Carbonado, WA
Carbonado, WA's wildfire risk, in USFS's own numbers
USFS scores Carbonado at the 56th national percentile for wildfire risk to structures (modestly above the national average for wildfire risk), a figure built from 397 real buildings rather than raw vegetation cover. (Source: USFS's Wildfire Risk to Communities methodology.)
Fire likelihood alone (USFS's burn-probability figure) ranks Carbonado at the 56th national percentile — 1 points below its risk-to-structures score, a gap driven by how much is actually built there.
Carbonado's building exposure, zone by zone
USFS puts 79.1% of Carbonado's 397 buildings in the Indirect exposure zone, versus 20.9% Direct and 0% Minimal — a place where the fire doesn't need to reach the structure directly for embers to.
Carbonado against the rest of the country
Carbonado's risk sits at a similar level relative to Washington (65th percentile statewide) as it does nationally (56th) — this place isn't unusual for its own state either way. Among the 31,521 US communities USFS scores, Carbonado ranks 13,727 for wildfire risk (1 is highest) and 19,287 by building count (1 is largest). Within Washington alone, it ranks 220 of 628 places by risk. See the full county-by-county picture for Washington on its state page.
Shopping for coverage in Carbonado
Carbonado's elevated rating (56th percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
Hardening a home in Carbonado
Carbonado's 79.1% Indirect-exposure share points at embers, not flame contact, as the main pathway — ember-resistant vents and non-combustible roofing rank ahead of defensible space here. Detail in the home-hardening guide.
Where Carbonado's figures come from
Carbonado's 56th-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 Carbonado's dominant indirect exposure actually means, with real examples from across the dataset.