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Short-term variations and influencing factors of suspended sediment concentrations at the Heisha Beach, Guangdong, China
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Jintang Ou1, 3, Haoyan Dong2, 3, Liangwen Jia1, 3, 4, *, Xiangxin Luo3, 4, 5, *, Zixiao He1, 3, Kanglin Chen2, 3, Jing Liu1, 3, Yitong Lin1, 3, Mingdong Yu2, 3, Mingen Liang1, 3
Acta Oceanologica Sinica | 2022, 41(5) : 51 - 63
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Acta Oceanologica Sinica | 2022, 41(5): 51-63
• Physical Oceanography, Marine Meteorology and Marine Physics •
Short-term variations and influencing factors of suspended sediment concentrations at the Heisha Beach, Guangdong, China
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Jintang Ou1, 3, Haoyan Dong2, 3, Liangwen Jia1, 3, 4, *, Xiangxin Luo3, 4, 5, *, Zixiao He1, 3, Kanglin Chen2, 3, Jing Liu1, 3, Yitong Lin1, 3, Mingdong Yu2, 3, Mingen Liang1, 3
Affiliations
  • 1 School of Marine Engineering and Technology, Sun Yat-sen University, Guangzhou 510275, China
  • 2 School of Marine Sciences, Sun Yat-sen University, Guangzhou 510275, China
  • 3 Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519080, China
  • 4 Guangdong Provincial Engineering Research Center of Coasts, Islands and Reefs, Guangzhou 510275, China
  • 5 Institute of Estuarine and Coastal Research, School of Marine Engineering and Technology, Sun Yat-sen University, Guangzhou 510275, China
Published: 2022-05-25 doi: 10.1007/s13131-021-1874-3
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Knowledge of sediment variation processes is essential to understand the evolution mechanism of beach morphology changes. Thus, a field measurement was conducted at the Heisha Beach, located on the west coast of the Zhujiang River (Pearl River) Estuary, to investigate the short-term variation in suspended sediment concentrations (SSCs) and the relationship between the SSC and turbulent kinetic energy, bottom shear stress (BSS), and relative wave height. Based on extreme event analysis results, extreme events have a greater influence on turbulent kinetic energy than SSC. Although a portion of the turbulent kinetic energy dissipates directly into the water column, it plays an important role in suspended sediment motion. Most of the time, the wave-current interaction is strong enough to drive sediment incipience and resuspension. When combined, the wave-current interaction and wave-induced BSSs have a greater influence on suspended sediment transport and SSC variation than current-induced BSS alone. The relative wave height also has a strong correlation with SSC, indicating that the combined effect of water depth and wave height significantly impacts SSC variation. Water depth is mainly controlled by the tide on the beaches; thus, the effects of tides and waves should be conjunctively considered when analyzing the factors influencing SSC.

Heisha Beach  /  suspended sediment concentration variation  /  turbulent kinetic energy  /  bottom shear stress  /  relative wave height
Jintang Ou, Haoyan Dong, Liangwen Jia, Xiangxin Luo, Zixiao He, Kanglin Chen, Jing Liu, Yitong Lin, Mingdong Yu, Mingen Liang. Short-term variations and influencing factors of suspended sediment concentrations at the Heisha Beach, Guangdong, China[J]. Acta Oceanologica Sinica, 2022 , 41 (5) : 51 -63 . DOI: 10.1007/s13131-021-1874-3
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1 Introduction
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Nearshore environments provide important habitats for coastal and marine life and support many human activities, including natural resource development and transportation. Knowledge of the physical and biogeochemical processes in these coastal environments is essential for their management, and for promoting sustainable development for these regions (Bolaños et al., 2012).
One of the most important processes in the nearshore environment is sediment transport, which affects the water quality, turbidity, biogeochemistry, and morphology evolution, especially for sandy beaches (Bolaños et al., 2012). The sediment transport process controls the morphodynamic state of the beach, and the cross-shore and alongshore sediment transport gradients are the main drivers of beach morphology changes, forming different beach states including dissipative, reflective, and intermediate beaches. Additionally, the different beach profile shapes and ensuing wave transformation patterns create different spatial sediment transport patterns (Aagaard et al., 2013). Thus, knowledge of sediment variation processes is essential to understand the evolution mechanism of beach morphology changes.
The sediment movement process on the beaches mainly depends on the hydrodynamic conditions and the corresponding suspended sediment concentrations (SSCs) (Pang et al., 2019). In the nearshore area, the hydrodynamic conditions control and affect sediment resuspension, mixing, and transport processes (Jing and Ridd, 1996); therefore, the roles of the different hydrodynamic factors on SSC need to be examined on different time scales.
Turbulent motion is one of the most critical contributors for sediment suspension and increasing SSCs (Aagaard and Hughes, 2006) by enhancing the current-induced bed shear stress (LeClaire and Ting, 2017), as has been confirmed by field observations and sediment transport models (Pang et al., 2020). A linear relationship was found between the net sediment flux and the net turbulent kinetic energy (TKE) flux over one wave cycle. Net onshore sediment transport is always associated with net positive (onshore) TKE flux (LeClaire and Ting, 2017). SSC in the water column under the waves depends on the balance between upward- and downward-directed sediment fluxes. In most sediment concentration models, upward fluxes depend on gradient diffusion and sediment pick-up (Nielsen, 1992). Gradient diffusion can be caused by small-scale turbulence due to friction between the (orbital) fluid motion and the (flat) seabed, whereas sediment pick-up can be caused by the lifting of coherent packages of sand by turbulent vortices produced by the interaction of fluid with bedforms, such as wave ripples (Aagaard and Hughes, 2010). Field observations and sediment transport models have confirmed that increased TKE intensity increases the SSC in the water (Hansen and Svendsen, 1984; Yoon and Cox, 2012). Therefore, to investigate the SSC variation mechanism on the beaches thoroughly, it is important to examine the influence of turbulent motion.
