GM2.1
Presentations: Thu, 26 May | Room K2
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.
The use of Inertial Measurement Units (IMUs) in geomorphological studies has exploded during the last decade. Scientists are deploying IMUs in a range of settings: from single grain flume experiments to full scale landslide motions and from capturing rock falls to measuring flows in glacial environments.
The vast majority of these experiments deploy sensing units that are partly customised for each application. However, there are limits to the level of IMU customisation geomorphologists can do as they rarely have access to bottom-up sensor assembly and production lines. Commercial IMUs and IMU components are built and calibrated for very different uses than the monitoring of dynamic sediment transport regimes, such as integration into electronic devices, wearables or Internet of Things applications.
Deploying commercial IMUs outside their nominal operational range has two main implications, the first being methodological. As the sensor is partly a "black box", we are obliged to do extensive testing in a trial-and-error manner and think deeply about the underlying physics of IMUs. If such difficulties are not acknowledged the results become difficult to interpret in the context of sediment movement.
The second implication concerns standardisation. The more our community uses commercial sensors and analytical tools, the more apparent becomes the need for open-source pre-processing and processing workflows that are fully validated and universally available to ensure comparability of published results.
This presentation aspires to contribute to this open debate about IMU sensors in geomorphology. The focus will be on the sensing requirements for grain motion detection, force capture and tracking by IMUs in the context of sediment transport. The presented calculations will use results published before the emergence of IMUs in geomorphology for a range of environments (fluvial, coastal, aeolian and glacial).
The above requirements capture will be accompanied by a meta-analysis of published IMU data in geomorphic applications which will be classified according to the exact type of sensor (accelerometer, full IMU, GPS (or equivalent)-aided IMU) and the sensors' specs (mainly sensing range and frequency).
Finally, this presentation will explore the case study of using a commercially available IMU for the capture of fluvial sediment interactions. The deployed IMU will be subjected to a series of simple physical experiments (e.g., drop tests) and then deployed to a flume setting designed to model grain-grain and grain-substrate collisions. The novelty here is the use of an independent very high-speed camera (1μs exposure frame rate) to monitor the sensor during calibration, which allows for the coherent propagation of uncertainty for all the experiments. All the results are presented within a processing workflow based on free, open-source R libraries.
How to cite: Gadd, C. and Maniatis, G.: Smart-pebbles in sediment transport studies: state of the art, future directions, and unsolved problems., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8757, https://doi.org/10.5194/egusphere-egu22-8757, 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.
Woody debris dams/leaky dams are an increasingly popular Natural Flood Management (NFM) measure in low order tributaries, with preliminary evidence suggesting that they are effective in attenuating flood peaks and reducing flood risk. However, the stability of these dams is not widely monitored, and thus there is a poor evidence base for best design practice with respect to the long-term integrity of such features. This is particularly pertinent given the threat posed to downstream infrastructure by woody debris carried in floodwaters after potentially catastrophic dam failure. There is also a lack of research into how effective dams of different designs are at holding back large wood and sediment transported by the flow and reducing the impact of flood debris on downstream infrastructure, including bridges, culverts etc. In the SENSUM project (Smart SENSing of landscapes Undergoing hazardous hydrogeomorphic Movement, https://sensum.ac.uk), we are developing and applying innovative sensor technology to assess the stability of different woody debris dam designs and build an evidence base to inform policy on this NFM practice locally and nationally. We also use these sensors to track woody debris and assess how effective dams are at trapping and retaining large wood debris and cobble-sized sediment. This paper addresses these questions at several field sites across the UK and in laboratory experiments to report quantitative data which evaluate the literal success/failure of NFM interventions and how these may impact the future design of such approaches.
How to cite: Egedusevic, M., Bennett, G., Roskilly, K., Sgarabotto, A., Manzella, I., Raby, A., Boulton, S. J., Clark, M., Curtis, R., Panici, D., and Brazier, R. E.: Monitoring the stability of leaky dams and their influence on debris transport with innovative sensor technology on the SENSUM project, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8642, https://doi.org/10.5194/egusphere-egu22-8642, 2022.
Bedload transport is a fundamental process by which coarse sediment is transferred through landscapes by river networks and may be well described stochastically by distributions of grain step length and rest time obtained through tracer studies. To date, none of these published tracer studies have specifically investigated the influence of large wood in the river channel on distributions of step length or rest time, limiting the applicability of stochastic sediment transport models in these settings. Large wood is a major component of many forested rivers and is increasing due to anthropogenic ‘Natural Flood Management’ (NFM) practices. This study aims to investigate and model the influence of large wood on grain-scale bedload transport.
