Overview

REddyProc package supports processing (half)hourly data from Eddy-Covariance sensors.

There is an online-formular to use the functionality of the package including description at https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProcWeb.

Installation

# Release stable version from CRAN
install.packages("REddyProc")

# The development version from GitHub using devtools:
# install.packages("devtools")
devtools::install_github("EarthyScience/REddyProc")

The REddyProc~package requires a quite recent versions of the tidyverse packages. On encountering problems on installations with older versions should run the following code before installing REddyProc.

install.packages("tidyverse")
update.packages(oldPkgs="dplyr")

Usage

A simple example performs Lookuptable-based gapfilling of Net-Ecosystem-Exchange (NEE) and plotting a fingerprint plot of the filled values.

library(REddyProc)
#+++ Input data from csv (example needs to be downloaded)
examplePath <- getExamplePath('Example_DETha98.txt', isTryDownload = TRUE)
if (length(examplePath)) {
  EddyData <- fLoadTXTIntoDataframe(examplePath)
} else {
  warning(
      "Could not find example text data file."
      ," In order to execute this example code,"
      ," please, allow downloading it from github. " 
      ," Type '?getExamplePath' for more information.")
  # using RData version distributed with the package instead
  EddyData <- Example_DETha98
}
#+++ If not provided, calculate VPD from Tair and rH
EddyData$VPD <- fCalcVPDfromRHandTair(EddyData$rH, EddyData$Tair)
#+++ Add time stamp in POSIX time format
EddyDataWithPosix <- EddyData %>% 
  filterLongRuns("NEE") %>% 
  fConvertTimeToPosix('YDH', Year = 'Year', Day = 'DoY', Hour = 'Hour')
#+++ Initalize R5 reference class sEddyProc for processing of eddy data
#+++ with all variables needed for processing later
EProc <- sEddyProc$new(
  'DE-Tha', EddyDataWithPosix, c('NEE','Rg','Tair','VPD', 'Ustar'))
#Location of DE-Tharandt
EProc$sSetLocationInfo(LatDeg = 51.0, LongDeg = 13.6, TimeZoneHour = 1)  
#
#++ Fill NEE gaps with MDS gap filling algorithm (without prior ustar filtering)
EProc$sMDSGapFill('NEE', FillAll = FALSE)
#
#++ Export gap filled and partitioned data to standard data frame
FilledEddyData <- EProc$sExportResults()
#
#++ Example plots of filled data to screen or to directory \plots
EProc$sPlotFingerprintY('NEE_f', Year = 1998)

Further examples are in vignette(useCase) and vignette(DEGebExample) and further md-files of the vignettes directory.

Reference

The methodology and benchmark of REddyProc 1.1.3 is described in the following paper:

Wutzler, T., Lucas-Moffat, A., Migliavacca, M., Knauer, J., Sickel, K., Šigut, L., Menzer, O., and Reichstein, M. (2018): Basic and extensible post-processing of eddy covariance flux data with REddyProc, Biogeosciences, 15, 5015-5030, https://doi.org/10.5194/bg-15-5015-2018.