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

IF 4.8 2区环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Environmental Modelling & Software Pub Date : 2025-03-05 DOI:10.1016/j.envsoft.2025.106415

Taeho Jeong , Mariia Kozlova , Leifur Thor Leifsson , Julian Scott Yeomans

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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.

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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。

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来源期刊

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.