Purpose & Methods

Why This Exists

This is a citizen science project. We built it because the people who live in the path of western wildfires and droughts deserve access to the same data and analysis that federal agencies and insurance companies use to make decisions about their lives. for free, without a paywall, without a login, without asking permission.

What We Commit To

Data accuracy above everything. Every number traces to a specific federal, state, or provincial data source. When we make a mistake, we correct it publicly. When a claim is uncertain, we say so.

Boots on the ground. This is not a dashboard built from a desk. We verify conditions in the field, cross-reference agency reports with local observations, and update analysis based on what’s actually happening.

Real policy analysis. We read the bills, track the budgets, and grade the agencies on whether their actions match the threats the data identifies.

A self-healing data network. When the government turns off a data source, we find alternatives. When those get turned off, we derive equivalent insights from satellite imagery, state agencies, academic repositories, allied nations.

Always free, always honest. No subscriptions. No premium tier. No sponsored content. No softening findings to protect relationships. The people in fire and drought country are the audience.

Open methods, public data. Every number on this site traces to a specific federal, state, or provincial data source. No proprietary data. No black boxes. The model, training code, and data pipelines will be published to a public repository once the 2026 validation period is complete. If you find an error, tell us.

Firewatch Modelv1.0 · Production release

Our prediction model. Firewatch, is a gradient-boosted decision tree model (XGBoost) trained on 37 million observations across the western fire corridor (11 states + BC). It draws on 26 years of daily climate at 4km resolution, 1,830 SNOTEL stations, 9,800+ large-fire perimeters as prediction targets, 312,000 fires for cause classification, fuel maps, soil moisture, snow disappearance timing, and topography to identify where large fires are most likely to burn.

0.911

AUC-ROC

Held-out test (2020–2024)

10.4x

Lift over base rate

Base rate: 0.1% of cells

37M

Training observations

50 features

AUC-ROC of 0.911 means: given a cell that burned and a cell that didn’t, the model ranks the burned cell higher 91% of the time. It is a ranking metric, not a hit rate, at the 0.1% base rate, most flagged cells will not burn in a given month. The model identifies where large fires (>1,000 acres) concentrate, not individual ignitions. 2026 is the live public validation period.

Top PredictorsSHAP importance · v1.0

1. Seasonality

6. Fuel moisture (current)

2. Monthly precipitation

7. VPD × slope interaction

3. Elevation

8. Peak VPD

4. Lightning fire history

9. VPD — 2-month lag

5. Fuel moisture — 3-month lag

10. Slope

15. Snow disappearance date (new to v1.0, after Westerling et al. 2006)

Data Sourcesv1.0 · All public

Dataset	Source	Coverage
Daily climate (VPD, fuel moisture, temp, precip, wind, BI)	gridMET (U of Idaho)	2000–2025, 4km, 32–49°N
Wind direction (east wind frequency)	gridMET, 4km daily	2000–2025, % summer easterly
Snowpack (daily SWE)	NRCS SNOTEL + SNODAS	1,830 stations, 11 states, 1981–2026
Snow disappearance date	Computed from SNOTEL	2000–2025 (novel feature)
Soil moisture (8-inch)	SNOTEL SMS sensors	55 stations, summer 2000–2025
Fire perimeters	NIFC WFIGS + MTBS	6,853 perimeters, 11 states, 1992–2024
Burn severity	USGS MTBS	7,949 fires >1,000 ac, 1984–2024
Fire cause	FPA-FOD	312,000 fires, lightning vs human
Canadian fires	NFDB	155,198 BC records + 1,878 polygons
Fuel type + canopy	LANDFIRE	FBFM40, EVT, canopy cover, 30m
Topography	GMTED2010 DEM	Elevation, slope, aspect, 250m
Climate indices	NOAA CPC	ENSO Niño 3.4, PDO, ENSO velocity
Reservoir storage	USBR Hydromet, USACE, CDEC	39 reservoirs, 11 states, real-time
Air quality (PM2.5)	EPA AQS + PurpleAir	700 monitors + 12,000+ sensors
Summer temperature	NOAA NCEI	131 years (1895–2025), 11 states
Smoke feedback	EPA AQS annual PM2.5	Prior-year smoke as predictor (novel)

