GM2.2 | New approaches for monitoring and modelling sediment transport
EDI PICO
New approaches for monitoring and modelling sediment transport
Co-organized by GI2
Convener: Rebecca Hodge | Co-conveners: Catherine Sanders, Anshul Yadav, James Christie
PICO
| Mon, 15 Apr, 10:45–12:30 (CEST)
 
PICO spot 3
Mon, 10:45
Sediment transport is a fundamental component of all geomorphic systems (including fluvial, aeolian, coastal, hillslopes and glacial), yet it is something that we still find surprisingly difficult both to monitor and to model. Robust data on where and how sediment transport occurs are needed to address outstanding research questions, including the spatial and temporal controls on critical shear stress, the influence of varying grain size distributions, and the impact of large magnitude events. Recent developments have provided a) new opportunities for measuring sediment transport in the field; and b) new ways to represent sediment transport in both physical laboratory models and in numerical models. These developments include (but are not limited to) the application of techniques such as seismic and acoustic monitoring, 3D imaging (e.g. CT and MRI scanning), deployment of sensors such as accelerometers, replication of field topography using 3D printing, use of luminescence as a sediment tracer, remote sensing of turbidity, discrete numerical modelling, and new statistical approaches.

In this session we welcome contributions from all areas of geomorphology that develop new methods for monitoring and modelling all types of sediment transport, or that showcase an application of such methods. Contributions from ECRs and underrepresented groups are particularly encouraged.

PICO: Mon, 15 Apr | PICO spot 3

Chairperson: Rebecca Hodge
10:45–10:50
10:50–10:52
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PICO3.1
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EGU24-9717
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ECS
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Highlight
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On-site presentation
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Chris Tomsett, Julian Leyland, Steve Darby, Tom Gernon, Dan Parsons, Thea Hincks, and Josh Wolstenholme

Sediment is an intrinsic component of the fluvial network, supplying material for floodplains and coastal landforms which provide resilience during flooding and storms. As a result, an understanding of the fluvial processes that control how much sediment moves through our river systems, and how this varies across the globe, is of fundamental importance.

For the purpose of estimating sediment delivery through the fluvial network, it is often assumed that rivers are well mixed through their vertical extent. However, empirical data reveals that there is frequently large variability in the concentration of sediment through the water column. Better understanding this variability is of interest to the geomorphological community to help explain variations in sediment transport and improve estimates of sediment flux.

In this research, we utilise a collection of Acoustic Doppler Current Profiler (ADCP) data from large rivers across the globe to investigate variations in the vertical distribution of suspended sediment. Calibrations of ADCP backscatter to Suspended Sediment Concentration (SSC) from the wider literature are used, alongside median grainsize and acoustic frequency, to create a Machine Learning (ML) model from which SSC from uncalibrated ADCPs can be estimated. This new ML model is subsequently implemented to explore the variations in the vertical mixing of suspended sediment both temporally and spatially. This variability is explored to identify the importance of catchment characteristics in determining variations in suspended sediment concentration within the water column. Comparison of multiple river systems and their catchment characteristics, both between sites and through time, enables the identification of key attributes which exert a greater control on this variation through the water column. Subsequently, this leads to an improved understanding of sediment flux through the river system, whereby knowing the variation in sediment concentration within the water column can help to better calibrate current methods of estimating flux.

How to cite: Tomsett, C., Leyland, J., Darby, S., Gernon, T., Parsons, D., Hincks, T., and Wolstenholme, J.: Vertical Mixing of Suspended Sediment in Big Rivers using ADCP data and Machine Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9717, https://doi.org/10.5194/egusphere-egu24-9717, 2024.

10:52–10:54
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PICO3.2
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EGU24-18899
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ECS
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On-site presentation
Jakob Höllrigl, Koen Blanckaert, David Hurther, Guillaume Fromant, and Florian R. Storck

Currently, the estimation of suspended sediment concentration (SSC) fluxes in rivers relies on river discharge and an average SSC, the latter is commonly determined through optical turbidity measurements at a single point in the river cross-section. This approach has limitations, such as the SSC data being extrapolated from a one-point measurement and indirectly determined depending on regular sampling and laboratory analysis, which is cost-intensive.


