{"title":"Simulation decomposition analysis of the Iowa food-water-energy system","authors":"Taeho Jeong , Mariia Kozlova , Leifur Thor Leifsson , Julian Scott Yeomans","doi":"10.1016/j.envsoft.2025.106415","DOIUrl":null,"url":null,"abstract":"<div><div>This study applies global sensitivity analysis (GSA) to the Iowa Food-Water-Energy system, focusing on nitrogen export into the Mississippi River. A binning method combined with <em>simulation decomposition</em> (SimDec) quantifies and visualizes the influence of crucial aggregate input variables — manure nitrogen (MN), commercial nitrogen (CN), grain nitrogen (GN), and fixation nitrogen (FN) — on nitrogen surplus (NS) at the county level. Unlike traditional Sobol’ indices, the binning method captures dependent variables. In addition, the SimDec procedure provides a detailed visual representation of how these dependencies and interactions drive the nitrogen variability. MN is identified as the most influential factor, followed by CN, with FN and GN having less impact. The study also performs GSA on the low-level input variables, enhancing the overall interpretability of the sensitivity analysis. This approach offers actionable insights for improving nitrogen management practices and contributes to GSA literature by showcasing the analysis of aggregate variables.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"188 ","pages":"Article 106415"},"PeriodicalIF":4.8000,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Modelling & Software","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1364815225000994","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
This study applies global sensitivity analysis (GSA) to the Iowa Food-Water-Energy system, focusing on nitrogen export into the Mississippi River. A binning method combined with simulation decomposition (SimDec) quantifies and visualizes the influence of crucial aggregate input variables — manure nitrogen (MN), commercial nitrogen (CN), grain nitrogen (GN), and fixation nitrogen (FN) — on nitrogen surplus (NS) at the county level. Unlike traditional Sobol’ indices, the binning method captures dependent variables. In addition, the SimDec procedure provides a detailed visual representation of how these dependencies and interactions drive the nitrogen variability. MN is identified as the most influential factor, followed by CN, with FN and GN having less impact. The study also performs GSA on the low-level input variables, enhancing the overall interpretability of the sensitivity analysis. This approach offers actionable insights for improving nitrogen management practices and contributes to GSA literature by showcasing the analysis of aggregate variables.
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.