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Spatial patterns and drivers for wildfire ignitions in California
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Spatial patterns and drivers for wildfire ignitions in California
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Journal Article
Spatial patterns and drivers for wildfire ignitions in California
2022
Overview
As a key component of wildfire activities, ignition is regulated by complex interactions among climate, fuel, topography, and humans. Considerable studies have advanced our knowledge on patterns and drivers of total areas burned and fire frequency, but much is less known about wildfire ignition. To better design effective fire prevention and management strategies, it is critical to understand contemporary ignition patterns and predict the probability of wildfire ignitions from different sources. We here modeled and analyzed human- and lightning-caused ignition probability across the whole state and sub-ecoregions of California, USA. We developed maximum entropy models to estimate wildfire ignition probability and understand the complex impacts of anthropogenic and biophysical drivers, based on a historical ignition database. The models captured well the spatial patterns of human and lightning started wildfire ignitions in California. The human-caused ignitions dominated the areas closer to populated regions and along the traffic corridors. Model diagnosis showed that precipitation, slope, human settlement, and road network shaped the statewide spatial distribution of human-started ignitions. In contrast, the lightning-caused ignitions were distributed more remotely in Sierra Nevada and North Interior, with snow water equivalent, lightning strike density, and fuel amount as primary drivers. Separate region-specific model results further revealed the difference in the relative importance of the key drivers among different sub-ecoregions. Model predictions suggested spatially heterogeneous decadal changes and an overall slight decrease in ignition probability between circa 2000 and 2010. Our findings reinforced the importance of varying humans vs biophysical controls in different fire regimes, highlighting the need for locally optimized land management to reduce ignition probability.
