Performance evaluation of multi-satellite rainfall products for analyzing rainfall variability in Abaya–Chamo basin: Southern Ethiopia

Abstract

Understanding the rainfall variability is crucial for managing water resources and mitigating agricultural hazards, particularly in poorly gauged regions like the Abaya–Chamo basin. This study compares various satellite-derived rainfall products, including Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Tropical Applications of Meteorology using Satellite data and ground-based observations (TAMSAT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and Climate Hazards Group Infrared Precipitation (CHIRP), with observed rainfall data from 1990 to 2019. Accordingly, this study evaluates the performance of these satellite rainfall products using multiple metrics at daily and monthly scales. The correlation coefficient (CC), mean square error (MSE), Nash-Sutcliffe efficiency (NSE), percent of bias (PBIAS), mean absolute error (MAE), and categorical analysis metrics such as probability of detection (POD), false alarm ratio (FAR) and critical success index (CSI) indicators were applied to evaluate the accuracy of these products. Among them, the CHIRPS satellite product demonstrates superior agreement with observed data, with CC = 0.871 and NSE = 0.925, warranting its selection for further analysis of seasonal and annual rainfall variability. The coefficient of variation (CV) and precipitation concentration index (PCI) were applied to investigate rainfall variability. The study indicates that precipitation patterns in the Abaya–Chamo basin exhibit moderate to high variability throughout the year, with a CV ranging from 20–30%. This suggests substantial variability in annual rainfall within the region, in some instances where the variability exceeds 30%. Moreover, the southern and northern regions of the basin experience a consistent moderate to high variation in precipitation throughout the entire season, while the lowest variability was observed in the central part of the basin. These findings underscore the importance of satellite-derived rainfall data, particularly the CHIRPS product, in understanding spatiotemporal rainfall patterns and making informed decisions in water resource management. This research contributes in advancing our knowledge of rainfall variability in the Abaya–Chamo basin and underscores the utility of satellite data in regions lacking adequate ground-based monitoring.