(a) Prediction of maximum incident heat flux due to direct flame contact to buildings during the Thomas Fire. (b) Prediction of maximum incident heat flux due to radiation to buildings during the Thomas Fire. The white line represents the observed fire perimeter. Credit: Dwi MJ Purnomo et al.
More than 6,000 wildfires have been recorded in California already this year, highlighting the need for better mitigation strategies to reduce their devastating impacts. Now, researchers have created a model that could shed light on how these fires spread through wildland-urban interface (WUI) communities, allowing us to better assess wildfire risk and take steps to build more resilient communities.
In a study recently published in the Proceedings of the Combustion Institute, a research team led by mechanical engineering professor Michael Gollner demonstrated how this new model simulates a wildfire spreading through a community.
By recreating two major historical wildfires, we were able to extract data describing fire behavior and gain insight into how embers, wildfire flames, and urban structures contribute to fire spread and destruction.
“This model goes beyond just recreating the fires, it tells us more about the processes that caused the fires to devastate these communities,” Gollner says. “We can also use the model to explore what mitigation strategies might work to protect these communities in the future.”
A comprehensive look at wildfire and urban landscape interactions
The researchers’ 2D model is the first to fully integrate wildland and urban fire spread processes. By visualizing the hopscotch-like interactions between wildfires and the urban landscape, it captures the complex dynamics of WUI fires and provides insight into the main mechanisms of fire spread.
In developing their model, the researchers aimed to fill a major gap in the current modeling tools used to assess risk. “Current wildfire models classify every community and every building as literally not burnable. That’s what we wanted to change,” says Gollner. “To make a more accurate risk assessment, we needed to bring in data that quantifies wildfire risk to buildings.”
This data includes structural attributes, such as building materials and surrounding vegetation, as well as the intensity of approaching flames. While structural attributes determine susceptibility, intensity and embers represent fire exposure, which together indicate the ability to ignite a structure. “By combining these factors, we can estimate structural damage that takes into account both the variability in attributes and the intensity of approaching flames,” Gollner said.
Additionally, the researchers designed a model that comprehensively addresses the three main ways that WUI fires spread through communities: direct flame contact (a structure ignites when it comes into direct contact with flames from an approaching wildfire), radiation (intense heat emitted from the flames increases the temperature of combustible materials on or within a structure), and spark ignition (when flammable vegetation or building materials break off and travel ahead of the advancing fire, as seen in embers).
They then seamlessly integrated their WUI model with ELMFIRE, an existing tool that simulates wildfire spread and is used by utilities and counties across the state for risk assessment. This integration allowed them to model the process by which wildfires spread into urban areas.
Learning from past wildfires
To test their model, the researchers simulated two historic and devastating fires in the WUI: the Tubbs Fire and the Thomas Fire. The Tubbs Fire broke out in Northern California in October 2017, killing 22 people, burning 36,810 acres, and destroying more than 5,643 structures and 5% of the housing stock in the city of Santa Rosa. The Thomas Fire raged through Southern California in December 2017, burning 281,893 acres and destroying 1,063 structures before being fully contained.
Despite incorporating some uncertainty, the model’s predictions achieved over 85% accuracy for fire extent and about 70% accuracy for damaged homes, with over 30% of homes ignited by fire sticks.
The accuracy of the model was assessed using fire boundary observation data from Geospatial Multi-Agency Coordination (GeoMAC). For structure damage and loss, the researchers used inspection data from the CAL FIRE Wildfire Damage Inspection (DINS) program, which keeps records of tens of thousands of homes destroyed by wildfires over the years.
The model output not only examined the role that urban structure plays in fire spread, but also provided new insights into how fires develop in these environments.
“Certainly, fires spread slower through communities than they do through forests and trees, but they’re still very destructive. They behave differently,” Gollner said. “Houses burn differently, and by putting that data into the model, we can now see the impact of each of those processes — houses burning, houses next to each other, weather — and how all those processes and factors interact.”
New tools for the community and industry
Through this research, lead author Dwi MJ Purnomo, a postdoctoral researcher in the Department of Mechanical Engineering, hopes to empower communities across California to take proactive steps to become more resilient. He envisions homeowners using this new tool to better understand their individual and collective risks and work with their neighbors to make their entire communities more resilient. This could include taking steps to protect or strengthen home structures by modifying materials, vents, roofs, and perimeter defensive spaces.
“Because we have the models, we can test millions of scenarios, change the data, and test them again to see what happens,” Purnomo says. “Ideally, communities can use this information to devise effective landscape management practices and put mitigation measures in place before the next fire.”
The models can also help investigate fires that have already happened: by re-creating them, we can learn what went well, what went wrong, and how to prevent such destruction in the future.
Improved accuracy and ease of use
Going forward, the researchers are looking into how to “democratize” the model. Currently, they only have the necessary data for a few regions in California. Their plan is to collect and load relevant data for the entire state.
“Our next step is to clean up the data pipeline and make it possible for anyone in any community to run these models and assess risk,” Gollner said.
The tool is open source and available online, but it can still be a bit complicated to use for non-experts, and over time the researchers hope to streamline some aspects and improve the model’s usability, making it more readily usable by practicing engineers and landscape planners.
Wildfires will always happen, but this model could help reduce the cycle of destruction. “If our communities become more resilient and adaptable to fire, then when fires do happen, they’ll be daytime events and won’t burn down our homes,” Gollner said. “Hopefully, we’re now one step closer to that scenario.”
Further information: Dwi MJ Purnomo et al., “Reconstructing failure modes in wildland-urban interface fires using semi-physical level set models,” Proceedings of the Combustion Institute (2024). DOI: 10.1016/j.proci.2024.105755
Courtesy of University of California, Berkeley
Source: Researchers Simulate Wildfire Spread in Communities (September 18, 2024) Retrieved September 18, 2024 from https://phys.org/news/2024-09-simulate-wildfires-communities.html
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