Xifu Sun, A. Jakeman, B. Croke, Stephen G. Roberts, J.D. Jakeman
In global sensitivity analysis (GSA) of a model, a proper convergence analysis of metrics is essential for ensuring a level of confidence or trustworthiness in sensitivity results obtained, yet is somewhat deficient in practice. The level of confidence in sensitivity measures, particularly in relation to their influence and support for decisions from scientific, social and policy perspectives, is heavily reliant on the convergence of GSA. We review the literature and summarize the available methods for monitoring and assessing convergence of sensitivity measures based on application purposes. The aim is to expose the various choices for convergence assessment and encourage further testing of available methods to clarify their level of robustness. Furthermore, the review identifies a pressing need for comparative studies on convergence assessment methods to establish a clear hierarchy of effectiveness and encourages the adoption of systematic approaches for enhanced robustness in sensitivity analysis.
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Key lessons about and limits to social-ecological systems (SES) modeling are widely available and frustratingly consistent over time. Prominent challenges include outdated perspectives about systems and models along with persistent disciplinary hegemony. The inherent complexity in SES means that an emphasis on discrete prediction is misplaced and has potentially reduced model efficacy for decision-making. Although computer models are definitely the tool to use to identify the complex relationships within SES, humans are messy and hence the ‘social’ in SES is often ignored, glossed over, or reduced to simplistic economic or demographic variables. This combination of factors has perpetuated biases in what is worth pursuing and/or publishing. In (re)visiting issues in SES modeling, including debates about model capabilities, data selection, and challenges in working across disciplinary lines, this reflection explores how the author’s experience aligns with extant literature as well as raises issues about what is absent from that body of work. The available lessons suggest that scholars and practitioners need to re-think how, why, and when to employ SES modeling in regulatory or other decision-making contexts.
关于社会生态系统（SES）建模的关键经验和局限性已广为流传，但令人沮丧的是，这些经验和局限性却始终如一。突出的挑战包括关于系统和模型的过时观点以及长期存在的学科霸权。SES 固有的复杂性意味着对离散预测的强调是错误的，有可能降低模型对决策的效用。尽管计算机模型无疑是用来识别社会经济地位中复杂关系的工具，但人类是杂乱无章的，因此社会经济地位中的 "社会 "往往被忽视、掩盖或简化为简单的经济或人口变量。在（重新）探究社会经济地位建模中的问题时，包括有关模型能力、数据选择和跨学科工作挑战的争论，本反思探讨了作者的经验如何与现有文献保持一致，同时也提出了有关这些工作中缺失的问题。现有经验表明，学者和从业人员需要重新思考如何、为何以及何时在监管或其他决策环境中使用 SES 模型。
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Key lessons about and limits to social-ecological systems (SES) modeling are widely available and frustratingly consistent over time. Prominent challenges include outdated perspectives about systems and models along with persistent disciplinary hegemony. The inherent complexity in SES means that an emphasis on discrete prediction is misplaced and has potentially reduced model efficacy for decision-making. Although computer models are definitely the tool to use to identify the complex relationships within SES, humans are messy and hence the ‘social’ in SES is often ignored, glossed over, or reduced to simplistic economic or demographic variables. This combination of factors has perpetuated biases in what is worth pursuing and/or publishing. In (re)visiting issues in SES modeling, including debates about model capabilities, data selection, and challenges in working across disciplinary lines, this reflection explores how the author’s experience aligns with extant literature as well as raises issues about what is absent from that body of work. The available lessons suggest that scholars and practitioners need to re-think how, why, and when to employ SES modeling in regulatory or other decision-making contexts.
关于社会生态系统（SES）建模的关键经验和局限性已广为流传，但令人沮丧的是，这些经验和局限性却始终如一。突出的挑战包括关于系统和模型的过时观点以及长期存在的学科霸权。SES 固有的复杂性意味着对离散预测的强调是错误的，有可能降低模型对决策的效用。尽管计算机模型无疑是用来识别社会经济地位中复杂关系的工具，但人类是杂乱无章的，因此社会经济地位中的 "社会 "往往被忽视、掩盖或简化为简单的经济或人口变量。在（重新）探究社会经济地位建模中的问题时，包括有关模型能力、数据选择和跨学科工作挑战的争论，本反思探讨了作者的经验如何与现有文献保持一致，同时也提出了有关这些工作中缺失的问题。现有经验表明，学者和从业人员需要重新思考如何、为何以及何时在监管或其他决策环境中使用 SES 模型。
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Katarina Radǐsić, E. Rouzies, C. Lauvernet, A. Vidard
The PESHMELBA model simulates water and pesticide transfers at the catchment scale. Its objective is to help the process of decision making in the common management of long-term water quality. Performing the global sensitivity analysis (GSA) of this type of model is necessary to trace the output variability to the input parameters. The goal of the present work is to perform a GSA, while considering the spatio-temporal nature and the high dimensionality of the model. The output considered is the surface moisture simulated over a two-month period on a catchment of assorted mesh elements (plots). The GSA is performed on the dynamical outputs, rewritten through their functional principal components. Sobol’ indices are then estimated through polynomial chaos expansion on each principal component. The analysis differs between the two types of behaviour observed in the surface moisture outputs. The hydrodynamic properties of the surface soil have a dominant influence on the average surface moisture. Nonetheless, the parameters describing deeper soil layers influence the output dynamics of those plots where the surface moisture is saturated. We obtain Sobol’ indices with high precision while using a limited number of model estimations and considering the models spatio-temporal nature. The physical interpretation of the GSA confirms and augments our knowledge on the model.
{"title":"Global sensitivity analysis of the dynamics of a distributed hydrological model at the catchment scale","authors":"Katarina Radǐsić, E. Rouzies, C. Lauvernet, A. Vidard","doi":"10.18174/sesmo.18570","DOIUrl":"https://doi.org/10.18174/sesmo.18570","url":null,"abstract":"The PESHMELBA model simulates water and pesticide transfers at the catchment scale. Its objective is to help the process of decision making in the common management of long-term water quality. Performing the global sensitivity analysis (GSA) of this type of model is necessary to trace the output variability to the input parameters. The goal of the present work is to perform a GSA, while considering the spatio-temporal nature and the high dimensionality of the model. The output considered is the surface moisture simulated over a two-month period on a catchment of assorted mesh elements (plots). The GSA is performed on the dynamical outputs, rewritten through their functional principal components. Sobol’ indices are then estimated through polynomial chaos expansion on each principal component. The analysis differs between the two types of behaviour observed in the surface moisture outputs. The hydrodynamic properties of the surface soil have a dominant influence on the average surface moisture. Nonetheless, the parameters describing deeper soil layers influence the output dynamics of those plots where the surface moisture is saturated. We obtain Sobol’ indices with high precision while using a limited number of model estimations and considering the models spatio-temporal nature. The physical interpretation of the GSA confirms and augments our knowledge on the model.","PeriodicalId":493105,"journal":{"name":"Socio-environmental systems modelling","volume":"51 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139600905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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