Understanding Determinants of Rapid Wildfire Spread Across California’s Diverse Ecosystems

Abstract:

Wildland fires at varying intensities and frequencies are a critical ecological process across ecosystems within the western United States. Variability in fire behavior is heavily influenced by dynamic and often complex interactions between meteorological and biophysical components. Humans also affect wildfire characteristics directly via fire suppression and indirectly via fuel management. We here used a machine learning approach to investigate what factors caused the rapid spread of large wildfires in the recent decade, across five distinct ecoregions in California. Continuous daily fire spread and area burned were derived from MODIS and VIIRS active fire products. The gradient boosting machine learning model explained 75% of variability in area burned and 63% of variability in the magnitude of spread at the daily time scale. Across ecoregions, the Sierra Nevada performed the best overall with r² of 0.85 and MSE of 3.45 ha, followed by Klamath Mountains, and Southern California, Baja, and Pine-Oak Mountains. Model diagnosis also showed the different drivers for fire spread across regions statewide. Fuel height, along with fuel-based characteristics were key determinants for large areal burns in the Klamath Mountains and Sierra Nevada, contributing to about a third of the total model gain. Increased rates of fire spread were also highly impacted by topography and weather. Fire spread in high-elevation montane regions was largely intensified in south-facing, mid-slope areas during conditions of heightened vapor pressure deficit and low relative humidity. In the Southern California and Baja region, increased area burned and magnitude were predominantly driven by high wind events and tended to be closer to human settlements. The results from this study provide insights on the efficacy of fuel management on reducing the rate of fire progression across ecologically diverse regions and help communities and managers to better anticipate and mitigate future risk of fast moving wildfires in the coming decades.