Changeability of movable bed‐surface particles in natural, gravel‐bed channels and its relation to bedload grain size distribution (scott river, svalbard)

2015 ◽  
Vol 97 (3) ◽  
pp. 507-521 ◽  
Author(s):  
Waldemar Kociuba ◽  
Grzegorz Janicki
Keyword(s):  
Grain Size ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Gravel Bed ◽  
Movable Bed

scholarly journals A 2D hydrodynamic-sedimentological model for gravel-bed rivers. Part I: theory and validation

2013 ◽  
Vol 44 (2s) ◽  
Author(s):  
Gabriel Kaless ◽  
Mario A. Lenzi ◽  
Luca Mao
Keyword(s):  
Grain Size ◽  
Sediment Transport ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Gravel Bed ◽  
Bed Elevation ◽  
Shallow Water Flows ◽  
Morphological Model ◽  
Gravel Bed Rivers ◽  
Transported Material

This paper presents a novel 2D-depth average model especially developed for gravel-bed rivers, named Lican-Leufú (Lican=pebble and Leufu=river, in Mapuche’s language, the native inhabitants of Central Patagonia, Argentina). The model consists of three components: a hydrodynamic, a sedimentological, and a morphological model. The flow of water is described by the depth-averaged Reynolds equations for unsteady, free-surface, shallow water flows. It includes the standard k-e model for turbulence closure. Sediment transport can be divided in different size classes (sand-gravel mixture) and the equilibrium approach is used for Exner’s equation. The amour layer is also included in the structure of the model and the surface grain size distribution is also allowed to evolve. The model simulates bank slides that enable channel widening. Models predictions were tested against a flume experiment where a static armour layer was developed under conditions of sediment starvations and general good agreements were found: the model predicted adequately the sediment transport, grain size of transported material, final armour grain size distribution and bed elevation.

scholarly journals Uniform grain-size distribution in the active layer of a shallow, gravel-bed, braided river (the Urumqi River, China) and implications for paleo-hydrology

2018 ◽  
Author(s):  
Laure Guerit ◽  
Laurie Barrier ◽  
Youcun Liu ◽  
Clément Narteau ◽  
Eric Lajeunesse ◽  
...  
Keyword(s):  
Grain Size ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Active Layer ◽  
Size Distributions ◽  
Gravel Bed ◽  
Surface Samples ◽  
River Bed ◽  
Braided Stream ◽  
Grain Size Distributions

Abstract. The grain-size distribution of ancient alluvial systems is commonly determined from surface samples of vertically exposed sections of gravel deposits. This method relies on the hypothesis that the grain-size distribution obtained from a vertical cross-section is equivalent to that of the river bed. We report a field test of this hypothesis on samples collected on an active, gravel-bed, braided stream: the Urumqi River in China. We compare data from volumetric samples of a trench excavated in an active thread and surface counts performed on the trench vertical faces. We show that the grain-size distributions obtained from all samples are similar and that the deposit is uniform at the scale of the river active layer, a layer extending from the surface to a depth of approximately ten times the size of the largest clasts.

scholarly journals Advanced Numerical Modeling of Sediment Transport in Gravel-Bed Rivers

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 550 ◽  
Author(s):  
Van Bui ◽  
Minh Bui ◽  
Peter Rutschmann
Keyword(s):  
Grain Size ◽  
Sediment Transport ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Morphological Changes ◽  
Relative Size ◽  
Size Fraction ◽  
New Model ◽  
Gravel Bed ◽  
Gravel Bed Rivers

Understanding the alterations of gravel bed structures, sediment transport, and the effects on aquatic habitat play an essential role in eco-hydraulic and sediment transport management. In recent years, the evaluation of changes of void in bed materials has attracted more concern. However, analyzing the morphological changes and grain size distribution that are associated with the porosity variations in gravel-bed rivers are still challenging. This study develops a new model using a multi-layer’s concept to simulate morphological changes and grain size distribution, taking into account the porosity variabilities in a gravel-bed river based on the mass conservation for each size fraction and the exchange of fine sediments between the surface and subsurface layers. The Discrete Element Method (DEM) is applied to model infiltration processes and to confirm the effects of the relative size of fine sediment to gravel on the infiltration depth. Further, the exchange rate and the bed porosity are estimated while using empirical formulae. The new model was tested on three straight channels. Analyzing the calculated results and comparing with the observed data show that the new model can successfully simulate sediment transport, grain sorting processes, and bed change in gravel-bed rivers.

scholarly journals Combination of Discrete Element Method and Artificial Neural Network for Predicting Porosity of Gravel-Bed River

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1461 ◽  
Author(s):  
Van Hieu Bui ◽  
Minh Duc Bui ◽  
Peter Rutschmann
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Grain Size ◽  
Discrete Element Method ◽  
Size Distribution ◽  
Grain Size Distribution ◽  
Discrete Element ◽  
Fine Sediment ◽  
Gravel Bed ◽  
Artificial Neural

In gravel-bed rivers, monitoring porosity is vital for fluvial geomorphology assessment as well as in r ecosystem management. Conventional porosity prediction methods are restricting in terms of the number of considered factors and are also time-consuming. We present a framework, the combination of the Discrete Element Method (DEM) and Artificial Neural Network (ANN), to study the relationship between porosity and the grain size distribution. DEM was applied to simulate the 3D structure of the packing gravel-bed and fine sediment infiltration processes under various forces. The results of the DEM simulations were verified with the experimental data of porosity and fine sediment distribution. Further, an algorithm was developed for calculating high-resolution results of porosity and grain size distribution in vertical and horizontal directions from the DEM results, which were applied to develop a Feed Forward Neural Network (FNN) to predict bed porosity based on grain size distribution. The reliable results of DEM simulation and FNN prediction confirm that our framework is successful in predicting porosity change of gravel-bed.

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