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Generation_NoIFR_10GCMs.Rmd
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Generation_NoIFR_10GCMs.Rmd
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---
title: "Generation"
author: "Gustavo Facincani Dourado"
date: "6/12/2020"
output: html_document
---
```{r}
library(lfstat)
library(extrafont)
library(reshape2)
library(dplyr)
library(ggplot2)
library(readr)
```
```{r}
#Merced
#Baseline 2045 - 1,2,6,10
#No IFR 2045 - 1,3,7,11
#Baseline 2045 - 1,4,8,12
#No IFR 2045 - 1,5,9,13
#Tuolumne
#Baseline 2045 - scenario
#No IFR 2045 - 1,3,7,11,15
#Baseline 2045 - 1,4,8,12,16
#No IFR 2045 - 1,5,9,13,17
#Stanislaus
#Baseline - scenario,18,22,26,30,34,38,42
#No IFR 2045 - 1,3,7,11,15,19,23,27,31,35,39,43
#Baseline 2045 - 1,4,8,12,16,20,24,28,32,36,40,44
#No IFR 2045 - 1,5,9,13,17,21,25,29,33,37,41,45
#USJ
#Baseline 2045 - scenario,18,22,26,30,34,38,42,46,50,54,58,62
#No IFR 2045 - 1,3,7,11,15,19,23,27,31,35,39,43,47,51,55,59,63
#Baseline 2045 - 1,4,8,12,16,20,24,28,32,36,40,44,48,52,56,60,64
#No IFR 2045 - 1,5,9,13,17,21,25,29,33,37,41,45,49,53,57,61,65
```
```{r}
scenario <- c(1,2,5,8)
Livneh_Gen_Mer <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/historical/Livneh/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)] %>%
filter(between(node, as.Date("1980-10-01"), as.Date("2010-09-30")))
Livneh_Gen_Mer <- Livneh_Gen_Mer %>%
mutate(`Livneh (Historical)` = rowSums(Livneh_Gen_Mer[c(2:4)]))
Livneh_Gen_Mer
CanES_Gen_Mer <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/CanESM2_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
CanES_Gen_Mer<- CanES_Gen_Mer %>%
mutate("CanESM2\u2020" = rowSums(CanES_Gen_Mer[c(2:4)]))
CanES_Gen_Mer
CNRM_Gen_Mer <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/CNRM-CM5_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
CNRM_Gen_Mer<- CNRM_Gen_Mer %>%
mutate("CNRM-CM5\u2020" = rowSums(CNRM_Gen_Mer[c(2:4)]))
CNRM_Gen_Mer
==
HadGEM_Gen_Mer <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/HadGEM2-ES_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
=
HadGEM_Gen_Mer<- HadGEM_Gen_Mer %>%
mutate("HadGEM2-ES\u2020" = rowSums(HadGEM_Gen_Mer[c(2:4)]))
HadGEM_Gen_Mer
MIROC_Gen_Mer <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/MIROC5_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
MIROC_Gen_Mer<- MIROC_Gen_Mer %>%
mutate("MIROC5\u2020" = rowSums(MIROC_Gen_Mer[c(2:4)]))
MIROC_Gen_Mer
HadGEMCC_Gen_Mer <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/HadGEM2-CC_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
HadGEMCC_Gen_Mer<- HadGEMCC_Gen_Mer %>%
mutate(`HadGEM2-CC` = rowSums(HadGEMCC_Gen_Mer[c(2:4)]))
HadGEMCC_Gen_Mer
CCSM4_Gen_Mer <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/CCSM4_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
CCSM4_Gen_Mer<- CCSM4_Gen_Mer %>%
mutate(CCSM4 = rowSums(CCSM4_Gen_Mer[c(2:4)]))
CCSM4_Gen_Mer
CMCC_Gen_Mer <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/CMCC-CMS_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
CMCC_Gen_Mer<- CMCC_Gen_Mer %>%
mutate(`CMCC-CMS` = rowSums(CMCC_Gen_Mer[c(2:4)]))
CMCC_Gen_Mer
CESM_Gen_Mer <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/CESM1-BGC_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
CESM_Gen_Mer<- CESM_Gen_Mer %>%
mutate(`CESM1-BGC` = rowSums(CESM_Gen_Mer[c(2:4)]))
CESM_Gen_Mer
ACCESS1_Gen_Mer <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/ACCESS1-0_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
ACCESS1_Gen_Mer<- ACCESS1_Gen_Mer %>%
mutate(`ACCESS1-0` = rowSums(ACCESS1_Gen_Mer[c(2:4)]))
ACCESS1_Gen_Mer
GFDL_Gen_Mer <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/GFDL-CM3_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
GFDL_Gen_Mer<- GFDL_Gen_Mer %>%
mutate(`GFDL-CM3` = rowSums(GFDL_Gen_Mer[c(2:4)]))
GFDL_Gen_Mer
```
```{r}
scenario <- c(1,3,6,9)
Livneh_Gen_Mer_SWRCB <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/historical/Livneh/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)] %>%
filter(between(node, as.Date("1980-10-01"), as.Date("2010-09-30")))
Livneh_Gen_Mer_SWRCB <- Livneh_Gen_Mer_SWRCB %>%
mutate(`Livneh (Historical)` = rowSums(Livneh_Gen_Mer_SWRCB[c(2:4)]))
Livneh_Gen_Mer_SWRCB
CanES_Gen_Mer_SWRCB <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/CanESM2_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
CanES_Gen_Mer_SWRCB<- CanES_Gen_Mer_SWRCB %>%
mutate("CanESM2\u2020" = rowSums(CanES_Gen_Mer_SWRCB[c(2:4)]))
CanES_Gen_Mer_SWRCB
CNRM_Gen_Mer_SWRCB <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/CNRM-CM5_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
CNRM_Gen_Mer_SWRCB<- CNRM_Gen_Mer_SWRCB %>%
mutate("CNRM-CM5\u2020" = rowSums(CNRM_Gen_Mer_SWRCB[c(2:4)]))
CNRM_Gen_Mer_SWRCB
HadGEM_Gen_Mer_SWRCB <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/HadGEM2-ES_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
HadGEM_Gen_Mer_SWRCB<- HadGEM_Gen_Mer_SWRCB %>%
mutate("HadGEM2-ES\u2020" = rowSums(HadGEM_Gen_Mer_SWRCB[c(2:4)]))
HadGEM_Gen_Mer_SWRCB
MIROC_Gen_Mer_SWRCB <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/MIROC5_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
MIROC_Gen_Mer_SWRCB<- MIROC_Gen_Mer_SWRCB %>%
mutate("MIROC5\u2020" = rowSums(MIROC_Gen_Mer_SWRCB[c(2:4)]))
MIROC_Gen_Mer_SWRCB
