Instrumented “smartrock” tracer clasts hold the potential to quantify unique and useful sediment transport statistics from the point of view of each grain--a Lagrangian reference frame.  In this presentation we synthesize lessons learned based on two successful smartrock field deployments in natural mountain rivers during snowmelt floods. Our sensors contain accelerometers, data loggers and batteries.  We have primarily used smartrock data to simply measure the exact timing of grain rests and motions, although future analyses and additional sensors could be used to measure many more aspects of transport.  In addition to methodological suggestions and challenges, we show how smartrock data can be used to measure (a) rest and hop time scaling over a range of timescales, and (b) changes in thresholds of motion through time as a function of discharge.  In data from Halfmoon Creek, Colorado, USA, and Reynolds Creek, Idaho, USA, rest duration scaling is heavy-tailed and varies systematically with both timescale and shear stress.  The shear stress dependence suggests that bedload clast dispersal becomes less superdiffusive as flood size becomes larger. We identify several likely diffusion regimes, and hypothesize how timescales of flow variability from turbulence to daily discharge cyclicity may cause scaling breaks over minutes to hours.  In addition, thresholds of motion tend to increase with cumulative flow (reducing transport rates over time), but also decrease with increases in discharge (increasing transport rates until grains restabilize at the higher flow). The threshold data are used to calibrate and partially validate a new model for discharge-dependent threshold evolution. Finally, we brainstorm ways in which smartrocks could be used to explore sediment transport questions in other Earth surface environments.

How to cite: Johnson, J., Pretzlav, K., Olinde, L., Bradley, D. N., and Masteller, C.: Rocks and Rivers that Remember:  Using Smartrocks To Constrain Bedload Transport Statistics and Evolving Thresholds of Motion in Natural Mountain Rivers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9016, https://doi.org/10.5194/egusphere-egu22-9016, 2022.

An increase in storminess under climate change and population pressure are resulting in an increase in landslide and flood events, in the UK and globally, and threatening the defences put in place to mitigate these hazards. Monitoring of unstable hillslopes and flood-prone rivers as well as structures designed to protect these is vital. Furthermore, as landslides and floods are both triggered by heavy rainfall, often occurring simultaneously, and may interact to generate cascading hazards, we need integrated approaches for their management.

A key objective of the SENSUM project (Smart SENSing of landscapes Undergoing hazardous hydrogeomorphic Movement, https://sensum.ac.uk) is to develop a smart sensor to be embedded within boulder and wood debris in landslide and flood prone sites to detect and track hazardous movement. These low-power, low-cost devices communicate this in near real time via Internet of Things networks. Several wireless sensor networks (WSNs) have been installed on landslides and in flood-prone rivers around the UK, involving insertion of devices into debris, installation of long-range wireless network gateways, and camera installation for validation of movements. The developed system architecture also permits straightforward integration of additional third-party sensors and open data. We aim to build a dataset with which hazardous movement can be detected using machine learning and communicated in near real time via alerts and web services to relevant stakeholders. This effort will be complemented by laboratory experiments.

How to cite: Roskilly, K., Bennett, G., Curtis, R., Egedusevic, M., Jones, J., Whitworth, M., Dini, B., Luo, C., Manzella, I., and Franco, A.: SENSUM project, Smart SENSing of landscapes Undergoing hazardous hydrogeomorphic Movement, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10289, https://doi.org/10.5194/egusphere-egu22-10289, 2022.

Events such as landslides, rockslides, debris flows, and flash floods can have destructive and possibly fatal outcomes. In these events, boulders and cobbles are carried downstream under the action of gravity and the study of their transport and movement can give important insight on the dynamics and hazards related to these processes. Recently, boulder motion has been investigated by the use of smart sensors in geomorphology applications both in lab and field experiments. Smart sensors are small and light-weighted devices that are able to collect different environmental data with low battery consumption communicating to a server through a wireless connection. However, the reliability of smart sensors still needs to be evaluated for monitoring purposes and for developing early warning systems.

In the present study, dedicated laboratory experiments were designed to assess the ability of the sensors to detect movements and distinguish between intensity and type of movement (e.g. sliding or rolling) within a well-constrained setting. For this purpose, a tag equipped with an accelerometer, a gyroscope, and a magnetometer sensor has been installed inside a cobble of 10.0 cm diameter within a borehole of 4.0 cm diameter, closed hermetically before each experiment. The experiments consisted in letting the cobble fall on an experimental table composed of an inclined plane of 1.5 m, followed by a horizontal one of 2.0 m. The inclined plane can be tilted at different angles (18˚- 55˚) and different types of movement have been generated by letting the cobble roll, bounce, or slide. Sliding was generated by embedding the cobble within a layer of sand. The position of the cobble travelling down the slope was derived from camera videos by a tracking algorithm developed within the study.

