Riverbank erosion is a significant contributor to sediment transport in rivers and a key factor shaping river ecosystems, which are affected by natural and human activities. Its dynamics depend on a variety of factors, including river flow, water levels, soil properties and composition, groundwater flow, topography, climate, soil moisture, temperature, and vegetation. The main drivers of riverbank erosion are particle detachment by water flow, gravity-induced mass failure, and seepage erosion. While these processes shape river channels and floodplain morphology and support ecological functions like habitat creation, they also pose risks such as land degradation and infrastructure damage. In seasonally frozen rivers, bank erosion dynamics are further complicated by unique climatic and hydrogeomorphic conditions, including temperature fluctuations and variations in groundwater flow. These additional processes can cause erosion by themselves and can interact with the other processes. Especially, the interactions between these factors remain poorly understood, hindering accurate predictions of bank erosion events. This study examines how topography, river stage changes, groundwater flow, soil moisture, and temperature variations affect riverbank erosion in seasonally frozen rivers. The research focuses on three objectives: (i) assessing how topography influences riverbank erosion, (ii) examining the role of river stage fluctuations and soil types in erosion processes, and (iii) analyzing the impact of freeze-thaw cycles on groundwater movement and soil stability, bank erosion. A two dimensional (2D) vertical bank erosion model was developed that integrates temperature dynamics with groundwater flow, allowing realistic simulations of temperature-induced changes in soil permeability and groundwater behavior. The framework applied with dynamic boundary conditions, offering novel insights into riverbank erosion mechanisms. The simulations were at this first stage performed by using a hypothetical riverbank geometry. First findings show that the interactions of processes can lead to temporally varying rates of erosion which cannot be understood in isolation. Bank geometry is expected to play a significant role, with some profiles more prone to collapse than others. Additionally, river stage fluctuations and dynamic soil conditions are likely to exacerbate erosion risks. These insights will support the development of predictive tools for sediment management, climate-resilient riverbank protection, and sustainable ecosystem management in cold-region rivers.
Keywords: Riverbank erosion, groundwater modeling, temperature, seasonally frozen rivers, numerical modeling, freeze-thaw cycles.
How to cite: Tedla, H. Z., Lotsari, E., and van Rooijen, E.: Riverbank erosion numerical modeling: groundwater and temperature dynamics in seasonally frozen rivers using a hypothetical bank geometry, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7240, https://doi.org/10.5194/egusphere-egu25-7240, 2025.
The hydrological and sediment transport regimes of Mediterranean river basins are critical for effective water resource management, especially in regions vulnerable to land degradation and erosion. This study investigates the Seman River Basin in Albania, employing the WASA-SED (Water Availability in Semi-Arid environments – SEDiments) model to simulate flow and sediment dynamics. Daily data on precipitation, temperature, soil properties, land use, discharge, and suspended sediment concentrations were used to quantify runoff and sediment yields in the basin. Results demonstrate a strong correlation between rainfall intensity, land surface cover, and sediment transport, with notable seasonal variations in runoff. Sediment deposition within the basin significantly reduces the storage capacity of local dams, aggravating water resource challenges. Additionally, land use changes, particularly deforestation and agricultural expansion, exacerbate sedimentation and impact the hydrological regime. This study provides valuable insights into the sediment dynamics and hydrological processes of Mediterranean river basins, offering a predictive tool for water resource management and sediment mitigation strategies. The findings underscore the need for sustainable land and water management practices in the Balkans and similar environments.
How to cite: Doko, A., Francke, T., and Bronstert, A.: Analyzing Hydrological and Sediment Transport Patterns in the Seman River Basin, Albania, Using the WASA-SED Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5927, https://doi.org/10.5194/egusphere-egu25-5927, 2025.
Morphological change in rivers is a dynamic and complex phenomenon affected by several environmental conditions and hydraulic processes, which are mainly related to the composition and the susceptibility of erosion of the river channel and watershed. While erosion occurs constantly at low rates most of the time, high flow events such as flash floods can lead to a severe increase in erosion and sedimentation rates, which can have negative effects on transportation infrastructures, residential areas and even the efficiency of hydropower projects. With the future projections of climate change showing an increase in the frequency of such events, a good understanding is important in assessing the impact they will have in already existing and planned riverside uses.
However, investigating sediment rates is a difficult task both in the field and through numerical modelling. Especially in small streams, quick events characterized by extreme flow, can produce a significant portion of the annual sediment load in the matter of a few days or hours. Satellite and drone data or acoustic/optical devices provide scarce observations and pointwise measurements respectively, thus lacking the time and spatial resolution necessary to capture the overall dynamics of the river. The development of 2D and 3D numerical models would also prove as a computationally demanding task when applied to larger scale areas such as a river reach and long simulation periods (i.e., tens of kilometers and decades). Utilizing a well-documented 1D model is a viable option due to the accessibility and low computational demands.
For these reasons, the objective of this study will be establishing a 1D sediment transport model through HEC-RAS, relying on evidences from case studies prone to hydrological quick events. The calibration procedure is deterministic parameter testing, such as sediment transport functions and cohesive factors, with the aim of reconstructing the sediment input and deposition based on the existing bathymetries and measured suspended sediment concentrations during floods.
The expected results would be assessing the sediment quantities during flood events, and the impact on the river morphology in the long term. Running various climate change scenarios, such as SSPs (Shared Socioeconomic Pathways) will provide the uncertainties of river morphology changes in the future, burden with increasing floods frequency,
How to cite: Lleshi, R., Guerrero, M., Conevski, S., Di Federico, V., and Rüther, N.: Large Scale Morphological Changes Due To Flash Floods In Small Streams Under Climate Change Scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16976, https://doi.org/10.5194/egusphere-egu25-16976, 2025.
Over the past two centuries, river regulation practices in Europe have significantly altered river systems through straightening, channelization and bedload retention. These modifications, coupled with the implementation of transverse structures such as hydropower facilities, have adversely affected riverine ecosystems, floodplains and sediment dynamics. River widening projects aim to address these challenges by creating more space for rivers, thereby improving the health of these natural systems. In Tyrol, Austria, between Stams and Rietz, a restoration project on the Inn River included the removal of most bank protection, the widening of the river up to 75 meters, and the creation of a dead branch and a side channel. On a length of 3 km, this measure re-established aquatic as well as terrestrial habitats. Shortly after completion, a 50-year flood event caused significant changes along the restoration zone, including the breaching of the dead branch, which subsequently connected to the main channel. These morphodynamic changes were documented using two airborne laser bathymetry (ALB) surveys and an echo-sounding survey for cross-sectional profiles.
Morphodynamic models are key tools for understanding sediment transport processes in rivers and providing insights into riverbed dynamics and sediment budgets over time. For this study, the Telemac2D hydrodynamic model, coupled with the Gaia sediment transport module, was employed to simulate the hydrograph of the HQ50 flood event. The model accounted for complex bedload behavior, including lateral slope effects and bank failure, which are essential processes in river restoration. A stable sediment budget with inflow rates equal to outflow rates was assumed due to the uncertainty in bedload inflow rates. A sensitivity analysis was conducted using various transport equations, including Meyer-Peter & Müller, Einstein & Brown, Hunziker, and Wilcock & Crowe. To assess model performance, metrics such as mean change in elevation (MCE), root mean square error (RMSE), and a newly developed erosion and deposition pattern index (EDPI) were analyzed. All transport equations replicated the general survey patterns, with the Meyer-Peter & Müller equation achieving the lowest MCE and RMSE errors and the highest EDPI values. Despite these promising results, unrealistic behavior was observed since none of the transport models accounted for the movement of the coarsest sediment fraction, leading to bed coarsening as fine material was preferentially transported out of the model. Furthermore, the Hunziker and Wilcock & Crowe equations yielded unrealistically low transport rates, resulting in reduced erosion and deposition compared to the Meyer-Peter & Müller and Einstein & Brown equations. These limitations highlight uncertainties in shear stress calculations for alpine rivers characterized by large particle sizes. Further research is recommended to address these issues and enhance the accuracy of sediment transport modeling in similar contexts.