We tagged 957 cobble – pebble sized particles (D50 = 73 mm) and 28 pieces of large wood (> 1 m in length) with RFID tracers in an alpine mountain stream. We monitored the transport distance of tracers annually over three years, building distributions of tracer transport distances. Empirical data was used in linear mixed modelling (LMM) statistical analysis, determining the relative influence proximity to wood had on likelihood of entrainment, deposition, and the transport distances of sediments.
Tracer sediments accumulated both up and downstream of large wood pieces, with LMM analysis confirming a reduction in the probability of entrainment of tracers closer to wood in all three years. Upon remobilisation, tracers entrained from positions closer to large wood had shorter subsequent transport distances in each year. In 2019, large wood also had a trapping effect, significantly reducing the transport distances of tracer particles entrained from upstream, i.e. forcing premature deposition of tracers. This study demonstrates the role of large wood in influencing bedload transport in alpine stream environments, with implications for both natural and anthropogenic addition of wood debris in fluvial environments.
How to cite: Clark, M., Bennett, G., Ryan, S., Sear, D., and Franco, A.: Capturing the Influence of Large Wood on Fluvial Bedload Transport with RFID Tracers and Linear Mixed Modelling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2894, https://doi.org/10.5194/egusphere-egu22-2894, 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.
Sand transport by wind displays dynamic structure and organisation in the form of streamers (aka ‘sand snakes’) that appear, meander and intertwine, and then dissipate as they are advected downwind. These patterns of saltating grain populations are thought to be initiated and controlled by eddies in the turbulent boundary layer airflow that scrape over the bed surface raking up sand into entrainment. Streamer behaviour is thus fundamental to understanding sand transport dynamics, in particular its strong spatio-temporal variability, and is equally relevant to granular transport in other geophysical flows (fluvial, submarine).
This paper presents findings on sand transport rates and streamer dynamics observed in a field experiment on a beach, by analysing imagery from 30Hz video footage, combined with 50Hz sand transport data from laser particle counters (‘Wenglors’), all taking place over an area of ~10 m2 and over periods of several minutes.
Mapping of streamers and saltation cloud density is compared with fluctuations in sand transport rate measured at the Wenglors. Large-Scale Particle Image Velocimetry (LSPIV) is applied to determine advection vectors that can be matched against in-situ measurements of airflow and sand transport. Analysing video-imagery of aeolian sand transport faces several challenges, however, most notably the difficulties of background subtraction to differentiate the moving streamers from the underlying beach surface.
How to cite: Baas, A.: Video-Imagery Analysis of Aeolian Sand Transport over a Beach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7639, https://doi.org/10.5194/egusphere-egu22-7639, 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.
Bedload transport estimation is required for a variety of engineering and ecological applications. Measurement of bedload transport by direct sampling is expensive and time-consuming and rarely captures the spatio-temporal variability of bedload transport. Recent research shows that passive acoustic technology, such as hydrophone, has the potential to monitor bedload transport by recording Self Generated Noise (SGN) resulting from particles collision. In this work, we present a calibration curve relating specific bedload flux to cross-sectional acoustic power for 40 experiments conducted on 13 French rivers. We present the measurement protocols for bedload transport and SGN, the results of the campaign, and discuss the physics of the relationship between the measured quantities.
How to cite: Nasr, M., Geay, T., Zanker, S., and Recking, A.: Multi-river Calibration Curve for Passive Acoustic Bedload Transport Monitoring., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2505, https://doi.org/10.5194/egusphere-egu22-2505, 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.
Flowing through the landscape, rivers generate high-frequency ground vibrations (> 1 Hz) by exerting force fluctuations on the bed. The well-established evidence that seismic sensors detect a wide variety of fluvial processes has motivated the use of seismology to indirectly measure sediment transport. In the last decade, numerous efforts have been dedicated to develop physically-based mechanistic models to investigate the link between the river-induced seismic signal and sediment transport properties such as the characteristic diameter of the transported sediments, bedload transport rate, debris flow thickness and velocity. However, most of the existing theories rely on simplistic descriptions of the transport dynamics that may not necessarily be sufficient to capture realistic behaviours. In particular, highly concentrated sediment flows are characterized by complex grain scale physical processes that could have a major impact on their seismic signature (Allstadt et al., 2020; Piantini et al., 2021).
