Numerical modelling of suspended-sediment transport in a geographically complex microtidal estuary: Sydney Harbour Estuary, NSW

Journal Pre-proof Numerical modelling of suspended-sediment transport in a geographically complex microtidal estuary: Sydney Harbour Estuary, NSW Z.Y. Xiao, X.H. Wang, D. Song, I. Jalón-Rojas, D. Harrison PII:

S0272-7714(19)30602-X

DOI:

https://doi.org/10.1016/j.ecss.2020.106605

Reference:

YECSS 106605

To appear in:

Estuarine, Coastal and Shelf Science

Received Date: 19 June 2019 Revised Date:

11 December 2019

Accepted Date: 14 January 2020

Please cite this article as: Xiao, Z.Y., Wang, X.H., Song, D., Jalón-Rojas, I., Harrison, D., Numerical modelling of suspended-sediment transport in a geographically complex microtidal estuary: Sydney Harbour Estuary, NSW, Estuarine, Coastal and Shelf Science (2020), doi: https://doi.org/10.1016/ j.ecss.2020.106605. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

Numerical modelling of suspended-sediment transport in a geographically complex microtidal estuary: Sydney Harbour Estuary, NSW Z. Y. Xiao1, 2, X. H. Wang1, 2, D. Song3,4, I. Jalón-Rojas 1,2 and D. Harrison5,6 1

The Sino-Australian Research Centre for Coastal Management, UNSW Canberra, Canberra,

ACT, Australia 2

School of Science, UNSW Canberra, Canberra, ACT, Australia

3

Key Laboratory of Physical Oceanography, Ministry of Education, at Ocean University of

China, Qingdao, 266100, China 4

Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266237, China

5

National Marine Science Centre, Southern Cross University, NSW 2450, Australia

6

Marine Studies Centre, School of Geosciences, University of Sydney, NSW 2006, Australia

Corresponding author: first and last name ([email protected]) Key points 

Spatial and temporal variability in sediment flux was induced by the interactions between channel topography, tidal forcing and vertical mixing;



Mean advection induced sediment flux results in a spatially fixed estuary turbidity maximum;



River discharges and intertidal frequencies dominated SSC variance at the estuarine head and mouth while spring-neap tidal range contributed most in the middle estuary

1

1

Abstract

2

A numerical study was conducted to investigate the sediment dynamics in a geographically

3

complex estuary, the Sydney Harbour Estuary (SHE). The SHE is a good example of a

4

microtidal estuary, with irregular shorelines and a complex bathymetry, characterized by

5

many headlands and islands forming a meandering main channel. Horizontal sediment

6

transport showed a local estuarine turbidity maximum (ETM) as a result of complex

7

topography, independent of salinity fields and river flows during dry weather. The along-

8

estuary advection of sediment was mainly driven by the mean advection, with a minor

9

contribution by tidal pumping. Mean advection associated with barotropic forcing drives

10

sediment flux seaward in the upper estuary and landward in the middle estuary, leading to a

11

longitudinal convergence of sediment transport, without upstream or downstream migration

12

of ETM during high river flows. The interactions between tidal currents, complex topography

13

and asymmetric vertical mixing led to spring-neap and flood-ebb variations in sediment

14

distribution. The Singular Spectrum Analysis (SSA) method was used to calculate the relative

15

contributions of the identified environmental forcing frequencies (tidal range, tidal frequency,

16

river discharges, wind stress) to the variability in suspended-sediment concentration. Tidal

17

frequency and river discharges were the major contributors to this variability. Tidal range

18

made the highest contribution in the middle estuary, where the ETM was located, driving the

19

spring-neap

cycle

of

2

the

ETM.

20

21

1. Introduction

22

Estuaries are efficient sediment traps between land and ocean, filtering cohesive and fine

23

particles, richly organic and prone to flocculate (Schubel & Carter, 1984; Dyer, 1995).

24

The region characterized by high suspended-sediment concentration (SSC), the so-called

25

estuarine turbidity maximum (ETM), is governed by numerous external forcings

26

including tidal currents, river discharges, salinity stratification, wind stress, current-wave

27

interactions, channel morphology, sediment properties, human activities and climate

28

change. One important mechanism in the formation of the ETM is related to the

29

longitudinal bottom convergence near the landward limit of salt intrusion, driven by the

30

residual gravitational circulation (Postma & Kalle, 1955; Festa & Hansen, 1976, 1978);

31

the location of the ETM can also be spatially fixed under local topographic effects

32

(Burchard et al., 2018; North & Houde, 2001; Kappenberg & Grabemann, 2001; Geyer et

33

al., 2001). ETMs in many estuaries are located near a rapid constriction or expansion of

34

channel topography, independent of salinity fields (Sommerfield & Wong, 2011; Hudson

35

et al., 2017).

36

To distinguish the relative importance of the different drivers of sediment transport and

37

ETM formation, several studies have decomposed total sediment fluxes into the

38

contributions of mean advection and tidal pumping (Geyer et al., 2001; Scully &

39

Friedrichs, 2007; Sommerfield & Wong, 2011; McSweeney et al., 2016). Tidal pumping

40

is understood to make a greater contribution to sediment transport in macrotidal estuaries

41

(Scully and Friedrichs, 2007; Li et al., 2014). The mean advection component, as

42

described by the product of tidally averaged tidal currents and suspended sediment 3

43

concentration, can be generated by barotropic runoff, wind straining, nonlinear

44

interactions between topography and tides, baroclinic forcing, and asymmetric vertical

45

mixing (Burchard and Hetland, 2010; Cheng et al., 2011 & 2013).

46

The variability in sediment dynamics is characterized by a range of time scales, from tidal

47

cycles, spring-neap, seasonal, annual-to-decadal to longer recurrence intervals for

48

extreme events, depending on the controlling forcing variability in space and time

49

(Schoellhamer, 2001, 2002). The time scales of sediment dynamic variability can be

50

linked to different environmental forcing frequencies, which helps to evaluate

51

quantitatively the influence of the forcings. Understanding the influence of environmental

52

forcings on SSC variability is important for a better understanding of the behaviour of the

53

system, and to anticipate its response to environmental changes. Singular Spectrum

54

Analysis (SSA) can be an effective tool for quantifying the relative contributions of

55

identified forcings on SSC variability (Jalón-Rojas et al., 2016a). The application of this

56

method to high-frequency multi-annual turbidity time series recorded in the macrotidal

57

Gironde and Loire estuaries revealed the relative contributions of the various

58

environmental forcings to turbidity variability in these systems (Jalón-Rojas et al., 2016a,

59

2017).

