Optical Observations of Suspended Particle Dynamics in a Tidal Wetland

OPTICAL OBSERVATIONS OF SUSPENDED PARTICLE DYNAMICS IN A TIDAL WETLAND

John Franco Saraceno B.S., University of California, Davis, 2006

THESIS

Submitted in partial satisfaction of the requirements for the degree of

MASTER OF SCIENCE

GEOLOGY

CALIFORNIA STATE UNIVERSITY, SACRAMENTO

SUMMER 2011

ii OPTICAL OBSERVATIONS OF SUSPENDED PARTICLE DYNAMICS IN A TIDAL WETLAND

A Thesis

John Franco Saraceno

Approved by:

______, Committee Chair David Evans, Ph.D.

______, Second Reader Brian Bergamaschi, Ph.D.

______, Third Reader Scott Wright, Ph.D.

______Date

iii

Student: John Franco Saraceno

I certify that this student has met the requirements for format contained in the University format manual, and that this thesis is suitable for shelving in the Library and credit is to be awarded for the thesis.

______, Department Chair ______David Evans, Ph.D. Date

Department of Geology

iv Abstract

OPTICAL OBSERVATIONS OF SUSPENDED PARTICLE DYNAMICS IN A TIDAL WETLAND

John Franco Saraceno

Suspended particulate matter (SPM) plays an important role in the

biogeochemistry of estuaries and wetlands. Recent wetland restoration efforts have

targeted tidal wetlands; yet, many questions pertaining to SPM dynamics s remain. A

paired in situ optical and acoustical sensor package was deployed for several weeks during the winter in the main channel at Browns Island, CA, in order to identify processes controlling estuarine and marsh sediment dynamics. Turbidity, particle volume concentration and particulate attenuation were employed as surrogates of SPM concentration. The slope of the particulate attenuation spectra, γ, and laser diffraction were used to study particle size dynamics and flocculation processes. Water samples were collected hourly over one tidal cycle at the end of the study period for SPM concentration and organic content in support of optical measurements. Together these observations helped elucidate the role of tidal wetlands in the cycling of estuarine particles. Tidal action was a driving force of particle dynamics at Browns Island, as fluctuations in tidal water levels controlled wetland inundation and current velocity. SPM surrogates were maximum at the height of the higher high (HH) flood tide and minimum at lower low (LL) tide water level, consistent with the loss of SPM through deposition

v and/or flocculation due to the interaction of estuarine water with the island. Following

HH slack tide, particles smaller than 133 µm flocculated into larger particle aggregates.

The largest aggregates reached a maximum size of 150 µm during the ebb tide. The controlling mechanism for flocculation was likely particle to particle collisions and differential settling in the presence of organic rich pore waters draining from the marsh.

The transport of organic rich flocs could be an important mechanism for the export of contaminants and organic matter off of the island and into the estuary.

______, Committee Chair David Evans, Ph.D.

______Date

vi ACKNOWLEDGEMENTS

I thank my committee members Dave Evans, Brian Bergamaschi and Scott

Wright for guidance and helpful suggestions. I am appreciative of Bryan Downing for his role in attaining high quality data and for stimulating insightful discussions. I could not have completed this work without the help and expert advice of Emmanuel Boss. I wish to thank the University of Maine at Orono In Situ Sound and Color Lab and the USGS

California Water Science Center for the use of the equipment used in this study. I express thanks to Neil Ganju and many other USGS personnel for the collection and processing of hydrodynamic and sediment data.

I express the deepest gratitude to my dear old friend Calvin Kenney for nudging me to pursue a graduate degree and in part making it possible through a gracious scholarship. Lastly, I extend my sincere appreciation to my wife, Stacy, for lending her ear, critical eye, and inexhaustible support.

