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 ArtiBcial Neural Net
works-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 perfor
mance of these satellite rainfall products using multiple metrics at daily and monthly scales. The cor
relation coefBcient (CC), mean square error (MSE), Nash-SutcliAe eDciency (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 coefBcient of variation (CV) and precipitation concentration
index (PCI) were applied to investigate rainfall variability. The study indicates that precipitation pat
terns 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 Bndings underscore the
importance of satellite-derived rainfall data, particularly the CHIRPS product, in understanding spa
tiotemporal 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.
