Probabilistic Identification of Debris‐Flow Pathways in Mountain Fans Within a Stochastic Framework

Abstract Debris flows are solid‐liquid mixtures originating in the upper part of mountain basins and routing downstream along incised channels. When the channel incises an open fan, the debris flow leaves the active channel and propagates downstream along a new pathway. This phenomenon is called an avulsion. We retrieve the most probable avulsion pathways leveraging a Monte Carlo approach based on using Digital Elevation Models (DEMs). Starting from LiDAR‐based DEMs, we build an ensemble of synthetic DEMs using a local Gaussian probability density function of local elevation values and obtain an ensemble of drainage networks using a gravity‐driven routing algorithm. The ensemble of drainage networks was used to obtain the most probable pathways of avulsions. We applied our methodology to a real monitored fan in the Dolomites (Northeastern Italian Alps) subjected to debris‐flow activity with avulsions. Our approach allows us to verify the consistency between the occurrence probability of a synthetic pathway and those that historically occurred. Furthermore, our approach can be used to predict future debris‐flow avulsions, assuming relevance in debris‐flow risk assessment and planning of debris‐flow control works.