Jordan Aaron

Last Name

Aaron

First Name

Jordan

ORCID

0000-0001-8414-106X

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09797 - Aaron, Jordan / Aaron, Jordan

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Publications1 - 10 of 33

Field Validation of the Superelevation Method for Debris-Flow Velocity Estimation Using High-Resolution Lidar and UAV Data
Åberg, Amanda; Aaron, Jordan; McArdell, Brian W.; et al. (2024)
Journal of Geophysical Research: Earth Surface
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.
LunarLeaper—A mission concept to explore the lunar subsurface with a small-scale legged robot
Kolvenbach, Hendrik; Mittelholz, Anna; Stähler, Simon Christian; et al. (2026)
Acta Astronautica
We present the LunarLeaper mission concept, which aims to robotically investigate volcanic pits on the lunar surface. Volcanic pits, or skylights, are collapse features that may provide access to subsurface lava tubes, which could serve as shelters for future human explorers and offer insight into the volcanic history of the Moon by exposing ancient lava flows. The existence and extent of large caves are still debated today and require in situ analysis. The Marius Hills site in particular offers a potential entry point to a cave system in a volcanic region on the lunar nearside. Our mission aims to deploy a payload-equipped 15 kg-class legged robot that can approach a pit, such as the Marius Hills pit, while taking measurements during the traverse. During the mission, measurements from a ground-penetrating radar (GPR) and a gravimeter will allow us to survey the subsurface and map any underlying lava tube, if present. The mission will investigate key questions regarding lunar volcanism, such as the existence and geometry of subsurface caves and the magnitude and timing of lava flows, while assessing the site's suitability for future human utilization and habitation. Furthermore, the mission will demonstrate key enabling technologies such as legged robots, serving as building blocks for the next generation of planetary missions.
Unravelling the evolution of the Frébouge polygenetic cone in Val Ferret (Mont Blanc Massif)
Dieleman, Catharina; Deline, Philip; Ivy Ochs, Susan; et al. (2025)
Boreas
Proglacial settings in the Alps are typically polygenetic, often characterized by a complex and discontinuous interplay between glacial, fluvial and gravitational processes. These processes yield high volumes of sediments, which usually exceed their transportation capacity. The excessive proglacial sediment load leads to accumulation on slopes, and thus, to subsequent failures such as rock avalanches. The northern slopes of the Ferret and Veny valleys in the Mont Blanc Massif are home to several polygenetic cones and are a stunning field laboratory for the exploration of the interplay between the glacial, fluvial and gravitational processes. This study investigates a well-preserved polygenetic cone, the Frébouge cone, to disentangle the geomorphic processes that contributed to its formation and to reconstruct its evolution. To achieve these goals, detailed field and remote mapping, 10Be surface exposure dating, and runout modelling with DAN3D® were used. The geomorphological map revealed complex interactions of glacial, fluvial, debris flow, as well as rock and snow avalanche processes. The established chronology indicates two major episodes of debris flows, the first one at c. 2 ka, and the second at c. 1 ka. In addition, a rock mass with a maximum volume of up to 12±3 Mm³ collapsed in the upper reaches of the cone at 1.3±0.1 ka and overran the cone, travelling more than 100 m up onto the opposite valley slope. Afterwards, the Frébouge Glacier overrode the cone several times leaving moraines and till, reaching its maximum extent c. 300 years ago. This study underscores the untwisting of the complex interaction of surface processes in the Alpine valleys, which are prone to hit the urban areas and infrastructures.
Optimising landslide initiation modelling with high-resolution saturation prediction based on soil moisture monitoring data
Halter, Tobias; Lehmann Grunder, Peter Ulrich; Wicki, Adrian; et al. (2025)
Landslides
