Same Pipeline, Different Fires
Kincade burned through Northern Sonoma County wine country in October 2019. Camp Fire leveled the town of Paradise in November 2018. Different terrain, different weather, radically different outcomes. We ran the identical four-level classification pipeline on properties from both fire domains.
| Metric | Kincade | Camp Fire |
|---|---|---|
| Properties | 316 | 155 |
| Properties burned | 21 (7%) | 131 (85%) |
| L0 classification | 96% HIGH | 100% HIGH |
| L3 classification | 86% EXTREME, 9% HIGH | 87% EXTREME, 12% HIGH |
| L0 → L3 reclassification | 91% | 88% |
| L3 sensitivity | 100% | 99% |
| L3 specificity | 6% | 0% |
| EAL divergence (L0 vs L3) | 133% | 128% |
EAL = Expected Annual Loss. Divergence = (L3 EAL − L0 EAL) / L0 EAL. Both fires use 200 MC draws.
L0 → L3 Reclassification Side by Side
Despite radically different burn outcomes — 7% of Kincade properties burned vs. 85% at Camp Fire — the reclassification rate is nearly identical. The zone-based model fails to differentiate in both environments.
How Much the Loss Estimate Changes
L0 understates Expected Annual Loss by 128–133% across both fires. This isn’t a rounding error — it’s more than doubling the loss estimate when the model accounts for terrain, fuel, wind, and uncertainty.
Not a Single-Fire Artifact
Why Camp Fire Is Different
Camp Fire destroyed nearly everything in Paradise. Under L0, all 155 properties are HIGH — but that’s actually correct for Camp Fire. The model’s value is that it goes further: 87% are EXTREME (almost certain to burn) while 12% are HIGH (probable but not certain). Even in a catastrophe, the fire model differentiates the last few percent.
Camp Fire’s 0% specificity makes sense: when 85% of properties burn, there are almost no true negatives to identify. The model isn’t wrong — the fire really did burn everything. What matters is the differentiation within the burn zone.
The Structural Effect of Fidelity
Both fires show the same qualitative pattern:
- L0 lumps properties into one tier (no differentiation)
- L1 adds the EXTREME tier (screening catches the worst)
- L2/L3 redistribute within HIGH/EXTREME (simulation refines the picture)
The reclassification rate is stable at 88–91%. The EAL divergence is stable at 128–133%. These are not artifacts — they’re the structural effect of fidelity.
The classification thresholds and loss rates are identical across both fires. The consistency comes from the fire model’s behavior, not from parameter tuning.