Research Database
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Dry Live Fuels Increase the Likelihood of Lightning-Caused Fires
Year: 2023
Live fuel moisture content (LFMC) is a key determinant of landscape ignition potential, but quantitative estimates of its effects on wildfire are lacking. We present a causal inference framework to isolate the effect of LFMC from other drivers like fuel type, fuel amount, and meteorology. We show that in California when LFMC is below a critical flammability threshold, the likelihood of fires is 1.8 times as high statewide (2.25% vs. 1.27%) and 2.5 times as high in shrubs, compared to when LFMC is greater than the threshold. This risk ratio is >2 times when LFMC is 10% less than the…
Publication Type: Journal Article
Lightning-Ignited Wildfires in the Western United States: Ignition Precipitation and Associated Environmental Conditions
Year: 2023
Cloud-to-ground lightning with minimal rainfall (“dry” lightning) is a major wildfire ignition source in the western United States (WUS). Although dry lightning is commonly defined as occurring with <2.5 mm of daily-accumulated precipitation, a rigorous quantification of precipitation amounts concurrent with lightning-ignited wildfires (LIWs) is lacking. We combine wildfire, lightning and precipitation data sets to quantify these ignition precipitation amounts across ecoprovinces of the WUS. The median precipitation for all LIWs is 2.8 mm but varies with vegetation and fire characteristics…
Publication Type: Journal Article
Projecting live fuel moisture content via deep learning
Year: 2023
Background: Live fuel moisture content (LFMC) is a key environmental indicator used to monitor for high wildfire risk conditions. Many statistical models have been proposed to predict LFMC from remotely sensed data; however, almost all these estimate current LFMC (nowcasting models). Accurate modelling of LFMC in advance (projection models) would provide wildfire managers with more timely information for assessing and preparing for wildfire risk. Aims: The aim of this study was to investigate the potential for deep learning models to predict LFMC across the continental United States 3 months…
Publication Type: Journal Article