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 (r.in.pism) 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.
Earlier I wrote more about how to calculate bioclim data layers in GRASS GIS. For people that do not use GRASS GIS, it is probably easier to generate these layers in DIVA GIS. You can get DIVA-GIS for free at http://www.diva-gis.org. Continue reading Using DIVA-GIS to create bioclim data layers
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
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