60

The aim of this work is to overview the temporal and spatial variability in SSC and the

61

ETM formation in a geometrically complex microtidal estuary, the Sydney Harbour

62

Estuary (SHE). The impact of the interactions between tidal currents, the complex

63

topography and asymmetric vertical mixing on spring-neap and flood-ebb sediment

64

dynamic asymmetries is also addressed. The relative contributions of the identified

65

forcings to the variability in sediment distribution is quantified. A three-dimensional (3D)

66

hydrodynamic-sediment dynamic model was calibrated with observed data and used to

67

evaluate the sediment fluxes in the SHE. Section 2 describes the study site, setup of the 4

68

numerical model, the flux decomposition and SSA methods. Section 3 describes the

69

calibration of the sediment model and the observed sediment flux at a mooring station in

70

the SHE. Sections 4.1 and 4.3 present a budget of the vertical and horizontal sediment

71

fluxes in monthly and hourly time steps over a month of simulation at different cross-

72

sections in the SHE. In Section 4.3 and 4.4, the sediment fluxes are decomposed to

73

determine the key processes forming an ETM. Section 4.2 details the sub-tidal and intra-

74

tidal variations in the ETM. The SSA results revealing the relative contributions of the

75

various forcing frequencies on SSC variability are discussed in Section 4.5.

STN A

T3

5

76

Figure 1: (a) Sydney Harbour Estuary, giving the locations of the CTD and turbidity profile

77

surveys in the estuary tributaries and embayments (black flags, T1−T7), the axial CTD and

78

turbidity profile surveys (blue flags, P1−P8) and the along- and cross-channel sections (red

79

lines, S1−S6) for the sediment flux calculations. Station A (solid black triangle) between S3

80

and S4 is selected to detail the bottom sediment flux within estuarine turbidity maximum. (b)

81

Magnified map of the cross-section S4 near Goat Island showing bathymetry; the red star

82

shows the location of the ADCP, CTD and bottom-turbidity mooring station near Goat Island.

83

(c) Mooring transect setup at S4. (d) Correlation between the CTD-mounted turbidity sensor

84

(NTU) measurements and the in-situ sediment concentration (mg/l) samples.

85

2. Methods

86

2.1 Study site

87

Located on the southeast coast of Australia, the SHE is a microtidal estuary (maximum tidal

88

range of 2.1 m) characterized by an irregular shoreline and complex bathymetry (Fig. 1). It

89

features a meandering main channel approximately 30 km in length and a number of large,

90

shallow embayments off the main channel. The SHE receives 60−90% of its fresh water from

91

three tributaries, the Parramatta River, the Lane Cove River and the Duck River (Lee et al.,

92

2011; Fig. 1a). Rainfall is evenly distributed throughout the year. The interannual variation in

93

rainfall is strongly driven by El Niño and La Niña events. Detailed numerical investigations

94

were conducted to determine the estuarine response to high-precipitation events (Lee et al.,

95

2011; Lee & Birch, 2012). Xiao et al. (2019) found the lateral circulation due to the complex

96

geometry creates tidal asymmetries in current magnitudes and vertical mixing during dry

97

periods. The SHE provides an ideal natural laboratory to assess the impact of complex

98

channel geometry on the hydrodynamics and consequently the sediment dynamics.

6

99 100

2.2 Numerical modelling 2.2.1 Sediment model

101

The finite-volume community ocean model (FVCOM) (Chen et al., 2003) was used to

102

simulate the hydrodynamics in the SHE. FVCOM simulates water surface elevation, velocity,

103

temperature and salinity by solving the equations of momentum, continuity, temperature,

104

salinity and density in an integrated form to conserve mass. The UNSW-Sed module (Wang,

105

2002) was two-way coupled to the SHE hydrodynamic model using the same grid in FVCOM

106

to simulate sediment dynamics. Based on the assumption of a constant settling velocity

107

suspended sediment and the continuity equation for salinity and temperature, the sediment

108

transport equation can be written as (Wang, 2002)

for

109 110

where C is the SSC and Kh is the vertical eddy diffusivity for C. A first-order iterative

111

upstream scheme was used for the horizontal diffusion term Fc to reduce implicit diffusion

112

with an anti-diffusive velocity (Smolarkiewicz, 1984). The UNSW-Sed module allows the

113

SSC to affect the seawater density and the bottom drag coefficient, and thus modulate the

114

estuarine circulation (Wang, 2002; Wang & Pinardi, 2002; Wang et al., 2005; Song & Wang,

115

2013). When the impact of SSC on the seawater density is considered, the seawater density is

116

given by

117

118

where ρw is clear seawater density and ρs sediment density. In the sediment-laden bottom

119

boundary layer (BBL), suspended sediment induced stratification suppresses the bottom

120

turbulence (Wang et al., 2005). By introducing a stability function (1+ARf) −1 into the bottom 7

121

drag coefficient Cd (Wang, 2002) (where A=5.5 is an empirical constant and Rf is the flux

122

Richardson number, an index of the vertical density stratification in the Mellor-Yamada

123

Level 2 approximation), the model includes the impact of sediment induced stratification on

124

the BBL dynamics. Cd and bottom stress

are given by:

125

126

,

(4)

127

where κ is the von Karman constant, zb the near-bottom layer thickness, z0b the bottom

128

roughness and

129

stratify the BBL; Cd is reduced as bottom turbulence is supressed. The minimum of Cd is

130

reached when the bottom turbulence is completely shut down at a critical value of Rf (Wang,

131

2002). In the current study, since the SSC is quite low (generally < 0.1 kg/m3) in the SHE,

132

seawater density is not greatly changed by the SSC.

133

The net vertical sediment flux at the bottom due to erosion and deposition Eb ( kg/m2/s) can

134

be expressed as (Ariathurai & Krone, 1976)

the bottom current velocity. When Rf >0, the suspended bottom sediment

135 136

where E0 (kg/m2/s) is the empirical erosion coefficient, Cb (kg/m3) the SSC in the bottom

137

boundary layer, τb (kg/m/s2) the bottom shear stress, τce and τcd (kg/m/s2) the critical shear

138

stress for erosion and deposition, respectively, and ws (m/s) the particle settling velocity,

139

positive upward and negative downward. An infinite sediment supply from the bed is

140

assumed; thus the SSC variations in the water column correspond to changes in the bottom 8

141

erosion rate. The parameters E0, ws, τce and τcd are well recognised to alter significantly within

142

very small spatial scales.

143

2.2.2 Model setup

144

The model grid consisted of 79,278 elements (triangles) and 43,584 nodes (of the triangles),

145

forming a mesh of triangles with variable cell width, ranging from 2,000 m at the open-ocean

146

boundary down to 15 m inside the estuary at sites where instruments were deployed. Over

147

50% of the cells were less than 50 m wide. A total of 15 sigma layers were applied in the

148

vertical direction, with a uniform thickness in the middle (11% of the total depth), and higher

149

resolution near the surface and bottom (1% of the total depth). The hydrodynamic forcings,

150

including tides, river discharge and wind field, are detailed in Xiao et al. (2019). The model

151

simulation commenced on the 15 Oct 2013, running until the 31 Dec 2013 with a focus on the

152

sediment transport during the dry period. The first 30 days of the simulation allowed for

153

model spinup. The simulation included two storm events on 10–13 Nov and the 16–19 Nov

154

2013. The impact of waves on the bottom sediment erosion rate were accounted for using

155

one-way coupling between the SHE hydrodynamic model and a SWAN model of the estuary

156

(Booij et al., 1999) which simulated typical significant wave height, wave direction and wave

157

duration.

