Increasing parameter identifiability through clustered time-varying sensitivity analysis

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Lu Wang , Yue-Ping Xu , Jiliang Xu , Haiting Gu , Zhixu Bai , Peng Zhou , Hongjie Yu , Yuxue Guo
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引用次数: 0

Abstract

Hydrological models are becoming progressively complex, leading to unclear internal model behavior, increasing uncertainty, and the risk of equifinality. Accordingly, our study provided a research framework based on global sensitivity analysis, aiming at unraveling the process-level behavior of high-complexity models, teasing out the main information, and ultimately exploiting its usage for model parameterization. The Distributed Hydrology-Soil-Vegetation Model implemented in a mountainous watershed was used. Results indicated that 5 soil parameters and 5 vegetation parameters were most important to control the streamflow responses, while their importance varied greatly throughout the simulation period. Four typical patterns of parameter importance corresponding to different watershed conditions (i.e., flood, short dry-to-wet, fast recession, and continuous dry periods) were successfully distinguished. Using this clustered information, parameters with short dominance times were more identifiable over the clusters (time periods) in which they were most important. The reduced posterior parameter space also slightly improved the model performance.

通过聚类时变敏感性分析提高参数可识别性
水文模型正变得越来越复杂,导致模型内部行为不清晰、不确定性增加以及等效性风险。因此,我们的研究提供了一个基于全局敏感性分析的研究框架,旨在揭示高复杂度模型的过程级行为,挖掘主要信息,并最终利用这些信息进行模型参数化。研究使用了在山区流域实施的分布式水文-土壤-植被模型。结果表明,5 个土壤参数和 5 个植被参数对控制溪流响应最为重要,而它们的重要性在整个模拟期间有很大差异。成功区分了与不同流域条件(即洪水期、干湿交替期、快速衰退期和持续干旱期)相对应的参数重要性的四种典型模式。利用这种聚类信息,在参数最重要的聚类(时间段)中,支配时间短的参数更容易识别。缩小后的参数空间也略微提高了模型的性能。
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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