Antecedent Hydrometeorological Conditions of Wildfire Occurrence in the Western US in a Changing Climate

Wildfires have significant hydrological and ecological impacts in the western U.S. Using a high-resolution regional climate simulation and wildfire observations for 1984-2018, this study investigates the antecedent hydrometeorological conditions (AHCs) of wildfires in the western U.S. During the warm season (April-September), the wildfire AHCs feature diverse surface pressure (PS), soil moisture, and longwave/shortwave radiation (LW/SW) conditions. K-means clustering classifies wildfires into four types with distinct AHCs: low-PS-type and high-PS-type with lower and higher PS anomalies, respectively, LW-type featuring intense LW but weak SW anomalies, and wet-soil-type with wet soil anomalies. Each fire cluster represents 22%-27% of all the wildfires, featuring different combinations of climate and vegetation conditions and their diverse relations to regional hydrometeorological conditions, with wet-soil-type fires often exhibiting opposite correlations with AHCs compared to those of the other three types. In five major Koppen climate zones over the western U.S., clustering-based predictions improve the seasonal wildfire prediction accuracy (R-2) by 10% compared to prediction without classification. Such improvement comes from separating the opposite relationships between wet-soil-type fires and their seasonal AHCs from the other three types, along with separating LW-type fires, which include most of the lightning-ignited fires that occur more randomly. Increases in wildfire occurrence during 1984-2018 are dominated by the increases in the LW-type fires, while the wet-soil-type fires have decreased, consistent with the long-term drying in the western U.S.

Automated summary

This study investigates the antecedent hydrometeorological conditions (AHCs) of wildfires in the western U.S. by using a high-resolution regional climate simulation and wildfire observations. The study classifies wildfires into four types based on their AHCs and finds that each type of wildfires has different climate and vegetation conditions and their diverse relations to regional hydrometeorological conditions. Clustering-based predictions improve the seasonal wildfire prediction accuracy, especially for wet-soil-type fires and LW-type fires. The increase in wildfire occurrence during 1984-2018 is dominated by the increases in LW-type fires.