A human-centered modeling approach to simulate best management practices and behaviors under uncertainty to meet water quality guidelines

以人为本的建模方法，用于模拟不确定情况下的最佳管理实践和行为，以满足水质准则

• 批准号: 2342309
• 负责人: Yi-Chen Yang
• 金额: $ 39.99万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-15 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2342309&HistoricalAwards=false
• 关键词: human; centered; modeling; approach; simulate

When people interact with their environment, they often induce harm to the water. Total Maximum Daily Load (TMDL) is a vital method for undertaking water quality issues. It sets limits on how much pollution a water body, like a river, can handle each day. Water quality best management practices (BMPs) are a popular method to prevent pollutants from entering the water bodies and to achieve the goals set by TMDL. This action usually requires cooperation among different experts, data sharing, and engaged stakeholder involvement. This NSF project aims to improve our understanding of how to manage water quality better at different spatial scales. Three theories regarding human-environment interactions will lay the groundwork for our analyses. This project combines modeling, data analysis, and surveys to study farmers’ BMP implementation decisions at different spatial scales.The project is structured around three research tasks (RTs). RT1 engages stakeholders and experts in the project advisory board via regular meetings. The research team interviews and surveys local farmers to evaluate factors influencing the implementation of BMPs in the study area. RT2 involves the development of a two-way coupled agent-based water quality model, in which farmers are agents. The model simulates the agents’ decisions and implementation of BMP using the Bayesian inference method based on socio-psychological theories. RT3 uses numerical experiments to evaluate the effect of future climate and socioeconomic scenarios and human behavior uncertainty on TMDL targets. These scenarios are co-developed with the project advisory board and disseminated in the project wrap-up workshop. This collaborative project with real-world practitioners in the Chesapeake Bay produces and shares results to inform the Scientific and Technical Advisory Committee of the Chesapeake Bay Program.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

当人们与环境互动时，常常会对水体造成损害，这是解决水质问题的重要方法，它限制了水体（如河流）可以处理的污染量。水质最佳管理实践 (BMP) 是防止污染物进入水体并实现 TMDL 设定目标的流行方法，该行动通常需要不同专家之间的合作、数据共享和利益相关者的参与。项目旨在提高我们对如何在不同的空间尺度上更好地管理水质。关于人类与环境相互作用的三种理论将为我们的分析奠定基础。该项目结合了建模、数据分析和调查，以研究农民在不同空间尺度上的 BMP 实施决策。 RT1 围绕三项研究任务 (RT) 进行，通过定期会议让利益相关者和专家参与到项目咨询委员会中。研究团队采访并调查了当地农民，以评估影响研究区域 BMP 实施的因素。基于代理的双向耦合RT3以农民为主体的水质模型，利用基于社会心理学理论的贝叶斯推理方法，模拟主体的决策和BMP的实施，通过数值实验来评估未来气候和社会经济情景以及人类行为的影响。这些情景是与项目咨询委员会共同开发的，并在项目总结研讨会上传播，该项目与切萨皮克湾的实际实践者合作产生并分享结果，以通知科学和技术咨询委员会。的切萨皮克湾计划。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。