Predicting burn severity for integration with post-fire debris-flow hazard assessment: a case study from the Upper Colorado River Basin, USA

Background. Burn severity significantly increases the likelihood and volume of post-wildfire debris flows. Pre-fire severity predictions can expedite mitigation efforts because precipitation contributing to these hazards often occurs shortly after wildfires, leaving little time for post-fire planning and management. Aim. The aim of this study was to predict burn severity using pre-fire conditions of individual wildfire events and estimate potential post-fire debris flow to unburned areas. Methods. We used random forests to model dNBR from pre-fire weather, fuels, topography, and remotely sensed data. We validated our model predictions against post-fire observations and potential post-fire debris-flow hazard estimates. Key results. Fuels, pre-fire weather, and topography were important predictors of burn severity, although predictor importance varied between fires. Post-fire debris-flow hazard rankings from predicted burn severity (pre-fire) were similar to hazard assessments based on observed burn severity (post fire). Conclusion. Predicted burn severity can serve as an input to post-fire debris-flow models before wildfires occur, antecedent to standard post-fire burn severity products. Assessing a larger set of fires under disparate conditions and landscapes will be needed to refine predictive models. Implications. Burn severity models based on pre-fire conditions enable the prediction of fire effects and identification of potential hazards to prioritise response and mitigation.

Automated summary

This study aimed to predict burn severity using pre-fire conditions and estimate the potential post-fire debris flow to unburned areas. Fuels, pre-fire weather, and topography were important predictors of burn severity. Predicted burn severity can be used to predict fire effects and identify potential hazards for prioritizing response and mitigation.