Raw sensor data allowed detection of movement and separation of two modes of movement, namely rolling and sliding. Furthermore, raw datasets approximated the magnitude of movement even without any calibration. On the other hand, by coupling smart-sensor measurements and camera-based positions, it was possible to develop a filter to derive reliable values for the position, orientation, velocity, and acceleration to fully represent cobble motions. These findings show how the raw data can provide information about the type and an indication of the magnitude of movement, and confirm the potential to use these sensors to improve early warning systems, although further studies are in progress to assess response time in a field setting. At the same time, the development of a filter that gives more precise and reliable data from the sensors enables assessment of the rotational and linear acceleration of the tracked element. If used in more sophisticated lab and field experiments, this has the potential to give new insights on the behaviour of cobbles within different types of processes and can shed new light on the dynamics of complex hazardous flows.

How to cite: Sgarabotto, A., Manzella, I., Roskilly, K., Luo, C., Clark, M., Franco, A. M. A., Bennett, G. L., and Raby, A.: Investigating boulder motions with smart sensors in lab experiments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10198, https://doi.org/10.5194/egusphere-egu22-10198, 2022.

Storm waves transport and sort coarse gravel along coasts. This fundamental process is important under changing sea-levels and increased storm frequency and intensity. However, limited information on intra-storm clast motion restricts theory development for coastal gravel sorting and coastal management of longshore transport. Here, we use ‘smart boulders’ equipped with loggers recording underwater, real-time, intra-storm clast motion, and measured longshore displacement of varied-mass marked boulders during storms. We utilize the unique setting of the Dead Sea shores where rapidly falling water levels allow isolating boulder transport during individual storms. Guided by these observations, we develop a new model quantifying the critical wave height for a certain clast mass mobilization. Then, we obtain an expression for the longshore clast displacement under the fluid-induced pressure impulse of a given wave. Finally, we formulate the sorting enforced by wave-height distributions during a storm, demonstrating how sorting is a direct manifestation of regional hydroclimatology.

How to cite: Eyal, H., Enzel, Y., Meiburg, E., Vowinckel, B., and G Lensky, N.: How does Coastal Gravel get Sorted under Stormy Longshore Transport?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6075, https://doi.org/10.5194/egusphere-egu22-6075, 2022.

Understanding sediment transport dynamics is key to understanding landscape evolution, and has important implications for engineering projects, aquatic ecosystem dynamics, and transmission of water-borne diseases. Multiple elevated temperature infra-red stimulated luminescence (MET-IRSL) has great potential to provide detailed information on the transport of sediments using infra-red light to stimulate the luminescence signal of feldspars. MET-IRSL uses a series of elevated temperature stimulations to access multiple signals with different characteristic rates of signal reduction by light exposure (bleaching), for example, during grain transport. During deposition and storage, trapped charge accumulates, leading to growth of the different IRSL signals, until the grain is again subject to transport. Applied in this manner, MET-IRSL measurements can constrain past sediment burial and exposure histories.

MET-IRSL measurements of different grain and clast sizes (e.g. silt, sand, pebbles and cobbles) can provide a range of sediment transport information, providing further constraint to sediment dynamics and system behaviour. Different clast size groups are associated with varied ways to structure the MET-IRSL measurements, e.g. depth bleaching profiles observed within pebbles and cobbles. In this presentation we demonstrate the potential of combining these approaches, and of constructing time-space equivalence models for real world situations, including the site of Allt Dubhaig, Perthshire, Scotland.

How to cite: Spano, T. M. C., Rhodes, E. J., and Hodge, R. A.: Tracing sediment movement using Infra-Red Stimulated Luminescence, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9698, https://doi.org/10.5194/egusphere-egu22-9698, 2022.

