Climate data sets, which one to select?

For species or vegetation modelling, one of the first choices to make is the selection of explanatory variables, which in most cases will include climatic or bioclimatic data sets. One of the most widely used global climate data sets in biogeographic and ecological research is from Worldclim (Hijmans et al., 2005). Alternative global rainfall data sets are from TAMSAT TARCAT (Maidment et al., 2014) and CHIRPS (Funk et al., 2014). The Worldclim data layers are based on an interpolation of average monthly climate data from weather stations. The other two data sets combine weather station data with satellite observations to improve accuracy where in situ rainfall measurements are sparse. All three data sets are available from the KITE resources website as part of the Africlim dataset (Platts et al. 2015). Continue reading “Climate data sets, which one to select?”


GRASS gis scripts to import data from Worldclim, CSFR and PISM

I just came across these GRASS GIS scripts by Julien Seguinot to import multiple files from the WorldClim current climate dataset, the  Climate Forecast System Reanalysis (CSFR) data and some other reanalysis data sets. Furthermore, there is a script ( to imports multiple raster maps from a NetCDF output file from PISM.

They are not in the GRASS addon 6 or GRASS addon 7 repositories so I am sharing the link here. If you are planning to work with these data sets, check out these scripts, they may make your life a whole lot easier.

Importing WorldClim climate .bil datalayers in GRASS GIS II

I uploaded an update to the R script I posted earlier to import data downloaded from WorldClim into GRASS. The main change is that it unzips and imports one grid at a time into GRASS, thus requiring much less free space on your hard-disk.

It moreover has the option to import and convert the layer to the region settings of the mapset you are working in. Alternatively, you can import rasters in their original extend and resolution. Continue reading “Importing WorldClim climate .bil datalayers in GRASS GIS II”

R script to import WorldClim datalayers in GRASS GIS

As I wrote before, you can download monthly climate data layers (rainfall, mean minimum and maximum temperature of the coldest and warmest month respectively) from WorldClim for current and future climate conditions. The latter can only be downloaded in generic grid (raster) format (.bil). Moreover, monthly data layers are compressed in one zip file.

I also posted a small R script that unzips the zip files with monthly data layers downloaded from WorldClim (.bil format) and imports it your current GRASS GIS Location and Mapset. It also corrects erroneous grid cell values as discussed in that post.

I updated the script, hopefully making it easier to use. Continue reading “R script to import WorldClim datalayers in GRASS GIS”

Importing WorldClim climate .bil datalayers in GRASS GIS

In a previous post I wrote how you can generate bioclimatic data layers based on monthly rainfall and temperature data in GRASS GIS. Monthly climate data for future conditions can be downloaded from WorldClim in generic grid (raster) format only. As indicated on the WorldClim website, ESRI software assumes that the data files (.bil) do not have negative values. These values (x) are replaced by (65535 + x); E.g., -10 becomes 65525. This also means that the nodata value of -9999 is not recognized. Unfortunately, gdal, used by GRASS GIS to import the data, seems to make the same assumption. As a workaround, WorldClim recommends to use Diva-GIS. Although free, it doesn’t run on Linux. So, then what? Continue reading “Importing WorldClim climate .bil datalayers in GRASS GIS”

Calculating bioclim variables in GRASS GIS

I am currently working on the modeling of the distribution of different vegetation types and associate species in eastern Africa. In absence of more detailed climate data for the region, a great source of global climatic data is the WorldClim website. Besides the usual monthly temperature and rainfall data, it provides bioclimatic variables which are derived from these monthly temperature and rainfall values in order to generate more biologically meaningful variables. However, it only does this for the current conditions (interpolations of observed data, representative of 1950-2000) but not yet (?) for the future conditions (downscaled data from global climate model (GCM) output, IPPC 3rd assessment). Thus, I had to calculate them myself, which I did using GRASS GIS and R. Continue reading “Calculating bioclim variables in GRASS GIS”