Collaborative Research: A Physics-Informed Flood Early Warning System for Agricultural Watersheds with Explainable Deep Learning and Process-Based Modeling

合作研究：基于物理的农业流域洪水预警系统，具有可解释的深度学习和基于过程的建模

• 批准号: 2243775
• 负责人: Chaoqun Lu
• 金额: $ 50万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2243775&HistoricalAwards=false
• 关键词: Collaborative; Research; Physics; Informed; Flood

Global floods and extreme rainfall events have surged by more than 50% this decade and are now occurring at a rate four times higher than in 1980. However, the capability of physical models in predicting flood events remains limited across spatial scales, especially in intensively managed agricultural systems like the Midwestern U.S. The apparent disparity between observed seasonal patterns of extreme precipitation and high streamflow events presents a challenge when using precipitation alone to predict flood occurrence and severity. This project addresses a fundamental question in hydrologic science: how do watershed characteristics and in-land management practices regulate the precipitation-runoff relationship across agriculture-dominated watersheds? The modeling framework in this project will integrate the complex impacts of watershed characteristics, human land use, and management practices into hydrological prediction. An early warning system will be developed for projecting flood occurrence at a granular level in a managed system and will be shared for further evaluation of the flood forecasting performance and uncertainty assessment.The overarching goal of the research is to develop a data-driven, physics-informed early warning system to predict flood occurrence and support communities in agriculture-dominated watersheds across the Midwestern United States. This project will develop a graph-based transformer deep learning approach integrated with process-based hydro-ecological modeling to improve flood prediction accuracy and keep the interpretable structure. The results of the project will be tested, shared, and deployed as a real-time prediction tool on a web-based platform that integrates mapping capabilities, advanced visualizations, and mobile access. The early warning system will be accessible to multiple users, especially underrepresented communities, concerning the direct impacts of flooding on life and property and the indirect effects on food security, economy, and livelihood of the communities.This project is jointly funded by Hydrologic Sciences, the Established Program to Stimulate Competitive Research (EPSCoR), and the Directorate for Geosciences to support AI/ML advancement in the geosciences.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.

全球洪水和极端降雨事件在这十年中激增了50%以上，现在的发生率是1980年的四倍。然而，物理模型在预测洪水事件的能力仍然有限的空间尺度，特别是在集中管理的农业系统，如美国中西部地区观察到的极端降水和高径流事件的季节性模式之间的明显差异提出了一个挑战时，单独使用降水预测洪水的发生和严重程度。该项目解决了水文科学中的一个基本问题：流域特征和土地管理实践如何调节农业主导流域的降水-径流关系？该项目的建模框架将把流域特征、人类土地利用和管理实践的复杂影响整合到水文预测中。将开发一个预警系统，用于在管理系统中细粒度预测洪水发生，并将共享该系统，以进一步评估洪水预报性能和不确定性评估。该研究的总体目标是开发一个数据驱动、物理信息的预警系统，以预测洪水发生并支持美国中西部以农业为主的流域的社区。该项目将开发一种基于图的Transformer深度学习方法，结合基于过程的水文生态建模，以提高洪水预测精度并保持可解释的结构。该项目的结果将作为实时预测工具在一个基于网络的平台上进行测试、共享和部署，该平台集成了制图功能、高级可视化和移动的访问。预警系统将提供给多个用户，特别是代表性不足的社区，关于洪水对生命和财产的直接影响以及对社区粮食安全，经济和生计的间接影响。该项目由水文科学，刺激竞争研究的既定计划（EPSCoR），该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。