Winter storms induced high suspended sediment concentration along the north offshore seabed of the Changjiang estuary

Estuarine, Coastal and Shelf Science 228 (2019) 106351

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Winter storms induced high suspended sediment concentration along the north offshore seabed of the Changjiang estuary

T

Jieping Tanga, Ya Ping Wanga,b,∗, Qingguang Zhuc, Jianjun Jiab, Jilian Xiongd, Peng Chenge, Hui Wub, Dezhi Chena, Hao Wua a

Ministry of Education Key Laboratory for Coast and Island Development, School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, 210093, China State Key Laboratory of Estuarine and Coastal Research, School of Marine Sciences, East China Normal University, Shanghai, 200241, China c Department of Environmental Sciences, University of Virginia, Charlottesville, 22904, USA d Virginia Institute of Marine Science, Gloucester Point, VA, 23062, USA e State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, 361102, China b

ARTICLE INFO

ABSTRACT

Keywords: Winter storm Suspended sediment concentration Advection Resuspension Fluid mud Changjiang estuary

Fine-grained sediments suspended in coastal waters play an important role in submarine topography evolution and associated environment changes. The convergence of suspended sediments concentrated near the seabed results in high sediment concentration, and contributes significantly to sediment transport. In order to investigate the mechanism triggering high suspended sediment concentration (SSC), we deployed a tripod to the seabed to obtain in situ bottom boundary layer measurements of sediment dynamics and a buoy to the sea surface to collect meteorological and wave data at the northern Changjiang River mouth from December 20, 2015 to January 20, 2016. The high SSC (e.g. > 3 g/L) events were observed together with fluid mud (thicknesses of 4–16 cm) near the seabed during neap tides when cold air intrusion generated winter storms and strong waves. Further, we found that the high SSC event was mainly resulted from wind waves and sediment resuspension supported by local benthic fluid mud, which was associated with three stages. At the setting up stage, the winter storm brought long duration of strong-waves (e.g. significant wave height > 1.5 m) more than 15 h, resulting in a maximum wave-current combined bottom shear stress of 3.2 Pa and the increase of SSC to > 1 g/L. At the reinforcement stage, the strong waves and bottom shear stress lasted for several hours, and further increased the SSC to > 3 g/L. At the final decay stage, wind waves and the maximum wave-current shear stresses decreased significantly, with the disappearance of high SSC. Thus, there was no high SSC event observed even with strong wind waves during spring tides because strong wave duration was too short for the reinforcement process. The sediment source for the high SSC events was mainly from bottom fluid mud resuspension, as well as the advection transport from the adjacent subaqueous Changjiang River delta.

1. Introduction The behavior of fine suspended sediments in coastal areas is fundamental to submarine topography, ecosystem and ocean engineering (Weeks et al., 1993; Gao and Collins, 2014). The suspended sediments concentrated near the seabed can alter the water density and result in density stratification that can damp out turbulence, change the turbulent structure, and influence the interaction between the flow and sea bed. A high suspended sediment concentration (SSC) event is defined as a phenomenon with SSC values > 3 g/L that can hinder settling velocity of fine particles, and with the occurrence of fluid mud layer near seabed (Dyer, 1986; Villaret and Trowbridge, 1991; Whitehouse et al.,

2000). The distribution and diffusion of SSC are influenced by sediment supply and complex hydrodynamic forces that vary over both time and space. The major dynamic forces that control suspended sediments movement are currents (driven by wind, wave, tide, and density gradient), waves, and their combinations (Ward et al., 1984; Huettel et al., 1996; Macvean and Lacy, 2014). The stresses caused by these forces are responsible for sustaining sediments in suspension and distributing them vertically. Therefore bed shear stress is a key parameter in estimating the sediment transport rate and SSC variation (Grant and Madsen, 1979; Cacchione et al., 1982; Wiberg, 1995; Wang and Pinardi, 2002; Brand et al., 2010; Heath et al., 2016). Although strong winds barely influence spatiotemporal variations of near-bed SSC in deep

∗ Corresponding author. Ministry of Education Key Laboratory for Coast and Island Development, School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, 210093, China. E-mail address: [email protected] (Y.P. Wang).

https://doi.org/10.1016/j.ecss.2019.106351 Received 16 April 2019; Received in revised form 7 August 2019; Accepted 23 August 2019 Available online 25 August 2019 0272-7714/ © 2019 Elsevier Ltd. All rights reserved.

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water condition (> 20 m) directly (de Jonge and van Beusekom, 1995; Green and Coco, 2014), it is observed that the wind-induced waves significantly contribute to SSC variations and are the dominant factor controlling near-bed SSC in shallow waters (Larcombe et al., 1995; Uncles and Stephens, 2010). High SSC events (SSC > 3 g/L) induced by waves near the bed have been observed on the Eel River continental shelf (Traykovski et al., 2000) and outside the mouth of the Rhine River (Flores et al., 2018). However, it is too difficult to deploy probes for insitu observation in the wave bottom boundary layer because it only has a limited thickness of several centimeters. Several models have been developed for studying the wave bottom boundary layer and the interaction between waves and the bed (Fredsøe and Deigaard, 1992). Localized resuspension and horizontal advection of sediment are two main sources for suspended sediment at a fixed observation point (Yu et al., 2012; Xiong et al., 2017). High SSC events in the Amazon continental shelf were found during sediment trapping, resuspension and advection driven by river discharge (Kineke et al., 1996). It was also reported that sediment from seabed can significantly enhance the SSC (> 8 g/L) in tidal flat through erosion and resuspension (Yu et al., 2017). Although decomposing time series data of SSC into components that represent contributions of resuspension and advection is difficult due to the complex characteristics of suspended particles and hydrodynamic conditions (Jago et al., 2006; Kalnejais et al., 2007; van der Hout et al., 2017), it is helpful to investigate sediment provenance. Shi et al. (2016) used empirical orthogonal function (EOF) analysis to estimate the relative contributions of erosion, deposition, and advection processes to SSC variations observed on tidal mudflats. Li et al. (2018) developed a box model to differentiate the effects of advection and resuspension on SSCs in a turbid estuary. In this study, we focus on the factors that determine the occurrence of high SSC events during winter storm periods accompanied with cold air intrusion, based on in situ meteorological and hydrodynamic data measurements at the north offshore area of the Changjiang River estuary.