The CALFED drinking water program funded this work.

vii TABLE OF CONTENTS Page Acknowledgements ...... vii List of Tables ...... x List of Figures ...... xi Chapter 1. INTRODUCTION ...... 12 1.1 The Ecological and Societal Importance of Estuaries and Tidal Wetlands ...... 12 1.2 Suspended Particulate Matter Biogeochemistry ...... 13 1.2.1 Size ...... 14 1.2.2 Flocculation...... 15 1.2.3 Settling Velocity ...... 16 1.2.4 Contaminate Transport...... 17 1.2.5 Optical Properties...... 17 1.3 Particle Cycling in Tidal Wetlands ...... 18 1.4 Previous Work ...... 19 2. SITE DESCRIPTION ...... 22 2.1 Geologic Setting ...... 22 3. METHODS ...... 24 3.1 Sampling Procedure and Instrumentation ...... 24 3.2 Turbidity ...... 25 3.3 Attenuation and Absorption ...... 25 3.4 ac9 Data Treatment ...... 26

3.5 cp Spectra and Particle Size ...... 28 3.6 In Situ Particle Size Distributions ...... 29 3.7 Discrete Measurements ...... 31 3.7.1 Sample Collection ...... 31 3.7.2 SPM Concentration ...... 31 3.7.3 Particle Organic Content ...... 31

viii 3.8 Time Series Analysis ...... 32 4. RESULTS ...... 33 4.1 Hydrodynamics ...... 33 4.2 Wind Velocity ...... 34 4.3 SPM Concentration Surrogates ...... 35 4.3.1 Turbidity ...... 35 4.3.2 Particulate Beam Attenuation ...... 36 4.3.3 Volume Concentration ...... 37 4.4 SPM, POM Concentration and Organic Content ...... 38 4.5 Particle Size ...... 39

4.5.1 cp Slope ...... 39 4.5.2 Mean and Median Particle Size ...... 39 4.5.3 Particle Size Distributions...... 39 5. INTERPRETATION...... 42 5.1 Hydrodynamics ...... 42 5.2 Drivers of SPM Concentration ...... 43 5.2.1 Precipitation ...... 43 5.2.2 Wind ...... 44 5.2.3 Tidal forcing...... 44 5.3 Drivers of SPM Size ...... 46 5.4 Conceptual Model ...... 49 6. CONCLUSIONS...... 50 6.1 Summary ...... 50 6.2 Conclusions ...... 51 6.3 Recommendations for Future Work ...... 51 References ...... 70

ix LIST OF TABLES Page

1. Table 1 Nomenclature ...... 57

2. Table 2 Suspended particulate matter organic content ...... 58

3. Table 3 Time series analysis results...... 59

x LIST OF FIGURES Page

1. Figure 1 Study area location map ...... 60

2. Figure 2 Instrumentation cage ...... 61

3. Figure 3 Sacramento River discharge and Mallard Island turbidity ...... 62

4. Figure 4 Browns Island hydrodynamics, wind velocity and turbidity ...... 63

5. Figure 5 Suisun wind velocity distribution ...... 64

6. Figure 6A Hydrodynamics, turbidity, attenuation and volume concentration...... 65

7. Figure 6B Hydrodynamics, and turbidity inset ...... 66

8. Figure 7 Hydrodynamics and particle volume concentration of three size bins ...... 67

9. Figure 8 Particle size and hydrodynamics ...... 68

10. Figure 9 Example particle size distributions ...... 69

xi 12

Chapter 1

INTRODUCTION

This study assesses measurements of suspended particulate matter (SPM) concentration and size dynamics in a tidal wetland. Identification of the major drivers of

SPM concentration and size variability was an important aspect of this study. SPM characterization using traditional methods is often impractical in rapidly changing environments, so optical surrogates of biogeochemical variables were employed.

Although optical measurements have been used successfully in oceans and coastal regions, the applicability of these methods in dynamic environments typical of tidal wetlands is largely unproven.

1.1 The Ecological and Societal Importance of Estuaries and Tidal Wetlands

Estuaries are the central hub of many communities. European immigrants originally settled estuaries because of their high bio-diversity, easy access to inland areas and dependable water supplies (Saenger, 1995). With the dawn of the industrial age, many estuarine margins were deforested, dredged and developed for the construction of ports and harbors, large scale farming operations and waterfront industry (Hodgkin,

1994). Contemporary societal dependence on estuaries includes multi-million dollar fisheries and recreational activities, shipping and real estate. More than 50% of the U.S. population now resides along narrow coastal waterways (Edgar et al., 2000; NOAA State of the Coast Report, 1998). Estuaries represent critical habitat for many endemic and endangered species. In the San Francisco Estuary (SFE) these species include the Delta smelt, California clapper rail and the salt marsh harvest mouse (Bennet, 2005).

