R/download_terraclimate.R

In cropDemand: Spatial Crop Water Demand for Brazil

#' Download of reference evapotranspiration (eto) and rainfall (ppt) from 'TerraClimate'
#'
#' @description This function will download the eto and ppt and will load a SpatRaster according to the the region of interest (Region and sub_region).
#' @param dir_out Directory where you want to save the raster images that you are goind to download.
#' @param variable Variable to download. This function will download the eto or ppt (SpatRaster).
#' @param region Use the "brazil" shapefile to extract the SpatRaster (variable) for one state (Brazilian state), or use the "biomes_brazil" to extract the SpatRaster (variable) for one biome of Brazil.
#' @param sub_region You have two options in this section, if you choice the brazil (in region parameter) you need to choice the Brazilian states, but if you choice the biomes_brazil (in region parameter) you must choice one of Brazilian biomes.
#' @param years The period in years that the function should download images.
#' @import terra
#' @import sf
#' @import ncdf4
#' @importFrom utils capture.output
#' @examples
#' \dontrun{
#'
#' ### Downloading eto based on Brazil states.
#' see_brazil_states()
#'
#' img<-download_terraclimate(variable = "eto",
#'                           years = c(2018:2019),
#'                           region = "brazil",
#'                           sub_region = 13)
#'
#' ### Downloading the ppt based on Brazil biomes.
#' see_brazil_biomes()
#'
#' img<-download_terraclimate(variable = "ppt",
#'                           years = c(2018:2019),
#'                           region = "biomes_brazil",
#'                           sub_region = 6)
#' }
#' @return Download for the region of interest the ppt (Rainfall) or eto (reference evapotranspiration) SpatRaster
#' @references
#' The images used in this package can be found in the paper: Abatzoglou, J.T., S.Z. Dobrowski, S.A. Parks, K.C. Hegewisch, 2018, Terraclimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015, Scientific Data.
#' @export

download_terraclimate <- function(dir_out, variable, years, region, sub_region){

  if(variable == "eto"){variable<-"pet"} else {}
  if(variable == "ppt"| variable == "pet"){

    for(i in 1:length(years)){
      baseurl <- paste0("http://thredds.northwestknowledge.net:8080/thredds/fileServer/TERRACLIMATE_ALL/data/TerraClimate_", variable, "_", years[i],".nc")
      name_img<- paste0("TerraClimate_", variable, "_", years[i], ".nc")
      outfile<-paste0(dir_out, "/", name_img)
      utils::download.file(url = baseurl, destfile = outfile, mode = "wb")
      img <- terra::rast(list.files(dir_out, pattern = name_img, full.names = T))
      shp_path <- system.file("extdata", paste0(region, ".shp"), package = "cropDemand")
      invisible(capture.output(shp <- sf::st_read(shp_path)))
      area <- shp[sub_region, ]
      area <- terra::vect(area)
      img <- terra::crop(img, area)
      img <- terra::mask(img, area)
      crs <- "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"
      terra::crs(img) <- crs
      unlink(outfile)
      terra::writeRaster(img, filename = paste0(dir_out, "/", paste0(substr(name_img, 1, 21), ".tif")), filetype = "GTiff")
      }

    setwd(dir_out)
    list_img <- lapply(list.files(dir_out, pattern = ".tif$", full.names = T), terra::rast)
    s <- terra::rast(list_img)
    return(s)
    } else{message("Please, pay attention. You should download the variable ppt or eto!")}
}