# Evaluation of Global Precipitation Products over Wabi Shebelle River Basin, Ethiopia
## 4 Result and Discussion
### Comparison of Global Precipitation Products at Temporal Scale
#### 4.1.2 Monthly Comparison  
The accuracy of the global precipitation products in replicating precipitation was further investigated at a monthly timescale, as shown in Figure 6 and Table 4. The results indicate that the performance of GPPs and their downscaled versions increased when daily data were aggregated to monthly data. These findings were also confirmed by [10; 58], which evaluated the performance accuracy of aggregated global precipitation products toward a coarser temporal resolution. For example, one study [36] investigated several global precipitation products over Burkina Faso with different temporal resolutions. The results indicated that the categorical and volumetric indicators significantly increased upon aggregating the timescale. Similarly, the authors of [59] evaluated the CHIRPS satellite precipitation estimates over eastern parts of the continent. In the comparison of CHIRPS estimates with ARC2 and TAMSTA, the findings exhibited reasonably better reference estimates at decadal and monthly timescales, with a better skill of detection and lower bias, while TAMSAT performed better at a daily timescale.  
Figure 4: The probability of detection for each station at a daily timescale.  
Figure 5: **(a)** Scatter plot of cumulative distribution function; **(b)** plots of observed rainfall against the global precipitation products.  
The downscaled GPPs outperformed their original coarser-resolution counterparts according to all statistical indicators of accuracy. CHIRPS-D performed better than other products with a minimum error value (RMSE = 53.734 mm) and higher correlation (CC = 0.748). The value of Pearson's correlation coefficient (CC) showed a good relationship for raw and downscaled global precipitation products. Scatter plots using average monthly timeseries gauge rainfall data against the three GPPs and their downscaled versions were generated. The highest coefficient (\(R^{2}\) = 0.418) was obtained by CHIRPS_D. As the time resolution  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Product** & **CHIRPSv2** & **PERSIANN-CDR** & **TAMSATv3** & **CHIRPS\_D** & **TAMSAT\_D** & **PERSIANN-CDR\_D** \\ \hline Correlation & 0.745 & 0.659 & 0.699 & 0.748 & 0.709 & 0.664 \\ PBIAS & \(-\)6.729 & 2.388 & \(-\)6.066 & \(-\)7.055 & \(-\)3179 & 1.999 \\ RMSE & 53.958 & 63.739 & 58.894 & 53.734 & 57.884 & 62.0345 \\ POD & 0.984 & 0.986 & 0.897 & 0.986 & 0.897 & 0.993 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Monthly statistical indicators of GPPs evaluated.  
Figure 6: Scatter charts of observed precipitation versus global precipitation products at monthly timescale.  
increased from days to months, the rainfall amount estimated by CHIRPSv2 became increasingly accurate. The best performance of CHIRPSv2 and its downscaled version could be due to the elimination of error as the data were aggregated to a coarser timescale. These findings are consistent with earlier investigations of CHIRPSv2 rainfall data at a monthly timescale [59; 60].  
PERSIANN-CDR showed the lowest values for RMSE, CC, and \(R^{2}\), as can be seen from Table 4 and Figure 6, on the daily timescale. PERSIANN_CDR_D showed relatively good performance with a lower, positive Pbias compared to other products (overestimate), with a value of 1.999%. PERSIANN_CDR_D resulted in the highest POD value of 0.993. CHIRPS_D and PERSIANN_CDR had the second better probability of detection (POD), whereas the TAMSAT group had the lowest value (Table 4). This implies that the performance of satellite estimates was influenced by the algorism and data source used.