Recent  

Vegetation influences how rivers change through time, yet the way in which we analyse vegetation is limited. Current methods collect detailed data at the individual plant level or determine dominant vegetation types across larger areas. Herein, we use UAVs to collect detailed vegetation datasets for a 1 km length of river and link vegetation properties to channel evolution occurring within the study site, providing a new method for investigating the influence of vegetation on river systems.

Predicting river morphology evolution is very complicated, especially for mountain rivers with complex morphologies such as the Lac des Gaves reach in France. A 2D hydromorphological model was developed to reproduce the channel's evolution and provide reliable volumetric predictions while revealing the challenge of choosing adapted sediment transport and friction laws. Our model can provide decision-makers with reliable predictions to design suitable restoration measures for this reach.

This paper proposes a method for describing large-wood behavior in terms of the convection equation and the storage equation, which are associated with active sediment erosion and deposition. Compared to the existing Lagrangian method, the proposed method can easily simulate the behavior of large wood in the flow field with active sediment transport. The method is applied to the flood disaster in the Akatani River in 2017, and the 2-D flood flow computations are successfully performed.

Seasonality in precipitation (P) and vegetation (V) influences catchment erosion (E), although which factor plays the dominant role is unclear. In this study, we performed a sensitivity analysis of E to P–V seasonality through numerical modeling. Our results suggest that P variations strongly influence seasonal variations in E, while the effect of seasonal V variations is secondary but significant. This is more pronounced in moderate and least pronounced in extreme environmental settings.

Predicting how shorelines change over time is a major challenge in coastal research. We here have turned to deep learning (DL), a data-driven modelling approach, to predict the movement of shorelines using observations from a camera system in New Zealand. The DL models here implemented succeeded in capturing the variability and distribution of the observed shoreline data. Overall, these findings indicate that DL has the potential to enhance the accuracy of current shoreline change predictions.

Debris flows are mixtures of mud and rocks that can travel at high speeds across steep landscapes. Here, we propose a new model to describe how landscapes are shaped by debris flow erosion over long timescales. Model results demonstrate that the shapes of channel profiles are sensitive to uplift rate, meaning that it may be possible to use topographic data from steep channel networks to infer how erosion rates vary across a landscape.

Packing models promise efficient and accurate porosity predictions of fluvial sediment deposits. In this study, three packing models were reviewed, calibrated, and validated. Only two of the models were able to handle the continuous and large grain size distributions typically encountered in rivers. We showed that an extension by a cohesion model is necessary and developed guidelines for successful predictions in different rivers.

A novel, artificial-intelligence-based riverbed sediment analysis methodology is introduced that uses underwater images to identify the characteristic sediment classes. The main novelties of the procedure are as follows: underwater images are used, the method enables continuous mapping of the riverbed along the measurement vessel’s route contrary to conventional techniques, the method is cost-efficient, and the method works without scaling.

River deltas encompass many depositional environments (like channels and wetlands) that interact to produce coastal environments that change through time. The processes leading to sedimentation in wetlands are often neglected from physical delta models. We show that wetland sedimentation constrains flow to the channels, changes sedimentation rates, and produces channels more akin to field-scale deltas. These results have implications for the management of these vulnerable coastal landscapes.

In glacial landscapes, debris supply rates vary spatially and temporally. Rockwall erosion rates derived from cosmogenic ¹⁰Be concentrations in medial moraine debris at five Swiss glaciers around Pigne d'Arolla indicate an increase in erosion from the end of the Little Ice Age towards deglaciation but temporally more stable rates over the last ∼100 years. Rockwall erosion rates are higher where rockwalls are steep and north-facing, suggesting a potential slope and temperature control.

We measured rock strength and amount of fracturing in the two different rock types, sandstones and carbonates, in Last Chance Canyon, New Mexico, USA. Where there is more carbonate bedrock, hills and channels steepen in Last Chance Canyon. This is because the carbonate-type bedrock tends to be more thickly bedded, is less fractured, and is stronger. The carbonate bedrock produces larger boulders than the sandstone bedrock, which can protect the more fractured sandstone bedrock from erosion.

Rivers can cut into rocks, and their strength modulates the river's erosion rates. Yet, which properties of the rock control its response to erosive action is poorly understood. Here, we describe parallel experiments to measure rock erosion rates under fluvial impact erosion and the rock's geotechnical properties such as fracture strength, elasticity, and density. Erosion rates vary over a factor of a million between different rock types. We use the data to improve current theory.

We investigated the influence of sediment transport modes on the formation of bedforms using theoretical analysis. The results of the theoretical analysis were verified with published data of plane beds obtained by fieldwork and laboratory experiments. We found that suspended sand particles can promote the formation of plane beds on a fine-grained bed, which suggests that the presence of suspended particles suppresses the development of dunes under submarine sediment-laden gravity currents.

Floods draining beneath an ice cap are hazardous events that generate six different short- or long-lasting types of seismic signals. We use these signals to see the collapse of the ice once the water has left the lake, the propagation of the flood front to the terminus, hydrothermal explosions and boiling in the bedrock beneath the drained lake, and increased water flow at rapids in the glacial river. We can thus track the flood and assess the associated hazards better in future flooding events.

Preservation bias often hampers the extraction of sea level changes from the stratigraphic record. In this contribution, we use a forward stratigraphic model to build three synthetic subtropical fringing reefs for a site in southwestern Madagascar (Indian Ocean). Each of the three synthetic reefs represents a different ice sheet melt scenario for the Pleistocene. We then evaluate each resultant reef sequence against the observed stratigraphic record.

Quantitative knowledge of water availability on high mountain rock slopes is very limited. We use a numerical model and field measurements to estimate the water balance at a steep rock wall site. We show that snowmelt is the main source of water at elevations >3600 m and that snowpack hydrology and sublimation are key factors. The new information presented here can be used to improve the understanding of thermal, hydrogeological, and mechanical processes on steep mountain rock slopes.

We combined data from satellite images with data measured from a kayak in order to understand the propagation of fine sediment in the Vjosa River. We were able to find some storm-activated and some permanent sources of sediment. We also estimated how much fine sediment is carried into the Adriatic Sea by the Vjosa River: approximately 2.5 Mt per year, which matches previous findings. With our work, we hope to show the potential of open-access satellite images.

We compare models of erosion against a global compilation of long-term erosion rates in order to find and interpret best-fit parameters using an iterative search. We find global signals among exponents which control the relationship between erosion rate and slope, as well as other parameters which are common in long-term erosion modelling. Finally, we analyse the global variability in parameters and find a correlation between precipitation and coefficients for optimised models.

We examine differences in glacier extent in the Olympic Mountains, USA, where modern precipitation in east-facing valleys is only 50 % of that in west-facing valleys. During the Last Glacial Period, there were very small glaciers in the east and very large glaciers in the west. We use climate data and glacier models to show that the modern spatial pattern of precipitation is likely to have been similar during the past glaciation and may be sufficient to explain the asymmetry of glacier extent.