R/readLUH2UrbanFuture.R

In pik-piam/mrland: MadRaT land data package

#' @title readLUH2UrbanFuture
#' @description read in gridded future urban land use datasets, from LUH2 Hurtt data
#' @return magpie object of gridded future urban land use in Mha, 2015-2100
#' @author David Chen, Patrick v. Jeetze
#' @seealso \code{\link{readSource}}
#' @importFrom magclass as.magpie
#' @importFrom ncdf4 nc_open
#' @importFrom terra ext rast aggregate extract ext<-
#' @importFrom magclass as.magpie mbind


readLUH2UrbanFuture <- function() {
  files <- list.files(path = paste0("./"), pattern = "states", all.files = TRUE, full.names = FALSE)

  # select years
  start_year <- 2015 # min 850
  end_year <- 2100 # max 2015
  timesteps <- 1
  offset <- 2015 # year 2016=1, y2100=86

  map <- toolGetMappingCoord2Country(pretty = TRUE)

  # land area
  carea <- rast("staticData_quarterdeg.nc")[["carea"]]
  ext(carea) <- c(-180, 180, -90, 90)

  data_sel <- "urban" # can change if ever other future LUH2v2 land use types are needed
  time_sel <- seq(start_year - offset, end_year - offset, by = timesteps)

  yrs <- paste0("y",seq(start_year,end_year,timesteps))
  ssps <- paste0("SSP", c(1:5))

  urbanMag <- NULL
  for (i in seq_len(length(files))) {
    tmpMag <- NULL
    for (t in seq_len(length(time_sel))) {
      shr <- rast(files[i], subds = data_sel)
      shr <- shr[[t]]
      x <- shr * carea
      x <- aggregate(x, fact = 2, fun = sum, na.rm = TRUE)
      x <- as.magpie(extract(x, map[c("lon", "lat")])[, 2], spatial = 1)
      dimnames(x) <- list(
        "x.y.iso" = paste(map$coords, map$iso, sep = "."),
        "t" = yrs[t],
        "data" = ssps[i]
      )
      tmpMag <- mbind(tmpMag, x)
    }
    urbanMag <- mbind(urbanMag, tmpMag)
  }

  # convert from km^2 to Mha
  urbanMag <- urbanMag / 10000

  out <- urbanMag

  return(out)
}