The observation site has a mean water depth of ~19.5 m. It is at the southern tip of a wedge-shaped channel (Dawanhong Channel). The Wulong sand ridge with a minimum water depth of less than 10 m is located towards the seaward side of the observation site. The wedge channel is roughly parallel to the coastline (~35° anticlockwise from north). The study area is dominated by a semidiurnal tide rotating in clockwise, and the flood current of the M2 tide is northwestward (Wu et al., 2018). The study area is controlled by the monsoon climate (Yang et al., 2008). Wind is strongest during winter, with a mean speed of ~4.3 m/s and mostly blowing from the northwest direction (Yang et al., 2008; He et al., 2010). In winter, the significant wave height is highly correlated with wind speed, and is greater than 1.0 m (He et al., 2010). The SSC in this region has significant seasonal variations with the minima (depth averaged SSCs: 0.005–0.020 g/L) in summer and maximum (depth averaged SSCs: 0.4–0.6 g/L) in winter (Xing et al., 2012), and tidally averaged SSC of surface layer in winter is ~0.2 g/L during neap tides and ~0.4 g/L during spring tides according to a numerical sediment model (Luo et al., 2017). 3. Materials and methods 3.1. Data collection A tripod mounted with oceanographic instruments was positioned at the seabed to observe sediment dynamic processes in the boundary layer outside the northern mouth of Changjiang River estuary from December 20, 2015 to January 20, 2016. The mean current and turbulence at a height of 0.3 m above bed (mab) were measured by an Acoustic Doppler Velocimeter (ADV) working in a duration of 512 s at 16 Hz every 20 min. The near-bottom velocity profiles (0–1.3 mab) were measured every 5 min by using a downward-looking Acoustic Doppler Current Profiler (ADCP) fixed on the tripod, with a high vertical resolution of 2 cm. Two optical backscatter sensors (OBS-3A, D&A Instrument Co.) were deployed at 0.3 mab and 0.8 mab, respectively, to obtain turbidity, temperature, and salinity data. In addition, a downward-looking Acoustic Backscatter System (ABS, Aquatec Group Limited) was deployed at 1.5 mab to measure the SSC profiles by sampling at 16 Hz for a duration of 8 s

2. Study area Our study area is located at the north offshore of the Changjiang River estuary, and is ~30 km away from the nearest coastline (Fig. 1).

Fig. 1. In situ observation site (blue dot). The study site is located in the shallow coastal area of Qidong, exterior to the northern branch of the Changjiang River. The tide rotates clockwise and the water depth is about 19.5 m. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.) 2

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every 5 min with 2 cm vertical resolution. The tripod was retrieved once from 14:00 to 16:00 on January 9, 2016 for maintenance. Thus, the invalid data covering this period was removed. Simultaneously, a buoy (AXYS Technologies Inc. of Canada) was moored above the sea surface to collect the meteorological (winds and air temperature) and wave data by measuring every 30 min. Wind data was collected from a height of 4 m above the sea surface. The observed wind data is highly correlated with the data from UCAR website during the same period (https:// rda.ucar.edu/datasets/ds094.1/?tdsourcetag=s_pctim_aiomsg). The seabed sediment samples were collected using a grab sampler and stored in sealed plastic bags. In addition, 600 1 L water samples were collected from the surface, middle and bottom layers of the water column every hour. All the collected samples were brought back to the laboratory for sediment grain-size analysis by using a Mastersizer2000 granulometer, Malvern Ltd. 3.2. SSC calibration The water samples were collected at the study site and taken back to the laboratory to obtain SSC by filtration, then we established the relationship between the SSC and turbidities (T) for each turbidity sensor. The relationship between SSCs and turbidities was almost linear when the SSC was less than 10 g/L (Kineke and Sternberg, 1992). Then, all turbidity data from optical sensors was converted to SSC based on the above calibration relationship. Coefficients of determination (R) values for all OBSs were greater than 0.99 in this study (Fig. 2a). The SSC profiles can be converted from ABS's voltage output after calibrating with SSC values from OBSs at the same time and water layer (Fig. 2b). High-frequency SSC was obtained by calibrating high-frequency ADV's acoustic signals (Wang et al., 2013; Li et al., 2018). The relationship between SSC and the ADV acoustic intensity value (Iadv in dB) can be expressed as log10 (SSC) = IADV + b (Thorne and Hanes, 2002), where and b are coefficients determined by linear regression analysis (Fig. 2c).

