- 1Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- 2Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, China
Bottom drag coefficient is one of the key parameters in quantifying shelf hydrodynamics and sediment transport processes. It varies markedly due to dynamic forcing and bed type differences, so a set of empirical values have been used for beds of coarse material where bedforms are often present. In comparison, dramatically fewer such rule-of-thumb values are available for muddy beds. Here, we present results of variations in bottom drag as calculated from in situ measurements by bottom-mounted tripods that were placed across the top of a muddy deposit during two different deployments, one in summer and another in winter. A tidal asymmetry of bottom drag was observed, most likely caused by variations of local bed roughness. For hydrodynamically smooth (
Introduction
Within the bottom boundary layer (BBL) on an oceanic shelf, bottom friction changes the velocity profile of the flow and the production/dissipation of turbulence, thus affecting the processes of physical, biological, chemical, and sediment transport (Trowbridge and Lentz, 2018). Among the numerous hydrodynamic and sediment dynamic models (Fringer et al., 2019), the current-related bed shear stress,
where
where
In the shallow water of continental shelves, the presence of surface waves (therefore wave-current interaction) impacts the hydrodynamics of the centimeter-scale wave BBL as well as the entire water column (Grant and Madsen, 1986). Wave actions change the velocity structure within the BBL and cause the flow to experience stronger drag (Grant and Madsen, 1979; Signell and List, 1997; Styles and Glenn, 2000; Nayak et al., 2015; Egan et al., 2019), but a recent study by Nelson and Fringer (2018) has shown that waves may lead to a decrease in drag on a smooth bed. Enhanced shear stress by energetic waves often leads to the resuspension of bed sediments (Brand et al., 2010; Egan et al., 2020b), which can further lead to stratification of suspended sediment and the drag-reduction effect on the flow (e.g., Wright et al., 1999; Peng et al., 2020). Therefore, the use of in situ data to obtain
Recent studies on sediment transport processes in the muddy areas of the East China Shelf Seas (ECSS) have often deployed numerical models such as the Regional Ocean Modeling System (e.g., Bian et al., 2013; Liu et al., 2015; Wang et al., 2019; Wang et al., 2020). The Regional Ocean Modeling System implements the simple quadratic drag-law approach for calculating BBL processes that require user input of the drag coefficient (Warner et al., 2008). This is often not an easy task because direct measurements of a drag coefficient are extremely scarce in the muddy areas of the ECSS due to a lack of in situ observations. Fan et al. (2019) derived the empirical relationship between
In this study, a field campaign was conducted to measure the BBL dynamics across the top of a mud deposit off the Shandong Peninsula in the Yellow Sea of China. Reynolds stress, therefore
Study Area
The scene of this study is around the depocenter of a muddy deposit in the coastal seas to the east of the Shandong Peninsula, China (Figure 1A). The water depths at the study stations are basically within 40 m, and their specific values are shown in Table 1. Previous sedimentary studies suggested that this muddy deposit was formed by sediments sourced from the Yellow River and transported by the Shandong Coastal Currents (SDCC), which flow out of the Bohai Sea to the Yellow Sea around the Shandong Peninsula (Figure 1A) (Alexander et al., 1991; Yang and Liu, 2007). Followed the Shepard scheme (Shepard, 1954), the surface sediment in the study area belongs to sandy silt with a mean grain size ranging from 5 to 6
FIGURE 1. (A) Topography and currents of the Bohai and Yellow seas. The isopaches of the muddy area were from Yang and Liu (2007), and the currents were based on Bian et al. (2013). These currents are the Shandong Coastal Current (SDCC), the Yellow Sea Warm Current (YSWC), and the Korea Coastal Current (KCC) (B) The location of the observation sites.
