Effects of suspended sediment concentration and turbulence on settling velocity of cohesive sediment

2010 ◽  
Vol 14 (2) ◽  
pp. 163-171 ◽  
Author(s):  
Ho Kyung Ha ◽  
Jerome P. -Y. Maa
Keyword(s):  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Settling Velocity ◽  
Sediment Concentration ◽  
Cohesive Sediment

Analysis of tidal average saturated suspended sediment concentration of cohesive sediment in the Yangtze Estuary

2021 ◽  
Author(s):  
Junyu Tao ◽  
Peng Hu ◽  
Wei Li ◽  
Zhiguo He
Keyword(s):  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Cohesive Sediment ◽  
Yangtze Estuary ◽  
Shear Stresses ◽  
The Yangtze Estuary ◽  
Erosion And Deposition ◽  
Deposition Fluxes ◽  
The Impact

<p>It is generally believed that sediment erosion and deposition can’t occur simultaneously, which is also reflected in the classical Partheniades-Krone formulas used to calculate erosion and deposition flux. In this study, the erosion and deposition fluxes of cohesive sediment are integrated in the tidal period respectively, and when they are equal, the corresponding suspended cohesive sediment concentration is called ‘tidal average saturated concentration of cohesive sediment’. Theoretical analysis of the factors affecting the saturated concentration indicates that a large erosion coefficient results in a high saturated concentration level. The corresponding critical erosion and deposition shear stresses (i.e., τ<sub>e </sub>and τ<sub>d</sub>) at saturated concentration have many possibilities. Therefore, it is understandable that good agreement of suspended sediment concentration between simulation and observation have been obtained by adjusting τ<sub>e </sub>and τ<sub>d </sub>in the previous numerical simulation calibration. According to the relative magnitude of τ<sub>e </sub>and τ<sub>d</sub> at saturated concentration, the erosion and deposition fluxes of cohesive sediment can be divided into four situations: weak erosion (i.e., τ<sub>e  ></sub> τ<sub>d</sub>), intense erosion (i.e., τ<sub>e  <</sub> τ<sub>d</sub>), intense deposition (i.e., τ<sub>e  <</sub> τ<sub>d</sub>), and weak deposition (i.e., τ<sub>e > </sub>τ<sub>d</sub> ). A two-dimensional numerical model is applied to calculate the temporal and spatial variation of the saturated concentration of cohesive sediment in the Yangtze Estuary. Simulation results shows the following findings. 1) The impact of the fraction of the kth size class in the surface (top) layer of bed material on erosion flux of non-uniform cohesive sediment is necessary to be considered. Otherwise, the calculated saturated concentration of cohesive sediment is greater than the measured. 2) The differences between saturated concentration and the field calculated/measured suspended sediment concentration can be applied to infer bed erosion/deposition characteristics to some extent, and compared it with the measured erosion/deposition result, which in turn verifies the values of  τ<sub>e </sub>and τ<sub>d</sub> in the model. This finding provides insights for the following research on transport and diffusion of cohesive sediment in estuary and coastal areas.</p>

scholarly journals HYDRODYNAMIC AND COHESIVE SEDIMENT TRANSPORT MODELING IN CHILIKA LAGOON

2018 ◽  
Vol IV-5 ◽  
pp. 141-149
Author(s):  
S. Pradhan ◽  
R. N. Samal ◽  
S. B. Choudhury ◽  
P. K. Mohanty
Keyword(s):  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Sediment Concentration ◽  
Cohesive Sediment ◽  
Chilika Lagoon ◽  
Thickness Change ◽  
North East ◽  
The North ◽  
Bed Thickness ◽  
Lagoon Environment

<p><strong>Abstract.</strong> Chilika lagoon, one of the largest brackish water lagoons in Asia located along the east coast of India. The rivers draining into the lagoon carry about 13 million tonnes of sediments annually. Because of the cohesiveness properties of the fine sediments, nutrients, heavy metals and other polluted substances tend to bind to the sediment’s surface. Consequently, pollutants can be concentrated in the inlets/estuaries, thus being of great environmental interest. In addition, the mudflats occurring are important biotopes for a large number of micro- and macro-faunal species and act as feeding places for a number of birds. To understand the cohesive sediment dynamics, a numerical model, MIKE 21 Mud Transport (MT) coupled with hydrodynamic (HD) was used. The model simulated the relative bed level height and suspended sediment concentrations. The sediment interchange and accumulation between each sectors and Bay of Bengal were evaluated. The suspended sediment concentration is high in the north-east portion of the lagoon while medium and low suspended loads are observed in the eastern and western portion of the lagoon. Bed thickness is very high in the north-western corner of the lagoon covered with Phragmites Karka which facilitate sediment trap. Total bed thickness change is very much pronounced in the northern sector which receives most of the sediments from the Mahanadi river systems as well along the periphery of the lagoon due to drainage. The eastern lagoon shows a net deposition accumulated fraction (5–15<span class="thinspace"></span>kg/m<sup>2</sup>) and hence gives enough indication of the sedimentation processes in the lagoon. Further, the results also warrant immediate attention to check and monitor suspended sediment concentration to find out the net deposition trend in the lagoon environment in order to take decisions in minimizing the sediment load.</p>

Neural networks approached for modelling river suspended sediment concentration due to tropical storms

2013 ◽  
Vol 11 (4) ◽  
pp. 457-466
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Suspended Sediment ◽  
Suspended Sediment Concentration ◽  
Time Series Data ◽  
Water Discharge ◽  
Sediment Concentration ◽  
Series Data ◽  
Generalized Regression Neural Network ◽  
Event Based

Artificial neural networks are one of the advanced technologies employed in hydrology modelling. This paper investigates the potential of two algorithm networks, the feed forward backpropagation (BP) and generalized regression neural network (GRNN) in comparison with the classical regression for modelling the event-based suspended sediment concentration at Jiasian diversion weir in Southern Taiwan. For this study, the hourly time series data comprised of water discharge, turbidity and suspended sediment concentration during the storm events in the year of 2002 are taken into account in the models. The statistical performances comparison showed that both BP and GRNN are superior to the classical regression in the weir sediment modelling. Additionally, the turbidity was found to be a dominant input variable over the water discharge for suspended sediment concentration estimation. Statistically, both neural network models can be successfully applied for the event-based suspended sediment concentration modelling in the weir studied herein when few data are available.

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