It has been widely recognised that the degree of soil wetness before precipitation events can be decisive for whether or not shallow rainfall-induced landslides occur. While there are methods to measure and/or model soil wetness in complex topography, they often exhibit limitations in spatial or temporal resolution, hindering their application in regional landside initiation modelling. In this study, we address the need for high-resolution predictions of initial saturation before rainfall events by employing data-driven linear regression models. The models were trained using in-situ soil moisture data collected from six measurement stations located in a landslide-prone region in Switzerland. Various topographic attributes, along with multiple antecedent rainfall and evapotranspiration variables were tested as input for the models. The final model consisted of five measurable variables, including cumulative antecedent rainfall, cumulative evapotranspiration, and the topographic wetness index (TWI). The model effectively reproduced the observed spatial and temporal variability of the in-situ measurements with a coefficient of determination R2 = 0.62 and a root mean square error RMSE = 0.07. Subsequently, we applied the regression model to predict the spatial soil saturation at the onset of actual landslide triggering rainfall events and integrated these patterns into the hydromechanical model STEP-TRAMM. The results demonstrate improvements in predicting observed landslide occurrences compared to simulations assuming spatially uniform initial saturation conditions, highlighting the importance of in-situ measurements and a realistic extrapolation of such data in space and time for accurate modelling of shallow landslide initiation.
Monitoring volcano landslides in an unresting caldera: the case of the Askja caldera, Iceland
Oestreicher, Nicolas Kyochi; Ruch, Joël; Panza, Elisabetta; et al. (2024)
14th International Symposium on Landslides: Book of abstracts
Calderas are volcanic edifices able to trigger massive eruptions and often engendering flank collapses. A large landslide occurred within the Askja caldera in the summer of 2014, triggering a tsunami within the 4 km diameter Öskjuvatn Lake, affecting a touristic site. The volcanic edifice has started a new phase of unrest since 2021 with a cumulative uplift of 0.6 m until August 2023, in the centre of the caldera. Monitoring the caldera flanks is a critical task for increasing the safety of visitors, in addition to its scientific values. The rough weather conditions, remoteness, and difficult-to-inaccessible terrain make the monitoring challenging. We describe here a monitoring system which was installed in August 2023, composed of three time-lapse cameras, three corner reflectors, two extensometers, a GNSS station and three drone surveys acquired in 2020, 2021 and 2023 around the caldera rim. We present preliminary results of surface deformation between 2020 and 2023. We identified several deforming zones using DInSAR and high-resolution photogrammetric DEM differencing. Our monitoring system will allow multi-instrumental slope deformation analysis and must resist extreme Icelandic weather conditions. Its open-source nature and robustness should serve as an example for equipping other landslides in harsh environments around the world.
Deep-learning-based 3D surge-wave detection and tracking in debris flows using cameras and LiDARs
Hirschberg, Jacob; Aaron, Jordan (2024)
14th International Symposium on Landslides: Book of abstracts
Debris-flow movement is rapid and complex. Recently developed LiDAR sensors allow for monitoring debris-flow dynamics at high spatial (<2 cm) and temporal (10 Hz) resolution. We present a framework consisting of object detection in debris flows (surge waves) using deep learning algorithms on 2D camera images, which are fused with LiDAR data to obtain 3D information. This allows for tracking objects and determining their size and velocity. Continued monitoring and application of this method will help to improve our understanding of debris-flow dynamics.
Hydro-Mechanical Interactions of a Rock Slope With a Retreating Temperate Valley Glacier
Hugentobler, Marc; Aaron, Jordan; Loew, Simon; et al. (2022)
Journal of Geophysical Research: Earth Surface
Rock slope failures often result from progressive rock mass damage which accumulates over long timescales. In deglaciating environments, rock slopes are affected by stress perturbations driven by mechanical unloading due to ice downwasting and concurrent changes in thermal and hydraulic boundary conditions. Since in-situ data are rare, the different processes and their relative contribution to slope damage remain poorly understood. Here, we analyze borehole monitoring data from a rock slope adjacent to the retreating Great Aletsch Glacier (Switzerland) and compare it to englacial water levels, climate data, and decreasing ice levels. Rock slope pore pressures show a seasonal signal controlled by infiltration events as well as effects from the connectivity to the englacial hydrological system. We find that reversible and irreversible strains are driven by: (a) hydromechanical effects caused by englacial pressure fluctuations and infiltration events, (b) stress transfer related to changing mechanical glacial loads from short-term englacial water level fluctuations and longer term ice downwasting, and (c) thermomechanical effects from annual temperature cycles penetrating the shallow subsurface, which primarily result in reversible deformation. We relate most observed irreversible strain (damage) to mechanical unloading from ice downwasting. Damage is strongest directly at the ice margin and moves through the slope at the pace of glacial retreat and advance. Locations with many retreat/advance cycles are very sensitive to landslide formation. The current climate warming impacts very sensitive valley sectors, which is confirmed by landslide distributions and activity in the study area.
