modeling
Human driven climate change increased the likelihood of the 2023 record area burned in Canada
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.
An optimization model to prioritize fuel treatments within a landscape fuel break network
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.
Global rise in forest fire emissions linked to climate change in the extratropics
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.
Tribal stewardship for resilient forest socio-ecosystems
The Yurok Tribe, along with other tribal communities in northwest California, non-profit organizations, universities, and governmental agencies are working to restore forests and woodlands to be more resilient to wildfires, drought, pests and diseases.
Probabilistic Forecasting of Lightning Strikes over the Continental USA and Alaska: Model Development and Verification
Lightning is responsible for the most area annually burned by wildfires in the extratropical region of the Northern Hemisphere. Hence, predicting the occurrence of wildfires requires reliable forecasting of the chance of cloud-to-ground lightning strikes during storms.
Predicting daily firefighting personnel deployment trends in the western United States
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.
A Wildfire Progression Simulation and Risk-Rating Methodology for Power Grid Infrastructure
As the frequency and intensity of power line-induced wildfires increase due to climate-, human- , and infrastructure-related risk drivers, maintaining power system resilience and reducing environmental impacts become increasingly crucial.
Visibility-informed mapping of potential firefighter lookout locations using maximum entropy modelling
Background
Situational awareness is an essential component of wildland firefighter safety. In the US, crew lookouts provide situational awareness by proxy from ground-level locations with visibility of both fire and crew members.
Aims
A model for rapid PM2.5 exposure estimates in wildfire conditions using routinely available data: rapidfire v0.1.3
Urban smoke exposure events from large wildfires have become increasingly common in California and throughout the western United States. The ability to study the impacts of high smoke aerosol exposures from these events on the public is limited by the availability of high-quality, spatially resolved estimates of aerosol concentrations.
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