Objective definition of discharge thresholds for post-fire debris flows

Runoff-generated debris flows are a common post-fire hazard in the western United States and a growing number of regions around the world. As wildfire continues to emerge across a broader range of geographic regions and plant communities, there is an increasing need for generalizable methods to predict post-fire debris-flow initiation. The prediction of post-fire debris flow during intense rainstorms has traditionally relied upon empirical rainfall thresholds. Rainfall intensity-duration thresholds are often developed based on rainfall data and the hydrologic response to those rainstorms. They are most applicable to the specific regions where data are collected. Here, we present a new predictive approach that utilises processes-based models with fundamental physics and machine learning methods to estimate discharge thresholds for runoff-generated debris-flow initiation in four recently burned areas in the western United States. We assess the performance of the objectively defined discharge threshold-based predictions for post-fire debris-flow initiation from our hybrid framework, which utilises debris-flow timing within rainstorms, physically based numerical simulations of runoff, and the support vector machines method. The proposed thresholds have a good balance between true and false predictions for debris flow and floods. Importantly, our method permits the direct estimation of rainfall intensity-duration thresholds for areas where post-fire debris flow observations are limited.