How to cite: Siedersleben, J., Zöschg, H., and Schletterer, M.: Analyzing morphodynamics along a river widening: Applicability of different transport equations in 2D numerical modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16570, https://doi.org/10.5194/egusphere-egu25-16570, 2025.
In river confluences, the two branches may have different water temperatures and sediment loads, which induce strong transverse density gradients. These gradients, in turn, drive the formation of secondary currents, which interact with those generated by channel curvature. Specifically, density gradients can either enhance or counteract curvature-induced secondary flows, and their impacts on flow and sediment transport require proper modelling approaches. Three-dimensional (3D) models naturally account for these dynamics and provide detailed predictions of flow and temperature fields, but cannot be applied to long-term morphodynamic simulations because of prohibitive computational demand. By contrast, traditional two-dimensional (2D) models are computationally more efficient, but do not account for 3D flow structures that are particularly relevant for river confluences. To fill the gap, a 2D depth-integrated hydro-morphodynamic model is enhanced, through appropriate parametrization, to account for the density-driven secondary flows and their effects on the flow field, mixing, sediment redistribution and, ultimately, on the morphodynamic evolution of the riverbed.
The enhanced 2D model is applied to the Yangtze River-Poyang Lake confluence, where field measurements have shown that temperature-induced density gradients play a critical role in shaping flow patterns, secondary currents, and the riverbed evolution. Interestingly, these effects vary throughout the year due to seasonal differences in temperature and discharge between the two branches of the confluence. Density-induced secondary currents, which superimpose or modify the curvature-induced helical flows, develop at the confluence apex where the two streams merge. Their inclusion in the 2D modelling framework improves the agreement of numerical results with ADCP field measurements, thus supporting the reliability of the model.
The efficiency of the 2D model, combined with its ability to represent key physical processes through the parametrization of density-driven effects, also allows to perform long-term simulations with mobile bed conditions. These simulations highlight the significant role of secondary flows, driven by both streamline curvature and spanwise density gradients, in sediment transport and bed morphology at the river confluence, confirming that the enhanced 2D model is a valuable tool for long-term morphodynamic studies in large river systems.
How to cite: Lazzarin, T., Xu, L., Yuan, S., Hoitink, T., and Viero, D. P.: Modelling Density-Driven Secondary Flow with a 2-D Depth-Integrated Model: Insights from the Yangtze River-Poyang Lake Confluence, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-820, https://doi.org/10.5194/egusphere-egu25-820, 2025.
This study aims to develop a Lagrangian stochastic model for simulating suspended sediment transport in open channel flows. The model focuses on a pair of particles, describing the trajectories of paired particles, which are dependent on the Reynolds number. It uses relative particle velocity as a foundation for tracking sediment motion, with key factors such as separation distance and relative velocity being critical in defining particle interactions and their role in separation processes. This model captures non-Gaussian turbulence features in Eulerian statistics to construct a relative velocity probability density function. A structure-function approach is employed to derive Eulerian velocity moments from velocity increments, ensuring stable dispersion by considering relevant scale properties. The model incorporates the Langevin equation for relative velocity, consisting a drift term defined by conditional acceleration and a Eulerian probability density function, and a random term defined by a scale-dependent diffusion coefficient influenced by viscous effects, exhibiting Brownian motion properties.
The model extends the fluid particle framework to sediment particles through the principles of force balance and accounts for the resuspension mechanism for sediment particles. In sediment transport, the influence of the resuspension mechanism on the two particles must be considered. This mechanism is different from those in fluid particle models and single-particle sediment models. Additionally, the relative velocity model is transformed into an absolute velocity model, and two-particle coefficients are introduced to determine particle motion. The Ornstein-Uhlenbeck (OU) process is employed to simulate velocity fluctuations for individual particles.
Compared to single-particle models, this two-particle stochastic model investigates turbulent sediment transport in terms of relative velocity and separation distance variations. We analyze the variation of the diffusion coefficient across scales by tuning specific parameters. Results are compared with direct numerical simulation (DNS) data across different Reynolds numbers to calibrate the model coefficients effectively. The initial findings provide valuable insights into the influence of turbulence characteristics on sediment behavior, particularly in relation to relative velocity and separation distance variations. This work contributes to a deeper understanding of the complex interactions governing sediment transport in turbulent open channel flows.
How to cite: Chen, H. Q. and Tsai, C.: Two-Particle Stochastic Model for Suspended Sediment Transport Using Spatial Relationship with Particles, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15745, https://doi.org/10.5194/egusphere-egu25-15745, 2025.
Sediment transport problems in rivers often arise under conditions involving a dynamic and complex free surface. Local scour around hydraulic structures can pose significant threats to the stability and safety of riverine infrastructure during extreme discharge events. To date, computational fluid dynamics (CFD) software using the two-phase approach are used for such scenarios, which comes at the cost of significant computational resources. This contribution presents a non-hydrostatic Navier-Stokes equations solver on a σ-coordinate grid that allows the grid to follow the variations of the free surface as well as the bottom. The approach is significantly more efficient then said CFD models. The model is developed within the open-source hydrodynamics framework REEF3D, which allows for use of the parallelization and high-order finite difference frameworks. For discretization, it uses a Godunov-type scheme for shock-capturing properties, allowing for stable and accurate representation of complex free surface conditions, such as hydraulic jumps. Bed load and suspended load transport formulations are implemented based on standard formulations. The possible sediment transport and scouring effects around the large bridge piers of a relatively old bridge over the river Nidelva in Trondheim, Norway are investigated. Due to the contraction effects of the piers, subcritical flow is forced for certain conditions. The numerical model captures the hydrodynamics and the free surface realistically, showing the possibility for a more efficient alternative to two-phase flow CFD simulations in such scenarios.
How to cite: Bihs, H. and Wang, W. W.: Non-Hydrostatic and Shock-Capturing Modeling of Free Surface Flow Driven Sediment Transport around Bridge Foundations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20462, https://doi.org/10.5194/egusphere-egu25-20462, 2025.
The diffusive wave or the zero-inertia (ZI) model for surface runoff modeling is derived by neglecting the local and convective acceleration terms from the two-dimensional (2D) depth-averaged shallow water equations (SWE). The ZI model is computationally efficient and highly accurate compared to the SW models for modeling low subcritical (Froude number < 0.5) flood propagation problems. The current study presents a Finite Volume (FV) method-based ZI flow model – zeroInertiaFlowFOAM, developed using the OpenFOAM® framework [1]. The model utilizes the implicit time discretization scheme and the Picard iteration scheme for the linearization of the non-linear momentum equation. The stabilized and adaptive time-stepping algorithm implemented in the present model adjusts the future time step size based on the convergence characteristics of the iterative scheme at the present time step, thereby enhancing the computational efficiency. The existing ZI model – surfaceFlowFOAM [2] suffered from high mass balance errors (MBE) and chequerboard oscillations while simulating flood flows due to high rainfall intensities over surfaces with steep bed-slopes. The present model is a modified version of surfaceFlowFOAM. In the present model, the velocity is calculated from the momentum equation at the element centroids of the collocated grid-system. The calculated velocity is used to solve the continuity equation, where the divergence of the flux term is discretized using the upwind scheme. This relates the gradient of the flow depth (∇h) to the values at the consecutive element centroids, thereby eliminating the possibility of chequerboard instability arising in the regions where the water-surface slope changes sharply. It significantly reduces the restrictions on mesh generation for such problems, thereby increasing the computational efficiency when compared to surfaceFlowFOAM. Moreover, the modified discretization technique adopted in zeroInertiaFlowFOAM has helped in achieving high mass balance accuracy which was another significant limitation in surfaceFlowFOAM. The applicability of zeroInertiaFlowFOAM has also been verified and validated against the standard benchmark problems from the literature.
