Evaluation and Comparison of Satellite-Derived Estimates of Rainfall in the Diverse Climate and Terrain of Central and Northeastern Ethiopia

Abstract

Understanding rainfall processes as the main driver of the hydrological cycle is important for formulating future water management strategies; however, rainfall data availability is challenging for countries such as Ethiopia. This study aims to evaluate and compare the satellite rainfall estimates (SREs) derived from tropical rainfall measuring mission (TRMM 3B43v7), rainfall estimation from remotely sensed information using artificial neural networks—climate data record (PERSIANN-CDR), merged satellite-gauge rainfall estimate (IMERG), and the Global Satellite Mapping of Precipitation (GSMaP) with ground-observed data over the varied terrain of hydrologically diverse central and northeastern parts of Ethiopia—Awash River Basin (ARB). Areal comparisons were made between SREs and observed rainfall using various categorical indices and statistical evaluation criteria, and a non-parametric Mann–Kendall (MK) trend test was analyzed. The monthly weighted observed rainfall exhibited relatively comparable results with SREs, except for the annual peak rainfall shifts noted in all SREs. The PERSIANN-CDR products showed a decreasing trend in rainfall at elevations greater than 2250 m above sea level in a river basin. This demonstrates that elevation and rainfall regimes may affect satellite rainfall data. On the basis of modified Kling–Gupta Efficiency, the SREs from IMERG v06, TRMM 3B43v7, and PERSIANN-CDR performed well in descending order over the ARB. However, GSMaP showed poor performance except in the upland sub-basin. A high frequency of bias, which led to an overestimation of SREs, was exhibited in TRMM 3B43v7 and PERSIANN-CDR products in the eastern and lower basins. Furthermore, the MK test results of SREs showed that none of the sub-basins exhibited a monotonic trend at 5% significance level except the GSMap rainfall in the upland sub-basin. In ARB, except for the GSMaP, all SREs can be used as alternative options for rainfall frequency-, flood-, and drought-monitoring studies. However, some may require bias corrections to improve the data quality.