# Evaluation of Global Precipitation Products over Wabi Shebelle River Basin, Ethiopia
## 4 Result and Discussion
### Comparison of Global Precipitation Products at Temporal Scale
#### 4.1.1 Daily Comparison  
The raw and downscaled global precipitation data were evaluated with observed rainfall at a daily timescale. Global precipitation products and their downscaled versions presented weak performance according to the majority of statistical indicator indices. The downscaled GPPs outperformed the original coarser resolutions as can be seen in Table 3. This result is similar to previous findings [54; 55]. This might be due to the accuracy of the original precipitation product and the spatial downscaling method [39]. The RMSE in global precipitation products and their downscaled versions was highest in the southern and northeastern parts of the basin, with values ranging from 4 to 13 mm, as can be seen in Figure 3b. TAMSAT-D performed better than other products with a minimum RMSE for a value of 6.926 mm. The value of Pearson's correlation coefficient (CC) showed a poor relationship for all global precipitation products, but the CC value was relatively higher in the southern and northern parts of the basin, with values between 0.05 and 0.5, as can be seen from Figure 3a. TAMSAT-D showed the best agreement with a higher CC (0.332). The highest coefficient (\(R^{2}\) = 0.039) was obtained by TAMSATv3 and TAMSAT_D, as can be seen Figure 5a. The high performance of daily rainfall estimates from TAMSTAv3 and its downscaled version could be due to the loss of localized convective precipitation with the specified threshold value of the study area. This discovery is in line with the findings of previous investigations. CHIRPSv2 and its downscaled version showed the worst performance, as can be seen in Table 3. This could be attributed to the areal discrepancy of gauge observations and satellite estimates, as well as of the retrieval algorithms in disaggregating pentadal data to daily values [56]. On the other hand, PERSIANN_CDR_D showed a relatively good performance with a lower, positive Pbias compared to other products (underestimate), with a value of 3.09%, as presented in Table 3. The spatial distribution of Pbias for PERSIANN_CDR_D (Figure 3c) showed better performance than most stations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & **GPPs** & \\ \hline \multirow{4}{*}{Station measurement} & \multirow{2}{*}{No Yes} & Yes & No & Total \\  & & Misses & Correct negative & Observed no \\  & & Yes & Hit & False alarm & Observed yes \\  & & Total & Estimate yes & Estimate no & Total \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Contingency table of any and non-rainy event prediction by global precipitation products. The ability of GPPs to detect the occurrence of precipitation events was also evaluated. In general, the downscaled products had better rainfall capability detection than the raw spatial resolution products in terms of the POD categorical statistical indicator. In this context, PERSIANN_CDR-D revealed a higher POD (0.691) than the PERSIANN_CDR precipitation product, as presented in Table 3. Both the raw and the downscaled precipitation products provided reasonably good PODs, varying between 0.25 and 0.893, as shown in Figure 3(a). The highest POD and low Pbias indicate that PERSIANN_CDR-D is suitable for capturing the behavior of extreme precipitation events in the Wabi Shebelle River Basin, Ethiopia. The same result was also confirmed by [57]. This could be due to the adjustment of PERSIANN_CDR using GPCP monthly 2.50 precipitation products [13]. CHIRPSv3 showed extremely poor performance according to the categorical statistical indicator values. Figure 3: The statistical parameter correlation coefficient (**a**), root-mean-square error (**b**), and percentage bias (**c**) for each station at a daily timescale. It can be observed that, in general, the downscaled and raw products presented poor agreement with the ground reference data (_r_ < 0.5). The scatter plots and cumulative distribution functions using the average daily timeseries gauge rainfall data against the GPPs were examined (Figure 5a,b).