Amanda Susanne Aaberg

Différents systèmes de surveillance sont utilisés dans le monde entier pour l'observation et la saisie de paramètres importants des laves torrentielles, mais ils ont tous une résolution limitée dans le temps et/ou dans l'espace. Dans cet article, nous présentons une nouvelle méthode de surveillance basée sur des scanners laser 3D (LiDAR), qui fournit des données à haute résolution temporelle et spatiale sur les laves torrentielles. Dans l'Illgraben, plusieurs scanners LiDAR de ce type ont été installés le long du chenal et ont déjà enregistré onze événements de laves torrentielles. Grâce à ces données 3D, nous avons pu mesurer avec une grande précision la vitesse d'objets individuels dans le mélange des laves torrentielles et déterminer les débits et les volumes. Nos analyses montrent que la technologie LiDAR peut apporter une contribution essentielle à une description plus détaillée de la dynamique des laves torrentielles, d'autant plus que les premiers résultats indiquent des différences significatives, par exemple dans la détermination du débit, entre cette méthode et les méthodes conventionnelles.

Zur Beobachtung und Erfassung wichtiger Kenngrössen von Murgängen kommen weltweit verschiedene Monitoring-Systeme zum Einsatz, die alle eine zeitlich und/oder räumlich beschränkte Auflösung haben. In diesem Artikel präsentieren wir eine neue Monitoring- Methode basierend auf 3D-Laserscannern (LiDAR), die zeitlich und räumlich hochaufgelöste Daten von Murgängen liefert. Im Illgraben wurden mehrere solche LiDAR-Scanner entlang des Gerinnes installiert und haben bereits elf Murgangereignisse erfasst. Anhand dieser 3D-Daten konnten wir Geschwindigkeiten einzelner Objekte in der Murgangmischung mit hoher Genauigkeit messen sowie Abflüsse und Volumina ermitteln. Unsere Analysen zeigen, dass die LiDAR-Technologie einen wesentlichen Beitrag zur detaillierteren Beschreibung der Murgangdynamik leisten kann, zumal erste Resultate auf massgebliche Unterschiede etwa in der Bestimmung des Abflusses zwischen dieser Methode und konventionellen Verfahren hindeuten.

Estimating flow velocities is key to assessing hazards associated with debris flows. One approach to post-event velocity estimation is the superelevation method, which uses debris-flow mudlines to measure the cross-channel surface inclination, or superelevation, produced by centripetal forces acting on the flow in a bend. Flow velocities are then calculated using a subjective parameterization of the forced vortex equation modified to include a debris-flow specific correction factor. Subjective parameterization of this equation leads to substantial variability and uncertainty in the resulting flow velocities. We present an analysis of the reliability of the superelevation method using a large UAV-based data set of 14 debris flows with front velocities of ∼0.8–6.5 m s⁻¹ and cross-channel surface inclinations of ∼0.6–8.5°, as well as a validation for a single debris flow measured using high-resolution, high-frequency 3D lidar data fused to video imagery. The validation event indicates that when the flow surface inclination can be measured directly, the forced vortex equation produces excellent results without needing a correction factor for Froude numbers ranging from 0.7 to 1.5. This finding indicates that the main challenge with the superelevation method lies in obtaining accurate measurements of superelevation from the mudlines, and that a correction factor may serve to compensate for measurement difficulties rather than variable flow properties. For very small and highly subcritical flows, the superelevation method may generate a large overestimation of flow velocities.

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.

The forced vortex equation, based on the cross-stream inclination of a flow surface as it passes through a bend, is a common approach to estimating debris flow velocities. Here, we present the preliminary results of a study of superelevation and the correction factor k, used to adapt the forced vortex equation to debris flows, based on data from the Illgraben torrent in Switzerland. The definition of the radius of curvature, a factor in the calculation of superelevation velocities, is not found to exercise a large influence on the calculated velocities when using high resolution aerial images, with the choice of cross-section location and k-factor exercising a more significant influence. The k-factors found here fall within the range previously reported in the literature, ranging from approximately 1 to 7, and a previously suggested non-linear relationship with Froude numbers is evident in the dataset. Following the debris flow season of 2022, the study will be continued with additional debris flow events and the investigative methods will be extended to include high-resolution LiDAR sensors installed along the Illgraben torrent.

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.