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.
Projects Updates/The Results
A beta version of the near-term fire risk forecast tool became available in May 2020 (pyregence.org). The tool displays forecasts for active fires and fire risk at a
five-day horizon. An API allows for flexible integration with an
organization's unique and existing workflows to assist in tactical fire
and ignition risk decision-making.
The research team also developed beta versions of experimental fire size distribution and fractional fire severity class models covering all of California. They utilized a sample of the beta version fractional fire severity models with observed historical fire sizes to begin testing and validating a bootstrapping procedure for expressing spatial autocorrelation in the clustering of high severity burn patches within fire perimeters. The second technical advisory meeting was held in May 2020.
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-seven-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.
The project seeks to improve IOU planning and decision-making related to wildfire risk, improving grid reliability and safety and lowering costs.
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. Examples of mitigation activities include fire-hardening (pole pretreatments, equipment replacements or upgrades) and measures to minimize de-energization impacts (such as investments in distributed energy resources).
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.