Melwood, MD
How exposed is Melwood to wildfire?
Out of every US place USFS scores, Melwood lands at the 32nd percentile for wildfire risk to structures — close to the middle of USFS's national wildfire-risk range — a figure built from its 1,629 buildings, not the land around them. (Source: USFS's Wildfire Risk to Communities methodology.)
USFS's separate burn-probability score — fire likelihood alone, before counting what's built there — puts Melwood at the 32nd percentile, close to its 32nd-percentile risk score.
What "at risk" means for the buildings here
Most of Melwood's buildings (48.7%) 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.
Where Melwood ranks
Melwood's 51st-percentile standing inside Maryland outpaces its 32nd national percentile — this is a hotter spot than most of its own state, even though the state as a whole runs cooler nationally. Among the 31,521 US communities USFS scores, Melwood ranks 21,301 for wildfire risk (1 is highest) and 8,654 by building count (1 is largest). Within Maryland alone, it ranks 262 of 527 places by risk. See the full county-by-county picture for Maryland on its state page.
Melwood and the insurance market
Melwood's moderate rating (32nd percentile nationally) sits outside the range where wildfire risk alone reshapes an insurance market — still, a homeowner here loses nothing by comparing rates.
What would actually reduce this score
Melwood's 48.7% 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 Melwood's figures come from
The methodology guide shows exactly how USFS turned 1,629 counted buildings into the percentiles shown above for Melwood. The exposure-zones guide covers what Melwood's dominant indirect exposure actually means, with real examples from across the dataset.