基于UQ-PyL的SWAT模型参数不确定性分析综合评估  

Comprehensive evaluation of parameter uncertainty analysis of SWAT model based on UQ-PyL

作　　者：肖渝 孙若辰 王琛 段青云 XIAO Yu;SUN Ruochen;WANG Chen;DUAN Qingyun(State Key Laboratory of Hydrology‐Water Resources and Hydraulic Engineering,Hohai University,Nanjing 210098,China;College of Hydrology and Water Resources,Hohai University,Nanjing 210098,China;China Meteorological Administration Hydro-Meteorology Key Laboratory,Hohai University,Nanjing 210098,China;South China Botanical Garden,Chinese Academy of Sciences,Guangzhou 510650,China)

机构地区：[1]河海大学水文水资源与水利工程科学国家重点实验室,南京210098 [2]河海大学水文水资源学院,南京210098 [3]中国气象局水文气象重点开放实验室,南京210098 [4]中国科学院华南植物园,广州510650

出　　处：《南水北调与水利科技（中英文）》2023年第2期233-247,共15页South-to-North Water Transfers and Water Science & Technology

基　　金：国家重点研发计划项目(2021YFC3201102);国家自然科学基金项目(42101046,51979004);水利部重大科技项目(SKS-2022001);河海大学引进高层次人才科研启动基金(522020012);广州市科技计划项目(202102020718)。

摘　　要：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 indire

关 键 词：SWAT模型 敏感性分析 参数优化 UQ-PyL SWAT-CUP 

分 类 号：TV214[水利工程—水文学及水资源]