## 2 Materials and Methods
### Methods
#### 2.4.4 Flood Simulation and Evaluation  
The flood simulations of the constructed models were performed using the hourly gauged and radar-based rainfall data for the selected eight flood events. For each model, the model parameters were adjusted to derive simulated hydrographs that closely matched \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Hydrologic** \\ **Element** \\ \end{tabular} } & **Calculation** & \multicolumn{2}{c}{**Methods**} \\ \cline{3-4}  & **Type** & **Gauged Data** & **Radar-Based Data** \\  & & **Simulation** & **Simulation** \\ \hline \hline \multirow{4}{*}{Subbasin} & Precipitation & \begin{tabular}{c} Gauge Weights \\ (Thessen polygon) \\ \end{tabular} & \begin{tabular}{c} Gridded data \\ (RAR, IMERG) \\ \end{tabular} \\ \cline{2-4}  & \begin{tabular}{c} Runoff-depth \\ \end{tabular} & \begin{tabular}{c} SCS Curve Number (CN) \\ \end{tabular} & \begin{tabular}{c} Gridded SCS CN \\ \end{tabular} \\ \cline{2-4}  & \begin{tabular}{c} Direct-runoff \\ (Transform) \\ \end{tabular} & \begin{tabular}{c} Clark Unit Hydrograph \\ (Clark) \\ \end{tabular} & \begin{tabular}{c} Modified Clark Method \\ (ModClark) \\ \end{tabular} \\ \cline{2-4}  & \
\begin{tabular}{c} Baseflow \\ \end{tabular} &
\begin{tabular}{c} Recession \\ \end{tabular} \\ \hline Reach & Routing & \multicolumn{2}{c}{Maskingum} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Calculation methods for the developed HEC-HMS models. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Data Processing and Program**} \\ \cline{2-4}  & **Conversion** & **Geo-Referencing** & **DSS File Generation** \\ \hline \hline \multirow{4}{*}{\begin{tabular}{c} RAR \\ \end{tabular} } & Binary to ASCII & \begin{tabular}{c} Lambert Conformal \\ Conic to SHG grid \\ \textless{}NCL script\textgreater{} \\ \end{tabular} & \
\begin{tabular}{c} HEC-GridUtil \\ \textless{}Python script\textgreater{} \\ \end{tabular} \\ \cline{1-1} \cline{2-4}  & IMERG & netCDF4 to ASCII 1 &
\begin{tabular}{c} WGS84 to SHG grid \\ (ITRF2000) 1 \\ \end{tabular} \\ \hline \hline \end{tabular}  
Note: 1 The same Python script in ArcGIS (ArcPy) is utilized for both processing steps.  
\end{table} 
Table 5: Procedures of data processing for RAR and IMERGE radar-based precipitation. with the discharges at the six flow measurement stations within the Youngdam Dam watershed. In this study, unlike the typical calibration process of using a single parameter set for rainfall-runoff prediction models, each parameter was individually adjusted for model verification. By implementing this, the approach aimed to assess the effectiveness and accuracy of the spatially distributed gridded rainfall data and the ModClark method in analyzing the flood characteristics of the mountainous dam watershed, compared to the conventional method that uses gauge points for deriving the areal average rainfall. Equations (3)-(5) introduced in the study are statistical indicators used to compare and evaluate the simulated hydrographs against the observed discharge values. These indicators include Nash-Sutcliffe efficiency (E\({}_{\text{NS}}\)), coefficient of determination (R\({}^{2}\)), and percent bias (PBIAS (%)) to assess the agreement between the observed and simulated discharge values.