Antecedent Hydrometeorological Conditions of Wildfire Occurrence in the Western U.S. in a Changing Climate

• Published in: Journal of geophysical research. Atmospheres 2023-11, Vol.128 (22), p.n/a
• Main Authors: Chen, Xiaodong, Leung, L. Ruby, Dong, Lu
• Format: Article
• Language: English
• Subjects: Accuracy; Anomalies; Classification; Climate; Climate and vegetation; Climate change; Clustering; Drying; ENVIRONMENTAL SCIENCES; extreme events; Fires; Forest & brush fires; Geophysics; hydroclimate; Hydrology; Hydrometeorology; Lightning; Long wave radiation; machine learning; Predictions; Pressure; Radiation; Regional climates; Seasons; Short wave radiation; Soil; Soil moisture; Surface pressure; Warm seasons; wildfire; Wildfires
• ISSN: 2169-897X; 2169-8996
• DOI: 10.1029/2023JD039136

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 Köppen climate zones over the western U.S., clustering‐based predictions improve the seasonal wildfire prediction accuracy (R2) 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. Plain Language Summary Wildfires have significant hydrological and ecological impacts in the western U.S. Using a long‐term 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, the wildfire AHCs feature diverse surface pressure (PS), 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, respectively, LW‐type featuring intense LW but weak SW, and wet‐soil‐type, each representing 22%–27% of all the wildfires. This classification strengthens the connections between the seasonal wildfire occurrence and the sea