Sand and mud may take many different pathways within a river as they travel from inland to the coast. During the trip, grains may be exposed to daylight, resetting a signal trapped within certain minerals. The signal can be measured in a laboratory to estimate the time since last light exposure. Here, we measure the trapped signal of sand and mud grains from the Mississippi River and its banks. We use this information to infer sediment pathways. Such knowledge is useful for delta management.

Slow-moving landslides are a major hazard but their movement mechanisms during earthquakes and rainstorms are not fully understood. We collected samples from a slow-moving landslide complex in New Zealand and subjected them to a range of porewater pressure and dynamic stress scenarios in a dynamic back-pressured shear box. Our results show how the complex movement patterns, observed in many large slow-moving landslides, may be mobilized by strong earthquakes and significant rain events.

We use a statistical earthquake generator to investigate the influence of fault activity on river profile development and on the formation of co-seismic knickpoints. We find that the magnitude distribution of knickpoints resulting from a purely seismic fault is homogeneous. Shallow aseismic slip favours knickpoints generated by large-magnitude earthquakes nucleating at depth. Accounting for fault burial by alluvial cover can modulate the topographic expression of earthquakes and fault activity.

We apply machine learning techniques to quantify and explain differences between recent palaeoclimates with regards to factors that are important in shaping the Earth's surface. We find that changes in ice cover, near-surface air temperature and rainfall duration create the most distinct differences. We also identify regions particularly prone to changes in rainfall and temperature-controlled erosion, which will help with the interpretation of erosion rates and geological archives.

Assessing the impact of glaciation at the Earth's surface requires simultaneous quantification of the impact of climate variability on past glacier fluctuations and on bedrock erosion. Here we present a new approach for evaluating post-glacial bedrock surface erosion in mountainous environments by combining two different surface exposure dating methods. This approach can be used to estimate how bedrock erosion rates vary spatially and temporally since glacier retreat in an alpine environment.

We performed seven physical experiments to explore terrace formation and sediment export from a braided alluvial river system that is perturbed by changes in water discharge, sediment supply, or base level. Each perturbation differently affects (1) the geometry of terraces and channels, (2) the timing of terrace formation, and (3) the transient response of sediment discharge. Our findings provide guidelines for interpreting fill terraces and sediment export from fluvial systems.

The paper describes a model that simultaneously evolves both the soil profile and the landform, creating a coupled soilscape–landscape evolution model. The physics in the model is presented and justified from physical processes. The behaviour of the model is then explored for a variety of process formulations for a one-dimensional hillslope consisting of a flat upslope, steep midslope, and flat lowlands, exploring the erosion and deposition behaviour, and soil profile evolution over time.

We used high-resolution topography collected over 4 years to investigate how two watersheds in Colorado responded to the June 2012 High Park Fire and an extreme flood in September 2013. After the fire, sediment was eroded from the hillslopes and deposited in valley bottoms, and the large flood eroded much of this deposited sediment. Patterns of erosion and deposition were related to landscape characteristics, burn severity, and rainfall, which may inform future post-fire treatment strategies.

We propose a new algorithm to solve the problem of flow routing across local depressions in the topography, one of the main computational bottlenecks in landscape evolution models. Our solution is more efficient than the state-of-the-art algorithms, with an optimal linear asymptotic complexity. The algorithm has been designed specifically to be used within landscape evolution models, and also suits more generally the efficient treatment of large digital elevation models.

This research has been conducted to develop the use of passive acoustic monitoring (PAM) for bedload monitoring in rivers. Monitored bedload acoustic signals depend on bedload characteristics (e.g., grain size distribution, fluxes) but are also affected by the environment in which the acoustic waves are propagated. This study focuses on the determination of propagation effects in rivers. An experimental approach has been conducted in several streams to estimate acoustic propagation laws.

A simple model is proposed to improve the sediment concentration monitoring in the large rivers of the Peruvian Amazon from an index concentration sampled in the flow. This powerful tool for optimizing the concentration sampling would allow for detailed uncertainty analysis on the sediment fluxes. It could be coupled with remote sensing and hydrological modeling to serve as a step toward the development of an integrated approach for assessing sediment fluxes in poorly monitored basins.

We used continuous seismic data from two small aperture geophone arrays deployed in the region above Davos in the eastern Swiss Alps to develop a machine learning workflow to automatically identify signals generated by snow avalanches. Our results suggest that the method presented could be used to identify major avalanche periods and highlight the importance of array processing techniques for the automatic classification of avalanches in seismic data.

Typically, we think of a shoreline growing with a smooth line separating the land and the water. If the growth is unstable, however, the land–water front will exhibit a roughness that grows with time. Here we ask whether the growth of deltaic shorelines cab be unstable. Through mathematical analysis we show that growth is unstable when the shoreline is building onto an adverse slope. The length scale of the unstable signal in such a case, however, might be obscured by other geomorphic processes.

Representing the surface of the Earth on an equally spaced grid leads to errors and uncertainties in derived slope and aspect. Using synthetic data, we develop a quality metric that can be used to compare the uncertainties in different datasets. We then apply this method to a real-world lidar dataset, and find that 1 m data have larger error bounds than lower-resolution data. The highest data resolution is not always the best choice – it is important to consider the quality of the data.