Hydro-acoustic echosounders are an alternative to derive SSC across an entire profile, for accurate conversion from backscatter intensity to SSC knowledge of particle size is a requirement. In this approach, we present a method utilizing multi-frequency hydro-acoustic echosounding in addition to velocity measurements via an ADCP. Operating on various acoustic frequencies allows for the direct estimation of mean particle size from backscatter data at different frequencies over a water profile. River in-situ measurements as well as laboratory experiments have been conducted in different concentration as well as particle size distribution regimes.

How to cite: Höllrigl, J., Blanckaert, K., Hurther, D., Fromant, G., and Storck, F. R.: Hydro-acoustic multi-frequency measurements of suspended sediment flux in rivers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18899, https://doi.org/10.5194/egusphere-egu24-18899, 2024.

10:54–10:56
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PICO3.3
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EGU24-20740
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ECS
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On-site presentation
Theodore Langhorst, Konstantinos Andreadis, and Tamlin Pavelsky

Fluvial sediment transport is an important component of the global sediment budget, yet in-situ monitoring is limited. Researchers and practitioners employ various methods to fill in these gaps, each with their own advantages and drawbacks. In this study, we compare four different models for estimating the total annual suspended solids and daily suspended sediment flux for the Sagavanirktok River in Alaska. These four models include: 1) in-situ turbidity calibration; 2) WBMsed global sediment flux estimates 3) optical remote sensing random forest model; and 4) Long-short term memory (LSTM) model trained on remote sensing and modeled inputs. We focus particularly on the summers of 2022 and 2023, when we have continuous validation data via a USGS discharge gage and turbidity sensors that we installed. We evaluate the accuracy, practicality, and shortcomings of each approach to reconstructing the total suspended sediment flux of the Sagavanirktok River. We highlight the necessity of high temporal resolution (approximately daily) for estimating suspended sediment flux in the Sag. River due to the frequency of high discharge events and variable hysteresis between discharge and sediment load. We find that, for the Sag. River, optical imagery alone does not have sufficient temporal resolution to estimate suspended sediment flux (due to orbit repeat and clouds), despite the accuracy of individual estimates. The geomorphic model, WBMsed, is not accurate enough for the unusual hydrology, but does produce daily estimates. Finally, the LSTM model shows promise in being able to bridge the temporal mismatch between satellite, in-situ, and modeled dataset. The LSTM can take advantage of daily discharge models, while incorporating the accuracy of optical satellite sediment models

How to cite: Langhorst, T., Andreadis, K., and Pavelsky, T.: Multi-Model Comparison of Suspended Sediment Flux in the Sagavanirktok River, Alaska. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20740, https://doi.org/10.5194/egusphere-egu24-20740, 2024.

10:56–10:58
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PICO3.4
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EGU24-3823
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ECS
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On-site presentation
Evaluation of continuously recorded fractional bedload transport rates in Swiss mountain streams
(withdrawn after no-show)
Danny Baldig and Dieter Rickenmann
10:58–11:00
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PICO3.5
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EGU24-11689
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Highlight
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On-site presentation
Adele Johannot, Florent Gimbert, Alain Recking, and Marco Piantini

Morphological changes in alluvial rivers are very active and remain very complex to predict because of the high spatio-temporal variability of bedload. This strongly limits the ability of river managers to assess risk or conduct ecological restoration. With the recent development of non-intrusive methods to monitor bedload, such as seismic or acoustic tools, acquisition of data has been highly facilitated compared to direct measurement methods involving in-situ sampling. The challenging task remains in the interpretation of the signals during phases of intense bedload transport which are responsible for major morphological changes. The analysis of such signals requires a good understanding of the underlying physics as well as in-situ field observations to confort interpretation. In this work, we combine seismic with timelapse camera observations with the objective to have a better understanding of bedload behavior and its consequences on the morphology during floods on an alluvial reach of the Severaisse river in the French Alps. Data consists in 3 seismic sensors continuously recording at 200Hz from upstream to downstream along the reach, as well as data from 2 cameras taking timelapse photos of the reach at a 10 min interval during flood. We We find that high frequency seismic power, attributed to bedload, exhibits a characteristic scaling relationship against discharge, materialized by two different phases: a scaling of about 5 from above the threshold of motion (around 12m3/s water discharge) up to a critical discharge of 25 m3/s, and a scaling of about 1.4 above 25 m3/s. We interpret the first scaling to be due to bedload occurring in a diluted regime as described in previous models, and the second scaling to be due to bedload in an intense transport phase. This shift only occur during floods where we observe channel shifting or important re-working of the bed and we suppose that it represents a phase of intense transport responsible for morphological changes. Interestingly, for the most extreme flood with a return period of 50-years, the seismic power versus discharge relationship shows a distinct behavior form the other floods, materialized by a particularly larger and singular hysteresis. Next steps include understanding why this distinct signature occurs, quantify the morphological changes by calculating indexes from image analysis and investigate how bedload and hence the morphological changes depends on the season, characterized by a snow-melting spring and summer and rainy autumn and winter through a multi-year scale.