Fig. 3. Wave-turbulence decomposition. Power Spectral Density (PSD) of (a) principal velocity and (b) vertical velocity before (gray line) and after wave removal (black line). Red dash lines show the −5/3 slope. The burst time is at 2016.01.19 12:00 and the location is at 0.3 mab. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

frequency velocities include mean currents (u , v , w ), wave motions (u˜, v˜, w˜ ), and turbulence (u , v , w ). Therefore, it is necessary to decompose these three motions to calculate c _ RS . The “phase method” (Bricker and Monismith, 2005), which linearly interpolates (in log space) the power spectral density (PSD) over the wave frequency range of 0.04 Hz - 0.5 Hz, is chosen to decompose turbulence and wave data (Macvean and Lacy, 2014). Fig. 3 illustrates the results of the waveturbulence decomposition which was conducted on one burst data collected on January 19, 2016. The gray lines represent the PSD of the flow fluctuations with wave and turbulence motions (u˜ + u and w˜ + w ); while, the black lines show the same (u and ) after the removal of waves. ID method: The bed shear stress based on the balance between shear production and energy dissipation with the 1D spectrum which is applicable to the inertial dissipation range is expressed as (Kim et al.,

3.3. Bed shear stress 3.3.1. Bed shear stress due to currents Bed shear stress due to currents can be estimated by three independent methods: the Reynolds stress method (RS), the inertial dissipation method (ID), and the logarithmic profile method (LP) (Sherwood et al., 2006). RS method: The bed shear stress c _ RS is calculated directly from turbulence fluctuations, and it is expressed as c _ RS

=

uw

(1)

where, is fluid density, and u and w are turbulence fluctuations in horizontal and vertical directions, respectively. The ADV's high-

Fig. 2. SSC calibration. (a) SSC calibration curves for turbidities (T) at 0.3 mab (red squars) and 0.8 mab (blue dots); (b) SSC calibration between values measured by ABS and OBS at 0.3 mab (blue squars) and 0.8 mab (red dots), respectively; (c) calibration curve to convert the acoustic signal (Iadv; dB) measured by ADV to SSC at 0.3 mab. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.) 3

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J. Tang, et al.

2000): c _ ID

where, H is the wave height, T is the wave period, h is the water depth,

2 kz U

=

2/3

ww

and L is the wave length (L =

(f)f 5/3 (2)

3

z L

cw _ max

w

(

+ 1

w s

) SSC , where

is seawater density (measured by

LP method is not applicable at all times, especially under low current velocity (i.e. slack water) because the major acceleration effects of flow occur near slack water (Dyer, 1986). Hence, only the velocity profiles with high correlation coefficient (R > 0.9) between U (z ) and ln z were adopted in the present study.

Y=

3.3.2. Bed shear stress due to waves The bed shear stress ( w ) caused by waves is derived from the bottom wave orbital velocity Uw and the wave friction factor, fw , as represented below: w

=

1 f Uw2 2 w

(5)

0.52

(6)

where, A = Uw T/2 is the semi-orbital excursion. The wave friction factor, fws , for smooth turbulent flow can be calculated as:

fws = BRw

N

R w = Uw A/ v

H Tsinh (2 h/ L)

Y11

Y1m

Yn1

Ynm

k

=

+

w

(11)

(12)

(7)

m i/

i=1

i i=1

(13)

In this research, we analyzed the first three modes accounting for more than 77% of the information extracted from the original data (e.g. > 77 %). The eigen weightings, W (W= VY ), represent the contribution of each parameter to the specific mode.

(8)

where, Rw is the wave Reynolds number, and v represents kinematic viscosity, which decreases as the temperature increases (Chen et al., 1973), and B = 2, N = 0.5, for Rw 500000 while B = 0.0521, N = 0.187, for Rw > 500000 . The kinematic viscosity can be obtained through Fig. 8 given on page 42 of Dynamics of Estuarine Mud (Whitehouse et al., 2000). Finally, the wave friction factor, fw , was taken as the greater one of fwr and fws (Whitehouse et al., 2000). According to the linear wave theory, the bottom wave orbital velocity, Uw , is represented as follows (Soulsby, 1997):

Uw =

c

(10)

3.2

w

1 + 1.2

c

)2 + ( w sin )2]1/2

where, Y is a n × m data matrix. n represents factors that potentially control SSC variation, and m is the observed time series. Subsequently, a standardized covariance matrix M( M= YY ) can be produced, in which, Y represents the transpose of matrix Y . The eigenvalues ( 1, …, n) and eigenvectors (Vn × n ) of matrix M can be calculated through matrix operation. Each eigenvalue corresponds to a column of eigenvectors. For example, the eigenvectors corresponding to 1 is the first mode (mode 1) of EOF analysis. Generally, a mode with a larger eigenvalue can serve as an indication of high percentage of parameter variations. The cumulative contribution of the first k eigenvalues of matrix, M, can be derived as follows:

The wave friction factor, fwr , for rough turbulent flow can be calculated as:

fwr = 1.39(A/ z 0 )

w cos

The Empirical Orthogonal Function (EOF) is a useful analytical technique for extracting main information from large datasets which contain complex spatial/temporal structures. After being introduced by Lorenz (1956) to the field of geophysics, this method has been widely applied in studies on sediment transport, morphological change, and suspended sediment dynamics (Liu and Lin, 2004; Liu et al., 2009; Dai et al., 2013; Shi et al., 2016). In the present study, the possible factors (current velocity [U], significant wave height [Hs], wave period [Tp], wind speed, water depth, air temperature [Tair], water temperature [Twater], and salinity) influencing SSC variations were analyzed by EOF method. Firstly, the values of these factors and SSC were standardized to form a single matrix Y

(4)

= u 2_ LP

+

3.4. Empirical orthogonal function (EOF) method

OBS) and s is sediment particle density (=2650 kg/m3). High-frequency fluid density accounting for high-frequency SSC was calculated from 16 Hz ADV acoustic signals. The bed shear stress is expressed as follows: c _ LP

cw _ m

where, cw _ max is the maximum value of the combined bed shear stress during a wave-cycle. It is used to determine the threshold of sediment motion and diffusion near the bed. cw _ m is the mean value of the combined bed shear stress over a wave-cycle. It is used for determining the average amount of friction which acts on the current and investigating diffusion of sediment into outer flow. is the angle between current direction and wave travel direction.

