scholarly journals The dominance of dispersion in the evolution of bed material waves in gravel-bed rivers

2001 ◽  
Vol 26 (13) ◽  
pp. 1409-1420 ◽  
Author(s):  
Thomas E. Lisle ◽  
Yantao Cui ◽  
Gary Parker ◽  
James E. Pizzuto ◽  
Annjanette M. Dodd
Keyword(s):  
Gravel Bed ◽  
Gravel Bed Rivers ◽  
Bed Material

scholarly journals The Prediction of Fine Sediment Distribution in Gravel-Bed Rivers Using a Combination of DEM and FNN

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1515
Author(s):  
Van Hieu Bui ◽  
Minh Duc Bui ◽  
Peter Rutschmann
Keyword(s):  
Feedforward Neural Networks ◽  
Fine Sediment ◽  
Sediment Distribution ◽  
Gravel Bed ◽  
Fine Sediments ◽  
Porosity Variation ◽  
Sediment Sorting ◽  
Gravel Bed Rivers ◽  
Bed Material ◽  
The Relationship

Large amounts of fine sediment infiltration into void spaces of coarse bed material have the ability to alter the morphodynamics of rivers and their aquatic ecosystems. Modelling the mechanisms of fine sediment infiltration in gravel-bed is therefore of high significance. We proposed a framework for calculating the sediment exchange in two layers. On the basis of the conventional approaches, we derived a two-layer fine sediment sorting, which considers the transportation of fine sediment in the form of infiltration into the void spaces of the gravel-bed. The relationship between the fine sediment exchange and the affected factors was obtained by using the discrete element method (DEM) in combination with feedforward neural networks (FNN). The DEM model was validated and applied for gravel-bed flumes with different sizes of fine sediments. Further, we developed algorithms for extracting information in terms of gravel-bed packing, grain size distribution, and porosity variation. On the basis of the DEM results with this extracted information, we developed an FNN model for fine sediment sorting. Analyzing the calculated results and comparing them with the available measurements showed that our framework can successfully simulate the exchange of fine sediment in gravel-bed rivers.

Evaluation of effective boundary roughness for gravel-bed rivers

1980 ◽  
Vol 7 (2) ◽  
pp. 392-397 ◽  
Author(s):  
D. I. Bray
Keyword(s):  
Average Velocity ◽  
Gravel Bed ◽  
Effective Boundary ◽  
Gravel Bed River ◽  
Gravel Bed Rivers ◽  
Definition Of ◽  
Bed Material ◽  
Boundary Roughness

In some cases the Keulegan equation is utilized to estimate the average velocity for in-bank flows for gravel-bed rivers. To apply the equation, the effective boundary roughness, k5, is usually estimated in terms of some characteristic bed material size.Data from 67 gravel-bed river reaches in Alberta are used to determine an optimum method of defining the effective boundary roughness in the Keulegan equation. The analysis indicates that k5 is best estimated as 6.8d50, 5.2d65, 3.5d84, or 3. Id90. These findings are compared with the work of other investigators who used gravel-bed river data from Britain and the United States.Errors in the estimation of average velocity by the Keulegan equation range from about 20% for D/d90 = 200 to about 100% for D/d50 = 5 if the characteristic bed material size, d0, is used to estimate the effective boundary roughness, k5, rather than the optimum definition of the effective boundary roughness, Cd0, as determined in this note.

Characteristics of Bed Material Shape of Gravel Bed Rivers in Youngdong Area

2014 ◽  
Vol 21 (1) ◽  
pp. 33-49
Author(s):  
Yang Jae-Jun ◽  
Park Sang-Deog ◽  
Shin Seung-Sook ◽  
Woo Tae-Young
Keyword(s):  
Gravel Bed ◽  
Gravel Bed Rivers ◽  
Bed Material

scholarly journals Morphology, bedload and sedimentology of active gravel bed rivers

2020 ◽  
Author(s):  
Peter Ashmore
Keyword(s):  
Morphological Change ◽  
Active Layer ◽  
Bedload Transport ◽  
Rate Of Change ◽  
Physical Models ◽  
Stream Power ◽  
Gravel Bed ◽  
Longitudinal Connectivity ◽  
Gravel Bed Rivers ◽  
Bed Material

<p>Morphology, bedload and sedimentology of morphologically active gravel bed rivers interact in fundamental ways. In braided and wandering rivers these interactions have distinct characteristics.  In these cases much of the bedload transfer is tied up in morphological change so that the bar and channel scale morpho-dynamics are, in effect, the bedload transport process. Physical models and field data reveal several inter-related aspects of this interaction.  We can define the morphological active layer as that in which erosion, deposition and bed particle exchange occur during channel-forming flows. The dimensions, complexity, and lateral and longitudinal connectivity of this layer increase with discharge in a given river and with channel-forming stream power between rivers. Bedload flux correlates strongly with the dimensions of the active layer and temporal variability of bedload at a given discharge is a consequence of bar-scale  variation in morphological change in complex morphology. Rates of planimetric change in braided channels also follow this morphological-bedload relationship. Higher rates of morphological change also correlate with greater bed material mobility, approaching equal mobility at the highest rate of change and the highest morphological active layer dimensions. Bed particle transfer distances and burial depths are also strongly controlled with the length scale and depth of the bar-scale morphology and active layer. The sedimentology reflects the channel morphological scale and processes in defining sedimentary unit thicknesses and geometry. The deposits of the active channel belt are almost homogenous with respect to particle size because of the ‘turnover’ of the bed material.  Morphology, bedload and sedimentology of morphologically active gravel bed rivers interact in fundamental ways that help to define the characteristics of these channel types. To what extent are these observations applicable in other channel types?</p>

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