How to cite: Johannot, A., Gimbert, F., Recking, A., and Piantini, M.: Using seismic and timelapse camera observations to study flood-induced morphological changes on an alpine gravel-bed reach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11689, https://doi.org/10.5194/egusphere-egu24-11689, 2024.

11:00–11:02
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EGU24-12924
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ECS
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Virtual presentation
Bronwyn Matthews, Mark Naylor, Hugh Sinclair, and Matthew Gervais

Bedload transport plays a crucial role in shaping landscapes, yet monitoring it is challenging. Seismic sensors have emerged as valuable tools for continuous and non-invasive bedload transport monitoring. However, isolating the seismic signal of bedload transport from other environmental signals such as flow turbulence remains a challenge. While seismic waves propagate both vertically and horizontally, previous seismic bedload transport studies focused solely on the vertical component. This was based on the assumption that the bedload transport signal was mainly contained in Rayleigh waves which propagate with both vertical and horizontal motion, as opposed to Love waves which propagate with only horizontal motion. We hypothesise that there may be a significant signal from horizontally-propagating waves that characterises the interactions of coarse bedload impacts, and that this signal will be strongest in a flow-parallel orientation.  

This study employs the Horizontal-to-Vertical Spectral Ratio (HVSR) which is a passive method, commonly used in engineering seismology, that determines the ratio between horizontal and vertical seismic signal components. In this study, we explore the potential of the HVSR method to isolate the dominant component in seismic bedload transport signals and its applicability for monitoring fluvial processes within rivers. Using seismic, hydroacoustic, and hydrological measurements from the River Feshie in Scotland, our findings challenge prior belief that the seismic signal of bedload transport predominantly resides in the vertical component; instead, the horizontal component contains significant fluvial and bedload transport information. Due to differences in seismic wave characteristics, the HVSR method acts as a tool to isolate signals of bedload transport and water turbulence.

Additionally, the HVSR method demonstrates promise in effectively filtering out meteorological signals that may contaminate raw river-induced seismic signals, enabling more accurate monitoring of bedload transport occurrences. However, we acknowledge that the contributions of horizontal and vertical signals greatly depend on sensor location and site characteristics. This study emphasises the significance of utilising horizontal seismic signals for comprehensive bedload transport monitoring, presenting an opportunity for this method to enhance our understanding of complex fluvial processes within river systems.

How to cite: Matthews, B., Naylor, M., Sinclair, H., and Gervais, M.: Exploring vertical component dominance in seismic bedload transport signals: Horizontal-to-Vertical Spectral Ratio (HVSR) analysis in the River Feshie, Scotland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12924, https://doi.org/10.5194/egusphere-egu24-12924, 2024.

11:02–11:04
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PICO3.6
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EGU24-11132
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On-site presentation
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Rebecca Hodge, Hal Voepel, Elowyn Yager, Julian Leyland, Joel Johnson, David Sear, and Sharif Ahmed

Understanding when gravel moves in river beds is essential for a range of different applications, but is still surprisingly hard to predict. The critical shear stress at which a grain will move depends on its relative size and structure within the bed, and spatial and temporal changes in grain-scale structure are likely to be a major driver of changes in critical shear stress. Consequently grain-structure metrics such as protrusion, pivot angle and contact with any surrounding fine grained matrix are used as parameters in models to predict critical shear stress, and so there is an increasing demand for measurements of these parameters in order to improve our predictive ability. However, we do not have established methods for measuring these parameters, nor do we know whether different methods provide consistent results. Here we present and compare new datasets of sediment structure metrics collected from eight locations in a small gravel-bed stream using three different methods: direct field-based measurements, terrestrial laser scanning (TLS), and computed tomography (CT) scanning. Using each method, we measure metrics including grain size distribution, grain protrusion and fine matrix content. We find that distributions of grain size are consistent between field-based and TLS data, but smaller in CT data. All three methods produce similar distributions of protrusion relative to grain size. There is also some consistency between field and CT measures of fine-grained matrix. However, the identification of similarity also depends on the type of analysis, and an alternative analysis shows less similarity in protrusion and fine-grained matrix between the different methods. Of the three methods, TLS-based approaches have potential to be most easily applied, and our analysis suggests that for grain-size and protrusion they perform as well as the alternative methods. However, they cannot currently be used for measuring fine-grained matrix content.

