Regional Models for Postfire Debris‐Flow Likelihood and Rainfall Thresholds Across the Western United States

ABSTRACT The U.S. Geological Survey (USGS) uses an empirical model developed with logistic regression (the ‘M1’ model) to rapidly assess debris‐flow likelihood and to identify quantitative rainfall thresholds for debris flows after wildfire in the western United States. The M1 model was calibrated to a debris‐flow inventory from southern California (United States) and has been applied throughout the western United States. Limited spatial coverage in the calibration dataset has motivated evaluation of M1 model accuracy outside the calibration region (e.g., the Sierra Nevada or the eastern Cascade Range, United States). Previous test cases showed that M1 overpredicts debris‐flow likelihood and underpredicts rainfall thresholds for some locations (e.g., Arizona, northern California, Colorado, New Mexico, United States). We sought to improve the regional applicability of a debris‐flow likelihood model by expanding the debris‐flow inventory used for calibration, testing multiple potential models and generating an updated model framework. The updated inventory includes 3788 observations from 67 burned areas paired with short duration rainfall ratios. The updated model framework consists of a modified model structure and sets of coefficients calibrated separately to the entire updated inventory and to subsets of the inventory that intersect three Environmental Protection Agency (EPA) Level 2 ecoregions (Mediterranean California, Upper Gila Mountains and Western Cordillera). Comparisons of predictions from the updated models with observed rainfall and debris‐flow activity show that the updated models outperform the M1 model by ~15%–60% and improve the uniformity of predictive performance across the western United States. The updated models also reduce false positive rates relative to M1 and generate rainfall thresholds that are better aligned with relative differences in regional climatology and debris‐flow activity.