R/readGMIA.R

In pik-piam/mrvalidation: madrat data preparation for validation purposes

#' @title {readGMIA }
#'
#' @description Read Global Irrigation Map
#'
#' Read the Data from Siebert et.al on Irrigated Areas for each Country. Data
#' contains total valuesas well as values from groundwater, surface water and
#' non convential water sources for the following Categories:
#' \itemize{
#' \item Area equipped for Irrigation (AEI)
#' \item Area acutally irrigated (AAI)
#' \item Consumption Irrigatoin Water use (ICU)
#' }
#' The following Data is also available spatialy explicit with a resolution of 5 arcmin:
#' \itemize{
#' \item AEI in Percent
#' \item AEI in Ha
#' \item AAI as percent of AEI
#' \item AEI from groundwater sources as percent of total AEI
#' \item AEI from surface water sources as percent of total AEI
#' \item AEI from non conventional sources as percent of total AEI
#' }
#' There also exists a correct function to aggregate the data to 0.5 degree resolution,
#' set convert="correctonly" to run.
#'
#'
#' @param subtype : Available subtypes are:
#' \itemize{
#' \item all_data_national : National Data on AEI (including differentiation by source
#' (Groundwater, Surface water and nonconventional)), AAI
#' \item aei_pct : AEI in Percent
#' \item aei_ha : AEI in ha
#' \item aai_pct_aei : AAI as percentage of AEI
#' \item aeigw_pct_aei AEI from Groundwater sources as Percentage of total AEI
#' \item aeisw_pct_aei : AEI from Surface water sources as Percentage of total AEI
#' \item aeinc_pct_aei : AEI from nonconventional sources as Percentage total AEI
#' }
#' @return magpie object of the Irrigated Area  data
#' @author Stephen Wirth
#' @seealso \code{\link{readSource}}
#' @examples
#' \dontrun{
#' a <- readSource("GlobalIrrigationMap", "all_data_national")
#' a <- readSource ("GMIA", "aei_pct", convert = FALSE)
#' a <- readSource ("GMIA", "aei_pct", convert = "correctonly")
#' }
#'
readGMIA <- function(subtype = NULL) {

  files <- c(all_data_national = "HESS_2010_159_Supplement_S2.csv",
             aei_pct = "gmia_v5_aei_pct.asc",
             aei_ha = "gmia_v5_aei_ha.asc",
             aai_pct_aei = "gmia_v5_aai_pct_aei.asc",
             aeigw_pct_aei = "gmia_v5_aeigw_pct_aei.asc",
             aeisw_pct_aei = "gmia_v5_aeisw_pct_aei.asc",
             aeinc_pct_aei = "gmia_v5_aeinc_pct_aei.asc")

  file <- toolSubtypeSelect(subtype, files)

  # check wether data is gridded for countries
  if (subtype == "all_data_national") {
    # read data for countries
    a <- read.csv("HESS_2010_159_Supplement_S2.csv", sep = ";", header = TRUE, skip = 3, stringsAsFactors = FALSE)
    a <- subset(a, select = -(2:5))
    a <- a[1:(length(a[, 1]) - 1), ]
    # change dots in columnames to underscores
    colnames(a) <- gsub("\\.{2}", ".", colnames(a))
    colnames(a) <- gsub("\\.", "_", colnames(a))
    # convert to magpieobject
    b <- as.magpie(a, spatial = 1)
    getItems(b, dim = 1) <- a[, 1]
    # since data range from 2000 to 2008 all data is used for every year
    c <- new.magpie(cells_and_regions = getItems(b, dim = 1), years = c(2000:2008),
                    names = getItems(b, dim = 3), sets = getSets(b), fill = 0)
    getItems(c, dim = 1) <- a[, 1]
    c[, c(2000:2008), ] <- b
  } else {
    # if data is gridded
    # read asci file and do some restructuring
    a <- read.table(file, skip = 6)
    a <- unlist(a)
    a <- as.data.frame(a)
    b <- as.magpie(a, spatial = 1)
    # new magpieobject with the correct dimensions
    c <- array(NA, dim = c(length(b), 9, 1))
    c[, 1:9, ] <- b
    c <- as.magpie(c, spatial = 1, temporal = 2)
    # change years and names
    getYears(c) <- c(2000:2008)
    getNames(c) <- paste0(subtype, " in 5 arcmin resolution")
    getCells(c) <- paste0("GLO.", 1:dim(c)[1]) #nolint
  }
  return(c)
}