How to cite: Hodge, R., Voepel, H., Yager, E., Leyland, J., Johnson, J., Sear, D., and Ahmed, S.: Comparing methods to quantify grain-scale sediment structure in gravel-bed rivers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11132, https://doi.org/10.5194/egusphere-egu24-11132, 2024.

11:04–11:06
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PICO3.7
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EGU24-558
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ECS
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On-site presentation
Anshul Yadav, Sumit Sen, Luca Mao, and Marwan A. Hassan

This study investigates sediment mobility and transport dynamics in two Himalayan rivers, the Aglar and Paligad Rivers, during both monsoon and non-monsoon flows. Employing the virtual velocity approach, key parameters such as bed proportional mobility (Y), active layer depth (ds), and displacement length were measured to estimate the virtual velocity of mobilized grains. Local parameters (0.5 m sub-sections) and wetted cross-sectional averages were utilized. Using local parameters, the total annual bed material transport was determined as 67,100 t (±20,400 t) and 18,400 t (±6,000 t) for the Aglar and Paligad Rivers, respectively, with nearly 60% occurring during the monsoon. The significant contribution of non-monsoonal flows (~ 40 %) could be ascertained to higher enough flows in specific sub-sections inducing partial or full mobility. Still, the contribution of partial transport (PT) remained lower (< 6%). In contrast, based on cross-section average parameters, total transport was estimated at 42,300 t (±15,800 t) and 12,200 t (±4,700 t) for the Aglar and Paligad Rivers, respectively, with approximately 79% and 68% occurring during the monsoon. The contribution of PT increased to nearly 18% and 29% for the Aglar and Paligad Rivers, respectively, attributed to the averaging effects of shallower sections. Furthermore, the interdependence of partial transport on Y and full transport on ds leads to discontinuities in transport curves, prompting the proposal of a unified function to represent transport extent for both partial and full transport conditions. The unified function ensured the generation of continuous transport curves, yielding similar transport patterns concerning the contribution of PT, FT, monsoonal, and non-monsoonal flows. The findings are particularly relevant for efficient river management as the region houses several hydropower plants and is highly susceptible to climate change.

Keywords:

Painted tracers, partial transport, full transport, active layer, monsoonal flows

How to cite: Yadav, A., Sen, S., Mao, L., and A. Hassan, M.: Estimating bed material transport in Himalayan streams using the virtual velocity approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-558, https://doi.org/10.5194/egusphere-egu24-558, 2024.

11:06–11:08
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PICO3.8
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EGU24-11623
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ECS
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On-site presentation
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Farhad Bahmanpouri, Silvia Barbetta, Christian Massari, Domenico De Santis, Ashutosh Sharma, Ankit Agarwal, and Sumit Sen

Abstract: Sediment transport is a natural process where sediment particles can be deposited downstream and exacerbate flooding. The movement of sediments can be observed in flows through rivers, canals, and coastal areas which include suspended load transport and bed-load transport. Bed-load transport occurs in the area close to the riverbed, which is of particular importance in shaping the riverbed. The present research aims to investigate the sediment transport process by applying the Entropy concept as a theoretical approach to the activities of the project ‘Probabilistic floods and sediment transport forecasting in the Himalayas during extreme events’, funded in the context of the Italy-India joint science and technology cooperation program.

Specifically, based on collected field data through the Alaknanda River at Srinagar in India by current meter, first, the Entropy theory was applied to obtain the cross-sectional distribution of the velocity (based on recent developments of Entropy theory in Bahmanpouri et al., 2022a, b). The calculated mean velocity and discharge were compared with the observed data collected by the Central Water Commission (CWC). Next, shear velocity was calculated for different cross-sections based on different flow conditions. Further, shear stress was calculated based on two terms induced by skin friction and bedforms, respectively. Finally, the shield parameter was obtained based on shear velocity distribution to find out if sediment particles have the potential to be transported or not. Overall, the findings of the current research highlighted the potential of the theoretical method of Entropy to calculate sediment transport in developing countries.

