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

CA

Recipient Location

7th

Senate District

16th

Assembly District

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$3,797,999

Amount Spent

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Active

Project Status

Project Update

The project has completed most of the innovative wildfire science tasks. These include a model to optimize the location of new weather stations to maximize coverage of the variability of fire weather and an analysis of the role of extreme weather on fire behavior. The researchers have also advanced the characterization and mapping of forest fuels. A new burn laboratory is being commissioned to investigate the burning behavior of the large logs that are resulting from the mass timber die-off during the recent drought. The near-term fire risk forecast tool became available in May 2020 (pyregence.org) and continues to be tested and refined with user input. The tool displays forecasts for active fires and fire risk at a five-day horizon and simulates millions of hypothetical fires daily at a seven-day horizon to provide situational awareness for utilities and first responders. A long-term wildfire projection modeling system has been developed to inform utility planning. This model uses climate projection data as a major driver of future fire. The climate data have just recently become available, so the team will begin running wildfire projections in support of California's Fifth Climate Change Assessment.

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 empirical and not well suited for predicting extreme fire behavior. Therefore, key stakeholders responsible for managing the grid -- including IOUs and state agencies -- lack tools and information that could improve near-term management and long-term planning decisions.

Project Innovation

The project is advancing wildfire science by incorporating the interaction of tree mortality and extreme fire weather into next-generation fire models. The project is developing zero-to-five-day risk forecasts for the grid with predictive capabilities, and computational efficiency and scalability. To support planning, the team is developing long-term fire projections using a coupled fire-climate-vegetation statistical and dynamical model to integrate the latest climate projections, tree mortality, development in the wildland-urban interface, and adaptation strategies. To integrate the models into electric utility management and planning, the team is facilitating workshops with IOUs. To support the California's Fifth Climate Change Assessment, the team is developing a web-based scenario analysis tool to visualize and explore the impacts of climate change and adaptation strategies on the grid.

Project Goals

Develop methodology to optimize weather station configuration.
Improve understanding of the relationship of extreme weather conditions and wildfire.
Improve understanding of fuels, fire behavior and how it is distributed across California.
Develop near-term fire risk and spread forecasts models at a 0-to-5-day temporal scale and at a fine spatial scale.
Develop coupled statistical/dynamical fire-climate-vegetation models to run long-term wildfire risk projections.

Project Benefits

The project will aid regulators and stakeholders in meeting statutory goals by addressing critical fire science gaps and applying the science to provide advanced forecasting capability. Specifically, the project will: 1) advance the science of measuring, modeling, and analyzing extreme weather events, tree mortality, and fire spread at scale; 2) advance risk modeling frameworks to include wind extrema, statewide maps of fuel loads, updated parameterizations, and indicators of where risk forecasting may underestimate fire risk due to gaps in science; and 3) advance the integration of science relating to vegetation dynamics, the wildland-urban interface, land-use, climate, and adaptation strategies, by building on existing models and comparing approaches.

Lower Costs

Affordability

The project seeks to improve IOU planning and decision-making related to wildfire risk, improving grid reliability and safety and lowering costs.

Greater Reliability

Reliability

With the use of more granular, dynamic fire-spread models, mitigation activities can be more targeted, and damages associated with fire and outages can be reduced.

Increase Safety

Safety

Safety will be improved as IOUs can better plan for maintenance cycles to avoid areas of elevated fire risk, reducing the risk of injury and loss of life.

Key Project Members

Romsos_Pic

Shane Romsos

Project Manager
Spatial Informatics Group
David Saah

David Saah

Principal Investigator
Spatial Informatics Group

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