By utilizing fast response instruments to monitor the amount of sediment in suspension (Brenninkmeyer, 1976), researchers have observed short periods of high SSC and TKE when sediments reach high levels (1 m) in the water column (Jaffe and Sallenger, 1992). These intense periods, called extreme events in this study, are separated by longer quiescent periods with a smaller magnitude of SSCs, and the suspension is confined near the bed. Field studies have confirmed that extreme events control sediment suspension in the surf zone. It was demonstrated that “large” suspension events, which exceeded the mean plus three standard deviations of the time series, accounted for 10% of the total record fraction, but these events contained approximately 15%–35% of the SSC and drove the onshore sediment transport (Jaffe and Sallenger, 1992). Furthermore, intermittent TKE events in plunging breakers, which accounted for 7% of the records, could contain 40% of the TKE for the coherent event (Cox and Anderson, 2001). The importance of intermittent suspension to sediment transport has been demonstrated by showing the significance of the coupled fluctuations of velocity and SSC in the sediment flux calculation (Ogston and Sternberg, 1995; Yoon and Cox, 2012). It was shown that the correlated TKE events (the simultaneous occurrence of extreme SSC and TKE events) accounted for 12.2% of the total TKE time records, and 38.1% of the total turbulent motions were associated with 42.9% and 40.0% of the SSC events and total sediment concentrations, respectively (Pang et al., 2020). Therefore, to elucidate the sediment suspension and transport processes, identifying the intermittent features of SSC and TKE is vital.
Bottom shear siress (BSS) also plays an important role in sediment mobilization and the resulting suspended sediment transport. Shear stress that exceeds a threshold indicates sediment entrainment and can result in beach morphology evolution (Maity and Maiti, 2016). Increased SSC usually corresponds with a large combined wave-current BSS. In shallow coastal waters, both wave and current movements are important for sediment transport and can enhance bed turbulence and increase BSS (Jing and Ridd, 1996). It has been shown (Grant and Madsen, 1979) that shear stresses are altered when waves and currents coexist in a region because the turbulence generated by the wave-current interaction (WCI) near the bed differs from the stresses that occur from only waves or currents. Thus, evaluating the effect of WCI on BSS calculations is necessary to analyze the relationship between BSS and SSC accurately.
Moreover, the importance of relative wave height to sediment suspension and the subsequent SSCs on the beaches also need to be further studied. Relative wave height (Hr) is the ratio of wave height to water depth (h), and the tide mainly controls h in the nearshore regions. Generally, wave height variations are the primary cause of variations in the average SSC in the water column due to their significant contribution to the sediment suspension mechanism compared to other factors, such as bed configuration and mean current (Aagaard and Greenwood, 1995; Liang et al., 2007; Pang et al., 2020). In the coastal zone, sand transport is strongly dependent on Hr, particularly under small velocity conditions (0.1–0.6 m/s) (Van Rijn, 2007). These findings indicate that Hr significantly affects suspended sediment transport on the beaches.
It has been proven that turbulence motion, BSS and Hr significantly influence sediment suspension on the beaches. In this paper, the effect of TKE, BSS, and Hr on SSC variation have been studied by evaluating the relationship between the intermittent extreme events of TKE and SSC, analyzing the contribution of BSS under the influence of WCI and examining the relative importance of tides and waves on SSC.
2 Study area
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Field observations were conducted at the Heisha Beach (Guangdong, China), located on the west coast of the Zhujiang River (Pearl River) Estuary, and the observation instrument was installed on the foreshore east of Heisha Beach (Fig. 1b). The Heisha Beach is composed of black sand with a gentle slope, and is located adjacent to the Huangmaohai Estuary in the east and the Taishan Nuclear Power Plant in the west (Fig. 1a). The surrounding islands include Dajin and Hebao Islands at the mouth of the Huangmaohai Estuary and Shangchuan and Xiachuan Islands to the west. From 1993 to 2001, approximately half of the beaches to the west of Heisha Beach were reclaimed to construct the Taishan Nuclear Power Plant.
According to data obtained from the Shangchuan Island tide gauge station, irregular semidiurnal tides prevail at the Heisha Beach, with a mean tidal range of 1.5–2.0 m.
The wave conditions in the dry and flood seasons were determined based on the 1982 measured wave data from the Hebao Island hydrological station. During the dry season, the highest 10% of the wave height ranges from 0.27–2.40 m with a mean value of approximately 1.26 m, and the wave period ranges from 3.4–7.8 s with a mean value of 5.6 s. During the flood season, the highest 10% of the wave height ranges from 0.48–2.81 m with a mean value of approximately 1.08 m, and the wave period ranges from 2.9–7.8 s with a mean value of 5.1 s. The wave directions are mainly southeast in the dry season, and in the flood season, they are primarily south and southeast, and sometimes east and southwest.
3 Methodology
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3.1 Instrument deployment
Field observations were conducted from August 9–12, 2019, at the foreshore of Heisha Beach. The observation interval (hereafter called “burst”) was 10 min, and 332 bursts with valid data were obtained.
