Rainfall data is a vital input for many ecosystem service modeling in general and hydrological modeling in particular. However, accurate rainfall data with sufficient spatiotemporal distribution is inadequate in the Blue Nile Basin (Ribb watershed) due to uneven distribution of rain gauge networks. Advances in remote sensing science have provided alternative sources of rainfall data with high spatiotemporal resolution. But the accuracies of different satellite rainfall datasets are not uniform across space and time that need to be checked. The overarching objective of this study is to evaluate the performance of four satellite-based rainfall products [Tropical Applications of Meteorology using Satellite and ground-based observations (TAMSAT-v2.0 and v3.0), Climate Hazards Group InfraRed Precipitation with Station data version two (CHIRPS-v2.0), and Tropical Rainfall Measuring Mission version seven (TRMM-3B43 v7.0)] in the data-scarce region of the Blue Nile Basin in Ethiopia. The evaluation was carried out through direct comparison with the observed rainfall and through simulation of annual water yield using InVEST model for monthly, seasonal, and annual time scales. In general, the results show that the performance of satellite rainfall differs in time scale, topography, and method of evaluation. CHIRPS v2.0 rainfall product shows good performance both at monthly (R2 = 0.83) and annual (r = 0.85) time scales regardless of elevation. TRMM-3B43 v7.0 well performed over the mountainous area, which makes it the best rainfall data than other products at seasonal time scale (r = 0.86). CHIRPS v2.0 and TAMSAT v3.0 are equally applicable to that of gauged rainfall data for annual water yield simulation (Bias = 1.01 and 1.08 respectively). The findings of this study indicated the best performance of CHIRPS v2.0 and TAMSAT v3.0 satellite rainfall products, and hence, these products can be used for water management and decision-making process, particularly in the data-scarce watersheds.
Keywords: InVEST model; Rain gauge; Remote sensing; Topography; Water yield.
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