Wildland Fire Weather-Behavior Support
Wildland Fire Weather-Behavior Support
Megafires have become the new normal for the American west. Attempting to manage them is like trying to manage a hurricane or tornado. These fires have devastated communities, especially those residing in the wildland-urban-interface (WUI), and risked the lives of first responders more profoundly than ever. Fire behavior is complex, shaped by interactions among heat output, humidity changes, wind, updrafts and downdrafts, fuel, and terrain, naming only a few. The intensity and aberrant behavior of the largest and most aggressive fires pose life-threatening challenges to fire-management efforts. Decision-makers need weather- and wildfire-prediction tools to develop more effective strategies to protect property and lives.
WRF-Fire: Wildland Fire Modeling
Basic weather prediction is not enough to effectively inform fire-management strategies. To fill this gap, RAL scientists and engineers extended the Weather Research and Forecasting (WRF) numerical weather prediction model to simulate how a wildfire behaves in response to weather, fuel conditions, and terrain. In turn, the wildfire’s effect on the local weather is also simulated. The model-generated fire and atmosphere continually co-evolve, predicting the fire’s extent and rate of spread, flame length, heat, and smoke, thereby alerting firefighting personnel and local agencies to respond accordingly. This coupled modeling platform is called WRF-Fire.
The LEAP-HI project convenes scientists and engineers to develop a new framework that offline couples WRF-Fire with an urban fire model, called Streamlined Wildland-Urban Interface Fire Tracing or SWUIFT, to predict wildfire spread and risk in the WUI. Our efforts in this collaborative project with multiple universities focus on enhancing fuel models through satellite-machine learning synergies, improving fire heat release in the wildland, accounting for ember spotting processes, and incorporating urban structure information. Ultimately, the coupled WRF-Fire-SWUIFT modeling approach allows for a more holistic understanding of fire behavior in the wildland-WUI-urban continuum.
We develop tools that predict the behavior of wildfires to help firefighters, local authorities, and resource managers direct their efforts more effectively. Tactically placing firefighting personnel, equipment, and promptly notifying the public saves property, lives, and livelihoods.
Contact
Please direct questions/comments about this page to:
Stefano Alessandrini
Project Scientist III