An aluminum frame was equipped with a 6 MHz Nortek acoustic Doppler velocimeter (ADV) and an optical backscatter sensor (RBR-Tu.Iwave), and a 1 MHz Nortek acoustic wave and current profiler (AWAC) was installed on a second frame. The ADV was used to record high-resolution 3D flow velocities in three directions: alongshore (u), cross-shore (v), and vertical (w). The ADV sensors faced downward and were located approximately 23 cm above the seabed. The velocity measurements were recorded every 10 min at 32 Hz for 5 min. The RBR-Tu.Iwave was utilized to measure turbidity and water depth (h). During Periods D1 and D2, the RBR-Tu.Iwave probe was located approximately 59 cm above the seabed, and measurements were performed at 0.2 Hz. During Period D3, the probe was located approximately 38 cm above the seabed, and measurements were performed at 6 Hz. The relationship between turbidity and SSC was calculated utilizing the linear least-squares fitting method, and the linear fitting value (R2) was 0.99 (Fig. 2). The AWAC, with its probe located approximately 50 cm above the seabed, was used to record the mean wave height (Hm0) and mean wave period (Tm0). Therefore, the study data included 332 segments of 10 min synchronous measurement data, including high-resolution 3D flow velocity, water turbidity, h, Hm0, and Tm0.
3.2 Methods
3.2.1 TKE calculation
Based on the data measured by ADV, the velocity direction is utilized to obtain the alongshore (u), cross-shore (v), and vertical (w) velocities. The TKE calculation equation utilized is as follows:
$ {\text{TKE = }}\frac{1}{2}\rho (u^{\prime ^2} + v^{\prime ^2} + w^{\prime ^2}) ,$
where u', v', and w' are the turbulent oscillation components of the three directions, and ρ is seawater density (1025 kg/m3).
The frequency cut-off method is utilized to estimate the turbulent oscillation components. The cut-off frequency is determined by the slope break of the power spectral density curve of the velocity in each direction (Foster et al., 2006; Ruessink, 2010; Smyth and Hay, 2003). For example, the power spectrum in Fig. 3 shows a slope break at approximately 1 Hz, which indicates a change in the dominance of wave and turbulent motion. This method has frequently been utilized in previous studies with different cut-off frequencies (Foster et al., 2000; Kos’yan et al., 1996; Smyth and Hay, 2003).
3.2.2 BSS calculation
The effect of the WCI on BSS was calculated based on the theory of Grant and Madsen (1979) , in which an enhanced drag exists at the outer edge of the wave-current boundary layer. This drag is a function of the near-bottom wave orbital velocity (Uw), mean current (Uc), wave frequency (ω), bottom roughness height ($ {z_{\text{0}}} $), and the angle between the wave propagation direction and the current (φc). The wave-current boundary layer thickness, called reference height (δcw) in this study, is estimated as 5% of the mean h (Jia et al., 2014) , which was 1.52 m during the observation period; therefore, δcw was approximately 0.08 m.
The maximum BSS (τb, max) for the wave-current combination is calculated as follows:
$ {\tau _{{\rm{b,\;max}}}} = {\tau _{\rm{w}}} + {\tau _{\rm{c}}} ,$
where τc and τw are the current- and wave-induced BSS, respectively. τw is calculated as follows:
$ {\tau _{\rm{w}}} = \rho u_{^*\rm{w}}^2 = \frac{1}{2}\rho {f_{\rm{w}}}U_{\rm{w}}^2, $
where ${u_{{\text{*w}}}} = \sqrt {\dfrac{1}{2}{f_{\text{w}}}} {U_{\text{w}}}$ is the wave-induced shear friction velocity. Based on the linear wave theory, Uw can be computed as follows:
$ {U_{\text{w}}} = \frac{{\text{π} {H_{{\text{m0}}}}}}{{{T_{{\text{m0}}}}\sinh (2{\text{π}}h/L)}} ,$
where Hm0 is the mean wave height, Tm0 is the mean wave period, h is the water depth and L is the wave length. L can be calculated by iterative computations through the linear wave dispersion relation:
$ L{\text{ = }}\frac{{{{g}}T_{{\text{m0}}}^2}}{{2\text{π} }}\tanh \left( {\frac{{2\text{π} h}}{L}} \right). $
The wave friction factor (fw) can be calculated via Eq. (6) (Jonsson, 1966) and Eq. (7) (Swart, 1974):
$ {f_{\text{w}}} = \exp \left[ { - 6 + 5.2{{\left( {\frac{{{A_{\textit{δ }}}}}{{{k_{\text{s}}}}}} \right)}^{ - 0.19}}} \right], $
$ {f_{\text{w}}} = 0.002\;51\exp \left[ {5.21{{\left( {\frac{{{A_{\textit{δ }}}}}{{{k_{\text{s}}}}}} \right)}^{ - 0.19}}} \right],\;{{{A_{\textit{δ }}}} / {{k_{\text{s}}} \leqslant }}1.57, $
where ${A_{\text{δ}}} = {U_{\text{w}}}T/(2\text{π}) $ is the near bottom excursion amplitude, T is the wave period, and ks is the bottom physical roughness, which is usually utilized to determine the $ {z_{\text{0}}} $ (${z_{\text{0}}} = {k_{\text{s}}}/30$ in the turbulent conditions). ks is composed of three physical roughness measurements: (1) the particle roughness ${k_{{\text{sg}}}} = 2.5D$; (2) the bed form roughness ${k_{{\text{sd}}}} = 27.7{H^2}/ \text{λ}$, where H and λ are the height and the length of sand wave respectively; (3) the bed load roughness, ${k_{{\text{sc}}}} = 1.142\;4D\dfrac{{{\tau _{{\rm{sf}}}}}}{{{\tau _{\rm{c}}} + 0.2{\tau _{{\rm{sf}}}}}},\;{\tau _{{\rm{sf}}}} > {\tau _{{\rm{cr}}}}$, in which τcr is the critical stress of sediment movement (as shown in Eq. (16)).
