## 3 Results and Discussion
### Flood Simulations  
In order to investigate the results of flood simulations in relation to model calibration, the initial values of key parameters for each HEC-HMS model are provided in Table 8.  
Figure 7: Spatial distributions of the total cumulative precipitation depth (mm) for eight flood events selected (# stands for event number): (**a**) RAR by KMA; (**b**) IMERG by NASA. Based on these values, flood simulations were performed using hourly areal-averaged rain gauges and gridded precipitation data (two radar products). Figure 9 represents whether or not the initial parameter values were adjusted for each model after the required calibrations using the eight selected storm events. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Hydroologic** \\ **Element** \\ \end{tabular} } & \multirow{2}{*}{**Process**} & \multicolumn{2}{c}{**Initial Parameter Values**} \\ \cline{3-4}  & & **Gauged Data** & **Radar-based Data** \\  & & **Simulation** & **Simulation** \\ \hline \hline \multirow{8}{*}{Subbasin} & \multirow{8}{*}{Loss} & SCS Curve Number (CN) & Gridded SCS CN \\  & & - CN: determined & - CN: determined \\ \cline{1-1}  & & - Initial abstraction (mm): 0 & - Ratio: 0.05 \\ \cline{1-1}  & & - Impervious (\%): 0 & - Factor: 1.0 \\ \cline{1-1} \cline{2-4}  & \multirow{8}{*}{Transform} & Clark Unit Hydrograph and ModClark \\ \cline{1-1}  & & - Time of concentration (HR): determined \\ \cline{1-1}  & & - Storage coefficient (HR): 2.0 \\ \cline{1-1} \cline{2-4}  & \multirow{8}{*}{Baseflow} & Recession \\ \cline{1-1}  & & - Initial discharge (m\({}^{3}\)/s): observed \\ \cline{1-1}  & & - Recession constant: 0.2 \\ \cline{1-1}  & & - Ratio to peak: 0.4 \\ \hline \hline \multirow{2}{*}{Reach} & \multirow{2}{*}{Routing} & Muskingum \\ \cline{1-1}  & & - Muskingum K (HR): 0.25 \\ \cline{1-1}  & & - Muskingum X: 0.25 \\ \cline{1-1}  & & - Number of subreaches: 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Initial parameter values for the developed HEC-HMS models. Figure 8: Developed two HEC-HMS models: (**a**) Clark model, red circles indicate the points where discharge data are inputted; (**b**) ModClark model for RAR (upper three figures) and IMERG (lower three figures), which represent the developed grid cells, the calculated flow lengths, and the gridded Curve Number for each grid, respectively. Upon examining the results, it is evident that the models utilizing gridded precipitation, particularly RAR data, required relatively less adjustment of parameters compared to simulations using areal-averaged precipitation. This can be attributed to the fact that in the processes of rainfall-runoff transformation (time of concentration, T\({}_{c}\), and storage coefficient, R, parameters used) and river routing (Muskingum K and X parameters used), which are associated with flow travel times, simulations were achieved without the need for adjustments of parameter values, yielding results similar to the observed hydrographs. In contrast, simulations using rain gauge observations and IMERG gridded precipitation, where the spatial distribution of precipitation across the entire watershed was not sufficiently considered, necessitated adjustments of parameter values related to travel times in almost all cases. This indicates the increased efficiency of flood discharge analysis when utilizing the detailed grid resolution of spatially distributed precipitation, which allows for the incorporation of localized rainfall patterns and amounts into model simulations. Particularly, in watersheds dominated by mountainous terrains, such as dam watersheds, where there are significant variations in rainfall across different regions, the introduction of data that considers spatial distribution, such as radar precipitation, is crucial. However, it should be noted that gridded precipitation data at the grid-cell level still tends to underestimate or overestimate the actual observed values.