References
[1] Jasak, H., A. Jemcov, Z. Tukovic. (2007). OpenFOAM: A C++ library for complex physics simulations. In Vol. 1000 of Proc., Int. Workshop on Coupled Methods in Numerical Dynamics,1–20. Dubrovnik, Croatia: Inter-University Center
[2] Dey, S., Dhar, A. (2024). Applicability of Zero-Inertia Approximation for Overland Flow Using a Generalized Mass-Conservative Implicit Finite Volume Framework. Journal of Hydrologic Engineering, 29(1), 04023042.
How to cite: Dey, S.: zeroInertiaFlowFOAM – a OpenFOAM®-based computationally efficient, mass-conservative, implicit zero-inertia flow model for flood inundation problems on collocated grid-systems. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17402, https://doi.org/10.5194/egusphere-egu25-17402, 2025.
Braided rivers are the most dynamic type of rivers, with a rapid and intricate morphological evolution. A limited understanding and inadequate algorithm implementation of specific morphological processes limits the prediction capabilities of physics-based models. The design of structures, infrastructure, and other interventions is consequently hampered. In recent years artificial intelligence (AI) techniques rapidly gained popularity across different contexts. Additionally, the availability of satellite images increased. This research sets a novel attempt to predict the planform evolution of braided rivers by means of deep learning and satellite images. The Brahmaputra-Jamuna River, in India and Bangladesh, was selected as case study. A convolutional neural network (CNN) with U-Net architecture was developed. The model was trained with the Global Surface Water Dataset (GSWD). The goal of the model was to classify each pixel as either "Non-water" or "Water". Four images, representative of the same month over four consecutive years, were used as input. The fifth-year image represented the target. The model demonstrated good skills in predicting the planform development. Processes like the migration of meanders, the abandonment of channels, and the evolution of confluences and bifurcations were often well captured. However, a lack of temporal patterns was noticed. More complex phenomena, like the formation and shifting of channels, were never predicted. The total areas of erosion and deposition were constantly underpredicted. Metrics such as precision, recall, F1-score, and critical success index (CSI) were tracked. Overall, our model achieved a 5-6% total improvement of these metrics compared to the benchmark method for which no morphological change is assumed to occur. Our model could be useful as a preliminary tool for water management authorities in India and Bangladesh. It can support the prioritisation of bank protection measures in areas subject to erosion or land reclamation projects in areas subject to deposition and assist inland navigation. Given the inherent tendency of the model to underpredict erosion, caution is always advised. More research is required to improve the current model. Despite this, deep-learning modelling could become a potentially valuable field of research. Testing alternative model architectures, increasing the datasets size, and incorporating additional data, such as water levels or river discharge, are some of the proposed strategies to improve the model performance.
How to cite: Magherini, A., Mosselman, E., Chavarrías, V., and Taormina, R.: Deep learning for planform predictions of braided rivers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-650, https://doi.org/10.5194/egusphere-egu25-650, 2025.
Deriving sediment yield (SY) from discharge (Q) and suspended sediment concentration (SSC) measurements is subject to multiple sources of error, including the sampling method, the sampling scheme and frequency, the load calculation method, and the measuring period. While the uncertainty from these individual sources of error has been studied in various contexts, their combined effect on SY calculations remains largely unquantified. This is mainly due to their complex and counteracting influences, the absence of detailed sampling protocol information, and the lack of true reference data. Still, estimating the total uncertainty on current and historical SY measurements is crucial to understand how these observational errors propagate in SY modelling and can impact subsequent model interpretation and decision-making.
Here, we aim to develop a tool that can provide realistic ranges of total uncertainty in SY observations worldwide for which limited metadata is reported. We do this by means of Monte Carlo simulations and machine learning. Using available long-term daily Q and SSC series, we quantify the effect of measurement-related sources of error, as well as their relative importance, on SY calculations. We apply this method on a (spatially) diverse selection of ∼180 gauging stations and further explore the relationship between SY uncertainty and catchment characteristics, including upstream area, land cover, and climate. Preliminary findings indicate that the range of uncertainty in SY calculations is mainly influenced by the sampling frequency, whereas the load calculation method and the sampling scheme can introduce important biases. Measuring errors on individual Q and SSC observations have relatively little impact on total SY uncertainty, provided that these measurements are unbiased. When considering long-term average SY, the length of the measuring period then becomes the most important source of uncertainty. Overall, the combined effect of these sources of error can lead to deviations up to three orders of magnitude from the true SY. Using these sampling-related variables and catchment characteristics derived from global hydro-environmental datasets, we further apply a gradient boosting algorithm to predict total uncertainty in annual SY and achieve a Nash-Sutcliffe model efficiency of ∼0.76. The model resulting from this work can thus provide scientists with realistic uncertainty estimates on existing SY observations with only basic metadata information available.
How to cite: Tan, F., Borrelli, P., de Oliveira Fagundes, H., and Vanmaercke, M.: How good are sediment measurements? An integrated approach to quantifying total uncertainty in metadata-limited annual suspended sediment yield observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9845, https://doi.org/10.5194/egusphere-egu25-9845, 2025.
A hydropeak is a rapid increase in river discharge induced by a hydroelectric dam when optimizing energy production. These flow fluctuations occur in many regulated rivers and can influence sediment transport and fluvial habitat. The present study investigates the relative importance of hydropeaks versus natural floods for bedload sediment transport in the Ésera River, Central Pyrenees, Spain. An acoustic Doppler current profiler (ADCP) was used to measure both stationary time series and spatial distributions of apparent bedload velocity, which is the bias induced in ADCP bottom track velocity (Doppler sonar) due to bedload transport. A Sontek RiverSurveyor M9® ADCP, coupled with a Leica GS15® Real-Time Global Navigation Satellite System (RTK-GNNS), was deployed on a tethered floating survey platform from the road bridge at the Santaliestra monitoring section, which is approximately 13 km downstream from the hydropower plant.
During two measurement campaigns in 2019 and 2020, a total of 29 of the stationary ADCP apparent bedload velocity measurements distributed across the channel section were coupled with synchronous adjacent physical bedload samples collected with a Helley-Smith sampler. Correlation of such paired samples can be used to develop a calibration relation between observed ADCP apparent bedload velocity (m/s) and bedload sediment transport rate (kg/m/s). The physical bedload samples were processed in the laboratory to obtain fractional bedload transport rates. The paired data set was insufficient to develop a strong overall calibration relation, but fractional results aligned with calibration relations developed in other rivers with similar mixed sand/gravel bed materials.
A total of 13 spatial surveys of apparent bedload velocity were obtained for different flow rates, during both hydropeaking events and natural floods. Initial observations suggest natural floods result in greater sediment transport in the Santaliestra section due to the input of sediment from tributaries. Nonethless, hydropeaks were observed to partially destabilize/mobilize the bed, and thus contribute to sediment transport and morphodynamic processes.
How to cite: Rennie, C. D., Ville, F., Vericat, D., and Batalla, R. J.: ADCP measurements of bedload due to hydropeaking versus natural floods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20662, https://doi.org/10.5194/egusphere-egu25-20662, 2025.
