# Evaluation of Global Precipitation Products over Wabi Shebelle River Basin, Ethiopia
###### Abstract
Global precipitation products (hereinafter GPPs) are impacted by exposure to important errors [15]. Such errors can be due to upscaling/downscaling the raw spatial resolution of global precipitation products with complicated terrain [16; 17; 18; 19; 20; 21] and temporal sampling constraints [22]. Before being used as input for the hydro-meteorological modeling, global precipitation products and their downscaled versions need to be evaluated against ground measurements. For investigations of climate extremes, climate change, and water potential assessment for local-scale applications, global precipitation data with fine spatial and temporal resolution going back in time (30+ years) are required for data-scarce local basins. The Wabi Shebelle river basin is Ethiopian's largest river basin in terms of its catchment area, but the surface water potential resource was reported as the minimum of all river basins in the master plan study [23]. The issue with this basin is that no compressive research has been conducted to determine water potential using accurate spatial and temporal rainfall datasets. Several studies have been undertaken to evaluate the performance of global precipitation estimates, which were concentrated in the Blue Nile Basin (for instance, [6; 10; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33]) and central parts of the country [22; 34; 35; 36; 37; 38], with a coarser resolution and limited time period. The performance of the global precipitation product is highly affected by spatial resolution, which is largely uncertain because of the scale discrepancy with point measurements. The authors of [39] validated 10 satellite precipitation estimates across 120 relatively dense gauge network highlands of Ethiopia. TAMSAT, CMORPH, and TRMM-3B 42 from the first (high-resolution) group had a strong performance. The authors of [40] evaluated CHIRPSv2, TRMM 3B43v7, CMORPH, ARC2, and TAMSAT across various rainfall regimes (eastern, central, western, and southern) of Ethiopia. The CHIRPSv2 precipitation products at a monthly timescale performed comparatively better across all rainfall regimes. In addition, eight satellite-based rainfall estimates were assessed over the Tekeze-Atbara Transboundary River Basin. TRMM, RFEv2, and CHIRPS precipitation products had superior performance to other products at all spatial resolutions (basin, sub-basin, and point) [14]. Similar research focused mainly on a grid-to-point technique in data-sparse regions [22; 25; 34; 41; 42]. Furthermore, this method can induce uncertainty in the performance of global precipitation products due to the comparison of two datasets on different spatial scales regardless of the location of gauges in the pixel. Uncertainty due to the mismatch of satellite-based spatial resolution scale with point measurements may affect the application of GPPs for climate study and hydrological modeling [43]. Although global precipitation products exist at a coarser resolution (larger pixel size) than required by climate studies and hydrological applications, they have to be downscaled to fine resolution for matching the sampling of GPPs with gauge data [44]. A comprehensive evaluation of global precipitation products and their downscaled versions, particularly with a spatial mismatch at different timescales, is needed for a better understanding of watershed hydrology; however, this has not been performed for the Wabi Shebelle River Basin to the best of our knowledge. Uncertainty related to the grid-to-point method can be addressed by avoiding the spatial mismatch between the global precipitation product and corresponding station measurement by downscaling the coarse resolution to a fine resolution. Therefore, this study attempted (i) a comprehensive evaluation of native and downscaled global precipitation products against ground reference rainfall data, and (ii) a quantification of the uncertainty associated with a grid-to-point approach for the spatial scale of global precipitation products at a selected pixel scale.