An initial current friction factor (fc) that does not consider the wave effect is calculated as follows:
$ {f_{\text{c}}} = 2{\left[ {\frac{\kappa }{{{\text{ln}}(30{\text{δ} _{{\rm{cw}}}}/{k_{\text{s}}})}}} \right]^2}, $
where κ, the von Karman’s constant, is 0.4.
The current-induced BSS and current-induced shear friction are calculated using Eqs (9) and (10), respectively, as follows:
$ {\tau _{\rm{c}}} = \rho u_{{\text{*c}}}^{\text{2}} = \frac{1}{2}\rho {f_{\text{c}}}u_{\text{c}}^{\text{2}} ,$
$ {u_{{\text{*c}}}} = \sqrt {\frac{1}{2}{f_{\text{c}}}} {u_{\text{c}}}, $
where uc is the current at δcw. The combined wave-current friction velocities are computed as follows:
$ {u_{{\text{*cw}}}} = {(u_{{\text{*c}}}^{\text{2}} + u_{{\text{*w}}}^{\text{2}} + 2{u_{{\text{*c}}}}{u_{{\text{*w}}}}\cos {\varphi _{\rm{c}}})^{1/2}} .$
The apparent bottom roughness (kb) , which indicates the turbulence level due to the wave-current boundary layer and physical bottom roughness, are calculated as follows (Signell et al., 1990):
$ k_{\rm{b}} = {k_{\rm{s}}}\left[ {24\frac{{{u_{{\rm{*cw}}}}}}{{{U_{\rm{w}}}}}\frac{{{A_{\delta}}}}{{{k_s}}}} \right]^{\beta} , $
where
$ \beta = 1 - \frac{{{u_{{\rm{*c}}}}}}{{{u_{{\rm{*cw}}}}}} .$
The resultant kb is utilized in Eq. (8) as ks to calculate the combined wave-current friction factor (fcw) for the next time step as follows:
$ {f_{{\rm{cw}}}} = 2{\left[ {\frac{\kappa }{{{\text{ln}}(30{\text{δ} _{{\rm{cw}}}}/{k_{\text{b}}})}}} \right]^2}. $
To obtain a stable fcw value, iterations of this process are performed, in which fcw is substituted for fc in Eq. (10). Then, iterations of Eqs (10)–(14) are performed until the difference between the computed results of the last and next to last time step is less than the preset threshold (10–6 in this study). Once the final fcw has been obtained, the current shear stress, ${\tau _{\rm{c}}} = \dfrac{1}{2}\rho {f_{{\text{cw}}}}u_{\text{c}}^{\text{2}}$ , is calculated in the presence of the wave.
The τb,max for the wave-current combination, which does not consider the effect of the wave and current propagation directions on the combined wave-current BSS, is calculated via Eq. (2). In this study, the nonlinear combined wave-current BSS proposed by Soulsby et al. (1993) is utilized, which is calculated as follows:
$ \left\{\begin{aligned} & {\tau _{{\rm{cw}}}} = {\left[ {{{\left( {{\tau _{\rm{m}}} + {\tau _{\rm{w}}}\left| {\cos {\varphi _{\rm{c}}}} \right|} \right)}^2} + {{\left( {{\tau _{\rm{w}}}\sin {\varphi _{\rm{c}}}} \right)}^2}} \right]^{0.5}} ,\\ & {\tau _{\rm{m}}} = {\tau _{\rm{c}}}\left[ {1 + 1.2{{\left( {\frac{{{\tau _{\rm{w}}}}}{{{\tau _{\rm{w}}} + {\tau _{\rm{c}}}}}} \right)}^{3.2}}} \right] ,\\ \end{aligned} \right.$
where τm is the mean BSS in the current direction and τcw is the maximum BSS within a wave period; thereby, the effects of the wave and current propagation directions are considered in Eq. (15).
Additionally, the critical BSS (τcr) for sediment motion initiation is obtained via the following equation (Soulsby, 1997):
$ {\tau _{{\rm{cr}}}} = {\theta _{{\text{cr}}}}({\rho _{\text{s}}} - \rho )g{D_{50}}, $
where ρs are the density of quartz grain (2650 kg/m3), D50 is the median grain size, and θcr is the critical Shield's parameter for sediment motion initiation. θcr is calculated via the following equation (Soulsby, 1997):
$ {\theta _{{\text{cr}}}} = \frac{{0.24}}{{{D_{\text{*}}}}} + 0.055[1 - \exp ( - 0.02{D_{\rm{*}}})], $
where $ {D_*} $ is the dimensionless grain diameter given as
$ {D_{\text{*}}} = {\left[ {\frac{{g(s - 1)}}{{{\nu ^2}}}} \right]^{1/3}}{D_{{\text{50}}}} ,$
where s is the ratio of the quartz grain and seawater densities (2.59) utilized in Eq. (16) and $ \nu $ is the kinematic viscosity of seawater (~1.0×10−6 m2/s).
3.2.3 Extreme event analysis
In this study, the extreme event is defined as the signals exceed the 75th percentile of the overall data for single variable (Pang et al., 2020). Other studies have defined an extreme event as signals exceeding the mean value (Jaffe and Sallenger, 1992) or the mean value plus one standard deviation (Cox and Kobayashi, 2000). In this study, n is the total number of data points, m is the mean value of the data points, n1 is the number of extreme event data points, and m1 is the mean value of the extreme event data points. If an extreme event occurs simultaneously for two variables, this is defined as a correlated extreme event, where n0 and m0 are the number and mean value of correlated extreme event data points, respectively. Therefore, n1/n is the fraction of extreme events to total data points, n0/n is the fraction of correlated extreme events to total data points, and n1m1/nm and n0m0/nm represent the influence of extreme events of a single variable and correlated extreme events to the total record, respectively. The correlation between the two variables can be evaluated via an extreme event analysis, which is applicable for high-resolution data; therefore, this analysis method was utilized in this study to analyze the correlation between SSC and TKE, BSS, h, Hm0, and Hr.