Materials and Methods
Data Collection
Tripods were deployed at three stations on the Inner Shelf of the Shandong Peninsula during two 10-day field campaigns, one in summer (August 18–27, 2017), and another in winter (February 23–March 2, 2018) (Figure 1B). One more winter deployment (January 6–26, 2020) was conducted to make up for a fallen instrument at S2 during a previous deployment. Each tripod was equipped with an upward-looking Teledyne/RDI 600 kHz Acoustic Doppler Current Profiler (ADCP), a Nortek Vector Acoustic Doppler Velocimeter (ADV), a conductivity/temperature (CT) sensor, and a turbidity sensor (OBS or RBR-TU) (Table 1). The sampling period of each instrument is shown in Table 1. In addition, for winter observations at S2, turbidity sensors were placed at 0.45, 0.9, and 1.34 m above the bottom (mab) to record sediment concentrations within the bottom boundary layer (Table 1). CTD (SeaBird 19) packaged with Niskin bottles and a turbidity sensor was cast to collect water samples and profiles of temperature, salinity, and turbidity periodically from the watching boats that guarded each instrument against being damaged by trawling nets of passing fishing boats. These water samples were used to calibrate and convert the observed turbidities into suspended sediment concentrations (SSCs). Detailed processes of data quality assurance/quality control and calibration can be found in Qi et al. (2022).
Most ADCPs are equipped with a waving module that enables wave measurements (Table 1). In addition, hourly significant wave height and wave period data from the WaveWatch III Global Wave Model (WW3) at approximately 0.5° (∼50 km) resolution (Tolman et al., 2014), is widely used in coastal studies (e.g., Duan et al., 2020; Silva et al., 2018), were downloaded. We compared the modeled and observed significant wave height (
Tidal Analysis and Wave Parameter Estimates
We used T_TIDE, a package of routines in MATLAB for harmonic analysis, to make a tidal prediction (Pawlowicz et al., 2002). In agreement with previous studies, the dominant tidal constituent was M2, flowing southwest during flood tide and northeast during ebb tide.
The bottom wave orbital velocity,
where
Wave–Turbulence Decomposition and Turbulence Quantities
In a wavy aquatic environment, the ADV measured velocity components (
where
FIGURE 2. (A) Power spectra of velocity fluctuation and turbulent fluctuation (
Following Feddersen and Williams (2007), we used the nondimensional integrated cospectrum (ogive) for controlling the quality of WTD and the Reynolds stress estimates. The ogive for
where
After obtaining the turbulent fluctuation in three directions, we calculated the Reynolds stress, turbulent kinetic energy (TKE), and turbulent energy dissipation rates (
where
Calculation of the Bottom Drag Coefficient
The Bottom drag coefficient was estimated following Egan et al. (2020a). As
The instantaneous
Combining Eqs. 1, 2, we can deduce the dependence of
Results
Tides, Currents, Waves, and Winds
Figure 3 shows the tidal ellipses of M2 tidal components in summer and winter for each of the three stations. According to the rotation rate of the tidal ellipse, tide currents in sites S2 and S3 were reversing currents, while the tide in Site 5 (S5) was rotary currents (Figure 3). The maximum tidal velocity had a certain difference for each site, and was generally satisfied: S2>S3>S5, where the maximum tidal velocity of S2 was approximately 0.5 m/s (Figure 3).
FIGURE 3. Tidal ellipses of M2 tidal components were calculated from the mean velocity at 1 mab in (A) summer and (B) winter for each of the three stations. The mean velocity was translated from ADV data by log law.
Figure 4 shows the magnitude of wave orbital velocities and wave directions. The maximum value of wave orbital velocity in summer was about 0.12 m/s (Figure 4A), and the average values were 0.01 m/s, 0.03 m/s, and 0.04 m/s at S2, S3, and S5, respectively. The wave propagation was mainly in the northwesterly direction in summer. It was not exactly matching with the direction of the instantaneous winds (Figure 5A) but was consistent with the trend of the prevailing southerly winds (Wu et al., 2019), indicating that the swell waves and background flow dominated the wave propagation.
FIGURE 4. Wave magnitude and direction for three stations (S2, S3, and S5) in (A) summer and (B) winter, with 0° corresponding to the northward propagating wave, and the radial axis representing the bottom wave-orbital velocity,
FIGURE 5. Rose diagrams of hourly averaged wind frequencies during (A) summer and (B) winter observations at S3. The greyscale indicates the categories of wind speed (m/s).
