Pawnee, OK
Pawnee wildfire risk explained
Pawnee sits at the 94th percentile nationally for wildfire risk to structures — in USFS's highest wildfire-risk band nationally — per USFS's Wildfire Risk to Communities model, built from its 1,474 counted buildings, not vegetation cover alone. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Pawnee at the 96th national percentile — 2 points above its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
Most of Pawnee's buildings (68.9%) sit in the Indirect zone, where wind-blown embers rather than flame front are the mechanism USFS is scoring — ember-resistant vents and non-combustible roofing are the interventions this pattern favors, more than defensible space alone.
Pawnee against the rest of the country
Pawnee ranks lower within Oklahoma (76th percentile statewide) than its 94th national percentile suggests alone — a calmer spot in a state where wildfire risk generally runs high. Among the 31,521 US communities USFS scores, Pawnee ranks 1,769 for wildfire risk (1 is highest) and 9,291 by building count (1 is largest). Within Oklahoma alone, it ranks 210 of 834 places by risk. See the full county-by-county picture for Oklahoma on its state page.
Pawnee and the insurance market
Pawnee's very high rating (94th percentile) puts it among the places where the national carrier-pullback trend is most relevant — not a guarantee of a coverage problem, but a reason to shop rather than assume.
Hardening a home in Pawnee
With ember exposure the dominant pattern in Pawnee (68.9% Indirect), vent screens and roofing material tend to matter more than lot clearing alone. The home-hardening guide covers both.
Where Pawnee's figures come from
The methodology guide shows exactly how USFS turned 1,474 counted buildings into the percentiles shown above for Pawnee. The exposure-zones guide covers what Pawnee's dominant indirect exposure actually means, with real examples from across the dataset.