Xifu Sun, A. Jakeman, B. Croke, Stephen G. Roberts, J.D. Jakeman
{"title":"Assessing convergence in global sensitivity analysis: a review of methods for assessing and monitoring convergence","authors":"Xifu Sun, A. Jakeman, B. Croke, Stephen G. Roberts, J.D. Jakeman","doi":"10.18174/sesmo.18678","DOIUrl":"https://doi.org/10.18174/sesmo.18678","url":null,"abstract":"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.","PeriodicalId":493105,"journal":{"name":"Socio-environmental systems modelling","volume":"115 26","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141665962","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}
{"title":"Reflections on SES modeling: Stop me if you’ve heard this","authors":"Kristan Cockerill","doi":"10.18174/sesmo.18658","DOIUrl":"https://doi.org/10.18174/sesmo.18658","url":null,"abstract":"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 Prominent challenges include outdated perspectives about systems and models along with persistent disciplinary hegemony.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.\u0000In (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.","PeriodicalId":493105,"journal":{"name":"Socio-environmental systems modelling","volume":"174 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139842974","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}
{"title":"Reflections on SES modeling: Stop me if you’ve heard this","authors":"Kristan Cockerill","doi":"10.18174/sesmo.18658","DOIUrl":"https://doi.org/10.18174/sesmo.18658","url":null,"abstract":"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.\u0000In (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.","PeriodicalId":493105,"journal":{"name":"Socio-environmental systems modelling","volume":"18 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139782845","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}
Katarina Radǐsić, E. Rouzies, C. Lauvernet, A. Vidard
{"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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