## 4 Results
### Parameter Optimization for Each DEM Grid Cell Size  
This study optimized input parameters of TOPMODEL depending on DEM resolutions and hydrologic events in the Dongkok and Ieemokjung watersheds in Figures 10 and 11. The input parameters are optimized based on the changes of DEM grid cell size and rainfall-runoff events. When the grid cell size changes, the simulated runoff shows a large change with parameter \(m\), a small change with parameters \(\ln\left(T_{0}\right)\) and \(T_{d}\), and no change with \(SR_{max}\) and \(SR_{0}\). Parameter \(m\) is the most sensitive because it is a parameter in an exponential equation, and the results in Figures 10 and 11 change  
Figure 9: Rainfall–runoff events by applying a constant parameter set depending on different DEM resolution values in the leemokjung watershed. **(a)** 25 May 1991. **(b)** 24 September 1992. **(c)** 1 August 1994. **(d)** 4 July 1996. **(e)** 25 August 1996. **(f)** 25 July 1999.  
significantly with changes in this parameter. The parameters \(\ln\left(T_{0}\right)\) and \(T_{d}\) increase as DEM grid size is greater. It demonstrates that an ultimate infiltration capacity, the soil-water permeability coefficient (\(\ln\left(T_{0}\right)\)) in this study, decreases as DEM grid cell resolution is coarser [38; 39]. Therefore, it is necessary to increase the parameter \(\ln\left(T_{0}\right)\) as well as the lag time in the unsaturated zone (\(T_{d}\)) to increase the actual infiltration capacity rate as DEM grid cell resolution decreases.  
Figure 11: Optimized parameters depending on DEM resolutions in each hydrologic event, the Ieemokjung watershed. (**a**) exponential storage parameter (m), (**b**) soil–water permeability coefficient (\(ln\left(T_{0}\right)\)), (**c**) unsaturated lag time (\(T_{d}\)), (**d**) maximum vegetation storage (\(SR_{max}\)), (**e**) Vegetation deficiency (\(SR_{0}\)), (**f**) model efficiency.  
Figure 10: Optimized parameters depending on DEM resolutions in each hydrologic event, the Dongkok watershed. (**a**) exponential storage parameter (m), (**b**) soil–water permeability coefficient (\(ln\left(T_{0}\right)\)), (**c**) unsaturated lag time (\(T_{d}\)), (**d**) maximum vegetation storage (\(SR_{max}\)), (**e**) Vegetation deficiency (\(SR_{0}\)), (**f**) model efficiency.  
Figures 12 and 13 show TOPMODEL hydrographs based on the optimization of the input parameters in the Dongkok and Ieemokjung watersheds depending on the DEM resolution. The NSEs of the Dongkok watershed shown in Figures 10 and 12a ranged from 0.88 to 0.90, 0.97 to 0.98, 0.94 to 0.96, and 0.77 to 0.80 depending on the events. In particular, in the DK960616 and DK970715 events in Figure 10, the generated peak flows were very close to observed peak flows depending on the resolution of the DEM grid cell size. Therefore, the result indicated that the TOPMODEL efficiencies were slightly different based on the DEM grid cell size resolution; however, the optimized input parameters were significantly different based on the hydrological events. The NSE of the Ieemokjung watershed shown in Figures 11 and 12b ranged from 0.89 to 0.94, 0.88 to 0.94, 0.88 to 0.93, and 0.8 to 0.86. In the IM960704 and IM960825 events, the estimated peak flow rates were very close to observed peak flows depending on the DEM resolution. In addition, similar to the Dongkok watershed, the NSE efficiencies significantly varied depending on the rainfall-runoff events instead of the DEM resolution.  
Figure 12: Rainfall–runoff events by applying optimized parameters in the Dongkok watershed depending on each DEM resolution. (**a**) 16 June 1996. (**b**) 8 August 1996. (**c**) 15 July 1997. (**d**) 15 August 1998.