 

Bahmanpouri, F., Barbetta, S., Gualtieri, C., Ianniruberto, M., Filizola, N., Termini, D., & Moramarco, T. (2022a). Prediction of river discharges at confluences based on entropy theory and surface-velocity measurements. Journal of Hydrology606, 127404.

Bahmanpouri, F., Eltner, A., Barbetta, S., Bertalan, L., & Moramarco, T. (2022b). Estimating the Average River Cross‐Section Velocity by Observing Only One Surface Velocity Value and Calibrating the Entropic Parameter. Water Resources Research58(10), e2021WR031821.

How to cite: Bahmanpouri, F., Barbetta, S., Massari, C., De Santis, D., Sharma, A., Agarwal, A., and Sen, S.: Application of Entropy theory to estimate the sediment transport, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11623, https://doi.org/10.5194/egusphere-egu24-11623, 2024.

11:08–11:10
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PICO3.9
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EGU24-13064
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ECS
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On-site presentation
Shawn Chartrand

We present results from a particle-scale numerical model inspired by the idea that a majority of the time during transport capable floods, bedload transport in rivers is rarefied, and a stochastic process. Physical experiments conducted by others to explore this idea suggest that the time varying particle activity N measured within a control area A above the bed surface is described by a Poisson probability mass function (pmf), assuming an absence of collective entrainment. This implies that particles are sporadically entrained from the bed surface at rate λ with no “memory” of prior entrainment events, when and where local flow conditions favor particle lift or dislodgement. In this context we developed a new open source kinematic particle-scale model written in Python (Zwiep and Chartrand, 2022). Notably, the model includes no information related to the bed surface shear stress or Shields conditions, and no sediment transport functions are used to drive the model.

The model domain measures a use specified length nD of the particle diameter D, with a width of 1D. At present we have tested the model with 30 simulations using a uniform particle diameter. Each simulation was run for 1 million iterations to explore the governing model parameters: SRe is the number of subregions within the domain length nD; En is the particle entrainment rate per iteration, which we randomly sample from a Poisson pmf for a specified value of λ; lt is the particle travel distance which we randomly sample from either a lognormal distribution or a truncated normal distribution for specified values of the distribution expected value and standard deviation; and Sh is the vertical particle stacking height ranging from 1-3D.

The model produces a time varying signal of particle flux counted at downstream points of internal subregion domains, and at the downstream end of the model domain. The simplified particle bed changes “relative” elevation distributions through particle stacking and downstream motions of travel distance. An implication of particle stacking within the context of a stochastic model framework is a time varying signal of the average “particle age” defined as the number of iterations since last entrainment, as well as the average “particle age range” defined as the difference of the maximum and minimum particle ages, both metrics calculated at each iteration and across all subregions. The age dynamics correlate with the magnitude of N following an initial period of particle bed organization. Our initial tests suggest that the relatively simple model logic captures the essence of rarefied particle transport. We believe the model can be used to ask basic science questions, and as a classroom tool to introduce students to bedload transport in a straightforward and illustrative manner.

References:

Zwiep, S., & Chartrand, S. M. (2022). pySBeLT: A Python software package for stochastic sediment transport under rarefied conditions. Journal of Open Source Software, 7(74), 4282. https://doi.org/10.21105/joss.04282.

How to cite: Chartrand, S.: A simplified Python-based kinematic model of particle transport in rivers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13064, https://doi.org/10.5194/egusphere-egu24-13064, 2024.

11:10–11:12
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PICO3.10
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EGU24-16886
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On-site presentation
Thomas Pähtz, Yulan Chen, Jiafeng Xie, Rémi Monthiller, Raphaël Maurin, Katharina Tholen, Hao-Che Ho, Peng Hu, Zhiguo He, and Orencio Durán

Bedload transport plays a vital role in shaping Earth’s environment by promoting the formation and growth of geological features of various scales, including ripples and dunes, deltas and fans, and laminations and cross-bedding. A key problem hampering our understanding of bedload-induced landscape evolution is the notoriously large variability commonly associated with measurements of bedload flux, even under controlled and highly idealized conditions in the laboratory, such as fully-developed, unidirectional open-channel flows over flat beds composed of grains of nearly uniform sizes. For example, two recent experimental studies report a nearly sixfold different nondimensionalized bedload flux at a comparable Shields number for spherical grains [1, 2]. The likely culprit is the immense difficulty experimentalists face in estimating the transport-driving bed shear stress. There is currently no universally accepted method of even determining the bed surface elevation in the presence of bedload transport, which is particularly problematic for shallow flows where small changes have a large effect. Neither is there agreement on how to account for the effects of sidewall friction, which become the stronger the smaller the width-to-depth ratio b/h of the open-channel flow. Standardly employed empirical sidewall corrections have arguably a greater resemblance to cooking recipes than to formal physically-derived methods. In addition to such experimental difficulties, there is the physical question of how grain shape, which usually is not controlled for in laboratory experiments, affects bedload flux. A recent prominently published study argued that grain shape is the predominant reason for bedload flux variability and put forward a semi-empirical, analytical bedload transport model to account for it [1].

