Wildfire Models Are Missing the Risk That Matters Most
Wildfires are no longer just an ecological concern. They are a financial, insurance, and infrastructure risk, and increasingly a tail-risk problem. Yet most wildfire models still answer the wrong question.
They tell us where fires burn, not how often catastrophic losses occur.
That gap matters.
Below, I explain why return-period–aware wildfire modeling is essential—and why its absence remains a critical blind spot in both research and practice.
1. Where Fires Burn ≠ Where Risk Is
Most wildfire maps focus on burned area.
When we look at county-level burned fractions, familiar patterns emerge: the Pacific Northwest, Northern California’s mountain regions, central Idaho and interior Nevada, parts of Florida, and the interior South. These areas burn frequently and extensively.
These regions burn frequently and extensively. But many are rural with limited economic exposure.
If risk were proportional to burned area, these places would dominate wildfire losses.
They don’t.
2. Where Assets Are Changes Everything
Mapping property exposure using housing units multiplied by median home value reverses the spatial pattern: risk concentrates in major metros and coastal counties.
These areas burn less frequently, but when fires occur, the resulting losses are disproportionately large.
3. Burned Area ≠ Loss
When fire frequency is combined with economic exposure to estimate annual property value at risk, a very different geography emerges, with hotspots concentrated in Pacific Northwest WUI zones, California’s coastal and metropolitan regions, Florida’s coastal counties, and Rocky Mountain WUI corridors. In contrast, areas such as interior Nevada or central Idaho—prominent in burned-area maps—largely fade once asset weighting is applied.
Big fires in empty places don’t bankrupt insurers. Medium fires in asset-dense places do.
4. Why Tail Risk Dominates Wildfire Losses
The world’s costliest wildfire events illustrate this clearly:
- Recent Los Angeles–area fires generated some of the largest wildfire losses globally, with insured losses estimated by Swiss Re near 40 Billion dollars and total economic losses esimtated by AccuWeather at $250–275 Billion. These were not the largest fire years by area burned.
They were rare, high-impact events hitting dense, expensive landscapes.
This is why insurers, reinsurers, and capital markets care about return periods, not averages.
5. Climate, Development, and Feedback Loops
Losses scale nonlinearly because multiple forces reinforce each other:
- Climate and fuel aridity
- Rapid WUI development
- Property value inflation
- Suppression limits during extreme weather
- Building codes and defensible-space variability
These feedbacks create fat-tailed loss distributions, exactly the regime where return periods matter most.
6. The Modeling Gap: What’s Missing Today
Most wildfire models do one of the following:
- Predict burned area
- Predict ignition probability
- Simulate fire spread for individual events
What they don’t do well is connect:
- Probability of large fires
- Spatial spread under weather extremes
- Interaction with urban form
- Resulting economic loss
- Frequency of catastrophic outcomes
Without that chain, we cannot answer:
Is this a 1-in-20 year loss… or a 1-in-200 year loss?
That distinction is everything for insurance pricing, capital adequacy, and resilience planning.
Return periods allow us to:
- Price insurance and reinsurance realistically
- Stress-test portfolios under rare fire years
- Compare wildfire risk across regions consistently
- Evaluate mitigation investments (codes, buffers, suppression)
- Align wildfire risk with other natural hazards (floods, hurricanes)
Yet return-period estimation is still rare in wildfire research, largely because it requires integrated, probabilistic, end-to-end modeling—from ignition to loss.
7. Toward Return-Period–Aware Wildfire Models
The path forward requires combining:
- Large-fire probability models
- High-resolution fire spread models
- Urban exposure and vulnerability models
- Ensemble weather and climate drivers
- Explicit loss modeling
Only then can we move from “where fires happen” to “how often catastrophic losses occur.”