158

The model was initialized with a zero-velocity field, uniform water temperature of 25°C and

159

an initial horizontal salinity gradient based on the CTD survey conducted on 15 Oct 2013

160

(detailed in Section 3). Twelve major inflow boundaries were identified as described in Xiao

161

et al. (2019). The river discharge calibration was conducted at the Parramatta River which is

162

the main river discharging into the SHE. The salinity and turbidity values of freshwater

163

inflow are lacking due to insufficient monitoring following rainfall to address infrequent high

164

precipitation conditions. Due to the fact that the estuary mostly experiences no-to-low flow,

9

165

we focus on quiescent conditions (< 5mm/day) to better understand sediment dynamics

166

during typical conditions. The model is likely to represent the observed behaviour during

167

most of the year.

168

Different values for the sediment model parameters ws, τce, τcd and E0 in the SHE were tested

169

to evaluate the sediment model performance. The suspended sediment is treated to be a single

170

group of fine cohesive sediment uniformly across the model domain (Wang, 2002). The

171

particle settling velocity ws was empirically demonstrated via experiment in Maggi (2013) to

172

be in a range of values between 1×10-4 m/s and 1×10-6 m/s for the fine cohesive fraction. The

173

ws was chosen through evaluation of model skill score (

174

the variable and

175

critical stress for both sediment erosion τce and deposition τcd in estuaries with fine cohesive

176

sediment has been observed to range from 0.1 kg/m/s2 to 1.0 kg/m/s2 (van Rijn, 1993). Given

177

the lack of field measurement in the SHE, the observed τce and τcd values in the Darwin

178

Harbour which constitutes similar sediment characteristics (Li et al., 2014), was tested in the

179

sediment model. The critical τce and τcd have been observed in the range 0.02 – 5.0 kg/m/s2

180

for erosion and 0.06 – 0.1 kg/m/s2 for deposition in Darwin Harbour (HR Wallinford, 2010b).

181

Thus, τce and τcd was set to be 0.2 kg/m/s2 which can reflect both the spring-neap and flood-

182

ebb fluctuations corresponding with the in-situ data in the SHE. The erosion rate E0 was

183

found to be not as sensitive as the critical erosion/deposition stress and particle settling

184

velocity in determining the SSC values in the SHE. The model parameters (see Table. 1)

185

were adjusted to best fit the variation trend in the SSC field data series. Flocculation and

186

deflocculation processes were not considered, as SSC in the SHE (generally < 0.1 kg/m3) is

187

much lower than the threshold value of 1 kg/m3 for making a significant contribution to the

188

sediment settling velocity (van Rijn, 1993).

, where X is

the temporal average) and set at an average value of 2×10-5 m/s. The

10

189

Table 1: Sediment model initial conditions and constants Parameters

Description

Sediment bed thickness

Infinite sediment bed thickness

Sediment type

Fine cohesive sediment

Settling velocity ws

2×10-5 m/s

Critical erosion stress τce

0.2 kg/m/s2

Critical deposition stress τcd

0.2 kg/m/s2

Erosion rate E0

2×10-5 kg/m2/s

190

2.3 Model data post-processing

191

2.3.1 Sediment flux computation and decomposition

192

The along-estuary sediment flux was decomposed into two components, advective flux

193

(tidally averaged) and tidal-pumping flux (tidally varying), following previous studies (Geyer

194

et al., 2001; Scully & Friedrichs, 2007; Sommerfield & Wong, 2011; McSweeney et al.,

195

2016). The depth-weighted velocity and SSC at six cross-sections (S1-S6; Fig. 1) were firstly

196

separated using a 36hr lanczos low-pass filter to obtain tidal averages, and tidal fluctuations

197

(McSweeney et al., 2016):

198

(6)

199

(7)

200

,

(8)

201

where Qsm is the mean-advection-induced sediment flux, Qst the tidal-pumping-induced

202

sediment flux and

203

sum of the mean-advective term Qsm and the tidal-pumping term Qst. In the along-estuary

the total water depth. The total sediment flux Qs was calculated as the

11

204

direction, positive values indicate transport down-estuary, negative values transport up-

205

estuary. Then the cross-sectional integrated longitudinal SSC flux was calculated at S1–S6 to

206

assess the along-estuary sediment flux.

207

2.3.2 Singular spectrum analysis

208

To understand the contribution environmental processes have on SSC throughout the SHE,

209

Singular Spectrum Analysis (SSA) was employed. The modelled SSC at the centre of each of

210

six cross-sections along the SHE channel (Fig. 1a) was used to quantify the relative

211

contributions of the environmental forcings on SSC variability in the different regions (Jalón-

212

Rojas et al., 2017). The SSA method decomposes a time series into so-called reconstructed

213

components (RCs) by sliding a window of width M, where M represents a window length of

214

time, over the series to give an autocorrelation matrix (Vautard et al., 1992). Each RC is

215

characterized by one or two periodic frequencies in the range 0.2M−M. One or two RCs will

216

have a frequency higher than M. The eigenvalues of the autocorrelation matrix give the

217

contribution of each RC to the variance of the analysed time series dataset. By adjusting the

218

size of the window M, Schoellhamer (2002) found over 80% of the total variability in SSC

219

could be attributed to specific environmental forcings, characterized by their RC frequencies.

220

These forcings included: (1) diurnal, semidiurnal and other higher-frequency tidal

221

constituents; (2) semi-monthly tidal cycles; (3) monthly tidal cycles; (4) semi-annual tidal

222

cycles; and (5) annual events such as river discharges. A more detailed description of the

223

SSA method is provided in Vautard et al. (1992) and Schoellhamer (2001). SSA analysis

224

was employed to investigate SSC and turbidity in two macrotidal systems, the Gironde and

225

Loire estuaries (Jalón-Rojas, 2016a). Jalón-Rojas (2017) applied SSA analysis to multiple

226

French sites located in coastal transitional waters. In contrast to the previous studies cited, we

227

applied the SSA method to analyse SSC time series data generated from the numerical model

228

of the SHE. SSA analyses the SSC time series from 45 days of simulations and sliding 12

229

windows of 30 hr and 360 hr (Schoellhamer, 2002) were used to identify both intertidal and

230

subtidal frequencies in the variability. Frequencies identified from SSA were linked to their

231

corresponding environmental forcing and their relative contribution to SSC variability

232

calculated.

233

2.3.3 Numerical experiment on topographic effect

234

In order to check the effects of channel bends and channel bathymetry variability on mean

235

advection in the SHE, three numerical experiments were designed (Table 2; Fig. 2). The

236

original model configuration (Case 0) was modified at S4 (where the ETM was found by the

237

model) as follows. Case 1: the impact of channel bends was omitted by removing headlands

238

and islands; Case 2: the impact of channel bathymetry variation was omitted by flattening the

239

bathymetry. In this way, the effects of channel bends and channel bathymetry on mean

240

advection can be evaluated independently.

241

Table 2. Description on model configuration of three numerical experiments Case

Description

0

Reference case with both channel bends and channel bathymetry variability

1

Channel bend at S4 were removed to eliminate curvature effect

2

Channel bathymetry at S4 and surrounding were smoothed to 15m

13

242

Figure 2: Numerical experiments on determining the role of channel bends (Case 1) and

243

channel bathymetry variability (Case 2) on along-estuary mean advection at transect S4.

