reddPrec (version 3.0.2)

gridPcp: 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

if (FALSE) {
# 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)
}