Detection of debris-flow initiation with seismic techniques for early-warning purposes

In mountain regions, debris flows are responsible for major damage to infrastructure and many casualties every year. Early Warning Systems (EWSs) based on sensor networks installed along the debris-flow channel have been implemented in some catchments around the world, including the Alps. Detecting the early phase of debris flows would allow expanding the lead time of an EWS compared to the monitoring of channelized flows upstream a vulnerable site. In this study, monitoring data gathered from 2019 to 2022 in the headwaters of the Gadria catchment, eastern Italian Alps, are analyzed. One active channel located at 2200 m a.s.l. was instrumented with a time-lapse video camera, a tipping-bucket rain gauge, and a 4.5-Hz vertical geophone. The dataset includes 5 debris-flow events that propagated from the monitored channel to the basin outlet and a large number of signals produced by other seismic sources (e.g., rockfalls, earthquakes, animals, wind). The peak amplitude, the duration and the frequency content of the seismic signals were analyzed with the support of video images to identify the different seismic sources. Results show that different seismic sources produce signals with different characteristics and that it is possible to discriminate the most intense channel processes by analyzing seismic data only. Adopting an approach similar to the rainfall thresholds, debris-flow and runoff events have been bounded by means of a power relationship between peak amplitude and signal duration. The next step of the research would be the development of an algorithm able to automatically classify the seismic sources and identify intense channel processes that can generate debris flows. A similar approach will be applied to the Blé catchment (Val Camonica, central Italian Alps) to study the triggering mechanisms and dynamics of debris flows and analyse whether the proposed approach is valid in other locations. The analysis of seismic data will be combined with the identification of triggering rainfall thresholds and the analysis of infrasound signals to develop reliable EWSs for debris flows.