HadGEMCC_Gen_Mer_SWRCB <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/HadGEM2-CC_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
HadGEMCC_Gen_Mer_SWRCB<- HadGEMCC_Gen_Mer_SWRCB %>%
mutate(`HadGEM2-CC` = rowSums(HadGEMCC_Gen_Mer_SWRCB[c(2:4)]))
HadGEMCC_Gen_Mer_SWRCB
CCSM4_Gen_Mer_SWRCB <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/CCSM4_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
CCSM4_Gen_Mer_SWRCB<- CCSM4_Gen_Mer_SWRCB %>%
mutate(CCSM4 = rowSums(CCSM4_Gen_Mer_SWRCB[c(2:4)]))
CCSM4_Gen_Mer_SWRCB
CMCC_Gen_Mer_SWRCB <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/CMCC-CMS_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
CMCC_Gen_Mer_SWRCB<- CMCC_Gen_Mer_SWRCB %>%
mutate(`CMCC-CMS` = rowSums(CMCC_Gen_Mer_SWRCB[c(2:4)]))
CMCC_Gen_Mer_SWRCB
CESM_Gen_Mer_SWRCB <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/CESM1-BGC_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
CESM_Gen_Mer_SWRCB<- CESM_Gen_Mer_SWRCB %>%
mutate(`CESM1-BGC` = rowSums(CESM_Gen_Mer_SWRCB[c(2:4)]))
CESM_Gen_Mer_SWRCB
ACCESS1_Gen_Mer_SWRCB <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/ACCESS1-0_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
ACCESS1_Gen_Mer_SWRCB<- ACCESS1_Gen_Mer_SWRCB %>%
mutate(`ACCESS1-0` = rowSums(ACCESS1_Gen_Mer_SWRCB[c(2:4)]))
ACCESS1_Gen_Mer_SWRCB
GFDL_Gen_Mer_SWRCB <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/GFDL-CM3_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
GFDL_Gen_Mer_SWRCB<- GFDL_Gen_Mer_SWRCB %>%
mutate(`GFDL-CM3` = rowSums(GFDL_Gen_Mer_SWRCB[c(2:4)]))
GFDL_Gen_Mer_SWRCB
```
```{r}
scenario <- c(1,4,7,10)
Livneh_Gen_Mer_FF <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/historical/Livneh/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)] %>%
filter(between(node, as.Date("1980-10-01"), as.Date("2010-09-30")))
Livneh_Gen_Mer_FF <- Livneh_Gen_Mer_FF %>%
mutate(`Livneh (Historical)` = rowSums(Livneh_Gen_Mer_FF[c(2:4)]))
Livneh_Gen_Mer_FF
CanES_Gen_Mer_FF <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/CanESM2_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
CanES_Gen_Mer_FF<- CanES_Gen_Mer_FF %>%
mutate("CanESM2\u2020" = rowSums(CanES_Gen_Mer_FF[c(2:4)]))
CanES_Gen_Mer_FF
CNRM_Gen_Mer_FF <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/CNRM-CM5_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
CNRM_Gen_Mer_FF<- CNRM_Gen_Mer_FF %>%
mutate("CNRM-CM5\u2020" = rowSums(CNRM_Gen_Mer_FF[c(2:4)]))
CNRM_Gen_Mer_FF
HadGEM_Gen_Mer_FF <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/HadGEM2-ES_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
HadGEM_Gen_Mer_FF<- HadGEM_Gen_Mer_FF %>%
mutate("HadGEM2-ES\u2020" = rowSums(HadGEM_Gen_Mer_FF[c(2:4)]))
HadGEM_Gen_Mer_FF
MIROC_Gen_Mer_FF <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/MIROC5_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
MIROC_Gen_Mer_FF<- MIROC_Gen_Mer_FF %>%
mutate("MIROC5\u2020" = rowSums(MIROC_Gen_Mer_FF[c(2:4)]))
MIROC_Gen_Mer_FF
HadGEMCC_Gen_Mer_FF <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/HadGEM2-CC_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
HadGEMCC_Gen_Mer_FF<- HadGEMCC_Gen_Mer_FF %>%
mutate(`HadGEM2-CC` = rowSums(HadGEMCC_Gen_Mer_FF[c(2:4)]))
HadGEMCC_Gen_Mer_FF
CCSM4_Gen_Mer_FF <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/CCSM4_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
CCSM4_Gen_Mer_FF<- CCSM4_Gen_Mer_FF %>%
mutate(CCSM4 = rowSums(CCSM4_Gen_Mer_FF[c(2:4)]))
CCSM4_Gen_Mer_FF
CMCC_Gen_Mer_FF <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/CMCC-CMS_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
CMCC_Gen_Mer_FF<- CMCC_Gen_Mer_FF %>%
mutate(`CMCC-CMS` = rowSums(CMCC_Gen_Mer_FF[c(2:4)]))
CMCC_Gen_Mer_FF
CESM_Gen_Mer_FF <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/CESM1-BGC_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
CESM_Gen_Mer_FF<- CESM_Gen_Mer_FF %>%
mutate(`CESM1-BGC` = rowSums(CESM_Gen_Mer_FF[c(2:4)]))
CESM_Gen_Mer_FF
ACCESS1_Gen_Mer_FF <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/ACCESS1-0_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
ACCESS1_Gen_Mer_FF<- ACCESS1_Gen_Mer_FF %>%
mutate(`ACCESS1-0` = rowSums(ACCESS1_Gen_Mer_FF[c(2:4)]))
ACCESS1_Gen_Mer_FF
GFDL_Gen_Mer_FF <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/All IFR scenarios - whole basin - 2022-12-05/merced/gcms/GFDL-CM3_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
GFDL_Gen_Mer_FF<- GFDL_Gen_Mer_FF %>%
mutate(`GFDL-CM3` = rowSums(GFDL_Gen_Mer_FF[c(2:4)]))
GFDL_Gen_Mer_FF
```
```{r}
library(reshape2)
library(lfstat)
#Can't cbind livneh as it has one day less (Feb 29) in its count
#Deal with Livneh separately
Livneh_Mer <- Livneh_Gen_Mer[-c(2,3,4)]
Livneh_Mer <- melt(Livneh_Mer, id = "node") %>% mutate(`Flow Scenario` = "Baseline")
Livneh_Mer
Livneh_Mer_SWRCB <- Livneh_Gen_Mer_SWRCB[-c(2,3,4)]
Livneh_Mer_SWRCB <- melt(Livneh_Mer_SWRCB, id = "node") %>% mutate(`Flow Scenario` = "40% Full Natural Flow")
Livneh_Mer_SWRCB
Livneh_Mer_FF <- Livneh_Gen_Mer_FF[-c(2,3,4)]
Livneh_Mer_FF <- melt(Livneh_Mer_FF, id = "node") %>% mutate(`Flow Scenario` = "Functional Flows")
Livneh_Mer_FF
Gen_Mer <- cbind(CanES_Gen_Mer[-c(2,3,4)], CNRM_Gen_Mer[-c(1,2,3,4)],
HadGEM_Gen_Mer[-c(1,2,3,4)], MIROC_Gen_Mer[-c(1,2,3,4)],
CMCC_Gen_Mer[-c(1,2,3,4)], ACCESS1_Gen_Mer[-c(1,2,3,4)],
