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

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，他们通常会影响对水的伤害。总每日总负荷（TMDL）是解决水质问题的重要方法。它设定了水体（如河流）每天可以处理的水体污染的限制。水质最佳管理实践（BMP）是防止污染物进入水体并实现TMDL设定的目标的流行方法。此行动通常需要在不同的专家，数据共享和敬业的利益相关者参与之间进行合作。这个NSF项目旨在提高我们对不同空间尺度上如何更好地管理水质的理解。有关人类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，以在不同的空间尺度研究农民的BMP实施决策。该项目围绕三个研究任务（RTS）结构。 RT1通过常规会议与项目咨询委员会的利益相关者和专家互动。研究小组采访并调查当地农民，以评估因素会影响研究领域中BMP的实施。 RT2涉及开发一个基于双向耦合剂的水质模型，其中农民是代理商。该模型使用基于社会心理学理论的贝叶斯推论方法模拟了代理商的决策和实施BMP。 RT3使用数值实验来评估未来气候和社会经济情景以及人类行为不确定性对TMDL目标的影响。这些方案与项目咨询委员会共同开发，并在项目总结研讨会中传播。这项与切萨皮克湾现实世界实践者的合作项目产生和分享结果，以告知切萨皮克湾计划的科学和技术咨询委员会。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来通过评估来诚实地支持。