3.2.4 Pearson correlation coefficient and Spearman rank correlation coefficient
The Pearson correlation coefficient (rp) is calculated as follows (Nahler, 2009):
$ \begin{split} {r_{\text{p}}} &= \frac{{\displaystyle\sum\limits_{i = 1}^n {({y_i} - \bar y)({x_i} - \bar x)} }}{{\left[\displaystyle\sum\limits_{i = 1}^n {{({y_i} - \bar y)}^2}\sum\limits_{i = 1}^n {{({x_i} - \bar x)}^2}\right]^{1/2} }} \\ & = \frac{{\displaystyle\sum\limits_{i = 1}^n {({y_i} - \bar y)({x_i} - \bar x)} }}{{n{\sigma _{{x}}}{\sigma _{{y}}}}} , \end{split} $
where rp is the Pearson correlation coefficient of two variables (x and y), n is the number of data points, and σx and σy are the standard deviation of x and y, respectively.
Spearman rank correlation (Spearman, 1904) is one of the oldest and best known of nonparametric functions. The Spearman rank correlation coefficient (rs) is generally expressed as follows (Zar, 1972):
$ {r_{\text{s}}} = 1 - \frac{{6\displaystyle\sum\limits_{i = 1}^n {{d_i}^2} }}{{n({n^2} - 1)}} ,$
where n is the number of measurements for the two correlated variables (x and y) and $ {d}_{i} $ = $ {\rm{rank}}\left({x}_{i}\right)-{\rm{rank}}\left({y}_{i}\right) $ is the ranked difference between the i-th measurement for the two variables (x and y).
The rp is utilized to evaluate the linear correlation between the two variables that are normally or nearly unimodally distributed, and rs is utilized to evaluate the monotonic correlation between the two variables that do not have repeated rank levels in the data point set. Compared with rp, rs can be applied to more situations and with fewer data quality limitations.
3.2.5 Surficial sediment particle size measurement
During the observation period, 12 surficial sediment samples (HSW01–HSW12) were collected at the sampling site (Fig. 4). A Mastersizer 3000 laser analyzer was used to measure the sediment grain size distribution of the samples, and electron microprobe analysis was performed to examine the composition of the sediment. According to the measured results, the Heisha Beach sediment is primarily composed of sand, with smaller amounts of gravel and silt. The proportion of fine and very fine sand is relatively high, reaching 90% in the foreshore, while medium and coarse sand accounts for approximately 10% (except HSW07).
The mean grain size (Dme), sorting coefficient (Sc), skewness (Sk) and kurtosis (Ku) are shown in Table 1, which are computed as follows (Folk and Ward, 1957):
$ {D_{{\text{me}}}} = \frac{{{d _{16}} + {d _{50}} + {d _{84}}}}{3}, $
$ {\rm{Sc}} = \frac{{{d _{84}} - {d _{16}}}}{4} + \frac{{{d _{95}} - {d _5}}}{{6.6}}, $
$ {\rm{Sk}} = \frac{{{d _{84}} + {d _{16}} - 2{d _{50}}}}{{2({d _{84}} - {d _{16}})}} + \frac{{{d _{95}} + {d _5} - 2{d _{50}}}}{{2({d _{95}} - {d _5})}} ,$
$ {\rm{Ku}} = \frac{{{d _{95}} - {d _5}}}{{2.44({d _{75}} - {d _{25}})}}, $
where ${d _{{j}}}$ represents the corresponding grain size values at j% of the cumulative frequency curve.
The median grain size (D50) range of the surficial sediment is 2.08${\text{ϕ}} $–2.88${\text{ϕ}}$, and at the instrument site (HSW12), D50 is approximately 2.83${\text{ϕ}}$ (Table 1). The sorting degree in the offshore area is better than that of the area close to the backshore. Generally, Heisha Beach sediment can be characterized as negative skewness and moderate kurtosis, and the main components of the sediment, determined via electron microprobe analysis, are quartz and glauconite, which account for about 50% and 40% in the component distribution respectively (Fig. 5).
4 Results
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4.1 Characteristics of the hydrodynamic conditions
Based on the tide data from the Shangchuan Island tide gauge station, all 332 bursts were divided into three periods: (1) Bursts 1–109 (Period D1) lasted from 16:10 on August 9 to 10:10 on August 10, (2) Bursts 110–213 (Period D2) lasted from 16:00 on August 10 to 9:10 on August 11, and (3) Bursts 214–332 (Period D3) lasted from 16:50 on August 11 to 12:30 on August 12. Each period contained a whole tidal cycle and lasted for about one day with continuous data gathering. Moreover, the tidal ranges of three periods (1.42 m, 1.70 m, and 1.95 m respectively) were apparently different (Fig. 6a), which indicated that the tide impacts of three periods were also different.
According to the data measured at the instrument site (Fig. 1b) during the observation period, except for the extremely low water period, h ranged from 0.75–2.14 m during the observation period (Fig. 6b). The Hm0 ranged from 0.24–1.14 m, and the maximum wave height occurred in Period D3. During Periods D2 and D3, the variation trends of h and Hm0 were similar (Fig. 6c). Tm0 ranged from 1.06–3.38 s and the Tm0 values were generally less than 2 s in Period D1, while in Periods D2 and D3, they were generally greater than 2 s (Fig. 6d).
