Predicting locations of post-fire debris-flow erosion in the San Gabriel Mountains of southern California

J. Gartner (), P. Santi and S. Cannon

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2015, vol. 77, issue 2, 1305-1321

Abstract: Timely hazard assessments are needed to assess post-fire debris flows that may impact communities located within and adjacent to recently burned areas. Implementing existing models for debris-flow probability and magnitude can be time-consuming because the geographic extent for applying the models is manually defined. In this study, a model is presented for predicting locations of post-fire debris-flow erosion. This model is further calibrated to identify the geographic extent for applying post-fire hazard assessment models. Aerial photographs were used to map locations of post-fire debris-flow erosion and deposition in the San Gabriel Mountains. Terrain, burn severity, and soil characteristics expected to influence debris-flow erosion and deposition were calculated for each mapped location using 10-m resolution DEMs, GIS data for burn severity, and soil surveys. Multiple logistic regression was used to develop a model that predicts the probability of erosion as a function of channel slope, planform curvature, and the length of the longest upstream flow path. The model was validated using an independent database of mapped locations of debris-flow erosion and deposition and found to make accurate and precise predictions. The model was further calibrated by identifying the average percentage of the drainage network classified as erosion for mapped locations where debris flows transitioned from eroding to depositing material. The calibrated model provides critical information for consistent and timely application of post-fire debris-flow hazard assessment models and the ability to identify locations of post-fire debris-flow erosion. Copyright US Government 2015

Keywords: Debris flow; Post-fire erosion; Hazard assessment; Logistic regression (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/s11069-015-1656-3

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