# Evaluation of Global Precipitation Products over Wabi Shebelle River Basin, Ethiopia
###### Abstract  
This study presents three global precipitation products and their downscaled versions (CHIRPSv2, TAMSATv3, PERISANN_CDR, CHIRPS_D, PERISAANN_CDR_D, and TAMSAT_D) estimated with observed values from 1983 to 2014. Performance evaluation of global precipitation products and their downscaled versions is important for accurate use of those measured values in water resource management, climate, and hydrological applications, particularly in the data-sparse Wabi Shebelle River Basin, Ethiopia. Categorical and quantitative evaluation index techniques were applied. The spatial downscaled global precipitation products outperformed raw spatial resolution estimates in all statistical indicators. TAMSAT-D had acceptable performance ratings in terms of RMSE, CC, and scatter plots (\(R^{2}\)). CHIRPSv2 showed the least performance at a daily timestep. Performance of global precipitation products and their downscaled versions increased when daily data were aggregated to the monthly data. CHIRPS-D performed better than other products with a minimum error value (RMSE) and higher CC at a monthly timestep. On the other hand, PERISANN_CDR_D showed a relatively good performance with a lower, positive Pbias and higher POD values compared to other products for daily and monthly timescales. For spatial mismatch analysis, the bias and RMSE from reference data (individual rain gauge station vs. the average of all available eight stations) against satellite rainfall estimates (PERISANN_CDR) had a significantly different weight, which could be related to the position of the gauge station to provide the \"true\" spatial rainfall amount. Overall, TAMSATv3 and CHIRPSv2 and their downscaled version satellite estimates showed good performance at daily and monthly timesteps, respectively. PERISANN_CDR performed best with low Pbias and the highest POD values. Thus, this study decided that the downscaled version of CHIRPSv2 and PERISANN_CDR-D satellite estimates could be applicable as an alternative to gauge data on a monthly timestep for hydrological and drought-monitoring applications, respectively. satellite; product; precipitation; rain gauge; evaluation; downscaling +
Footnote †: journal: _Article Evaluation of Global Precipitation Products over Wabi Shebelle River Basin, Ethiopia_  
world [5]. The current meteorological network are inadequate and have poor maintenance for water resource assessment and climate studies in most tropical regions including Ethiopia [6]. The Wabi Shebelle River Basin (hereinafter WSRB) suffers from the scarce and uneven distribution of the gauge network, inadequate and low-accuracy precipitation, and incoherence of rainfall records [7]. The study area also experienced sociopolitical instability during the civil war in the region, resulting in precipitation measurements not being taken continuously. The gauge network spatial distribution is below the World Meteorological Organization (WMO) recommendations as a guideline for checking the adequacy of a meteorological network for the different physiographic units (one station per 575 km\({}^{2}\) interior plains and 250 km\({}^{2}\) for the mountainous regions) [8]. Therefore, satellite precipitation products are considered a significant alternative source for obtaining precipitation datasets for the nonexistence of observed data in filling spatiotemporal gaps [9; 10]. Several higher global precipitation products exist at the regional and global level, including Tropical Applications of Meteorology using SATellite version three (hereinafter TAMSATv3) [11], Climate Hazards Group Infrared Precipitation with station data version two (hereinafter CHIRPSv2) [12], and Multisource and Precipitation Estimation from Remotely Sensed Information using Artificial Neutral Network Climate Data Record (PERSIANN-CDR) [13]. However, the products from these algorithms and assimilation models need to be evaluated as their precision is impacted by gauge density, orography, rainfall regime, temporal and spatial resolution, and algorithms used [14].