Water demand protections // Gridded population data // Plots of regional temperature and precipitation now on DKRZ server


Posted by Lila Warszawski on Nov. 15, 2012

Water demand projections

Further water demand projections have been uploaded by the H08 and WaterGAP teams, including some based on changing population and GDP according to SSP2. Note that in WaterGAP, non-irrigation water uses (e.g. domestic or livestock-related) are independent of the climate model, and the corresponding data are therefore filed under WaterGAP/NOT_APPLICABLE/ instead of under a specific GCM directory. For H08, on the other hand, these data are filed under the respective GCM directory, just as all other output data. A related explanation by the H08 team has been posted on the ISI-MIP data blog, so please have a look there.

2. Gridded population data

We are very grateful to Felipe Colon and Adrian Tompkins (VECTRI model), who have prepared gridded population data to be used in ISI-MIP. They are currently available for SSP2 and can be downloaded from:

http://clima-dods.ictp.it/d10/fcolon_g/SSP2/

That folder contains:

- Total population .nc files for 2010-2100 at 0.5 deg resolution
- A shapefile mask with the country boundaries
- A national identification grid at a 2.5 arc minutes resolution (used for upscaling)
- A csv file with a comparison of the global annual values in the GPWv3 (2.5 min and 0.5 deg) and SSP datasets
- Total population .nc files for 2010-2100 at 2.5 arc min resolution

We will post a short documentation of the method from Felipe on our website www.isimip.org -> Input data -> SSP data.

3. Plots of regional temperature and precipitation now on DKRZ server

We have plotted the regionally averaged (Giorgi regions) temperature and precipitation for all models and have placed these on the DKRZ server. As discussed at the workshop in Reading, there were concerns that the GFDL model exhibits very high variability in precipitation. As can be seen in the plots, the precipitation variability in the GFDL model in some regions is as much as twice as large as the others, whereas in others it is approximately the same. We will therefore ask you to simply treat the discrepancies in variability across the models as a characteristic of the climate data ensemble.

They are available here: data/controlplots/input.uncorrected/interGCM/