Much of California and the U.S. West has now been declared drought-free, but there’s no time to celebrate.
Recent precipitation, including atmospheric rivers that bring rounds of flooding to the state, is setting the stage for another daunting wildfire season. “Although winter storms are mitigating drought conditions in California, they are also creating a ‘wildfire paradox,’” explains AiDash Co-Founder and CEO Abhishek Vinod Singh. “Wet conditions allowing abundant vegetation growth will give way to the dry summer season, causing that growth to morph into highly flammable fuel for wildfires.”
AiDash confirmed this cycle in its analysis of winter precipitation and the number of acres burned by wildfires in California from 2000-2022. Specifically, records from the winters of 2016-2017 and 2018-2019 show that exceptionally wet conditions led to a profusion of vegetation growth, which fueled a series of devastating wildfires throughout the state.
As spring begins, lush vegetation has already been seen in Southern California. Considering this factor, as well as more than 20 other parameters , AiDash was able to determine the top 10 California counties facing wildfire risks in 2023: Ventura, Santa Barbara, Los Angeles, San Diego, Riverside, Tehama, Glenn, Colusa, Yolo, and Siskiyou.
“Satellite- and AI-powered solutions for vegetation management can address the wildfire paradox by supporting resilience and identifying high-growth areas that utilities need to watch or clear before fire season,” says Singh.
The proprietary AiDash predictive model uses data from satellites to consider static factors such as topography (aspect, slope, and elevation), and dynamic factors like meteorological data (precipitation, wind speed, humidity, temperature). It also looks at other data sources like powerline and road networks, wildland-urban interface (WUI) tiers, and vegetation health. AiDash provides a unique tool, its Intelligent Vegetation Management System, to support vegetation risk mitigation, including mitigation of wildfire risk.