Model Historyv0.1 → v1.0

Version	AUC	Key change
v0.1–0.3	0.67–0.72	PNW annual grid, added topography + LANDFIRE + fire cause
v0.4	0.841	Monthly resolution (biggest single improvement)
v0.5–0.6	0.90–0.91	Full western corridor, lag features, interaction terms, ensemble
v0.7	0.913*	SNODAS gridded SWE. *Leaked metric — test set used during tuning.
v0.8	0.901	Proper 3-way split, ENSO/PDO, held-out test.
v0.9	0.909	11-state SNOTEL, SWE anomaly, VPD×SWE interaction.
v1.0	0.911	Snow disappearance, east wind, prior-year precip, reservoir anomaly, ENSO velocity, soil moisture, smoke feedback. 50 features. First public production release.

Roadmap

Planned · Not yet in production

Target	Component	Gap it closes
v1.1	Live forecast + fire tracking	Daily 4km risk grids for 2026 fire season. VIIRS satellite hotspot detection (375m) for near-real-time active fire tracking.
v1.2	Fuel + vegetation	LANDFIRE fuel loading maps and NDVI-derived live fuel moisture. Closes the biggest input gap in the current model.
v1.3	Wind + smoke dispersion	Sub-daily HRRR winds to capture Diablo, Santa Ana, and east wind events. Atmospheric mixing height for real smoke transport modeling. Fuel-composition smoke toxicity.
v2.0	Data quality + coverage	Resolve SWE measurement discontinuity at 2010 (SNOTEL station interpolation vs. SNODAS gridded). Expand Canadian coverage with ECCC station data and CFS reanalysis.
v2.1	Consequence modeling	Fire behavior (rate of spread, flame length, spotting). WUI structure exposure mapping. Post-fire burn severity (dNBR) for damage assessment and model retraining.

Version targets are goals, not commitments. Order may shift based on data availability, contributor expertise, and what fire season reveals. If you can help close one of these gaps, get in touch.

Compared to Published Models

NFDRS/WFAS0.780

Station-level, no spatial prediction

SMLFire1.0 (Buch et al. 2023)0.800

25km global, monthly (GMD). Uses r not AUC-ROC,0.80 is approx.

Burn-P3 (Canada)0.750

Monte Carlo simulation

FireCast (UCLA)0.780

Daily, 375m, not public

Firewatch v1.00.911

4km monthly, 50 features, held-out test, snow disappearance, soil moisture, ENSO velocity, smoke feedback, public data only

Known Limitations

•Predicts large fires (>1,000 acres) only — not ignitions or small fires.

•Wind at 4km daily average — extreme events (2020 Labor Day) underrepresented.

•SWE methodological discontinuity at 2010 (SNOTEL IDW → SNODAS gridded).

•BC predictions use nearest-grid climate interpolation.

•Smoke forecast is distance-based, not fuel-composition-aware. A proper smoke model requires mapped fuel condition, plant stress, live fuel moisture (NDVI), and sub-4km atmospheric mixing height — all on the roadmap.

•This is a research platform, not an operational tactical tool for evacuation or life-safety decisions.

Contact & Support

This platform is free, independent, and updated daily. No paywalls, no ads, no sponsors to answer to. Just the data. If it’s useful to you, here’s how to get involved.

Fund the work

Your support covers hosting, data access, and the time it takes to maintain a platform that anyone can use. A coffee goes a long way.

Support PCSN

Send a tip or correction

Spotted an error? Have a data source we should know about? Media inquiry or partnership proposal? We read everything and fix mistakes the same day.

Send a message

Contribute expertise

We’re building a network of named expert contributors, scientists, modelers, and domain specialists who maintain specific components of the platform.

Get in touch

Data corrections If you spot an error in our data, sourcing, or analysis, email us. We fix mistakes the same day and credit the correction.

