Wildfires are growing in destructive power, and accurately predicting the spread and intensity of wildland fire is essential for managing ecological and societal impacts.
Background: In response to increasing risk of extreme wildfire across western North America, forest managers are proactively implementing fuel treatments.
Background: Daily fire progression information is crucial for public health studies that examine the relationship between population-level smoke exposures and subsequent health events. Issues with remote sensing used in fire emissions inventories (FEI) lead to the possibility of missed exposures that impact the results of acute health effects studies.
In recent decades, bark beetle outbreaks have caused mass tree mortality in western US forests, which has led to altered wildfire characteristics, hydrological processes, and forest carbon dynamics.
Background: Wildfire simulation models are used to derive maps of burn probability (BP) based on fuels, weather, topography and ignition locations, and BP maps are key components of wildfire risk assessments.
In 2023, wildfires burned 15 million hectares in Canada, more than doubling the previous record. These wildfires caused a record number of evacuations, unprecedented air quality impacts across Canada and the northeastern United States, and substantial strain on fire management resources.
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.
Climate change increases fire-favorable weather in forests, but fire trends are also affected by multiple other controlling factors that are difficult to untangle. We use machine learning to systematically group forest ecoregions into 12 global forest pyromes, with each showing distinct sensitivities to climatic, human, and vegetation controls.