Destruction caused by wildfires in the US has significantly increased in the past two decades. While the federal government?s spending on wildfire fighting has been steadily increasing, wildfire severity has also been on the rise. The focus of this Leading Engineering for America's Prosperity, Health, and Infrastructure (LEAP-HI) project is the creation an overarching computational platform for wildfire risk management at multiple space and time scales. This vision will be accomplished by creating and integrating transdisciplinary scientific techniques in the fields of data analytics, computational modeling, and model-based inference. The objective is to develop scientific foundations for a live digital platform that evolves with new data and dynamically updates the long-term (seasons/months ahead) to short-term (weeks/days ahead) pre-ignition fire risks at regional and community scales, and predicts the post-ignition fire behavior in near-real-time at the fire front. Once developed, the computational platform will increase the efficiency of wildfire management process by providing actionable information to decision-makers for pre-ignition risk mitigation and post-ignition emergency response management. Involvement of key stakeholders and utility companies, preparation of future workforce, and K-12 outreach programs are integral parts of the project.
The research promises to provide a rigorous computational approach to quantifying and predicting wildfire risk. Scientific advancements that are anticipated include: (i) an overarching computational platform for probabilistic wildfire loss assessment at different spatial and temporal scales that evolves with data as they become available; (ii) an integrated simulation framework including a wildfire model, urban-fire model, and socioeconomic model to predict the wildfire loss in terms of economic and social losses; (iii) a novel data-driven modeling approach for urban-fire simulation, and a new empirical model for change in quality-of-life (QoL) due to wildfire; (iv) new data collection modules and advanced data processing techniques to collect refined data, process and infuse different sources of data, and quantify uncertainty in the measurement data; and, (v) a Bayesian model inference framework to quantify modeling uncertainties and update the fire spread in ear-real-time by integrating new measurement data modules with the wildfire model. In perspective, the project aims to lay the scientific foundations of a holistic new computational platform to predict and monitor wildfire risk. The resulting technology has the potential to positively influence the wildfire management process, including the development of accurate actuarial strategies.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.