Previous experiments on the restoration of sediment cover in semi-alluvial channels with irregular boundaries have shown that coarse size fractions of the bedload are dispersed faster over a bare bed than the finer fractions, and that the coarse fraction helps to build a set of skeleton bars that are later covered by finer sediment. Unsteady flow experiments in the same channel confirmed these trends over a bed of mobile sediment and further indicate strong spatial gradients in bedload transport and deposition. Despite these advances, a methodological gap remains in the tracking of bedload sediment during the experiments. In this study we advance a tracking technique for obtaining vectors of particle displacements during unsteady flow experiments. Methods involve painting the coarsest three sediment fractions with different colours of fluorescent paint and illuminating a region of interest within the flume with ultraviolet lights (wavelength 400-410 nm) during the experiment, which results in the painted gravel appearing in bright neon colors while the water remains transparent and dark (i.e. the ‘Disco’). We use a Panasonic BGH1 camera recording at 60 fps and a resolution of 1080 x 1920 pixels to film a region of interest in the channel roughly 0.25 m wide 1.0 m long.  With this technique we are able to identify the displacements of the two coarsest size fractions. For the third size fraction the tracers were too numerous and too small to be tracked with confidence. Analysis of the videos occurred in three steps: 1) color segmentation to isolate the size class of interest, 2) application of TracTrac algorithm (Heyman, 2019) to identify particle paths, and 3) post-processing to reduce two types of error.  The errors are likely related to the irregular water surface, which can result in particles appearing to ‘vibrate’ in place when they are not moving, and also result in a continuous tracer path being broken into a series of shorter discontinuous paths.  Overall the technique appears to be useful for characterizing spatial variability at the threshold of motion and delimiting preferential transport pathways. Future improvements in resolution and tracer concentration should help to reduce the minimum size of tracer that can be tracked with confidence.

How to cite: Montakhab, F. A., Iun, M., and MacVicar, B.: Disco Gravel: Image-based bedload tracking in shallow water flume experiments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10761, https://doi.org/10.5194/egusphere-egu22-10761, 2022.

Accurate measurements of bedload flux in mountain rivers remain an important issue in hydraulic engineering. Diverse acoustic-based monitoring devices have been utilized to record continuous vibration signals triggered by bedload particle impacts, aiming to translate bedload information such as transport rates and grain size distributions from the generated signals. However, the spatial variability of bedload impacts on the river bed (or on an impact plate) contributes to uncertainty in the calibration relationship between the recorded signal and bedload flux.

The present study develops an acoustic model based on microphone data to determine the characteristics of the air shock waves induced by the bedload particle impacts on the bed. A phased microphone array (PMA) system is established on the plane underside of an impact plate flush with the river bed, which includes a number of mini microphone elements set apart from each other at a specific spacing distance. The model allows for a calculation of the cross-power matrix of the air vibrations recorded by each microphone of the array. The acoustic vibrations recorded on the PMA plane are subsequently reconstructed and transformed to an acoustic image of the sound source on a scanning plane of the plate surface, considering different air propagation models corresponding to monopole, multipole and moving sources. As a result, the locations of the bedload particle impacts can be detected, connecting to the central coordinates of the reconstructed sound source. The signal amplitude extracted from the sound intensity in the reconstructed acoustic image potentially provides a better way for classifying bedload particle size than just utilizing the raw data recorded by one of the microphone elements.

The findings of this study contribute to the measurement and monitoring of the bedload transport with an acoustic system, illustrating a promising way to identify bedload impact locations, which could be helpful in grain size classification during the transport process.

How to cite: Chen, Z., He, S., Nicollier, T., Ammann, L., Badoux, A., and Rickenmann, D.: An acoustic model for monitoring bedload transport with microphones array, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6702, https://doi.org/10.5194/egusphere-egu22-6702, 2022.

Bed load transport is a critical parameter in the study of landscape evolution and also provides valuable information for problems in the fields of ecology, river and landuse management, and civil engineering. Bed load transport is difficult to assess due to its stochastic nature and highly variable transport rates, and traditional measurement techniques have struggled to capture the spatial and temporal variability of bed load transport. In recent years, bed load monitoring based on seismological observations has emerged, which allows non-invasive and continuous indirect measurements. However, there still remains a significant challenge to independently characterise the seismic signature of bed load from other sources of noise, such as turbulence. Our study aims to explore seismic data recorded at the highly braided River Feshie in Scotland, which has undergone significant morphological change in its history and has been highly monitored over the last couple decades through Digital Elevation Models. Since the deployment of our seismometers in December 2020 we have captured three independent high flow events plus an isolated earthquake, which are being used to determine the environmental signals and the site specific signal characteristics. In some previous studies, an observed hysteretic relationship between seismic power and hydrological parameters has been interpreted as being characteristic of bed load transport. From the data we have gathered we have observed a hysteresis in the signals, and through Shields calculations it is suggested that bed load transport would be expected during these events. However, without independent constraints we do not feel we can be absolutely certain that this behaviour is a result of bed load transport. Our ongoing study therefore aims to combine multiple physical measurement techniques, such as hydroacoustic measurements, time-lapse imagery and seismic observations, to try and pinpoint what is contributing to the seismic signals recorded and how we can isolate the bed load transport component.

