Fire risk methods overview

Fire risk methods overview#

This page explains the high-level methodology used to compute building-level wildfire risk scores for CONUS. It provides a conceptual overview with references to more detailed documentation pages. For a full description of our methods, read the methods document that accompanied our explainer article.

Summary#

The Open Climate Risk fire risk model computes building-level wildfire risk scores by:

  1. Taking baseline burn probability data from Riley et al. 2025 and spreading it into developed areas using wind-informed blurring

  2. Multiplying the wind-adjusted burn probability by Scott et al. 2024 conditional risk values (cRPS)

  3. Sampling the resulting risk surface at building locations from the Overture Maps Foundation buildings dataset

The model produces risk scores (RPS: risk to potential structures) representing the expected net value change for a hypothetical structure at each location. The scores account for directional fire spread patterns driven by prevailing winds.

Conceptual framework#

Risk to potential structures (RPS)#

The model calculates risk to potential structures (RPS), defined as:

\[ \text{RPS} = \text{BP} \times \text{cRPS} \]

Where:

  • BP (burn probability): Annual likelihood that a given pixel burns, derived from wildfire simulations in Riley et al. 2025

  • cRPS (Conditional risk to potential structures): The conditional net value change for a hypothetical structure if it were to burn, from Scott et al. 2024

RPS represents the expected net value change per year for a generic structure at each location. It combines both probability (how likely fire is) and consequence (how much damage would occur).

Key limitation

This approach models risk to a hypothetical “potential structure” rather than actual buildings with specific characteristics. Building-level attributes (materials, retrofits, defensible space) are not included.

Wind-adjusted fire spread#

A key innovation is incorporating wind-driven fire spread patterns into the burn probability:

  1. Fire weather analysis: Identify wind directions during high fire weather conditions (99th percentile FFWI) from Rasmussen et al. 2023

  2. Elliptical spread kernels: Apply oval-shaped blurring filters, inspired by Richards (1990), pointing in eight cardinal/ordinal directions

  3. Upwind spreading: For each pixel, calculate zonal mean BP from upwind areas weighted by fire-weather wind direction frequencies

  4. Iterate spreading: Repeat blurring 3 times to spread BP up to ~1.5 km into non-burnable (developed) areas

This differs from the uniform circular blurring in Scott et al. 2024. The wind-informed approach better represents how embers transport fire downwind from wildland into developed areas.

Processing workflow#

1. Fire weather wind analysis#

  • Calculate Fosberg Fire Weather Index (FFWI) for every hour 1979-2022

  • For each 4km pixel, identify 99th percentile FFWI threshold

  • Extract wind directions for all hours exceeding that threshold (“fire-weather winds”)

  • Bin fire-weather winds into 8 cardinal/ordinal directions

  • Create distribution of fire-weather wind directions for each pixel

2. Upscale and prepare BP#

  • Convert 270m BP raster from Riley et al. 2025 to 30m resolution

  • Identify “non-burnable” pixels (where BP = 0 in Riley et al. data)

3. Wind-informed BP spreading#

For each 30m pixel:

  • Extract nearest-neighbor 4km fire-weather wind distribution

  • Create 8 oval-shaped blurring filters (elliptical wavelets) pointing in 8 directions

    • Each filter represents wind coming FROM that direction (spreading BP downwind TO the pixel)

    • Distance from pixel to far side of oval along major axis: 510m

  • For each direction, apply upwind filter to calculate zonal mean BP

  • Weight the 8 smeared BP values by fire-weather wind direction frequencies

  • Repeat this process 3 times → maximum spread of ~1.5 km into non-burnable areas

4. Calculate RPS#

  • Multiply wind-adjusted BP by 30m cRPS raster

  • Result: 30m RPS (risk to potential structures) for present and future

  • RPS = expected net value change per year for a hypothetical structure

5. Sample at building locations#

6. Convert to categorical scores#

  • Convert continuous RPS values to categorical risk scores (0-10 scale)

  • Scores are calculated using percentile-based RPS bins defined here

Score

Criteria

0

RPS == 0

1

0 < RPS < 0.01

2

0.01 <= RPS < 0.02

3

0.02 <= RPS < 0.035

4

0.035 <= RPS < 0.06

5

0.06 <= RPS < 0.1

6

0.1 <= RPS < 0.2

7

0.2 <= RPS < 0.5

8

0.5 <= RPS < 1

9

1 <= RPS < 3

10

3 <= RPS <= 100

Spatial processing architecture#

The model uses a spatial chunking system for efficient parallel processing:

  • CONUS is divided into 595 processing regions (30m resolution chunks)

  • Each region is processed independently using distributed compute (Coiled/Dask)

  • Outputs are stored in Icechunk (for rasters) and GeoParquet (for vectors)

  • Failed regions can be reprocessed without affecting completed work

See Horizontal scaling via spatial chunking for details on the parallelization strategy.

Outputs#

The pipeline produces data outputs in several formats:

Option

Description

Formats

Raster (tensor) data

Full gridded dataset spanning CONUS

Icechunk (Zarr-based)

Vector (polygon) data

Full buildings dataset spanning CONUS

GeoParquet

Regional statistics

Summary statistics for regions (state, county, census tract, census block) within CONUS

CSV, GeoJSON

For more information about each data output type, see the Access data page.