coupled models, though there are still biases in simulating the patterns of maximum variability. Typical resolutions of atmospheric component models contained in coupled models are now usually around 2.5degrees latitude-longitude, with the ocean components often having about twice the atmospheric model resolution, with even higher resolution in the equatorial Tropics. Some new-generation coupled models have atmospheric resolutions of around 1.5degrees latitude - longitude. Modeling groups now routinely run the CMIP control and 1\% CO2 simulations in addition to twentieth- and twenty-first-century climate simulations with a variety of forcings [e.g., volcanoes, solar variability, anthropogenic sulfate aerosols, ozone, and greenhouse gases, with the anthropogenic forcings for future climate as well]. However, persistent systematic errors noted in previous generations of global coupled models are still present in the current generation (e.g., overextensive equatorial Pacific cold tongue, double ITCZ). This points to the next challenge for the global coupled climate modeling community. Planning and commencement of the Intergovernmental Panel on Climate Change Fourth Assessment Report (AR4) has prompted rapid coupled model development, which is leading to an expanded CMIP-like activity to collect and analyze results for the control, 1\% CO2, and twentieth-, twenty-first, and twenty-second-century simulations performed for the AR4. The international climate community is encouraged to become involved in this analysis effort.}, - language = {English}, - number = {1}, - journal = {Bulletin of the American Meteorological Society}, - author = {Meehl, G. A. and Covey, C. and McAvaney, B. and Latif, M. and Stouffer, R. J.}, - month = jan, - year = {2005}, - pages = {89--93}, - annote = {896YITimes Cited:0Cited References Count:5}, - annote = {The following values have no corresponding Zotero field:auth-address: Meehl, GA Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 USA Natl Ctr Atmospher Res, Boulder, CO 80307 USA Program Climate Model Diagnosis \& Intercomparison, Livermore, CA USA Bur Meteorol Res Ctr, Melbourne, Vic, Australia Leibniz Inst Meereswissensch, Kiel, Germany Geophys Fluid Dynam Lab, Princeton, NJ USAaccession-num: ISI:000226970100024}, -} - -@article{meehl_understanding_2005, - title = {Understanding future patterns of increased precipitation intensity in climate model simulations}, - volume = {32}, - journal = {Geophysical Research Letters}, - author = {Meehl, G. A. and Arblaster, J. A. and Tebaldi, C.}, - year = {2005}, - pages = {L18719, doi:10.1029/2005GL023680}, -} - -@incollection{mearns_climate_2001, - address = {New York}, - title = {Climate scenario development}, - booktitle = {Climate {Change} 2001: {The} {Scientific} {Basis}}, - publisher = {Cambridge University Press}, - author = {Mearns, L. O. and Hulme, M. and Carter, T. R. and Leemans, R. and Lal, M. and Whetton, P.}, - editor = {Houghton, J. T. et al}, - year = {2001}, - pages = {739--768}, -} - -@article{mearns_regional_2009, - title = {A regional climate change assessment program for {North} {America}}, - volume = {90}, - number = {36}, - journal = {Eos Trans. AGU}, - author = {Mearns, L. O. and Gutowski, W. and Jones, R. and Leung, R. and McGinnis, S. and Nunes, A. and Qian, Y.}, - year = {2009}, - pages = {311}, -} - -@article{mearns_analysis_1995, - title = {Analysis of daily variability or precipitation in a nested regional climate model: comparison with observations and doubled {CO2} results}, - volume = {10}, - journal = {Global and Planetary Change}, - author = {Mearns, L. O. and Giorgi, F. and McDaniel, L. and Shield, C.}, - year = {1995}, - pages = {55--78}, -} - -@article{mearns_comparison_1999, - title = {Comparison of climate change scenarios generated from regional climate model experiments and statistical downscaling}, - volume = {104}, - abstract = {We compare regional climate change scenarios (temperature and precipitation) over eastern Nebraska produced by a semiempirical statistical downscaling (SDS) technique and regional climate model (RegCM2) experiments, both using large scale information from the same coarse resolution general circulation model (GCM) control and 2 x CO2 simulations. The SDS method is based on the circulation pattern classification technique in combination with stochastic generation of daily time series of temperature and precipitation. It uses daily values of 700 mbar geopotential heights as the large-scale circulation variable. The regional climate model is driven by initial and lateral boundary conditions from the GCM, The RegCM2 exhibited greater spatial variability than the SDS method for change in both temperature and precipitation. The SDS method produced a seasonal cycle of temperature change with a much larger amplitude than that of the RegCM2 or the GCM. Daily variability of temperature mainly decreased for both downscaling methods and the GCM. Changes in mean daily precipitation varied between SDS and RegCM2. The RegCM2 simulated both increases and decreases in the probability of precipitation, while the SDS method produced only increases. We explore possible dynamical and physical reasons for the differences in the scenarios produced by the two methods and the GCM.}, - number = {D6}, - journal = {Journal of Geophysical Research-Atmospheres}, - author = {Mearns, L. O. and Bogardi, I. and Giorgi, F. and Matyasovszky, I. and Palecki, M.}, - month = mar, - year = {1999}, - pages = {6603--6621}, - annote = {180AYJ GEOPHYS RES-ATMOS}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Geophys. Res.-Atmos.accession-num: ISI:000079363700038}, -} - -@article{meehl_factors_2001, - title = {Factors that affect the amplitude of {El} {Nino} in global coupled climate models}, - volume = {17}, - issn = {0930-7575}, - abstract = {Historically, El Nine-like events simulated in global coupled climate models have had reduced amplitude compared to observations. Here, El Nino-like phenomena are compared in ten sensitivity experiments using two recent global coupled models. These models have various combinations of horizontal and vertical ocean resolution, ocean physics. and atmospheric model resolution. It is demonstrated that the lower the value of the ocean background vertical diffusivity, the greater the amplitude of EI Nine variability which is related primarily to a sharper equatorial thermocline. Among models with low background vertical diffusivity, stronger equatorial zonal wind stress is associated with relatively higher amplitude El Nine variability along with more realistic east-west sea surface temperature (SST) gradient alone the equator. The SST seasonal cycle in the eastern tropical Pacific has too much of a semiannual component with a double intertropical convergence zone (ITCZ) in all experiments. and thus does not affect. nor is it affected by, the amplitude of El Nine variability. Systematic errors affecting the spatial variability of El Nine in the experiments are characterized by the eastern equatorial Pacific cold tongue regime extending too far westward into the warm pool. The time scales of interannual variability (as represented by time series of Nino3 SSTs) show significant power in the 3-4 year ENSO band and 2-2.5 year tropospheric biennial oscillation (TBO) band in the model experiments. The TBO periods in the models agree well with the observations, while the ENSO periods are near the short end of the range of 3-6 years observed during the period 1950-94. The close association between interannual variability of equatorial eastern Pacific SSTs and large-scale SST patterns is represented by significant correlations between Nino3 time series and the PC time series of the first EOFs of near-global SSTs in the models and observations.}, - language = {English}, - number = {7}, - journal = {Climate Dynamics}, - author = {Meehl, G. A. and Gent, P. R. and Arblaster, J. M. and Otto-Bliesner, B. L. and Brady, E. C. and Craig, A.}, - month = apr, - year = {2001}, - keywords = {simulation, precipitation, variability, southern oscillation, component, general-circulation models, ocean-atmosphere gcm, seasonal cycle, system-model, tropical pacific}, - pages = {515--526}, - annote = {427AZTimes Cited:42Cited References Count:42}, - annote = {The following values have no corresponding Zotero field:auth-address: Meehl, GA Natl Ctr Atmospher Res, POB 3000, Boulder, CO 80307 USA Natl Ctr Atmospher Res, Boulder, CO 80307 USAaccession-num: ISI:000168383800003}, -} - -@article{meehl_wcrp_2007, - title = {The {WCRP} {CMIP3} multimodel dataset: {A} new era in climate change research}, - volume = {88}, - journal = {Bulletin of the American Meteorological Society}, - author = {Meehl, G. A. and C. Covey and T. Delworth and M. Latif and B. McAvaney and J. F.B. Mitchell and R. J. Stouffer and K. E. Taylor}, - year = {2007}, - pages = {1383--1394}, -} - -@article{mearns_mean_1997, - title = {Mean and {Variance} {Change} in {Climate} {Scenarios}: {Methods}, {Agricultural} {Applications}, and {Measures} of {Uncertainty}}, - volume = {35}, - issn = {1573-1480}, - doi = {10.1023/a:1005358130291}, - abstract = {Our central goal is to determine the importance of including both mean and variability changes in climate change scenarios in an agricultural context. By adapting and applying a stochastic weather generator, we first tested the sensitivity of the CERES-Wheat model to combinations of mean and variability changes of temperature and precipitation for two locations in Kansas. With a 2°C increase in temperature with daily (and interannual) variance doubled, yields were further reduced compared to the mean only change. In contrast, the negative effects of the mean temperature increase were greatly ameliorated by variance decreased by one-half. Changes for precipitation are more complex, since change in variability naturally attends change in mean, and constraining the stochastic generator to mean change only is highly artificial. The crop model is sensitive to precipitation variance increases with increased mean and variance decreases with decreased mean. With increased mean precipitation and a further increase in variability Topeka (where wheat cropping is not very moisture limited) experiences decrease in yield after an initial increase from the 'mean change only’ case. At Goodland Kansas, a moisture-limited site where summer fallowing is practiced, yields are decreased with decreased precipitation, but are further decreased when variability is further reduced. The range of mean and variability changes to which the crop model is sensitive are within the range of changes found in regional climate modeling (RegCM) experiments for a CO2 doubling (compared to a control run experiment). We then formed two types of climate change scenarios based on the changes in climate found in the control and doubled CO2 experiments over the conterminous U. S. of RegCM: (1) one using only mean monthly changes in temperature, precipitation, and solar radiation; and (2) another that included these mean changes plus changes in daily (and interannual) variability. The scenarios were then applied to the CERES-Wheat model at four locations (Goodland, Topeka, Des Moines, Spokane) in the United States. Contrasting model responses to the two scenarios were found at three of the four sites. At Goodland, and Des Moines mean climate change increased mean yields and decreased yield variability, but the mean plus variance climate change reduced yields to levels closer to their base (unchanged) condition. At Spokane mean climate change increased yields, which were somewhat further increased with climate variability change. Three key aspects that contribute to crop response are identified: the marginality of the current climate for crop growth, the relative size of the mean and variance changes, and timing of these changes. Indices for quantifying uncertainty in the impact assessment were developed based on the nature of the climate scenario formed, and the magnitude of difference between model and observed values of relevant climate variables.}, - number = {4}, - journal = {Climatic Change}, - author = {Mearns, L. O. and Rosenzweig, C. and Goldberg, R.}, - year = {1997}, - pages = {367--396}, - annote = {The following values have no corresponding Zotero field:label: ref1work-type: journal article}, -} - -@book{mccuen_modeling_2003, - address = {Washington, D.C.}, - title = {Modeling {Hydrologic} change: {Statistical} {Methods}}, - publisher = {Lewis/CRC}, - author = {McCuen, R. H.}, - year = {2003}, -} - -@article{mccabe_decadal_1999, - title = {Decadal variations in the strength of {ENSO} teleconnections with precipitation in the western {U}.{S}.}, - volume = {19}, - journal = {International Journal of Climatology}, - author = {McCabe, G. J. and Dettinger, M. D.}, - year = {1999}, - pages = {1399--1410}, -} - -@article{maurer_detection_2007, - title = {Detection, attribution, and sensitivity of trends toward earlier streamflow in the {Sierra} {Nevada}}, - volume = {112}, - journal = {Journal of Geophysical Research}, - author = {Maurer, E. P. and Stewart, I. T. and Bonfils, C. and Duffy, P. B. and Cayan, D. 112}, - year = {2007}, - pages = {D11118, doi:10.1029/2006JD008088}, -} - -@article{maurer_bias_2014, - title = {Bias correction can modify climate model simulated precipitation changes without adverse effect on the ensemble mean}, - volume = {18}, - issn = {1607-7938}, - doi = {10.5194/hess-18-915-2014}, - number = {3}, - journal = {Hydrol. Earth Syst. Sci.}, - author = {Maurer, E. P. and Pierce, D. W.}, - year = {2014}, - pages = {915--925}, - annote = {HESS}, - annote = {The following values have no corresponding Zotero field:publisher: Copernicus Publications}, -} - -@article{maurer_evaluation_2001, - title = {Evaluation of the land surface water budget in {NCEP}/{NCAR} and {NCEP}/{DOE} reanalyses using an off-line hydrologic model}, - volume = {106}, - number = {D16}, - journal = {Journal of Geophysical Research}, - author = {Maurer, E. P. and O'Donnell, G. M. and Lettenmaier, D. P. and Roads, J. O.}, - year = {2001}, - pages = {17841--17862}, -} - -@article{maurer_predictability_2003, - title = {Predictability of seasonal runoff in the {Mississippi} {River} basin}, - volume = {108}, - number = {D16}, - journal = {Journal of Geophysical Research}, - author = {Maurer, E. P. and Lettenmaier, D. P.}, - year = {2003}, - pages = {8607 doi:10.1029/2002JD002555}, -} - -@article{mccabe_spatial_2014, - title = {Spatial and temporal patterns in conterminous {United} {States} streamflow characteristics}, - volume = {41}, - issn = {1944-8007}, - doi = {10.1002/2014gl061980}, - number = {19}, - journal = {Geophysical Research Letters}, - author = {McCabe, Gregory J. and Wolock, David M.}, - year = {2014}, - keywords = {streamflow, 1616 Climate variability, 1833 Hydroclimatology, 1860 Streamflow, conterminous United States, spatiotemporal variability}, - pages = {6889--6897}, - annote = {The following values have no corresponding Zotero field:modified-date: 2014gl061980}, -} - -@article{maurer_long-term_2002, - title = {A long-term hydrologically-based data set of land surface fluxes and states for the conterminous {United} {States}}, - volume = {15}, - number = {22}, - journal = {Journal of Climate}, - author = {Maurer, E. P. and Wood, A. W. and Adam, J. C. and Lettenmaier, D. P. and Nijssen, B.}, - year = {2002}, - pages = {3237--3251}, -} - -@article{maurer_evaluation_2003, - title = {Evaluation of the snow-covered area data product from {MODIS}}, - volume = {17}, - issn = {1099-1085}, - doi = {10.1002/hyp.1193}, - number = {1}, - journal = {Hydrological Processes}, - author = {Maurer, E. P. and Rhoads, J. D. and Dubayah, R. O. and Lettenmaier, D. P.}, - year = {2003}, - keywords = {MODIS, NOHRSC, remote sensing, snow-covered area}, - pages = {59--71}, - annote = {The following values have no corresponding Zotero field:publisher: John Wiley \& Sons, Ltd.}, -} - -@article{maurer_variability_2004, - title = {Variability and predictability of {North} {American} runoff}, - volume = {40}, - number = {9}, - journal = {Water Resources Research}, - author = {Maurer, E. P. and Lettenmaier, D. P. and Mantua, N. J.}, - year = {2004}, - pages = {W09306 doi:10.1029/2003WR002789}, -} - -@article{edwin_p_maurer_adjusting_2018, - title = {Adjusting {Flood} {Peak} {Frequency} {Changes} to {Account} for {Climate} {Change} {Impacts} in the {Western} {United} {States}}, - volume = {144}, - doi = {doi:10.1061/(ASCE)WR.1943-5452.0000903}, - number = {3}, - journal = {Journal of Water Resources Planning and Management}, - author = {Edwin P. Maurer and Gretchen Kayser and Laura Doyle and Andrew W. Wood}, - year = {2018}, - pages = {05017025}, -} - -@article{maurer_uncertainty_2005, - title = {Uncertainty in projections of streamflow changes due to climate change in {California}}, - volume = {32}, - issn = {0094-8276}, - abstract = {[1] Understanding the uncertainty in the projected impacts of climate change on hydrology will help decision-makers interpret the confidence in different projected future hydrologic impacts. We focus on California, which is vulnerable to hydrologic impacts of climate change. We statistically bias correct and downscale temperature and precipitation projections from 10 GCMs participating in the Coupled Model Intercomparison Project. These GCM simulations include a control period ( unchanging CO2 and other forcing) and perturbed period (1\%/ year CO2 increase). We force a hydrologic model with the downscaled GCM data to generate streamflow at strategic points. While the different GCMs predict significantly different regional climate responses to increasing atmospheric CO2, hydrological responses are robust across models: decreases in summer low flows and increases in winter flows, and a shift of flow to earlier in the year. Summer flow decreases become consistent across models at lower levels of greenhouse gases than increases in winter flows do.}, - language = {English}, - number = {3}, - journal = {Geophysical Research Letters}, - author = {Maurer, E. P. and Duffy, P. B.}, - month = feb, - year = {2005}, - keywords = {impacts, responses, hydrology}, - pages = {L03704, doi:10.1029/2004GL021462}, - annote = {895YMTimes Cited:0Cited References Count:16}, - annote = {The following values have no corresponding Zotero field:auth-address: Maurer, EP Santa Clara Univ, Dept Civil Engn, Santa Clara, CA 95053 USA Santa Clara Univ, Dept Civil Engn, Santa Clara, CA 95053 USA Lawrence Livermore Natl Lab, Div Atmospher Sci, Livermore, CA USAaccession-num: ISI:000226901000001}, -} - -@article{maurer_utility_2010, - title = {The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in {California}}, - volume = {14}, - journal = {Hydrology and Earth System Sciences}, - author = {Maurer, E. P. and Hidalgo, H. G. and Das, T. and Dettinger, M. D. and Cayan, D. R.}, - year = {2010}, - pages = {1125--1138, doi:10.5194/hess--14--1125--2010}, -} - -@article{maurer_utility_2008, - title = {Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods}, - volume = {12}, - journal = {Hydrology and Earth System Sciences}, - author = {Maurer, E. P. and Hidalgo, H. G.}, - year = {2008}, - pages = {551--563}, -} - -@article{maurer_enhanced_2014, - title = {An enhanced archive facilitating climate impacts and adaptation analysis}, - issn = {0003-0007}, - doi = {10.1175/bams-d-13-00126.1}, - journal = {Bulletin of the American Meteorological Society}, - author = {Maurer, E. P. and Brekke, L. and Pruitt, T. and Thrasher, B. and Long, J. and Duffy, P. and Dettinger, M. and Cayan, D. and Arnold, J.}, - year = {2014}, - annote = {doi: 10.1175/BAMS-D-13-00126.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/BAMS-D-13-00126.1}, -} - -@article{matyasovszky_impact_1995, - title = {Impact of {Global} {Climate}-{Change} on {Temperature} and {Precipitation} in {Greece}}, - volume = {71}, - abstract = {A stochastic space-time model is used at four locations in Greece for estimating the effect of global climate change on daily temperature and precipitation. The approach is based on a semi-empirical downscaling of simulated daily atmospheric Circulation Patterns (CP) of General Circulation Models (GCM). Historical data and a 10-year outputs of the Max Planck Institute GCM for the 1 x CO2 and 2 x CO2 cases are used. Nine CP types for the winter and summer half years are obtained to characteriz;e large-scale climatic forcing in Greece. Local temperature and precipitation appear to be highly dependent on CP types. The space-time response of daily temperature to global climate change is slightly variable in Greece. In general, a warmer climate will imply nearly 3 degrees C increase in fall and in winter. The variability within the month will not change considerably. A slight but statistically significant increase of precipitation is obtained at one location and an insignificant increase is found at the other three locations.}, - number = {2-3}, - journal = {Applied Mathematics and Computation}, - author = {Matyasovszky, I. and Bogardi, I. and Ganoulis, J.}, - month = sep, - year = {1995}, - pages = {119--150}, - annote = {RN166APPL MATH COMPUT}, - annote = {The following values have no corresponding Zotero field:alt-title: Appl. Math. Comput.accession-num: ISI:A1995RN16600002}, -} - -@article{maurer_errors_2013, - title = {Errors in climate model daily precipitation and temperature output: time invariance and implications for bias correction}, - volume = {17}, - issn = {1607-7938}, - doi = {10.5194/hess-17-2147-2013}, - number = {6}, - journal = {Hydrol. Earth Syst. Sci.}, - author = {Maurer, E. P. and Das, T. and Cayan, D. R.}, - year = {2013}, - pages = {2147--2159}, - annote = {HESS}, - annote = {The following values have no corresponding Zotero field:publisher: Copernicus Publications}, -} - -@article{maurer_fine-resolution_2007, - title = {Fine-resolution climate change projections enhance regional climate change impact studies}, - volume = {88}, - number = {47}, - journal = {Eos Trans. AGU}, - author = {Maurer, E. P. and Brekke, L. D. and Pruitt, T. and Duffy, P. B.}, - year = {2007}, - pages = {504, doi:10.1029/2007EO470006}, -} - -@article{maurer_contrasting_2010, - title = {Contrasting {Lumped} and {Distributed} {Hydrology} {Models} for {Estimating} {Climate} {Change} {Impacts} on {California} {Watersheds1}}, - volume = {46}, - issn = {1752-1688}, - doi = {10.1111/j.1752-1688.2010.00473.x}, - number = {5}, - journal = {JAWRA Journal of the American Water Resources Association}, - author = {Maurer, E. P. and Brekke, L. D. and Pruitt, T.}, - year = {2010}, - keywords = {surface water hydrology, climate variability/change, runoff, recharge}, - pages = {1024--1035}, - annote = {The following values have no corresponding Zotero field:publisher: Blackwell Publishing Ltd}, -} - -@article{maurer_climate_2009, - title = {Climate model based consensus on the hydrologic impacts of climate change to the {Rio} {Lempa} basin of {Central} {America}}, - volume = {13}, - issn = {1027-5606}, - abstract = {Temperature and precipitation from 16 climate models each using two emissions scenarios (lower B1 and mid-high A2) were used to characterize the range of potential climate changes for the Rio Lempa basin of Central America during the middle (2040-2069) and end (2070-2099) of the 21st century. A land surface model was applied to investigate the hydrologic impacts of these changes, focusing on inflow to two major hydropower reservoirs. By 2070-2099 the median warming relative to 1961-1990 was 1.9 degrees C and 3.4 degrees C under B1 and A2 emissions, respectively. For the same periods, the models project median precipitation decreases of 5.0 \% (B1) and 10.4 \% (A2). Median changes by 2070-2099 in reservoir inflow were 13 \% (B1) and 24 \% (A2), with largest flow reductions during the rising limb of the seasonal hydrograph, from June through September. Frequency of low flow years increases, implying decreases in firm hydropower capacity of 33 \% to 53 \% by 2070-2099.}, - language = {English}, - number = {2}, - journal = {Hydrol. Earth Syst. Sci.}, - author = {Maurer, E. P. and Adam, J. C. and Wood, A. W.}, - year = {2009}, - keywords = {model, simulation, streamflow, variability, water-resources, part i, colorado river-basin, coupled, doubled co2, emissions, midsummer drought}, - pages = {183--194}, - annote = {ISI Document Delivery No.: 414BMTimes Cited: 2Cited Reference Count: 74Maurer, E. P. Adam, J. C. Wood, A. W.Santa Clara University ; Office of Science, U. S. Department of EnergyWe are indebted to Jacqueline Cativo and Ismael Sanchez of the Departamento de Ciencias Energeticas y Fluidicas at the Universidad Centroamericana in San Salvador for their support and assistance in this study. We are grateful to Mauricio Martinez of the Servicio Nacional de Estudios Territoriales (SNET), San Salvador, El Salvador, and Rodolfo Caceres of the Comision Ejecutiva Hidroelectrica del Rio Lempa (CEL) for generously sharing their time and helping to acquire the data essential to this study. The first author received the generous support of an Arthur Vining Davis grant through Santa Clara University. We acknowledge the modeling groups for making their simulations available for analysis, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for collecting and archiving the CMIP3 model output, and the WCRP's Working Group on Coupled Modeling (WGCM) for organizing the model data analysis activity. The WCRP CMIP3 multi-model dataset is supported by the Office of Science, U. S. Department of Energy. We are grateful for the thorough review and helpful comments of two anonymous reviewers who helped improve this manuscript.Copernicus publicationsKathlenburg-lindau}, - annote = {The following values have no corresponding Zotero field:auth-address: [Maurer, E. P.] Santa Clara Univ, Dept Civil Engn, Santa Clara, CA 95053 USA. [Adam, J. C.] Washington State Univ, Dept Civil \& Environm Engn, Pullman, WA 99164 USA. [Wood, A. W.] Tier Grp, Seattle, WA USA. Maurer, EP, Santa Clara Univ, Dept Civil Engn, Santa Clara, CA 95053 USA. emaurer@engr.scu.edualt-title: Hydrol. Earth Syst. Sci.accession-num: ISI:000263838900009work-type: Article}, -} - -@article{matulla_climate_2002, - title = {Climate change scenarios at {Austrian} {National} {Forest} {Inventory} sites}, - volume = {22}, - abstract = {Regional risk assessments for the potential effects of climate change rely on plausible small-scale climate change scenario data. To bridge the gap between the coarse scale of general circulation models and the local scale of approximately 11000 sample sites of the Austrian National Forest Inventory (AFI), a seasonally stratified statistical downscaling procedure was applied to a control run and 2 transient experiments of ECHAM4/OPYC3, which are based on the trace gas only or trace gas plus sulphate scenario IPCC IS92a. We fitted multiple linear regression (MLR) models for the micro-scale monthly precipitation and temperature for each AFI point. The meteorological data at the AFI sites were obtained by interpolation of measurements from the dense network of Austrian weather stations for the period 1961-1995. The macro- scale predictors were principal components of monthly NCEP/NCAR reanalysis data (850 and 700 hPa geopotential. height, 850 hPa temperature and 700 hPa relative humidity). The results show spatial and temporal heterogeneity for both temperature and precipitation. In case of temperature MLR leads to increases from +1.4 to +4.0degreesC (trace gas only integration) and from +1.1 to +2.9degreesC (trace gas plus sulphate integration) for a period of about 55 yr relative to the 1961-1995 climatology. The regionalized precipitation changes are both negative and positive. Values range from -44 to + 26 \% (trace gas only integration) and from -29 to + 26 \% (trace gas plus sulphate integration). As expected, the explained variability for temperature was higher than for precipitation and depended on the season. From a validation experiment for model calibration we conclude that MLR shows reliable results for temperature. Even in the case of precipitation the method seems to yield plausible results. Both temperature and precipitation were better reproduced for winter than for summer.}, - number = {2}, - journal = {Climate Research}, - author = {Matulla, C. and Groll, N. and Kromp-Kolb, H. and Scheifinger, H. and Lexer, M. J. and Widmann, M.}, - month = sep, - year = {2002}, - pages = {161--173}, - annote = {608EWCLIMATE RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:000178833200006}, -} - -@article{matti_variability_2016, - title = {On the variability of cold region flooding}, - volume = {534}, - issn = {0022-1694}, - doi = {http://dx.doi.org/10.1016/j.jhydrol.2016.01.055}, - journal = {Journal of Hydrology}, - author = {Matti, Bettina and Dahlke, Helen E. and Lyon, Steve W.}, - year = {2016}, - keywords = {Flood generation, Gumbel distribution, Mann–Kendall test, Sweden sub-arctic, Trend analysis}, - pages = {669--679}, -} - -@article{hidalgo_caribbean_2015, - title = {The {Caribbean} {Low}-{Level} {Jet}, the {Inter}-{Tropical} {Convergence} {Zone} and {Precipitation} {Patterns} in the {Intra}-{Americas} {Sea}: {A} {Proposed} {Dynamical} {Mechanism}}, - volume = {97}, - issn = {1468-0459}, - doi = {10.1111/geoa.12085}, - number = {1}, - journal = {Geografiska Annaler: Series A, Physical Geography}, - author = {Hidalgo, Hugo G. and Durán-Quesada, Ana M. and Amador, Jorge A. and Alfaro, Eric J.}, - year = {2015}, - keywords = {precipitation, Atlantic Ocean, Caribbean low-level jet, Central America climatology, moisture transport, Pacific Ocean, Western Hemisphere Warm Pool}, - pages = {41--59}, -} - -@article{hoff_spatial_2001, - title = {Spatial and temporal persistence of mean monthly temperature on two {GCM} grid cells}, - volume = {21}, - abstract = {The study investigates a simple statistically based scheme for translating large scale general circulation model (GCM) information to local scale where natural and socio-economic environments are dependent on climatic variables. Two GCM grid cells were chosen, one centred on Montpellier (France), the other on Sevilla (Spain). For each meteorological station over these cells, the relative values of mean monthly temperature compared with the GCM grid cell mean were expressed as percentages, and were called 'relative temperatures' (RT). Using geostatistical procedures, the spatial structure of RT was sought on both GCM grid cells, with the altitude as external drift. The months of January and July were studied during the 1979-1993 period. For each month, the characteristics of the variograms of RT are close for each year of the period. There is also a good inter-annual persistence of spatial patterns of RT when the correlation between altitude and RT is significant. The results will be of great interest for easily estimating local values of mean monthly temperature at any place within each grid cell without further calculation. Copyright (C) 2001 Royal Meteorological Society.}, - number = {6}, - journal = {International Journal of Climatology}, - author = {Hoff, C.}, - month = may, - year = {2001}, - pages = {731--744}, - annote = {439PLINT J CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:000169126200005}, -} - -@article{hodgkins_changes_2003, - title = {Changes in the timing of high flows in {New} {England} over the 20th century}, - volume = {278}, - journal = {Journal of Hydrology}, - author = {Hodgkins, G. A. and Dudley, R. W. and Huntington, T. G.}, - year = {2003}, - pages = {244--252}, -} - -@article{hirabayashi_global_2013, - title = {Global flood risk under climate change}, - volume = {3}, - issn = {1758-678X}, - doi = {10.1038/nclimate1911 http://www.nature.com/nclimate/journal/v3/n9/abs/nclimate1911.html#supplementary-information}, - number = {9}, - journal = {Nature Clim. Change}, - author = {Hirabayashi, Yukiko and Mahendran, Roobavannan and Koirala, Sujan and Konoshima, Lisako and Yamazaki, Dai and Watanabe, Satoshi and Kim, Hyungjun and Kanae, Shinjiro}, - year = {2013}, - pages = {816--821}, - annote = {The following values have no corresponding Zotero field:publisher: Nature Publishing Groupwork-type: Letter}, -} - -@article{hidalgo_enso_2003, - title = {{ENSO} and {PDO} {Effects} on {Hydroclimatic} {Variations} of the {Upper} {Colorado} {River} {Basin}}, - volume = {4}, - issn = {1525-755X}, - doi = {10.1175/1525-7541(2003)004<0005:eapeoh>2.0.co;2}, - number = {1}, - journal = {Journal of Hydrometeorology}, - author = {Hidalgo, Hugo G. and Dracup, John A.}, - month = feb, - year = {2003}, - pages = {5--23}, - annote = {doi: 10.1175/1525-7541(2003)004{\textless}0005:EAPEOH{\textgreater}2.0.CO;2}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/1525-7541(2003)004{\textless}0005:EAPEOH{\textgreater}2.0.CO;2}, -} - -@techreport{hidalgo_downscaling_2008, - address = {Sacramento, CA}, - title = {Downscaling with constructed analogues: daily precipitation and temperature fields over the {United} {States}.}, - institution = {California Energy Commission, Public Interest Energy Research Program}, - author = {Hidalgo, H. G. and Dettinger, M. D. and Cayan, D. R.}, - month = jan, - year = {2008}, - pages = {62}, - annote = {The following values have no corresponding Zotero field:volume: CEC-500-2007-123}, -} - -@article{hidalgo_hydrological_2013, - title = {Hydrological climate change projections for {Central} {America}}, - volume = {495}, - issn = {0022-1694}, - doi = {http://dx.doi.org/10.1016/j.jhydrol.2013.05.004}, - abstract = {Summary Runoff climate change projections for the 21st century were calculated from a suite of 30 General Circulation Model (GCM) simulations for the A1B emission scenario in a 0.5° × 0.5° grid over Central America. The GCM data were downscaled using a version of the Bias Correction and Spatial Downscaling (BCSD) method and then used in the Variable Infiltration Capacity (VIC) macroscale hydrological model. The VIC model showed calibration skill in Honduras, Nicaragua, Costa Rica and Panama, but the results for some of the northern countries (Guatemala, El Salvador and Belize) and for the Caribbean coast of Central America was not satisfactory. Bias correction showed to remove effectively the biases in the GCMs. Results of the projected climate in the 2050–2099 period showed median significant reductions in precipitation (as much as 5–10\%) and runoff (as much as 10–30\%) in northern Central America. Therefore in this sub-region the prevalence of severe drought may increase significantly in the future under this emissions scenario. Northern Central America could warm as much as 3 °C during 2050–2099 and southern Central America could reach increases as much as 4 °C during the same period. The projected dry pattern over Central America is consistent with a southward displacement of the Intertropical Convergence Zone (ITCZ). In addition, downscaling of the NCEP/NCAR Reanalysis data from 1948 to 2012 and posterior run in VIC, for two locations in the northern and southern sub-regions of Central America, suggested that the annual runoff has been decreasing since ca. 1980, which is consistent with the sign of the runoff changes of the GCM projections. However, the Reanalysis 1980–2012 drying trends are generally much stronger than the corresponding GCM trends. Among the possible reasons for that discrepancy are model deficiencies, amplification of the trends due to constructive interference with natural modes of variability in the Reanalysis data, errors in the Reanalysis (modeled) precipitation data, and that the drying signal is more pronounced than predicted by the emissions scenario used. A few studies show that extrapolations of future climate from paleoclimatic indicators project a wetter climate in northern Central America, which is inconsistent with the modeling results presented here. However, these types of extrapolations should be done with caution, as the future climate responds to an extra forcing mechanism (anthropogenic) that was not present prehistorically and therefore the response could also be different than in the past.}, - number = {0}, - journal = {Journal of Hydrology}, - author = {Hidalgo, Hugo G. and Amador, Jorge A. and Alfaro, Eric J. and Quesada, Beatriz}, - year = {2013}, - keywords = {Caribbean Low-Level Jet (CLLJ), Hydrometeorology, Intertropical Convergence Zone (ITCZ), Mid-Summer Drought (MSD), Surface water hydrology, Variable Infiltration Capacity Model}, - pages = {94--112}, -} - -@article{hidalgo_observed_2017, - title = {Observed (1970–1999) climate variability in {Central} {America} using a high-resolution meteorological dataset with implication to climate change studies}, - volume = {141}, - issn = {1573-1480}, - doi = {10.1007/s10584-016-1786-y}, - abstract = {High spatial resolution of precipitation (P) and average air temperature (Tavg) datasets are ideal for determining the spatial patterns associated with large-scale atmospheric and oceanic indexes, and climate change and variability studies, however such datasets are not usually available. Those datasets are particularly important for Central America because they allow the conception of climate variability and climate change studies in a region of high climatic heterogeneity and at the same time aid the decisionmaking process at the local scale (municipalities and districts). Tavg data from stations and complementary gridded datasets at 50 km resolution were used to generate a high-resolution (5 km grid) dataset for Central America from 1970 to 1999. A highresolution P dataset was used along with the new Tavg dataset to study climate variability and a climate change application. Consistently with other studies, it was found that the 1970-1999 trends in P are generally non-significant, with the exception of a few small locations. In the case of Tavg, there were significant warming trends in most of Central America, and cooling trends in Honduras and northern Panama. When the sea surface temperature anomalies between the Tropical Pacific and the Tropical Atlantic have different (same) sign, they are a good indicator of the sign of P (Tavg) annual anomalies. Even with non-significant trends in precipitation, the significant warming trends in Tavg in most of Central America can have severe consequences in the hydrology and water availability of the region, as the warming would bring increases in evapotranspiration, drier soils and higher aridity.}, - number = {1}, - journal = {Climatic Change}, - author = {Hidalgo, H. G. and Alfaro, E. J. and Quesada-Montano, B.}, - year = {2017}, - pages = {13--28}, - annote = {The following values have no corresponding Zotero field:label: Hidalgo2017work-type: journal article}, -} - -@article{hoerling_regional_2010, - title = {Regional {Precipitation} {Trends}: {Distinguishing} {Natural} {Variability} from {Anthropogenic} {Forcing}}, - volume = {23}, - issn = {0894-8755}, - doi = {10.1175/2009jcli3420.1}, - number = {8}, - journal = {Journal of Climate}, - author = {Hoerling, Martin and Eischeid, Jon and Perlwitz, Judith}, - month = apr, - year = {2010}, - pages = {2131--2145}, - annote = {doi: 10.1175/2009JCLI3420.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/2009JCLI3420.1}, -} - -@article{hidalgo_skill_2015, - title = {Skill of {CMIP5} climate models in reproducing 20th century basic climate features in {Central} {America}}, - volume = {35}, - issn = {1097-0088}, - doi = {10.1002/joc.4216}, - number = {12}, - journal = {International Journal of Climatology}, - author = {Hidalgo, Hugo G. and Alfaro, Eric J.}, - year = {2015}, - keywords = {GCM, Central America, Caribbean Low-Level Jet, climate model, ITCZ, skill}, - pages = {3397--3421}, - annote = {The following values have no corresponding Zotero field:publisher: John Wiley \& Sons, Ltd}, -} - -@article{hewitson_interrogating_2013, - title = {Interrogating empirical-statistical downscaling}, - issn = {0165-0009}, - doi = {10.1007/s10584-013-1021-z}, - language = {English}, - journal = {Climatic Change}, - author = {Hewitson, B. C. and Daron, J. and Crane, R. G. and Zermoglio, M. F. and Jack, C.}, - month = dec, - year = {2013}, - pages = {1--16}, - annote = {The following values have no corresponding Zotero field:alt-title: Climatic Changepublisher: Springer Netherlands}, -} - -@article{herrera_airsea_2015, - title = {Air–sea interactions and dynamical processes associated with the midsummer drought}, - volume = {35}, - issn = {1097-0088}, - doi = {10.1002/joc.4077}, - number = {7}, - journal = {International Journal of Climatology}, - author = {Herrera, Eduardo and Magaña, Víctor and Caetano, Ernesto}, - year = {2015}, - keywords = {midsummer drought, Caribbean low-level jet, air–sea interaction, tropical Americas precipitation}, - pages = {1569--1578}, - annote = {The following values have no corresponding Zotero field:publisher: John Wiley \& Sons, Ltd}, -} - -@article{hellstrom_comparison_2001, - title = {Comparison of climate change scenarios for {Sweden} based on statistical and dynamical downscaling of monthly precipitation}, - volume = {19}, - abstract = {Two dynamically and statistically downscaled precipitation scenarios for Sweden are compared with respect to changes in the mean, The dynamically downscaled scenarios are generated by a 44 km version of the Rossby Centre regional climate model (RCM). The RCM is driven by data from 2 global greenhouse gas simulations sharing a 2.6degreesC global warming, one made by the HadCM2 and the other by the ECHAM4 general circulation model (GCM). The statistical downscaling model driven by the same GCMs is regression-based and incorporates large-scale circulation indices of the 2 geostrophic wind components (u and v), total vorticity (xi) and large-scale humidity at 850 hPa (q850) as predictors. The precipitation climates of the GCMs, RCMs and statistical models from the control runs are compared with respect to their ability to reproduce the observed seasonal cycle. Great improvements in the simulation of the seasonal cycle by all the downscaling models compared to the GCMs significantly increase the credibility of the downscaling models, The precipitation changes produced by the statistical models result from changes in all predictors, but the change in 4 is the greatest contributor in southern Sweden followed by q850 and u, while changes in q850 have greater effects in the northern parts of the country. The temporal and spatial variability of precipitation changes are higher in the statistically downscaled scenarios than in the dynamically downscaled ones. Comparisons of the 4 scenarios show that the spread of the scenarios created by the statistical model is on average larger than that between the RCM scenarios. The relatively large average spread is mainly due to the large differences found in summer. The seasonally averaged difference of the dynamical and statistical scenarios for the ECHAM4-based downscaled scenarios is 12\%, and for the HadCM2 downscaled scenarios 21\%. The differences in annual precipitation change are smaller, on average 4.5\% among the HadCM2-based downscaled scenarios, and 6.9\% among the ECHAM4-based downscaling scenarios.}, - number = {1}, - journal = {Climate Research}, - author = {Hellstrom, C. and Chen, D. L. and Achberger, C. and Raisanen, J.}, - month = nov, - year = {2001}, - pages = {45--55}, - annote = {510RZCLIMATE RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:000173222200005}, -} - -@article{haylock_trends_2006, - title = {Trends in {Total} and {Extreme} {South} {American} {Rainfall} in 1960–2000 and {Links} with {Sea} {Surface} {Temperature}}, - volume = {19}, - issn = {0894-8755}, - doi = {10.1175/jcli3695.1}, - number = {8}, - journal = {Journal of Climate}, - author = {Haylock, M. R. and Peterson, T. C. and Alves, L. M. and Ambrizzi, T. and Anunciação, Y. M. T. and Baez, J. and Barros, V. R. and Berlato, M. A. and Bidegain, M. and Coronel, G. and Corradi, V. and Garcia, V. J. and Grimm, A. M. and Karoly, D. and Marengo, J. A. and Marino, M. B. and Moncunill, D. F. and Nechet, D. and Quintana, J. and Rebello, E. and Rusticucci, M. and Santos, J. L. and Trebejo, I. and Vincent, L. A.}, - month = apr, - year = {2006}, - pages = {1490--1512}, - annote = {doi: 10.1175/JCLI3695.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/JCLI3695.1}, -} - -@article{heyen_statistical_1996, - title = {Statistical downscaling of monthly mean {North} {Atlantic} air- pressure to sea level anomalies in the {Baltic} {Sea}}, - volume = {48}, - abstract = {The term ''downscaling'' describes a procedure in which information about a process with a certain characteristic scale is derived from other processes with larger scales. The present paper identifies a relationship between the main components of the North Atlantic air-pressure anomalies at sea-level (characteristic length-scale {\textgreater} 1000 km) and the sea level anomalies at several Baltic Sea gauges (10 km-100 km) in winter. Monthly means from 20 observed winters are used to fit a statistical model. that describes the dependence between both parameters. Further observations from this century are used to validate this model, which is able to estimate sea level anomalies from the air-pressure held to a good level of approximation. Sea level anomalies with periods from months to decades are reproduced well. As main Forcing For the sea level anomalies, wind-stress with a strong zonal component is identified. For the past 89 years, we found that a slight decrease of mean sea level was induced by air-pressure. A slight increase is found when air-pressure from a GCM ''greenhouse'' experiment is downscaled.}, - number = {2}, - journal = {Tellus Series a-Dynamic Meteorology and Oceanography}, - author = {Heyen, H. and Zorita, E. and vonStorch, H.}, - month = mar, - year = {1996}, - pages = {312--323}, - annote = {The following values have no corresponding Zotero field:alt-title: Tellus Ser. A-Dyn. Meteorol. Oceanol.accession-num: ISI:A1996UA75600008}, - annote = {UA756TELLUS A-DYN METEOROL OCEANOG}, -} - -@article{hewitson_self-organizing_2002, - title = {Self-organizing maps: applications to synoptic climatology}, - volume = {22}, - abstract = {Self organizing maps (SOMs) are used to locate archetypal points that describe the multi-dimensional distribution function of a gridded sea level pressure data set for the northeast United States. These points-nodes on the SOM-identify the primary features of the synoptic-scale circulation over the region. In effect, the nodes represent a non-linear distribution of overlapping, non-discreet, circulation types. The circulation patterns are readily visualized in a 2- dimensional array (the SOM) that places similar types adjacent to one another and very different types far apart in the SOM space. The SOM is used to describe synoptic circulation changes over time, and to relate the circulation to January station precipitation data (for State College, Pennsylvania) in the center of the domain. The paper focuses on the methodology; however, the analysis suggests that circulation systems that promote precipitation have decreased over the last 40 yr- although January precipitation at State College has actually increased. Further analysis with the SOM indicates that this is due to a change in precipitation characteristics of the synoptic-scale circulation features, rather than to their frequency of occurrence.}, - number = {1}, - journal = {Climate Research}, - author = {Hewitson, B. C. and Crane, R. G.}, - month = aug, - year = {2002}, - pages = {13--26}, - annote = {597DNCLIMATE RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:000178207000002}, -} - -@article{hewitson_developing_2003, - title = {Developing perturbations for climate change impact assessments}, - volume = {84}, - number = {35}, - journal = {EOS,Transactions, American Geophysical Union}, - author = {Hewitson, B.}, - year = {2003}, - pages = {337--341, DOI: 10.1029/2003EO350001}, -} - -@article{hevesi_precipitation_1992, - title = {Precipitation {Estimation} in {Mountainous} {Terrain} {Using} {Multivariate} {Geostatistics}. {Part} {I}: {Structural} {Analysis}}, - volume = {31}, - issn = {0894-8763}, - doi = {10.1175/1520-0450(1992)031<0661:peimtu>2.0.co;2}, - number = {7}, - journal = {Journal of Applied Meteorology}, - author = {Hevesi, Joseph A. and Istok, Jonathan D. and Flint, Alan L.}, - month = jul, - year = {1992}, - pages = {661--676}, - annote = {doi: 10.1175/1520-0450(1992)031{\textless}0661:PEIMTU{\textgreater}2.0.CO;2}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/1520-0450(1992)031{\textless}0661:PEIMTU{\textgreater}2.0.CO;2}, -} - -@book{helsel_statistical_2002, - address = {Reston, VA, USA}, - title = {Statistical {Methods} in {Water} {Resources}, {Techniques} of {Water} {Resources} {Investigations}, {Book} 4, chapter {A3}}, - publisher = {U.S. Geological Survey}, - author = {Helsel, D. R. and Hirsch, R. M.}, - year = {2002}, -} - -@article{haylock_downscaling_2006, - title = {Downscaling heavy precipitation over the {UK}: a comparison of dynamical and statistical methods and their future scenarios}, - volume = {26}, - number = {10}, - journal = {International Journal of Climatology}, - author = {Haylock, M. R. and Cawley, G. C. and Harpham, C. and Wilby, R. L. and Goodess, C. M.}, - year = {2006}, - pages = {1397--1415}, -} - -@article{hayhoe_past_2007, - title = {Past and future changes in climate and hydrological indicators in the {US} {Northeast}}, - volume = {28}, - number = {4}, - journal = {Climate Dynamics}, - author = {Hayhoe, K. and Wake, C. P. and Huntington, T. G. and Luo, L. and Schwartz, M. D. and Sheffield, J. and Wood, E. and Anderson, B. and Bradbury, J. and Degaetano, A. and Troy, T. J. and Wolfe, D.}, - year = {2007}, - pages = {381--407}, -} - -@article{hayhoe_regional_2008, - title = {Regional climate change projections for the {Northeast} {USA}}, - volume = {13}, - issn = {1381-2386}, - doi = {10.1007/s11027-007-9133-2}, - number = {5}, - journal = {Mitigation and Adaptation Strategies for Global Change}, - author = {Hayhoe, Katharine and Wake, Cameron and Anderson, Bruce and Liang, Xin-Zhong and Maurer, Edwin and Zhu, Jinhong and Bradbury, James and DeGaetano, Art and Stoner, Anne and Wuebbles, Donald}, - year = {2008}, - keywords = {Earth and Environmental Science}, - pages = {425--436}, - annote = {The following values have no corresponding Zotero field:publisher: Springer Netherlands}, -} - -@article{hayhoe_whats_2004, - title = {What's in store for 2004 - and beyond?}, - volume = {51}, - issn = {0049-3155}, - abstract = {T he beginning of the year is a good time to look ahead, so I'd like to offer some observations about trends in our profession, from my perspective as a practitioner-turned-academic here in the United States-These comments are based on my reading, conversations with others in the field, and personal biases. They are mostly U.S.-centric because I don't know much about the economies and job markets of countries other than my own. For these and other reasons, your mileage may vary.}, - language = {English}, - number = {1}, - journal = {Technical Communication}, - author = {Hayhoe, G. F.}, - month = feb, - year = {2004}, - pages = {9--10}, - annote = {778ZFTimes Cited:0Cited References Count:0}, - annote = {The following values have no corresponding Zotero field:accession-num: ISI:000189270600001}, -} - -@article{hayes_monitoring_1999, - title = {Monitoring the 1996 {Drought} {Using} the {Standardized} {Precipitation} {Index}}, - volume = {80}, - abstract = {Droughts are difficult to detect and monitor. Drought indices, most commonly the Palmer Drought Severity Index (PDSI), have been used with limited success as operational drought monitoring tools and triggers for policy responses. Recently, a new index, the Standardized Precipitation Index (SPI), was developed to improve drought detection and monitoring capabilities. The SPI has several characteristics that are an improvement over previous indices, including its simplicity and temporal flexibility, that allow its application for water resources on all timescales. In this article, the 1996 drought in the southern plains and southwestern United States is examined using the SPI. A series of maps are used to illustrate how the SPI would have assisted in being able to detect the onset of the drought and monitor its progression. A case study investigating the drought in greater detail for Texas is also given. The SPI demonstrated that it is a tool that should be used operationally as part of a state, regional, or national drought watch system in the United States. During the 1996 drought, the SPI detected the onset of the drought at least 1 month in advance of the PDSI. This timeliness will be invaluable for improving mitigation and response actions of state and federal government to drought-affected regions in the future.}, - number = {3}, - journal = {Bulletin of the American Meteorological Society}, - author = {Hayes, Michael J. and Svoboda, Mark D. and Wilhite, Donald A. and Vanyarkho, Olga V.}, - month = mar, - year = {1999}, - pages = {429--438}, -} - -@article{hay_comparison_2000, - title = {A comparison of delta change and downscaled {GCM} scenarios for three mountainous basins in the {United} {States}}, - volume = {36}, - abstract = {Simulated daily precipitation, temperature, and runoff time series were compared in three mountainous basins in the United States: (1) the Animas River basin in Colorado, (2) the East Fork of the Carson River basin in Nevada and California, and (3) the Cle Elum River basin in Washington State. Two methods of climate scenario generation were compared: delta change and statistical downscaling. The delta change method uses differences between simulated current and future climate conditions from the Hadley Centre for Climate Prediction and Research (HadCM2) General Circulation Model (GCM) added to observed time series of climate variables. A statistical downscaling (SDS) model was developed for each basin using station data and output from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis regridded to the scale of HadCM2. The SDS model was then used to simulate local climate variables using HadCM2 output for current and future conditions. Surface climate variables from each scenario were used in a precipitation-runoff model. Results from this study show that, in the basins tested, a precipitation-runoff model can simulate realistic runoff series for current conditions using statistically downscaled NCEP output. But, use of downscaled HadCM2 output for current or future climate assessments are questionable because the GCM does not produce accurate estimates of the surface variables needed for runoff in these regions. Given the uncertainties in the GCMs ability to simulate current conditions based on either the delta change or downscaling approaches, future climate assessments based on either of these approaches must be treated with caution.}, - number = {2}, - journal = {J. Am. Water Resour. Assoc.}, - author = {Hay, L. E. and Wilby, R. J. L. and Leavesley, G. H.}, - month = apr, - year = {2000}, - pages = {387--397}, - annote = {315LJJ AM WATER RESOUR ASSOC}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Am. Water Resour. Assoc.}, -} - -@article{hay_step_2006, - title = {Step wise, multiple objective calibration of a hydrologic model for a snowmelt dominated basin}, - volume = {42}, - issn = {1093-474X}, - abstract = {The ability to apply a hydrologic model to large numbers of basins for forecasting purposes requires a quick and effective calibration strategy. This paper presents a step wise, multiple objective, automated procedure for hydrologic model calibration. This procedure includes the sequential calibration of a model's simulation of solar radiation (SR), potential evapotranspiration (PET), water balance, and daily runoff. The procedure uses the Shuffled Complex Evolution global search algorithm to calibrate the U.S. Geological Survey's Precipitation Runoff Modeling System in the Yampa River basin of Colorado. This process assures that intermediate states of the model (SR and PET on a monthly mean basis), as well as the water balance and components of the daily hydrograph are simulated consistently with measured values.}, - language = {English}, - number = {4}, - journal = {J. Am. Water Resour. Assoc.}, - author = {Hay, L. E. and Leavesley, G. H. and Clark, M. P. and Markstrom, S. L. and Viger, R. J. and Umemoto, M.}, - month = aug, - year = {2006}, - keywords = {potential evapotranspiration, optimization, runoff, AUTOMATIC CALIBRATION, Colorado, GLOBAL, OPTIMIZATION, Precipitation Runoff Modeling System, RAINFALL-RUNOFF MODELS, Shuffled Complex Evolution, solar radiation, UNITED-STATES, VALIDATION, water balance}, - pages = {877--890}, - annote = {ISI Document Delivery No.: 086ACTimes Cited: 4Cited Reference Count: 39}, - annote = {The following values have no corresponding Zotero field:auth-address: US Geol Survey, Lakewood, CO 80225 USA. Univ Colorado, Cooperat Inst Res Environm Sci, Boulder, CO 80309 USA. Hay, LE, US Geol Survey, Box 25046,MS 412, Lakewood, CO 80225 USA. lhay@usgs.govalt-title: J. Am. Water Resour. Assoc.accession-num: ISI:000240644500005work-type: Article}, -} - -@article{hay_use_2003, - title = {Use of statistically and dynamically downscaled atmospheric model output for hydrologic simulations in three mountainous basins in the western {United} {States}}, - volume = {282}, - issn = {0022-1694}, - number = {1–4}, - journal = {Journal of Hydrology}, - author = {Hay, L. E. and Clark, M. P.}, - year = {2003}, - keywords = {Statistical downscaling, Dynamical downscaling, Hydrologic modelling, NCEP/NCAR reanalysis}, - pages = {56--75}, - annote = {The following values have no corresponding Zotero field:work-type: doi: 10.1016/S0022-1694(03)00252-X}, -} - -@article{hayhoe_emissions_2004, - title = {Emissions pathways, climate change, and impacts on {California}}, - volume = {101}, - issn = {0027-8424}, - abstract = {The magnitude of future climate change depends substantially on the greenhouse gas emission pathways we choose. Here we explore the implications of the highest and lowest Intergovernmental Panel on Climate Change emissions pathways for climate change and associated impacts in California. Based on climate projections from two state-of-the-art climate models with low and medium sensitivity (Parallel Climate Model and Hadley Centre Climate Model, version 3, respectively), we find that annual temperature increases nearly double from the lower B1 to the higher A1fi emissions scenario before 2100. Three of four simulations also show greater increases in summer temperatures as compared with winter. Extreme heat and the associated impacts on a range of temperature-sensitive sectors are substantially greater under the higher emissions scenario, with some interscenario differences apparent before midcentury. By the end of the century under the B1 scenario, heatwaves and extreme heat in Los Angeles quadruple in frequency while heat-related mortality increases two to three times; alpine/subalpine forests are reduced by 50-75\%; and Sierra snowpack is reduced 30-70\%. Under A1fi, heatwaves in Los Angeles are six to eight times more frequent, with heat-related excess mortality increasing five to seven times; alpine/subalpine forests are reduced by 75-90\%; and snowpack declines 73-90\%, with cascading impacts on runoff and streamflow that, combined with projected modest declines in winter precipitation, could fundamentally disrupt California's water rights system. Although interscenario differences in climate impacts and costs of adaptation emerge mainly in the second half of the century, they are strongly dependent on emissions from preceding decades.}, - language = {English}, - number = {34}, - journal = {Proceedings of the National Academy of Sciences}, - author = {Hayhoe, K. and Cayan, D. and Field, C. B. and Frumhoff, P. C. and Maurer, E. P. and Miller, N. L. and Moser, S. C. and Schneider, S. H. and Cahill, K. N. and Cleland, E. E. and Dale, L. and Drapek, R. and Hanemann, R. M. and Kalkstein, L. S. and Lenihan, J. and Lunch, C. K. and Neilson, R. P. and Sheridan, S. C. and Verville, J. H.}, - month = aug, - year = {2004}, - keywords = {model, united-states, responses, water-resources, change scenarios, mortality, potential impacts, river-basins, san-francisco estuary, weather}, - pages = {12422--12427}, - annote = {850FITimes Cited:2Cited References Count:34}, - annote = {The following values have no corresponding Zotero field:auth-address: Hayhoe, K ATMOS Res \& Consulting, 809 W Colfax Ave, South Bend, IN 46601 USA ATMOS Res \& Consulting, South Bend, IN 46601 USA Scripps Inst Oceanog, Climate Res Div, Div Water Resources, La Jolla, CA 92093 USA Carnegie Inst Washington, Dept Global Ecol, Stanford, CA 94305 USA Union Concerned Sci, Cambridge, MA 02238 USA Santa Clara Univ, Dept Civil Engn, Santa Clara, CA 95053 USA Lawrence Berkeley Natl Lab, Div Earth Sci, Atmosphere \& Ocean Sci Grp, Berkeley, CA 94720 USA Natl Ctr Atmospher Res, Environm \& Societal Impacts Grp, Boulder, CO 80307 USA Stanford Univ, Dept Biol Sci, Stanford, CA 94305 USA Stanford Univ, Inst Int Studies, Stanford, CA 94305 USA USDA, Forest Serv, Corvallis Forestry Sci Lab, Corvallis, OR 97331 USA Univ Calif Berkeley, Dept Agr \& Resource Econ, Berkeley, CA 94720 USA Univ Delaware, Dept Geog, Clin Res Ctr, Newark, DE 19716 USA Kent State Univ, Dept Geog, Kent, OH 44242 USAaccession-num: ISI:000223596200007}, -} - -@article{hay_spatial_2002, - title = {Spatial variability in water-balance model performance in the conterminous {United} {States}}, - volume = {38}, - issn = {1093-474X}, - abstract = {A monthly water-balance (WB) model was tested in 44 river basins from diverse physiographic and climatic regions across the conterminous United States (U.S.). The WB model includes the concepts of climatic water supply and climatic water demand, seasonality in climatic water supply and demand, and soil-moisture storage. Exhaustive search techniques were employed to determine the optimal set of precipitation and temperature stations, and the optimal set of WB model parameters to use for each basin. It was found that the WB model worked best for basins with: (1) a mean elevation less than 450 meters or greater than 2000 meters, and/or (2) monthly runoff that is greater than 5 millimeters (mm) more than 80 percent of the time. In a separate analysis, a multiple linear regression (MLR) was computed using the adjusted R-square values obtained by comparing measured and estimated monthly runoff of the original 44 river basins as the dependent variable, and combinations of various independent variables [streamflow gauge latitude, longitude, and elevation; basin area, the long-term mean and standard deviation of annual precipitation; temperature and runoff, and low-flow statistics (i.e., the percentage of months with monthly runoff that is less than 5 mm)]. Results from the MLR study showed that the reliability of a WB model for application in a specific region can be estimated from mean basin elevation and the percentage of months with gauged runoff less than 5 mm. The MLR equations were subsequently used to estimate adjusted R-square values for 1,646 gauging stations across the conterminous U.S. Results of this study indicate that WB models can be used reliably to estimate monthly runoff in the eastern U.S., mountainous areas of the western U.S., and the Pacific Northwest, Applications of monthly WB models in the central U.S. can lead to uncertain estimates of runoff.}, - language = {English}, - number = {3}, - journal = {J. Am. Water Resour. Assoc.}, - author = {Hay, L. E. and McCabe, G. J.}, - month = jun, - year = {2002}, - keywords = {surface water hydrology, CLIMATE-CHANGE, exhaustive search, monthly runoff, multiple linear regression, RIVER BASIN, SCENARIOS, United States, VARIABILITY, water-balance model}, - pages = {847--860}, - annote = {ISI Document Delivery No.: 572HWTimes Cited: 5Cited Reference Count: 26}, - annote = {The following values have no corresponding Zotero field:auth-address: US Geol Survey, Denver Fed Ctr, Lakewood, CO 80225 USA. Hay, LE, US Geol Survey, Denver Fed Ctr, Box 25046,MS 412, Lakewood, CO 80225 USA.alt-title: J. Am. Water Resour. Assoc.accession-num: ISI:000176769300019work-type: Article}, -} - -@article{hawkins_potential_2011, - title = {The potential to narrow uncertainty in projections of regional precipitation change}, - volume = {37}, - issn = {0930-7575}, - doi = {10.1007/s00382-010-0810-6}, - number = {1-2}, - journal = {Climate Dynamics}, - author = {Hawkins, Ed and Sutton, Rowan}, - year = {2011}, - keywords = {Earth and Environmental Science}, - pages = {407--418}, - annote = {The following values have no corresponding Zotero field:publisher: Springer Berlin / Heidelberg}, -} - -@techreport{hastings_global_1999, - address = {Boulder, CO, USA}, - title = {Global land one-kilometer base elevation ({GLOBE}) digital elevation model, documentation, volume 1.0.}, - institution = {National Oceanic and Atmospheric Administration, National Geophysical Data Center}, - author = {Hastings, D. A. and Dunbar, P. K.}, - year = {1999}, -} - -@article{hannah_regional_2017, - title = {Regional modeling of climate change impacts on smallholder agriculture and ecosystems in {Central} {America}}, - volume = {141}, - issn = {1573-1480}, - doi = {10.1007/s10584-016-1867-y}, - abstract = {Climate change will have serious repercussions for agriculture, ecosystems, and farmer livelihoods in Central America. Smallholder farmers are particularly vulnerable due to their reliance on agriculture and ecosystem services for their livelihoods. There is an urgent need to develop national and local adaptation responses to reduce these impacts, yet evidence from historical climate change is fragmentary. Modeling efforts help bridge this gap. Here, we review the past decade of research on agricultural and ecological climate change impact models for Central America. The results of this review provide insights into the expected impacts of climate change and suggest policy actions that can help minimize these impacts. Modeling indicates future climate-driven changes, often declines, in suitability for Central American crops. Declines in suitability for coffee, a central crop in the regional economy, are noteworthy. Ecosystem models suggest that climate-driven changes are likely at low- and high-elevation montane forest transitions. Modeling of vulnerability suggests that smallholders in many parts of the region have one or more vulnerability factors that put them at risk. Initial adaptation policies can be guided by these existing modeling results. At the same time, improved modeling is being developed that will allow policy action specifically targeted to vulnerable groups, crops, and locations. We suggest that more robust modeling of ecological responses to climate change, improved representation of the region in climate models, and simulation of climate influences on crop yields and diseases (especially coffee leaf rust) are key priorities for future research.}, - number = {1}, - journal = {Climatic Change}, - author = {Hannah, Lee and Donatti, Camila I. and Harvey, Celia A. and Alfaro, Eric and Rodriguez, Daniel Andres and Bouroncle, Claudia and Castellanos, Edwin and Diaz, Freddy and Fung, Emily and Hidalgo, Hugo G. and Imbach, Pablo and Läderach, Peter and Landrum, Jason P. and Solano, Ana Lucía}, - year = {2017}, - pages = {29--45}, - annote = {The following values have no corresponding Zotero field:label: Hannah2017work-type: journal article}, -} - -@article{hassan_lake_1998, - title = {Lake stratification and temperature profiles simulated using downscaled {GCM} output}, - volume = {38}, - abstract = {A mathematical in-lake water temperature model (WATEMP-Lake) was developed to investigate future responses of lake stratification and temperature profiles to future climate change due to rising concentrations of atmospheric greenhouse gases (GHGs). The model was used to simulate daily water temperature profiles and stratification characteristics in summer (June, July, and August -JJA) for Suwa Lake in Japan as a case study. For future assessments, the model uses surface climate variables obtained from a downscaling method that was applied to the UK Hadley Centre's coupled ocean/atmosphere model forced by combined CO2 and sulphate aerosol changes (HadCM2SUL). The downscaling method employed mean sea level surface pressure to derive three airflow indices identified as: the total shear vorticity (Z) -a measure of cyclonicity -, the strength of the resultant flow (F), and the overall flow direction (D). Statistical relationships between these indices and seven daily meteorological time series were formulated to represent climate variable series at sites around Suwa Lake. These relationships were used to downscale the observed climatology of 1979-1995 and that of 2080-2099 using HadCM2SUL outputs. (C) 1998 IAWQ Published by Elsevier Science Ltd. All rights reserved.}, - number = {11}, - journal = {Water Science and Technology}, - author = {Hassan, H. and Aramaki, T. and Hanaki, K. and Matsuo, T. and Wilby, R.}, - year = {1998}, - pages = {217--226}, - annote = {154FFWATER SCI TECHNOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Water Sci. Technol.accession-num: ISI:000077877500027}, -} - -@incollection{hartmann_use_2005, - title = {Use of climate information in water resources management}, - booktitle = {Encyclopedia of {Hydrological} {Sciences}}, - publisher = {John Wiley \& Sons, Ltd.}, - author = {Hartmann, H.}, - editor = {Anderson, M. G.}, - year = {2005}, - pages = {202, 10.1002/0470848944.hsa213}, - annote = {The following values have no corresponding Zotero field:section: Part 17, Climate Change}, -} - -@incollection{d_l_hartmann_observations_2013, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Observations: {Atmosphere} and {Surface}}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {D. L. Hartmann and A. M.G. Klein Tank and M. Rusticucci and L. V. Alexander and S. Brönnimann and Y. Charabi and F. J. Dentener and E. J. Dlugokencky and D. R. Easterling and A. Kaplan and B. J. Soden and P. W. Thorne and M. Wild and P. M. Zhai}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley}, - year = {2013}, - pages = {159--254}, - annote = {The following values have no corresponding Zotero field:section: 2electronic-resource-num: 10.1017/CBO9781107415324.008}, -} - -@article{harpham_multi-site_2005, - title = {Multi-site downscaling of heavy daily precipitation occurrence and amounts}, - volume = {312}, - journal = {Journal of Hydrology}, - author = {Harpham, C. and Wilby, R. L.}, - year = {2005}, - pages = {235--255}, -} - -@article{hantel_physical_1998, - title = {Physical aspects of the weather generator}, - volume = {213}, - abstract = {One task in BAHC is to develop a physical understanding of the weather generator (WG). The core of the WG concept is scale interaction, represented by upscaling and downscaling, between the gridscale atmospheric fields and the subgrid-scale hydrological and ecosystem patterns. This paper considers scale interaction in terms of the convective ( = latent plus sensible heat) and rain fluxes. Earth's surface and atmosphere are coupled through these fluxes. However, horizontal scale interaction in the free atmosphere is physically different from that at the surface. Upscaling atmospheric fluxes generates new sub-gridscale fluxes at the lower grid resolution, caused by the gridscale fluxes resolved at the higher grid resolution. Downscaling has to recover these secondary circulations, usually through mesoscale submodels. The same gridscale eddy effect is zero for surface fluxes because there is no cross- surface mass flux of air. The atmospheric fluxes on the various scales are quantified here with an atmospheric diagnostic model (DIAMOD) coupled to a surface flux model (SURFMOD). Two convectively active periods over Europe tone disturbed, one undisturbed) are considered. Upscaling from 100 to 1000 km horizontal resolution generates additional grid-scale eddy fluxes. However, these amount, in both cases, to just 10\% of the diagnosed fluxes that are sub-gridscale at 100 km; this suggests that the additional gridscale eddy effect may be negligible in convective situations. We further apply a simple downscaling recipe for disturbed periods (scale the rain flux profiles with the observed surface rain) and for undisturbed periods (scale the convective flux profiles with the observed surface latent plus sensible heat flux). At the Earth's surface, we study the upscaling-downscaling mechanism, not with diagnosed, but with modeled latent and sensible heat fluxes. With a simplified version of SURFMOD, plus the probability density function of soil moisture, we reproduce the fatal impact of taking mean soil moisture for calculating mean evaporation: evaporation can be underestimated by 25\% in dry situations and overestimated by 10\% in moist situations. We demonstrate how to completely remove this bias. The technique presented here can be generalized to a wide class of deterministic and statistical models and offers a rational framework for the aggregation problem and the WG problem in general. (C) 1998 Elsevier Science B.V. All rights reserved.}, - number = {1-4}, - journal = {Journal of Hydrology}, - author = {Hantel, M. and Acs, F.}, - month = dec, - year = {1998}, - pages = {393--411}, - annote = {151LVJ HYDROL}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Hydrol.accession-num: ISI:000077722300026}, -} - -@article{hansen_global_2000, - title = {Global land cover classification at 1 km spatial resolution using a classification tree approach}, - volume = {21}, - journal = {International Journal of Remote Sensing}, - author = {Hansen, M. C. and DeFries, R. S. and Townshend, J. R. G. and Sohlberg, R.}, - year = {2000}, - pages = {1331--1364}, -} - -@article{harris_multiscale_2001, - title = {Multiscale statistical properties of a high-resolution precipitation forecast}, - volume = {2}, - abstract = {Small-scale (less than;15 km) precipitation variability significantly affects the hydrologic response of a basin and the accurate estimation of water and energy fluxes through coupled land-atmosphere modeling schemes. It also affects the radiative transfer through precipitating clouds and thus rainfall estimation from microwave sensors. Because both land- atmosphere and cloud-radiation interactions are nonlinear and occur over a broad range of scales (from a few centimeters to several kilometers), it is important that, over these scales, cloud-resolving numerical models realistically reproduce the observed precipitation variability. This issue is examined herein by using a suite of multiscale statistical methods to compare the scale dependence of precipitation variability of a numerically simulated convective storm with that observed by radar. In particular, Fourier spectrum, structure function, and moment-scale analyses are used to show that, although the variability of modeled precipitation agrees with that observed for scales larger than approximately 5 times the model resolution, the model shows a falloff in variability at smaller scales. Thus, depending upon the smallest scale at which variability is considered to be important for a specific application, one has to resort either to very high resolution model runs (resolutions 5 times higher than the scale of interest) or to stochastic methods that can introduce the missing small-scale variability. The latter involve upscaling the model output to a scale approximately 5 times the model resolution and then stochastically downscaling it to smaller scales. The results of multiscale analyses, such as those presented herein, are key to the implementation of such stochastic downscaling methodologies.}, - number = {4}, - journal = {Journal of Hydrometeorology}, - author = {Harris, D. and Foufoula-Georgiou, E. and Droegemeier, K. K. and Levit, J. J.}, - year = {2001}, - pages = {406--418}, - annote = {461ZQJ HYDROMETEOROL}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Hydrometeorol.accession-num: ISI:000170394100006}, -} - -@article{hanson_methodology_2004, - title = {A methodology to asess relations between climatic variability and variations in hydrologic time series in the southwestern {United} {States}}, - volume = {287}, - issn = {0022-1694}, - abstract = {A new method for frequency analysis of hydrologic time series was developed to facilitate the estimation and reconstruction of individual or groups of frequencies from hydrologic time-series and facilitate the comparison of these isolated time-series components across data types, between different hydrologic settings within a watershed, between watersheds, and across frequencies. While climate-related variations in inflow to and outflow from aquifers have often been neglected, the development and management of ground-water and surface-water resources has required the inclusion of the assessment of the effects of climatic variability on the supply and demand and sustainability of use. The regional assessment of climatic variability of surface-water and ground-water flow throughout the southwestern United States required this new systematic method of hydrologic time-series analysis. -To demonstrate the application of this new method, six hydrologic time-series from the Mojave River Basin, California were analyzed. The results indicate that climatic variability exists in all the data types and are partially coincident with known climate cycles such as the Pacific Decadal Oscillation and the El Nino-Southern Oscillation. The time-series also indicate lagged correlations between tree-ring indices, streamflow, stream base flow, and ground-water levels. These correlations and reconstructed time-series can be used to better understand the relation of hydrologic response to climatic forcings and to facilitate the simulation of streamflow and ground-water recharge for a more realistic approach to water-resource management. Published by Elsevier B.V.}, - language = {English}, - number = {1-4}, - journal = {Journal of Hydrology}, - author = {Hanson, R. T. and Newhouse, M. W. and Dettinger, M. D.}, - month = feb, - year = {2004}, - keywords = {california, climate, recharge, climate cycles, discharge, ground-water, surface-water}, - pages = {252--269}, - annote = {802GZTimes Cited:0Cited References Count:36}, - annote = {The following values have no corresponding Zotero field:auth-address: Hanson, RT US Geol Survey, Div Water Resources, 5735 Kearny Villa Rd,Suite 0, San Diego, CA 92123 USA US Geol Survey, Div Water Resources, San Diego, CA 92123 USAaccession-num: ISI:000220153300015}, -} - -@article{hamlet_effects_2007, - title = {Effects of 20th century warming and climate variability on flood risk in the western {U}.{S}}, - volume = {43}, - issn = {1944-7973}, - doi = {10.1029/2006WR005099}, - number = {6}, - journal = {Water Resources Research}, - author = {Hamlet, Alan F. and Lettenmaier, Dennis P.}, - year = {2007}, - keywords = {global warming, California, 1616 Climate variability, 1821 Floods, Pacific Northwest, 1600 Global Change, 9350 North America, Climate variability, Colorado River Basin, flood risk, flooding, Great Basin, precipitation variability, western U.S.}, - pages = {W06427}, -} - -@article{hanak_adapting_2012, - title = {Adapting {California}’s water management to climate change}, - volume = {111}, - issn = {0165-0009}, - number = {1}, - journal = {Climatic Change}, - author = {Hanak, Ellen and Lund, Jay R.}, - year = {2012}, - pages = {17--44}, -} - -@article{hamlet_effects_2005, - title = {Effects of temperature and precipitation variability on snowpack trends in the western {U}.{S}.}, - volume = {18}, - number = {21}, - journal = {Journal of Climate}, - author = {Hamlet, A. F. and Mote, P. W. and Clark, M. P. and Lettenmaier, D. P.}, - year = {2005}, - pages = {4545--4561}, -} - -@article{hamlet_effects_1999, - title = {Effects of climate change on hydrology and water resources in the {Columbia} {River} {Basin}}, - volume = {35}, - abstract = {As part of the National Assessment of Climate Change, the implications of future climate predictions derived from four global climate models (GCMs) were used to evaluate possible future changes to Pacific Northwest climate, the surface water response of the Columbia River basin, and the ability of the Columbia River reservoir system to meet regional water resources objectives. Two representative GCM simulations from the Hadley Centre (HC) and Max Planck Institute (MPI) were selected from a group of GCM simulations made available via the National Assessment for climate change. Although the two GCM simulations showed somewhat different seasonal patterns for temperature change, in general the simulations show reasonably consistent basin average increases in temperature of about 1.8-2.1 degree C for 2025, and about 2.3-2.9 degree C for 2045. The HC simulations predict an annual average temperature increase of about 4.5 degree C for 2095. Changes in basin averaged winter precipitation range from -1 percent to +20 percent for the HC and MPI scenarios, and summer precipitation is also variously affected. These changes in climate result in significant increases in winter runoff volumes due to increased winter precipitation and warmer winter temperatures, with resulting reductions in snowpack. Average March 1 basin average snow water equivalents are 75 to 85 percent of the base case for 2025, and 55 to 65 percent of the base case by 2045. By 2045 the reduced snowpack and earlier snow melt, coupled with higher evapotranspiration in early summer, would lead to earlier spring peak flows and reduced runoff volumes from April-September ranging from about 75 percent to 90 percent of the base case. Annual runoff volumes range from 85 percent to 110 percent of the base case in the simulations for 2045. These changes in streamflow create increased competition for water during the spring, summer, and early fall between non-firm energy production, irrigation, instream flow, and recreation. Flood control effectiveness is moderately reduced for most of the scenarios examined, and desirable navigation conditions on the Snake are generally enhanced or unchanged. Current levels of winter-dominated firm energy production are only significantly impacted for the MPI 2045 simulations.}, - number = {6}, - author = {Hamlet, A. F. and Lettenmaier, D. P.}, - year = {1999}, - keywords = {Precipitation, Streamflow, Hydrology, River basins, Stream flow, Climate, Climate prediction, Climatic Changes, Environmental impact, Evapotranspiration, Instream Flow, Precipitation (Atmospheric), Runoff, Snow cover, Snowpack, USA, Columbia R., USA, Columbia R. Basin, Water Resources}, - pages = {1597--1624}, - annote = {Journal of the American Water Resources Association [J. Am. Water Resour. Assoc.]. Vol. 35, no. 6, pp. 1597-1624. Dec 1999.}, - annote = {The following values have no corresponding Zotero field:pub-location: Journal of the American Water Resources Association}, -} - -@article{hamlet_columbia_1999, - title = {Columbia {River} streamflow forecasting based on {ENSO} and {PDO} climate signals}, - volume = {125}, - issn = {0733-9496}, - abstract = {A simple method has been devised to incorporate the Fl Nino Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) climate signals into the well-known extended streamflow prediction forecasting approach. Forecasts of ENSO are currently available up to a year or more in advance, which facilitates forecasting of the streamflow response to this climate signal at interannual forecast lead times. The biomodal phase of the PDO can be identified in real time using a combination of assumed persistence of the existing phase and the tracking of extreme events to identify transitions. The technique makes use of a gridded meteorological data set to drive a macroscale hydrology model at 1 degrees spatial resolution over the Columbia River Basin above The Dalles. A streamflow forecast ensemble is created by resampling from the historical meteorological data according to six predefined PDO/ENSO categories. Given a forecast of the ENSO climate signal for the coming water year and the existing phase of the PDO, these meteorological ensembles are then used to drive the hydrology model based on the initial soil and snow conditions as of the forecast date. To evaluate the technique, a retrospective forecast of the historic record was prepared (1989-1998), using October-September as the forecast period, as well as an ensemble forecast for water years 1999 and 2000 that were prepared on June 1, 1998 and May 10, 1999, respectively. The results demonstrate the increase in lead time and forecast specificity over climatology that can be achieved by using PDO and ENSO climate information to condition the forecast ensembles.}, - language = {English}, - number = {6}, - journal = {Journal of Water Resources Planning and Management-Asce}, - author = {Hamlet, A. F. and Lettenmaier, D. P.}, - month = dec, - year = {1999}, - keywords = {el-nino, nino southern oscillation}, - pages = {333--341}, - annote = {248MHTimes Cited:35Cited References Count:17}, - annote = {The following values have no corresponding Zotero field:auth-address: Hamlet, AF Univ Washington, Dept Civil \& Environm Engn, Box 352700, Seattle, WA 98195 USA Univ Washington, Dept Civil \& Environm Engn, Seattle, WA 98195 USAaccession-num: ISI:000083278600004}, -} - -@article{hamlet_economic_2002, - title = {Economic {Value} of {Long}-{Lead} {Streamflow} {Forecasts} for {Columbia} {River} {Hydropower}}, - volume = {128}, - number = {2}, - journal = {ASCE Journal of Water Resources Planning and Management}, - author = {Hamlet, A. F. and Huppert, D. and Lettenmaier, D. P.}, - year = {2002}, - pages = {91--101}, -} - -@article{hamlet_overview_2013, - title = {An {Overview} of the {Columbia} {Basin} {Climate} {Change} {Scenarios} {Project}: {Approach}, {Methods}, and {Summary} of {Key} {Results}}, - volume = {51}, - issn = {0705-5900}, - doi = {10.1080/07055900.2013.819555}, - abstract = {The Columbia Basin Climate Change Scenarios Project (CBCCSP) was conceived as a comprehensive hydrologic database to support climate change planning, impacts assessment, and adaptation in the Pacific Northwest (PNW) by a diverse user community with varying technical capacity over a wide range of spatial scales. The study has constructed a state-of-the-art, end-to-end data processing sequence from ?raw? climate model output to a suite of hydrologic modelling products that are served to the user community from a web-accessible database. A calibrated 1/16 degree latitude-longitude resolution implementation of the VIC hydrologic model over the Columbia River basin was used to produce historical simulations and 77 future hydrologic projections associated with three different statistical downscaling methods and three future time periods (2020s, 2040s, and 2080s). Key products from the study include summary data for about 300 river locations in the PNW and monthly Geographic Information System products for 21 hydrologic variables over the entire study domain. Results from the study show profound changes in spring snowpack and fundamental shifts from snow and mixed-rain-and-snow to rain-dominant behaviour across most of the domain. Associated shifts in streamflow timing from spring and summer to winter are also evident in basins with significant snow accumulation in winter (for the current climate). Potential evapotranspiration increases over most of the PNW in summer because of rising temperatures; however, actual evapotranspiration is reduced in all but a few areas of the domain because evapotranspiration is mostly water limited in summer, and summer precipitation decreases in the simulations. Simulated widespread increases in soil moisture recharge in fall and winter in areas with significant snow accumulation in winter (for the current climate) support hypotheses of increased landslide risk and sediment transport in winter in the future. Simulations of floods and extreme low flows increase in intensity for most of the river sites included in the study. The largest increases in flooding are in mixed-rain-and-snow basins whose current mid-winter temperatures are within a few degrees of freezing. The CBCCSP database has been a valuable public resource that has dramatically reduced costs in a number of high-visibility studies in the PNW and western United States focused on technical coordination and planning. RÉSUMÉ?[Traduit par la rédaction] Le projet de scénarios de changement climatique du bassin du Columbia (CBCCSP) a été conçu comme une base de données hydrologiques complète pour appuyer les activités de planification, d?évaluation des répercussions et d'adaptation dans la région pacifique nord?ouest menées par une communauté d'utilisateurs diversifiée disposant de capacités techniques variées dans une large gamme d?échelles spatiales. L?étude a produit une séquence de traitements de données de bout en bout, à la fine pointe, partant d'une sortie « brute » de modèle climatique pour aboutir à une série de produits de modélisation hydrologique, qui sont offerts à la communauté d'utilisateurs via une base de données Web. Nous avons implémenté une résolution latitude?longitude calibrée à 1/16 de degré dans le modèle à capacité d'infiltration variable (VIC) et avons appliqué dans le modèle bassin du fleuve Columbia pour produire des simulations historiques et 77 projections hydrologiques futures correspondant à trois méthodes de réduction d?échelle statistique et trois périodes futures (les décennies 2020, 2040 et 2080). Les principaux produits de l?étude comprennent des données sommaires pour environ 300 sites fluviaux dans la région pacifique nord?ouest et des produits mensuels de Système d'information géographique pour 21 variables hydrologiques couvrant tout le domaine à l?étude. Les résultats de l?étude montrent de profonds changements dans l'accumulation de neige au printemps et des déplacements radicaux de « neige ou pluie et neige mêlées » vers « princ palement pluie » dans presque tout le domaine. Des déplacements correspondants des caractéristiques d?écoulement fluvial du printemps et de l?été vers l'hiver sont également évidents dans les bassins où l'accumulation de neige est importante en hiver (sous le climat actuel). L?évapotranspiration potentielle augmente dans la majeure partie de la région du Pacifique et du Nord?Ouest en été à cause des températures plus élevées; cependant, l?évaporation réelle est réduite dans presque tous les secteurs du domaine parce que l?évapotranspiration est principalement limitée par l'eau en été et les précipitations estivales diminuent dans les simulations. Des accroissements généralisés simulés de la réhumidification du sol en automne et en hiver dans les secteurs où l'accumulation de neige en hiver est importante (sous le climat actuel) appuient les hypothèses de risque accru de glissement de terrain et de transport de sédiments durant l'hiver dans le futur. Les simulations d?écoulements de crue et d?étiage augmentent en intensité pour la plupart des sites fluviaux compris dans cette étude. Les plus fortes augmentations dans les crues sont dans les bassins de pluie et neige mêlées dont les températures actuelles au milieu de l'hiver sont à quelques degrés du point de congélation. La base de données du CBCCSP s'est avérée une ressource publique précieuse qui a permis de réduire énormément les coûts liés à un certain nombre d?études de haute visibilité dans la région pacifique nord?ouest et dans l'ouest des États?Unis axées sur la coordination technique et la planification.}, - number = {4}, - journal = {Atmosphere-Ocean}, - author = {Hamlet, Alan F. and Elsner, Marketa McGuire and Mauger, Guillaume S. and Lee, Se-Yeun and Tohver, Ingrid and Norheim, Robert A.}, - month = sep, - year = {2013}, - pages = {392--415}, - annote = {The following values have no corresponding Zotero field:publisher: Taylor \& Francis}, -} - -@misc{hall_updated_2011, - title = {Updated daily. {MODIS}/{Terra} {Snow} {Cover} 8-{Day} {L3} {Global} 0.05deg {CMG} {V005}, {Digital} media}, - author = {Hall, D. K. and Riggs, G. S. and Salomonson, V. V.}, - month = dec, - year = {2011}, - annote = {The following values have no corresponding Zotero field:pub-location: Boulder, Colorado USApublisher: National Snow and Ice Data Center}, -} - -@article{hall_improving_2008, - title = {Improving predictions of summer climate change in the {United} {States}}, - volume = {35}, - number = {L01702}, - journal = {Geophysical Research Letters}, - author = {Hall, A. and Qu, X. and Neelin, J. D.}, - year = {2008}, - pages = {doi:10.1029/2007GL032012}, -} - -@incollection{haleakala_assessing_2017, - title = {Assessing {Climate} {Change} {Impacts} on {Water} {Supply} {Reliability} for {Santa} {Clara} {County}, {California}}, - booktitle = {World {Environmental} and {Water} {Resources} {Congress} 2017}, - publisher = {ASCE/EWRI}, - author = {Haleakala, K. and Maurer, E. and Greene, S.}, - year = {2017}, - pages = {347--359}, - annote = {The following values have no corresponding Zotero field:electronic-resource-num: doi:10.1061/9780784480618.034}, -} - -@article{hagemann_impact_2011, - title = {Impact of a {Statistical} {Bias} {Correction} on the {Projected} {Hydrological} {Changes} {Obtained} from {Three} {GCMs} and {Two} {Hydrology} {Models}}, - volume = {12}, - issn = {1525-755X}, - doi = {10.1175/2011jhm1336.1}, - number = {4}, - journal = {Journal of Hydrometeorology}, - author = {Hagemann, Stefan and Chen, Cui and Haerter, Jan O. and Heinke, Jens and Gerten, Dieter and Piani, Claudio}, - month = aug, - year = {2011}, - pages = {556--578}, - annote = {doi: 10.1175/2011JHM1336.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/2011JHM1336.1}, -} - -@article{hall_using_2006, - title = {Using the current seasonal cycle to constrain snow albedo feedback in future climate change}, - volume = {33}, - issn = {0094-8276}, - doi = {10.1029/2005gl025127}, - abstract = {Differences in simulations of climate feedbacks are sources of significant divergence in climate models' temperature response to anthropogenic forcing. Snow albedo feedback is particularly critical for climate change prediction in heavily-populated northern hemisphere land masses. Here we show its strength in current models exhibits a factor-of-three spread. These large intermodel variations in feedback strength in climate change are nearly perfectly correlated with comparably large intermodel variations in feedback strength in the context of the seasonal cycle. Moreover, the feedback strength in the real seasonal cycle can be measured and compared to simulated values. These mostly fall outside the range of the observed estimate, suggesting many models have an unrealistic snow albedo feedback in the seasonal cycle context. Because of the tight correlation between simulated feedback strength in the seasonal cycle and climate change, eliminating the model errors in the seasonal cycle will lead directly to a reduction in the spread of feedback strength in climate change. Though this comparison to observations may put the models in an unduly harsh light because of uncertainties in the observed estimate that are difficult to quantify, our results map out a clear strategy for targeted observation of the seasonal cycle to reduce divergence in simulations of climate sensitivity.}, - number = {3}, - journal = {Geophysical Research Letters}, - author = {Hall, Alex and Qu, Xin}, - year = {2006}, - keywords = {1616 Global Change: Climate variability, 1626 Global Change: Global climate models, 1621 Global Change: Cryospheric change}, - pages = {L03502}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{haerter_statistical_2015, - title = {Statistical precipitation bias correction of gridded model data using point measurements}, - volume = {42}, - issn = {1944-8007}, - doi = {10.1002/2015gl063188}, - number = {6}, - journal = {Geophysical Research Letters}, - author = {Haerter, Jan O. and Eggert, Bastian and Moseley, Christopher and Piani, Claudio and Berg, Peter}, - year = {2015}, - keywords = {precipitation, 1626 Global climate models, extreme events, 1637 Regional climate change, statistical bias correction, 1817 Extreme events, 1854 Precipitation, climate model, rain gauge}, - pages = {1919--1929}, - annote = {The following values have no corresponding Zotero field:modified-date: 2015gl063188}, -} - -@article{guttman_accepting_1999, - title = {Accepting the {Standardized} {Precipitation} {Index}: a calculation algorithm}, - volume = {35}, - number = {2}, - journal = {Journal of the American Water Resources Association}, - author = {Guttman, N. B.}, - year = {1999}, - pages = {311--322}, -} - -@article{gutmann_intercomparison_2014, - title = {An intercomparison of statistical downscaling methods used for water resource assessments in the {United} {States}}, - volume = {50}, - journal = {Water Resources Research}, - author = {Gutmann, E. and Pruitt, T. and Clark, M. and Brekke, L. and Arnold, J. R. and Raff, D. A. and Rasmussen, R. M.}, - year = {2014}, - pages = {7167--7186, DOI: 10.1002/2014WR015559}, -} - -@article{haerter_climate_2011, - title = {Climate model bias correction and the role of timescales}, - volume = {15}, - issn = {1607-7938}, - doi = {10.5194/hess-15-1065-2011}, - number = {3}, - journal = {Hydrol. Earth Syst. Sci.}, - author = {Haerter, J. O. and Hagemann, S. and Moseley, C. and Piani, C.}, - year = {2011}, - pages = {1065--1079}, - annote = {HESS}, - annote = {The following values have no corresponding Zotero field:publisher: Copernicus Publications}, -} - -@article{haerter_heavy_2010, - title = {Heavy rain intensity distributions on varying time scales and at different temperatures}, - volume = {115}, - issn = {2156-2202}, - doi = {10.1029/2009jd013384}, - number = {D17}, - journal = {Journal of Geophysical Research: Atmospheres}, - author = {Haerter, J. O. and Berg, P. and Hagemann, S.}, - year = {2010}, - keywords = {precipitation, temperature, 1655 Water cycles, 1719 Hydrology, 4468 Probability distributions, heavy and fat-tailed, distributions}, - pages = {D17102}, -} - -@article{r_j_haarsma_high_2016, - title = {High {Resolution} {Model} {Intercomparison} {Project} ({HighResMIP} v1.0) for {CMIP6}}, - volume = {9}, - doi = {10.5194/gmd-9-4185-2016}, - abstract = {Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. The role of enhanced horizontal resolution in improved process representation in all components of the climate system is of growing interest, particularly as some recent simulations suggest both the possibility of significant changes in large-scale aspects of circulation as well as improvements in small-scale processes and extremes. {\textless}br{\textgreater}{\textless}br{\textgreater} However, such high-resolution global simulations at climate timescales, with resolutions of at least 50\&\#8239;km in the atmosphere and 0.25\&\#176; in the ocean, have been performed at relatively few research centres and generally without overall coordination, primarily due to their computational cost. Assessing the robustness of the response of simulated climate to model resolution requires a large multi-model ensemble using a coordinated set of experiments. The Coupled Model Intercomparison Project\&\#160;6 (CMIP6) is the ideal framework within which to conduct such a study, due to the strong link to models being developed for the CMIP DECK experiments and other model intercomparison projects (MIPs). {\textless}br{\textgreater}{\textless}br{\textgreater} Increases in high-performance computing (HPC) resources, as well as the revised experimental design for CMIP6, now enable a detailed investigation of the impact of increased resolution up to synoptic weather scales on the simulated mean climate and its variability. {\textless}br{\textgreater}{\textless}br{\textgreater} The High Resolution Model Intercomparison Project (HighResMIP) presented in this paper applies, for the first time, a multi-model approach to the systematic investigation of the impact of horizontal resolution. A coordinated set of experiments has been designed to assess both a standard and an enhanced horizontal-resolution simulation in the atmosphere and ocean. The set of HighResMIP experiments is divided into three tiers consisting of atmosphere-only and coupled runs and spanning the period 1950\&\#8211;2050, with the possibility of extending to 2100, together with some additional targeted experiments. This paper describes the experimental set-up of HighResMIP, the analysis plan, the connection with the other CMIP6 endorsed MIPs, as well as the DECK and CMIP6 historical simulations. HighResMIP thereby focuses on one of the CMIP6 broad questions, \&\#8220;what are the origins and consequences of systematic model biases?