Tidal wetlands lie at estuary margins which are sharp and dynamic interfaces between the land and water. Tidal wetlands provide ecological services that include filtering upstream waters and buffering and armoring sensitive inland areas/waters from large and rapid sea level fluctuations (Mitsch and Gosselink, 2000). Due to their largely depositional nature, tidal wetlands can be regional sinks for particulate matter as well as any adsorbed nutrients and contaminants (Gordon et al., 1985; Mitsch and Gosselink,

2000). Tidal wetlands commonly contain a high diversity of aquatic vegetation, especially aquatic macrophytes, and represent important habitats for fish and waterfowl

(Atwater, 1977). Besides tropical mangrove swamps, salt, tidal and freshwater marshes experience the highest rates of net primary productivity (NPP) of any wetland types in the world (3700, 2300 and 5500 g dry wt. m-2 yr -1, respectively (Mitsch and Gosselink, 2000 and references therein). In dormant winter months, algal productivity may dominate NPP, providing up to 80 % of NPP by mass in some estuarine marshes (Mitsch and Reeder,

1991). High microbial activity can slow the rate at which material is gained in the marsh

through high rates high rates of decomposition (Mitsch and Gosselink, 2000 and

references therein).

1.2 Suspended Particulate Matter Biogeochemistry

SPM is an important fraction of the sediment pool in estuaries, wetlands and

marshes as it influences their optical properties and water quality as well as primary

production, biogeochemical cycling, carbon fluxes and contaminant transport (e.g. Eisma

et al., 1990; Bergamaschi et al., 2011). Therefore, many studies have focused on SPM

properties, variability and drivers in these landscapes (e.g. Eisma, 1986; Temmerman et

14 al. 2005; Oubelkheir et al., 2006; Murphy and Voulgaris, 2006; Verney et al., 2009).

SPM is composed of both living and dead algae, intact and decomposing plant litter, and biogenic, authigenic or eroded mineral material. The concentration and size distribution of SPM can directly impact estuarine primary productivity (Cole and Cloern, 1987) by filtering sunlight (Belzile et al., 2002). In addition, SPM can influence the pelagic food web by interfering with zooplankton grazing (Gasparini, 2009). The fate and transport of chemically toxic materials in many systems is directly related to the fate and transport of

SPM (Horowitz and Elrick, 1987; Domagalski and Kuivila, 1993; Fugate et al., 2003;

Bergamaschi et al. 2001; Bergamaschi et al. 2011). SPM supply and transport is central to the morphology and evolution of shallow intertidal mudflats, wetlands, marshes and islands. These bodies persist only while the rate of sediment accumulation through trapping and peat accretion exceeds local sea level and subsidence (Madsen et al., 2007).

A long term reduction in estuarine sediment supply due to upstream reservoir trapping has resulted in large scale bathymetric changes in the SFE (Ganju and Schoelhammer,

2006). Siltation in the estuary can result in costly port and navigational channel dredging operations (Shi and Zhou, 2004).

1.2.1 Size

SPM size varies widely and can affect SPM optical activity, chemical and physical reactivity, source and fate. The size within natural estuarine particle populations may span many orders of magnitude from sub micron to several hundred microns in size

(Guo and He, 2011 and references therein). In many environments, SPM is present as aggregates, which are loosely bound masses of smaller component particles (e.g. Eisma,

1986; Kranck and Milligan, 1992; Hill et al., 1998; Verney et al., 2009).The terms

aggregate and floc are commonly used interchangeably. We follow the classification

system of Eisma (1986), defining component particles as having a diameter less than 33

µm, microflocs (loosely bound groups of component particles) as having diameters

greater than 33 µm but less than 133 µm and macroflocs (groups of microflocs) having

diameters greater than 133 µm (Manning, 2004; Mikkleson et al. 2006).

