Comprehensive Open Source Development of Next Generation Wildfire Models for Grid Resiliency
The development of next-generation wildfire risk forecasting models to inform effective near-term management and long-term planning decisions.
Spatial Informatics Group, LLC
Recipient
Pleasanton, CA
Recipient Location
7th
Senate District
16th
Assembly District
$3,941,577
Amount Spent
Active
Project Status
Project Update
All technical tasks from the project have been completed, and the Final Report is being completed. This project investigated drivers of catastrophic wildfires and their relevance to wildfire modeling and demonstrated and/or developed advanced wildfire models and tools to enhance the resilience of California’s electric grid. The project was organized into four technical workgroups that advanced fire science and models across different spatial and temporal scales. These included: (1) optimizing weather monitoring systems and understanding extreme fire weather patterns; (2) improving representation of fuels and tree mortality in fire behavior models; (3) developing the PyreCast platform (https://pyrecast.org/) for near-term fire forecasting; and (4) building integrated, long-term risk projection models that coupled climate, vegetation, and fire dynamics. In each area, the project emphasized open-source development, data, and products, stakeholder engagement, and scientific rigor to ensure tools were both credible and usable in operational contexts.
The Issue
Many aspects of wildfires in California have changed in the past several decades, including climate patterns and the development of human infrastructure near wildlands. The impacts of wildfire on the electric grid have resulted in increased costs and reduced safety and reliability. Understanding the risks associated with wildfire remains challenging. Operational wildfire behavior models are not readily available to users nor well suited for predicting extreme fire behavior. Therefore, key stakeholders responsible for managing the grid, including investor-owned utilities (IOU) and state agencies, lack tools and information that could improve near-term situational awareness and longer-term management and planning decisions.
Project Innovation
The project produced innovations that advance wildfire science and risk forecasting for electric utilities and state agencies. A major innovation was the development of a multi-scale, multi-model framework that integrates dynamic vegetation modeling with statistical and physical fire behavior models to forecast long-term wildfire risk. The team also created and operationalized PyreCast, an open-source platform for near-term wildfire spread and risk forecasting, which delivers probabilistic risk information with fine spatial and temporal resolution. In parallel, the project advanced the understanding of fire behavior in extreme wind and fuel conditions using simulations with an advanced coupled atmosphere-wildfire model (CAWFE), developed a model to identify the optimal placement of weather monitoring instruments, and deployed an upper-air wind profiler to evaluate its use for real-time fire weather monitoring. Novel field measurements and laboratory experiments were also conducted to study smoldering combustion and the role of tree mortality in altering fuelbed structure and fire persistence.
Project Goals
Project Benefits
The project delivers a range of benefits that enhance utility wildfire mitigation, public safety, and cost-effectiveness. Open-source tools like PyreCast increase transparency and public trust by providing accessible, real-time wildfire risk information to consumers and agencies. Improved forecasting allows utilities to better target suppression efforts and Public Safety Power Shutoff (PSPS) events, reducing operational costs and avoiding unnecessary outages. Long-term fire risk projections help prioritize mitigation actions, reducing future damage and expenditures. Enhanced models improve situational awareness and planning, increasing grid reliability and supporting safer operations. Finally, the project strengthens grid security by informing proactive infrastructure hardening and reducing exposure to wildfire threats.
Affordability
The project can improve IOU planning and decision-making related to wildfire risk, which could lower costs by reducing damages associated with fire and more targeted mitigation efforts.
Reliability
With the use of high resolution, dynamic fire-spread models, mitigation activities can be more targeted, and damages associated with fire and PSPS outages can be reduced.
Safety
With improved information on extreme weather and fire behavior and on long-term shifts in wildfire risks, utilities, residents, and wildfire responders can develop more effective safety measures, both in real-time and in longer-term investment.
Key Project Members
Shane Romsos
David Saah
Subrecipients
The Regents of the University of California, on behalf of the Berkeley Campus
Sonoma Technology, Inc.
The Brattle Group
The Regents of the University of California, Merced
Eagle Rock Analytics, Inc.
Reax Engineering Inc.
University Corporation for Atmospheric Research
Salo Sciences, Inc.
University of San Francisco
Missoula Fire Sciences Laboratory
Prometheus Fire Consulting
Deer Creek Resources, Inc.
Clere, Inc.
Pyrologix, LLC
Vibrant Planet
USGS- Geosciences and Environmental Change Science Center
University of New Mexico
Drew Consulting, Inc.
Match Partners
The Regents of the University of California, Merced
Technical Support Unknown
Eagle Rock Analytics, Inc.
US Geological Society (USGS)
Spatial Informatics Group, LLC
Reax Engineering Inc.
University Corporation for Atmospheric Research
Salo Sciences, Inc.
Missoula Fire Sciences Laboratory
Pyrologix, LLC
USGS- Geosciences and Environmental Change Science Center
Lumen Energy Strategy, LLC