Sediment flocculation in subaqueous environments is vital for morphodynamics, biogeochemical cycles, and ecological processes; however, the effects of biophysical cohesion on flocs are not well understood or quantified. This study provides quantitative field evidence that suspended sediments on a coastal algal reef primarily flocculate due to bio-cohesion. Measurements of mass and volume concentrations of suspended sediment and turbulent Reynolds stresses were performed at various heights above the seabed using Optical Backscatter Sensors (OBSs), Laser In-Situ Scattering and Transmissometry (LISSTs), and Acoustic Doppler Velocimeters (ADVs). Observed mass concentration profiles were compared with Rouse's law. Results indicate that while mass concentration decreases as expected with height, volume concentration increases away from the bed. Notably, mass concentration profiles align with the Rouse formula when assuming a settling velocity for flocculated sediment rather than non-cohesive sediment. Microscope images confirmed sediment flocculation, likely due to bio-cohesion. Direct measurements showed that particle effective density depends on mean particle diameter. Regression analysis determines a three-dimensional fractal dimension of 2.18. The reduced effective density and low fractal dimension are characteristic of flocs comprising lower-density saltwater and organic materials. The organic content was determined using the weight loss on ignition method. We found that organic content negatively correlates with effective density and positively correlates with the mean particle diameter, reinforcing the role of bio-cohesion in flocculation. Further information on the findings is published in "Field evidence of flocculated sediments on a coastal algal reef," Volume 6, Article 8, Communications Earth & Environment, 2025.
How to cite: Huang, Z.-C., Hsu, T.-J., and Ly, T. N.: Field Observations on Flocculation of Suspended Sediment in Coastal Algal Reef Environments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2995, https://doi.org/10.5194/egusphere-egu25-2995, 2025.
Accurately predicting, modelling and measuring bedload transport rate remains a challenge even after a century of research and various approaches. Traditionally measurements of bedload transport rate in field setting have been done with labor-intensive and difficult to use mechanical equipment such as bedload samplers or traps. Mechanical devices are often intrusive, meaning that the devices’ presence can influence the shape of riverbed and the measured bedload transport rate during the measurement. These devices are also limited in their capability to capture the spatial and temporal fluctuations of bedload transport and only describe dimensionless mean transport rate from a point or a section. Image processing techniques such as particle image velocimetry and optical flow combined with background subtraction and image labeling methods enable continuous, non-intrusive two-dimensional bedload velocity and bedload transport rate measurements over large areas. These image processing techniques have been previously successfully applied in lab conditions to measure bedload transport rates from video data sets but not in field conditions.
The focus of this study is 1) to apply image processing techniques to underwater video data sets to measure seasonal bedload transport rates in various sediment transport conditions and 2) to understand and compare the seasonal variation in bedload transport measured with image processing techniques and traditional mechanical measurements.
To cover various sediment transport conditions, the study is based on field data collected over various years (2021-2024), seasons (winter, spring, autumn), and flow conditions (open channel and ice-covered) at sub-arctic Pulmanki river, which is in northern Finland (~70°N latitude) and drains to the Arctic Sea. The novel results are presented and show that the method is promising in enhancing the understanding of sediment transport processes and the seasonal transported amounts in sub-arctic river conditions.
How to cite: Välimäki, J.-M., Lotsari, E., Eltner, A., and Takala, T.: Measuring seasonal bedload transport rates in a sub-arctic river using image processing techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18955, https://doi.org/10.5194/egusphere-egu25-18955, 2025.
Suspended sediment plays a crucial role in shaping stream morphology and maintaining ecological balance, yet the main controls on its sources and dynamics in forested mountain catchments are still poorly documented. In this study we aimed at assessing suspended sediment spatio-temporal sources and transport dynamics in a Mediterranean mountain catchment. A relevant role might be played by large wood debris in suspended sediment retention and release: large wood structures can significantly influence sediment dynamics by trapping sediments and creating stable habitats for aquatic organisms. This aspect is particularly relevant in forested mountain streams where wood accumulation can alter flow patterns and sediment transport mechanisms.
The experimental activities were carried out in the densely forested Re della Pietra catchment located in Tuscany, Central Italy.
To assess suspended sediment spatio-temporal dynamics, field measurements were conducted since December 2024, including monitoring of turbidity at the catchment outlet using a high-definition turbidimeter, stream stage measurements, soil moisture measurements at two depths, and the main meteorological variables.
Preliminary results show a significant correlation between turbidity, rainfall intensity and stage variation, suggesting that rainfall intensity is crucial in suspended sediment release and transport patterns. Notably, pronounced turbidity peaks were observed during moderate to intense storm events occurred during the wet season but did not correlated to meteorological variables. The analysis of the hysteresis loops between turbidity and stream stage (as a proxy of discharge) reported that the 15% of the loops were clockwise, suggesting that suspended sediment primarily originates from local sources, mostly during the wet season.
The study highlights the relationships between suspended sediment transport, large wood debris, and hydrological variables, emphasizing the need for further investigation of the factors affecting suspended sediment transport in forested mountain environments. The determination of flow rating curve is in progress, and future analysis will consider suspended sediment concentration and discharge data. The large wood impact will be studied through the visual analysis of the photographic documentation produced by cameras located at the catchment outlet.
How to cite: Chirici, D., Murgia, I., Verdone, M., Innocenti, L., Nigro, M., Manca, F., Dani, A., Preti, F., Belli, G., Gheri, D., Mao, L., Marchetti, E., Solari, L., and Penna, D.: Investigating Suspended Sediment And Large Wood Dynamics in a Mountain Forested Catchment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19543, https://doi.org/10.5194/egusphere-egu25-19543, 2025.
In recent decades, global watershed surface processes have undergone significant alterations due to a complex interplay of factors, including global warming and human-induced stressors. Changes in river morphology serve as crucial indicators of the long-term dynamic evolution of rivers and their associated environmental impacts.
Existing studies have predominantly focused on changes in river morphology, with limited attention given to regions exhibiting lateral stability. This study introduces a novel river morphology change index and applies it to overlay analysis of river morphology in the Pearl River basin spanning several decades. By quantifying areas of stable, expanding, and diminishing river morphology, the study unveils the patterns of morphological change in the region. The findings reveal a trend of increasing stability in the Pearl River's morphology, extending from its source to its mouth, driven by progressive human intervention. While such interventions enhance river stability in the short term, the hardening of channels may reduce their effectiveness in managing floods during extreme climatic events (e.g., heavy rainfall), potentially exacerbating flood risks.
Through a case study of the Pearl River Basin, this paper underscores the vital importance of retaining wide river corridors and restoring natural riverbed morphology configurations in maintaining natural geomorphology. It further proposes recommendations for optimizing river management and disaster prevention and mitigation.
How to cite: yang, Z.: Satellite observations of surface water dynamics and channel morphology in the Pearl River, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1335, https://doi.org/10.5194/egusphere-egu25-1335, 2025.
Drone imagery can efficiently perform large-scale riverbed grain size measurements. However, its applicability is significantly constrained by image resolution limitations. This issue is especially critical in mountainous areas, where sediments exhibit a wide range of grain sizes and spatial heterogeneity. To address this issue, this paper develops a new fluvial sediment measurement technique for UAV images using a deep learning technique super-resolution (SR). We first used RTK-based UAV technology to collect high-resolution riverbed grain orthophotos of different types of mountain rivers, with the collected UAV images having a resolution between 3~5 mm/pixel. Four types of super-resolution models Nearest Neighbor, Lanczos filter, SRCNN and SRGAN were trained to restore the high-resolution images from low-resolution riverbed images. Three automated grain sizing methods BASEGRAIN, GrainID and ImageGrains were applied to the images restored by SR models, and 113,456 manual grain labels are created as grain size baseline for model evaluation. The efficacy of all three models diminishes with decreasing resolution, with BASEGRAIN being the most robust and GrainID the most sensitive. Application of all four SR models model significantly increase the efficacy of grain size measurement, and SRGAN models with upscaling factor of 4 (SRGAN×4) outperform other models. Further analysis shows the minimum detectable sediment particle size of SRGAN×4 is 1 pixel, which exceed the minimal human vision limitation for detecting grain size. The SR technology proposed in this paper makes it more feasible to rapidly obtain the riverbed grain size over a wide range in mountainous rivers.
