Input data set: Population & GDP

Direct human forcings.

Historical (1950-2009) and future projections (2010-2100) of country-level and gridded population data based on the Shared Socio-economic Pathways (SSPs) for SSP1-SSP5.

The SSP folders contain:
- 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

The national identification grid was obtained from the GPWv3 site. The ID number for each country was modified to follow the order of the Iso3v10 (i.e. country code) variable in the shapefile mask (also obtained from the GPWv3 site). To match the GPWv3 population for the baseline year (2010) with the SSP national estimations I obtained population for each country using the national identification grid. The SSP/GPWv3 ratio was used to scale up the population in the GPWv3 dataset. The 37 countries not present in the SSP dataset were assigned a meanscaling factor taking the mean of the scalingfactors for the 193 countries in the SSP dataset. I ran some tests to a randomly selected number of years and I had a margin of error of about maximum plus/minus ten individuals globally per year before adding the population of those countries not present in the SSP dataset.

Population for intervening years not present in the SSPs was estimated using a linear interpolation. Then, yearly population increases were estimated dividing the population of year 'X' by the population of 2010 for each country in the SSP dataset. Countries not present in the SSP dataset (37 out of 230) were assigned a mean population increase taking the mean of the population increase in the SSP dataset.

Historic data:

The historical folder contains:

- Total population netcdf files for 1950-2009 at a 2.5 arc min and 0.5 deg resolution
- A csv file with a comparison of the global annual values in the netcdf files (0.5 deg) and the UNWPP dataset

The same national identification grid as in the SSP scenario was used. The ID number for each country was modified to follow the order of the Iso3v10 (i.e. country code) variable in the shapefile mask (also obtained from the GPWv3 site).

As in the SSP run, for matching the GPWv3 population for the baseline year (2010) with the UNWPP national estimations I obtained population for each country using the national identification grid. The UNWPP/GPWv3 ratio was used to scale up the population in the UNWPP dataset. The 6 countries not present in the UNWPP dataset were assigned a mean scaling factor taking the mean of the scaling factors for the countries present in such dataset.

Yearly population increases were estimated dividing the population of year 'X' by the population of 2010 for each country in the UNWPP data. Countries not present in the dataset were assigned a mean population increase taking the mean of the global population increase.

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