DOI: 10.5285/c6ba2edc-09f0-4cdc-9aa7-9a108ca91d54
title: Ensemble of land and freshwater ecosystem services mapped for Lake Victoria’s catchment area, 2000-2022

description: This dataset presents modelled predictions (spanning 2000-2022) for several important ecosystem services in the Lake Victoria basin in eastern Africa. The catchment includes areas of Kenya, Tanzania, Uganda, Rwanda, and Burundi (196,883 sq. km, excluding Lake Victoria itself). The dataset variables include carbon storage across multiple land pools, erosion avoided by the presence of vegetation cover, nitrogen and phosphorus retention by the landscape, annual baseflow (subsurface contribution to river flow) and annual quickflow (surface runoff). These data were generated by UKCEH scientists using the Integrated Valuation of Ecosystem Services & Trade-offs (InVEST) platform of GIS models. The purpose of this dataset is to allow users to investigate how these ecosystems services vary spatially across the landscape and also how they may change over time. Users can also investigate where synergies and trade offs among different ecosystem services occur, which can help inform management decisions such as where to best target the planting of new trees for maximum environmental benefit.

The dataset comes in the form of 30, single-band geotiff raster files. For each of the six modelled variables, there five files (one file per year: 2000, 2005, 2010, 2015 and 2022), at 90 x 90 metre spatial resolution, using the ESRI:102022 - Albers for Africa equal area projection.
description: Models from the Integrated Valuation of Ecosystem Services & Trade-offs (InVEST) platform were used to simulate the spatial distribution of ecosystem service provision across Lake Victoria’s full catchment area. Models used were:
Carbon Storage and Sequestration (CSS) model to calculate total carbon storage on land
Sediment Delivery Ratio (SDR) model to calculate avoided soil erosion as a result of land cover and management
Nutrient Delivery Ratio (NDR) model to calculate rates of nitrogen and phosphorus retention on the land surface
Seasonal Water Yield (SWY) model to calculate baseflow (slower moving subsurface water flows) and quickflow (faster moving surface runoff flows)

Model input data consisted of information on soils, geomorphology, climate, vegetative coverage and management practices. This included using large-scale third-party spatial datasets (for example, land cover data, precipitation, potential evapotranspiration, digital soil maps and digital elevation model data (DEM). Each model also requires a biophysical inputs table with land-cover specific values. Values were derived from spatially explicit global datasets, FAOSTAT (for crop production and crop nutrient balance) and published literature for the study area.

The units for each variable in the dataset are described in the Supporting Documentation.

Quality control checks were carried out on the dataset variables prior to submission. Model outputs for sediment and nutrient loads were compared with observational data from several Lake Victoria sub-catchments (for the years 2000 and 2005) and shown to be reasonably accurate. Most predicted N, P and sediment loads from rivers in the Lake Victoria catchment were found to lie close to the 1:1 line. No independent data on carbon storage could be found to compare the CCS model outputs against; however, a comparison against independent applications of the InVEST carbon model to countries around Lake Victoria reveals that there does seem to at least be agreement that total carbon stocks have declined since 2000 (although the level of decline varies between studies). The SWY model outputs were evaluated by comparing them with independent hydrological modelling data for selected sub-catchments in the region, with results suggesting that the model captures the seasonal dynamics of flow reasonably well across different sub-catchments.

To keep file sizes to a minimum, a conversion factor has been applied to each raster layer, allowing storage as integer rasters, which are much smaller than floating point formats. See the Supporting Documentation for more details on the conversion factors the user should divide the raster layers by to get the “true” values in the proper units listed above. All rasters were harmonised to exactly the same spatial extents as the DEM layer used to drive most of the modelling to allow for direct comparison with one another.