Fuels and Fuel Treatments

Background 

Background

Background

Altered fire regimes are a global challenge, increasingly exacerbated by climate change, which modifies fire weather and prolongs fire seasons. These changing conditions heighten the vulnerability of ecosystems and human populations to the impacts of wildfires on the environment, society, and the economy.

Wildfire structure losses are increasing globally and particularly in California, USA. Losses can be mitigated in part by changes to the Home Ignition Zone (HIZ), including both home hardening and defensible space. In the United States, there are local, nation-wide, and industry-based home mitigation standards that are enforced or recommended.

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.

Background: In response to increasing risk of extreme wildfire across western North America, forest managers are proactively implementing fuel treatments.

In many parts of the western United States, wildfires are becoming larger and more severe, threatening the persistence of forest ecosystems. Understanding the ways in which management activities such as prescribed fire and managed wildfire can mitigate fire severity is essential for developing effective forest conservation strategies.

Drivers of forest wildfire severity include fuels, topography and weather. However, because only fuels can be actively managed, quantifying their effects on severity has become an urgent research priority. Here we employed GEDI spaceborne lidar to consistently assess how pre-fire forest fuel structure affected wildfire severity across 42 California wildfires between 2019–2021.

We present a mixed integer programming model for prioritizing fuel treatments within a landscape fuel break network to maximize protection against wildfires, measured by the total fire size reduction or the sum of Wildland Urban Interface areas avoided from burning. This model uses a large dataset of simulated wildfires in a large landscape to inform fuel break treatment decisions.