HadGEMCC_Gen_Mer[-c(1,2,3,4)], CESM_Gen_Mer[-c(1,2,3,4)],
CCSM4_Gen_Mer[-c(1,2,3,4)], GFDL_Gen_Mer[-c(1,2,3,4)]) %>% mutate(`Flow Scenario` = "Baseline")
Gen_Mer_SWRCB <- cbind(CanES_Gen_Mer_SWRCB[-c(2,3,4)], CNRM_Gen_Mer_SWRCB[-c(1,2,3,4)],
HadGEM_Gen_Mer_SWRCB[-c(1,2,3,4)], MIROC_Gen_Mer_SWRCB[-c(1,2,3,4)],
CMCC_Gen_Mer_SWRCB[-c(1,2,3,4)], ACCESS1_Gen_Mer_SWRCB[-c(1,2,3,4)],
HadGEMCC_Gen_Mer_SWRCB[-c(1,2,3,4)], CESM_Gen_Mer_SWRCB[-c(1,2,3,4)],
CCSM4_Gen_Mer_SWRCB[-c(1,2,3,4)], GFDL_Gen_Mer_SWRCB[-c(1,2,3,4)]) %>% mutate(`Flow Scenario` = "40% Full Natural Flow")
Gen_Mer_FF <- cbind(CanES_Gen_Mer_FF[-c(2,3,4)], CNRM_Gen_Mer_FF[-c(1,2,3,4)],
HadGEM_Gen_Mer_FF[-c(1,2,3,4)], MIROC_Gen_Mer_FF[-c(1,2,3,4)],
CMCC_Gen_Mer_FF[-c(1,2,3,4)], ACCESS1_Gen_Mer_FF[-c(1,2,3,4)],
HadGEMCC_Gen_Mer_FF[-c(1,2,3,4)], CESM_Gen_Mer_FF[-c(1,2,3,4)],
CCSM4_Gen_Mer_FF[-c(1,2,3,4)], GFDL_Gen_Mer_FF[-c(1,2,3,4)]) %>% mutate(`Flow Scenario` = "Functional Flows")
#Gen_Mer <- Gen_Mer %>%
# mutate(Ensemble = rowMeans(as.matrix((.[-1])))) %>%
# filter(between(node, as.Date("2029-10-01"), as.Date("2060-09-30")))
#Gen_Mer
#Gen_Mer
Gen_Mer_Total <- rbind(Gen_Mer, Gen_Mer_SWRCB, Gen_Mer_FF)
Gen_Mer1 <- melt(Gen_Mer_Total, id = c("node", "Flow Scenario")) #, "Basin", "Year", "Month")) #"Annual_Can", "Annual_CNRM", "Annual_Had", "Annual_MIR")
# Month = as.Date(cut(node, breaks = "month")),
# mutate(Month = as.factor(format(as.Date(node, format = "%B"), "%b")),
# Year = as.Date(cut(node, breaks = "year")))
Gen_Mer1
Gen_Mer2 <- rbind(Gen_Mer1, Livneh_Mer, Livneh_Mer_SWRCB, Livneh_Mer_FF) %>%
mutate(Month = as.factor(format(as.Date(node, format = "%B"), "%b")),
#Year = as.Date(cut(node, breaks = "year")),
Year = water_year(node, origin = "usgs"),
Scenario = as.factor("RCP 8.5")) %>%
group_by(Year, Month, variable, `Flow Scenario`) %>%
dplyr::summarize(MonthlyTotal = sum(value))
Gen_Mer2
Gen_Mer2$variable <- factor(Gen_Mer2$variable, levels = c("Livneh (Historical)", "ACCESS1-0", "CMCC-CMS", "MIROC5\u2020", "GFDL-CM3", "CCSM4", "HadGEM2-CC", "HadGEM2-ES\u2020","CESM1-BGC", "CanESM2\u2020", "CNRM-CM5\u2020"))
Gen_Mer3 <- Gen_Mer2 %>%
group_by(Year, variable, `Flow Scenario`) %>%
dplyr::summarize(AnnualTotal = sum(MonthlyTotal))
Gen_Mer3
Gen_Mer4 <- Gen_Mer3
#df[3:ncol(df)] <- df[3:ncol(df)]-df[,2]
```
#Merced annual generation
Gen_Mer2 %>%
filter(variable == "MIROC5") %>%
ggplot() +
theme_bw(base_size=12, base_family='Times New Roman') + #change font to Times New Roman, 12pt, Bold
geom_boxplot(aes( x = Month, y = value), outlier.alpha = 0.3) + #plot monthly observed data in greenish blue
#geom_line(aes(x = node, y = value)) +
scale_y_continuous(limits = c(0, NA),
expand = c(0, NA)) +
scale_x_discrete(limits=c("Oct","Nov","Dec", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep")) +
labs(title = "Merced River",
subtitle = "MIROC5",
x = element_blank(),
y = "Total Electricity Generation (MWh)") + #name of x axis
theme(legend.title = element_blank(),
legend.position = "bottom",
legend.direction = "horizontal",
legend.box.margin = margin(t = -19),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
strip.placement = "outside",
strip.background = element_blank(),
panel.spacing.y = unit(0, "lines"))+
png("Mer_generation_MIROC5_March.png", units ="in", width=8, height=5, res = 300)
library(readxl)
NoIFRs_perc <- read_xlsx("C:/Users/gusta/Desktop/PhD/Classes/ES207/Merced_TotalAnnual_Generation_RCP4.5_Baseline.xlsx", sheet = 2)
NoIFRs_perc$Month <- factor(NoIFRs_perc$Month, levels = c("Oct", "Nov", "Dec", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep"))
Baseline2 <- melt(NoIFRs_perc, id = c("Year", "Month"))
Baseline2
ggplot(Baseline2) +
theme_bw(base_size=12, base_family='Times New Roman') + #change font to Times New Roman, 12pt, Bold
geom_boxplot(aes( x = Month, y = value*100, fill = variable), outlier.alpha = 0.3) + #plot monthly observed data in greenish blue
#geom_line(aes(x = node, y = value)) +
scale_y_continuous(limits = c(-100, NA),
expand = c(0, NA),
labels=comma) +
scale_x_discrete(limits=c("Oct","Nov","Dec", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep"))+
scale_fill_manual(values = c("#A50026", "#D73027", "#F46D43", "chocolate1", "gold1", "cadetblue2", "deepskyblue2", "dodgerblue2", "#4575B4", "#313695" )) +
labs(fill = "RCP 8.5",
title = "Baseline",
subtitle = "Merced River",
x = element_blank(),
y = "Δ Total Electricity Generation (%)") + #name of x axis
theme(legend.position = "bottom",
legend.direction = "horizontal",
legend.box.margin = margin(t = -17),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
strip.placement = "outside",
strip.background = element_blank(),
panel.spacing.y = unit(0, "lines"))+
png("Mer_generation_10GCMs_NoIFRs_percdiff_March.png", units ="in", width=10, height=5, res = 300)
Generation <- merge(Gen_Mer3, WYT_SJVI, by = c("variable", "Year")) %>%
filter(!RCP == "rcp85")
Generation
ggplot(Generation)+# %>% filter(variable == "ACCESS1-0" | variable == "GFDL-CM3")) +
theme_bw(base_size=12, base_family='Times New Roman') + #change font to Times New Roman, 12pt, Bold
geom_rect(aes(fill = WYT, xmin = as.numeric(Year)-0.5, xmax = as.numeric(Year)+0.5, ymin = -Inf, ymax = Inf))+
scale_y_continuous(limits = c(0, NA),
expand = c(0, NA),
labels=scales::comma) +
