16 Wildland Fire: An Agent of Geomorphic, Ecologic, and Societal Change

This session will consider the impacts of wildland fire on landscapes and society and invites presentations from a wide variety of disciplines, such as water quality management, post-fire hazard prediction, fire ecology, soil science, and landscape and community recovery.

Sunday, 19 October 2025
8:00 AM - 12:00 PM
HBGCC, 301C

Modeling Post-fire Debris-flow Likelihood in the Southwest USA.

Paper number: 16-9
Program: Technical Sessions
Time: 10:10 AM – 10:25 AM

Authors

  • Ana Isabel Fernandez Sirgo Presenting Author
    University of Arizona, Department of Mathematics, Program in Applied Mathematics, Tucson, AZ
  • Luke McGuire
    University of Arizona, Department of Geosciences, Tucson, AZ
  • Ann Youberg
    University of Arizona, Arizona Geological Survey, Tucson, AZ
  • Tao Liu
    University of Arizona, Los Alamos, NM

Abstract

       Enhanced runoff and erosion in the first several years following fire can increase the likelihood of runoff-generated debris flows. These post-fire debris flows threaten life and infrastructure and significantly contribute to sediment supply in upland channels, impacting water quality, stream habitat, and landscape evolution. Models designed to assess post-fire debris-flow likelihood at the watershed scale in response to design or forecast rainstorms are beneficial for identifying and mitigating post-fire debris-flow hazards, and for modeling the long-term evolution of steep, fire-prone landscapes. Here, we develop classification models for predicting post-fire debris-flow likelihood at the watershed scale. We compiled a new dataset of post-fire debris-flow observations from the southwest USA, specifically from Arizona and New Mexico. The dataset includes information related to rainfall, terrain, fire severity, soil properties, and debris flow response for 3144 rainfall events over 200 watersheds. We use these data to develop a logistic regression model based on two features that combine peak 15-minute rainfall accumulation with mean watershed slope and a fire severity metric. The model achieves a threat score of 0.41, a true positive rate of 0.63, a false negative rate of 0.07, and an F1 Score of 0.57. We evaluated alternative algorithms for model development, namely linear discriminant analysis, random forest, and XGBoost;  however, these performed similarly or worse when compared to logistic regression. We demonstrate the model’s ability to improve assessments of post-fire debris-flow hazards in the southwest USA by applying it to predict debris-flow activity during observed rainstorms following the 2022 Contreras Fire, the 2020 Tadpole Fire, and the 2017 Pinal Fire.

Geological Society of America Abstracts with Programs. Vol. 57, No. 6

doi: 10.1130/abs/2025AM-413234

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