How to cite: Chen, X., Liu, Y., Wang, J., Ma, H., Hassan, M. A., and Fu, X.: Super-Resolution for Enhanced Fluvial Sediment Measurement in UAV Images , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20149, https://doi.org/10.5194/egusphere-egu25-20149, 2025.
Physical models are valuable tools for investigating changes in river morphology while also allowing the analysis of three-dimensional processes such as scour in bends or the vicinity of hydraulic structures. An important aspect is an accurate assessment of the river bed and morphological structures that occur, which today is often based on optical measurement systems, such as LiDAR, or photogrammetric techniques like Structure for Motion (SfM). These sensors are usually mounted on tripods, requiring multiple look angles to cover the whole model, and therefore need to be manually repositioned several times. Alternatively, they can be mounted on overhead tracks, limiting the possible look angles and requiring expansive installation.
To overcome these limitations, this study utilized a drone equipped with a high-resolution camera to survey morphological bed changes of a 70 m long and up to 6 m wide physical model with a movable bed and fixed rip-rap embankments. The bed material consisted of coarse sand and fine gravel with a mean diameter of 2.1 mm. Several surveys covering a total area of 180 m² were carried out and drone-based SfM results were compared with data obtained using a terrestrial laser scanner (Leica RTC360). The DJI Mavic Mini 3 Pro drone was equipped with a 48 MP camera, featuring a 1/1.3'' CMOS sensor which captured up to 240 camera positions from three vertical angles within 30 minutes. This was a similar acquisition time required by the tripod-mounted laser scanner to cover the whole model with six setups. Post-processing, from ground control point detection and tie point matching to cloud construction and digital elevation model (DEM) generation, was automated in this study to reduce processing time.
By comparing the DEMs produced by SfM and the RTC360, it became obvious that SfM cannot only map morphological structures but also produces denser point clouds, with a mean surface point density of 127 pts/cm² compared to 75 pts/cm² by the laser scan. The mean absolute cloud-to-cloud distance for the model bed is 1.8 mm, with a standard deviation of 1.5 mm. This compares favorably to the accuracy of the RTC360 of 1.9 mm at a distance of 10 meters.
Notably, there are disagreements between the SfM model and the laser scan, especially in areas with coarser materials, e.g. rip-rap at the embankments, or areas with low-feature texture, e.g. plastic structures or smooth concrete faces. The final calculated volume differences from the resulting DEMs before and after an experimental trial also show good agreement, with a 3 % discrepancy in the volume difference.
The results of this study showed that the accuracy of drone-based SfM-generated DEMs is similar to that of an RTC360 with much lower equipment costs. Furthermore, the mobility of drones offers the advantage of achieving a wider range of look angles, which improves the quality of the resulting SfM model. Hence, the application of drone-based SfM for morphological measurements in laboratory experiments is a promising technique for a wide range of measurements of morphological processes.
How to cite: Pirker, M., Haun, S., and Schneider, J.: Evaluating drone-based photogrammetry for morphologic mapping of a hydraulic model with a mobile bed, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20898, https://doi.org/10.5194/egusphere-egu25-20898, 2025.
Drawdown flushing in reservoirs is a key scheme for reservoirs water and sediment regulation in Yellow River basin, making it possible to maintain the effective storage capacity of the reservoirs. In early September of 2023 and 2024, joint operation of key reservoirs in the upper and middle reaches of the Yellow River was carried out to implement sediment discharge. In 2023, the water and sediment regulation resulted in an outflow of 610 million m³ from Liujiaxia Reservoir. Four reservoirs, including Qingtongxia, Haibowan, Wanjiazhai, and Longkou, discharged a total of 75 million tons of sediment, consuming 8 m³ of water per ton of sediment. In 2024, the water and sediment regulation resulted in a water output of 1.09 billion m³ from Liujiaxia Reservoir. The five reservoirs of Shapotou, Qingtongxia, Haibowan, Wanjiazhai, and Longkou Reservoirs discharged a total of 144 million tons of sediment, consuming 7.5 m³ of water per ton of sediment. Through the practice of water and sediment regulation, it has been proven that flushing is a systematic response of reservoir deposition to the drawdown of water level. The flushing process is unsteady, and the sediment concentration at the outlet increases rapidly at first and then decreases rapidly. The scouring amount is positively correlated with the magnitude of inflow discharge and duration of drawdown. The larger the discharge, the longer the duration, and the more sediment discharge.
How to cite: Yang, F., Yan, X., and Wang, Q.: Water and sediment regulation in past two years at upper and middle Yellow River, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14657, https://doi.org/10.5194/egusphere-egu25-14657, 2025.
Abstract: Under the combined operation of upstream cascade reservoirs with the Three Gorges Project as the core and the comprehensive impact of climate change, the water and sediment conditions in the middle and lower reaches of the Yangtze River have undergone significant changes, which have had a profound impact on the evolution and pattern of river-lake system. This paper takes the confluence reach of the Yangtze River Mainstream and Dongting Lake as the research object. Through the physical model(plane scale 1:400, vertical scale 1:100) experiments, the confluence process of flow and sediment and the evolution rule of erosion and deposition of the Yangtze River Mainstream and Dongting Lake outlet channel under different silt-discharge and river lake boundary conditions were explored, and the influence mechanism of different erosion and deposition conditions on the river-lake relationship was analyzed. The results indicate that considering the further scouring development of the main stream of the Yangtze River in the future, under the condition of controlling the water level at the model outlet (Luoshan station) to drop by 3m, the longitudinal gradient of the water surface in the confluence channel of Dongting Lake increased by about 20%, showing a scouring trend, and the emptying effect of the Yangtze River Mainstream on the Dongting Lake was enhanced. In addition, under the condition of controlling the flood flow of 50000m3/s at the outlet, with the increase of the confluence ratio between Dongting Lake and the Yangtze River Mainstream, the scouring intensity in the river and lake confluence area increased, and the elevation difference between the main stream and tributary riverbed increases, resulting in the increasing jacking effect of Dongting Lake outflow on the main stream. The relevant achievements can provide new insights and decision-making references for maintaining the healthy river-lake system pattern and interaction relationship in the middle and lower reaches of the Yangtze River.
Key words: flow and sediment variation; river-lake relations; physical model experiments; Dongting Lake; Middle reaches of the Yangtze River
How to cite: Wang, H., Yao, S., Guo, X., and Guo, C.: Influence mechanism of erosion and deposition evolution of Dongting Lake confluence channel on river-lake relationship under new water and sediment conditions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12100, https://doi.org/10.5194/egusphere-egu25-12100, 2025.
A vortex tube silt ejector is employed to extract sediments from the canal. It consists of a duct laid across the whole bed of the canal with a slit along its top edge, and compared to the other alternative sediment-extraction devices, it is very efficient and economical. A Vortex Tube silt ejector is a device that is used to remove unwanted sediment from the irrigation and power canal. The vortex tube basically removes the sediment particle with the rotational action of the entered flow and receives it to the escaped channel. In this study, M5P, M5Rules, Random Forest (RF), and Gradient Boosting Method (GBM) approaches were employed to predict the trapping efficiency of the vortex tube ejector. Data was obtained by conducting experiments on the vortex tube silt ejector. The input data set consists of sediment size (mm), the concentration of sediment (ppm), the ratio of slit thickness and diameter of the tube (t/d), and extraction ratio (%), whereas trapping efficiency (%) was considered as output. The comparative analyses with conventional models reveal that the GBM outperforms the other ML models, achieving a Coefficient of Correlation (CC) of 0.9985, Root Mean Square Error (RMSE) of 0.769, and a Mean Absolute Error (MAE) of 0.531, indicating superior accuracy with lessor errors.