Here, we carry out laboratory experiments to explore the seismic signature of highly concentrated sediment flows. Our scaled experimental setup allows the self-triggering and propagation of sediment pulses in a steep channel (slope of 18%), using a wide bimodal grain size distribution typical of mountain streams. We monitor physical parameters such as flow surface elevation, outlet solid discharge and the corresponding granulometric composition, together with seismically relevant quantities such as basal force fluctuations and flume vibrations using force and ultrasonic sensors, respectively. We observe transport conditions that range from the dilute transport of big grains (sediment pulse front) to dense sediment flows (sediment pulse body). Consistent with previous studies, the passage of the unsaturated front exerts the highest force fluctuations and seismic power. However, we also find that the body, despite having an amount of coarse particles similar to the front, becomes dramatically quieter when bulk density increases and the content of fine particles is maximum. We explain this latter behaviour by two main processes. First, flow stratification prevents a large part of the transported sediments from generating direct impacts to the fixed channel bed. Second, fines allow the formation of a conveyor belt that transport big particles with reduced collisions, as manifested by a considerable increase in their downstream velocity. These findings argue that internal stratification and the presence of a high content of fines may exert a major control on the seismic signature of highly concentrated sediment flows.
References
Allstadt, K. E., Farin, M., Iverson, R. M., Obryk, M. K., Kean, J. W., Tsai, V. C., Rapstine, T. D., and Logan, M.: Measuring Basal Force Fluctuations of Debris Flows Using Seismic Recordings and Empirical Green’s Functions, J. Geophys. Res.-Earth Surf., 125, 9, https://doi.org/10.1029/2020JF005590, 2020
Piantini, M., Gimbert, F., Bellot, H., and Recking, A.: Triggering and propagation of exogenous sediment pulses in mountain channels: insights from flume experiments with seismic monitoring, Earth Surf. Dynam., 9, 1423–1439, https://doi.org/10.5194/esurf-9-1423-2021, 2021
How to cite: Piantini, M., Gimbert, F., Korkolis, E., Rousseau, R., Bellot, H., and Recking, A.: What are the key elements that control the seismic signature of highly concentrated sediment flows?, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8480, https://doi.org/10.5194/egusphere-egu22-8480, 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 size distribution of sediments together with their shape inform on their transport history, are important factors controlling the efficiency of erosion and transport, and control the quality of aquatic ecosystems. However, the size distribution of sediments is generally assessed using poorly representative field measurements and determining the grain-scale shape of sediments remains a real challenge in geomorphology. To tackle this issue, we develop a new methodological approach based on the segmentation and geomorphological fitting of 3D point clouds. Point cloud segmentation into individual grains is performed using a watershed algorithm applied here to 3D point clouds. Once the grains are individualized into several sub-clouds, the morphology of each grain is determined by fitting a 3D ellipsoid to each sub-cloud. These 3D models are then used to extract the size distribution and the grain-scale shape of the sediment population. The algorithm is validated against field data acquired by Wolman counts in coastal and fluvial environments. The main benefits of this automatic and non-destructive method are that it provides, with a fast and efficient approach, access to 1) an un-biased estimate of surface grain-size distribution on a large range of scales, from centimeters to tens of meters; 2) a very large number of data, only limited by the number of grains in the point-cloud dataset; 3) the 3D morphology of grains, in turn allowing to develop new metrics characterizing the size and shape of grains; and 4) the in-situ orientation and organization of grains and grain clusters. The main limit of this method is that it is only able to detect grains with a characteristic size significantly greater than the resolution of the point cloud.
How to cite: Guerit, L., Steer, P., Lague, D., Crave, A., and Gourdon, A.: Fast and automatic measurement of grain geometries from 3D point clouds, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6321, https://doi.org/10.5194/egusphere-egu22-6321, 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.
Today, erosion is increasing in many intensively used agricultural regions with fertile soils. At the same time, scientists expect that the intensity of heavy precipitation events, their erosivity, drought intensity and persistance will increase significantly through climate change. In combination with more strict regulations to protect the natural environment from nutrients and hazardous substances (such as herbicides and micro-plastics), it is challenging to balance the interests of food (and energy) production and environmental protection.
Therefore, we design and establish a worldwide unique measurement plot at the Bavarian Agricultural Institute (LfL) to assess different combinations of four- and six-year crop rotation schemes and machining methods concerning their long-term soil fertility, stability and resilience against climate change effects and environmental impacts, focusing on compound effects. The plot to measure and compare soil-water retention, nutrient fluxes, surface runoff, and erosion masses has an area of four acres and 14 parallel crop strips. Crop cultivation, experiments and measurements with and without artificial rain will be performed for more than ten years after a three-year set-up phase, will have a 4D (3D spatial plus temporal) high-resolution design and combine established and innovative measurement and management techniques, such as artificial intelligence, neural networks, deep learning, and robotics. Finally, up-to-date process-based hydrological modelling will incorporate the measurement data to increase our process understanding and enable upscaling to catchment scales.
This contribution to EGU 2022 will inform and include the scientific community during the set-up phase about the running and planned activities to build an international scientific network, discuss our approaches, efficiently use the existing scientific knowledge, and initiate future collaborations around the measurement financed by the German federal state of Bavaria.
