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

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 the 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.

Award Number: 2611747
Principal Investigator: Yusuf Sermet
Funds Obligated: $92,927
State: LA