1.2.2 Flocculation

Particle collisions result in particle size growth, flocculation, while shear stress

results in particle size reduction through break up (Manning, 2004).Eisma (1986) observed that the upper limit of floc size (hindered of microns to millimeters) was the same as the size of the smallest turbulent eddies. Size-resolved aggregation/disaggregation simulations by Xu et al., (2008) support these observations.

However, in modest levels, increases in turbulence can actually favor flocculation.

Manning et al. (2008) observed an even distribution of microflocs and macroflocs at low concentrations and low shear stress (0.1 N m-2). As stress increased to 0.45 N m-2, the

proportion of macroflocs increased to 80% of the population by mass. Stresses above this

critical value resulted in deflocculation (break-up), suggesting there is a critical stress

range for macrofloc stability. In addition to turbulence driven growth, flocs actively

“scavenge” other particles to build in size through differential settling (Stolzenbach et al.,

1993). The presence of organics increases the rates of floc formation as well as their

persistence (Verney et al., 2009), since aggregate formation efficiency would be lower

without their presence (Ziervogel, 2003). Diatoms, microbes and algal mats exude sticky

organic polymers that can precipitate or aide in flocculation (Eisma, 1980; Eisma et al.,

1986; Verney et al., 2009). Though macroflocs are generally fragile, organics can make

them stronger and resistant to break up (Verney et al., 2009), potentially increasing their

lifetime and transport potential.

1.2.3 Settling Velocity

Accurate knowledge of suspended-particle settling rates is an important to

generate reasonable sediment transport models (e.g.Sherwood et al., 1994;Wiberg et al.,

1996).Sediment transport is related to the state of flocculation due to the relation between

floc size and settling velocity. The relation arises from the observation that the effective

density of flocs generally decreases with increasing floc size (e.g. Mikkleson and Perjup,

2001; Fugate and Chant, 2005; Curran et al., 2007). However, this relationship can be

complex since often complex Shi and Zhou (2004) reviewed published estuarine

particles settling velocities (0.01 – 3.5 mm s-1) and attributed the wide range in values to

variations in methodology, turbulence, shear stress, and density. Manning (2004) showed that macroflocs (2.2 mm s-1) in the Tamar Estuary settle three times as quickly as

microflocs (0.8 mm s-1) of the same composition. Manning et al. (2008) supported the

findings of Manning (2004) with flume based experimental evidence. Verney et al.,

(2009) added organic matter to estuarine sediment and observed an increase in settling

velocity of up to 400% coincident with an increase in floc size. In that study, floccs

reached sizes greater than 400 µm.

1.2.4 Contaminate Transport

The ability of particles to adsorb and transport contaminates is affected by particle

size and effective surface area (e.g. Horowitz and Elrick, 1987). For example, the particle

size distribution shape parameter improved modeling of particulate MeHg concentration

and transport at Browns Island, CA, a tidal wetland (Bergamaschi et al., 2011). In the

same study, Bergamaschi et al. (2011) found particles to dominate the flux of MeHg from

the island in the winter. Since small particles have a higher surface area to mass ratio than larger particles of the same composition, they have traditionally been considered a central component in contaminant transport models. However, due to their fractal geometries and high surface area, aggregates are also significant in contaminate fate and transport. For

example, macroflocs are the dominant conduits of metals and organics to the deep sea

(Fowler and Knauer, 1986). Aggregates have been shown to host dense microbial

communities, suggesting they are potentially important sites for chemical cycling within

the water column (e.g Simon et al., 2002)

1.2.5 Optical Properties

The scattering properties of particles depend to a large degree on particle size

(Conner and DeVisser, 1992; Gibbs and Wolanski, 1992; Hatcher et al., 2001; Downing

et al., 2006). The conventional view of small component particles is that for the same

mass, are more efficient light scatterers than larger particles (van de Hulst, 1957;

Stramski and Keifer, 1991; Uloa et al., 1994; Peng and Effer, 2007). This observation has resulted in the disproportionate focus on small in the fields of water clarity and remote sensing. Braithwaite et al., (2010) showed that particles less than 10 µm in diameter

18 contributed nearly 60 % of the total scattering across a large size range (2 to 500 µm).

