## 3 Results
### Identification of Sensitive Parameters  
To integrate the results of four analytical methods, the 30 parameters were first ranked according to the index calculated with each sensitivity analysis method, so that each parameter had four ranking numbers. Then, four sort numbers for each parameter were added to obtain the total sort number and order it from smallest to largest. The smaller the sort number is, the more sensitive the parameter is. Lastly, the top ten parameters were taken as sensitive parameters: SMTMP, SOL_AWC, SFTMP, RCHRG_DP, SOL_K, SOL_Z, GWQMN, SOL_BD, ALPHA_BNK, and HRU_SLP. The calculated parameters were highly sensitive and representative of various methods. The ten parameters above were optimized 100 times in the constructed SWAT model with the sequential uncertainty fitting procedure version 2 (SUFI-2). Table 4 describes the upper and lower boundary ranges of crucial parameter values and the optimal parameter values obtained with calibration. To demonstrate the necessity of parameter sensitivity analysis, 30 parameters (Table 1) related to runoff simulated by the SWAT model without sensitivity analysis were optimized under the condition that other things were equal. Moreover, to compare the sensitive parameters calculated with a single analytical method with those calculated by integrating the results of four analytical methods, the parameters in Table 3 were also optimized. Figure 13 compares the observed streamflow and the simulation results for the Yinglu-oxia hydrological station at a monthly scale under six optimization parameter conditions. The simulation effect of the SWAT model under the C10 scenario was better than that under other scenarios, especially in low-flow simulation. Figure 14 describes the statistical comparison between simulated and measured flow values under six parameter optimization scenarios. The closer the values of NSE and Kling-Gupta efficiency coefficient (KGE) were to 1, the better the simulation effect was. The closer the mean square error (MSE) and percentage bias (PBIAS) were to 0, the better the simulation effect was. The values of NSE, MSE, PBIAS, and KGE of the C10 scenario were 0.89, 200, 8.60, and 0.90, respectively, indicating that the C10 scenario achieved the best performance among all scenarios. By comparing the statistical indices of the C10 and C30 scenarios, defining sensitive parameters was proven necessary to improve the simulation effect of the SWAT model. Comparing the statistical indices of the C10-Morris, C10-Sobol, C10-FAST, and C10-EFAST scenarios shows that the performance of optimizing sensitive parameters defined by a single analysis method was not stable. For example, the c10-EFAST scenario achieved good performance in PBIAS and KGE with 10.80 and 0.84, respectively, while poor performance in NSE and MSE with 0.85 and 200, respectively. Further comparison of the statistical indices of these scenarios and the C30 scenario shows that only using a single analytical method to analyze the sensitivity of SWAT model parameters had no noticeable improvement on the simulation effect, and even had adverse effects. For example, the PBIAS of the C10-Sobol and C10-FAST scenarios was even higher than that of the C30 scenario. Comparing the statistical indices of these scenarios and the C10 scenario proves that combining the results of the four analytical methods to define the SWAT model's sensitive parameters could significantly improve the model's simulation effect. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Ranking** & **Parameter** & **Value Range** & **Optimal Value** & **Ranking** & **Parameter** & **Value Range** & **Optimal Value** \\ \hline
1 & SMTMP & \((-5,5)\) & \(-4.65\) & 6 & SOL\_Z & \((0,1000)\) & 625 \\
2 & SOL\_AWC & \((0,1)\) & 0.075 & 7 & GWQMN & \((0,1000)\) & 535 \\
3 & SFTMP & \((-5,5)\) & 2.05 & 8 & SOL\_BD & \((0.9,2.5)\) & 1.116 \\
4 & RCHRG\_DP & \((0,1)\) & 0.945 & 9 & ALPHA\_BNK & \((0,1)\) & 0.695 \\
5 & SOL\_K & \((0,100)\) & 5.5 & 10 & HRU\_SLP & \((0,1)\) & 0.845 \\ \hline \end{tabular}
\end{table}