## 3 Materials and Methods
### Water Supply Estimation
#### 3.4.1 Calibration and Validation of SWAT Model  
To determine the optimal local model parameters, the Khlong Bang Yai watershed in the study area was chosen as a reference watershed because of the availability of long-term observed hydrologic data. The hydrologic response unit (HRU) was first generated using 20 percent land use, 10 percent soil, and 20 percent slope thresholds, which are suitable for most applications [35; 36]. Weather data (precipitation, temperature, solar radiation, wind speed, and humidity) collected by six meteorological stations of the Thai Meteorological Department and Royal Irrigation Department between 1996 and 2019 were prepared and input into the model. Then, a sensitivity analysis was conducted to determine the influence of parameters on estimating total flow to identify the most sensitive parameters in the study area. Seven critical parameters--curve number at moisture condition II, available soil water capacity, soil evaporation compensation factor, surface runoff lag coefficient, baseflow alpha factor, groundwater \"rexp\" coefficient, and groundwater delay, which affect surface runoff and baseflow as suggested by many researchers [6; 37; 38; 39; 40; 41; 42; 43]--were analyzed using t-statistics in the SWAT-CUP software, with _p_-values considered significant at the 5% significance level. After that, these parameters were systematically calibrated to identify the optimal local model parameters for water yield estimation according to annual runoff data at the X.191 station of the Khlong Bang Yai watershed under dry and wet year conditions. Dry and wet year conditions were defined based on the long-term mean annual runoff between 1999 and 2019 at the X.191 station (Figure 3). Any year in which annual runoff was higher than the mean annual runoff was identified as a wet year. Conversely, any year in which the annual runoff was less than the mean annual runoff was identified as a dry year. Then, the local model parameters identified in the calibration phase were further applied to validate the model. Table 2 summarizes the essential data required for the model calibration and validation periods under dry and wet year conditions. Furthermore, the RMSE-observations standard deviation ratio (RSR) [44], Nash-Sutcliffe efficiency (NSE) [45], and percent bias (PBIAS) [46], as shown in Equations (2)-(4), were determined to evaluate the performance of the model, where good performance is defined by expected threshold values of \(\leq\)0.60, \(\geq\)0.65, and \(\leq\)\(\pm\)15% in the model calibration and validation phases (Table 3). \[\text{PSR}=\frac{\text{RMSE}}{\text{STDEV}_{\text{obs}}}=\frac{\sqrt{\sum_{i= 1}^{\text{n}}(\text{O}_{i}-\text{E}_{i})^{2}}}{\sqrt{\sum_{i=1}^{\text{n}} \big{(}\text{O}_{i}-\overline{\text{O}}\big{)}^{2}}} \tag{2}\]  
\[\text{NSE}=1-\frac{\sum_{i=1}^{\text{n}}(\text{O}_{i}-\text{E}_{i})^{2}}{\sum _{i=1}^{\text{n}}\big{(}\text{O}_{i}-\overline{\text{O}}\big{)}^{2}} \tag{3}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Required Data**} & \multicolumn{2}{c}{**Dry Year Condition**} & \multicolumn{2}{c}{**Wet Year Condition**} \\ \cline{2-5}  & **Calibration** & **Validation** & **Calibration** & **Validation** \\ \hline LULC data & 2014 & 2019 & 2002 & 2014 \\ Weather data & 2006–2010 & 2015–2019 & 1996–2000 & 2013–2017 \\ Model warming period & 2006–2008 & 2015–2017 & 1996–1998 & 2013–2015 \\ Estimated water yield & 2009–2010 & 2018–2019 & 1999–2000 & 2016–2017 \\ Observed water yield & 2009–2010 & 2018–2019 & 1999–2000 & 2016–2017 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Essential data required for model calibration and validation under dry and wet year conditions. Figure 3: Distribution of annual runoff in the dry and wet year conditions at the X.191 station. \[\text{PBIAS}=\frac{\sum_{i=1}^{\text{n}}(\text{O}_{\text{i}}-\text{E}_{\text{i}}) \times 100}{\sum_{i=1}^{\text{n}}(\text{O}_{\text{i}})} \tag{4}\]  
where E\({}_{\text{i}}\) is the estimated value, and O\({}_{\text{i}}\) is the observed value at time i.