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.

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.

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.

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.

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; E_n is the particle entrainment rate per iteration, which we randomly sample from a Poisson pmf for a specified value of λ; l_t 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 S_h 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.

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.

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.