(3)

w

= [(

=

cw _ m

where, u*_ LP is bed shear velocity, k is von Karman constant (k = 0.4), z0 is the bed roughness length, is 4.7–5.2, L = U 3LP /(kgw ) is known as the Monin-Obukov length, and g is gravitation acceleration. In this study, we calculate the fluid density by the equation

=

h/ L) ).

3.3.3. Bed shear stress due to the combined currents and waves According to the Soulsby (1997) model, bed shear stress caused by the combined currents and waves can be expressed as follows:

where, ww (f) is the spectral density of the vertical turbulence component at the frequency of 1.5–3 Hz, 3 is the Kolmogorov constant ( 3=~0.69) (Green, 1992), U is the average current velocity of each burst. The vertical turbulent fluctuations were used to estimate c _ ID because there were almost no interferences from wave fluctuations in the vertical direction near the seabed and the spectral density of vertical turbulence agreed well with the −5/3 slope line (Fig. 3b). LP Method: The mean velocity U(z) at a height z above the bed can be represented by the von Karman-Prandtl equation, U (z ) = (u*_ LP / k )ln(z/z0) . After accounting for the effect of density stratification on the velocity profile, the von Karman-Prandtl equation was modified to (Dyer, 1986)

U (z ) 1 z = ln + U _ LP k z0

gT 2 tanh (2 2

4. Results 4.1. Meteorology and wave data Four storm events including cold air intrusion and strong winds were observed during observation periods. The storm events which occurred during neap tides were represented with RWN1 and RWN2 (Fig. 4, yellow stripes), and the events during spring tides were denoted as RWS1 and RWS2 (Fig. 4, gray stripes). The durations of four events

(9) 4

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Fig. 4. Meteorological, hydrodynamic, and water characteristics data. (a) Wind; (b) significant wave height, Hs, (black line) and wave periods (red line); (c) bottom wave orbital velocity, Uw, (black line) and wave direction (red stars); (d) temperature of air (red line) and bottom water at 0.3 mab (black line); (e) salinity at 0.3 mab; (f) current velocity (blue arrows) and water depth (black line); (g) SSC at 0.3 mab (black line) and 0.8 mab (red line). Four storm events are represented through gray (RWS1 and RWS2) and yellow (RWN1 and RWN2) stripes, respectively. Two high SSC events occurred during RWN1 and RWN2 events are denoted as N1 and N2 events. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

5

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(when Hs > 1.5 m) were about 39 h (RWN1), 54 h (RWN2), 12 h (RWS1), and 15 h (RWS2), respectively (Fig. 9). The maximum wind speed was 13.9 m/s (direction: 47°) during RWN1 event, 11.4 m/s (direction: 343°) during RWN2 event, 11.2 m/s (direction: 52°) during RWS1 event, and 11.7 m/s (direction: 35°) during RWS2 event (Fig. 4a and 9). Wave heights increased significantly during the storm events. The maximum significant wave height (Hs) of each storm event was between 2.0 and 2.8 m, and the wave periods ranged between 4 and 10.5 s (Fig. 4b). Air temperature was between 0.5 °C and 13.1 °C throughout the observation period. Due to cold air intrusion, the air temperature dropped 4.8 °C, 4.4 °C, 8.5 °C, and 8.7 °C during RWS1, RWN1, RWS2, and RWN2 events, respectively (Fig. 4d). All the extreme winds were accompanied by a decrease in air temperature, and all of the strong waves were accompanied by strong winds.

(29.7–31.6 PSU) and temperatures (7.3–12.2 °C) near the seabed. The bottom layer of water salinity and temperature tended to decline during storms since strong wind waves enhanced the vertical mixing within the water column and brought lower salinity and temperature surface water into the bottom. Water temperature showed a good linear relationship with air temperature (R = 0.8), although the drop in water temperature (1.44 °C for RWS1 event, 1.34 °C for RWN1 event, 1.83 °C for RWS2 event, and 2.67 °C for RWN2 event) was smaller than air temperature drop. 4.3. SSC and grain size of sediments During the entire observation period, the SSCs derived from turbidities remained between 0.03 and 7.40 g/L at 0.3mab and 0.04–4.1 g/ L at 0.80 mab. Peak SSCs of each tidal cycle were recorded near the maximum ebb and flood current speed (Fig. 4g). Both tide and wave signals were visible in the SSC variations, and this indicated that SSC variations were influenced by tides and waves. The maximum SSCs of each tidal cycle during neap tides were between 4.6 and 7.4 g/L at 0.3 mab for RWN1 and RWN2 events; however, for RWS1 and RWS2 during spring tides, the maximum SSCs of each tidal cycle were between 1.3 and 2.1 g/L, which were much smaller than SSCs during neap tides (Figs. 5 and 6). High SSC events (N1 and N2) were found during RWN1 and RWN2 events. During N1 event, high SSC (> 3 g/L) could only be observed near the maximum flood period, however, during N2 event, high SSC values were both recorded during the maximum flood and ebb periods. All SSCs were small (< 1 g/L) during slack water. Furthermore, the ABS fixed on the tripod recorded the presence of a fluid mud layer when the maximum SSC in the layer was higher than 10 g/L (Ross and Mehta, 1989; Kineke et al., 1996).

