Identify the impacts of waves and tides to coastal suspended sediment concentration based on high-frequency acoustic observations

Fan, Renfu; Wei, Hao; Zhao, Liang; Wei, Zhao; Jiang, Chengfei; Nie, Hongtao

doi:10.1016/j.margeo.2018.12.005

Cited by 20 publications

(12 citation statements)

References 31 publications

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“…where u ′ w and u ′ t denote the wave and turbulent components, respectively (Bian et al, 2018;Bricker & Monismith, 2007;Fan et al, 2019;MacVean & Lacy, 2014). The energy spectra of velocity fluctuations for two selected data segments are examined.…”

Section: Estimation Of Bottom Shear Stressmentioning

confidence: 99%

“…Taking u as an example, we have

u = \overset{u}{true} + u^{'},

where

\overset{u}{true}

denotes the mean component averaged over 600 s and u ′ denotes the fluctuation component. In the presence of waves, u ′ can be further decomposed into

u^{'} = u_{w}^{'} + u_{t}^{'},

where

u_{w}^{'}

and

u_{t}^{'}

denote the wave and turbulent components, respectively (Bian et al, ; Bricker & Monismith, ; Fan et al, ; MacVean & Lacy, ).…”

Section: Estimation Of Bottom Shear Stressmentioning

confidence: 99%

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Impacts of Currents and Waves on Bottom Drag Coefficient in the East China Shelf Seas

Fan

Zhao

et al. 2019

JGR Oceans

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High-frequency measurements of waves, currents, and turbulence were made using the bottom-mounted tripod equipped with the Nortek 6-MHz acoustic Doppler velocimetry at eight mooring stations in the East China Shelf Seas. The observational data are analyzed to estimate the bottom drag coefficient and also the contributions from currents and waves. Variations of bottom drag coefficient caused by currents show no obvious relationship with water depth. Generally, the current-induced drag coefficient decreases with the strengthening currents, and the wave-induced drag coefficient increases with the enhancing waves. Synthesis analyses of the observational data reveal a scale relationship between the current-induced drag coefficient and the turbulent Reynolds number and that between the wave-induced drag coefficient and the bottom wave orbital velocity. The analyses show no significant impacts of waves on turbulent Reynolds stress, and the total bottom drag coefficient is the sum of that due to currents and waves. The empirical formula for bottom drag coefficient derived from this study may help to improve the simulation of tides, storm surges, and sediment transport in coastal seas.Plain Language Summary Ocean floor exerts a frictional force on ocean currents, and this force is particularly important in coastal oceans. This force is difficult to measure directly and is usually parameterized using the near-bottom currents and a bottom drag coefficient. In this study, we derive estimates of bottom drag coefficient from observed currents and turbulence at eight mooring stations in the East China Shelf Seas. The estimated bottom drag coefficient is not a constant. Empirical relationships are established to link the impacts of currents and waves on bottom drag coefficient to separate dynamic parameters, that is, the turbulent Reynolds number and the bottom wave orbital velocity. The total bottom drag coefficient can be taken as the sum of the two components. The empirical formula for bottom drag coefficient derived from this study may help to improve the simulation of tides, storm surges, and sediment transport in coastal seas.

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Section: Estimation Of Bottom Shear Stressmentioning

confidence: 99%

“…Taking u as an example, we have

u = \overset{u}{true} + u^{'},

where

\overset{u}{true}

denotes the mean component averaged over 600 s and u ′ denotes the fluctuation component. In the presence of waves, u ′ can be further decomposed into

u^{'} = u_{w}^{'} + u_{t}^{'},

where

u_{w}^{'}

and

u_{t}^{'}

denote the wave and turbulent components, respectively (Bian et al, ; Bricker & Monismith, ; Fan et al, ; MacVean & Lacy, ).…”

Section: Estimation Of Bottom Shear Stressmentioning

confidence: 99%

Impacts of Currents and Waves on Bottom Drag Coefficient in the East China Shelf Seas

Fan

Zhao

et al. 2019

JGR Oceans

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“…Besides the sea surface layer, the SSC in the middle and bottom layers could also increase several times or dozens of times during the extreme weather in winter in the shallow waters [26,44]. In general, besides the wind intensity, the growth of wave also depends on the wind duration and fetch [45]. During the northerly wind in winter, the southern BH had the longer fetch which was better for wave growth.…”

Section: Changes Of Ssc In Bh and Bhs Due To Synoptic Eventsmentioning

confidence: 99%

Periodic Oscillation of Sediment Transport Influenced by Winter Synoptic Events, Bohai Strait, China

Duan

et al. 2020

Water

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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.

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“…Therefore, real-time and quantitative measurement of these sediment parameters is indispensable. Over the past decades, numerous techniques have been developed for real-time sediment monitoring based on turbidity, − acoustic measurement, , laser diffraction, , photoimaging, etc. Although great progress is expected with these techniques, their application is still limited .…”

Section: Introductionmentioning

confidence: 99%

Particle-Laden Droplet-Driven Triboelectric Nanogenerator for Real-Time Sediment Monitoring Using a Deep Learning Method

Yang

Wang

Zhao

et al. 2020

ACS Appl. Mater. Interfaces

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Continuous information on the suspended sediment in the water system is critical in various areas of industry and hydrological studies. However, because of the high variation of suspended sediment flow, challenges still remain in developing new techniques implementing simple, reliable, and real-time sediment monitoring. Herein, we report a potential method to realize real-time sediment monitoring by introducing a particle-laden droplet-driven triboelectric nanogenerator (PLDD-TENG) combined with a deep learning method. The PLDD-TENG was operated under the single-electrode mode with a triboelectric layer of polytetrafluoroethylene (PTFE) thin film. The working mechanism of the PLDD-TENG was proved to be induced by liquid–PTFE contact electrification and sand particle–electrode electrostatic induction. Then, its performance was explored under various particle parameters, and the results indicated that the output signal of the PLDD-TENG was very sensitive to the sand particle size and mass fraction. A convolutional neural network-based deep learning method was finally adopted to identify the particle parameters based on the output signal. High identifying accuracies over 90% were achieved in most of the cases by the proposed method, which sheds light on the application of the PLDD-TENG in real-time sediment monitoring.

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Identify the impacts of waves and tides to coastal suspended sediment concentration based on high-frequency acoustic observations

Cited by 20 publications

References 31 publications

Impacts of Currents and Waves on Bottom Drag Coefficient in the East China Shelf Seas

Impacts of Currents and Waves on Bottom Drag Coefficient in the East China Shelf Seas

Periodic Oscillation of Sediment Transport Influenced by Winter Synoptic Events, Bohai Strait, China

Particle-Laden Droplet-Driven Triboelectric Nanogenerator for Real-Time Sediment Monitoring Using a Deep Learning Method

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