Abstract
This study presents three global precipitation products and their downscaled versions (CHIRPSv2, TAMSATv3, PERSIANN_CDR, CHIRPS_D, PERSIANNN_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 (R2). 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, PERSIANN_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 (PERSIANN_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. PERSIANN_CDR performed best with low Pbias and the highest POD values. Thus, this study decided that the downscaled version of CHIRPSv2 and PERSIANN_CDR-D satellite estimates could be applicable as an alternative to gauge data on a monthly timestep for hydrological and drought-monitoring applications, respectively.
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