ERA5-Land Data: New Possibilities in Hydrological Modelling and Water Resources Assessment in the Data-Scarce Regions of Sub-Saharan Africa

Successful application of hydrological models requires data to assess the validity, as well as the inherent uncertainty, of the outputs, most importantly streamflow. In parts of Sub-Saharan Africa (SSA), such data is often lacking. Therefore, it is frequently challenging to find the necessary resources for setting up a robust hydrological model and hydrological monitoring platforms. In data-scarce regions within SSA, ground data required to model and make water resources decisions are not always available and therefore, some form of alternative data sources and simplified modelling approaches are required. In recent years, satellite and climate reanalysis data have been intensely explored for watershed modelling in poorly gauged regions with variables such as precipitation, evapotranspiration, soil moisture, runoff etc. Very good potential is provided by the ERA5-Land dataset which is considered one of the best freely available global products for hydrology given its 0.1° x 0.1° spatial resolution and an hourly to monthly temporal resolution spanning from 1950 till present. Here, ERA-5 Land input on a monthly resolution was assessed in the Berg River Basin, South Africa using the Modified PITMAN model. Total precipitation, runoff, and potential evapotranspiration for each of the basin’s 12 quaternary catchments were retrieved using the Google Earth Engine platform for a study period of 40 years (1981-2021). A validation period of 20 years (1985-2005) was used corresponding to the freely available streamflow data. The assimilation of ERA5-Land precipitation data showed satisfactory results across the basin with the best results in the upstream catchment (G10A) with a 0.634 coefficient of efficiency and 0.404 KGE during the initial run. However, runoff for the downstream catchments (G10K) gave positive biases in high-flow months. This paper gives a detailed analysis of the performance of remotely sensed datasets (ERA5-Land) on catchments with varying climatic, land use and cover, water use, and geomorphological characteristics, therefore, offering a valuable reference for its applications in understanding hydrological processes in different river basins across SSA.