\&\#8221;, but we also discuss how it addresses the World Climate Research Program (WCRP) grand challenges.}, - number = {1}, - journal = {Geoscientific Model Development}, - author = {R. J. Haarsma and M. J. Roberts and P. L. Vidale and C. A. Senior and A. Bellucci and Q. Bao and P. Chang and S. Corti and N. S. Fukar and V. Guemas and J. von Hardenberg and W. Hazeleger and C. Kodama and T. Koenigk and L. R. Leung and J. Lu and J. J. Luo and J. Mao and M. S. Mizielinski and R. Mizuta and P. Nobre and M. Satoh and E. Scoccimarro and T. Semmler and J. Small and J. S. von Storch}, - year = {2016}, - pages = {4185--4208}, -} - -@book{haan_statistical_2002, - address = {Ames, IA, USA}, - title = {Statistical {Methods} in {Hydrology}, second edition}, - publisher = {Iowa State Press}, - author = {Haan, C. T.}, - year = {2002}, -} - -@article{gupta_parameter_1999, - title = {Parameter estimation of a land surface scheme using multicriteria methods}, - volume = {104}, - number = {D16}, - journal = {Journal of Geophysical Research}, - author = {Gupta, H. V. and Bastidas, L. A. and Sorooshian, S. and Shuttleworth, W. J. and Yang, Z. L.}, - year = {1999}, - pages = {19491--19503, doi:10.1029/1999JD900154}, -} - -@article{gutierrez_reassessing_2013, - title = {Reassessing {Statistical} {Downscaling} {Techniques} for {Their} {Robust} {Application} under {Climate} {Change} {Conditions}}, - volume = {26}, - issn = {0894-8755}, - doi = {10.1175/jcli-d-11-00687.1}, - number = {1}, - journal = {Journal of Climate}, - author = {Gutiérrez, J. M. and San-Martín, D. and Brands, S. and Manzanas, R. and Herrera, S.}, - month = jan, - year = {2013}, - pages = {171--188}, - annote = {doi: 10.1175/JCLI-D-11-00687.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/JCLI-D-11-00687.1}, -} - -@article{guilyardi_first_2012, - title = {A first look at {ENSO} in {CMIP5}}, - volume = {58}, - journal = {CLIVAR Exchanges}, - author = {Guilyardi, E. and Bellenger, H. and Collins, M. and Ferrett, S. and Cai, W. and Wittenberg, A. T.}, - year = {2012}, - pages = {29--32}, -} - -@article{gudmundsson_technical_2012, - title = {Technical {Note}: {Downscaling} {RCM} precipitation to the station scale using statistical transformations - a comparison of methods}, - volume = {16}, - issn = {1607-7938}, - doi = {10.5194/hess-16-3383-2012}, - number = {9}, - journal = {Hydrol. Earth Syst. Sci.}, - author = {Gudmundsson, L. and Bremnes, J. B. and Haugen, J. E. and Engen-Skaugen, T.}, - year = {2012}, - pages = {3383--3390}, - annote = {HESS}, - annote = {The following values have no corresponding Zotero field:publisher: Copernicus Publications}, -} - -@article{groves_developing_2008, - title = {Developing and applying uncertain global climate change projections for regional water management planning}, - volume = {44}, - journal = {Water Resources Research}, - author = {Groves, D. G. and Yates, D. and Tebaldi, C.}, - year = {2008}, - pages = {W12413, doi:10.1029/2008WR006964}, -} - -@article{groisman_heavy_2001, - title = {Heavy precipitation and high streamflow in the contiguous {United} {States}: {Trends} in the twentieth century}, - volume = {82}, - issn = {0003-0007}, - abstract = {Changes in several components of the hydrological cycle over the contiguous United States have been documented during the twentieth century: an increase of precipitation, especially heavy and very heavy precipitation, and a significant retreat in spring snow cover extent over western regions during the last few decades. These changes have affected streamflow, including the probability of high flow. In the eastern half of the United States a significant relationship is found between the frequency of heavy precipitation and high streamflow events both annually and during the months of maximum streamflow. Two factors contributed to finding such a relation: 1) the relatively small contribution of snowmelt to heavy runoff in the eastern United States (compared to the west), and 2) the presence of a sufficiently dense network of streamflow and precipitation gauges available for analysis. An increase of spring heavy precipitation events over the eastern United States indicates with high probability that during the twentieth century an increase of high streamflow conditions has also occurred. In the West, a statistically significant reduction of snow cover extent has complicated the relation between heavy precipitation and streamflow. Increases in peak stream flow have not been observed here, despite increases in heavy precipitation events, and less extensive snow cover is the likely cause.}, - language = {English}, - number = {2}, - journal = {Bulletin of the American Meteorological Society}, - author = {Groisman, P. Y. and Knight, R. W. and Karl, T. R.}, - month = feb, - year = {2001}, - pages = {219--246}, - annote = {398ECTimes Cited:48Cited References Count:42}, - annote = {The following values have no corresponding Zotero field:auth-address: Groisman, PY Natl Climat Data Ctr, 151 Patton Ave, Asheville, NC 28801 USA Natl Climat Data Ctr, Asheville, NC 28801 USAaccession-num: ISI:000166742900002}, -} - -@article{groisman_trends_2005, - title = {Trends in intense precipitation in the climate record}, - volume = {18}, - issn = {0894-8755}, - abstract = {Observed changes in intense precipitation (e.g., the frequency of very heavy precipitation or the upper 0.3\% of daily precipitation events) have been analyzed for over half of the land area of the globe. These changes have been linked to changes in intense precipitation for three transient climate model simulations. all with greenhouse gas concentrations increasing during the twentieth and twenty-first centuries and doubling in the later part of the twenty-first century. It was found that both the empirical evidence from the period of instrumental observations and model projections of a greenhouse-enriched atmosphere indicate an increasing probability of intense precipitation events for many extratropical regions including the United States. Although there can be ambiguity as to the impact of more frequent heavy precipitation events, the thresholds of the definitions of these events were raised here, such that they are likely to be disruptive. Unfortunately, reliable assertions of very heavy and extreme precipitation changes are possible only for regions with dense networks due to the small radius of correlation for many intense precipitation events.}, - language = {English}, - number = {9}, - journal = {Journal of Climate}, - author = {Groisman, P. Y. and Knight, R. W. and Easterling, D. R. and Karl, T. R. and Hegerl, G. C. and Razuvaev, V. A. N.}, - month = may, - year = {2005}, - keywords = {hydrological cycle, greenhouse-gas, surface air-temperature, change simulation, atlantic convergence zone, brazilian amazon basin, contiguous united-states, extreme precipitation, heavy precipitation, interannual variability}, - pages = {1326--1350}, - annote = {928QVTimes Cited:1Cited References Count:107}, - annote = {The following values have no corresponding Zotero field:auth-address: Groisman, PY UCAR, Natl Climat Data Ctr, NCDC, Fed Bldg,151 Patton Ave, Asheville, NC 28801 USA Univ Corp Atmospher Res, Boulder, CO USA NOAA, Natl Climate Data Ctr, Asheville, NC USA Univ N Carolina Asheville, Asheville, NC USA Duke Univ, Nicholas Sch Environ \& Earth Sci, Durham, NC USA Russian Inst Hydrometeorol Informat, Obninsk, Russiaaccession-num: ISI:000229287700003}, -} - -@article{groisman_contemporary_2004, - title = {Contemporary changes of the hydrological cycle over the contiguous {United} {States}: {Trends} derived from in situ observations}, - volume = {5}, - issn = {1525-755X}, - abstract = {Over the contiguous United States, precipitation, temperature, streamflow, and heavy and very heavy precipitation have increased during the twentieth century. In the east, high streamflow has increased as well. Soil wetness (as described by the Keetch-Byram Drought index) has increased over the northern and eastern regions of the United States, but in the southwestern quadrant of the country soil dryness has increased, making the region more susceptible to forest fires. In addition to these changes during the past 50 yr, increases in evaporation, near-surface humidity, total cloud cover, and low stratiform and cumulonimbus clouds have been observed. Snow cover has diminished earlier in the year in the west, and a decrease in near-surface wind speed has also occurred in many areas. Much of the increase in heavy and very heavy precipitation has occurred during the past three decades.}, - language = {English}, - number = {1}, - journal = {Journal of Hydrometeorology}, - author = {Groisman, P. Y. and Knight, R. W. and Karl, T. R. and Easterling, D. R. and Sun, B. M. and Lawrimore, J. H.}, - month = feb, - year = {2004}, - keywords = {streamflow, variability, impact, surface air-temperature, extreme precipitation, heavy precipitation, former ussr, secular trends, snow cover, summer}, - pages = {64--85}, - annote = {775TLTimes Cited:6Cited References Count:62}, - annote = {The following values have no corresponding Zotero field:auth-address: Groisman, PY NCDC, 151 Patton Ave, Asheville, NC 28801 USA NCDC, Asheville, NC 28801 USAaccession-num: ISI:000189076700005}, -} - -@article{groisman_changes_1999, - title = {Changes in the probability of heavy precipitation: {Important} indicators of climatic change}, - volume = {42}, - issn = {0165-0009}, - abstract = {A simple statistical model of daily precipitation based on the gamma distribution is applied to summer (JJA in Northern Hemisphere, DJF in Southern Hemisphere) data from eight countries: Canada, the United States, Mexico, the former Soviet Union, China, Australia, Norway, and Poland. These constitute more than 40\% of the global land mass, and more than 80\% of the extratropical land area. It is shown that the shape parameter of this distribution remains relatively stable, while the scale parameter is most variable spatially and temporally. This implies that the changes in mean monthly precipitation totals tend to have the most influence on the heavy precipitation rates in these countries. Observations show that in each country under consideration (except China), mean summer precipitation has increased by at least 5\% in the past century. In the USA, Norway, and Australia the frequency of summer precipitation events has also increased, but there is little evidence of such increases in any of the countries considered during the past fiffty years. A scenario is considered, whereby mean summer precipitation increases by 5\% with no change in the number of days with precipitation or the shape parameter. When applied in the statistical model, the probability of daily precipitation exceeding 25.4 mm (1 inch) in northern countries (Canada, Norway, Russia, and Poland) or 50.8 mm (2 inches) in mid-latitude countries (the USA, Mexico, China, and Australia) increases by about 20\% (nearly four times the increase in mean). The contribution of heavy rains (above these thresholds) to the total 5\% increase of precipitation is disproportionally high (up to 50\%), while heavy rain usually constitutes a significantly smaller fraction of the precipitation events and totals in extratropical regions (but up to 40\% in the tropics, e.g., in southern Mexico). Scenarios with moderate changes in the number of days with precipitation coupled with changes in the scale parameter were also investigated and found to produce smaller increases in heavy rainfall but still support the above conclusions. These scenarios give changes in heavy rainfall which are comparable to those observed and are consistent with the greenhouse-gas-induced increases in heavy precipitation simulated by some climate models for the next century. In regions with adequate data coverage such as the eastern two-thirds of contiguous United States, Norway, eastern Australia, and the European part of the former USSR, the statistical model helps to explain the disproportionate high changes in heavy precipitation which have been observed.}, - language = {English}, - number = {1}, - journal = {Climatic Change}, - author = {Groisman, P. Y. and Karl, T. R. and Easterling, D. R. and Knight, R. W. and Jamason, P. F. and Hennessy, K. J. and Suppiah, R. and Page, C. M. and Wibig, J. and Fortuniak, K. and Razuvaev, V. N. and Douglas, A. and Forland, E. and Zhai, P. M.}, - month = may, - year = {1999}, - keywords = {simulation, north-america, frequency, trends, variability, rainfall, temperature, continental united-states, extreme events, canada}, - pages = {243--283}, - annote = {236JQTimes Cited:74Cited References Count:66}, - annote = {The following values have no corresponding Zotero field:auth-address: Groisman, PY US Natl Climat Data Ctr, Univ Corp Atmospher Res Visiting Scientist, 151 Patton Ave, Asheville, NC 28801 USA US Natl Climat Data Ctr, Univ Corp Atmospher Res Visiting Scientist, Asheville, NC 28801 USA US Natl Climat Data Ctr, Asheville, NC 28801 USA CSIRO Atmospher Res, Aspendale, Vic 3195, Australia Univ Lodz, Dept Meteorol \& Climatol, PL-90568 Lodz, Poland Res Inst Hydrometeorol Informat, Obninsk 601240, Russia Creighton Univ, Dept Atmospher Sci, Omaha, NE 68178 USA Norwegian Meteorol Inst, N-0313 Oslo, Norway Natl Climate Ctr, Beijing 100081, Peoples R Chinaaccession-num: ISI:000082596800015}, -} - -@article{griffiths_climate_2009, - title = {Climate variability and the design flood problem}, - volume = {48}, - number = {1}, - journal = {Journal of Hydrology (New Zealand)}, - author = {Griffiths, George A. and Pearson, Charles P. and McKerchar, Alistair I.}, - year = {2009}, - pages = {29--38}, -} - -@unpublished{gordon_csiro_2002, - address = {Victoria, Australia}, - title = {The {CSIRO} {Mk3} climate system model, {CSIRO} {Atmospheric} {Research} {Technical} {Paper} {No}.60}, - author = {Gordon, H. B. and Rotstayn, L. D. and McGregor, J. L. and Dix, M. R. and Kowalczyk, E. A. and O’Farrell, S. P. and Waterman, L. J. and Hirst, A. C. and Wilson, S. G. and Collier, M. A. and Watterson, I. G. and Elliott, T. I.}, - year = {2002}, - annote = {The following values have no corresponding Zotero field:publisher: CSIRO. Division of Atmospheric Research}, -} - -@article{gordon_simulation_2000, - title = {The simulation of {SST}, sea ice extents and ocean heat transports in a version of the {Hadley} {Centre} coupled model without flux adjustments}, - volume = {16}, - issn = {0930-7575}, - abstract = {Results are presented from a new version of the Hadley Centre coupled model (HadCM3) that does not require flux adjustments to prevent large climate drifts in the simulation. The model has both an improved atmosphere and ocean component. In particular, the ocean has a 1.25 degrees x 1.25 degrees degree horizontal resolution and leads to a considerably improved simulation of ocean heat transports compared to earlier versions with a coarser resolution ocean component. The model does not have any spin up procedure prior to coupling and the simulation has been run for over 400 years starting from observed initial conditions. The sea surface temperature (SST) and sea ice simulation are shown to be stable and realistic. The trend in global mean SST is less than 0.009 degrees C per century. In part, the improved simulation is a consequence of a greater compatibility of the atmosphere and ocean model heat budgets. The atmospheric model surface heat and momentum budget are evaluated by comparing with climatological ship-based estimates. Similarly the ocean model simulation of poleward heat transports is compared with direct ship-based observations for a number of sections across the globe. Despite the limitations of the observed datasets, it is shown that the coupled model is able to reproduce many aspects of the observed heat budget.}, - language = {English}, - number = {2-3}, - journal = {Climate Dynamics}, - author = {Gordon, C. and Cooper, C. and Senior, C. A. and Banks, H. and Gregory, J. M. and Johns, T. C. and Mitchell, J. F. B. and Wood, R. A.}, - month = feb, - year = {2000}, - keywords = {variability, impact, parameterization, general-circulation models, atmosphere model, climate-system-model, mixed layer, north-atlantic, numerical-models, south}, - pages = {147--168}, - annote = {285DPTimes Cited:216Cited References Count:87}, - annote = {The following values have no corresponding Zotero field:auth-address: Gordon, C Hadley Ctr Climate Predict \& Res, Bracknell, Berks, England Hadley Ctr Climate Predict \& Res, Bracknell, Berks, Englandaccession-num: ISI:000085374600005}, -} - -@article{goodess_links_2002, - title = {Links between circulation and changes in the characteristics of {Iberian} rainfall}, - volume = {22}, - abstract = {Investigation of the links between atmospheric circulation patterns and rainfall is important for the understanding of climatic variability and for the development of empirical circulation-based downscaling methods. Here, spatial and temporal variations in circulation-rainfall relationships over the Iberian Peninsula during the period 1958-97 are explored using an automated circulation classification scheme and daily rainfall totals for 18 stations. Links between the circulation classification scheme and the North Atlantic oscillation (NAO) are also considered, as are the direct links between rainfall and the NAO. Trends in rainfall and circulation-type frequency are explored. A general tendency towards decreasing mean seasonal rainfall over the peninsula, with the exception of the southeastern Mediterranean coast, hides larger changes in wet day amount and rainfall probability. There is a tendency towards more, less-intensive rain days across much of Iberia, with a tendency towards more, more-intensive rain days along the southeastern Mediterranean coast, both of which are reflected in changes in rainfall amount quantiles. A preliminary analysis indicates that these changes may have occurred systematically across all circulation types. Comparison of the trends in rainfall and in circulation-type frequency suggests possible links. These links are supported by linear regression analyses using circulation-type frequencies as predictor variables and rainfall totals for winter months as the predictands. The selected predictor variables reflect the main circulation features influencing winter rainfall across the peninsula, i.e. the strong influence of Atlantic westerly and southwesterly airmasses over much of the peninsula, of northerly and northwesterly surface flow over northern/northwestern Spain and northern Portugal and the stronger effect of Mediterranean rather than Atlantic influences in southeastern Spain. The observed rainfall changes cannot, however, be explained by changes in circulation alone. Copyright (C) 2002 Royal Meteorological Society.}, - number = {13}, - journal = {International Journal of Climatology}, - author = {Goodess, C. M. and Jones, P. D.}, - month = nov, - year = {2002}, - pages = {1593--1615}, - annote = {621FZINT J CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:000179579000002}, -} - -@article{gonzalez-rouco_agreement_2000, - title = {Agreement between observed rainfall trends and climate change simulations in the southwest of {Europe}}, - volume = {13}, - abstract = {The lowest spatial scale at which current climate models are considered to be skillful is on the order of 1000 km because of resolution and computer capabilities. The estimation of the regional changes caused by anthropogenic emissions of greenhouse gases and aerosols therefore is problematic. Here a statistical downscaling scheme is used to study the relationship between large-scale sea lever pressure and regional precipitation in southwestern Europe, both in observed data and in outputs from a general circulation model (GCM) forced with increasing levers of greenhouse gases and sulfate aerosols. The results indicate that the GCM does reproduce the main aspects of the large- to local-scale coupled variability. Furthermore, these large- to local-scale relationships remain stable in the scenario simulations. The GCM runs predict increases of advection of oceanic air masses to the Iberian Peninsula that will produce a slight decrease of precipitation amounts in the north coast and the opposite effect in the rest of the territory, with values that could reach 10 mm decade(-1) in the south. In the homogenized historical records, the obtained pattern of change is very similar. These results support estimations of future regional trends simulated by the GCM under future emission scenarios.}, - number = {17}, - journal = {J. Clim.}, - author = {Gonzalez-Rouco, J. F. and Heyen, H. and Zorita, E. and Valero, E.}, - month = sep, - year = {2000}, - pages = {3057--3065}, - annote = {359GMJ CLIMATE}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Clim.accession-num: ISI:000089603200002}, -} - -@article{golubyatnikov_stochastic_2002, - title = {Stochastic generator of monthly precipitation and monthly average temperature}, - volume = {38}, - abstract = {A method is proposed for simulating the monthly precipitation and monthly average temperature of air near the earth's surface. The proposed algorithm is based on the method of inverse functions. The climatic parameters used in simulations were derived from the UEA/CRU database. The method was applied to simulate time series of monthly precipitation and monthly average temperatures of air over a dry land territory with a spatial resolution of 0.5 x 0.5degrees in latitude and longitude. The time series of the climatic parameters obtained with the proposed stochastic generator appear statistically adequate to actual data series for any time interval in months and years.}, - number = {1}, - journal = {Izvestiya Atmospheric and Oceanic Physics}, - author = {Golubyatnikov, L. L. and Yamagata, Y. and Aleksandrov, G. A.}, - month = feb, - year = {2002}, - pages = {40--47}, - annote = {523QWIZV ATMOS OCEAN PHYS}, - annote = {The following values have no corresponding Zotero field:alt-title: Izv. Atmos. Ocean. Phys.accession-num: ISI:000173967300004}, -} - -@article{gleick_regional_1987, - title = {Regional hydrologic consequences of increases in atmospheric {CO2} and other trace gases}, - volume = {10}, - number = {2}, - journal = {Climatic Change}, - author = {Gleick, P. H.}, - year = {1987}, - pages = {137 -- 160}, -} - -@article{glahn_use_1972, - title = {The use of model output statistics ({MOS}) in objective weather forecasting}, - volume = {11}, - journal = {Journal of Applied Meteorology}, - author = {Glahn, H. R. and Lowry, D. A.}, - year = {1972}, - pages = {1203--1211}, -} - -@article{gleick_peak_2010, - title = {Peak water limits to freshwater withdrawal and use}, - doi = {10.1073/pnas.1004812107}, - abstract = {Freshwater resources are fundamental for maintaining human health, agricultural production, economic activity as well as critical ecosystem functions. As populations and economies grow, new constraints on water resources are appearing, raising questions about limits to water availability. Such resource questions are not new. The specter of “peak oil”—a peaking and then decline in oil production—has long been predicted and debated. We present here a detailed assessment and definition of three concepts of “peak water”: peak renewable water, peak nonrenewable water, and peak ecological water. These concepts can help hydrologists, water managers, policy makers, and the public understand and manage different water systems more effectively and sustainably. Peak renewable water applies where flow constraints limit total water availability over time. Peak nonrenewable water is observable in groundwater systems where production rates substantially exceed natural recharge rates and where overpumping or contamination leads to a peak of production followed by a decline, similar to more traditional peak-oil curves. Peak “ecological” water is defined as the point beyond which the total costs of ecological disruptions and damages exceed the total value provided by human use of that water. Despite uncertainties in quantifying many of these costs and benefits in consistent ways, more and more watersheds appear to have already passed the point of peak water. Applying these concepts can help shift the way freshwater resources are managed toward more productive, equitable, efficient, and sustainable use.}, - journal = {Proceedings of the National Academy of Sciences}, - author = {Gleick, Peter H. and Palaniappan, Meena}, - month = may, - year = {2010}, -} - -@article{gleick_development_1987, - title = {Development and {Testing} of a {Water} {Balance} {Model} for {Climate} {Impact} {Assessment}: {Modeling} the {Sacramento} {Basin}}, - volume = {23}, - abstract = {Within the next few decades, changes in global temperature and precipitation patterns caused by increasing atmospheric concentrations of carbon dioxide and other trace gases are likely to appear. At present, the regional hydrologic impacts of such climatic changes cannot be evaluated with any certainty. Using modified water balance methods, a model of a critical hydrologic basin, the Sacramento Basin in California, is developed and tested for the purpose of investigating the effects on water availability of changes in climate. This basin was chosen because of the importance of its water supplies to agricultural and industrial productivity and because of the quality and quantity of the hydrologic data available. The water balance model is capable of reproducing both the magnitude and the timing of monthly and seasonal runoff, as well as changes in soil moisture conditions. The results suggest that the application of such models may provide considerably more information on regional hydrologic effects of climatic changes than is currently available. Such information is likely to have important ramifications for long-range water resource planning, for agricultural water resource development and conservation, and for industrial water use over the next several decades. (Author 's abstract)}, - number = {6}, - journal = {Water Resources Research}, - author = {Gleick, P. H.}, - year = {1987}, - keywords = {California, water, Runoff, Air pollution, Air pollution effects, Available, Climatic data, Climatic impact, Climatology, Data, Environmental effects, Hydrologic aspects, Hydrologic budget, Hydrologic data, Hydrologic models, interpretation, Mathematical equations, Model studies, Model testing, Sacramento Basin, Soil water, Temperature effects, Watersheds}, - pages = {1049--1061}, - annote = {Water Resources Research WRERAQ Vol. 23, No. 6, p 1049-1061, June 1987. 6 fig, 6 tab, 55 ref.}, -} - -@article{gleckler_performance_2008, - title = {Performance metrics for climate models}, - volume = {113}, - abstract = {Objective measures of climate model performance are proposed and used to assess simulations of the 20th century, which are available from the Coupled Model Intercomparison Project (CMIP3) archive. The primary focus of this analysis is on the climatology of atmospheric fields. For each variable considered, the models are ranked according to a measure of relative error. Based on an average of the relative errors over all fields considered, some models appear to perform substantially better than others. Forming a single index of model performance, however, can be misleading in that it hides a more complex picture of the relative merits of different models. This is demonstrated by examining individual variables and showing that the relative ranking of models varies considerably from one variable to the next. A remarkable exception to this finding is that the so-called “mean model” consistently outperforms all other models in nearly every respect. The usefulness, limitations and robustness of the metrics defined here are evaluated 1) by examining whether the information provided by each metric is correlated in any way with the others, and 2) by determining how sensitive the metrics are to such factors as observational uncertainty, spatial scale, and the domain considered (e.g., tropics versus extra-tropics). An index that gauges the fidelity of model variability on interannual time-scales is found to be only weakly correlated with an index of the mean climate performance. This illustrates the importance of evaluating a broad spectrum of climate processes and phenomena since accurate simulation of one aspect of climate does not guarantee accurate representation of other aspects. Once a broad suite of metrics has been developed to characterize model performance it may become possible to identify optimal subsets for various applications.}, - journal = {J. Geophys. Res.}, - author = {Gleckler, P. J. and Taylor, K. E. and Doutriaux, C.}, - year = {2008}, - keywords = {1610 Global Change: Atmosphere, 1616 Global Change: Climate variability, 1626 Global Change: Global climate models, 1620 Global Change: Climate dynamics, 1630 Global Change: Impacts of global change, climate models, model evaluation, Performance metrics}, - pages = {D06104, doi:10.1029/2007JD008972}, - annote = {The following values have no corresponding Zotero field:publisher: American Geophysical Union}, -} - -@article{giorgi_coordinated_2016, - title = {Coordinated {Experiments} for {Projections} of {Regional} {Climate} {Change}}, - volume = {2}, - issn = {2198-6061}, - doi = {10.1007/s40641-016-0046-6}, - abstract = {We review coordinated efforts for producing regional climate projections through dynamical and statistical downscaling tools driven by global climate model output. Such projections are affected by multiple sources of uncertainty both at the global model and at the downscaling levels. The characterization of these uncertainties and the production of robust regional to local projections for use in impact studies require the completion of properly designed large ensembles of experiments. Toward this purpose, several regional coordinated efforts have been conducted in the past, particularly involving regional climate models, but because of the lack of a common experiment protocol, the transfer of know-how across them has been difficult. This problem is being addressed in the Coordinated Regional Downscaling Experiment (CORDEX), a framework designed to produce the next generation of worldwide high-resolution regional climate projections through a fully coordinated experiment protocol.}, - number = {4}, - journal = {Current Climate Change Reports}, - author = {Giorgi, Filippo and Gutowski, William J.}, - month = dec, - year = {2016}, - pages = {202--210}, - annote = {The following values have no corresponding Zotero field:label: Giorgi2016}, -} - -@article{girvetz_applied_2009, - title = {Applied {Climate}-{Change} {Analysis}: {The} {Climate} {Wizard} {Tool}}, - volume = {4}, - doi = {10.1371/journal.pone.0008320.}, - abstract = {{\textless}sec{\textgreater} {\textless}title{\textgreater}Background{\textless}/title{\textgreater} {\textless}p{\textgreater}Although the message of “global climate change” is catalyzing international action, it is local and regional changes that directly affect people and ecosystems and are of immediate concern to scientists, managers, and policy makers. A major barrier preventing informed climate-change adaptation planning is the difficulty accessing, analyzing, and interpreting climate-change information. To address this problem, we developed a powerful, yet easy to use, web-based tool called Climate Wizard ({\textless}ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://ClimateWizard.org" xlink:type="simple"{\textgreater}http://ClimateWizard.org{\textless}/ext-link{\textgreater}) that provides non-climate specialists with simple analyses and innovative graphical depictions for conveying how climate has and is projected to change within specific geographic areas throughout the world.{\textless}/p{\textgreater} {\textless}/sec{\textgreater}{\textless}sec{\textgreater} {\textless}title{\textgreater}Methodology/Principal Findings{\textless}/title{\textgreater} {\textless}p{\textgreater}To demonstrate the Climate Wizard, we explored historic trends and future departures (anomalies) in temperature and precipitation globally, and within specific latitudinal zones and countries. We found the greatest temperature increases during 1951–2002 occurred in northern hemisphere countries (especially during January–April), but the latitude of greatest temperature change varied throughout the year, sinusoidally ranging from approximately 50°N during February-March to 10°N during August-September. Precipitation decreases occurred most commonly in countries between 0–20°N, and increases mostly occurred outside of this latitudinal region. Similarly, a quantile ensemble analysis based on projections from 16 General Circulation Models (GCMs) for 2070–2099 identified the median projected change within countries, which showed both latitudinal and regional patterns in projected temperature and precipitation change.{\textless}/p{\textgreater} {\textless}/sec{\textgreater}{\textless}sec{\textgreater} {\textless}title{\textgreater}Conclusions/Significance{\textless}/title{\textgreater} {\textless}p{\textgreater}The results of these analyses are consistent with those reported by the Intergovernmental Panel on Climate Change, but at the same time, they provide examples of how Climate Wizard can be used to explore regionally- and temporally-specific analyses of climate change. Moreover, Climate Wizard is not a static product, but rather a data analysis framework designed to be used for climate change impact and adaption planning, which can be expanded to include other information, such as downscaled future projections of hydrology, soil moisture, wildfire, vegetation, marine conditions, disease, and agricultural productivity.{\textless}/p{\textgreater} {\textless}/sec{\textgreater}}, - number = {12}, - journal = {PLoS ONE}, - author = {Girvetz, E. H. and Zganjar, C. and Raber, G. T. and Maurer, E. P. and Kareiva, P. and Lawler, J. J.}, - year = {2009}, - pages = {e8320}, - annote = {The following values have no corresponding Zotero field:publisher: Public Library of Science}, -} - -@article{giorgi_approaches_1991, - title = {Approaches to the {Simulation} of {Regional} {Climate} {Change} - a {Review}}, - volume = {29}, - abstract = {The increasing demand by the scientific community, policy makers, and the public for realistic projections of possible regional impacts of future climate changes has rendered the issue of regional climate simulation critically important. The problem of projecting regional climate changes can be identified as that of representing effects of atmospheric forcings on two different spatial scales: large-scale forcings, i.e., forcings which modify the general circulation and determine the sequence of weather events which characterize the climate regime of a given region (for example, greenhouse gas abundance), and mesoscale forcings, i.e., forcings which modify the local circulations, thereby regulating the regional distribution of climatic variables (for example, complex mountainous systems). General circulation models (GCMs) are the main tools available today for climate simulation. However, they are run and will likely be run for the next several years at resolutions which are too coarse to adequately describe mesoscale forcings and yield accurate regional climate detail. This paper presents a review of these approaches. They can be divided in three broad categories: (1) Purely empirical approaches, in which the forcings are not explicitly accounted for, but regional climate scenarios are constructed by using instrumental data records or paleoclimatic analogues; (2) semiempirical approaches, in which GCMs are used to describe the atmospheric response to large-scale forcings of relevance to climate changes, and empirical techniques account for the effect of mesoscale forcings; and (3) modeling approaches, in which mesoscale forcings are described by increasing the model resolution only over areas of interest. Since they are computationally inexpensive, empirical and semiempirical techniques have been so far more widely used. Their application to regional climate change projection is, however, limited by their own empiricism and by the availability of data sets of adequate quality. More recently, a nested GCM-limited area model methodology for regional climate simulation has been developed, with encouraging preliminary results. As it is physically, rather than empirically, based, the nested modeling framework has a wide range of applications.}, - number = {2}, - journal = {Reviews of Geophysics}, - author = {Giorgi, F. and Mearns, L. O.}, - month = may, - year = {1991}, - pages = {191--216}, - annote = {FL559REV GEOPHYS}, - annote = {The following values have no corresponding Zotero field:alt-title: Rev. Geophys.accession-num: ISI:A1991FL55900004}, -} - -@incollection{giorgi_regional_2001, - address = {Cambridge, UK}, - title = {Regional {Climate} {Information}- {Evaluation} and {Projections}}, - booktitle = {Climate {Change} 2001: {The} {Scientific} {Basis}. {Contribution} of {Working} {Group} {I} to the {Third} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {Giorgi, F. and Hewitson, B. and Christensen, J. and Hulme, M. and von Storch, H. and Whetton, P. and Jones, R. and Mearns, L. and Fu, C.}, - editor = {Houghton, J. T.}, - year = {2001}, - pages = {583--638}, - annote = {The following values have no corresponding Zotero field:section: 10}, -} - -@article{f_giorgi_regional_2015, - title = {Regional {Dynamical} {Downscaling} and the {CORDEX} {Initiative}}, - volume = {40}, - doi = {10.1146/annurev-environ-102014-021217}, - abstract = {We review the challenges and future perspectives of regional climate model (RCM), or dynamical downscaling, activities. Among the main technical issues in need of better understanding are those of selection and sensitivity to the model domain and resolution, techniques for providing lateral boundary conditions, and RCM internal variability. The added value (AV) obtained with the use of RCMs remains a central issue, which needs more rigorous and comprehensive analysis strategies. Within the context of regional climate projections, large ensembles of simulations are needed to better understand the models and characterize uncertainties. This has provided an impetus for the development of the Coordinated Regional Downscaling Experiment (CORDEX), the first international program offering a common protocol for downscaling experiments, and we discuss how CORDEX can address the key scientific challenges in downscaling research. Among the main future developments in RCM research, we highlight the development of coupled regional Earth system models and the transition to very high-resolution, cloud-resolving models.}, - number = {1}, - journal = {Annual Review of Environment and Resources}, - author = {F. Giorgi and W. J. Gutowski}, - year = {2015}, - keywords = {regional climate modeling,dynamical downscaling,regional climate,CORDEX,RCM,climate change,model evaluation,downscaling added value,regional Earth system modeling,high-resolution modeling}, - pages = {467--490}, -} - -@article{giorgi_does_2010, - title = {Does the model regional bias affect the projected regional climate change? {An} analysis of global model projections}, - volume = {100}, - issn = {0165-0009}, - doi = {10.1007/s10584-010-9864-z}, - abstract = {An analysis is presented of the dependence of the regional temperature and precipitation change signal on systematic regional biases in global climate change projections. The CMIP3 multi-model ensemble is analyzed over 26 land regions and for the A1B greenhouse gas emission scenario. For temperature, the model regional bias has a negligible effect on the projected regional change. For precipitation, a significant correlation between change and bias is found in about 30\% of the seasonal/regional cases analyzed, covering a wide range of different climate regimes. For these cases, a performance-based selection of models in producing climate change scenarios can affect the resulting change estimate, and it is noted that a minimum of four to five models is needed to obtain robust precipitation change estimates. In a number of cases, models with largely different precipitation biases can still produce changes of consistent sign. Overall, it is assessed that in the present generation of models the regional bias does not appear to be a dominant factor in determining the simulated regional change in the majority of cases.}, - language = {English}, - number = {3-4}, - journal = {Clim. Change}, - author = {Giorgi, F. and Coppola, E.}, - month = jun, - year = {2010}, - keywords = {uncertainty, ensemble, simulations, aogcm, change prediction}, - pages = {787--795}, - annote = {ISI Document Delivery No.: 602AWTimes Cited: 0Cited Reference Count: 20Giorgi, Filippo Coppola, ErikaOffice of Science, U.S. Department of EnergyWe acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP's Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy. We also thank two anonymous reviewers for their helpful comments and suggestions.SpringerDordrecht}, - annote = {The following values have no corresponding Zotero field:auth-address: [Giorgi, Filippo; Coppola, Erika] Int Ctr Theoret Phys, Earth Syst Phys Sect, Trieste, Italy. Giorgi, F, Int Ctr Theoret Phys, Earth Syst Phys Sect, Trieste, Italy. giorgi@ictp.italt-title: Clim. Changeaccession-num: ISI:000278111500025work-type: Article}, -} - -@article{giorgi_regional_2005, - title = {Regional changes in surface climate interannual variability for the 21st century from ensembles of global simulations}, - volume = {L13701}, - journal = {Geophysical Research Letters}, - author = {Giorgi, F. and Bi, X.}, - year = {2005}, - pages = {doi:10.1029/2005GL023002}, -} - -@article{giorgi_climate_2006, - title = {Climate change hot-spots}, - volume = {33}, - journal = {Geophysical Research Letters}, - author = {Giorgi, F.}, - year = {2006}, - pages = {L08707, doi:10.1029/2006GL025734}, -} - -@article{giorgi_dependence_2002, - title = {Dependence of the surface climate interannual variability on spatial scale}, - volume = {29}, - abstract = {[1] This paper examines the scale dependence of surface climate interannual variability over six regions of the world using a gridded observed dataset of precipitation and surface air temperature for the period 1961-1990. The scales analysed cover the range of 0.5 to 3.5 degrees in longitude/latitude. The interannual variability of precipitation exhibits a strong sensitivity to spatial scale, especially at the finer scales analysed, while the interannual variability of temperature shows a weak sensitivity to spatial scale. The scale sensitivity of interannual variability is maximum in warm climate regimes, such as over tropical and subtropical regions and in the summer season. These results have important implications for climate predictability (e.g., at seasonal and longer temporal scales) and for the identification of climatic trends and changes. They indicate that the prediction and detection of precipitation changes becomes a substantially more difficult task as the scale of interest is increasingly refined. Conversely, for temperature, since the variability is characterized by broader spatial scales, scale refinement is a less important factor.}, - number = {23}, - journal = {Geophysical Research Letters}, - author = {Giorgi, F.}, - month = dec, - year = {2002}, - pages = {art. no.--2101}, - annote = {646YPGEOPHYS RES LETT}, - annote = {The following values have no corresponding Zotero field:alt-title: Geophys. Res. Lett.accession-num: ISI:000181064700004}, -} - -@article{gershunov_heavy_2003, - title = {Heavy daily precipitation frequency over the contiguous {United} {States}: {Sources} of climatic variability and seasonal predictability}, - volume = {16}, - issn = {0894-8755}, - abstract = {By matching large-scale patterns in climate fields with patterns in observed station precipitation, this work explores seasonal predictability of precipitation in the contiguous United States for all seasons. Although it is shown that total seasonal precipitation and frequencies of less-than-extreme daily precipitation events can be predicted with much higher skill, the focus of this study is on frequencies of daily precipitation above the seasonal 90th percentile (P90), a variable whose skillful prediction is more challenging. Frequency of heavy daily precipitation is shown to respond to ENSO as well as to non-ENSO interannual and interdecadal variability in the North Pacific. -Specification skill achieved by a statistical model based on contemporaneous SST forcing with and without an explicit dynamical atmosphere is compared and contrasted. Statistical models relating the SST forcing patterns directly to observed station precipitation are shown to perform consistently better in all seasons than hybrid (dynamical-statistical) models where the SST forcing is first translated to atmospheric circulation via three separate general circulation models and the dynamically computed circulation anomalies are statistically related to observed precipitation. Skill is summarized for all seasons, but in detail for January-February-March, when it is shown that predictable patterns are spatially robust regardless of the approach used. Predictably, much of the skill is due to ENSO. While the U. S. average skill is modest, regional skill levels can be quite high. It is also found that non-ENSO-related skill is significant, especially for the extreme Southwest and that this is due mostly to non-ENSO interannual and decadal variability in the North Pacific SST forcing. -Although useful specification skill is achieved by both approaches, hybrid predictability is not pursued further in this effort. Rather, prognostic analysis is carried out with the purely statistical approach to analyze P90 predictability based on antecedent SST forcing. Skill at various lead times is investigated and it is shown that significant regional skill can be achieved at lead times of several months even in the absence of strong ENSO forcing.}, - language = {English}, - number = {16}, - journal = {Journal of Climate}, - author = {Gershunov, A. and Cayan, D. R.}, - month = aug, - year = {2003}, - keywords = {arctic oscillation, enso influence, interdecadal modulation, intraseasonal extreme rainfall, north-atlantic oscillation, pacific-ocean, regional simulations, sst anomalies, surface-temperature, temperature frequencies}, - pages = {2752--2765}, - annote = {708HATimes Cited:2Cited References Count:48}, - annote = {The following values have no corresponding Zotero field:auth-address: Gershunov, A Univ Calif San Diego, Scripps Inst Oceanog, Div Climate Res, 9500 Gilman Dr, La Jolla, CA 92093 USA Univ Calif San Diego, Scripps Inst Oceanog, Div Climate Res, La Jolla, CA 92093 USA US Geol Survey, La Jolla, CA USAaccession-num: ISI:000184560100007}, -} - -@article{gershunov_predicting_2000, - title = {Predicting and downscaling {ENSO} impacts on intraseasonal precipitation statistics in {California}: {The} 1997/98 event}, - volume = {1}, - issn = {1525-755X}, - abstract = {Three long-range forecasting methods have been evaluated for prediction and downscaling of seasonal and intraseasonal precipitation statistics in California. Full-statistical, hybrid-dynamical-statistical and full-dynamical approaches have been used to forecast Fl Nino-Southern Oscillation (ENSO)-related total precipitation, daily precipitation frequency, and average intensity anomalies during the January-March season. For El Nino winters, the hybrid approach emerges as the best performer, while La Nina forecasting skill is poor. The full-statistical forecasting method features reasonable forecasting skill for both La Nina and El Nino winters. The performance of the full-dynamical approach could not be evaluated as rigorously as that of the other two forecasting schemes. Although the full-dynamical forecasting approach is expected to outperform simpler forecasting schemes in the long run, evidence is presented to conclude that, at present, the full-dynamical forecasting approach is the least viable of the three, at least in California. The authors suggest that operational forecasting of any intraseasonal temperature, precipitation, or streamflow statistic derivable from the available-records is possible now for ENSO-extreme years.}, - language = {English}, - number = {3}, - journal = {Journal of Hydrometeorology}, - author = {Gershunov, A. and Barnett, T. P. and Cayan, D. R. and Tubbs, T. and Goddard, L.}, - month = jun, - year = {2000}, - keywords = {predictability, contiguous united-states, temperature frequencies, extreme rainfall, regional spectral model, teleconnections}, - pages = {201--210}, - annote = {357DMTimes Cited:7Cited References Count:17}, - annote = {The following values have no corresponding Zotero field:auth-address: Gershunov, A Univ Calif San Diego, Scripps Inst Oceanog, Div Climate Res, 9500 Gilman Dr, La Jolla, CA 92093 USA Univ Calif San Diego, Scripps Inst Oceanog, Div Climate Res, La Jolla, CA 92093 USA US Geol Survey, Div Water Resources, La Jolla, CA USA Univ Calif San Diego, Scripps Inst Oceanog, Int Res Inst Climate Predict, La Jolla, CA 92093 USAaccession-num: ISI:000089483300001}, -} - -@article{georgakakos_soil_2001, - title = {Soil moisture tendencies into the next century for the conterminous {United} {States}}, - volume = {106}, - abstract = {A monthly snowpack and soil-moisture-accounting model is formulated for application to each of the climate divisions of the conterminous United States for use in climate impacts assessment studies. Statistical downscaling and bias adjustment components complement the model for the assimilation of large- scale global climate model precipitation and temperature. The model produces monthly streamflow that is broadly consistent with observed streamflow from several drainage basins in the United States for the period 1931-1998. Simulated historical soil moisture fields reproduce several features of the available observed soil moisture in the Midwest. The simulations produce large-scale coherent seasonal patterns of soil moisture field moments over the conterminous United States, with high soil moisture means over divisions in the Ohio Valley, the northeastern United States, and the Pacific Northwest, and with pronounced low means in most of the western U.S. climate divisions. Characteristically low field standard deviations are produced for the Ohio Valley and northeastern United States, the Pacific Northwest in winter, and the southwestern United States in summer. Differences in extreme standardized anomalies of soil moisture over the historical record possess high values (2.5-3) in the central United States, where the available water capacity of the soils is high. Application of the methodology for future periods using output from the Canadian coupled global climate model (CGCM1) shows that for at least the first few decades of the 21st century, somewhat drier-than-p resent soil conditions are projected, with highest drying trends found in the southeastern United States. The soil moisture deficits in most areas are of the same order of magnitude as the soil moisture field standard deviations arising from historical natural variability. Brumbelow and Georgakakos [this issue] study the implications for crop yield in the United States.}, - number = {D21}, - journal = {Journal of Geophysical Research-Atmospheres}, - author = {Georgakakos, K. P. and Smith, D. E.}, - month = nov, - year = {2001}, - pages = {27367--27382}, - annote = {494XZJ GEOPHYS RES-ATMOS}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Geophys. Res.-Atmos.accession-num: ISI:000172312300011}, -} - -@article{garreaud_present-day_2009, - title = {Present-day {South} {American} climate}, - volume = {281}, - issn = {0031-0182}, - number = {3–4}, - journal = {Palaeogeography, Palaeoclimatology, Palaeoecology}, - author = {Garreaud, René D. and Vuille, Mathias and Compagnucci, Rosa and Marengo, José}, - year = {2009}, - keywords = {Precipitation, South America, Climate variability, Climate, Atmospheric circulation}, - pages = {180--195}, - annote = {The following values have no corresponding Zotero field:work-type: doi: 10.1016/j.palaeo.2007.10.032}, -} - -@article{fuss_betting_2014, - title = {Betting on negative emissions}, - volume = {4}, - issn = {1758-678X}, - doi = {10.1038/nclimate2392}, - number = {10}, - journal = {Nature Clim. Change}, - author = {Fuss, Sabine and Canadell, Josep G. and Peters, Glen P. and Tavoni, Massimo and Andrew, Robbie M. and Ciais, Philippe and Jackson, Robert B. and Jones, Chris D. and Kraxner, Florian and Nakicenovic, Nebosja and Le Quere, Corinne and Raupach, Michael R. and Sharifi, Ayyoob and Smith, Pete and Yamagata, Yoshiki}, - year = {2014}, - pages = {850--853}, - annote = {The following values have no corresponding Zotero field:publisher: Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.work-type: Commentary}, -} - -@article{furevik_description_2003, - title = {Description and evaluation of the bergen climate model: {ARPEGE} coupled with {MICOM}}, - volume = {21}, - number = {1}, - journal = {Climate Dynamics}, - author = {Furevik, T. and Bentsen, M. and Drange, H. and Kindem, I. K. T. and Kvamstø, N. G.}, - year = {2003}, - pages = {27--51}, -} - -@article{fuentes_improved_2000, - title = {An improved statistical-dynamical downscaling scheme and its application to the alpine precipitation climatology}, - volume = {65}, - abstract = {An improved statistical-dynamical downscaling method for the regionalization of large-scale climate analyses or simulations is introduced. The method is based on the disaggregation of a multi-year time-series of large-scale meteorological data into multi-day episodes of quasistationary circulation. The episodes are subsequently grouped into a defined number of classes. A regional model is used to simulate the evolution of weather during the most typical episode of each class. These simulations consider the effects of the regional topography. Finally, the regional model results are statistically weighted with the climatological frequencies of the respective circulation classes in order to provide regional climate patterns. The statistical-dynamical downscaling procedure is applied to large-scale analyses for a 12-year climate period 1981-1992. The performance of the new method is demonstrated for winter precipitation in the Alpine region. With the help of daily precipitation analyses it was possible to validate the results and to assess the different sources of errors. It appeared that the main error originates from the regional model, whereas the error of the procedure itself was relatively unimportant. This new statistical-dynamical downscaling method turned out to be an efficient alternative to the commonly used method of nesting a regional model continuously within a general circulation model (dynamical downscaling).}, - number = {3-4}, - journal = {Theoretical and Applied Climatology}, - author = {Fuentes, U. and Heimann, D.}, - year = {2000}, - pages = {119--135}, - annote = {309CTTHEOR APPL CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Theor. Appl. Climatol.accession-num: ISI:000086750600001}, -} - -@article{holger_fritze_shifts_2011, - title = {Shifts in {Western} {North} {American} {Snowmelt} {Runoff} {Regimes} for the {Recent} {Warm} {Decades}}, - volume = {12}, - doi = {10.1175/2011jhm1360.1}, - abstract = {AbstractClimate change–driven shifts in streamflow timing have been documented for western North America and are expected to continue with increased warming. These changes will likely have the greatest implications on already short and overcommitted water supplies in the region. This study investigated changes in western North American streamflow timing over the 1948–2008 period, including the very recent warm decade not previously considered, through (i) trends in streamflow timing measures, (ii) two second-order linear models applied simultaneously over the region to test for the acceleration of these changes, and (iii) changes in runoff regimes. Basins were categorized by the percentage of snowmelt-derived runoff to enable the comparison of groups of streams with similar runoff characteristics and to quantify shifts in snowmelt-dominated regimes. Results indicate that streamflow has continued to shift to earlier in the water year, most notably for those basins with the largest snowmelt runoff component. However, an acceleration of these streamflow timing changes for the recent warm decades is not clearly indicated. Most coastal rain-dominated and some interior basins have experienced later timing. The timing changes are connected to area-wide warmer temperatures, especially in March and January, and to precipitation shifts that bear subregional signatures. Notably, a set of the most vulnerable basins has experienced runoff regime changes, such that basins that were snowmelt dominated at the beginning of the observational period shifted to mostly rain dominated in later years. These most vulnerable regions for regime shifts are in the California Sierra Nevada, eastern Washington, Idaho, and northeastern New Mexico. Snowmelt regime changes may indicate that the time available for adaptation of water supply systems to climatic changes in vulnerable regions are shorter than previously recognized.}, - number = {5}, - journal = {Journal of Hydrometeorology}, - author = {Holger Fritze and Iris T. Stewart and Edzer Pebesma}, - year = {2011}, - keywords = {Snowmelt,Decadal variability,North America,Runoff,Climate change,Streamflow}, - pages = {989--1006}, -} - -@article{friedlingstein_update_2010, - title = {Update on {CO2} emissions}, - volume = {3}, - issn = {1752-0894}, - number = {12}, - journal = {Nature Geosci}, - author = {Friedlingstein, P. and Houghton, R. A. and Marland, G. and Hackler, J. and Boden, T. A. and Conway, T. J. and Canadell, J. G. and Raupach, M. R. and Ciais, P. and Le Quere, C.}, - year = {2010}, - pages = {811--812}, - annote = {10.1038/ngeo1022}, - annote = {The following values have no corresponding Zotero field:publisher: Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.work-type: 10.1038/ngeo1022}, -} - -@article{francl_ecosystem_2010, - title = {Ecosystem {Adaptation} to {Climate} {Change}: {Small} {Mammal} {Migration} {Pathways} in the {Great} {Lakes} {States}}, - volume = {36}, - issn = {0380-1330}, - doi = {10.1016/j.jglr.2009.09.007}, - number = {sp2}, - journal = {Journal of Great Lakes Research}, - author = {Francl, K. E. and Hayhoe, K. and Saunders, M. and Maurer, E. P.}, - month = jul, - year = {2010}, - pages = {86--93, doi: 10.1016/j.jglr.2009.09.007}, - annote = {doi: 10.1016/j.jglr.2009.09.007}, - annote = {The following values have no corresponding Zotero field:publisher: International Association for Great Lakes Research}, -} - -@incollection{g_flato_evaluation_2013, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Evaluation of {Climate} {Models}}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {G. Flato and J. Marotzke and B. Abiodun and P. Braconnot and S. C. Chou and W. Collins and P. Cox and F. Driouech and S. Emori and V. Eyring and C. Forest and P. Gleckler and E. Guilyardi and C. Jakob and V. Kattsov and C. Reason and M. Rummukainen}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley}, - year = {2013}, - pages = {741--866}, - annote = {The following values have no corresponding Zotero field:section: 9electronic-resource-num: 10.1017/CBO9781107415324.020}, -} - -@article{fowler_implications_2003, - title = {Implications of changes in seasonal and annual extreme rainfall}, - volume = {30}, - issn = {0094-8276}, - doi = {10.1029/2003gl017327}, - abstract = {Future projections from climate models and recent observations show a worldwide increase in both the frequency and intensity of heavy rainfall, coinciding with widespread flooding and landslides in Europe. It is estimated, using regional frequency analysis, that the magnitude of extreme rainfall has increased two-fold over parts of the UK since the 1960s. Intensities previously experienced, on average, every 25 years now occur at 6 year intervals; a consequence of both increased event frequency and changes in seasonality. These climatic changes may be explained by persistent atmospheric circulation anomalies and have huge economic and social implications in terms of increased flooding.}, - number = {13}, - journal = {Geophysical Research Letters}, - author = {Fowler, H. J. and Kilsby, C. G.}, - year = {2003}, - keywords = {1854 Hydrology: Precipitation, 1821 Hydrology: Floods, 3364 Meteorology and Atmospheric Dynamics: Synoptic-scale meteorology, 9335 Information Related to Geographic Region: Europe}, - pages = {1720}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{koster_regions_2004, - title = {Regions of {Strong} {Coupling} {Between} {Soil} {Moisture} and {Precipitation}}, - volume = {305}, - doi = {10.1126/science.1100217}, - abstract = {Previous estimates of land-atmosphere interaction (the impact of soil moisture on precipitation) have been limited by a lack of observational data and by the model dependence of computational estimates. To counter the second limitation, a dozen climate-modeling groups have recently performed the same highly controlled numerical experiment as part of a coordinated comparison project. This allows a multimodel estimation of the regions on Earth where precipitation is affected by soil moisture anomalies during Northern Hemisphere summer. Potential benefits of this estimation may include improved seasonal rainfall forecasts.}, - number = {5687}, - journal = {Science}, - author = {Koster, Randal D. and Dirmeyer, Paul A. and Guo, Zhichang and Bonan, Gordon and Chan, Edmond and Cox, Peter and Gordon, C. T. and Kanae, Shinjiro and Kowalczyk, Eva and Lawrence, David and Liu, Ping and Lu, Cheng-Hsuan and Malyshev, Sergey and McAvaney, Bryant and Mitchell, Ken and Mocko, David and Oki, Taikan and Oleson, Keith and Pitman, Andrew and Sud, Y. C. and Taylor, Christopher M. and Verseghy, Diana and Vasic, Ratko and Xue, Yongkang and Yamada, Tomohito}, - month = aug, - year = {2004}, - pages = {1138--1140}, -} - -@article{kollat_estimating_2012, - title = {Estimating the {Impacts} of {Climate} {Change} and {Population} {Growth} on {Flood} {Discharges} in the {United} {States}}, - volume = {138}, - issn = {0733-9496}, - doi = {10.1061/(asce)wr.1943-5452.0000233}, - number = {5}, - journal = {Journal of Water Resources Planning and Management}, - author = {Kollat, J. and Kasprzyk, J. and Thomas, W. and Miller, A. and Divoky, D.}, - month = sep, - year = {2012}, - pages = {442--452}, - annote = {doi: 10.1061/(ASCE)WR.1943-5452.0000233}, - annote = {The following values have no corresponding Zotero field:publisher: American Society of Civil Engineerswork-type: doi: 10.1061/(ASCE)WR.1943-5452.0000233}, -} - -@article{koster_interplay_1997, - title = {The {Interplay} between {Transpiration} and {Runoff} {Formulations} in {Land} {Surface} {Schemes} {Used} with {Atmospheric} {Models}}, - volume = {10}, - abstract = {The Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) has shown that different land surface models (LSMs) driven by the same meteorological forcing can produce markedly different surface energy and water budgets, even when certain critical aspects of the LSMs (vegetation cover, albedo, turbulent drag coefficient, and snowcover) are carefully controlled. To help explain these differences, the authors devised a monthly water balance model that successfully reproduces the annual and seasonal water balances of the different PILPS schemes. Analysis of this model leads to the identification of two quantities that characterize an LSM\&\#8217;s formulation of soil water balance dynamics: 1) the efficiency of the soil\&\#8217;s evaporation sink integrated over the active soil moisture range, and 2) the fraction of this range over which runoff is generated. Regardless of the LSM\&\#8217;s complexity, the combination of these two derived parameters with rates of interception loss, potential evaporation, and precipitation provides a reasonable estimate for the LSM\&\#8217;s simulated annual water balance. The two derived parameters shed light on how evaporation and runoff formulations interact in an LSM, and the analysis as a whole underscores the need for compatibility in these formulations.}, - number = {7}, - journal = {Journal of Climate}, - author = {Koster, Randal D. and Milly, P. C. D.}, - month = jul, - year = {1997}, - pages = {1578--1591}, -} - -@article{knutti_robustness_2013, - title = {Robustness and uncertainties in the new {CMIP5} climate model projections}, - volume = {3}, - issn = {1758-678X}, - number = {4}, - journal = {Nature Clim. Change}, - author = {Knutti, R. and Sedlacek, J.}, - year = {2013}, - pages = {369--373, doi:10.1038/nclimate1716}, - annote = {10.1038/nclimate1716}, - annote = {The following values have no corresponding Zotero field:publisher: Nature Publishing Group}, -} - -@article{knutti_review_2008, - title = {A review of uncertainties in global temperature projections over the twenty-first century}, - volume = {21}, - issn = {0894-8755}, - doi = {10.1175/2007jcli2119.1}, - abstract = {Quantification of the uncertainties in future climate projections is crucial for the implementation of climate policies. Here a review of projections of global temperature change over the twenty-first century is provided for the six illustrative emission scenarios from the Special Report on Emissions Scenarios (SRES) that assume no policy intervention, based on the latest generation of coupled general circulation models, climate models of intermediate complexity, and simple models, and uncertainty ranges and probabilistic projections from various published methods and models are assessed. Despite substantial improvements in climate models, projections for given scenarios on average have not changed much in recent years. Recent progress has, however, increased the confidence in uncertainty estimates and now allows a better separation of the uncertainties introduced by scenarios, physical feedbacks, carbon cycle, and structural uncertainty. Projection uncertainties are now constrained by observations and therefore consistent with past observed trends and patterns. Future trends in global temperature resulting from anthropogenic forcing over the next few decades are found to be comparably well constrained. Uncertainties for projections on the century time scale, when accounting for structural and feedback uncertainties, are larger than captured in single models or methods. This is due to differences in the models, the sources of uncertainty taken into account, the type of observational constraints used, and the statistical assumptions made. It is shown that as an approximation, the relative uncertainty range for projected warming in 2100 is the same for all scenarios. Inclusion of uncertainties in carbon cycle-climate feedbacks extends the upper bound of the uncertainty range by more than the lower bound.}, - language = {English}, - number = {11}, - journal = {J. Clim.}, - author = {Knutti, R. and Allen, M. R. and Friedlingstein, P. and Gregory, J. M. and Hegerl, G. C. and Meehl, G. A. and Meinshausen, M. and Murphy, J. M. and Plattner, G. K. and Raper, S. C. B. and Stocker, T. F. and Stott, P. A. and Teng, H. and Wigley, T. M. L.}, - month = jun, - year = {2008}, - keywords = {model, ensembles, atmospheric co2, feedbacks, scenarios, carbon, climate system properties, increasing co2, ocean, sensitivities, uptake}, - pages = {2651--2663}, - annote = {ISI Document Delivery No.: 311TLTimes Cited: 21Cited Reference Count: 47Knutti, R. Allen, M. R. Friedlingstein, P. Gregory, J. M. Hegerl, G. C. Meehl, G. A. Meinshausen, M. Murphy, J. M. Plattner, G. -K. Raper, S. C. B. Stocker, T. F. Stott, P. A. Teng, H. Wigley, T. M. L.Amer meteorological socBoston}, - annote = {The following values have no corresponding Zotero field:auth-address: [Knutti, R.] ETH, Inst Atmospher \& Climate Sci, CH-8092 Zurich, Switzerland. [Knutti, R.; Meehl, G. A.; Teng, H.; Wigley, T. M. L.] Natl Ctr Atmospher Res, Boulder, CO 80307 USA. [Allen, M. R.] Univ Oxford, Dept Phys, Oxford, England. [Friedlingstein, P.] IPSL LSCE, Gif Sur Yvette, France. [Gregory, J. M.] Univ Reading, Walker Inst, Reading, Berks, England. [Gregory, J. M.; Murphy, J. M.; Stott, P. A.] Hadley Ctr, Met Off, Exeter, Devon, England. [Hegerl, G. C.] Univ Edinburgh, Sch Geosci, Edinburgh, Midlothian, Scotland. [Meinshausen, M.] Potsdam Inst Climate Impact Res, Potsdam, Germany. [Plattner, G. -K.; Stocker, T. F.] Univ Bern, Bern, Switzerland. [Plattner, G. -K.] ETH, Inst Biogeochem \& Pollutant Dynam, CH-8092 Zurich, Switzerland. [Raper, S. C. B.] Manchester Metropolitan Univ, CATE, Manchester M15 6BH, Lancs, England. Knutti, R, ETH, Inst Atmospher \& Climate Sci, Univ Str 16, CH-8092 Zurich, Switzerland. reto.knutti@env.ethz.chalt-title: J. Clim.accession-num: ISI:000256623200019work-type: Article}, -} - -@book{knutti_good_2010, - address = {University of Bern, Bern, Switzerland}, - series = {Meeting {Report} of the {Intergovernmental} {Panel} on {Climate} {Change} {Expert} {Meeting} on {Assessing} and {Combining} {Multi} {Model} {Climate} {Projections}}, - title = {Good {Practice} {Guidance} {Paper} on {Assessing} and {Combining} {Multi} {Model} {Climate} {Projections}}, - publisher = {IPCC Working Group I Technical Support Unit}, - author = {Knutti, R. and Abramowitz, G. and Collins, M. and Eyring, V. and Gleckler, P. J. and Hewitson, B. and Mearns, L.}, - editor = {Stocker, T. F. and Qin, D. and Plattner, G.-K. and Tignor, M. and Midgley, P. M.}, - year = {2010}, -} - -@article{knowles_trends_2006, - title = {Trends in snowfall versus rainfall for the {Western} {United} {States}}, - volume = {19}, - number = {18}, - journal = {Journal of Climate}, - author = {Knowles, N. and Dettinger, M. and Cayan, D.}, - year = {2006}, - pages = {4545--4559}, -} - -@article{knowles_responses_2018, - title = {Responses of {Unimpaired} {Flows}, {Storage}, and {Managed} {Flows} to {Scenarios} of {Climate} {Change} in the {San} {Francisco} {Bay}-{Delta} {Watershed}}, - volume = {54}, - issn = {0043-1397}, - doi = {10.1029/2018wr022852}, - number = {10}, - journal = {Water Resources Research}, - author = {Knowles, N. and Cronkite-Ratcliff, C. and Pierce, D. W. and Cayan, D. R.}, - month = oct, - year = {2018}, - pages = {7631--7650}, - annote = {Knowles, N. Cronkite-Ratcliff, C. Pierce, D. W. Cayan, D. R.Knowles, Noah/A-3911-2019Knowles, Noah/0000-0001-5652-10491944-7973}, - annote = {The following values have no corresponding Zotero field:accession-num: WOS:000450726000029}, -} - -@article{knowles_elevational_2004, - title = {Elevational dependence of projected hydrologic changes in the {San} {Francisco} {Estuary} and watershed}, - volume = {62}, - issn = {0165-0009}, - abstract = {California's primary hydrologic system, the San Francisco Estuary and its upstream watershed, is vulnerable to the regional hydrologic consequences of projected global climate change. Previous work has shown that a projected warming would result in a reduction of snowpack storage leading to higher winter and lower spring-summer streamflows and increased spring-summer salinities in the estuary. The present work shows that these hydrologic changes exhibit a strong dependence on elevation, with the greatest loss of snowpack volume in the 1300 - 2700 m elevation range. Exploiting hydrologic and estuarine modeling capabilities to trace water as it moves through the system reveals that the shift of water in mid-elevations of the Sacramento river basin from snowmelt to rainfall runoff is the dominant cause of projected changes in estuarine inflows and salinity. Additionally, although spring-summer losses of estuarine inflows are balanced by winter gains, the losses have a stronger influence on salinity since longer spring-summer residence times allow the inflow changes to accumulate in the estuary. The changes in inflows sourced in the Sacramento River basin in approximately the 1300 - 2200 m elevation range thereby lead to a net increase in estuarine salinity under the projected warming. Such changes would impact ecosystems throughout the watershed and threaten to contaminate much of California's freshwater supply.}, - language = {English}, - number = {1-3}, - journal = {Climatic Change}, - author = {Knowles, N. and Cayan, D. R.}, - month = feb, - year = {2004}, - keywords = {model, california, climate, joaquin river-basin, resources}, - pages = {319--336}, - annote = {768FATimes Cited:4Cited References Count:25}, - annote = {The following values have no corresponding Zotero field:auth-address: Knowles, N Univ Calif San Diego, Scripps Inst Oceanog, Div Climate Res, 9500 Gilman Dr, La Jolla, CA 92093 USA Univ Calif San Diego, Scripps Inst Oceanog, Div Climate Res, La Jolla, CA 92093 USA US Geol Survey, Div Water Resources, Menlo Pk, CA 94025 USAaccession-num: ISI:000188531900013}, -} - -@techreport{klein_tank_guidelines_2009, - title = {Guidelines on {Analysis} of extremes in a changing climate in support of informed decisions for adaptation. {Climate} {Data} and {Monitoring} {WCDMP}-{No}. 72, {WMO}-{TD} {No}. 1500}, - author = {Klein Tank, A. M. G. and Zwiers, F. W. and Zhang, X.}, - year = {2009}, - pages = {56}, -} - -@article{kittel_intercomparison_1998, - title = {Intercomparison of regional biases and doubled {CO2}-sensitivity of coupled atmosphere-ocean general circulation model experiments}, - volume = {14}, - issn = {0930-7575}, - abstract = {We compared regional biases and transient doubled CO2 sensitivities of nine coupled atmosphere-ocean general circulation models (GCMs) from six international climate modeling groups. We evaluated biases and responses in winter and summer surface air temperatures and precipitation for seven subcontinental regions, including those in the 1990 Intergovernmental Panel on Climate Change (IPCC) Scientific Assessment. Regional biases were large and exceeded the variance among four climatological datasets, indicating that model biases were not primarily due to uncertainty in observations. Model responses to altered greenhouse forcing were substantial (average temperature change = 2.7 +/- 0.9 degrees C, range of precipitation change = - 35 to + 120\% of control). While coupled models include more climate system feedbacks than earlier GCMs implemented with mixed-layer ocean models, inclusion of a dynamic ocean alone did not improve simulation of long-term mean climatology nor increase convergence among model responses to altered greenhouse gas forcing. On the other hand, features of some of the coupled models including flux adjustment (which may have simply masked simulation errors), high horizontal resolution, and estimation of screen height temperature contributed to improved simulation of long-term surface climate. The large range of model responses was partly accounted for by inconsistencies in forcing scenarios and transient-simulation averaging periods. Nonetheless, the models generally had greater agreement in their sensitivities than their controls did with observations. This suggests that consistent, large-scale response features from an ensemble of model sensitivity experiments may not depend on details of their representation of present-day climate.}, - language = {English}, - number = {1}, - journal = {Climate Dynamics}, - author = {Kittel, T. G. F. and Giorgi, F. and Meehl, G. A.}, - month = jan, - year = {1998}, - keywords = {precipitation, co2, temperature, greenhouse gases, transient climate-change, gradual changes, increasing carbon-dioxide, low-frequency variability, mean response, spatial variability}, - pages = {1--15}, - annote = {The following values have no corresponding Zotero field:auth-address: Kittel, TGF Natl Ctr Atmospher Res, Climat \& Global Dynam Div, Pob 3000, Boulder, CO 80307 USA Natl Ctr Atmospher Res, Climat \& Global Dynam Div, Boulder, CO 80307 USA Univ Corp Atmospher Res, Climate Syst Modeling Program, Boulder, CO 80307 USAaccession-num: ISI:000072740800001}, - annote = {Zd938Times Cited:29Cited References Count:65}, -} - -@article{klein_objective_1959, - title = {Objective prediction of five-day mean temperature during winter}, - volume = {16}, - journal = {Journal of Meteorology}, - author = {Klein, W. H. and Lewis, B. M. and Enger, I.}, - year = {1959}, - pages = {672--682}, -} - -@article{klein_tank_trends_2003, - title = {Trends in {Indices} of {Daily} {Temperature} and {Precipitation} {Extremes} in {Europe}, 1946–99}, - volume = {16}, - doi = {doi:10.1175/1520-0442(2003)016<3665:TIIODT>2.0.CO;2}, - number = {22}, - journal = {Journal of Climate}, - author = {Klein Tank, A. M. G. and Können, G. P.}, - year = {2003}, - pages = {3665--3680}, -} - -@article{klein_diagnosis_2006, - title = {Diagnosis of the summertime warm and dry bias over the {U}.{S}. {Southern} {Great} {Plains} in the {GFDL} climate model using a weather forecasting approach}, - volume = {33}, - issn = {0094-8276}, - doi = {10.1029/2006gl027567}, - abstract = {Weather forecasts started from realistic initial conditions are used to diagnose the large warm and dry bias over the United States Southern Great Plains simulated by the GFDL climate model. The forecasts exhibit biases in surface air temperature and precipitation within 3 days which appear to be similar to the climate bias. With the model simulating realistic evaporation but underestimated precipitation, a deficit in soil moisture results which amplifies the initial temperature bias through feedbacks with the land surface. The underestimate of precipitation may be associated with an inability of the model to simulate the eastward propagation of convection from the front-range of the Rocky Mountains and is insensitive to an increase of horizontal resolution from 2° to 0.5° latitude.}, - number = {18}, - journal = {Geophysical Research Letters}, - author = {Klein, Stephen A. and Jiang, Xianan and Boyle, Jim and Malyshev, Sergey and Xie, Shaocheng}, - year = {2006}, - keywords = {3354 Atmospheric Processes: Precipitation, 1631 Global Change: Land/atmosphere interactions, 1876 Hydrology: Water budgets, 3314 Atmospheric Processes: Convective processes, 4928 Paleoceanography: Global climate models}, - pages = {L18805}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{kistler_ncep-ncar_2001, - title = {The {NCEP}-{NCAR} 50-year reanalysis: monthly means {CD}-{ROM} and documentation}, - volume = {82}, - number = {2}, - journal = {Bulletin of the American Meteorological Society}, - author = {Kistler, R. and Kalnay, E. and Collins, W. and Saha, S. and White, G. and Woollen, J. and Chelliah, M. and Ebisuzaki, W. and Kanamitsu, M. and Kousky, V. and van den Dool, H. and Jenne, R. and Fiorino, M.}, - year = {2001}, - pages = {247--268, doi:10.1175/1520--0477(2001)082}, -} - -@article{kingston_uncertainty_2009, - title = {Uncertainty in the estimation of potential evapotranspiration under climate change}, - volume = {36}, - issn = {0094-8276}, - doi = {10.1029/2009gl040267}, - abstract = {21st century climate change is projected to result in an intensification of the global hydrological cycle, but there is substantial uncertainty in how this will impact freshwater availability. A relatively overlooked aspect of this uncertainty pertains to how different methods of estimating potential evapotranspiration (PET) respond to changing climate. Here we investigate the global response of six different PET methods to a 2°C rise in global mean temperature. All methods suggest an increase in PET associated with a warming climate. However, differences in PET climate change signal of over 100\% are found between methods. Analysis of a precipitation/PET aridity index and regional water surplus indicates that for certain regions and GCMs, choice of PET method can actually determine the direction of projections of future water resources. As such, method dependence of the PET climate change signal is an important source of uncertainty in projections of future freshwater availability.}, - number = {20}, - journal = {Geophysical Research Letters}, - author = {Kingston, Daniel G. and Todd, Martin C. and Taylor, Richard G. and Thompson, Julian R. and Arnell, Nigel W.}, - year = {2009}, - keywords = {1818 Hydrology: Evapotranspiration, potential evapotranspiration, climate change, uncertainty, 1833 Hydrology: Hydroclimatology, 3305 Atmospheric Processes: Climate change and variability, 1876 Hydrology: Water budgets, 1836 Hydrology: Hydrological cycles and budgets}, - pages = {L20403}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@incollection{b_kirtman_near-term_2013, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Near-term {Climate} {Change}: {Projections} and {Predictability}}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {B. Kirtman and S. B. Power and J. A. Adedoyin and G. J. Boer and R. Bojariu and I. Camilloni and F. J. Doblas-Reyes and A. M. Fiore and M. Kimoto and G. A. Meehl and M. Prather and A. Sarr and C. Schär and R. Sutton and G. J. van Oldenborgh and G. Vecchi and H. J. Wang}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley}, - year = {2013}, - pages = {953--1028}, - annote = {The following values have no corresponding Zotero field:section: 11electronic-resource-num: 10.1017/CBO9781107415324.023}, -} - -@article{kirshen_adapting_2015, - title = {Adapting {Urban} {Infrastructure} to {Climate} {Change}: {A} {Drainage} {Case} {Study}}, - volume = {141}, - doi = {doi:10.1061/(ASCE)WR.1943-5452.0000443}, - number = {4}, - journal = {Journal of Water Resources Planning and Management}, - author = {Kirshen, P. and Caputo, L. and Vogel, R. M. and Mathisen, P. and Rosner, A. and Renaud, T.}, - year = {2015}, -} - -@incollection{kimball_responses_2002, - address = {New York}, - title = {Responses of agricultural crops to free-air {CO2} enrichment}, - volume = {77}, - booktitle = {Advances in {Agronomy}}, - publisher = {Academic Press}, - author = {Kimball, B. A. and Kobayashi, K. and Bindi, M.}, - year = {2002}, - pages = {293--368}, -} - -@article{kim_projection_2005, - title = {A projection of the effects of the climate change induced by increased {CO2} on extreme hydrologic events in the western {US}}, - volume = {68}, - issn = {0165-0009}, - abstract = {The effects of increased atmospheric CO2 on the frequency of extreme hydrologic events in the Western United States (WUS) for the 10-yr period of 2040-2049 are examined using dynamically downscaled regional climate change signals. For assessing the changes in the occurrence of hydrologic extremes, downscaled climate change signals in daily precipitation and runoff that are likely to indicate the occurrence of extreme events are examined. Downscaled climate change signals in the selected indicators suggest that the global warming induced by increased CO2 is likely to increase extreme hydrologic events in the WUS. The indicators for heavy precipitation events show largest increases in the mountainous regions of the northern California Coastal Range and the Sierra Nevada. Increased cold season precipitation and increased rainfall-portion of precipitation at the expense of snowfall in the projected warmer climate result in large increases in high runoff events in the Sierra Nevada river basins that are already prone to cold season flooding in today's climate. The projected changes in the hydrologic characteristics in the WUS are mainly associated with higher freezing levels in the warmer climate and increases in the cold season water vapor influx from the Pacific Ocean.}, - language = {English}, - number = {1-2}, - journal = {Climatic Change}, - author = {Kim, J.}, - month = jan, - year = {2005}, - keywords = {model, simulation, streamflow, united-states, precipitation, trends, variability, california, clouds, soil hydrology}, - pages = {153--168}, - annote = {899ZFTimes Cited:0Cited References Count:34}, - annote = {The following values have no corresponding Zotero field:auth-address: Kim, J Univ Calif Los Angeles, Dept Atmospher Sci, Los Angeles, CA 90024 USA Univ Calif Los Angeles, Dept Atmospher Sci, Los Angeles, CA 90024 USAaccession-num: ISI:000227183700009}, -} - -@article{kiehl_national_1998, - title = {The {National} {Center} for {Atmospheric} {Research} {Community} {Climate} {Model}: {CCM3}}, - volume = {11}, - journal = {Journal of Climate}, - author = {Kiehl, J. T. and Hack, J. J. and Bonan, G. B. and Boville, B. B. and Williamson, D. L. and Rasch, P. J.}, - year = {1998}, - pages = {1131--1149}, -} - -@article{kharin_changes_2013, - title = {Changes in temperature and precipitation extremes in the {CMIP5} ensemble}, - volume = {119}, - issn = {0165-0009}, - doi = {10.1007/s10584-013-0705-8}, - language = {English}, - number = {2}, - journal = {Climatic Change}, - author = {Kharin, V. V. and Zwiers, F. W. and Zhang, X. and Wehner, M.}, - month = jul, - year = {2013}, - pages = {345--357}, - annote = {The following values have no corresponding Zotero field:alt-title: Climatic Changepublisher: Springer Netherlands}, -} - -@article{kim_impacts_2002, - title = {Impacts of increased atmospheric {CO2} on the hydroclimate of the western {United} {States}}, - volume = {15}, - issn = {0894-8755}, - abstract = {Regional-scale projections of climate change signals due to increases in atmospheric CO2 are generated for the western United States using a regional climate model (RCM) nested within two global scenarios from a GCM. The downscaled control climate improved the local accuracy of the GCM results substantially. The downscaled control climate is reasonably close to the results of an 8-yr regional climate hindcast using the same RCM nested within the NCEP-NCAR reanalysis, despite wet biases in high-elevation regions along the Pacific coast. -The downscaled near-surface temperature signal ranges from 3 to 5 K in the western United States. The projected warming signals generally increase with increasing elevation, consistent with earlier studies for the Swiss Alps and the northwestern United States. In addition to the snow-albedo feedback, seasonal variations of the low-level flow and soil moisture appear to play important roles in the spatial pattern of warming signals. Projected changes in precipitation characteristics are mainly associated with increased moisture fluxes from the Pacific Ocean and the increase in elevation of freezing levels during the cold season. Projected cold season precipitation increases substantially in mountainous areas along the Pacific Ocean. Most of the projected precipitation increase over the Sierra Nevada and the Cascades is in rainfall, while snowfall generally decreases except above 2500 m. Projected changes in summer rainfall are small. The snow budget signals are characterized by decreased (increased) cold season snowfall (snowmelt) and reduced snowmelt during spring and summer. The projected cold season runoff from high-elevation regions increases substantially in response to increased cold season rainfall and snowmelt, while the spring runoff decreases due to an earlier depletion of snow, except above 2500 m.}, - language = {English}, - number = {14}, - journal = {Journal of Climate}, - author = {Kim, J. and Kim, T. K. and Arritt, R. W. and Miller, N. L.}, - month = jul, - year = {2002}, - keywords = {precipitation, prediction, simulations, gcm, california, river flow, soil hydrology, regional climate model, snow budget, winter season}, - pages = {1926--1942}, - annote = {570JQTimes Cited:1Cited References Count:43}, - annote = {The following values have no corresponding Zotero field:auth-address: Kim, J Univ Calif Los Angeles, Dept Atmospher Sci, Los Angeles, CA 90095 USA Univ Calif Berkeley, Lawrence Berkeley Lab, Berkeley, CA 94720 USA Iowa State Univ, Dept Agron, Ames, IA USAaccession-num: ISI:000176655500008}, -} - -@article{kilsby_predicting_1998, - title = {Predicting rainfall statistics in {England} and {Wales} using atmospheric circulation variables}, - volume = {18}, - abstract = {Regression models are developed to predict point rainfall statistics, with potential application to downscaling general circulation model (GCM) output for future climates. The models can be used to predict the mean daily rainfall amount and the proportion of dry days for each calendar month at any site in England and Wales, and use the following explanatory variables: (i) geographical (altitude, geographic coordinates, and distance from nearest coast); and (ii) atmospheric circulation variables (mean values of air-flow indices derived from mean sea-level pressure grids). Values predicted by the models, for 10-km grid squares covering the whole of England and Wales, are in reasonable agreement with the 1961-1990 climatology of Barrow et al. (1993). The potential use of the models in hydrological climate change impact studies is discussed. (C) 1998 Royal Meteorological Society.}, - number = {5}, - journal = {International Journal of Climatology}, - author = {Kilsby, C. G. and Cowpertwait, P. S. P. and O'Connell, P. E. and Jones, P. D.}, - month = apr, - year = {1998}, - pages = {523--539}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:000073433000003}, - annote = {ZL439INT J CLIMATOL}, -} - -@article{kidson_comparison_1998, - title = {A comparison of statistical and model-based downscaling techniques for estimating local climate variations}, - volume = {11}, - abstract = {The respective merits of statistical and regional modeling techniques for downscaling GCM predictions have been evaluated over New Zealand, a small mountainous country surrounded by ocean. The boundary conditions were supplied from twice-daily European Centre for Medium-Range Weather Forecasts analyses at 2.5 degrees resolution for the period 1980-94, which were taken as the output of a "perfect" climate model. Daily and monthly estimates of minimum and maximum temperature and precipitation From both techniques were validated against readings from a network of 78 climate stations. The statistical estimates were made by a screening regression technique using the EOFs of the regional height fields at 1000 and 500 hPa, and local variables derived from these fields, as predictors. The model interpolations made use of the RAMS model developed at Colorado State University running at 50-km resolution for 1990-94 only. The model values at the nearest grid point to each station were rescaled using a simple linear regression to give the best fit to the station values. The results show both methods to have comparable skill in estimating daily and monthly station anomalies of temperatures and rainfall. Statistical estimates of monthly departures were better obtained directly from monthly mean forcing than from a combination of daily estimates; however, daily values are needed if one wishes to estimate variability. While there are good physical grounds for using the modeling technique to estimate the likely effects of climate change, the statistical technique requires considerably less computational effort and may be preferred for many applications.}, - number = {4}, - journal = {J. Clim.}, - author = {Kidson, J. W. and Thompson, C. S.}, - month = apr, - year = {1998}, - pages = {735--753}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Clim.accession-num: ISI:000073338900017}, - annote = {ZK575J CLIMATE}, -} - -@article{kharin_changes_2007, - title = {Changes in temperature and precipitation extremes in the {IPCC} ensemble of global coupled model simulations}, - volume = {20}, - journal = {Journal of Climate}, - author = {Kharin, V. V. and Zwiers, F. W. and Zhang, X. and Hegerl, G. C.}, - year = {2007}, - pages = {1419--1444}, -} - -@article{karnauskas_simple_2013, - title = {A simple mechanism for the climatological midsummer drought along the {Pacific} coast of {Central} {America}}, - volume = {26}, - number = {2}, - journal = {Atmósfera}, - author = {Karnauskas, K. B. and Seager, R. and Giannini, A. and Busalacchi, A. J.}, - year = {2013}, - pages = {261--281}, -} - -@article{karmalkar_climate_2011, - title = {Climate change in {Central} {America} and {Mexico}: regional climate model validation and climate change projections}, - volume = {37}, - issn = {0930-7575}, - doi = {10.1007/s00382-011-1099-9}, - language = {English}, - number = {3-4}, - journal = {Climate Dynamics}, - author = {Karmalkar, AmbarishV and Bradley, RaymondS and Diaz, HenryF}, - month = aug, - year = {2011}, - keywords = {Central America, Biodiversity, Regional climate change}, - pages = {605--629}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim Dynpublisher: Springer-Verlag}, -} - -@article{kerr_greenhouse_2012, - title = {The {Greenhouse} {Is} {Making} the {Water}-{Poor} {Even} {Poorer}}, - volume = {336}, - doi = {10.1126/science.336.6080.405}, - number = {6080}, - journal = {Science}, - author = {Kerr, Richard A.}, - month = apr, - year = {2012}, - pages = {405}, -} - -@article{kharin_intercomparison_2005, - title = {Intercomparison of near-surface temperature and precipitation extremes in {AMIP}-2 simulations, reanalyses, and observations}, - volume = {8}, - number = {24}, - journal = {Journal of Climate}, - author = {Kharin, V. V. and Zwiers, F. W. and Zhang, X.}, - year = {2005}, - pages = {5201--5223}, -} - -@article{keyantash_quantification_2002, - title = {The {Quantification} of {Drought}: {An} {Evaluation} of {Drought} {Indices}}, - volume = {83}, - abstract = {Indices for objectively quantifying the severity of meteorological, agricultural, and hydrological forms of drought are discussed. Indices for each drought form are judged according to six weighted evaluation criteria: robustness, tractability, transparency, sophistication, extendability, and dimensionality. The indices considered most promising for succinctly summarizing drought severity are computed for two climate divisions in Oregon for 24 water years, 1976-99. The assessment determined that the most valuable indices for characterizing meteorological, hydrological, and agricultural droughts are rainfall deciles, total water deficit, and computed soil moisture, respectively.}, - number = {8}, - journal = {Bulletin of the American Meteorological Society}, - author = {Keyantash, John and Dracup, John A.}, - month = aug, - year = {2002}, - pages = {1167--1180}, -} - -@article{kaushal_increased_2005, - title = {Increased salinization of fresh water in the northeastern {United} {States}}, - volume = {102}, - doi = {10.1073/pnas.0506414102}, - abstract = {Chloride concentrations are increasing at a rate that threatens the availability of fresh water in the northeastern United States. Increases in roadways and deicer use are now salinizing fresh waters, degrading habitat for aquatic organisms, and impacting large supplies of drinking water for humans throughout the region. We observed chloride concentrations of up to 25\% of the concentration of seawater in streams of Maryland, New York, and New Hampshire during winters, and chloride concentrations remaining up to 100 times greater than unimpacted forest streams during summers. Mean annual chloride concentration increased as a function of impervious surface and exceeded tolerance for freshwater life in suburban and urban watersheds. Our analysis shows that if salinity were to continue to increase at its present rate due to changes in impervious surface coverage and current management practices, many surface waters in the northeastern United States would not be potable for human consumption and would become toxic to freshwater life within the next century.}, - number = {38}, - journal = {Proceedings of the National Academy of Sciences of the United States of America}, - author = {Kaushal, Sujay S. and Groffman, Peter M. and Likens, Gene E. and Belt, Kenneth T. and Stack, William P. and Kelly, Victoria R. and Band, Lawrence E. and Fisher, Gary T.}, - month = sep, - year = {2005}, - pages = {13517--13520}, -} - -@article{katz_statistics_2002, - title = {Statistics of extremes in hydrology}, - volume = {25}, - abstract = {The statistics of extremes have played an important role in engineering practice for water resources design and management. How recent developments in the statistical theory of extreme values can be applied to improve the rigor of hydrologic applications and to make such analyses more physically meaningful is the central theme of this paper. Such methodological developments primarily relate to maximum likelihood estimation in the presence of covariates, in combination with either the block maxima or peaks over threshold approaches. Topics that are treated include trends in hydrologic extremes, with the anticipated intensification of the hydrologic cycle as part of global climate change. In an attempt to link downscaling (i.e., relating large-scale atmosphere-ocean circulation to smaller-scale hydrologic variables) with the statistics of extremes, statistical downscaling of hydrologic extremes is considered. Future challenges are reviewed, such as the development of more rigorous statistical methodology for regional analysis of extremes, as well as the extension of Bayesian methods to more fully quantify uncertainty in extremal estimation. Examples include precipitation and streamflow extremes, as well as economic damage associated with such extreme events, with consideration of trends and dependence on patterns in atmosphere-ocean circulation (e.g., El Nino phenomenon). (C) 2002 Elsevier Science Ltd. All rights reserved.}, - number = {8-12}, - journal = {Advances in Water Resources}, - author = {Katz, R. W. and Parlange, M. B. and Naveau, P.}, - month = dec, - year = {2002}, - pages = {1287--1304}, - annote = {629VWADV WATER RESOUR}, - annote = {The following values have no corresponding Zotero field:alt-title: Adv. Water Resour.accession-num: ISI:000180073300020}, -} - -@article{katz_mixtures_1996, - title = {Mixtures of stochastic processes: {Application} to statistical downscaling}, - volume = {7}, - abstract = {Analyses of mixtures of stochastic processes have begun to appear in climate research in recent years. Some general properties of mixtures that are well known within statistics, but not ordinarily utilized in complete generality in climate applications, are reviewed. How these issues apply in certain types of statistical downscaling is described. An important distinction is drawn between 'conditional' models, sometimes utilized in downscaling, and 'unconditional' models, utilized in more traditional approaches. Through a combination of the individual conditional models, a single overall (or 'induced') model is obtained. Among other things, the mixture concept suggests physically plausible mechanisms by which more complex stochastic models could arise in climate applications. As an application, the stochastic modeling of time series of daily precipitation amount conditional on a monthly index of large- (or regional) scale atmospheric circulation patterns is considered. Chain-dependent processes are used both as conditional and unconditional models of precipitation. For illustrative purposes, precipitation measurements for a site in California, USA, were fitted. How the mixture approach can aid in determining the properties of climate change scenarios produced by downscaling is demonstrated in this example. In particular, changes in the relative frequency of occurrence of the states of the circulation index would be associated not just with changes in mean precipitation, but with changes in its variance as well.}, - number = {2}, - journal = {Climate Research}, - author = {Katz, R. W. and Parlange, M. B.}, - month = nov, - year = {1996}, - pages = {185--193}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:A1996WE47400007}, - annote = {WE474CLIMATE RES}, -} - -@article{kergoat_impact_2002, - title = {Impact of doubled {CO2} on global-scale leaf area index and evapotranspiration: {Conflicting} stomatal conductance and {LAI} responses}, - volume = {107}, - number = {D24}, - journal = {Journal of Geophysical Research}, - author = {Kergoat, L. and Lafont, S. and Douville, H. and Berthelot, B. and Dedieu, G. and Planton, S. and Royer, J.-F.}, - year = {2002}, - pages = {doi:10.1029/2001JD001245, 2002}, -} - -@article{kenney_is_2012, - title = {Is {Urban} {Stream} {Restoration} {Worth} {It}?1}, - issn = {1752-1688}, - doi = {10.1111/j.1752-1688.2011.00635.x}, - journal = {JAWRA Journal of the American Water Resources Association}, - author = {Kenney, Melissa A. and Wilcock, Peter R. and Hobbs, Benjamin F. and Flores, Nicholas E. and Martínez, Daniela C.