gridPcp: Gridded dataset creation

In reddPrec: Reconstruction of Daily Data - Precipitation

Gridded dataset creation

Description

This function creates a gridded precipitation dataset from a station-based dataset.

Usage

gridPcp(
  prec,
  grid,
  dyncovars = NULL,
  sts,
  model_fun,
  dates,
  ncpu,
  thres,
  neibs,
  coords,
  crs,
  coords_as_preds,
  dir_name = NA
)

Arguments

prec	matrix or data.frame containing the original (cleaned) precipitation data. Each column represents one station. The names of columns must coincide with the names of the stations.
grid	SpatRaster. Collection of rasters representing each one of the predictors.
dyncovars	SpatRasterDataset. Collection of variables acting as dynamic predictors (changing each day). Each dataset inside the sds object (corresponding to each variable) must have the same rasters as number of days to grid. See Details.
sts	matrix or data.frame. A column "ID" (unique ID of stations) is required. The rest of the columns (all of them) will act as predictors of the model.
model_fun	function. A function that integrates the statistical hybrid model (classification and regression). The default is learner_glm, which is the original model. Other models are also available (learner_rf and learner_xgboost). Users can create their functions with different models as well.
dates	vector of class "Date" with all days of observations (yyyy-mm-dd).
ncpu	number of processor cores used to parallel computing.
thres	numeric. Maximum radius (in km) where neighboring stations will be searched. NA value uses the whole spatial domain.
neibs	integer. Number of nearest neighbors to use.
coords	vector of two character elements. Names of the fields in "sts" containing longitude and latitude.
crs	character. Coordinates system in EPSG format (e.g.: "EPSG:4326").
coords_as_preds	logical. If TRUE (default), "coords" are also taken as predictors.
dir_name	character. Name of the of the folder in which the data will be saved. Default NA uses the original names.

Details

All the rasters provided in "grid" and "dyncovars" must have the same spatial characteristics (resolution, crs, extent, etc.). The function estimates precipitation based on the nearest observations ("sts" and "prec") using as covariates the predictors contained in "grid" and "dyncovars". Predictors of "grid" are used for all days while those on "dyncovars" are selected depending on the day. For instance, to model the first day the algorithm considers all rasters in "grid" and only those corresponding to the first day of each variable.

Examples

## Not run: 
# fixed covariates (elevation, latitude, longitude)
alt <- terra::rast(volcano, crs = 'EPSG:4326')
terra::ext(alt) <- c(-1,3,38,42)
lon <- terra::rast(cbind(terra::crds(alt),terra::crds(alt)[,1]),type='xyz',crs='EPSG:4326')
lat <- terra::rast(cbind(terra::crds(alt),terra::crds(alt)[,2]),type='xyz',crs='EPSG:4326')
dcoast <- terra::costDist(alt,target=min(terra::values(alt)))/1000
grid <- c(alt, lon, lat, dcoast)
names(grid) <- c('alt', 'lon', 'lat', 'dcoast')

# Dynamic covariates (Variable 1, Variable 2, Variable 3)
foo <- alt
terra::values(foo) <- runif(length(terra::values(alt)))
dyncovars1 <- rep(foo, 7)
names(dyncovars1) <- paste('dynvar1.day',1:terra::nlyr(dyncovars1), 
                      sep = "_") # not use blank space!
dyncovars2 <- dyncovars1*0.0234
names(dyncovars2) <- paste('dynvar2.day',1:terra::nlyr(dyncovars2), 
                      sep = "_") # not use blank space!
dyncovars3 <- dyncovars1*10502
names(dyncovars3) <- paste('dynvar3.day',1:terra::nlyr(dyncovars3), 
                      sep = "_") # not use blank space!
dyncovars <- terra::sds(dyncovars1, dyncovars2, dyncovars3)
 
# precipitation and stations generation
set.seed(123)
prec <- round(matrix(rnorm(7*25, mean = 1.2, sd = 4), 7, 25), 1)+1
prec[prec<0] <- 0
colnames(prec) <- paste0('sts_',1:25)
sts <- data.frame(ID = paste0('sts_',1:25), as.data.frame(terra::spatSample(grid, 25)))


gridPcp(prec = prec, 
        grid = grid, 
        sts = sts, 
        model_fun = learner_glm, 
        dates = seq.Date(as.Date('2023-04-01'),as.Date('2023-04-07'),by='day'), 
        ncpu = 4, 
        thres = NA, 
        neibs = 15,
        coords = c('lon','lat'), 
        crs = 'EPSG:4326', 
        coords_as_preds = TRUE)

r <- terra::rast(c('./pred/20230401.tif','./err/20230401.tif'))
terra::plot(r)

## End(Not run)

reddPrec documentation built on June 8, 2025, 1:55 p.m.