scholarly journals Hydrodynamics of steep streams with planar coarse-grained beds: Turbulence, flow resistance, and implications for sediment transport

2017 ◽  
Vol 53 (3) ◽  
pp. 2240-2263 ◽  
Author(s):  
Michael P. Lamb ◽  
Fanny Brun ◽  
Brian M. Fuller
Keyword(s):  
Sediment Transport ◽  
Flow Resistance ◽  
Coarse Grained ◽  
Turbulence Flow

scholarly journals Clast imbrications in coarse-grained sediments suggest changes from upper to lower flow regime conditions

2018 ◽  
Author(s):  
Fritz Schlunegger ◽  
Philippos Garefalakis
Keyword(s):  
Sediment Transport ◽  
Flow Regime ◽  
Shear Force ◽  
Foreland Basin ◽  
Coarse Grained ◽  
Late Oligocene ◽  
Incipient Motion ◽  
Material Transport ◽  
Bed Roughness ◽  
Large Flow

Abstract. Clast imbrications are presumably the most conspicuous sedimentary structures in coarse-grained clastic deposits. In this paper, we test whether the formation of such a fabric is related to changes from lower to upper flow regime conditions in streams. To this extent, we calculate the Froude number at the incipient motion of coarse-grained bedload for various values of relative bed roughness and stream gradient. We then compare the results with data from modern streams and stratigraphic records. The calculations show that upper flow regime conditions most likely establish where average stream gradients exceed c. 0.5 ± 0.1°, and where relative bed roughness values are larger than ∼ 0.06 ± 0.01. Similarly, data from modern streams reveal that imbricated clasts are found where channels are steeper than c. 0.5 ± 0.2°, and where relative bed roughness values exceed ∼ 0.07. Likewise, imbricated conglomerates are encountered in late Oligocene foreland basin sequences where paleo-slopes were greater than 0.4°. We use these relationships to propose that clast imbrications occur where channel gradients exceed a threshold, which appears large enough for upper flow regime conditions to establish. We finally relate the formation of an imbricated arrangement of clasts to a mechanism where material transport occurs through rolling, or pivoting. This process requires a large shear force and thus a large flow velocity upon transport, which is likely to be associated with shifts from the lower to the upper flow regime. Our results thus suggest that clast imbrications are suitable recorders of upper flow regime conditions upon sediment transport.

Hydro-morphodynamic modelling of floodplains: the role of vegetation in suspended sediment transport

2020 ◽  
Author(s):  
Daniel A. S. Conde ◽  
Robert M. Boes ◽  
David F. Vetsch
Keyword(s):  
Sediment Transport ◽  
Suspended Sediment ◽  
Flow Resistance ◽  
Laboratory Data ◽  
Anthropogenic Factors ◽  
Future Research ◽  
Suspended Sediment Transport ◽  
Compound Channels ◽  
Closure Models

<p>Riverine environments are amongst the most complex ecosystems on the planet. As several anthropogenic factors have increasingly disrupted the natural dynamics of rivers, namely through stream regulation, the need for re-establishing the ecological role of these systems has gained relevance.</p><p>Of particular interest are floodplains in compound channels, primarily regarded for safety against floods, but which also comprise an extensive realm for ecological functions and establishment of various species. Floodplain vegetation affects flow resistance and dispersion, playing a fundamental role in erosion and deposition of suspended sediment.</p><p>The present work aims at quantifying the interaction between vegetation and suspended sediment transport on floodplains in compound channels by numerical simulations. The employed numerical tool is BASEMENT v3, a GPU-accelerated hydro-morphodynamic 2D model developed at the Laboratory of Hydraulics, Hydrology and Glaciology of ETH Zurich. In the context of the present study, the model is extended with turbulence and suspended sediment transport capabilities. The implemented closure models for turbulence pertain to three major groups, namely (i) mixing-length, (ii) production-dissipation and (iii) algebraic stress models. For suspended sediment transport, the main classical formulations from fluvial hydraulics were implemented in the numerical model.</p><p>Laboratory data from flume experiments featuring suspended sediment load and vegetation-like proxies are used for model validation. The numerical results are compared with the observed water depths, velocities and sediment concentrations for different sets of experiments with varying properties, such as density and submergence. The implemented closure models for flow resistance, turbulence and suspended sediment are then combined, calibrated and classified in terms of numerical output quality.</p><p>The obtained results from this modelling effort mainly contribute to understanding the applicability of 2D (depth-averaged) models to complex eco-morphodynamics scenarios. The calibration and rating of well-known closure models for turbulence and sediment transport provides relevant guidelines for both future research and practice in fluvial modelling.</p>

Flow resistance and sediment transport by concentrated overland flow in a grassland valley

Geomorphology ◽  
1995 ◽  
Vol 13 (1-4) ◽  
pp. 71-86 ◽  
Author(s):  
Ian P. Prosser ◽  
William E. Dietrich ◽  
Janelle Stevenson
Keyword(s):  
Sediment Transport ◽  
Flow Resistance ◽  
Overland Flow

scholarly journals A New Framework for Modelling Fine Sediment Transport in Rivers Includes Flocculation to Inform Reservoir Management in Wildfire Impacted Watersheds

