On November 8, 2018, a destructive wildfire broke out in Los Angeles and Ventura Counties in California. Named the Woolsey Fire, it burned 96,949 acres, destroyed 1,643 structures, killed three people, and prompted the evacuation of more than 295,000 people.
The WIFIRE Lab, a collaboration between the San Diego Supercomputer Center (SDSC) and the Qualcomm Institute (QI) at UC San Diego (UCSD), develops systems for natural hazards monitoring, simulation, and response. A simulation platform the Lab developed helped inform the evacuation of the Malibu and Topanga Canyon areas during the Woolsey Fire that helped save lives.
Jessica Block is associate director for operational programs of the WIFIRE Lab. Science Node caught up with Jessica at the QI offices in San Diego to find out more.
Tell us about some of the work you do as associate director.
I work with first responders and agencies around the country to find out what data is available, what data is needed, and how we can use simulation tools in real time to fight fires more effectively. I do GIS and remote sensing data integration to the work as well. I also support the dissemination of fire science to practice.
How do you collaborate with the Los Angeles Fire Department?
While we were a National Science Foundation-funded project, the LA fire chief found us and asked to use our tools for fighting and predicting fires throughout the city.
Through that partnership, we built a web-based platform called Firemap. It's now being used in a pilot program across multiple fire departments throughout Southern California.
Does the lab work with other natural disasters beyond fires?
We’re looking towards expanding to landslide, flood hazards, and air quality and pollution. We're bringing in multiple weather forecasts to our predictive models, which can inform many other natural disasters, so these coupled disasters are organically becoming part of what we do.
How does predictive modeling work?
Predictive modeling involves inputting real weather data into simulation equations that then propagate fire on the landscape with the vegetation and topography taken into account. One of the problems with predictive modeling—for example, a weather forecast—is it's based only on the data that we're able to collect.
What's exciting about right now is that we are sensing our world in such detail that we're finding innovative ways to make use of this highly networked, very dense information about our environment. We then can take advantage of predictive data and simulated tools and create the workflows that can leverage all the data that's available.
What kind of data do you collect that helps predict wildfire?
The simulations use topography and what's on the ground—things like types of vegetation, and the weather. Utility companies in California have been building out hundreds of weather stations, and now we can know very precisely what the weather is like in a canyon versus on a mountain. We're also taking advantage of high-resolution satellite imagery and tracking in much higher resolution what's happening on the ground.
If there's a new urban development, we can feed that information into the simulation. If there's been a fire recently, or if trees have been cut down or have died, we can take that into account, making the simulation more accurate.
By being able to run those simulations much faster, firefighters can use them for their initial attack. So, for the Woolsey Fire in Malibu, our simulation platform helped with the evacuation of the Malibu and Topanga Canyon areas.
How do visualizations help keep cities and firefighters safe?
We have two different ways we visualize our work. The Firemap platform is a web-based tool that enables anybody to see what the weather is like, where the fire is now, and even to infer where the fire is going. That platform has data from many different sources.
And then, here at the Qualcomm Institute, we have virtual reality facilities and large tile display walls, where we're working on ways to create virtual environments and simulate past fires. We can recreate a past event for firefighters to experience virtually.
We take 3D GIS data, look at topography from aerial imagery, and fly through a past fire scar. Firefighters that were there could explain why certain areas burned and why certain areas didn't. Firefighters tell a lot of their experience through word of mouth, and sometimes it's better to have a picture to describe your experience.
Now, we're taking that to the next level with hypothetical simulations that use photorealistic tools to give researchers and firefighters the sense of fire propagation and a better understanding of relative risk.
There has been a lot of talk about using virtual environments to train firefighters since the Yarnell fire in Arizona, which killed an entire Hotshot crew.
It seems like there’s a lot at stake in your research
I'm really passionate about understanding how we can make our cities resilient to disasters like wildfire. There are different ways we can learn to respect our environment and to urbanize our environment in a way that can make us more sustainable.
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