How to cite: Matthews, B., Naylor, M., Sinclair, H., Dietze, M., Williams, R., and Cuthill, C.: Isolating bed load transport from river induced seismic signals, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7633, https://doi.org/10.5194/egusphere-egu22-7633, 2022.

Measuring the concentration and grain size of suspended sand in rivers continuously remains a scientific challenge due to its pronounced spatio-temporal variability. Vertical and lateral gradients within a river cross-section require spatially-distributed water sampling at multiple verticals and depths. However, this classical approach is time-consuming and offers limited temporal resolution. Sampling is particularly difficult in presence of a bimodal suspension composed of fine sediment and a sand fraction, notably if the fine/sand ratio varies with time. The aim of this study is to establish time series of sand concentration and grain size by improving temporal resolution using an acoustic multi-frequency method based on acoustic attenuation and backscatter to measure the suspension indirectly. Experiences of Moore et al. (2012) and Topping & Wright (2016) with Horizontal Acoustic Doppler Current Profilers (HADCPs) show that dual-frequency inversion can separate the fine sediment fraction dominating acoustic attenuation from the sand fraction dominating acoustic backscatter. Concentration and grain size of suspended sediment, both the fine and sand fraction, can be quantified by signal inversion after correction for transmission losses.

Applying existing dual-frequency, semi-empirical methods in a typical Piedmont river (River Isère, France) remains a challenge due to the high concentrations and a broad bimodal distribution. Two monostatic HADCPs of 400 and 1000 kHz were installed at a hydrometric station of the Isère at Grenoble Campus where discharge and turbidity have been recorded for more than 20 years. Using frequent isokinetic water samples obtained with US P-72 and US P-06 samplers close to the ensonified volume, a relation between acoustic signal and the sediment concentration and grain size can be determined. Simultaneously, total sand flux and grain size distribution are calculated performing solid gaugings using Delft bottle samples and ADCP measurements in the entire cross-section. The method using index concentration and grain size in the HADCP measurement area is then used to evaluate the total sand flux and average grain size time-series in the cross-section.

First results show good correlations between the fine sediment concentration and the sediment attenuation for both frequencies. Specific extreme events (e.g. debris flows, dam flushes or spring floods) show distinct signatures in acoustic attenuation, backscatter and ratio between the two frequencies. During a debris flow (concentration up to 5.3 g/l), attenuation reached 1.6 and 3 dB/m for 400 respectively 1000 kHz, but no peak in backscatter intensity, whereas a spring flood (up to 4 g/l with at least 50 % sand) caused major peaks in attenuation and backscatter. Pronounced hysteresis during the events and time-varying ratio between attenuation due to sediments measured by 400 and 1000 kHz indicate suggest that the grain size distribution may vary. Relating sand concentration from physical samples with beam-averaged backscatter may elucidate changes in grain size more precisely. Existing heterogeneities of concentration and grain size along the acoustic beam contradict the homogeneous distribution supposed by the method and require local analysis based on local concentration and grain size characteristics.

How to cite: Laible, J., Camenen, B., Le Coz, J., Dramais, G., Lauters, F., and Pierrefeu, G.: Establishing time series of flux and grain size of suspended sand in rivers using an acoustic method, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7344, https://doi.org/10.5194/egusphere-egu22-7344, 2022.

The structure and function of rivers is directly related to bedload transport which is difficult to measure due to its spatial heterogeneity and the logistic constraints of field measurements. These difficulties have given rise to the morphological method wherein sediment transport is inferred from changes in morphology and estimates of the distance traveled by sediment during a flood, its path length. However, current methods for estimating path length are time and labor intensive, have low recovery rates, and are limited to some morphological units. We propose a method to estimate path length from repeat digital elevation models (DEM’s of difference i.e. DoDs) which are requisite for the morphological method. We interpret the pattern of erosion and deposition downstream as a signal and apply Variational Mode Decomposition (VMD), a signal processing method, to quantify the periodicity as a proxy for path length. We developed this method using flume experiments with measured sediment flux and applied it to published field data with tracer measurements for validation. The preliminary results provide a range of values on the same order of magnitude as measured tracer and flux data and are coherent with channel geometry. This method provides a reasonable estimation of path length based solely on remotely sensed data and a range of plausible sediment fluxes associated with specific channel morphological processes through DoD interpretation.