244

3. Field observations and model calibration

245

Hydrodynamic monitoring was undertaken at one fixed mooring station in the central estuary

246

and additional 15 stations along the main estuary channel and embayments every month in

247

2013 (Fig 1a). The mooring station near Goat Island (GI, Figs. 1b,c) included: (a) a bottom-

248

fixed upward-looking ADCP; (b) a CTD profiling system set at depths of 1.3 m, 7.3 m, 10.6

249

m and 13.7 m; and (c) a turbidimeter 2m above the estuary bed. The mooring station recorded

250

surface water level, temperature, density, current and bottom turbidity at five-minute

251

sampling intervals over a 10-day period from 1 Dec to 10 Dec, 2013 (including part of a

252

spring-neap tidal cycle).

253

At the additional 15 stations (Fig1a, P1-P8, T1-T7), CTD profiles were obtained monthly in

254

2013, with turbidity data collected down the water column. Concurrent with the turbidity data,

255

surface water samples were obtained at each station. Given the limited spatial data capturing

256

SSC, turbidity data from depths nearest water sampling depths were compared to SSC data.

257

The resulting equation corresponding to the highest correlation coefficient (R2 = 0.89; Fig. 1d)

258

was then employed to convert turbidity (NTU) data to SSC (mg/l). During Nov 2013, a series

259

of complex surface troughs and lows resulted in a succession of rainy days in the Sydney

260

catchment. Between the 10 Nov and 13 Nov, a total of 70 mm rainfall was recorded at

261

Sydney Observatory Hill (BOM station: 66062; www.bom.gov.au ). A second period of rain

262

occurred between the 16 Nov and 19 Nov, resulting in total rainfall of 90 mm at Sydney

263

Observatory Hill station. The sampling period followed this succession of rainy days on the

264

20 Nov and 21 Nov. During the two-day survey CTD-turbidity data were collected down the

265

water column at 15 sites during a flood to ebb period of a spring tidal cycle.

14

266

The root-mean-square (RMS) errors of modelled SSC were calculated to be less than 3.5 mg/l

267

and the SS of modelled SSC was calculated as 0.45 (Figs. 3e, 4a) over the 10-day period from

268

1 Dec to 10 Dec, 2013. The SSC distribution in the longitudinal profiles P1–P7 was

269

compared to the model at the corresponding tidal stage. Highest SSC was measured on the 20

270

– 21 Nov 2013, following a period of rainy weather in the catchment beginning on the 16–19

271

Nov 2013 (Figs. 4b,c). The sediment model reproduced the spring-neap and the semi-diurnal

272

bottom SSC variability well. It is therefore suitable for a fundamental study of the first-order

273

sediment transport processes in the SHE.

274

Various processes not addressed in this study have the potential to modify further the

275

sediment flux patterns in the SHE. The model here was forced by wind fields on a

276

0.125°×0.125° grid, which was too coarse to represent the local small-scale topographic

277

effects on wind patterns. A higher spatial resolution of the wind field is required to better

278

understand wind-stress-induced residual sediment flux. For cohesive sediment, the

279

flocculation process can determine the settling velocity of suspended sediment and influence

280

the residual sediment transport (Van Oplphen, 1977). The SSC is quite low in the SHE

281

compared to the threshold value of 1 kg/m3 at which flocculation starts to become significant

282

(van Rijn, 1993). However, it was found from water samples in earlier studies that

283

flocculation occurred at very low salinity near headwaters and embayments (Irvine & Birch,

284

1998). Increased shipping activities in the SHE also add complexity to the sediment

285

fluctuations. The key sediment model parameters are set to be constant which is a simplified

286

representation of the realistic sediment bed. Further field measurements would allow better

287

calibration of the sediment dynamic simulations.

15

288

Figure 3: Observed (black line) and modelled (red line) data from Goat Island: (a) surface

289

water level (m); (b) bottom along-estuary velocity U (m/s) (positive indicates ebbing); (c)

290

bottom cross-estuary velocity V (m/s) (positive indicates northward); (d) bottom density

291

(kg/m3); (e) bottom SSC (mg/l); (f) bottom gradient Richardson number log(Rig/0.25)

292

(positive indicates stratification, negative mixing). Ebb tides are indicated by the shaded

293

background. SS: Skill Scores. Bottom indicates 2m from the bed.

16

294

Figure 4: (a) Observed (blue line) and modelled (red line) SSC profiles at the survey sites

295

(left to right: T1−T7); (b) along-estuary distribution of SSC (mg/l) from the axial CTD

296

survey (P1−P7), top panel observation, bottom panel model. The survey was conducted on

297

20–21 Nov 2013 during a flood-ebb cycle over spring tide.

298

4. Model results and discussion

299

4.1 Bottom sediment resuspension and deposition

300

Based on the numerical modelling of SSC dynamics from 15 November to 15 December, we

301

investigated the SSC variations in SHE. The predicted temporal and spatial variations in the

302

depth-averaged SSC along the estuary channel are shown in Fig. 5. The SSC in the SHE was 17

303

relatively low (less than 6 mg/l) during dry weather, strongly modulated by the semidiurnal

304

tidal cycles and spring-neap tidal range (Fig. 5, 29 Nov – 12 Dec). In the middle estuary,

305

highest near-bottom current velocities occurred during flood tides (Fig. 3b), however near-

306

bed SSC was highest during ebb tides. This suggests that bottom SSC in the SHE was not

307

only the result of sediment resuspension, but was also related to sediment settling of material

308

supplied through advection and tidal mixing process. Xiao et al. (2019) showed that intra-

309

tidal asymmetric vertical mixing in the SHE contributes to stratification of the water column

310

during flood tides, and mixing of the water column during ebb tides. Increased vertical

311

sediment fluxes due to vertical mixing explains the higher depth-averaged SSC observed

312

during spring ebb tides (Fig. 5).

313

18

314 315

Figure 5: Left panel: river flow (m3/s); Right panel: Model predicted temporal and spatial

316

distributions of depth-averaged SSC (mg/l, colour scale) along the longitudinal profile from

317

15 Nov to 15 Dec 2013; the black line shows the tidal range; the labels (a) – (d) are snapshots

318

during spring flood, spring ebb, neap flood and neap ebb in Fig. 7, respectively.

319

The vertical sediment flux is an important parameter in the suspended sediment transport

320

budgets. Figure 6 shows the monthly accumulated deposition or erosion of sediment on the

321

estuary bed over the whole estuary. Bottom sediment erosion maximum is in the main

322

channel, where it is deep and experiences the highest current speeds. Bottom sediment

19

323

deposition prevails in the shallow embayment. In the along-estuary profile, highest rates of

324

monthly accumulated sediment erosion (up to 35 g/m2) occurred in the middle estuary

325

between S3 and S4 (Fig. 6c). Sediment accumulation in the upper estuary was less than 10

326

g/m2, and there was no significant erosion or deposition in the lower estuary toward the

327

estuary mouth (Figs. 6a,c). At transect S4, in the central estuary, the predicted sediment

328

erosion rate reached up to 15 g/m2 in the deeper part of the channel near the northern

329

shoreline. From the central channel to the shallower southern end of S4, less than 2 g/m 2 of

330

deposition was predicted. In the SHE, the main driver of sediment erosion is found to be the

331

strong tidal currents in the main channel causing enhanced bottom shear stress for erosion.