However, past studies don’t typically consider the process of aggregation on particle optical properties. Aggregates behave differently than single particles of the same size and evidence shows “that aggregates are much more efficient scatterers per mass than previously expected based on Mie theory for single particles” (Slade et al., 2011). Boss et al., (2009a) suggested that the discrepancy between the outputs of co-located transmissometers with different acceptance angles could be explained by changes in particle size attributable to the presence or formation of flocs. These are important considerations when using optical measurements as surrogates for biogeochemical variables.

1.3 Particle Cycling in Tidal Wetlands

Despite the importance of SPM, little is known about the relative proportions and behavior of primary particles and aggregates in rivers and estuarine environments, where flocculation and fine grain sediment dynamics determine particle source and fate.

Sediment deposition in tidal wetlands is spatially and temporally variable (Wang et al.,

1993). Due to shallow water depths, tidal circulation is the temporally dominate mechanism of sediment transport in tidal wetlands, though episodic wind and watershed precipitation events are also important factors (Ganju et al 2005; Fettweis et al., 2006;

Bergamaschi et al., 2011). Tidal wetlands may collect sediment from many sources including rivers, bed and channel sediments and primary production. In certain environments, such as the SFE, tides cause semidiurnal inundation of the marsh surface.

Friction with vegetation slows down water velocities, causing sediment to deposit and

build up the marsh surface (Kadlec, 1990). Reduced turbulence and increased organic

matter levels following marsh inundation provides ideal conditions for floc formation.

And despite the observation that in general, tidal wetlands are depositional environments, tidal wetlands may export material. For example, Findlay et al. (1990) could not explain the removal of organic detritus from a tidal marsh, totaling 25% of the net primary productivity by mass, using local biodegradation rates, and so concluded that the material could have been exported with the ebb tide.

1.4 Previous Work

SPM behavior is dynamic and typically proceeds through a continuous cycle of aggregation, deposition, resuspension, break-up and dispersion over short timescales (e.g.

Schoelhammer, 2002). Hence, these processes are rarely observed directly. Traditional grab samples are commonly collected at a frequency to low to understand these dynamic processes and local temporal variability. In addition, invasive grab sampling can disturbed SPM and bias the results (Kranck and Milligan, 1992; Eisma, 1986). These methodological shortcomings have imparted significant errors to the understanding of particle behavior and have hampered models of particle transport (Hill et al., 1998).

In situ optical instruments have been used successfully to study particle dynamics in environments where traditional sampling is inadequate for understanding site variability. For example, in situ optical instrumentation has been used to study SPM behavior in regions where variability is often dynamic and poorly understood (e.g.

Mikkleson et al., 2006; Fettweis et al., 2006; Boss et al., 2001; Curran et al., 2007).

Oubelkheir et al. (2006) observed spatially variable and tidally driven particle dynamics

in a macro tidal system. Schoelhammer et al., (2002) utilized 10 years of high frequency,

turbidity data to indicate the main sources of and processes affecting sediment

concentration variability throughout the San Francisco Estuary.

The beam attenuation coefficient is likely the oldest optical parameter used to

quantify SPM in water (e.g. Baker and Lavelle, 1984). Variation in the beam attenuation

coefficient varies to first order on the concentration of particles in water and to second

order their size and composition (e.g. Oubelkheir et al., 2006). Baker and Lavelle (1984)

used beam attenuation to assess particle volume and mass concentration at coastal and

deep ocean sites and found that the calibration slope was sensitive to particle size. Boss

and Behrenfeld (2003) found beam attenuation to be a suitable predictor of algal biomass

in the ocean. Twardowski et al. (2001) identified and fingerprinted suspended particles in

contrasting water types using spectral attenuation and scattering measurements. Boss et

al. (2001) studied particle size variability in the marine bottom boundary layer by means

of the shape of the particulate attenuation spectrum. Slade et al., (2011) used optical sensors to understand the light scattering properties of aggregates.