The overall intensity of waves was significantly higher in winter than that in summer (Figure 4). The maximum value of wave orbital velocity in winter was about 0.37 m/s, and the average values at S2, S3, and S5 were 0.01 m/s, 0.05 m/s, and 0.06 m/s, respectively. The wave direction was modulated by the local wind field. During periods of weak wind (wind speed less than 10 m/s), the wind direction was not fixed and was dominated alternately by southwesterly and northeasterly winds (Figure 5B). Therefore, when the orbital velocity was smaller than 0.1 m/s, the wave propagation direction was mainly southwestward or northeastward (Figure 4B). However, the stronger wind events (wind speed greater than 10 m/s) that occurred during the observation period were dominated by northerly winds (Figure 5B). Therefore, the wave propagation direction was mainly southwesterly when the orbital velocity was greater than 0.1 m/s (Figure 4B). Wave orbital velocities were higher at S3 and S5 than that at S2 because of the shallower water depth (Table 1). We used data in winter to discuss wave-current interactions in the rest of the article.
Bottom Drag Coefficient
First, we estimated time-series
FIGURE 6. Time series of (A,C,E) ADV’s burst mean velocity (
FIGURE 7. Time series of (A,C) ADV’s burst mean velocity (
FIGURE 8. Twenty-day time series during the winter deployment at S2 showing measurements of (A) ADV’s burst mean velocity (
To recognize the flood–ebb tidal asymmetry
FIGURE 9. Correlation between measured turbulent Reynolds stress,
Discussion
Variation of During an S2 Winter Event
During event periods, the SSC at 0.45 mab was significantly higher than that of 1.34 mab, which caused significant suspended sediment stratification (Figure 8C). We used the buoyancy frequency squared,
Figure 10 shows the estimated
FIGURE 10. Correlation between measured turbulent Reynolds stress,
Flood–Ebb Asymmetry of
The variation of
Combined with the above analysis, variation in the tidal asymmetry
where
FIGURE 11. Scatter plots of drag coefficient estimated at 1 mab,
TABLE 2. The mean value of
Bottom Drag Enhanced by Waves
In addition to the change in roughness, the bottom drag experienced by currents could be enhanced by waves, which do affect the flood–ebb asymmetry
FIGURE 12. Correlation between measured turbulent Reynolds stress,
To quantify the effect of waves, we used a one-dimensional time-dependent model, referred to as the Grant-Madsen (GM) model, that incorporates the combined effects of a steady current in the presence of oscillatory waves (Grant and Madsen, 1979). In the GM model, the friction velocity combined waves and currents (
where
where
Based on the observed data, we calculated the
Figure 13A shows the estimates
FIGURE 13. (A) Scatter plots of
Conclusion
In situ observations of currents, waves, and suspended sediment concentration at three stations on the muddy deposits off the Shandong Peninsula was conducted to investigate the variation of the bottom drag coefficient. Data collected in both summer and winter highlight the tidal variations and the effect of winter storm events. The results show that the estimated
In this work, we provide an accurate estimate of
Data Availability Statement
The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author.
Author Contributions
JX and ZL: conceptualization, methodology, and reviewing. FQ: conceptualization, methodology, data curation, analyses, and original draft preparation.
Funding
The authors gratefully acknowledge the financial support of the Key Special Project for Introduced Talents Team of the Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (GML2019ZD0210), Guangdong Basic and Applied Basic Research Foundation (2021B1515120080) and the National Natural Science Foundation of China (NSFC, Grant No. 41530966).
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Acknowledgments
Special thanks go to Baoduo Wang, Meng Liu, Zhiwen Wang, and Xiaohang Liu for their support in fieldwork.
Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feart.2022.921995/full#supplementary-material
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Keywords: continental shelf, drag coefficient, muddy deposit, tidal asymmetry, wave–current interaction
Citation: Qi F, Liu Z and Xu J (2022) Bottom Drag Variations Under Waves and Currents: A Case Study on a Muddy Deposit off the Shandong Peninsula. Front. Earth Sci. 10:921995. doi: 10.3389/feart.2022.921995
Received: 17 April 2022; Accepted: 12 May 2022;
Published: 01 July 2022.
Edited by:
Ya Ping Wang, East China Normal University, ChinaCopyright © 2022 Qi, Liu and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jingping Xu, xujp@sustech.edu.cn