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.
Background
As fire seasons in the Western US intensify and lengthen, fire managers have been grappling with increases in simultaneous, significant incidents that compete for response resources and strain capacity of the current system.
Aims
Arid forests in the American West contend with overly dense stands and a need to reduce fuels and restore more natural fire regimes. Forest restoration efforts include fuel treatments, such as thinning and prescribed burning, that can reduce ground and ladder fuels.
Across the Western United States, human development into the wildland urban interface (WUI) is contributing to increasing wildfire damage. Given that natural disasters often cause greater harm within socio-economically vulnerable groups, research is needed to explore the potential for disproportionate impacts associated with wildfire.
Public agencies and organizations often deliver financial assistance through cost sharing, in which recipients contribute some portion toward total costs. However, cost sharing might raise equity concerns if it reduces participation among populations with lower incomes.