NSF R2I2: Wildfire-Resilient and Insurable Utah
- Institution
- University of Utah, UT
- Principal investigator
- William R Anderegg
- Funding
- $500K
- Duration
- 2025–2027
Moonbase score
Still being scored
Abstract
Wildfires are growing more frequent and severe in the western United States, threatening lives, infrastructure, health, and economic stability. This project brings together scientists, insurers, government officials, and community members to co-develop solutions for reducing wildfire risks and protecting insurability in Utah, where wildfire risk is rising and insurers are withdrawing coverage or raising premiums. The team will use a community-based approach to produce wildfire risk models tailored to local areas, test the effectiveness of mitigation strategies, and design new insurance approaches that reward resilience. A set of strategies and tools will be developed to guide local decision-makers, particularly in high-risk and high-vulnerability areas of multiple Utah counties with extensive wildland-urban interface. Educational activities—including a new undergraduate research stream, course materials, and a speaker series—will help train future leaders. By aligning science, public engagement, and policy innovation, this project will strengthen the ability of communities to prepare for and recover from wildfires, supporting the national interest in public welfare, economic security, and scientific advancement. This interdisciplinary project integrates Earth system modeling, fire and ecosystem modeling, machine learning, insurance analytics, and community planning to develop actionable strategies for wildfire resilience and financial protection. Three high-resolution wildfire models will be tested, compared, and validated using historical burn data for the entire state of Utah, with more detailed maps for Summit, Salt Lake, and Utah counties. These models will be linked to detailed exposure maps of homes, infrastructure, and economic assets, enabling precise risk assessments and cost-benefit analyses of mitigation actions vetted through community engagement. Machine learning techniques will be used to identify at-risk assets and simulate the impacts of mitigation strategies such as defensible space, infrastructure hardening, and utility risk reduction. Concurrently, the project will assess insurance coverage gaps and test innovative solutions—including parametric products, community-scale covers, and public-private partnerships—to improve affordability and coverage. Through iterative co-design with community partners, the research will produce scalable tools and strategies for use across Utah and the Southwest region. This work will advance the scientific foundations of environmental risk modeling, bottom-up socio-technical adaptation, and adaptive insurance frameworks for wildfire and other hazards. 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.
Research team
- William R AndereggPI
- John Lin
- Divya Chandrasekhar
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