Laser diffraction has been used to characterize aquatic particles for over two

decades (e.g. Bale and Morris, 1987). The laser in situ particle sizer and transmissometer

(LISST) has provided a new and routine approach to in situ particle sizing (e.g. Agrawal

and Pottsmith, 2000) .The strengths and weaknesses of this approach are discussed in the

literature (Traykovski et al., 1999; Agrawal and Pottsmith, 2000; Gartner et al., 2001;

Fugate and Friedrichs, 2003; Agrawal et al., 2008). Researchers have used the LISST to

study SPM concentration and size (e.g. Mikkleson et al., 2005; 2006, 2007; Curran et al.,

2007), particle cross sectional area (e.g. Bowers et al., 2011), particle density (e.g.

Braithwaite et al., 2010), floc density (e.g. Fugate and Chen, 2003) and fractal dimension

(e.g. Ganju et al., 2007), and settling velocities (e.g. Mikkleson and Perjup, 2001; Fugate et al., 2002; Fugate and Chant, 2005; Curran et al., 2007). The LISST has performed remarkably well in a range of aquatic environments and has routinely enabled the direct observation of SPM flocculation patterns in response to physical processes (e.g.

Mikkleson et al., 2006; 2007; Braithwaite et al., 2007; Curran et al., 2007; Fettweis,

2008; Baeye et al. 2010; Guo and He, 2011).

Though optical instrumentation use among researchers has recently become routine in estuaries, little work has focused on tidal marshes and wetlands where knowledge of particle cycling is sorely needed. Though many studies have focused on the processes controlling SPM deposition in the marshes of tidal wetlands (e.g. Christiansen et al., 2000; Temmerman et al., 2005), much uncertainty exists concerning general SPM behavior and dynamics in response to tidal forces. The role that tidal wetlands play in controlling estuarine particle composition and size is especially unclear. Here, modern in situ novel optical sensors were deployed in a tidal wetland channel for several weeks in the winter to examine SPM concentration and size variability in order to understand the various processes driving estuarine particle cycling. This study hypothesized that tidal wetlands enhance estuarine particle size through the process of flocculation due to the interaction of particles with organic matter rich pore waters.

Chapter 2

SITE DESCRIPTION

2.1 Geologic Setting

Following the end of the last ice age (8-10 ka), sea levels began to rise sharply, providing the necessary gradient for the formation of the San Francisco Estuary (SFE), which was fully established by 6 ka (Atwater et al., 1977). As of 1850, tidal marshes covered an expansive area of nearly 2,200 km2. Since then, regional marsh and wetland area has been reduced by up to 90% (Josselyn, 1983).

In the winter months, the largest influx of sediment to the San Francisco Estuary is from rivers (Wright and Schoelhammer, 2005). Discharge records at Rio Vista and sediment measurements made at Mallard Island are good indicators of freshwater contributions to the San Francisco Estuary (e.g. Wright and Schoelhammer, 2005). This sediment is temporarily stored in shallow bays and inlets. Benthic organisms cycle these particles into the food web (Luoma, 1996), possibly changing their quality. Large tidal velocities, spring tides, and wind waves in shallow water embayments, such as Susuin and Honker Bays, can resuspend and transport fine clay and silt bottom sediments

(Schoelhammer, 1996; Ganju and Schoelhammer, 2007). Surrounding marshes and tidal wetlands accumulate these sediments. Ganju et al., (2005) cited ebb tide resuspension and advection as the main mode of particle concentration variability in a Browns, Island a large tidal wetland and regional shoreline. Despite troubled water balance calculations and a poor turbidity – sediment concentration relationship, Ganju et al., (2005) noted that the island was a net sink of particulate matter in the Fall.

Browns Island is a natural tidal wetland in the San Francisco Estuary. The 2.8 km2

wetland is located near the confluence of the Sacramento River, San Joaquin River, and

Suisun Bay, approximately 77 km from the estuary mouth. The island is home to six

species of rare and endangered plants and is dominated by tules/hardstem bulrushes

(Scirpus californicus and S. acutus), cattails (Typha angustifolia), saltgrass (Distichlis

spicata) and other sedges growing on deep peat soils.