scale_x_discrete(#limits=c("Oct","Nov","Dec", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep")
expand = c(0, 0))+
geom_line(aes( x = as.numeric(Year), y = AnnualTotal/1000)) + #plot monthly observed data in greenish blue
#geom_line(aes(x = node, y = value)) +
#scale_color_manual(values = c("#5A5A5A", "#A50026", "#D73027", "#F46D43", "chocolate1", "gold1", "cadetblue2", "deepskyblue2", "dodgerblue2", "#4575B4", "#313695" )) +
scale_fill_manual(values = c("tomato2", "orange", "#FEDC56", "cyan3", "dodgerblue2"))+
labs(fill = "RCP 8.5",
title = "No Instream2045 prices)",
subtitle = "Merced River",
x = element_blank(),
y = "Total Electricity Generation (GWh/month)") + #name of x axis
theme(legend.position = "bottom",
legend.direction = "horizontal",
legend.box.margin = margin(t = -17),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
strip.placement = "outside",
strip.background = element_blank(),
panel.spacing.y = unit(0, "lines"))+
facet_wrap(~variable, ncol = 3, scales ="free_x")
#png("Mer_generation_rcp85_timeseries_Report_March.png", units ="in", width=10, height=5, res = 300)
```{r}
library(scales)
#Merced annual generation
#Gen_Mer2 %>%
# filter(variable == "CanESM2") %>%
ggplot(Gen_Mer2) +
theme_bw(base_size=12, base_family='Times New Roman') + #change font to Times New Roman, 12pt, Bold
geom_boxplot(aes( x = Month, y = MonthlyTotal/1000, fill = variable), outlier.alpha = 0.3) + #plot monthly observed data in greenish blue
#geom_line(aes(x = node, y = value)) +
scale_y_continuous(limits = c(0, NA),
expand = c(0, NA),
labels=comma) +
scale_x_discrete(limits=c("Oct","Nov","Dec", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep"))+
scale_fill_manual(values = c("#5A5A5A", "#A50026", "#D73027", "#F46D43", "chocolate1", "gold1", "cadetblue2", "deepskyblue2", "dodgerblue2", "#4575B4", "#313695" )) +
labs(fill = "RCP 8.5",
title = "Baseline",
subtitle = "Merced River",
x = element_blank(),
y = "Total Electricity Generation (GWh/month)") + #name of x axis
theme(legend.position = "bottom",
legend.direction = "horizontal",
legend.box.margin = margin(t = -17),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
strip.placement = "outside",
strip.background = element_blank(),
panel.spacing.y = unit(0, "lines"))+
png("Mer_generation_rcp85_egu.png", units ="in", width=10, height=5, res = 300)
```
```{r}
#annual aggreagate
#Gen_Mer3 %>%
# filter(variable == "Livneh (Historical)" | variable == "ACCESS1-0" | variable == "CNRM-CM5") %>%
ggplot(Gen_Mer3) +
theme_bw(base_size=12, base_family='Times New Roman') + #change font to Times New Roman, 12pt, Bold
geom_violin(draw_quantiles = c(.25, .5, .75), aes( x = variable, y = AnnualTotal/1000, fill = variable)) + #plot monthly observed data in greenish blue
#geom_line(aes(x = node, y = value)) +
scale_y_continuous(limits = c(0, NA),
expand = c(0, NA),
labels = comma,
breaks= c(0,200,400, 600, 800)) +
# scale_x_discrete(limits=c(c("Livneh (Historical)", "ACCESS1-0",# "CMCC-CMS", "MIROC5", "GFDL-CM3", "CCSM4", "HadGEM2-ES", "HadGEM2-CC", "CanESM2", "CESM1-BGC",
# "CNRM-CM5")))+
scale_fill_manual(values = c("#5A5A5A", "#A50026", "#D73027", "#F46D43", "chocolate1", "gold1", "cadetblue2", "deepskyblue2", "dodgerblue2", "#4575B4", "#313695" )) +
labs(fill = "RCP 8.5", title = "Merced River",
subtitle = "Baseline",
x = element_blank(),
y = "Total Electricity Generation (GWh/year)") + #name of x axis
theme(legend.position = "none",
#legend.direction = "horizontal",
#legend.box.margin = margin(t = -19),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
strip.placement = "outside",
strip.background = element_blank(),
panel.spacing.y = unit(0, "lines"))+
png("Mer_annual_generation_rcp85_egu2.png", units ="in", width=11.5, height=4.5, res = 300)
```
ggplot(Gen_Mer1) +
theme_bw(base_size=12, base_family='Times New Roman') + #change font to Times New Roman, 12pt, Bold
geom_boxplot(width = 0.4, aes( x = variable, y = value, fill = variable), outlier.alpha = 0.3) + #plot monthly observed data in greenish blue
#geom_line(aes(x = node, y = value)) +
scale_y_continuous(limits = c(0, NA),
expand = c(0, NA)) +
# scale_x_discrete(limits=c("Livneh", "HadGEM2-ES", "CanESM2/u2020", "MIROC5", "CNRM-CM5"))+
# scale_fill_manual(values = c("#5A5A5A", "#E69F00", "#F0E442", "#009E73", "#56B4E9")) +
labs(fill = "RCP 8.5",
title = "Merced River",
x = element_blank(),
y = "Total Electricity Generation (MWh)") + #name of x axis
theme(legend.position = "none",
#legend.direction = "horizontal",
#legend.box.margin = margin(t = -19),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
strip.placement = "outside",
strip.background = element_blank(),
panel.spacing.y = unit(0, "lines"))
#png("Mer_generation_GCMs3_March.png", units ="in", width=10, height=4, res = 300)
ggplot(Gen_Mer2) +
theme_bw(base_size=12, base_family='Times New Roman') + #change font to Times New Roman, 12pt, Bold
geom_boxplot(aes( x = WaterYear, y = value, fill = variable), outlier.alpha = 0.3) + #plot monthly observed data in greenish blue
#geom_line(aes(x = node, y = value)) +
scale_y_continuous(limits = c(0, NA),
expand = c(0, NA)) +
scale_x_discrete(breaks = c("1981", "1991", "2001", "2010", "2031", "2041", "2051", "2060"))+
# scale_fill_manual(values = c("#5A5A5A", "#E69F00", "#F0E442", "#009E73", "#56B4E9")) +
labs(fill = "RCP 8.5",
title = "Merced River",
