
Special Seminar: Makoto Kelp
Title: Efficacy of Recent Prescribed Burning and Land Management on Wildfire Burn Severity and Smoke Emissions in the Western United States
Abstract: Wildfires in the western United States increasingly threaten infrastructure, air quality, and public health. Prescribed (“Rx”) fire is often proposed as a strategy to mitigate future wildfires, but treatments remain limited, and few studies quantify their effectiveness on recent major wildfires. We investigate the effects of Rx fire treatments on subsequent burn severity across different western US ecoregions and particulate matter (PM 2.5 ) emissions in California. Using high-resolution (30-meter) satellite imagery, land management records, and fire emissions data, we employ a quasi-experimental design to compare Rx fire-treated areas with adjacent untreated areas to estimate the impacts of recent Rx fires (Fall 2018 – Spring 2020) on the extreme 2020 wildfire season. We find that within 2020 wildfire burn areas where Rx fires were used prior to 2020, burn severity was -15.6% (p<0.001) lower and smoke PM 2.5 emissions were reduced by -101 kg per acre (p<0.1). Rx fires in the wildland-urban interface (“WUI”) were less effective in reducing burn severity and smoke emissions than those outside the WUI. Overall, Rx fires led to a -14% net reduction in PM 2.5 emissions, including those from the Rx fires themselves. Our analysis provides comprehensive estimates of the net benefits of Rx fire on subsequent burn severity and air quality in the western US, valuable constraints for future modeling, and an empirical basis for evaluating proposed Rx fire expansions.
Makoto Kelp is a NOAA Climate & Global Change Postdoctoral Fellow in the Climate and Earth System Dynamics group at Stanford University. Starting in January 2026, he will join the Department of Atmospheric Sciences at the University of Utah as an Assistant Professor. Makoto holds a Ph.D. in Atmospheric Chemistry from Harvard University and a B.A. in Chemistry from Reed College. His research leverages data- driven methods, including machine learning and computational sensing, to uncover new insights into atmospheric chemistry and its interactions between fires, climate, and society.