Wildfire risk modeling

Wildfire risk modeling estimates the likelihood and potential severity of fire at a given location by combining fuel conditions, vegetation type, density and moisture content, weather variables like wind, temperature and humidity, topography, and historical ignition patterns, whether from lightning or human activity. Unlike flood or earthquake hazard, wildfire risk depends heavily on vegetation management that changes year to year, not just fixed geography.

Models typically simulate fire spread across a landscape grid using fire-behavior equations that account for how fuel, slope and wind direction accelerate or slow a fire's edge, calibrated against observed fires and updated with current fuel-moisture conditions often derived from remote sensing. This differs from a general catastrophe model built on long historical event records, since wildfire's growing frequency in many regions makes recent years a poor guide to future risk, pushing modelers toward forward-looking, conditions-based simulation.

Wildfire risk scores drive property insurance underwriting and pricing, utility infrastructure hardening decisions, and evacuation and resource-allocation planning, and results are often expressed as a return period-style probability alongside expected damage, feeding the same kind of exposure data-based loss calculation used in other catastrophe modeling. A key pitfall is relying on historical fire frequency alone in a hazard whose statistics are visibly shifting, and ignoring that model skill varies between well-studied regions like the western United States and fire-prone areas with sparser data.