Water ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 2319
Author(s):  
Micheal Stone ◽  
Bommanna G. Krishnappan ◽  
Uldis Silins ◽  
Monica B. Emelko ◽  
Chris H. S. Williams ◽  
...  
Keyword(s):  
Particle Size ◽  
Sediment Transport ◽  
Water Resources ◽  
River Sediment ◽  
Numerical Models ◽  
Cohesive Sediment ◽  
Fine Sediment ◽  
Coarse Grained ◽  
Size Distributions ◽  
Particle Size Distributions

Fine-grained cohesive sediment is the primary vector for nutrient and contaminant redistribution through aquatic systems and is a critical indicator of land disturbance. A critical limitation of most existing sediment transport models is that they assume that the transport characteristics of fine sediment can be described using the same approaches that are used for coarse-grained non-cohesive sediment, thereby ignoring the tendency of fine sediment to flocculate. Here, a modelling framework to simulate flow and fine sediment transport in the Crowsnest River, the Castle River, the Oldman River and the Oldman Reservoir after the 2003 Lost Creek wildfire in Alberta, Canada was developed and validated. It is the first to include explicit description of fine sediment deposition/erosion processes as a function of bed shear stress and the flocculation process. This framework integrates four existing numerical models: MOBED, RIVFLOC, RMA2 and RMA4 using river geometry, flow, fine suspended sediment characteristics and bathymetry data. Sediment concentration and particle size distributions computed by RIVFLOC were used as the upstream boundary condition for the reservoir dispersion model RMA4. The predicted particle size distributions and mass of fine river sediment deposited within various sections of the reservoir indicate that most of the fine sediment generated by the upstream disturbance deposits in the reservoir. Deposition patterns of sediment from wildfire-impacted landscapes were different than those from unburned landscapes because of differences in settling behaviour. These differences may lead to zones of relatively increased internal loading of phosphorus to reservoir water columns, thereby increasing the potential for algae proliferation. In light of the growing threats to water resources globally from wildfire, the generic framework described herein can be used to model propagation of fine river sediment and associated nutrients or contaminants to reservoirs under different flow conditions and land use scenarios. The framework is thereby a valuable tool to support decision making for water resources management and catchment planning.

Recent advances on bedform research and application: Process-based to machine learning

2021 ◽  
Author(s):  
Sanjay Giri ◽  
Amin Shakya ◽  
Mohamed Nabi ◽  
Suleyman Naqshband ◽  
Toshiki Iwasaki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sediment Transport ◽  
Flow Resistance ◽  
Large Scale ◽  
Data Science ◽  
Numerical Models ◽  
Hybrid Approach ◽  
Water Model ◽  
Micro Scale ◽  
Cfd Models

<p>Evolution and transition of bedforms in lowland rivers are micro-scale morphological processes that influence river management decisions. This work builds upon our past efforts that include physics-based modelling, physical experiments and the machine learning (ML) approach to predict bedform features, states as well as associated flow resistance. We revisit our past works and efforts on developing and applying numerical models, from simple to sophisticated, starting with a multi-scale shallow-water model with a dual-grid technique. The model incorporates an adjustment of the local bed shear stress by a slope effect and an additional term that influences bedform feature. Furthermore, we review our work on a vertical two-dimensional model with a free surface flow condition. We explore the effects of different sediment transport approaches such as equilibrium transport with bed slope correction and a non-equilibrium transport with pick-up and deposition. We revisit a sophisticated three-dimensional Large Eddy Simulation (LES) model with an improved sediment transport approach that includes sliding, rolling, and jumping based on a Lagrangian framework. Finally, we discuss about bedform states and transition that are studied using laboratory experiments as well as a theory-guided data science approach that assures logical reasoning to analyze physical phenomena with large amounts of data. A theoretical evaluation of parameters that influence bedform development is carried out, followed by classification of bedform type by using a neural network model.</p><p>In second part, we focus on practical application, and discuss about large-scale numerical models that are being applied in river engineering and management practices. Such models are found to have noticeable inaccuracies and uncertainties associated with various physical and non-physical reasons. A key physical problem of these large-scale numerical models is related to the prediction of evolution and transition of micro-scale bedforms, and associated flow resistance. The evolution and transition of bedforms during rising and falling stages of a flood wave have a noticeable impact on morphology and flow levels in low-land alluvial rivers. The interaction between flow and micro-scale bedforms cannot be considered in a physics-based manner in large-scale numerical models due to the incompatibility between the resolution of the models and the scale of morphological changes. The dynamics of bedforms and the corresponding changes in flow resistance are not captured. As a way forward, we propse a hydrid approach that includes application of the CFD models, mentioned above, to generate a large amount of data in complement with field and laboratory observations, analysis of their reliability based on which developing a ML model. The CFD models can replicate bedform evolution and transition processes as well as associated flow resistance in physics-based manner under steady and varying flow conditions. The hybrid approach of using CFD and ML models can offer a better prediction of flow resistance that can be coupled with large-scale numerical models to improve their performance. The reseach is in progress.</p>

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