Wanblee, SD
Wanblee, SD's wildfire risk, in USFS's own numbers
USFS's Wildfire Risk to Communities model puts Wanblee at the 81st national percentile for risk to structures, in USFS's highest wildfire-risk band nationally — a score built from 225 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Wanblee at the 79th national percentile — 2 points below its risk-to-structures score, a gap driven by how much is actually built there.
What "at risk" means for the buildings here
USFS puts 64.9% of Wanblee's 225 buildings in the Indirect exposure zone, versus 35.1% Direct and 0% Minimal — a place where the fire doesn't need to reach the structure directly for embers to.
How Wanblee compares
There's little gap between Wanblee's 81st national percentile and its 80th percentile inside South Dakota, which means the state comparison mostly confirms what the national score already shows. Among the 31,521 US communities USFS scores, Wanblee ranks 6,123 for wildfire risk (1 is highest) and 23,963 by building count (1 is largest). Within South Dakota alone, it ranks 87 of 439 places by risk. See the full county-by-county picture for South Dakota on its state page.
What this risk score means for insurance
Wanblee's very high rating (81st 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.
Lowering exposure, not just insuring around it
Because 64.9% of Wanblee's buildings sit in the Indirect zone, sealing the ember pathway (vents, roofing, gutters) is the intervention this page's data actually supports — see the home-hardening guide.
Where Wanblee's figures come from
Wanblee's 81st-percentile score and its burn-probability figure both come from the same USFS workbook, documented in the methodology guide. The exposure-zones guide covers what Wanblee's dominant indirect exposure actually means, with real examples from across the dataset.