Lucas Bair James R. Meldrum Todd Hawbaker Aaron Daniel Russell 2026 <div class="title">Background</div><p>Wildfire risk in the United States is rising and remains a land management priority. The quantitative wildfire risk assessment (QWRA) framework integrates fuels, topography, weather and values at risk to estimate the potential change in value from wildfire. Within this, response functions (RFs) represent how values respond to fire intensity. These are often based on expert judgment, but variation across assessments is unclear.</p><div class="title">Aims</div><p>This study uses data from the US Geological Survey (USGS) Wildfire Hazard and Risk Assessment Clearinghouse to characterize consistency and variation across categories and contexts.</p><div class="title">Methods</div><p>We applied descriptive statistics to summarize RFs, using tables, box-and-whisker plots and heat maps stratified by highly valued resource or asset (HVRA) category and spatial scale.</p><div class="title">Key results</div><p>RFs and value definitions vary, especially for ecosystem-related resources. Some functions, such as for buildings in the wildland–urban interface (WUI), translate well across contexts, while others require more input.</p><div class="title">Conclusions</div><p>Some functions are broadly transferable, while others need customization. This analysis provides references and starting points for improvement to RFs in QWRAs.</p><div class="title">Interpretations</div><p>Expanding the clearinghouse and dataset and building more transparency in expert elicitation can build trust among communities, agencies and end-users, and can support efficient use of limited resources to mitigate wildfire risk.</p> application/pdf 10.1071/WF25219 en CSIRO Publishing Variability and consistency in wildfire susceptibility: Insights from a national compilatioI article