Fighting Wildfires: A Data-Informed, Physics-Based Computational Framework for Probabilistic Risk

In this project, our vision is to develop an overarching computational platform for wildfire risk management at different spatial, temporal, and uncertainty scales. The vision will be accomplished by creating and integrating transdisciplinary scientific knowledge and techniques in the fields of data (data harnessing: collection, processing, fusion, and uncertainty quantification), computational modeling (wildfire, urban-fire, and social quality-of-life models), stochastic simulation, and model-based inference. The objective is to develop scientific foundations for a live digital platform that evolves with data as they become available, to dynamically update the pre-ignition wildfire risk from long-term (seasons-month ahead) to short-term (weeks-days ahead) at regional and community scales, and the post-ignition wildfire behavior at near-real-time (hours-days) for situational awareness.