How to cite: Ebertseder, F., Mitterer, J., and Disse, M.: Moving the frontier of comparative erosion measurements under different agricultural schemes – Development of a long-term, high-resolution, 4D erosion measurement site of the Bavarian Agricultural Institute in Lower Bavaria (Germany), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9500, https://doi.org/10.5194/egusphere-egu22-9500, 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.
Abstract: Climate change and land-use change impact the sediment flux and grainsize delivered to rivers which influences channel morphologies and hence modifies flood risk; this is particularly the case where channels are fed by high mountain catchments. Here, We studied the Nakkhu River which is the largest southern tributary of the Kathmandu basin, Nepal. The mobility of the channel is well documented in response to bank erosion, down-cutting, and accumulation of bar forms; these processes are particularly important during extreme flood events. Comparing satellite images from 2003 to 2020, the river course, which has a medium channel width of 15 m, has migrated laterally up to 130m. Bank erosion and down-cutting reduce the inundation and water storage upstream, whereas aggradation of river bar forms downstream reduces the channel’s conveyance capacity. These vertical and lateral geomorphological alterations result in significant impact on flood risk downstream.
In this research, we investigate how changes in sediment supply, and grain size affect river morphology and flood inundation in the Nakkhu River. We use the landscape evolution model, CAESAR-Lisflood, combined with a newly generated (2019) 10 m digital elevation model, field-derived grainsize data and 20 years (2001 to 2020) of daily discharge data, to simulate erosion and deposition along a 14 km reach of the river. In a set of experiments, we compare river bed cross-sections, flood extent, and water depths for 15 model scenarios where we vary sediment supply and grain size from fine sand to coarse gravel dominated distributions assessing the geomorphic uncertainty of observation of sediment data.
The model results show that channel morphologies are sensitive to changes in sediment grainsize distribution. The study suggests that lack of consideration of sediment impact in flood hazard mapping could lead to increased flood risk. In addition, this study highlights some of the challenges regarding the significance of grain size parameter and uncertainty to the landscape evolution model that need to be addressed in current research.
Keywords: River morphology, sediment flux, grainsize, flood modelling, Nepal
How to cite: Thapa, S., Sinclair, H. D., Creed, M., Mudd, S. M., Attal, M., Muthusamy, M., and Sharma, B.: Modelling the sensitivity of changes in sediment flux and grainsize distributions on flooding in the Kathmandu basin, Nepal, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6865, https://doi.org/10.5194/egusphere-egu22-6865, 2022.
Seismic shaking in mountain environments introduces the potential for complex fluvial response from a multitude of landslide sources. Stream networks may be impacted in multiple branches, introducing the possibility of interacting sedimentary ‘pulses’ moving through the system. Large quantities of mobile sediment added to the stream network from multiple sources during and after a co-seismic event can overload susceptible river reaches, causing changes in sediment transport and storage. Although past research works have addressed dynamic sediment movement in river networks and identification of sedimentary hotspots, the physiographic factors (e.g. canyons, bends, fans, slope change) that prompt such change remain unexplored. The catchment settings and reach sequences that contribute most to delay/acceleration of the sediment in the active mountain environments are investigated in order to improve hazard assessment in susceptible terrain. In this work, we employ the one-dimensional River Network Bed-Material Sediment model (Czuba & Foufoula-Georgiou, 2014) to explore the landscape factors that may lead to hotspot behaviour for the very coarse sand fraction (2mm), followed by multi-criteria analysis of four basic stream network parameters: slope, sinuosity, channel confinement and tributary influence. Patterns of network topology associated with delay and accumulation of river sediment in the model were systematically identified in 75,400 stream links from 16 major drainages (135 to 6425 km2) of New Zealand’s upper South Island, as assessed by sediment travel time and the cluster persistence index (CPI). Catchment size determines the number of sediment sources, and thus ultimately the magnitude of the sedimentary hotspots i.e., bigger catchments can accommodate more landslides which increases the sediment input, along with the chances of sediment accumulation at susceptible locations. Multi-criteria analysis of the top 10 reaches with highest CPI values in each catchment (160 sites, total), showed that about 30% of the hotspots occurred in partly-confined valley settings with gentle slope (<0.02m/m), moderate sinuosity (1-1.1), downstream from the confluence of two or more tributaries. This combination emerged as the most likely setting for the occurrence of sedimentary hotspots in active mountain river networks. This approach may provide a simple means to map out susceptible sites based upon reach characteristics, which will not only contribute to improved catchment hazard assessment but may also help to augment more sophisticated models of catchment response to co-seismic landslide events.
How to cite: Tamang, N. B. and Tunnicliffe, J.: Network-scale analysis of sedimentary hotspots in dynamic, seismically-active steepland rivers, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1395, https://doi.org/10.5194/egusphere-egu22-1395, 2022.
