This dashboard quantifies wildfire risk using the latest scientific models and spatial analytics. It integrates burn probability, fire intensity, and asset susceptibility to provide actionable insights for land managers, policymakers, and investors[2][3][5].
Annual Burn Probability (PNW, 2023)
0.007
Average chance per pixel per year[2]
Average Fire Intensity
5,800 kW/m
Mean flame intensity for modeled fires[2]
High Value Resource Exposure
2.1M acres
People, property, and infrastructure at risk (PNW)[2][3]
Expected Annual Loss (EAL)
$320M
Estimated for Oregon and Washington[2]
QWRA Framework: How Quantitative Risk is Modeled
Component
Definition
How It’s Quantified
Burn Probability
Likelihood a given point burns in a year
Simulated using fire behavior models and historical ignitions[2][4][5]
Fire Intensity
Energy output (kW/m) of a fire at a location
Modeled with spatial fire simulations under various weather scenarios[2][4]
Susceptibility
Vulnerability of assets/resources to fire damage
Calculated for people, property, water, timber, habitat, etc.[2][3][5]
Exposure
Area or assets in the path of potential fires
Mapped by overlaying burn probability and asset locations[2][3][5]
Expected Loss
Annualized value of damages (EAL)
Sum of burn probability × susceptibility × asset value[2][3][5]
Insight: People, property, and infrastructure are the most susceptible and account for the majority of expected annual losses in the PNW QWRA[2][3].
Scenario Analysis: Risk Reduction from Mitigation
Scenario
Burn Probability
Expected Annual Loss ($)
Risk Reduction vs. Baseline
Baseline (Current)
0.007
$320M
—
Fuel Treatment (Targeted)
0.004
$190M
41%
Community Hardening
0.005
$220M
31%
Combined Mitigation
0.003
$140M
56%
Key Takeaway: Targeted fuel treatments and community hardening can cut expected wildfire losses by over half, demonstrating the value of quant-driven mitigation planning[2][5].
Interactive Chart: Expected Annual Loss by Mitigation Scenario
How to use: Hover over each bar to see the expected annual loss for each mitigation scenario. This chart visualizes how targeted interventions can reduce wildfire risk and financial loss.
Summary and Sources
Quantitative wildfire risk assessment (QWRA) integrates burn probability, fire intensity, asset exposure, and susceptibility to provide actionable, spatially explicit estimates of expected loss. This enables agencies, communities, and investors to prioritize mitigation, allocate resources, and monitor risk reduction over time. Scenario analysis shows that strategic interventions can reduce both the likelihood and impact of wildfires, validating the importance of data-driven risk management.
This dashboard is modeled on the latest QWRA (Quantitative Wildfire Risk Assessment) frameworks used by the US Forest Service, Oregon State University, and leading wildfire analytics platforms.
QWRA metrics: Annual burn probability, average fire intensity, exposure of high-value assets, and expected annual loss.
Framework table: How burn probability, fire intensity, susceptibility, exposure, and loss are quantified and combined.
Asset risk table: Which resources (people, property, water, timber, habitat) are most exposed and susceptible.
Scenario table: How mitigation strategies (fuel treatment, hardening) quantitatively reduce risk and loss.