Upper Parramatta River

332

Figure 6: Model predicted monthly cumulative bottom sediment deposition and erosion rates

333

(g/m2): (a) spatial distribution in the estuary; (b) along the S1−S6 cross-sections; (c) along

334

the longitudinal profile.

335

4.2 Sub-tidal and intra-tidal variability in the ETM

20

336

The SSC distribution along the longitudinal profile varied significantly over the spring-neap

337

and the flood-ebb tidal cycles. A low-concentration ETM (less than 10 mg/l) was found

338

between S3 and S4 during spring tides (Figs. 7a,b), where maximum erosion of bottom

339

sediment was predicted due to enhanced vertical mixing. Resuspended bottom sediment

340

accumulated about 10 km downstream of the landward limit of the salinity intrusion (Fig. 7b).

341

The occurrence of a spring-tide ETM at high salinities, indicates that the sediment trapping

342

might be caused by topographic effects rather than gravitational circulation towards the

343

landward limit of salt intrusion. Model simulation indicated that unlike many estuaries in

344

which river discharge affects the position of the ETM (Postma, 1967; Uncles & Stephen,

345

1989; Mitchell et al., 2012; Jalón-Rojas et al., 2015), high river flow did not cause the ETM

346

to migrate down-estuary in the SHE (Fig. 5). The storms on 16–19 Nov discharged large

347

amounts of fluvial sediment into the estuary and increased the upper estuary SSC, between

348

S1 and S3. The ETM between S3 and S4 was consistently present for about seven days

349

during spring tides showing reduced subtidal-tidal variability due to the influence of river

350

discharge. Burchard et al. (2018) suggested that the topographic trapping of sediment

351

provides a mechanism to generate an ETM by local effects. The key process driving

352

horizontal sediment flux in the SHE is investigated in Section 4.3.

353

Station A in the ETM between S3 and S4 (labelled in Figs. 1a,7a) was selected to show the

354

variations in bottom sediment flux and its mechanisms due to tidal cycles (Fig. 8). The

355

bottom current speed at station A showed flood-ebb asymmetries, inducing variations in the

356

bottom shear stress (Figs. 8b,c). During spring flood, the bottom shear stress was

357

strengthened (>0.2 kg/m/s2), triggering bottom sediment erosion (Fig. 8c). Bottom SSC

358

values were increased when the near-bed tidal currents were at their maximum (Fig. 8e).

359

Suspended sediment was mostly contained below the stratified water column in the BBL (Fig.

360

7e). During spring ebb, the intensified surface ebb currents and vertical mixing maximized 21

361

the resuspended sediment concentration in the water column (Fig. 7b). The bottom shear

362

stress was below the critical shear stress (0.2 kg/m/s2) due to weakened bottom currents

363

during ebb, and thus bottom sediment deposition occurred (Fig. 8c). During neap tides,

364

bottom current is not strong enough to erode bottom sediment. The reduced turbulent mixing

365

in the BBL was further suppressed by sediment-induced stratification, as indicated by the

366

increased bottom flux Richardson number Rf (Fig. 8d). The bottom drag coefficient was

367

reduced, according to Eq. (3), leading to a slippery bottom boundary layer with low bottom

368

shear stress. The ETM with a low SSC was no longer present between S3 and S4 and the low

369

bottom shear stress allowed sediment to deposit on the bed (Figs. 7c,d, 8c).

370

Figure 7: Left column: Model predicted suspended-sediment concentration (mg/l) along the

371

longitudinal profile; Model predicted isohalines (psu) are shown as white lines. Right

372

column: The stability of shear generated turbulence is indicated by simulated Gradient

373

Richardson numbers

, where red values indicate negative values (increasing 22

374

shear generate turbulence) and blue values indicate positive values (decreasing shear

375

generated turbulence). (a, e) spring flood; (b, f) spring ebb; (c, g) neap flood; (d, h) neap ebb;

376

corresponding to the timesteps indicated in Fig 5. Station A selected in the ETM is labelled

377

on the top horizontal axis in black.

23

378

Figure 8: Simulated time series at station A in the ETM: (a) tidal elevations (m; blue) and river discharge rate in the Parramatta River (m3/s;

379

red); (b) bottom along-estuary current speed

(m/s; blue) and cross-estuary current speed 0

(m/s; red); (c) bottom shear stress

(kg/ms2;

380

blue deposition; red erosion); (d) bottom flux Richardson number Rf ; (e) bottom SSC (mg/l). The red labels (a) – (d) in top panel are snapshots

381

during spring flood, spring ebb, neap flood and neap ebb in Fig. 7, respectively.

1

382

4.3 Along-estuary sediment flux decomposition

383

Fig. 9 shows the model predicted temporal variations in the total cross-sectionally integrated

384

sediment fluxes Qs at S1–S6 where the total sediment flux is the sum of the mean-advection

385

flux Qsm and the tidal-pumping flux Qst.

386

Qs in upstream cross-sections was dominated by river flow induced Qsm. Following the storm

387

on 16–19 Nov 2013, up to 600 g/s of suspended sediment passed down estuary through the

388

S1 cross section. During periods of low river flow, sediment flux was mainly driven by tidal

389

pumping (less than 10 g/s) in the down-estuary direction. In the middle estuary, advection

390

driven sediment flux transitioned from down-estuary export, to up-estuary import (up to 20 g

391

s-1), while tides continued to promote down-estuary sediment flux. The interaction of these

392

two competing processes caused sediment to remain in the water column and become trapped

393

between transects S3 and S4 (Fig. 9g). In the lower estuary, between S5 and S6, Qst worked

394

in concert with Qsm to transport the sediment out of the estuary. Tidal pumping contributed 5–

395

20 g/s of sediment export, becoming the dominant mechanisms for sediment transport as tidal

396

currents became stronger instead (Figs. 9a–f). Note that at S4, the upstream sediment

397

transport was found to be driven by a landward residual flow generated by the nonlinear

398

interactions between channel bends and tidal currents in the middle estuary, detailed in

399

Section 4.4.

0

400

Figure 9: Cross-sectionally integrated along-estuary SSC fluxes (g/s): total (Qs, blue area);

401

tidal-pumping component (Qst, red line); and mean-advection component (Qsm, blue dashed

402

line). (a–f): SSC flux time series during spring tides (grey shading) and neap tides (no

1

403

shading) along the cross-sections S1–S6; (g): monthly-mean cross-sectionally integrated SSC

404

flux along the longitudinal profile at S1 – S6.

