Recent  

In this paper, we use machine learning to automatically outline landforms based on their characteristics. We test several methods to identify the most accurate and then proceed to develop the most accurate to improve its accuracy further. We manage to outline landforms with 65 %–75 % accuracy, at a resolution of 10 m, thanks to high-quality/high-resolution elevation data. We find that it is possible to run this method at a country scale to quickly produce landform inventories for future studies.

We evaluated the intensity and spatial extent of landscape change in the Hengduan Mountains by identifying areas where river network reorganization is occurring or expected in the future. We combine four metrics that measure topographic imbalances at different spatial and temporal scales. Our study provides a deeper understanding of the dynamic nature of the Hengduan Mountains landscape and associated drivers, such as tectonic uplift, and insights for applying similar methods elsewhere.

Accurately identifying the presence and absence of landforms is important to inferring processes and testing numerical models of landscape evolution. Using synthetic scenarios, I show that the Matthews correlation coefficient (MCC) should be favored over the F₁ score when comparing accuracy across scenes where landform abundances vary. Despite the resilience of MCC to imbalanced data, strong sensitivity to the size and shape of features can still occur when truth and model data are misaligned.

This study investigates subsurface weathering zones, revealing their structure through shear wave velocity variations. The research focuses on the arid climate of Pan de Azúcar National Park, Chile, using seismic ambient noise recordings to construct pseudo-3D models. The resulting models show the subsurface structure, including granite gradients and mafic dike intrusions. Comparison with other sites emphasizes the intricate relationship between climate, geology, and weathering depth.

Ice cliffs are melt hot spots that increase melt rates on debris-covered glaciers which otherwise see a reduction in melt rates. In this study, we show how surface runoff streams contribute to the generation, evolution, and survival of ice cliffs by carving into the glacier and transporting rocky debris. On Kennicott Glacier, Alaska, 33 % of ice cliffs are actively influenced by streams, while nearly half are within 10 m of streams.

Understanding how volcanic edifices develop drainage basins remains unexplored in landscape evolution. Using digital evolution models of volcanoes with varying ages, we quantify the geometries of their edifices and associated drainage basins through time. We find that these metrics correlate with edifice age and are thus useful indicators of a volcano’s history. We then develop a generalized model for how volcano basins develop and compare our results to basin evolution in other settings.

In this paper, we propose a framework for generating risk maps that provide the probabilities of erosion due to river migration. This framework uses concepts from probability theory to learn the river migration model's parameter values from satellite data while taking into account parameter uncertainty. Our analysis shows that such geomorphic risk estimation is more reliable than models that do not explicitly consider various sources of variability and uncertainty.

In the Grands Causses area (Southern France), we study the relationship between the evolution of the river, its incision through time, and the location of the nearby caves. It is commonly accepted that horizontal caves are formed during a period of river stability (no incision) at the elevation of the river. Our original results show that it is wrong in our case study. Therefore, another model of cave formation is proposed that does not rely on direct river control over cave locations.

We show that Icelandic long-runout landslides with longitudinal ridges represent good analogues of Martian landforms. The large record of long-runout landslides with longitudinal ridges emplaced after the Last Glacial Maximum in Iceland offers a unique opportunity to study the possible relation between the development of these landforms and environmental conditions. This could have implications for reconstructing Martian paleoclimatic and paleoenvironmental conditions.

Natural disasters such as landslides and rockfalls are mostly difficult to study because of the impossibility of making in situ measurements due to their destructive nature and spontaneous occurrence. Seismology is able to record the occurrence of such events from a distance and in real time. In this study, we show that, by using a machine learning approach, the mass and velocity of rockfalls can be estimated from the seismic signal they generate.

This study explores the creation of submarine canyons and hanging-wall fans on active faults, which can be defined by gravity-dominated breaching and underflow-dominated diffusion processes. The study reveals the self-similarity in canyon–fan long profiles, uncovers Hack’s scaling relationship and proposes a formula to estimate fan volume using canyon length. This is validated by global data from source-to-sink systems, providing insights into deep-water sedimentary processes.

This study examined how vegetation growing in marshes affects the formation of tidal channel networks. Experiments were conducted to imitate marsh development, both with and without vegetation. The results show interdependency between biotic and abiotic factors in channel development. They mainly play a role when the landscape changes from bare to vegetated. Overall, the study suggests that abiotic factors are more important near the sea, while vegetation plays a larger role closer to the land.

This study investigates the profound impact of deforestation in the Amazon on sediment dynamics. Novel remote sensing data and statistical analyses reveal significant changes, especially in heavily deforested regions, with rapid effects within a year. In less disturbed areas, a 1- to 2-year lag occurs, influenced by natural sediment shifts and human activities. These findings highlight the need to understand the consequences of human activity for our planet's future.

We investigate the reaction of salt structures on ice sheet transgressions. We used a series of sandbox models that enabled us to experiment with scaled-down versions of salt bodies from northern Germany. The strongest reactions occurred when large salt pillows were partly covered by the ice load. Subsurface salt structures may play an important role in the energy transition, e.g., as energy storage. Thus, it is important to understand all processes that affect their stability. 

Benthos is recognized to strongly influence sediment stability, deposition, and erosion. This is well studied on small scales, but large-scale impact on morphological change is largely unknown. We quantify the large-scale impact of benthos by modeling the evolution of a tidal basin. Results indicate a profound impact of benthos by redistributing sediments on large scales. As confirmed by measurements, including benthos significantly improves model results compared to an abiotic scenario.

Pebbles become increasingly rounded during downstream transport in rivers due to abrasion. This study quantifies pebble roundness along the length of two Himalayan rivers. We demonstrate that roundness increases with downstream distance and that the rates are dependent on rock type. We apply this to reconstructing travel distances and hence the size of ancient Himalaya. Results show that the ancient river network was larger than the modern one, indicating that there has been river capture.

Fluvial valleys are ubiquitous landforms, and understanding their formation and evolution affects a wide range of disciplines from archaeology and geology to fish biology. Here, we develop a model to predict the width of fluvial valleys for a wide range of geographic conditions. In the model, fluvial valley width is controlled by the two competing factors of lateral channel mobility and uplift. The model complies with available data and yields a broad range of quantitative predictions.

Rock exhumation from the Earth's interior to the surface is important information for better understanding many geological problems, ranging from mountain building and its decay to resource and hydrocarbon evaluation and exploration. We propose a new stepwise inverse modeling strategy for optimizing the model parameters to mitigate the model dependencies on the initial parameters that are required to simulate the rock exhumation processes.