4.2. Tides, currents, and water characteristics Tidal range was between 0.9 and 4.2 m at the observation site, which was modulated by the spring-neap tidal cycle. The average water depth was ~19.5 m (Fig. 4f). A semi-diurnal tide which rotated in clockwise direction with a tidal ellipse rate of 0.27 (the ratio of short axis to long axis of a tidal ellipse) dominated the study area (Fig. 1). The maximum current velocity of each tidal cycle varied from 0.3 m/s during neap tide to 0.7 m/s during spring tide at 0.3 mab. The averaged tidal current velocities were between 0.28 and 0.33 m/s for RWS1 event, 0.17–0.21 m/s for RWN1 event, 0.34–0.43 m/s for RWS2 event, and 0.20–0.24 m/s for RWN2 event (Figs. 4f, 5a, 5d, 6a, and 6d). The main tidal current direction was almost parallel to the wedgeshaped channel. Tidal and meteorological factors influenced the water salinity

Fig. 5. Across-shore current (offshore is positive, black dash lines) and along-shore current (southeastward is positive, blue lines) during (a) N1 and (d) N2 events; bottom wave orbital velocity, Uw, during (b) N1 and (e) N2 events; and SSC Profiles during (c) N1 and (f) N2 events. SSCs at 0.3 mab and 0.8 mab measured by OBSs are shown with red lines and contour lines represented 3 g/L are shown with green lines in Figure c and f. The level of zero bed was set to 1.5 m below the ABS probe. During 14:00–16:00 in January 9, 2016, the tripod was taken out and redeployed to the same location. Influenced by currents, the distance between the ABS probe and the real seabed changed before and after redeploying the tripod. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.) 6

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Fig. 6. Across-shore current (black dash lines) and along-shore current (blue lines) during (a) RWS1 and (d) RWS2 events; Uw during (b) RWS1 and (e) RWS2 events; SSC Profiles during (c) RWS1 and (f) RWS2 events. SSCs at 0.3 and 0.8 mab measured by OBSs are shown with red lines in Figure c and f. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

The thickness of the fluid mud layer was 4–6 cm during N1 event and 8–16 cm during N2 event (Fig. 5c and f). The median grain size (d50) of seabed sediment samples collected from the observation site was 5.6Φ, which was coarser than the size of the suspended sediment samples (d50: 6.3Φ) (Fig. 7). The grain size distribution was unimodal and the proportion of mud in bed sediment samples was more than 80%. Such a high proportion of mud made the bed sediments cohesive and affected the erodibility of the seabed (Bass et al., 2002).

was noticeable that the results obtained through the LP method were higher than those derived from the ID and RS methods during high SSC events (Fig. 8). Bed shear stress calculated by RS and ID methods was based on the turbulent fluctuation at a fixed position of 0.3 mab. They could effectively assess the suspended sediment diffusion due to turbulence at the same layer where high SSC was observed. So, the mean values of c _ ID and c _ RS were chosen to evaluate bed shear stress due to current in this paper. The LP method is optimal to evaluate the shear stress of the entire boundary layer. Even though the LP method can include the influence of density stratification, it provides limited information about the sediment diffusion at the fixed layer compared with the RS and ID methods when fluid mud layer occurred near the bed during high SSC events. The maximum currents and waves induced-bed shear stress, cw _ max were between 0.20 and 3.28 Pa (average 0.61 Pa) for RWN1 event, 0.19–2.98 Pa (average 0.78 Pa) for RWN2 event, 0.18–2.67 Pa (average 0.98 Pa) for RWS1 event, and 0.22–2.29 Pa (average 0.62 Pa) for RWS2

4.4. Bed shear stress The LP, ID, and RS methods were used to estimate the bed shear stress induced by tides and waves. During the N1 event, average values of c _ LP , c _ ID , and c _ RS were 0.56 Pa, 0.15 Pa, and 0.05 Pa, respectively (Fig. 8a). On the other hand, averaged values of c _ LP , c _ ID , and c _ RS were 1.47 Pa, 0.18 Pa, and 0.08 Pa during the N2 event, respectively. It

Fig. 7. Grain size distribution of (a) suspended and (b) bed sediment samples collected from the observation site. 7

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Fig. 8. Bed shear stress. (a) bed shear stress induced by currents as calculated by LP ( c _ LP , gray line), RS ( c _ RS , black line), and ID ( c _ ID , red dash line) methods; (b) bed shear stress, w , induced by waves; (c) the mean ( cw _ m , gray line) and maximum ( cw _ max , black line) shear stress induced by the combination of currents and waves. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 9. SSC (black lines), Hs (red lines), wind speed (blue lines) and current speed (green lines) during (a) RWN1, (b) RWN2, (c) RWS1, and (d) RWS2 events; (e) the relationship between durations of Hs > 1.5 m and maximum SSCs. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

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event. cw _ max is the dominantly parameter driving sediment motion and diffusion near the bed (Whitehouse et al., 2000). The maximum values of cw _ max during four storm events were all high (2.29–3.28 Pa), and the duration of storm events during neap tides was two time longer than the durations in spring tides (Fig. 9). When wave height decreased, cw _ max decayed significantly (maximum value 0.4 Pa) during N1 event with the disappearance of high SSC.

relationship with air temperature in the first three modes during the four events. There was also a positive relationship between air and water temperature.

