UC Irvine

An improved understanding of the mechanisms that regulate wildfire risk at local to regional scales is needed for the design of effective fire and ecosystem management. We investigated the spatial distribution of burned area in Southern California during 1960-2009 using five different data-driven methods: multiple linear regression, generalized additive models (GAMs), GAMs with spatial autocorrelation, non-linear multiplicative models, and random forest models. We used each method to separately develop burned area risk maps for Southern California's two distinct wildfire regimes: Santa Ana (SA fires), which occur during high wind events mostly in autumn, and non-Santa Ana fires (non-SA fires), which occur mostly during the hot and dry Mediterranean-climate summer. The different methods explained 38-63% of the spatial variance in burned area for SA fires and 21-48% for non-SA fires. The two fire regimes had contrasting drivers, with Fosberg fire weather index, relative humidity, minimum temperature, and distance to housing most important for SA fires, and shrub cover, road density, and distance to minor and major roads most important for non-SA fires. Our modeling framework carries implications for the strategic placement of fire suppression resources, and for prevention planning in areas facing increasing human and climate pressures.