COLUMBIA, MD -- As destructive wildfires increasingly threaten communities across the globe, a new report from UL Research Institutes' Fire Safety Research Institute examines the strengths and limitations of current wildfire risk models and provides a roadmap for more comprehensive wildfire risk intelligence.

"Wildfire Risk Modeling Evaluation Findings and Community Perspectives" details findings from the 2026 Wildfire Risk Modeling Exercise, a first-of-its-kind collaborative effort that evaluated a diverse set of wildfire risk models using common scenarios and datasets. The exercise included approximately 20 organizations across academia, government, national laboratories, nonprofits, and the private sector.

For fire service leaders, emergency managers, and public safety officials, the report provides a practical framework for evaluating which modeling capabilities are best suited to specific community needs and decisions. Applied effectively, these tools can help practitioners identify where risk is greatest, prioritize mitigation investments, strengthen evacuation and preparedness planning, and make more informed decisions before, during, and after a wildfire.

"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 the investments needed to advance wildfire risk intelligence," said Rebecca Harned, principal research scientist for Fire Safety Research Institute and the report's lead author. "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."

Key Findings From the Wildfire Risk Modeling Exercise
The results from the exercise demonstrate that no single model can capture the full complexity of wildfire risk, nor should one be expected to. Instead, the report identifies an opportunity to build a new generation of wildfire risk intelligence by connecting complementary models, datasets, and scientific approaches. Doing so can help close critical gaps between modeling wildfire hazard and understanding what ultimately matters to communities — which people, property, and assets are at risk, why they are vulnerable, what the consequences could be, and which actions can most effectively reduce those risks.

To realize that vision, the report recommends building an interoperable modeling ecosystem spanning the full wildfire risk assessment pathway, from ignition and landscape fire spread through structure exposure, community impacts, and economic consequences. It also calls for stronger shared core data and standards, improved transparency and explainability, expanded decision-support tools, and greater use of ensemble modeling. Together, these advances could transform wildfire modeling from a collection of specialized capabilities into an integrated risk intelligence framework that better supports decisions before, during, and after wildfire events.