How to cite: Kumar, S. and Ojha, C. S. P.: Application of Machine Learning for Predicting the Trapping Efficiency of Vortex Tube Silt Ejectors, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-528, https://doi.org/10.5194/egusphere-egu25-528, 2025.
Human interventions in riverine systems, such as the construction of cross-drainage structures, significantly alter hydrodynamic and related sediment transport processes, leading to siltation, riverbank instability, and sediment flushing. These changes disrupt bed sediment characteristics and near-bed flow turbulence, thereby imposing a considerable modification to bed roughness and flow-induced transport mechanisms that enforce a threat to the overall natural health of the river. This is particularly pertinent during the present-day Anthropocene era, while human interventions and activities are imposing a significant impact on riverine systems. Despite numerous studies on sediment sorting in non-cohesive beds, there is limited understanding of the physical processes governing the initial movement of heterogeneous non-uniform sediment beds composed of silt, sand, and gravel on sloping beds. The present laboratory-based flume study aims to investigate the incipient motion and critical Shields parameter for sloping sediment beds inclined at 4.8° towards downstream. The sediment bed consists of mixtures of fine sand, coarse sand, and gravel. A fixed discharge generated unidirectional flow, and the evolution of bed morphology was monitored until a quasi-equilibrium state was reached. Instantaneous velocity data was acquired using a 16 MHz micro-ADV, while high-precision video recording captured particle motion. The spatial and morphological characteristics of evolved bed forms were measured with a digital vernier gauge. The study reveals that sediment composition and near-bed flow turbulence strongly influence the critical Shields parameter and incipient motion thresholds. The variation in sediment sorting, bed form evolution, and flow turbulence enhances non-uniform flow conditions, contributing to significant changes in sediment transport dynamics. The findings provide insights into sediment bed behavior, helping inform engineering practices to mitigate siltation at dam and barrage sites.
How to cite: Jha, S., Chaudhuri, S., Das, V. K., Mazumder, B. S., and Debnath, K.: Study of Grain Size Variation of Non-Uniform Sloping Sediment Bed and Associated Flow Turbulence, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1045, https://doi.org/10.5194/egusphere-egu25-1045, 2025.
The biggest Brazilian rainfall event occurred in May 2024 affecting millions of people. The damage caused includes several deaths and other social issues, billions in economic losses, and massive environmental devastation with more than 10,000 landslides and sediment settling in flat areas. In this context of extreme events, we aimed to estimate the suspended sediment transport in the Guaíba basin during this unprecedented flood. We used the model for large basins MGB-SED and daily precipitation to compute sediment erosion, transport and deposition. The model was calibrated considering the historical period, prioritizing the adequate representation of extreme events, resulting in KGE values higher than 0.4 in the main sediment stations. For the first time, our results provided sediment yield estimates for this event: 5 million tons of suspended sediment were delivered to Guaíba from April 27 to June 17, 2024. The Taquari River was the tributary that transported the most suspended sediment, reaching a peak of 554,500 tons on May 2, which is five times greater than the highest simulated peak in the historical period. After the event, a large deposition of coarse sediments in the lowland areas, silting up the rivers and islands was widely reported. Despite this, we observed from satellite images that the morphological changes (e.g. bank erosion and the appearance/ changes in sand banks) along the main channels were insignificant compared to the event scale. We conclude that, even in the face of the unprecedented sediment and water flow, these rivers demonstrate a bank stability condition and high suspended sediment transport capacity, even suggesting an equilibrium condition.
How to cite: Fagundes, H., Fassoni-Andrade, A., Maciel, D., Silva, V., Rossoni, R., Cavalcanti, J. R., Fagundes, M., Paixão, M., and Fan, F.: Suspended sediment transport during the unprecedented flood in southern Brazil, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3151, https://doi.org/10.5194/egusphere-egu25-3151, 2025.
Hydroelectric dams represent the primary source of renewable energy in France but have a significant impact on the proper functioning of aquatic ecosystems by disrupting the ecological continuity of rivers. Indeed, these structures interfere with sediment transport by reducing sediment availability in downstream sections and causing major disruptions to the morphology and ecology of the environment. To offset this deficit, spawning ground restoration operations can be carried out to replenish sediments. Predicting sediment transport, particularly the remobilization of these sediments, is of paramount importance in order to accurately assess the durability of the inputs and their ecological effectiveness.
To better understand and quantify these phenomena, which are often difficult to measure in reality, experimental laboratory models are developed to replicate the hydrodynamic and sedimentary conditions observed in the field through scaling techniques. This development of the experimental model relies on preserving the Froude number (hydraulic) and the Shields parameter (sedimentary). These parameters enable the reproduction of flood hydrographs, river roughness, and the size of recharging sediments at the study site on a laboratory scale. A characterization of the hydrodynamic parameters on a rough bottom was then carried out using Particle image velocimetry (PIV), enabling the bottom shear stress to be estimated and compared with the displacement of localized sediment input.
How to cite: Grattepanche, F., Gomit, G., Calluaud, D., Courret, D., and Sagnes, P.: modeling laboratory-scale unsteady flow hydrographs: bed shear stress and sediment transport on rough surfaces, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3768, https://doi.org/10.5194/egusphere-egu25-3768, 2025.
Accurately predicting river water levels is essential for effective environmental and water resource management, particularly in flood mitigation, drought forecasting, and infrastructure planning. Two-dimensional (2D) hydraulic models are widely used for simulating water levels, but achieving high accuracy remains a challenge due to uncertainties related to input data, model structures, technical configurations like mesh design, and parameters such as roughness. Mesh configuration plays a pivotal role in shaping bathymetry, influencing discharge capacity, and determining water levels. While high-resolution meshes deliver greater accuracy, they often come at the cost of longer computational times. In contrast, low-resolution meshes are computationally efficient but can introduce significant errors, requiring complex calibrations that may struggle to handle extreme flow conditions effectively.
This study adapts a novel developed algorithm, first for hypothetical river systems, and then to real-world applications. The method adjusts the elevation of individual mesh nodes, ensuring that the flow volume in low-resolution meshes aligns with high-resolution riverbed data. By improving mesh accuracy while maintaining computational efficiency, this innovative approach addresses mesh-related errors and enhances model reliability. The modified low-resolution mesh was tested through hydraulic simulations and validated against real-world measurements.
Results demonstrate that the modified low-resolution mesh produces water level predictions up to 50% closer to observed measurements compared to the original low-resolution mesh. This significant improvement underscores the potential of the algorithm to enhance prediction accuracy. The findings contribute to advancing hydraulic modeling by optimizing mesh configurations and hold broader implications for flood management and water resource planning. By improving the reliability of water level simulations, this research supports more informed and effective environmental management strategies.
How to cite: khorsandi kuhanestani, P., Bomers, A., Booij, M., and Hulscher, S.: Improving Water Level Predictions in 2D Hydraulic Models: Optimizing Low-Resolution Meshes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4353, https://doi.org/10.5194/egusphere-egu25-4353, 2025.