405

4.4 Estuarine turbidity-maximum formation

406

The decomposition of sediment flux suggests that the mean advection drove the along-estuary

407

sediment transport and trapped sediment in the ETM. The tidally averaged along-estuary

408

current Umean was further decomposed into residual flows induced by the combined effect of

409

the estuarine gravitational circulation (sum of river-induced UR and density-induced flow

410

(UD), asymmetric tidal mixing (UA) and tidal nonlinearities (UN), following Cheng et al.

411

(2011). The three-layer vertical structure of UA responded to the asymmetries of tidal mixing

412

(stratified flood and mixing ebb) but was an order of magnitude less than the other two

413

mechanisms inducing residual flows, and hence can be less significant (Fig. 10b). Residual

414

flows driven by the estuarine gravitational circulation showed strong magnitude in the upper

415

outward and bottom inward flows, as the primary force driving sediment flux (Fig. 10a).

416

Tidal nonlinearity generated residual flows with a similar vertical structure to those generated

417

by gravitational circulation in the upper and lower estuary, but a distinctive landward flow

418

through the water column in the middle estuary (Fig. 10c). The tidal-nonlinearity-induced

419

mean flow reinforced the landward flow at the bottom and weakened the seaward flow at the

420

surface, primarily driven by gravitational circulation. Thus, a longitudinal convergence point

421

of sediment flux is generated between S3 and S4 under a combined effect of gravitational

422

circulation and tidal nonlinear mean advection.

423

A better understanding of the effect of channel bends and channel bathymetry variability on

424

tidal nonlinear mean advection would be particularly useful in explaining the topographic

425

effect on mean advection and thus the ETM formation. A set of numerical experiments was

426

setup at S4 as explained in Section 2.4.3 (Fig. 2). Figure 11 compares the tidally-averaged

2

427

decomposed along-estuary mean-advection components (UR, UD, UN and UA) in the vertical

428

profiles at S4 (sectional-averaged) for Cases 0 (existing case with both curvature effect and

429

topographic effect), case 1 (curvature effect omitted) and case 2 (topographic effect omitted).

430

In Case 1, the seaward residual flow induced by the tidal non-linearities (UN) occurred in the

431

lower water column, which weakened the gravitational bottom landward flows (Fig. 11b).

432

Consequently, the sum of the decomposed components weighted by vertical sigma-layer

433

thickness was stretched more seaward (Fig. 11b). In both Case 0 and Case 2, in which the

434

curvature effect was included, the landward UN was persistent throughout the whole water

435

column and reinforced the gravitational bottom landward flows, leading to a net landward

436

mean advection (Figs. 11a,c). Indicating the channel bends caused up-estuary nonlinear

437

advection between S3 and S4, leading to net up-estuary sediment flux which, combined with

438

the river-induced down-estuary sediment flux between S1 and S2, provided sufficient

439

sediment in the ETM for erosion and deposition.

3

440

Figure 10: Decomposition of tidally averaged along-estuary current Umean (m/s) into the

441

residual flow induced by: (a) estuarine gravitational circulation; (b) asymmetric tidal mixing;

442

(c) tidal nonlinearities.

443

Figure 11: Decomposed sectional-averaged decomposed along-estuary current at S4: (a)

444

with channel bends and channel bathymetry (Case 0); (b) idealized straight channel with

445

channel bathymetry (Case 1); (c) idealized flat channel with channel bends (Case 2). Black

446

solid line indicates sum of decomposed Umean components

447

dotted line UN ; blue dashed line with triangles UD ; blue dashed line with crosses UA ; blue

448

solid line UR.

449

4.5 Overview of environmental-forcing impacts on SSC variability

450

River discharges and tidal forcing are key environmental forcings impacting SSC variability

451

in the SHE. To what extent they impact SSC distribution and the relative contributions to the

452

total SSC variability from other associated forcings were unknown. The SSA method was

453

applied to the 1.5 month modelled SSC time series at the centre station of each of six cross-

454

sections along the estuary. Figure 12 shows the SSA decomposition of the SSC time series

455

from the mooring station at S4 and estimates of the contributions from each mode (RC) to the

456

total SSC variability. Six significant modes, containing 92.7% of the total variance, were

457

identified and assigned to different environmental forcing frequencies: river-flow variability 4

sigma layer thickness; blue

458

(Mode 1); semi-monthly variability (Mode 2); semi-diurnal variability (Mode 3); quarter-

459

diurnal variability (Mode 4); and diurnal variability (Mode 5). Modes 2 to 5 are therefore

460

associated with tidal forcings.

461

Mode 1 was found to be associated with river flow from the Paramatta River (Fig. 12b), with

462

a phase lag due to the distance between the river station and the S4 station. The river-flow

463

variability explained 48.7% and the spring-neap tidal-range variability (Mode 2) 35% of the

464

total SSC variance. The SSC variability associated with intertidal frequencies was 8.6%, the

465

semi-diurnal (Mode 3) 6.6%, the quarter-diurnal (Mode 4) 1.4% and the diurnal (Mode 5)

466

tidal frequencies 0.6% of the total SSC variance. The remaining variance (7.3%) in our

467

simulation is likely explained by wind and swell-wave-induced turbulence.

468

Figure 12: Singular spectrum analysis (SSA) applied to the 1.5-month of the simulated SSC

469

time series at the mooring station. (a) Modes 1−6 combined. The forcings associated with the

470

individual modes are: (b) river discharge (Mode 1); (c) semi-monthly tidal variability (Mode

5

471

2); (d) semi-diurnal tidal variability (Mode 3); (e) quarter-diurnal tidal variability (Mode 4);

472

(f) diurnal tidal variability (Mode 5).

473

The contributions to the total SSC variance from the identified forcing frequencies at each

474

station are summarized in Table 3. The influence of intertidal frequency (sum of semi-diurnal,

475

quarter-diurnal and diurnal tidal frequencies) on the total SSC variability increased down-

476

estuary, from 4.9% at the estuary head to 50% at the estuary mouth, with a sharp jump

477

between S4 and S5 (Table 3). In contrast, the river discharge made a higher contribution (up

478

to 77.5%) upstream, and decreased down-estuary (35.1%), with a sharp drop between S3 and

479

S4 (Table 3). The variations in intertidal frequencies and river discharge both showed rapid

480

change in the central and lower estuary (within 12km of the mouth), but little variation in the

481

remainder of the estuary. The spring-neap tidal range exerted a noticeably greater influence

482

on total SSC variation at S4, which could indicate the presence of an ETM in this area. The

483

spring-neap asymmetry in tidal currents controlled the processes of sediment erosion,

484

resuspension and advection, and thus the spring-neap time scale of the ETM at S4 (Fig. 12c).

485

In contrast, the influence of the spring-neap tides on SSC variability was reduced in the lower

486

estuary, probably due to lack of sediment sources, while variability in the upper estuary was

487

due to variability in river discharge. The contributions of wind and swell-wave-induced

488

turbulence increased from 7.6% at the estuary mouth to 21.2% at the estuary head, a

489

shallower region where wind wave induced turbulence becomes significant.