x = element_blank(),
y = "Total Electricity Generation (MWh)") + #name of x axis
theme(legend.position = "none",
#legend.direction = "horizontal",
#legend.box.margin = margin(t = -19),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
strip.placement = "outside",
strip.background = element_blank(),
panel.spacing.y = unit(0, "lines"))+
facet_wrap(~variable, ncol=1, scales= "free")
# png("Mer_generation_GCMs4_March.png", units ="in", width=8, height=5, res = 300)
```{r}
scenario <- c(1,2,5,8,11)
Livneh_Gen_Tuo <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results//All IFR scenarios - whole basin - 2022-12-05/tuolumne/historical/Livneh/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
Livneh_Gen_Tuo
Livneh_Gen_Tuo <- Livneh_Gen_Tuo %>%
mutate(`Livneh (Historical)` = rowSums(Livneh_Gen_Tuo[c(2:5)])) %>%
filter(between(node, as.Date("1950-10-01"), as.Date("2011-09-30")))
Livneh_Gen_Tuo
CanES_Gen_Tuo <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results//All IFR scenarios - whole basin - 2022-12-05/tuolumne/gcms/CanESM2_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
CanES_Gen_Tuo <- CanES_Gen_Tuo %>%
mutate("CanESM2\u2020" = rowSums(CanES_Gen_Tuo[c(2:5)]))
CanES_Gen_Tuo
CNRM_Gen_Tuo <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results//All IFR scenarios - whole basin - 2022-12-05/tuolumne/gcms/CNRM-CM5_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
CNRM_Gen_Tuo <- CNRM_Gen_Tuo %>%
mutate("CNRM-CM5\u2020" = rowSums(CNRM_Gen_Tuo[c(2:5)]))
CNRM_Gen_Tuo
HadGEM_Gen_Tuo <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results//All IFR scenarios - whole basin - 2022-12-05/tuolumne/gcms/HadGEM2-ES_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
HadGEM_Gen_Tuo <- HadGEM_Gen_Tuo %>%
mutate("HadGEM2-ES\u2020" = rowSums(HadGEM_Gen_Tuo[c(2:5)]))
HadGEM_Gen_Tuo
MIROC_Gen_Tuo <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results//All IFR scenarios - whole basin - 2022-12-05/tuolumne/gcms/MIROC5_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
MIROC_Gen_Tuo <- MIROC_Gen_Tuo %>%
mutate("MIROC5\u2020" = rowSums(MIROC_Gen_Tuo[c(2:5)]))
MIROC_Gen_Tuo
HadGEMCC_Gen_Tuo <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results//All IFR scenarios - whole basin - 2022-12-05/tuolumne/gcms/HadGEM2-CC_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
HadGEMCC_Gen_Tuo<- HadGEMCC_Gen_Tuo %>%
mutate(`HadGEM2-CC` = rowSums(HadGEMCC_Gen_Tuo[c(2:5)]))
HadGEMCC_Gen_Tuo
CCSM4_Gen_Tuo <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results//All IFR scenarios - whole basin - 2022-12-05/tuolumne/gcms/CCSM4_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
CCSM4_Gen_Tuo<- CCSM4_Gen_Tuo %>%
mutate(CCSM4 = rowSums(CCSM4_Gen_Tuo[c(2:5)]))
CCSM4_Gen_Tuo
CMCC_Gen_Tuo <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results//All IFR scenarios - whole basin - 2022-12-05/tuolumne/gcms/CMCC-CMS_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
CMCC_Gen_Tuo<- CMCC_Gen_Tuo %>%
mutate(`CMCC-CMS` = rowSums(CMCC_Gen_Tuo[c(2:5)]))
CMCC_Gen_Tuo
CESM_Gen_Tuo <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results//All IFR scenarios - whole basin - 2022-12-05/tuolumne/gcms/CESM1-BGC_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
CESM_Gen_Tuo<- CESM_Gen_Tuo %>%
mutate(`CESM1-BGC` = rowSums(CESM_Gen_Tuo[c(2:5)]))
CESM_Gen_Tuo
ACCESS1_Gen_Tuo <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results//All IFR scenarios - whole basin - 2022-12-05/tuolumne/gcms/ACCESS1-0_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
ACCESS1_Gen_Tuo<- ACCESS1_Gen_Tuo %>%
mutate(`ACCESS1-0` = rowSums(ACCESS1_Gen_Tuo[c(2:5)]))
ACCESS1_Gen_Tuo
GFDL_Gen_Tuo <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results//All IFR scenarios - whole basin - 2022-12-05/tuolumne/gcms/GFDL-CM3_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario)]
GFDL_Gen_Tuo<- GFDL_Gen_Tuo %>%
mutate(`GFDL-CM3` = rowSums(GFDL_Gen_Tuo[c(2:5)]))
GFDL_Gen_Tuo
```
```{r}
Livneh_Tuo <- Livneh_Gen_Tuo[-c(2:5)]
Livneh_Tuo <- melt(Livneh_Tuo, id = "node")
Livneh_Tuo
Gen_Tuo <- cbind(CanES_Gen_Tuo[-c(2:5)], CNRM_Gen_Tuo[-c(1:5)],
HadGEM_Gen_Tuo[-c(1:5)], MIROC_Gen_Tuo[-c(1:5)],
CMCC_Gen_Tuo[-c(1:5)], ACCESS1_Gen_Tuo[-c(1:5)],
HadGEMCC_Gen_Tuo[-c(1:5)], CESM_Gen_Tuo[-c(1:5)],
CCSM4_Gen_Tuo[-c(1:5)], GFDL_Gen_Tuo[-c(1:5)])
#Gen_Mer
#Gen_Tuo <- Gen_Tuo %>%
# mutate(Ensemble = rowMeans(as.matrix((.[-1])))) %>%
# filter(between(node, as.Date("2029-10-01"), as.Date("2060-09-30")))
#Gen_Tuo
Gen_Tuo1 <- melt(Gen_Tuo, id = "node")
Gen_Tuo1
Gen_Tuo2 <- rbind(Gen_Tuo1, Livneh_Tuo) %>%
mutate(Month = as.factor(format(as.Date(node, format = "%B"), "%b")),
Year = as.factor(format(as.Date(node, format = "%Y"), "%Y")),
Year = water_year(node, origin = "usgs"),
Scenario = as.factor("RCP 8.5")) %>%
group_by(Year, Month, variable) %>%
dplyr::summarize(MonthlyTotal = sum(value))
Gen_Tuo2
Gen_Tuo2$variable <- factor(Gen_Tuo2$variable, levels = c("Livneh (Historical)", "ACCESS1-0", "CMCC-CMS", "MIROC5\u2020", "GFDL-CM3", "CCSM4", "HadGEM2-CC", "HadGEM2-ES\u2020","CESM1-BGC", "CanESM2\u2020", "CNRM-CM5\u2020"))
Gen_Tuo3 <- Gen_Tuo2 %>%
group_by(Year, variable) %>%
dplyr::summarize(AnnualTotal = sum(MonthlyTotal))
Gen_Tuo3
```
#Merced annual generation
Gen_Tuo2 %>%
filter(variable == "CanESM2") %>%
ggplot() +