Hydraulic jumps play a critical role in the design of energy dissipation stilling basins. Although hydraulic jumps are inherently three-dimensional (3D) phenomena, they have traditionally been studied using one-dimensional (1D) approaches. In previous, enginners and researchers have relied primarily on 1D and 3D models to analyze hydraulic jumps. While 3D models provide high accuracy, they are also computationally expensive, and 1D models some how fail to capture the vortices and lateral flow effects inherent in hydraulic jumps. Two-dimensional (2D) models offer a balance between computational efficiency and accuracy, making them a promising alternative. This study seeks to evaluate the capability of a 2D hydraulic model to simulate experiments on abrupt expansion stilling basins, as well as to assess its applications and limitations. By focusing on critical design parameters, such as the expansion width ratio, inlet eccentricity, and inlet angle, the research aims to identify optimal designs that maximize energy dissipation through simulations of various parameter combinations. Preliminary findings reveal that energy dissipation efficiency stabilizes once the expansion width ratio surpasses a certain threshold, showing no significant further improvement. In the case of inlet eccentricity, adjustments are evaluated individually to ensure that the inlet is not centered and that the inlet wall does not overlap with the outlet wall. For the inlet angle, an optimal configuration is observed to vary based on the tailwater conditions. Ongoing work aims to validate the relationships between energy dissipation efficiency and the interplay of these parameters.
How to cite: Lin, P.-J. and You, J.-Y.: The Investigation of a Two-Dimensional Numerical Model for Characterizing Energy Dissipation Efficiency in Stilling Basins, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5739, https://doi.org/10.5194/egusphere-egu25-5739, 2025.
The unexpected retreat of riverbanks can significantly influence adjacent infrastructure, floodplain usage, and ecological systems. In our increasingly populated world, where rivers are closely linked with sensitive environments, sudden morphological changes have to be incorporated into planning processes. Changes in the riverbank depend on various factors, including soil composition, pore-water pressure, ship waves, and vegetation. This results in complex erosion mechanisms which are challenging to represent in numerical models. A review of existing studies shows that cohesive banks and the occurring processes, such as the variety of failure mechanisms, the deposition of failed material, or pore-water pressure, are often neglected. This gap limits the predictive accuracy of current models in the case of cohesive banks. Therefore, representing these mechanisms with a 2D model based on the software TELEMAC-2D and considering the random instabilities would enhance the understanding of the morphological development of rivers with cohesive banks. This leads to more accurate predictions and informed decisions that benefit both human activities and ecological systems.
How to cite: Baumann, D., Kopmann, R., and Rüther, N.: Numerical Modeling of Cohesive Riverbanks: Current methods, challenges, and prospects, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8100, https://doi.org/10.5194/egusphere-egu25-8100, 2025.
Understanding sediment transport and hydrodynamic processes is critical for managing riverine and coastal systems, influencing navigation, flood risk, and sustainable sediment management. Traditional measurement approaches often rely on physical sediment sampling and manual data interpretation, which can be labour-intensive, spatially constrained, and time-consuming. This study presents a novel framework that combines Acoustic Doppler Current Profiler (ADCP)-derived data with machine learning (ML) techniques to enhance the monitoring and analysis of both sediment transport and hydrodynamics in open water environments.
Our dataset includes comprehensive hydrodynamic and acoustic parameters, such as bottom track velocity (BT), signal-to-noise ratio (SNR), acoustic backscatter (AB), depth, velocity standard deviation (SD), and mean flow speed. Exploratory analysis reveals significant relationships among these features, with BT, SNR emerging as key proxies for sediment transport and hydrodynamic variability. Notably, BT shows moderate correlations with depth (r = 0.55) and SD (r = 0.36), underscoring its utility for characterizing flow conditions and sediment dynamics.
A machine learning framework is under development to analyse these relationships and predict sediment transport and hydrodynamic parameters. Initial exploratory findings highlight patterns in hydrodynamic variability and sediment transport proxies, laying the groundwork for advanced modeling efforts. Clustering algorithms reveal distinct flow regimes, and feature correlations suggest potential for predictive modeling of sediment dynamics.
This study demonstrates the potential of leveraging ADCP data for scalable and resource-efficient sediment and hydrodynamic monitoring. By integrating laboratory and field datasets, the proposed approach aims to enhance measurement capabilities and support the calibration and validation of numerical models. The findings hold significant implications for sustainable water resource management and the development of real-time hydro-morphological monitoring frameworks in diverse open water environments.
How to cite: Tuhin, M. T. H. and Mudersbach, C.: Integrating Machine Learning with ADCP Data for Advanced Sediment Transport and Hydrodynamics Monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8296, https://doi.org/10.5194/egusphere-egu25-8296, 2025.
Our research focused on the river regulation in Hungary in the 19th century, as it resulted in significant morphodynamic changes. The Tisza River was significantly meandering in its contemporary state and the regulations were used to cut through these meanders. The main question is what bank geometry and flow conditions were formed in the natural state of the river, before the regulation works?
In our research, we wanted to study a selected section of the Hungarian Tisza, where a detailed literature search revealed that there was not enough data on the contemporary condition to suitably describe the natural morphology. This led to the need to develop a procedure that would be able to map the pre-regulation condition in a way. To this end, a novel method for estimating the geometry of the riverbed was used. pyRiverbed is a tool that estimates the bankfull bed geometry based on the river's centerline and the average channel depth and width. Using the 'synthetic' geometry produced in this way, we were able to calculate flow characteristics (e.g. typical flow velocity and bed shear stress, reach-averaged bed resistance) for the pre- and post-regulation conditions using 2D flow models and compare these parameters.
Our research also aims to show the importance of studying the sediment budget of a river and its role in the variation of riverbed geometry. We believe that such a method could play a major role in future regulatory work and could also help in the preparation of regulatory plans.
How to cite: Nyiri, E., Török, G. T., and Homoródi, K.: Investigation of the natural morphodynamic state of a Tisza river section in Hungary, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9624, https://doi.org/10.5194/egusphere-egu25-9624, 2025.
Predicting flow in rough-bed rivers, which are a common and important feature of upland river networks, is crucial for improved river management, as changes in flow discharge can affect sediment transport, cause flooding, and disrupt habitats. Standard grain-size approaches that predict flow resistance (e.g., D84) do not perform well in rough-bed rivers because they are unable to account for the coarse topographic features that rough-bed rivers possess, such as exposed bedrock, potholes, bedrock ribs and boulders. The flow resistance derives from the interaction of the flow and the bed topography, which causes spatial variations in pressure and form drag, and modulates the shear stress available for sediment transport. However, the extent to which different components of the bed topography affect the flow resistance is not well understood. Thus, improvements in flow resistance prediction are necessary.
One feasible approach to fill this gap is to use computational fluid dynamics (CFD), as it is able to numerically resolve the pressure at the bed, at the resolution of the grid representing the bed topography. In this work, we developed a robust, validated numerical model to simulate a series of flow discharges over three different rough-bed river beds. Our CFD model fully resolves the Navier-Stokes equations in a three-dimensional, cartesian-gridded domain. The model captures the adjustment of the free surface at the air-water interface with the volume-of-fluid method, such that the simulated flows are not constrained by low Froude numbers. The three different bed sections were reconstructed from high-resolution topographic data from bedrock rivers with smooth, intermediate and rough topography (standard deviation of the bed elevation equal to 0.043 m, 0.083 m and 0.131 m at the chosen sections, respectively). As the CFD are replicating scaled flume experiments performed with the same bed topography, the topography in the CFD has also been scaled by 1:10 from the field dimensions. For each bed, we assess the pressure distribution, pressure gradient and form drag over the beds under five different flow depths and discharges.
How to cite: Yamasaki, T., Hodge, R., Hardy, R., Houseago, R., Whitfield, D., Rice, S., Ferguson, R., Hackney, C., Yager, E., Johnson, J., and Hoey, T.: How can we better predict flow resistance in rough-bed rivers? A numerical (CFD) approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12233, https://doi.org/10.5194/egusphere-egu25-12233, 2025.
