Reprint of Mechanisms of maintaining high suspended sediment concentration over tide-dominated offshore shoals in the southern Yellow Sea

2018 ◽  
Vol 206 ◽  
pp. 2-13 ◽  
Author(s):  
Jilian Xiong ◽  
Xiao Hua Wang ◽  
Ya Ping Wang ◽  
Jingdong Chen ◽  
Benwei Shi ◽  
...  
Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 986 ◽  
Author(s):  
Haiqin Duan ◽  
Jingping Xu ◽  
Xiao Wu ◽  
Houjie Wang ◽  
Zhiqiang Liu ◽  
...  

Instruments on two bottom-mount platforms deployed in the Bohai Strait during a cruise from January 6–13, 2018 recorded an intense northerly wind event. The responses of hydrodynamic and hydrographical characteristics in Bohai Sea and Yellow Sea to the wind event were analyzed aided by the wind, wave, sea surface suspended sediment concentration and sea surface height datasets from open sources. It is shown that the strong wind event had a significant impact on the redistribution of sea surface height, regional wave conditions, regional circulations and the accompanying sediment transport pattern. Specifically, the sediment transport through the Bohai Strait may be divided into two chronological phases related to the wind event: (1) the enhanced sediment transport phase during the buildup and peak of the wind event when both the Northern Shandong Coastal Current and regional suspended sediment concentration were sharply increased; and (2) the relaxation phase when the northerly wind subsided or even reversed, accompanied by the enhanced Yellow Sea Warm Current with lowered suspended sediment concentration. Such results at synoptic scale would improve our capability of quantifying sediment exchange between the Bohai and Yellow sea, through the Bohai Strait and provide valuable reference for the study of other similar environments worldwide.


2020 ◽  
Vol 419 ◽  
pp. 106067
Author(s):  
Chongguang Pang ◽  
Dongliang Yuan ◽  
Man Jiang ◽  
Fushuo Chu ◽  
Yao Li

2013 ◽  
Vol 11 (4) ◽  
pp. 457-466

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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