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It would be great if you could add also CHELSA (https://chelsa-climate.org/) for terrestrial climate. Comparison papers have found it superior to WorldClim, probably because it is based on statistical downscaling rather than spatial interpolation of very irregularly distributed weather stations. Cheers!
The text was updated successfully, but these errors were encountered:
Hi @AMBarbosa. Thank you for opening the issue, I was not aware of the existence of this dataset and indeed seems a valuable addition for sdmpredictors.
Unfortunately I don't have time to get to know the CHELSA dataset. I was wondering if you could help? If you come up with a list of the layers to be included in sdmpredictors following the structure of layers.csv and layers_future.csv (most important: the url to the layer. This link is an example) I can then update sdmpredictors.
sdmpredictors currently supports layers as .tif, or anything that can be easily read with raster::raster(). Multidimensional formats like NetCDF are not supported yet.
It would be great if you could add also CHELSA (https://chelsa-climate.org/) for terrestrial climate. Comparison papers have found it superior to WorldClim, probably because it is based on statistical downscaling rather than spatial interpolation of very irregularly distributed weather stations. Cheers!
The text was updated successfully, but these errors were encountered: