WildfireRiskFinder

Noonan, ND

Noonan wildfire risk explained

Elevated
56thpercentile nationally

Out of every US place USFS scores, Noonan lands at the 56th percentile for wildfire risk to structures — modestly above the national average for wildfire risk — a figure built from its 225 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)

Separately from the risk score above, Noonan's burn probability — fire likelihood with no building count factored in — sits at the 52nd percentile nationally.

What "at risk" means for the buildings here

225Total buildings
13.3%Direct exposure
86.7%Indirect exposure
0%Minimal exposure

225 buildings are counted in Noonan, and 86.7% of them are Indirect exposure — ember-driven risk rather than the 13.3% in Direct exposure or the 0% rated Minimal.

Noonan against the rest of the country

Noonan's risk sits at a similar level relative to North Dakota (64th 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, Noonan ranks 14,035 for wildfire risk (1 is highest) and 23,958 by building count (1 is largest). Within North Dakota alone, it ranks 146 of 402 places by risk. See the full county-by-county picture for North Dakota on its state page.

Shopping for coverage in Noonan

At the 56th national percentile, Noonan rates elevated for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.

Hardening a home in Noonan

Noonan's 86.7% 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 Noonan's figures come from

The methodology guide shows exactly how USFS turned 225 counted buildings into the percentiles shown above for Noonan. The exposure-zones guide covers what Noonan's dominant indirect exposure actually means, with real examples from across the dataset.