}, - year = {2012}, - keywords = {nonpoint source pollution, watershed management, best management practices (BMPs), natural resource economics, restoration, rivers/streams, stormwater management, urban areas}, - pages = {no--no}, - annote = {The following values have no corresponding Zotero field:publisher: Blackwell Publishing Ltd}, -} - -@article{kay_comparison_2009, - title = {Comparison of uncertainty sources for climate change impacts: flood frequency in {England}}, - volume = {92}, - journal = {Climatic Change}, - author = {Kay, A. L. and Davies, H. N. and Bell, V. A. and Jones, R. G.}, - year = {2009}, - pages = {41--63, doi:10.1007/s10584--008--9471--4}, -} - -@article{katz_extreme_1999, - title = {Extreme value theory for precipitation: sensitivity analysis for climate change}, - volume = {23}, - abstract = {Extreme value theory for the maximum of a time series of daily precipitation amount is described. A chain-dependent process is assumed as a stochastic model for daily precipitation, with the intensity distribution being the gamma. To examine how the effective return period for extreme high precipitation amounts would change as the parameters of the chain-dependent process change (i.e., probability of a wet day, shape and scale parameters of the gamma distribution), a sensitivity analysis is performed. This sensitivity analysis is guided by some results from statistical downscaling that relate patterns in large-scale atmospheric circulation to local precipitation, providing a physically plausible range of changes in the parameters. For the particular location considered in the example, the effective return period is most sensitive to the scale parameter of the intensity distribution. (C) 1999 Elsevier Science Ltd. All rights reserved.}, - number = {2}, - journal = {Advances in Water Resources}, - author = {Katz, R. W.}, - month = oct, - year = {1999}, - pages = {133--139}, - annote = {245MGADV WATER RESOUR}, - annote = {The following values have no corresponding Zotero field:alt-title: Adv. Water Resour.accession-num: ISI:000083112300004}, -} - -@article{karl_modern_2003, - title = {Modern global climate change}, - volume = {302}, - journal = {Science}, - author = {Karl, T. R. and Trenberth, K. E.}, - year = {2003}, - pages = {1719--1723}, -} - -@book{karl_global_2009, - address = {New York}, - title = {Global climate change impacts in the {United} {States}}, - publisher = {Cambridge University Press}, - author = {Karl, T. R. and Melillo, J. M. and Peterson, D. H.}, - year = {2009}, -} - -@article{karl_new_1993, - title = {A {New} {Perspective} on {Recent} {Global} {Warming}: {Asymmetric} {Trends} of {Daily} {Maximum} and {Minimum} {Temperature}}, - volume = {74}, - issn = {0003-0007}, - doi = {10.1175/1520-0477(1993)074<1007:anporg>2.0.co;2}, - number = {6}, - journal = {Bulletin of the American Meteorological Society}, - author = {Karl, Thomas R. and Knight, Richard W. and Gallo, Kevin P. and Peterson, Thomas C. and Jones, Philip D. and Kukla, George and Plummer, Neil and Razuvayev, Vyacheslav and Lindseay, Janette and Charlson, Robert J.}, - month = jun, - year = {1993}, - pages = {1007--1023}, - annote = {doi: 10.1175/1520-0477(1993)074{\textless}1007:ANPORG{\textgreater}2.0.CO;2}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/1520-0477(1993)074{\textless}1007:ANPORG{\textgreater}2.0.CO;2}, -} - -@article{karamaz_reliability-based_2013, - title = {Reliability-based flood management in urban watersheds considering climate change impacts}, - journal = {J. Water Resour. Plann. Manage.}, - author = {Karamaz, M. and Nazif, S.}, - year = {2013}, - pages = {520}, -} - -@article{kalnay_estimation_2006, - title = {Estimation of the impact of land-surface forcings on temperature trends in eastern {United} {States}}, - volume = {111}, - journal = {Journal of Geophysical Research}, - author = {Kalnay, E. and Cai, M. and Li, H. and Tobin, J.}, - year = {2006}, - pages = {D06106, doi:10.1029/2005JD006555}, -} - -@article{karl_clivargcoswmo_1999, - title = {Clivar/{GCOS}/{WMO} {Workshop} on {Indices} and {Indicators} for {Climate} {Extremes} {Workshop} {Summary}}, - volume = {42}, - issn = {0165-0009}, - doi = {10.1023/a:1005491526870}, - number = {1}, - journal = {Climatic Change}, - author = {Karl, Thomas R. and Nicholls, Neville and Ghazi, Anver}, - year = {1999}, - keywords = {Earth and Environmental Science}, - pages = {3--7}, - annote = {The following values have no corresponding Zotero field:publisher: Springer Netherlands}, -} - -@article{karl_secular_1998, - title = {Secular trends of precipitation amount, frequency, and intensity in the {United} {States}}, - volume = {79}, - issn = {0003-0007}, - abstract = {Twentieth century trends of precipitation are examined by a variety of methods to more fully describe how precipitation has changed or varied. Since 1910, precipitation has increased by about 10\% across the contiguous United States. The increase in precipitation is reflected primarily in the heavy and extreme daily precipitation events. For example, over half (53\%) of the total increase of precipitation is due to positive trends in the upper 10 percentiles of the precipitation distribution. These trends are highly significant, both practically and statistically. The increase has arisen for two reasons. First, an increase in the frequency of days with precipitation [6 days (100 yr)(-1)] has occurred for all categories of precipitation amount. Second, for the extremely heavy precipitation events, an increase in the intensity of the events is also significantly contributing (about half) to the precipitation increase. As a result, there is a significant trend in much of the United States of the highest daily year-month precipitation amount, but with no systematic national trend of the median precipitation amount. -These data suggest that the precipitation regimes in the United States are changing disproportionately across the precipitation distribution. The proportion of total precipitation derived from extreme and heavy events is increasing relative to more moderate events. These changes have an impact on the area of the United States affected by a much above-normal (upper 10 percentile) proportion of precipitation derived from very heavy precipitation events, for example, daily precipitation events exceeding 50.8 mm (2 in.).}, - language = {English}, - number = {2}, - journal = {Bulletin of the American Meteorological Society}, - author = {Karl, T. R. and Knight, R. W.}, - month = feb, - year = {1998}, - keywords = {variability, snowfall}, - pages = {231--241}, - annote = {The following values have no corresponding Zotero field:auth-address: Karl, TR NOAA, NESDIS, Natl Climat Data Ctr, 151 Patton Ave, Asheville, NC 28801 USA NOAA, NESDIS, Natl Climat Data Ctr, Asheville, NC 28801 USAaccession-num: ISI:000072303900003}, - annote = {Yz883Times Cited:170Cited References Count:14}, -} - -@article{kampf_framework_2007, - title = {A framework for classifying and comparing distributed hillslope and catchment hydrologic models}, - volume = {43}, - journal = {Water Resources Research}, - author = {Kampf, S. K. and Burges, S. J.}, - year = {2007}, - pages = {W05423, doi:10.1029/2006WR005370}, -} - -@article{kalnay_ncepncar_1996, - title = {The {NCEP}/{NCAR} 40-year reanalysis project}, - volume = {77}, - number = {3}, - journal = {Bulletin of the American Meteorological Society}, - author = {Kalnay, E. and Kanamitsu, M. and Kistler, R. and Collins, W. and Deaven, D. and Gandin, L. and Iredell, M. and Saha, S. and White, G. and Woollen, J. and Zhu, Y. and Leetmaa, A. and Reynolds, B.}, - year = {1996}, - pages = {437--472}, -} - -@article{kalnay_impact_2003, - title = {Impact of urbanization and land-use change on climate}, - volume = {423}, - journal = {Nature}, - author = {Kalnay, E. and Cai, M.}, - year = {2003}, - pages = {528--531}, -} - -@article{kaleris_case_2001, - title = {Case study on impact of atmospheric circulation changes on river basin hydrology: uncertainty aspects}, - volume = {245}, - abstract = {Significance of predictions concerning the climate change impact on basin hydrology in case of doubled CO2 concentration in the atmosphere (2 x CO2-case) is investigated within the context of a case study for the Mesohora basin in Greece. Circulation patterns (CPs) characterizing the weather in the region are defined based on wind direction and on barometric conditions and the modification of the large-scale atmospheric variables (geopotential height, occurrence probability and duration of CPs) in 2 x CO2-case is estimated. The effect of the atmospheric-variables modification on temperature and precipitation on basin scale is predicted by means of a semiempirical, purely circulation-based downscaling model. Temperature modification in 2 x CO2-case has been found to be significant for eight months of the year with a maximal increase of mean monthly temperature approximately 2 degreesC. For precipitation, significant change has been predicted for one of the precipitation stations considered. Owing to the fact that this precipitation change does not influence mean precipitation of the basin, climate change impact on basin runoff has been investigated only for temperature effects. For this purpose a hydrological model has been used. It has been found that the error of hydrologic model is significantly larger than climate change impact so that modifications of the river basin hydrology for the 2 x CO2-case can be hardly predicted. (C) 2001 Elsevier Science B.V. All rights reserved.}, - number = {1-4}, - journal = {Journal of Hydrology}, - author = {Kaleris, V. and Papanastasopoulos, D. and Lagas, G.}, - month = may, - year = {2001}, - pages = {137--152}, - annote = {427CKJ HYDROL}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Hydrol.accession-num: ISI:000168387100010}, -} - -@article{kahya_us_1993, - title = {U.{S}. streamflow patterns in relation to {El} {Niño}/{Southern} {Oscillation}}, - volume = {29}, - journal = {Water Resources Research}, - author = {Kahya, E. and Dracup, J. A.}, - year = {1993}, - pages = {2491--2503}, -} - -@techreport{k-1_model_developers_k-1_2004, - address = {Tokyo, Japan}, - title = {K-1 coupled model ({MIROC}) description’, {K}-1 technical report, 1}, - institution = {Center for Climate System Research, University of Tokyo}, - author = {K-1 model developers}, - editor = {Hasumi, H. and Emori, S.}, - year = {2004}, - pages = {34}, -} - -@article{jungclaus_ocean_2006, - title = {Ocean circulation and tropical variability in the {AOGCM} {ECHAM5}/{MPI}-{OM}}, - volume = {19}, - number = {16}, - journal = {Journal of Climate}, - author = {Jungclaus, J. H. and Botzet, M. and Haak, H. and Keenlyside, N. and Luo, J.-J. and Latif, M. and Marotzke, J. and Mikolajewicz, U. and Roeckner, E.}, - year = {2006}, - pages = {3952--3972}, -} - -@article{juras_common_1994, - title = {Some common features of probability distributions for precipitation}, - volume = {49}, - number = {2}, - journal = {Theoretical and Applied Climatology}, - author = {Juras, J.}, - year = {1994}, - pages = {69--76, DOI: 10.1007/BF00868191}, -} - -@article{joubert_simulating_1997, - title = {Simulating present and future climates of southern {Africa} using general circulation models}, - volume = {21}, - abstract = {The current state of regional climate and climate change modelling using GCMs is reviewed for southern Africa, and several approaches to regional climate change prediction which have been applied to southern Africa are assessed. Confidence in projected regional changes is based on the ability of a range of models to simulate present regional climate, and is greatest where intermodel consensus in terms of the nature of projected changes is highest. Both equilibrium and transient climate change projections for southern Africa are considered. Warming projected over southern Africa is within the range of globally averaged estimates. Uncertainties associated with the parameterization of convection ensure that projected changes in rainfall at GCM grid scales remain unreliable. However, empirical downscaling approaches produce rainfall changes consistent with synoptic-scale circulation. Both downscaling and grid-scale approaches indicate a 10-15\% decrease in summer rainfall over the central interior which may have important implications for surface hydrology. Climate change may be manifested as a change in variability, and not in mean climate. Over southern Africa, increases in the variability and intensity of daily rainfall events are indicated.}, - number = {1}, - journal = {Progress in Physical Geography}, - author = {Joubert, A. M. and Hewitson, B. C.}, - year = {1997}, - pages = {51--78}, - annote = {The following values have no corresponding Zotero field:alt-title: Prog. Phys. Geogr.accession-num: ISI:A1997XA44600004}, - annote = {XA446PROG PHYS GEOG}, -} - -@techreport{jones_generating_2004, - address = {Exeter, UK}, - title = {Generating {High} {Resolution} {Climate} {Change} {Scenarios} {Using} {PRECIS}}, - institution = {Met Office Hadley Centre}, - author = {Jones, R. G. and Noguer, M. and Hassell, D. C. and Hudson, D. and Wilson, S. S. and Jenkins, G. J. and Mitchell, J. F. B.}, - year = {2004}, - pages = {40pp.}, -} - -@article{jones_influence_2000, - title = {The influence of climate on air and precipitation chemistry over {Europe} and downscaling applications to future acidic deposition}, - volume = {14}, - abstract = {Atmospheric circulations, and related climate conditions, exert a strong influence on the transport and ultimate deposition of atmospheric pollutants. One potential outcome of climate change is alteration of atmospheric circulation patterns. The first stage in the development of a downscaling methodology to assess the influence of these possible future atmospheric circulation changes on transport and deposition of atmospheric pollutants over Europe is described. Firstly, the main modes of regional- scale circulation over a domain covering the North Atlantic and Europe are determined using principal components analysis of sea level pressure patterns. To determine whether the principal components represent circulation types found in reality, and show realistic relationships with surface temperature and precipitation amount, they are compared to the Lamb Weather Types, Central Eng land Temperature, and England and Wales Rainfall. There are statistically significant relationships between some of the principal components and aqueous and ambient pollutant concentrations at 5 selected stations from the European Monitoring and Evaluation Programme monitoring network. Although, for any one combination of principal component and pollutant variable, the level of explanation can be quite small, notwithstanding some levels of variance explained for individual combinations being up to 69 \% (45 \% for the winter season, described in detail here). Because the pollution climate at any one location is a function of many combinations of circulation/pollution relationships, despite the relatively small magnitude of variance explained for individual combinations in this study. the results confirm the utility of atmospheric circulation principal components in deriving downscaling relationships for surface pollution behaviour (via eventual multiple regressions, not reported in this paper), as well as giving further insight into the climatic influences on air and precipitation chemistry over Europe.}, - number = {1}, - journal = {Climate Research}, - author = {Jones, J. M. and Davies, T. D.}, - month = jan, - year = {2000}, - pages = {7--24}, - annote = {302RTCLIMATE RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:000086380000002}, -} - -@article{jenicek_modeling_2018, - title = {Modeling of {Future} {Changes} in {Seasonal} {Snowpack} and {Impacts} on {Summer} {Low} {Flows} in {Alpine} {Catchments}}, - volume = {54}, - doi = {doi:10.1002/2017WR021648}, - abstract = {Abstract It is expected that an increasing proportion of the precipitation will fall as rain in alpine catchments in the future. Consequently, snow storage is expected to decrease, which, together with changes in snowmelt rates and timing, might cause reductions in spring and summer low flows. The objectives of this study were (1) to simulate the effect of changing snow storage on low flows during the warm seasons and (2) to relate drought sensitivity to the simulated snow storage changes at different elevations. The Swiss Climate Change Scenarios 2011 data set was used to derive future changes in air temperature and precipitation. A typical bucket-type catchment model, HBV-light, was applied to 14 mountain catchments in Switzerland to simulate streamflow and snow in the reference period and three future periods. The largest relative decrease in annual maximum SWE was simulated for elevations below 2,200 m a.s.l. (60–75\% for the period 2070–2099) and the snowmelt season shifted by up to 4 weeks earlier. The relative decrease in spring and summer minimum runoff that was caused by the relative decrease in maximum SWE (i.e., elasticity), reached 40–90\% in most of catchments for the reference period and decreased for the future periods. This decreasing elasticity indicated that the effect of snow on summer low flows is reduced in the future. The fraction of snowmelt runoff in summer decreased by more than 50\% at the highest elevations and almost disappeared at the lowest elevations. This might have large implications on water availability during the summer.}, - number = {1}, - journal = {Water Resources Research}, - author = {Jenicek, Michal and Seibert, Jan and Staudinger, Maria}, - year = {2018}, - pages = {538--556}, -} - -@article{johannesson_climate_1995, - title = {Climate change scenarios for the {Nordic} countries}, - volume = {5}, - abstract = {A climate change scenario for the Nordic countries has been defined for application in hydrological models in the Nordic research project 'Climate Change and Energy Production'. The scenario is based on a subjective evaluation of several recent results from global coupled atmosphere and ocean general circulation models (GCMs) and on a statistical downscaling of the model results. The scenario specifies a warming rate which ranges from 0.3 degrees C per decade for Iceland and the Faeroe Islands to 0.45 degrees C per decade for eastern Finland and the northernmost parts of Sweden. There are marked seasonal and regional differences in the warming rate. Summer warming is relatively uniform over the area, ranging from 0.25 degrees C per decade in Iceland and the Faeroe Islands to 0.3 degrees C per decade in Finland. Winter warming is more variable, ranging from 0.35 degrees C per decade in the North Atlantic area to 0.6 degrees C per decade in Finland. Precipitation is increased by 3 to 6\% per degree of warming, yielding an increase ranging from 1\% per decade during the summer season in Finland, Sweden and eastern Norway to 2.5\% per decade during the winter season in western Norway. Climate in the Nordic countries is characterized by low-frequency natural variability on decadal time scales which is thought to be partly driven by changes in the thermohaline circulation of the North Atlantic. Future behaviour of the North Atlantic ocean circulation is highly uncertain, and model predictions of climate development in this part of the world are perhaps more difficult than for most other regions. Other sources of uncertainty in GCM model computations apply to the North Atlantic region as well as to other regions of the world, e.g. coarse model resolution, somewhat arbitrary coupling of the ocean and the atmosphere and uncertain parameterizations of clouds and precipitation. In view of these difficulties, the scenario must be considered an extremely uncertain, although plausible, description of what might happen in the future rather than a prediction of what most likely will happen.}, - number = {3}, - journal = {Climate Research}, - author = {Johannesson, T. and Jonsson, T. and Kallen, E. and Kaas, E.}, - month = dec, - year = {1995}, - pages = {181--195}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:A1995TV24700001}, - annote = {TV247CLIMATE RES}, -} - -@inproceedings{jeuken_climate_2016, - address = {Munich, Germany}, - title = {Climate {Risk} {Informed} {Decision} {Analysis} ({CRIDA}): {A} novel practical guidance for {Climate} {Resilient} {Investments} and {Planning}}, - volume = {18}, - publisher = {European Geophysical Union}, - author = {Jeuken, Ad and Mendoza, Guillermo and Matthews, John and Ray, Patrick and Haasnoot, Marjolijn and Gilroy, Kristin and Olsen, Rolf and Kucharski, John and Stakhiv, Gene and Cushing, Janet}, - year = {2016}, - pages = {8797}, -} - -@techreport{ipsl_new_2005, - address = {Paris, France}, - title = {The new {IPSL} climate system model: {IPSL}-{CM4}}, - institution = {Institut Pierre Simon Laplace des Sciences de l’Environnement Global}, - author = {IPSL}, - year = {2005}, - pages = {73}, -} - -@incollection{ipcc_annex_2013, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Annex {VI}: {Expert} {Reviewers} of the {IPCC} {WGI} {Fifth} {Assessment} {Report}}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {IPCC}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley}, - year = {2013}, - pages = {1497--1522}, - annote = {The following values have no corresponding Zotero field:section: AVIelectronic-resource-num: 10.1017/CBO9781107415324}, -} - -@incollection{ipcc_annex_2013-1, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Annex {IV}: {Acronyms}}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {IPCC}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley}, - year = {2013}, - pages = {1467--1476}, - annote = {The following values have no corresponding Zotero field:section: AIVelectronic-resource-num: 10.1017/CBO9781107415324}, -} - -@article{islam_modeling_2012, - title = {Modeling the impacts of climate change on irrigated corn production in the {Central} {Great} {Plains}}, - volume = {110}, - issn = {0378-3774}, - doi = {http://dx.doi.org/10.1016/j.agwat.2012.04.004}, - number = {0}, - journal = {Agricultural Water Management}, - author = {Islam, Adlul and Ahuja, Lajpat R. and Garcia, Luis A. and Ma, Liwang and Saseendran, Anapalli S. and Trout, Thomas J.}, - year = {2012}, - keywords = {Climate change, Evapotranspiration, Corn yield, Multi-model scenarios, Water use efficiency}, - pages = {94--108}, -} - -@book{ipcc_climate_2014, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Climate {Change} 2014: {Impacts}, {Adaptation}, and {Vulnerability}. {Part} {B}: {Regional} {Aspects}. {Contribution} of {Working} {Group} {II} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {IPCC}, - editor = {Barros, V. R. and Field, C. B. and Dokken, D. J. and Mastrandrea, M. D. and Mach, K. J. and Bilir, T. E. and Chatterjee, M. and Ebi, K. L. and Estrada, Y. O. and Genova, R. C. and Girma, B. and Kissel, E. S. and Levy, A. N. and MacCracken, S. and Mastrandrea, P. R. and White, L. L.}, - year = {2014}, -} - -@incollection{ipcc_index_2013, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Index}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {IPCC}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley}, - year = {2013}, - pages = {1523--1535}, - annote = {The following values have no corresponding Zotero field:section: Indexelectronic-resource-num: 10.1017/CBO9781107415324}, -} - -@incollection{ipcc_annex_2013-2, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Annex {V}: {Contributors} to the {IPCC} {WGI} {Fifth} {Assessment} {Report}}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {IPCC}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley}, - year = {2013}, - pages = {1477--1496}, - annote = {The following values have no corresponding Zotero field:section: AVelectronic-resource-num: 10.1017/CBO9781107415324}, -} - -@incollection{ipcc_annex_2013-3, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Annex {II}: {Climate} {System} {Scenario} {Tables}}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {IPCC}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley and M. Prather and G. Flato and P. Friedlingstein and C. Jones and J. -F. Lamarque and H. Liao and P. Rasch}, - year = {2013}, - pages = {1395--1446}, - annote = {The following values have no corresponding Zotero field:section: AIIelectronic-resource-num: 10.1017/CBO9781107415324.030}, -} - -@incollection{ipcc_annex_2013-4, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Annex {I}: {Atlas} of {Global} and {Regional} {Climate} {Projections}}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {IPCC}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley and G. J. van Oldenborgh and M. Collins and J. Arblaster and J. H. Christensen and J. Marotzke and S. B. Power and M. Rummukainen and T. Zhou}, - year = {2013}, - pages = {1311--1394}, - annote = {The following values have no corresponding Zotero field:section: AIelectronic-resource-num: 10.1017/CBO9781107415324.029}, -} - -@incollection{ipcc_annex_2013-5, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Annex {III}: {Glossary}}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {IPCC}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley and S. Planton}, - year = {2013}, - pages = {1447--1466}, - annote = {The following values have no corresponding Zotero field:section: AIIIelectronic-resource-num: 10.1017/CBO9781107415324.031}, -} - -@incollection{ipcc_summary_2013, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Summary for {Policymakers}}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {IPCC}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley}, - year = {2013}, - pages = {1--30}, - annote = {The following values have no corresponding Zotero field:section: SPMelectronic-resource-num: 10.1017/CBO9781107415324.004}, -} - -@book{ipcc_managing_2012, - address = {Cambridge, UK}, - title = {Managing the {Risks} of {Extreme} {Events} and {Disasters} to {Advance} {Climate} {Change} {Adaptation}. {A} {Special} {Report} of {Working} {Groups} {I} and {II} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {IPCC}, - editor = {Field, C. B. and Barros, V. and Stocker, T. F. and Qin, D. and Dokken, D. J. and Ebi, K. L. and Mastrandrea, M. D. and Mach, K. J. and Plattner, G.-K. and Allen, S. K. and Tignor, M. and Midgley, P. M.}, - year = {2012}, -} - -@book{ipcc_intergovernmental_2011, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Intergovernmental {Panel} on {Climate} {Change} {Special} {Report} on {Managing} the {Risks} of {Extreme} {Events} and {Disasters} to {Advance} {Climate} {Change} {Adaptation}, {Summary} for {Policymakers}}, - publisher = {Cambridge University Press}, - author = {IPCC}, - editor = {Field, C. B. and Barros, V. and Stocker, T. F. and Qin, D. and Dokken, D. and Ebi, K. L. and Mastrandrea, M. D. and Mach, K. J. and Plattner, G.-K. and Allen, S. K. and Tignor, M. and P. M. Midgley}, - year = {2011}, -} - -@incollection{ipcc_gaps_2008, - address = {Geneva}, - title = {Gaps in knowledge and suggestions for further work}, - booktitle = {Climate {Change} and {Water}: {Technical} {Paper} {VI}}, - publisher = {IPCC Secretariat}, - author = {IPCC}, - editor = {Bates, B. C. and Kundzewicz, Z. W. and Wu, S. and Palutikof, J. P.}, - year = {2008}, - pages = {133--137}, -} - -@book{ipcc_contribution_2007, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Contribution of {Working} {Group} {I} to the {Fourth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {IPCC}, - editor = {Solomon, S. and Qin, D. and Manning, M. and Chen, Z. and Marquis, M. and Averyt, K. B. and Tignor, M. and Miller, H. L.}, - year = {2007}, -} - -@book{ipcc_climate_2007, - address = {Geneva, Switzerland}, - title = {Climate change 2007: {Impacts}, adaptation and vulnerability, working group {II} contribution to the {Intergovernmental} {Panel} on {Climate} {Change} {Fourth} {Assessment} {Report}}, - publisher = {Intergovernmental Panel on Climate Change}, - author = {IPCC}, - year = {2007}, -} - -@book{ipcc_climate_2013, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - isbn = {ISBN 978-1-107-66182-0}, - publisher = {Cambridge University Press}, - author = {IPCC}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley}, - year = {2013}, - annote = {The following values have no corresponding Zotero field:electronic-resource-num: 10.1017/CBO9781107415324}, -} - -@book{ipcc_climate_2007-1, - address = {Geneva, Switzerland}, - title = {Climate {Change} 2007: {The} physical science basis, summary for policymakers}, - publisher = {Intergovernmental Panel on Climate Change}, - author = {IPCC}, - year = {2007}, -} - -@techreport{ipcc_climate_2001, - address = {Cambridge,United Kingdom}, - title = {Climate {Change} 2001: {Synthesis} {Report}, {A} {Contribution} of {Working} {Groups} {I}, {II}, and {III} to the {Third} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - institution = {Cambridge University Press}, - author = {IPCC}, - editor = {Watson, R. T.}, - year = {2001}, - pages = {398}, -} - -@article{iorio_effects_2004, - title = {Effects of model resolution and subgrid-scale physics on the simulation of precipitation in the continental {United} {States}}, - volume = {23}, - number = {3-4}, - journal = {Climate Dynamics}, - author = {Iorio, J. P. and Duffy, P. B. and Govindasamy, B. and Thompson, S. L. and Khairoutdinov, M. and Randall, D.}, - year = {2004}, - pages = {243--258, doi:10.1007/s00382--004--0440--y}, -} - -@article{ines_bias_2006, - title = {Bias correction of daily {GCM} rainfall for crop simulation studies}, - volume = {138}, - issn = {0168-1923}, - number = {1–4}, - journal = {Agricultural and Forest Meteorology}, - author = {Ines, Amor V. M. and Hansen, James W.}, - year = {2006}, - keywords = {Precipitation, Corn (Zea mays), Crop simulation model, General circulation model (GCM), Seasonal climate prediction}, - pages = {44--53}, - annote = {The following values have no corresponding Zotero field:work-type: doi: 10.1016/j.agrformet.2006.03.009}, -} - -@article{imbach_climate_2017, - title = {Climate change, ecosystems and smallholder agriculture in {Central} {America}: an introduction to the special issue}, - volume = {141}, - issn = {1573-1480}, - doi = {10.1007/s10584-017-1920-5}, - number = {1}, - journal = {Climatic Change}, - author = {Imbach, Pablo and Beardsley, Megan and Bouroncle, Claudia and Medellin, Claudia and Läderach, Peter and Hidalgo, Hugo and Alfaro, Eric and Van Etten, Jacob and Allan, Robert and Hemming, Debbie and Stone, Roger and Hannah, Lee and Donatti, Camila I.}, - year = {2017}, - pages = {1--12}, - annote = {The following values have no corresponding Zotero field:label: Imbach2017work-type: journal article}, -} - -@techreport{iacwd_guidelines_1982, - address = {Reston, Virginia}, - title = {Guidelines for determining flood flow frequency, {Hydrology} {Subcommittee} {Bulletin} {17B}}, - institution = {Interagency Advisory Committee on Water Data, U.S. Geological Survey, Office of Water Data Coordination}, - author = {IACWD}, - year = {1982}, - pages = {194 pp.}, -} - -@article{hwang_development_2013, - title = {Development and comparative evaluation of a stochastic analog method to downscale daily {GCM} precipitation}, - volume = {17}, - issn = {1607-7938}, - doi = {10.5194/hess-17-4481-2013}, - number = {11}, - journal = {Hydrol. Earth Syst. Sci.}, - author = {Hwang, S. and Graham, W. D.}, - year = {2013}, - pages = {4481--4502}, - annote = {HESS}, - annote = {The following values have no corresponding Zotero field:publisher: Copernicus Publications}, -} - -@article{huth_simulation_2003, - title = {Simulation of surface air temperature by {GCMs}, statistical downscaling and weather generator: {Higher}-order statistical moments}, - volume = {47}, - number = {1}, - journal = {Studia Geophysica Et Geodaetica}, - author = {Huth, R. and Kysely, J. and Dubrovsky, M.}, - month = jan, - year = {2003}, - pages = {203--216}, - annote = {642BKSTUD GEOPHYS GEOD}, - annote = {The following values have no corresponding Zotero field:alt-title: Stud. Geophys. Geod.accession-num: ISI:000180783900011}, -} - -@article{huth_time_2001, - title = {Time structure of observed, {GCM}-simulated, downscaled, and stochastically generated daily temperature series}, - volume = {14}, - abstract = {The time structure of simulated daily maximum and minimum temperature series, produced by several different methods, is compared with observations at six stations in central Europe. The methods are statistical downscaling, stochastic weather generator, and general circulation models (GCMs). Outputs from control runs of two GCMs are examined: ECHAM3 and CCCM2. Four time series are constructed by statistical downscaling using multiple linear regression of 500-hPa heights and 1000-/500-hPa thickness: (i) from observations with variance reproduced by the inflation technique, (ii) from observations with variance reproduced by adding a white noise process, and (iii) from the two GCMs. Two runs of the weather generator were performed, one considering and one neglecting the annual cycle of lag-0 and lag-1 correlations among daily weather characteristics. Standard deviation and skewness of day-to-day temperature changes and lag-1 autocorrelations are examined. For heat and cold waves, the occurrence frequency, mean duration, peak temperature, and mean position within the year are studied. Possible causes of discrepancies between the simulated and observed time series are discussed and identified. They are shown to stem, among others, from (i) the absence of physics in downscaled and stochastically generated series, (ii) inadequacies of treatment of physical processes in GCMs, (iii) assumptions of linearity in downscaling equations, and (iv) properties of the underlying statistical model of the weather generator. In downscaling, variance inflation is preferable to the white noise addition in most aspects as the latter results in highly overestimated day-to-day variability. The inclusion of the annual cycle of correlations into the weather generator does not lead to an overall improvement of the temperature series produced. None of the methods appears to be able to reproduce all the characteristics of time structure correctly.}, - number = {20}, - journal = {J. Clim.}, - author = {Huth, R. and Kysely, J. and Dubrovsky, M.}, - year = {2001}, - pages = {4047--4061}, - annote = {478JXJ CLIMATE}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Clim.accession-num: ISI:000171344200004}, -} - -@article{huth_constructing_2000, - title = {Constructing site-specific climate change scenarios on a monthly scale using statistical downscaling}, - volume = {66}, - abstract = {Monthly mean temperature and monthly precipitation totals in two small catchments in the Czech Republic are estimated from large-scale 500 hPa height and 1000/500 hPa thickness fields using statistical downscaling. The method used is multiple lineal regression. Whereas precipitation can be determined from large-scale fields with some confidence in only a few months of the year, temperature can be determined successfully. Principal components calculated separately from the height and thickness anomalies are identified as the best predictor set. The method is most accurate if the regression is performed using seasons based on three months. The test on an independent sample, consisting of warm seasons, confirms that the method successfully reproduces the difference in mean temperature between two climatic states, which indicates that this downscaling method is applicable for constructing scenarios of a future climate change. The ECHAM3 GCM is used for scenario construction. The CCM is shown to simulate surface temperature and precipitation with low accuracy, whereas the large-scale atmospheric fields are reproduced well; this justifies the downscaling approach. The observed regression equations are applied to 2xCO(2) GCM output so that the model's bias is eleminated. This procedure is then discussed and finally, temperature scenarios for the 2xCO(2) climate are constructed for the two catchments.}, - number = {1-2}, - journal = {Theoretical and Applied Climatology}, - author = {Huth, R. and Kysely, J.}, - year = {2000}, - pages = {13--27}, - annote = {335YZTHEOR APPL CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Theor. Appl. Climatol.accession-num: ISI:000088276000002}, -} - -@article{huth_statistical_2002, - title = {Statistical downscaling of daily temperature in {Central} {Europe}}, - volume = {15}, - abstract = {Statistical downscaling methods and potential large-scale predictors are intercompared for winter daily mean temperature in a network of stations in central and western Europe. The methods comprise (i) canonical correlation analysis (CCA), (ii) singular value decomposition analysis, (iii) multiple linear regression (MLR) of predictor principal components (PCs) with stepwise screening, (iv) MLR of predictor PCs without screening (i. e., all PCs are forced to enter the regression model), and (v) MLR of gridpoint values with stepwise screening (pointwise regression). The potential predictors include two circulation variables (sea level pressure and 500-hPa heights) and two temperature variables (850-hPa temperature and 1000-500-hPa thickness). The methods are evaluated according to the accuracy of specification (in terms of rmse and variance explained), their temporal structure (characterized by lag-1 autocorrelations), and their spatial structure (characterized by spatial correlations and objectively defined divisions into homogeneous regions). The most accurate specification and best approximation of the temporal structure are achieved by the pointwise regression; the spatial structure is best captured by CCA. The best choice of predictors appears to be a pair of one circulation and one temperature predictor. Of the two ways of reproducing the original variance, inflation yields more realistic both temporal and spatial variability than randomization. The size of the domain on which predictors are defined plays a rather negligible role.}, - number = {13}, - journal = {J. Clim.}, - author = {Huth, R.}, - month = jul, - year = {2002}, - pages = {1731--1742}, - annote = {564QRJ CLIMATE}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Clim.accession-num: ISI:000176325500015}, -} - -@article{huth_statistical_1999, - title = {Statistical downscaling in central {Europe}: evaluation of methods and potential predictors}, - volume = {13}, - abstract = {Several statistical downscaling methods and large-scale predictors are evaluated to ascertain their potential to determine daily mean temperatures at 39 stations in central Europe. The methods include canonical correlation analysis, singular value decomposition, and 3 multiple regression models. The potential large-scale predictors are 500 hPa heights, sea level pressure, 850 hPa temperature and 1000-500 hPa thickness. The performance of the methods is evaluated using cross- validation and root-mean-squared error as a measure of accuracy. The stepwise screening of gridpoint data is found to be the statistical model that performed the best. Among the predictors, temperature variables yield more accurate results than circulation variables. The best predictor is the combination of 500 hPa heights and 850 hPa temperature. Geographical variations of the specification skill, mainly the differences between the elevated and lowland stations, are also discussed.}, - number = {2}, - journal = {Climate Research}, - author = {Huth, R.}, - month = oct, - year = {1999}, - pages = {91--101}, - annote = {277DMCLIMATE RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:000084917500001}, -} - -@article{huth_disaggregating_2001, - title = {Disaggregating climatic trends by classification of circulation patterns}, - volume = {21}, - abstract = {The trends in occurrence frequencies of circulation types over Europe and in nine climate variables in the Czech Republic conditioned by the types are examined for period 1949-1980. The circulation types are determined by an objective procedure from daily 500 hPa heights. Both in summer and winter, anticyclonic types have become more frequent at the expense of cyclonic types. The circulation changes are shown to be unrelated to the trends in surface climate elements in summer, whereas in winter, trends in circulation explain a part of the observed warming and strengthening of southerly winds. The trends in climate elements are not uniform among circulation types. In summer, the trend pattern consisting of decreasing maximum and daily mean temperatures, daily temperature range (DTR) and sunshine duration, and increasing cloudiness and relative humidity is observed under the cyclonic types and the types with a well-pronounced jet, but is missing under types with a blocking anticyclone over Europe. Two possible mechanisms causing this trend pattern are proposed: increasing cloudiness, and a process responsible for the reduction of sunshine without a concurrent increase of cloudiness. The latter mechanism can possibly be identified with increasing aerosol concentrations. In winter, the degree of warming is governed by changes in zonal wind. The mechanism of change in DTR seems to vary with elevation: at the lowland station (Prague-Klementinum), the increase in DTR is related to the warming trend, and consequently with zonal wind changes, while at the mountain station (Milesovka), the increase in DTR reflects the increase in precipitating clouds. The changes in DTR are related much more to mid-tropospheric circulation than to cloud cover in summer, whereas in winter, cloud cover plays a more important role in affecting DTR trends. Copyright (C) 2001 Royal Meteorological Society.}, - number = {2}, - journal = {International Journal of Climatology}, - author = {Huth, R.}, - month = feb, - year = {2001}, - pages = {135--153}, - annote = {406VZINT J CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:000167238000001}, -} - -@article{huffman_trmm_2007, - title = {The {TRMM} {Multisatellite} {Precipitation} {Analysis} ({TMPA}): {Quasi}-{Global}, {Multiyear}, {Combined}-{Sensor} {Precipitation} {Estimates} at {Fine} {Scales}}, - volume = {8}, - issn = {1525-755X}, - doi = {10.1175/jhm560.1}, - number = {1}, - journal = {Journal of Hydrometeorology}, - author = {Huffman, George J. and Bolvin, David T. and Nelkin, Eric J. and Wolff, David B. and Adler, Robert F. and Gu, Guojun and Hong, Yang and Bowman, Kenneth P. and Stocker, Erich F.}, - month = feb, - year = {2007}, - pages = {38--55}, - annote = {doi: 10.1175/JHM560.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/JHM560.1}, -} - -@article{huffman_global_2001, - title = {Global {Precipitation} at {One}-{Degree} {Daily} {Resolution} from {Multisatellite} {Observations}}, - volume = {2}, - issn = {1525-755X}, - doi = {10.1175/1525-7541(2001)002<0036:gpaodd>2.0.co;2}, - number = {1}, - journal = {Journal of Hydrometeorology}, - author = {Huffman, George J. and Adler, Robert F. and Morrissey, Mark M. and Bolvin, David T. and Curtis, Scott and Joyce, Robert and McGavock, Brad and Susskind, Joel}, - month = feb, - year = {2001}, - pages = {36--50}, - annote = {doi: 10.1175/1525-7541(2001)002{\textless}0036:GPAODD{\textgreater}2.0.CO;2}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/1525-7541(2001)002{\textless}0036:GPAODD{\textgreater}2.0.CO;2}, -} - -@article{huth_intercomparison_1996, - title = {An intercomparison of computer-assisted circulation classification methods}, - volume = {16}, - abstract = {Five different methods that have been used for classification of circulation patterns (correlation method, sums-of-squares method, average linkage, K-means, and rotated principal component analysis) are examined as to their ability to detect dominant circulation types. The performance of the methods is evaluated according to the degree of meeting the following demands made on the groups formed: The groups should (i) be consistent when preset parameters are changed (ii) be well separated both from each other and from the entire data set, (iii) be stable in space and time, and (iv) reproduce the predefined types. All the methods proved to be capable of yielding meaningful classifications. None of them can be thought of as the best in all aspects. Which method to use will depend mainly on the aim of the classification. Nevertheless, the principal component analysis is most successful in reproducing the predefined types and is therefore considered as the most promising method among those examined.}, - number = {8}, - journal = {International Journal of Climatology}, - author = {Huth, R.}, - month = aug, - year = {1996}, - pages = {893--922}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:A1996VF59100004}, - annote = {VF591INT J CLIMATOL}, -} - -@article{huntingford_link_2012, - title = {The link between a global 2 °{C} warming threshold and emissions in years 2020, 2050 and beyond}, - volume = {7}, - issn = {1748-9326}, - abstract = {In the Copenhagen Accord, nations agreed on the need to limit global warming to two degrees to avoid potentially dangerous climate change, while in policy circles negotiations have placed a particular emphasis on emissions in years 2020 and 2050. We investigate the link between the probability of global warming remaining below two degrees (above pre-industrial levels) right through to year 2500 and what this implies for emissions in years 2020 and 2050, and any long-term emissions floor. This is achieved by mapping out the consequences of alternative emissions trajectories, all in a probabilistic framework and with results placed in a simple-to-use set of graphics. The options available for carbon dioxide-equivalent (CO 2 e) emissions in years 2020 and 2050 are narrow if society wishes to stay, with a chance of more likely than not, below the 2 °C target. Since cumulative emissions of long-lived greenhouse gases, and particularly CO 2 , are a key determinant of peak warming, the consequence of being near the top of emissions in the allowable range for 2020 is reduced flexibility in emissions in 2050 and higher required rates of societal decarbonization. Alternatively, higher 2020 emissions can be considered as reducing the probability of limiting warming to 2 °C. We find that the level of the long-term emissions floor has a strong influence on allowed 2020 and 2050 emissions for two degrees of global warming at a given probability. We place our analysis in the context of emissions pledges for year 2020 made at the end of and since the 2009 COP15 negotiations in Copenhagen.}, - number = {1}, - journal = {Environmental Research Letters}, - author = {Huntingford, C. and Lowe, J. A. and Gohar, L. K. and Bowerman, N. H. A. and Allen, M. R. and Raper, S. C. B. and Smith, S. M.}, - year = {2012}, - pages = {014039}, -} - -@article{hubbard_estimating_2003, - title = {Estimating daily dew point temperature for the northern {Great} {Plains} using maximum and minimum temperature}, - volume = {95}, - abstract = {Dew point temperature (T-d) is a precise measure of atmospheric moisture. A significant number of models for studying crop- climate interactions and earth processes require daily T-d as an input. However, limited availability of T-d data is a major barrier for applications of these models. In this paper, we present a daily T-d estimation method for the northern Great Plains (NGP). The daily T-d estimation method presented here requires daily maximum, minimum, and mean temperature data. Data from six sites in the NGP were used for the study. These sites record hourly T-d data from relative humidity. Length of the time series is 14 yr (1986-1999). Four different regression-based approaches were adopted and applied to all sites. Eventually, the best method was adopted based on its performance. The model evaluation statistics show that the selected model performs satisfactorily for these six sites. For example, root mean square error (RMSE), mean absolute error (MAE), and d index (ranges between 0 and 1, where 1 indicates no model error) values for North Platte, NE, application are 3.23, 2.55, and 0.97, respectively. The selected method was further applied to five additional locations in the NGP, and again it performed satisfactorily. For example, RMSE, MAE, and d index values for McCook, NE, application are 2.6, 2.0, and 0.98, respectively. From the model evaluation, we conclude that the model performed satisfactorily and will be quite useful in estimating. T-d.}, - number = {2}, - journal = {Agronomy Journal}, - author = {Hubbard, K. G. and Mahmood, R. and Carlson, C.}, - month = apr, - year = {2003}, - pages = {323--328}, - annote = {659XRAGRON J}, - annote = {The following values have no corresponding Zotero field:alt-title: Agron. J.accession-num: ISI:000181804400011}, -} - -@article{huang_analysis_1996, - title = {Analysis of {Model}-{Calculated} {Soil} {Moisture} over the {United} {States} (1931–1993) and {Applications} to {Long}-{Range} {Temperature} {Forecasts}}, - volume = {9}, - doi = {doi:10.1175/1520-0442(1996)009<1350:AOMCSM>2.0.CO;2}, - number = {6}, - journal = {Journal of Climate}, - author = {Huang, Jin and van den Dool, Huug M. and Georgarakos, Konstantine P.}, - year = {1996}, - pages = {1350--1362}, -} - -@article{howat_climate_2005, - title = {Climate sensitivity of spring snowpack in the {Sierra} {Nevada}}, - volume = {110}, - number = {F04021}, - journal = {Journal of Geophysical Research}, - author = {Howat, I. M. and Tulaczyk, S.}, - year = {2005}, - pages = {doi:10.1029/2005JF000356}, -} - -@book{houghton_climate_2001, - address = {Cambridge, UK}, - title = {Climate {Change} 2001: {The} scientific basis. contribution of {Working} {Group} {I} to the third assessment report of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {Houghton, J. T.}, - year = {2001}, -} - -@article{holland_polar_2003, - title = {Polar amplification of climate change in coupled models}, - volume = {21}, - journal = {Climate Dynamics}, - author = {Holland, M. M. and Bitz, C. M.}, - year = {2003}, - pages = {221--232}, -} - -@article{holden_assessment_2002, - title = {An assessment of the potential impact of climate change on grass yield in {Ireland} over the next 100 years}, - volume = {41}, - abstract = {The impact of climate change on grassland agriculture may affect yield, livestock numbers, winter housing, slurry storage and land spreading, and the production system. The potential change in grass yield in Ireland due to climate change was estimated using simulation modelling. The estimation took account of the spatial variation of soil and climate. The approach adopted was to: (i) use statistically downscaled climate data derived from the Hadley HADCM3 climate model; (ii) simulate daily weather for 30 separate years from monthly climate data (baseline, 2055 and 2075) using a stochastic weather generator; (iii) simulate 30 years of grass yields for each climate period using the Johnstown Castle Grass Model; (iv) summarize the 30 yield predictions for each climate period as a mean response to climate; and (v) quantify the change in yield as a result of climate change. Grass yield was predicted to decrease in the east of the country due to summer drought stress. In the west grass yield was predicted to increase. The major impacts of yield change were considered to be: (i) grass may cease to be a viable crop in the southeast and east if it requires irrigation to compensate for drought; (ii) theoretical turnout date may become earlier in the season; (iii) stock may have to remain housed at times when currently grazed outside, thus extending storage requirements; and (iv) alternative forage crops may become more suitable for winter feed conservation. No catastrophic impacts are foreseen, but the exact implications will also depend on regional agricultural policy.}, - number = {2}, - journal = {Irish Journal of Agricultural and Food Research}, - author = {Holden, N. M. and Brereton, A. J.}, - month = dec, - year = {2002}, - pages = {213--226}, - annote = {663TCIRISH J AGR FOOD RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Irish J. Agr. Food Res.accession-num: ISI:000182021000005}, -} - -@article{maurer_uncertainty_2007, - title = {Uncertainty in hydrologic impacts of climate change in the {Sierra} {Nevada}, {California} under two emissions scenarios}, - volume = {82}, - number = {3-4}, - journal = {Climatic Change}, - author = {Maurer, E. P.}, - year = {2007}, - pages = {309--325, doi:10.1007/s10584--006--9180--9}, -} - -@article{matyasovszky_downscaling_1996, - title = {Downscaling two versions of a general circulation model to estimate local hydroclimatic factors under climate change}, - volume = {41}, - abstract = {The regional hydroclimatological effect of global climate change has been estimated and compared using a semi-empirical downscaling method with two versions (T21 and T42) of the general circulation model (GCM) developed at the Max Planck Institute for Meteorology, Germany. The comparisons were performed with daily mean temperature and daily precipitation amounts for the continental climate of the state of Nebraska, USA. Both the T21 and the T42 versions resulted in an increase of daily mean temperature under a 2 x CO2 climate. The magnitude of warming was substantially greater for T21 than for T42, except for February and June and at some stations in July where the T42 model suggested greater warming. Both GCMs resulted in a slight decrease in precipitation frequency and an increase in the amount of precipitation on wet days. Here, the T42 model again led to smaller changes. Different locations within Nebraska exhibited somewhat different temperature and precipitation responses with both GCM versions.}, - number = {1}, - journal = {Hydrological Sciences Journal-Journal Des Sciences Hydrologiques}, - author = {Matyasovszky, I. and Bogardi, I.}, - month = feb, - year = {1996}, - pages = {117--129}, - annote = {The following values have no corresponding Zotero field:alt-title: Hydrol. Sci. J.-J. Sci. Hydrol.accession-num: ISI:A1996TV97600008}, - annote = {TV976HYDROLOG SCI J}, -} - -@article{masson_spatial-scale_2011, - title = {Spatial-{Scale} {Dependence} of {Climate} {Model} {Performance} in the {CMIP3} {Ensemble}}, - volume = {24}, - issn = {0894-8755}, - doi = {10.1175/2011jcli3513.1}, - number = {11}, - journal = {Journal of Climate}, - author = {Masson, D. and Knutti, R.}, - month = jun, - year = {2011}, - pages = {2680--2692}, - annote = {doi: 10.1175/2011JCLI3513.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/2011JCLI3513.1}, -} - -@article{martin_downscaling_1996, - title = {Downscaling of general circulation model outputs: {Simulation} of the snow climatology of the {French} {Alps} and sensitivity to climate change}, - volume = {13}, - abstract = {A downscaling method was developed to simulate the seasonal snow cover of the French Alps from general circulation model outputs under various scenarios. It consists of an analogue procedure, which associates a real meteorological situation to a model output. It is based on the comparison between simulated upper air fields and meteorological analyses from the European Centre for Medium-Range Weather Forecasts. The selection uses a nearest neighbour method at a daily time-step. In a second phase, the snow cover is simulated by the snow model CROCUS at several elevations and in the different regions of the French Alps by using data from the real meteorological situations. The method is tested with real data and applied to various ARPEGE/Climat simulations: the present climate and two climate change scenarios.}, - number = {1}, - journal = {Climate Dynamics}, - author = {Martin, E. and Timbal, B. and Brun, E.}, - month = dec, - year = {1996}, - pages = {45--56}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Dyn.accession-num: ISI:A1996WE96000005}, - annote = {WE960CLIM DYNAM}, -} - -@book{maraun_statistical_2018, - address = {Cambridge, UK}, - title = {Statistical downscaling and bias correction for climate research}, - publisher = {Cambridge University Press}, - author = {Maraun, Douglas and Widmann, Martin}, - year = {2018}, -} - -@article{maraun_towards_2017, - title = {Towards process-informed bias correction of climate change simulations}, - volume = {7}, - doi = {10.1038/nclimate3418}, - journal = {Nature Climate Change}, - author = {Maraun, Douglas and Shepherd, Theodore G. and Widmann, Martin and Zappa, Giuseppe and Walton, Daniel and Gutiérrez, José M. and Hagemann, Stefan and Richter, Ingo and Soares, Pedro M. M. and Hall, Alex and Mearns, Linda O.}, - year = {2017}, - pages = {764}, - annote = {The following values have no corresponding Zotero field:publisher: Nature Publishing Groupwork-type: Perspective}, -} - -@incollection{v_masson-delmotte_information_2013, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Information from {Paleoclimate} {Archives}}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {V. Masson-Delmotte and M. Schulz and A. Abe-Ouchi and J. Beer and A. Ganopolski and J. F. González Rouco and E. Jansen and K. Lambeck and J. Luterbacher and T. Naish and T. Osborn and B. Otto-Bliesner and T. Quinn and R. Ramesh and M. Rojas and X. Shao and A. Timmermann}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley}, - year = {2013}, - pages = {383--464}, - annote = {The following values have no corresponding Zotero field:section: 5electronic-resource-num: 10.1017/CBO9781107415324.013}, -} - -@article{maraun_representation_2015, - title = {The representation of location by regional climate models in complex terrain}, - volume = {12}, - issn = {1812-2116}, - doi = {10.5194/hessd-12-3011-2015}, - number = {3}, - journal = {Hydrol. Earth Syst. Sci. Discuss.}, - author = {Maraun, D. and Widmann, M.}, - year = {2015}, - pages = {3011--3028}, - annote = {HESSD}, - annote = {The following values have no corresponding Zotero field:publisher: Copernicus Publications}, -} - -@article{maraun_precipitation_2010, - title = {Precipitation downscaling under climate change: {Recent} developments to bridge the gap between dynamical models and the end user}, - volume = {48}, - issn = {8755-1209}, - doi = {10.1029/2009rg000314}, - abstract = {Precipitation downscaling improves the coarse resolution and poor representation of precipitation in global climate models and helps end users to assess the likely hydrological impacts of climate change. This paper integrates perspectives from meteorologists, climatologists, statisticians, and hydrologists to identify generic end user (in particular, impact modeler) needs and to discuss downscaling capabilities and gaps. End users need a reliable representation of precipitation intensities and temporal and spatial variability, as well as physical consistency, independent of region and season. In addition to presenting dynamical downscaling, we review perfect prognosis statistical downscaling, model output statistics, and weather generators, focusing on recent developments to improve the representation of space-time variability. Furthermore, evaluation techniques to assess downscaling skill are presented. Downscaling adds considerable value to projections from global climate models. Remaining gaps are uncertainties arising from sparse data; representation of extreme summer precipitation, subdaily precipitation, and full precipitation fields on fine scales; capturing changes in small-scale processes and their feedback on large scales; and errors inherited from the driving global climate model.}, - number = {3}, - journal = {Reviews of Geophysics}, - author = {Maraun, D. and Wetterhall, F. and Ireson, A. M. and Chandler, R. E. and Kendon, E. J. and Widmann, M. and Brienen, S. and Rust, H. W. and Sauter, T. and Themeßl, M. and Venema, V. K. C. and Chun, K. P. and Goodess, C. M. and Jones, R. G. and Onof, C. and Vrac, M. and Thiele-Eich, I.}, - year = {2010}, - keywords = {climate change, dynamical downscaling, precipitation, statistical downscaling, 1616 Global Change: Climate variability, 1817 Hydrology: Extreme events, 1637 Global Change: Regional climate change, 1854 Hydrology: Precipitation, 1807 Hydrology: Climate impacts}, - pages = {RG3003}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{maraun_reply_2014, - title = {Reply to “{Comment} on ‘{Bias} {Correction}, {Quantile} {Mapping}, and {Downscaling}: {Revisiting} the {Inflation} {Issue}’”}, - volume = {27}, - issn = {0894-8755}, - doi = {10.1175/jcli-d-13-00307.1}, - abstract = {AbstractIn his comment, G. Bürger criticizes the conclusion that inflation of trends by quantile mapping is an adverse effect. He assumes that the argument would be ?based on the belief that long-term trends and along with them future climate signals are to be large scale.? His line of argument reverts to the so-called inflated regression. Here it is shown, by referring to previous critiques of inflation and standard literature in statistical modeling as well as weather forecasting, that inflation is built upon a wrong understanding of explained versus unexplained variability and prediction versus simulation. It is argued that a sound regression-based downscaling can in principle introduce systematic local variability in long-term trends, but inflation systematically deteriorates the representation of trends. Furthermore, it is demonstrated that inflation by construction deteriorates weather forecasts and is not able to correctly simulate small-scale spatiotemporal structure.}, - number = {4}, - journal = {Journal of Climate}, - author = {Maraun, Douglas}, - month = feb, - year = {2014}, - pages = {1821--1825}, - annote = {doi: 10.1175/JCLI-D-13-00307.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/JCLI-D-13-00307.1}, -} - -@article{maraun_bias_2013, - title = {Bias {Correction}, {Quantile} {Mapping}, and {Downscaling}: {Revisiting} the {Inflation} {Issue}}, - volume = {26}, - issn = {0894-8755}, - doi = {10.1175/jcli-d-12-00821.1}, - number = {6}, - journal = {Journal of Climate}, - author = {Maraun, D.}, - month = mar, - year = {2013}, - pages = {2137--2143}, - annote = {doi: 10.1175/JCLI-D-12-00821.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/JCLI-D-12-00821.1}, -} - -@article{mantua_pacific_2002, - title = {The {Pacific} {Decadal} {Oscillation}}, - volume = {58}, - number = {1}, - journal = {Journal of Oceanography}, - author = {Mantua, N. J. and Hare, S. R.}, - year = {2002}, - pages = {35--44}, -} - -@article{manning_misrepresentation_2010, - title = {Misrepresentation of the {IPCC} {CO2} emission scenarios}, - volume = {3}, - issn = {1752-0894}, - number = {6}, - journal = {Nature Geosci}, - author = {Manning, M. R. and Edmonds, J. and Emori, S. and Grubler, A. and Hibbard, K. and Joos, F. and Kainuma, M. and Keeling, R. F. and Kram, T. and Manning, A. C. and Meinshausen, M. and Moss, R. and Nakicenovic, N. and Riahi, K. and Rose, S. K. and Smith, S. and Swart, R. and van Vuuren, D. P.}, - year = {2010}, - pages = {376--377}, - annote = {10.1038/ngeo880}, - annote = {The following values have no corresponding Zotero field:publisher: Nature Publishing Groupwork-type: 10.1038/ngeo880}, -} - -@article{mann_climate_2015, - title = {Climate change and {California} drought in the 21st century}, - volume = {112}, - issn = {0027-8424}, - number = {13}, - journal = {Proceedings of the National Academy of Sciences}, - author = {Mann, Michael E. and Gleick, Peter H.}, - year = {2015}, - pages = {3858--3859}, -} - -@article{mallakpour_changing_2015, - title = {The changing nature of flooding across the central {United} {States}}, - volume = {5}, - issn = {1758-678X}, - doi = {10.1038/nclimate2516 http://www.nature.com/nclimate/journal/v5/n3/abs/nclimate2516.html#supplementary-information}, - number = {3}, - journal = {Nature Clim. Change}, - author = {Mallakpour, Iman and Villarini, Gabriele}, - year = {2015}, - pages = {250--254}, - annote = {The following values have no corresponding Zotero field:publisher: Nature Publishing Groupwork-type: Letter}, -} - -@article{maraun_nonstationarities_2012, - title = {Nonstationarities of regional climate model biases in {European} seasonal mean temperature and precipitation sums}, - volume = {39}, - issn = {0094-8276}, - doi = {10.1029/2012gl051210}, - abstract = {Bias correcting climate models implicitly assumes stationarity of the correction function. This assumption is assessed for regional climate models in a pseudo reality for seasonal mean temperature and precipitation sums. An ensemble of regional climate models for Europe is used, all driven with the same transient boundary conditions. Although this model-dependent approach does not assess all possible bias non-stationarities, conclusions can be drawn for the real world. Generally, biases are relatively stable, and bias correction on average improves climate scenarios. For winter temperature, bias changes occur in the Alps and ice covered oceans caused by a biased forcing sensitivity of surface albedo; for summer temperature, bias changes occur due to a biased sensitivity of cloud cover and soil moisture. Precipitation correction is generally successful, but affected by internal variability in arid climates. As model sensitivities vary considerably in some regions, multi model ensembles are needed even after bias correction.}, - number = {6}, - journal = {Geophysical Research Letters}, - author = {Maraun, D.}, - year = {2012}, - keywords = {bias correction, climate change, 1637 Global Change: Regional climate change (4321), downscaling, 1616 Global Change: Climate variability (1635, 3305, 3309, 4215, 4513), pseudo reality, stationarity, validation}, - pages = {L06706}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{mantua_pacific_1997, - title = {A {Pacific} interdecadal climate oscillation with impacts on salmon production}, - volume = {78}, - journal = {Bulletin of the American Meteorological Society}, - author = {Mantua, N. J. and Hare, S. R. and Zhang, Y. and Wallace, J. M. and Francis, R. C.}, - year = {1997}, - pages = {1069--1079}, -} - -@article{maloney_north_2014, - title = {North {American} {Climate} in {CMIP5} {Experiments}: {Part} {III}: {Assessment} of 21st {Century} {Projections}}, - volume = {27}, - journal = {Journal of Climate}, - author = {Maloney, E. D. and Camargo, S. J. and Chang, E. and Colle, B. and Fu, R. and Geilw, K. L. and Hu, Q. and Jiang, X. and Johnson, N. and Karnauskas, K. B. and Kinter, J. and Kirtman, B. and Kumar, S. and Langenbrunner, B. and Lombardo, K. and Long, L. and Mariotti, A. and Meyerson, J. E. and Mo, K. and Neelin, J. D. and Pan, Z. and Seager, R. and Serraw, Y. and Seth, A. and Sheffield, J. and Thibeault, J. and Xie, S.-P. and Wang, C. and Wyman, B. and Zhao, M.}, - year = {2014}, - pages = {2230--2270}, -} - -@article{maldonado_interannual_2016, - title = {Interannual variability of the midsummer drought in {Central} {America} and the connection with sea surface temperatures}, - volume = {42}, - issn = {1680-7359}, - doi = {10.5194/adgeo-42-35-2016}, - journal = {Advances in Geosciences}, - author = {Maldonado, T. and Rutgersson, A. and Alfaro, E. and Amador, J. and Claremar, B.}, - year = {2016}, - pages = {35--50}, - annote = {ADGEO}, - annote = {The following values have no corresponding Zotero field:publisher: Copernicus Publications}, -} - -@article{mailhot_design_2010, - title = {Design {Criteria} of {Urban} {Drainage} {Infrastructures} under {Climate} {Change}}, - volume = {136}, - issn = {0733-9496}, - doi = {10.1061/(asce)wr.1943-5452.0000023}, - abstract = {Actual projections provided by climate models suggest that the probability of occurrence of intense rainfall will increase in a future climate due to increasing concentrations of greenhouse gases. Considering that the design of urban drainage systems is based on statistical analysis of past events, an increase in the intensity and frequency of extreme rainfall events will most probably result in more frequent flooding. The design criteria must therefore be revised to take into consideration possible changes induced by climate change. A procedure is proposed to revise the design criteria of urban drainage infrastructures. This procedure integrates information about (1) climate projections for extreme rainfall over the region under consideration; (2) expected level of performance (or acceptable level of risk); and (3) expected lifetime of the infrastructure/system. The resulting design criterion ensures that the service level remains above the selected "acceptable" level over a predefined portion of the infrastructure lifetime. It is argued that the definition of new design criteria should be part of a global adaptation strategy combining various measures to maintain an acceptable level of service in a long-term perspective. Defining this level of service is however a challenge in a context where uncertainties on projected changes in intense rainfall are still important.}, - language = {English}, - number = {2}, - journal = {Journal of Water Resources Planning and Management-Asce}, - author = {Mailhot, A. and Duchesne, S.}, - month = apr, - year = {2010}, - keywords = {regional climate, impacts, Climate change, temperature, ensemble, simulations, extreme rainfall, Design criteria, Extreme rainfall event, future changes, Integrated stormwater management, management, model integrations, Return period, Urban drainage, Urban infrastructure}, - pages = {201--208}, - annote = {ISI Document Delivery No.: 555NDTimes Cited: 7Cited Reference Count: 44Mailhot, Alain Duchesne, SophieAsce-amer soc civil engineersReston}, - annote = {The following values have no corresponding Zotero field:auth-address: [Mailhot, Alain; Duchesne, Sophie] Inst Natl Rech Sci, Ctr Eau Terre Environm 490, De La Couronne, PQ G1K 9A9, Canada. Mailhot, A (reprint author), Inst Natl Rech Sci, Ctr Eau Terre Environm 490, De La Couronne, PQ G1K 9A9, Canada alain.mailhot@ete.inrs.ca sophie.duchesne@ete.inrs.caalt-title: J. Water Resour. Plan. Manage.-ASCEaccession-num: WOS:000274517800007work-type: Article}, -} - -@article{mahlstein_emerging_2012, - title = {Emerging local warming signals in observational data}, - volume = {39}, - issn = {0094-8276}, - doi = {10.1029/2012gl053952}, - abstract = {The global average temperature of the Earth has increased, but year-to-year variability in local climates impedes the identification of clear changes in observations and human experience. For a signal to become obvious in data records or in a human lifetime it needs to be greater than the noise of variability and thereby lead to a significant shift in the distribution of temperature. We show that locations with the largest amount of warming may not display a clear shift in temperature distributions if the local variability is also large. Based on observational data only we demonstrate that large parts of the Earth have experienced a significant local shift towards warmer temperatures in the summer season, particularly at lower latitudes. We also show that these regions are similar to those that are found to be significant in standard detection methods, thus providing an approach to link locally significant changes more closely to impacts.}, - number = {21}, - journal = {Geophysical Research Letters}, - author = {Mahlstein, Irina and Hegerl, Gabriele and Solomon, Susan}, - year = {2012}, - keywords = {observations, 1637 Global Change: Regional climate change (4321), interannual variability, 1616 Global Change: Climate variability (1635, 3305, 3309, 4215, 4513), emerging signal}, - pages = {L21711}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{macadam_ranking_2010, - title = {Ranking climate models by performance using actual values and anomalies: {Implications} for climate change impact assessments}, - volume = {37}, - issn = {0094-8276}, - doi = {L1670410.1029/2010gl043877}, - abstract = {Reducing uncertainty in climate projections can involve giving less credence to Atmosphere-Ocean General Circulation Models (AOGCMs) for which the simulated future climate is judged to be unreliable. Reliability is commonly assessed by comparing AOGCM output with observations. A desirable property of any AOGCM skill score is that resulting AOGCM-performance rankings should show some consistency when derived using observations from different time periods. Notably, earlier work has demonstrated inconsistency between rankings obtained for 20-year periods in the 20th century based on global and regional comparisons of simulated and observed near-surface temperature anomalies. Here, we demonstrate that AOGCM-performance rankings derived from actual temperatures, which incorporate AOGCM biases in climatological means, can be used to identify AOGCMs that perform consistently well or poorly across multiple 20-year periods in the 20th century. This result supports the use of comparisons of simulated and observed actual values of climate variables when assessing the reliability of AOGCMs. Citation: Macadam, I., A. J. Pitman, P. H. Whetton, and G. Abramowitz (2010), Ranking climate models by performance using actual values and anomalies: Implications for climate change impact assessments, Geophys. Res. Lett., 37, L16704, doi:10.1029/2010GL043877.}, - language = {English}, - journal = {Geophysical Research Letters}, - author = {Macadam, I. and Pitman, A. J. and Whetton, P. H. and Abramowitz, G.}, - month = aug, - year = {2010}, - keywords = {temperature, projections, australia}, - annote = {ISI Document Delivery No.: 641ORTimes Cited: 0Cited Reference Count: 15Macadam, I. Pitman, A. J. Whetton, P. H. Abramowitz, G.CSIRO Climate Adaptation National Research Flagship ; Australian Research Council [LP0883296]We acknowledge the modelling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP's Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, US Department of Energy. We thank Janice Bathols of CSIRO for assistance with the CSIRO archive of CMIP3 data and John Kennedy of the Met Office for advice on the HadCRUT3 dataset. This work was partly funded by the CSIRO Climate Adaptation National Research Flagship and by the Australian Research Council via LP0883296. Two anonymous reviewers provided valuable comments.Amer geophysical unionWashington}, - annote = {The following values have no corresponding Zotero field:auth-address: [Macadam, I.; Pitman, A. J.; Abramowitz, G.] Univ New S Wales, Climate Change Res Ctr, Sydney, NSW 2052, Australia. [Whetton, P. H.] CSIRO, Ctr Australian Weather \& Climate Res, Mordialloc, Vic 3195, Australia. Macadam, I, Univ New S Wales, Climate Change Res Ctr, Sydney, NSW 2052, Australia. i.macadam@unsw.edu.aualt-title: Geophys. Res. Lett.accession-num: ISI:000281138200001work-type: Article}, -} - -@incollection{magrin_central_2014, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Central and {South} {America}}, - booktitle = {Climate {Change} 2014: {Impacts}, {Adaptation}, and {Vulnerability}. {Part} {B}: {Regional} {Aspects}. {Contribution} of {Working} {Group} {II} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} of {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {Magrin, G. O. and Marengo, J. A. and Boulanger, J. P. and Buckeridge, M. S. and Castellanos, E. and Poveda, G. and Scarano, F. R. and Vicuña, S.}, - editor = {Barros, V. R. and Field, C. B. and Dokken, D. J. and Mastrandrea, M. D. and Mach, K. J. and Bilir, T. E. and Chatterjee, M. and Ebi, K. L. and Estrada, Y. O. and Genova, R. C. and Girma, B. and Kissel, E. S. and Levy, A. N. and MacCracken, S. and Mastrandrea, P. R. and White, L. L.}, - year = {2014}, - pages = {1499--1566}, - annote = {The following values have no corresponding Zotero field:section: 27}, -} - -@incollection{magrin_latin_2007, - address = {Cambridge, UK}, - title = {Latin {America}. {Climate} {Change} 2007: {Impacts}, {Adaptation} and {Vulnerability}.}, - booktitle = {Contribution of {Working} {Group} {II} to the {Fourth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {Magrin, G. and Gay García, C. and Cruz Choque, D. and Giménez, J. C. and Moreno, A. R. and Nagy, G. J. and Nobre, C. and Villamizar, A.}, - editor = {Parry, M. L. and Canziani, O. F. and Palutikof, J. P. and van der Linden, P. J. and Hanson, C. E.}, - year = {2007}, - pages = {581--615}, - annote = {The following values have no corresponding Zotero field:section: 13}, -} - -@article{victor_magana_midsummer_1999, - title = {The {Midsummer} {Drought} over {Mexico} and {Central} {America}}, - volume = {12}, - doi = {10.1175/1520-0442(1999)012<1577:tmdoma>2.0.co;2}, - abstract = {Abstract The annual cycle of precipitation over the southern part of Mexico and Central America exhibits a bimodal distribution with maxima during June and September–October and a relative minimum during July and August, known as the midsummer drought (MSD). The MSD is not associated with the meridional migration of the intertropical convergence zone (ITCZ) and its double crossing over Central America but rather with fluctuations in the intensity and location of the eastern Pacific ITCZ. During the transition from intense to weak (weak to intense) convective activity, the trade winds over the Caribbean strengthen (weaken). Such acceleration in the trade winds is part of the dynamic response of the low-level atmosphere to the magnitude of the convective forcing in the ITCZ. The intensification of the trade winds during July and August and the orographic forcing of the mountains over most of Central America result in maximum precipitation along the Caribbean coast and minimum precipitation along the Pacific coast of Central America. Changes in the divergent (convergent) low-level winds over the “warm pool” off the west coast of southern Mexico and Central America determine the evolution of the MSD. Maximum deep convective activity over the northern equatorial eastern Pacific, during the onset of the summer rainy season, is reached when sea surface temperatures exceed 29°C (around May). After this, the SSTs over the eastern Pacific warm pool decrease around 1°C due to diminished downwelling solar radiation and stronger easterly winds (during July and August). Such SST changes near 28°C result in an substantial decrease in deep convective activity, associated with the nonlinear interaction between SST and deep tropical convection. Decreased deep tropical convection allows increased downwelling solar radiation and a slight increase in SSTs, which reach a second maximum (∼28.5°C) by the end of August and early September. This increase in SST results once again in stronger low-level convergence, enhanced deep convection, and, consequently, in a second maximum in precipitation. The MSD signal can also be detected in other variables such as minimum and maximum surface temperature and even in tropical cyclone activity over the eastern Pacific.}, - number = {6}, - journal = {Journal of Climate}, - author = {Victor Magaña and Jorge A. Amador and Socorro Medina}, - year = {1999}, - pages = {1577--1588}, -} - -@article{madsen_regional_2002, - title = {Regional estimation of rainfall intensity-duration-frequency curves using generalized least squares regression of partial duration series statistics}, - volume = {38}, - abstract = {A general framework for regional analysis and modeling of extreme rainfall characteristics is presented. The model is based on the partial duration series (PDS) method that includes in the analysis all events above a threshold level. In the PDS model the average annual number of exceedances, the mean value of the exceedance magnitudes, and the coefficient of L variation (LCV) are considered as regional variables. A generalized least squares (GLS) regression model that explicitly accounts for intersite correlation and sampling uncertainties is applied for evaluating the regional heterogenity of the PDS parameters. For the parameters that show a significant regional variability the GLS model is subsequently adopted for describing the variability from physiographic and climatic characteristics. For determination of a proper regional parent distribution L moment analysis is applied for discriminating between the exponential distribution and various two-parameter distributions in the PDS model. The resulting model can be used for estimation of rainfall intensity-duration-frequency curves at an arbitrary location in a region. For illustration, the regional model is applied to rainfall data from a rain gauge network in Denmark.}, - number = {11}, - journal = {Water Resources Research}, - author = {Madsen, H. and Mikkelsen, P. S. and Rosbjerg, D. and Harremoes, P.}, - month = nov, - year = {2002}, - pages = {art. no.--1239}, - annote = {637RXWATER RESOUR RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Water Resour. Res.accession-num: ISI:000180527800021}, -} - -@article{maccracken_climate_2003, - title = {Climate change scenarios for the {U}.{S}. national assessment}, - volume = {84}, - number = {12}, - journal = {Bulletin of the American Meteorological Society}, - author = {MacCracken, M. C. and Barron, E. J. and Easterling, D. R. and Felzer, B. S. and Karl, T. R.}, - year = {2003}, - pages = {1711--1723, DOI: 10.1175/BAMS--84--12--1711}, -} - -@article{maak_statistical_1997, - title = {Statistical downscaling of monthly mean air temperature to the beginning of flowering of {Galanthus} nivalis {L}. in {Northern} {Germany}}, - volume = {41}, - abstract = {We have examined the relationship between phenological data and concurrent large-scale meterological data. As phenological data we have chosen the beginning of the flowering of Galanthus nivalis L. (flowering date) in Northern Germany, and as large- scale meteorological data we use monthly mean near-surface air temperatures for January, February and March. By means of canonical cell-elation analysis (CCA), a strong linear correlation between both sets of variables is identified. Twenty years of observed data are used to build the statistical model. To validate the derived relationship, the flowering date is downscaled from air temperature observations of an independent period. The statistical model is found to reproduce the observed flowering dates well, both in terms of variability as well as amplitude. Air temperature data from a general circulation model of climate change are used to estimate the flowering date in the case of increasing atmospheric carbon dioxide concentration. We found that at a time of doubled CO2 concentration (expected by about 2035) G. nivalis L. in Northern Germany will flower similar to 2 weeks and at the time of tripled CO2 concentration (expected by about 2085) similar to 4 weeks earlier than presently.}, - number = {1}, - journal = {International Journal of Biometeorology}, - author = {Maak, K. and vonStorch, H.}, - month = jul, - year = {1997}, - pages = {5--12}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Biometeorol.accession-num: ISI:A1997XX01600002}, - annote = {XX016INT J BIOMETEOROL}, -} - -@article{lussier_activated_1994, - title = {Activated carbon from cherry stones}, - volume = {32}, - issn = {0008-6223}, - number = {8}, - journal = {Carbon}, - author = {Lussier, Michael G. and Shull, Jeffrey C. and Miller, Dennis J.}, - year = {1994}, - keywords = {Activated carbon, cherry stones}, - pages = {1493--1498}, - annote = {The following values have no corresponding Zotero field:work-type: doi: 10.1016/0008-6223(94)90144-9}, -} - -@article{lundquist_onset_2006, - title = {Onset of snowmelt and streamflow in 2004 in the western {United} {States}: how shading may affect spring streamflow timing in a warmer world}, - volume = {7}, - journal = {Journal of Hydrometeorology}, - author = {Lundquist, J. D. and Flint, A. L.}, - year = {2006}, - pages = {1199--1217}, -} - -@article{lundquist_spring_2004, - title = {Spring onset in the {Sierra} {Nevada}: {When} is snowmelt independent of elevation?}, - volume = {5}, - issn = {1525-755X}, - abstract = {Short-term climate and weather systems can have a strong influence on mountain snowmelt, sometimes overwhelming the effects of elevation and aspect. Although most years exhibit a spring onset that starts first at lowest and moves to highest elevations, in spring 2002, flow in a variety of streams within the Tuolumne and Merced River basins of the southern Sierra Nevada all rose synchronously on 29 March. Flow in streams draining small high-altitude glacial subcatchments rose at the same time as that draining much larger basins gauged at lower altitudes, and streams from north- and south-facing cirques rose and fell together. Historical analysis demonstrates that 2002 was one among only 8 yr with such synchronous flow onsets during the past 87 yr, recognized by having simultaneous onsets of snowmelt at over 70\% of snow pillow sites, having discharge in over 70\% of monitored streams increase simultaneously, and having temperatures increase over 12degreesC within a 5-day period. Synchronous springs tend to begin with a low pressure trough over California during late winter, followed by the onset of a strong ridge and unusually warm temperatures. Synchronous springs are characterized by warmer than average winters and cooler than average March temperatures in California. In the most elevation-dependent, nonsynchronous years, periods of little or no storm activity, with warmer than average March temperatures, precede the onset of spring snowmelt, allowing elevation and aspect to influence snowmelt as spring arrives gradually.}, - language = {English}, - number = {2}, - journal = {Journal of Hydrometeorology}, - author = {Lundquist, J. D. and Cayan, D. R. and Dettinger, M. D.}, - month = apr, - year = {2004}, - keywords = {model, california, runoff, basins, switzerland, western united-states}, - pages = {327--342}, - annote = {810GWTimes Cited:0Cited References Count:30}, - annote = {The following values have no corresponding Zotero field:auth-address: Lundquist, JD Univ Calif San Diego, Scripps Inst Oceanog, 9500 Gilman Dr, La Jolla, CA 92093 USA Univ Calif San Diego, Scripps Inst Oceanog, La Jolla, CA 92093 USA US Geol Survey, La Jolla, CA USAaccession-num: ISI:000220693800005}, -} - -@article{lozar_climate_2011, - title = {Climate {Change} {Impacts} and {Adaptation} on {CONUS} {Military} {Installations}}, - volume = {Part 3}, - journal = {NATO Science for Peace and Security Series C: Environmental Security}, - author = {Lozar, R. C. and Hiett, M. D. and Westervelt, J. D.}, - year = {2011}, - pages = {333--371, DOI: 10.1007/978--94--007--1770--1\_19}, -} - -@article{j_a_lowe_how_2009, - title = {How difficult is it to recover from dangerous levels of global warming?}, - volume = {4}, - issn = {1748-9326}, - abstract = {Climate models provide compelling evidence that if greenhouse gas emissions continue at present rates, then key global temperature thresholds (such as the European Union limit of two degrees of warming since pre-industrial times) are very likely to be crossed in the next few decades. However, there is relatively little attention paid to whether, should a dangerous temperature level be exceeded, it is feasible for the global temperature to then return to safer levels in a usefully short time. We focus on the timescales needed to reduce atmospheric greenhouse gases and associated temperatures back below potentially dangerous thresholds, using a state-of-the-art general circulation model. This analysis is extended with a simple climate model to provide uncertainty bounds. We find that even for very large reductions in emissions, temperature reduction is likely to occur at a low rate. Policy-makers need to consider such very long recovery timescales implicit in the Earth system when formulating future emission pathways that have the potential to 'overshoot' particular atmospheric concentrations of greenhouse gases and, more importantly, related temperature levels that might be considered dangerous.}, - number = {1}, - journal = {Environmental Research Letters}, - author = {J. A. Lowe and C. Huntingford and S. C. B. Raper and C. D. Jones and S. K. Liddicoat and L. K. Gohar}, - year = {2009}, - pages = {014012}, -} - -@article{lopez-moreno_influence_2004, - title = {Influence of snow accumulation and snowmelt on streamflow in the central {Spanish} {Pyrenees}}, - volume = {49}, - number = {5}, - journal = {Hydrological Sciences}, - author = {López-Moreno, J. I. and García-Ruiz, J. M.}, - year = {2004}, - pages = {787--802}, -} - -@article{lopez_climate_2009, - title = {From climate model ensembles to climate change impacts and adaptation: {A} case study of water resource management in the southwest of {England}}, - volume = {45}, - issn = {1944-7973}, - doi = {10.1029/2008wr007499}, - number = {8}, - journal = {Water Resources Research}, - author = {Lopez, Ana and Fung, Fai and New, Mark and Watts, Glenn and Weston, Alan and Wilby, Robert L.}, - year = {2009}, - keywords = {climate change, 1884 Water supply, 1807 Climate impacts, 1873 Uncertainty assessment, 1880 Water management, probabilistic climate change predictions, water resources management}, - pages = {W08419}, -} - -@article{long_atmospheric_2010, - title = {Atmospheric carbon dioxide removal: long-term consequences and commitment}, - volume = {5}, - issn = {1748-9326}, - abstract = {Carbon capture from ambient air has been proposed as a mitigation strategy to counteract anthropogenic climate change. We use an Earth system model to investigate the response of the coupled climate–carbon system to an instantaneous removal of all anthropogenic CO 2 from the atmosphere. In our extreme and idealized simulations, anthropogenic CO 2 emissions are halted and all anthropogenic CO 2 is removed from the atmosphere at year 2050 under the IPCC A2 CO 2 emission scenario when the model-simulated atmospheric CO 2 reaches 511 ppm and surface temperature reaches 1.8 °C above the pre-industrial level. In our simulations a one-time removal of all anthropogenic CO 2 in the atmosphere reduces surface air temperature by 0.8 °C within a few years, but 1 °C surface warming above pre-industrial levels lasts for several centuries. In other words, a one-time removal of 100\% excess CO 2 from the atmosphere offsets less than 50\% of the warming experienced at the time of removal. To maintain atmospheric CO 2 and temperature at low levels, not only does anthropogenic}, - number = {2}, - journal = {Environmental Research Letters}, - author = {Long, Cao and Ken, Caldeira}, - year = {2010}, - pages = {024011}, -} - -@article{lolis_spatial_2002, - title = {Spatial and temporal 850 {hPA} air temperature and sea-surface temperature covariances in the {Mediterranean} region and their connection to atmospheric circulation}, - volume = {22}, - abstract = {The spatial and temporal covariability between the lower troposphere and sea surface temperatures (SSTs) are Studied in the Mediterranean basin for the period 1958-98. Monthly air temperature anomalies for the 850 hPa pressure level (T-850hPa) at 2.5degrees x 2.5degrees grid points and SST anomalies in 5degrees x 5degrees grid boxes are utilized. As a first step, factor analysis is applied oil both sets of data in order to reduce their dimensionality. Then, canonical correlation analysis is applied and this leads to one statistically significant pair of canonical variates for winter and to two pairs for summer. In winter, a teleconnection (see-saw) between western Europe and the eastern Mediterranean at the 850 hPa level is revealed, and a corresponding weaker one between the areas of central-west and eastern Mediterranean for SST. The correlation between T-850hPa and SST appears higher over the eastern Mediterranean. In summer, the first pair of canonical variates reveals a covariability between T-850hPa and SST in the western Mediterranean, and the second one shows a covariability in the eastern Mediterranean, without the existence of any strong spatial teleconnection. The analysis is repeated, using time lags of I month, or longer, in order to detect any possible non-synchronous relation. Statistically significant results are found only when T-850hPa leads SST with a time lag of I month. In particular, the results are statistically significant for winter only, and the findings are similar to those of the first analysis. Therefore, the existence of a I month time scale SST persistence is detected for winter months. Copyright (C) 2002 Royal Meteorological Society.}, - number = {6}, - journal = {International Journal of Climatology}, - author = {Lolis, C. J. and Bartzokas, A. and Katsoulis, B. D.}, - month = may, - year = {2002}, - pages = {663--676}, - annote = {562BKINT J CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:000176177400003}, -} - -@article{loikith_characteristics_2012, - title = {Characteristics of {Observed} {Atmospheric} {Circulation} {Patterns} {Associated} with {Temperature} {Extremes} over {North} {America}}, - issn = {0894-8755}, - doi = {10.1175/jcli-d-11-00709.1}, - journal = {Journal of Climate}, - author = {Loikith, Paul C. and Broccoli, Anthony J.}, - year = {2012}, - annote = {doi: 10.1175/JCLI-D-11-00709.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/JCLI-D-11-00709.1}, -} - -@article{lohmann_large-scale_1996, - title = {A large-scale horizontal routing model to be coupled to land surface parameterization schemes}, - volume = {48A}, - journal = {Tellus}, - author = {Lohmann, D. and Nolte-Holube, R. and Raschke, E.}, - year = {1996}, - pages = {708--721}, -} - -@article{lohmann_streamflow_2004, - title = {Streamflow and water balance intercomparisons of four land surface models in the {North} {American} {Land} {Data} {Assimilation} {System} project}, - volume = {109}, - abstract = {This paper is part of a series of papers about the multi-institutional North American Land Data Assimilation System (NLDAS) project. It compares and evaluates streamflow and water balance results from four different land surface models (LSMs) within the continental United States. These LSMs have been run for the retrospective period from 1 October 1996 to 30 September 1999 forced by atmospheric observations from the Eta Data Assimilation System (EDAS) analysis, measured precipitation, and satellite-derived downward solar radiation. These model runs were performed on a common 1/8° latitude-longitude grid and used the same database for soil and vegetation classifications. We have evaluated these simulations using U.S. Geological Survey (USGS) measured daily streamflow data for 9 large major basins and 1145 small- to medium-sized basins from 23 km2 to 10,000 km2 distributed over the NLDAS domain. Model runoff was routed with a common distributed and a lumped optimized linear routing model. The diagnosis of the model water balance results demonstrates strengths and weaknesses in the models, our insufficient knowledge of ad hoc parameters used for the model runs, the interdependence of model structure and model physics, and the lack of good forcing data in parts of the United States, especially in regions with extended snow cover. Overall, the differences between the LSM water balance terms are of the same magnitude as the mean water balance terms themselves. The modeled mean annual runoff shows large regional differences by a factor of up to 4 between models. The corresponding difference in mean annual evapotranspiration is about a factor of 2. The analysis of runoff timing for the LSMs demonstrates the importance of correct snowmelt timing, where the resulting differences in streamflow timing can be up to four months. Runoff is underestimated by all LSMs in areas with significant snowfall.}, - journal = {Journal of Geophysical Research}, - author = {Lohmann, D. and Mitchell, K. E. and Houser, P. R. and Wood, E. F. and Schaake, J. C. and Robock, A. and Cosgrove, B. A. and Sheffield, J. and Duan, Q. and Luo, L. and Higgins, R. W. and Pinker, R. T. and Tarpley, J. D.}, - year = {2004}, - keywords = {1818 Hydrology: Evapotranspiration, 1836 Hydrology: Hydrologic budget, 1860 Hydrology: Runoff and streamflow, 1863 Hydrology: Snow and ice, 1878 Hydrology: Water/energy interactions}, - pages = {D07S91, doi:10.1029/2003JD003517}, - annote = {10.1029/2003JD003517}, - annote = {The following values have no corresponding Zotero field:publisher: American Geophysical Union}, -} - -@article{lobell_impacts_2006, - title = {Impacts of future climate change on {California} perennial crop yields: {Model} projections with climate and crop uncertainties}, - volume = {141}, - journal = {Agricultural and Forest Meteorology}, - author = {Lobell, D. B. and Field, C. B. and Cahill, K. N. and Bonfils, C.}, - year = {2006}, - pages = {208--218, doi:10.1016/j.agrformet.2006.10.006}, -} - -@article{loaiciga_sea_2012, - title = {Sea {Water} {Intrusion} by {Sea}-{Level} {Rise}: {Scenarios} for the 21st {Century}}, - volume = {50}, - issn = {1745-6584}, - doi = {10.1111/j.1745-6584.2011.00800.x}, - number = {1}, - journal = {Ground Water}, - author = {Loáiciga, Hugo A. and Pingel, Thomas J. and Garcia, Elizabeth S.}, - year = {2012}, - pages = {37--47}, - annote = {The following values have no corresponding Zotero field:publisher: Blackwell Publishing Ltd}, -} - -@article{livneh_long-term_2013, - title = {A {Long}-{Term} {Hydrologically} {Based} {Dataset} of {Land} {Surface} {Fluxes} and {States} for the {Conterminous} {United} {States}: {Update} and {Extensions}*}, - volume = {26}, - issn = {0894-8755}, - doi = {10.1175/jcli-d-12-00508.1}, - number = {23}, - journal = {Journal of Climate}, - author = {Livneh, Ben and Rosenberg, Eric A. and Lin, Chiyu and Nijssen, Bart and Mishra, Vimal and Andreadis, Kostas M. and Maurer, Edwin P. and Lettenmaier, Dennis P.}, - month = dec, - year = {2013}, - pages = {9384--9392}, - annote = {doi: 10.1175/JCLI-D-12-00508.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/JCLI-D-12-00508.1}, -} - -@article{livezey_statistical_1983, - title = {Statistical {Field} {Significance} and its {Determination} by {Monte} {Carlo} {Techniques}}, - volume = {111}, - doi = {doi:10.1175/1520-0493(1983)111<0046:SFSAID>2.0.CO;2}, - number = {1}, - journal = {Monthly Weather Review}, - author = {Livezey, Robert E. and Chen, W. Y.}, - year = {1983}, - pages = {46--59}, -} - -@article{lin_explicit_1997, - title = {An explicit flux-form semi-{Lagrangian} shallow water model on the sphere}, - volume = {123}, - journal = {Quarterly Journal of the Royal Meteorological Society}, - author = {Lin, S. J. and Rood, R. B.}, - year = {1997}, - pages = {2531--2533}, -} - -@article{lobanov_empirical-statistical_2001, - title = {Empirical-statistical methodology and methods for modeling and forecasting of climate variability of different temporal scales}, - volume = {18}, - abstract = {Main problem of modern climatology is to assess the present as well as future climate change, For this aim two approaches are used: physic-mathematic modeling on the basis of GCMs and palaeoclimatic analogues. The third approach is based on the empirical-statistical methodology and is developed in this paper. This approach allows to decide two main problems: to give a real assessment of climate changes by observed data for climate monitoring and extrapolation of obtained climate tendencies to the nearest future (10-15 years) and give the empirical basis for further development of physic-mathematical models. The basic theory and methodology of empirical-statistic approach have been developed as well as a common model for description of space-time climate variations taking into account the processes of different time scales. The way of decreasing of the present and future uncertainty is suggested as the extraction of long-term climate changes components in the particular time series and spatial generalization of the same climate tendencies in the obtained homogeneous regions. Algorithm and methods for realization of empirical-statistic methodology have been developed along with methods for generalization of intraannual fluctuations, methods for extraction of homogeneous components of different time scales (interannual, decadal, century), methodology and methods for spatial generalization and modeling, methods for extrapolation on the basis of two main kinds of time models: stochastic and deterministic-stochastic. Some applications of developed methodology and methods are given for the longest time series of temperature and precipitation over the world and for spatial generalization over the European area.}, - number = {5}, - journal = {Advances in Atmospheric Sciences}, - author = {Lobanov, V. A.