Ke Yi , Pan Yang , Siyuan Yang , Shenxu Bao , Zhihao Xu , Qian Tan
{"title":"On the accuracy requirement of surrogate models for adequate global sensitivity analysis of urban low-impact development model","authors":"Ke Yi , Pan Yang , Siyuan Yang , Shenxu Bao , Zhihao Xu , Qian Tan","doi":"10.1016/j.jhydrol.2025.133102","DOIUrl":null,"url":null,"abstract":"<div><div>Global sensitivity analysis (GSA) is crucial for understanding, simplifying, and applying high fidelity process-based (Hifi) hydrological models. Its application has been hindered by the extensive model evaluations needed for convergence and the associated computational cost. Surrogate models (SMs) can significantly reduce the computational burden of GSA, but its approximation errors can introduce usually uninvestigated GSA errors. We address this gap by investigating SMs-induced GSA error in parameter screening, sensitivity ranking, and sensitivity index valuation. By comparing the converged GSA results from a support vector regression surrogate model (SVR-SM) and the storm water management model (SWMM) in simulating the hydrological response of a small urban watershed to changes in low-impact development (LID) parameters, this study finds that SMs-induced GSA errors increase with higher SMs approximation errors. The relationship between SMs-induced GSA error and SMs approximation error (measured by R<sup>2</sup>) is consistent across various flow metrics and rainfall intensities. SMs can adequately reproduce the converged GSA results of a Hifi SWMM with only one-thousandth of the original computation time. However, SMs-induced GSA errors may become unacceptable if the R<sup>2</sup> of SVR-SMs is below 0.96. Our findings highlight the importance of surrogate models’ accuracy in GSA and provide valuable guidance for future GSA applications.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"657 ","pages":"Article 133102"},"PeriodicalIF":5.9000,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Hydrology","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022169425004408","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0
Abstract
Global sensitivity analysis (GSA) is crucial for understanding, simplifying, and applying high fidelity process-based (Hifi) hydrological models. Its application has been hindered by the extensive model evaluations needed for convergence and the associated computational cost. Surrogate models (SMs) can significantly reduce the computational burden of GSA, but its approximation errors can introduce usually uninvestigated GSA errors. We address this gap by investigating SMs-induced GSA error in parameter screening, sensitivity ranking, and sensitivity index valuation. By comparing the converged GSA results from a support vector regression surrogate model (SVR-SM) and the storm water management model (SWMM) in simulating the hydrological response of a small urban watershed to changes in low-impact development (LID) parameters, this study finds that SMs-induced GSA errors increase with higher SMs approximation errors. The relationship between SMs-induced GSA error and SMs approximation error (measured by R2) is consistent across various flow metrics and rainfall intensities. SMs can adequately reproduce the converged GSA results of a Hifi SWMM with only one-thousandth of the original computation time. However, SMs-induced GSA errors may become unacceptable if the R2 of SVR-SMs is below 0.96. Our findings highlight the importance of surrogate models’ accuracy in GSA and provide valuable guidance for future GSA applications.
期刊介绍:
The Journal of Hydrology publishes original research papers and comprehensive reviews in all the subfields of the hydrological sciences including water based management and policy issues that impact on economics and society. These comprise, but are not limited to the physical, chemical, biogeochemical, stochastic and systems aspects of surface and groundwater hydrology, hydrometeorology and hydrogeology. Relevant topics incorporating the insights and methodologies of disciplines such as climatology, water resource systems, hydraulics, agrohydrology, geomorphology, soil science, instrumentation and remote sensing, civil and environmental engineering are included. Social science perspectives on hydrological problems such as resource and ecological economics, environmental sociology, psychology and behavioural science, management and policy analysis are also invited. Multi-and interdisciplinary analyses of hydrological problems are within scope. The science published in the Journal of Hydrology is relevant to catchment scales rather than exclusively to a local scale or site.