为解决 SWAT?(soil?and?water?assessment?tool) 模型在复杂情形下的参数不确定性分析问题，引入参数不确定 性分析平台 UQ-PyL(Uncertainty?Quantification?Python?Laboratory)，开发 UQ-PyL 与 SWAT 模型的耦合模块，使得 UQ-PyL 中的各种算法能够方便快捷地应用于 SWAT 模型的参数不确定性分析。为验证 UQ-PyL 用于 SWAT 模 型参数不确定性分析的效果，在我国不同气候条件下的 4 个流域构建 SWAT 模型，综合对比评估 UQ-PyL 与 SWAT-CUP 对模型参数的不确定性分析结果。结果表明：UQ-PyL 多种敏感性分析方法筛选出的敏感参数比 SWAT-CUP 单一方法筛选的结果更加合理；使用 UQ-PyL 率定的参数在 4 个流域应用中都表现良好，优化后模拟 结果的纳什效率系数均在 0.55 以上，收敛次数在 550 次以内；在 4 个流域的模拟中，UQ-PyL 能提供计算效率更高 的算法 ASMO，也能提供模拟结果更准确的算法 SCE。综上，与 SWAT 模型相耦合的 UQ-PyL 能够支持 SWAT 模 型用户在不同系统下对模型参数进行更高效的不确定性分析研究。

The SWAT model is a widely used hydrological model that offers a range of simulation capabilities. However, it is well-established that the accuracy of model simulations is heavily dependent on the proper specification of SWAT model parameters. While the official SWAT-CUP software is widely used for parameter uncertainty quantification of SWAT model, it has several limitations. For example, it relies on simple sensitivity analysis methods, lacks flexibility in terms of additional options, and its parameter optimization methods are computationally inefficient. Furthermore, as a closed-source software, SWAT-CUP can only be used on the Windows platform, which hampers the applicability of the SWAT model and may compromise simulation results. To overcome these issues, the Uncertainty Quantification Python Laboratory (UQ-PyL) platform, which offers a comprehensive toolset for parameter uncertainty analysis. In addition, a new module has been developed to couple UQ-PyL with the SWAT model, providing a user-friendly and efficient way to perform parameter uncertainty analysis using various algorithms offered by UQ-PyL.To assess the efficacy of UQ-PyL in analyzing parameter uncertainty of SWAT models, four distinct SWAT models across different watersheds in China were constructed, each subjected to varying climatic conditions. The results of parameter uncertainty analysis were comprehensively evaluated by comparing UQ-PyL with SWAT-CUP. In terms of sensitivity analysis, four different methods (Morris, MARS, DT, and Sobol') in UQ-PyL, and qualitative sensitivity analysis in SWAT-CUP were employed to analyze model parameters. The selection of sensitive parameters between UQ-PyL and SWAT-CUP was compared in terms of rationality, by the Sobol' method as a reference to test the validity of the results from the four qualitative methods of sensitivity analysis. Additionally, the SCE-UA algorithm was used to optimize the sensitive parameter groups selected by UQ-PyL and SWAT-CUP separately, and the final converged objective function values was compared, thereby indirectly validating the appropriateness of the selected sensitive parameters by both software tools. Regarding optimization effectiveness, the sensitive parameters using ASMO, SCE-UA of UQ-PyL, and SUFI-2, which is the most widely used algorithm in SWAT-CUP. The computational efficiency and accuracy of different optimization algorithms were compared by evaluating the number of runs required for the final objective function to converge, and the value of the objective function when it converged. Moreover, the applicability of UQ-PyL in watersheds with different climate zones was further validated .The findings reveal that, among the four sensitivity analysis techniques, MARS exhibits the strongest performance, followed by Morris, DT and the SWAT-CUP sensitivity analysis method. Moreover, when utilizing the SCE-UA optimization algorithm to optimize the sensitive parameters identified by UQ-PyL and SWAT-CUP, the optimization outcomes of the UQ-PyL parameter group are relatively superior to those of the SWAT-CUP parameter group across the four watersheds. In terms of parameter optimization, the ASMO optimization algorithm in UQ-PyL demonstrates a higher level of computing efficiency, while the SCE-UA optimization algorithm yields greater accuracy compared to the SUFI-2 algorithm. Additionally, when optimizing independent processes, UQ-PyL solutions offer higher efficiency and accuracy compared to SWAT-CUP solutions. Moreover, UQ-PyL outperformed SWAT-CUP in terms of overall performance across the four watersheds, indicating its robustness .In summary, compared to the single sensitivity analysis method in SWAT-CUP, UQ-PyL offers both quantitative sensitivity analysis using the Sobol' algorithm, as well as qualitative sensitivity analysis using the MARS, Morris, and DT algorithms. This enables a more comprehensive and reasonable screening of sensitive parameters. In terms of parameter optimization, UQ-PyL outperforms the SUFI-2 algorithm in SWAT-CUP by providing two optimization algorithms with better computational efficiency (ASMO) and higher accuracy (SCE-UA). In the four watersheds, UQ-PyL demonstrated superior performance to SWAT-CUP, with the best results observed in humid watersheds and slightly lower performance in drier watersheds.