}, - year = {2001}, - pages = {844--863}, - annote = {475VNADV ATMOS SCI}, - annote = {The following values have no corresponding Zotero field:alt-title: Adv. Atmos. Sci.accession-num: ISI:000171190400018}, -} - -@article{livneh_spatially_2015, - title = {A spatially comprehensive, hydrometeorological data set for {Mexico}, the {U}.{S}., and {Southern} {Canada} 1950–2013}, - volume = {2}, - doi = {10.1038/sdata.2015.42}, - journal = {Scientific Data}, - author = {Livneh, Ben and Bohn, Theodore J. and Pierce, David W. and Munoz-Arriola, Francisco and Nijssen, Bart and Vose, Russell and Cayan, Daniel R. and Brekke, Levi}, - year = {2015}, - pages = {150042}, - annote = {The following values have no corresponding Zotero field:publisher: Macmillan Publishers Limited}, -} - -@article{liu_future_2015, - title = {Future property damage from flooding: sensitivities to economy and climate change}, - volume = {132}, - issn = {0165-0009}, - doi = {10.1007/s10584-015-1478-z}, - language = {English}, - number = {4}, - journal = {Climatic Change}, - author = {Liu, Jing and Hertel, ThomasW and Diffenbaugh, NoahS and Delgado, MichaelS and Ashfaq, Moetasim}, - month = oct, - year = {2015}, - pages = {741--749}, - annote = {The following values have no corresponding Zotero field:alt-title: Climatic Changepublisher: Springer Netherlands}, -} - -@article{lins_regional_1997, - title = {Regional streamflow regimes and hydroclimatology of the {United} {States}}, - volume = {33}, - journal = {Water Resources Research}, - author = {Lins, H. F.}, - year = {1997}, - pages = {1655-- 1667}, -} - -@article{lin_finite-volume_1997, - title = {A finite-volume integration scheme for computing pressure-gradient forces in general vertical coordinates}, - volume = {123}, - journal = {Quarterly Journal of the Royal Meteorological Society}, - author = {Lin, S. J.}, - year = {1997}, - pages = {1749--1762}, -} - -@article{liebmann_daily_2005, - title = {Daily {Precipitation} {Grids} for {South} {America}}, - volume = {86}, - issn = {0003-0007}, - doi = {10.1175/bams-86-11-1567}, - number = {11}, - journal = {Bulletin of the American Meteorological Society}, - author = {Liebmann, Brant and Allured, Dave}, - month = nov, - year = {2005}, - pages = {1567--1570}, - annote = {doi: 10.1175/BAMS-86-11-1567}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/BAMS-86-11-1567}, -} - -@article{liang_one-dimensional_1996, - title = {One-dimensional statistical dynamic representation of subgrid spatial variability of precipitation in the two-layer variable infiltration capacity model}, - volume = {101}, - number = {D16}, - journal = {Journal of Geophysical Research}, - author = {Liang, X. and Lettenmaier, D. P. and Wood, E. F.}, - year = {1996}, - pages = {21403--21422}, -} - -@article{liang_simple_1994, - title = {A simple hydrologically based model of land surface water and energy fluxes for general circulation models}, - volume = {99}, - number = {D7}, - journal = {Journal of Geophysical Research}, - author = {Liang, X. and Lettenmaier, D. P. and Wood, E. and Burges, S. J.}, - year = {1994}, - pages = {14415--14428}, -} - -@article{lin_multidimensional_1996, - title = {Multidimensional flux form semi-{Lagrangian} transport schemes}, - volume = {124}, - journal = {Monthly Weather Review}, - author = {Lin, S. J. and Rood, R. B.}, - year = {1996}, - pages = {2046--2070}, -} - -@article{li_application_2000, - title = {Application of tree-structured regression for regional precipitation prediction using general circulation model output}, - volume = {16}, - abstract = {This study presents a tree-structured regression (TSR) method to relate daily precipitation with a variety of free-atmosphere variables. Historical data were used to identify distinct weather patterns associated with differing types of precipitation events. Models were developed using 67 \% of the data for training and the remaining data for model validation. Seasonal models were built for each of 2 US sites: San Francisco, California, and San Antonio, Texas. The average correlation between observed and simulated daily precipitation data series is 0.75 for the training set and 0.68 for the validation set. Relative humidity was found to be the dominant variable in these TSR models. Output from an NCAR CSM (climate system model) transient simulation of climate change were then used to drive the TSR models in the prediction of precipitation characteristics under climate change. A preliminary screening of the GCM output variables for current climate, however, revealed significant problems for the San Antonio site. Specifically, the CSM missed the annual trends in humidity for the grid cell containing this site. CSM output for the San Francisco site was found to be much more reliable. Therefore, we present future precipitation estimates only for the San Francisco site.}, - number = {1}, - journal = {Climate Research}, - author = {Li, X. S. and Sailor, D.}, - month = nov, - year = {2000}, - pages = {17--30}, - annote = {388NGCLIMATE RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:000166186700002}, -} - -@article{li_evaluating_2015, - title = {Evaluating the effect of climate change on areal reduction factors using regional climate model projections}, - volume = {528}, - issn = {0022-1694}, - doi = {http://dx.doi.org/10.1016/j.jhydrol.2015.06.067}, - journal = {Journal of Hydrology}, - author = {Li, Jingwan and Sharma, Ashish and Johnson, Fiona and Evans, Jason}, - year = {2015}, - keywords = {Areal reduction factor, Climate impacts, Extreme rainfall, GEV distribution, Regional climate simulation}, - pages = {419--434}, -} - -@article{li_bias_2010, - title = {Bias correction of monthly precipitation and temperature fields from {Intergovernmental} {Panel} on {Climate} {Change} {AR4} models using equidistant quantile matching}, - volume = {115}, - issn = {0148-0227}, - doi = {10.1029/2009jd012882}, - abstract = {A new quantile-based mapping method is developed for the bias correction of monthly global circulation model outputs. Compared to the widely used quantile-based matching method that assumes stationarity and only uses the cumulative distribution functions (CDFs) of the model and observations for the baseline period, the proposed method incorporates and adjusts the model CDF for the projection period on the basis of the difference between the model and observation CDFs for the training (baseline) period. Thus, the method explicitly accounts for distribution changes for a given model between the projection and baseline periods. We demonstrate the use of the new method over northern Eurasia. We fit a four-parameter beta distribution to monthly temperature fields and discuss the sensitivity of the results to the choice of distribution range parameters. For monthly precipitation data, a mixed gamma distribution is used that accounts for the intermittent nature of rainfall. To test the fidelity of the proposed method, we choose 1970\&\#8211;1999 as the baseline training period and then randomly select 30 years from 1901\&\#8211;1999 as the projection test period. The bootstrapping is repeated 30 times to mimic different climate conditions that may occur, and the results suggest that both methods are comparable when applied to the 20th century for both temperature and precipitation for the examined quartiles. We also discuss the dependence of the bias correction results on the choice of time period for training. This indicates that the remaining biases in the bias-corrected time series are directly tied to the model's performance during the training period, and therefore care should be taken when using a particular training time period. When applied to the Intergovernmental Panel on Climate Change fourth assessment report (AR4) A2 climate scenario projection, the data time series after bias correction from both methods exhibit similar spatial patterns. However, over regions where the climate model shows large changes in projected variability, there are discernable differences between the methods. The proposed method is more sensitive to a reduction in variability, exemplified by wintertime temperature. Further synthetic experiments using the lower 33\% and upper 33\% of the full data set as the validation data suggest that the proposed equidistance quantile-matching method is more efficient in reducing biases than the traditional CDF mapping method for changing climates, especially for the tails of the distribution. This has important consequences for the occurrence and intensity of future projected extreme events such as heat waves, floods, and droughts. As the new method is simple to implement and does not require substantial computational time, it can be used to produce auxiliary ensemble scenarios for various climate impact-oriented applications.}, - number = {D10}, - journal = {J. Geophys. Res.}, - author = {Li, Haibin and Sheffield, Justin and Wood, Eric F.}, - year = {2010}, - keywords = {bias correction, climate change, statistical downscaling, 1637 Global Change: Regional climate change, 3305 Atmospheric Processes: Climate change and variability, 1869 Hydrology: Stochastic hydrology, cumulative distribution function, quantile mapping}, - pages = {D10101}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{li_evaluation_2007, - title = {Evaluation of {Intergovernmental} {Panel} on {Climate} {Change} {Fourth} {Assessment} soil moisture simulations for the second half of the twentieth century}, - volume = {112}, - number = {D06106}, - journal = {Journal of Geophysical Research}, - author = {Li, H. and Robock, A. and Wild, M.}, - year = {2007}, - pages = {doi:10.1029/2006JD007455}, -} - -@article{li_how_2017, - title = {How much runoff originates as snow in the western {United} {States}, and how will that change in the future?}, - volume = {44}, - doi = {doi:10.1002/2017GL073551}, - abstract = {Abstract In the western United States, the seasonal phase of snow storage bridges between winter-dominant precipitation and summer-dominant water demand. The critical role of snow in water supply has been frequently quantified using the ratio of snowmelt-derived runoff to total runoff. However, current estimates of the fraction of annual runoff generated by snowmelt are not based on systematic analyses. Here based on hydrological model simulations and a new snowmelt tracking algorithm, we show that 53\% of the total runoff in the western United States originates as snowmelt, despite only 37\% of the precipitation falling as snow. In mountainous areas, snowmelt is responsible for 70\% of the total runoff. By 2100, the contribution of snowmelt to runoff will decrease by one third for the western U.S. in the Intergovernmental Panel on Climate Change Representative Concentration Pathway 8.5 scenario. Snowmelt-derived runoff currently makes up two thirds of the inflow to the region's major reservoirs. We argue that substantial impacts on water supply are likely in a warmer climate.}, - number = {12}, - journal = {Geophysical Research Letters}, - author = {Li, Dongyue and Wrzesien, Melissa L. and Durand, Michael and Adam, Jennifer and Lettenmaier, Dennis P.}, - year = {2017}, - pages = {6163--6172}, -} - -@article{levis_large-scale_2000, - title = {Large-scale vegetation feedbacks on a doubled-{CO2} climate}, - volume = {13}, - journal = {Journal of Climate}, - author = {Levis, S. and Foley, J. A. and Pollard, D.}, - year = {2000}, - pages = {1313--1325}, -} - -@article{leung_potential_1999, - title = {Potential climate chance impacts on mountain watersheds in the {Pacific} {Northwest}}, - volume = {35}, - issn = {1093-474X}, - abstract = {Global climate change due to the buildup of greenhouse gases in the atmosphere has serious potential impacts on water resources in the Pacific Northwest. Climate scenarios produced by general circulation models (GCMs) do not provide enough spatial specificity for studying water resources in mountain watersheds. This study uses dynamical downscaling with a regional climate model (RCM) driven by a GCM to simulate climate change scenarios. The RCM uses a subgrid parameterization of orographic precipitation and land surface cover to simulate surface climate at the spatial scale suitable for the representation of topographic effects over mountainous regions. Numerical experiments have been performed to simulate the present-day climatology and the climate conditions corresponding to a doubling of atmospheric CO2 concentration. The RCM results indicate an average warming of about 2.5 degrees C, and precipitation generally increases over the Pacific Northwest and decreases over California. These simulations were used to drive a distributed hydrology model of two snow dominated watersheds, the American River and Middle Fork Flathead, in the Pacific Northwest to obtain more detailed estimates of the sensitivity of water resources to climate change. Results show that as more precipitation falls as rain rather than snow in the warmer climate, there is a 60 percent reduction in snowpack and a significant shift in the seasonal pattern of streamflow in the American River. Much less drastic changes are found in the Middle Fork Flathead where snowpack is only reduced by 18 percent and the seasonal pattern of streamflow remains intact. This study shows that the impacts of climate change on water resources are highly region specific. Furthermore, under the specific climate change scenario, the impacts are largely driven by the warming trend rather than the precipitation trend, which is small.}, - language = {English}, - number = {6}, - journal = {Journal of the American Water Resources Association}, - author = {Leung, L. R. and Wigmosta, M. S.}, - month = dec, - year = {1999}, - keywords = {model, dynamical downscaling, surface water hydrology, atmospheric co2, climate change impacts, distributed hydrologic modeling, mountain water resources, orographic precipitation, snowpack, water resources planning}, - pages = {1463--1471}, - annote = {271ZBTimes Cited:17Cited References Count:19}, - annote = {The following values have no corresponding Zotero field:auth-address: Leung, LR Pacific NW Natl Lab, POB 999, Richland, WA 99352 USA Pacific NW Natl Lab, Richland, WA 99352 USAaccession-num: ISI:000084624600016}, -} - -@article{leung_mid-century_2004, - title = {Mid-century ensemble regional climate change scenarios for the western {United} {States}}, - volume = {62}, - issn = {0165-0009}, - abstract = {To study the impacts of climate change on water resources in the western U. S., global climate simulations were produced using the National Center for Atmospheric Research/Department of Energy (NCAR/DOE) Parallel Climate Model (PCM). The Penn State/NCAR Mesoscale Model (MM5) was used to downscale the PCM control ( 20 years) and three future ( 2040 - 2060) climate simulations to yield ensemble regional climate simulations at 40 km spatial resolution for the western U. S. This paper describes the regional simulations and focuses on the hydroclimate conditions in the Columbia River Basin (CRB) and Sacramento- San Joaquin River (SSJ) Basin. Results based on global and regional simulations show that by mid-century, the average regional warming of 1 to 2.5 degreesC strongly affects snowpack in the western U. S. Along coastal mountains, reduction in annual snowpack was about 70\% as indicated by the regional simulations. Besides changes in mean temperature, precipitation, and snowpack, cold season extreme daily precipitation increased by 5 to 15 mm/day (15 - 20\%) along the Cascades and the Sierra. The warming resulted in increased rainfall at the expense of reduced snowfall, and reduced snow accumulation ( or earlier snowmelt) during the cold season. In the CRB, these changes were accompanied by more frequent rain-on-snow events. Overall, they induced higher likelihood of wintertime flooding and reduced runoff and soil moisture in the summer. Changes in surface water and energy budgets in the CRB and SSJ basin were affected mainly by changes in surface temperature, which were statistically significant at the 0.95 confidence level. Changes in precipitation, while spatially incoherent, were not statistically significant except for the drying trend during summer. Because snow and runoff are highly sensitive to spatial distributions of temperature and precipitation, this study shows that ( 1) downscaling provides more realistic estimates of hydrologic impacts in mountainous regions such as the western U. S., and ( 2) despite relatively small changes in temperature and precipitation, changes in snowpack and runoff can be much larger on monthly to seasonal time scales because the effects of temperature and precipitation are integrated over time and space through various surface hydrological and land-atmosphere feedback processes. Although the results reported in this study were derived from an ensemble of regional climate simulations driven by a global climate model that displays low climate sensitivity compared with most other models, climate change was found to significantly affect water resources in the western U. S. by the mid twenty-first century.}, - language = {English}, - number = {1-3}, - journal = {Climatic Change}, - author = {Leung, L. R. and Qian, Y. and Bian, X. D. and Washington, W. M. and Han, J. G. and Roads, J. O.}, - month = feb, - year = {2004}, - pages = {75--113}, - annote = {768FATimes Cited:8Cited References Count:37}, - annote = {The following values have no corresponding Zotero field:auth-address: Leung, LR Pacific NW Natl Lab, POB 999, Richland, WA 99352 USA Pacific NW Natl Lab, Richland, WA 99352 USA Natl Ctr Atmospher Res, Boulder, CO 80307 USA Univ Calif San Diego, Scripps Inst Oceanog, La Jolla, CA 92093 USAaccession-num: ISI:000188531900005}, -} - -@article{leung_regional_2003, - title = {Regional climate research - {Needs} and opportunities}, - volume = {84}, - issn = {0003-0007}, - language = {English}, - number = {1}, - journal = {Bulletin of the American Meteorological Society}, - author = {Leung, L. R. and Mearns, L. O. and Giorgi, F. and Wilby, R. L.}, - month = jan, - year = {2003}, - pages = {89--95}, - annote = {642BMTimes Cited:12Cited References Count:1}, - annote = {The following values have no corresponding Zotero field:auth-address: Leung, LR Pacific NW Natl Lab, POB 999, Richland, WA 99352 USA Pacific NW Natl Lab, Richland, WA 99352 USA Natl Ctr Atmospher Res, Boulder, CO 80307 USA Abdus Salam Int Ctr Theoret Phys, Trieste, Italy Univ London Kings Coll, London WC2R 2LS, Englandaccession-num: ISI:000180784200016}, -} - -@article{leung_hydroclimate_2003, - title = {Hydroclimate of the western {United} {States} based on observations and regional climate simulation of 1981-2000. part {I}: {Seasonal} statistics}, - volume = {16}, - issn = {0894-8755}, - abstract = {The regional climate of the western United States shows clear footprints of interaction between atmospheric circulation and orography. The unique features of this diverse climate regime challenges climate modeling. This paper provides detailed analyses of observations and regional climate simulations to improve our understanding and modeling of the climate of this region. The primary data used in this study are the 1/8degrees gridded temperature and precipitation based on station observations and the NCEP - NCAR global reanalyses. These data were used to evaluate a 20-yr regional climate simulation performed using the fifth-generation Pennsylvania State University - National Center for Atmospheric Research (Penn State - NCAR) Mesoscale Model (MM5) driven by large-scale conditions of the NCEP - NCAR reanalyses. Regional climate features examined include seasonal mean and extreme precipitation; distribution of precipitation rates; and precipitation intensity, frequency, and seasonality. The relationships between precipitation and surface temperature are also analyzed as a means to evaluate how well regional climate simulations can be used to simulate surface hydrology, and relationships between precipitation and elevation are analyzed as diagnostics of the impacts of surface topography and spatial resolution. The latter was performed at five east - west transects that cut across various topographic features in the western United States. -These analyses suggest that the regional simulation realistically captures many regional climate features. The simulated seasonal mean and extreme precipitation are comparable to observations. The regional simulation produces precipitation over a wide range of precipitation rates comparable to observations. Obvious biases in the simulation include the oversimulation of precipitation in the basins and intermountain West during the cold season, and the undersimulation in the Southwest in the warm season. There is a tendency of reduced precipitation frequency rather than intensity in the simulation during the summer in the Northwest and Southwest, which leads to the insufficient summer mean precipitation in those areas. Because of the general warm biases in the simulation, there is also a tendency for more precipitation events to be associated with warmer temperatures, which can affect the simulation of snowpack and runoff.}, - language = {English}, - number = {12}, - journal = {Journal of Climate}, - author = {Leung, L. R. and Qian, Y. and Bian, X. D.}, - month = jun, - year = {2003}, - keywords = {variability, impacts, pacific-northwest, oscillation, budget, extremes, mesoscale model, north-american monsoon, storms, subgrid orographic precipitation}, - pages = {1892--1911}, - annote = {688WKTimes Cited:12Cited References Count:35}, - annote = {The following values have no corresponding Zotero field:auth-address: Leung, LR Pacific NW Natl Lab, 902 Battelle Blvd,POB 999, Richland, WA 99352 USA Pacific NW Natl Lab, Richland, WA 99352 USAaccession-num: ISI:000183460100002}, -} - -@article{leung_simulations_1999, - title = {Simulations of the {ENSO} hydroclimate signals in the {Pacific} {Northwest} {Columbia} {River} basin}, - volume = {80}, - issn = {0003-0007}, - abstract = {Natural fluctuations in the atmosphere-ocean system related to the El Nino-Southern Oscillation (ENSO) induce climate variability over many parts of the world that is potentially predictable with lead times from seasons to decades. This study examines the potential of using a model nesting approach to provide seasonal climate and streamflow forecasts suitable for water resources management. Two ensembles of perpetual January simulations were performed with a regional climate model driven by a general circulation model (GCM), using observed climatological sea surface temperature (SST) and the mean SST of the warm ENSO years between 1950 and 1994. The climate simulations were then used to drive a macroscale hydrology model to simulate streamflow. The differences between the two ensembles of simulations are defined as the warm ENSO signals. -The simulated hydroclimate signals were compared with observations. The analyses focus on the Columbia River basin in the Pacific Northwest. Results show that the global and regional models simulated a warming over the Pacific Northwest that is quite close to the observations. The models also correctly captured the strong wet signal over California and the weak dry signal over the Pacific Northwest during warm ENSO years. The regional climate model consistently performed better than the GCM in simulating the spatial distribution of regional climate and climate signals. When the climate simulations were used to drive a macroscale hydrology model at the Columbia River basin, the simulated streamflow signal resembles that derived from hydrological simulations driven by observed climate. The streamflow simulations were considerably improved when a simple bias correction scheme was applied to the climate simulations. The coupled regional climate and macroscale hydrologic simulations demonstrate the prospect for generating and utilizing seasonal climate forecasts for managing reservoirs.}, - language = {English}, - number = {11}, - journal = {Bulletin of the American Meteorological Society}, - author = {Leung, L. R. and Hamlet, A. F. and Lettenmaier, D. P. and Kumar, A.}, - month = nov, - year = {1999}, - keywords = {temperature, parameterization, el-nino, skill, spatial variability, regional climate model, orographic precipitation, lateral boundary-conditions, northern-hemisphere, predictions}, - pages = {2313--2329}, - annote = {253NGTimes Cited:18Cited References Count:37}, - annote = {The following values have no corresponding Zotero field:auth-address: Leung, LR Pacific NW Lab, POB 999, Richland, WA 99352 USA Pacific NW Lab, Richland, WA 99352 USA Univ Washington, Dept Civil Engn, Seattle, WA 98195 USA NOAA, NCEP, EMC, Climate Modeling Branch, Washington, DC USAaccession-num: ISI:000083562200009}, -} - -@article{lettenmaier_hydro-climatological_1994, - title = {Hydro-climatological trends in the continental {United} {States}, 1948-1988}, - volume = {7}, - journal = {Journal of Climate}, - author = {Lettenmaier, D. P. and Wood, E. F. and Wallis, J. R.}, - year = {1994}, - pages = {586--607}, -} - -@incollection{lettenmaier_hydrologic_1993, - address = {New York, NY, USA}, - title = {Hydrologic {Forecasting}}, - booktitle = {Handbook of {Hydrology}}, - publisher = {McGraw-Hill Inc.}, - author = {Lettenmaier, D. P. and Wood, E. F.}, - editor = {Maidment, D. R.}, - year = {1993}, - pages = {26.1--26.30}, -} - -@article{lettenmaier_water_1999, - title = {Water resources implications of global warming: {A} {US} regional perspective}, - volume = {43}, - abstract = {The implications of global warming for the performance of six U.S. water resource systems are evaluated. The six case study sites represent a range of geographic and hydrologic, as well as institutional and social settings. Large, multi-reservoir systems (Columbia River, Missouri River, Apalachicola- Chatahoochee-Flint (ACF) Rivers), small, one or two reservoir systems (Tacoma and Boston) and medium size systems (Savannah River) are represented. The river basins range from mountainous to low relief and semi-humid to semi-arid, and the system operational purposes range from predominantly municipal to broadly multi-purpose. The studies inferred, using a chain of climate downscaling, hydrologic and water resources systems models, the sensitivity of six water resources systems to changes in precipitation, temperature and solar radiation. The climate change scenarios used in this study are based on results from transient climate change experiments performed with coupled ocean-atmosphere General Circulation Models (GCMs) for the 1995 Intergovernmental Panel on Climate Change (IPCC) assessment. An earlier doubled-CO2 scenario from one of the GCMs was also used in the evaluation. The GCM scenarios were transferred to the local level using a simple downscaling approach that scales local weather variables by fixed monthly ratios (for precipitation) and fixed monthly shifts (for temperature). For those river basins where snow plays an important role in the current climate hydrology (Tacoma, Columbia, Missouri and, to a lesser extent, Boston) changes in temperature result in important changes in seasonal streamflow hydrographs. In these systems, spring snowmelt peaks are reduced and winter flows increase, on average. Changes in precipitation are generally reflected in the annual total runoff volumes more than in the seasonal shape of the hydrographs. In the Savannah and ACF systems, where snow plays a minor hydrological role, changes in hydrological response are linked more directly to temperature and precipitation changes. Effects on system performance varied from system to system, from GCM to GCM, and for each system operating objective (such as hydropower production, municipal and industrial supply, flood control, recreation, navigation and instream flow protection). Effects were generally smaller for the transient scenarios than for the doubled CO2 scenario. In terms of streamflow, one of the transient scenarios tended to have increases at most sites, while another tended to have decreases at most sites. The third showed no general consistency over the six sites. Generally, the water resource system performance effects were determined by the hydrologic changes and the amount of buffering provided by the system's storage capacity. The effects of demand growth and other plausible future operational considerations were evaluated as well. For most sites, the effects of these non-climatic effects on future system performance would about equal or exceed the effects of climate change over system planning horizons.}, - number = {3}, - journal = {Clim. Change}, - author = {Lettenmaier, D. P. and Wood, A. W. and Palmer, R. N. and Wood, E. F. and Stakhiv, E. Z.}, - month = nov, - year = {1999}, - pages = {537--579}, - annote = {237PWCLIMATIC CHANGE}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Changeaccession-num: ISI:000082666100003}, -} - -@article{leung_pacific_1999, - title = {Pacific {Northwest} climate sensitivity simulated by a regional climate model driven by a {GCM}. {Part} {I}: {Control} simulations}, - volume = {12}, - issn = {0894-8755}, - abstract = {A model nesting approach has been used to simulate the regional climate over the Pacific Northwest. The present-day global climatology is first simulated using the NCAR Community Climate Model (CCM3) driven by observed sea surface temperature and sea ice distribution at T42 (2.8 degrees) resolution. This large-scale simulation is used to provide lateral boundary conditions for driving the Pacific Northwest National Laboratory Regional Climate Model (RCM). One notable feature of the RCM is the use of subgrid parameterizations of orographic precipitation and vegetation cover, in which subgrid variations of surface elevation and vegetation are aggregated to a limited number of elevation-vegetation classes. An airflow model and a thermodynamic model are used to parameterize the orographic uplift/descent as air parcels cross over mountain barriers or valleys. -The 7-yr climatologies as simulated by CCM3 and RCM are evaluated and compared in terms of large-scale spatial patterns and regional means. Biases are found in the simulation of large-scale circulations, which also affect the regional model simulation. Therefore, the regional simulation is not very different from the CCM3 simulation in terms of large-scale features. However, the regional model greatly improves the simulation of precipitation, surface temperature, and snow cover at the local scales. This is shown by improvements in the spatial correlation between the observations and simulations. The RCM simulation is further evaluated using station observations of surface temperature and precipitation to compare the simulated and observed relationships between surface temperature-precipitation and altitude. The model is found to correctly capture the surface temperature-precipitation variations as functions of surface topography over different mountain ranges, and under different climate regimes.}, - language = {English}, - number = {7}, - journal = {Journal of Climate}, - author = {Leung, L. R. and Ghan, S. J.}, - month = jul, - year = {1999}, - keywords = {general-circulation model, parameterization, spatial variability, orographic precipitation, boundary-layer, ccm3, lake, resolution}, - pages = {2010--2030}, - annote = {214WNTimes Cited:32Cited References Count:39}, - annote = {The following values have no corresponding Zotero field:auth-address: Leung, LR Pacific NW Natl Lab, POB 999, Richland, WA 99352 USA Pacific NW Natl Lab, Richland, WA 99352 USAaccession-num: ISI:000081350700010}, -} - -@article{lemos_narrowing_2012, - title = {Narrowing the climate information usability gap}, - volume = {2}, - issn = {1758-678X}, - doi = {http://www.nature.com/nclimate/journal/v2/n11/abs/nclimate1614.html#supplementary-information}, - number = {11}, - journal = {Nature Clim. Change}, - author = {Lemos, Maria Carmen and Kirchhoff, Christine J. and Ramprasad, Vijay}, - year = {2012}, - pages = {789--794}, - annote = {10.1038/nclimate1614}, - annote = {The following values have no corresponding Zotero field:publisher: Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.work-type: 10.1038/nclimate1614}, -} - -@article{leipprand_global_2006, - title = {Global effects of doubled atmospheric {CO2} content on evapotranspiration, soil moisture and runoff under potential natural vegetation}, - volume = {51}, - number = {1}, - journal = {Hydrological Sciences Journal-Journal Des Sciences Hydrologiques}, - author = {Leipprand, A. and Gerten, D.}, - year = {2006}, - pages = {171--185}, -} - -@techreport{lehner_hydrosheds_2006, - address = {Washington, DC.}, - title = {{HydroSHEDS} {Technical} {Documentation}}, - author = {Lehner, B. and Verdin, K. and Jarvis, A.}, - editor = {World Wildlife Fund US}, - year = {2006}, - pages = {[http://hydrosheds.cr.usgs.gov]}, -} - -@article{lee_effect_2011, - title = {Effect of climate change on field crop production in {California}’s {Central} {Valley}}, - volume = {109}, - issn = {0165-0009}, - doi = {10.1007/s10584-011-0305-4}, - language = {English}, - number = {1}, - journal = {Climatic Change}, - author = {Lee, Juhwan and De Gryze, Steven and Six, Johan}, - month = dec, - year = {2011}, - pages = {335--353}, - annote = {The following values have no corresponding Zotero field:alt-title: Climatic Changepublisher: Springer Netherlands}, -} - -@article{lebassi_observed_2009, - title = {Observed 1970–2005 {Cooling} of {Summer} {Daytime} {Temperatures} in {Coastal} {California}}, - volume = {22}, - doi = {doi:10.1175/2008JCLI2111.1}, - number = {13}, - journal = {Journal of Climate}, - author = {Lebassi, Bereket and González, Jorge and Fabris, Drazen and Maurer, Edwin and Miller, Norman and Milesi, Cristina and Switzer, Paul and Bornstein, Robert}, - year = {2009}, - pages = {3558--3573}, -} - -@article{lettenmaier_hydrologic_1990, - title = {Hydrologic sensitivities of the {Sacramento}-{San} {Joaquin} {River} {Basin}, {California}, to global warming}, - volume = {26}, - abstract = {The recent advent of climate simulations using physically based general circulation models (GCMs) provides a method of generating future climate scenarios to evaluate the reliability of surface water resources. Using GCMs, the hydrologic sensitivities of four medium-sized mountainous catchments in the Sacramento and San Joaquin river basins to long-term global warming were analyzed. The study catchments were selected to represent the geographic, climatic, and hydrologic diversity of the Sacramento-San Joaquin River basin. In addition, the following selection criteria were used: (1) diversions and storage upstream of the gaging station should be minimal; (2) the stream gage should be rated ' good ' or better, with the record including the years 1951-1980; and (3) the annual runoff should be highly correlated with the summed annual inflows to the reservoir system. The hydrologic response of these catchments, all of which are dominated by spring snowmelt runoff , were simulated by the coupling of the snowmelt and the soil moisture accounting models of the U.S. National Weather Service River Forecast System. In all four catchments the global warming pattern, which was indexed to CO2 doubling scenarios simulated by three (global) general circulation models, produced a major seasonal shift in the snow accumulation pattern. Under the alternative climate scenarios more winter precipitation fell as rain instead of snow, and winter runoff increased while spring snowmelt runoff decreased. In addition, large increases in the annual flood maxima were simulated, primarily due to an increase in rain-on-snow events, with the time of occurrence of many large floods shifting from spring to winter. (Tappert-PTT)}, - number = {1}, - journal = {Water Resources Research}, - author = {Lettenmaier, D. P. and Gan, T. Y.}, - year = {1990}, - keywords = {Streamflow, California, Sensitivity analysis, Evapotranspiration, Climatology, accumulation, forecasting, Future planning, Global warming, Greenhouse effect, Model, Snow, Snowmelt, Stream discharge, Stream gages, Streamflow data, Streamflow forecasting, studies, Weather}, - pages = {69--86}, - annote = {Water Resources Research WRERAQ Vol. 26, No. 1, p 69-86, January 1990. 15 fig, 11 tab, 28 ref. USEPA Cooperative Agreement CR814637.}, -} - -@article{lehmann_increased_2015, - title = {Increased record-breaking precipitation events under global warming}, - volume = {132}, - issn = {0165-0009}, - doi = {10.1007/s10584-015-1434-y}, - language = {English}, - number = {4}, - journal = {Climatic Change}, - author = {Lehmann, Jascha and Coumou, Dim and Frieler, Katja}, - month = oct, - year = {2015}, - pages = {501--515}, - annote = {The following values have no corresponding Zotero field:alt-title: Climatic Changepublisher: Springer Netherlands}, -} - -@techreport{legutke_hamburg_1999, - address = {Hamburg, Germany}, - title = {The {Hamburg} atmosphere-ocean coupled circulation model {ECHO}-{G}, {Technical} report, {No}. 18.}, - institution = {German Climate Computer Centre (DKRZ)}, - author = {Legutke, S. and Voss, R.}, - year = {1999}, - pages = {62}, -} - -@article{leathers_characteristics_1995, - title = {Characteristics of {Temperature} {Depressions} {Associated} with {Snow} {Cover} across the {Northeast} {United} {States}}, - volume = {34}, - number = {2}, - journal = {Journal of Applied Meteorology}, - author = {Leathers, D. J. and Ellis, A. W. and Robinson, D. A.}, - year = {1995}, - pages = {381--390, DOI:10.1175/1520--0450--34.2.381}, -} - -@article{lawler_projected_2009, - title = {Projected climate-induced faunal change in the {Western} {Hemisphere}}, - volume = {90}, - issn = {0012-9658}, - abstract = {Climate change is predicted to be one of the greatest drivers of ecological change in the coming century. Increases in temperature over the last century have clearly been linked to shifts in species distributions. Given the magnitude of projected future climatic changes, we can expect even larger range shifts in the coming century. These changes will, in turn, alter ecological communities and the functioning of ecosystems. Despite the seriousness of predicted climate change, the uncertainty in climate-change projections makes it difficult for conservation managers and planners to proactively respond to climate stresses. To address one aspect of this uncertainty, we identified predictions of faunal change for which a high level of consensus was exhibited by different climate models. Specifically, we assessed the potential effects of 30 coupled atmosphere-ocean general circulation model (AOGCM) future-climate simulations on the geographic ranges of 2954 species of birds, mammals, and amphibians in the Western Hemisphere. Eighty percent of the climate projections based on a relatively low greenhouse-gas emissions scenario result in the local loss of at least 10\% of the vertebrate fauna over much of North and South America. The largest changes in fauna are predicted for the tundra, Central America, and the Andes Mountains where, assuming no dispersal constraints, specific areas are likely to experience over 90\% turnover, so that faunal distributions in the future will bear little resemblance to those of today.}, - language = {English}, - number = {3}, - journal = {Ecology}, - author = {Lawler, J. J. and Shafer, S. L. and White, D. and Kareiva, P. and Maurer, E. P. and Blaustein, A. R. and Bartlein, P. J.}, - month = mar, - year = {2009}, - keywords = {climate change, future, impacts, projections, change, distributions, amphibian declines, amphibians, biodiversity, birds, climate envelope models, extinctions, mammals, models, patterns, potential changes, range shifts, species distributions, species-richness}, - pages = {588--597}, - annote = {ISI Document Delivery No.: 413EXTimes Cited: 35Cited Reference Count: 57Lawler, Joshua J. Shafer, Sarah L. White, Denis Kareiva, Peter Maurer, Edwin P. Blaustein, Andrew R. Bartlein, Patrick J.Ecological soc amerWashington}, - annote = {The following values have no corresponding Zotero field:auth-address: [Lawler, Joshua J.; Blaustein, Andrew R.] Oregon State Univ, Dept Zool, Corvallis, OR 97331 USA. [Shafer, Sarah L.] US Geol Survey, Corvallis, OR 97331 USA. [White, Denis] US EPA, Corvallis, OR 97333 USA. [Kareiva, Peter] Nature Conservancy, Seattle, WA 98105 USA. [Maurer, Edwin P.] Santa Clara Univ, Dept Civil Engn, Santa Clara, CA 95053 USA. [Bartlein, Patrick J.] Univ Oregon, Dept Geog, Eugene, OR 97403 USA. Lawler, JJ, Univ Washington, Coll Forest Resources, Seattle, WA 98195 USA. jlawler@u.washington.edualt-title: Ecologyaccession-num: ISI:000263776800002work-type: Article}, -} - -@article{lauren_step_2006, - title = {{STEP} {WISE}, {MULTIPLE} {OBJECTIVE} {CALIBRATION} {OF} {A} {HYDROLOGIC} {MODEL} {FOR} {A} {SNOWMELT} {DOMINATED} {BASIN}$^{\textrm{1}}$}, - volume = {42}, - issn = {1752-1688}, - number = {4}, - journal = {Journal of the American Water Resources Association}, - author = {Lauren, E. Hay and George, H. Leavesley and Martyn, P. Clark and Steve, L. Markstrom and Roland, J. Viger and Makiko, Umemoto}, - year = {2006}, - pages = {877--890}, - annote = {10.1111/j.1752-1688.2006.tb04501.x}, -} - -@article{langenbrunner_analyzing_2013, - title = {Analyzing {ENSO} {Teleconnections} in {CMIP} {Models} as a {Measure} of {Model} {Fidelity} in {Simulating} {Precipitation}}, - volume = {26}, - issn = {0894-8755}, - doi = {10.1175/jcli-d-12-00542.1}, - number = {13}, - journal = {Journal of Climate}, - author = {Langenbrunner, Baird and Neelin, J. David}, - month = jul, - year = {2013}, - pages = {4431--4446}, - annote = {doi: 10.1175/JCLI-D-12-00542.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/JCLI-D-12-00542.1}, -} - -@article{lawford_advancing_2004, - title = {Advancing {Global}-and {Continental}-{Scale} {Hydrometeorology}: {Contributions} of {GEWEX} {Hydrometeorology} {Panel}}, - volume = {85}, - issn = {0003-0007}, - doi = {10.1175/bams-85-12-1917}, - number = {12}, - journal = {Bulletin of the American Meteorological Society}, - author = {Lawford, R. G. and Stewart, R. and Roads, J. and Isemer, H. J. and Manton, M. and Marengo, J. and Yasunari, T. and Benedict, S. and Koike, T. and Williams, S.}, - month = dec, - year = {2004}, - pages = {1917--1930}, - annote = {doi: 10.1175/BAMS-85-12-1917}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/BAMS-85-12-1917}, -} - -@article{lau_canonical_2013, - title = {A canonical response of precipitation characteristics to {Global} {Warming} from {CMIP5} models}, - issn = {1944-8007}, - doi = {10.1002/grl.50420}, - journal = {Geophysical Research Letters}, - author = {Lau, William K. M. and Wu, H. T. and Kim, K. M.}, - year = {2013}, - keywords = {precipitation, global warming, 1626 Global climate models, 1854 Precipitation, 1655 Water cycles}, - pages = {n/a--n/a}, -} - -@article{lapp_climate_2005, - title = {Climate warming impacts on snowpack accumulation in an alpine watershed}, - volume = {25}, - number = {4}, - journal = {International Journal of Climatology}, - author = {Lapp, S. and Byrne, J. and Townshend, I. and Kienzle, S.}, - year = {2005}, - pages = {521--536}, -} - -@article{lapp_linking_2002, - title = {Linking global circulation model synoptics and precipitation for western {North} {America}}, - volume = {22}, - abstract = {Synoptic downscaling from global circulation models (GCMs) has been widely used to develop local and regional-scale future precipitation scenarios under global warming. This paper presents an analysis of the linkages between the Canadian Centre for Climate Modelling and Analysis first version of the Canadian Global Coupled Model (CCCma CGCM1) 2000 model output and local/regional precipitation time series. The GCM 500 hPa geopotential heights were visually classified for Synoptic patterns using a geographical information system. The pattern frequencies were statistically compared with historical data from Chan-non et al. (1993. Monthly, Weather Review 121: 633- 647) for the winter period 1961-85, The CGCM1 synoptic frequencies compare favourably with the historical data. and they represent a Substantial improvement over the 1992 Canadian Climate Centre Global Circulation Model Synoptic climatology Output. The CGCM1 output was used to forecast future winter precipitation scenarios for five geographically diverse climate stations in western North America. Copyright (C) 2002 Royal Meteorological Society.}, - number = {15}, - journal = {International Journal of Climatology}, - author = {Lapp, S. and Byrne, J. and Kienzle, S. and Townshend, I.}, - month = dec, - year = {2002}, - pages = {1807--1817}, - annote = {631GDINT J CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:000180159000001}, -} - -@article{lapen_spatial_2003, - title = {Spatial analysis of seasonal and annual temperature and precipitation normals in southern {Ontario}, {Canada}}, - volume = {29}, - issn = {0380-1330}, - abstract = {This study presents some univariate and multivariate low-pass filtering methodologies (ordinary kriging, ordinary cokriging, modified residual kriging, inverse distance weighting) to spatially model seasonal temperature and precipitation normals in an environment where climate patterns are variably modified by regional water bodies and physiography. Cross-validation and maps of modeled climate patterns are used as a criteria to assess particular climate realizations. The results of the exercise show how an agro-climatologist might use these methods to assess spatial representations of the modeled phenomena, the relevance of covariates, and the shortcomings and strengths of different techniques to characterize lake-physiography-climate interactions on a seasonal basis. The methods are also useful for evaluating station networks for capturing important Great Lakes climate patterns.}, - language = {English}, - number = {4}, - journal = {Journal of Great Lakes Research}, - author = {Lapen, D. R. and Hayhoe, H. N.}, - year = {2003}, - keywords = {model, precipitation, evapotranspiration, air temperature, elevation, geostatistics, great lakes, mountainous terrain, multivariate geostatistics}, - pages = {529--544}, - annote = {761ZYTimes Cited:1Cited References Count:25}, - annote = {The following values have no corresponding Zotero field:auth-address: Lapen, DR Agr \& Agri Food Canada, Eastern Cereal \& Oilseed Res Ctr, Ottawa, ON K1A 0C6, Canada Agr \& Agri Food Canada, Eastern Cereal \& Oilseed Res Ctr, Ottawa, ON K1A 0C6, Canadaaccession-num: ISI:000187946400001}, -} - -@article{landman_statistical_2000, - title = {Statistical downscaling of monthly forecasts}, - volume = {20}, - abstract = {Canonical correlation analysis (CCA) is used to downscale large-scale circulation forecasts by the Centre for Ocean-Land- Atmosphere studies (COLA) T30 general circulation model (GCM) statistically to regional rainfall in South Africa. Monthly GCM ensemble forecasts available from 1979 to 1995 have been generated using NCEP reanalysis data as initial input and globally observed sea-surface temperature (SST) data at the lower boundary. Altogether, 51 30-day cases of CCM simulations, spanning 17 years, within the target season of December- February (DJF), are produced. This period is very important for agriculture and maize, in particular. A model output statistics (MOS) procedure is used to downscale GCM forecast sea-level pressure and 500 hPa height fields to regional rainfall for 30- day periods over South Africa. The CCA model is trained on the first 31 cases (up to February 1989) and forecasts are subsequently made for the remaining 20 cases. These retro- active real-time forecasts have a high potential (correlations {\textgreater} 0.5) over most of the interior of South Africa and, furthermore, the prediction of extreme events seems feasible. CCA diagnostics of the GCM-output against rainfall reveal that favourable rainfall over most of the interior is associated with low pressure systems at the surface over the west coast, with an associated ridging high. This is supported by other observational studies. Copyright (C) 2000 Royal Meteorological Society.}, - number = {13}, - journal = {International Journal of Climatology}, - author = {Landman, W. A. and Tennant, W. J.}, - month = nov, - year = {2000}, - pages = {1521--1532}, - annote = {376EPINT J CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:000165445500001}, -} - -@article{landman_statistical_2001, - title = {Statistical downscaling of {GCM} simulations to streamflow}, - volume = {252}, - abstract = {A multi-tiered forecast procedure is employed to simulate real- time operational seasonal forecasts of categorized (below- normal, near-normal and above-normal) streamflow at the inlets of twelve dams of the Vaal and upper Tugela river catchments in South Africa. Forecasts are made for the December to February (DJF) season over an 8-year independent period from 1987/ 1988 to 1994/1995. A physically based model of the atmosphere system, known as a general circulation model (GCM), is used to simulate atmospheric variability over southern Africa, the output of which is statistically downscaled to streamflow. The GCM used is the COLA T30, and is forced at the boundary with predicted monthly-mean global sea-surface temperatures. The monthly-mean sea-surface temperature fields are first predicted over lead-times of several months using a canonical correlation analysis (CCA) model. GCM simulations are then obtained for an area including most of southern Africa and adjacent oceans. The GCM simulations are downscaled to catchment level from coarse resolution gridded climate variables, using a perfect prognosis approach: bias-corrected GCM simulations are substituted into the perfect prognosis equations to provide the downscaled categorized streamflow forecasts. Although surface characteristics of each catchment that affect the variability of streamflow are not considered in the proposed downscaling system, successful forecasts of streamflow categories were obtained for some of the years forecast independently. The scheme's operational utility is thus demonstrated. albeit over short lead-times. (C) 2001 Elsevier Science B.V. All rights reserved.}, - number = {1-4}, - journal = {Journal of Hydrology}, - author = {Landman, W. A. and Mason, S. J. and Tyson, P. D. and Tennant, W. J.}, - month = oct, - year = {2001}, - pages = {221--236}, - annote = {469VQJ HYDROL}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Hydrol.accession-num: ISI:000170837100015}, -} - -@article{landman_statistical_2002, - title = {Statistical recalibration of {GCM} forecasts over southern {Africa} using model output statistics}, - volume = {15}, - abstract = {A technique for producing regional rainfall forecasts for southern Africa is developed that statistically maps or "recalibrates'' large-scale circulation features produced by the ECHAM3.6 general circulation model (GCM) to observed regional rainfall for the December-February (DJF) season. The recalibration technique, model output statistics (MOS), relates archived records of GCM fields to observed DJF rainfall through a set of canonical correlation analysis (CCA) equations. After screening several potential predictor fields, the 850-hPa geopotential height field is selected as the single predictor field in the CCA equations that is subsequently used to produce MOS-recalibrated rainfall patterns. The recalibrated forecasts outscore area-averaged GCM-simulated rainfall anomalies, as well as forecasts produced using a simple linear forecast model. The MOS recalibration is applied to two sets of GCM experiments: for the "simulation'' experiment, simultaneous observed sea surface temperature (SST) serves as the lower boundary forcing; for the "hindcast'' experiment, the prescribed SSTs are obtained by persisting the previous month's SST anomaly through the forecast period. Pattern analyses performed on the predictor-predictand pairs confirm a robust relationship between the GCM 850-hPa height fields and the rainfall fields. The structure and variability of the large- scale circulation is well characterized by the GCM in both simulation and hindcast mode. Measures of retroactive skill for a 9-yr independent period (1991/92-1999/2000) using the hindcast MOS are obtained for both deterministic and probabilistic forecasts, suggesting that a probabilistic representation of MOS forecasts is potentially more valuable. Finally, MOS is employed to investigate its potential to downscale the GCM large-scale circulation to more specific forecasts of land surface characteristics such as streamflow.}, - number = {15}, - journal = {J. Clim.}, - author = {Landman, W. A. and Goddard, L.}, - month = aug, - year = {2002}, - pages = {2038--2055}, - annote = {576LRJ CLIMATE}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Clim.accession-num: ISI:000177007200003}, -} - -@article{kysely_comparison_2002, - title = {Comparison of extremes in {GCM}-simulated, downscaled and observed central-{European} temperature series}, - volume = {20}, - abstract = {This study concentrates on the comparison of 20 and 50 yr return values of annual maximum and minimum temperatures in (1) observations, (2) general circulation model (GCM) simulated control climates, (3) statistical downscaling from observations, and (4) statistical downscaling from GCMs. Temperature series at 4 sites in central Europe and in the nearest GCM gridpoints corresponding to the stations are analysed. Extreme value analysis was performed by fitting the generalized extreme value (GEV) distribution using L moment and maximum likelihood estimators. The comparison does not appear to be sensitive to which statistical method of the estimation of parameters of the GEV distribution is used, although individual return values are influenced by the choice of method. The skill of both GCMs in reproducing extreme high and low temperatures is limited, and statistical downscaling from GCMs tends to improve their performance, although it generally yields extremes that are too moderate compared to observed values (this holds for downscaling both from observations and GCMs). White noise addition in downscaling from observations leads to more realistic return values of annual maximum and minimum temperatures than variance inflation.}, - number = {3}, - journal = {Climate Research}, - author = {Kysely, J.}, - month = apr, - year = {2002}, - pages = {211--222}, - annote = {563CDCLIMATE RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:000176235900004}, -} - -@article{kuo_potential_2015, - title = {Potential impact of climate change on intensity duration frequency curves of central {Alberta}}, - volume = {130}, - issn = {1573-1480}, - doi = {10.1007/s10584-015-1347-9}, - abstract = {Under the effect of climate change, warming likely means that there will be more water vapour in the atmosphere and extreme storms are expected to occur more frequently and with greater severity, resulting in municipal Intensity-Duration-Frequency (IDF) curves with higher intensities and shorter return periods. A regional climate model, MM5 (the Pennsylvania State University / National Center for Atmospheric Research numerical model), was set up in a one-way, three-domain nested framework to simulate future summer (May to August) precipitation of central Alberta. MM5 is forced with climate data of four Global Climate Models, CGCM3, ECHAM5, CCSM3, and MIROC3.2, for the baseline 1971–2000 and 2011–2100 based on the Special Report on Emissions Scenarios A2, A1B, and B1 of Intergovernmental Panel on Climate Change. Due to the bias of MM5’s simulations, a quantile-quantile bias correction method and a regional frequency analysis is applied to derive projected grid-based IDF curves for central Alberta. In addition, future trends of air temperature and precipitable water, which affect storm pattern and intensity, are investigated. Future IDF curves show a wide range of increased intensities especially for storms of short durations (≤1-h). Conversely, future IDF curves are expected to shift upward because of increased air temperature and precipitable water which are projected to be about 2.9 °C and 29 \% in average by 2071–2100, respectively. Our results imply that the impact of climate change could increase the future risk of flooding in central Alberta.}, - number = {2}, - journal = {Climatic Change}, - author = {Kuo, Chun-Chao and Gan, Thian Yew and Gizaw, Mesgana}, - year = {2015}, - pages = {115--129}, - annote = {The following values have no corresponding Zotero field:label: Kuo2015work-type: journal article}, -} - -@article{kunkel_monitoring_2013, - title = {Monitoring and {Understanding} {Trends} in {Extreme} {Storms}: {State} of {Knowledge}}, - volume = {94}, - issn = {0003-0007}, - doi = {10.1175/bams-d-11-00262.1}, - abstract = {The state of knowledge regarding trends and an understanding of their causes is presented for a specific subset of extreme weather and climate types. For severe convective storms (tornadoes, hailstorms, and severe thunderstorms), differences in time and space of practices of collecting reports of events make using the reporting database to detect trends extremely difficult. Overall, changes in the frequency of environments favorable for severe thunderstorms have not been statistically significant. For extreme precipitation, there is strong evidence for a nationally averaged upward trend in the frequency and intensity of events. The causes of the observed trends have not been determined with certainty, although there is evidence that increasing atmospheric water vapor may be one factor. For hurricanes and typhoons, robust detection of trends in Atlantic and western North Pacific tropical cyclone (TC) activity is significantly constrained by data heterogeneity and deficient quantification of internal variability. Attribution of past TC changes is further challenged by a lack of consensus on the physical link- ages between climate forcing and TC activity. As a result, attribution of trends to anthropogenic forcing remains controversial. For severe snowstorms and ice storms, the number of severe regional snowstorms that occurred since 1960 was more than twice that of the preceding 60 years. There are no significant multidecadal trends in the areal percentage of the contiguous United States impacted by extreme seasonal snowfall amounts since 1900. There is no distinguishable trend in the frequency of ice storms for the United States as a whole since 1950.}, - number = {4}, - journal = {Bulletin of the American Meteorological Society}, - author = {Kunkel, Kenneth E. and Karl, Thomas R. and Brooks, Harold and Kossin, James and Lawrimore, Jay H. and Arndt, Derek and Bosart, Lance and Changnon, David and Cutter, Susan L. and Doesken, Nolan and Emanuel, Kerry and Groisman, Pavel Ya and Katz, Richard W. and Knutson, Thomas and O'Brien, James and Paciorek, Christopher J. and Peterson, Thomas C. and Redmond, Kelly and Robinson, David and Trapp, Jeff and Vose, Russell and Weaver, Scott and Wehner, Michael and Wolter, Klaus and Wuebbles, Donald}, - month = apr, - year = {2013}, - pages = {499--514}, - annote = {doi: 10.1175/BAMS-D-11-00262.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/BAMS-D-11-00262.1}, -} - -@incollection{kundzewicz_freshwater_2007, - address = {Cambridge, UK}, - title = {Freshwater resources and their management}, - booktitle = {Climate {Change} 2007: {Impacts}, {Adaptation} and {Vulnerability}. {Contribution} of {Working} {Group} {II} to the {Fourth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {Kundzewicz, Z. W. and Mata, L. J. and Arnell, N. W. and Döll, P. and Kabat, P. and Jiménez, B. and Miller, K. A. and Oki, T. and Sen, Z. and Shiklomanov, I. A.}, - editor = {Parry, M. L. and Canziani, O. F. and Palutikof, J. P. and van der Linden, P. J. and Hanson, C. E.}, - year = {2007}, - pages = {173--210}, -} - -@article{kundzewicz_flood_2014, - title = {Flood risk and climate change: global and regional perspectives}, - volume = {59}, - issn = {0262-6667}, - doi = {10.1080/02626667.2013.857411}, - number = {1}, - journal = {Hydrological Sciences Journal}, - author = {Kundzewicz, Zbigniew W. and Kanae, Shinjiro and Seneviratne, Sonia I. and Handmer, John and Nicholls, Neville and Peduzzi, Pascal and Mechler, Reinhard and Bouwer, Laurens M. and Arnell, Nigel and Mach, Katharine and Muir-Wood, Robert and Brakenridge, G. Robert and Kron, Wolfgang and Benito, Gerardo and Honda, Yasushi and Takahashi, Kiyoshi and Sherstyukov, Boris}, - month = jan, - year = {2014}, - pages = {1--28}, - annote = {doi: 10.1080/02626667.2013.857411}, - annote = {The following values have no corresponding Zotero field:publisher: Taylor \& Franciswork-type: doi: 10.1080/02626667.2013.857411}, -} - -@article{krause_comparison_2005, - title = {Comparison of different efficiency criteria for hydrological model assessment}, - volume = {5}, - journal = {Advances in Geosciences}, - author = {Krause, P. and Boyle, D. P. and Bäse, F.}, - year = {2005}, - pages = {89--97}, -} - -@article{lafon_bias_2012, - title = {Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods}, - issn = {1097-0088}, - doi = {10.1002/joc.3518}, - journal = {International Journal of Climatology}, - author = {Lafon, Thomas and Dadson, Simon and Buys, Gwen and Prudhomme, Christel}, - year = {2012}, - keywords = {bias correction, daily precipitation, downscaling, regional climate model, cross-validation, UK}, - pages = {1--15, doi: 10.1002/joc.3518}, - annote = {The following values have no corresponding Zotero field:publisher: John Wiley \& Sons, Ltd.}, -} - -@article{koutroulis_evaluation_2016, - title = {Evaluation of precipitation and temperature simulation performance of the {CMIP3} and {CMIP5} historical experiments}, - volume = {47}, - issn = {1432-0894}, - doi = {10.1007/s00382-015-2938-x}, - abstract = {The fifth phase of the Coupled Model Intercomparison Project (CMIP5) is the most recent coordinated experiment of global climate modeling. Compared to its predecessor CMIP3, the fifth phase of the homonymous experiment—CMIP5 involves a greater number of GCMs, run at higher resolutions with more complex components. Here we use daily GCM data from both projects to test their efficiency in representing precipitation and temperature parameters with the use of a state of the art high resolution gridded global dataset for land areas and for the period 1960–2005. Two simple metrics, a comprehensive histogram similarity metric based on the match of simulated and observed empirical pdfs and a metric for the representation of the annual cycle were employed as performance indicators. The metrics were used to assess the skill of each GCM at the entire spectrum of precipitation and temperature pdfs but also for the upper and lower tails of it. Results are presented globally and regionally for 26 land regions that represent different climatic regimes, covering the total earth’s land surface except for Antarctica. Compared to CMIP3, CMIP5 models perform better in simulating precipitation including relatively intense events and the fraction of wet days. For temperature the improvement is not as clear except for the upper and lower hot and cold events of the distribution. The agreement of model simulations is also considerably increased in CMIP5. Substantial improvement in intense precipitation is observed over North Europe, Central and Eastern North America and North East Europe. Nevertheless, in both ensembles some models clearly perform better than others from a histogram similarity point of view. The derived skill score metrics provide essential information for impact studies based on global or regional land area multi-model ensembles.}, - number = {5}, - journal = {Climate Dynamics}, - author = {Koutroulis, A. G. and Grillakis, M. G. and Tsanis, I. K. and Papadimitriou, L.}, - month = sep, - year = {2016}, - pages = {1881--1898}, - annote = {The following values have no corresponding Zotero field:label: Koutroulis2016}, -} - -@article{koster_observational_2003, - title = {Observational evidence that soil moisture variations affect precipitation}, - volume = {30}, - number = {5}, - journal = {Geophysical Research Letters}, - author = {Koster, R. D. and Suarez, M. J. and Higgins, R. W. and Van den Dool, H. M.}, - year = {2003}, - pages = {1241, doi:10.1029/2002GL016571}, -} - -@article{koster_simple_1999, - title = {A simple framework for examining the interannual variability of land surface moisture fluxes}, - volume = {12}, - journal = {Journal of Climate}, - author = {Koster, R. D. and Suarez, M. J.}, - year = {1999}, - pages = {1911--1917}, -} - -@article{knutti_challenges_2010, - title = {Challenges in {Combining} {Projections} from {Multiple} {Climate} {Models}}, - volume = {23}, - issn = {0894-8755}, - doi = {10.1175/2009jcli3361.1}, - abstract = {Recent coordinated efforts, in which numerous general circulation climate models have been run for a common set of experiments, have produced large datasets of projections of future climate for various scenarios. Those multimodel ensembles sample initial conditions, parameters, and structural uncertainties in the model design, and they have prompted a variety of approaches to quantifying uncertainty in future climate change. International climate change assessments also rely heavily on these models. These assessments often provide equal-weighted averages as best-guess results, assuming that individual model biases will at least partly cancel and that a model average prediction is more likely to be correct than a prediction from a single model based on the result that a multimodel average of present-day climate generally outperforms any individual model. This study outlines the motivation for using multimodel ensembles and discusses various challenges in interpreting them. Among these challenges are that the number of models in these ensembles is usually small, their distribution in the model or parameter space is unclear, and that extreme behavior is often not sampled. Model skill in simulating present-day climate conditions is shown to relate only weakly to the magnitude of predicted change. It is thus unclear by how much the confidence in future projections should increase based on improvements in simulating present-day conditions, a reduction of intermodel spread, or a larger number of models. Averaging model output may further lead to a loss of signal-for example, for precipitation change where the predicted changes are spatially heterogeneous, such that the true expected change is very likely to be larger than suggested by a model average. Last, there is little agreement on metrics to separate "good'' and "bad'' models, and there is concern that model development, evaluation, and posterior weighting or ranking are all using the same datasets. While the multimodel average appears to still be useful in some situations, these results show that more quantitative methods to evaluate model performance are critical to maximize the value of climate change projections from global models.}, - language = {English}, - number = {10}, - journal = {J. Clim.}, - author = {Knutti, R. and Furrer, R. and Tebaldi, C. and Cermak, J. and Meehl, G. A.}, - month = may, - year = {2010}, - keywords = {averaging rea method, ensembles, seasonal forecasts, temperature, quantifying uncertainty, surface-temperature, predictions, bayesian-approach, maximum, minimum temperature, multimodel, perturbed physics ensembles}, - pages = {2739--2758}, - annote = {ISI Document Delivery No.: 611AKTimes Cited: 2Cited Reference Count: 100Knutti, Reto Furrer, Reinhard Tebaldi, Claudia Cermak, Jan Meehl, Gerald A.Office of Science, U.S. Department of Energy (DOE) ; National Science Foundation ; NOAA ; Office of Science (BER), U.S. DOE [DE-FC02-97ER62402]Discussions with a large number of colleagues and comments from three reviewers helped improve the manuscript. We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI), and the WCRP's Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multimodel dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy (DOE). Portions of this study were supported by the Office of Science (BER), U.S. DOE, Cooperative Agreement DE-FC02-97ER62402, and the National Science Foundation. The National Center for Atmospheric Research is sponsored by the National Science Foundation. Members of the International Detection and Attribution Working Group (IDAG) acknowledge support from the DOE's Office of Science (BER) and NOAA's Climate Program Office.Amer meteorological socBoston}, - annote = {The following values have no corresponding Zotero field:auth-address: [Knutti, Reto; Cermak, Jan] ETH, Inst Atmospher \& Climate Sci, CH-8092 Zurich, Switzerland. [Furrer, Reinhard] Univ Zurich, Inst Math, CH-8001 Zurich, Switzerland. [Furrer, Reinhard] Colorado Sch Mines, Golden, CO 80401 USA. [Tebaldi, Claudia] Climate Cent, Palo Alto, CA USA. [Meehl, Gerald A.] Natl Ctr Atmospher Res, Boulder, CO 80307 USA. Knutti, R, ETH, Inst Atmospher \& Climate Sci, Univ Str 16, CH-8092 Zurich, Switzerland. reto.knutti@env.ethz.chalt-title: J. Clim.accession-num: ISI:000278782600018work-type: Article}, -} - -@article{zhang_avoiding_2005, - title = {Avoiding {Inhomogeneity} in {Percentile}-{Based} {Indices} of {Temperature} {Extremes}}, - volume = {18}, - issn = {0894-8755}, - doi = {10.1175/jcli3366.1}, - number = {11}, - journal = {Journal of Climate}, - author = {Zhang, X. and Hegerl, G. C. and Zwiers, F. W. and Kenyon, J.}, - month = jun, - year = {2005}, - pages = {1641--1651}, - annote = {doi: 10.1175/JCLI3366.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/JCLI3366.1}, -} - -@article{zhang_trends_2005, - title = {Trends in {Middle} {East} climate extreme indices from 1950 to 2003}, - volume = {110}, - issn = {0148-0227}, - doi = {10.1029/2005jd006181}, - abstract = {A climate change workshop for the Middle East brought together scientists and data for the region to produce the first area-wide analysis of climate extremes for the region. This paper reports trends in extreme precipitation and temperature indices that were computed during the workshop and additional indices data that became available after the workshop. Trends in these indices were examined for 1950\&\#8211;2003 at 52 stations covering 15 countries, including Armenia, Azerbaijan, Bahrain, Cyprus, Georgia, Iran, Iraq, Israel, Jordan, Kuwait, Oman, Qatar, Saudi Arabia, Syria, and Turkey. Results indicate that there have been statistically significant, spatially coherent trends in temperature indices that are related to temperature increases in the region. Significant, increasing trends have been found in the annual maximum of daily maximum and minimum temperature, the annual minimum of daily maximum and minimum temperature, the number of summer nights, and the number of days where daily temperature has exceeded its 90th percentile. Significant negative trends have been found in the number of days when daily temperature is below its 10th percentile and daily temperature range. Trends in precipitation indices, including the number of days with precipitation, the average precipitation intensity, and maximum daily precipitation events, are weak in general and do not show spatial coherence. The workshop attendees have generously made the indices data available for the international research community.}, - number = {D22}, - journal = {J. Geophys. Res.}, - author = {Zhang, X. and Aguilar, Enric and Sensoy, Serhat and Melkonyan, Hamlet and Tagiyeva, Umayra and Ahmed, Nader and Kutaladze, Nato and Rahimzadeh, Fatemeh and Taghipour, Afsaneh and Hantosh, T. H. and Albert, Pinhas and Semawi, Mohammed and Karam Ali, Mohammad and Said Al-Shabibi, Mansoor Halal and Al-Oulan, Zaid and Zatari, Taha and Al Dean Khelet, Imad and Hamoud, Saleh and Sagir, Ramazan and Demircan, Mesut and Eken, Mehmet and Adiguzel, Mustafa and Alexander, Lisa and Peterson, Thomas C. and Wallis, Trevor}, - year = {2005}, - keywords = {1616 Global Change: Climate variability, 1637 Global Change: Regional climate change, 3305 Atmospheric Processes: Climate change and variability, BVOC, inventory, uncertainty assessment}, - pages = {D22104}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{yukimoto_new_2001, - title = {The new {Meteorological} {Research} {Institute} coupled {GCM} ({MRI}-{CGCM2}), {Model} climate and variability}, - volume = {51}, - journal = {Papers in Meteorology and Geophysics}, - author = {Yukimoto, S. and Noda, A. and Kitoh A and Sugi, M. and Kitamura, Y. and Hosaka, M. and Shibata, K. and Maeda, S. and Uchiyama, T.}, - year = {2001}, - pages = {47--88}, -} - -@article{yuan_downscaling_2012, - title = {Downscaling precipitation or bias-correcting streamflow? {Some} implications for coupled general circulation model ({CGCM})-based ensemble seasonal hydrologic forecast}, - volume = {48}, - issn = {1944-7973}, - doi = {10.1029/2012WR012256}, - number = {12}, - journal = {Water Resources Research}, - author = {Yuan, Xing and Wood, Eric F.}, - year = {2012}, - keywords = {streamflow, 1854 Precipitation, downscaling, 1860 Streamflow, 1627 Coupled models of the climate system, 1816 Estimation and forecasting, CFS, CGCM, ensemble prediction, seasonal hydrologic forecast}, - pages = {W12519}, -} - -@article{liping_zhang_simulated_2016, - title = {Simulated {Response} of the {Pacific} {Decadal} {Oscillation} to {Climate} {Change}}, - volume = {29}, - doi = {10.1175/jcli-d-15-0690.1}, - abstract = {AbstractThe impact of climate change on the Pacific decadal oscillation (PDO) is studied using a fully coupled climate model. The model results show that the PDO has a similar spatial pattern in altered climates, but its amplitude and time scale of variability change in response to global warming or cooling. In response to global warming the PDO amplitude is significantly reduced, with a maximum decrease over the Kuroshio–Oyashio Extension (KOE) region. This reduction appears to be associated with a weakened meridional temperature gradient in the KOE region. In addition, reduced variability of North Pacific wind stress, partially due to reduced air–sea feedback, also helps to weaken the PDO amplitude by reducing the meridional displacements of the subtropical and subpolar gyre boundaries. In contrast, the PDO amplitude increases in response to global cooling.In the control simulations the model PDO has an approximately bidecadal peak. In a warmer climate the PDO time scale becomes shorter, changing from {\textasciitilde}20 to {\textasciitilde}12 yr. In a colder climate the time scale of the PDO increases to {\textasciitilde}34 yr. Physically, global warming (cooling) enhances (weakens) ocean stratification. The increased (decreased) ocean stratification acts to increase (reduce) the phase speed of internal Rossby waves, thereby altering the time scale of the simulated PDO.}, - number = {16}, - journal = {Journal of Climate}, - author = {Liping Zhang and Thomas L. Delworth}, - year = {2016}, - keywords = {Variability,Decadal variability}, - pages = {5999--6018}, -} - -@article{zhang_joint_2012, - title = {Joint variable spatial downscaling}, - volume = {111}, - issn = {0165-0009}, - doi = {10.1007/s10584-011-0167-9}, - language = {English}, - number = {3-4}, - journal = {Climatic Change}, - author = {Zhang, Feng and Georgakakos, ArisP}, - month = apr, - year = {2012}, - pages = {945--972}, - annote = {The following values have no corresponding Zotero field:alt-title: Climatic Changepublisher: Springer Netherlands}, -} - -@incollection{young_rapid_2012, - address = {Chicago, IL.}, - title = {Rapid assessment of plant and animal vulnerability to climate change}, - booktitle = {Wildlife {Conservation} in a {Changing} {Climate}}, - publisher = {University of Chicago Press}, - author = {Young, B. E. and Hall, K. R. and Byers, E. and Gravuer, K. and Hammerson, G. and Redder, A. and Szabo. K}, - editor = {Brodie, J. and Post, E. and Doak, D.}, - year = {2012}, - pages = {129--152}, -} - -@article{yin_consistent_2005, - title = {A consistent poeward shift of the storm tracks in simulations of 21st century climate}, - volume = {32}, - journal = {Geophysical Research Letters}, - author = {Yin, J. H.}, - year = {2005}, - pages = {L18701, doi:10.1029/2005GL023684}, -} - -@book{yevjevich_probability_1972, - title = {Probability and {Statistics} in {Hydrology}}, - publisher = {Water Resources Publications, Ft. Collins, CO, USA}, - author = {Yevjevich, V.}, - year = {1972}, - annote = {The following values have no corresponding Zotero field:section: 302}, -} - -@article{yapo_multi-objective_1998, - title = {Multi-objective global optimization for hydrologic models}, - volume = {204}, - journal = {Journal of Hydrology}, - author = {Yapo, P. O. and Gupta, H. V. and Sorooshian, S.}, - year = {1998}, - pages = {83--97}, -} - -@article{xoplaki_interannual_2003, - title = {Interannual summer air temperature variability over {Greece} and its connection to the large-scale atmospheric circulation and {Mediterranean} {SSTs} 1950-1999}, - volume = {20}, - abstract = {The interannual and decadal variability of summer (June to September) air temperature in the northeastern Mediterranean is analysed for the period 1950 to 1999. Extremely hot and cool summers are illustrated by means of composite analysis. The combined influence of the large-scale atmospheric circulation and thermic predictors on local temperature is assessed by means of an objective approach based on empirical orthogonal functions and canonical correlation analysis. Monthly values of sea level pressure, geopotential heights, atmospheric thickness and Mediterranean sea surface temperatures are used as predictor fields and air temperature from 24 observational sites spread over Greece and western Turkey constitute the predictand variable. Results indicate that more than 50\% of the total summer temperature variability can be explained linearly by the combination of eight large-scale predictor fields on two canonical correlation modes. The first canonical mode is related to a more meridional circulation at the upper tropospheric levels, which favours local land-sea contrasts in the associated local temperature pattern. Variations of this mode are found to be responsible for the occurrence of extreme events and decadal trends in regional temperature, the latter being characterized by a cooling in the early 1960s and a warming in the early 1990s. The second canonical mode pictures variations in the intensity of the zonal circulation over the Atlantic area that drive temperature anomalies affecting mainly the Aegean Sea and the west of Greece. Our results suggest the potential of statistical downscaling for Greek summer temperature with reliable climate forecasts for planetary-scale anomalies.}, - number = {5}, - journal = {Climate Dynamics}, - author = {Xoplaki, E. and Gonzalez-Rouco, J. F. and Gyalistras, D. and Luterbacher, J. and Rickli, R. and Wanner, H.}, - month = mar, - year = {2003}, - pages = {537--554}, - annote = {671UNCLIM DYNAM}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Dyn.accession-num: ISI:000182482600008}, -} - -@article{ye_hydrologic_2014, - title = {Hydrologic post-processing of {MOPEX} streamflow simulations}, - volume = {508}, - issn = {0022-1694}, - journal = {Journal of Hydrology}, - author = {Ye, Aizhong and Duan, Qingyun and Yuan, Xing and Wood, Eric F. and Schaake, John}, - year = {2014}, - pages = {147--156}, -} - -@article{yang_intensity_2003, - title = {Intensity of hydrological cycles in warmer climates}, - volume = {16}, - issn = {0894-8755}, - abstract = {The fact that the surface and tropospheric temperatures increase with increasing CO2 has been well documented by numerical model simulations; however, less agreement is found for the changes in the intensity of precipitation and the hydrological cycle. Here, it is demonstrated that while both the radiative heating by increasing CO2 and the resulting higher sea surface temperatures contribute to warm the atmosphere, they act against each other in changing the hydrological cycle. As a consequence, in a warmer climate forced by increasing CO2 the intensity of the hydrological cycle can be either more or less intense depending upon the degree of surface warming.}, - language = {English}, - number = {14}, - journal = {Journal of Climate}, - author = {Yang, F. L. and Kumar, A. and Schlesinger, M. E. and Wang, W. Q.}, - month = jul, - year = {2003}, - keywords = {model, temperature, greenhouse-gas}, - pages = {2419--2423}, - annote = {700EUTimes Cited:7Cited References Count:15}, - annote = {The following values have no corresponding Zotero field:auth-address: Yang, FL NASA, Goddard Space Flight Ctr, Code 913, Greenbelt, MD 20771 USA NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA Nalt Ctr Environm Predict, Climate Predict Ctr, Washington, DC USA Univ Illinois, Dept Atmospher Sci, Urbana, IL 61801 USA Natl Ctr Environm Predict, SAIC, Environm Modeling Ctr, Washington, DC USAaccession-num: ISI:000184100300009}, -} - -@article{xu_gcms_1999, - title = {From {GCMs} to river flow: a review of downscaling methods and hydrologic modelling approaches}, - volume = {23}, - abstract = {The scientific literature of the past decade contains a large number of reports detailing the development of downscaling methods and the use of hydrologic models to assess the potential effects of climate change on a variety of water resource issues. This article reviews the current state of methodologies for simulating hydrological responses to global climate change. Emphasis is given to recent advances in climatic downscaling and the problems related to the practical application of appropriate models in impact studies. Following a discussion of the advantages and deficiencies of the various approaches, challenges for the future study of the hydrological impacts of climate change are identified.}, - number = {2}, - journal = {Progress in Physical Geography}, - author = {Xu, C. Y.}, - month = jun, - year = {1999}, - pages = {229--249}, - annote = {204MJPROG PHYS GEOG}, - annote = {The following values have no corresponding Zotero field:alt-title: Prog. Phys. Geogr.accession-num: ISI:000080767800004}, -} - -@article{xia_stochastic_1996, - title = {A stochastic weather generator applied to hydrological models in climate impact analysis}, - volume = {55}, - abstract = {A pattern recognition methodology for estimating local climate variables such as regional precipitation and air temperature using local observation and scenario information provided by GCMs is presented. We have adopted a three step approach: (a) Feature information extraction of climate variables, where weather patterns are expanded by the Karhunen-Loeve (K-L) orthogonal functional series; (b) Grey associative clustering of the feature vectors; (3) Stochastic weather generation by a Monte Carlo simulation. The methods described in this paper were verified using the temperature and precipitation data set of Wuhan, Yangtze river basin and the Shun Tian catchment, Dongjiang River in China. The proposed method yields good stochastic simulations and also provides useful information on temporal or spatial downscaling and uncertainty.}, - number = {1-4}, - journal = {Theoretical and Applied Climatology}, - author = {Xia, J.}, - year = {1996}, - pages = {177--183}, - annote = {The following values have no corresponding Zotero field:alt-title: Theor. Appl. Climatol.accession-num: ISI:A1996WA75500016}, - annote = {WA755THEOR APPL CLIMATOL}, -} - -@article{wuebbles_cmip5_2014, - title = {{CMIP5} {Climate} {Model} {Analyses}: {Climate} {Extremes} in the {United} {States}}, - volume = {95}, - issn = {0003-0007}, - doi = {10.1175/bams-d-12-00172.1}, - abstract = {This is the fourth in a series of four articles on historical and projected climate extremes in the United States. Here, we examine the results of historical and future climate model experiments from the phase 5 of the Coupled Model Intercomparison Project (CMIP5) based on work presented at the World Climate Research Programme (WCRP) Workshop on CMIP5 Climate Model Analyses held in March 2012. Our analyses assess the ability of CMIP5 models to capture observed trends, and we also evaluate the projected future changes in extreme events over the contiguous Unites States. Consistent with the previous articles, here we focus on model-simulated historical trends and projections for temperature extremes, heavy precipitation, large-scale drivers of precipitation variability and drought, and extratropical storms. Comparing new CMIP5 model results with earlier CMIP3 simulations shows that in general CMIP5 simulations give similar patterns and magnitudes of future temperature and precipitation extremes in the United States relative to the projections from the earlier phase 3 of the Coupled Model Intercomparison Project (CMIP3) models. Specifically, projections presented here show significant changes in hot and cold temperature extremes, heavy precipitation, droughts, atmospheric patterns such as the North American monsoon and the North Atlantic subtropical high that affect interannual precipitation, and in extratropical storms over the twenty-first century. Most of these trends are consistent with, although in some cases (such as heavy precipitation) underestimate, observed trends.}, - number = {4}, - journal = {Bulletin of the American Meteorological Society}, - author = {Wuebbles, Donald and Meehl, Gerald and Hayhoe, Katharine and Karl, Thomas R. and Kunkel, Kenneth and Santer, Benjamin and Wehner, Michael and Colle, Brian and Fischer, Erich M. and Fu, Rong and Goodman, Alex and Janssen, Emily and Kharin, Viatcheslav and Lee, Huikyo and Li, Wenhong and Long, Lindsey N. and Olsen, Seth C. and Pan, Zaitao and Seth, Anji and Sheffield, Justin and Sun, Liqiang}, - month = apr, - year = {2014}, - pages = {571--583}, - annote = {doi: 10.1175/BAMS-D-12-00172.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/BAMS-D-12-00172.1}, -} - -@techreport{wuca_options_2009, - address = {Boulder, CO}, - title = {Options for improving climate modeling to assist water utility planning for climate change}, - author = {WUCA}, - editor = {Water Utility Climate Alliance (WUCA)}, - year = {2009}, - pages = {129}, -} - -@article{wu_changing_2015, - title = {Changing characteristics of precipitation for the contiguous {United} {States}}, - issn = {0165-0009}, - doi = {10.1007/s10584-015-1453-8}, - language = {English}, - journal = {Climatic Change}, - author = {Wu, Shuang-Ye}, - month = jul, - year = {2015}, - pages = {1--16}, - annote = {The following values have no corresponding Zotero field:alt-title: Climatic Changepublisher: Springer Netherlands}, -} - -@article{wu_human_2005, - title = {Human influence on increasing {Arctic} river discharges}, - volume = {32}, - journal = {Geophysical Research Letters}, - author = {Wu, P. and Wood, R. and Stott, P.}, - year = {2005}, - pages = {L02703, doi:10.1029/2004GL021570}, -} - -@techreport{wrf_joint_2012, - address = {Denver, Colorado, USA}, - title = {Joint {Front} {Range} {Climate} {Vulnerability} {Study}}, - author = {WRF}, - editor = {Water Research Foundation}, - year = {2012}, - pages = {148}, -} - -@article{wood_long-range_2002, - title = {Long-range experimental hydrologic forecasting for the eastern {United} {States}}, - volume = {107}, - issn = {0747-7309}, - abstract = {We explore a strategy for long-range hydrologic forecasting that uses ensemble climate model forecasts as input to a macroscale hydrologic model to produce runoff and streamflow forecasts at spatial and temporal scales appropriate for water management. Monthly ensemble climate model forecasts produced by the National Centers for Environmental Prediction/Climate Prediction Center global spectral model (GSM) are bias corrected, downscaled to 1/8degrees horizontal resolution, and disaggregated to a daily time step for input to the Variable Infiltration Capacity hydrologic model. Bias correction is effected by evaluating the GSM ensemble forecast variables as percentiles relative to the GSM model climatology and then extracting the percentiles' associated variable values instead from the observed climatology. The monthly meteorological forecasts are then interpolated to the finer hydrologic model scale, at which a daily signal that preserves the forecast anomaly is imposed through resampling of the historic record. With the resulting monthly runoff and streamflow forecasts for the East Coast and Ohio River basin, we evaluate the bias correction and resampling approaches during the southeastern United States drought from May to August 2000 and also for the El Nino conditions of December 1997 to February 1998. For the summer 2000 study period, persistence in anomalous initial hydrologic states predominates in determining the hydrologic forecasts. In contrast, the El Nino-condition hydrologic forecasts derive direction both from the climate model forecast signal and the antecedent land surface state. From a qualitative standpoint the hydrologic forecasting strategy appears successful in translating climate forecast signals to hydrologic variables of interest for water management.}, - language = {English}, - number = {D20}, - journal = {Journal of Geophysical Research-Atmospheres}, - author = {Wood, A. W. and Maurer, E. P. and Kumar, A. and Lettenmaier, D. P.}, - month = oct, - year = {2002}, - keywords = {simulation, local climate, climate downscaling, catchment-based approach, circulation model output, eastern united states, hydrologic forecast, regional climate-change, river basin experiment, seasonal forecast, seasonal predictions, spectral model, streamflow forecast, surface parameterization schemes, vic-2l model}, - pages = {4429, doi:10.1029/2001JD000659}, - annote = {636PYTimes Cited:17Cited References Count:44}, - annote = {The following values have no corresponding Zotero field:auth-address: Wood, AW Univ Washington, Dept Civil \& Environm Engn, Box 352700, Seattle, WA 98195 USA Univ Washington, Dept Civil \& Environm Engn, Seattle, WA 98195 USA NOAA, Natl Ctr Environm Predict, Climate Predict Ctr, Camp Springs, MD 20748 USAaccession-num: ISI:000180466200086}, -} - -@article{wood_hydrologic_2004, - title = {Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs}, - volume = {62}, - issn = {0165-0009}, - number = {1-3}, - journal = {Climatic Change}, - author = {Wood, A. W. and Leung, L. R. and Sridhar, V. and Lettenmaier, D. P.}, - year = {2004}, - pages = {189--216}, -} - -@article{wood_assessing_1997, - title = {Assessing climate change implications for water resources planning}, - volume = {37}, - abstract = {Numerous recent studies have shown that existing water supply systems are sensitive to climate change. One apparent implication is that water resources planning methods should be modified accordingly. Few of these studies, however, have attempted to account for either the chain of uncertainty in projecting water resources system vulnerability to climate change, or the adaptability of system operation resulting from existing planning strategies. Major uncertainties in water resources climate change assessments lie in a) climate modeling skill; b) errors in regional downscaling of climate model predictions; and c) uncertainties in future water demands. A simulation study was designed to provide insight into some aspects of these uncertainties. Specifically, the question that is addressed is whether a different decision would be made in a reservoir reallocation decision if knowledge about future climate were incorporated (i.e., would planning based on climate change information be justified?). The case study is possible reallocation of flood storage to conservation (municipal water supply) on the Green River, WA. We conclude that, for the case study, reservoir reallocation decisions and system performance would not differ significantly if climate change information were incorporated in the planning process.}, - number = {1}, - journal = {Clim. Change}, - author = {Wood, A. W. and Lettenmaier, D. P. and Palmer, R. N.}, - month = sep, - year = {1997}, - pages = {203--228}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Changeaccession-num: ISI:A1997XV60400012}, - annote = {XV604CLIMATIC CHANGE}, -} - -@article{wouters_relevance_2000, - title = {The {Relevance} and {Role} of {Water} {Law} in the {Sustainable} {Development} of {Freshwater} -- {From} "{Hydrosovereignty}" to "{Hydrosolidarity}"}, - volume = {25}, - issn = {0250-8060}, - number = {2}, - journal = {Water International}, - author = {Wouters, Patricia}, - year = {2000}, - pages = {202 -- 207}, - annote = {The following values have no corresponding Zotero field:publisher: Routledge}, -} - -@article{wobus_estimating_2014, - title = {Estimating monetary damages from flooding in the {United} {States} under a changing climate}, - volume = {7}, - issn = {1753-318X}, - doi = {10.1111/jfr3.12043}, - number = {3}, - journal = {Journal of Flood Risk Management}, - author = {Wobus, C. and Lawson, M. and Jones, R. and Smith, J. and Martinich, J.}, - year = {2014}, - keywords = {precipitation, Climate change, modelling}, - pages = {217--229}, -} - -@article{winter_alpine_2017, - title = {The {Alpine} snow-albedo feedback in regional climate models}, - volume = {48}, - issn = {1432-0894}, - doi = {10.1007/s00382-016-3130-7}, - abstract = {The effect of the snow-albedo feedback (SAF) on 2m temperatures and their future changes in the European Alps is investigated in the ENSEMBLES regional climate models (RCMs) with a focus on the spring season. A total of 14 re-analysis-driven RCM experiments covering the period 1961–2000 and 10 GCM-driven transient climate change projections for 1950–2099 are analysed. A positive springtime SAF is found in all RCMs, but the range of the diagnosed SAF is large. Results are compared against an observation-based SAF estimate. For some RCMs, values very close to this estimate are found; other models show a considerable overestimation of the SAF. Net shortwave radiation has the largest influence of all components of the energy balance on the diagnosed SAF and can partly explain its spatial variability. Model deficiencies in reproducing 2m temperatures above snow and ice and associated cold temperature biases at high elevations seem to contribute to a SAF overestimation in several RCMs. The diagnosed SAF in the observational period strongly influences the estimated SAF contribution to twenty first century temperature changes in the European Alps. This contribution is subject to a clear elevation dependency that is governed by the elevation-dependent change in the number of snow days. Elevations of maximum SAF contribution range from 1500 to 2000 m in spring and are found above 2000 m in summer. Here, a SAF contribution to the total simulated temperature change between 0 and 0.5 °C until 2099 (multi-model mean in spring: 0.26 °C) or 0 and 14 \% (multi-model mean in spring: 8 \%) is obtained for models showing a realistic SAF. These numbers represent a well-funded but only approximate estimate of the SAF contribution to future warming, and a remaining contribution of model-specific SAF misrepresentations cannot be ruled out.}, - number = {3}, - journal = {Climate Dynamics}, - author = {Winter, Kevin J.-P. M. and Kotlarski, Sven and Scherrer, Simon C. and Schär, Christoph}, - month = feb, - year = {2017}, - pages = {1109--1124}, - annote = {The following values have no corresponding Zotero field:label: Winter2017work-type: journal article}, -} - -@techreport{wmo_manual_2009, - address = {Geneva, Switzerland}, - title = {Manual on {Low}-flow {Estimation} and {Prediction}, {Operational} {Hydrology} {Report} {No}. 50}, - institution = {World Meteorological Organization}, - author = {WMO}, - year = {2009}, - pages = {138}, - annote = {The following values have no corresponding Zotero field:number: WMO-No. 1029}, -} - -@book{wisner_handbook_2012, - address = {New York, NY}, - title = {Handbook of {Hazards} and {Disaster} {Risk} {Reduction}}, - isbn = {978-0-415-59065-5}, - publisher = {Routledge}, - author = {Wisner, B. and Gaillard, J. C. and Kelman, I.}, - year = {2012}, - annote = {The following values have no corresponding Zotero field:section: 876}, -} - -@article{michael_winton_surface_2006, - title = {Surface {Albedo} {Feedback} {Estimates} for the {AR4} {Climate} {Models}}, - volume = {19}, - doi = {10.1175/jcli3624.1}, - abstract = {Abstract A technique for estimating surface albedo feedback (SAF) from standard monthly mean climate model diagnostics is applied to the 1\% yr−1 carbon dioxide (CO2)-increase transient climate change integrations of 12 Intergovernmental Panel on Climate Change (IPCC) fourth assessment report (AR4) climate models. Over the 80-yr runs, the models produce a mean SAF at the surface of 0.3 W m−2 K−1 with a standard deviation of 0.09 W m−2 K−1. Relative to 2 × CO2 equilibrium run estimates from an earlier group of models, both the mean SAF and the standard deviation are reduced. Three-quarters of the model mean SAF comes from the Northern Hemisphere in roughly equal parts from the land and ocean areas. The remainder is due to Southern Hemisphere ocean areas. The SAF differences between the models are shown to stem mainly from the sensitivity of the surface albedo to surface temperature rather from the impact of a given surface albedo change on the shortwave budget.}, - number = {3}, - journal = {Journal of Climate}, - author = {Michael Winton}, - year = {2006}, - pages = {359--365}, -} - -@article{winkler_simulation_1997, - title = {The simulation of daily temperature time series from {GCM} output .2. {Sensitivity} analysis of an empirical transfer function methodology}, - volume = {10}, - abstract = {Empirical transfer functions have been proposed as a means for ''downscaling'' simulations from general circulation models (GCMs) to the local scale. However, subjective decisions made during the development of these functions may influence the ensuing climate scenarios. This research evaluated the sensitivity of a selected empirical transfer function methodology to 1) the definition of the seasons for which separate specification equations are derived, 2) adjustments for known departures of the GCM simulations of the predictor variables from observations, 3) the length of the calibration period, 4) the choice of function form, and 5) the choice of predictor variables. A modified version of the Climatological Projection by Model Statistics method was employed to generate control (1 X CO3 and perturbed (2 X CO2) scenarios of daily maximum and minimum temperature for two locations with diverse climates (Alcantarilla, Spain, and Eau Claire, Michigan). The GCM simulations used in the scenario development were from the Canadian Climate Centre second-generation model (CCC GCMII). Variations in the downscaling methodology were found to have a statistically significant impact on the 2 X CO2 climate scenarios, even though the 1 X CO2 scenarios for the different transfer function approaches were often similar. The daily temperature scenarios for Alcantarilla and Eau Claire were most sensitive to the decision to adjust for deficiencies in the GCM simulations, the choice of predictor variables, and the seasonal definitions used to derive the functions (i.e., fixed seasons, floating seasons, or no seasons). The scenarios were less sensitive to the choice of function form (i.e., linear versus nonlinear) and to an increase in the length of the calibration period. The results of Part I, which identified significant departures of the CCC GCMII simulations of two candidate predictor variables from observations, together with those presented here in Part II, 1) illustrate the importance of detailed comparisons of observed and GCM 1 X CO2 series of candidate predictor variables as an initial step in impact analysis, 2) demonstrate that decisions made when developing the transfer functions can have a substantial influence on the 2 X CO2 scenarios and their interpretation, 3) highlight the uncertainty in the appropriate criteria for evaluating transfer function approaches, and 4) suggest that automation of empirical transfer function methodologies is inappropriate because of differences in the performance of transfer functions between sites and because of spatial differences in the GCM's ability to adequately simulate the predictor variables used in the functions.}, - number = {10}, - journal = {J. Clim.}, - author = {Winkler, J. A. and Palutikof, J. P. and Andresen, J. A. and Goodess, C. M.