theme_bw(base_size=12, base_family='Times New Roman') + #change font to Times New Roman, 12pt, Bold
geom_boxplot(aes( x = Month, y = value), outlier.alpha = 0.3) + #plot monthly observed data in greenish blue
#geom_line(aes(x = node, y = value)) +
scale_y_continuous(limits = c(0, NA),
expand = c(0, NA)) +
scale_x_discrete(limits=c("Oct","Nov","Dec", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep")) +
labs(title = "Stanislaus River",
subtitle = "CanESM2",
x = element_blank(),
y = "Total Electricity Generation (MWh)") + #name of x axis
theme(legend.title = element_blank(),
legend.position = "bottom",
legend.direction = "horizontal",
legend.box.margin = margin(t = -19),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
strip.placement = "outside",
strip.background = element_blank(),
panel.spacing.y = unit(0, "lines"))
#Merced annual generation
#Gen_Mer2 %>%
# filter(variable == "CanESM2") %>%
```{r}
ggplot(Gen_Tuo2) +
theme_bw(base_size=12, base_family='Times New Roman') + #change font to Times New Roman, 12pt, Bold
geom_boxplot(aes( x = Month, y = MonthlyTotal/1000, fill = variable), outlier.alpha = 0.3) + #plot monthly observed data in greenish blue
#geom_line(aes(x = node, y = value)) +
scale_y_continuous(limits = c(0, NA),
expand = c(0, NA),
labels=comma) +
scale_x_discrete(limits=c("Oct","Nov","Dec", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep"))+
scale_fill_manual(values = c("#5A5A5A", "#A50026", "#D73027", "#F46D43", "chocolate1", "gold1", "cadetblue2", "deepskyblue2", "dodgerblue2", "#4575B4", "#313695" )) +
labs(fill = "RCP 8.5",
title = "No Instreamflow Requirement (2045 prices)",
subtitle = "Tuolumne River",
x = element_blank(),
y = "Total Electricity Generation (GWh/month)") + #name of x axis
theme(legend.position = "bottom",
legend.direction = "horizontal",
legend.box.margin = margin(t = -17),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
strip.placement = "outside",
strip.background = element_blank(),
panel.spacing.y = unit(0, "lines"))+
png("Tuo_generation_rcp85_NoIFRs_2045prices_Report_March.png", units ="in", width=10, height=5, res = 300)
```
```{r}
#annual aggreagate
#annual aggreagate
ggplot(Gen_Tuo3) +
theme_bw(base_size=12, base_family='Times New Roman') + #change font to Times New Roman, 12pt, Bold
geom_violin(draw_quantiles = c(.25, .5, .75), aes( x = variable, y = AnnualTotal/1000, fill = variable)) + #plot monthly observed data in greenish blue
#geom_line(aes(x = node, y = value)) +
scale_y_continuous(limits = c(0, NA),
expand = c(0, NA),
labels = comma,
breaks= c(0,1000,2000, 3000)) +
# scale_x_discrete(limits=c(c("Livneh (Historical)", "ACCESS1-0",# "CMCC-CMS", "MIROC5", "GFDL-CM3", "CCSM4", "HadGEM2-ES", "HadGEM2-CC", "CanESM2", "CESM1-BGC",
# "CNRM-CM5")))+
scale_fill_manual(values = c("#5A5A5A", "#A50026", "#D73027", "#F46D43", "chocolate1", "gold1", "cadetblue2", "deepskyblue2", "dodgerblue2", "#4575B4", "#313695" )) +
labs(fill = "RCP 8.5", title = "Tuolumne River",
subtitle = "Baseline - RCP 8.5 (2045 prices)",
x = element_blank(),
y = "Total Electricity Generation (GWh/year)") + #name of x axis
theme(legend.position = "none",
#legend.direction = "horizontal",
#legend.box.margin = margin(t = -19),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
strip.placement = "outside",
strip.background = element_blank(),
panel.spacing.y = unit(0, "lines"))+
png("Tuo_annual_generation_rcp85_NoIFRs_2045prices_Report_March.png", units ="in", width=11.5, height=4.5, res = 300)
```
```{r}
Livneh_Gen_Stn <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/Binary IFRs x Prices - 2021-03-29/stanislaus/historical/Livneh/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c( scenario,18,22,26,30,34,38,42)] %>%
filter(between(node, as.Date("1950-10-01"), as.Date("2011-09-30")))
Livneh_Gen_Stn <- Livneh_Gen_Stn %>%
mutate(`Livneh (Historical)` = rowSums(Livneh_Gen_Stn[c(2:12)]))
Livneh_Gen_Stn
CanES_Gen_Stn <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/Binary IFRs x Prices - 2021-03-30/stanislaus/gcms/CanESM2_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c( scenario,18,22,26,30,34,38,42)]
CanES_Gen_Stn<- CanES_Gen_Stn %>%
mutate("CanESM2\u2020" = rowSums(CanES_Gen_Stn[c(2:12)]))
CanES_Gen_Stn
CNRM_Gen_Stn <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/Binary IFRs x Prices - 2021-03-30/stanislaus/gcms/CNRM-CM5_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c( scenario,18,22,26,30,34,38,42)]
CNRM_Gen_Stn<- CNRM_Gen_Stn %>%
mutate("CNRM-CM5\u2020" = rowSums(CNRM_Gen_Stn[c(2:12)]))
CNRM_Gen_Stn
HadGEM_Gen_Stn <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/Binary IFRs x Prices - 2021-03-30/stanislaus/gcms/HadGEM2-ES_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c( scenario,18,22,26,30,34,38,42)]
HadGEM_Gen_Stn<- HadGEM_Gen_Stn %>%
mutate("HadGEM2-ES\u2020" = rowSums(HadGEM_Gen_Stn[c(2:12)]))
HadGEM_Gen_Stn
MIROC_Gen_Stn <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/Binary IFRs x Prices - 2021-03-30/stanislaus/gcms/MIROC5_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c( scenario,18,22,26,30,34,38,42)]
MIROC_Gen_Stn<- MIROC_Gen_Stn %>%
mutate("MIROC5\u2020" = rowSums(MIROC_Gen_Stn[c(2:12)]))
MIROC_Gen_Stn
HadGEMCC_Gen_Stn <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/Binary IFRs x Prices - 2021-03-30/stanislaus/gcms/HadGEM2-CC_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c( scenario,18,22,26,30,34,38,42)]