490

The estuarine region was characterized by ranking the environmental forcing impacts on SSC

491

variability (Table.3). In the lower estuary (S5–S6), tidal forcing dominated the SSC

492

variability, followed by river discharge, which indicates the impact the river discharge

493

exerted on the whole estuary (Table. 3). In the upper estuary (S1–S3), river discharge was the

494

main forcing, followed by wind and swell-wave-induced turbulence, then tidal frequencies

6

495

(Table. 3). In the middle estuary (S3–S4), SSC variability was controlled by both river

496

discharge and the spring-neap tidal cycle (Table. 3).

497

Table 3: Relative contributions (%) to the total SSC variability of the different environmental

498

forcings, estimated using SSA on a 1.5-month SSC time series at the centre of each of the six

499

cross-sections. Position is the distance (km) from the estuary mouth. Position Tidal River Spring/neap Wind/Swell (km) frequency discharge tidal range waves (%) (%) (%) induced turbulence (%) S1 S2 S3 S4 S5 S6

28.1 25.7 21.5 13.0 8.5 1.1

7.3 4.9 6.6 8.6 44.9 50.0

71.5 77.5 70.1 48.7 37.5 35.1

11.4 35.4 12.1 7.3

21.2 17.6 11.9 7.3 5.5 7.6

500

5

501

This numerical study focused on the sediment dynamics in a periodic-mixed microtidal

502

estuary with a complex geometry, the Sydney Harbour Estuary (SHE). Topographic effects

503

influence the generation of the estuarine turbidity maximum (ETM), without significant

504

influence by river discharges and salinity fields. The model results suggest the potential for

505

an ETM to be generated in the central estuary near S4 and the flood-ebb and spring-neap

506

variations of the ETM. Increased tidal current speeds during spring tide enhanced bottom

507

shear stress to erode sediment, and periodic vertical mixing resuspended bottom sediment to

508

form the ETM. The ETM was not present during neap tide due to low tidal current speeds and

509

sediment-induced stratification in the bottom boundary layer, which reduced the bottom shear

510

stress and caused sediment to deposit. The results of these model simulations suggest a mid-

511

estuary ETM location which is supported by the limited field data collected during this study.

Conclusions

7

512

Along-estuary sediment transport in the SHE was primarily driven by mean advection in the

513

upper and middle estuary, with increasing contributions from tidal pumping towards the

514

estuary mouth. In the upper estuary, river-runoff-induced mean advection dominated the

515

down-estuary sediment fluxes. In the middle estuary, mean advection led up-estuary sediment

516

fluxes to form a longitudinal convergence zone, which provided significant amounts of

517

sediment trapped in the ETM. In the lower estuary, mean advection and tidal pumping make

518

equal contributions to the down-estuary sediment flux.

519

The application of the Singular Spectrum Analysis method to a simulated suspended

520

sediment concentration (SSC) time series along the estuary helped describe the influences on

521

the SSC throughout the system. River discharge contributed around 77.5% of the surface SSC

522

variability in estuary headwaters, decreasing to 35.1% towards the open ocean. Tidal

523

frequency explained 50% of the SSC variability at the estuary mouth, decreasing to 7.3% in

524

the upper estuary. The spring-neap tidal cycle became more important in the middle estuary,

525

where

river

discharge

and

tidal

frequency

8

were

both

weakened.

526 527

Acknowledgements

528

Oceanography Field Services Pty Ltd kindly provided survey data. This paper benefited from

529

editorial review by Dr Peter McIntyre from UNSW Canberra. This work was supported by

530

the National Computational Infrastructure Facility at the Australian National University. This

531

is publication no. 55 of the Sino-Australian Research Centre for Coastal Management at

532

UNSW Canberra. Z. Y. Xiao and D. P. Harrison were supported by Australian Postgraduate

533

Awards and X. H. Wang by the UNSW Special Study Program. Comments from the two

534

anonymous

reviewers

have

improved

9

the

paper

535

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Festa, J. F., & Hansen, D. V. (1976). A two-dimensional numerical model of estuarine circulation: The effects of altering depth and river discharge. Estuarine and Coastal Marine Science, 4(3), 309-323. doi: 10.1016/0302-3524(76)90063-3. Festa, J. F., & Hansen, D. V. (1978). Turbidity maxima in partially mixed estuaries: A twodimensional numerical model. Estuarine and Coastal Marine Science, 7(4), 347-359. doi: 10.1016/0302-3524(78)90087-7. Fugate, D. C., Friedrichs, C. T., & Sanford, L. P. (2007). Lateral dynamics and associated transport of sediment in the upper reaches of a partially mixed estuary, Chesapeake Bay, USA. Continental Shelf Research, 27(5), 679-698. doi: 10.1016/j.csr.2006.11.012. Gao, G.D., Wang, X. H., Bao, X. W., Song, D., Lin, X. P., & Qiao, L. L. (2017). The impacts of land reclamation on suspended-sediment dynamics in Jiaozhou Bay, Qingdao, China. Estuarine, Coastal and Shelf Science. doi: 10.1016/j.ecss.2017.01.012. Geyer, W. R. (1993). The importance of suppression of turbulence by stratification on the estuarine turbidity maximum. Estuaries, 16(1), 113-125, doi: 10.2307/1352769. Geyer, W. R., Signell, R. P., & Kineke, G. C. (1998). Lateral trapping of sediment in a partially mixed estuary. Physics of Estuaries and Coastal Seas, pp. 115-126. Geyer, W. R., Woodruff, J. D., & Traykovski, P. (2001). Sediment transport and trapping in the Hudson River estuary. Estuaries Coasts, 24, 670-79. Howard, L. (1961). Note on a paper of John W. Miles. Journal of Fluid Mechanics, 10(4), 509-512. doi: 10.1017/S0022112061000317. HR Wallingford. (2010b). Ichthys Gas Field Development Project: Dredging and spoil disposal modelling, report, INPEX Browse Ltd, Perth, Australia.

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Uncles, R. J., & Stephens, J. A. (1989). Distributions of suspended sediment at high water in a macrotidal estuary. Journal of Geophysical Research: Oceans, 94, 14,395-14,405. Van Olphen, H. (1977). An Introduction to Clay Colloid Chemistry. New York: Wiley & Sons. Van Rijn, L. C. (1993). Principles of sediment transport in rivers. Aqua Publ. Vautard, R., Yiou, P., & Ghil, M. (1992). Singular-spectrum analysis: A toolkit for short, noisy chaotic signals. Physica D: Nonlinear Phenomena, 58(1), 95-126. doi: 10.1016/01672789(92)90103-T. Wang, X. H. (2002). Tide-induced sediment resuspension and the bottom boundary layer in an idealized estuary with a muddy bed. Journal of Physical Oceanography, 32(11), 3113-3131. doi: 10.1175/1520-0485(2002)032<3113:TISRAT>2.0.CO;2. Wang, X. H., & Pinardi, N. (2002). Modeling the dynamics of sediment transport and resuspension in the northern Adriatic Sea, Journal of Geophysical Research: Oceans, 107(C12), 3225. doi: 10.1029/2001JC001303. Wang, X. H., Byun, D. S., Wang, X, L., & Cho, Y. K. (2005). Modelling tidal currents in a sediment stratified idealized estuary. Continental Shelf Research, 25, 655-665. doi: 10.1016/j.csr.2004.10.013. Xiao, Z. Y., Wang, X. H., Roughan, M., & Harrison, D. (2019). Numerical modelling of the Sydney Harbour Estuary, New South Wales: lateral circulation and asymmetric vertical mixing. Estuarine, Coastal and Shelf Science, 217, p 132-147. doi:10.1016/j.ecss.2018.11.004. Yu, Q., Wang, Y. P., Gao, J. H., Gao, S., & Flemming, B. W. (2014). Turbidity maximum formation in a well-mixed macro-tidal estuary: The role of tidal pumping. Journal of Geophysical Research: Oceans, 119(11), 7705e7724. doi: 10.1002/2014JC010228.

