Department of Civil and Environmental Engineering
University of Nevada, Reno (UNR)
LEAP-HI: Fighting Wildfires: A Data-Informed, Physics-Based Computational Framework for Probabilistic Risk Assessment and Mitigation and Emergency Response Manageme
Destruction caused by wildfires in the U.S. has significantly increased in the past two decades. An average of 72,400 wildfires burned an average of 7 million acres of U.S. land each year since 2000, more than double the average number of acres scorched by wildfires from 1980 to 1999. The federal government spent more than $3 billion of taxpayers’ money putting out fires in 2018. The field of wildfire science has now matured to a point that technological and computational tools, as well as different modalities of thinking and modeling from a variety of disciplines can be now beneficially employed to enable a rigorous assessment of wildfire risks. These risks can be characterized at unprecedented granularity—providing probable event magnitudes as well as potential economic and social losses—to make informed decisions on a quantitative stochastic basis.
We propose the creation of an overarching computational platform for wildfire risk management at multiple spatial and temporal scales, each of which bears different levels of modeling and input data uncertainties. This vision will be accomplished by creating and integrating transdisciplinary scientific knowledge and techniques in the fields of data harnessing (viz., collection, processing, fusion, and uncertainty quantification), computational modeling (viz., wildfire, urban-fire, and social quality-of-life models), stochastic simulation, and model-based inference. The objective is to develop scientific foundations for an eventual 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 the post-ignition fire behavior at near-real-time.
Once developed, the computational platform will provide actionable information to decision-makers at different spatial and temporal scales for pre-ignition wildfire risk mitigation and post-ignition emergency response.
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