}, - month = oct, - year = {1997}, - pages = {2514--2532}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Clim.accession-num: ISI:A1997YC70500008}, - annote = {YC705J CLIMATE}, -} - -@article{winkler_possible_2002, - title = {Possible impacts of projected temperature change on commercial fruit production in the {Great} {Lakes} region}, - volume = {28}, - abstract = {Commercial fruit production is a primary revenue source for many locations within the Great Lakes region. Projected climate change may have a profound impact on this highly climate sensitive activity, which owes its existence to the moderating influence of the Great Lakes. Downscaled daily maximum and minimum temperature series provided by the U.S. National Assessment were utilized to evaluate 1) possible changes in the frequency and timing of several agronomically-relevant temperature threshold events and 2) potential interactions between crop phenology and a commercially-important insect pest (Cydia pornonella (L.)). The analyses are for the two future decades of 2025 to 2034 and 2090 to 2099. The assessments for 2025 to 2034 suggest that fruit-growing areas in the Great Lakes region will experience a moderate increase in growing season length and seasonal heat accumulation and a decrease in the frequency of freezing temperatures. In addition, important growth stages will occur earlier in the calendar year than at present. Very large changes in the temperature threshold parameters are projected for 2090 to 2099. However, it is unclear for both assessment decades whether fruit production will be more or less susceptible to damage from cold temperatures after critical growth stages are reached. Projected warming may also result in increases in the number of generations per season of a primary insect pest and the number of necessary pesticide applications.}, - number = {4}, - journal = {Journal of Great Lakes Research}, - author = {Winkler, J. A. and Andresen, J. A. and Guentchev, G. and Kriegel, R. D.}, - year = {2002}, - pages = {608--625}, - annote = {635QVJ GREAT LAKES RES}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Gt. Lakes Res.accession-num: ISI:000180411100010}, -} - -@article{wilsey_tools_2013, - title = {Tools for {Assessing} {Climate} {Impacts} on {Fish} and {Wildlife}}, - volume = {4}, - issn = {1944-687X}, - doi = {10.3996/062012-jfwm-055}, - number = {1}, - journal = {Journal of Fish and Wildlife Management}, - author = {Wilsey, Chad B. and Lawler, Joshua J. and Maurer, Edwin P. and McKenzie, Donald and Townsend, Patricia A. and Gwozdz, Richard and Freund, James A. and Hagmann, Keala and Hutten, Karen M.}, - month = jun, - year = {2013}, - pages = {220--241}, - annote = {doi: 10.3996/062012-JFWM-055}, - annote = {The following values have no corresponding Zotero field:publisher: US Fish \& Wildlife Servicework-type: doi: 10.3996/062012-JFWM-055}, -} - -@techreport{willmott_terrestrial_2001, - address = {Newark, DE, USA}, - title = {Terrestrial air temperature and precipitation: monthly and annual time series (1950 - 1999) ({Version} 1.02)}, - institution = {Center for Climatic Research, University of Delaware}, - author = {Willmott, C. J. and Matsuura, K.}, - year = {2001}, -} - -@article{williams_what_1996, - title = {What future for saline lakes?}, - volume = {38}, - number = {9}, - journal = {Environment}, - author = {Williams, William David}, - year = {1996}, - pages = {12--24}, - annote = {ARROW Discovery Service [http://search.arrow.edu.au/apps/ArrowUI/OAIHandler] (Australia) ER}, -} - -@article{wilks_weather_1999, - title = {The weather generation game: a review of stochastic weather models}, - volume = {23}, - abstract = {This article reviews the historical development of statistical weather models, from simple analyses of runs of consecutive rainy and dry days at single sites, through to multisite models of daily precipitation. Weather generators have been used extensively in water engineering design and in agricultural, ecosystem and hydrological impact studies as a means of in- filling missing data or for producing indefinitely long synthetic weather series from finite station records. We begin by describing the statistical properties of the rainfall occurrence and amount processes which are necessary precursors to the simulation of other (dependent) meteorological variables. The relationship between these daily weather models and lower-frequency variations in climate statistics is considered next, noting that conventional weather generator techniques often fail to capture wholly interannual variability Possible solutions to this deficiency - such as the use of mixtures of slowly and rapidly varying conditioning variables are discussed. Common applications of weather generators are then described. These include the modelling of climate- sensitive systems, the simulation of missing weather data and statistical downscaling of regional climate change scenarios. Finally, we conclude by considering ongoing advances in the simulation of spatially correlated weather series at multiple sites, the downscaling of interannual climate variability and the scope for using nonparametric techniques to synthesize weather series.}, - number = {3}, - journal = {Progress in Physical Geography}, - author = {Wilks, D. S. and Wilby, R. L.}, - month = sep, - year = {1999}, - pages = {329--357}, - annote = {235RWPROG PHYS GEOG}, - annote = {The following values have no corresponding Zotero field:alt-title: Prog. Phys. Geogr.accession-num: ISI:000082557200002}, -} - -@article{wilks_field_2006, - title = {On “{Field} {Significance}” and the {False} {Discovery} {Rate}}, - volume = {45}, - doi = {doi:10.1175/JAM2404.1}, - number = {9}, - journal = {Journal of Applied Meteorology and Climatology}, - author = {Wilks, D. S.}, - year = {2006}, - pages = {1181--1189}, -} - -@article{wilks_multisite_1999, - title = {Multisite downscaling of daily precipitation with a stochastic weather generator}, - volume = {11}, - abstract = {Stochastic models of daily precipitation are useful both for characterizing different precipitation climates and for stochastic simulation of these climates in conjunction with agricultural, hydrological, or other response models. A simple stochastic precipitation model is used to downscale-i.e. disaggregate from area-average to individual station- precipitation statistics for 6 groups of 5 U.S. stations, in a way that is consistent with observed relationships between the area-averaged series and their constituent station series. Each group of stations is located within a General Circulation Model grid-box-sized area, and collectively they exhibit a broad range of precipitation climates. The downscaling procedure is validated using natural climate variability in the observed precipitation records as an analog for climate change, by alternately considering collections of the driest and wettest seasons as 'base' and 'future' climates, and comparing the 2 sets of downscaled station parameters to those fit directly to the respective withheld observations. The resulting downscaled stochastic model parameters can be readily used for local-scale simulation of climate-change impacts.}, - number = {2}, - journal = {Climate Research}, - author = {Wilks, D. S.}, - month = mar, - year = {1999}, - pages = {125--136}, - annote = {192FJCLIMATE RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:000080067600003}, -} - -@article{willems_revision_2013, - title = {Revision of urban drainage design rules after assessment of climate change impacts on precipitation extremes at {Uccle}, {Belgium}}, - volume = {496}, - issn = {0022-1694}, - doi = {http://dx.doi.org/10.1016/j.jhydrol.2013.05.037}, - number = {0}, - journal = {Journal of Hydrology}, - author = {Willems, P.}, - year = {2013}, - keywords = {Climate change, Statistical downscaling, Design storms, Rainfall extremes, Storage capacity}, - pages = {166--177}, -} - -@book{wilks_statistical_2006, - address = {New York, NY, USA}, - title = {Statistical {Methods} in the {Atmospheric} {Sciences}, 2 ed.}, - publisher = {Academic Press}, - author = {Wilks, D. S.}, - year = {2006}, -} - -@article{wilks_rainfall_1989, - title = {Rainfall {Intensity}, the {Weibull} {Distribution}, and {Estimation} of {Daily} {Surface} {Runoff}}, - volume = {28}, - issn = {0894-8763}, - doi = {10.1175/1520-0450(1989)028<0052:ritwda>2.0.co;2}, - number = {1}, - journal = {Journal of Applied Meteorology}, - author = {Wilks, D. S.}, - month = jan, - year = {1989}, - pages = {52--58}, - annote = {doi: 10.1175/1520-0450(1989)028{\textless}0052:RITWDA{\textgreater}2.0.CO;2}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/1520-0450(1989)028{\textless}0052:RITWDA{\textgreater}2.0.CO;2}, -} - -@article{wilby_adapting_2012, - title = {Adapting to flood risk under climate change}, - volume = {36}, - doi = {10.1177/0309133312438908}, - abstract = {Flooding is the most common natural hazard and third most damaging globally after storms and earthquakes. Anthropogenic climate change is expected to increase flood risk through more frequent heavy precipitation, increased catchment wetness and sea level rise. This paper reviews steps being taken by actors at international, national, regional and community levels to adapt to flood risk from tidal, fluvial, surface and groundwater sources. We refer to existing inventories, national and sectoral adaptation plans, flood inquiries, building and planning codes, city plans, research literature and international policy reviews. We distinguish between the enabling environment for adaptation and specific implementing measures to manage flood risk. Enabling includes routine monitoring, flood forecasting, data exchange, institutional reform, bridging organizations, contingency planning for disasters, insurance and legal incentives to reduce vulnerability. All such activities are ‘low regret’ in that they yield benefits regardless of the climate scenario but are not cost-free. Implementing includes climate safety factors for new build, upgrading resistance and resilience of existing infrastructure, modifying operating rules, development control, flood forecasting, temporary and permanent retreat from hazardous areas, periodic review and adaptive management. We identify evidence of both types of adaptation following the catastrophic 2010/11 flooding in Victoria, Australia. However, significant challenges remain for managing transboundary flood risk (at all scales), protecting existing property at risk from flooding, and ensuring equitable outcomes in terms of risk reduction for all. Adaptive management also raises questions about the wider preparedness of society to systematically monitor and respond to evolving flood risks and vulnerabilities.}, - number = {3}, - journal = {Progress in Physical Geography}, - author = {Wilby, Robert L. and Keenan, Rod}, - month = jun, - year = {2012}, - pages = {348--378}, -} - -@article{wilby_statistical_1998, - title = {Statistical downscaling of general circulation model output: {A} comparison of methods}, - volume = {34}, - abstract = {A range of different statistical downscaling models was calibrated using both observed and general circulation model (GCM) generated daily precipitation time series and intercompared. The GCM used was the U.K. Meteorological Office, Hadley Centre's coupled ocean/atmosphere model (HadCM2) forced by combined CO, and sulfate aerosol changes. Climate model results for 1980-1999 (present) and 2080-2099 (future) were used, for six regions across the United States. The downscaling methods compared were different weather generator techniques (the standard "WGEN" method, and a method based on spell-length durations), two different methods using grid point vorticity data as an atmospheric predictor variable (B-Circ and C-Circ), and two variations of an artificial neural network (ANN) transfer function technique using circulation data and circulation plus temperature data as predictor variables. Comparisons of results were facilitated by using standard sets of observed and GCM-derived predictor variables and by using a standard suite of diagnostic statistics. Significant differences in the level of skill were found among the downscaling methods. The weather generation techniques, which are able to fit a number of daily precipitation statistics exactly, yielded the smallest differences between observed and simulated daily precipitation. The ANN methods performed poorly because of a failure to simulate wet-day occurrence statistics adequately. Changes in precipitation between the present and future scenarios produced by the statistical downscaling methods were generally smaller than those produced directly by the GCM. Changes in daily precipitation produced by the GCM between 1980-1999 and 2080-2099 were therefore judged not to be due primarily to changes in atmospheric circulation. In the light of these results and detailed model comparisons, suggestions for future research and model refinements are presented.}, - number = {11}, - journal = {Water Resources Research}, - author = {Wilby, R. L. and Wigley, T. M. L. and Conway, D. and Jones, P. D. and Hewitson, B. C. and Main, J. and Wilks, D. S.}, - month = nov, - year = {1998}, - pages = {2995--3008}, - annote = {132WAWATER RESOUR RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Water Resour. Res.accession-num: ISI:000076652600020}, -} - -@article{wilby_future_2002, - title = {Future changes in the distribution of daily precipitation totals across {North} {America}}, - volume = {29}, - abstract = {[1] Shape and scale parameters of the two-parameter gamma distribution were estimated for daily distributions of precipitation at individual grid-points in two General Circulation Models (HadCM2 and CSM). Maps of changing parameter distributions under anthropogenic forcing show the extent to which future precipitation scenarios for North America are consistent with observed trends in the 20th century. The scale parameter is found to be more variable, both spatially and temporally, than the shape parameter. Patterns of changes in mean wet-day amounts are strongly correlated with changes in the scale parameter. While the two models show quite different results for the spatial patterns of change, both indicate that the proportion of total precipitation derived from extreme and heavy events will continue to increase relative to moderate and light precipitation events.}, - number = {7}, - journal = {Geophysical Research Letters}, - author = {Wilby, R. L. and Wigley, T. M. L.}, - month = apr, - year = {2002}, - pages = {art. no.--1135}, - annote = {609DEGEOPHYS RES LETT}, - annote = {The following values have no corresponding Zotero field:alt-title: Geophys. Res. Lett.accession-num: ISI:000178886700025}, -} - -@article{wilby_precipitation_2000, - title = {Precipitation predictors for downscaling: {Observed} and general circulation model relationships}, - volume = {20}, - abstract = {Because of the coarse resolution of general circulation models (GCM), 'downscaling' techniques have emerged as a means of relating meso-scale atmospheric variables to grid- and sub- grid-scale surface variables. This study investigates these relationships. As a precursor, inter-variable correlations were investigated within a suite of 15 potential downscaling predictor variables on a daily time-scale for six regions in the conterminous USA, and observed correlations were compared with those based on the HadCM2 coupled ocean/atmosphere GCM. A comparison was then made of observed and model correlations between daily precipitation occurrence (a time series of zeroes and ones) and wet-day amounts and the 15 predictors. These two analyses provided new insights into model performance and provide results that are central to the choice of predictor variables in downscaling of daily precipitation. Also determined were the spatial character of relationships between observed daily precipitation and both mean sea-level pressure (mslp) and atmospheric moisture and daily precipitation for selected regions. The question of whether the same relationships are replicated by HadCM2 was also examined. This allowed the assessment of the spatial consistency of key predictor-predictand relationships in observed and HadCM2 data. Finally, the temporal stability of these relationships in the GCM was examined. Little difference between results for 1980- 1999 and 2080-2099 was observed. For correlations between predictor variables, observed and model results were generally similar, providing strong evidence of the overall physical realism of the model. For correlations with precipitation, the results are less satisfactory. For example, model precipitation is more strongly dependent on surface divergence and specific humidity than observed precipitation, while the latter has a stronger link to 500 hPa divergence than is evident in the model. These results suggest possible deficiencies in the model precipitation process, and may indicate that the model overestimates future changes in precipitation. Correlation field patterns for mslp versus precipitation are remarkably similar for observed data and HadCM2 output. Differences in the correlation fields for specific humidity are more noticeable, especially in summer. in many cases, maximum correlations between precipitation and mslp occurred away from the grid box; whereas correlations with specific humidity were largest when the data were propinquitous. This suggests that the choice of predictor variable and the corresponding predictor domain, in terms of location and spatial extent, are critical factors affecting the realism and stability of downscaled precipitation scenarios. Copyright (C) 2000 Royal Meteorological Society.}, - number = {6}, - journal = {International Journal of Climatology}, - author = {Wilby, R. L. and Wigley, T. M. L.}, - month = may, - year = {2000}, - pages = {641--661}, - annote = {314HAINT J CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:000087049300003}, -} - -@article{wilby_downscaling_1997, - title = {Downscaling general circulation model output: a review of methods and limitations}, - volume = {21}, - journal = {Progress in Physical Geography}, - author = {Wilby, R. L. and Wigley, T. M. L.}, - year = {1997}, - pages = {530--548}, -} - -@article{wilby_comparison_1999, - title = {A comparison of downscaled and raw {GCM} output: implications for climate change scenarios in the {San} {Juan} {River} basin, {Colorado}}, - volume = {225}, - abstract = {The fundamental rationale for statistical downscaling is that the raw outputs of climate change experiments from General Circulation Models (GCMs) are an inadequate basis for assessing the effects of climate change on land-surface processes at regional scales. This is because the spatial resolution of GCMs is too coarse to resolve important sub-grid scale processes (most notably those pertaining to the hydrological cycle) and because GCM output is often unreliable at individual and sub- grid box scales. By establishing empirical relationships between grid-box scale circulation indices (such as atmospheric vorticity and divergence) and sub-grid scale surface predictands (such as precipitation), statistical downscaling has been proposed as a practical means of bridging this spatial difference. This study compared three sets of current and future rainfall-runoff scenarios. The scenarios were constructed using: (1) statistically downscaled GCM output; (2) raw GCM output; and (3) raw GCM output corrected for elevational biases. Atmospheric circulation indices and humidity variables were extracted from the output of the UK Meteorological Office coupled ocean-atmosphere GCM (HadCM2) in order to downscale daily precipitation and temperature series for the Animas River in the San Juan River basin, Colorado. Significant differences arose between the modelled snowpack and how regimes of the three future climate scenarios. Overall, the raw GCM output suggests larger reductions in winter/spring snowpack and summer runoff than the downscaling, relative to current conditions. Further research is required to determine the generality of the water resource implications for other regions, GCM outputs and downscaled scenarios. (C) 1999 Elsevier Science B.V. All rights reserved.}, - number = {1-2}, - journal = {Journal of Hydrology}, - author = {Wilby, R. L. and Hay, L. E. and Leavesley, G. H.}, - month = nov, - year = {1999}, - pages = {67--91}, - annote = {257NAJ HYDROL}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Hydrol.accession-num: ISI:000083787000004}, -} - -@article{wilby_hydrological_2000, - title = {Hydrological responses to dynamically and statistically downscaled climate model output}, - volume = {27}, - number = {8}, - journal = {Geophysical Research Letters}, - author = {Wilby, R. L. and Hay, L. E. and Gutowski, W. J. and Arritt, R. W. and Takle, E. S. and Pan, Z. and Leavesley, G. H. and Clark, M. P.}, - year = {2000}, - pages = {1199--1202}, -} - -@article{wilby_statistical_1998-1, - title = {Statistical downscaling of hydrometeorological variables using general circulation model output}, - volume = {205}, - abstract = {Empirical relationships between daily hydrometeorological variables for a catchment in Nagano prefecture, Japan and three indices of regional atmospheric circulation are examined with a view to assessing their use in General Circulation Model (GCM) downscaling. The indices (vorticity, flow strength and angular direction of airflow) were calculated by using daily grid-point sea-level pressure data derived from: (a) the National Centers for Environmental Prediction, NCEP Re-analysis data set (1979- 1995); and (b) the UK Meteorological Office, Hadley Centre coupled ocean-atmosphere model (HadCM2SUL) for two periods indicative of present (1980-1999) and future greenhouse gas plus sulfate aerosol forcing (2080-2099). Statistical models of the surface variables were then "forced" by using the three airflow indices obtained from HadCM2SUL. The differences between the NCEP and HadCM2SUL "present" downscaled variables were generally greater than those arising between the downscaling of the two GCM airflow scenarios. The lack of change in downscaled surface variables between the 1980-1999 and 2080-2099 data was attributed to the low sensitivity of atmospheric circulation patterns in HadCM2SUL to greenhouse gas forcing. (C) 1998 Elsevier Science B.V.}, - number = {1-2}, - journal = {Journal of Hydrology}, - author = {Wilby, R. L. and Hassan, H. and Hanaki, K.}, - month = feb, - year = {1998}, - pages = {1--19}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Hydrol.accession-num: ISI:000072678100001}, - annote = {ZD367J HYDROL}, -} - -@article{wilby_framework_2006, - title = {A framework for assessing uncertainties in climate change impacts: low-flow scenarios for the {River} {Thames}, {UK}}, - volume = {42}, - journal = {Water Resources Research}, - author = {Wilby, R. L. and Harris, I.}, - year = {2006}, - pages = {W02419, doi:10.1029/2005WR004065}, -} - -@article{wilby_sdsm_2002, - title = {{SDSM} - a decision support tool for the assessment of regional climate change impacts}, - volume = {17}, - abstract = {General Circulation Models (GCMs) suggest that rising concentrations of greenhouse gases will have significant implications for climate at global and regional scales. Less certain is the extent to which meteorological processes at individual sites will be affected. So-called `downscaling' techniques are used to bridge the spatial and temporal resolution gaps between what climate modellers are currently able to provide and what impact assessors require. This paper describes a decision support tool for assessing local climate change impacts using a robust statistical downscaling technique. Statistical DownScaling Model (SDSM) facilitates the rapid development of multiple, low-cost, single-site scenarios of daily surface weather variables under current and future regional climate forcing. Additionally, the software performs ancillary tasks of predictor variable pre-screening, model calibration, basic diagnostic testing, statistical analyses and graphing of climate data. The application of SDSM is demonstrated with respect to the generation of daily temperature and precipitation scenarios for Toronto, Canada by 2040-2069. (C) 2002 Elsevier Science Ltd. All rights reserved.}, - number = {2}, - journal = {Environmental Modelling \& Software}, - author = {Wilby, R. L. and Dawson, C. W. and Barrow, E. M.}, - year = {2002}, - pages = {147--159}, - annote = {524JMENVIRON MODELL SOFTW}, - annote = {The following values have no corresponding Zotero field:alt-title: Environ. Modell. Softw.accession-num: ISI:000174009600004}, -} - -@article{wilby_seasonal_2001, - title = {Seasonal forecasting of river flows in the {British} {Isles} using {North} {Atlantic} pressure patterns}, - volume = {15}, - abstract = {A potentially useful forecasting relationship is demonstrated between the North Atlantic Oscillation index in winter (January-February) and the following summer (July-September) monthly mean flows for selected rivers in the British Isles. The relationship was strongest in August when up to 40\% of the variance in monthly mean flow may be explained. For two rivers in southern and eastern England, positive phases of the winter North Atlantic Oscillation index were found to precede summer flows which were nearly 50\% of long-term average. A regression equation which was established to predict August flows in the Great Stour produced a correlation score of 0.6 at a lead time of six months. Further research is needed to determine the significance of catchment characteristics and geographic location relative to forecasting skill.}, - number = {1}, - journal = {Journal of the Chartered Institution of Water and Environmental Management}, - author = {Wilby, R. L.}, - month = mar, - year = {2001}, - pages = {56--63}, - annote = {448QVJ CHART INST WATER ENV MANAGE}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Chart. Inst. Water. Environ. Manage.accession-num: ISI:000169639900010}, -} - -@article{wilby_statistical_1998-2, - title = {Statistical downscaling of daily precipitation using daily airflow and seasonal teleconnection indices}, - volume = {10}, - abstract = {Monthly or seasonal climate variability is seldom captured adequately by high-resolution statistical downscaling models. However, such deficiences may, in fact, be an artefact of the failure of many downscaling models to incorporate appropriate low-frequency predictor variables. The present study explores the possibility of using variables that characterise both the high- and low-frequency variability of daily precipitation at selected sites in the British Isles. Accordingly, 3 statistical downscaling models were calibrated by regressing daily precipitation data for sites at Durham and Kempsford, UK, against regional climate predictors for the period 1881-1935. Model 1 employed only 1 predictor, the daily vorticity obtained from daily grid-point mean-sea-level pressure over the British Isles. Model 2 employed both daily vorticity and seasonal North Atlantic Oscillation Indices (NAOI) as predictors. Finally, Model 3 employed daily vorticity and seasonal North Atlantic sea-surface temperature (SST) anomalies as predictors. All 3 models were validated using daily and monthly precipitation statistics at the same stations for the period 1936-1990. Although Models 2 and 3 did yield improvements in the downscaling of the monthly precipitation diagnostics, the enhancement was only modest relative to Model 1 (the vorticity- only model). Nonetheless, the preliminary results suggest that there may be some merit in using North Atlantic SST series as a downscaling predictor variable for daily/monthly precipitation in the UK. However, further research is required to determine whether or not the inclusion of teleconnection indices in downscaling schemes leads to better representations of low- frequency variability in both present and future climates when General Circulation Model outputs are employed.}, - number = {3}, - journal = {Climate Research}, - author = {Wilby, R. L.}, - month = dec, - year = {1998}, - pages = {163--178}, - annote = {167XPCLIMATE RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:000078660400001}, -} - -@article{wilby_non-stationarity_1997, - title = {Non-stationarity in daily precipitation series: {Implications} for {GCM} down-scaling using atmospheric circulation indices}, - volume = {17}, - abstract = {The paper addresses three issues relating to the design and calibration of precipitation models for down-scaling General Circulation Model (GCM) output. First, the significance of non- stationary daily circulation-pattern-precipitation relationships will be described for two UK sites, each with records beginning in 1881. Second, the cause(s) of the observed non-stationarity will be investigated using lengthy air flow, synoptic, temperature, and standardized teleconnection indices. Third, the prospects for incorporating these factors within down-scaling methods will be discussed with reference to several alternative techniques. Finally, a multivariate approach incorporating teleconnection and airflow indices within precipitation models is advocated. (C) 1997 by the Royal Meteorological Society.}, - number = {4}, - journal = {International Journal of Climatology}, - author = {Wilby, R. L.}, - month = mar, - year = {1997}, - pages = {439--454}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:A1997WT23100006}, - annote = {WT231INT J CLIMATOL}, -} - -@article{wilby_stochastic_1994, - title = {Stochastic {Weather} {Type} {Simulation} for {Regional} {Climate}-{Change} {Impact} {Assessment}}, - volume = {30}, - abstract = {A stochastic model is developed for the synthesis of daily precipitation by weather type analysis. Daily rainfall at two sites in southern England is related to the Lamb weather types by using conditional probabilities. Time series of circulation patterns and hence rainfall are then generated using a Markov representation of matrices of transition probabilities between weather types. The model reproduces key aspects of the historic rainfall regime at daily, monthly, and annual temporal resolutions. The general validity of the methodology as a means of disaggregating general circulation model predictions into finer spatial scales for water resource studies is discussed. Several limitations are identified of which the subjectivity introduced by the weather type classification system and the nonstationarity of the precipitation characteristics are the most serious.}, - number = {12}, - journal = {Water Resources Research}, - author = {Wilby, R. L.}, - month = dec, - year = {1994}, - pages = {3395--3403}, - annote = {PU142WATER RESOUR RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Water Resour. Res.accession-num: ISI:A1994PU14200015}, -} - -@article{wilby_robust_2010, - title = {Robust adaptation to climate change}, - volume = {65}, - issn = {1477-8696}, - doi = {10.1002/wea.543}, - number = {7}, - journal = {Weather}, - author = {Wilby, R. L. and Dessai, S.}, - year = {2010}, - pages = {180--185}, - annote = {The following values have no corresponding Zotero field:publisher: John Wiley \& Sons, Ltd.}, -} - -@article{wilby_rainfall_1995, - title = {Rainfall {Variability} {Associated} with {Lamb} {Weather} {Types} - the {Case} for {Incorporating} {Weather} {Fronts}}, - volume = {15}, - abstract = {The value of weather generators for down-scaling the output of general circulation models for regional impact assessments is limited by the assumption of stationary-rainfall-circulation- pattern relationships. This note assesses the potential of incorporating weather front data into existing stochastic precipitation models to describe intraweather pattern rainfall variability.}, - number = {11}, - journal = {International Journal of Climatology}, - author = {Wilby, R. L. and Barnsley, N. and Ohare, G.}, - month = nov, - year = {1995}, - pages = {1241--1252}, - annote = {TG705INT J CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:A1995TG70500004}, -} - -@article{wilby_modelling_1998, - title = {Modelling low-frequency rainfall events using airflow indices, weather patterns and frontal frequencies}, - volume = {213}, - abstract = {Low-frequency, high-magnitude daily rainfall amounts recorded at several sites in central and southern England were related to the prevailing Lamb Weather Types (LWTs), daily weather front and airflow data. Three statistically distinct weather- type clusters were indentified and used to construct a simplified frontal model of daily precipitation occurrence/amount. The model was calibrated against station data for the period 1970-1990 and used to reconstruct observed daily precipitation between 1875 and 1969 given the historic sequence of LWTs. Although the model reproduced the incidence of low-frequency, high-magnitude events, it failed to capture variations in mean wet day probabilities and wet/dry spell persistence. This inability was attributed to the general limitations of the weather classification methodology, which did not capture all aspects of the precipitation regime with equal levels of proficiency. Therefore, the prospects for downscaling high-resolution precipitation series directly from indices of mean sea-lever pressure rather than via weather patterns was discussed. Preliminary results indicate that relationships can be established between mean daily precipitation occurrence and airflow indices such as vorticity and strength of air how. However, further research is required to establish the value of such indices for modelling low- frequency, high-magnitude precipitation events. (C) 1998 Published by Elsevier Science B.V. All rights reserved.}, - number = {1-4}, - journal = {Journal of Hydrology}, - author = {Wilby, R. L.}, - month = dec, - year = {1998}, - pages = {380--392}, - annote = {151LVJ HYDROL}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Hydrol.accession-num: ISI:000077722300025}, -} - -@article{wilby_simulation_1995, - title = {Simulation of {Precipitation} by {Weather} {Pattern} and {Frontal} {Analysis}}, - volume = {173}, - abstract = {Daily rainfall from two sites in central and southern England was stratified according to the presence or absence of weather fronts and then cross-tabulated with the prevailing Lamb Weather Type (LWT). A semi-Markov chain model was developed for simulating daily sequences of LWTs from matrices of transition probabilities between weather types for the British Isles 1970- 1990. Daily and annual rainfall distributions were then simulated from-the prevailing LWTs using historic conditional probabilities for precipitation occurrence and frontal frequencies. When compared with a conventional rainfall generator the frontal model produced improved estimates of the, overall size distribution of daily rainfall amounts and in particular the incidence of low-frequency high-magnitude totals. Further research is required to establish the contribution of individual frontal sub-classes to daily rainfall totals and of long-term fluctuations in frontal frequencies to conditional probabilities.}, - number = {1-4}, - journal = {Journal of Hydrology}, - author = {Wilby, R.}, - month = dec, - year = {1995}, - pages = {91--109}, - annote = {TG713J HYDROL}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Hydrol.accession-num: ISI:A1995TG71300006}, -} - -@article{wigley_interpretation_2001, - title = {Interpretation of high projections for global-mean warming}, - volume = {293}, - journal = {Science}, - author = {Wigley, T. M. L. and Raper, S. C. B.}, - year = {2001}, - pages = {451--454}, -} - -@article{wigley_influences_1985, - title = {Influences of precipitation changes and direct {CO2} effects on streamflow}, - volume = {314}, - number = {6007}, - journal = {Nature}, - author = {Wigley, T. M. L. and Jones, P. D.}, - year = {1985}, - pages = {149--152}, - annote = {10.1038/314149a010.1038/314149a0}, -} - -@article{wigley_effect_2009, - title = {The effect of changing climate on the frequency of absolute extreme events}, - volume = {97}, - issn = {0165-0009}, - doi = {10.1007/s10584-009-9654-7}, - language = {English}, - number = {1-2}, - journal = {Clim. Change}, - author = {Wigley, T. M. L.}, - month = nov, - year = {2009}, - keywords = {model}, - pages = {67--76}, - annote = {ISI Document Delivery No.: 508ZMTimes Cited: 1Cited Reference Count: 10Wigley, T. M. L.SpringerDordrecht}, - annote = {The following values have no corresponding Zotero field:auth-address: Natl Ctr Atmospher Res, Climate \& Global Dynam Div, Boulder, CO 80307 USA. Wigley, TML, Natl Ctr Atmospher Res, Climate \& Global Dynam Div, POB 3000, Boulder, CO 80307 USA. wigley@ucar.edualt-title: Clim. Changeaccession-num: ISI:000270979600006work-type: Article}, -} - -@techreport{wigley_input_2004, - address = {Sacramento, CA}, - title = {Input needs for downscaling of climate data}, - institution = {California Energy Commission}, - author = {Wigley, T. M. L.}, - month = jan, - year = {2004}, - pages = {29}, - annote = {The following values have no corresponding Zotero field:number: 500-04-027}, -} - -@misc{white_house_executive_2015, - title = {Executive {Order} 13960 -{Establishing} a {Federal} {Flood} {Risk} {Management} {Standard} and a {Process} for {Further} {Soliciting} and {Considering} {Stakeholder} {Input}.}, - volume = {80}, - author = {White House}, - year = {2015}, - annote = {The following values have no corresponding Zotero field:number: 23publisher: Federal Register}, -} - -@article{whateley_selecting_2016, - title = {Selecting {Stochastic} {Climate} {Realizations} to {Efficiently} {Explore} a {Wide} {Range} of {Climate} {Risk} to {Water} {Resource} {Systems}}, - volume = {142}, - doi = {doi:10.1061/(ASCE)WR.1943-5452.0000631}, - number = {6}, - journal = {Journal of Water Resources Planning and Management}, - author = {Whateley, S. and Steinschneider, S. and Brown, C.}, - year = {2016}, -} - -@article{widmann_statistical_2003, - title = {Statistical precipitation downscaling over the {Northwestern} {United} {States} using numerically simulated precipitation as a predictor}, - volume = {16}, - abstract = {This study investigates whether GCM-simulated precipitation is a good predictor for regional precipitation over Washington and Oregon. In order to allow for a detailed comparison of the estimated precipitation with observations, the simulated precipitation is taken from the NCEP-NCAR reanalysis, which nearly perfectly represents the historic pressure, temperature, and humidity, but calculates precipitation according to the model physics and parameterizations. Three statistical downscaling methods are investigated: (i) local rescaling of the simulated precipitation, and two newly developed methods, namely, (ii) downscaling using singular value decomposition (SVD) with simulated precipitation as the predictor, and (iii) local rescaling with a dynamical correction. Both local scaling methods are straightforward to apply to GCMs that are used for climate change experiments and seasonal forecasts, since they only need control runs for model fitting. The SVD method requires for model fitting special reanalysis-type GCM runs nudged toward observations from a historical period (selection of analogs from the GCM chosen to optimally match the historical weather states might achieve similar results). The precipitation-based methods are compared with conventional statistical downscaling using SVD with various large-scale predictors such as geopotential height, temperature, and humidity. The skill of the different methods for reconstructing historical wintertime precipitation (1958-94) over Oregon and Washington is tested on various spatial scales as small as 50 km and on temporal scales from months to decades. All methods using precipitation as a predictor perform considerably better than the conventional downscaling. The best results using conventional methods are obtained with geopotential height at 1000 hPa or humidity at 850 hPa as predictors. In these cases correlations of monthly observed and reconstructed precipitation on the 50-km scale range from 0.43 to 0.65. The inclusion of several predictor fields does not improve the reconstructions, since they are all highly correlated. Local rescaling of simulated precipitation yields much higher correlations between 0.7 and 0.9, with the exception of the rain shadow of the Cascade Mountains in the Columbia Basin (eastern Washington). When the simulated precipitation is used as a predictor in SVD-based downscaling correlations also reach 0.7 in eastern Washington. Dynamical correction improves the local scaling considerably in the rain shadow and yields correlations almost as high as with the SVD method. Its combination of high skill and ease to use make it particularly attractive for GCM precipitation downscaling.}, - number = {5}, - journal = {J. Clim.}, - author = {Widmann, M. and Bretherton, C. S. and Salathe, E. P.}, - month = mar, - year = {2003}, - pages = {799--816}, - annote = {645FJJ CLIMATE}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Clim.accession-num: ISI:000180965300002}, -} - -@article{widmann_validation_2000, - title = {Validation of mesoscale precipitation in the {NCEP} reanalysis using a new grid-cell precipitation dataset for the {Northwestern} {United} {States}}, - volume = {13}, - journal = {Journal of Climate}, - author = {Widmann, M. and Bretherton, C. S.}, - year = {2000}, - pages = {1936--1950}, -} - -@article{wettstein_influence_2002, - title = {The influence of the {North} {Atlantic}-{Arctic} {Oscillation} on mean, variance, and extremes of temperature in the northeastern {United} {States} and {Canada}}, - volume = {15}, - abstract = {An analysis of detailed relationships between the North Atlantic Oscillation-Arctic Oscillation (NAO-AO) and local temperature response throughout the northeastern United States and neighboring areas of Canada is presented. In particular, the study focuses on how contrasts in the mean and daily variance, based on AO phase, are associated with contrasts in the frequency and intensity of extreme temperature events in both winter and spring. In this region, notable contrasts in mean temperatures in winter and daily variance in spring, which influence the pattern of extremes, are associated with phases of the NAO-AO. Warmer temperatures in New England and cooler temperatures in Quebec, Canada, result during winter with increases in the NAO-AO index. The mean temperature response is weaker in spring, but the response of daily variance of temperature is stronger; variance increases with the NAO-AO index. Relationships between these effects help explain significant increases in maximum temperature extremes during winter in New England and in minimum temperature extremes during spring in Quebec for high NAO-AO index years. Diurnal temperature range tends to be larger in AO-positive winters and springs throughout the region. This study helps put other work on the trends in regional extreme events into the context of large-scale climate variability.}, - number = {24}, - journal = {J. Clim.}, - author = {Wettstein, J. J. and Mearns, L. O.}, - month = dec, - year = {2002}, - pages = {3586--3600}, - annote = {625JYJ CLIMATE}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Clim.accession-num: ISI:000179814400004}, -} - -@article{wetherald_simulation_2002, - title = {Simulation of hydrologic changes associated with global warming}, - volume = {107}, - issn = {0148-0227}, - doi = {10.1029/2001jd001195}, - abstract = {Using the results obtained from a coupled ocean-atmosphere-land model with medium computational resolution, we investigated how the hydrology of the continents changes in response to the combined increases of greenhouse gases and sulfate aerosols in the atmosphere, which are determined based upon the IS92a scenario. In order to extract the forced response from natural, internal variability, the difference between the mean of an eight-member ensemble of numerical experiments and a control experiment are used for the present analysis. The global mean surface air temperature of the coupled model increases by about 2.3°C above the preindustrial level by the middle of the 21st century. Accompanying the warming, the global mean rates of both precipitation and evaporation increase by 5.2\%, yielding the average increase in the rate of runoff by approximately 7.3\%. The increase in the rate of runoff simulated by the model is particularly large in high northern latitudes, where the runoff from some rivers such as the Mackenzie and Ob\&\#8242; may increase by as much as 20\%. Runoff from many European rivers increases by more than 20\%. Runoff also increases substantially in some tropical rivers such as the Amazon and Ganges. However, the percentage changes in simulated runoff from many other tropical rivers and middle latitude rivers are smaller with both positive and negative signs. In middle and high latitudes in the Northern Hemisphere, soil moisture tends to decrease in summer, whereas it increases in winter. However, in many semi-arid regions in subtropical and middle latitudes, soil moisture is reduced during most of a year. These semi-arid regions include the southwestern part of North America, the northeastern part of China in the Northern Hemisphere, and the region in the vicinity of the Kalahari Desert and southern part of Australia in the Southern Hemisphere. Since a semi-arid region usually surrounds a desert, the reduction of soil moisture in such a region often results in the expansion of the desert. Soil moisture is also reduced during the dry season in many semi-arid regions. For example, it is reduced in the savannahs of Africa and South America during winter and early spring in the Southern Hemisphere. In the Northern Hemisphere, it is reduced at the Mediterranean coast of Europe in summer.}, - number = {D19}, - journal = {J. Geophys. Res.}, - author = {Wetherald, Richard T. and Manabe, Syukuro}, - year = {2002}, - keywords = {3309 Meteorology and Atmospheric Dynamics: Climatology, 1866 Hydrology: Soil moisture, 3322 Meteorology and Atmospheric Dynamics: Land/atmosphere interactions, 1860 Hydrology: Runoff and streamflow, 3354 Meteorology and Atmospheric Dynamics: Precipitation}, - pages = {4379}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{wetherald_changes_2009, - title = {Changes of {Variability} in {Response} to {Increasing} {Greenhouse} {Gases}. {Part} {II}: {Hydrology}}, - volume = {22}, - abstract = {This paper examines hydrological variability and its changes in two different versions of a coupled ocean\&\#8211;atmosphere general circulation model developed at the National Oceanic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory and forced with estimates of future increases of greenhouse gas and aerosol concentrations. This paper is the second part, documenting potential changes in variability as greenhouse gases increase. The variance changes are examined using an ensemble of 8 transient integrations for an older model version and 10 transient integrations for a newer model. Monthly and annual data are used to compute the mean and variance changes. Emphasis is placed on computing and analyzing the variance changes for the middle of the twenty-first century and compared with those found in the respective control integrations.The hydrologic cycle intensifies because of the increase of greenhouse gases. In general, precipitation variance increases in most places. This is the case virtually everywhere the mean precipitation rate increases and many places where the precipitation decreases. The precipitation rate variance decreases in the subtropics, where the mean precipitation rate also decreases. The increased precipitation rate and variance, in middle to higher latitudes during late fall, winter, and early spring leads to increased runoff and its variance during that period.On the other hand, the variance changes of soil moisture are more complicated, because soil moisture has both a lower and upper bound that tends to reduce its fluctuations. This is particularly true in middle to higher latitudes during winter and spring, when the soil moisture is close to its saturation value at many locations. Therefore, changes in its variance are limited. Soil moisture variance change is positive during the summer, when the mean soil moisture decreases and is close to the middle of its allowable range. In middle to high northern latitudes, an increase in runoff and its variance during late winter and spring plus the decrease in soil moisture and its variance during summer lend support to the hypothesis stated in other publications that a warmer climate can cause an increasing frequency of both excessive discharge and drier events, depending on season and latitude.}, - number = {22}, - journal = {Journal of Climate}, - author = {Wetherald, Richard T.}, - month = nov, - year = {2009}, - pages = {6089--6103}, -} - -@article{weigel_risks_2010, - title = {Risks of {Model} {Weighting} in {Multimodel} {Climate} {Projections}}, - volume = {23}, - issn = {0894-8755}, - doi = {10.1175/2010jcli3594.1}, - abstract = {Multimodel combination is a pragmatic approach to estimating model uncertainties and to making climate projections more reliable. The simplest way of constructing a multimodel is to give one vote to each model ("equal weighting"), while more sophisticated approaches suggest applying model weights according to some measure of performance ("optimum weighting"). In this study, a simple conceptual model of climate change projections is introduced and applied to discuss the effects of model weighting in more generic terms. The results confirm that equally weighted multimodels on average outperform the single models, and that projection errors can in principle be further reduced by optimum weighting. However, this not only requires accurate knowledge of the single model skill, but the relative contributions of the joint model error and unpredictable noise also need to be known to avoid biased weights. If weights are applied that do not appropriately represent the true underlying uncertainties, weighted multimodels perform on average worse than equally weighted ones, which is a scenario that is not unlikely, given that at present there is no consensus on how skill-based weights can be obtained. Particularly when internal variability is large, more information may be lost by inappropriate weighting than could potentially be gained by optimum weighting. These results indicate that for many applications equal weighting may be the safer and more transparent way to combine models. However, also within the presented framework eliminating models from an ensemble can be justified if they are known to lack key mechanisms that are indispensable for meaningful climate projections.}, - language = {English}, - number = {15}, - journal = {J. Clim.}, - author = {Weigel, A. P. and Knutti, R. and Liniger, M. A. and Appenzeller, C.}, - month = aug, - year = {2010}, - keywords = {averaging rea method, ensembles, temperature, uncertainty, probability, simulations, gcm, skill, combination, ensemble forecasts, seasonal prediction}, - pages = {4175--4191}, - annote = {ISI Document Delivery No.: 645WXTimes Cited: 1Cited Reference Count: 55Weigel, Andreas P. Knutti, Reto Liniger, Mark A. Appenzeller, ChristofSwiss National Science Foundation through the National Centre for Competence in Research (NCCR) Climate ; EU [GOCE-CT-2003-505539]This study was supported by the Swiss National Science Foundation through the National Centre for Competence in Research (NCCR) Climate and by the ENSEMBLES project (EU FP6, Contract GOCE-CT-2003-505539). Helpful comments of Andreas Fischer are acknowledged.Amer meteorological socBoston}, - annote = {The following values have no corresponding Zotero field:auth-address: [Weigel, Andreas P.; Liniger, Mark A.; Appenzeller, Christof] MeteoSwiss, Fed Off Meteorol \& Climatol, CH-8044 Zurich, Switzerland. [Knutti, Reto] ETH, Inst Atmospher \& Climate Sci, Zurich, Switzerland. Weigel, AP, MeteoSwiss, Fed Off Meteorol \& Climatol, Krahbuhlstr 58,POB 514, CH-8044 Zurich, Switzerland. andreas.weigel@meteoswiss.chalt-title: J. Clim.accession-num: ISI:000281498000009work-type: Article}, -} - -@article{weichert_linear_1998, - title = {Linear versus nonlinear techniques in downscaling}, - volume = {10}, - abstract = {Standard linear and nonlinear downscaling models are compared using identical atmospheric circulation forcing fields. The target variables chosen were observed daily values of average temperature (TAV), precipitation (PRC), and vapor pressure (HPR) at a Central European station. Being without much sophistication, both models show acceptable performance on this time scale only for TAV and HPR; PRC, which behaves in a predominantly nonlinear fashion, handled very poorly. By considerably refining the evaluation it is nevertheless possible to distinguish significant differences between the 2 models and, with the nonlinear model, to describe specific rainfall conditions. We argue that this difference is caused by the limitations of the linear approach, and discuss how this might affect the downscaling of nonlinear quantities in general.}, - number = {2}, - journal = {Climate Research}, - author = {Weichert, A. and Burger, G.}, - month = aug, - year = {1998}, - pages = {83--93}, - annote = {132FNCLIMATE RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:000076619800001}, -} - -@article{wehner_sources_2010, - title = {Sources of uncertainty in the extreme value statistics of climate data}, - volume = {13}, - issn = {1386-1999}, - doi = {10.1007/s10687-010-0105-7}, - abstract = {We investigate three sources of uncertainty in the calculation of extreme value statistics for observed and modeled climate data. Inter-model differences in formulation, unforced internal variability and choice of statistical model all contribute to uncertainty. Using fits to the GEV distribution to obtain 20 year return values, we quantify these uncertainties for the annual maximum daily mean surface air temperatures of pre-industrial control runs from 15 climate models in the CMIP3 dataset.}, - language = {English}, - number = {2}, - journal = {Extremes}, - author = {Wehner, M.}, - month = jun, - year = {2010}, - keywords = {temperature, ensemble, simulations, gcm, Uncertainty, Climate models, Extreme temperature, Return value}, - pages = {205--217}, - annote = {ISI Document Delivery No.: 617PATimes Cited: 0Cited Reference Count: 22Wehner, MichaelSpringerNew york}, - annote = {The following values have no corresponding Zotero field:auth-address: Univ Calif Berkeley, Lawrence Berkeley Lab, Berkeley, CA 94720 USA. Wehner, M, Univ Calif Berkeley, Lawrence Berkeley Lab, 1 Cyclotron Rd,MS 50F, Berkeley, CA 94720 USA. mfwehner@lbl.govalt-title: Extremesaccession-num: ISI:000279291700006work-type: Article}, -} - -@article{watterson_what_2014, - title = {What {Influences} the {Skill} of {Climate} {Models} over the {Continents}?}, - volume = {95}, - issn = {0003-0007}, - doi = {10.1175/bams-d-12-00136.1}, - abstract = {Climate modeling groups from four continents have submitted simulations as part of phase 5 of the Coupled Model Intercomparison Project (CMIP5). With climate impact assessment in mind, we test the accuracy of the seasonal averages of temperature, precipitation, and mean sea level pressure, compared to two observational datasets. Nondimensional skill scores have been generated for the global land and six continental domains. For most cases the 25 models analyzed perform well, particularly the models from Europe. Overall, this CMIP5 ensemble shows improved skill over the earlier (ca. 2005) CMIP3 ensemble of 24 models. This improvement is seen for each variable and continent, and in each case it is largely consistent with the increased resolution on average of CMIP5, given the correlation between scores and grid length found across the combined ensemble. From this apparent influence on skill, the smaller average score for the 13 Earth system models in CMIP5 is consistent with their mostly lower resolution. There is some variation in the ranking of models by skill score for the global, versus continental, measures of skill, and this prompts consideration of the potential influence of a regional focus that model developers might have. While some models rank considerably better in their ?home? continent than globally, most have similar ranks in the two domains. Averaging over each ensemble, the home rank is better by only one or two ranks, indicating that the location of development is only a minor influence.}, - number = {5}, - journal = {Bulletin of the American Meteorological Society}, - author = {Watterson, I. G. and Bathols, J. and Heady, C.}, - month = may, - year = {2014}, - pages = {689--700}, - annote = {doi: 10.1175/BAMS-D-12-00136.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/BAMS-D-12-00136.1}, -} - -@article{watanabe_intercomparison_2012, - title = {Intercomparison of bias-correction methods for monthly temperature and precipitation simulated by multiple climate models}, - volume = {117}, - issn = {0148-0227}, - doi = {10.1029/2012jd018192}, - abstract = {Bias-correction methods applied to monthly temperature and precipitation data simulated by multiple General Circulation Models (GCMs) are evaluated in this study. Although various methods have been proposed recently, an intercomparison among them using multiple GCM simulations has seldom been reported. Moreover, no previous methods have addressed the issue how to adequately deal with the changes of the statistics of bias-corrected variables from the historical to future simulations. In this study, a new method which conserves the changes of mean and standard deviation of the uncorrected model simulation data is proposed, and then five previous bias-correction methods as well as the proposed new method are intercompared by applying them to monthly temperature and precipitation data simulated from 12 GCMs in the Coupled Model Intercomparison Project (CMIP3) archives. Parameters of each method are calibrated by using 1948\&\#8211;1972 observed data and validated in the 1974\&\#8211;1998 period. These methods are then applied to the GCM future simulations (2073\&\#8211;2097) and the bias-corrected data are intercompared. For the historical simulations, negligible difference can be found between observed and bias-corrected data. However, the differences in future simulations are large dependent on the characteristics of each method. The new method successfully conserves the changes in the mean, standard deviation and the coefficient of variation before and after bias-correction. The differences of bias-corrected data among methods are discussed according to their respective characteristics. Importantly, this study classifies available correction methods into two distinct categories, and articulates important features for each of them.}, - number = {D23}, - journal = {J. Geophys. Res.}, - author = {Watanabe, Satoshi and Kanae, Shinjiro and Seto, Shinta and Yeh, Pat J. F. and Hirabayashi, Yukiko and Oki, Taikan}, - year = {2012}, - keywords = {bias correction, climate change, GCM, 1626 Global Change: Global climate models (3337, 4928), 1807 Hydrology: Climate impacts (4321), 1854 Hydrology: Precipitation (3354)}, - pages = {D23114}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{walton_hybrid_2015, - title = {A {Hybrid} {Dynamical}–{Statistical} {Downscaling} {Technique}. {Part} {I}: {Development} and {Validation} of the {Technique}}, - volume = {28}, - issn = {0894-8755}, - doi = {10.1175/JCLI-D-14-00196.1}, - abstract = {AbstractIn this study (Part I), the mid-twenty-first-century surface air temperature increase in the entire CMIP5 ensemble is downscaled to very high resolution (2 km) over the Los Angeles region, using a new hybrid dynamical?statistical technique. This technique combines the ability of dynamical downscaling to capture finescale dynamics with the computational savings of a statistical model to downscale multiple GCMs. First, dynamical downscaling is applied to five GCMs. Guided by an understanding of the underlying local dynamics, a simple statistical model is built relating the GCM input and the dynamically downscaled output. This statistical model is used to approximate the warming patterns of the remaining GCMs, as if they had been dynamically downscaled. The full 32-member ensemble allows for robust estimates of the most likely warming and uncertainty resulting from intermodel differences. The warming averaged over the region has an ensemble mean of 2.3°C, with a 95\% confidence interval ranging from 1.0° to 3.6°C. Inland and high elevation areas warm more than coastal areas year round, and by as much as 60\% in the summer months. A comparison to other common statistical downscaling techniques shows that the hybrid method produces similar regional-mean warming outcomes but demonstrates considerable improvement in capturing the spatial details. Additionally, this hybrid technique incorporates an understanding of the physical mechanisms shaping the region?s warming patterns, enhancing the credibility of the final results.}, - number = {12}, - journal = {Journal of Climate}, - author = {Walton, Daniel B. and Sun, Fengpeng and Hall, Alex and Capps, Scott}, - month = jun, - year = {2015}, - pages = {4597--4617}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Society}, -} - -@article{waugh_quantitative_2008, - title = {Quantitative performance metrics for stratospheric-resolving chemistry-climate models}, - volume = {8}, - issn = {1680-7316}, - doi = {10.5194/acp-8-5699-2008}, - number = {18}, - journal = {Atmos. Chem. Phys.}, - author = {Waugh, D. W. and Eyring, V.}, - year = {2008}, - pages = {5699--5713}, - annote = {ACP}, - annote = {The following values have no corresponding Zotero field:publisher: Copernicus Publications}, -} - -@article{washington_parallel_2000, - title = {Parallel climate model ({PCM}) control and transient simulations}, - volume = {16}, - issn = {0930-7575}, - abstract = {The Department of Energy (DOE) supported Parallel Climate Model (PCM) makes use of the NCAR Community Climate Model (CCM3) and Land Surface Model (LSM) for the atmospheric and land surface components, respectively, the DOE Los Alamos National Laboratory Parallel Ocean Program (POP) for the ocean component, and the Naval Postgraduate School sea-ice model. The PCM executes on several distributed and shared memory computer systems. The coupling method is similar to that used in the NCAR Climate System Model (CSM) in that a flux coupler ties the components together, with interpolations between the different grids of the component models. Flute adjustments are not used in the PCM. The ocean component has 2/3 degrees average horizontal grid spacing with 32 vertical levels and a free surface that allows calculation of sea level changes. Near the equator, the grid spacing is approximately 1/2 degrees in latitude to better capture the ocean equatorial dynamics. The North Pole is rotated over northern North America thus producing resolution smaller than 3/3 degrees in the North Atlantic where the sinking part of the world conveyor circulation largely takes place. Because this ocean model component does not have a computational Feint at the North pole, the Arctic Ocean circulation systems are more realistic and similar to the observed. The elastic viscous plastic sea ice model has a grid spacing of 27 km to represent small-scale features such as ice transport through the Canadian Archipelago and the East Greenland current region. Results from a 300 year present-day coupled climate control simulation are presented, as well as for a transient 1\% per year compound CO2 increase experiment which shows a global warming of 1.27 degreesC for a 10 year average at the doubling point of CO2 and 2.89 degreesC at the quadrupling point. There is a gradual warming beyond the doubling and quadrupling points with CO2 held constant. Globally averaged sea level rise at the time of CO2 control simulation are 1\% per year transient 1\% doubling is approximately 7 cm and at the time of quadrupling it is 23 cm. Some of the regional sea level changes are larger and reflect the adjustments in the temperature, salinity, internal ocean dynamics, surface heat flux, and wind stress on the ocean. A 0.5\% per year CO2 increase experiment also was performed showing a global warming of 1.5 degreesC around the time of CO2 doubling and a similar warming pattern to the 1\% CO2 per year increase experiment. El Nino and La Nina events in the tropical Pacific show approximately the observed frequency distribution and amplitude, which leads to near observed levels of variability on interannual time scales.}, - language = {English}, - number = {10-11}, - journal = {Climate Dynamics}, - author = {Washington, W. M. and Weatherly, J. W. and Meehl, G. A. and Semtner, A. J. and Bettge, T. W. and Craig, A. P. and Strand, W. G. and Arblaster, J. and Wayland, V. B. and James, R. and Zhang, Y.}, - month = oct, - year = {2000}, - keywords = {sensitivity, ocean circulation models, parameterization, system-model, ccm3, design, high-resolution, increased atmospheric co2, sea-ice dynamics}, - pages = {755--774}, - annote = {368LDTimes Cited:93Cited References Count:52}, - annote = {The following values have no corresponding Zotero field:auth-address: Washington, WM Natl Ctr Atmospher Res, 1850 Table Mesa Dr, Boulder, CO 80307 USA Natl Ctr Atmospher Res, Boulder, CO 80307 USA USA, Cold Reg Res \& Engn Lab, Hanover, NH 03755 USA USN, Postgrad Sch, Washington, DC USAaccession-num: ISI:000090117700003}, -} - -@article{daniel_b_walton_incorporating_2017, - title = {Incorporating {Snow} {Albedo} {Feedback} into {Downscaled} {Temperature} and {Snow} {Cover} {Projections} for {California}’s {Sierra} {Nevada}}, - volume = {30}, - doi = {10.1175/jcli-d-16-0168.1}, - abstract = {AbstractCalifornia’s Sierra Nevada is a high-elevation mountain range with significant seasonal snow cover. Under anthropogenic climate change, amplification of the warming is expected to occur at elevations near snow margins due to snow albedo feedback. However, climate change projections for the Sierra Nevada made by global climate models (GCMs) and statistical downscaling methods miss this key process. Dynamical downscaling simulates the additional warming due to snow albedo feedback. Ideally, dynamical downscaling would be applied to a large ensemble of 30 or more GCMs to project ensemble-mean outcomes and intermodel spread, but this is far too computationally expensive. To approximate the results that would occur if the entire GCM ensemble were dynamically downscaled, a hybrid dynamical–statistical downscaling approach is used. First, dynamical downscaling is used to reconstruct the historical climate of the 1981–2000 period and then to project the future climate of the 2081–2100 period based on climate changes from five GCMs. Next, a statistical model is built to emulate the dynamically downscaled warming and snow cover changes for any GCM. This statistical model is used to produce warming and snow cover loss projections for all available CMIP5 GCMs. These projections incorporate snow albedo feedback, so they capture the local warming enhancement (up to 3°C) from snow cover loss that other statistical methods miss. Capturing these details may be important for accurately projecting impacts on surface hydrology, water resources, and ecosystems.}, - number = {4}, - journal = {Journal of Climate}, - author = {Daniel B. Walton and Alex Hall and Neil Berg and Marla Schwartz and Fengpeng Sun}, - year = {2017}, - keywords = {Complex terrain,Climate change,Feedback,Snow cover,Temperature,Regional models}, - pages = {1417--1438}, -} - -@article{daniel_walton_assessment_2018, - title = {An {Assessment} of {High}-{Resolution} {Gridded} {Temperature} {Datasets} over {California}}, - volume = {31}, - doi = {10.1175/jcli-d-17-0410.1}, - abstract = {AbstractHigh-resolution gridded datasets are in high demand because they are spatially complete and include important finescale details. Previous assessments have been limited to two to three gridded datasets or analyzed the datasets only at the station locations. Here, eight high-resolution gridded temperature datasets are assessed two ways: at the stations, by comparing with Global Historical Climatology Network–Daily data; and away from the stations, using physical principles. This assessment includes six station-based datasets, one interpolated reanalysis, and one dynamically downscaled reanalysis. California is used as a test domain because of its complex terrain and coastlines, features known to differentiate gridded datasets. As expected, climatologies of station-based datasets agree closely with station data. However, away from stations, spread in climatologies can exceed 6°C. Some station-based datasets are very likely biased near the coast and in complex terrain, due to inaccurate lapse rates. Many station-based datasets have large unphysical trends ({\textgreater}1°C decade−1) due to unhomogenized or missing station data—an issue that has been fixed in some datasets by using homogenization algorithms. Meanwhile, reanalysis-based gridded datasets have systematic biases relative to station data. Dynamically downscaled reanalysis has smaller biases than interpolated reanalysis, and has more realistic variability and trends. Dynamical downscaling also captures snow–albedo feedback, which station-based datasets miss. Overall, these results indicate that 1) gridded dataset choice can be a substantial source of uncertainty, and 2) some datasets are better suited for certain applications.}, - number = {10}, - journal = {Journal of Climate}, - author = {Daniel Walton and Alex Hall}, - year = {2018}, - keywords = {Complex terrain,North America,Climate variability,Climatology,Temperature,Trends}, - pages = {3789--3810}, -} - -@article{walton_understanding_2019, - title = {Understanding differences in climate projections produced by dynamical and statistical downscaling (in review)}, - journal = {Journal of Applied Meteorology and Climatology}, - author = {Walton, Daniel and Berg, N. and Pierce, D. W. and Maurer, E. P. and Hall, A. and Cayan, D.}, - year = {2019}, -} - -@article{walker_characterizing_2011, - title = {Characterizing {Climate}-{Change} {Impacts} on the 1.5-yr {Flood} {Flow} in {Selected} {Basins} across the {United} {States}: {A} {Probabilistic} {Approach}}, - volume = {15}, - doi = {10.1175/2010ei379.1}, - abstract = {AbstractThe U.S. Geological Survey Precipitation-Runoff Modeling System (PRMS) model was applied to basins in 14 different hydroclimatic regions to determine the sensitivity and variability of the freshwater resources of the United States in the face of current climate-change projections. Rather than attempting to choose a most likely scenario from the results of the Intergovernmental Panel on Climate Change, an ensemble of climate simulations from five models under three emissions scenarios each was used to drive the basin models.Climate-change scenarios were generated for PRMS by modifying historical precipitation and temperature inputs; mean monthly climate change was derived by calculating changes in mean climates from current to various future decades in the ensemble of climate projections. Empirical orthogonal functions (EOFs) were fitted to the PRMS model output driven by the ensemble of climate projections and provided a basis for randomly (but representatively) generating realizations of hydrologic response to future climates. For each realization, the 1.5-yr flood was calculated to represent a flow important for sediment transport and channel geomorphology. The empirical probability density function (pdf) of the 1.5-yr flood was estimated using the results across the realizations for each basin. Of the 14 basins studied, 9 showed clear temporal shifts in the pdfs of the 1.5-yr flood projected into the twenty-first century. In the western United States, where the annual peak discharges are heavily influenced by snowmelt, three basins show at least a 10\% increase in the 1.5-yr flood in the twenty-first century; the remaining two basins demonstrate increases in the 1.5-yr flood, but the temporal shifts in the pdfs and the percent changes are not as distinct. Four basins in the eastern Rockies/central United States show at least a 10\% decrease in the 1.5-yr flood; the remaining two basins demonstrate decreases in the 1.5-yr flood, but the temporal shifts in the pdfs and the percent changes are not as distinct. Two basins in the eastern United States show at least a 10\% decrease in the 1.5-yr flood; the remaining basin shows little or no change in the 1.5-yr flood.}, - number = {18}, - journal = {Earth Interactions}, - author = {Walker, John F. and Hay, Lauren E. and Markstrom, Steven L. and Dettinger, Michael D.}, - month = jun, - year = {2011}, - pages = {1--16}, - annote = {doi: 10.1175/2010EI379.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/2010EI379.1}, -} - -@article{vrac_statistical_2007, - title = {Statistical downscaling of precipitation through nonhomogeneous stochastic weather typing}, - volume = {34}, - issn = {0936-577X}, - doi = {10.3354/cr00696}, - number = {3}, - journal = {Climate Research}, - author = {Vrac, M. and Stein, M. and Hayhoe, K.}, - month = sep, - year = {2007}, - pages = {169--184}, - annote = {The following values have no corresponding Zotero field:accession-num: WOS:000250184800001}, - annote = {Vrac, M. Stein, M. Hayhoe, K.}, -} - -@article{vose_impact_2004, - title = {Impact of land-use change on climate}, - volume = {427}, - journal = {Nature}, - author = {Vose, R. S. and Karl, T. R. and Easterling, D. R. and Williams, C. N. and Menne, M. J.}, - year = {2004}, - pages = {213--214}, -} - -@article{von_storch_use_1999, - title = {On the use of "inflation" in statistical downscaling}, - volume = {12}, - abstract = {The technique of "inflating" in downscaling, which makes the downscaled climate variable have the right variance, is based on the assumption that all local variability can be traced back to large-scale variability. For practical situations this assumption is not valid, and inflation is an inappropriate technique. Instead, additive, randomized approaches should be adopted.}, - number = {12}, - journal = {J. Clim.}, - author = {von Storch, H.}, - month = dec, - year = {1999}, - pages = {3505--3506}, - annote = {264BXJ CLIMATE}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Clim.accession-num: ISI:000084161100012}, -} - -@article{walsh_global_2008, - title = {Global {Climate} {Model} {Performance} over {Alaska} and {Greenland}}, - volume = {21}, - doi = {citeulike-article-id:3854779}, - abstract = {The performance of a set of 15 global climate models used in the Coupled Model Intercomparison Project is evaluated for Alaska and Greenland, and compared with the performance over broader pan-Arctic and Northern Hemisphere extratropical domains. Root-mean-square errors relative to the 1958\&\#8211;2000 climatology of the 40-yr ECMWF Re-Analysis (ERA-40) are summed over the seasonal cycles of three variables: surface air temperature, precipitation, and sea level pressure. The specific models that perform best over the larger domains tend to be the ones that perform best over Alaska and Greenland. The rankings of the models are largely unchanged when the bias of each model\&\#8217;s climatological annual mean is removed prior to the error calculation for the individual models. The annual mean biases typically account for about half of the models\&\#8217; root-mean-square errors. However, the root-mean-square errors of the models are generally much larger than the biases of the composite output, indicating that the systematic errors differ considerably among the models. There is a tendency for the models with smaller errors to simulate a larger greenhouse warming over the Arctic, as well as larger increases of Arctic precipitation and decreases of Arctic sea level pressure, when greenhouse gas concentrations are increased. Because several models have substantially smaller systematic errors than the other models, the differences in greenhouse projections imply that the choice of a subset of models may offer a viable approach to narrowing the uncertainty and obtaining more robust estimates of future climate change in regions such as Alaska, Greenland, and the broader Arctic.}, - number = {23}, - journal = {Journal of Climate}, - author = {Walsh, John and Chapman, William and Romanovsky, Vladimir and Christensen, Jens and Stendel, Martin}, - year = {2008}, - pages = {6156--6174}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Society}, -} - -@article{vrugt_effective_2003, - title = {Effective and efficient algorithm for multiobjective optimization of hydrologic models}, - volume = {39}, - issn = {0043-1397}, - doi = {10.1029/2002wr001746}, - abstract = {Practical experience with the calibration of hydrologic models suggests that any single-objective function, no matter how carefully chosen, is often inadequate to properly measure all of the characteristics of the observed data deemed to be important. One strategy to circumvent this problem is to define several optimization criteria (objective functions) that measure different (complementary) aspects of the system behavior and to use multicriteria optimization to identify the set of nondominated, efficient, or Pareto optimal solutions. In this paper, we present an efficient and effective Markov Chain Monte Carlo sampler, entitled the Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm, which is capable of solving the multiobjective optimization problem for hydrologic models. MOSCEM is an improvement over the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm, using the concept of Pareto dominance (rather than direct single-objective function evaluation) to evolve the initial population of points toward a set of solutions stemming from a stable distribution (Pareto set). The efficacy of the MOSCEM-UA algorithm is compared with the original MOCOM-UA algorithm for three hydrologic modeling case studies of increasing complexity.}, - number = {8}, - journal = {Water Resources Research}, - author = {Vrugt, Jasper A. and Gupta, Hoshin V. and Bastidas, Luis A. and Bouten, Willem and Sorooshian, Soroosh}, - year = {2003}, - keywords = {1836 Hydrology: Hydrologic budget, 1869 Hydrology: Stochastic processes, 1894 Hydrology: Instruments and techniques}, - pages = {1214}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{vonstorch_downscaling_1993, - title = {Downscaling of {Global} {Climate}-{Change} {Estimates} to {Regional} {Scales} - an {Application} to {Iberian} {Rainfall} in {Wintertime}}, - volume = {6}, - abstract = {A statistical strategy to deduct regional-scale features from climate general circulation model (GCM) simulations has been designed and tested. The main idea is to interrelate the characteristic patterns of observed simultaneous variations of regional climate parameters and of large-scale atmospheric flow using the canonical correlation technique. The large-scale North Atlantic sea level pressure (SLP) is related to the regional, variable, winter (DJF) mean Iberian Peninsula rainfall. The skill of the resulting statistical model is shown by reproducing, to a good approximation, the winter mean Iberian rainfall from 1900 to present from the observed North Atlantic mean SLP distributions. It is shown that this observed relationship between these two variables is not well reproduced in the output of a general circulation model (GCM). The implications for Iberian rainfall changes as the response to increasing atmospheric greenhouse-gas concentrations simulated by two GCM experiments are examined with the proposed statistical model. In an instantaneous '' 2 CO2'' doubling experiment, using the simulated change of the mean North Atlantic SLP field to predict Iberian rainfall yields, there is an insignificant increase of area-averaged rainfall of 1 mm/month, with maximum values of 4 mm/month in the northwest of the peninsula. In contrast, for the four GCM grid points representing the Iberian Peninsula, the change is - 10 mm/ month, with a minimum of - 19 mm/ month in the southwest. In the second experiment, with the IPCC scenario A (''business as usual'') increase of CO2, the statistical-model results partially differ from the directly simulated rainfall changes: in the experimental range of 100 years, the area-averaged rainfall decreases by 7 mm/month (statistical model), and by 9 mm/month (GCM); at the same time the amplitude of the interdecadal variability is quite different.}, - number = {6}, - journal = {J. Clim.}, - author = {Vonstorch, H. and Zorita, E. and Cubasch, U.}, - month = jun, - year = {1993}, - pages = {1161--1171}, - annote = {LK397J CLIMATE}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Clim.accession-num: ISI:A1993LK39700014}, -} - -@techreport{von_engelen_towards_2008, - address = {The Netherlands}, - title = {Towards an operational system for assessing observed changes in climate extremes: {European} {Climate} {Assessment} \& {Dataset} ({ECA}\&{D})}, - institution = {KNMI}, - author = {von Engelen, A. and Klein Tank, A. and ven de Schrier, G. and Klok, L.}, - year = {2008}, - pages = {40}, - annote = {The following values have no corresponding Zotero field:number: Publication 224}, -} - -@article{richard_m_vogel_regional_1999, - title = {Regional {Regression} {Models} of {Annual} {Streamflow} for the {United} {States}}, - volume = {125}, - number = {3}, - journal = {Journal of Irrigation and Drainage Engineering}, - author = {Richard M. Vogel and Ian Wilson and Chris Daly}, - year = {1999}, - pages = {148--157}, - annote = {The following values have no corresponding Zotero field:publisher: ASCE}, -} - -@article{vinukollu_multimodel_2012, - title = {Multimodel {Analysis} of {Energy} and {Water} {Fluxes}: {Intercomparisons} between {Operational} {Analyses}, a {Land} {Surface} {Model}, and {Remote} {Sensing}}, - volume = {13}, - issn = {1525-755X}, - doi = {10.1175/2011jhm1372.1}, - number = {1}, - journal = {Journal of Hydrometeorology}, - author = {Vinukollu, Raghuveer K. and Sheffield, Justin and Wood, Eric F. and Bosilovich, Michael G. and Mocko, David}, - month = feb, - year = {2012}, - pages = {3--26}, - annote = {doi: 10.1175/2011JHM1372.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/2011JHM1372.1}, -} - -@article{vidal_multimodel_2008, - title = {Multimodel projections of catchment-scale precipitation regime}, - volume = {353}, - issn = {0022-1694}, - number = {1-2}, - journal = {Journal of Hydrology}, - author = {Vidal, Jean-Philippe and Wade, Steven D.}, - year = {2008}, - keywords = {Climate change, Precipitation, Catchment, Downscaling method, Multimodel}, - pages = {143--158}, - annote = {The following values have no corresponding Zotero field:work-type: doi: DOI: 10.1016/j.jhydrol.2008.02.003}, -} - -@misc{vicuna_sensitivity_2007, - title = {The sensitivity of {California} water resources to climate change scenarios}, - publisher = {Wiley Online Library}, - author = {Vicuna, Sebastian and Maurer, Edwin P. and Joyce, Brian and Dracup, John A. and Purkey, David}, - year = {2007}, - annote = {The following values have no corresponding Zotero field:isbn: 1752-1688}, -} - -@article{venugopal_space-time_1999, - title = {A space-time downscaling model for rainfall}, - volume = {104}, - abstract = {Interpretation of the impact of climate change or climate variability on water resources management requires information at scales much smaller than the current resolution of regional climate models. Subgrid-scale variability of precipitation is typically resolved by running nested or variable resolution models or by statistical downscaling, the latter being especially attractive in ensemble predictions due to its computational efficiency. Most existing precipitation downscaling schemes are based on spatial disaggregation of rainfall patterns, independently at different times, and do not properly account for the temporal persistence of rainfall at the subgrid spatial scales. Such a temporal persistence in rainfall directly relates to the spatial variability of accumulated local soil moisture and might be important if the downscaled values were to be used in a coupled atmospheric- hydrologic model. In this paper we propose a rainfall downscaling model which utilizes the presence of dynamic scaling in rainfall [ Venugopal et al., 1999] and which in conjunction with a spatial disaggregation scheme preserves both the temporal and spatial correlation structure of rainfall at the subgrid scales.}, - number = {D16}, - journal = {Journal of Geophysical Research-Atmospheres}, - author = {Venugopal, V. and Foufoula-Georgiou, E. and Sapozhnikov, V.}, - month = aug, - year = {1999}, - pages = {19705--19721}, - annote = {230CYJ GEOPHYS RES-ATMOS}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Geophys. Res.-Atmos.accession-num: ISI:000082235300053}, -} - -@article{velazquez_evaluating_2015, - title = {Evaluating the {Time}-{Invariance} {Hypothesis} of {Climate} {Model} {Bias} {Correction}: {Implications} for {Hydrological} {Impact} {Studies}}, - volume = {16}, - issn = {1525-755X}, - doi = {10.1175/JHM-D-14-0159.1}, - abstract = {AbstractThe bias correction of climate model outputs is based on the main assumption of the time invariance of the bias, in which the statistical relationship between observations and climate model outputs in the historical period stays constant in the future period. The present study aims to assess statistical bias correction under nonstationary bias conditions and its implications on the simulated streamflow over two snowmelt-driven Canadian catchments. A pseudoreality approach is employed in order to derive a proxy of future observations. In this approach, CRCM?ECHAM5 ensemble simulations are used as pseudoreality observations to correct for bias in the CRCM?CGCM3 ensemble simulations in the reference (1971?2000) period. The climate model simulations are then bias corrected in the future (2041?70) period and compared with the future pseudoreality observations. This process demonstrates that biases (precipitation and temperature) remain after the bias correction. In a second step, the uncorrected and bias-corrected CRCM?CGCM3 simulations are used to drive the Soil and Water Assessment Tool (SWAT) hydrological model in both periods. The bias correction decreases the error on mean monthly streamflow over the reference period; such findings are more mixed over the future period. The results of various hydrological indicators show that the climate change signal on streamflow obtained with uncorrected and bias-corrected simulations is similar in both its magnitude and its direction for the mean monthly streamflow only. Regarding the indicators of extreme hydrological events, more mixed results are found with site dependence. All in all, bias correction under nonstationary bias is an additional source of uncertainty that cannot be neglected in hydrological climate change impact studies.}, - number = {5}, - journal = {Journal of Hydrometeorology}, - author = {Velázquez, Juan Alberto and Troin, Magali and Caya, Daniel and Brissette, François}, - month = oct, - year = {2015}, - pages = {2013--2026}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Society}, -} - -@article{veijalainen_national_2010, - title = {National scale assessment of climate change impacts on flooding in {Finland}}, - volume = {391}, - issn = {0022-1694}, - doi = {10.1016/j.jhydrol.2010.07.035}, - abstract = {This paper provides a general overview of changes in flooding caused by climate change in Finland for the periods 2010-2039 and 2070-2099. Changes in flooding were evaluated at 67 sites in Finland with variable sizes of runoff areas using a conceptual hydrological model and 20 climate scenarios from both global and regional climate models with the delta change approach. Floods with a 100-year return period were estimated with frequency analysis using the Gumbel distribution. At four study sites depicting different watershed types and hydrology, the inundation areas of the 100-year floods were simulated with a 2D hydraulic model. The results demonstrate that the impacts of climate change are not uniform within Finland due to regional differences in climatic conditions and watershed properties. In snowmelt-flood dominated areas, annual floods decreased or remained unchanged due to decreasing snow accumulation. On the other hand, increased precipitation resulted in growing floods in major central lakes and their outflow rivers. The changes in flood inundation did not linearly follow the changes in 100-year discharges, due to varying characteristics of river channels and floodplains. The results highlight the importance of comprehensive climatological and hydrological knowledge and the use of several climate scenarios in estimation of climate change impacts on flooding. Generalisations based on only a few case studies, or large scale flood assessments using only a few climate scenarios should be avoided in countries with variable hydrological conditions. (c) 2010 Elsevier B.V. All rights reserved.}, - language = {English}, - number = {3-4}, - journal = {Journal of Hydrology}, - author = {Veijalainen, N. and Lotsari, E. and Alho, P. and Vehvilainen, B. and Kayhko, J.}, - month = sep, - year = {2010}, - keywords = {regional climate, Climate change, river-basin, european climate, model projections, simulations, hydrology, water-resources, 21st-century, assessing uncertainties, Finland, Flood, Flood inundation area, Hydraulic modelling, Hydrological modelling, inundation}, - pages = {333--350}, - annote = {ISI Document Delivery No.: 657EITimes Cited: 0Cited Reference Count: 77Veijalainen, Noora Lotsari, Ehisa Alho, Petteri Vehvilainen, Bertel Kayhko, JukkaAcademy of Finland ; Finnish Ministry of Agriculture and Forestry ; Nordic Energy Research ; TEKES ; Maj \& Tor Nessling Foundation ; Finnish Meteorological Institute ; EUThis study was carried out in the Freshwater centre of the Finnish Environment Institute and in the Department of Geography of the University of Turku. The study is part of the Future of Floods in Finland (TULeVAT) research project financed by the Academy of Finland, WaterAdapt research project financed by the Finnish Ministry of Agriculture and Forestry as part of the Finnish Climate Change Adaptation Research Programme ISTO, Climate and Energy Systems (CES) research project funded by the Nordic Energy Research, GIFLOOD research project funded by TEKES and FLOODA-WARE research project funded by Maj \& Tor Nessling Foundation. We gratefully acknowledge the support of the Finnish Meteorological Institute and the EU FP6 Integrated Project ENSEMBLES, who provided the climate scenario data used in this study. We also thank Regional Environment Centres for providing us data sets and Esko Kuusisto, Hanne Laine-Kaulio, Harri Koivusalo, Johanna Korhonen, Juho Jakkila and the reviewers for their valuable comments on this article.Elsevier science bvAmsterdam}, - annote = {The following values have no corresponding Zotero field:auth-address: [Veijalainen, Noora; Vehvilainen, Bertel] Finnish Environm Inst, Freshwater Ctr, FI-00251 Helsinki, Finland. [Lotsari, Ehisa; Alho, Petteri; Kayhko, Jukka] Univ Turku, Dept Geog, FI-20014 Turku, Finland. Veijalainen, N, Finnish Environm Inst, Freshwater Ctr, Mechelininkatu 34A,POB 140, FI-00251 Helsinki, Finland. Noora.Veijalainen@ymparisto.fialt-title: J. Hydrol.accession-num: ISI:000282395600011work-type: Article}, -} - -@article{vogel_co-producing_2016, - title = {Co-producing actionable science for water utilities}, - volume = {2–3}, - issn = {2405-8807}, - doi = {https://doi.org/10.1016/j.cliser.2016.06.003}, - journal = {Climate Services}, - author = {Vogel, Jason and McNie, Elizabeth and Behar, David}, - year = {2016}, - keywords = {Actionable science, Climate model information, Co-production, Vulnerability assessment, Water resources management}, - pages = {30--40}, -} - -@article{vicuna_climate_2011, - title = {Climate change impacts on the hydrology of a snowmelt driven basin in semiarid {Chile}}, - volume = {105}, - journal = {Climatic Change}, - author = {Vicuna, S. and Garreaud, René D. and McPhee, J.}, - year = {2011}, - pages = {469--488, doi: 10.1007/s10584--010--9888--4}, -} - -@article{vicuna_basin-scale_2010, - title = {Basin-scale water system operations with uncertain future climate conditions: {Methodology} and case studies}, - volume = {46}, - journal = {Water Resources Research}, - author = {Vicuna, S. and Dracup, J. A. and Lund, J. R. and Dale, L. L. and Maurer, E. P.}, - year = {2010}, - pages = {W04505, doi:10.1029/2009WR007838}, -} - -@techreport{van_der_zee_arias_estudio_2012, - address = {Tegulcigalpa, Honduras}, - title = {Estudio de la caracterización del {Corredor} {Seco} {Centroamericano}}, - institution = {Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO)}, - author = {van der Zee Arias, A. and van der Zee, J. and Meyrat, A. and Poveda, C. and Picado, L.}, - year = {2012}, - pages = {92}, -} - -@article{tang_have_2020, - title = {Have satellite precipitation products improved over last two decades? {A} comprehensive comparison of {GPM} {IMERG} with nine satellite and reanalysis datasets}, - volume = {240}, - issn = {0034-4257}, - url = {https://www.sciencedirect.com/science/article/pii/S0034425720300663}, - doi = {10.1016/j.rse.2020.111697}, - abstract = {The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) produces the latest generation of satellite precipitation estimates and has been widely used since its release in 2014. IMERG V06 provides global rainfall and snowfall data beginning from 2000. This study comprehensively analyzes the quality of the IMERG product at daily and hourly scales in China from 2000 to 2018 with special attention paid to snowfall estimates. The performance of IMERG is compared with nine satellite and reanalysis products (TRMM 3B42, CMORPH, PERSIANN-CDR, GSMaP, CHIRPS, SM2RAIN, ERA5, ERA-Interim, and MERRA2). Results show that the IMERG product outperforms other datasets, except the Global Satellite Mapping of Precipitation (GSMaP), which uses daily-scale station data to adjust satellite precipitation estimates. The monthly-scale station data adjustment used by IMERG naturally has a limited impact on estimates of precipitation occurrence and intensity at the daily and hourly time scales. The quality of IMERG has improved over time, attributed to the increasing number of passive microwave samples. SM2RAIN, ERA5, and MERRA2 also exhibit increasing accuracy with time that may cause variable performance in climatological studies. Even relying on monthly station data adjustments, IMERG shows good performance in both accuracy metrics at hourly time scales and the representation of diurnal cycles. In contrast, although ERA5 is acceptable at the daily scale, it degrades at the hourly scale due to the limitation in reproducing the peak time, magnitude and variation of diurnal cycles. IMERG underestimates snowfall compared with gauge and reanalysis data. The triple collocation analysis suggests that IMERG snowfall is worse than reanalysis and gauge data, which partly results in the degraded quality of IMERG in cold climates. This study demonstrates new findings on the uncertainties of various precipitation products and identifies potential directions for algorithm improvement. The results of this study will be useful for both developers and users of satellite rainfall products.}, - journal = {Remote Sensing of Environment}, - author = {Tang, Guoqiang and Clark, Martyn P. and Papalexiou, Simon Michael and Ma, Ziqiang and Hong, Yang}, - month = apr, - year = {2020}, - keywords = {Diurnal cycle, Error analysis, IMERG, Reanalysis precipitation, Satellite precipitation, Snowfall}, - pages = {111697}, -} - -@techreport{magfor_compendio_2010, - address = {Managua, Nicaragua}, - title = {Compendio de mapas: {Uso} {Potencial} de la {Tierra}}, - institution = {Nicaragua Ministerio Agropecuario y Forestal}, - author = {MAGFOR}, - year = {2010}, - pages = {148}, -} - -@article{romero_standardized_2020, - title = {Standardized {Drought} {Indices} for {Pre}-{Summer} {Drought} {Assessment} in {Tropical} {Areas}}, - volume = {11}, - copyright = {http://creativecommons.org/licenses/by/3.0/}, - url = {https://www.mdpi.com/2073-4433/11/11/1209}, - doi = {10.3390/atmos11111209}, - abstract = {The main climatic indices used for the determination of pre-summer drought severity were developed for temperate zones with very different climatic conditions from those found in the tropical climate zones, particularly with respect to seasonal rainfall variations. The temporal evolution of pre-summer drought leads the authors to compute the indices for each year over a defined period according to the climatic normals of each meteorological station and to consider the months inside the dry episode differently, according to the law of emptying the water reserves. As a function of this, standardized drought indices are proposed for the evaluation of the pre-summer drought in tropical zone. Two new indices were tested: one developed from precipitation and the other also considering temperature. These indices were validated by correlation with Advanced very-high-resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) time series and used to identify the most severe drought conditions in the Yucatan Peninsula. The comparison between the indices and their temporal variations highlighted the importance of temperature in the most critical events and left indications of the impact of global warming on the phenomenon.}, - language = {en}, - number = {11}, - urldate = {2021-06-30}, - journal = {Atmosphere}, - author = {Romero, David and Alfaro, Eric and Orellana, Roger and Hernandez Cerda, Maria-Engracia}, - month = nov, - year = {2020}, - note = {Number: 11 -Publisher: Multidisciplinary Digital Publishing Institute}, - keywords = {precipitation, temperature, drought, Meso America, tropical climate}, - pages = {1209}, - file = {Full Text PDF:C\:\\Users\\EdMaurer\\Zotero\\storage\\9KEXBXDQ\\Romero et al. - 2020 - Standardized Drought Indices for Pre-Summer Drough.pdf:application/pdf;Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\PTKTICFG\\1209.html:text/html}, -} - -@article{corrales-suastegui_mid-summer_2020, - title = {The mid-summer drought over {Mexico} and {Central} {America} in the 21st century}, - volume = {40}, - copyright = {© 2019 Royal Meteorological Society}, - issn = {1097-0088}, - url = {https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.6296}, - doi = {10.1002/joc.6296}, - abstract = {The southern Mexico and Central America (SMCA) region shows a dominant well-defined precipitation annual cycle. The rainy season usually begins in May and ends in October, with a relatively dry period in July and August known as the mid-summer drought (MSD); notable exceptions are the Caribbean coast of Honduras and Costa Rica. This MSD phenomenon is expected to be affected as the SMCA experiences an enhanced differential warming between the Pacific and Atlantic Oceans (PO-AO) towards the end of the 21st century. Previous studies have suggested that this differential warming will induce a strengthening of the westward Caribbean low-level jet (CLLJ) and that this heightened CLLJ will shift precipitation westwards, falling on the PO instead that within the SMCA region causing a severe drought. In this work we examine this scenario with a new model, the Rossby Center Regional Climate Model (RCA4), for the COordinated Regional climate Downscaling EXperiment (CORDEX) Central America domain, forced with different general circulation models (GCMs) and for different representative concentration paths (RCPs). We consider 25-year periods as “present conditions” (1981–2005) and “future scenario” (2071–2095), focusing on the “extended summer” season (May–October). Results suggest that in the future the spatial extension of the MSD will decrease and that in certain areas the MSD will be more intense but less frequent compared to present conditions. Also, the oceanic differential warming, the intensification of the CLLJ, and the reduction in regional precipitation in the future scenario, suggested by previous works, were verified in this study.}, - language = {en}, - number = {3}, - urldate = {2021-06-30}, - journal = {International Journal of Climatology}, - author = {Corrales-Suastegui, Arturo and Fuentes-Franco, Ramón and Pavia, Edgar G.