HadGEMCC_Gen_Stn<- HadGEMCC_Gen_Stn %>%
mutate(`HadGEM2-CC` = rowSums(HadGEMCC_Gen_Stn[c(2:12)]))
HadGEMCC_Gen_Stn
CCSM4_Gen_Stn <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/Binary IFRs x Prices - 2021-03-30/stanislaus/gcms/CCSM4_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c( scenario,18,22,26,30,34,38,42)]
CCSM4_Gen_Stn<- CCSM4_Gen_Stn %>%
mutate(CCSM4 = rowSums(CCSM4_Gen_Stn[c(2:12)]))
CCSM4_Gen_Stn
CMCC_Gen_Stn <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/Binary IFRs x Prices - 2021-03-30/stanislaus/gcms/CMCC-CMS_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c( scenario,18,22,26,30,34,38,42)]
CMCC_Gen_Stn<- CMCC_Gen_Stn %>%
mutate(`CMCC-CMS` = rowSums(CMCC_Gen_Stn[c(2:12)]))
CMCC_Gen_Stn
CESM_Gen_Stn <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/Binary IFRs x Prices - 2021-03-30/stanislaus/gcms/CESM1-BGC_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c( scenario,18,22,26,30,34,38,42)]
CESM_Gen_Stn<- CESM_Gen_Stn %>%
mutate(`CESM1-BGC` = rowSums(CESM_Gen_Stn[c(2:12)]))
CESM_Gen_Stn
ACCESS1_Gen_Stn <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/Binary IFRs x Prices - 2021-03-30/stanislaus/gcms/ACCESS1-0_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c( scenario,18,22,26,30,34,38,42)]
ACCESS1_Gen_Stn<- ACCESS1_Gen_Stn %>%
mutate(`ACCESS1-0` = rowSums(ACCESS1_Gen_Stn[c(2:12)]))
ACCESS1_Gen_Stn
GFDL_Gen_Stn <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/Binary IFRs x Prices - 2021-03-30/stanislaus/gcms/GFDL-CM3_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c( scenario,18,22,26,30,34,38,42)]
GFDL_Gen_Stn<- GFDL_Gen_Stn %>%
mutate(`GFDL-CM3` = rowSums(GFDL_Gen_Stn[c(2:12)]))
GFDL_Gen_Stn
```
```{r}
Livneh_Stn_Gen <- Livneh_Gen_Stn[-c(2:12)]
Livneh_Stn_Gen <- melt(Livneh_Stn_Gen, id = "node")
Livneh_Stn_Gen
```
```{r}
Gen_Stn <- cbind(CanES_Gen_Stn[-c(2:12)], CNRM_Gen_Stn[-c(1:12)],
HadGEM_Gen_Stn[-c(1:12)], MIROC_Gen_Stn[-c(1:12)],
CMCC_Gen_Stn[-c(1:12)], ACCESS1_Gen_Stn[-c(1:12)],
HadGEMCC_Gen_Stn[-c(1:12)], CESM_Gen_Stn[-c(1:12)],
CCSM4_Gen_Stn[-c(1:12)], GFDL_Gen_Stn[-c(1:12)])
Gen_Stn
#Gen_Stn <- Gen_Stn %>%
# mutate(Ensemble = rowMeans(as.matrix((.[-1])))) %>%
# filter(between(node, as.Date("2029-10-01"), as.Date("2060-09-30")))
#Gen_Stn
Gen_Stn2 <- melt(Gen_Stn, id = "node")
Gen_Stn2
Gen_Stn2 <- rbind(Gen_Stn2, Livneh_Stn_Gen) %>%
mutate(Month = as.factor(format(as.Date(node, format = "%B"), "%b")),
Year = water_year(node, origin = "usgs"),
Scenario = as.factor("RCP 8.5")) %>%
group_by(Year, Month, variable) %>%
dplyr::summarize(MonthlyTotal = sum(value))
Gen_Stn2$variable <- factor(Gen_Stn2$variable, levels = c("Livneh (Historical)", "ACCESS1-0", "CMCC-CMS", "MIROC5\u2020", "GFDL-CM3", "CCSM4", "HadGEM2-CC", "HadGEM2-ES\u2020","CESM1-BGC", "CanESM2\u2020", "CNRM-CM5\u2020"))
Gen_Stn3 <- Gen_Stn2 %>%
group_by(Year, variable) %>%
dplyr::summarize(AnnualTotal = sum(MonthlyTotal))
Gen_Stn2
```
Gen_Stn2 %>%
filter(variable == "CanESM2") %>%
ggplot() +
theme_bw(base_size=12, base_family='Times New Roman') + #change font to Times New Roman, 12pt, Bold
geom_boxplot(aes( x = Month, y = value), outlier.alpha = 0.3) + #plot monthly observed data in greenish blue
#geom_line(aes(x = node, y = value)) +
scale_y_continuous(limits = c(0, NA),
expand = c(0, NA)) +
scale_x_discrete(limits=c("Oct","Nov","Dec", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep")) +
labs(title = "Stanislaus River",
subtitle = "CanESM2",
x = element_blank(),
y = "Total Electricity Generation (MWh)") + #name of x axis
theme(legend.title = element_blank(),
legend.position = "bottom",
legend.direction = "horizontal",
legend.box.margin = margin(t = -19),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
strip.placement = "outside",
strip.background = element_blank(),
panel.spacing.y = unit(0, "lines"))
```{r}
ggplot(Gen_Stn2) +
theme_bw(base_size=12, base_family='Times New Roman') + #change font to Times New Roman, 12pt, Bold
geom_boxplot(aes( x = Month, y = MonthlyTotal/1000, fill = variable), outlier.alpha = 0.3) + #plot monthly observed data in greenish blue
#geom_line(aes(x = node, y = value)) +
scale_y_continuous(limits = c(0, NA),
expand = c(0, NA),
labels=comma) +
scale_x_discrete(limits=c("Oct","Nov","Dec", "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep"))+
scale_fill_manual(values = c("#5A5A5A", "#A50026", "#D73027", "#F46D43", "chocolate1", "gold1", "cadetblue2", "deepskyblue2", "dodgerblue2", "#4575B4", "#313695" )) +
labs(fill = "RCP 8.5",
title = "No Instreamflow Requirement (2045 prices)",
subtitle = "Stanislaus River",
x = element_blank(),
y = "Total Electricity Generation (GWh/month)") + #name of x axis
theme(legend.position = "bottom",
legend.direction = "horizontal",
legend.box.margin = margin(t = -17),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
strip.placement = "outside",
strip.background = element_blank(),
panel.spacing.y = unit(0, "lines"))+
png("Stn_generation_rcp85_NoIFRs_2045prices_Report_March.png", units ="in", width=10, height=5, res = 300)
```
```{r}
#annual aggreagate
#annual aggreagate
ggplot(Gen_Stn3) +
theme_bw(base_size=12, base_family='Times New Roman') + #change font to Times New Roman, 12pt, Bold
