DOI: 10.5285/86d07d98-2956-4395-8b02-29dd5d98e6be
title: Predicted soil erosion rates, nutrient fluxes and topsoil lifespans, modelled for Kenya at a 30 metre resolution

description: This dataset presents predicted soil erosion rates (t ha-1 yr-1) and its impact on topsoils, including lifespans (yr) assuming erosion rates remain constant and there is no replacement of soil; flux rates of soil organic carbon via erosion (t SOC ha-1 yr-1); flux rates of soil nitrogen via erosion (t N ha-1 yr-1); and flux rates of soil phosphorous via erosion (t P ha-1 yr-1). The dataset comes in the form of  three multi-band raster GeoTiff files, structured as follows:

LC16_Results.tif:  Model predictions generated under the 2016 Copernicus Land Cover Map at 30-metre resolution (five bands)
Mitigation_scenarios.tif:  Predicted reductions in erosion rates in the event of implementing mitigation scenarios described in sixteen  different scenarios (sixteen bands).
PNV_Results.tif: Same structure as LC16_Results.tif, but stores predictions generated under the Potential Natural Vegetation cover map for East Africa at 30-metre resolution (five bands)
description: Topsoil (0-20 cm) erosion rates were predicted by applying the Revised Universal Soil Loss Equation to 4 spatial layers (full details are provided in the supporting documentation file).
In the case of Mitigation_scenarios.tif, we multiplied the erosion rate predictions in LC16_Results.tif by coefficients that represent various mitigation scenarios on cropland. These predictions were then subtracted from the LC16_Results.tif erosion rate predictions to produce estimated erosion rate reductions under sustainable land-use scenarios.
Topsoil nutrient concentration maps were combined with erosion rate predictions to estimate rates of soil organic carbon, nitrogen and phosphorous fluxes with soil loss. Topsoil bulk density predictions were combined with erosion rate predictions to estimate topsoil lifespans across Kenya. Predicted soil erosion rates and topsoil lifespans were evaluated against available observational measurements and shown to be reasonably accurate. No observational data on soil organic carbon, nitrogen or phosphorous fluxes via erosion were available for evaluation purposes.
To reduce the overall sizes of each data file, we converted the data from floating point to integer format. Before converting our data, we multiplied each raster layer (individual band in our GeoTiffs) by a conversion factor appropriate to the number of decimal places for each property (see Supporting Documentation for more details). The user of our data will thus need to divide the values stored in our raster layers by a conversion factor to get the “true” values.
After converting our data into integer format, each raster band was harmonised to the same coordinate reference system and spatial extent before stacking into multi-band GeoTiff files. Please note that while within each of the 3 files, the spatial extents are identical, between the 3 files, the spatial extents differ. This is because the LC16_Results.tif layers include predictions for the Ilemi Triangle in northwest Kenya, whereas the PNV_Results.tif layers do not. The Mitigation_scenarios.tif layers meanwhile cover only Kenya’s croplands.