The variation in the mean current velocity of the bursts is presented in Figs 6e and f. Generally, u was slightly greater than v with ranges of −0.11 m/s to 0.35 m/s, and −0.23 m/s to 0.07 m/s, respectively. The current flowed offshore most of the time, and offshore velocity was greater than onshore velocity (Fig. 6e). During Period D1, u generally ranged from −0.1 m/s to 0.1 m/s, and during Periods D2 and D3, u typically exceeded 0.1 m/s (Fig. 6f).
4.2 The variation of SSCs
As shown in Fig. 7, during the observation period, the SSC ranged from 0.70–9.55 kg/m3. Generally, the SSC increased from Period D1 to D3 and was significantly higher in Periods D2 and D3 compared to Period D1. The SSC decreased between Bursts 1–100 and increased between Bursts 101–264 with mean values of 1.40 kg/m3 and 4.78 kg/m3, respectively. The SSCs from Bursts 235–264 were significantly higher than the other bursts, with a mean value of 7.93 kg/m3, and the SSC decreased slowly from Bursts 265–332, with a mean value of 6.24 kg/m3, which is higher than the mean value of Bursts 1–100.
The extreme SSC events were analyzed in this study, and the results are presented in Fig. 8. The extreme SSC event fractions (hereafter referred to as n1/n(SSC)) and the SSC percentage contained in the extreme events (hereafter referred to as n1m1/nm(SSC)) had minimum values of 0%, which mainly occurred between Bursts 1–109 and 176–211, and the maximum values of 100% which occurred six times after Burst 211. Moreover, as shown in Fig. 8, the extreme SSC events occurred more frequently between Bursts 235–318, with mean n1/n(SSC) and n1m1/nm(SSC) values of 40.81% and 45.27%, respectively, which were significantly larger than those of the total bursts (10.88% and 12.57% for n1/n(SSC) and n1m1/nm(SSC), respectively, shown in Table 2). This period could be divided by Burst 264. The mean n1/n(SSC) and n1m1/nm(SSC) values of Bursts 235–264 were 67.81% and 69.74%, respectively, which were larger than those of Bursts 265–318 (25.82% and 31.68% for n1/n(SSC) and n1m1/nm(SSC), respectively), indicating the extreme SSC events occurred most frequently between Bursts 235–264.
4.3 Influence of TKE on SSC
As shown in Fig. 9, the mean TKE values ranged from 0.14–33.98 kg/(m·s2). The TKE was significantly lower during Period D1 (Bursts 1–109), with a mean value of 0.36 kg/(m·s2), compared to the other periods. The TKE variation was small during Period D2 (Bursts 110–231); however, the mean TKE value increased to 2.37 kg/(m·s2), which was remarkably higher than that of Period D1. During Period D3 (Bursts 232–332), the TKE was significantly higher than those of the other periods, with a mean value of 6.02 kg/(m·s2), and the maximum TKE occurred between Bursts 239–278 with a mean value of 9.63 kg/(m·s2).
The extreme TKE events were also analyzed, and the results are presented in Fig. 10. The fractions of extreme TKE events (hereafter referred to as n1/n(TKE)) ranged from 0%–80.67%, and the TKE percentage contained in the extreme events (hereafter referred to as n1m1/nm(TKE)) ranged from 0%–99.09%. The minimum n1/n(TKE) and n1m1/nm(TKE) values mainly occurred between Bursts 54–98, while the maximum values occurred in Burst 243. Based on the calculated n1/n(TKE) results, the extreme TKE events occurred more frequently between Bursts 110–332 and 239–278, which had mean n1/n(TKE) values of 21.75% and 42.65%, respectively, with the latter value being 14.79% greater than that of the total bursts. As shown in Table 2, the TKE had a smaller standard deviation of n1/n than the SSC, indicating the distribution of extreme TKE events during the observation period was less concentrated than the distribution of extreme SSC events. Therefore, it can be concluded that the TKE had a longer high incidence period of extreme events than the SSC (Bursts 235–318) during the observation period. Moreover, based on the n1m1/nm(TKE) calculation results, n1m1/nm(TKE) was significantly larger than n1/n(TKE) during extreme TKE events, indicating that during extreme events, TKE differed from that in normal events. Furthermore, extreme events had a greater effect on TKE than SSC.
Generally, the SSC and TKE variation trends were similar; however, the TKE variation range was significantly larger than that of the SSC. As shown in Figs 9 and 10, the extreme TKE events occurred most frequently, and the TKE was significantly higher among Bursts 239–278 than the other bursts; therefore, the extreme event influence was probably more significant on TKE than SSC. In the high incidence period of extreme events, the TKE increase was significantly larger than during the other periods.
Utilizing the high-resolution TKE and SSC data, an extreme event analysis was performed to investigate their relationship. As shown in Fig. 11a, the fraction of correlated extreme events to extreme SSC events (hereafter referred to as n0/n1(SSC)) had minimum values of 0%, which mainly occurred in the same periods as the n1/n(SSC) (Bursts 1–109 and 176–211), and maximum values of 100% which occurred at Bursts 114, 116, 230, and 278. From Bursts 239–280, the correlated extreme events occurred more frequently, and the n0/n1(SSC) values, with a mean value of 45.00%, were significantly larger than those of the other bursts. Furthermore, the SSC percentage contained in the correlated extreme events (hereafter referred to as n0m0/nm(SSC)) had minimum values of 0%, which mainly occurred in the same periods as the n1/n(SSC), and the maximum value of 78.30% occurred at Burst 243. The n0m0/nm(SSC) values among Bursts 235–264, with a mean value of 29.00%, were larger than those of the other bursts and occurred during the same high incidence period as the extreme SSC events.
