This dataset contains fluvial flood maps of the present day 1 in 20 year return period, and corresponding flood extents for 3 SSP (Shared Socioeconomic Pathway)/RCP(Representative Concentration Pathway) scenarios for the future (2070-2100). Change in flood return periods are estimated using CMIP6 projections and subsequently used to extract flood maps from a global flood model. Full details about this dataset can be found at https://doi.org/10.5285/0d5d69ae-7f50-40ee-a0c9-2522de138f27

This dataset presents model predictions of soil erosion impacts on land and freshwater systems in the Lake Victoria Basin. These impacts include: (1) soil nutrient balances as a function of nitrogen (N) and phosphorus (P) loads to the land surface from fertiliser, manure and atmospheric deposition, minus rates of nutrient loss via soil erosion; and (2) total sediment and nutrient exports to water aggregated per sub-catchment (t/yr), which combines nutrients lost in surface runoff and subsurface flows with additional losses associated with erosion of existing soil nutrient stocks. These data are particularly useful for better understanding terrestrial N and P dynamics, and should provide valuable data for analysis of soil fertility, food security and water quality. These data were generated for each year (2000, 2005, 2010, 2015 & 2022) using two models from the InVEST ecosystem services platform: the Nutrient Delivery Ratio (NDR) and Sediment Delivery Ratio (SDR) models. Each of these models relies on a set of simple empirical functions, which has the advantage of requiring few model inputs that are easy to provide and allow for rapid modelling of large spatial areas, but come with the disadvantage of missing potentially important processes. For example, the SDR model only considers soil erosion via rills and sheetwash; it does not capture gullies, landslides or river channel change. Additionally, it was not possible to calibrate either model due to a scarcity of observational data. However, the models do produce generally expected patterns of sediment and nutrient dynamics. Hence, the outputs should still be useful for identifying areas of relatively high and low values and for assessing relative changes over time. The outputs themselves consist of raster gridded maps at 90 x 90 metre resolution, as well as vector polygons representing sub-catchments. The sub-catchments aggregate the total sediment, N and P exports from land to water for all grid cells present within each sub-catchment's boundary. All maps cover the full catchment area (196,883 sq. km excluding the lake itself) of the Lake Victoria Basin. Full details about this dataset can be found at https://doi.org/10.5285/02977a5d-00a0-44f8-baee-d2e0eecb5df0

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. Full details about this dataset can be found at https://doi.org/10.5285/c6ba2edc-09f0-4cdc-9aa7-9a108ca91d54