WildfireRiskFinder

Harrold, SD

Harrold wildfire risk explained

Elevated
59thpercentile nationally

USFS's Wildfire Risk to Communities model puts Harrold at the 59th national percentile for risk to structures, modestly above the national average for wildfire risk — a score built from 193 actual buildings. (Source: USFS's Wildfire Risk to Communities methodology.)

USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Harrold at the 51st percentile, close to its 59th-percentile risk score.

What "at risk" means for the buildings here

193Total buildings
10.9%Direct exposure
0%Indirect exposure
89.1%Minimal exposure

Most of Harrold's buildings (89.1% of 193) fall outside USFS's Direct and Indirect zones entirely — that doesn't zero out the score above, it means the risk is regional, not structure-by-structure.

Harrold against the rest of the country

Harrold scores 59th nationally and 44th within South Dakota — close enough that its state context doesn't change the picture the national number already gives. Among the 31,521 US communities USFS scores, Harrold ranks 13,063 for wildfire risk (1 is highest) and 25,136 by building count (1 is largest). Within South Dakota alone, it ranks 247 of 439 places by risk. See the full county-by-county picture for South Dakota on its state page.

Shopping for coverage in Harrold

At the 59th national percentile, Harrold rates elevated for wildfire risk — well short of the threshold where insurers have been withdrawing coverage, though comparing a quote costs nothing either way.

Lowering exposure, not just insuring around it

Harrold's 89.1% Minimal-exposure share means structure-level hardening matters less here than it would elsewhere — still worth the low-cost basics, per the home-hardening guide.

Where Harrold's figures come from

Every one of the two percentiles behind Harrold's 13,063-place national rank traces to USFS's Wildfire Risk to Communities dataset — see the methodology guide. The exposure-zones guide covers what Harrold's dominant minimal exposure actually means, with real examples from across the dataset.