## 1 Introduction  
As an essential tool for simulating runoff, the hydrological model can quantitatively reflect the evolution and physical mechanisms of hydrological phenomena [1, 2]. Hydrological models can be classified into lumped, semidistributed, and distributed according to the degree of the description of spatial characteristics. With the development of hydrological theory and computer technology, the research and application of distributed hydrological models are increasingly extensive [3, 4]. Developing hydrological models is significant to water resource management and planning, hydrological forecasting, and optimal reservoir operation [5]. Compared with the traditional lumped model, the distributed hydrological model based on the physical mechanism demands more input parameters [6]. Moreover, the spatial difference of parameters in the hydrological land surface process, errors in the acquisition process, and the difficulties in parameter evaluation give the input of the initial parameter values of the model significant uncertainties [7; 8; 9; 10] that reduce the operational efficiency and simulation accuracy of the distributed hydrological model. In addition, the insufficient understanding of the sensitivity of model parameters is an essential factor affecting the application of distributed hydrological models [11; 12]. Therefore, it is necessary to understand the influence of each parameter in the model on the simulation results. Parameter sensitivity analysis can improve the efficiency of model calibration and increase the reliability of model application [13; 14; 15; 16]. Sensitivity analysis usually includes local and global sensitivity analyses. Many studies showed that the global sensitivity analysis method is more suitable for multiparameter hydrological model research [17; 18; 19; 20]. Common global analysis methods include Morris screening [21; 22], Sobol analysis [23], the Fourier amplitude sensitivity test (FAST) [24; 25; 26], and the extended Fourier amplitude sensitivity test (EFAST) [27]. These sensitivity analysis methods have been widely used in hydrological models. The most critical parameters of physical distributed model SHETRAN were identified using the Morris screening method [6]. [PERSON] et al. used Sobol sensitivity analysis to identify parameter sensitivities of the variable infiltration capacity (VIC) snow model, a physical model [28]. [PERSON] et al. used the variance-based FAST method to quantitatively identify the Pitman semidistributed hydrologic model's parametric uncertainty and sensitivity [29]. The EFAST method was used to conduct global sensitivity analyses for three modules in the hydrological soil-water-atmosphere-plant-world-food study (SWAP-WOFOST) model to identify the parameters that significantly influenced the objective variables [30]. However, it is not clear which method performs better, as these studies lack a comparison of the sensitivity analyses above. The Soil and Water Assessment Tool (SWAT) is a widely used hydrological model that has had successful application in many fields [31; 32; 33; 34]. The SWAT model has many parameters, rendering calibrating the parameters time-consuming and laborious. Therefore, some studies were carried out on SWAT model parameters to define the most sensitive parameters, and reduce parameter calibration, and computational and temporal costs [13; 35; 36; 37; 38; 39]. Due to the different methods that researchers adopt, the selected sensitivity parameters are different and lack representation to some extent, not allowing for a comparison to determine the best approach. The upper reach of Heihe River is located in the semiarid alpine area of the Qilian Mountains in Northwest China. As a typical alpine river basin, the upper reaches of Heihe River, with complex underlying surface properties, diversiform climate conditions, and various runoff sources [40; 41; 42] can be used as a representative example to analyze the parameter sensitivity of the SWAT model.