## 4 Discussion  
Previous studies indicated that, when parameter sensitivity analysis methods are different, the calculated parameter sensitivity also has differences; the simulation effect of the SWAT model is also different [13; 16]. In this study, four parameter sensitivity analysis methods were adopted for calculation. Under the circumstance that other conditions of the SWAT model were consistent, although some of the sensitivity parameters obtained were the same, there were still differences in most of the parameters. The different sampling processes, sensitivity indices, and calculation principles of each method may contribute to the above phenomenon.  
The sampling method of [PERSON] is the MOAT sampling method, that of [PERSON] is the Sobol sequence sampling method, and that of EFAST and FAST is the FAST sampling method. The comparison of Figures 3, 6 and 9 shows that the three sampling methods had apparent differences. From the perspective of the sensitivity index, the Morris method calculated the mean value and variance of the elementary effect. In contrast, the three other methods decomposed the variance of the model output and then calculated the ratio. This shows that the [PERSON] method differs from the other three methods. The calculation principles of the four methods were also significantly different. The Morris method mainly calculates the elementary effect. The Sobol method primarily regards breaking down the variance of the model output. The FAST and EFAST methods specifically decompose the variance in terms of the Fourier transform. These differences among the four methods lead to significant differences in the analyzed sensitivity parameters.  
The simulation results of the SWAT model prove that the sensitive parameters determined by combining the results of multiple analytical methods are more reasonable than those specified by a single method. Therefore, when discussing the sensitivity of model parameters, a combination of various approaches can be used to reduce the uncertainty brought by the approach.  
Among the 10 selected sensitive parameters, SMTMP and SFTMP belong to the snowmelt process parameters, and are sensitive because the UHRB is at a high altitude and covered with snowpacks, resulting in the snow-melt water being an important supply source for the Heihe River [17; 37; 62]. SOL_AWC, RCHRG_DP, SOL_K, SOL_Z, GWQMN, and SOL_BD control groundwater recharge and discharge processes. The UHRB is located in an arid inland area of Northwest China, with little precipitation but massive evaporation. As a result, the runoff generation and confluence processes of the HRB are affected by groundwater [17; 37; 62]. ALPHA_BNK belongs to the baseflow decline process. In the months with less precipitation in the HRB, the amount of river flow is entirely dependent on the baseflow, and its decline speed distinctly impacts the Heihe River flow [17; 37; 62]. Combined with the high altitude and complex terrain of the UHRB, the channel confluence velocity of the upper reaches of the Heihe River is affected by topography. HRU_SLP is a terrain parameter that significantly impacts the channel confluence of the upper reaches of Heihe River [17; 37; 62]. The 10 parameters above are sensitive because of the unique climatic and topographical conditions in the HRB from a physical point of view. This also indicates that, in analyzing the sensitivity of model parameters, sensitivity index should be considered from a mathematical aspect, and comprehensive analysis should be performed from the underlying surface of the basin, climatic conditions, and other aspects [63; 64; 65].