geom_violin(draw_quantiles = c(.25, .5, .75), aes( x = variable, y = AnnualTotal/1000, fill = variable)) + #plot monthly observed data in greenish blue
#geom_line(aes(x = node, y = value)) +
scale_y_continuous(limits = c(0, NA),
expand = c(0, NA))+
# labels = comma,
# breaks= c(0,200,400, 600, 800)) +
# scale_x_discrete(limits=c(c("Livneh (Historical)", "ACCESS1-0",# "CMCC-CMS", "MIROC5", "GFDL-CM3", "CCSM4", "HadGEM2-ES", "HadGEM2-CC", "CanESM2", "CESM1-BGC",
# "CNRM-CM5")))+
scale_fill_manual(values = c("#5A5A5A", "#A50026", "#D73027", "#F46D43", "chocolate1", "gold1", "cadetblue2", "deepskyblue2", "dodgerblue2", "#4575B4", "#313695" )) +
labs(fill = "RCP 8.5", title = "Stanislaus River",
subtitle = "No Instreamflow Requirement (2045 prices)",
x = element_blank(),
y = "Total Electricity Generation (GWh/year)") + #name of x axis
theme(legend.position = "none",
#legend.direction = "horizontal",
#legend.box.margin = margin(t = -19),
plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5),
strip.placement = "outside",
strip.background = element_blank(),
panel.spacing.y = unit(0, "lines"))+
png("Stn_annual_generation_rcp85_NoIFRs_2045prices_Report_March.png", units ="in", width=11.5, height=4.5, res = 300)
```
```{r}
Livneh_Gen_USJ <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/Binary IFRs x Prices - 2021-03-29/upper_san_joaquin/historical/Livneh/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario,18,22,26,30,34,38,42,46,50,54,58,62)]
Livneh_Gen_USJ
Livneh_Gen_USJ <-Livneh_Gen_USJ %>%
mutate(`Livneh (Historical)` = rowSums(Livneh_Gen_USJ[c(2:17)])) %>%
filter(between(node, as.Date("1950-10-01"), as.Date("2011-09-30")))
Livneh_Gen_USJ
CanES_Gen_USJ <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/Binary IFRs x Prices - 2021-03-29/upper_san_joaquin/gcms/CanESM2_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario,18,22,26,30,34,38,42,46,50,54,58,62)]
CanES_Gen_USJ
CanES_Gen_USJ <- CanES_Gen_USJ %>%
mutate("CanESM2\u2020" = rowSums(CanES_Gen_USJ[c(2:17)]))
CanES_Gen_USJ
CNRM_Gen_USJ <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/Binary IFRs x Prices - 2021-03-29/upper_san_joaquin/gcms/CNRM-CM5_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario,18,22,26,30,34,38,42,46,50,54,58,62)]
CNRM_Gen_USJ
CNRM_Gen_USJ <- CNRM_Gen_USJ %>%
mutate("CNRM-CM5\u2020" = rowSums(CNRM_Gen_USJ[c(2:17)]))
CNRM_Gen_USJ
HadGEM_Gen_USJ <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/Binary IFRs x Prices - 2021-03-29/upper_san_joaquin/gcms/HadGEM2-ES_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario,18,22,26,30,34,38,42,46,50,54,58,62)]
HadGEM_Gen_USJ
HadGEM_Gen_USJ <- HadGEM_Gen_USJ %>%
mutate("HadGEM2-ES\u2020" = rowSums(HadGEM_Gen_USJ[c(2:17)]))
HadGEM_Gen_USJ
MIROC_Gen_USJ <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/Binary IFRs x Prices - 2021-03-29/upper_san_joaquin/gcms/MIROC5_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario,18,22,26,30,34,38,42,46,50,54,58,62)]
MIROC_Gen_USJ
MIROC_Gen_USJ <- MIROC_Gen_USJ %>%
mutate("MIROC5\u2020"= rowSums(MIROC_Gen_USJ[c(2:17)]))
MIROC_Gen_USJ
HadGEMCC_Gen_USJ <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/Binary IFRs x Prices - 2021-03-29/upper_san_joaquin/gcms/HadGEM2-CC_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario,18,22,26,30,34,38,42,46,50,54,58,62)]
HadGEMCC_Gen_USJ<- HadGEMCC_Gen_USJ %>%
mutate(`HadGEM2-CC` = rowSums(HadGEMCC_Gen_USJ[c(2:17)]))
HadGEMCC_Gen_USJ
CCSM4_Gen_USJ <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/Binary IFRs x Prices - 2021-03-29/upper_san_joaquin/gcms/CCSM4_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario,18,22,26,30,34,38,42,46,50,54,58,62)]
CCSM4_Gen_USJ<- CCSM4_Gen_USJ %>%
mutate(CCSM4 = rowSums(CCSM4_Gen_USJ[c(2:17)]))
CCSM4_Gen_USJ
CMCC_Gen_USJ <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/Binary IFRs x Prices - 2021-03-29/upper_san_joaquin/gcms/CMCC-CMS_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario,18,22,26,30,34,38,42,46,50,54,58,62)]
CMCC_Gen_USJ<- CMCC_Gen_USJ %>%
mutate(`CMCC-CMS` = rowSums(CMCC_Gen_USJ[c(2:17)]))
CMCC_Gen_USJ
CESM_Gen_USJ <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/Binary IFRs x Prices - 2021-03-29/upper_san_joaquin/gcms/CESM1-BGC_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario,18,22,26,30,34,38,42,46,50,54,58,62)]
CESM_Gen_USJ<- CESM_Gen_USJ %>%
mutate(`CESM1-BGC` = rowSums(CESM_Gen_USJ[c(2:17)]))
CESM_Gen_USJ
ACCESS1_Gen_USJ <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/Binary IFRs x Prices - 2021-03-29/upper_san_joaquin/gcms/ACCESS1-0_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario,18,22,26,30,34,38,42,46,50,54,58,62)]
ACCESS1_Gen_USJ<- ACCESS1_Gen_USJ %>%
mutate(`ACCESS1-0` = rowSums(ACCESS1_Gen_USJ[c(2:17)]))
ACCESS1_Gen_USJ
GFDL_Gen_USJ <- read_csv("C:/Users/gusta/Box/VICE Lab/RESEARCH/PROJECTS/CERC-WET/Task7_San_Joaquin_Model/pywr_models/results/Binary IFRs x Prices - 2021-03-29/upper_san_joaquin/gcms/GFDL-CM3_rcp85/Hydropower_Energy_MWh.csv", col_types = cols(node = col_date(), .default = col_double()))[-c(1:2), c(scenario,18,22,26,30,34,38,42,46,50,54,58,62)]
GFDL_Gen_USJ<- GFDL_Gen_USJ %>%
mutate(`GFDL-CM3` = rowSums(GFDL_Gen_USJ[c(2:17)]))
GFDL_Gen_USJ
```
```{r}
Livneh_USJ_Gen <- Livneh_Gen_USJ[-c(2:17)]
Livneh_USJ_Gen <- melt(Livneh_USJ_Gen, id = "node")
Livneh_USJ_Gen
```