3D object detection and tracking in debris flows with cameras and LiDARs
Hirschberg, Jacob; Bickel, Valentin Tertius; Aaron, Jordan (2024)
EGUsphere
Debris flows are destructive mixtures of water and sediments. In mountain regions, debris flows are a relevant hazard as they threaten people and infrastructure. They move with rapid to extremely-rapid velocities, and often feature a coarse-grained front followed by a liquefied tail. Recently developed LiDAR sensors allow for long-term monitoring of debris-flow dynamics at high spatial (<2 cm) and temporal (10 Hz) resolution in the field, and provide the necessary high-quality data to improve our fundamental understanding of the complex debris-flow behavior. Here, we present a framework for object detection in debris flows using deep learning algorithms which are trained on 2D camera images, and the results were then fused with LiDAR data to obtain 3D information. We used the YOLOv5 architecture to train a detector of breaking and diffuse surge waves, woody debris, boulders and rolling boulders. The detected objects were then tracked using the SORT algorithm. By subsequently reprojecting the image detections and tracks on the point clouds, 3D information such as velocity was determined. The detector performs very well on the different surge wave types with mean average precisions exceeding 0.9 in the test dataset. The other object types such as woody debris are more difficult to detect and track but still result in mean average precisions around 0.7. Finally, we show how surge waves interact with other objects of the flow by speeding them up and increasing their potential destructive impact. Continued monitoring and application of this method to more debris-flow events will result in an extensive dataset, which would be nearly impossible to obtain with a human operator only. Ultimately, our work will help to improve our understanding of debris-flow dynamics and reduce the hazard associated with this destructive process.
LiDAR-based investigation of debris flow superelevation and velocity
Åberg, Amanda; Aaron, Jordan; Hirschberg, Jacob; et al. (2023)
EGUsphere
Deciphering the Dynamics of a Younger Dryas Rock Avalanche in the Bernese Alps
Bucher, Giacomina; Dieleman, Catharina; Ivy-Ochs, Susan; et al. (2024)
Research Square
Large rock avalanches play a key role in shaping alpine landscapes. However, the complex interplay between mass movement and other surface processes poses challenges in identifying these deposits and understanding the underlying process controls. Here, we focus on the rock avalanche deposits of the Lurnigalp Valley in the Bernese Alps (Switzerland), originally mapped as till. The Lurnigalp is a U-shaped tributary valley located in the southwest of Adelboden, Canton Bern. To explore the timing and dynamics of the rock avalanche event, we employed detailed remote and field mapping, sedimentary petrology, surface exposure dating with cosmogenic 36Cl, and runout modelling with DAN3D®. For the reconstruction of the chronology, we analyzed cosmogenic 36Cl in surface samples from 15 boulders of the rock avalanche deposit. We developed three distinct scenarios to investigate the dynamics and contextual conditions of the rock avalanche event. In the first two scenarios, we hypothesize that the rock avalanche occurred in a valley devoid of ice. In contrast, the third scenario presents a different setting where a hypothetical glacier is posited to have occupied the upper sections of the valley. In brief, we anticipate that a rock mass of ca. 6 Mm3 of rock mass composed of limestone and sandstone was released from a limestone cliff at 12 ± 2 ka during the Younger Dryas. The collapsed rock mass fell into the ice-free valley floor, ran up the opposite valley side and was deflected towards the northeast following the valley orientation. The rock mass stopped after 2.2 km leaving approximately 6.4 Mm3 deposits spread across the entire valley floor. Subsequently, most of the rock avalanche deposits have been reworked by periglacial activity. We conclude that structural features (bedding, fault), lithology and glacial erosion were involved in the weakening of the in-situ bedrock that finally led to the collapse. Our study not only enhances our understanding of rock avalanche mechanisms and their profound impact on Alpine landscape evolution but also elucidates the complex interplay of geological processes that led to the collapse and altered the rock avalanche deposits afterwards.

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