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Active STANDARD GRANT National Science Foundation (US)

RAISE: Flood resilience in rural Texas communities

$10M USD

Funder National Science Foundation (US)
Recipient Organization William Marsh Rice University
Country United States
Start Date Mar 01, 2025
End Date Feb 29, 2028
Duration 1,095 days
Number of Grantees 4
Roles Principal Investigator; Co-Principal Investigator
Data Source National Science Foundation (US)
Grant ID 2440167
Grant Description

This project will develop and implement a framework that leverages community knowledge and priorities to improve flood hazard assessments and mitigation efforts in rural Texas. Floods are the costliest natural hazard in the United States, causing loss of life, billions in damages per year, and widespread disruption to society. Rural communities face unique and persistent challenges in mitigating their flooding risk because flood hazards are not accurately delineated in rural regions and community priorities often differ from state or federal policies.

Rural communities need new data as well as novel computer models and frameworks that integrate Earth system science with community-centered decision making. This project will create a framework to help rural communities better manage their repetitive flooding hazards and improve their resilience to evolving hazards. The framework will be implemented in two rural communities, with potential to replicate in other Texas counties.

The proposed research brings together expertise across scientific fields to address the limitations hindering understanding of flood risk drivers and the design of effective mitigation pathways. This project creates a novel Earth systems flood risk framework based on principles of performance-based design. Within each component of the framework, the research will advance novel methodologies for revealing complex system dynamics and interactions.

This research will establish a new model for integrating community knowledge into characterizing and evaluating floods and their impacts. The proposed work will also develop advanced statistical techniques for integrating hydroclimate variability in flood risk assessment. It will provide new workflows for integrating artificial intelligence and machine learning in Earth systems science research.

The research will propose methods for combining multi-scale models and observations, and establish decision frameworks for generating robust flood solutions.

This project is jointly funded by the Division of Research, Innovation, Synergies, and Education in the Directorate for Geosciences, and the Office of Advanced Cyberinfrastructure through the National Discovery Cloud for Climate initiative.

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.

All Grantees

William Marsh Rice University

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