In order to study the influence of the presence of floodplain vegetations on the flow structure of compound open channels, a three-dimensional large eddy simulation model for compound open-channel flows with vegetations was established, and the flow motion of the compound open channel under the action of non-submerged vegetations was simulated. The simulation results are in good agreement with the previous numerical simulation results, indicating that the model established in this paper is reliable and effective in the numerical simulation of compound open-channel flows with vegetations. The influence of the presence of floodplain vegetations on the flow structure of compound open channels and the influence of different vegetation densities on the flow structure of compound open channels were analyzed. The results show that the presence of floodplain vegetations significantly changes the flow structure within the compound open-channel flow. Due to the presence of vegetations, the longitudinal average velocity of the floodplain decreases, the longitudinal average velocity of the main channel increases, the maximum values of turbulent kinetic energy and Reynolds stress at the junction of the main channel and the floodplain increase, the boundary shear stress on the main channel and the floodplain decreases, and the apparent shear stress increases. The higher the vegetation density, the smaller the longitudinal average velocity of the floodplain, the greater the intensity of the leftward shift of the maximum longitudinal average velocity of the main channel, the stronger the secondary flow, the greater the maximum values of turbulent kinetic energy and Reynolds stress near the confluence of the main channel and the floodplain, while the boundary shear stress of the main channel and the floodplain decreases, and the peak value of the apparent shear stress near the confluence of the main channel and the floodplain increases.
How to cite: Lei, Y. and Liu, G.: Large eddy simulation of flow structure of compound open channels with non-vegetated floodplain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14656, https://doi.org/10.5194/egusphere-egu25-14656, 2025.
study concentrates on proposing a stochastic turbulent diffusivity for fluid particles derived from diffusing diffusivity, a unique stochastic process defined as the square of the Ornstein–Uhlenbeck (OU) process. The diffusing diffusivity is assigned to be the square of time-dependent velocity fluctuations in a turbulent flow and is modeled using the fractional OU process with fractional Brownian motion (FBM). Three crucial properties from various fields are tied into this model: (1) mean-reverting behavior derived from the OU process, (2) long-term memory attributed to FBM, and (3) stochastic turbulent diffusivity for fluid particles. The first four ensemble statistics—mean, variance, skewness, and kurtosis—are provided for the diffusing diffusivity to identify non-Gaussian behavior, measure the variability, and investigate the deviation from classical deterministic models.
The highlight of this study is the proposal of stochastic turbulent diffusivity for fluid particles in a turbulent flow. It is defined by multiplying the diffusing diffusivity with the Lagrangian timescale, thereby linking small-scale temporal fluctuations captured by diffusing diffusivity to the macroscopic mixing effects of turbulent diffusivity. This approach ensures dimensional consistency with deterministic turbulent diffusivity while preserving its stochastic characteristics. Additionally, higher-order structure functions and wavelet-based intermittency measures are provided to examine intermittency in turbulent flows. The former provides evidence of intermittency, and the latter captures energy bursts across scales associated with turbulent diffusing diffusivity. On the other hand, the validation is conducted against the Ergodicity Breaking parameter from theoretical stochastic analysis and turbulent velocity fluctuation data from experiments, confirming the applicability of bridging diffusing diffusivity to stochastic turbulent diffusivity.
How to cite: Shen, S. W. and Tsai, C. W.: Proposing Stochastic Turbulent Diffusivity from Diffusing Diffusivity with Fractional Ornstein–Uhlenbeck Process and Fractional Brownian Motion, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16285, https://doi.org/10.5194/egusphere-egu25-16285, 2025.
The retreat of alpine glaciers impacts and intensifies geomorphological processes in proglacial zones, driven by increased sediment availability and altered hydrological regimes. These dynamic systems transfer sediment from glacial sources to downstream fluvial networks, profoundly influencing sediment flux and fluvial morphology. This study investigates sediment dynamics within proglacial gravel plains of the Jamtal Valley (Tyrol, Austria), focusing on DEM of difference (DoD) analysis combined with grain size distribution (GSD) mapping as key tools for understanding sediment redistribution. Both are used as input to calibrate hydromorphological models. A multi-method approach was employed, integrating UAV-based photogrammetry, LiDAR surveys and manual ground-truth sampling. High-resolution local ground truth data were upscaled to large areas by applying a Random Forest Regressor, expanding spatial coverage and reducing dependence on labor-intensive field methods. This approach enables efficient and frequent monitoring even in alpine terrain that is difficult to access. Initial results demonstrate the effectiveness of integrating photogrammetry with semi-automated grain size detection algorithms to capture spatiotemporal variations in sediment properties. The temporal changes in elevation and GSD mapped since 2021 offer insights into sediment redistribution mechanisms. Glacial runoff and associated introduction of bed load trigger the transport and redistribution mechanisms in the glacier forefield. Whereas aggradation and surficial coarsening is assumed to occur under sediment supply-dominated conditions, contrasting to periods leading to a net erosion in the forefield. These dynamics underline the role of proglacial zones in buffering and modulating sediment fluxes connecting downstream river reaches. The hydraulic implications of GSD variability were analyzed using multiple roughness models within a 2D hydrodynamic framework, including Manning’s, Nikuradse’s and Ferguson’s roughness model. Applying the approach by Ferguson (2007) accounts for macro-roughness and variable submergence and revealed significant velocity variations and minor changes in flood extents. However, spatially differentiated roughness exhibited limited impact on water levels under varying discharge conditions, highlighting the nuanced influence of surficial sediment distribution on hydrodynamic behavior. To assess the morphodynamic behavior, the associated 2D sediment transport model is being used to evaluate the use of GSD maps in advancing our understanding of sediment dynamics and hydromorphological feedback in proglacial environments. This work in progress focuses on impacts of various inflow conditions and model setups on shifts in sediment transport and spatial redistribution patterns.
How to cite: Leistner, S., Hiller, C., Schulte, F., Winiwarter, L., Glas, S., Helfricht, K., and Achleitner, S.: Sediment Dynamics in Proglacial Zones: Insights from Grain Size Distribution Mapping and Hydrodynamic Modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19336, https://doi.org/10.5194/egusphere-egu25-19336, 2025.
Posters virtual: Thu, 1 May, 14:00–15:45 | vPoster spot A
EGU25-15513 | ECS | Posters virtual | VPS10
Monitoring Geomorphological Changes in the Peruvian Coast Using UAVs and PPK TechniquesThu, 01 May, 14:00–15:45 (CEST) | vPA.11
The Peruvian coast is undergoing significant landscape transformations driven by environmental and climatic factors, with extreme precipitation events exerting a pivotal influence on the morphology of river channels and floodplains. This study leverages advanced technologies, including unmanned aerial vehicles (UAVs) and post-processing kinematic (PPK) techniques, to address these dynamic changes. The methodology involves co-registering point clouds using ground control points (GCPs) to produce high-resolution and temporally stable digital elevation models (DEMs).The research focuses on a 0.5 km² area within a coastal basin in Peru, with data collection scheduled across two distinct timeframes. The primary objective is to identify areas exhibiting minimal elevation changes and quantify rates of erosion and sediment deposition over a defined period. Specifically, the study measures erosion in gullies and riverbanks, as well as sediment deposition, enabling the estimation of volumetric changes in cubic meters (m³). These findings are critical for advancing the understanding of regional geomorphological processes and informing the development of effective management and mitigation strategies. By employing UAVs and PPK techniques, this research delivers actionable insights into sediment dynamics, supporting sustainable water resource management and land use planning in Peru’s coastal basins. Ultimately, the study contributes to mitigating the adverse impacts of extreme precipitation on the region’s landscapes.
How to cite: Cubas-Arteaga, E. and Cárdenas-Gaudry, M.: Monitoring Geomorphological Changes in the Peruvian Coast Using UAVs and PPK Techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15513, https://doi.org/10.5194/egusphere-egu25-15513, 2025.