}, - year = {2020}, - note = {\_eprint: https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.6296}, - keywords = {Central America, regional climate model, Mexico, mid-summer drought, RCA4, summer precipitation}, - pages = {1703--1715}, - file = {Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\XTI5KRZA\\joc.html:text/html}, -} - -@article{maggioni_review_2016, - title = {A {Review} of {Merged} {High}-{Resolution} {Satellite} {Precipitation} {Product} {Accuracy} during the {Tropical} {Rainfall} {Measuring} {Mission} ({TRMM}) {Era}}, - volume = {17}, - issn = {1525-7541, 1525-755X}, - url = {https://journals.ametsoc.org/view/journals/hydr/17/4/jhm-d-15-0190_1.xml}, - doi = {10.1175/JHM-D-15-0190.1}, - abstract = {{\textless}section class="abstract"{\textgreater}{\textless}h2 class="abstractTitle text-title my-1" id="d1333e2"{\textgreater}Abstract{\textless}/h2{\textgreater}{\textless}p{\textgreater}A great deal of expertise in satellite precipitation estimation has been developed during the Tropical Rainfall Measuring Mission (TRMM) era (1998–2015). The quantification of errors associated with satellite precipitation products (SPPs) is crucial for a correct use of these datasets in hydrological applications, climate studies, and water resources management. This study presents a review of previous work that focused on validating SPPs for liquid precipitation during the TRMM era through comparisons with surface observations, both in terms of mean errors and detection capabilities across different regions of the world. Several SPPs have been considered: TMPA 3B42 (research and real-time products), CPC morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP; both the near-real-time and the Motion Vector Kalman filter products), PERSIANN, and PERSIANN–Cloud Classification System (PERSIANN-CCS). Topography, seasonality, and climatology were shown to play a role in the SPP’s performance, especially in terms of detection probability and bias. Regions with complex terrain exhibited poor rain detection and magnitude-dependent mean errors; low probability of detection was reported in semiarid areas. Winter seasons, usually associated with lighter rain events, snow, and mixed-phase precipitation, showed larger biases.{\textless}/p{\textgreater}{\textless}/section{\textgreater}}, - language = {EN}, - number = {4}, - urldate = {2020-12-20}, - journal = {Journal of Hydrometeorology}, - author = {Maggioni, Viviana and Meyers, Patrick C. and Robinson, Monique D.}, - month = apr, - year = {2016}, - note = {Publisher: American Meteorological Society -Section: Journal of Hydrometeorology}, - pages = {1101--1117}, - file = {Full Text PDF:C\:\\Users\\EdMaurer\\Zotero\\storage\\2GEY5RPE\\Maggioni et al. - 2016 - A Review of Merged High-Resolution Satellite Preci.pdf:application/pdf;Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\ELRIYT3N\\jhm-d-15-0190_1.html:text/html}, -} - -@article{nogueira_inter-comparison_2020, - title = {Inter-comparison of {ERA}-5, {ERA}-interim and {GPCP} rainfall over the last 40 years: {Process}-based analysis of systematic and random differences}, - volume = {583}, - issn = {0022-1694}, - url = {http://www.sciencedirect.com/science/article/pii/S0022169420300925}, - doi = {10.1016/j.jhydrol.2020.124632}, - abstract = {This study presents a comprehensive inter-comparison between two widely used rainfall datasets – the Global Precipitation Climatology Project (GPCP) and the ERA-Interim reanalysis – and the recently released ERA-5, which will replace ERA-Interim as the main European Centre for Medium-Range Weather and Forecasting (ECMWF) reanalysis by 2020. Systematic and random error components were computed for the reanalysis datasets over the 1979–2018 period using GPCP as reference. The analysis was taken at monthly timescale at 2.5° spatial resolution, limited by GPCP. Despite its resolution limitations and complex algorithms, GPCP has been extensively used for model evaluation due to its global and long-term (over 40 years) coverage. Over most of the tropics, and over localized mid-latitude regions, ERA-5 showed lower bias and unbiased root-mean squared error (ubRMSE), as well as higher correlations, compared to ERA-Interim. Throughout the rest of the globe, these error metrics displayed similar values between both reanalysis, except over localized regions of the eastern tropical Pacific, the Andes and the Himalayas, where ERA-Interim outperformed ERA-5. A process-based analysis revealed that ERA-Interim tended to overestimate deep convection and moisture flux convergence over the tropical oceans and land, leading to excessive rainfall. Similarly, ERA-Interim showed significant rainfall underestimation over the mid-latitude oceans due to underestimation of deep convection and moisture flux convergence. Both cases were significantly improved in ERA-5, likely due to its improved parameterizations and higher resolution. Indeed, the differences in monthly rainfall between the two reanalysis were mainly due to improved dynamical (circulation) rather than thermodynamical (Clausius-Clapeyron) processes or surface evaporation changes. Nonetheless, the results also revealed improved representation of the moisture sink/source patterns over the tropical oceans in ERA-5. Finally, there were significant differences in the long-term rainfall trend patterns amongst the three datasets (with differences reaching {\textasciitilde}30\%/decade), that can extend even to the sign of the trend, with the most notorious differences occurring over tropical Africa. Furthermore, ERA-5 didn’t show improved representation of these trends. In fact, the trend of global-mean rainfall in ERA-Interim was closer to GPCP than ERA-5.}, - journal = {Journal of Hydrology}, - author = {Nogueira, Miguel}, - month = apr, - year = {2020}, - keywords = {Climate, Dynamics, Rainfall datasets, Rainfall variability, Reanalysis}, - pages = {124632}, -} - -@article{albergel_era-5_2018, - title = {{ERA}-5 and {ERA}-{Interim} driven {ISBA} land surface model simulations: which one performs better?}, - volume = {22}, - issn = {1607-7938}, - url = {https://hess.copernicus.org/articles/22/3515/2018/}, - doi = {10.5194/hess-22-3515-2018}, - number = {6}, - journal = {Hydrol. Earth Syst. Sci.}, - author = {Albergel, C. and Dutra, E. and Munier, S. and Calvet, J.-C. and Munoz-Sabater, J. and de Rosnay, P. and Balsamo, G.}, - month = jun, - year = {2018}, - note = {Publisher: Copernicus Publications}, - pages = {3515--3532}, -} - -@article{heim_review_2002, - title = {A {Review} of {Twentieth}-{Century} {Drought} {Indices} {Used} in the {United} {States}}, - volume = {83}, - issn = {0003-0007, 1520-0477}, - url = {https://journals.ametsoc.org/view/journals/bams/83/8/1520-0477-83_8_1149.xml}, - doi = {10.1175/1520-0477-83.8.1149}, - abstract = {{\textless}section class="abstract"{\textgreater}{\textless}p{\textgreater}The monitoring and analysis of drought have long suffered from the lack of an adequate definition of the phenomenon. As a result, drought indices have slowly evolved during the last two centuries from simplistic approaches based on some measure of rainfall deficiency, to more complex problem-specific models. Indices developed in the late nineteenth and early twentieth century included such measures as percent of normal precipitation over some interval, consecutive days with rain below a given threshold, formulae involving a combination of temperature and precipitation, and models factoring in precipitation deficits over consecutive days. The incorporation of evapotranspiration as a measure of water demand by Thornthwaite led to the landmark development in 1965 by Palmer of a water budget-based drought index that is still widely used. Drought indices developed since the 1960s include the Surface Water Supply Index, which supplements the Palmer Index by integrating snowpack, reservoir storage, streamflow, and precipitation at high elevations; the Keetch–Byram Drought Index, which is used by fire control managers; the Standardized Precipitation Index; and the Vegetation Condition Index, which utilizes global satellite observations of vegetation condition. These models continue to evolve as new data sources become available. The twentieth century concluded with the development of the Drought Monitor tool, which incorporates Palmer's index and several other (post Palmer) indices to provide a universal assessment of drought conditions across the entire United States. By putting the development of these drought indices into a historical perspective, this paper provides a better understanding of the complex Palmer Index and of the nature of measuring drought in general.{\textless}/p{\textgreater}{\textless}/section{\textgreater}}, - language = {en}, - number = {8}, - urldate = {2020-12-19}, - journal = {Bulletin of the American Meteorological Society}, - author = {Heim, Richard R.}, - month = aug, - year = {2002}, - note = {Publisher: American Meteorological Society -Section: Bulletin of the American Meteorological Society}, - pages = {1149--1166}, - file = {Full Text PDF:C\:\\Users\\EdMaurer\\Zotero\\storage\\BYKKJYC4\\Heim - 2002 - A Review of Twentieth-Century Drought Indices Used.pdf:application/pdf;Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\JQ58DA56\\1520-0477-83_8_1149.html:text/html;Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\VV6KB2BX\\1520-0477-83_8_1149.html:text/html}, -} - -@article{stagge_candidate_2015, - title = {Candidate {Distributions} for {Climatological} {Drought} {Indices} ({SPI} and {SPEI})}, - volume = {35}, - copyright = {© 2015 The Authors. International Journal of Climatology published by John Wiley \& Sons Ltd on behalf of the Royal Meteorological Society.}, - issn = {1097-0088}, - url = {https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.4267}, - doi = {https://doi.org/10.1002/joc.4267}, - abstract = {The Standardized Precipitation Index (SPI), a well-reviewed meteorological drought index recommended by the World Meteorological Organization (WMO), and its more recent climatic water balance variant, the Standardized Precipitation-Evapotranspiration Index (SPEI), both rely on selection of a univariate probability distribution to normalize the index, allowing for comparisons across climates. Choice of an improper probability distribution may impart bias to the index values, exaggerating or minimizing drought severity. This study compares a suite of candidate probability distributions for use in SPI and SPEI normalization using the 0.5° × 0.5° gridded Watch Forcing Dataset (WFD) at the continental scale, focusing on Europe. Several modifications to the SPI and SPEI methodology are proposed, as well as an updated procedure for evaluating SPI/SPEI goodness of fit based on the Shapiro–Wilk test. Candidate distributions for SPI organize into two groups based on their ability to model short-term accumulation (1–2 months) or long-term accumulation ({\textgreater}3 months). The two-parameter gamma distribution is recommended for general use when calculating SPI across all accumulation periods and regions within Europe, in agreement with previous studies. The generalized extreme value distribution is recommended when computing the SPEI, in disagreement with previous recommendations.}, - language = {en}, - number = {13}, - urldate = {2020-12-19}, - journal = {International Journal of Climatology}, - author = {Stagge, James H. and Tallaksen, Lena M. and Gudmundsson, Lukas and Loon, Anne F. Van and Stahl, Kerstin}, - year = {2015}, - note = {\_eprint: https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.4267}, - keywords = {potential evapotranspiration, drought index, probability distribution, SPEI, SPI, Standardized Precipitation Index}, - pages = {4027--4040}, - file = {Full Text PDF:C\:\\Users\\EdMaurer\\Zotero\\storage\\EINKV9BX\\Stagge et al. - 2015 - Candidate Distributions for Climatological Drought.pdf:application/pdf;Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\5L35L5L5\\joc.html:text/html}, -} - -@article{bennett_metsim_2020, - title = {{MetSim}: {A} {Python} package for estimation and disaggregation of meteorological data}, - volume = {5}, - issn = {2475-9066}, - shorttitle = {{MetSim}}, - url = {https://joss.theoj.org/papers/10.21105/joss.02042}, - doi = {10.21105/joss.02042}, - abstract = {Bennett et al., (2020). MetSim: A Python package for estimation and disaggregation of meteorological data. Journal of Open Source Software, 5(47), 2042, https://doi.org/10.21105/joss.02042}, - language = {en}, - number = {47}, - urldate = {2020-12-19}, - journal = {Journal of Open Source Software}, - author = {Bennett, Andrew R. and Hamman, Joseph J. and Nijssen, Bart}, - month = mar, - year = {2020}, - pages = {2042}, - file = {Full Text PDF:C\:\\Users\\EdMaurer\\Zotero\\storage\\YZ4JNSUT\\Bennett et al. - 2020 - MetSim A Python package for estimation and disagg.pdf:application/pdf;Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\TNJXTJEP\\joss.html:text/html}, -} - -@article{thornton_improved_1999, - title = {An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation}, - volume = {93}, - issn = {0168-1923}, - url = {http://www.sciencedirect.com/science/article/pii/S0168192398001269}, - doi = {10.1016/S0168-1923(98)00126-9}, - abstract = {We present a reformulation of the Bristow–Campbell model for daily solar radiation, developed using daily observations of radiation, temperature, humidity, and precipitation, from 40 stations in contrasting climates. By expanding the original model to include a spatially and temporally variable estimate of clear-sky transmittance, and applying a small number of other minor modifications, the new model produces better results than the original over a wider range of climates. Our method does not require reparameterization on a site-by-site basis, a distinct advantage over the original approach. We do require observations of dewpoint temperature, which the original model does not, but we suggest a method that could eliminate this dependency. Mean absolute error (MAE) for predictions of clear-sky transmittance was improved by 28\% compared to the original model formulation. Aerosols and snowcover probably contribute to variation in clear-sky transmittance that remains unexplained by our method. MAE and bias for prediction of daily incident radiation were about 2.4MJm−2day−1 and +0.5MJm−2day−1, respectively. As a percent of the average observed values of incident radiation, MAE and bias are about 15\% and +4\%, respectively. The lowest errors and smallest biases (percent basis) occurred during the summer. The highest prediction biases were associated with stations having a strong seasonal concentration of precipitation, with underpredictions at summer-precipitation stations, and overpredictions at winter-precipitation stations. Further study is required to characterize the behavior of this method for tropical climates.}, - number = {4}, - journal = {Agricultural and Forest Meteorology}, - author = {Thornton, Peter E. and Running, Steven W.}, - month = mar, - year = {1999}, - keywords = {Air temperature, Atmospheric transmittance, Daily, Ecosystem process simulation, Humidity, Snowcover, Solar radiation}, - pages = {211--228}, -} - -@article{kimball_improved_1997, - title = {An improved method for estimating surface humidity from daily minimum temperature}, - volume = {85}, - issn = {0168-1923}, - url = {http://www.sciencedirect.com/science/article/pii/S0168192396023660}, - doi = {10.1016/S0168-1923(96)02366-0}, - abstract = {Minimum daily air temperature (Tn) is often used as a surrogate for mean daily dew point (TI,day) to estimate near-surface humidity. This method is unreliable under arid conditions where nightly minimum temperatures may remain well above the dew point. Daily meteorological data from 52 weather stations within the continental US and Alaska were evaluated. Daily differences between Td,day and Tn were large in arid climates, with corresponding vapor pressure differences averaging between 0.8 and 1.2 kPa on an annual basis. Sites with semi-arid characteristics showed a large degree of seasonality in the results with average vapor pressure differences ranging from 0.1 to 0.6 kPa between winter and summer months. Sites in other climate regimes generally showed small differences between Tn and Td,day over the entire year, corresponding to average vapor pressure differences of less than 0.3 kPa. An empirical model was developed to improve the accuracy of Tn based humidity estimates using daily air temperature, annual precipitation and estimated daily potential evapotranspiration. The model reduced humidity estimation errors by up to 80\% at semi-arid and arid sites and had minimal effects when Tn based humidity estimates were relatively accurate. The ratio of annual precipitation to estimated annual evapotranspiration was useful for distinguishing sites where Tn based humidity estimates were relatively accurate from sites where estimation errors were large. The results of this investigation provide a simple, more accurate method than Tn for estimating humidity in arid and semi-arid regions using general weather station data.}, - number = {1}, - journal = {Agricultural and Forest Meteorology}, - author = {Kimball, J.S. and Running, S.W. and Nemani, R.}, - month = jun, - year = {1997}, - pages = {87--98}, -} - -@book{mckee_relationship_1993, - title = {The {Relationship} of {Drought} {Frequency} and {Duration} to {Time} {Scales}}, - author = {McKee, Thomas B. and Doesken, Nolan J. and Kleist, John}, - year = {1993}, - file = {Citeseer - Full Text PDF:C\:\\Users\\EdMaurer\\Zotero\\storage\\LNSGK2Y7\\McKee et al. - 1993 - The Relationship of Drought Frequency and Duration.pdf:application/pdf;Citeseer - Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\GF6CFD77\\summary.html:text/html;Citeseer - Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\J6Z9K7CH\\summary.html:text/html}, -} - -@article{vicente-serrano_multiscalar_2010, - title = {A {Multiscalar} {Drought} {Index} {Sensitive} to {Global} {Warming}: {The} {Standardized} {Precipitation} {Evapotranspiration} {Index}}, - volume = {23}, - url = {https://journals.ametsoc.org/view/journals/clim/23/7/2009jcli2909.1.xml}, - doi = {10.1175/2009JCLI2909.1}, - language = {English}, - number = {7}, - journal = {Journal of Climate}, - author = {Vicente-Serrano, Sergio M. and Beguería, Santiago and López-Moreno, Juan I.}, - month = apr, - year = {2010}, - note = {Place: Boston MA, USA -Publisher: American Meteorological Society}, - pages = {1696--1718}, -} - -@misc{noauthor_multiscalar_nodate, - title = {A {Multiscalar} {Drought} {Index} {Sensitive} to {Global} {Warming}: {The} {Standardized} {Precipitation} {Evapotranspiration} {Index} in: {Journal} of {Climate} {Volume} 23 {Issue} 7 (2010)}, - url = {https://journals.ametsoc.org/view/journals/clim/23/7/2009jcli2909.1.xml?tab_body=fulltext-display}, - urldate = {2020-12-19}, - file = {A Multiscalar Drought Index Sensitive to Global Warming\: The Standardized Precipitation Evapotranspiration Index in\: Journal of Climate Volume 23 Issue 7 (2010):C\:\\Users\\EdMaurer\\Zotero\\storage\\JMJCM2N8\\2009jcli2909.1.html:text/html}, -} - -@article{s_m_vicente-serrano_multiscalar_0000, - title = {A multiscalar drought index sensitive to global warming: {The} standardized precipitation evapotranspiration index}, - volume = {23}, - journal = {J. Climate}, - author = {{S. M. Vicente-Serrano} and {S. Beguería} and {J. I. López-Moreno}}, - year = {0000}, - pages = {1696--1718}, -} - -@article{vicente-serrano_performance_2012, - title = {Performance of {Drought} {Indices} for {Ecological}, {Agricultural}, and {Hydrological} {Applications}}, - volume = {16}, - url = {https://journals.ametsoc.org/view/journals/eint/16/10/2012ei000434.1.xml}, - doi = {10.1175/2012EI000434.1}, - abstract = {{\textless}section class="abstract"{\textgreater}{\textless}h2 class="abstractTitle text-title my-1" id="d658e2"{\textgreater}Abstract{\textless}/h2{\textgreater}{\textless}p{\textgreater}In this study, the authors provide a global assessment of the performance of different drought indices for monitoring drought impacts on several hydrological, agricultural, and ecological response variables. For this purpose, they compare the performance of several drought indices [the standardized precipitation index (SPI); four versions of the Palmer drought severity index (PDSI); and the standardized precipitation evapotranspiration index (SPEI)] to predict changes in streamflow, soil moisture, forest growth, and crop yield. The authors found a superior capability of the SPEI and the SPI drought indices, which are calculated on different time scales than the Palmer indices to capture the drought impacts on the aforementioned hydrological, agricultural, and ecological variables. They detected small differences in the comparative performance of the SPI and the SPEI indices, but the SPEI was the drought index that best captured the responses of the assessed variables to drought in summer, the season in which more drought-related impacts are recorded and in which drought monitoring is critical. Hence, the SPEI shows improved capability to identify drought impacts as compared with the SPI. In conclusion, it seems reasonable to recommend the use of the SPEI if the responses of the variables of interest to drought are not known a priori.{\textless}/p{\textgreater}{\textless}/section{\textgreater}}, - language = {EN}, - number = {10}, - urldate = {2020-12-19}, - journal = {Earth Interactions}, - author = {Vicente-Serrano, Sergio M. and Beguería, Santiago and Lorenzo-Lacruz, Jorge and Camarero, Jesús Julio and López-Moreno, Juan I. and Azorin-Molina, Cesar and Revuelto, Jesús and Morán-Tejeda, Enrique and Sanchez-Lorenzo, Arturo}, - month = sep, - year = {2012}, - note = {Publisher: American Meteorological Society -Section: Earth Interactions}, - pages = {1--27}, - file = {Full Text PDF:C\:\\Users\\EdMaurer\\Zotero\\storage\\XSIETAUZ\\Vicente-Serrano et al. - 2012 - Performance of Drought Indices for Ecological, Agr.pdf:application/pdf;Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\558FXSML\\2012ei000434.1.html:text/html}, -} - -@article{wilcoxon_individual_1945, - title = {Individual {Comparisons} by {Ranking} {Methods}}, - volume = {1}, - issn = {00994987}, - url = {http://www.jstor.org/stable/3001968}, - doi = {10.2307/3001968}, - number = {6}, - urldate = {2020-12-19}, - journal = {Biometrics Bulletin}, - author = {Wilcoxon, Frank}, - year = {1945}, - note = {Publisher: [International Biometric Society, Wiley]}, - pages = {80--83}, -} - -@misc{noaa_national_centers_for_environmental_information_state_2020, - title = {State of the {Climate}: {Global} {Climate} {Report} for {July} 2020}, - url = {https://www.ncdc.noaa.gov/sotc/global/202007}, - urldate = {2020-09-15}, - author = {NOAA National Centers for Environmental Information}, - year = {2020}, -} - -@misc{reliefweb_central_2020, - title = {Central {America}: {Drought} - 2014-2017}, - url = {https://reliefweb.int/disaster/dr-2014-000132-hnd}, - urldate = {2020-09-01}, - author = {ReliefWeb}, - year = {2020}, -} - -@article{greve_aridity_2019, - title = {The aridity {Index} under global warming}, - volume = {14}, - issn = {1748-9326}, - url = {https://doi.org/10.1088/1748-9326/ab5046}, - doi = {10.1088/1748-9326/ab5046}, - abstract = {Aridity is a complex concept that ideally requires a comprehensive assessment of hydroclimatological and hydroecological variables to fully understand anticipated changes. A widely used (offline) impact model to assess projected changes in aridity is the aridity index (AI) (defined as the ratio of potential evaporation to precipitation), summarizing the aridity concept into a single number. Based on the AI, it was shown that aridity will generally increase under conditions of increased CO2 and associated global warming. However, assessing the same climate model output directly suggests a more nuanced response of aridity to global warming, raising the question if the AI provides a good representation of the complex nature of anticipated aridity changes. By systematically comparing projections of the AI against projections for various hydroclimatological and ecohydrological variables, we show that the AI generally provides a rather poor proxy for projected aridity conditions. Direct climate model output is shown to contradict signals of increasing aridity obtained from the AI in at least half of the global land area with robust change. We further show that part of this discrepancy can be related to the parameterization of potential evaporation. Especially the most commonly used potential evaporation model likely leads to an overestimation of future aridity due to incorrect assumptions under increasing atmospheric CO2. Our results show that AI-based approaches do not correctly communicate changes projected by the fully coupled climate models. The solution is to directly analyse the model outputs rather than use a separate offline impact model. We thus urge for a direct and joint assessment of climate model output when assessing future aridity changes rather than using simple index-based impact models that use climate model output as input and are potentially subject to significant biases.}, - language = {en}, - number = {12}, - urldate = {2020-12-19}, - journal = {Environmental Research Letters}, - author = {Greve, P. and Roderick, M. L. and Ukkola, A. M. and Wada, Y.}, - month = nov, - year = {2019}, - note = {Publisher: IOP Publishing}, - pages = {124006}, - file = {IOP Full Text PDF:C\:\\Users\\EdMaurer\\Zotero\\storage\\QU99BNCP\\Greve et al. - 2019 - The aridity Index under global warming.pdf:application/pdf}, -} - -@article{mccabe_variability_2010, - title = {Variability and trends in dry day frequency and dry event length in the southwestern {United} {States}}, - volume = {115}, - copyright = {Copyright 2010 by the American Geophysical Union.}, - issn = {2156-2202}, - url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2009JD012866}, - doi = {https://doi.org/10.1029/2009JD012866}, - abstract = {Daily precipitation from 22 National Weather Service first-order weather stations in the southwestern United States for water years 1951 through 2006 are used to examine variability and trends in the frequency of dry days and dry event length. Dry events with minimum thresholds of 10 and 20 consecutive days of precipitation with less than 2.54 mm are analyzed. For water years and cool seasons (October through March), most sites indicate negative trends in dry event length (i.e., dry event durations are becoming shorter). For the warm season (April through September), most sites also indicate negative trends; however, more sites indicate positive trends in dry event length for the warm season than for water years or cool seasons. The larger number of sites indicating positive trends in dry event length during the warm season is due to a series of dry warm seasons near the end of the 20th century and the beginning of the 21st century. Overall, a large portion of the variability in dry event length is attributable to variability of the El Niño–Southern Oscillation, especially for water years and cool seasons. Our results are consistent with analyses of trends in discharge for sites in the southwestern United States, an increased frequency in El Niño events, and positive trends in precipitation in the southwestern United States.}, - language = {en}, - number = {D7}, - urldate = {2020-12-19}, - journal = {Journal of Geophysical Research: Atmospheres}, - author = {McCabe, Gregory J. and Legates, David R. and Lins, Harry F.}, - year = {2010}, - note = {\_eprint: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2009JD012866}, - keywords = {climate change, dry events, southwestern United States}, - file = {Full Text PDF:C\:\\Users\\EdMaurer\\Zotero\\storage\\U2LEJEBD\\McCabe et al. - 2010 - Variability and trends in dry day frequency and dr.pdf:application/pdf;Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\F7IV7SX5\\2009JD012866.html:text/html}, -} - -@article{liu_observed_2015, - title = {Observed changes in dry day frequency and prolonged dry episodes in {Northeast} {China}}, - volume = {35}, - copyright = {© 2014 Royal Meteorological Society}, - issn = {1097-0088}, - url = {https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.3972}, - doi = {https://doi.org/10.1002/joc.3972}, - abstract = {Daily precipitation data from 34 weather stations in Northeast China from 1960 to 2008 are used to examine trends in the occurrence of dry days and prolonged dry episodes. Trends in the mean length and frequency of prolonged dry episodes, as well as the number of dry days within prolonged dry episodes (DDPDE), are computed regionally and for individual stations at the threshold levels of 1 and 2 mm day−1. Regionally, the number of dry days is increasing in the summer half-year and decreasing in the winter half-year. DDPDE is significantly increasing in the summer half-year; the increased frequency of dry spells has the greatest effect. The mean length of prolonged dry episodes in the winter half-year has decreased significantly. Our results indicate that neither the frequency nor the mean length of prolonged dry episodes, alone, can adequately describe the short-term dry conditions of an area. Multiple characteristics of dry spells, including frequency, length, and the number of DDPDE should be considered. DDPDE is a function of both the frequency and length of dry episodes, but may be more influenced by one or the other characteristics of dry spells, depending on the season or region.}, - language = {en}, - number = {2}, - urldate = {2020-12-19}, - journal = {International Journal of Climatology}, - author = {Liu, Xiaodong and Liu, Binhui and Henderson, Mark and Xu, Ming and Zhou, Daowei}, - year = {2015}, - note = {\_eprint: https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/joc.3972}, - keywords = {climate change, precipitation, dry day, Northeast China, prolonged dry episode, trend analysis}, - pages = {196--214}, - file = {Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\TSRCJTPY\\joc.html:text/html;Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\2VDA7G6T\\joc.html:text/html}, -} - -@article{zhao_evaluating_2019, - title = {Evaluating the {Drought}-{Monitoring} {Utility} of {Four} {Satellite}-{Based} {Quantitative} {Precipitation} {Estimation} {Products} at {Global} {Scale}}, - volume = {11}, - copyright = {http://creativecommons.org/licenses/by/3.0/}, - url = {https://www.mdpi.com/2072-4292/11/17/2010}, - doi = {10.3390/rs11172010}, - abstract = {This study simultaneously analyzed and evaluated the meteorological drought-monitoring utility of the following four satellite-based, quantitative precipitation estimation (QPE) products: the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis 3B43V7 (TRMM-3B43), the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), the Climate Prediction Center Morphing Technique gauge-satellite blended product (CMORPH-BLD), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). Data from 2000 to 2016 was used at global scale. The global Climate Research Unit (CRU) Version 4.02 was used as reference data to assess QPE products. The Standardized Precipitation Evapotranspiration Index (SPEI) drought index was chosen as an example to evaluate the drought utility of four QPE products. The results indicate that CHIRPS has the best performance in Europe, Oceania, and Africa; the PERSIANN-CDR has the best performance in North America, South America, and Asia; the CMORPH-BLD has the worst statistical indices in all continents. Although four QPE products showed satisfactory performance for most of the world according to SPEI statistics, poor drought monitoring ability occurred in Southeast Asia, Central Africa, the Tibetan plateau, the Himalayas, and Amazonia. The PERSIANN-CDR achieves the best performance of the four QPE products in most regions except for Africa; CHIRPS and TRMM-3B43 have comparable performances. According to the spatial probability of detection (POD) and false alarm ratio (FAR) of the SPEI, more than 50\% of all drought events cannot be accurately identified by QPE products in regions with sparse gauge distribution. In other regions, such as the southeastern USA, southeastern China, and South Africa, QPE products capture more than 75\% of drought events. Temporally, all datasets (except for CMORPH-BLD) can detect all typical drought events, namely, in the southeastern US in 2007, western Europe in 2003, Kenya in 2006, and Central Asia in 2008. The study concludes that CHIRPS and TRMM-3B43 can be used as near-real-time drought monitoring techniques whereas PERSIANN-CDR might be more suitable for long-term historical drought analysis.}, - language = {en}, - number = {17}, - urldate = {2020-12-19}, - journal = {Remote Sensing}, - author = {Zhao, Haigen and Ma, Yanfei}, - month = jan, - year = {2019}, - note = {Number: 17 -Publisher: Multidisciplinary Digital Publishing Institute}, - keywords = {precipitation, CHIRPS, CMORPH-BLD, drought utility, global scale, PERSIANN-CDR, TRMM-3B43}, - pages = {2010}, -} - -@article{carvalho_assessing_2020, - title = {Assessing precipitation trends in the {Americas} with historical data: {A} review}, - volume = {11}, - copyright = {© 2019 Wiley Periodicals, Inc.}, - issn = {1757-7799}, - shorttitle = {Assessing precipitation trends in the {Americas} with historical data}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/wcc.627}, - doi = {https://doi.org/10.1002/wcc.627}, - abstract = {North, Central, and South America (collectively referred to as the Americas) extend across two hemispheres, and together cover approximately 28\% of Earth's land area and are home to about 13\% of the world's population. Unique ecosystems, diversified cultures, and communities that inhabit the region rely on precipitation delivered yearly by multiple systems, including mid-latitudes storms, the North and South American Monsoons, and tropical storms and hurricanes. The rapid warming of the Earth's atmosphere and oceans combined with internal variability of the climate system, have modified precipitation patterns from the tropics to high latitudes. In the Americas, instrumental records have shown evidence of upward trends in extreme precipitation (amount, intensity, and frequency) in many areas. The most consistent evidence of precipitation trends occurs in mid-latitudes of North America and in the subtropics of South America. Recent studies have indicated a poleward shift of heavy precipitation associated with South American Monsoon. Nonetheless, the deficient network of rain gauges in vast areas over tropical Americas limits the assessment of trends in regions with heavy rainfall amounts. Additionally, observed trends in the North America monsoon precipitation are difficult to separate from the contribution of tropical storms and hurricanes. Furthermore, coupled modes such as the El Nino/Southern Oscillation, the Pacific Decadal Oscillation, and the Atlantic Multidecadal Oscillation modulate precipitation in the Americas, from the tropics to the extratropics, and these teleconnections are relevant to assess precipitation trends using historical records. This review evaluates all these complex issues focusing on observations based on instrumental datasets. This article is categorized under: Paleoclimates and Current Trends {\textgreater} Modern Climate Change}, - language = {en}, - number = {2}, - urldate = {2020-12-19}, - journal = {WIREs Climate Change}, - author = {Carvalho, Leila M. V.}, - year = {2020}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/wcc.627}, - keywords = {South America, Central America, extreme precipitation trends, North America, precipitation trends}, - pages = {e627}, - file = {Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\59PWIDKD\\wcc.html:text/html}, -} - -@article{hidalgo_detection_2009, - title = {Detection and {Attribution} of {Streamflow} {Timing} {Changes} to {Climate} {Change} in the {Western} {United} {States}}, - volume = {22}, - issn = {0894-8755, 1520-0442}, - url = {https://journals.ametsoc.org/view/journals/clim/22/13/2009jcli2470.1.xml}, - doi = {10.1175/2009JCLI2470.1}, - abstract = {{\textless}section class="abstract"{\textgreater}{\textless}h2 class="abstractTitle text-title my-1" id="d116e2"{\textgreater}Abstract{\textless}/h2{\textgreater}{\textless}p{\textgreater}This article applies formal detection and attribution techniques to investigate the nature of observed shifts in the timing of streamflow in the western United States. Previous studies have shown that the snow hydrology of the western United States has changed in the second half of the twentieth century. Such changes manifest themselves in the form of more rain and less snow, in reductions in the snow water contents, and in earlier snowmelt and associated advances in streamflow “center” timing (the day in the “water-year” on average when half the water-year flow at a point has passed). However, with one exception over a more limited domain, no other study has attempted to formally attribute these changes to anthropogenic increases of greenhouse gases in the atmosphere. Using the observations together with a set of global climate model simulations and a hydrologic model (applied to three major hydrological regions of the western United States—the California region, the upper Colorado River basin, and the Columbia River basin), it is found that the observed trends toward earlier “center” timing of snowmelt-driven streamflows in the western United States since 1950 are detectably different from natural variability (significant at the {\textless}em{\textgreater}p{\textless}/em{\textgreater} \< 0.05 level). Furthermore, the nonnatural parts of these changes can be attributed confidently to climate changes induced by anthropogenic greenhouse gases, aerosols, ozone, and land use. The signal from the Columbia dominates the analysis, and it is the only basin that showed a detectable signal when the analysis was performed on individual basins. It should be noted that although climate change is an important signal, other climatic processes have also contributed to the hydrologic variability of large basins in the western United States.{\textless}/p{\textgreater}{\textless}/section{\textgreater}}, - language = {EN}, - number = {13}, - urldate = {2020-12-19}, - journal = {Journal of Climate}, - author = {Hidalgo, H. G. and Das, T. and Dettinger, M. D. and Cayan, D. R. and Pierce, D. W. and Barnett, T. P. and Bala, G. and Mirin, A. and Wood, A. W. and Bonfils, C. and Santer, B. D. and Nozawa, T.}, - month = jul, - year = {2009}, - note = {Publisher: American Meteorological Society -Section: Journal of Climate}, - pages = {3838--3855}, - file = {Full Text PDF:C\:\\Users\\EdMaurer\\Zotero\\storage\\RAW67STU\\Hidalgo et al. - 2009 - Detection and Attribution of Streamflow Timing Cha.pdf:application/pdf;Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\MZS7UKC8\\2009jcli2470.1.html:text/html}, -} - -@article{pierce_attribution_2008, - title = {Attribution of {Declining} {Western} {U}.{S}. {Snowpack} to {Human} {Effects}}, - volume = {21}, - issn = {0894-8755, 1520-0442}, - url = {https://journals.ametsoc.org/view/journals/clim/21/23/2008jcli2405.1.xml}, - doi = {10.1175/2008JCLI2405.1}, - abstract = {{\textless}section class="abstract"{\textgreater}{\textless}h2 class="abstractTitle text-title my-1" id="d801e2"{\textgreater}Abstract{\textless}/h2{\textgreater}{\textless}p{\textgreater}Observations show snowpack has declined across much of the western United States over the period 1950–99. This reduction has important social and economic implications, as water retained in the snowpack from winter storms forms an important part of the hydrological cycle and water supply in the region. A formal model-based detection and attribution (D–A) study of these reductions is performed. The detection variable is the ratio of 1 April snow water equivalent (SWE) to water-year-to-date precipitation ({\textless}em{\textgreater}P{\textless}/em{\textgreater}), chosen to reduce the effect of {\textless}em{\textgreater}P{\textless}/em{\textgreater} variability on the results. Estimates of natural internal climate variability are obtained from 1600 years of two control simulations performed with fully coupled ocean–atmosphere climate models. Estimates of the SWE/{\textless}em{\textgreater}P{\textless}/em{\textgreater} response to anthropogenic greenhouse gases, ozone, and some aerosols are taken from multiple-member ensembles of perturbation experiments run with two models. The D–A shows the observations and anthropogenically forced models have greater SWE/{\textless}em{\textgreater}P{\textless}/em{\textgreater} reductions than can be explained by natural internal climate variability alone. Model-estimated effects of changes in solar and volcanic forcing likewise do not explain the SWE/{\textless}em{\textgreater}P{\textless}/em{\textgreater} reductions. The mean model estimate is that about half of the SWE/{\textless}em{\textgreater}P{\textless}/em{\textgreater} reductions observed in the west from 1950 to 1999 are the result of climate changes forced by anthropogenic greenhouse gases, ozone, and aerosols.{\textless}/p{\textgreater}{\textless}/section{\textgreater}}, - language = {EN}, - number = {23}, - urldate = {2020-12-18}, - journal = {Journal of Climate}, - author = {Pierce, David W. and Barnett, Tim P. and Hidalgo, Hugo G. and Das, Tapash and Bonfils, Céline and Santer, Benjamin D. and Bala, Govindasamy and Dettinger, Michael D. and Cayan, Daniel R. and Mirin, Art and Wood, Andrew W. and Nozawa, Toru}, - month = dec, - year = {2008}, - note = {Publisher: American Meteorological Society -Section: Journal of Climate}, - pages = {6425--6444}, - file = {Full Text PDF:C\:\\Users\\EdMaurer\\Zotero\\storage\\BHLHN5Y8\\Pierce et al. - 2008 - Attribution of Declining Western U.S. Snowpack to .pdf:application/pdf;Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\JL7RE87L\\2008jcli2405.1.html:text/html}, -} - -@techreport{funk_quasi-global_2014, - address = {Reston, VA}, - type = {Report}, - title = {A quasi-global precipitation time series for drought monitoring}, - url = {http://pubs.er.usgs.gov/publication/ds832}, - language = {English}, - number = {832}, - author = {Funk, Chris C. and Peterson, Pete J. and Landsfeld, Martin F. and Pedreros, Diego H. and Verdin, James P. and Rowland, James D. and Romero, Bo E. and Husak, Gregory J. and Michaelsen, Joel C. and Verdin, Andrew P.}, - year = {2014}, - doi = {10.3133/ds832}, - pages = {12}, -} - -@misc{sorooshian_noaa_2014, - title = {{NOAA} {Climate} {Data} {Record} ({CDR}) of {Precipitation} {Estimation} from {Remotely} {Sensed} {Information} using {Artificial} {Neural} {Networks} ({PERSIANN}-{CDR}), {Version} 1 {Revision} 1.}, - url = {https://www.ncei.noaa.gov/data/precipitation-persiann/access/}, - urldate = {2019-09-01}, - author = {Sorooshian, Soroosh and Hsu, Kuolin and Braithwaite, Dan and Ashouri, Hamed and NOAA CDR Program}, - year = {2014}, -} - -@misc{copernicus_climate_change_service_c3s_era5_nodate, - title = {{ERA5}: {Fifth} generation of {ECMWF} atmospheric reanalyses of the global climate, {Copernicus} {Climate} {Change} {Service} {Climate} {Data} {Store} ({CDS})}, - url = {https://cds.climate.copernicus.eu/cdsapp#!/home}, - urldate = {2019-09-09}, - author = {Copernicus Climate Change Service (C3S)}, -} - -@misc{global_modeling_and_assimilation_office_gmao_merra-2_nodate, - title = {{MERRA}-2 {tavgM}\_2d\_int\_Nx: 2d,{Monthly} mean,{Time}-{Averaged},{Single}-{Level},{Assimilation},{Vertically} {Integrated} {Diagnostics} {V5}.12.4 ({M2TMNXINT} 5.12.4)}, - url = {https://disc.gsfc.nasa.gov/datasets/M2TMNXINT_5.12.4/summary}, - urldate = {2019-12-18}, - author = {Global Modeling {and} Assimilation Office (GMAO)}, - file = {GES DISC Dataset\: MERRA-2 tavgM_2d_int_Nx\: 2d,Monthly mean,Time-Averaged,Single-Level,Assimilation,Vertically Integrated Diagnostics V5.12.4 (M2TMNXINT 5.12.4):C\:\\Users\\EdMaurer\\Zotero\\storage\\EB8KUI7S\\summary.html:text/html}, -} - -@misc{schneider_gpcc_2018, - title = {{GPCC} {Monitoring} {Product}: {Near} {Real}-{Time} {Monthly} {Land}-{Surface} {Precipitation} from {Rain}-{Gauges} based on {SYNOP} and {CLIMAT} data}, - url = {http://dx.doi.org/10.5676/DWD_GPCC/MP_M_V6_100}, - author = {Schneider, Udo and Becker, Andreas and Finger, Peter and Meyer-Christoffer, Anja and Ziese, Markus}, - year = {2018}, -} - -@book{ipcc_climate_2014-1, - address = {Cambridge}, - title = {Climate {Change} 2013 – {The} {Physical} {Science} {Basis}: {Working} {Group} {I} {Contribution} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - isbn = {978-1-107-05799-9}, - url = {https://www.cambridge.org/core/books/climate-change-2013-the-physical-science-basis/BE9453E500DEF3640B383BADDC332C3E}, - abstract = {This Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) will again form the standard scientific reference for all those concerned with climate change and its consequences, including students and researchers in environmental science, meteorology, climatology, biology, ecology and atmospheric chemistry. It provides invaluable material for decision makers and stakeholders at international, national and local level, in government, businesses, and NGOs. This volume provides:An authoritative and unbiased overview of the physical science basis of climate changeA more extensive assessment of changes observed throughout the climate system than ever beforeNew dedicated chapters on sea-level change, biogeochemical cycles, clouds and aerosols, and regional climate phenomenaExtensive coverage of model projections, both near-term and long-term climate projectionsA detailed assessment of climate change observations, modelling, and attribution for every continentA new comprehensive atlas of global and regional climate projections for 35 regions of the world}, - publisher = {Cambridge University Press}, - author = {{IPCC}}, - year = {2014}, - doi = {10.1017/CBO9781107415324}, -} - -@techreport{shukla_climate_2020, - title = {Climate {Change} and {Land}: an {IPCC} special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems}, - institution = {IPCC}, - author = {Shukla, P.R. and Skea, J. and Slade, R. and van Diemen, R. and Haughey, E. and Malley, J. and Pathak, M. and Portugal Pereira, J.}, - year = {2020}, -} - -@article{dinku_validation_2018, - title = {Validation of the {CHIRPS} satellite rainfall estimates over eastern {Africa}}, - volume = {144}, - copyright = {© 2018 Royal Meteorological Society}, - issn = {1477-870X}, - url = {https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3244}, - doi = {https://doi.org/10.1002/qj.3244}, - abstract = {Long and temporally consistent rainfall time series are essential in climate analyses and applications. Rainfall data from station observations are inadequate over many parts of the world due to sparse or non-existent observation networks, or limited reporting of gauge observations. As a result, satellite rainfall estimates have been used as an alternative or as a supplement to station observations. However, many satellite-based rainfall products with long time series suffer from coarse spatial and temporal resolutions and inhomogeneities caused by variations in satellite inputs. There are some satellite rainfall products with reasonably consistent time series, but they are often limited to specific geographic areas. The Climate Hazards Group Infrared Precipitation (CHIRP) and CHIRP combined with station observations (CHIRPS) are recently produced satellite-based rainfall products with relatively high spatial and temporal resolutions and quasi-global coverage. In this study, CHIRP and CHIRPS were evaluated over East Africa at daily, dekadal (10-day) and monthly time-scales. The evaluation was done by comparing the satellite products with rain-gauge data from about 1,200 stations. The CHIRP and CHIRPS products were also compared with two similar operational satellite rainfall products: the African Rainfall Climatology version 2 (ARC2) and the Tropical Applications of Meteorology using Satellite data (TAMSAT). The results show that both CHIRP and CHIRPS products are significantly better than ARC2 with higher skill and low or no bias. These products were also found to be slightly better than the latest version of the TAMSAT product at dekadal and monthly time-scales, while TAMSAT performed better at the daily time-scale. The performance of the different satellite products exhibits high spatial variability with weak performances over coastal and mountainous regions.}, - language = {en}, - number = {S1}, - urldate = {2020-12-18}, - journal = {Quarterly Journal of the Royal Meteorological Society}, - author = {Dinku, Tufa and Funk, Chris and Peterson, Pete and Maidment, Ross and Tadesse, Tsegaye and Gadain, Hussein and Ceccato, Pietro}, - year = {2018}, - note = {\_eprint: https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/qj.3244}, - keywords = {climate, remote sensing, validation, East Africa, rainfall estimation, satellite}, - pages = {292--312}, - file = {Full Text PDF:C\:\\Users\\EdMaurer\\Zotero\\storage\\74CH75I9\\Dinku et al. - 2018 - Validation of the CHIRPS satellite rainfall estima.pdf:application/pdf;Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\P7RDY2RX\\qj.html:text/html;Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\K5GRX89S\\qj.html:text/html}, -} - -@article{prakash_performance_2019, - title = {Performance assessment of {CHIRPS}, {MSWEP}, {SM2RAIN}-{CCI}, and {TMPA} precipitation products across {India}}, - volume = {571}, - issn = {0022-1694}, - url = {http://www.sciencedirect.com/science/article/pii/S0022169419301039}, - doi = {10.1016/j.jhydrol.2019.01.036}, - abstract = {Accurate long-term estimates of precipitation at fine spatiotemporal resolution are vital for several applications ranging from hydrometeorology to climatology. The availability of a good network of rain gauges, and high precipitation variability associated with two annual monsoon systems and complex topography make India a suitable test-bed to assess the performance of any satellite-based precipitation product. This study assesses the performance of latest versions of four multi-satellite precipitation products: (i) Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), (ii) Multi-Source Weighted-Ensemble Precipitation (MSWEP), (iii) SM2RAIN-Climate Change Initiative (SM2RAIN-CCI), and (iv) TRMM Multisatellite Precipitation Analysis (TMPA) across India using gauge-based observations for the period of 1998–2015 at monthly scale. These four multi-satellite precipitation products are essentially based on different algorithms and input data sets. Among these multi-satellite precipitation products, SM2RAIN-CCI is the only product that does not use rain gauge observations for bias adjustment. Results indicate that CHIRPS and TMPA are comparable to gauge-based precipitation estimates at all-India and sub-regional scales followed by MSWEP estimates. However, SM2RAIN-CCI largely underestimates precipitation across the country as compared to gauge-based observations. The systematic error component in SM2RAIN-CCI dominates as compared to random error component, which suggests the need of a suitable bias correction to SM2RAIN-CCI before integrating it in any application. The overall results indicate that CHIRPS data set could be used for long-term precipitation analyses with rather higher confidence.}, - language = {en}, - urldate = {2020-12-18}, - journal = {Journal of Hydrology}, - author = {Prakash, Satya}, - month = apr, - year = {2019}, - keywords = {Precipitation, Error decomposition, Multi-satellite, Rain gauge, Soil moisture}, - pages = {50--59}, - file = {ScienceDirect Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\RQMCYKFZ\\S0022169419301039.html:text/html}, -} - -@article{alfaro-cordoba_aridity_2020, - title = {Aridity {Trends} in {Central} {America}: {A} {Spatial} {Correlation} {Analysis}}, - volume = {11}, - copyright = {http://creativecommons.org/licenses/by/3.0/}, - shorttitle = {Aridity {Trends} in {Central} {America}}, - url = {https://www.mdpi.com/2073-4433/11/4/427}, - doi = {10.3390/atmos11040427}, - abstract = {Trend analyses are common in several types of climate change studies. In many cases, finding evidence that the trends are different from zero in hydroclimate variables is of particular interest. However, when estimating the confidence interval of a set of hydroclimate stations or gridded data the spatial correlation between can affect the significance assessment using for example traditional non-parametric and parametric methods. For this reason, Monte Carlo simulations are needed in order to generate maps of corrected trend significance. In this article, we determined the significance of trends in aridity, modeled runoff using the Variable Infiltration Capacity Macroscale Hydrological model, Hagreaves potential evapotranspiration (PET) and near-surface temperature in Central America. Linear-regression models were fitted considering that the predictor variable is the time variable (years from 1970 to 1999) and predictand variable corresponds to each of the previously mentioned hydroclimate variables. In order to establish if the temporal trends were significantly different from zero, a Mann Kendall and a Monte Carlo test were used. The spatial correlation was calculated first to correct the variance of each trend. It was assumed in this case that the trends form a spatial stochastic process that can be modeled as such. Results show that the analysis considering the spatial correlation proposed here can be used for identifying those extreme trends. However, a set of variables with strong spatial correlation such as temperature can have robust and widespread significant trends assuming independence, but the vast majority of the stations can still fail the Monte Carlo test. We must be vigilant of the statistically robust changes in key primary parameters such as temperature and precipitation, which are the driving sources of hydrological alterations that may affect social and environmental systems in the future.}, - language = {en}, - number = {4}, - urldate = {2020-12-18}, - journal = {Atmosphere}, - author = {Alfaro-Córdoba, Marcela and Hidalgo, Hugo G. and Alfaro, Eric J.}, - month = apr, - year = {2020}, - note = {Number: 4 -Publisher: Multidisciplinary Digital Publishing Institute}, - keywords = {variability, trend analysis, aridity, Central American climate, spatial correlation}, - pages = {427}, - file = {Full Text PDF:C\:\\Users\\EdMaurer\\Zotero\\storage\\9Y8G9UYR\\Alfaro-Córdoba et al. - 2020 - Aridity Trends in Central America A Spatial Corre.pdf:application/pdf;Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\TUUBHHD6\\427.html:text/html;Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\TH65R9I8\\427.html:text/html}, -} - -@article{bouroncle_mapping_2017, - title = {Mapping climate change adaptive capacity and vulnerability of smallholder agricultural livelihoods in {Central} {America}: ranking and descriptive approaches to support adaptation strategies}, - volume = {141}, - issn = {1573-1480}, - shorttitle = {Mapping climate change adaptive capacity and vulnerability of smallholder agricultural livelihoods in {Central} {America}}, - url = {https://doi.org/10.1007/s10584-016-1792-0}, - doi = {10.1007/s10584-016-1792-0}, - abstract = {Climate change is one of the main threats to rural livelihoods in Central America, especially for small and medium-sized farmers. Climate change vulnerability assessment (CCVA) integrates biophysical and socioeconomic information to support policy decisions. We present a CCVA of agricultural livelihoods of four countries in Central America, at the municipality level. We use the IPCC definition of vulnerability, and address the potential impact of climate change on suitability for major crops and adaptive capacity using indicators of basic human needs, as well as resources for innovation and action framed in a livelihoods approach. Adaptive capacity was estimated using ranking techniques for municipalities and descriptive multivariate analysis. Projected changes in climate suitability for crops show a wide variation between Guatemala, El Salvador, Honduras and Nicaragua, and within each country. Cluster analysis of adaptive capacity values shows a gradient between higher values close to urban areas and lower values in agricultural frontier areas and in those prone to drought. Municipalities with a high proportional area under subsistence crops tend to have less resources to promote innovation and action for adaptation. Our results suggest that a full spectrum of adaptation levels and strategies must be considered in the region to achieve different adaptation goals. They also show that the adaptive capacity ranking and characterization are complementary and support geographical prioritization and identification of adaptation strategies, respectively.}, - language = {en}, - number = {1}, - urldate = {2020-12-18}, - journal = {Climatic Change}, - author = {Bouroncle, Claudia and Imbach, Pablo and Rodríguez-Sánchez, Beatriz and Medellín, Claudia and Martinez-Valle, Armando and Läderach, Peter}, - month = mar, - year = {2017}, - pages = {123--137}, - file = {Springer Full Text PDF:C\:\\Users\\EdMaurer\\Zotero\\storage\\AYC8IRFA\\Bouroncle et al. - 2017 - Mapping climate change adaptive capacity and vulne.pdf:application/pdf}, -} - -@article{baca_integrated_2014, - title = {An {Integrated} {Framework} for {Assessing} {Vulnerability} to {Climate} {Change} and {Developing} {Adaptation} {Strategies} for {Coffee} {Growing} {Families} in {Mesoamerica}}, - volume = {9}, - issn = {1932-6203}, - url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0088463}, - doi = {10.1371/journal.pone.0088463}, - abstract = {The Mesoamerican region is considered to be one of the areas in the world most vulnerable to climate change. We developed a framework for quantifying the vulnerability of the livelihoods of coffee growers in Mesoamerica at regional and local levels and identify adaptation strategies. Following the Intergovernmental Panel on Climate Change (IPCC) concepts, vulnerability was defined as the combination of exposure, sensitivity and adaptive capacity. To quantify exposure, changes in the climatic suitability for coffee and other crops were predicted through niche modelling based on historical climate data and locations of coffee growing areas from Mexico, Guatemala, El Salvador and Nicaragua. Future climate projections were generated from 19 Global Circulation Models. Focus groups were used to identify nine indicators of sensitivity and eleven indicators of adaptive capacity, which were evaluated through semi-structured interviews with 558 coffee producers. Exposure, sensitivity and adaptive capacity were then condensed into an index of vulnerability, and adaptation strategies were identified in participatory workshops. Models predict that all target countries will experience a decrease in climatic suitability for growing Arabica coffee, with highest suitability loss for El Salvador and lowest loss for Mexico. High vulnerability resulted from loss in climatic suitability for coffee production and high sensitivity through variability of yields and out-migration of the work force. This was combined with low adaptation capacity as evidenced by poor post harvest infrastructure and in some cases poor access to credit and low levels of social organization. Nevertheless, the specific contributors to vulnerability varied strongly among countries, municipalities and families making general trends difficult to identify. Flexible strategies for adaption are therefore needed. Families need the support of government and institutions specialized in impacts of climate change and strengthening of farmer organizations to enable the adjustment of adaptation strategies to local needs and conditions.}, - language = {en}, - number = {2}, - urldate = {2020-12-18}, - journal = {PLOS ONE}, - author = {Baca, María and Läderach, Peter and Haggar, Jeremy and Schroth, Götz and Ovalle, Oriana}, - month = feb, - year = {2014}, - note = {Publisher: Public Library of Science}, - keywords = {Climate change, Nicaragua, Urban infrastructure, Mexico, Agricultural workers, Crops, El Salvador, Guatemala}, - pages = {e88463}, - file = {Full Text PDF:C\:\\Users\\EdMaurer\\Zotero\\storage\\4USU4R97\\Baca et al. - 2014 - An Integrated Framework for Assessing Vulnerabilit.pdf:application/pdf;Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\NRTSQIMW\\article.html:text/html}, -} - -@article{perdigonmorales_midsummer_2018, - title = {The midsummer drought in {Mexico}: perspectives on duration and intensity from the {CHIRPS} precipitation database}, - volume = {38}, - number = {5}, - journal = {International Journal of Climatology}, - author = {Perdigón‐Morales, Juliet and Romero‐Centeno, Rosario and Pérez, Paulina Ordóñez and Barrett, Bradford S.}, - year = {2018}, - note = {Publisher: Wiley Online Library}, - pages = {2174--2186}, -} - -@book{wani_rainfed_2009, - title = {Rainfed agriculture: unlocking the potential}, - volume = {7}, - number = {Book, Whole}, - publisher = {CABI}, - author = {Wani, Suhas Pralhad and Rockström, Johan and Oweis, Theib Yousef}, - year = {2009}, -} - -@article{schneider_evaluating_2017, - title = {Evaluating the hydrological cycle over land using the newly-corrected precipitation climatology from the {Global} {Precipitation} {Climatology} {Centre} ({GPCC})}, - volume = {8}, - number = {3}, - journal = {Atmosphere}, - author = {Schneider, Udo and Finger, Peter and Meyer-Christoffer, Anja and Rustemeier, Elke and Ziese, Markus and Becker, Andreas}, - year = {2017}, - note = {Publisher: Multidisciplinary Digital Publishing Institute}, - pages = {52}, -} - -@article{reichle_land_2017, - title = {Land surface precipitation in {MERRA}-2}, - volume = {30}, - number = {5}, - journal = {Journal of Climate}, - author = {Reichle, Rolf H. and Liu, Q. and Koster, Randal D. and Draper, Clara S. and Mahanama, Sarith PP and Partyka, Gary S.}, - year = {2017}, - pages = {1643--1664}, -} - -@article{reichle_assessment_2017, - title = {Assessment of {MERRA}-2 land surface hydrology estimates}, - volume = {30}, - number = {8}, - journal = {Journal of Climate}, - author = {Reichle, Rolf H. and Draper, Clara S. and Liu, Qing and Girotto, Manuela and Mahanama, Sarith PP and Koster, Randal D. and De Lannoy, Gabrielle JM}, - year = {2017}, - pages = {2937--2960}, -} - -@article{quesada-hernandez_dynamical_2019, - title = {Dynamical delimitation of the {Central} {American} {Dry} {Corridor} ({CADC}) using drought indices and aridity values}, - volume = {43}, - number = {5}, - journal = {Progress in Physical Geography: Earth and Environment}, - author = {Quesada-Hernandez, Luis Eduardo and Calvo-Solano, Oscar David and Hidalgo, Hugo G. and Perez-Briceno, Paula M. and Alfaro, Eric J.}, - year = {2019}, - note = {Publisher: SAGE Publications Sage UK: London, England}, - pages = {627--642}, -} - -@book{pennington_neotropical_2006, - title = {Neotropical savannas and seasonally dry forests: plant diversity, biogeography, and conservation}, - number = {Book, Whole}, - publisher = {CRC press}, - author = {Pennington, R. Toby and Ratter, James A.}, - year = {2006}, -} - -@article{perez-briceno_distribucion_2016, - title = {Distribución espacial de impactos de eventos hidrometeorológicos en {América} {Central}}, - volume = {16}, - number = {Journal Article}, - journal = {Revista de Climatología}, - author = {Pérez-Briceño, Paula M. and Alfaro, Eric J. and Hidalgo, Hugo G. and Jiménez, Francisco}, - year = {2016}, - pages = {63--75}, -} - -@article{peterson_climate_2015, - title = {The {Climate} {Hazards} {Group} {InfraRed} {Precipitation} with {Stations} ({CHIRPS}) v2. 0 {Dataset}: 35 year {Quasi}-{Global} {Precipitation} {Estimates} for {Drought} {Monitoring}}, - volume = {2015}, - number = {Journal Article}, - journal = {AGUFM}, - author = {Peterson, P. and Funk, C. C. and Landsfeld, M. F. and Pedreros, D. H. and Shukla, S. and Husak, G. J. and Harrison, L. and Verdin, J. P.}, - year = {2015}, - pages = {NH41D--05}, -} - -@article{yue_power_2002, - title = {Power of the {Mann}–{Kendall} and {Spearman}'s rho tests for detecting monotonic trends in hydrological series}, - volume = {259}, - number = {1-4}, - journal = {Journal of hydrology}, - author = {Yue, Sheng and Pilon, Paul and Cavadias, George}, - year = {2002}, - note = {Publisher: Elsevier}, - pages = {254--271}, -} - -@article{van_der_zee_arias_no_2012, - title = {No title}, - number = {Journal Article}, - journal = {Estudio de caracterización del corredor seco centroamericano}, - author = {Van der Zee Arias, Amparo and Meyrat, Alain and Picado, Luis and Poveda, Carlos and Van der Zee, Jaap}, - year = {2012}, - note = {Publisher: FAO}, -} - -@article{peralta_buenas_2012, - title = {Buenas prácticas para la seguridad alimentaria y la gestión de riesgos}, - volume = {53}, - number = {Journal Article}, - journal = {Publicado por: organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO)}, - author = {Peralta, O. and Carrazón, J. and Zelaya, C. A.}, - year = {2012}, -} - -@article{maurer_projected_2017, - title = {Projected twenty-first-century changes in the {Central} {American} mid-summer drought using statistically downscaled climate projections}, - volume = {17}, - issn = {1436-3798}, - doi = {10.1007/s10113-017-1177-6}, - abstract = {In addition to periodic long-term drought, much of Central America experiences a rainy season with two peaks separated by a dry period of weeks to over a month in duration, termed the mid-summer drought (MSD). Farmers in the region have adapted their activities to accommodate this phenomenon, anticipating its arrival and estimating its duration. Among the many impacts of global warming on the region are projected changes in precipitation amount, variability, and timing, with potential to affect agriculture and food security. Using gridded daily precipitation for a historic period with future projections, we characterize the MSD across much of Central America using four measures: onset date, duration, intensity, and minimum, and test for significant changes by the end of the twenty-first century. Our findings indicate that the most significant changes are for the duration, which is projected to increase by an average of over a week, and the MSD minimum precipitation, which is projected to decrease by an average of over 26\%, with statistically significant changes for the mountains and Pacific side in most of Nicaragua, Honduras, El Salvador, and Guatemala (assuming a higher emissions pathway through the twenty-first century). These changes could portend important impacts on food security for vulnerable communities through the region. We find that for the four metrics, the changes in interannual variability are small compared to historical variability and are generally statistically insignificant.}, - number = {8}, - journal = {Regional Environmental Change}, - author = {Maurer, Edwin and Roby, Nicholas and Stewart-Frey, Iris and Bacon, Christopher}, - year = {2017}, - note = {Place: Berlin/Heidelberg -Publisher: Springer Berlin Heidelberg}, - pages = {2421--2432}, -} - -@article{munozjimenez_spatial_2019, - title = {Spatial and temporal patterns, trends and teleconnection of cumulative rainfall deficits across {Central} {America}}, - volume = {39}, - number = {4}, - journal = {International Journal of Climatology}, - author = {Muñoz‐Jiménez, Rudy and Giraldo‐Osorio, Juan Diego and Brenes‐Torres, Alonso and Avendaño‐Flores, Isabel and Nauditt, Alexandra and Hidalgo‐León, Hugo G. and Birkel, Christian}, - year = {2019}, - note = {Publisher: Wiley Online Library}, - pages = {1940--1953}, -} - -@article{maldonado_review_2018, - title = {A review of the main drivers and variability of {Central} {America}’s {Climate} and seasonal forecast systems}, - volume = {66}, - number = {1-1}, - journal = {Revista de Biología Tropical}, - author = {Maldonado, Tito and Alfaro, Eric J. and Hidalgo, Hugo G.}, - year = {2018}, - pages = {S153--S175}, -} - -@article{kreft_global_2013, - title = {Global climate risk index 2014}, - volume = {1}, - number = {Journal Article}, - journal = {Who suffers most from extreme weather events}, - author = {Kreft, Sönke and Eckstein, David and Melchior, Inga}, - year = {2013}, -} - -@article{gotlieb_central_2019, - title = {The {Central} {American} {Dry} {Corridor}: {A} consensus statement and its background}, - volume = {3}, - number = {Journal Article}, - journal = {Rev.Yu’am}, - author = {Gotlieb, Yosef and Pérez-Briceño, Paula M. and Hidalgo, H. G. and Alfaro, E. J.}, - year = {2019}, - pages = {42--51}, -} - -@article{funk_high-resolution_2019, - title = {A {High}-{Resolution} 1983–2016 {T} max {Climate} {Data} {Record} {Based} on {Infrared} {Temperatures} and {Stations} by the {Climate} {Hazard} {Center}}, - volume = {32}, - number = {17}, - journal = {Journal of Climate}, - author = {Funk, Chris and Peterson, Pete and Peterson, Seth and Shukla, Shraddhanand and Davenport, Frank and Michaelsen, Joel and Knapp, Kenneth R. and Landsfeld, Martin and Husak, Gregory and Harrison, Laura}, - year = {2019}, - pages = {5639--5658}, -} - -@article{gelaro_modern-era_2017, - title = {The modern-era retrospective analysis for research and applications, version 2 ({MERRA}-2)}, - volume = {30}, - number = {14}, - journal = {Journal of Climate}, - author = {Gelaro, Ronald and McCarty, Will and Suárez, Max J. and Todling, Ricardo and Molod, Andrea and Takacs, Lawrence and Randles, Cynthia A. and Darmenov, Anton and Bosilovich, Michael G. and Reichle, Rolf}, - year = {2017}, - pages = {5419--5454}, -} - -@article{magana_midsummer_1999, - title = {The midsummer drought over {Mexico} and {Central} {America}}, - volume = {12}, - number = {6}, - journal = {Journal of Climate}, - author = {Magaña, Victor and Amador, Jorge A. and Medina, Socorro}, - year = {1999}, - pages = {1577--1588}, -} - -@article{kling_runoff_2012, - title = {Runoff conditions in the upper {Danube} basin under an ensemble of climate change scenarios}, - volume = {424}, - number = {Journal Article}, - journal = {Journal of Hydrology}, - author = {Kling, Harald and Fuchs, Martin and Paulin, Maria}, - year = {2012}, - note = {Publisher: Elsevier}, - pages = {264--277}, -} - -@article{jones_longterm_2016, - title = {Long‐term trends in precipitation and temperature across the {Caribbean}}, - volume = {36}, - number = {9}, - journal = {International Journal of Climatology}, - author = {Jones, Philip D. and Harpham, Colin and Harris, Ian and Goodess, Clare M. and Burton, Aidan and Centella‐Artola, Abel and Taylor, Michael A. and Bezanilla‐Morlot, Arnoldo and Campbell, Jayaka D. and Stephenson, Tannecia S.}, - year = {2016}, - note = {Publisher: Wiley Online Library}, - pages = {3314--3333}, -} - -@article{hersbach_era_2018, - title = {{ERA} report series}, - number = {Journal Article}, - author = {Hersbach, Hans and Peubey, Carole and Simmons, Adrian and Poli, Paul and Dee, Dick and Berrisford, Paul}, - year = {2018}, - note = {Publisher: DOI}, -} - -@article{gupta_decomposition_2009, - title = {Decomposition of the mean squared error and {NSE} performance criteria: {Implications} for improving hydrological modelling}, - volume = {377}, - number = {1-2}, - journal = {Journal of hydrology}, - author = {Gupta, Hoshin V. and Kling, Harald and Yilmaz, Koray K. and Martinez, Guillermo F.}, - year = {2009}, - note = {Publisher: Elsevier}, - pages = {80--91}, -} - -@article{hidalgo_leon_precursors_2019, - title = {Precursors of quasi-decadal dry-spells in the {Central} {America} {Dry} {Corridor}}, - number = {Journal Article}, - author = {Hidalgo León, Hugo G. and Alfaro Martínez, Eric J. and Amador Astúa, Jorge Alberto and Bastidas Pacheco, Álvaro}, - year = {2019}, -} - -@article{funk_recognizing_2019, - title = {Recognizing the {Famine} {Early} {Warning} {Systems} {Network}: {Over} 30 {Years} of {Drought} {Early} {Warning} {Science} {Advances} and {Partnerships} {Promoting} {Global} {Food} {Security}}, - volume = {100}, - number = {6}, - journal = {Bulletin of the American Meteorological Society}, - author = {Funk, Chris and Shukla, Shraddhanand and Thiaw, Wassila Mamadou and Rowland, James and Hoell, Andrew and McNally, Amy and Husak, Gregory and Novella, Nicholas and Budde, Michael and Peters-Lidard, Christa}, - year = {2019}, - pages = {1011--1027}, -} - -@article{beck_daily_2019, - title = {Daily evaluation of 26 precipitation datasets using {Stage}-{IV} gauge-radar data for the {CONUS}}, - volume = {23}, - issn = {1607-7938}, - url = {https://search.datacite.org/works/10.5194/hess-23-207-2019}, - doi = {10.5194/hess-23-207-2019}, - abstract = {New precipitation (P) datasets are released regularly, following innovations in weather forecasting models, satellite retrieval methods, and multi-source merging techniques. Using the conterminous US as a case study, we evaluated the performance of 26 gridded (sub-)daily P datasets to obtain insight into the merit of these innovations. The evaluation was performed at a daily timescale for the period 2008–2017 using the Kling–Gupta efficiency (KGE), a performance metric combining correlation, bias, and variability. As a reference, we used the high-resolution (4 km) Stage-IV gauge-radar P dataset. Among the three KGE components, the P datasets performed worst overall in terms of correlation (related to event identification). In terms of improving KGE scores for these datasets, improved P totals (affecting the bias score) and improved distribution of P intensity (affecting the variability score) are of secondary importance. Among the 11 gauge-corrected P datasets, the best overall performance was obtained by MSWEP V2.2, underscoring the importance of applying daily gauge corrections and accounting for gauge reporting times. Several uncorrected P datasets outperformed gauge-corrected ones. Among the 15 uncorrected P datasets, the best performance was obtained by the ERA5-HRES fourth-generation reanalysis, reflecting the significant advances in earth system modeling during the last decade. The (re)analyses generally performed better in winter than in summer, while the opposite was the case for the satellite-based datasets. IMERGHH V05 performed substantially better than TMPA-3B42RT V7, attributable to the many improvements implemented in the IMERG satellite P retrieval algorithm. IMERGHH V05 outperformed ERA5-HRES in regions dominated by convective storms, while the opposite was observed in regions of complex terrain. The ERA5-EDA ensemble average exhibited higher correlations than the ERA5-HRES deterministic run, highlighting the value of ensemble modeling. The WRF regional convection-permitting climate model showed considerably more accurate P totals over the mountainous west and performed best among the uncorrected datasets in terms of variability, suggesting there is merit in using high-resolution models to obtain climatological P statistics. Our findings provide some guidance to choose the most suitable P dataset for a particular application.}, - number = {1}, - journal = {Hydrology and earth system sciences}, - author = {Beck, Hylke E. and Pan, Ming and Roy, Tirthankar and Weedon, Graham P. and Pappenberger, Florian and van Dijk, Albert I. J. M and Huffman, George J. and Adler, Robert F. and Wood, Eric F.}, - year = {2019}, - note = {Place: Katlenburg-Lindau -Publisher: Copernicus GmbH}, - pages = {207--224}, -} - -@article{avelino_coffee_2015, - title = {The coffee rust crises in {Colombia} and {Central} {America} (2008–2013): impacts, plausible causes and proposed solutions}, - volume = {7}, - issn = {1876-4525}, - url = {https://search.datacite.org/works/10.1007/s12571-015-0446-9}, - doi = {10.1007/s12571-015-0446-9}, - abstract = {Coffee rust is a leaf disease caused by the fungus, Hemileia vastatrix. Coffee rust epidemics, with intensities higher than previously observed, have affected a number of countries including: Colombia, from 2008 to 2011; Central America and Mexico, in 2012–13; and Peru and Ecuador in 2013. There are many contributing factors to the onset of these epidemics e.g. the state of the economy, crop management decisions and the prevailing weather, and many resulting impacts e.g. on production, on farmers’ and labourers’ income and livelihood, and on food security. Production has been considerably reduced in Colombia (by 31 \% on average during the epidemic years compared with 2007) and Central America (by 16 \% in 2013 compared with 2011–12 and by 10 \% in 2013–14 compared with 2012–13). These reductions have had direct impacts on the livelihoods of thousands of smallholders and harvesters. For these populations, particularly in Central America, coffee is often the only source of income used to buy food and supplies for the cultivation of basic grains. As a result, the coffee rust epidemic has had indirect impacts on food security. The main drivers of these epidemics are economic and meteorological. All the intense epidemics experienced during the last 37 years in Central America and Colombia were concurrent with low coffee profitability periods due to coffee price declines, as was the case in the 2012–13 Central American epidemic, or due to increases in input costs, as in the 2008–11 Colombian epidemics. Low profitability led to suboptimal coffee management, which resulted in increased plant vulnerability to pests and diseases. A common factor in the recent Colombian and Central American epidemics was a reduction in the diurnal thermal amplitude, with higher minimum/lower maximum temperatures (+0.1 °C/-0.5 °C on average during 2008–2011 compared to a low coffee rust incidence period, 1991–1994, in Chinchiná, Colombia; +0.9 °C/-1.2 °C on average in 2012 compared with prevailing climate, in 1224 farms from Guatemala). This likely decreased the latency period of the disease. These epidemics should be considered as a warning for the future, as they were enhanced by weather conditions consistent with climate change. Appropriate actions need to be taken in the near future to address this issue including: the development and establishment of resistant coffee cultivars; the creation of early warning systems; the design of crop management systems adapted to climate change and to pest and disease threats; and socio-economic solutions such as training and organisational strengthening.}, - number = {2}, - journal = {Food security}, - author = {Avelino, Jacques and Cristancho, Marco and Georgiou, Selena and Imbach, Pablo and Aguilar, Lorena and Bornemann, Gustavo and Läderach, Peter and Anzueto, Francisco and Hruska, Allan J. and Morales, Carmen}, - year = {2015}, - note = {Place: Dordrecht -Publisher: Springer Science and Business Media LLC}, - pages = {303--321}, -} - -@article{ashouri_assessing_2016, - title = {Assessing the efficacy of high-resolution satellite-based {PERSIANN}-{CDR} precipitation product in simulating streamflow}, - volume = {17}, - number = {7}, - journal = {Journal of Hydrometeorology}, - author = {Ashouri, Hamed and Nguyen, Phu and Thorstensen, Andrea and Hsu, Kuo-lin and Sorooshian, Soroosh and Braithwaite, Dan}, - year = {2016}, - pages = {2061--2076}, -} - -@article{ashouri_persiann-cdr_2015, - title = {{PERSIANN}-{CDR}: {Daily} precipitation climate data record from multisatellite observations for hydrological and climate studies}, - volume = {96}, - number = {1}, - journal = {Bulletin of the American Meteorological Society}, - author = {Ashouri, Hamed and Hsu, Kuo-Lin and Sorooshian, Soroosh and Braithwaite, Dan K. and Knapp, Kenneth R. and Cecil, L. Dewayne and Nelson, Brian R. and Prat, Olivier P.}, - year = {2015}, - pages = {69--83}, -} - -@article{funk_climate_2015, - title = {The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes}, - volume = {2}, - number = {1}, - journal = {Scientific data}, - author = {Funk, Chris and Peterson, Pete and Landsfeld, Martin and Pedreros, Diego and Verdin, James and Shukla, Shraddhanand and Husak, Gregory and Rowland, James and Harrison, Laura and Hoell, Andrew}, - year = {2015}, - note = {Publisher: Nature Publishing Group}, - pages = {1--21}, -} - -@article{barnett_human-induced_2008, - title = {Human-{Induced} {Changes} in the {Hydrology} of the {Western} {United} {States}}, - volume = {319}, - issn = {1095-9203}, - url = {https://search.datacite.org/works/10.1126/science.1152538}, - doi = {10.1126/science.1152538}, - abstract = {Observations have shown that the hydrological cycle of the western United States changed significantly over the last half of the 20th century. We present a regional, multivariable climate change detection and attribution study, using a high-resolution hydrologic model forced by global climate models, focusing on the changes that have already affected this primarily arid region with a large and growing population. The results show that up to 60\% of the climate-related trends of river flow, winter air temperature, and snow pack between 1950 and 1999 are human-induced. These results are robust to perturbation of study variates and methods. They portend, in conjunction with previous work, a coming crisis in water supply for the western United States.}, - number = {5866}, - journal = {Science (American Association for the Advancement of Science)}, - author = {Barnett, T. P. and Pierce, D. W. and Hidalgo, H. G. and Bonfils, C. and Santer, B. D. and Das, T. and Bala, G. and Wood, A. W. and Nozawa, T. and Mirin, A. A. and Cayan, D. R. and Dettinger, M. D.}, - year = {2008}, - note = {Place: United States -Publisher: American Association for the Advancement of Science (AAAS)}, - pages = {1080--1083}, -} - -@article{bacon_explaining_2014, - title = {Explaining the ‘hungry farmer paradox’: {Smallholders} and fair trade cooperatives navigate seasonality and change in {Nicaragua}'s corn and coffee markets}, - volume = {25}, - issn = {0959-3780}, - url = {https://search.datacite.org/works/10.1016/j.gloenvcha.2014.02.005}, - doi = {10.1016/j.gloenvcha.2014.02.005}, - abstract = {•Seasonal hunger is common among coffee smallholders in Nicaragua.•Domestic maize prices peak during the hungry months of June, July and August.•The number of lean months correlated negatively with corn harvests, fruit trees, improved grain storage, gross income and farm area.•Currently used agro-environmental practices showed no effect on lean months.•Fair trade cooperatives, farmers and other stakeholders can develop agroecological and local food system approaches that increase food access and sustainable land management.•Crop losses from the recent coffee leaf rust outbreak and falling commodity prices suggest that another coffee crisis is brewing in the Central America. Latin American smallholder coffee farmers linked with fair trade and organic markets are frequently cited as models for sustainable food systems. Yet many experience seasonal hunger, which is a very common, but understudied, form of food insecurity. Northern Nicaragua's highlands include well-organized cooperatives, high rural poverty rates, and rain dependent farms, offering a compelling study area to understand what factors are associated with seasonal hunger. This participatory mixed methods study combines data from observations, interviews and focus groups with results from a survey of 244 cooperative members. It finds that seasonal hunger is influenced by multiple factors, including: (1) annual cycles of precipitation and rising maize prices during the lean months; (2) inter annual droughts and periodic storms; and (3) the long-term inability of coffee harvests and prices to provide sufficient income. Sampled households experienced an average of about 3 months of seasonal hunger in 2009. A series of five least squares regression models find the expected significant impacts of corn harvest quantity, farm area, improved grain storage, and household incomes, all inversely correlated with lean months. Unanticipated results include the finding that households with more fruit trees reported fewer lean months, while the predominant environmentally friendly farming practices had no discernable impacts. The presence of hunger among producers challenges sustainable coffee marketing claims. We describe one example of a partnership-based response that integrates agroecological farm management with the use of fair trade cooperative institutions to re-localize the corn distribution system. Increased investments and integrated strategies will be needed to reduce threats to food security, livelihoods, and biodiversity associated with the rapid spread of coffee leaf rust and falling commodity prices.}, - number = {Journal Article}, - journal = {Global environmental change}, - author = {Bacon, Christopher M. and Sundstrom, William A. and Flores Gómez, María Eugenia and Ernesto Méndez, V. and Santos, Rica and Goldoftas, Barbara and Dougherty, Ian}, - year = {2014}, - note = {Publisher: Elsevier BV}, - pages = {133--149}, -} - -@article{alfaro_martinez_observed_2015, - title = {Observed (1970-1999) climate variability in {Central} {America} using a high-resolution meteorological dataset with potential for climate change studies}, - number = {Journal Article}, - author = {Alfaro Martínez, Eric J. and Quesada Montano, Beatriz and Hidalgo León, Hugo G.}, - year = {2015}, -} - -@article{sen_estimates_1968, - title = {Estimates of the regression coefficient based on {Kendall}'s tau}, - volume = {63}, - number = {324}, - journal = {Journal of the American statistical association}, - author = {Sen, Pranab Kumar}, - year = {1968}, - note = {Publisher: Taylor \& Francis Group}, - pages = {1379--1389}, -} - -@article{siegel_robust_1982, - title = {Robust regression using repeated medians}, - volume = {69}, - number = {1}, - journal = {Biometrika}, - author = {Siegel, Andrew F.}, - year = {1982}, - note = {Publisher: Oxford University Press}, - pages = {242--244}, -} - -@article{mann_nonparametric_1945, - title = {Nonparametric tests against trend}, - number = {Journal Article}, - journal = {Econometrica: Journal of the econometric society}, - author = {Mann, Henry B.}, - year = {1945}, - note = {Publisher: JSTOR}, - pages = {245--259}, -} - -@article{ziese_gpcc_2018, - title = {{GPCC} full data daily version. 2018 at 1.0◦: {Daily} land-surface precipitation from rain-gauges built on {GTS}-based and historic data}, - number = {Journal Article}, - journal = {GPCC Full Data Daily Version.2018 at 1.0°: Daily Land-Surface Precipitation from Rain-Gauges Built on GTS-Based and Historic Data}, - author = {Ziese, Markus and Rauthe-Schöch, Armin and Becker, Andreas and Finger, Peter and Meyer-Christoffer, Anja and Schneider, Udo}, - year = {2018}, -} - -@article{sun_review_2018, - title = {A review of global precipitation data sets: {Data} sources, estimation, and intercomparisons}, - volume = {56}, - number = {1}, - journal = {Reviews of Geophysics}, - author = {Sun, Qiaohong and Miao, Chiyuan and Duan, Qingyun and Ashouri, Hamed and Sorooshian, Soroosh and Hsu, Kuo‐Lin}, - year = {2018}, - note = {Publisher: Wiley Online Library}, - pages = {79--107}, -} - -@article{marvel_twentieth-century_2019, - title = {Twentieth-century hydroclimate changes consistent with human influence}, - volume = {569}, - number = {7754}, - journal = {Nature}, - author = {Marvel, Kate and Cook, Benjamin I. and Bonfils, Céline JW and Durack, Paul J. and Smerdon, Jason E. and Williams, A. Park}, - year = {2019}, - note = {Publisher: Nature Publishing Group}, - pages = {59--65}, -} - -@article{kendall_rank_1948, - title = {Rank correlation methods.}, - number = {Journal Article}, - author = {Kendall, Maurice George}, - year = {1948}, - note = {Publisher: Griffin}, -} - -@article{hersbach_era5_2020, - title = {The {ERA5} global reanalysis}, - volume = {146}, - number = {730}, - journal = {Quarterly Journal of the Royal Meteorological Society}, - author = {Hersbach, Hans and Bell, Bill and Berrisford, Paul and Hirahara, Shoji and Horányi, András and Muñoz‐Sabater, Joaquín and Nicolas, Julien and Peubey, Carole and Radu, Raluca and Schepers, Dinand}, - year = {2020}, - note = {Publisher: Wiley Online Library}, - pages = {1999--2049}, -} - -@article{chen_inter-comparison_2020, - title = {Inter-comparison of spatiotemporal features of precipitation extremes within six daily precipitation products}, - volume = {54}, - number = {1}, - journal = {Climate Dynamics}, - author = {Chen, Shiling and Liu, Bingjun and Tan, Xuezhi and Wu, Yi}, - year = {2020}, - note = {Publisher: Springer}, - pages = {1057--1076}, -} - -@article{cavazos_climatic_2020, - title = {Climatic trends and regional climate models intercomparison over the {CORDEX}-{CAM} ({Central} {America}, {Caribbean}, and {Mexico}) domain}, - volume = {40}, - issn = {0899-8418}, - url = {https://doi.org/10.1002/joc.6276}, - doi = {10.1002/joc.6276}, - abstract = {Abstract An intercomparison of three regional climate models (RCMs) (PRECIS-HadRM3P, RCA4, and RegCM4) was performed over the Coordinated Regional Dynamical Experiment (CORDEX)?Central America, Caribbean, and Mexico (CAM) domain to determine their ability to reproduce observed temperature and precipitation trends during 1980?2010. Particular emphasis was given to the North American monsoon (NAM) and the mid-summer drought (MSD) regions. The three RCMs show negative (positive) temperature (precipitation) biases over the mountains, where observations have more problems due to poor data coverage. Observations from the Climate Research Unit (CRU) and ERA-Interim show a generalized warming over the domain. The most significant warming trend (≥0.34°C/decade) is observed in the NAM, which is moderately captured by the three RCMs, but with less intensity; each decade from 1970 to 2016 has become warmer than the previous ones, especially during the summer (mean and extremes); this warming appears partially related to the positive Atlantic Multidecadal Oscillation (+AMO). CRU, GPCP, and CHIRPS show significant decreases of precipitation (less than ?15\%/decade) in parts of the southwest United States and northwestern Mexico, including the NAM, and a positive trend (5?10\%/decade) in June?September in eastern Mexico, the MSD region, and northern South America, but longer trends (1950?2017) are not statistically significant. RCMs are able to moderately simulate some of the recent trends, especially in winter. In spite of their mean biases, the RCMs are able to adequately simulate inter-annual and seasonal variations. Wet (warm) periods in regions affected by the MSD are significantly correlated with the +AMO and La Niña events (+AMO and El Niño). Summer precipitation trends from GPCP show opposite signs to those of CRU and CHIRPS over the Mexican coasts of the southern Gulf of Mexico, the Yucatan Peninsula, and Cuba, possibly due to data limitations and differences in grid resolutions.}, - number = {3}, - journal = {International Journal of Climatology}, - author = {Cavazos, Tereza and Luna-Niño, Rosa and Cerezo-Mota, Ruth and Fuentes-Franco, Ram and Méndez, Matías and Pineda Martínez, Luis Felipe and Valenzuela, Ernesto}, - year = {2020}, - note = {Publisher: John Wiley \& Sons, Ltd}, - pages = {1396--1420}, - annote = {doi: 10.1002/joc.6276; 30}, -} - -@article{jones_long-term_2016, - title = {Long-term trends in precipitation and temperature across the {Caribbean}}, - volume = {36}, - issn = {0899-8418}, - url = {https://doi.org/10.1002/joc.4557}, - doi = {10.1002/joc.4557}, - abstract = {ABSTRACT This study considers long-term precipitation and temperature variability across the Caribbean using two gridded data sets (CRU TS 3.21 and GPCCv5). We look at trends across four different regions (Northern, Eastern, Southern and Western), for three different seasons (May to July, August to October and November to April) and for three different periods (1901?2012, 1951?2012 and 1979?2012). There are no century-long trends in precipitation in either data set, although all regions (with the exception of the Northern Caribbean) show decade-long periods of wetter or drier conditions. The most significant of these is for the Southern Caribbean region which was wetter than the 1961?1990 average from 1940 to 1956 and then drier from 1957 to 1965. Temperature in contrast shows statistically significant warming everywhere for the periods 1901?2012, 1951?2012 and for over half the area during 1979?2012. Data availability is a limiting issue over much of the region and we also discuss the reliability of the series we use in the context of what is known to be available in the CRU TS 3.21 data set. More station data have been collected but have either not been fully digitized yet or not made freely available both within and beyond the region.}, - number = {9}, - journal = {International Journal of Climatology}, - author = {Jones, Philip D. and Harpham, Colin and Harris, Ian and Goodess, Clare M. and Burton, Aidan and Centella-Artola, Abel and Taylor, Michael A. and Bezanilla-Morlot, Arnoldo and Campbell, Jayaka D. and Stephenson, Tannecia S. and Joslyn, Ottis and Nicholls, Keith and Baur, Timo}, - year = {2016}, - note = {Publisher: John Wiley \& Sons, Ltd}, - pages = {3314--3333}, - annote = {doi: 10.1002/joc.4557; 03}, -} - -@article{zhu_evaluation_2004, - title = {Evaluation of hydrologically relevant {PCM} climate variables and large-scale variability over the continental {U}.{S}.}, - volume = {62}, - number = {1-3}, - journal = {Climatic Change}, - author = {Zhu, C. and Pierce, D. W. and Barnett, T. P. and Wood, A. W. and Lettenmaier, D. P.}, - year = {2004}, - pages = {45--74}, -} - -@article{zorita_analog_1999, - title = {The analog method as a simple statistical downscaling technique: {Comparison} with more complicated methods}, - volume = {12}, - abstract = {The derivation of local scale information from integrations of coarse-resolution general circulation models (GCM) with the help of statistical models fitted to present observations is generally referred to as statistical downscaling. In this paper a relatively simple analog method is described and applied for downscaling purposes. According to this method the large-scale circulation simulated by a GCM is associated with the local variables observed simultaneously with the most similar large scale circulation pattern in a pool of historical observations. The similarity of the large-scale circulation patterns is defined in terms of their coordinates in the space spanned by the leading observed empirical orthogonal functions. The method can be checked by replicating the evolution of the local variables in an independent period. Its performance for monthly and daily winter rainfall in the Iberian Peninsula is compared to more complicated techniques, each belonging to one of the broad families of existing statistical downscaling techniques: a method based on canonical correlation analysis, as representative of linear methods; a method based on classification and regression trees, as representative of a weather generator based on classification methods; and a neural network, as an example of deterministic nonlinear methods. It is found in these applications that the analog method performs in general as well as the more complicated methods, and it can be applied to both normally and nonnormally distributed local variables. Furthermore, it produces the right level of variability of the local variable and preserves the spatial covariance between local variables. On the other hand linear multivariate methods offer a clearer physical interpretation that supports more strongly its validity in an altered climate. Classification and neural networks are generally more complicated methods and do not directly offer a physical interpretation.}, - number = {8}, - journal = {J. Clim.}, - author = {Zorita, E. and von Storch, H.}, - month = aug, - year = {1999}, - pages = {2474--2489}, - annote = {2232MMJ CLIMATE}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Clim.accession-num: ISI:000082374600002}, -} - -@article{zhu_analysis_2012, - title = {Analysis of potential impacts of climate change on intensity–duration–frequency ({IDF}) relationships for six regions in the {United} {States}}, - volume = {3}, - doi = {10.2166/wcc.2012.045}, - abstract = {Potential changes in climate are expected to lead to future changes in the characteristics of precipitation events, including extreme rainfall intensity in most regions. In order for government agencies and design engineers to incorporate these trends and future changes into assessment and design processes, tools for planning and design should be capable of considering nonstationary climate conditions. In this work, potential changes are investigated in intensity–duration–frequency (IDF) curves, which are often used for assessment of extreme rainfall events, using historic data and future climate projections. An approach is proposed for calculating IDF curves that incorporates projected changes in rainfall intensity at a range of locations in the United States. The results elucidate strong regional patterns in projected changes in rainfall intensity, which are influenced by the rainfall characteristics of the region. Therefore, impacts of climate change on extreme hydrologic events will be highly regional and thus such assessments should be performed for specific project locations.}, - number = {3}, - journal = {Journal of Water and Climate Change}, - author = {Zhu, Jianting and Stone, Mark C. and Forsee, William}, - month = sep, - year = {2012}, - pages = {185--196}, -} - -@techreport{zhang_rclimdex_2004, - address = {Downsview, Ontario, Canada.}, - title = {{RClimDex} (1.0) {User} {Guide}}, - institution = {Climate Research Branch, Environment Canada}, - author = {Zhang, X. and Yang, F.}, - year = {2004}, -} - -@article{zierl_global_2005, - title = {Global change impacts on hydrological processes in {Alpine} catchments}, - volume = {41}, - issn = {0043-1397}, - abstract = {[1] This analysis assesses the impacts of global change on hydrological processes in Alpine catchments. We applied the regional hydroecological simulation system RHESSys to five case studies from different climatic zones in the European Alps. Seven scenarios based on the so-called SRES emission scenarios of the Intergovernmental Panel on Climate Change were used to describe changes in climate and land use. Shifts in runoff regimes and annual and summer runoff were investigated. The focus was on analyzing differences in the hydrological responses of different Alpine climate zones, exploring differences between SRES scenarios ( A1FI, A2, B1, and B2), and analyzing the importance of applying several global circulation models ( HadCM3, CGCM2, CSIRO2, and PCM). Under all scenarios, rising temperatures were projected to strongly affect runoff regimes via the impact on snow cover. The overall effect is a substantial runoff regime shift across the Alps, with specific differences depending on the climatic zone considered.}, - language = {English}, - number = {2}, - journal = {Water Resources Research}, - author = {Zierl, B. and Bugmann, H.}, - month = feb, - year = {2005}, - keywords = {climate-change, variability, temperature, evapotranspiration, models, forest ecosystem processes, glacier national-park, high-elevation, regions, snow interception}, - pages = {W02028, doi: 10.1029/2004WR003447}, - annote = {904WJTimes Cited:0Cited References Count:46}, - annote = {The following values have no corresponding Zotero field:auth-address: Bugmann, H ETH Zentrum, Dept Environm Sci, Ramistr 101, CH-8092 Zurich, Switzerland ETH Zentrum, Dept Environm Sci, CH-8092 Zurich, Switzerlandaccession-num: ISI:000227528500002}, -} - -@techreport{zier_spatial_1980, - title = {On the spatial interpolation of air pollution fields}, - abstract = {Air pollution fields are considered as inhomogeneous, anisotropic random fields. Using quantiles of the cumulative frequency distribution of measuring data as characteristic parameters, the space and time structure of these fields are described by means of special correlation parameters. Such correlation parameters are used in a spatial interpolation method for determining the long term mean value of air pollution in a point apart from measuring stations, using short time series from this point and the measuring points and the long term mean values of the measuring points. The method is illustrated by gravimetric measurement of suspended dust from the air pollution measuring network of East Germany. The interpolation errors are discussed and compared with those of a regression interpolation method. Accuracies are approximately equal.}, - number = {N-8312621 GermanyThu Feb 07 05:32:46 EST 2008NTIS MF A01- WMO, Geneva, Switzerland.ERA-08-042168; EDB-83-134152English}, - author = {Zier, M.