As shown in Fig. 11b, the fraction of correlated extreme events to TKE extreme events (hereafter referred to as n0/n1(TKE)) had minimum values of 0%, which mainly occurred between Bursts 1–211, and maximum values of 100% which occurred 6 times after Burst 211. The TKE percentage contained in the correlated extreme events (hereafter referred to as n0m0/nm(TKE)) had minimum values of 0%, which mainly occurred in the same period as the minimum n0/n1(TKE), and the maximum value of 78.30% occurred at Burst 240. Based on the SSC extreme event calculation results, extreme SSC events rarely occurred from Bursts 1–211, causing an n0 value of zero for most bursts. However, from Bursts 235–318, the extreme SSC and TKE events occurred more frequently, and the number of extreme SSC events was significantly higher than that of TKE, which increased the n0/n1(TKE) value. As shown in Table 3, the mean n0/n1(TKE) and n0m0/nm(TKE) values from Bursts 235–318 were 41.60% and 34.25%, respectively, and both were greater than those of all the other bursts. Therefore, it can be concluded that the extreme SSC event distribution was more concentrated than the extreme TKE event distribution during the observation period.
As shown in Table 3, from Bursts 235–318, the mean n0/n1(SSC) value was 30.72%, and the mean n0m0/nm(SSC) value was smaller than the mean n1m1/nm(SSC) value, indicating that approximately 15% of the sediment suspension motion was associated with turbulent motion, and other factors could influence sediment motion. Furthermore, the mean n0/n1(TKE) value was 41.60%, and the mean n0m0/nm(TKE) value was smaller than the mean n1m1/nm(TKE) value, indicating that in the high incidence period of extreme events, approximately 34% of the turbulent motion contributed to sediment suspension, and portions of the TKE dissipated directly into the water column. Generally, the fraction of correlated extreme events to the total data points (n0/n) was 14.35%, indicating that in addition to TKE, other factors affect the SSC variation.
4.4 Influence of BSS on SSC
The mean SSC values and the corresponding wave-induced BSS (τw), current-induced BSS (τc), and combined wave-current BSS (τcw) of each burst were calculated to evaluate the relationship between SSC and BSS. The results showed that τw and τc ranged from 0.002–3.060 Pa (mean value=1.50 Pa) and 0.001–0.900 Pa (mean value=0.31 Pa), respectively, showing τw was an order of magnitude larger than τc. Additionally, τcw ranged from 0.002–3.60 Pa (mean value=1.80 Pa) and the critical BSS (τcr) was 0.16 Pa. As shown in Fig. 12, from Bursts 67–101, τcw was smaller than τcr for some bursts, which corresponds with the low SSC value period; however, the majority of the time, τcw was an order of magnitude larger than τcr, which indicates the WCI was strong enough to drive sediment incipience and resuspension.
As shown in Table 4, based on the extreme event analysis results, τcw had the greatest correlated extreme events with an n0/n of 20.86%, while the n0/n of τc was significantly less than those of τcw and τw. Moreover, based on the n0m0/nm(SSC) values, during extreme event periods, in which the maximum n0m0/nm(SSC) occurred, approximately 36% and 30% of the sediment suspension motion were correlated with τcw and τw, respectively. The τc variation had less influence on the SSC than the τw influence because τc was an order of magnitude smaller than τw (Fig. 12), and the n0m0/nm of τc was 25.04%. Based on these results, the τcw and τw influences were much greater than that of τc on the SSC variation.
Based on the linear regression results between SSC and BSS (Fig. 13), τcw had the best fitting effect with an R2 value of 0.70, while the R2 values of τw and τc were similar. These results demonstrate a statistically significant relationship between SSC and BSS, and SSC is best correlated with τcw.
The data quality of each group satisfied the rp and rs calculation requirements. As shown in Table 5, there was a good correlation between SSC and BSS, and the rp and rs values were both approximately 0.8, and τcw had the highest correlation with rp and rs values of 0.84 and 0.89, respectively.
The variation of SSCs and BSS during the observation were shown in Fig. 12. As mentioned above, τc was smaller than τw, which indicates that the τcw variation was mainly affected by τw. Generally, τcw and τw had similar variation trends, with both significantly increasing during the observation period. The τcw and τw values were significantly larger from Bursts 101–332 than from Bursts 1–100, similar to the SSC variation trend. Based on these findings, it can be concluded that τcw has a significant effect on sediment mobilization and the resulting suspended sediment entrainment.
5 Discussion
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Previous studies have demonstrated that wave conditions significantly influence suspended sediment variation and that wave type and the distance from the breakpoint may govern SSC in the surf zone (Beach and Sternberg, 1996). The dominant wave conditions are determined by relative wave height (Hr), which can be calculated as follows:
$ {H_{\text{r}}} = \frac{{{H_{{\text{m0}}}}}}{h}. $
Some studies have shown that breaking waves are the sediment-suspending mechanism, which is consistent with previous studies that have determined that SSC and TKE are significantly larger under breaking wave (0.3<Hr<0.6) and surf bore conditions (Hr>0.6) than under non-breaking wave conditions (Hr<0.3) (Conley and Beach, 2003; Hansen and Svendsen, 1984). During the observation period of this study, the instrument site was under breaking wave conditions most of the time (Fig. 14); therefore, the wave conditions should significantly influence SSC, and the wave effect can be evaluated utilizing h and Hm0.
