## 4 Results
### Parameter Sensitivity
#### 4.1.1 Global Sensitivity
Consequently, they do not warrant a high priority in the subsequent parameter range adjustment process. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ID** & \begin{tabular}{c} **Parameter** \\ **Name** \\ \end{tabular} & **Description** & **T-Stat** & \(p\)**-Value** & \
\begin{tabular}{c} **Fitted** \\ **Value** \\ \end{tabular} & **Min Value** &
\begin{tabular}{c} **Max** \\ **Value** \\ \end{tabular} \\ \hline
1 & SOL\_BD & Moist bulk density (Mg/cm\({}^{3}\)) & 5.66 & 0.00 & 0.37 & 0.30 & 0.50 \\
2 & ALPHA\_BF * & Baseflow alpha factor (days) & \(-4.11\) & 0.00 & 0.04 & 0.00 & 0.20 \\  & Baseflow distribution factor & & & & & & \\
3 & BFLO\_DIST & for sub-daily simulation & \(-3.53\) & 0.00 & \(-0.97\) & \(-1.00\) & \(-0.90\) \\  & & (fraction) & & & & & \\
4 & CN2 & SCS runoff curve number & & & & & \\  & & (dimensionless) & & & & & \\
5 & ESCO * & Soil evaporation & \(-1.37\) & 0.18 & 0.98 & 0.90 & 1.00 \\  & compensation factor (fraction) & & & & & & \\
6 & SOL\_AWC & Available water capacity of & & & & & \\  & the soil layer (fraction) & 1.19 & 0.24 & \(-0.66\) & \(-0.70\) & \(-0.60\) \\  & Threshold depth of water in & & & & & & \\
7 & GWQMN * & the shallow aquifer required & 1.07 & 0.29 & 304.00 & 100.00 & 500.00 \\  & for return flow to occur (mm) & & & & & & \\
8 & OV\_N * & Manning’s \"n\" value for & & & & & \\  & & & & & & & \\
9 & EPCO * & Plant uptake compensation & & & & & \\  & factor (fraction) & & & & & & \\
10 & CANMX * & Maximum canopy storage & & & & & \\  & & (mm) & & & & & \\
11 & HRU\_SLP & Average slope steepness & \(-0.37\) & 0.72 & \(-0.80\) & \(-0.80\) & \(-0.70\) \\  & & (m/m) & & & & & \\
12 & SOL\_Z & Depth from soil surface to & & & & & \\  & & bottom of layer (mm) & & & & & \\
13 & SOL\_K & & & & & & \\  & & conductivity (mm/hr) & & & & & \\
14 & GW\_DELAY * & Groundwater delay (days) & 0.10 & 0.92 & 0.75 & 0.00 & 3.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameter calibration results and sensitivity analysis ranking. Figure 3 displays the distribution of 14 parameter values and NSE values in a global sensitivity analysis. By analyzing the linear relationship between the distribution of each parameter and the simulation effect (NSE), this provides suggestions for adjusting the parameter range. The positive correlation between the parameter distribution and the simulation effect indicates that increasing the maximum or minimum value of the parameter may be conducive to improving the simulation effect, while the negative correlation between the parameter distribution and the simulation effect indicates that decreasing the maximum or minimum value may be conducive to improving the simulation effect. Parameters exhibiting a negative linear relationship include CN2, ALPHA_BF, GW_DELAY, SOL_Z, HRU_SLP, and BFLO_DIST. Conversely, other parameters demonstrate a positive correlation. The degree of scatter dispersion is linked to parameter sensitivity, with highly sensitive parameters showing more clustered scatter plots, such as SOL_BD and ALPHA_BF. For these parameters, the scattered points predominantly cluster within the 0.5-0.6 interval of NSE, indicating that the parameter range we ultimately selected closely aligns with the optimal range following multiple calibration efforts. Interactions and influences among parameters within the SWAT model may introduce potential sources of unreliability in sensitivity analysis results. Consequently, we carried out a correlation analysis on the simulated parameter values (Figure 4). Notably, significant correlations were observed among certain parameters; for instance, the correlation coefficient between SOL_BD and GW_DELAY was found to be \(-0.4\), the correlation coefficient between SOL_K and ALPHA_BF stood at \(-0.3\), and the correlation coefficient between SOL_AWC and SOL_Z reached 0.3. The correlations between parameters lead to parameter redundancy, which is part of model uncertainty. For its uncertainty, we performed another parametric sensitivity analysis, namely one-at-a-time sensitivity.