4.5. EOF analysis

During the entire observation period, high SSC events occurred in those neap tide cycles which were accompanied with winter storms. The maximum decrease of water temperature caused by cold air intrusion was ~2 °C resulting in an 8% increase of water viscosity (Whitehouse et al., 2000). EOF analysis clearly explained that the waves were driven by winds which were accompanied by cold air intrusion (Fig. 10), and waves contributed greater to SSC variation than currents during high SSC events. At the beginning, when winter storms generated strong waves (Hs > 1.5 m) and maximum SSC at 0.3 mab increased to more than 1 g/L (1–1.5 tidal cycles), we defined this period as the setting up stage (ST1). The reinforcement stage (ST2) was defined as the period when strong waves lasted for more than 15 h, and maximum SSC of > 3 g/L was observed. As Hs decreased to less than 1.2 m, the high SSC event disappeared and the SSC decreased (Fig. 9a), and we defined this period as the decay stage (ST3). If the strong wave duration was too short to support the reinforcement stage, high SSC event will miss like the observation during RWS1 and RWS2 events in spring tides (Fig. 9c and d). During these two events, Hs responsible for the last peak SSC value was 1.27 m, which is less than 1.5 m threshold that can force occurrence of high SSCevent. During slack water of high SSC events, minimum SSC at 0.3 mab was less than 1 g/L, indicating that sediment settling was predominant rather than vertical diffusion into upper layer. Therefore, wind-waves dominantly influenced the occurrence of high SSC events during winter storms with the help of currents (driven by winds, waves and tides). Sufficient duration of strong waves was essential for the occurrence of high SSC event (Fig. 9e). Water temperature, salinity produced minor direct effects on high SSCs based on EOF analysis.

5. Discussion 5.1. Hydrodynamic and environmental factors influencing high SSCs

SSC variations are highly influenced by weather conditions such as air temperatures, strong winds, and storms (Schoellhamer, 1995; Valipour et al., 2017). In addition, they are determined by hydrodynamic conditions like currents (driven by winds, waves and tides) and waves (Wang et al., 2006; Zhu et al., 2014), as well as environmental factors like water temperature and salinity (Xing et al., 2012). EOF method was used to identify the major modes of correlated variance using time series of weather parameters, SSC, hydrodynamic and environmental factors which were collected during the four storm events. The time windows of the four events and time series of SSC, Hs, wind speeds, and currents used for analysis are shown in Fig. 9. Based on the EOF results, the first three modes explained 77.6% (RWN1 event), 84.2% (RWN2 event), 77.2% (RWS1 event), and 80.4% (RWS2 event) of the different datasets (Fig. 10). The eigenvectors showed the grouping of variables, and the eigen weighting series indicated the temporal distributions of the variables. Mode 1 was the most important mode, which explained 44.3%–59.5% of the data. This observation showed that SSC had a negative relationship with air temperature, water temperature and salinity. However, it shared a positive relationship with winds and waves. SSC also demonstrated a minor relationship with flow velocity during the four storm events (Fig. 10). Results of mode 1 revealed that strong wind-waves had a larger contribution to the amplitude of SSC variations as compared to tidal currents. Taken together, the modes 2 and 3 could explain 24.7%–36.1% of the data. SSC variations in mode 2 and 3 showed a positive relationship with current speed but a negative relationship with winds and waves for the RWN1, RWN2, and RWS2 events (Fig. 10a, b, d). Additionally, waves showed a positive relationship with winds and a negative

Fig. 10. Results of EOF analysis that can help to determine the factors influencing local SSC variations. The eigenvectors and eigen weightings of the first three eigenmodes for (a and e) RWN1; (b and f) RWN2; (c and g) RWS1; and (d and h) RWS2 events are shown. These eigenmodes represent the main information extracted from the original data (cumulative contribution > 77%). U represents current speed; Hs represents significant wave height; Tp represents wave periods; Twater represents water temperature at 0.3 mab; Tair represents air temperature above sea surface. Both salinity and SSC were measured at 0.3 mab. 9

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Fig. 11. Fast Fourier Transformation (FFT) analysis of SSC (black lines), current velocity (U, red lines), and the modulus of current velocity (|U|, blue lines) during (a) RWN1, (b) RWS1, (c) RWN2, and (d) RWS2 events. The along-shore velocity was chosen to represent current velocity, U (southeastward is positive). The modulus of current velocity was calculated using the formula u 2 + v 2 + w 2 . (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

5.2. Sediment sources of suspended sediment

analysis for N1 event in which advection contributed greater than resuspension. However, during N2 event, substantial sediment deposited on the bed (Fig. 5f), indicating that the major sediment source for the high SSC event is not from the local seabed but from the fluid mud between the seabed and the observation layer. Thus, a three-layer structure (water with high SSC-fluid mud-stationary seabed arrangement) was formed in the bottom boundary layer. FFT analysis of SSC recorded during RWS1 and RWS2 events showed that the amplitude of SSC attained two peaks at F1 and F2 frequencies corresponding to U and |U|, respectively. The linear correlation coefficients between cw _ max and SSC were 0.59 for RWS1 event and 0.38 for RWS2 event (Fig. 12). Hence, SSC variation during RWS1 and RWS2 events was controlled by both advection and resuspension. Fig. 6c and f showed that there was no evidently fluid mud observed near the bed to supply sediment for formation of high SSC event. The higher correlation coefficient between w and SSC (0.62 for RWS1 event, and 0.28 for RWS2 event) showed that waves contributed more to SSC variations than currents. Thus, high SSC events were mainly resulted from wind waves. SSC variation during N1 event contributed from advection and wave induced sediment resuspension, and advection was predominant. N2 event was mainly caused by sediment resuspension. The local benthic fluid mud forced by waves supplied sediment for the occurrence of high SSC events through resuspension (Fig. 14). During RWS1 and RWS2 events, short durations of strong waves cannot erode and resuspend enough sediment from the bed to form evident fluid mud on the seabed and supply sediment for the upper water layer to form high SSC event even in spring tides.

