Bioclimatic Variables from Monthly Climate Data

Computes the 19 standard bioclimatic variables (BIO01-BIO19) from monthly climate data. The variable set was originally proposed by Nix (1986, ISBN:978-0-644-04887-3) for the BIOCLIM modelling system and is also distributed with the CHELSA climatologies (Karger et al., 2017 ). Provides both individual variable functions and a unified interface to compute all 19 variables at once. Designed as an R implementation of the 'xbioclim' C++ library (Robles Fernandez, 2026 < https://github.com/alrobles/xbioclimcpp>). Supports single-pixel vectors and block-based raster processing via 'terra' for memory-efficient handling of large spatial datasets. Includes helpers to transform ERA5-Land hourly reanalysis data (Muñoz-Sabater et al., 2021 ) into monthly climate inputs.

xbioclim

An R package for computing the 19 standard bioclimatic variables (BIO01–BIO19) from monthly climate data, following the WorldClim specification. This is an R implementation of the xbioclimcpp C++ library.

Installation

Install the development version from GitHub:

# install.packages("remotes")
remotes::install_github("alrobles/xbioclim")

Building from source

xbioclim uses configure and src/Makevars.in to detect optional GDAL and CUDA support. For a production-quality build, use R CMD build (which automatically runs the cleanup script) and then install from the tarball:

R CMD build .
R CMD INSTALL --configure-args='--without-cuda' xbioclim_*.tar.gz

Cleaning after `roxygen2::roxygenise()`

roxygen2::roxygenise() loads the package with debug compilation flags (-g -O0 -UNDEBUG) to extract Rd and NAMESPACE entries. This leaves src/*.o files compiled without optimization. A later R CMD INSTALL . may reuse those object files and install an unoptimized shared library.

If you run roxygen2::roxygenise(), remove the stale debug objects before R CMD INSTALL:

./cleanup

Then install as usual:

R CMD INSTALL --configure-args='--without-cuda' .

For production and CI, always use R CMD build (which runs cleanup) followed by R CMD INSTALL from the tarball.

Usage

Single-pixel (vector) interface

library(xbioclim)

# Monthly climate data (12 values, one per month)
tas    <- c(5, 7, 10, 14, 18, 22, 25, 24, 20, 15, 10, 6)
tasmax <- c(8, 10, 14, 18, 23, 28, 32, 31, 26, 19, 13, 9)
tasmin <- c(1, 3, 6, 10, 13, 17, 20, 19, 15, 10, 6, 2)
pr     <- c(60, 55, 50, 40, 30, 15, 5, 10, 25, 45, 55, 65)

# Compute all 19 bioclimatic variables at once
result <- bioclim(tas, tasmax, tasmin, pr)
print(result)

# Or compute individual variables
bio01(tas)         # Mean Annual Temperature
bio12(pr)          # Annual Precipitation
bio04(tas)         # Temperature Seasonality
bio15(pr)          # Precipitation Seasonality

Raster (SpatRaster) interface

For large rasters, bioclim_raster() uses terra's block-loop architecture to process data one block at a time, keeping memory use bounded regardless of raster size. Multi-core processing within each block is supported via the ncores argument.

library(xbioclim)
library(terra)

# Each SpatRaster must have exactly 12 layers (one per month)
# tas    <- rast("path/to/monthly_tas.tif")
# tasmax <- rast("path/to/monthly_tasmax.tif")
# tasmin <- rast("path/to/monthly_tasmin.tif")
# pr     <- rast("path/to/monthly_pr.tif")

# Sequential (memory-efficient block processing)
bio <- bioclim_raster(tas, tasmax, tasmin, pr)

# Write directly to file to avoid loading the full result into RAM
bio <- bioclim_raster(tas, tasmax, tasmin, pr,
                       filename = "bioclim_output.tif",
                       overwrite = TRUE)

# Multi-core: process cells within each block in parallel
bio <- bioclim_raster(tas, tasmax, tasmin, pr, ncores = 4L)

nlyr(bio)    # 19
names(bio)   # "bio01" ... "bio19"

Native GDAL engine (`bioclim_engine`)

For maximum control and minimal file sizes, bioclim_engine() reads climate data directly via GDAL, writes each bioclimatic variable to a separate single-band GeoTIFF inside an output directory, and lets you select which of the 19 variables to compute.

library(xbioclim)

# Compute all 19 variables — one file each in a directory
result <- bioclim_engine(
  "tas.tif", "tasmax.tif", "tasmin.tif", "pr.tif",
  output = "bioclim_output/",
  overwrite = TRUE
)
list.files("bioclim_output/")
# "bio01.tif" "bio02.tif" ... "bio19.tif"

# Compute only BIO01 (mean annual temp) and BIO12 (annual precip)
result <- bioclim_engine(
  "tas.tif", "tasmax.tif", "tasmin.tif", "pr.tif",
  output    = "bioclim_subset/",
  variables = c(1L, 12L),
  overwrite = TRUE
)
names(result)  # "bio01" "bio12"