Here, we compile data from existing experiments and existing and new DNS-DEM, LES-DEM, and RANS-DEM numerical simulations of turbulent bedload transport of shape-controlled grains, in which b/h varies between 0.1 and infinity (periodic boundary conditions in simulations). After employing a non-empirical sidewall correction, which we derived from the phenomenological theory of turbulence, and a granular-physics-based method to determine the bed surface elevation, all data for spherical grains of sufficient size collapse onto a single curve, resolving the experimental problem of bed shear stress determination. Furthermore, the combined data for spherical and non-spherical grains are in strong disagreement with the model of Ref. [1] but support our alternative analytical bedload model across grain shapes, bed slopes, flow strengths, and channel widths.

[1] Deal et al., Nature 613, 298 (2023). https://doi.org/10.1038/s41586-022-05564-6

[2] Ni & Capart, Geophysical Research Letters 45, 7000 (2018). https://doi.org/10.1029/2018GL077571

How to cite: Pähtz, T., Chen, Y., Xie, J., Monthiller, R., Maurin, R., Tholen, K., Ho, H.-C., Hu, P., He, Z., and Durán, O.: Resolving bedload flux variability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16886, https://doi.org/10.5194/egusphere-egu24-16886, 2024.

11:12–11:14
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PICO3.11
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EGU24-18979
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ECS
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On-site presentation
Xiuqi Wang, Geert Campmans, Thomas Weinhart, Anthony Thornton, Stefan Luding, and Kathelijne Wijnberg

Moisture is a crucial environmental factor that shapes the dynamics of aeolian sediment transport along coastal beaches. Despite the existence of empirical formulations, little is known about the mechanism through which moisture influences this dynamic process. To address this knowledge gap, we present a numerical modelling framework implemented in the open-source software package MercuryDPM [1].
This framework combines a discrete particle model, a one-dimensional airflow model and a liquid migration model. The two-way coupling between the discrete particle model and the airflow model can accurately represent the momentum exchange between these phases, yeilding reasonable sediment transport rates [2]. The inter-particle moisture distribution is modelled by a liquid migration law, which governs the presence of liquid films covering the particle surfaces and liquid bridges spanning the particle contacts [3]. The liquid bridge model introduces a static capillary force as well as a dynamic lubrication force, which is necessary to model the dynamic effects of moisture. This comprehensive model effectively captures particle behaviour under moist conditions and demonstrates the dependence of bed erodibility on particle impact and wind entrainment for varying moisture levels.
Our approach provides valuable insights on the moisture effect in aeolian sediment transport. It advances our understanding of this complex phenomenon, and gives insights on the development of geomorphological patterns at coastal sandy areas. With its flexilibity and versatility, it can be extended to study many more specific processes related to sediment transport.


[1] Weinhart, T., Orefice, L., Post, M., et al (2020). Fast, flexible particle simulations—an introduction to MercuryDPM. Computer physics communications, 249, 107129.
[2] Campmans, G., & Wijnberg, K. (2022). Modelling the vertical grain size sorting process in aeolian sediment transport using the discrete element method. AeolianResearch, 57, 100817.
[3] Mani, R., Kadau, D., Or, D., & Herrmann, H. J. (2012). Fluid depletion in shear bands. Physical review letters, 109 (24), 248001.

How to cite: Wang, X., Campmans, G., Weinhart, T., Thornton, A., Luding, S., and Wijnberg, K.: Exploring moisture-constrained aeolian sediment transport through a discrete particle modelling framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18979, https://doi.org/10.5194/egusphere-egu24-18979, 2024.

