UC Merced

Concerns over the potential impacts of increasing wildfires with continued climate change are growing. Comprehensive inventories of severity and emissions are essential for assessing these impacts and setting appropriate management strategies. To improve the availability of accurate fire severity and emissions estimates, we developed the Wildfire Burn Severity and Emissions Inventory (WBSE). WBSE is a retrospective spatial burn severity and emissions inventory at 30-m resolution for event-based assessment and 500-m resolution for daily emissions calculation. We applied the WBSE framework to calculate burn severity and emissions for large wildfires that burned during 1984-2020 in the state of California, U.S., a substantially more extended period than existing inventories. The framework described here can be applied to estimate severity for smaller wildfires and can also be used to estimate emissions for fires simulated in California for future climate and land-use scenarios. Based on the data generated with WBSE, we analyzed spatial and temporal patterns of burn severity and emissions from large wildfires burning in California during 1984-2020. Results show vegetation and severity play critical roles in controlling the spatial and seasonal distribution of emissions. California’s annual area burned, and emissions are increasing, notably in early and late parts of what once was the typical fire season. Emissions and areas burned in moderate to high severity were particularly high and increasing in North Coast and Sierra Nevada forests. The year of 2020–with the most megafires in history–had fifteen times the annual average emissions of 1984-2015. Another key aim of this dissertation is to explore whether the existing health and wildfire emissions inventory data are sufficient to develop computationally efficient statistical methods that link wildfire pollution emissions to health impacts, to support the simulation of wildfire-related health impacts for very large numbers of future climate and land use scenarios. We presented our effort of developing a logistic Generalized Additive Model (GAM) and a negative binomial GAM model based on 36-years data in California to predict the probability of presence and numbers of respiratory hospitalizations using wildfire PM2.5 emissions inventory data at ZIP Code Tabulation Areas (ZCTAs) spatial scale and semimonthly temporal scale.