R/ET_from_gleam.R

In marcianito/gravitySynth: Errors due to global gravity correction methods

#' @title Actual Evapotranspiration data (daily) from GLEAM
#'
#' @description test
#'
#' @param test test
#' 
#' @return test
#' 
#' @details Datasets are provided by: 
#' https://www.gleam.eu/
#' @references Marvin Reich (2018), mreich@@posteo.de
#' @import ncdf4, ggplot2
#' @examples missing

ET_from_gleam = function(
    input_dir,
    filenames,
    lon,
    lat,
    output_cumsum = F,
    plotting = T
){
    # DEBUGGING: inspect file
    # file_nc = ncdf4::nc_open(paste0(dir_input, file_name))
    # lon = loc_x
    # lat = loc_y
    # 
    # run loop over all input files
    # and stich output
    ET_data_siteXY = data.frame()
    for(i in 1:length(filenames)){
    # open connectin to file
    file_nc = ncdf4::nc_open(paste0(input_dir, filenames[i]))
    # read variables (data) of netcdf-file
    # print(file_nc)
    # ET_attr = ncdf4::ncatt_get(file_nc, "ET")
    ET_data = ncdf4::ncvar_get(file_nc, "E")
    time_data = ncdf4::ncvar_get(file_nc, "time")
    lon_range = ncdf4::ncvar_get(file_nc, "lon")
    lat_range = ncdf4::ncvar_get(file_nc, "lat")
    
    # time conversion
    time_units = ncatt_get(file_nc, "time", attname="units")
    time_resolution = unlist(strsplit(time_units$value, " "))[1]
    time_origin = unlist(strsplit(time_units$value, " "))[3]
    # convert "time_data" to seconds 
    switch(time_resolution,
           "days" = {time_data = time_data * 3600 * 24},
           "hours" = {time_data = time_data * 3600}
           )
    # close connection to file
    ncdf4::nc_close(file_nc)
    ## get time series from location X, Y
    # library(rgdal)
    # loc_utm = SpatialPoints(randompoints, proj4string=CRS("+proj=utm +zone=32 +datum=WGS84")  
    # loc_degree = spTransform(sputm, CRS("+proj=longlat +datum=WGS84"))
    
    # loc_x = 12.884216
    # loc_y = 49.152940
    # loc_x_index = which(lon_range == loc_x)
    # loc_y_index = which(lat_range == loc_y)
    
    # find closest location match
    lon_range_dif = lon_range - lon
    lat_range_dif = lat_range - lat
    
    lon_index = which(abs(lon_range_dif) == min(abs(lon_range_dif)))
    lat_index = which(abs(lat_range_dif) == min(abs(lat_range_dif)))
    
    ET_data_siteXY_temp = data.frame(
                datetime = as.POSIXct(time_data, origin = time_origin),
                lon = lon,
                lat = lat,
                value = ET_data[lat_index, lon_index,]
                )
    # combine datasets over different files
    ET_data_siteXY = rbind(ET_data_siteXY, ET_data_siteXY_temp)
    }
    ## output either as actual value or
    # cumulative sum
    if(output_cumsum){
        # check if NAs exist and set them to 0 (no precipitation)
        # otherwise there is a problem with the cumsum-function
        ET_data_siteXY = ET_data_siteXY %>%
            dplyr::mutate(value = ifelse(is.na(value), 0, value))
        # calculate cummulative sum
        ET_data_siteXY$value = cumsum(ET_data_siteXY$value)
    }
    # plot if desired
    if(plotting){
        print(
          ggplot(ET_data_siteXY, aes(x = datetime, y = value)) + geom_line()
          )
    }
    # return datset
    return(ET_data_siteXY)
}