Report Provides Unified Guidance for Advancing Wildfire Risk Modeling
The latest milestone in its national fire risk research addressing the growing wildfire threat, UL Research Institutes’ Fire Safety Research Institute has released “Wildfire Risk Modeling Evaluation Findings and Community Perspectives.”
The report presents key learnings from the first-of-its-kind collaborative research exercise evaluating wildfire risk modeling approaches. It goes on to provide an actionable roadmap for advancing wildfire risk modeling science and improving its usability for emergency managers, fire service leaders, public safety officials, and other practitioners. With this publication, the wildfire risk intelligence community now has unified guidance on how to meaningfully advance risk modeling and better protect at-risk communities.
Key Findings From the Wildfire Risk Modeling Exercise
The report’s central finding is that advancements in wildfire risk modeling should focus on creating a modular, interoperable modeling ecosystem that spans the full wildfire risk pathway rather than developing a universal “best” model. This enables various model specializations, datasets, and analytical methods to work together — leveraging their unique strengths to better support wildfire risk assessment and operational decision-making.
Supporting findings include:
Diversity of Modeling Approaches Is a Strength: Model submissions employed a wide range of methodologies and techniques. No single model consistently outperformed the rest; instead, each excelled in specific wildfire risk applications.
Hazard Modeling Dominates Current Capabilities: Models primarily focus on hazard exposure such as fire spread and ignition probability while lacking coverage of other important areas, including mitigation effectiveness, consequence severity, and economic impact.
Risk Is Not Consistently Defined: Models define risk parameters and terminology in myriad ways, limiting the ability to effectively compare models and create a unified risk assessment framework.
Science-to-Operations Translation Remains Challenging: Model outputs are often highly complex, making them difficult to interpret and limiting their usefulness for emergency managers, community stakeholders, and fire practitioners.
Data Inconsistencies Limit Comparability: Differences in building attribute data, exposure datasets, vulnerability information, and historical and observational wildfire records limits the inputs available to support wildfire risk modeling.
“The Wildfire Risk Modeling Exercise was a convergent test and evaluation of the diverse models available to characterize wildfire risk today. These results revealed broader insights about the state of the science, prioritized gaps, and identified the investments needed to advance wildfire risk intelligence.
The findings provide a critical foundation for shaping the next decade of research, innovation, and operational capability. This report also serves as an important resource for local and state fire service and public safety agencies, providing an evidence-based framework for selecting wildfire risk modeling capabilities and applying them effectively to assess and manage community risk.”
— Rebecca Harned
Principal Research Scientist
UL Research Institutes | Fire Safety Research Institute
Recommendations and Roadmap for Advancing Wildfire Risk Modeling
Based on the exercise’s findings and identification of opportunity gaps, the report recommends the following course of action for the wildfire risk intelligence community:
1. Establish end-to-end wildfire risk intelligence frameworks to create decision-centered risk assessments capable of identifying who and what are most at risk, why risk exists, and how risk can be reduced.
2. Create an integrated landscape-to-community fire modeling ecosystem to enable comprehensive modeling of fire progression, from ignition through community impacts and urban conflagration.
3. Advance exposure and vulnerability modeling for improved accuracy in estimating structure ignition probability, damage likelihood, and community impacts.
4. Expand wildfire risk modeling into decision support systems to have operationally relevant decision-support tools that inform mitigation, preparedness, response, and recovery activities.
5. Develop comprehensive consequence and economic risk modeling capabilities to enable comprehensive assessments of wildfire consequences that support risk-informed investments and policy decisions.
6. Improve visualization, communication, and explainability to increase usability, transparency, and adoption of wildfire risk intelligence products by technical and non-technical stakeholders.
7. Establish interoperability standards and shared data frameworks to build a federated modeling ecosystem that enables collaboration, model integration, and workflow automation.
8. Advance ensemble modeling as the future state of wildfire risk assessment for more robust, transparent, and scientifically defensible predictions that leverage the strengths of multiple modeling approaches.
