Predicting burn severity for integration with post-fire debris-flow hazard assessment: a case study from the Upper Colorado River Basin, USA
Adam G. Wells
A * , Todd J. Hawbaker B , J. Kevin Hiers C F , Jason Kean D , Rachel A. Loehman E and Paul F. Steblein C
+ Author Affiliations
- Author Affiliations
A U.S. Geological Survey, Southwest Biological Science Center, Flagstaff, AZ 86001, USA.
B U.S. Geological Survey, Geosciences and Environmental Change Science Center, Denver Federal Center, MS980, Denver, CO 80225, USA.
C U.S. Geological Survey, Ecosystems Mission Area, USGS National Center, 12201 Sunrise Valley Drive, Reston, VA 20192, USA.
D U.S. Geological Survey, Geologic Hazards Science Center, Golden, CO 80401, USA.
E U.S. Geological Survey, Alaska Science Center, Anchorage, AK 99508, USA.
F Present address: Texas A&M Natural Resources Institute, Texas A&M University, 1747 Pennsylvania Avenue, N.W., Suite 400, Washington, D.C. 20006, USA.
* Correspondence to: agwells@usgs.gov
International Journal of Wildland Fire 32(9) 1315-1331 https://doi.org/10.1071/WF22200
Submitted: 1 October 2022 Accepted: 24 July 2023 Published: 14 August 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
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.
Keywords: burn severity, Burned Area Reflectance Classification (BARC), debris flow, differenced Normalised Burn Ratio (dNBR), fuels, Landsat 8, machine learning, Monitoring Trends in Burn Severity (MTBS), pre-fire, Sentinel-2, Wildland fire.
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