Weeks et al. (1993) had suggested that SSC variations corresponding with the periodic movements of current velocity (U) were caused by advection which occurred along horizontal SSC gradient. SSC variations matching with the periodic movements of the modulus of current velocity (|U|) were found to be caused by local resuspension. In the present study, Fast Fourier Transformation (FFT) technique was used to capture the dominant frequency of SSC variability and current velocity, and analyze the relationship between the two. During N1 event, the amplitude of SSC had two peaks at F1 (2 Cycles Per Day, CPD) and F2 (4CPD) frequencies which corresponded to U and |U|, respectively (Fig. 11a). The SSC amplitude which corresponded to F1 frequency was slightly higher than that corresponding with F2 frequency. Hence, both horizontal advection and local resuspension contributed to the occurrence of N1 event, and horizontal advection contributed more SSC variations than resuspension (Fig. 11a). During N2 event, the amplitude of SSC attained only one peak which corresponded to |U|, indicating that sediment resuspension contributed significantly to SSC variations and resulted in high SSC event (Fig. 11c). Maximum bed shear stress, cw _ max , was applied to estimate the rate of sediment resuspension near the bed (Whitehouse et al., 2000). During N1 event, cw _ max showed a poor correlation with SSC variation (R = 0.29); thereby, proving that SSC variations are not dominated by local sediment resuspension (Fig. 12e). The positive correlation between cw _ max and SSC (R = 0.65) during the N2 event indicated that suspended sediments were mainly resuspended from the lower layer (Fig. 12f). This is consistent with the results derived from the FFT analysis for SSC and current velocity. The positive correlation between w and SSC (0.33 for N1 event, and 0.75 for N2 event) showed that wave stresses accounted for a greater proportion of SSC than current stresses which had negative relationship with SSC (Fig. 12a and b). This result again agreed with our EOF analysis. During high SSC events, high SSC value at 0.3 mab did not respond immediately to high value of cw _ max (> 1 Pa), but lagged for more than 15 h. After cw _ max decreased to 0.4 Pa, high SSC disappeared. In addition, bed elevation was used to determine whether SSC variations were caused by horizontal advection or localized sediment resuspension. Generally, sediment eroded from the seabed is resuspended into water column, and this will cause the decrease of bed elevation. Fig. 5c showed that erosion and deposition both occurred during N1 event. This observation does not conflict with the above

5.3. Sediment transport under winter storm conditions Both residual current and sediment transport were in the northwestward direction during the calm weather conditions, further indicating that sustained sediment transport between the Changjiang River Delta and the sand ridge system in Jiangsu (Wu et al., 2014, 2018; Xuan et al., 2016). This sediment transport process benefited from upshelf residual current due to the shoreward tidal stress produced by the Poincare tidal wave coming from the East China Sea as a result of the Earth's rotation (Wu et al., 2018). However, during the four storm events, residual current near the seabed was southeastward under northerly winds and southwesterly waves. Along-shelf wind produces along shore current near the coast (Csanady & T., 1978; Wu et al., 10

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Fig. 12. The relationship between SSC and c (R1, gray circles) and the relationship between SSC and w (R2, black dots) during (a) RWN1, (b) RWN2, (c) RWS1, (d) and RWS2 events. The relationship between SSC and cw _ m (gray circles) and the relationship between SSC and cw _ max (R3, black dots) during (e) RWN1, (f) RWN2, (g) RWS1, (h) and RWS2 events.

Fig. 13. (a) Along-shore (southeast is positive, red line) and across-shore (offshore is positive, blue dash line) residual current at 0.3 mab, and (b) along-shore (southeast is positive, red line) and across-shore (offshore is positive, blue dash line) sediment transport rate at 0.3 mab. The results were obtained through low-pass filtering (34 h) of the time series of current and sediment transport. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

2018). For simplicity, the wind stress is set to parallel to the coast, and the cross shore current is zero, then, in the steady state, the major momentum balance in the along-shelf direction is the frictional balance between surface wind stress sy and bottom stress by , i.e. 1 y ( y b ) = 0 . Based on the above theory, Wu et al. (2018) found the h s along shore current was 0.05 m/s under the wind stress of 0.05 N/m2

and tidal current magnitude of 0.5 m/s, and, wind driven flow became dominant during neap tides. The wind shear stress was estimated as the wind velocity squared multiplied by the density of air (1 kg/m3) and a drag coefficient (0.0012) (Garratt, 1977). The observed along shore residual current was southeastward under northwesterly winds during four storm events (Figs. 4a and 13a). Especially, during RWN2 event, the maximum values of southeastward residual current reached 0.07 m/s 11

