Evaluating a simulation-based wildfire burn probability map for the conterminous US
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Abstract
Wildfire simulation models are used to derive maps of burn probability (BP) based on fuels, weather, topography and ignition locations, and BP maps are key components of wildfire risk assessments.
Few studies have compared BP maps with real-world fires to evaluate their suitability for near-future risk assessment. Here, we evaluated a BP map for the conterminous US based on the large fire simulation model FSim.
We compared BP with observed wildfires from 2016 to 2022 across 128 regions representing similar fire regimes (‘pyromes’). We evaluated the distribution of burned areas across BP values, and compared burned area distributions among fire size classes.
Across all pyromes, mean BP was moderately correlated with observed burned area. An average of 71% of burned area occurred in higher-BP classes, vs 79% expected. BP underpredicted burned area in the Mountain West, especially for extremely large fires.
The FSim BP map was useful for estimating subsequent wildfire hazard, but may have underestimated burned areas where input data did not reflect recent climate change, vegetation change or human ignition patterns.
Our evaluations indicate that caution is needed when relying on simulation-based BP maps to inform management decisions. Our results also highlight potential opportunities to improve model estimates.
Study Area
Publication type | Article |
---|---|
Publication Subtype | Journal Article |
Title | Evaluating a simulation-based wildfire burn probability map for the conterminous US |
Series title | International Journal of Wildland Fire |
DOI | 10.1071/WF23196 |
Volume | 34 |
Issue | 1 |
Year Published | 2025 |
Language | English |
Publisher | CSIRO Publishing |
Contributing office(s) | Fort Collins Science Center, Geosciences and Environmental Change Science Center, Office of the AD Ecosystems, Southwest Biological Science Center |
Description | WF23196, 16 p. |
Country | United States |
Other Geospatial | Conterminous United States |
Google Analytic Metrics | Metrics page |