Performance evaluation of multi-satellite rainfall products in the Gidabo catchment, Rift Valley Basin, Ethiopia

Satellite rainfall products are good options to overcome shorter records, record challenges, and inconsistencies with rain gauges. However, satellites' rainfall retrieval algorithms are region- and time scale-specific; hence, its key concern is the selection of appropriate satellite products. Accordingly, this study evaluates the performance of five high-resolution satellites' rainfall qualitatively, using multiple categorical metrics, and quantitatively by hybrid techniques at multiple metrics for daily and monthly scales. The result showed that Climate Prediction Center (CPC) Morphing Algorithm (CMORPH.CPC) performed better by scoring: qualitatively; Critical Success Index (CSI = 0.856), Probability of Detection (POD = 0.911), Frequency Bias Index (FBI = 0.974), and quantitatively; correlation coefficient (CC = 0.375), Root Mean Square Error (RMSE ≈ 575), and Volumetric Critical Success Index (VCSI = 0.958) at a daily scale. At a monthly scale, Climate Hazards Group Infrared Precipitation with Stations (CHIRPS.v2) performed better by scoring CSI = 0.983, POD = 1 and FBI = 0.975 qualitatively, and quantitatively, CC = 0.836 with strong VCSI = 0.981 and better RMSE (≈125) than daily. The daily rainfall of these satellites needs value-improving techniques before using them in place of Gidabo's rain gauge rainfall, while the rainfall of CHIRPS.v2 at a monthly scale can be an alternative source of rainfall data. Finally, it ensured that for the Gidabo catchment and elsewhere with similar features, the performance of satellite rainfall products was more effective at a monthly scale than at a daily scale.