Understanding Urban and Wildland Fire Dynamics
Jeffrey Mirocha | 20-ERD-034
Wildfire poses a significant and rapidly expanding threat to life and property that will only get worse in the coming years and decades. We must simultaneously learn to coexist with wildfires while developing appropriate pathways to ameliorate their impacts. However, we currently lack adequate computational approaches of the necessary scale and fidelity to understand their behavior in relation to complex surface and atmospheric drivers or accurately predict their propagation and impacts at landscape scales. To address this gap, the present study examined the efficacy of a state-of-the-art coupled atmosphere-fire simulation framework to simulate and forecast wildfire spread and emissions at various spatial scales. We also examined the supply chain of information required to initialize and force such simulations.
Our research identified the strengths and weaknesses of current best practices, while also demonstrating several pathways to improve the state of the art, including i) improved characterization of surface fuels in both urban and natural landscapes, which we approached using machine learning, ii) improved fire combustion and emission coefficients for different fuels, which we approached using chemical combustion simulations, iii) improved fire rate of spread, which we approached using satellite-based fire-detection algorithms, iv) improved emissions transport and fate, which we approached via coupling of wildfire emissions with an atmospheric-chemistry solver, and v) improved pyrocumulus representation, which we approached using modified fuel categories based on observed vegetation conditions. The project successfully established a strong basis of fundamental understanding of wildfire-simulation requirements and capabilities in complex settings, while demonstrating several pathways that promise significant future improvements.
This project supported the examination and development of computational tools and workflows to better understand and predict a growing, high-visibility risk to lives, property, and critical infrastructure: wildfire. Information on wildfire risk and mitigation is critical to ensuring the security and resilience of the nation and its critical infrastructure from fires and their impacts. The work provides capability to multiple programs at LLNL involving fundamental atmospheric and energy sciences, providing i) tools for assessment of health and climate impacts of smoke, both locally as well as long-range transport and global cooling when lofted into the stratosphere, ii) renewable energy generation, iii) energy, water, and other critical infrastructure investments, and iv) input to conduct risk assessment and examine mitigation strategies such as firebreaks, controlled burns, and forest thinning. The project also enhanced LLNL's visibility in the wildfire-science community and provided for the development of relationships with researchers at other institutions, as well as potential sponsors of future work.
Publications, Presentations, and Patents
Lassman, W., et al. 2023. "Using Satellite-Derived Fire Arrival Times for Coupled Wildfire-Air Quality Simulations at Regional Scales of the 2020 California Wildfire Season." Journal of Geophysical Research: Atmospheres, (Accepted) 2023; doi: 10.1029/2022JD037062.
Saggese, C., et al. "Numerical Study of the Effects of Particle Size and Moisture on Biomass Pyrolysis." 18th International Conference on Numerical Combustion, San Diego, CA, May 2022.
Lassman, W., et al. "Connecting Wildfire Dynamics to Air Quality: A Case Study of the 2020 Northern California Wildfire Season." 2nd Annual Wildfire Induced Air Pollution Assessment & Mitigation Symposium, Virtual. March 2022.
Mirocha, J. "Improving the Fidelity of Coupled Atmosphere-Fire Simulations to Manage Our Fiery Future." Stanford University Wildland Fire Seminar Series 9, Virtual. February 2022.
Lassman, W., et al. "Connecting Fire Behavior to Air Quality: A Case Study of the 2020 Northern California Wildfire Season." Meteorology and Climate: Modelling for Air Quality Conference, Virtual.September 2021.
Tian, Y., et al. "Using Machine Learning for Fine-Scale Surface Fuel Conditions." 2021 Fire Weather Research Workshop, Virtual. April 2021.
Lassman, W. "How Does Smoke Impact Fire Dynamics and Rate of Spread, and How Do Fire Dynamics and Rate of Spread Impact Smoke? Integrated Simulations of Gas and Aerosol Chemistry Coupled with Atmospheric and Fire Dynamics." 101st Annual Meeting of the American Meteorological Society, Virtual. January 2021.