How to cite: Capito, L., Bizzi, S., Surian, N., and Bertoldi, W.: Particle path length estimation: a signal processing approach , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1378, https://doi.org/10.5194/egusphere-egu22-1378, 2022.

Taiwan is located on the convergent boundary of the Philippine Sea Plate and the Eurasian Plate. Due to the active orogenic movement, the rock formations are fragmented and the weak joints are developed. In recent years, heavy rainfall accompanied with the occurrence of river surges carry a large amount of broken sand and gravel to the downstream. The accumulation of a large amount of sand and gravel in the river may threaten the safety along the river bank, such as channel diversion and flooding. Therefore, the river channel needs to be dredged regularly to reduce the risk to the residents and properties. Because the dredging area is scattered and difficult to reach, on-site measurement has become a time-consuming and labor-intensive method. With the improvement of satellite technology, it is feasible to use efficient remote sensing technology to generate point clouds and a surface elevation model (DSM) for monitoring purposes. However, several problems still exist in this technology, including the scatteredness of control points and feature points, instability of the platform, varying imaging conditions, and time differences in the matching process. To solve the DSM errors caused by these problems, this study uses 3-D point cloud registration method to align the horizontal and vertical directions and tries to reduce elevation system error due to the failure of co-registration. First, feature description, extraction, and feature matching are performed. Second, the iterative closest point algorithm (ICP) is used to closely match two sets of point clouds after coarse alignment. Finally, elevation difference between two dets of DSM is verified with ground measurement data and the accuracy of the point cloud registration is assessed. We use a dredging area in Laonong River, Taiwan, as an example to monitor gravel volume change in river channel by high resolution Pléiades images and UAV in different time periods. Our preliminary results show that the spaceborne technology could achieve submeter level accuracy in monitoring height changes in each transect.

How to cite: Lin, L.-S. and Tseng, K.-H.: Monitoring Gravel Volume Change by Very High Resolution Satellite Image Stereopairs, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11367, https://doi.org/10.5194/egusphere-egu22-11367, 2022.

We have measured unit sediment fluxes and their relationship to unit water discharges over 7 orders of magnitude on hillslopes of up to 350 m in length in Arizona. Unit sediment and water fluxes were measured using a novel combination of instrumented monitoring plots and repeat photogrammetric surveys analyzed volumetrically. The monitoring plots, which are ideal for measuring sediment fluxes in relatively planar portions of the landscape dominated by slope-wash erosion, funnel water and sediment into a detention basin where bedload sediment fluxes are measured and then into a flume where water discharges and suspended sediment fluxes are measured at 1-minute intervals using a pressure transducer and calibrated turbidity sensor. Repeat photogrammetric surveys complement the monitoring plots by measuring sediment fluxes in rills that tend to form in areas of convergent flow during intense rain events. The volumetric change in each pixel is digitally routed to determine the volumetric sediment flux in each pixel associated with rilling during a rain event. Unit water discharges for every pixel cannot be measured directly but are estimated using a rainfall-runoff model calibrated to the monitoring plot data. The relationship between unit sediment fluxes and unit water dischargees exhibits two piecewise power-function relationships with different exponents characterizing the slope-wash and rill-dominated regimes. We developed a novel landscape evolution model, inspired by the SIBERIA model but improved in specific ways optimized for hillslopes, that uses the measured piecewise power-function relationship between unit sediment fluxes and unit water discharges to predict hillslope evolution from time scales of individual events to decades. The predictions of the model are validated using ten years of observation of rill development at the study site. We provide equations for estimating the parameters of the piecewise power-function relationship for other hillslopes with different cover characteristics. This measurement and modeling framework must be tested at more study sites but is potentially useful for predicting the erosion of any hillslope, including alternative designs for landscape rehabilitations following mining or other anthropogenic disturbances.   

How to cite: Pelletier, J., Abramson, N., Chataut, S., Ananthanarayan, S., and Ludwick, D.: Measurement and modeling of slope-wash and rill erosion on hillslopes using a novel combination of instrumented plots and remote sensing, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6666, https://doi.org/10.5194/egusphere-egu22-6666, 2022.