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Fig. 14. Mechanisms of high SSC events. One high SSC event (N1 event) occurred due to advection and resuspension, and advection contributed greater to SSC variations than sediment resuspension. Another high SSC event (N2 event) was mainly resulted from resuspension of sediment supplied from the local benthic fluid mud induced by waves.

due to strong along shore winds (> 7 m/s) which lasted for more than 2 days (Fig. 13a). The along-shore residual current matched well through observation and theory. A downwelling-favorable along-shelf wind stress or an onshore wind stress over an along-shelf uniform unstratified water column forces a steady across-shelf circulation pattern with onshore flow near the surface and an offshore return flow in the lower layer (Ekman, 1905). The calculation process of the across-shelf volume transport in the surface boundary layer is gave by Lentz and Fewings (2012), and the results are similar with that based on the Ekman theory. However, vertical distribution of the wind-driven flow in the lower layer is unclear. The Ekman theory can give the vertical profile of the wind-driven flow in the lower layer, but it doesn't match with the observed results. The cross-shelf circulation pattern indicates the offshore cross-shelf volume transport exists in the lower layer under a downwelling-favorable along-shelf wind or an onshore wind stress, and its value should be in a similar order of magnitude with the surface crossshelf volume transport. The observed, offshore residual current near the bed during RWS1, RWN1, and RWS2 events agreed with the cross-shelf circulation pattern. Except for the RWN2 event, the onshore residual current near the bed (maximum values: 0.06 m/s) occurred under the downwelling-favorable along-shelf wind (northwesterly wind), it may be due to the occurrence of fluid mud flowing onshore along the slope of the sand ridge. Over the inner shelf, the net wave-driven transport forced by surface gravity waves is near zero over most of the water column, since the Stokes drift tends to offset the Stokes-Coriolis-induced Eulerian velocity or undertow (Lentz and Fewings, 2012). The two high SSC events significantly contributed to the sediment transport flux. During N1 event, sediment was transported northwestward (peak along-shore sediment transport rate = 0.28 kg/m/s) near the bed under northeasterly winds and waves (Fig. 13). And the net sediment transport during N1 event accounted for 80% of sediment transport between January 1, 2016 and January 8, 2016. In the case of N2 event, northwesterly winds and northeasterly waves resulted in onshore sediment transport (average transport rate = 0.10 kg/m/s), and along-shore suspended sediment transport was southeastward at the beginning but northwestward towards the end; therefore, the net northwestward sediment transport was relatively weak (average transport rate = 0.02 kg/m/s). N2 event contributed to 99% of across-shore sediment transport and 60% of net sediment transport from January 13, 2016 to January 20, 2016.

Therefore, high SSC events played a significant role in sediment transport close to the seabed. 6. Conclusion High SSC events (SSC > 3 g/L) were observed in the neap tide cycles during winter storms generated by cold air intrusion. Wind wave was the main force to drive the occurrence of high SSC events that were supported by fluid mud layer (thickness of 4–16 cm). The changes of temperature and salinity played minor effects on SSC variations. During the two high SSC events observed in the neap tide, the SSCs at 0.3 mab can increase to 7.4 g/L and they were observed with the flood and ebb peak flows. The high SSC events had gone through three stages: the setting up stage, the reinforcement stage, and the decay stage. The high SSC > 3 g/L was only observed in the reinforcement stage. While in the setting up stage, the bottom SSC increased to more than 1 g/L with occurrence of strong waves, but its value was less than 3 g/L. This indicated that it took times for the occurrence of high SSC values driven by wave-induced resuspension. During slack water, SSCs decreased to less than 1 g/L, indicating that sediment settling was predominant rather than sediment diffusion driven by waves. Further, the two high SSC events had different formation mechanisms (Fig. 14). N1 event was formed through advection and resuspension with advection contributing greater than sediment resuspension to SSC variations. N2 event was mainly resulted from resuspension with sediment from the benthic fluid mud supported by strong waves. The fluid mud also supplied abundant sediment for erosion and advection. However, during winter storm events (RWS1 and RWS2) in spring tides, the maximum SSCs were 2.1 g/L and much lower than that observed in neap tides, because of the missing of the reinforcement stage and the lack of evident fluid mud layer. Thus, long duration of strong waves and adequate sediment source are necessary for occurrence of high SSC (> 3 g/L) events. Although the factors influencing high SSCs during storms are analyzed in this paper, new issues including formation mechanisms of the benthonic fluid mud and the effects of resuspended sediment induced density stratification in the bottom boundary layer structure are yet to be solved. 12

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Acknowledgements

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This study was supported by the National Natural Science Foundation of China (41625021, 41876092), Innovation Program of Shanghai Municipal Education Commission (2019-01-07-00-05E00027), and the Laboratory for Marine Geology, Qingdao National Laboratory for Marine Science and Technology (MGQNLM-KF201714). We thank Jingdong Chen, Changchen Ji, Gaolei Cheng, Benwei Shi, Hui Sheng, Danxi Chen, Yang Yang, Gaocong Li, Yifei Liu, Tinglu Cai, Renzhe Chen for their assistance in the field works and laboratory measurements. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecss.2019.106351. References Bass, S.J., Aldridge, J.N., Mccave, I.N., Vincent, C.E., 2002. Phase relationships between fine sediment suspensions and tidal currents in coastal seas. J. Geophys. Res. Ocean. 107 (C10). https://doi.org/10.1029/2001JC001269. (C10). Brand, A., Lacy, J.R., Hsu, K., Hoover, D., Gladding, S., Stacey, M.T., 2010. 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