}, - year = {1980}, - pages = {Medium: X; Size: Pages: 11}, - annote = {The following values have no corresponding Zotero field:accession-num: OSTI ID: 5921244}, -} - -@incollection{zhao_xinanjiang_1980, - title = {The {Xinanjiang} model}, - booktitle = {Hydrological {Forecasting}, {Proceedings}, {Oxford} {Symposium}}, - publisher = {IAHS Publ. 129}, - author = {Zhao, R.-J. and Fang, L.-R. and Liu, X.-R. and Zhang, Q.-S.}, - year = {1980}, - pages = {351--356}, -} - -@article{zhang_enso_2012, - title = {{ENSO} anomalies over the {Western} {United} {States}: present and future patterns in regional climate simulations}, - volume = {110}, - issn = {0165-0009}, - doi = {10.1007/s10584-011-0088-7}, - language = {English}, - number = {1-2}, - journal = {Climatic Change}, - author = {Zhang, Yongxin and Qian, Yun and Dulière, Valérie and Salathé, EricP and Leung, L. Ruby}, - month = jan, - year = {2012}, - pages = {315--346}, - annote = {The following values have no corresponding Zotero field:alt-title: Climatic Changepublisher: Springer Netherlands}, -} - -@article{zhang_subgrid-scale_1997, - title = {Subgrid-scale rainfall variability and its effects on atmospheric and surface variable predictions}, - volume = {102}, - abstract = {A new approach, which combines the Penn State/National Center for Atmospheric Research mesoscale model MM5 with a recently developed statistical downscaling scheme, has been investigated for the prediction of rainfall over scales (grid sizes) ranging from the atmospheric model scale ({\textgreater} 10 km) to subgrid scale (around 1 km). The innovation of the proposed dynamical/statistical hybrid approach lies on having unraveled a link between larger-scale dynamics of the atmosphere and smaller-scale statistics of the rainfall fields [Perica and Foufoula-Georgiou, 1996a], which then permits the coupling of a mesoscale dynamical model with a small-scale statistical parameterization of rainfall. This coupling is two-way interactive and offers the capability of investigating the feedback effects of subgrid-scale rainfall spatial variability on the further development of a rainfall system and on the surface energy balance and water partitioning over the MM5 model grids. The results of simulating rainfall in a strong convection system observed during the Oklahoma-Kansas Preliminary Regional Experiment for STORM-Central (PRESTORM) on June 10-11, 1985 show that (1) the dynamical/statistical hybrid approach is a useful and cost-effective scheme to predict rainfall at subgrid scales (around 1 km) based on larger-scale atmospheric model predictions, and (2) the inclusion of the subgrid-scale rainfall spatial variability can significantly affect the surface temperature distribution and the short-term ({\textless} 24 hour) prediction of rainfall intensity.}, - number = {D16}, - journal = {Journal of Geophysical Research-Atmospheres}, - author = {Zhang, S. X. and FoufoulaGeorgiou, E.}, - month = aug, - year = {1997}, - pages = {19559--19573}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Geophys. Res.-Atmos.accession-num: ISI:A1997XT93400014}, - annote = {XT934J GEOPHYS RES-ATMOS}, -} - -@article{schmidli_downscaling_2006, - title = {Downscaling from {GCM} precipitation: {A} benchmark for dynamical and statistical downscaling}, - volume = {26}, - journal = {International Journal of Climatology}, - author = {Schmidli, J. and Frei, C. and Vidale, P. L.}, - year = {2006}, - pages = {679--689}, -} - -@incollection{schneider_assessing_2007, - address = {Cambridge, UK}, - title = {Assessing key vulnerabilities and the risk from climate change}, - booktitle = {Climate {Change} 2007: {Impacts}, {Adaptation} and {Vulnerability}. {Contribution} of {Working} {Group} {II} to the {Fourth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {Schneider, S. H. and Semenov, S. and Patwardhan, A. and Burton, I. and Magadza, C. H. D. and Oppenheimer, M. and Pittock, A. B. and Rahman, A. and Smith, J. B. and Suarez, A. and Yamin, F.}, - editor = {Parry, M. and Canziani, O. F. and Palutikof, J. P. and van der Linden, P. J. and Hanson, C. E.}, - year = {2007}, - pages = {779--810}, -} - -@article{schaeffer_shifts_2005, - title = {Shifts of means are not a proxy for changes in extreme winter temperatures in climate projections}, - volume = {25}, - issn = {0930-7575}, - doi = {10.1007/s00382-004-0495-9}, - number = {1}, - journal = {Climate Dynamics}, - author = {Schaeffer, M. and Selten, F. and Opsteegh, J.}, - year = {2005}, - keywords = {Earth and Environmental Science}, - pages = {51--63}, - annote = {The following values have no corresponding Zotero field:publisher: Springer Berlin / Heidelberg}, -} - -@article{saunders_using_1999, - title = {Using synoptic surface and geopotential height fields for generating grid-scale precipitation}, - volume = {19}, - abstract = {Grid-scale precipitation data were derived from synoptic-scale fields of 50 kPa geopotentials by a simple method that relates synoptic-scale circulation to local climate. The study addressed the concerns that: (i) different sizes of grids might affect the results; (ii) different gridpoint spacing might affect the results; (iii) days that remain unclassified might be important to precipitation delivery. Three different grids were used. Grid A is 45 points with a 3.7 degrees x 3.7 degrees spacing with limited spatial coverage; Grid B is 117 points on a 5 degrees x 5 degrees grid which covered most of North America and the north-eastern Pacific Ocean; and Grid C is 35 points on a 10 degrees x 10 degrees grid with the same area as Grid B. The Kirchhofer technique was applied, and generated 20 (Grid A), 18 (Grid B), and 14 (Grid C) different synoptic types from a 10-year sample data set (1960-1969). Comparisons between simulated and observed precipitation for 1970-1989 showed that the method produced compatible precipitation totals regardless of which grid was employed, but the simulated variability was too low. Grid B gave the closest agreement between simulated and observed precipitation, with Grids C and A being ranked second and third, respectively. The spatial coverage of the grid was therefore deemed to be more important than the number of grid points within it. Similar results were found when comparing observed precipitation with that generated using general circulation model (GCM) geopotential height data. Precipitation regimes generated from the GCM suggest that little change in the synoptic control of precipitation would arise from a doubling of atmospheric CO2. Copyright (C) 1999 Royal Meteorological Society.}, - number = {11}, - journal = {International Journal of Climatology}, - author = {Saunders, I. R. and Byrne, J. M.}, - month = sep, - year = {1999}, - pages = {1165--1176}, - annote = {235LWINT J CLIMATOL}, - annote = {The following values have no corresponding Zotero field:alt-title: Int. J. Climatol.accession-num: ISI:000082544000001}, -} - -@article{santer_identification_2004, - title = {Identification of anthropogenic climate change using a second-generation reanalysis}, - volume = {109}, - issn = {0148-0227}, - abstract = {[ 1] Changes in the height of the tropopause provide a sensitive indicator of human effects on climate. A previous attempt to identify human effects on tropopause height relied on information from 'first-generation' reanalyses of past weather observations. Climate data from these initial model-based reanalyses have well-documented deficiencies, raising concerns regarding the robustness of earlier detection work that employed these data. Here we address these concerns using information from the new second-generation ERA-40 reanalysis. Over 1979 to 2001, tropopause height increases by nearly 200 m in ERA-40, partly due to tropospheric warming. The spatial pattern of height increase is consistent with climate model predictions of the expected response to anthropogenic influences alone, significantly strengthening earlier detection results. Atmospheric temperature changes in two different satellite data sets are more highly correlated with changes in ERA-40 than with those in a first-generation reanalysis, illustrating the improved quality of temperature information in ERA-40. Our results provide support for claims that human activities have warmed the troposphere and cooled the lower stratosphere over the last several decades of the 20th century, and that both of these changes in atmospheric temperature have contributed to an overall increase in tropopause height.}, - language = {English}, - number = {D21}, - journal = {Journal of Geophysical Research-Atmospheres}, - author = {Santer, B. D. and Wigley, T. M. L. and Simmons, A. J. and Kallberg, P. W. and Kelly, G. A. and Uppala, S. M. and Ammann, C. and Boyle, J. S. and Bruggemann, W. and Doutriaux, C. and Fiorino, M. and Mears, C. and Meehl, G. A. and Sausen, R. and Taylor, K. E. and Washington, W. M. and Wehner, M. F. and Wentz, F. J.}, - month = nov, - year = {2004}, - keywords = {climate change, variability, 20th-century, reanalysis, simulations, temperature trends, greenhouse-gas, el-nino, atmosphere-ocean gcm, detection, satellite data, tropical tropopause, tropopause height}, - pages = {--}, - annote = {870CJTimes Cited:2Cited References Count:71}, - annote = {The following values have no corresponding Zotero field:auth-address: Santer, BD Lawrence Livermore Natl Lab, Program Climate Model Diag \& Intercomparison, Livermore, CA 94550 USA Lawrence Livermore Natl Lab, Program Climate Model Diag \& Intercomparison, Livermore, CA 94550 USA Natl Ctr Atmospher Res, Boulder, CO 80303 USA European Ctr Medium Range Weather Forecasts, Reading RG2 9AX, Berks, England Univ Birmingham, Sch Math \& Stat, Birmingham B15 2TT, W Midlands, England Remote Sensing Syst, Santa Rosa, CA 95401 USA Deutsch Zentrum Luft \& Raumfahrt, Inst Phys Atmosphare, D-82234 Wessling, Germany Univ Calif Berkeley, Lawrence Berkeley Lab, Berkeley, CA 94720 USAaccession-num: ISI:000225031300007}, -} - -@article{santer_forced_2006, - title = {Forced and unforced ocean temperature changes in {Atlantic} and {Pacific} tropical cyclogenesis regions}, - volume = {103}, - number = {38}, - journal = {Proceedings National Academy of Sciences}, - author = {Santer, B. D. and Wigley, T. M. L. and Glecker, P. J. and Bonfils, C. and Wehner, M. F. and AchutaRao, K. and Barnett, T. P. and Boyle, J. S. and Bruggemann, W. and Florino, M. and Gillett, N. and Hansen, J. E. and Jones, P. D. and Klein, S. A. and Meehl, G. A. and Raper, S. C. B. and Reynolds, R. W. and Taylor, K. E. and Washington, W. M.}, - year = {2006}, - pages = {13905--13910}, -} - -@article{santer_incorporating_2009, - title = {Incorporating model quality information in climate change detection and attribution studies}, - volume = {106}, - doi = {10.1073/pnas.0901736106}, - abstract = {In a recent multimodel detection and attribution (D\&A) study using the pooled results from 22 different climate models, the simulated “fingerprint” pattern of anthropogenically caused changes in water vapor was identifiable with high statistical confidence in satellite data. Each model received equal weight in the D\&A analysis, despite large differences in the skill with which they simulate key aspects of observed climate. Here, we examine whether water vapor D\&A results are sensitive to model quality. The “top 10” and “bottom 10” models are selected with three different sets of skill measures and two different ranking approaches. The entire D\&A analysis is then repeated with each of these different sets of more or less skillful models. Our performance metrics include the ability to simulate the mean state, the annual cycle, and the variability associated with El Niño. We find that estimates of an anthropogenic water vapor fingerprint are insensitive to current model uncertainties, and are governed by basic physical processes that are well-represented in climate models. Because the fingerprint is both robust to current model uncertainties and dissimilar to the dominant noise patterns, our ability to identify an anthropogenic influence on observed multidecadal changes in water vapor is not affected by “screening” based on model quality.}, - number = {35}, - journal = {Proceedings of the National Academy of Sciences}, - author = {Santer, B. D. and Taylor, K. E. and Gleckler, P. J. and Bonfils, C. and Barnett, T. P. and Pierce, D. W. and Wigley, T. M. L. and Mears, C. and Wentz, F. J. and Brüggemann, W. and Gillett, N. P. and Klein, S. A. and Solomon, S. and Stott, P. A. and Wehner, M. F.}, - month = sep, - year = {2009}, - pages = {14778--14783}, -} - -@article{sanso_nonstationary_2000, - title = {A nonstationary multisite model for rainfall}, - volume = {95}, - abstract = {Estimation and prediction of the amount of rainfall in time and space is a problem of fundamental importance in many applications in agriculture, hydrology, and ecology. Stochastic simulation of rainfall data is also an important step in the development of stochastic downscaling: methods where large- scale climate information is considered as an additional explanatory variable of rainfall behavior at the local scale. Simulated rainfall has also been used as input data for many agricultural, hydrological, and ecological models, especially when rainfall measurements are not available for locations of interest or when historical records are not of sufficient length to evaluate important rainfall characteristics as extreme values. Rainfall estimation and prediction were carried out for an agricultural region of Venezuela in the central plains state of Guarico, where rainfall for 10-day periods is available for 80 different locations. The measurement network is relatively sparse for some areas, and aggregated rainfall at time resolutions of days or less is of very poor quality or nonexistent. We consider a model for rainfall based on a truncated normal distribution that has been proposed in the literature. We assume that the data y(it), where i indexes location and t indexes time, correspond to normal random variates w(it) that have been truncated and transformed. According to this model, the dry periods correspond to the (unobserved) negative values and the wet periods correspond to a transformation of the positive ones. The serial structure present in series of rainfall data can be modeled by considering a stochastic process for w(it) We use a dynamic linear model on w(t) = (w(1t),...,w(Nt)) that includes a Fourier representation to allow for the seasonality of the data that is assumed to be the same for all sites, plus a linear combination of functions of the location of each site. This approach captures year-to-year variability and provides a tool for short-term forecasting. The model is fitted using a Markov chain Monte Carlo method that uses latent variables to handle dry periods and missing values.}, - number = {452}, - journal = {Journal of the American Statistical Association}, - author = {Sanso, B. and Guenni, L.}, - month = dec, - year = {2000}, - pages = {1089--1100}, - annote = {376RJJ AMER STATIST ASSN}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Am. Stat. Assoc.accession-num: ISI:000165470300007}, -} - -@article{sankarasubramanian_climate_2001, - title = {Climate elasticity of streamflow in the {United} {States}}, - volume = {37}, - issn = {1944-7973}, - doi = {10.1029/2000wr900330}, - number = {6}, - journal = {Water Resources Research}, - author = {Sankarasubramanian, A. and Vogel, R. M. and Limbrunner, J. F.}, - year = {2001}, - keywords = {1833 Hydroclimatology, 1860 Streamflow, 1836 Hydrological cycles and budgets, 3322 Land/atmosphere interactions}, - pages = {1771--1781}, -} - -@article{sankarasubramanian_hydroclimatology_2003, - title = {Hydroclimatology of the continental {United} {States}}, - volume = {30}, - number = {7}, - journal = {Geophysical Research Letters}, - author = {Sankarasubramanian, A. and Vogel, R. M.}, - year = {2003}, - pages = {1363, doi:10.1029/2002GL015937}, -} - -@article{sanderson_towards_2007, - title = {Towards constraining climate sensitivity by linear analysis of feedback patterns in thousands of perturbed-physics {GCM} {Simulations}}, - volume = {30}, - number = {2-3}, - journal = {Climate Dynamics}, - author = {Sanderson, B. M. and Piani, C. and Ingram, W. J. and Stone, D. S. and Allen, M. R.}, - year = {2007}, - pages = {175--190, DOI: 10.1007/s00382--007--0280--7}, -} - -@article{salathe_estimates_2014, - title = {Estimates of {Twenty}-{First}-{Century} {Flood} {Risk} in the {Pacific} {Northwest} {Based} on {Regional} {Climate} {Model} {Simulations}}, - volume = {15}, - issn = {1525-755X}, - doi = {10.1175/jhm-d-13-0137.1}, - abstract = {AbstractResults from a regional climate model simulation show substantial increases in future flood risk (2040?69) in many Pacific Northwest river basins in the early fall. Two primary causes are identified: 1) more extreme and earlier storms and 2) warming temperatures that shift precipitation from snow to rain dominance over regional terrain. The simulations also show a wide range of uncertainty among different basins stemming from localized storm characteristics. While previous research using statistical downscaling suggests that many areas in the Pacific Northwest are likely to experience substantial increases in flooding in response to global climate change, these initial estimates do not adequately represent the effects of changes in heavy precipitation. Unlike statistical downscaling techniques applied to global climate model scenarios, the regional model provides an explicit, physically based simulation of the seasonality, size, location, and intensity of historical and future extreme storms, including atmospheric rivers. This paper presents climate projections from the ECHAM5/Max Planck Institute Ocean Model (MPI-OM) global climate model dynamically downscaled using the Weather Research and Forecasting (WRF) Model implemented at 12-km resolution for the period 1970?2069. The resulting daily precipitation and temperature data are bias corrected and used as input to a physically based Variable Infiltration Capacity (VIC) hydrologic model. From the daily time step simulations of streamflow produced by the hydrologic model, probability distributions are fit to the extreme events extracted from each water year and flood statistics for various return intervals are estimated.}, - number = {5}, - journal = {Journal of Hydrometeorology}, - author = {Salathé, Eric P. and Hamlet, Alan F. and Mass, Clifford F. and Lee, Se-Yeun and Stumbaugh, Matt and Steed, Richard}, - month = oct, - year = {2014}, - pages = {1881--1899}, - annote = {doi: 10.1175/JHM-D-13-0137.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/JHM-D-13-0137.1}, -} - -@article{sanderson_interpretation_2012, - title = {On the interpretation of constrained climate model ensembles}, - volume = {39}, - issn = {0094-8276}, - doi = {10.1029/2012gl052665}, - abstract = {An ensemble of models can be interpreted in two ways. The first treats each model as an approximation of the true system with some random error. Alternatively, the true system can be interpreted as a sample drawn from a distribution of models, such that model and truth are statistically indistinguishable. Both interpretations are ubiquitous and have different consequences for the uncertainty of model projections, but are rarely defended. Here we argue that the two seemingly conflicting views are in fact complementary, and the interpretation of the ensemble may evolve seamlessly from the former to the latter. We show some \&\#8216;truth plus error\&\#8217; like properties exist for historical and present day climate simulations in the CMIP archive, and that they can be explained by the ensemble design and tuning to observations, although both models and tuning are imperfect. For future projections, structural differences in model response arise which are independent of the present day state and thus the \&\#8216;indistinguishable\&\#8217; interpretation is increasingly favored. Our inability to define performance metrics that identify \&\#8216;good\&\#8217; and \&\#8216;bad\&\#8217; models can be explained by the models having largely exploited the available observations. The remaining model error is largely structural and the observations are often uninformative to further reduce model biases or reduce the range of projections covered by the ensemble. The discussion here is motivated by the use of multi model ensembles in climate projections, but the arguments are generic to any situation where multiple different models constrained by observations are used to describe the same system.}, - number = {16}, - journal = {Geophysical Research Letters}, - author = {Sanderson, Benjamin M. and Knutti, Reto}, - year = {2012}, - keywords = {ensembles, uncertainty, 1626 Global Change: Global climate models (3337, 4928), 3275 Mathematical Geophysics: Uncertainty quantification (1873, 1990), 3333 Atmospheric Processes: Model calibration (1846), CMIP}, - pages = {L16708}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{salathe_review_2007, - title = {Review of scenario selection and downscaling methods for the assessment of climate change impacts on hydrology in the {United} {States} pacific northwest}, - volume = {27}, - issn = {1097-0088}, - number = {12}, - journal = {International Journal of Climatology}, - author = {Salathé, E. P. and Mote, P. W. and Wiley, M. W.}, - year = {2007}, - pages = {1611--1621}, - annote = {The following values have no corresponding Zotero field:auth-address: Climate Impacts Group, Center for Science in the Earth System, University of Washington, USA; 3TIER Environmental Forecast Group, Seattle, Washington, USAcustom1: 10.1002/joc.1540}, -} - -@article{salathe_downscaling_2005, - title = {Downscaling simulations of future global climate with application to hydrologic modelling}, - volume = {25}, - journal = {International Journal of Climatology}, - author = {Salathé, E. P.}, - year = {2005}, - pages = {419--436}, -} - -@article{salathe_comparison_2003, - title = {Comparison of various precipitation downscaling methods for the simulation of streamflow in a rainshadow river basin}, - volume = {23}, - journal = {International Journal of Climatology}, - author = {Salathé, E. P.}, - year = {2003}, - pages = {887--901}, -} - -@article{sakaguchi_hindcast_2012, - title = {The hindcast skill of the {CMIP} ensembles for the surface air temperature trend}, - volume = {117}, - issn = {0148-0227}, - doi = {10.1029/2012jd017765}, - abstract = {Linear trends of the surface air temperature (SAT) simulated by selected models from the Coupled Model Intercomparison Project (CMIP3 and CMIP5) historical experiments are evaluated using observations to document (1) the expected range and characteristics of the errors in hindcasting the \&\#8216;change\&\#8217; in SAT at different spatiotemporal scales, (2) if there are \&\#8216;threshold\&\#8217; spatiotemporal scales across which the models show substantially improved performance, and (3) how they differ between CMIP3 and CMIP5. Root Mean Square Error, linear correlation, and Brier score show better agreement with the observations as spatiotemporal scale increases but the skill for the regional (5° × 5° \&\#8211; 20° × 20° grid) and decadal (10 \&\#8211; \&\#8764;30-year trends) scales is rather limited. Rapid improvements are seen across 30° × 30° grid to zonal average and around 30 years, although they depend on the performance statistics. Rather abrupt change in the performance from 30° × 30° grid to zonal average implies that averaging out longitudinal features, such as land-ocean contrast, might significantly improve the reliability of the simulated SAT trend. The mean bias and ensemble spread relative to the observed variability, which are crucial to the reliability of the ensemble distribution, are not necessarily improved with increasing scales and may impact probabilistic predictions more at longer temporal scales. No significant differences are found in the performance of CMIP3 and CMIP5 at the large spatiotemporal scales, but at smaller scales the CMIP5 ensemble often shows better correlation and Brier score, indicating improvements in the CMIP5 on the temporal dynamics of SAT at regional and decadal scales.}, - number = {D16}, - journal = {J. Geophys. Res.}, - author = {Sakaguchi, Koichi and Zeng, Xubin and Brunke, Michael A.}, - year = {2012}, - keywords = {1610 Global Change: Atmosphere (0315, 0325), 1627 Global Change: Coupled models of the climate system, 3305 Atmospheric Processes: Climate change and variability (1616, 1635, 3309, 4215, 4513), 3337 Atmospheric Processes: Global climate models (1626, 4928), CMIP3, CMIP5, spatiotemporal scale, surface air temperature trend}, - pages = {D16113}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{salas-melia_description_2006, - title = {Description and validation of the {CNRM}-{CM3} global coupled model}, - journal = {Climate Dynamics}, - author = {Salas-Mélia, D. and Chauvin, F. and Déqué, M. and Douville, H. and Gueremy, J. F. and Marquet, P. and Planton, S. and Royer, J. F. and Tyteca, S.}, - year = {2006}, -} - -@article{salas_special_2012, - title = {Special {Section} on {Climate} {Change} and {Water} {Resources}: {Climate} {Nonstationarity} and {Water} {Resources} {Management}}, - volume = {138}, - issn = {0733-9496}, - doi = {10.1061/(asce)wr.1943-5452.0000279}, - number = {5}, - journal = {Journal of Water Resources Planning and Management}, - author = {Salas, J. and Rajagopalan, B. and Saito, L. and Brown, C.}, - month = sep, - year = {2012}, - pages = {385--388}, - annote = {doi: 10.1061/(ASCE)WR.1943-5452.0000279}, - annote = {The following values have no corresponding Zotero field:publisher: American Society of Civil Engineerswork-type: doi: 10.1061/(ASCE)WR.1943-5452.0000279}, -} - -@article{sailor_neural_2000, - title = {A neural network approach to local downscaling of {GCM} output for assessing wind power implications of climate change}, - volume = {19}, - abstract = {A methodology is presented for downscaling General Circulation Model (GCM) output to predict surface wind speeds at scales of interest in the wind power industry under expected future climatic conditions. The approach involves a combination of Neural Network tools and traditional weather forecasting techniques. A Neural Network transfer function is developed to relate local wind speed observations to large scale GCM predictions of atmospheric properties under current climatic conditions. By assuming the invariability of this transfer function under conditions of doubled atmospheric carbon dioxide, the resulting transfer function is then applied to GCM output for a transient run of the National Center for Atmospheric Research coupled ocean-atmosphere GCM. This methodology is applied to three test sites in regions relevant to the wind power industry-one in Texas and two in California. Changes in daily mean wind speeds at each location are presented and discussed with respect to potential implications for wind power generation. (C) 1999 Elsevier Science Ltd. All rights reserved.}, - number = {3}, - journal = {Renewable Energy}, - author = {Sailor, D. J. and Hu, T. and Li, X. and Rosen, J. N.}, - month = mar, - year = {2000}, - pages = {359--378}, - annote = {250VMRENEWABLE ENERGY}, - annote = {The following values have no corresponding Zotero field:alt-title: Renew. Energyaccession-num: ISI:000083409500002}, -} - -@article{russell_response_1999, - title = {Response to {CO2} transient increase in the {GISS} coupled model: {Regional} coolings in a warming climate}, - volume = {12}, - issn = {0894-8755}, - abstract = {The GISS coupled atmosphere-ocean model is used to investigate the effect of increased atmospheric CO2 by comparing a compounded 1\% CO2 increase experiment with a control simulation. After 70 yr of integration, the global surface air temperature in the 1\% CO2 experiment is 1.43 degrees C warmer. In spite of this global warming, there are two distinct regions, the northern Atlantic Ocean and the southern Pacific Ocean, where the surface air temperature is up to 4 degrees C cooler. This situation is maintained by two positive feedbacks: a local effect on convection in the South Pacific and a nonlocal impact on the meridional circulation in the North Atlantic. The poleward transport of latent energy and dry static energy by the atmosphere is greater in the 1\% CO2 experiment, caused by warming and therefore increased water vapor and greater greenhouse capacity at lower latitudes. The larger atmospheric transports tend to reduce upward vertical fluxes of heat and moisture from the ocean surface at high latitudes, which has the effect of stabilizing the ocean, reducing both convection and the thermohaline circulation. With less convection, less warm water is brought up from below, and with a reduced North Atlantic thermohaline circulation (by 30\% at time of CO2 doubling), the poleward energy transport by the oceans decreases. The colder water then leads to further reductions in evaporation, decreases of salinity at high latitudes, continued stabilization of the ocean, and maintenance of reduced convection and meridional overturning. Although sea ice decreases globally, it increases in the cooling regions, which reduces the overall climate sensitivity, especially in the Southern Hemisphere. Tropical warming has been observed over the past several decades; if modeling studies such as this and others that have produced similar effects are valid, these processes may already be beginning.}, - language = {English}, - number = {2}, - journal = {Journal of Climate}, - author = {Russell, G. L. and Rind, D.}, - month = feb, - year = {1999}, - keywords = {variability, ocean-atmosphere model, aogcm, mean response, ice, sea-surface temperature}, - pages = {531--539}, - annote = {174LYTimes Cited:27Cited References Count:29}, - annote = {The following values have no corresponding Zotero field:auth-address: Russell, GL NASA, Goddard Inst Space Studies, Goddard Space Flight Ctr, 2880 Broadway, New York, NY 10025 USA NASA, Goddard Inst Space Studies, Goddard Space Flight Ctr, New York, NY 10025 USAaccession-num: ISI:000079037700015}, -} - -@article{russell_comparison_2000, - title = {Comparison of model and observed regional temperature changes during the past 40 years}, - volume = {105}, - issn = {0747-7309}, - abstract = {Results are presented fur six simulations of the Goddard Institute for Space Studies (GISS) global atmosphere-ocean model for the years 1950-2099. There are two control simulations with constant 1950 atmospheric composition from different initial states, two greenhouse gas (GHG) experiments with observed greenhouse gases Lip to 1990 and compounded 0.5\% CO2 annual increases thereafter, and two greenhouse gas plus sulfate (GHG + SO4) experiments with the same varying greenhouse gases plus varying tropospheric sulfate aerosols. Surface air temperature trends in the two GHG experiments (with tl-le control simulations climate drift subtracted out) are compared between themselves and with changes in the observed temperature record between 1960 and 1998. All comparisons show high positive spatial correlation in the Northern Hemisphere except in summer when the greenhouse signal is weakest. The GHG + SO4 experiments show weaker correlations. In the Southern Hemisphere, correlations between any experiments ol observations are either weak or negative, in part because the model's interannual variability of southern sea ice cover is unrealistic. The model results imply that temperature changes due to forcing by increased greenhouse gases have risen above the level of regional interannual temperature variability in the Northern Hemisphere over the past 40 years. This period is thus an important test of the reliability of coupled climate models.}, - language = {English}, - number = {D11}, - journal = {Journal of Geophysical Research-Atmospheres}, - author = {Russell, G. L. and Miller, J. R. and Rind, D. and Ruedy, R. A. and Schmidt, G. A. and Sheth, S.}, - month = jun, - year = {2000}, - keywords = {climate-change, surface}, - pages = {14891--14898}, - annote = {326LBTimes Cited:21Cited References Count:23}, - annote = {The following values have no corresponding Zotero field:auth-address: Russell, GL NASA, Goddard Inst Space Studies, 2880 Broadway, New York, NY 10025 USA NASA, Goddard Inst Space Studies, New York, NY 10025 USAaccession-num: ISI:000087736000039}, -} - -@article{rubio-alvarez_patterns_2010, - title = {Patterns of spatial and temporal variability in streamflow records in south central {Chile} in the period 1952–2003}, - volume = {46}, - journal = {Water Resources Research}, - author = {Rubio-Álvarez, E. and McPhee, J.}, - year = {2010}, - pages = {W05514, doi:10.1029/2009WR007982}, -} - -@article{ryu_interannual_2008, - title = {Interannual variability of evapotranspiration and energy exchange over an annual grassland in {California}}, - volume = {113}, - journal = {Journal of Geophysical Research}, - author = {Ryu, Y. and Baldocchi, D. D. and Ma, S. and Hehn, T.}, - year = {2008}, - pages = {D09104, doi:10.1029/2007JD009263}, -} - -@article{roy_projecting_2012, - title = {Projecting {Water} {Withdrawal} and {Supply} for {Future} {Decades} in the {U}.{S}. under {Climate} {Change} {Scenarios}}, - volume = {46}, - issn = {0013-936X}, - doi = {10.1021/es2030774}, - number = {5}, - journal = {Environmental Science \& Technology}, - author = {Roy, Sujoy B. and Chen, Limin and Girvetz, Evan H. and Maurer, Edwin P. and Mills, William B. and Grieb, Thomas M.}, - year = {2012}, - pages = {2545--2556}, - annote = {doi: 10.1021/es2030774}, - annote = {The following values have no corresponding Zotero field:publisher: American Chemical Societywork-type: doi: 10.1021/es2030774}, -} - -@article{rossel_spatial_2001, - title = {Spatial variability and downscalling of precipitation}, - volume = {26}, - abstract = {To fully take advantage of regional climate forecast information for agricultural applications, the relationship between regional and station scale precipitation characteristics must be quantified. The spatial variability of precipitation within a region is defined by differences between station and regional standardised values. The spatial variability is quantified at the monthly, seasonal and yearly time scales for the central Oklahoma "super climate division" and the four "climate divisions" included in. For the super climate division, the standard deviation of the differences between station and regional standardised values ranges from minimum values during the winter around 0,55 to maximum values during the summer around 0,80. The decrease of the spatial variability associated with the increase of the time scale is of about 35\% from month to season, of about 45\% from season to year. Whereas the decrease of spatial variability associated with the decrease in space scale is of about 35\% from super- climate division to climate division. An analysis of the distribution of the differences allows to define confidence envelope for local precipitation around the regional value. This study clearly demonstrates the critical influence of the spatial variability of precipitation and the additional uncertainty it introduces in the downscaling of regional information to local applications. (C) 2001 Elsevier Science Ltd. All rights reserved.}, - number = {11-12}, - journal = {Physics and Chemistry of the Earth Part B-Hydrology Oceans and Atmosphere}, - author = {Rossel, F. and Garbrecht, J.}, - year = {2001}, - pages = {863--867}, - annote = {488VFPHYS CHEM EARTH P B-HYDROL OC}, - annote = {The following values have no corresponding Zotero field:alt-title: Phys. Chem. Earth Pt B-Hydrol. Oceans Atmos.accession-num: ISI:000171956200003}, -} - -@techreport{ross_nino_1998, - address = {Asheville, NC}, - type = {{NCDC} {Technical} {Report}}, - title = {The {El} {Niño} winter of '97-'98}, - number = {98-02}, - institution = {National Climatic Data Center}, - author = {Ross, T. and Lott, N. and McCown, S. and Quinn, D.}, - year = {1998}, - pages = {12}, -} - -@article{rosenberg_possible_1999, - title = {Possible {Impacts} of {Global} {Warming} on the {Hydrology} of the {Ogallala} {Aquifer} {Region}}, - volume = {42}, - issn = {0165-0009}, - doi = {10.1023/a:1005424003553}, - number = {4}, - journal = {Climatic Change}, - author = {Rosenberg, Norman J. and Epstein, Daniel J. and Wang, David and Vail, Lance and Srinivasan, Raghavan and Arnold, Jeffrey G.}, - year = {1999}, - keywords = {Earth and Environmental Science}, - pages = {677--692}, - annote = {The following values have no corresponding Zotero field:publisher: Springer Netherlands}, -} - -@article{rosenberg_precipitation_2010, - title = {Precipitation extremes and the impacts of climate change on stormwater infrastructure in {Washington} {State}}, - volume = {102}, - issn = {0165-0009}, - doi = {10.1007/s10584-010-9847-0}, - language = {English}, - number = {1-2}, - journal = {Climatic Change}, - author = {Rosenberg, EricA and Keys, PatrickW and Booth, DerekB and Hartley, David and Burkey, Jeff and Steinemann, AnneC and Lettenmaier, DennisP}, - month = sep, - year = {2010}, - pages = {319--349}, - annote = {The following values have no corresponding Zotero field:alt-title: Climatic Changepublisher: Springer Netherlands}, -} - -@article{rood_flood_2016, - title = {Flood moderation: {Declining} peak flows along some {Rocky} {Mountain} rivers and the underlying mechanism}, - volume = {536}, - issn = {0022-1694}, - doi = {http://dx.doi.org/10.1016/j.jhydrol.2016.02.043}, - journal = {Journal of Hydrology}, - author = {Rood, Stewart B. and Foster, Stephen G. and Hillman, Evan J. and Luek, Andreas and Zanewich, Karen P.}, - year = {2016}, - keywords = {Climate change, North America, Historic hydrology, Hydrographic apex, River floods}, - pages = {174--182}, -} - -@article{rogelj_global_2012, - title = {Global warming under old and new scenarios using {IPCC} climate sensitivity range estimates}, - volume = {2}, - issn = {1758-678X}, - doi = {http://www.nature.com/nclimate/journal/v2/n4/abs/nclimate1385.html#supplementary-information}, - number = {4}, - journal = {Nature Clim. Change}, - author = {Rogelj, Joeri and Meinshausen, Malte and Knutti, Reto}, - year = {2012}, - pages = {248--253}, - annote = {10.1038/nclimate1385}, - annote = {The following values have no corresponding Zotero field:publisher: Nature Publishing Groupwork-type: 10.1038/nclimate1385}, -} - -@article{roe_why_2007, - title = {Why is climate sensitivity so unpredictable?}, - volume = {318}, - issn = {0036-8075}, - doi = {10.1126/science.1144735}, - abstract = {Uncertainties in projections of future climate change have not lessened substantially in past decades. Both models and observations yield broad probability distributions for long-term increases in global mean temperature expected from the doubling of atmospheric carbon dioxide, with small but finite probabilities of very large increases. We show that the shape of these probability distributions is an inevitable and general consequence of the nature of the climate system, and we derive a simple analytic form for the shape that fits recent published distributions very well. We show that the breadth of the distribution and, in particular, the probability of large temperature increases are relatively insensitive to decreases in uncertainties associated with the underlying climate processes.}, - language = {English}, - number = {5850}, - journal = {Science}, - author = {Roe, G. H. and Baker, M. B.}, - month = oct, - year = {2007}, - keywords = {ensemble, simulations, feedbacks, uncertainties, models, earth}, - pages = {629--632}, - annote = {ISI Document Delivery No.: 223XLTimes Cited: 76Cited Reference Count: 23Roe, Gerard H. Baker, Marcia B.Amer assoc advancement scienceWashington}, - annote = {The following values have no corresponding Zotero field:auth-address: Univ Washington, Dept Earth \& Space Sci, Seattle, WA 98195 USA. Roe, GH, Univ Washington, Dept Earth \& Space Sci, Seattle, WA 98195 USA. gerard@ess.washington.edualt-title: Scienceaccession-num: ISI:000250409200042work-type: Article}, -} - -@article{rodriguez-puebla_winter_2001, - title = {Winter precipitation over the {Iberian} peninsula and its relationship to circulation indices}, - volume = {5}, - abstract = {Winter precipitation variability over the Iberian peninsula was investigated by obtaining the spatial and temporal patterns. Empirical Orthogonal Functions were used to describe the variance distribution and to compress the precipitation data into a few modes. The corresponding spatial patterns divide the peninsula into climatic regions according to precipitation variations. The associated time series were related to large scale circulation indices and tropical sea surface temperature anomalies by using lag cross-correlation and cross-spectrum. The major findings are: the most influential indices for winter precipitation were the North Atlantic Oscillation and the East Atlantic/West Russian pattern. coherent oscillations were detected at about eight years between precipitation and the North Atlantic Oscillation and some dynamic consequences of the circulation on precipitation over the Iberian peninsula were examined during drought and wet spells. In the end statistical methods have been proposed to downscale seasonal precipitation prediction.}, - number = {2}, - journal = {Hydrol. Earth Syst. Sci.}, - author = {Rodriguez-Puebla, C. and Encinas, A. H. and Saenz, J.}, - month = jun, - year = {2001}, - pages = {233--244}, - annote = {SI469AHHYDROL EARTH SYST SCI}, - annote = {The following values have no corresponding Zotero field:alt-title: Hydrol. Earth Syst. Sci.accession-num: ISI:000170790000010}, -} - -@article{rind_change_1989, - title = {Change in climate variability in the 21st century}, - volume = {14}, - issn = {1573-1480}, - doi = {10.1007/bf00140173}, - abstract = {As climate changes due to the increase of greenhouse gases, there is the potential for climate variability to change as well. The change in variability of temperature and precipitation in a transient climate simulation, where trace gases are allowed to increase gradually, and in the doubled CO2 climate is investigated using the GISS general circulation model. The current climate control run is compared with observations and with the climate change simulations for variability on three time-scales: interannual variability, daily variability, and the amplitude of the diurnal cycle. The results show that the modeled variability is often larger than observed, especially in late summer, possibly due to the crude ground hydrology. In the warmer climates, temperature variability and the diurnal cycle amplitude usually decrease, in conjunction with a decrease in the latitudinal temperature gradient and the increased greenhouse inhibition of radiative cooling. Precipitation variability generally changes with the same sign as the mean precipitation itself, usually increasing in the warmer climate. Changes at a particular grid box are often not significant, with the prevailing tendency determined from a broader sampling. Little change is seen in daily persistence. The results are relevant to the continuing assessments of climate change impacts on society, though their use should be tempered by appreciation of the model deficiencies for the current climate.}, - number = {1}, - journal = {Climatic Change}, - author = {Rind, D. and Goldberg, R. and Ruedy, R.}, - year = {1989}, - pages = {5--37}, - annote = {The following values have no corresponding Zotero field:label: ref1work-type: journal article}, -} - -@article{reichler_how_2008, - title = {How {Well} {Do} {Coupled} {Models} {Simulate} {Today}'s {Climate}?}, - volume = {89}, - number = {3}, - journal = {Bulletin of the American Meteorological Society}, - author = {Reichler, T. and Kim, J.}, - year = {2008}, - pages = {303--311, doi:10.1175/BAMS--89--3--303}, -} - -@article{eytan_rocheta_can_2017, - title = {Can {Bias} {Correction} of {Regional} {Climate} {Model} {Lateral} {Boundary} {Conditions} {Improve} {Low}-{Frequency} {Rainfall} {Variability}?}, - volume = {30}, - doi = {10.1175/jcli-d-16-0654.1}, - abstract = {AbstractGlobal climate model simulations inherently contain multiple biases that, when used as boundary conditions for regional climate models, have the potential to produce poor downscaled simulations. Removing these biases before downscaling can potentially improve regional climate change impact assessment. In particular, reducing the low-frequency variability biases in atmospheric variables as well as modeled rainfall is important for hydrological impact assessment, predominantly for the improved simulation of floods and droughts. The impact of this bias in the lateral boundary conditions driving the dynamical downscaling has not been explored before. Here the use of three approaches for correcting the lateral boundary biases including mean, variance, and modification of sample moments through the use of a nested bias correction (NBC) method that corrects for low-frequency variability bias is investigated. These corrections are implemented at the 6-hourly time scale on the global climate model simulations to drive a regional climate model over the Australian Coordinated Regional Climate Downscaling Experiment (CORDEX) domain. The results show that the most substantial improvement in low-frequency variability after bias correction is obtained from modifying the mean field, with smaller changes attributed to the variance. Explicitly modifying monthly and annual lag-1 autocorrelations through NBC does not substantially improve low-frequency variability attributes of simulated precipitation in the regional model over a simpler mean bias correction. These results raise questions about the nature of bias correction techniques that are required to successfully gain improvement in regional climate model simulations and show that more complicated techniques do not necessarily lead to more skillful simulation.}, - number = {24}, - journal = {Journal of Climate}, - author = {Eytan Rocheta and Jason P. Evans and Ashish Sharma}, - year = {2017}, - keywords = {Rainfall,Bias,Regional models,Interannual variability}, - pages = {9785--9806}, -} - -@article{robertson_large-scale_1999, - title = {Large-scale weather regimes and local climate over the {Western} {United} {States}}, - volume = {12}, - journal = {Journal of Climate}, - author = {Robertson, A. W. and Ghil, M.}, - year = {1999}, - pages = {1796--1813}, -} - -@incollection{m_rhein_observations_2013, - address = {Cambridge, United Kingdom and New York, NY, USA}, - title = {Observations: {Ocean}}, - isbn = {ISBN 978-1-107-66182-0}, - booktitle = {Climate {Change} 2013: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fifth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {M. Rhein and S. R. Rintoul and S. Aoki and E. Campos and D. Chambers and R. A. Feely and S. Gulev and G. C. Johnson and S. A. Josey and A. Kostianoy and C. Mauritzen and D. Roemmich and L. D. Talley and F. Wang}, - editor = {T. F. Stocker and D. Qin and G. -K. Plattner and M. Tignor and S. K. Allen and J. Boschung and A. Nauels and Y. Xia and V. Bex and P. M. Midgley}, - year = {2013}, - pages = {255--316}, - annote = {The following values have no corresponding Zotero field:section: 3electronic-resource-num: 10.1017/CBO9781107415324.010}, -} - -@article{reifen_climate_2009, - title = {Climate projections: {Past} performance no guarantee of future skill?}, - volume = {36}, - issn = {0094-8276}, - doi = {10.1029/2009gl038082}, - abstract = {The principle of selecting climate models based on their agreement with observations has been tested for surface temperature using 17 of the IPCC AR4 models. Those models simulating global mean, Siberian and European 20th Century surface temperature with a lower error than the total ensemble for one period on average do not do so for a subsequent period. Error in the ensemble mean decreases systematically with ensemble size, N, and for a random selection as approximately 1/N \&\#945; , where \&\#945; lies between 0.6 and 1. This is larger than the exponent of a random sample (\&\#945; = 0.5) and appears to be an indicator of systematic bias in the model simulations. There is no evidence that any subset of models delivers significant improvement in prediction accuracy compared to the total ensemble.}, - number = {13}, - journal = {Geophysical Research Letters}, - author = {Reifen, C. and Toumi, R.}, - year = {2009}, - keywords = {1637 Global Change: Regional climate change, 3305 Atmospheric Processes: Climate change and variability, multi-model ensemble, 3337 Atmospheric Processes: Global climate models, climate model selection, model skill}, - pages = {L13704}, - annote = {The following values have no corresponding Zotero field:publisher: AGU}, -} - -@article{reichert_statistical_1999, - title = {A statistical modeling approach for the simulation of local paleoclimatic proxy records using general circulation model output}, - volume = {104}, - abstract = {A statistical modeling approach is proposed for the simulation of local paleoclimatic proxy records using general circulation model (GCM) output, A method for model-consistent statistical downscaling to local weather conditions is developed which can be used as input for process-based proxy models in order to investigate to what extent climate variability obtained from proxy data can be represented by a GCM, and whether, for example, the response of glaciers to climatic change can be reproduced. Downscaling is based on a multiple linear forward regression model using daily sets of operational weather station data and large-scale predictors at various pressure levels obtained from reanalyses of the European Centre for Medium-Range Weather Forecasts, Composition and relative impact of predictors vary significantly for individual. stations within the area of investigation. Owing to a strong dependence on individual synoptic-scale patterns, daily data give the highest performance which can be further increased by developing seasonal-specific relationships. The model is applied to a long integration of a GCM coupled to a mixed layer ocean (ECHAM4/MLO) simulating present-day and preindustrial climate variability. Patterns of variability are realistically simulated compared to observed station data within an area of Norway for the period 1868-1993.}, - number = {D16}, - journal = {Journal of Geophysical Research-Atmospheres}, - author = {Reichert, B. K. and Bengtsson, L. and Akesson, O.}, - month = aug, - year = {1999}, - pages = {19071--19083}, - annote = {230CYJ GEOPHYS RES-ATMOS}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Geophys. Res.-Atmos.accession-num: ISI:000082235300005}, -} - -@article{regonda_seasonal_2005, - title = {Seasonal {Cycle} {Shifts} in {Hydroclimatology} over the {Western} {United} {States}}, - volume = {18}, - number = {2}, - journal = {Journal of Climate}, - author = {Regonda, S. K. and Rajagopalan, B. and Clark, M. and Pitlick, J.}, - year = {2005}, - pages = {372--384}, -} - -@article{rauscher_extension_2008, - title = {Extension and intensification of the {Meso}-{American} mid-summer drought in the twenty-first century}, - volume = {31}, - number = {5}, - journal = {Climate Dynamics}, - author = {Rauscher, S. A. and Giorgi, F. and Diffenbaugh, N. S. and Seth, A.}, - year = {2008}, - pages = {551--571, doi:10.1007/s00382--007--0359--1}, -} - -@article{reddy_predicting_2000, - title = {Predicting crop yields under climate change conditions from monthly {GCM} weather projections}, - volume = {15}, - abstract = {Estimation of changes in crop yields is currently based on projections of atmospheric General Circulation Models (GCM) and the use of crop simulators. Crop simulators require daily input of environmental variables. GCMs produce monthly projections of climatic variables. Our objective was to explore the possibility of using monthly weather projections in yield estimates. We considered atmospheric CO2 level, total solar radiation, average maximum and minimum temperature, and rainfall for five months of the growing season. The group method of data handling (GMDH) was applied to relate crop yields to these variables. Projections of GCMs were downscaled to provide daily weather variables for the Mississippi Delta, and weather patterns were obtained in 50 replications for each GCM. The soybean crop simulator GLYCIM was used to generate crop yields on sandy loam, loam and silt loam soils. The equations built with GMDH explained 81-85\% of yield variability, acid included solar radiation in July and August, CO2 level, minimum temperature in June and August, and rainfall in August. Published by Elsevier Science Ltd.}, - number = {1}, - journal = {Environmental Modelling \& Software}, - author = {Reddy, V. R. and Pachepsky, Y. A.}, - year = {2000}, - pages = {79--86}, - annote = {267MKENVIRON MODELL SOFTW}, - annote = {The following values have no corresponding Zotero field:alt-title: Environ. Modell. Softw.accession-num: ISI:000084361600007}, -} - -@techreport{reclamation_colorado_2012, - address = {Denver, Colorado, USA}, - title = {Colorado {River} {Basin} {Water} {Supply} and {Demand} {Study}}, - institution = {U.S. Department of Interior, Bureau of Reclamation}, - author = {Reclamation}, - year = {2012}, - pages = {99}, -} - -@techreport{reclamation_west-wide_2011, - address = {Denver, Colorado}, - title = {West-{Wide} {Climate} {Risk} {Assessments}: {Bias}-{Corrected} and {Spatially} {Downscaled} {Surface} {Water} {Projections}, {Technical} {Memorandum} {No}. 86-68210–2011-01}, - institution = {U.S. Dept. of Interior – Bureau of Reclamation, Technical Service Center}, - author = {Reclamation}, - year = {2011}, - pages = {138}, -} - -@article{reardon_rural_2001, - title = {Rural {Nonfarm} {Employment} and {Incomes} in {Latin} {America}: {Overview} and {Policy} {Implications}}, - volume = {29}, - issn = {0305-750X}, - doi = {http://dx.doi.org/10.1016/S0305-750X(00)00112-1}, - number = {3}, - journal = {World Development}, - author = {Reardon, Thomas and Berdegué, Julio and Escobar, Germán}, - year = {2001}, - keywords = {Latin America, rural development, rural nonfarm employment and incomes}, - pages = {395--409}, -} - -@techreport{reclamation_downscaled_2016, - address = {Denver, Colorado, USA}, - title = {Downscaled {CMIP3} and {CMIP5} {Climate} {Projections} - {Addendum}, {Release} of {Downscaled} {CMIP5} {Climate} {Projections} ({LOCA}) and {Comparison} with {Preceding} {Information}}, - institution = {U.S. Department of the Interior, Bureau of Reclamation, Technical Service Center}, - author = {Reclamation}, - month = jul, - year = {2016}, - pages = {34}, -} - -@techreport{reclamation_downscaled_2014, - address = {Denver, Colorado, USA}, - title = {Downscaled {CMIP3} and {CMIP5} {Hydrology} {Projections}: {Release} of {Hydrology} {Projections}, {Comparison} with {Preceding} {Information}, and {Summary} of {User} {Needs}}, - institution = {U.S. Department of the Interior, Bureau of Reclamation, Technical Service Center}, - author = {Reclamation}, - month = jul, - year = {2014}, - pages = {111, available at http://gdo--dcp.ucllnl.org/downscaled\_cmip\_projections/techmemo/BCSD5HydrologyMemo.pdf}, -} - -@techreport{reclamation_downscaled_2013, - address = {Denver, Colorado, USA}, - title = {Downscaled {CMIP3} and {CMIP5} {Climate} {Projections}: {Release} of {Downscaled} {CMIP5} {Climate} {Projections}, {Comparison} with {Preceding} {Information}, and {Summary} of {User} {Needs}}, - institution = {U.S. Department of the Interior, Bureau of Reclamation, Technical Service Center}, - author = {Reclamation}, - year = {2013}, - pages = {116, available at: http://gdodcp.ucllnl.org/downscaled\_cmip\_projections/techmemo/downscaled\_climate.pdf}, -} - -@incollection{reclamation_sensitivity_2008, - address = {Sacramento, California, USA}, - title = {Sensitivity of {Future} {CVP}/{SWP} {Operations} to {Potential} {Climate} {Change} and {Associated} {Sea} {Level} {Rise}, {Appendix} {R}}, - booktitle = {Sensitivity of {Future} {CVP}/{SWP} {Operations} to {Potential} {Climate} {Change} and {Associated} {Sea} {Level} {Rise}}, - publisher = {U.S. Department of Interior, Bureau of Reclamation, Mid-Pacific Region}, - author = {Reclamation}, - year = {2008}, - pages = {1016}, -} - -@article{rathgeber_simulated_2000, - title = {Simulated responses of {Pinus} halepensis forest productivity to climatic change and {CO2} increase using a statistical model}, - volume = {26}, - abstract = {Tree ring chronologies provide long-term records of growth in natural environmental conditions and may be used to evaluate impacts of climatic change and CO2 increase on forest productivity. This study focuses on 21 Pinus halepensis forest stands in calcareous Provence (in the south-east of France). A chronology of net primary productivity (NPP) both for the 20th century and for each stand was estimated using tree ring data (width and density). The response of each stand to climate in terms of NPP was statistically modelled using response functions. Anomalies between estimated NPP and NPP reconstructed by response functions were calculated to evaluate the fertilising effect of CO2 increase on tree growth. The changes in anomalies during the 20th century were attributed to the effect of CO2 increase. A multiplying factor (beta) linking CO2 concentration and stand productivity was then calculated, on the basis of the trend observed during the 20th century. In this study, the value of the beta factor obtained under natural conditions (beta = 0.50) is consistent with those from controlled CO2 enrichment experiments. Both response functions and the beta factor were used to predict NPP changes for a 2 X CO2 scenario. The 2 X CO2 climate was obtained using predictions from Meteo France's ARPEGE atmospheric general circulation model (AGCM) downscaled to Marseilles meteorological station. NPP increased significantly for nine stands solely when the climatic effect was taken into account. The main factors responsible for this enhancement were increased winter and early spring temperatures. When the fertilising effect of the CO2 increase was added, NPP was significantly enhanced for 14 stands (i.e. NPP enhancement ranged from 8\% to 55\%). Although the effects of global change were slightly detectable during the 20th century, their acceleration is likely to lead to great changes in the future productivity of P. halepensis forests. (C) 2000 Elsevier Science B.V. All rights reserved.}, - number = {4}, - journal = {Global and Planetary Change}, - author = {Rathgeber, C. and Nicault, A. and Guiot, J. and Keller, T. and Guibal, F. and Roche, P.}, - month = dec, - year = {2000}, - pages = {405--421}, - annote = {385WJGLOBAL PLANET CHANGE}, - annote = {The following values have no corresponding Zotero field:alt-title: Glob. Planet. Changeaccession-num: ISI:000166027900005}, -} - -@incollection{randall_climate_2007, - address = {Cambridge, UK}, - title = {Climate models and their evaluation}, - booktitle = {Climate {Change} 2007: {The} {Physical} {Science} {Basis}. {Contribution} of {Working} {Group} {I} to the {Fourth} {Assessment} {Report} of the {Intergovernmental} {Panel} on {Climate} {Change}}, - publisher = {Cambridge University Press}, - author = {Randall, D. A. and Wood, R. A. and Bony, S. and Colman, R. and Fichefet, T. and Fyfe, J. and Kattsov, V. and Pitman, A. and Shukla, J. and Srinivasan, J. and Sumi, A. and Stouffer, R. J. and Taylor, K. E.}, - editor = {Solomon, S. and Qin, D. and Manning, M. and Chen, Z. and Marquis, M. and Averyt, K. B. and Tignor, M. and Miller, H. L.}, - year = {2007}, - pages = {591--648}, -} - -@article{rajczak_robust_2015, - title = {Robust climate scenarios for sites with sparse observations: a two-step bias correction approach}, - issn = {1097-0088}, - doi = {10.1002/joc.4417}, - journal = {International Journal of Climatology}, - author = {Rajczak, Jan and Kotlarski, Sven and Salzmann, Nadine and Schär, Christoph}, - year = {2015}, - keywords = {bias correction, climate change, statistical downscaling, projections, climate impact assessment, regional climate models}, - pages = {n/a--n/a}, - annote = {The following values have no corresponding Zotero field:publisher: John Wiley \& Sons, Ltd}, -} - -@article{raupach_global_2007, - title = {Global and regional drivers of accelerating {CO2} emissions}, - volume = {104}, - number = {24}, - journal = {Proceedings National Academy of Sciences}, - author = {Raupach, M. R. and Marland, G. and Ciais, P. and LeQuere, C. and Canadell, J. G. and Field, C. B.}, - year = {2007}, - pages = {10288--10293, doi:10.1073/pnas.0700609104}, -} - -@article{jouni_raisanen_co2-induced_2002, - title = {{CO2}-{Induced} {Changes} in {Interannual} {Temperature} and {Precipitation} {Variability} in 19 {CMIP2} {Experiments}}, - volume = {15}, - doi = {doi:10.1175/1520-0442(2002)015<2395:CICIIT>2.0.CO;2}, - number = {17}, - journal = {Journal of Climate}, - author = {Jouni Räisänen}, - year = {2002}, - pages = {2395--2411}, -} - -@article{rahmstorf_ocean_2002, - title = {Ocean circulation and climate during the past 120,000 years}, - volume = {419}, - journal = {Nature}, - author = {Rahmstorf, S.}, - year = {2002}, - pages = {207--214}, -} - -@article{raff_framework_2009, - title = {A framework for assessing flood frequency based on climate projection information}, - volume = {13}, - issn = {1607-7938}, - doi = {10.5194/hess-13-2119-2009}, - number = {11}, - journal = {Hydrol. Earth Syst. Sci.}, - author = {Raff, D. A. and Pruitt, T. and Brekke, L. D.}, - year = {2009}, - pages = {2119--2136}, - annote = {HESS}, - annote = {The following values have no corresponding Zotero field:publisher: Copernicus Publications}, -} - -@article{racherla_added_2012, - title = {The added value to global model projections of climate change by dynamical downscaling: {A} case study over the continental {U}.{S}. using the {GISS}-{ModelE2} and {WRF} models}, - volume = {117}, - issn = {2156-2202}, - doi = {10.1029/2012jd018091}, - number = {D20}, - journal = {Journal of Geophysical Research: Atmospheres}, - author = {Racherla, P. N. and Shindell, D. T. and Faluvegi, G. S.}, - year = {2012}, - keywords = {dynamical downscaling, regional climate change, 1637 Regional climate change, 1622 Earth system modeling, model assessment}, - pages = {n/a--n/a}, -} - -@article{quintana_segui_comparison_2010, - title = {Comparison of three downscaling methods in simulating the impact of climate change on the hydrology of {Mediterranean} basins}, - volume = {383}, - issn = {0022-1694}, - number = {1–2}, - journal = {Journal of Hydrology}, - author = {Quintana Seguí, P. and Ribes, A. and Martin, E. and Habets, F. and Boé, J.}, - year = {2010}, - keywords = {Uncertainty, Hydrology, Downscaling, Impacts, Mediterranean, Regional climate}, - pages = {111--124}, - annote = {The following values have no corresponding Zotero field:work-type: doi: 10.1016/j.jhydrol.2009.09.050}, -} - -@article{quinn_integrated_2001, - title = {An integrated modeling system for environmental impact analysis of climate variability and extreme weather events in the {San} {Joaquin} {Basin}, {California}}, - volume = {5}, - abstract = {This collaborative research project has two main objectives: to assess the vulnerability of water supply, water demand, water quality, ecosystem health and socioeconomic welfare within the San Joaquin River Basin as a function of climate variability and extreme weather events; and to provide guidance in the formulation of effective management strategies to mitigate the range of potential impacts due to climate variability and extreme weather. The project involves updating and advancing previous studies on climate change in California. Climate data are based on new Global Circulation Model output from the statistical downscaling that converts GCM climate forecasts into local weather forecasts. The project applies these climate data to perturb an existing 72-year historical hydrologic time series of the San Joaquin Basin to develop an integrated impacts analysis of climate change/variability on the water, economic and social resources of the Basin. Previous studies focused only on water resource impacts. A decision support system (DSS) is under development that will provide assistance to CALFED (a joint California State and Federal program designed to resolve water issues in the Bay-Delta) in water resource and ecosystem management of the San Joaquin Basin. (C) 2001 Elsevier Science Ltd. AR rights reserved.}, - number = {4}, - journal = {Advances in Environmental Research}, - author = {Quinn, N. W. T. and Miller, N. L. and Dracup, J. A. and Brekke, L. and Grober, L. F.}, - month = nov, - year = {2001}, - pages = {309--317}, - annote = {482VFADV ENVIRON RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Adv. Environ. Res.accession-num: ISI:000171597400002}, -} - -@article{qian_simulation_2006, - title = {Simulation of {Global} {Land} {Surface} {Conditions} from 1948 to 2004. {Part} {I}: {Forcing} {Data} and {Evaluations}}, - volume = {7}, - abstract = {Because of a lack of observations, historical simulations of land surface conditions using land surface models are needed for studying variability and changes in the continental water cycle and for providing initial conditions for seasonal climate predictions. Atmospheric forcing datasets are also needed for land surface model development. The quality of atmospheric forcing data greatly affects the ability of land surface models to realistically simulate land surface conditions. Here a carefully constructed global forcing dataset for 1948\&\#8211;2004 with 3-hourly and T62 (\&\#8764;1.875\&\#176;) resolution is described, and historical simulations using the latest version of the Community Land Model version 3.0 (CLM3) are evaluated using available observations of streamflow, continental freshwater discharge, surface runoff, and soil moisture. The forcing dataset was derived by combining observation-based analyses of monthly precipitation and surface air temperature with intramonthly variations from the National Centers for Environmental Prediction\&\#8211;National Center for Atmospheric Research (NCEP\&\#8211;NCAR) reanalysis, which is shown to have spurious trends and biases in surface temperature and precipitation. Surface downward solar radiation from the reanalysis was first adjusted for variations and trends using monthly station records of cloud cover anomaly and then for mean biases using satellite observations during recent decades. Surface specific humidity from the reanalysis was adjusted using the adjusted surface air temperature and reanalysis relative humidity. Surface wind speed and air pressure were interpolated directly from the 6-hourly reanalysis data. Sensitivity experiments show that the precipitation adjustment (to the reanalysis data) leads to the largest improvement, while the temperature and radiation adjustments have only small effects. When forced by this dataset, the CLM3 reproduces many aspects of the long-term mean, annual cycle, interannual and decadal variations, and trends of streamflow for many large rivers (e.g., the Orinoco, Changjiang, Mississippi, etc.), although substantial biases exist. The simulated long-term-mean freshwater discharge into the global and individual oceans is comparable to 921 river-based observational estimates. Observed soil moisture variations over Illinois and parts of Eurasia are generally simulated well, with the dominant influence coming from precipitation. The results suggest that the CLM3 simulations are useful for climate change analysis. It is also shown that unrealistically low intensity and high frequency of precipitation, as in most model-simulated precipitation or observed time-averaged fields, result in too much evaporation and too little runoff, which leads to lower than observed river flows. This problem can be reduced by adjusting the precipitation rates using observed-precipitation frequency maps.}, - number = {5}, - journal = {Journal of Hydrometeorology}, - author = {Qian, Taotao and Dai, Aiguo and Trenberth, Kevin E. and Oleson, Keith W.}, - month = oct, - year = {2006}, - pages = {953--975}, -} - -@article{qian_comparison_2004, - title = {Comparison of {LARS}-{WG} and {AAFC}-{WG} stochastic weather generators for diverse {Canadian} climates}, - volume = {26}, - issn = {0936-577X}, - abstract = {Two weather generators -LARS-WG, developed at Long Ashton Research Station (UK), and AAFC-WG, developed at Agriculture and Agri-Food Canada-were compared in order to gauge their capabilities of reproducing probability distributions, means and variances of observed daily precipitation, maximum temperature and minimum temperature for diverse Canadian climates. Climatic conditions, such as wet and dry spells, interannual variability and agroclimatic indices, were also used to assess the performance of the 2 weather generators. AAFC-WG performed better in simulating temperature -related statistics, while it did almost as well as LARS-WG for statistics associated with daily precipitation. Using empirical distributions in AAFC-WG for daily maximum and minimum temperatures helped to improve the temperature statistics, especially in cases where local temperatures did not follow normal distributions. However, both weather generators had overdispersion problems, i.e. they underestimated interannual variability, especially for temperatures. Overall, AAFC-WG performed better.}, - language = {English}, - number = {3}, - journal = {Climate Research}, - author = {Qian, B. D. and Gameda, S. and Hayhoe, H. and De Jong, R. and Bootsma, A.}, - month = jun, - year = {2004}, - keywords = {model, simulation, daily precipitation, rainfall, temperature, climate scenarios, agriculture, scenarios, canada, atmospheric circulation patterns, impact studies, latent evaporation, multiple sites, solar-radiation, weather generators}, - pages = {175--191}, - annote = {847LJTimes Cited:2Cited References Count:37}, - annote = {The following values have no corresponding Zotero field:auth-address: Qian, BD Agr \& Agri Food Canada, Eastern Cereal \& Oilseed Res Ctr, 960 Carling Ave, Ottawa, ON, Canada Agr \& Agri Food Canada, Eastern Cereal \& Oilseed Res Ctr, Ottawa, ON, Canadaaccession-num: ISI:000223393700001}, -} - -@techreport{pyke_meteorological_1972, - address = {Berkeley, CA}, - title = {Some meteorological aspects of the seasonal distribution of precipitation in the western {United} {States} and {Baja} {California}, {University} of {California} {Water} {Resources} {Center} {Contribution} 139}, - author = {Pyke, C. B.}, - year = {1972}, - pages = {205}, -} - -@article{prudhomme_downscaling_2002, - title = {Downscaling of global climate models for flood frequency analysis: where are we now?}, - volume = {16}, - abstract = {The issues of downscaling the results from global climate models (GCMs) to a scale relevant for hydrological impact studies are examined. GCM outputs, typically at a spatial resolution of around 3degrees latitude and 4degrees longitude, are currently not considered reliable at time scales shorter than I month. Continuous rainfall-runoff modelling for flood regime assessment requires input at the daily or even hourly time-step. A review of the different methodologies suggested in the literature to downscale GCM results at smaller spatial and temporal resolutions is presented. The methods, from simple interpolation to more sophisticated dynamical modelling, through multiple regression and weather generators, are, however, mostly based directly on GCM outputs, sometimes at daily time-step. The approach adopted is a simple, empirical methodology based on modelled monthly changes from the HadCM2 greenhouse gases experiment for the time horizon 2050s. Three daily rainfall scenarios are derived from the same set of monthly changes, representing different possible changes in the rainfall regime. The first scenario represents an increase of the occurrence of frontal systems, corresponding to a decrease in the rainfall intensity; the second corresponds to an increase in convective storm-type rainfall, characterized by extreme events with higher intensity; the third one assumes an increase in the monthly rainfall without any change in rainfall variability. A continuous daily rainfall-runoff model, calibrated for the Severn catchment, was used to generate daily flow series for the 1961-90 baseline period and the 2050s, and a peaks-over-threshold analysis was undertaken to produce flood frequency distributions for the two time horizons. Though the three scenarios lead to an increase in the magnitude and the frequency of the extreme flood events, the impact is strongly influenced by the type of daily rainfall scenario applied. We conclude that if the next generation of GCMs produce more reliable rainfall variance estimates, then more appropriate ways of deriving rainfall scenarios could be developed using weather generators rather than empirical methods. Copyright (C) 2002 John Wiley Sons, Ltd.}, - number = {6}, - journal = {Hydrological Processes}, - author = {Prudhomme, C. and Reynard, N. and Crooks, S.}, - month = apr, - year = {2002}, - pages = {1137--1150}, - annote = {SI535HNHYDROL PROCESS}, - annote = {The following values have no corresponding Zotero field:alt-title: Hydrol. Process.accession-num: ISI:000174639300002}, -} - -@article{prudhomme_assessing_2009, - title = {Assessing uncertainties in climate change impact analyses on the river flow regimes in the {UK}. {Part} 1: baseline climate}, - volume = {93}, - issn = {1573-1480}, - doi = {10.1007/s10584-008-9464-3}, - abstract = {Assessing future climate and its potential implications on river flows is a key challenge facing water resource planners. Sound, scientifically-based advice to decision makers also needs to incorporate information on the uncertainty in the results. Moreover, existing bias in the reproduction of the ‘current’ (or baseline) river flow regime is likely to transfer to the simulations of flow in future time horizons, and it is thus critical to undertake baseline flow assessment while undertaking future impacts studies. This paper investigates the three main sources of uncertainty surrounding climate change impact studies on river flows: uncertainty in GCMs, in downscaling techniques and in hydrological modelling. The study looked at four British catchments’ flow series simulated by a lumped conceptual rainfall–runoff model with observed and GCM-derived rainfall series representative of the baseline time horizon (1961–1990). A block-resample technique was used to assess climate variability, either from observed records (natural variability) or reproduced by GCMs. Variations in mean monthly flows due to hydrological model uncertainty from different model structures or model parameters were also evaluated. Three GCMs (HadCM3, CCGCM2, and CSIRO-mk2) and two downscaling techniques (SDSM and HadRM3) were considered. Results showed that for all four catchments, GCM uncertainty is generally larger than downscaling uncertainty, and both are consistently greater than uncertainty from hydrological modelling or natural variability. No GCM or downscaling technique was found to be significantly better or to have a systematic bias smaller than the others. This highlights the need to consider more than one GCM and downscaling technique in impact studies, and to assess the bias they introduce when modelling river flows.}, - number = {1}, - journal = {Climatic Change}, - author = {Prudhomme, Christel and Davies, Helen}, - year = {2009}, - pages = {177--195}, - annote = {The following values have no corresponding Zotero field:label: Prudhomme2009work-type: journal article}, -} - -@book{press_numerical_2007, - address = {New York}, - title = {Numerical {Recipes}: {The} {Art} of {Scientific} {Computing}, 3rd {Edition}}, - publisher = {Cambridge University Press}, - author = {Press, W. H. and Teukolsky, S. A. and Vetterling, W. T. and Flannery, B. P.}, - year = {2007}, -} - -@article{postel_enetering_2000, - title = {Enetering an era of water scarcity: {The} challenges ahead}, - volume = {10}, - doi = {doi:10.1890/1051-0761(2000)010[0941:EAEOWS]2.0.CO;2}, - number = {4}, - journal = {Ecological Applications}, - author = {Postel, Sandra L.}, - year = {2000}, - pages = {941--948}, -} - -@article{pongracz_fuzzy_2001, - title = {Fuzzy rule-based prediction of monthly precipitation}, - volume = {26}, - abstract = {Monthly precipitation in Hungary is modeled using the Hess- Brezowsky atmospheric circulation pattern types and an ENSO index as forcing functions or inputs. The weakness of the statistical dependence between these individual inputs and precipitation prevents the use of a multivariate regression analysis for reproducing the probability distribution function of observed precipitation. In order to utilize the existing relationship between forcing functions and precipitation a fuzzy rule-based modeling technique is used. The first part of the observed input and precipitation data is used as the learning set to identify the fuzzy rules. Then, the second part of the data is used to validate the rules by comparing the frequency distributions of precipitation calculated respectively with the fuzzy rules and observed data. Example results are presented for two different climatic regions of Hungary. One of them represents a wetter climate while the other refers to the drier conditions of the Hungarian Plains. The fuzzy rule-based model reproduces the empirical frequency distributions in every season. However, as expected, the statistical prediction is better in winter, spring and fall than in the summer. The potential of the approach is important in climate change studies when the fuzzy rules obtained as described above can be used with input data stemming from GCM to predict regional/local precipitation reflecting GCM scenarios. (C) 2001 Elsevier Science Ltd. All rights reserved.}, - number = {9}, - journal = {Physics and Chemistry of the Earth Part B-Hydrology Oceans and Atmosphere}, - author = {Pongracz, R. and Bartholy, J. and Bogardi, I.}, - year = {2001}, - pages = {663--667}, - annote = {469ZXPHYS CHEM EARTH P B-HYDROL OC}, - annote = {The following values have no corresponding Zotero field:alt-title: Phys. Chem. Earth Pt B-Hydrol. Oceans Atmos.accession-num: ISI:000170846900005}, -} - -@article{polade_natural_2013, - title = {Natural climate variability and teleconnections to precipitation over the {Pacific}-{North} {American} region in {CMIP3} and {CMIP5} models}, - volume = {40}, - issn = {1944-8007}, - doi = {10.1002/grl.50491}, - number = {10}, - journal = {Geophysical Research Letters}, - author = {Polade, Suraj D. and Gershunov, Alexander and Cayan, Daniel R. and Dettinger, Michael D. and Pierce, David W.}, - year = {2013}, - keywords = {1626 Global climate models, 1637 Regional climate change, 3305 Climate change and variability, 1616 Climate variability, ENSO, CMIP5, 4928 Global climate models, natural climate variability, North American hydroclimate, PDO, southwest USA hydroclimate}, - pages = {2296--2301}, -} - -@article{plaut_large-scale_2001, - title = {Large-scale circulation classification, weather regimes, and local climate over {France}, the {Alps} and {Western} {Europe}}, - volume = {17}, - abstract = {By applying the dynamical cluster algorithm to large-scale circulation patterns at various tropospheric levels (Z700, Z500, and SLP [sea-level pressure]), we obtain the so-called weather regimes (WRs). WRs are the cluster central patterns; there is no subjectivity at all involved in the procedure, A red noise test allows one to select the best number of clusters at each level. A comparison is performed between the different level classifications, and highly significant correlations are found. While previous attempts to classify daily circulations in a fully objective way were concerned with mid-tropospheric levels and could not go further back than 50 yr, here we classify 120 yr of SLP patterns. We arrive at the important conclusion that the same 5 WRs are found for the 3 periods 1880-1918, 1919-1957 and 1958-1997. The linkage between these WRs and the local tangible weather is then investigated for both temperatures and precipitation. It is found that the instantaneous departure of local weather from average climate is highly correlated with the WRs, making this approach a challenging and coherent description of local climates. The atmosphere does not merely evolve around its mean state, but instead spends more time around a few peculiar (large-scale) states with specific consequences for local weather. As a result, the WRs may provide a reliable (and moreover fully objective) framework for building downscaling algorithms appropriate for local climate change studies.}, - number = {3}, - journal = {Climate Research}, - author = {Plaut, G. and Simonnet, E.}, - month = aug, - year = {2001}, - pages = {303--324}, - annote = {478UECLIMATE RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:000171363100006}, -} - -@article{david_w_pierce_statistical_2014, - title = {Statistical {Downscaling} {Using} {Localized} {Constructed} {Analogs} ({LOCA})}, - volume = {15}, - doi = {doi:10.1175/JHM-D-14-0082.1}, - abstract = {AbstractA new technique for statistically downscaling climate model simulations of daily temperature and precipitation is introduced and demonstrated over the western United States. The localized constructed analogs (LOCA) method produces downscaled estimates suitable for hydrological simulations using a multiscale spatial matching scheme to pick appropriate analog days from observations. First, a pool of candidate observed analog days is chosen by matching the model field to be downscaled to observed days over the region that is positively correlated with the point being downscaled, which leads to a natural independence of the downscaling results to the extent of the domain being downscaled. Then, the one candidate analog day that best matches in the local area around the grid cell being downscaled is the single analog day used there. Most grid cells are downscaled using only the single locally selected analog day, but locations whose neighboring cells identify a different analog day use a weighted combination of the center and adjacent analog days to reduce edge discontinuities. By contrast, existing constructed analog methods typically use a weighted average of the same 30 analog days for the entire domain. By greatly reducing this averaging, LOCA produces better estimates of extreme days, constructs a more realistic depiction of the spatial coherence of the downscaled field, and reduces the problem of producing too many light-precipitation days. The LOCA method is more computationally expensive than existing constructed analog techniques, but it is still practical for downscaling numerous climate model simulations with limited computational resources.}, - number = {6}, - journal = {Journal of Hydrometeorology}, - author = {David W. Pierce and Daniel R. Cayan and Bridget L. Thrasher}, - year = {2014}, - keywords = {Hydrometeorology,Climate models,Hydrologic models,Land surface model,Atmosphere-land interaction,Regional effects}, - pages = {2558--2585}, -} - -@article{pierce_key_2013, - title = {The key role of heavy precipitation events in climate model disagreements of future annual precipitation changes in {California}}, - volume = {26}, - issn = {0894-8755}, - doi = {10.1175/jcli-d-12-00766.1}, - number = {16}, - journal = {Journal of Climate}, - author = {Pierce, D. W. and Cayan, D. R. and Das, T. and Maurer, E. P. and Miller, N. L. and Bao, Y. and Kanamitsu, M. and Yoshimura, K. and Snyder, M. A. and Sloan, L. C. and Franco, G. and Tyree, M.}, - month = aug, - year = {2013}, - pages = {5879--5896}, - annote = {doi: 10.1175/JCLI-D-12-00766.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/JCLI-D-12-00766.1}, -} - -@article{pilling_impact_2002, - title = {The impact of future climate change on seasonal discharge, hydrological processes and extreme flows in the {Upper} {Wye} experimental catchment, mid-{Wales}}, - volume = {16}, - abstract = {Analysing the impact of future climate change on hydrological regimes is hampered by the disparity of scales between general circulation model (GCM) output and the spatial resolution required by catchment-scale hydrological simulation models. In order to overcome this, statistical relationships were established between three indices of atmospheric circulation (vorticity and the strength and direction of geostrophic windflow) and daily catchment precipitation and potential evapotranspiration (PET) to downscale from the HadCM2 GCM to the Upper Wye experimental catchment in mid-Wales. The atmospheric circulation indices were calculated from daily grid point sea-level pressure data for: (a) the Climatic Research Unit observed data set (1975-90); (b) the HadCM2SUL simulation representing the present climate (1980-99); and (c) the HadCM2SUL simulation representing future climate conditions (2080-99). The performance of the downscaling approach was evaluated by comparing diagnostic statistics from the three downscaled precipitation and PET scenarios with those recorded from the Upper Wye catchment. The most significant changes between the downscaled HadCM2SUL 1980-99 and 2080-99 scenarios are decreases in precipitation occurrence and amount in summer and autumn combined with a shortening of mean wet spell length, which is most pronounced in autumn. A hydrological simulation model (HYSIM) was calibrated on recorded flow data for the Upper Wye catchment and 'forced' with the three downscaled precipitation and PET scenarios to model changes in river flow and hillslope hydrological processes. Results indicate increased seasonality of flows, with markedly drier summers. Analysis of extreme events suggests significant increases in the frequency of both high- and low-flow events. Copyright (C) 2002 John Wiley Sons, Ltd.}, - number = {6}, - journal = {Hydrological Processes}, - author = {Pilling, C. G. and Jones, J. A. A.}, - month = apr, - year = {2002}, - pages = {1201--1213}, - annote = {SI535HNHYDROL PROCESS}, - annote = {The following values have no corresponding Zotero field:alt-title: Hydrol. Process.accession-num: ISI:000174639300005}, -} - -@article{pierce_probabilistic_2013, - title = {Probabilistic estimates of future changes in {California} temperature and precipitation using statistical and dynamical downscaling}, - volume = {1-18}, - number = {40}, - journal = {Climate Dynamics}, - author = {Pierce, D. W. and Das, T. and Cayan, D. and Maurer, E. P. and Bao, Y. and Kanamitsu, M. and Yoshimura, K. and Snyder, M. A. and Sloan, L. C. and Franco, G. and Tyree, M.}, - year = {2013}, - pages = {839--856, DOI: 10.1007/s00382--012--1337--9}, -} - -@article{pierce_improved_2015, - title = {Improved {Bias} {Correction} {Techniques} for {Hydrological} {Simulations} of {Climate} {Change}}, - volume = {16}, - issn = {1525-755X}, - doi = {10.1175/JHM-D-14-0236.1}, - abstract = {AbstractGlobal climate model (GCM) output typically needs to be bias corrected before it can be used for climate change impact studies. Three existing bias correction methods, and a new one developed here, are applied to daily maximum temperature and precipitation from 21 GCMs to investigate how different methods alter the climate change signal of the GCM. The quantile mapping (QM) and cumulative distribution function transform (CDF-t) bias correction methods can significantly alter the GCM?s mean climate change signal, with differences of up to 2°C and 30\% points for monthly mean temperature and precipitation, respectively. Equidistant quantile matching (EDCDFm) bias correction preserves GCM changes in mean daily maximum temperature but not precipitation. An extension to EDCDFm termed PresRat is introduced, which generally preserves the GCM changes in mean precipitation. Another problem is that GCMs can have difficulty simulating variance as a function of frequency. To address this, a frequency-dependent bias correction method is introduced that is twice as effective as standard bias correction in reducing errors in the models? simulation of variance as a function of frequency, and it does so without making any locations worse, unlike standard bias correction. Last, a preconditioning technique is introduced that improves the simulation of the annual cycle while still allowing the bias correction to take account of an entire season?s values at once.}, - number = {6}, - journal = {Journal of Hydrometeorology}, - author = {Pierce, David W. and Cayan, Daniel R. and Maurer, Edwin P. and Abatzoglou, John T. and Hegewisch, Katherine C.}, - month = dec, - year = {2015}, - pages = {2421--2442}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Society}, -} - -@article{pierce_selecting_2009, - title = {Selecting global climate models for regional climate change studies}, - volume = {106}, - number = {21}, - journal = {Proceedings National Academy of Sciences}, - author = {Pierce, D. W. and Barnett, T. P. and Santer, B. D. and Gleckler, P. J.}, - year = {2009}, - pages = {8441--8446}, -} - -@article{piechota_western_1997, - title = {Western {US} streamflow and atmospheric circulation patterns during {El} {Niño}-{Southern} {Oscillation}}, - volume = {201}, - journal = {Journal of Hydrology}, - author = {Piechota, T. C. and J. A. Dracup and R. G. Fovell}, - year = {1997}, - pages = {249--271}, -} - -@article{piao_changes_2007, - title = {Changes in climate and land use have a larger direct impact than rising {CO2} on global river runoff trends}, - volume = {104}, - doi = {10.1073/pnas.0707213104}, - abstract = {The significant worldwide increase in observed river runoff has been tentatively attributed to the stomatal “antitranspirant” response of plants to rising atmospheric CO [Gedney N, Cox PM, Betts RA, Boucher O, Huntingford C, Stott PA (2006) 439: 835–838]. However, CO also is a plant fertilizer. When allowing for the increase in foliage area that results from increasing atmospheric CO levels in a global vegetation model, we find a decrease in global runoff from 1901 to 1999. This finding highlights the importance of vegetation structure feedback on the water balance of the land surface. Therefore, the elevated atmospheric CO concentration does not explain the estimated increase in global runoff over the last century. In contrast, we find that changes in mean climate, as well as its variability, do contribute to the global runoff increase. Using historic land-use data, we show that land-use change plays an additional important role in controlling regional runoff values, particularly in the tropics. Land-use change has been strongest in tropical regions, and its contribution is substantially larger than that of climate change. On average, land-use change has increased global runoff by 0.08 mm/year and accounts for ≈50\% of the reconstructed global runoff trend over the last century. Therefore, we emphasize the importance of land-cover change in forecasting future freshwater availability and climate.}, - number = {39}, - journal = {Proceedings of the National Academy of Sciences}, - author = {Piao, S. and Friedlingstein, P. and Ciais, P. and de Noblet-Ducoudre, N. and Labat, D. and Zaehle, S.}, - month = sep, - year = {2007}, - pages = {15242--15247}, -} - -@article{peterson_monitoring_2013, - title = {Monitoring and {Understanding} {Changes} in {Heat} {Waves}, {Cold} {Waves}, {Floods}, and {Droughts} in the {United} {States}: {State} of {Knowledge}}, - volume = {94}, - issn = {0003-0007}, - doi = {10.1175/bams-d-12-00066.1}, - abstract = {Weather and climate extremes have been varying and changing on many different time scales. In recent decades, heat waves have generally become more frequent across the United States, while cold waves have been decreasing. While this is in keeping with expectations in a warming climate, it turns out that decadal variations in the number of U.S. heat and cold waves do not correlate well with the observed U.S. warming during the last century. Annual peak flow data reveal that river flooding trends on the century scale do not show uniform changes across the country. While flood magnitudes in the Southwest have been decreasing, flood magnitudes in the Northeast and north-central United States have been increasing. Confounding the analysis of trends in river flooding is multiyear and even multidecadal variability likely caused by both large-scale atmospheric circulation changes and basin-scale ?memory? in the form of soil moisture. Droughts also have long-term trends as well as multiyear and decadal variability. Instrumental data indicate that the Dust Bowl of the 1930s and the drought in the 1950s were the most significant twentieth-century droughts in the United States, while tree ring data indicate that the megadroughts over the twelfth century exceeded anything in the twentieth century in both spatial extent and duration. The state of knowledge of the factors that cause heat waves, cold waves, floods, and drought to change is fairly good with heat waves being the best understood.}, - number = {6}, - journal = {Bulletin of the American Meteorological Society}, - author = {Peterson, Thomas C. and Heim, Richard R. and Hirsch, Robert and Kaiser, Dale P. and Brooks, Harold and Diffenbaugh, Noah S. and Dole, Randall M. and Giovannettone, Jason P. and Guirguis, Kristen and Karl, Thomas R. and Katz, Richard W. and Kunkel, Kenneth and Lettenmaier, Dennis and McCabe, Gregory J. and Paciorek, Christopher J. and Ryberg, Karen R. and Schubert, Siegfried and Silva, Viviane B. S. and Stewart, Brooke C. and Vecchia, Aldo V. and Villarini, Gabriele and Vose, Russell S. and Walsh, John and Wehner, Michael and Wolock, David and Wolter, Klaus and Woodhouse, Connie A. and Wuebbles, Donald}, - month = jun, - year = {2013}, - pages = {821--834}, - annote = {doi: 10.1175/BAMS-D-12-00066.1}, - annote = {The following values have no corresponding Zotero field:publisher: American Meteorological Societywork-type: doi: 10.1175/BAMS-D-12-00066.1}, -} - -@article{peters_challenge_2013, - title = {The challenge to keep global warming below 2 [deg]{C}}, - volume = {3}, - issn = {1758-678X}, - doi = {http://www.nature.com/nclimate/journal/v3/n1/abs/nclimate1783.html#supplementary-information}, - number = {1}, - journal = {Nature Clim. Change}, - author = {Peters, Glen P. and Andrew, Robbie M. and Boden, Tom and Canadell, Josep G. and Ciais, Philippe and Le Quere, Corinne and Marland, Gregg and Raupach, Michael R. and Wilson, Charlie}, - year = {2013}, - pages = {4--6}, - annote = {10.1038/nclimate1783}, - annote = {The following values have no corresponding Zotero field:publisher: Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.work-type: 10.1038/nclimate1783}, -} - -@techreport{peck_catchment_1976, - address = {Washington, D.C.}, - title = {Catchment modeling and initial parameter estimation for the {National} {Weather} {Service} {River} {Forecast} {System}}, - institution = {U.S. National Weather Service, National Oceanic and Atmospheric Administration, U.S. Department of Commerce}, - author = {Peck, E. L.}, - year = {1976}, -} - -@article{piani_statistical_2010, - title = {Statistical bias correction for daily precipitation in regional climate models over {Europe}}, - volume = {99}, - issn = {0177-798X}, - doi = {10.1007/s00704-009-0134-9}, - number = {1}, - journal = {Theoretical and Applied Climatology}, - author = {Piani, C. and Haerter, J. and Coppola, E.}, - year = {2010}, - keywords = {Earth and Environmental Science}, - pages = {187--192}, - annote = {The following values have no corresponding Zotero field:publisher: Springer Wien}, -} - -@article{pfizenmayer_anthropogenic_2001, - title = {Anthropogenic climate change shown by local wave conditions in the {North} {Sea}}, - volume = {19}, - abstract = {In the central North Sea we have observed an increase in the frequency of eastwardly propagating waves in the last 4 decades. To assess the significance of this change, wave statistics for the 20th century were reconstructed with a statistical model With a linear multivariate technique (redundancy analysis), monthly mean air pressure fields over the North Atlantic and Western Europe were downscaled on the intramonthly frequency of directional wave propagation. When compared against this reference, the recent change appears statistically significant at the 5\% level. In order to investigate the reason for this local climatic change, the reconstruction was compared with the downscaled results of control and transient GCM scenarios (ECHAM4-OPYC3) and with the results obtained in a high-resolution time-slice experiment with increased concentrations of greenhouse gases and aerosols. Both estimates are qualitatively consistent with the changes observed in the last 4 decades, We suggest that the recent increase in eastward propagation is a local manifestation of anthropogenic global climate change.}, - number = {1}, - journal = {Climate Research}, - author = {Pfizenmayer, A. and von Storch, H.}, - month = nov, - year = {2001}, - pages = {15--23}, - annote = {510RZCLIMATE RES}, - annote = {The following values have no corresponding Zotero field:alt-title: Clim. Res.accession-num: ISI:000173222200002}, -} - -@article{peterson_global_1998, - title = {Global historical climatology network ({GHCN}) quality control of monthly temperature data}, - volume = {18}, - issn = {1097-0088}, - doi = {10.1002/(sici)1097-0088(199809)18:11<1169::aid-joc309>3.0.co;2-u}, - number = {11}, - journal = {International Journal of Climatology}, - author = {Peterson, Thomas C. and Vose, Russell and Schmoyer, Richard and Razuvaëv, Vyachevslav}, - year = {1998}, - keywords = {quality control, global historical climatology network (GHCN), homogeneity tests, outliers, temperature, monthly}, - pages = {1169--1179}, - annote = {The following values have no corresponding Zotero field:publisher: John Wiley \& Sons, Ltd.}, -} - -@article{perkins_evaluation_2007, - title = {Evaluation of the {AR4} climate models' simulated daily maximum temperature, minimum temperature, and precipitation over {Australia} using probability density functions}, - volume = {20}, - issn = {0894-8755}, - doi = {10.1175/jcli4253.1}, - abstract = {The coupled climate models used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change are evaluated. The evaluation is focused on 12 regions of Australia for the daily simulation of precipitation, minimum temperature, and maximum temperature. The evaluation is based on probability density functions and a simple quantitative measure of how well each climate model can capture the observed probability density functions for each variable and each region is introduced. Across all three variables, the coupled climate models perform better than expected. Precipitation is simulated reasonably by most and very well by a small number of models, although the problem with excessive drizzle is apparent in most models. Averaged over Australia, 3 of the 14 climate models capture more than 80\% of the observed probability density functions for precipitation. Minimum temperature is simulated well, with 10 of the 13 climate models capturing more than 80\% of the observed probability density functions. Maximum temperature is also reasonably simulated with 6 of 10 climate models capturing more than 80\% of the observed probability density functions. An overall ranking of the climate models, for each of precipitation, maximum, and minimum temperatures, and averaged over these three variables, is presented. Those climate models that are skillful over Australia are identified, providing guidance on those climate models that should be used in impacts assessments where those impacts are based on precipitation or temperature. These results have no bearing on how well these models work elsewhere, but the methodology is potentially useful in assessing which of the many climate models should be used by impacts groups.}, - language = {English}, - number = {17}, - journal = {J. Clim.}, - author = {Perkins, S. E. and Pitman, A. J. and Holbrook, N. J. and McAneney, J.