The National Science Foundation has awarded Assistant Professor Hamed Ebrahimian a $2 million grant to develop novel wildfire risk assessment and mitigation tools.
On November 8, 2018, the deadliest wildfire in California’s history ignited in Butte County outside the city of Paradise. When it was declared contained 17 days later, the Camp Fire had burned more than 150,000 acres, destroyed 18,000 buildings and taken 86 lives.
Like many, Hamed Ebrahimian, assistant professor in the College of Engineering, was moved by this tragedy. And when he discovered the fire was part of a growing trend of wildfire danger—for the last twenty years, on average, seven million acres of U.S. land have burned in wildfires annually—he got to work.
Harnessing his expertise in computational modeling in civil engineering, Ebrahimian began pursuing a better way to understand fire risk. He assembled a multi-institutional group of researchers with a similar desire to use science and technology to reduce the chances that the world would suffer from another wildfire of the magnitude of the Camp Fire. Now, with the help of a 5-year, $2 million grant from the National Science Foundation’s LEAP-HI program, Ebrahimian is ready to realize his vision.
“Some of the most tragic fatalities in the Camp Fire were due to unpredicted fire behavior, which surprised the victims and eliminated the proper reaction time. I told myself that we are in a digital and technology era and our lives should not be sacrificed this easily,” Ebrahimian said. “Two years later, I am grateful to be part of a solid team and to have received the support to execute this vision.”
The vision: A computational platform for multi-level wildfire risk assessment
Researchers at the Desert Research Institute (DRI), UCLA, University at Buffalo, National Center for Atmospheric Research in Boulder (NCAR), and the University of Nevada, Reno Colleges of Science and Business are gathered together under the leadership of the University’s College of Engineering to redefine wildfire risk monitoring and management through the development of a new computational platform. The platform is intended for use by wildfire managers, emergency responders and utility companies to plan for, respond to, and mitigate the risk of wildfires.
“This is an interdisciplinary intervention with a diverse team to blend different thinking modalities and to build a digital platform that can be used to monitor the risk of wildfire on a spectrum of spatial resolution and time,” Ebrahimian said. “Once developed, the computational platform will increase the efficiency of the wildfire management process by providing timely actionable information to decision-makers.”
The research project envisions 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 for risk management, and the post-ignition fire behavior at near-real-time (hours-days) for situational awareness.
Ebrahimian explained, “Our objective is to develop a systematic framework to quantify the risk of wildfires to wildland-urban-interface communities in terms of the total probability of loss. Loss is defined as a combination of monetary damage and the change in the quality of life of people. The risk, thus, depends, on one hand, on the characteristics of the community, its structure, and location and, on the other hand, on the wildland and the factors affecting the fire ignition and spread, such as topography, climate conditions, fuel type and moisture. Now, we want to have the capability to combine all these factors and predict the seasons-month ahead to weeks-days-ahead risk for different communities and regions.”
This goal will be accomplished by creating and integrating transdisciplinary scientific knowledge and techniques in the fields of data harnessing (collection, processing, fusion, and uncertainty quantification), computational modeling (wild- and urban-fire initiation and spread, as well as social quality-of-life models), stochastic simulation, and model-based inference.
“This is a complex undertaking and requires the integration of various sources of data with a hierarchy of data-driven and physics-based models,” Ebrahimian continued. “The core idea is inspired by the many years of research advancement in the field of earthquake risk assessment and disaster resilience. Once developed and validated, the framework will be crucial to help make informed decisions and take preventive actions in order to scientifically reduce the risk of fires, and therefore, their effects on our communities and people. This can help reduce the risk of fires but the risk can never be eliminated. Therefore, another component of our computational platform is focused on predicting how active fires will behave and propagate. This will be instrumental to help the ground-zero firefighting activities.”
