## 4 Results and Discussion
### Sensitivity Analysis and Model Calibration and Validation  
The results of the sensitivity analysis are reported in Table 12. According to the results, available soil water capacity (SOL_AWC) is the most sensitive parameter in the Khlong Bang Yai watershed. Thus, the available soil water capacity, which is the soil's capacity to hold water available for plant use and reflects the soil's capacity for water storage [60; 61], was the main value adjusted in the model calibration stage. In addition, the curve number at moisture condition II (CN2), soil evaporation compensation factor (ESCO), and groundwater \"revap\" coefficient (GW_REVAP), which relate to surface runoff and baseflow, were slightly modified in this study. The optimal model parameters in the calibration period under dry and wet year conditions are reported in Table 13. The results indicate that the optimal value of available soil water capacity (SOL_AWC) varies according to soil type. Likewise, the curve number at moisture condition II (CN2) depends on land use, soil, and slope. Furthermore, Table 14 and Figure 8 summarize the SWAT model's performance for the dry year condition in the calibration and validation periods based on the observed and estimated hydrologic data at the X.191 station. According to the three statistical values, the model shows good performance in the calibration period and very good performance in the validation period, according to the model performance scale of [PERSON] et al. [47]. The R\({}^{2}\) values in the calibration and validation periods are 0.88 and 0.89, as shown in Figure 9. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Unit**} & \multirow{2}{*}{**Range**} & \multicolumn{2}{c}{**Calibration Period**} \\ \cline{3-4}  & & & **Dry Year Condition** & **Wet Year Condition** \\ \hline SOL\_AWC a & & 0–1 & 0.19–0.30 & 0.05–0.16 \\ SURLAG & day & 0–10 & 4 & 4 \\ GW\_DELAY & day & 0–100 & 31 & 31 \\ ESCO & & 0–1 & 0.80 & 0.96 \\ CN2 b & & 30–100 & 30–68 & 30–85 \\ GW\_REVAP & & 0–1 & 0.050 & 0.157 \\ ALPHA\_BF & day\(-\)1 & 0–1 & 0.048 & 0.048 \\ \hline \hline \end{tabular} Note: a  
\end{table} 
Table 13: Optimal parameter values of SWAT model for hydrologic component estimation under dry and wet year conditions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Abbreviation** & **Parameter** & **t-Stat** & \(p\)**-Value** \\ \hline SOL\_AWC & Available soil water capacity & 5.7839 & 0.0286 \\ SURLAG & Surface runoff lag coefficient & 2.6590 & 0.1171 \\ GW\_DELAY & Groundwater delay & \(-\)2.2600 & 0.1523 \\ ESCO & Soil evaporation compensation factor & \(-\)2.2570 & 0.1526 \\ CN2 & Curve number at moisture condition II & \(-\)1.0655 & 0.3983 \\ GW\_REVAP & Groundwater “revap” coefficient & 1.0291 & 0.4116 \\ ALPHA\_BF & Baseflow alpha factor & \(-\)0.4692 & 0.6851 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: SWAT parameter sensitivity to monthly streamflow at the X.191 station. The SWAT model's performance for the wet year condition in the calibration and validation periods based on the estimated and observed hydrologic data at the X.191 station is summarized in Table 15 and Figure 10. According to the three statistical values, the model shows good performance in the calibration and validation periods, as suggested by [PERSON] et al. [47]. Additionally, the R\({}^{2}\) values in the calibration and validation periods are 0.70 and 0.89, as shown in Figure 11. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Indicator**} & \multicolumn{2}{c}{**Calibration (Year)**} & \multicolumn{2}{c}{**Validation (Year)**} \\ \cline{2-5}  & **In 1999–2000** & **Performance Rating** & **In 2016–2017** & **Performance Rating** \\ \hline RSR & 0.58 & Good & 0.59 & Good \\ NSE & 0.66 & Good & 0.65 & Good \\ PBIAS & 14.90 & Good & \(-13.84\) & Good \\ \hline \hline \end{tabular} Note: Model performance rating scale by [PERSON] et al. [47].  
\end{table}