## 3 Results and Discussion
### Flood Simulations
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & & \multicolumn{2}{c}{**Gauged Data**} & \multicolumn{2}{c}{**Ground-Borne Radar Data**} & \multicolumn{2}{c}{**Space-Borne Radar Data**} \\ \cline{2-10}
**Storm Events \#** & & \multicolumn{2}{c}{**Simulation**} & \multicolumn{2}{c}{**Simulation**} & \multicolumn{2}{c}{**Simulation**} & \multicolumn{2}{c}{**Simulation**} \\ \cline{2-10}  & \(\mathbf{E_{NS}}\) & \(\mathbf{R^{2}}\) & **PBIAS (\%)** & \(\mathbf{E_{NS}}\) & \(\mathbf{R^{2}}\) & **PBIAS (\%)** & \(\mathbf{E_{NS}}\) & \(\mathbf{R^{2}}\) & **PBIAS (\%)** \\ \hline
1 & 0.914 & 0.937 & 16.97 & 0.914 & 0.914 & 4.01 & 0.878 & 0.878 & \(-\)1.20 \\
2 & 0.941 & 0.945 & \(-\)5.61 & 0.919 & 0.925 & 9.54 & 0.842 & 0.843 & \(-\)3.14 \\
3 & 0.844 & 0.853 & \(-\)9.05 & 0.716 & 0.752 & \(-\)18.06 & 0.663 & 0.697 & \(-\)13.90 \\
4 & 0.920 & 0.928 & \(-\)7.35 & 0.921 & 0.920 & \(-\)2.57 & 0.906 & 0.905 & \(-\)0.99 \\
5 & 0.893 & 0.902 & \(-\)6.69 & 0.912 & 0.921 & \(-\)7.28 & 0.611 & 0.629 & 14.35 \\
6 & 0.800 & 0.812 & 0.38 & 0.763 & 0.765 & \(-\)0.63 & 0.773 & 0.790 & 9.05 \\
7 & 0.930 & 0.948 & 11.87 & 0.895 & 0.896 & \(-\)0.18 & 0.670 & 0.673 & \(-\)7.34 \\
8 & 0.921 & 0.925 & 1.43 & 0.865 & 0.891 & \(-\)17.64 & 0.861 & 0.878 & \(-\)7.19 \\ \hline Avg. * & 0.895 & 0.906 & 7.42 & 0.863 & 0.873 & 7.49 & 0.776 & 0.787 & 7.15 \\ \hline \end{tabular} Note: * Average: \(\mathrm{E_{NS}}\) and \(\mathbf{R^{2}}\) arithmetic mean; PBIAS arithmetic mean of absolute value.  
\end{table} 
Table 9: Statistical results of flood simulation for the developed models. Figure 10: Hydrographs of flood simulation for the developed models (# stands for event number); X-axis represents simulation time (hours) and Y-axis represents discharge (m\({}^{3}\)/sec). Additionally, from Table 9, it can be inferred that relatively small catchments may experience a greater incidence of missing or inaccurate discharge data compared to the measured data at the outlet point, which poses challenges in calibrating the models. However, it is crucial to acknowledge that the same occurrence can also arise due to errors in the precipitation data used in this study. Consequently, the flood analysis utilizing various precipitation datasets with diverse production sources in this study revealed simulation results that are not significantly different from the major findings of previously discussed studies in the introduction; notably less accurate for the satellite-based data (i.e., space-borne radar precipitation). However, considering the primary objective of this research, which is to evaluate the usability of satellite-based precipitation products for flood applications, the anticipation of suboptimal simulation results was intrinsic. Nonetheless, for watersheds with available observed discharge data, this study performed an investigation to improve the usability of satellite precipitation data. This was achieved through hydrological model calibration,  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Som**} & \multicolumn{4}{c}{**Gauged Data**} & \multicolumn{4}{c}{**Ground-Borne Radar Data**} & \multicolumn{4}{c}{**Space-Borne Radar Data**} \\  & \multicolumn{4}{c}{**Simulation**} & \multicolumn{4}{c}{**Simulation**} & \multicolumn{4}{c}{**Simulation**} & \multicolumn{4}{c}{**Simulation**} \\ \cline{2-19}  & **CC** & **DH** & **DC** & **SJ** & **JC** & **CC** & **DH** & **DC** & **SJ** & **JC** & **CC** & **DH** & **DC** & **SJ** & **JC** \\ \hline  
with the aim of determining the extent to which it can be effectively employed for accurate flood analysis.