Media & press Journalists, researchers, and producers are welcome to use our data and visualizations with attribution to PCSN / pnw-firemap.org.

Partnerships Emergency managers, state agencies, water utilities, and nonprofits working on fire and drought resilience in the western corridor.

Season Severity Index (SSI)

The headline label on the home page (LOW / MODERATE / HIGH / VERY HIGH / EXTREME) and thecomposite_score in public/threat_level.json are produced by a documented weighted formula in scripts/build_threat_level.py, run daily by the public GitHub Actions workflow. No hand-editing.

Component	Weight	Input	Scoring rule
Snowpack collapse	0.20	11-state mean SWE % of normal	100 − 0.7 × (% of normal), clamped
Early-season fires	0.20	WFIGS YTD acres	Percentile rank vs historical full-year acres
Drought / precip	0.15	Water-year precip % of 1991–2020 normal	Same as snowpack rule
VPD anomaly	0.15	Annual VPD time series	30 + 25 × z-score vs early baseline
ENSO state	0.10	NOAA Niño 3.4 annual anomaly	20 + 40 × \|anomaly\|, clamped
Capacity loss	0.10	Qualitative (USFS workforce, budget)	Static config, reviewed monthly
Air quality	0.05	% PurpleAir sensors > AQI 100	10 + 4.5 × pct, clamped
Insurance stress	0.05	Qualitative (FAIR plan, non-renewals)	Static config, reviewed monthly

Composite = Σ (component score × weight). Weights sum to 1.00. Level mapping:0–20 LOW, 21–40 MODERATE, 41–60 HIGH, 61–80 VERY HIGH, 81–100 EXTREME.

Computed components draw from public/*.json generated by the same daily workflow. Qualitative components live in scripts/threat_components_static.json; both files are in the public repo, history is auditable. Past hand-edited values for these have been retired.

Editorial standardsCorrections welcome

PCSN is a single-contributor citizen science project. The site is researched, written, and maintained by one editor of record; errors of fact or interpretation are the editor’s. The site does not run an editorial board and does not claim institutional review.

Sourcing. Every numeric claim links to or names a primary federal, provincial, or peer-reviewed source: NOAA, NRCS, USGS, USFS, NIFC, MTBS, NSIDC, NRCan, FEMA, EPA, BLS, NDMC. Where a figure depends on the Firewatch model output, the model card and training period are linked. Hand-edited values are flagged in site_stats.json with a _last_reviewed date and a source citation.

Language. AUC-ROC is reported as AUC-ROC, not “accuracy.” The model predicts where fire is most likely, not where fire will occur. “Live” only describes data fetched at view time; static snapshots are labeled with their date. The Smoke layer on the dashboard map is a retrospective historical fit, not a forecast; for live smoke and AQI use fire.airnow.gov or NOAA HRRR-Smoke.

Life safety. This site is not an alert system and is not an operational tool for evacuation or fire-line decisions. Life-safety content (/emergency, /prepare, /lite) cites Ready.gov, EPA, CDC, NWS, and state emergency-management agencies and links directly to official sources. When the site’s data pipeline is stale or down, those pages still work.

Reproducibility. Every published artifact is generated by a script committed to the public repository. The daily data pipeline runs on GitHub Actions on a public schedule (13:00 UTC), and every commit by the pipeline bot is auditable in git history. Model code, training data specifications, and feature engineering live in a companion repository.

Corrections. If you find a factual error, broken citation, or methodological concern, email corrections@pnw-firemap.org with the URL and a description. The site keeps an updates timeline on the homepage; substantive corrections are dated and noted there.

Independence. PCSN is independent, not affiliated with any federal, state, tribal, provincial, or commercial agency. It accepts no advertising, no sponsored placement, and no undisclosed compensation. All data sources are public and itemized in the model card and the per-page source notes.

All data from publicly available federal, state, and provincial sources. Model trained on 2000–2019, validated on held-out test years 2020–2024 never seen during training.