Simulation decomposition analysis of the Iowa food-water-energy system

IF 4.6 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Environmental Modelling & Software Pub Date : 2025-04-01 Epub Date: 2025-03-05 DOI:10.1016/j.envsoft.2025.106415
Taeho Jeong , Mariia Kozlova , Leifur Thor Leifsson , Julian Scott Yeomans
{"title":"Simulation decomposition analysis of the Iowa food-water-energy system","authors":"Taeho Jeong ,&nbsp;Mariia Kozlova ,&nbsp;Leifur Thor Leifsson ,&nbsp;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":4.6000,"publicationDate":"2025-04-01","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":"2025/3/5 0:00:00","PubModel":"Epub","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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
爱荷华州食物-水-能源系统的模拟分解分析
本研究将全球敏感性分析(GSA)应用于爱荷华州食物-水-能源系统,重点关注氮出口到密西西比河。结合模拟分解(SimDec)的分仓方法量化和可视化了关键的总输入变量——粪肥氮(MN)、商品氮(CN)、粮食氮(GN)和固定氮(FN)对县级氮盈余(NS)的影响。与传统的Sobol索引不同,开始方法捕获因变量。此外,SimDec过程提供了这些依赖关系和相互作用如何驱动氮可变性的详细可视化表示。MN是影响最大的因素,其次是CN, FN和GN的影响较小。本研究还对低层次输入变量进行了GSA,增强了敏感性分析的整体可解释性。这种方法为改善氮管理实践提供了可行的见解,并通过展示总变量的分析为GSA文献做出了贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
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.
期刊最新文献
Emulating the Global Change Analysis Model with deep learning: An energy sector case study RiverSTICH: Sewing together 3D rivers from only a few loose threads of transect data Dynamic integration of water quality simulation into Pywr Converging human intelligence with AI systems to advance flood evacuation decision Data assimilation-based correction of flood forecasts in a large-scale river network: A case study of the Pearl River Basin
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1