# Assessing Groundwater Level with a Unified Seasonal Outlook and Hydrological Modeling Projection
## 3 Study Site and Modeling Configurations
### Modeling Configuration and Calibrations  
Generally, adequate initial conditions of 1D routing are either obtained from measurements or steady-state simulations using the WASH123D model. The 2D ground domain was assumed to be initially dry for all case runs. The well-observed records were interpolated into a 3D domain, and then a steady-state pre-run process was conducted as the initial setup for the subsurface flow simulation. Thus, zero depth and fixed pressure head-dependent boundary conditions were assigned in the 2D and 3D simulations, respectively. Simulations for the years 2013-2015 were conducted to determine the appropriate parameters for the hydrological models and evaluate the model performance. Then, the initial loss, lag time, CN values, and Manning's coefficients of the 1D stream-river network and 2D overland regime were determined. The hydraulic conductivity was determined on the basis of the long-term period for the groundwater simulation. The modeling parameters and simulation errors are listed in Tables 1 and 2. [PERSON] and [PERSON] [7] confirmed the good agreement between the simulation and observations results for both surface water and groundwater tables, in which error indicators of \(R^{2}\), root-mean-square error (RMSE), and efficiency coefficient (CE) were 0.850, 0.279m, and 0.824, respectively, for the river stage and 0.607, 0.281, and 0.621 for the groundwater table. Therefore, the simulations adopted the setups by the above research to further improve the feasibility of this study. The flow patterns of the rainfall-induced stream hydrograph and groundwater table were quite different, as presented in Figure 5, which illustrates the simulated hydrographs of both surface water and groundwater from 4 July to 17 July in 2013 and 12 May to 27 May in 2015. These two periods, 2013 and 2015, were selected due to the insufficient rainfall data in 2014. In Figure 5, it can be seen that  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**HEC-HMS** & \multicolumn{3}{c}{**WASH123D**} \\ \hline SCS curve & Channels (1D) & Surface land (2D) & Groundwater layers (3D) \\  & \(Mn\) & \(Mn\) & \(Ks\) (m/s) \\ \hline Initial loss (4.0 mm) & & & \\ Non-infiltration covers & & Metropolitan (0.120) & \begin{tabular}{c} Gravel (\(1\times 10^{-3}\)) \\ Mud or fine silt (\(4\times 10^{-6}\)) \\ River sediment (\(2\times 10^{-5}\)) \\ \end{tabular}  \\
2.0.036–0.029 & 0.036–0.029 & 0.036–0.028 & 0.036–0.028 & 0.  
the river stage corresponded quite well to the rainfall intensity. The time lag was almost 1 h between the maximum rainfall intensity and peak flow. This response is typical for watersheds of Taiwan, which are characterized by rugged and steep topography. Thus, it is difficult to capture and store surface water for human consumption. However, the groundwater level has a different pattern with a relatively mild fluctuation in comparison to the river stages. The studies found there was a 2 day average lag (approximately 45 to 52 h) for the groundwater to reach the peak level after the occurrence of extreme rainfall. Therefore, the exact evaluation of the groundwater level can contribute to the effective utilization of water resources in Taiwan.