}, - month = sep, - year = {2007}, - keywords = {variability, events, ensemble, performance, surface-temperature, extremes, changing climate, grid-box, i, mean temperature, part}, - pages = {4356--4376}, - annote = {ISI Document Delivery No.: 207FTTimes Cited: 30Cited Reference Count: 41Perkins, S. E. Pitman, A. J. Holbrook, N. J. McAneney, J.Amer meteorological socBoston}, - annote = {The following values have no corresponding Zotero field:auth-address: Univ New S Wales, Climate Change Res Ctr, Sydney, NSW 2052, Australia. Macquarie Univ, Dept Phys Geog, Sydney, NSW 2109, Australia. Macquarie Univ, Risk Frontiers Res Ctr, Sydney, NSW, Australia. Pitman, AJ, Univ New S Wales, Climate Change Res Ctr, Red Culture Bldg, Sydney, NSW 2052, Australia. a.pitman@unsw.edu.aualt-title: J. Clim.accession-num: ISI:000249237700004work-type: Article}, -} - -@article{pennell_effective_2011, - title = {On the {Effective} {Number} of {Climate} {Models}}, - volume = {24}, - doi = {doi:10.1175/2010JCLI3814.1}, - number = {9}, - journal = {Journal of Climate}, - author = {Pennell, Christopher and Reichler, Thomas}, - year = {2011}, - pages = {2358--2367}, -} - -@article{pathirana_multifractal_2002, - title = {Multifractal modelling and simulation of rain fields exhibiting spatial heterogeneity}, - volume = {6}, - abstract = {Spatial multifractals are statistically homogeneous random fields. While being useful to model geophysical fields exhibiting a high degree of variability and discontinuity and including rainfall, they ignore the spatial trends embedded in the variability that are evident from large temporal aggregation of spatial fields. The modelling of rain fields using multifractals causes the information related to spatial heterogeneity, immensely important at some spatial scales, to be lost in the modelling process. A simple method to avoid this loss of the heterogeneity information is proposed. Instead of modelling rain fields directly as multifractals, a derived field M is modelled; this is the product of filtering observed rainfall snapshots with spatial heterogeneity as indicated by long term accumulations of rain fields. The validity of considering the field M as multifractal is investigated empirically. The applicability of the proposed method is demonstrated using a discrete cascade model on gauge-calibrated radar rainfall of central Japan at a daily scale. Important parameters of spatial rainfall, like the distribution of wet areas, spatial autocorrelation and rainfall intensity distributions at different geographic locations with different amounts of average rainfall, were faithfully reproduced by the proposed method.}, - number = {4}, - journal = {Hydrol. Earth Syst. Sci.}, - author = {Pathirana, A. and Herath, S.}, - month = aug, - year = {2002}, - pages = {695--708}, - annote = {601CJHYDROL EARTH SYST SCI}, - annote = {The following values have no corresponding Zotero field:alt-title: Hydrol. Earth Syst. Sci.accession-num: ISI:000178428300008}, -} - -@article{payne_mitigating_2004, - title = {Mitigating the effects of climate change on the water resources of the {Columbia} {River} {Basin}}, - volume = {62}, - issn = {0165-0009}, - abstract = {The potential effects of climate change on the hydrology and water resources of the Columbia River Basin (CRB) were evaluated using simulations from the U. S. Department of Energy and National Center for Atmospheric Research Parallel Climate Model (DOE/NCAR PCM). This study focuses on three climate projections for the 21st century based on a 'business as usual' (BAU) global emissions scenario, evaluated with respect to a control climate scenario based on static 1995 emissions. Time-varying monthly PCM temperature and precipitation changes were statistically downscaled and temporally disaggregated to produce daily forcings that drove a macroscale hydrologic simulation model of the Columbia River basin at 1/4-degree spatial resolution. For comparison with the direct statistical downscaling approach, a dynamical downscaling approach using a regional climate model (RCM) was also used to derive hydrologic model forcings for 20-year subsets from the PCM control climate ( 1995 - 2015) scenario and from the three BAU climate ( 2040 - 2060) projections. The statistically downscaled PCM scenario results were assessed for three analysis periods ( denoted Periods 1 - 3: 2010 - 2039, 2040 - 2069, 2070 - 2098) in which changes in annual average temperature were + 0.5, + 1.3 and + 2.1 degreesC, respectively, while critical winter season precipitation changes were - 3, + 5 and + 1 percent. For RCM, the predicted temperature change for the 2040 - 2060 period was + 1.2 degreesC and the average winter precipitation change was - 3 percent, relative to the RCM control climate. Due to the modest changes in winter precipitation, temperature changes dominated the simulated hydrologic effects by reducing winter snow accumulation, thus shifting summer streamflow to the winter. The hydrologic changes caused increased competition for reservoir storage between firm hydropower and instream flow targets developed pursuant to the Endangered Species Act listing of Columbia River salmonids. We examined several alternative reservoir operating policies designed to mitigate reservoir system performance losses. In general, the combination of earlier reservoir refill with greater storage allocations for instream flow targets mitigated some of the negative impacts to flow, but only with significant losses in firm hydropower production ( ranging from - 9 percent in Period 1 to - 35 percent for RCM). Simulated hydropower revenue changes were less than 5 percent for all scenarios, however, primarily due to small changes in annual runoff.}, - language = {English}, - number = {1-3}, - journal = {Climatic Change}, - author = {Payne, J. T. and Wood, A. W. and Hamlet, A. F. and Palmer, R. N. and Lettenmaier, D. P.}, - month = feb, - year = {2004}, - keywords = {model, sensitivity, impacts, simulations, california, western united-states}, - pages = {233--256}, - annote = {768FATimes Cited:10Cited References Count:32}, - annote = {The following values have no corresponding Zotero field:auth-address: Lettenmaier, DP Univ Washington, Dept Civil Engn, 164 Wilcox Hall,POB 352700, Seattle, WA 98195 USA Univ Washington, Dept Civil Engn, Seattle, WA 98195 USAaccession-num: ISI:000188531900010}, -} - -@article{parris_downscaling_2001, - title = {Downscaling climate change assessments}, - volume = {43}, - number = {4}, - journal = {Environment}, - author = {Parris, T. M.}, - month = may, - year = {2001}, - pages = {3--3}, - annote = {425PMENVIRONMENT}, - annote = {The following values have no corresponding Zotero field:alt-title: Environmentaccession-num: ISI:000168300200002}, -} - -@article{pandey_hybrid_2000, - title = {A hybrid orographic plus statistical model for downscaling daily precipitation in northern {California}}, - volume = {1}, - issn = {1525-755X}, - abstract = {A hybrid (physical-statistical) scheme is developed to resolve the finescale distribution of daily precipitation over complex terrain. The scheme generates precipitation by combining information from the upper-air conditions and From sparsely distributed station measurements: thus, it proceeds in two steps. First, an initial estimate of the precipitation is made using a simplified orographic precipitation model. It is a steady-state, multilayer, and two-dimensional model following the concepts of Rhea, The model is driven by the 2.5 degrees x 2.5 degrees gridded National Oceanic and Atmospheric Administration-National Centers for Environmental Prediction upper-air profiles, and its parameters are tuned using the observed precipitation structure of the region, Precipitation is generated assuming a forced lifting of the air parcels as they cross the mountain barrier following a straight trajectory. Second, the precipitation is adjusted using errors between derived precipitation and observations from nearby sites. The study area covers the northern half of California, including coastal mountains, central valley, and the Sierra Nevada. The model is run for a 5-km rendition of terrain for days of January-March over the period of 1988-95. A jackknife analysis demonstrates the validity of the approach. The spatial and temporal distributions of the simulated precipitation field agree well with the observed precipitation, Further, a mapping of model performance indices (correlation coefficients, model bias, root-mean-square error, and threat scores) from an array of stations from the region indicates that the model performs satisfactorily in resolving daily precipitation at 5-km resolution.}, - language = {English}, - number = {6}, - journal = {Journal of Hydrometeorology}, - author = {Pandey, G. R. and Cayan, D. R. and Dettinger, M. D. and Georgakakos, K. P.}, - month = dec, - year = {2000}, - pages = {491--506}, - annote = {397NFTimes Cited:5Cited References Count:27}, - annote = {The following values have no corresponding Zotero field:auth-address: Pandey, GR Dept Water Resource, Delta Modeling Sect, 1416 9th St,Room 215-15, Sacramento, CA 95481 USA Calif Dept Water Resources, Sacramento, CA USA US Geol Survey, La Jolla, CA USA Univ Calif San Diego, Scripps Inst Oceanog, Div Climate Res, La Jolla, CA 92093 USA Hydrol Res Ctr, San Diego, CA USAaccession-num: ISI:000166701200002}, -} - -@article{palmer_quantifying_2002, - title = {Quantifying the risk of extreme seasonal precipitation events in a changing climate}, - volume = {415}, - issn = {0028-0836}, - number = {6871}, - journal = {Nature}, - author = {Palmer, T. N. and Raisanen, J.}, - year = {2002}, - pages = {512--514}, - annote = {10.1038/415512a}, - annote = {The following values have no corresponding Zotero field:work-type: 10.1038/415512a}, -} - -@article{overland_relation_2002, - title = {The relation of surface forcing of the {Bering} {Sea} to large- scale climate patterns}, - volume = {49}, - abstract = {Climate fluctuations, or modes, are largely manifested in terms of coherent, large-scale (similar to 3000 km) patterns of anomalous sea-level pressure or geopotential height at various altitudes. It is worthwhile to investigate how these modes relate to the specific processes associated with atmospheric forcing of the ocean, in this case for the southeast Bering Sea. This approach has been termed "downscaling." Climate-scale patterns in this study are derived from covariance-based empirical orthogonal functions (EOFs) of low-pass filtered (10- day cut-oft) 700-mb geopotential height fields for 1958-1999. By design, this EOF analysis elicits sets of patterns for characterizing the variability in the large-scale atmospheric circulation centered on the Bering Sea. Four modes are considered for each of three periods, January-March, April-May, and June-July. These modes are compared with atmospheric circulation patterns formed by compositing 700-mb height anomalies based on the individual elements constituting the local forcing, i.e. the surface heat and momentum fluxes. In general, different aspects of local forcing are associated with different climate modes. In winter, the modes dominating the forcing of sea-ice include considerable interannual variability, but no discernible long-term trends. A prominent shift did occur around 1977 in the sign of a winter mode resembling the Pacific North American pattern; this mode is most significantly related to the local wind-stress curl. In spring, forcing of currents and stratification are related to the two leading climate modes, one resembling the North Pacific (NP) pattern and one reflecting the strength of the Aleutian low; both exhibit long-term trends with implications for the Bering Sea. In summer, an NP-like mode and a mode featuring a center over the Bering Sea include long-term trends with impacts on surface heating and wind mixing, respectively. Rare events, such as a persistent period of strong high pressure or a major storm, also can dominate the summer Bering Sea forcing in particular years. (C) 2002 Elsevier Science Ltd. All rights reserved.}, - number = {26}, - journal = {Deep-Sea Research Part Ii-Topical Studies in Oceanography}, - author = {Overland, J. E. and Bond, N. A. and Adams, J. M.}, - year = {2002}, - pages = {5855--5868}, - annote = {620WFDEEP-SEA RES PT II-TOP ST OCE}, - annote = {The following values have no corresponding Zotero field:alt-title: Deep-Sea Res. Part II-Top. Stud. Oceanogr.accession-num: ISI:000179555400005}, -} - -@book{panofsky_applications_1968, - address = {University Park, PA, USA}, - title = {Some {Applications} of {Statistics} to {Meteorology}}, - publisher = {The Pennsylvania State University}, - author = {Panofsky, H. A. and Brier, G. W.}, - year = {1968}, -} - -@article{palutikof_generating_2002, - title = {Generating rainfall and temperature scenarios at multiple sites: {Examples} from the {Mediterranean}}, - volume = {15}, - abstract = {A statistical downscaling methodology was implemented to generate daily time series of temperature and rainfall for point locations within a catchment, based on the output from general circulation models. The rainfall scenarios were constructed by a two-stage process. First, for a single station, a conditional first-order Markov chain was used to generate wet and dry day successions. Then, the multisite scenarios were constructed by sampling from a benchmark file containing a daily time series of multiple-site observations, classified by season, circulation weather type, and whether the day is wet or dry at the reference station. The temperature scenarios were constructed using deterministic transfer functions initialized by free atmosphere variables. The relationship between the temperature and rainfall scenarios is established in two ways. First, sea level pressure fields define the circulation weather types underpinning the rainfall scenarios and are used to construct predictor variables in the temperature scenarios. Second, separate temperature transfer functions are developed for wet and dry days. The methods were evaluated in two Mediterranean catchments. The rainfall scenarios were always too dry, despite the application of Monte Carlo techniques in an attempt to overcome the problem. The temperature scenarios were generally too cool. The scenarios were used to explore the occurrence of extreme events, and the changes predicted in response to climate change, taking the example of temperature. The nonlinear relationship between changes in the mean and changes at the extremes was clearly demonstrated.}, - number = {24}, - journal = {J. Clim.}, - author = {Palutikof, J. P. and Goodess, C. M. and Watkins, S. J. and Holt, T.}, - month = dec, - year = {2002}, - pages = {3529--3548}, - annote = {625JYJ CLIMATE}, - annote = {The following values have no corresponding Zotero field:alt-title: J. Clim.accession-num: ISI:000179814400001}, -} - -@article{overland_considerations_2011, - title = {Considerations in the {Selection} of {Global} {Climate} {Models} for {Regional} {Climate} {Projections}: {The} {Arctic} as a {Case} {Study}*}, - volume = {24}, - doi = {doi:10.1175/2010JCLI3462.1}, - number = {6}, - journal = {Journal of Climate}, - author = {Overland, James E. and Wang, Muyin and Bond, Nicholas A. and Walsh, John E. and Kattsov, Vladimir M. and Chapman, William L.}, - year = {2011}, - pages = {1583--1597}, -} - -@article{osland_coastal_2011, - title = {Coastal {Freshwater} {Wetland} {Plant} {Community} {Response} to {Seasonal} {Drought} and {Flooding} in {Northwestern} {Costa} {Rica}}, - volume = {31}, - issn = {0277-5212}, - doi = {10.1007/s13157-011-0180-9}, - language = {English}, - number = {4}, - journal = {Wetlands}, - author = {Osland, MichaelJ and González, Eugenio and Richardson, CurtisJ}, - month = aug, - year = {2011}, - keywords = {Climate change, Palo Verde National Park, Plant life forms, Seasonal hydrology, Seed bank, Tropical dry wetland}, - pages = {641--652}, - annote = {The following values have no corresponding Zotero field:alt-title: Wetlandspublisher: Springer Netherlands}, -} - -@article{ogorman_physical_2009, - title = {The physical basis for increases in precipitation extremes in simulations of 21st-century climate change}, - volume = {106}, - doi = {10.1073/pnas.0907610106}, - abstract = {Global warming is expected to lead to a large increase in atmospheric water vapor content and to changes in the hydrological cycle, which include an intensification of precipitation extremes. The intensity of precipitation extremes is widely held to increase proportionately to the increase in atmospheric water vapor content. Here, we show that this is not the case in 21st-century climate change scenarios simulated with climate models. In the tropics, precipitation extremes are not simulated reliably and do not change consistently among climate models; in the extratropics, they consistently increase more slowly than atmospheric water vapor content. We give a physical basis for how precipitation extremes change with climate and show that their changes depend on changes in the moist-adiabatic temperature lapse rate, in the upward velocity, and in the temperature when precipitation extremes occur. For the tropics, the theory suggests that improving the simulation of upward velocities in climate models is essential for improving predictions of precipitation extremes; for the extratropics, agreement with theory and the consistency among climate models increase confidence in the robustness of predictions of precipitation extremes under climate change.}, - number = {35}, - journal = {Proceedings of the National Academy of Sciences}, - author = {O'Gorman, Paul A. and Schneider, Tapio}, - month = sep, - year = {2009}, - pages = {14773--14777}, -} - -@article{olsson_statistical_2001, - title = {Statistical atmospheric downscaling of short-term extreme rainfall by neural networks}, - volume = {26}, - abstract = {Statistical atmospheric rainfall downscaling, that is, statistical estimation of local or regional rainfall on the basis of large-scale atmospheric circulation, has been advocated to make the output from global and regional climate models more accurate for a particular location or basin. Neural networks (NNs) have been used for such downscaling, but their application has proved problematic, mainly due to the numerous zero-values present in short-term rainfall time series. In the present study, using serially coupled NNs was tested as a way to improve performance. Mean 12-hour rainfall in the Chikugo River basin, Kyushu Island, Southern Japan, was downscaled from observations of precipitable water and zonal and meridional wind speed at 850 hPa, averaged over areas within which the temporal variation was found to be significantly correlated with basin rainfall. Basin rainfall was ranked into tour categories: no-rain (0) and low (1), high (2) and extreme (3) intensity, A series of NN experiments showed that the best overall performance in terms of hit rates was achieved by a two-stage approach in which a first NN distinguished between no-rain (0) and rain (1-3), and a second NN distinguished between low, high, and extreme rainfalls. Using either a single NN to distinguish between all four categories or three NNs to successively detect extreme values proved inferior. The results demonstrate the need for an elaborate configuration when using NNs for short-term, downscaling, and the importance of including physical considerations in the NN application. (C) 2001 Elsevier Science Ltd. All rights reserved.}, - number = {9}, - journal = {Physics and Chemistry of the Earth Part B-Hydrology Oceans and Atmosphere}, - author = {Olsson, J. and Uvo, C. B. and Jinno, K.}, - year = {2001}, - pages = {695--700}, - annote = {469ZXPHYS CHEM EARTH P B-HYDROL OC}, - annote = {The following values have no corresponding Zotero field:alt-title: Phys. Chem. Earth Pt B-Hydrol. Oceans Atmos.accession-num: ISI:000170846900010}, -} - -@article{alfaro_characteristics_2002, - title = {Some {Characteristics} of the {Precipitation} {Annual} {Cycle} in {Central} {America} {And} their {Relationships} with its {Surrounding} {Tropical} {Oceans}}, - volume = {7}, - abstract = {Resumen. Usando 94 estaciones pluviométricas de registro diario, se calcularon los ciclos anuales promedio dominantes en la región centroamericana por medio del análisis de funciones ortogonales empíricas. Esto permitió estimar aspectos importantes del ciclo anual tales como el inicio y término de la estación lluviosa así como el período de veranillo o canícula, notándose una variación latitudinal de los mismos. Se encontró que la región está dominada por un ciclo anual promedio que captura cerca del 72\% de la varianza. Este ciclo anual está explicado por una combinación de sistemas y parámetros que involucran la migración latitudinal de la ZCIT, la variación estacional de la radiación solar, que influye sobre el flujo de calor latente, y el viento en bajo nivel y su interacción con la orografía, principalmente. El segundo ciclo anual en importancia explica sólo el 8\% de la varianza en la región. Este dominó en estaciones ubicadas sobre la costa Caribe de Honduras, Costa Rica y Panamá. La precipitación en Centroamérica presentó relaciones distintas con los océanos Atlántico y Pacífico tropical en las escalas interanuales y decadales. En la escala interanual, los años más húmedos (secos) en la región estuvieron relacionados en general con anomalías de la temperatura superficial del mar más cálidas (frías) en el Atlántico Tropical que en el Pacífico Tropical Este. Mientras que en la escala interdecadal la precipitación mostró correlaciones positivas con ambas regiones oceánicas tropicales.}, - journal = {Tóp. Meteorol. Oceanogr.}, - author = {Alfaro, Eric}, - month = jan, - year = {2002}, - file = {Full Text PDF:C\:\\Users\\EdMaurer\\Zotero\\storage\\PHC4VSZE\\Alfaro - 2002 - Some Characteristics of the Precipitation Annual C.pdf:application/pdf}, -} - -@article{alfaro_improved_2018, - title = {Improved seasonal prediction skill of rainfall for the {Primera} season in {Central} {America}}, - volume = {38}, - issn = {0899-8418}, - url = {https://doi.org/10.1002/joc.5366}, - doi = {10.1002/joc.5366}, - abstract = {ABSTRACT This study explores the predictive skill of seasonal rainfall characteristics for the first rainy (and planting) season, May?June, in Central America. Statistical predictive models were built using a Model Output Statistics (MOS) technique based on canonical correlation analysis, in which variables that forecast with the Climate Forecast System version 2 (CFSv2) were used as candidate predictors for the observed total precipitation, frequency of rainy days and mean number of extremely dry and wet events in the season. CFSv2 initializations from February to April were explored. The CFSv2 variables used in the study consist of rainfall, as in a typical MOS technique, and a combination of low-level winds and convective available potential energy (CAPE), a blend that has been previously shown to be a good predictor for convective activity. The highest predictive skill was found for the seasonal frequency of rainy days, followed by the mean frequency of dry events. In terms of candidate predictors, the zonal transport of CAPE (uCAPE) at 925?hPa offers higher skill across Central America than rainfall, which is attributed in part to the high model uncertainties associated with precipitation in the region. As expected, dynamical model predictors initialized in February provide lower skill than those initialized later. Nonetheless, the skill is comparable for March and April initializations. These results suggest that the National Meteorological and Hydrological Services in Central America, and the Central American Regional Climate Outlook Forum, can produce earlier more skilful forecasts for May?June rainfall characteristics than previously stated.}, - number = {S1}, - urldate = {2021-10-28}, - journal = {International Journal of Climatology}, - author = {Alfaro, Eric J. and Chourio, Xandre and Muñoz, Ángel G. and Mason, Simon J.}, - month = apr, - year = {2018}, - note = {Publisher: John Wiley \& Sons, Ltd}, - keywords = {precipitation, Central America, canonical correlation analysis, MOS predictive schemes, seasonal climate prediction, statistical models}, - pages = {e255--e268}, - annote = {https://doi.org/10.1002/joc.5366}, -} - -@article{almazroui_projected_2021, - title = {Projected {Changes} in {Temperature} and {Precipitation} {Over} the {United} {States}, {Central} {America}, and the {Caribbean} in {CMIP6} {GCMs}}, - volume = {5}, - issn = {2509-9434}, - url = {https://doi.org/10.1007/s41748-021-00199-5}, - doi = {10.1007/s41748-021-00199-5}, - abstract = {The Coupled Model Intercomparison Project Phase 6 (CMIP6) dataset is used to examine projected changes in temperature and precipitation over the United States (U.S.), Central America and the Caribbean. The changes are computed using an ensemble of 31 models for three future time slices (2021–2040, 2041–2060, and 2080–2099) relative to the reference period (1995–2014) under three Shared Socioeconomic Pathways (SSPs; SSP1-2.6, SSP2-4.5, and SSP5-8.5). The CMIP6 ensemble reproduces the observed annual cycle and distribution of mean annual temperature and precipitation with biases between − 0.93 and 1.27 °C and − 37.90 to 58.45\%, respectively, for most of the region. However, modeled precipitation is too large over the western and Midwestern U.S. during winter and spring and over the North American monsoon region in summer, while too small over southern Central America. Temperature is projected to increase over the entire domain under all three SSPs, by as much as 6 °C under SSP5-8.5, and with more pronounced increases in the northern latitudes over the regions that receive snow in the present climate. Annual precipitation projections for the end of the twenty-first century have more uncertainty, as expected, and exhibit a meridional dipole-like pattern, with precipitation increasing by 10–30\% over much of the U.S. and decreasing by 10–40\% over Central America and the Caribbean, especially over the monsoon region. Seasonally, precipitation over the eastern and central subregions is projected to increase during winter and spring and decrease during summer and autumn. Over the monsoon region and Central America, precipitation is projected to decrease in all seasons except autumn. The analysis was repeated on a subset of 9 models with the best performance in the reference period; however, no significant difference was found, suggesting that model bias is not strongly influencing the projections.}, - number = {1}, - journal = {Earth Systems and Environment}, - author = {Almazroui, Mansour and Islam, M. Nazrul and Saeed, Fahad and Saeed, Sajjad and Ismail, Muhammad and Ehsan, Muhammad Azhar and Diallo, Ismaila and O’Brien, Enda and Ashfaq, Moetasim and Martínez-Castro, Daniel and Cavazos, Tereza and Cerezo-Mota, Ruth and Tippett, Michael K. and Gutowski, William J. and Alfaro, Eric J. and Hidalgo, Hugo G. and Vichot-Llano, Alejandro and Campbell, Jayaka D. and Kamil, Shahzad and Rashid, Irfan Ur and Sylla, Mouhamadou Bamba and Stephenson, Tannecia and Taylor, Michael and Barlow, Mathew}, - month = jan, - year = {2021}, - pages = {1--24}, -} - -@article{anderson_multiscale_2019, - title = {Multiscale trends and precipitation extremes in the {Central} {American} {Midsummer} {Drought}}, - volume = {14}, - number = {12}, - journal = {Environmental Research Letters}, - author = {Anderson, Talia and Anchukaitis, Kevin and Pons, Diego and Taylor, Matthew}, - year = {2019}, - pages = {124016}, -} - -@article{bombardi_sub-seasonal_2017, - title = {Sub-seasonal {Predictability} of the {Onset} and {Demise} of the {Rainy} {Season} over {Monsoonal} {Regions}}, - volume = {5}, - issn = {2296-6463}, - url = {https://www.frontiersin.org/article/10.3389/feart.2017.00014}, - doi = {10.3389/feart.2017.00014}, - abstract = {Sub-seasonal to seasonal (S2S) retrospective forecasts from three global coupled models are used to evaluate the predictability of the onset and demise dates of the rainy season over monsoonal regions. The onset and demise dates of the rainy season are defined using only precipitation data. The forecasts of the onset and demise dates of the rainy season are based on a hybrid methodology that combines observations and simulations. Although skillful model precipitation predictions remain challenging in many regions, our results show that they are skillful enough to identify onset and demise dates of the rainy season in many monsoon regions at sub-seasonal ({\textasciitilde}30 days) lead-times in retrospective forecasts. We verify sub-seasonal prediction skill for the onset and demise dates of the rainy season over South America, East Asia, and Northern Australia. However, we find low prediction skill for the onset and demise of the rainy season on sub-seasonal scales over the Indian monsoon region. This information would be valuable to sectors related to water management.}, - journal = {Frontiers in Earth Science}, - author = {Bombardi, Rodrigo J. and Pegion, Kathy V. and Kinter, James L. and Cash, Benjamin A. and Adams, Jennifer M.}, - year = {2017}, - pages = {14}, -} - -@article{colorado-ruiz_climate_2018, - title = {Climate change projections from {Coupled} {Model} {Intercomparison} {Project} phase 5 multi-model weighted ensembles for {Mexico}, the {North} {American} monsoon, and the mid-summer drought region}, - volume = {38}, - issn = {0899-8418}, - url = {https://doi.org/10.1002/joc.5773}, - doi = {10.1002/joc.5773}, - abstract = {Two versions of the ?reliability ensemble averaging? (REA and REA Xu) method and an unweighted mean were used to generate multi-model ensembles of 14 general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) for a baseline (1971?2000) period and future scenarios (RCP4.5 and RCP8.5) for the 21st century. To test the consistency of the REA ensembles at different scales, one ensemble was area-averaged at regional scale and two were obtained at 50-km gridpoints. Climatic metrics of temperature and precipitation during the baseline period served to evaluate the performance of the GCMs and the ensembles in the North American monsoon (NAM) and the mid-summer drought (MSD) regions. The metrics of the three REA ensembles are very similar among them in each region and show a better performance than the unweighted multi-model ensemble (U-MME). The majority of the GCMs overestimate winter precipitation in the NAM and fail to retract the end of the monsoon in autumn; REA ensembles reduce this overestimation. All ensembles capture well the MSD's double peak of rainfall, but underestimate summer precipitation. According to all ensembles, temperature increases of 1.5?2 °C in the two regions may be reached between 2035 and 2055 relative to the baseline, and by 2070?2099 temperature (precipitation) in Mexico may increase (decrease) between 2° C (5\%) and 5.8 °C (10\%) in the RCP4.5 and RCP8.5 scenarios, respectively. Annual changes of precipitation show a north (positive) and south of 35°N (negative) pattern. The largest impacts are expected during summer with a possible decrease of {\textasciitilde}13\% (up to ?1.5 mm/day), especially in southern Mexico, Central America, and the Caribbean, while autumn precipitation may slightly increase. Future projections from the REA Xu (by gridpoint) ensemble show spatial patterns similar to the U-MME, but with more regional detail, which could be an added value for regional climate impact studies.}, - number = {15}, - urldate = {2021-08-10}, - journal = {International Journal of Climatology}, - author = {Colorado-Ruiz, Gabriela and Cavazos, Tereza and Salinas, José Antonio and De Grau, Pamela and Ayala, Rosario}, - month = dec, - year = {2018}, - note = {Publisher: John Wiley \& Sons, Ltd}, - keywords = {climate change, projections, Mexico, CMIP5, monsoon, MSD, REA, weighted ensembles}, - pages = {5699--5716}, - annote = {https://doi.org/10.1002/joc.5773}, -} - -@article{curtis_regional_2008, - title = {Regional variations of the {Caribbean} mid-summer drought}, - volume = {94}, - issn = {1434-4483}, - url = {https://doi.org/10.1007/s00704-007-0342-0}, - doi = {10.1007/s00704-007-0342-0}, - abstract = {Pentad satellite-based precipitation estimates were input into a wavelet analysis to quantify the length, timing, and strength of the mid-summer drought (MSD) for the Caribbean Sea and surrounding regions. For most of the Caribbean the time between the first and second summer precipitation maxima is 98 to 117 days (∼3 to 4 months). The MSD appears in early-June over Puerto Rico and Hispaniola, and develops progressively later in the summer season towards the west, finally occurring in early-October over the Gulf of Mexico. The MSD is most intense in the eastern Pacific, strong and significant in the western Caribbean, and almost nonexistent in the eastern Caribbean. Forcing mechanisms are examined to help explain the regional variability in the Caribbean. A July increase in surface pressure and surface divergence, caused by the changing wind field, appears to contribute to a strong concurrent MSD over the waters bounded by Jamaica, Cuba, and the Yucatan peninsula. Finally, the island of Jamaica itself appears to block the flow of the tradewinds as they migrate northward and intensify into mid-summer, thus enhancing the divergence, and in turn MSD, immediately to the west.}, - number = {1}, - journal = {Theoretical and Applied Climatology}, - author = {Curtis, S. and Gamble, D. W.}, - month = sep, - year = {2008}, - pages = {25--34}, -} - -@article{curtis_diurnal_2004, - title = {Diurnal cycle of rainfall and surface winds and the mid-summer drought of {Mexico}/{Central} {America}}, - volume = {27}, - issn = {0936577X, 16161572}, - url = {http://www.jstor.org/stable/24868728}, - abstract = {[ABSTRACT:This study uses a novel data set of satellite-derived precipitation and reanalysis winds to describe variations in the diurnal cycle during the summer season in southern Mexico, Central America, and adjacent oceans, with the purpose of elucidating local forcing mechanisms of the midsummer drought (MSD). It is well established that precipitation peaks in the evening over land, while just off the Pacific coast the maximum is during the day. However, little is understood about the diurnal cycle of rainfall and winds associated with the MSD, a climatological phenomenon that impacts the local population. First, the MSD was quantified with the precipitation data and found to be strongest over Guatemala and El Salvador, consistent with previous studies. Over Guatemala the strength of the diurnal cycle follows the evolution of the MSD. Here evening precipitation rates are 25\% higher at the beginning and end of the summer compared to mid-summer, while daytime rates remain fairly constant throughout the season. To the south, over the Pacific, both daytime and nighttime precipitation rates are higher at the beginning and end of summer as compared to mid-summer. In June, surface winds in the vicinity of the Gulf of Tehuantepec are southerly during the evening and northerly during the day, dynamically consistent with the diurnal cycle of convection. In July-August and September, tradewind systems disturb this apparent local circulation cell bringing conditions conducive to reduced and enhanced rainfall respectively. In neighboring areas where the MSD is weak, precipitation and wind change little over land and ocean for the summer season. Thus, these results support earlier studies linking solar forcing of sea surface temperatures and changes in the large-scale circulation in the development of a strong but localized MSD.]}, - number = {1}, - urldate = {2021-10-28}, - journal = {Climate Research}, - author = {Curtis, Scott}, - year = {2004}, - note = {Publisher: Inter-Research Science Center}, - pages = {1--8}, -} - -@article{garcia-oliva_mid-summer_2021, - title = {The mid-summer drought spatial variability over {Mesoamerica}}, - volume = {34}, - copyright = {Copyright (c) 2020 Atmósfera}, - issn = {2395-8812}, - url = {https://www.revistascca.unam.mx/atm/index.php/atm/article/view/ATM.52790}, - doi = {10.20937/ATM.52790}, - abstract = {The region that includes southern Mexico and Central America is known as Mesoamerica. The annual cycle of precipitation characteristic of the Pacific slope of this region presents a bimodal distribution on summertime, with two maxima and a relative intraseasonal minimum, known as the Mid-Summer Drought (MSD). In this study, the small-scale (tens of kilometers) variability of the MSD is analyzed. Numerical simulations are performed using the regional climate model RegCM4 over a Mesoamerican domain for a six-year period. ERA Interim reanalysis data is used as initial and lateral boundary conditions. The domain is subdivided into smaller areas and the average annual cycle of precipitation distribution is computed for each of them. The MSD pattern is found to present a high spatial variability in the intensity of its two maxima and even the total absence of its characteristic minimum. Such behavior is attributed to soil-atmosphere and terrain topography interactions that are better resolved with a regional climate model.}, - language = {en}, - number = {2}, - urldate = {2021-10-28}, - journal = {Atmósfera}, - author = {García-Oliva, Lilian C. and Pazos, Enrique}, - month = mar, - year = {2021}, - note = {Number: 2}, - keywords = {regional model}, - pages = {227--232}, - file = {Full Text PDF:C\:\\Users\\EdMaurer\\Zotero\\storage\\SWDKY6X8\\García-Oliva and Pazos - 2021 - The mid-summer drought spatial variability over Me.pdf:application/pdf}, -} - -@techreport{helsel_statistical_2020, - address = {Reston, VA}, - type = {{USGS} {Numbered} {Series}}, - title = {Statistical methods in water resources}, - url = {http://pubs.er.usgs.gov/publication/tm4A3}, - abstract = {This text began as a collection of class notes for a course on applied statistical methods for hydrologists taught at the U.S. Geological Survey (USGS) National Training Center. Course material was formalized and organized into a textbook, first published in 1992 by Elsevier as part of their Studies in Environmental Science series. In 2002, the work was made available online as a USGS report.The text has now been updated as a USGS Techniques and Methods Report. It is intended to be a text in applied statistics for hydrology, environmental science, environmental engineering, geology, or biology that addresses distinctive features of environmental data. For example, water resources data tend to have many variables with a lower bound of zero, tend to be more skewed than data from many other disciplines, commonly contain censored data (less than values), and assumptions that the data are normally distributed are not appropriate. Computer-intensive methods (bootstrapping and permutation tests) now improve upon and replace the dependence on t-intervals, t-tests, and analysis of variance. A new chapter on sampling design addresses questions such as “How many observations do I need?” The chapter also presents distribution-free methods to help plan sampling efforts. The trends chapter has been updated to include the WRTDS (Weighted Regressions on Time, Discharge, and Season) method for analysis of water-quality data. This new version contains updated graphics and updated guidance on the use of statistical techniques. The text utilizes R, a programming language and open-source software environment, for all exercises and most graphics, and the R code used to generate figures and examples is provided for download.}, - number = {4-A3}, - urldate = {2021-10-28}, - institution = {U.S. Geological Survey}, - author = {Helsel, Dennis R. and Hirsch, Robert M. and Ryberg, Karen R. and Archfield, Stacey A. and Gilroy, Edward J.}, - year = {2020}, - doi = {10.3133/tm4A3}, - note = {Code Number: 4-A3 -Code: Statistical methods in water resources -Publication Title: Statistical methods in water resources -Reporter: Statistical methods in water resources -Series: Techniques and Methods -IP-089727}, - pages = {484}, - file = {Full Text:C\:\\Users\\EdMaurer\\Zotero\\storage\\4XZYGAGJ\\Helsel et al. - 2020 - Statistical methods in water resources.pdf:application/pdf}, -} - -@article{hidalgo_precursors_2019, - title = {Precursors of quasi-decadal dry-spells in the {Central} {America} {Dry} {Corridor}}, - volume = {53}, - issn = {1432-0894}, - url = {https://doi.org/10.1007/s00382-019-04638-y}, - doi = {10.1007/s00382-019-04638-y}, - abstract = {Although the hydric stress in Central America is generally low, there is a region relatively drier and prone to drought known as the Central America Dry Corridor (CADC). The area of interest is located mainly in the Pacific slope of Central America, from Chiapas in southern Mexico, to the Nicoya Peninsula in the Costa Rican North Pacific. Most of the region has experienced significant warming trends (1970–1999). On the contrary precipitation and the Palmer Drought Severity Index (PDSI) have mainly displayed non-significant trends. Analysis using the Standardized Precipitation Index and PDSI in the CADC, suggests a significant periodicity of severe and sustained droughts of around 10 years. The drought response has been associated with tropical heating that drives an atmospheric response through strengthening of the Hadley cell, which in turn produces higher pressure in the subtropical highs, and intensification of the trade winds (indexed by the Caribbean Low Level Jet). It is important to determine the commonness of severe and sustained droughts in the CADC to improve water resources planning, as this is a region that depends on subsistence agriculture and presents high social and economic vulnerabilities.}, - number = {3}, - journal = {Climate Dynamics}, - author = {Hidalgo, Hugo G. and Alfaro, Eric J. and Amador, Jorge A. and Bastidas, Álvaro}, - month = aug, - year = {2019}, - pages = {1307--1322}, -} - -@article{hidalgo_caribbean_2015-1, - title = {The caribbean low‐level jet, the inter‐tropical convergence zone and precipitation patterns in the intra‐americas sea: a proposed dynamical mechanism}, - volume = {97}, - number = {1}, - journal = {Geografiska Annaler: Series A, Physical Geography}, - author = {Hidalgo, Hugo G. and Durán‐Quesada, Ana M. and Amador, Jorge A. and Alfaro, Eric J.}, - year = {2015}, - note = {Publisher: Taylor \& Francis}, - pages = {41--59}, -} - -@article{maloney_north_2014-1, - title = {North {American} {Climate} in {CMIP5} {Experiments}: {Part} {III}: {Assessment} of {Twenty}-{First}-{Century} {Projections}}, - volume = {27}, - url = {https://journals.ametsoc.org/view/journals/clim/27/6/jcli-d-13-00273.1.xml}, - doi = {10.1175/JCLI-D-13-00273.1}, - number = {6}, - journal = {Journal of Climate}, - author = {Maloney, Eric D. and Camargo, Suzana J. and Chang, Edmund and Colle, Brian and Fu, Rong and Geil, Kerrie L. and Hu, Qi and Jiang, Xianan and Johnson, Nathaniel and Karnauskas, Kristopher B. and Kinter, James and Kirtman, Benjamin and Kumar, Sanjiv and Langenbrunner, Baird and Lombardo, Kelly and Long, Lindsey N. and Mariotti, Annarita and Meyerson, Joyce E. and Mo, Kingtse C. and Neelin, J. David and Pan, Zaitao and Seager, Richard and Serra, Yolande and Seth, Anji and Sheffield, Justin and Stroeve, Julienne and Thibeault, Jeanne and Xie, Shang-Ping and Wang, Chunzai and Wyman, Bruce and Zhao, Ming}, - year = {2014}, - note = {Place: Boston MA, USA -Publisher: American Meteorological Society}, - pages = {2230 -- 2270}, -} - -@article{mehta_network_1983, - title = {A {Network} {Algorithm} for {Performing} {Fisher}'s {Exact} {Test} in r × c {Contingency} {Tables}}, - volume = {78}, - issn = {01621459}, - url = {http://www.jstor.org/stable/2288652}, - doi = {10.2307/2288652}, - abstract = {[An exact test of significance of the hypothesis that the row and column effects are independent in an r × c contingency table can be executed in principle by generalizing Fisher's exact treatment of the 2 × 2 contingency table. Each table in a conditional reference set of r × c tables with fixed marginal sums is assigned a generalized hypergeometric probability. The significance level is then computed by summing the probabilities of all tables that are no larger (on the probability scale) than the observed table. However, the computational effort required to generate all r × c contingency tables with fixed marginal sums severely limits the use of Fisher's exact test. A novel technique that considerably extends the bounds of computational feasibility of the exact test is proposed here. The problem is transformed into one of identifying all paths through a directed acyclic network that equal or exceed a fixed length. Some interesting new optimization theorems are developed in the process. The numerical results reveal that for sparse contingency tables Fisher's exact test and Pearson's χ$^{\textrm{2}}$ test frequently lead to contradictory inferences concerning row and column independence.]}, - number = {382}, - urldate = {2021-10-28}, - journal = {Journal of the American Statistical Association}, - author = {Mehta, Cyrus R. and Patel, Nitin R.}, - year = {1983}, - note = {Publisher: [American Statistical Association, Taylor \& Francis, Ltd.]}, - pages = {427--434}, -} - -@article{rauscher_role_2011, - title = {The {Role} of {Regional} {SST} {Warming} {Variations} in the {Drying} of {Meso}-{America} in {Future} {Climate} {Projections}}, - volume = {24}, - url = {https://journals.ametsoc.org/view/journals/clim/24/7/2010jcli3536.1.xml}, - doi = {10.1175/2010JCLI3536.1}, - number = {7}, - journal = {Journal of Climate}, - author = {Rauscher, Sara A. and Kucharski, Fred and Enfield, David B.}, - year = {2011}, - note = {Place: Boston MA, USA -Publisher: American Meteorological Society}, - pages = {2003 -- 2016}, -} - -@article{stewart_recent_2021, - title = {Recent evidence for warmer and drier growing seasons in climate sensitive regions of {Central} {America} from multiple global datasets}, - volume = {n/a}, - issn = {1097-0088}, - url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/joc.7310}, - doi = {10.1002/joc.7310}, - abstract = {Smallholder livelihoods throughout Central America are built on rain-fed agriculture and depend on seasonal variations in temperature and precipitation. Recent climatic shifts in this highly diverse region are not well understood due to sparse observations, and as the skill of global climate products have not been thoroughly evaluated. We examine the performance for several reanalysis and satellite-based global climate data products (CHIRPS/CHIRTS, ERA5, MERRA-2, PERSIANN-CDR) as compared to the observation-based GPCC precipitation dataset. These datasets are then used to evaluate the magnitude and spatial extent of hydroclimatic shifts and changes in aridity and drought over the last four decades. We focus on water-limited regions that are important for rain-fed agriculture and particularly vulnerable to further drying, and newly delineate those regions for Central America and Mexico by adapting prior definitions of the Central American Dry Corridor. Our results indicate that the CHIRPS dataset exhibits the greatest skill for the study area. A general warming of 0.2–0.8°C·decade−1 was found across the region, particularly for spring and winter, while widespread drying was indicated by several measures for the summer growing season. Changes in annual precipitation have been inconsistent, but show declines of 20–25\% in eastern Honduras/Nicaragua and in several parts of Mexico. Some regions most vulnerable to drying have been subject to statistically significant trends towards summer drying, increases in drought and aridity driven by precipitation declines, and/or a lengthening of the winter dry season, highlighting areas where climate adaptation measures may be most urgent.}, - language = {en}, - number = {n/a}, - urldate = {2021-10-28}, - journal = {International Journal of Climatology}, - author = {Stewart, Iris T. and Maurer, Edwin P. and Stahl, Kerstin and Joseph, Kenneth}, - year = {2021}, - note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/joc.7310}, - keywords = {climate change, precipitation, Central America, drought, dry corridor}, - pages = {1--19}, - file = {Full Text PDF:C\:\\Users\\EdMaurer\\Zotero\\storage\\PA4FW4R8\\Stewart et al. - Recent evidence for warmer and drier growing seaso.pdf:application/pdf;Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\XPIE3B7C\\joc.html:text/html}, -} - -@article{vichot-llano_projected_2021, - title = {Projected changes in precipitation and temperature regimes and extremes over the {Caribbean} and {Central} {America} using a multiparameter ensemble of {RegCM4}}, - volume = {41}, - issn = {0899-8418}, - url = {https://doi.org/10.1002/joc.6811}, - doi = {10.1002/joc.6811}, - abstract = {Abstract Regional climate projections are developed four Central America and the Caribbean, based on a multiparameter ensemble formed by four configurations with different convective cumulus schemes with the regional model RegCM4 at 25?km grid spacing, driven by the HadGEM2-ES global model under the RCP4.5 and RCP8.5 scenarios. The precipitation change projections indicate drier conditions compared to present in the near future (2020?2049) and far future (2070?2099) time slices. These drier conditions are statistically significant at the 90\% confidence level over the eastern Caribbean, central Atlantic Ocean, and the western Pacific Ocean and are more pronounced in the far future time slice. For temperature, the warmer conditions are statistically significant at the 95\% confidence level over the study region. The drier and warmer signals are greater in extension and magnitude in the RCP8.5 than for RCP4.5. Projected changes in precipitation and temperature extreme indices show a reduction of consecutive wet days and an increment of consecutive dry days, with a reduction of cold days and nights and an increase of warm days and nights. Lower precipitation along with increased intensity of extreme events are projected over the Greatest Antilles region.}, - number = {2}, - urldate = {2021-08-10}, - journal = {International Journal of Climatology}, - author = {Vichot-Llano, Alejandro and Martinez-Castro, Daniel and Bezanilla-Morlot, Arnoldo and Centella-Artola, Abel and Giorgi, Filippo}, - month = feb, - year = {2021}, - note = {Publisher: John Wiley \& Sons, Ltd}, - keywords = {precipitation, ensembles, Caribbean, projections, extremes, indices, RegCM4}, - pages = {1328--1350}, - annote = {https://doi.org/10.1002/joc.6811}, -} - -@article{zhao_evaluation_2021, - title = {Evaluation of methods to detect and quantify the bimodal precipitation over {Central} {America} and {Mexico}}, - volume = {41}, - issn = {0899-8418}, - url = {https://doi.org/10.1002/joc.6736}, - doi = {10.1002/joc.6736}, - abstract = {Abstract Bimodal precipitation is a globally observed and regionally significant event that has a significant influence on the agriculture, public health, and insurance needs of associated regions. Many studies have focused on the mechanisms behind the generation and development of this event; however, little research into its characteristics exists due to a lack of a widely accepted method for accurate detection and quantification. Using a function collection containing various methods, different methods can be compared in terms of their performance in the detection and quantification of bimodal precipitation signals, allowing the proposal of appropriate criteria for method choice in various study types. Five methods (Mosiño and García, 1966; Curtis, 2002; Angeles et al., 2010; Karnauskas et al., 2013; Zhao et al., 2020) are adapted to the Climate Prediction Centre data during 1979?2017 in the domain of southern Mexico and Central America, and their performances are evaluated and compared. While outputs from the five methods reach general consistence for strong bimodal features over the Pacific side of Central America and Yucatán Peninsula, some biases are identified, specifically shown by the fact that methods using monthly climatological data demonstrates bimodal precipitation over the Caribbean side of Central America, while those using daily annual data indicate the existence of bimodal precipitation over the Pacific side of southern Mexico. By comparing two typical algorithms, we determined that this bias was induced by the limitation of temporal resolution in monthly climatological data and the nature of algorithms applying daily annual data. As part of a case study, a cluster algorithm was applied to outputs from an algorithm using daily annual precipitation, and a classification algorithm was used to test clustering performance. The resultant general high accuracy shows that annual bimodal signals offer good adaption to cluster and other potential machine learning algorithms.}, - number = {S1}, - urldate = {2021-04-20}, - journal = {International Journal of Climatology}, - author = {Zhao, Zijie and Zhang, Xihan}, - month = jan, - year = {2021}, - note = {Publisher: John Wiley \& Sons, Ltd}, - keywords = {midsummer drought, bimodal precipitation, intraseasonal variability}, - pages = {E897--E911}, - annote = {https://doi.org/10.1002/joc.6736}, -} - -@article{du_fei_life_2013, - title = {Life {Cycle} {Analysis} for {Water} and {Wastewater} {Pipe} {Materials}}, - volume = {139}, - url = {https://doi.org/10.1061/(ASCE)EE.1943-7870.0000638}, - doi = {10.1061/(ASCE)EE.1943-7870.0000638}, - number = {5}, - urldate = {2021-11-08}, - journal = {Journal of Environmental Engineering}, - author = {{Du Fei} and {Woods Gwendolyn J.} and {Kang Doosun} and {Lansey Kevin E.} and {Arnold Robert G.}}, - month = may, - year = {2013}, - note = {Publisher: American Society of Civil Engineers}, - pages = {703--711}, - annote = {doi: 10.1061/(ASCE)EE.1943-7870.0000638}, -} - -@techreport{usgcrp_climate_nodate, - title = {Climate {Science} {Special} {Report}}, - url = {https://science2017.globalchange.gov/chapter/appendix-b/}, - abstract = {This report is an authoritative assessment of the science of climate change, with a focus on the United States. It represents the first of two volumes of the Fourth National Climate Assessment, mandated by the Global Change Research Act of 1990.}, - language = {en}, - urldate = {2022-09-12}, - institution = {U.S. Global Change Research Program, Washington, DC}, - author = {{USGCRP}}, - pages = {1--470}, - file = {Full Text PDF:C\:\\Users\\EdMaurer\\Zotero\\storage\\7QHZ6I6Z\\USGCRP - Climate Science Special Report.pdf:application/pdf;Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\X546D6I7\\USGCRP - Climate Science Special Report.html:text/html}, -} - -@techreport{usgcrp_climate_nodate-1, - title = {Climate {Science} {Special} {Report}}, - url = {https://science2017.globalchange.gov/chapter/appendix-b/}, - abstract = {This report is an authoritative assessment of the science of climate change, with a focus on the United States. It represents the first of two volumes of the Fourth National Climate Assessment, mandated by the Global Change Research Act of 1990.}, - language = {en}, - urldate = {2022-09-12}, - institution = {U.S. Global Change Research Program, Washington, DC}, - author = {{USGCRP}}, - pages = {1--470}, - file = {Full Text PDF:C\:\\Users\\EdMaurer\\Zotero\\storage\\FTEKSCPP\\USGCRP - Climate Science Special Report.pdf:application/pdf;Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\Z5SW2MTX\\USGCRP - Climate Science Special Report.html:text/html}, -} - -@article{anderson_modeling_2018, - title = {Modeling multiple sea level rise stresses reveals up to twice the land at risk compared to strictly passive flooding methods}, - volume = {8}, - issn = {2045-2322}, - url = {https://doi.org/10.1038/s41598-018-32658-x}, - doi = {10.1038/s41598-018-32658-x}, - abstract = {Planning community resilience to sea level rise (SLR) requires information about where, when, and how SLR hazards will impact the coastal zone. We augment passive flood mapping (the so-called “bathtub” approach) by simulating physical processes posing recurrent threats to coastal infrastructure, communities, and ecosystems in Hawai‘i (including tidally-forced direct marine and groundwater flooding, seasonal wave inundation, and chronic coastal erosion). We find that the “bathtub” approach, alone, ignores 35–54 percent of the total land area exposed to one or more of these hazards, depending on location and SLR scenario. We conclude that modeling dynamic processes, including waves and erosion, is essential to robust SLR vulnerability assessment. Results also indicate that as sea level rises, coastal lands are exposed to higher flood depths and water velocities. The prevalence of low-lying coastal plains leads to a rapid increase in land exposure to hazards when sea level exceeds a critical elevation of {\textasciitilde}0.3 or 0.6 m, depending on location. At {\textasciitilde}1 m of SLR, land that is roughly seven times the total modern beach area is exposed to one or more hazards. Projected increases in extent, magnitude, and rate of persistent SLR impacts suggest an urgency to engage in long-term planning immediately.}, - number = {1}, - journal = {Scientific Reports}, - author = {Anderson, Tiffany R. and Fletcher, Charles H. and Barbee, Matthew M. and Romine, Bradley M. and Lemmo, Sam and Delevaux, Jade M.S.}, - month = sep, - year = {2018}, - pages = {14484}, -} - -@techreport{wmo_wmo_2017, - address = {Geneva, Switzerland}, - title = {{WMO} {Guidelines} on the {Calculation} of {Climate} {Normals} {Report} {No}. 1203}, - institution = {World Meteorological Organization}, - author = {WMO}, - year = {2017}, - pages = {29}, -} - -@techreport{sanderson_appendix_2017, - title = {Appendix {B}: {Model} {Weighting} {Strategy}. {Climate} {Science} {Special} {Report}: {Fourth} {National} {Climate} {Assessment}, {Volume} {I}}, - shorttitle = {Appendix {B}}, - url = {https://science2017.globalchange.gov/chapter/appendix-b/}, - urldate = {2022-09-12}, - institution = {U.S. Global Change Research Program}, - author = {Sanderson, B.M. and Wehner, M.F. and Wuebbles, D.J. and Fahey, D.W. and Hibbard, K.A. and Dokken, D.J. and Stewart, B.C. and Maycock, T.K.}, - year = {2017}, - doi = {10.7930/J06T0JS3}, - file = {Full Text:C\:\\Users\\EdMaurer\\Zotero\\storage\\DVE2TB9G\\Sanderson et al. - 2017 - Appendix B Model Weighting Strategy. Climate Scie.pdf:application/pdf}, -} - -@article{sanderson_skill_2017, - title = {Skill and independence weighting for multi-model assessments}, - volume = {10}, - issn = {1991-9603}, - url = {https://gmd.copernicus.org/articles/10/2379/2017/}, - doi = {10.5194/gmd-10-2379-2017}, - abstract = {Abstract. We present a weighting strategy for use with the CMIP5 multi-model archive in the fourth National Climate Assessment, which considers both skill in the climatological performance of models over North America as well as the inter-dependency of models arising from common parameterizations or tuning practices. The method exploits information relating to the climatological mean state of a number of projection-relevant variables as well as metrics representing long-term statistics of weather extremes. The weights, once computed can be used to simply compute weighted means and significance information from an ensemble containing multiple initial condition members from potentially co-dependent models of varying skill. Two parameters in the algorithm determine the degree to which model climatological skill and model uniqueness are rewarded; these parameters are explored and final values are defended for the assessment. The influence of model weighting on projected temperature and precipitation changes is found to be moderate, partly due to a compensating effect between model skill and uniqueness. However, more aggressive skill weighting and weighting by targeted metrics is found to have a more significant effect on inferred ensemble confidence in future patterns of change for a given projection.}, - language = {en}, - number = {6}, - urldate = {2022-09-12}, - journal = {Geoscientific Model Development}, - author = {Sanderson, Benjamin M. and Wehner, Michael and Knutti, Reto}, - month = jun, - year = {2017}, - pages = {2379--2395}, - file = {Full Text:C\:\\Users\\EdMaurer\\Zotero\\storage\\NQXTFBCG\\Sanderson et al. - 2017 - Skill and independence weighting for multi-model a.pdf:application/pdf}, -} - -@article{sanderson_skill_2017-1, - title = {Skill and independence weighting for multi-model assessments}, - volume = {10}, - issn = {1991-959X}, - url = {https://gmd.copernicus.org/articles/10/2379/2017/gmd-10-2379-2017-relations.html}, - doi = {10.5194/gmd-10-2379-2017}, - abstract = {{\textless}p{\textgreater}{\textless}strong class="journal-contentHeaderColor"{\textgreater}Abstract.{\textless}/strong{\textgreater} We present a weighting strategy for use with the CMIP5 multi-model archive in the fourth National Climate Assessment, which considers both skill in the climatological performance of models over North America as well as the inter-dependency of models arising from common parameterizations or tuning practices. The method exploits information relating to the climatological mean state of a number of projection-relevant variables as well as metrics representing long-term statistics of weather extremes. The weights, once computed can be used to simply compute weighted means and significance information from an ensemble containing multiple initial condition members from potentially co-dependent models of varying skill. Two parameters in the algorithm determine the degree to which model climatological skill and model uniqueness are rewarded; these parameters are explored and final values are defended for the assessment. The influence of model weighting on projected temperature and precipitation changes is found to be moderate, partly due to a compensating effect between model skill and uniqueness. However, more aggressive skill weighting and weighting by targeted metrics is found to have a more significant effect on inferred ensemble confidence in future patterns of change for a given projection.{\textless}/p{\textgreater}}, - language = {English}, - number = {6}, - urldate = {2022-09-12}, - journal = {Geoscientific Model Development}, - author = {Sanderson, Benjamin M. and Wehner, Michael and Knutti, Reto}, - month = jun, - year = {2017}, - note = {Publisher: Copernicus GmbH}, - pages = {2379--2395}, - file = {Full Text PDF:C\:\\Users\\EdMaurer\\Zotero\\storage\\62KTRGA7\\Sanderson et al. - 2017 - Skill and independence weighting for multi-model a.pdf:application/pdf;Snapshot:C\:\\Users\\EdMaurer\\Zotero\\storage\\2MZSP4C6\\gmd-10-2379-2017-relations.html:text/html}, -} - -@article{cannon_reductions_2020, - title = {Reductions in daily continental-scale atmospheric circulation biases between generations of global climate models: {CMIP5} to {CMIP6}}, - volume = {15}, - issn = {1748-9326}, - shorttitle = {Reductions in daily continental-scale atmospheric circulation biases between generations of global climate models}, - url = {https://iopscience.iop.org/article/10.1088/1748-9326/ab7e4f}, - doi = {10.1088/1748-9326/ab7e4f}, - abstract = {Abstract - This study evaluates and compares historical simulations of daily sea-level pressure circulation types over 6 continental-scale regions (North America, South America, Europe, Africa, East Asia, and Australasia) by 15 pairs of global climate models from modeling centers that contributed to both Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6. Atmospheric circulation classifications are constructed using two different methodologies applied to two reanalyses. Substantial improvements in performance, taking into account internal variability, are found between CMIP5 and CMIP6 for both frequency (24\% reduction in global error) and persistence (12\% reduction) of circulation types. Improvements between generations are robust to different methodological choices and reference datasets. A modest relationship between model resolution and skill is found. While there is large intra-ensemble spread in performance, the best performing models from CMIP6 exhibit levels of skill close to those from the reanalyses. In general, the latest generation of climate models should provide less biased simulations for use in regional dynamical and statistical downscaling efforts than previous generations.}, - number = {6}, - urldate = {2022-09-12}, - journal = {Environmental Research Letters}, - author = {Cannon, Alex J}, - month = jun, - year = {2020}, - pages = {064006}, - file = {Full Text:C\:\\Users\\EdMaurer\\Zotero\\storage\\9FTXJRUE\\Cannon - 2020 - Reductions in daily continental-scale atmospheric .pdf:application/pdf}, -} - -@techreport{dwr_-_california_department_of_water_resources_perspectives_2015, - address = {Sacramento, CA}, - title = {Perspectives and {Guidance} for {Climate} {Change} {Analysis}}, - author = {{DWR - California Department of Water Resources} and {Climate Change Technical Advisory Group}}, - month = aug, - year = {2015}, - pages = {142}, -} - -@article{thrasher_nasa_2022, - title = {{NASA} {Global} {Daily} {Downscaled} {Projections}, {CMIP6}}, - volume = {9}, - issn = {2052-4463}, - url = {https://www.nature.com/articles/s41597-022-01393-4}, - doi = {10.1038/s41597-022-01393-4}, - abstract = {Abstract - We describe the latest version of the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6). The archive contains downscaled historical and future projections for 1950–2100 based on output from Phase 6 of the Climate Model Intercomparison Project (CMIP6). The downscaled products were produced using a daily variant of the monthly bias correction/spatial disaggregation (BCSD) method and are at 1/4-degree horizontal resolution. Currently, eight variables from five CMIP6 experiments (historical, SSP126, SSP245, SSP370, and SSP585) are provided as procurable from thirty-five global climate models.}, - language = {en}, - number = {1}, - urldate = {2022-09-12}, - journal = {Scientific Data}, - author = {Thrasher, Bridget and Wang, Weile and Michaelis, Andrew and Melton, Forrest and Lee, Tsengdar and Nemani, Ramakrishna}, - month = dec, - year = {2022}, - pages = {262}, - file = {Full Text:C\:\\Users\\EdMaurer\\Zotero\\storage\\VXD5HZFP\\Thrasher et al. - 2022 - NASA Global Daily Downscaled Projections, CMIP6.pdf:application/pdf}, -} - -@article{williams_global_2016, - title = {A {Global} {Repository} for {Planet}-{Sized} {Experiments} and {Observations}}, - volume = {97}, - issn = {0003-0007, 1520-0477}, - url = {https://journals.ametsoc.org/doi/10.1175/BAMS-D-15-00132.1}, - doi = {10.1175/BAMS-D-15-00132.1}, - abstract = {Abstract - Working across U.S. federal agencies, international agencies, and multiple worldwide data centers, and spanning seven international network organizations, the Earth System Grid Federation (ESGF) allows users to access, analyze, and visualize data using a globally federated collection of networks, computers, and software. Its architecture employs a system of geographically distributed peer nodes that are independently administered yet united by common federation protocols and application programming interfaces (APIs). The full ESGF infrastructure has now been adopted by multiple Earth science projects and allows access to petabytes of geophysical data, including the Coupled Model Intercomparison Project (CMIP)—output used by the Intergovernmental Panel on Climate Change assessment reports. Data served by ESGF not only include model output (i.e., CMIP simulation runs) but also include observational data from satellites and instruments, reanalyses, and generated images. Metadata summarize basic information about the data for fast and easy data discovery.}, - language = {en}, - number = {5}, - urldate = {2022-09-12}, - journal = {Bulletin of the American Meteorological Society}, - author = {Williams, Dean N. and Balaji, V. and Cinquini, Luca and Denvil, Sébastien and Duffy, Daniel and Evans, Ben and Ferraro, Robert and Hansen, Rose and Lautenschlager, Michael and Trenham, Claire}, - month = may, - year = {2016}, - pages = {803--816}, - file = {Full Text:C\:\\Users\\EdMaurer\\Zotero\\storage\\YFMEA3DS\\Williams et al. - 2016 - A Global Repository for Planet-Sized Experiments a.pdf:application/pdf}, -} - -@article{raoult_climate_2017, - title = {Climate service develops user-friendly data store}, - url = {https://www.ecmwf.int/node/18188}, - doi = {10.21957/P3C285}, - urldate = {2022-09-12}, - author = {Raoult, Baudouin and Bergeron, Cedric and López Alós, Angel and Thépaut, Jean-Noël and Dee, Dick}, - year = {2017}, - note = {Publisher: ECMWF}, -} - -@article{allen_nino-like_2017, - title = {El {Niño}-like teleconnection increases {California} precipitation in response to warming}, - volume = {8}, - issn = {2041-1723}, - url = {http://www.nature.com/articles/ncomms16055}, - doi = {10.1038/ncomms16055}, - language = {en}, - number = {1}, - urldate = {2022-09-12}, - journal = {Nature Communications}, - author = {Allen, Robert J. and Luptowitz, Rainer}, - month = dec, - year = {2017}, - pages = {16055}, - file = {Full Text:C\:\\Users\\EdMaurer\\Zotero\\storage\\9AT3LLSI\\Allen and Luptowitz - 2017 - El Niño-like teleconnection increases California p.pdf:application/pdf}, -} - -@article{pierce_evaluating_2021, - title = {Evaluating {Global} {Climate} {Models} for {Hydrological} {Studies} of the {Upper} {Colorado} {River} {Basin}}, - issn = {1093-474X, 1752-1688}, - url = {https://onlinelibrary.wiley.com/doi/10.1111/1752-1688.12974}, - doi = {10.1111/1752-1688.12974}, - language = {en}, - urldate = {2022-09-12}, - journal = {JAWRA Journal of the American Water Resources Association}, - author = {Pierce, David W. and Cayan, Daniel R. and Goodrich, Jordan and Das, Tapash and Munévar, Armin}, - month = nov, - year = {2021}, - pages = {1752--1688.12974}, -} - -@article{abatzoglou_evaluating_2017, - title = {Evaluating climate model simulations of drought for the northwestern {United} {States}}, - volume = {37}, - issn = {0899-8418, 1097-0088}, - url = {https://onlinelibrary.wiley.com/doi/10.1002/joc.5046}, - doi = {10.1002/joc.5046}, - language = {en}, - number = {S1}, - urldate = {2022-09-12}, - journal = {International Journal of Climatology}, - author = {Abatzoglou, John T. and Rupp, David E.}, - month = aug, - year = {2017}, - pages = {910--920}, -} - -@article{brunner_quantifying_2019, - title = {Quantifying uncertainty in {European} climate projections using combined performance-independence weighting}, - volume = {14}, - issn = {1748-9326}, - url = {https://iopscience.iop.org/article/10.1088/1748-9326/ab492f}, - doi = {10.1088/1748-9326/ab492f}, - abstract = {Abstract - Uncertainty in model projections of future climate change arises due to internal variability, multiple possible emission scenarios, and different model responses to anthropogenic forcing. To robustly quantify uncertainty in multi-model ensembles, inter-dependencies between models as well as a models ability to reproduce observations should be considered. Here, a model weighting approach, which accounts for both independence and performance, is applied to European temperature and precipitation projections from the CMIP5 archive. Two future periods representing mid- and end-of-century conditions driven by the high-emission scenario RCP8.5 are investigated. To inform the weighting, six diagnostics based on three observational estimates are used to also account for uncertainty in the observational record. Our findings show that weighting the ensemble can reduce the interquartile spread by more than 20\% in some regions, increasing the reliability of projected changes. The mean temperature change is most notably impacted by the weighting in the Mediterranean, where it is found to be 0.35 °C higher than the unweighted mean in the end-of-century period. For precipitation the largest differences are found for Northern Europe, with a relative decrease in precipitation of 2.4\% and 3.4\% for the two future periods compared to the unweighted case. Based on a perfect model test, it is found that weighting the ensemble leads to an increase in the investigated skill score for temperature and precipitation while minimizing the probability of overfitting.}, - number = {12}, - urldate = {2022-09-12}, - journal = {Environmental Research Letters}, - author = {Brunner, Lukas and Lorenz, Ruth and Zumwald, Marius and Knutti, Reto}, - month = dec, - year = {2019}, - pages = {124010}, - file = {Full Text:C\:\\Users\\EdMaurer\\Zotero\\storage\\ECFAT42Z\\Brunner et al. - 2019 - Quantifying uncertainty in European climate projec.pdf:application/pdf}, -} - -@article{langenbrunner_paretooptimal_2017, - title = {Pareto‐{Optimal} {Estimates} of {California} {Precipitation} {Change}}, - volume = {44}, - issn = {0094-8276, 1944-8007}, - url = {https://onlinelibrary.wiley.com/doi/10.1002/2017GL075226}, - doi = {10.1002/2017GL075226}, - language = {en}, - number = {24}, - urldate = {2022-09-12}, - journal = {Geophysical Research Letters}, - author = {Langenbrunner, Baird and Neelin, J. David}, - month = dec, - year = {2017}, -} - -@article{simpson_emergent_2021, - title = {Emergent constraints on the large scale atmospheric circulation and regional hydroclimate: do they still work in {CMIP6} and how much can they actually constrain the future?}, - issn = {0894-8755, 1520-0442}, - shorttitle = {Emergent constraints on the large scale atmospheric circulation and regional hydroclimate}, - url = {https://journals.ametsoc.org/view/journals/clim/aop/JCLI-D-21-0055.1/JCLI-D-21-0055.1.xml}, - doi = {10.1175/JCLI-D-21-0055.1}, - abstract = {Abstract - An ‘emergent constraint’ (EC) is a statistical relationship, across a model ensemble, between a measurable aspect of the present day climate (the predictor) and an aspect of future projected climate change (the predictand). If such a relationship is robust and understood, it may provide constrained projections for the real world. Here, Coupled Model Intercomparison Project 6 (CMIP6) models are used to revisit several ECs that were proposed in prior model intercomparisons with two aims: (1) to assess whether these ECs survive the partial out-of-sample test of CMIP6 and (2) to more rigorously quantify the constrained projected change than previous studies. To achieve the latter, methods are proposed whereby uncertainties can be appropriately accounted for, including the influence of internal variability, uncertainty on the linear relationship, and the uncertainty associated with model structural differences, aside from those described by the EC. Both least squares regression and a Bayesian Hierarchical Model are used. Three ECs are assessed: (a) the relationship between Southern Hemisphere jet latitude and projected jet shift, which is found to be a robust and quantitatively useful constraint on future projections; (b) the relationship between stationary wave amplitude in the Pacific-North American sector and meridional wind changes over North America (with extensions to hydroclimate), which is found to be robust but improvements in the predictor in CMIP6 result in it no longer substantially constrains projected change in either circulation or hydroclimate; and (c) the relationship between ENSO teleconnections to California and California precipitation change, which does not appear to be robust when using historical ENSO teleconnections as the predictor.}, - urldate = {2022-09-12}, - journal = {Journal of Climate}, - author = {Simpson, Isla R. and McKinnon, Karen A. and Davenport, Frances V. and Tingley, Martin and Lehner, Flavio and Al Fahad, Abdullah and Chen, Di}, - month = may, - year = {2021}, - pages = {1--62}, - file = {Submitted Version:C\:\\Users\\EdMaurer\\Zotero\\storage\\E43YJLBM\\Simpson et al. - 2021 - Emergent constraints on the large scale atmospheri.pdf:application/pdf}, -} - -@article{li_wetter_2022, - title = {Wetter {California} {Projected} by {CMIP6} {Models} {With} {Observational} {Constraints} {Under} a {High} {GHG} {Emission} {Scenario}}, - volume = {10}, - issn = {2328-4277, 2328-4277}, - url = {https://onlinelibrary.wiley.com/doi/10.1029/2022EF002694}, - doi = {10.1029/2022EF002694}, - language = {en}, - number = {4}, - urldate = {2022-09-12}, - journal = {Earth's Future}, - author = {Li, Fa and Zhu, Qing and Riley, William J. and Yuan, Kunxiaojia and Wu, Huayi and Gui, Zhipeng}, - month = apr, - year = {2022}, - file = {Full Text:C\:\\Users\\EdMaurer\\Zotero\\storage\\XGX7TSQ3\\Li et al. - 2022 - Wetter California Projected by CMIP6 Models With O.pdf:application/pdf}, -} - -@article{eyring_taking_2019, - title = {Taking climate model evaluation to the next level}, - volume = {9}, - issn = {1758-678X, 1758-6798}, - url = {http://www.nature.com/articles/s41558-018-0355-y}, - doi = {10.1038/s41558-018-0355-y}, - language = {en}, - number = {2}, - urldate = {2022-09-13}, - journal = {Nature Climate Change}, - author = {Eyring, Veronika and Cox, Peter M. and Flato, Gregory M. and Gleckler, Peter J. and Abramowitz, Gab and Caldwell, Peter and Collins, William D. and Gier, Bettina K. and Hall, Alex D. and Hoffman, Forrest M. and Hurtt, George C. and Jahn, Alexandra and Jones, Chris D. and Klein, Stephen A. and Krasting, John P. and Kwiatkowski, Lester and Lorenz, Ruth and Maloney, Eric and Meehl, Gerald A. and Pendergrass, Angeline G. and Pincus, Robert and Ruane, Alex C. and Russell, Joellen L. and Sanderson, Benjamin M. and Santer, Benjamin D. and Sherwood, Steven C. and Simpson, Isla R. and Stouffer, Ronald J. and Williamson, Mark S.}, - month = feb, - year = {2019}, - pages = {102--110}, - file = {Full Text:C\:\\Users\\EdMaurer\\Zotero\\storage\\B94CQLBN\\Eyring et al. - 2019 - Taking climate model evaluation to the next level.pdf:application/pdf}, -} - -@article{planton_evaluating_2021, - title = {Evaluating {Climate} {Models} with the {CLIVAR} 2020 {ENSO} {Metrics} {Package}}, - volume = {102}, - issn = {0003-0007, 1520-0477}, - url = {https://journals.ametsoc.org/view/journals/bams/102/2/BAMS-D-19-0337.1.xml}, - doi = {10.1175/BAMS-D-19-0337.1}, - abstract = {Abstract - El Niño–Southern Oscillation (ENSO) is the dominant mode of interannual climate variability on the planet, with far-reaching global impacts. It is therefore key to evaluate ENSO simulations in state-of-the-art numerical models used to study past, present, and future climate. Recently, the Pacific Region Panel of the International Climate and Ocean: Variability, Predictability and Change (CLIVAR) Project, as a part of the World Climate Research Programme (WCRP), led a community-wide effort to evaluate the simulation of ENSO variability, teleconnections, and processes in climate models. The new CLIVAR 2020 ENSO metrics package enables model diagnosis, comparison, and evaluation to 1) highlight aspects that need improvement; 2) monitor progress across model generations; 3) help in selecting models that are well suited for particular analyses; 4) reveal links between various model biases, illuminating the impacts of those biases on ENSO and its sensitivity to climate change; and to 5) advance ENSO literacy. By interfacing with existing model evaluation tools, the ENSO metrics package enables rapid analysis of multipetabyte databases of simulations, such as those generated by the Coupled Model Intercomparison Project phases 5 (CMIP5) and 6 (CMIP6). The CMIP6 models are found to significantly outperform those from CMIP5 for 8 out of 24 ENSO-relevant metrics, with most CMIP6 models showing improved tropical Pacific seasonality and ENSO teleconnections. Only one ENSO metric is significantly degraded in CMIP6, namely, the coupling between the ocean surface and subsurface temperature anomalies, while the majority of metrics remain unchanged.}, - number = {2}, - urldate = {2022-09-13}, - journal = {Bulletin of the American Meteorological Society}, - author = {Planton, Yann Y. and Guilyardi, Eric and Wittenberg, Andrew T. and Lee, Jiwoo and Gleckler, Peter J. and Bayr, Tobias and McGregor, Shayne and McPhaden, Michael J. and Power, Scott and Roehrig, Romain and Vialard, Jérôme and Voldoire, Aurore}, - month = feb, - year = {2021}, - pages = {E193--E217}, - file = {Full Text:C\:\\Users\\EdMaurer\\Zotero\\storage\\XRNZVQC8\\Planton et al. - 2021 - Evaluating Climate Models with the CLIVAR 2020 ENS.pdf:application/pdf}, -} - -@article{herger_selecting_2018, - title = {Selecting a climate model subset to optimise key ensemble properties}, - volume = {9}, - issn = {2190-4987}, - url = {https://esd.copernicus.org/articles/9/135/2018/}, - doi = {10.5194/esd-9-135-2018}, - abstract = {Abstract. End users studying impacts and risks caused by human-induced climate change -are often presented with large multi-model ensembles of climate projections -whose composition and size are arbitrarily determined. An efficient and -versatile method that finds a subset which maintains certain key properties -from the full ensemble is needed, but very little work has been done in this -area. Therefore, users typically make their own somewhat subjective subset -choices and commonly use the equally weighted model mean as a best estimate. -However, different climate model simulations cannot necessarily be regarded -as independent estimates due to the presence of duplicated code and shared -development history. Here, we present an efficient and flexible tool that makes better use of the -ensemble as a whole by finding a subset with improved mean performance -compared to the multi-model mean while at the same time maintaining the -spread and addressing the problem of model interdependence. Out-of-sample -skill and reliability are demonstrated using model-as-truth experiments. This -approach is illustrated with one set of optimisation criteria but we also -highlight the flexibility of cost functions, depending on the focus of -different users. The technique is useful for a range of applications that, -for example, minimise present-day bias to obtain an accurate ensemble mean, -reduce dependence in ensemble spread, maximise future spread, ensure good -performance of individual models in an ensemble, reduce the ensemble size -while maintaining important ensemble characteristics, or optimise several of -these at the same time. As in any calibration exercise, the final ensemble is -sensitive to the metric, observational product, and pre-processing steps used.}, - language = {en}, - number = {1}, - urldate = {2022-09-13}, - journal = {Earth System Dynamics}, - author = {Herger, Nadja and Abramowitz, Gab and Knutti, Reto and Angélil, Oliver and Lehmann, Karsten and Sanderson, Benjamin M.}, - month = feb, - year = {2018}, - pages = {135--151}, - file = {Full Text:C\:\\Users\\EdMaurer\\Zotero\\storage\\2M7EHH9Z\\Herger et al. - 2018 - Selecting a climate model subset to optimise key e.pdf:application/pdf}, -} - -@article{fasullo_evaluating_2020, - title = {Evaluating simulated climate patterns from the {CMIP} archives using satellite and reanalysis datasets using the {Climate} {Model} {Assessment} {Tool} ({CMATv1})}, - volume = {13}, - issn = {1991-9603}, - url = {https://gmd.copernicus.org/articles/13/3627/2020/}, - doi = {10.5194/gmd-13-3627-2020}, - abstract = {Abstract. An objective approach is presented for scoring coupled climate simulations through an evaluation -against satellite and reanalysis datasets during the satellite era (i.e., since 1979). The approach -is motivated, described, and applied to available Coupled Model Intercomparison Project (CMIP) -archives and the Community Earth System Model (CESM) Version 1 Large Ensemble archives with the -goal of robustly benchmarking model performance and its evolution across CMIP generations. A -scoring system is employed that minimizes sensitivity to internal variability, external forcings, -and model tuning. Scores are based on pattern correlations of the simulated mean state, seasonal -contrasts, and ENSO teleconnections. A broad range of feedback-relevant fields is considered and -summarized on discrete timescales (climatology, seasonal, interannual) and physical realms (energy -budget, water cycle, dynamics). Fields are also generally chosen for which observational -uncertainty is small compared to model structural differences. Highest mean variable scores across models are reported for well-observed fields such as sea level -pressure, precipitable water, and outgoing longwave radiation, while the lowest scores are reported -for 500 hPa vertical velocity, net surface energy flux, and precipitation minus -evaporation. The fidelity of models is found to vary widely both within and across CMIP -generations. Systematic increases in model fidelity in more recent CMIP generations are -identified, with the greatest improvements occurring in dynamic and energetic fields. Such -examples include shortwave cloud forcing and 500 hPa eddy geopotential height and relative -humidity. Improvements in ENSO scores with time are substantially greater than for climatology or -seasonal timescales. Analysis output data generated by this approach are made freely available online from a broad range -of model ensembles, including the CMIP archives and various single-model large ensembles. These -multimodel archives allow for an expeditious analysis of performance across a range of -simulations, while the CESM large ensemble archive allows for estimation of the influence of -internal variability on computed scores. The entire output archive, updated and expanded -regularly, can be accessed at http://webext.cgd.ucar.edu/Multi-Case/CMAT/index.html (last access: 18 August 2020).}, - language = {en}, - number = {8}, - urldate = {2022-09-13}, - journal = {Geoscientific Model Development}, - author = {Fasullo, John T.}, - month = aug, - year = {2020}, - pages = {3627--3642}, - file = {Full Text:C\:\\Users\\EdMaurer\\Zotero\\storage\\2WHZ6N59\\Fasullo - 2020 - Evaluating simulated climate patterns from the CMI.pdf:application/pdf}, -} - -@article{bock_quantifying_2020, - title = {Quantifying {Progress} {Across} {Different} {CMIP} {Phases} {With} the {ESMValTool}}, - volume = {125}, - issn = {2169-897X, 2169-8996}, - url = {https://onlinelibrary.wiley.com/doi/10.1029/2019JD032321}, - doi = {10.1029/2019JD032321}, - language = {en}, - number = {21}, - urldate = {2022-09-13}, - journal = {Journal of Geophysical Research: Atmospheres}, - author = {Bock, L. and Lauer, A. and Schlund, M. and Barreiro, M. and Bellouin, N. and Jones, C. and Meehl, G. A. and Predoi, V. and Roberts, M. J. and Eyring, V.}, - month = nov, - year = {2020}, - file = {Full Text:C\:\\Users\\EdMaurer\\Zotero\\storage\\Y5NDI8GB\\Bock et al. - 2020 - Quantifying Progress Across Different CMIP Phases .pdf:application/pdf}, -} - -@article{parding_gcmeval_2020, - title = {{GCMeval} – {An} interactive tool for evaluation and selection of climate model ensembles}, - volume = {18}, - issn = {24058807}, - url = {https://linkinghub.elsevier.com/retrieve/pii/S2405880720300194}, - doi = {10.1016/j.cliser.2020.100167}, - language = {en}, - urldate = {2022-09-13}, - journal = {Climate Services}, - author = {Parding, Kajsa M. and Dobler, Andreas and McSweeney, Carol F. and Landgren, Oskar A. and Benestad, Rasmus and Erlandsen, Helene B. and Mezghani, Abdelkader and Gregow, Hilppa and Räty, Olle and Viktor, Elisabeth and El Zohbi, Juliane and Christensen, Ole B. and Loukos, Harilaos}, - month = apr, - year = {2020}, - pages = {100167}, - file = {Full Text:C\:\\Users\\EdMaurer\\Zotero\\storage\\H34NZISB\\Parding et al. - 2020 - GCMeval – An interactive tool for evaluation and s.pdf:application/pdf}, -} - -@article{sherwood_assessment_2020, - title = {An {Assessment} of {Earth}'s {Climate} {Sensitivity} {Using} {Multiple} {Lines} of {Evidence}}, - volume = {58}, - issn = {8755-1209, 1944-9208}, - url = {https://onlinelibrary.wiley.com/doi/10.1029/2019RG000678}, - doi = {10.1029/2019RG000678}, - language = {en}, - number = {4}, - urldate = {2022-09-19}, - journal = {Reviews of Geophysics}, - author = {Sherwood, S. C. and Webb, M. J. and Annan, J. D. and Armour, K. C. and Forster, P. M. and Hargreaves, J. C. and Hegerl, G. and Klein, S. A. and Marvel, K. D. and Rohling, E. J. and Watanabe, M. and Andrews, T. and Braconnot, P. and Bretherton, C. S. and Foster, G. L. and Hausfather, Z. and Heydt, A. S. and Knutti, R. and Mauritsen, T. and Norris, J. R. and Proistosescu, C. and Rugenstein, M. and Schmidt, G. A. and Tokarska, K. B. and Zelinka, M. D.}, - month = dec, - year = {2020}, - file = {Full Text:C\:\\Users\\EdMaurer\\Zotero\\storage\\9YG5MS6S\\Sherwood et al. - 2020 - An Assessment of Earth's Climate Sensitivity Using.pdf:application/pdf}, -} - -@article{hausfather_climate_2022, - title = {Climate simulations: recognize the ‘hot model’ problem}, - volume = {605}, - issn = {0028-0836, 1476-4687}, - shorttitle = {Climate simulations}, - url = {https://www.nature.com/articles/d41586-022-01192-2}, - doi = {10.1038/d41586-022-01192-2}, - language = {en}, - number = {7908}, - urldate = {2022-09-19}, - journal = {Nature}, - author = {Hausfather, Zeke and Marvel, Kate and Schmidt, Gavin A. and Nielsen-Gammon, John W. and Zelinka, Mark}, - month = may, - year = {2022}, - pages = {26--29}, - file = {Full Text:C\:\\Users\\EdMaurer\\Zotero\\storage\\AUQGA2JX\\Hausfather et al. - 2022 - Climate simulations recognize the ‘hot model’ pro.pdf:application/pdf}, -} - -@article{meehl_context_2020, - title = {Context for interpreting equilibrium climate sensitivity and transient climate response from the {CMIP6} {Earth} system models}, - volume = {6}, - issn = {2375-2548}, - url = {https://www.science.org/doi/10.1126/sciadv.aba1981}, - doi = {10.1126/sciadv.aba1981}, - abstract = {A historical context is provided for interpreting the equilibrium climate sensitivity (ECS) and transient climate response (TCR). - , - - For the current generation of earth system models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6), the range of equilibrium climate sensitivity (ECS, a hypothetical value of global warming at equilibrium for a doubling of CO - 2 - ) is 1.8°C to 5.6°C, the largest of any generation of models dating to the 1990s. Meanwhile, the range of transient climate response (TCR, the surface temperature warming around the time of CO - 2 - doubling in a 1\% per year CO - 2 - increase simulation) for the CMIP6 models of 1.7°C (1.3°C to 3.0°C) is only slightly larger than for the CMIP3 and CMIP5 models. Here we review and synthesize the latest developments in ECS and TCR values in CMIP, compile possible reasons for the current values as supplied by the modeling groups, and highlight future directions. Cloud feedbacks and cloud-aerosol interactions are the most likely contributors to the high values and increased range of ECS in CMIP6.}, - language = {en}, - number = {26}, - urldate = {2022-09-19}, - journal = {Science Advances}, - author = {Meehl, Gerald A. and Senior, Catherine A. and Eyring, Veronika and Flato, Gregory and Lamarque, Jean-Francois and Stouffer, Ronald J. and Taylor, Karl E. and Schlund, Manuel}, - month = jun, - year = {2020}, - pages = {eaba1981}, - file = {Full Text:C\:\\Users\\EdMaurer\\Zotero\\storage\\9SMSAF28\\Meehl et al. - 2020 - Context for interpreting equilibrium climate sensi.pdf:application/pdf}, -} - -@article{brunner_reduced_2020, - title = {Reduced global warming from {CMIP6} projections when weighting models by performance and independence}, - volume = {11}, - issn = {2190-4987}, - url = {https://esd.copernicus.org/articles/11/995/2020/}, - doi = {10.5194/esd-11-995-2020}, - abstract = {Abstract. The sixth Coupled Model Intercomparison Project (CMIP6) constitutes the latest update on expected future climate change based on a new generation of climate models. To extract reliable estimates of future warming and related uncertainties from these models, the spread in their projections is often translated into probabilistic estimates such as the mean and likely range. Here, we use a model weighting approach, which accounts for the models' historical performance based on several diagnostics as well as model interdependence within the CMIP6 ensemble, to calculate constrained distributions of global mean temperature change. We investigate the skill of our approach in a perfect model test, where we use previous-generation CMIP5 models as pseudo-observations in the historical period. The performance of the distribution weighted in the abovementioned manner with respect to matching the pseudo-observations in the future is then evaluated, and we find a mean increase in skill of about 17 \% compared with the unweighted distribution. In addition, we show that our independence metric correctly clusters models known to be similar based on a CMIP6 “family tree”, which enables the application of a weighting based on the degree of inter-model dependence. We then apply the weighting approach, based on two observational estimates (the fifth generation of the European Centre for Medium-Range Weather Forecasts Retrospective Analysis – ERA5, and the Modern-Era Retrospective analysis for Research and Applications, version 2 – MERRA-2), to constrain CMIP6 projections under weak (SSP1-2.6) and strong (SSP5-8.5) climate change scenarios (SSP refers to the Shared Socioeconomic Pathways). Our results show a reduction in the projected mean warming for both scenarios because some CMIP6 models with high future warming receive systematically lower performance weights. The mean of end-of-century warming (2081–2100 relative to 1995–2014) for SSP5-8.5 with weighting is 3.7 ∘C, compared with 4.1 ∘C without weighting; the likely (66\%) uncertainty range is 3.1 to 4.6 ∘C, which equates to a 13 \% decrease in spread. For SSP1-2.6, the weighted end-of-century warming is 1 ∘C (0.7 to 1.4 ∘C), which results in a reduction of −0.1 ∘C in the mean and −24 \% in the likely range compared with the unweighted case.}, - language = {en}, - number = {4}, - urldate = {2022-09-19}, - journal = {Earth System Dynamics}, - author = {Brunner, Lukas and Pendergrass, Angeline G. and Lehner, Flavio and Merrifield, Anna L. and Lorenz, Ruth and Knutti, Reto}, - month = nov, - year = {2020}, - pages = {995--1012}, - file = {Full Text:C\:\\Users\\EdMaurer\\Zotero\\storage\\MLWLI4TF\\Brunner et al. - 2020 - Reduced global warming from CMIP6 projections when.pdf:application/pdf}, -} - -@article{phillips_evaluating_2014, - title = {Evaluating {Modes} of {Variability} in {Climate} {Models}}, - volume = {95}, - issn = {00963941}, - url = {http://doi.wiley.com/10.1002/2014EO490002}, - doi = {10.1002/2014EO490002}, - language = {en}, - number = {49}, - urldate = {2022-09-20}, - journal = {Eos, Transactions American Geophysical Union}, - author = {Phillips, Adam S. and Deser, Clara and Fasullo, John}, - month = dec, - year = {2014}, - pages = {453--455}, -} - -@article{chen_model_2020, - title = {Model {Biases} in the {Simulation} of the {Springtime} {North} {Pacific} {ENSO} {Teleconnection}}, - volume = {33}, - issn = {0894-8755, 1520-0442}, - url = {https://journals.ametsoc.org/doi/10.1175/JCLI-D-19-1004.1}, - doi = {10.1175/JCLI-D-19-1004.1}, - abstract = {Abstract - The wintertime ENSO teleconnection over the North Pacific region consists of an intensified (weakened) low pressure center during El Niño (La Niña) events both in observations and in climate models. Here, it is demonstrated that this teleconnection persists too strongly into late winter and spring in the Community Earth System Model (CESM). This discrepancy arises in both fully coupled and atmosphere-only configurations, when observed SSTs are specified, and is shown to be robust when accounting for the sampling uncertainty due to internal variability. Furthermore, a similar problem is found in many other models from piControl simulations of the Coupled Model Intercomparison Project (23 out of 43 in phase 5 and 11 out of 20 in phase 6). The implications of this bias for the simulation of surface climate anomalies over North America are assessed. The overall effect on the ENSO composite field (El Niño minus La Niña) resembles an overly prolonged influence of ENSO into the spring with anomalously high temperatures over Alaska and western Canada, and wet (dry) biases over California (southwest Canada). Further studies are still needed to disentangle the relative roles played by diabatic heating, background flow, and other possible contributions in determining the overly strong springtime ENSO teleconnection intensity over the North Pacific.}, - number = {23}, - urldate = {2022-09-20}, - journal = {Journal of Climate}, - author = {Chen, Ruyan and Simpson, Isla R. and Deser, Clara and Wang, Bin}, - month = dec, - year = {2020}, - pages = {9985--10002}, -} - @book{xie_bookdown_2017, address = {Boca Raton}, series = {The {R} series}, @@ -13858,19 +83,6 @@ @book{peterka_alvin_j_hydraulic_1978 year = {1978}, } -@book{finnemore_fluid_2002, - address = {Boston}, - edition = {10th ed}, - series = {The {McGraw}-{Hill} series in civil and environmental engineering}, - title = {Fluid mechanics with engineering applications}, - isbn = {978-0-07-243202-2 978-0-07-112196-5}, - publisher = {McGraw-Hill}, - author = {Finnemore, E. John and Franzini, Joseph B.}, - year = {2002}, - keywords = {Fluid mechanics}, - annote = {Franzini's name appears first on the earlier ed}, -} - @article{wigley_thermal_1987, title = {Thermal expansion of sea water associated with global warming}, volume = {330}, @@ -13918,14 +130,6 @@ @book{searcy_double-mass_1960 year = {1960}, } -@book{searcy_double-mass_1960-1, - title = {Double-mass curves}, - number = {1541}, - publisher = {US Government Printing Office}, - author = {Searcy, James Kincheon and Hardison, Clayton H}, - year = {1960}, -} - @misc{mccuen_hydrologic_2016, title = {Hydrologic analysis and design, 4th}, publisher = {Pearson Education}, @@ -14004,9 +208,6 @@ @techreport{england_jf_guidelines_2019 pages = {148}, } -@article{noauthor_notitle_nodate, -} - @article{marshall_framing_2005, title = {Framing the {Elusive} {Concept} of {Sustainability}: {A} {Sustainability} {Hierarchy}}, volume = {39}, @@ -14036,7 +237,7 @@ @techreport{helsel_dr_statistical_2020 pages = {458}, } -@article{tebaldi_modelling_2012-1, +@article{tebaldi_modelling_2012, title = {Modelling sea level rise impacts on storm surges along {US} coasts}, volume = {7}, issn = {1748-9326}, @@ -14118,13 +319,29 @@ @book{sturm_open_2021 year = {2021}, } -@book{finnemore_fluid_2023, +@article{camp_design_1946, + title = {Design of sewers to facilitate flow}, + volume = {18}, + issn = {0096-9362}, + language = {eng}, + journal = {Sewage Works Journal}, + author = {Camp, T. R.}, + month = jan, + year = {1946}, + pmid = {21011592}, + keywords = {Humans, Sewage, SEWAGE/disposal}, + pages = {3--16}, +} + +@book{finnemore_fluid_2024, address = {New York}, - edition = {11}, + edition = {Eleventh edition}, title = {Fluid mechanics with civil engineering applications}, - isbn = {978-1-264-78729-6}, - abstract = {"This eleventh edition of the classic textbook, Fluid Mechanics with Civil Engineering Applications, continues and improves on its tradition of explaining the physical phenomena of fluid mechanics and applying its basic principles in the simplest and clearest possible manner without the use of complicated mathematics. It focuses on civil, environmental, and agricultural engineering problems, although mechanical engineering topics are also strongly represented. The book is written as a text for a first course in fluid mechanics for civil and related engineering students, with sufficient breadth of coverage that it can serve in a number of ways for a second course if desired"--}, - publisher = {McGraw Hill}, - editor = {Finnemore, E. John and Maurer, Edwin}, - year = {2023}, + isbn = {978-1-264-78736-4}, + abstract = {A complete guide to fluid mechanics for engineers--fully updated for current standards This thoroughly revised, classic guide clearly explains the principles and applications of fluid mechanics and hydraulics in a straightforward manner, without using complicated mathematics. While aimed at undergraduate students, practicing engineers will also benefit from the hands-on information covered. You will explore fluid mechanics fundamentals, pipe and open channel flow, unsteady flow, and much more. Written by a pair of experienced engineering educators, Fluid Mechanics with Civil Engineering Applications, Eleventh Edition focuses on reducing and streamlining content while retaining its traditional approach to teaching fundamental concepts by solving engineering problems. This overhauled edition features new practical sample problems and exercises and incorporates digital resources while removing some more advanced topics less essential to civil engineering. Contains new and extensively updated content to meet current standards Incorporates new examples and problems Includes a new online problem and solutions manual as well as additional resources for students and instructors"--Publisher's description}, + language = {eng}, + publisher = {McGraw-Hill}, + author = {Finnemore, E. 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