Pile burning of thinned residues is a critical tool to dispose of fuels and to reduce wildfire risk in overstocked, fire-prone forests globally. However, cost estimates of pile burning are limited.
In the fire-prone and fire-adapted landscape of the Rogue River Basin of southwestern Oregon, communities mobilize to prepare, respond, and recover from wildfire while modifying the current social and ecological system.
Wildfires are having disproportionate impacts on U.S. households. Notably, in California, over half of wildfire-destroyed homes (54%) are in low-income areas. We investigate the relationship between social vulnerability and wildfire community preparedness using building permits from 16 counties in California with 2.9 million buildings (2013–2021) and the U.S.
Managed burning of forests can provide benefits to society including mitigated wildfire risk, improved habitat, and more. However, adverse outcomes of escaped fire or smoke pose risks. I reviewed the evolution of the law regulating forest management burns, explored the current legal architecture, and analyzed the economic incentives for involved actors, in order to identify policy options.
Wildfires typically have devastating impacts on communities, both in urban and rural areas, resulting in property loss, psychological distress, physical injuries, and loss of life. A notable gap in the literature is the spatial and temporal disproportionate impact of wildfires on underrepresented communities.
The most destructive and deadly wildfires in US history were also fast. Using satellite data, we analyzed the daily growth rates of more than 60,000 fires from 2001 to 2020 across the contiguous US. Nearly half of the ecoregions experienced destructive fast fires that grew more than 1620 hectares in 1 day.
Major power outages have risen over the last two decades, largely due to more extreme weather conditions. However, there is a lack of knowledge on the distribution of power outages and its relationship to social vulnerability and co-occurring hazards.
Projected increases in wildfire frequency, size, and severity may further stress already scarce firefighting resources in the western United States that are in high demand. Machine learning is a promising field with the ability to model firefighting resource usage without compromising dataset size or complexity.
Quantitative wildfire risk assessments increasingly are used to prioritize areas for investments in wildfire risk mitigation actions. However, current assessments of wildfire risk derived from fire models built primarily on biophysical data do not account for socioeconomic contexts that influence community vulnerability to wildfire.