Dr. Pierre Huguenin

Powder snow avalanches (PSAs) radiate infrasound energy, yet the source mechanism remains unclear, limiting hazard monitoring and mitigation with infrasound-based technologies. Here, we analyze a unique data set from a large PSA to improve the understanding of the source mechanism. Through comparison of cluster activity within the airborne layers of the PSA and the recorded infrasound signal in the frequency domain, we demonstrate that infrasound is mainly generated from particle clusters suspended by turbulent eddies or ejected from the denser basal layer. Further correlating infrasound amplitudes with radar-derived spatial distributions of these clusters, we reveal a distributed source extending hundreds of meters behind the avalanche front. Additionally, we establish a relationship between infrasound and kinetic energy of suspended particles. These findings deepen our understanding of the complex dynamics of infrasound generation, offering valuable insights for avalanche detection and early warning strategies, and fundamental comprehension of PSA dynamics.

Avalanches and other hazardous mass movements pose a danger to the population and critical infrastructure in alpine areas. Hence, understanding and continuously monitoring mass movements are crucial to mitigate their risk. We propose to use Distributed Acoustic Sensing (DAS) to measure strain rate along a fiber-optic cable to characterize ground deformation induced by avalanches. We recorded 12 snow avalanches of various dimensions at the Vallée de la Sionne test site in Switzerland, utilizing existing fiber-optic infrastructure and a DAS interrogation unit during the winter 2020/2021. By training a Bayesian Gaussian Mixture Model, we automatically characterize and classify avalanche-induced ground deformations using physical properties extracted from the frequency-wavenumber and frequency-velocity domain of the DAS recordings. The resulting model can estimate the probability of avalanches in the DAS data and is able to differentiate between the avalanche-generated seismic near-field, the seismo-acoustic far-field, and the mass movement propagating on top of the fiber. By analyzing the mass-movement propagation signals, we are able to identify group velocity packages within an avalanche that propagate faster than the phase velocity of the avalanche front, indicating complex internal structures. Importantly, we show that the seismo-acoustic far-field can be detected before the avalanche reaches the fiber-optic array, highlighting DAS as a potential research and early warning tool for hazardous mass movements.

Debris flows can grow greatly in size and hazardous potential by eroding bed and bank materials. However, erosion mechanisms are poorly understood because debris flows are complex hybrids between a fluid flow and a moving mass of colliding particles, bed erodibility varies between events, and field measurements are hard to obtain. Here, we (i) quantify the spatio-temporal patterns of erosion and deposition and (ii) identify the key controls on debris-flow erosion in the Illgraben (CH). We use a dataset that combines information on flow properties, antecedent rainfall, and bed and bank erosion for 13 debris flows that occurred between 2019 and 2021. We show that spatio-temporal patterns of erosion and deposition in natural debris-flow torrents can be highly variable and dynamic, and we identify a memory effect where erosion is strong at locations of strong deposition during previous flows and vice versa. We find that flow conditions and antecedent rainfall (affecting bed wetness) jointly control debris-flow erosion. We find statistically significant correlations between channel erosion/deposition and a wide range of flow conditions, including frontal flow depth, velocity, and discharge, and flow volume, cumulative shear stress and seismic energy, as well as antecedent rainfall. Overall, flow conditions describing the cumulative forces exerted at the bed during an event, such as cumulative shear stress and flow volume, best explain erosion. A shear-stress approach accounting for bed erodibility may therefore be applicable for modelling and predicting debris-flow erosion. This work can provide input for model development by identifying correlations of flow and bed conditions with erosion that models should oblige.

Debris flows can grow greatly in size and hazardous potential by eroding bed and bank materials. However, erosion mechanisms are poorly understood because debris flows are complex hybrids between a fluid flow and a moving mass of colliding particles, bed erodibility varies between events, and field measurements are hard to obtain. Here, we identify the key controls on debris-flow erosion based on a field data set that combines information on flow properties, bed conditions, and bed and bank erosion. We show that flow conditions and bed wetness jointly control debris-flow erosion. Flow conditions describing the cumulative forces exerted at the bed during an event best explain erosion. Shear forces and particle-impact forces are strongly correlated and act in conjunction in the erosion process. A shear-stress approach accounting for bed erodibility may therefore be applicable for modeling and predicting debris-flow erosion. This work provides a foundation for developing effective debris-flow erosion models.

Im Illgraben bei Leuk (VS) wurde 2003 die weltweit erste Murgangwaage gebaut. Sie lieferte ab Sommer 2004 bis zu ihrer Zerstörung im Juli 2016 wertvolle Daten über die dynamischen Kräfte von Murgängen. Im Rahmen eines Investitionsprojektes der Eidg. Forschungsanstalt WSL wird nun eine ähnlich konzipierte Messeinrichtung am selben Standort unter der Kantonsstrassenbrücke über den Illgraben gebaut. Erfahrungen aus dem bisherigen Betrieb fliessen zusammen mit den Ergebnissen aus den Tests im Labor in eine optimierte optimierte Version ein, welche uns ab Mai 2019 weitere interessante Einblicke in die Fliessdynamik von Murgängen ermöglichen wird.