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Jalón-Rojas, I., Schmidt, S., & Sottolichio, A. (2016a). Evaluation of spectral methods for highfrequency multiannual time series in coastal transitional waters: Advantages of combined analyses. Limnol. Oceanogr. Methods, 14: 381-396. doi: 10.1002/lom3.10097. Kappenberg, J., & Grabemann, I. (2001). Variability of the mixing zones and estuarine turbidity maxima in the Elbe and Weser Estuaries. Estuaries, 24(5), 699-706. doi:10.2307/1352878. Lee, S. B., & Birch, G. F. (2012). Utilising monitoring and modelling of estuarine environments to investigate catchment conditions responsible for stratification events in a typically well-mixed urbanised estuary. Estuarine, Coastal and Shelf Science, 111, 1-16. doi: 10.1016/j.ecss.2012.05.034. Lee, S. B., Birch, G. F., & Lemckert, C. J. (2011). Field and modelling investigations of fresh-water plume behaviour in response to infrequent high-precipitation events, Sydney Estuary, Australia. Estuarine, Coastal and Shelf Science, 92(3), 389-402. doi: 10.1016/j.ecss.2011.01.013. Li, L., Wang X. H., Andutta, F., & Williams, D. (2014). Effects of mangroves and tidal flats on suspended-sediment dynamics: Observational and numerical study of Darwin Harbour, Australia. Journal of Geophysical Research: Oceans, 119(9), 5854-5873. doi: 10.1002/2014JC009987. Maggi, F. (2013). The settling velocity of mineral, biomineral, and biological particles and aggregates in water. Journal of Geophysical Research: Oceans, 118, 2118 – 2132, doi:10.1002/jgrc.20086. McSweeney J. M., Chant R. J., & Sommerfield, C. K. (2016). Lateral variability of sediment transport in the Delaware Estuary. Journal of Geophysical Research: Oceans, 121(1), 725-744. doi: 10.1002/2015JC010974. Mitchell, S., Akesson, L., & Uncles, R. (2012). Observations of turbidity in the Thames Estuary, United Kingdom. Water Environ. J., 26, 511- 520. doi: 10.1111/j.1747-6593.2012.00311.x.

North, E. W., & Houde, E. D. (2001). Retention of white perch and striped bass larvae: biologicalphysical interactions in Chesapeake Bay estuarine turbidity maximum. Estuaries Coasts, 24, 756-69. Postma, H., & Kalle, K. (1955). Die Entstehung von Trübungszonen in Unterlauf der Flüsse, speziell im Hinblick auf der Verhaltnisse in der Unterelbe. Deutsche Hydrographische Zeitschrift, 8, 137144. Postma, H. (1967). Sediment transport and sedimentation in the estuaries environment. Estuaries, 158179. Schoellhamer, D. H. (2002). Variability of suspended-sediment concentration at tidal to annual time scales in San Francisco Bay, USA. Continental Shelf Research, 22(11-13), 1857-1866. doi: 10.1016/S0278-4343(02)00042-0. Schoellhamer, D.H. (2001). Singular spectrum analysis for time series with missing data. Geophys. Res. Lett., 28, 3187-3190. Schubel, J. R., & Carter, H. H. (1984). The estuary as a filter for fine-grained suspended sediment. The Estuary As a Filter. Academic Press, New York, pp. 81-105. Scully, M. E., & Friedrichs, C. T. (2007). Sediment pumping by tidal asymmetry in a partially mixed estuary. Journal of Geophysical Research: Oceans, 112, C07028. doi: 10.1029/2006JC003784. Smolarkiewicz, P. K. (1984). A fully multidimensional positive definite advection transport algorithm with small implicit diffusion. Journal of Computational Physics, 54, 325-362. doi:10.1016/00219991(84)90121-9.

Sommerfield, C. K., & Wong, K. C. (2011). Mechanisms of sediment flux and turbidity maintenance in the Delaware Estuary. Journal of Geophysical Research: Oceans, 116, C01005. doi: 10.1029/2010JC006462. Song, D., & Wang, X. H. (2013). Suspended sediment transport in the Deepwater Navigation Channel, Yangtze River Estuary, China, in the dry season 2009: 2. Numerical simulations. Journal of Geophysical Research: Oceans, 118(10), 5568-5590. doi: 10.1002/jgrc.20411. Uncles, R. J., & Stephens, J. A. (1989). Distributions of suspended sediment at high water in a macrotidal estuary. Journal of Geophysical Research: Oceans, 94, 14,395-14,405. Van Olphen, H. (1977). An Introduction to Clay Colloid Chemistry. New York: Wiley & Sons. Van Rijn, L. C. (1993). Principles of sediment transport in rivers. Aqua Publ. Vautard, R., Yiou, P., & Ghil, M. (1992). Singular-spectrum analysis: A toolkit for short, noisy chaotic signals. Physica D: Nonlinear Phenomena, 58(1), 95-126. doi: 10.1016/0167-2789(92)90103-T. Wang, X. H. (2002). Tide-induced sediment resuspension and the bottom boundary layer in an idealized estuary with a muddy bed. Journal of Physical Oceanography, 32(11), 3113-3131. doi: 10.1175/1520-0485(2002)032<3113:TISRAT>2.0.CO;2. Wang, X. H., & Pinardi, N. (2002). Modeling the dynamics of sediment transport and resuspension in the northern Adriatic Sea, Journal of Geophysical Research: Oceans, 107(C12), 3225. doi: 10.1029/2001JC001303. Wang, X. H., Byun, D. S., Wang, X, L., & Cho, Y. K. (2005). Modelling tidal currents in a sediment stratified idealized estuary. Continental Shelf Research, 25, 655-665. doi: 10.1016/j.csr.2004.10.013.

Xiao, Z. Y., Wang, X. H., Roughan, M., & Harrison, D. (2019). Numerical modelling of the Sydney Harbour Estuary, New South Wales: lateral circulation and asymmetric vertical mixing. Estuarine, Coastal and Shelf Science, 217, p 132-147. doi:10.1016/j.ecss.2018.11.004.

Ziyu Xiao: Conceptualization; Methodology; Software; Validation; Formal Analysis; Investigation; Data Curation; Writing-original draft; Xiao Hua Wang: Conceptualization; Supervision; Writing – review&editing Dehai Song: Software; Isabel Jalón-Rojas: Methodology; Daniel Harrison : Resources; Writing – review&editing

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☒The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

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