11:14–11:16
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PICO3.12
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EGU24-1825
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ECS
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On-site presentation
Islam Koa, Alain Recking, and Florent Gimbert

Sediment transport can occur in a so-called “debris flow” form, where concentrations are high and movement is driven by gravity. Previous studies have predominantly used simple rheological fluids or uniform granular materials to study the characteristics of debris flows. However, a fundamental question remains regarding the characteristics of the complex granular debris flow, and the transition from granular debris flow to bedload remains poorly understood. In this contribution, we present an experiment in the laboratory where this phenomenon could be studied. Our experiment setup, a 6-meter-long wooden flume, involved a 1 m-long low-slope trapezoidal storage area and a 5 m-long and 0.1 m-wide wooden flume channel inclined at 33%, equipped with a force plate and hydrometer sensors. Our observations show that self-formed, highly concentrated sediment accumulation in the storage area, influenced by flow rate, generates pulses that exhibit three phases: the tail phase containing sand particles, the body phase containing a mixture of particles, and the front phase containing coarse particles. As discharge was dynamically increased, two distinct domains controlled by the forefront coarse particles were observed. Firstly, at low flow (0.14-0.16 l/sec), a static-dynamic domain is identified, characterized by a high sediment concentration and very low velocity. This generates a high resultant force magnitude that affects the forefront coarse particles, resulting in debris-flow-like pulses controlled by the sediment density. Secondly, at higher flows (0.17–0.24 l/sec), a full-dynamic domain is identified, characterized by a lower sediment concentration and very high velocities. This behavior generates hyperconcenrated flow-like pulses controlled by momentum transfer between the pulse phases. We demonstrated that the transition from debris and hyperconcentrated flow to bedload is controlled by the coarse particle’s mobility, whose threshold discharge in clear water was 0.22 l/sec. The important role played by the sand fraction is also demonstrated, which permits the static dynamics behavior by ensuring momentum transfer either directly, by mass transfer, or indirectly by reducing the medium porosity.

How to cite: Koa, I., Recking, A., and Gimbert, F.: The Transition from Granular Debris Flow to Bedload: a force balance perspective, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1825, https://doi.org/10.5194/egusphere-egu24-1825, 2024.

11:16–11:18
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PICO3.13
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EGU24-394
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ECS
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Highlight
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On-site presentation
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Kate Newby, Georgina Bennett, Kyle Roskilly, Alessandro Sgarabotto, Chunbo Luo, and Irene Manzella

Landslides present a substantial hazard across coastal and mountainous regions in Europe and worldwide, and are becoming increasingly prevalent due to extreme rainfall linked to climate change. There is a need to develop new technologies for landslide monitoring and early warning systems, as traditional approaches alone are insufficient due to low temporal resolution and high costs. The SENSUM project (smart SENSing of landscapes Undergoing hazardous hydrogeologic Movement) has deployed manmade boulders, called SlideCubes, that monitor landslide movement in real-time across two coastal slow-moving landslide sites in southern England (Lyme Regis and Isle of Wight).

SlideCubes are embedded with low-power low-cost sensors that comprise an inertial measurement unit (IMU with accelerometers and gyroscopes) and magnetometers. The SlideCubes are part of a wireless sensor network (WSN) that communicates via Long Range Wide Area Network (LoRaWAN) and Internet of Things (IoT) technologies. Rain gauges and other third-party sensors can be easily integrated into the network to provide additional data sources. Our novel WSN allows for near real-time wireless monitoring of the landslides, only requiring field visits to replace sensor batteries every 9-12 months. The sensors are motion-triggered, significantly saving battery power, meaning the WSN requires little and less frequent maintenance than other sensor-based monitoring approaches. This allows long-term remote measurement of landslide kinematics (inferred from SlideCubes) and initiation of movement, which is key for early warning.

In the present work, initial findings from the SlideCubes installed at two UK-based sites are discussed. The movement events detected and recorded over 2 years are validated by periodic GNSS and drone imagery surveys. We present an overview of temporal and spatial motion across both landslide sites and evaluate sensor performance. Using gyroscope and accelerometer readings from field and laboratory data, we demonstrate how types of motion (e.g. rolling, sliding) can begin to be categorised, which is not possible with the accelerometer alone. This research will be developed in future with machine learning to detect hazardous movement including large magnitude catastrophic events. These findings will be integrated into a SENSUM early warning online portal, in development, for use by stakeholders.

How to cite: Newby, K., Bennett, G., Roskilly, K., Sgarabotto, A., Luo, C., and Manzella, I.: Smart boulders for real-time detection of hazardous movement on landslides, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-394, https://doi.org/10.5194/egusphere-egu24-394, 2024.

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