The extreme event analysis method was used to evaluate the relationship among SSCs and water depth, mean wave height, relative wave height. As shown in Table 6, the relative wave height had most correlated extreme events with n0/n reaching 18.10%, which was close to the n0/n of combined wave-current BSS and wave-induced BBS (20.86% and 18.71%, respectively), while the n0/n of water depth and mean wave height were apparently smaller. Furthermore, based on the calculation results of n0m0/nm(SSC), during extreme events period, approximately 30% of sediment suspension motion was correlated with relative wave height, which was also close to the n0m0/nm(SSC) of combined wave-current BSS and wave-induced BBS (35.61% and 31.35%, respectively). Therefore, it could be considered that compared with combined wave-current BSS and wave-induced BBS, relative wave height had the similar influence degree on SSC variation.
Based on the linear regression results (Fig. 15), h and Hm0 were poorly correlated with SSC (R2=0.15 and 0.06, respectively); however, Hr was highly correlated with SSC with an R2 value of 0.62, which was similar to those of τcw and τw (R2=0.61 and 0.70, respectively, Fig. 13) and higher than the h and Hm0 R2 values.
The rp and rs calculation results were consistent with the linear regression results. As shown in Table 7, Hr had a higher correlation degree with SSC (rp=0.82, rs=0.84) than with h or Hm0, and was similar to those of τcw and τw.
The SSC and Hr variations during the observation period are presented in Fig. 14. Generally, Hr significantly increased during the observation period. Except for a few early period bursts that were significantly higher, the Hr of Bursts 1–100 was smaller than that of Bursts 101–332, similar to the SSC variation trend.
If h and Hm0 are analyzed separately, they have little influence on SSC; however, if the relative importance of h and Hm0 is considered by calculating Hr, and Hr is determined to impact SSC variation significantly and has a similar correlation degree with those of τcw and τw. According to a previous study (Van Rijn, 2007), in coastal zones with small velocities (0.1–0.6 m/s), suspended sediment transport can be strongly dependent on Hr; therefore, the combined effects of h and wave height can have significant influence on SSC variation. When analyzing the SSC influence factors, the effects of h and wave height should be considered conjunctively, which can affect SSC variation in the ways different from TKE and BSS.
Generally, wave height variations are the major cause of variations in the mean SSC in the water column and influence the sediment entrainment due to their significant contribution to the sediment suspension mechanism compared to other factors, such as bed configuration and mean current (Aagaard and Greenwood, 1995; Liang et al., 2007; Pang et al., 2020). Both waves and tidal current can lead to sediment resuspension (Fan et al., 2019). During the observation period, as the tidal range increased, the impact of the tide was enhanced, and the lowest tide level decreased gradually, resulting in a reduced minimum h from Period D1 to D3 (Fig. 6a). During the ebb tide period among Bursts 214–269, h was smaller than in the other periods, while SSC increased significantly and the maximum SSC value occurred. The measure results indicate that strong tide effect may enhance the local resuspension and cause high SSC.
In addition, it has been determined that tidal flow plays an important role in horizontal advection which is the main contributor to sediment transport and high SSCs (Xiong et al., 2017; Yang et al., 2016; Yu et al., 2012). In the radial sand ridge system of the southern Yellow Sea, the model simulation suggested that the distribution of SSC was strongly affected by the tidal current (Xing et al., 2012). Moreover, under insignificant wave conditions, the tidal reciprocating flow can affect the sediment resuspension process and cause SSC flood-ebb asymmetry, resulting in SSC variation (Fan et al., 2019). Therefore, further studies are necessary to comprehensively explore the way how tide influences SSC on the beaches.
6 Conclusions
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The findings from this study are summarized below:
(1) TKE is higher during extreme event periods than during normal events, and extreme events have a greater effect on TKE than on SSC.
(2) TKE is one of the critical factors affecting SSC variation. Based on the field observation data, approximately 15% of the sediment suspension motion was associated with turbulent motion during the high incidence period of extreme events, approximately 34% of the turbulent motion contributed to sediment suspension, and a portion of the TKE dissipated directly into the water column. The correlated extreme events accounted for 14.35% of the total observation period.
(3) During the observation period, the WCI was strong enough to drive sediment incipience and resuspension most of the time, and τcw and τw have a greater influence on suspended sediment transport and SSC variation compared with that of τc. During extreme event periods, approximately 36% and 30% of the sediment suspension motion were attributed to τcw and τw, respectively.
(4) Hr is highly correlated with SSC, indicating that the combined effects of h and wave height can significantly affect SSC variation. In the nearshore regions, h is mainly controlled by the tide; therefore, when analyzing the factors influencing SSC, the effects of tides and waves should be considered conjunctively.
Funding
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  • The National Key Research and Development Program of China under contract No. 2016YFC0402603; the Guangdong Provincial Department of Natural Resources Project under contract No. 42090038; the Guangdong Provincial Department of Ocean and Fisheries Project under contract No. 42090033.
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Year 2022 volume 41 Issue 5
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doi: 10.1007/s13131-021-1874-3
  • Receive Date：2021-05-07
  • Online Date：2025-11-21
  • Published：2022-05-25
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  • Received：2021-05-07
  • Accepted：2021-06-21
Funding
The National Key Research and Development Program of China under contract No. 2016YFC0402603; the Guangdong Provincial Department of Natural Resources Project under contract No. 42090038; the Guangdong Provincial Department of Ocean and Fisheries Project under contract No. 42090033.
Affiliations
    1 School of Marine Engineering and Technology, Sun Yat-sen University, Guangzhou 510275, China
    2 School of Marine Sciences, Sun Yat-sen University, Guangzhou 510275, China
    3 Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519080, China
    4 Guangdong Provincial Engineering Research Center of Coasts, Islands and Reefs, Guangzhou 510275, China
    5 Institute of Estuarine and Coastal Research, School of Marine Engineering and Technology, Sun Yat-sen University, Guangzhou 510275, China

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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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