Browns Island contains a well-defined main channel with numerous side channels

(Figure 1). San Francisco Estuary tides are generally mixed semidiurnal, with a

maximum spring tidal range of 1.8 m and a minimum neap tide range of 0.20 m (Ganju et

al., 2005). At high water, the main channel is approximately 17 m wide with a maximum

depth of 4.5 m (Ganju et al., 2005). Just north of the main channel mouth is a shallow

mudflat; a known source of sediment to the channel (Bergamaschi et al., 2011). The main channel opens to the west and terminates at a lagoon in the eastern wetland interior. The main-channel sampling site was upstream of major side channels approximately 300 m from the channel mouth (Downing et al., 2009; Bergamaschi et al., 2011).

Chapter 3

METHODS

3.1 Sampling Procedure and Instrumentation

Co-located acoustical and optical instrument systems collected data at Browns

Island in the main channel approximately 15 m apart for a three week period in the winter from January 11th to February 2nd, 2006. The systems were left unattended on battery power except for weekly service visit to download data, replace depleted batteries and clean optical sensors. Instrumentation was moored on the channel bottom at the center of the main drainage channel while data was collected and processed according to (Downing et al., 2009 and Bergamaschi et al., 2011). The instrumentation package consisted of both acoustical and optical sensors. The acoustical instrumentation and methods follow Ganju et al. (2005) while the optical instrumentation follows Downing et al. (2009) (with the exception of the LISST-100X type B particle sizer).The details of the deployment are summarized below for reference.

The optical sampling package consisted of a stainless steel cage containing submersible sampling impeller pumps, nylon membrane filters, and optical and hydrographic sensors (Figure 2). A WETLabs DH4 data logger (WET Labs, Philomath,

OR, USA) controlled the pumping, powering and logging of the SeaBird Electronics

SBE-37 Microcat CTD (Sea-Bird Electronics, Bellevue, WA, USA) and WETLabs ac9 data. Every thirty minutes the system woke from low power sleep mode, warmed up the sensors and flushed the pumps for two minutes in order to purge stagnant water and accumulated particles out of the system prior to sampling. The sensors were burst

sampled for 30 seconds at their respective sampling rates (ac9 at 6 Hz and the CTD at 4

Hz). The burst from each sensor was median filtered and merged to a common fifteen

minute timestamp on Pacific Standard Time (PST). An upward looking acoustic Doppler

velocity meter (ADVM) measured water depth and velocity channel averaged velocity

according to Ganju et al., (2005). Positive values for velocity indicate current onto the

island. Frequently used nomenclature is provided in Table 1 for explanation and quick

reference.

3.2 Turbidity

Turbidity is a measure of water clarity, and in general, quantifies the effect of

dissolved and suspended materials to cause light to be scattered and absorbed rather than

transmitted in straight lines through the water. Turbidity was measured by illuminating

water samples with infrared (IR) (850 nm) LED light, and measuring the light scattered at

90 degrees using an IR filtered photodiode (Downing et al., 2006). Turbidity was

measured once every 30 minutes using a Hydrolabs DS-4 nephelometer (Hydrolabs,

HACH Hydromet, Loveland, CO) from January 13th to February 2nd, 2006. Although

optical backscatter is the preferred surrogate for retrieving continuous records of

suspended particulate matter (Boss et al., 2009a), turbidity has been shown to be a

suitable surrogate for SPM in many freshwater and brackish settings, such as in the SFE

(e.g. Buchanan and Lionberger, 2009).

3.3 Attenuation and Absorption

Particles absorb and scatter light. The combined effect of both of these processes

is attenuation and is measured with a transmissometer. Specifically, a transmissometer

measures the drop in the intensity of a monochromatic light beam that has passed a

known distance though dissolved and particulate substances. Theoretically, all of the

transmitted light is detected, though practically, a fraction is unaccounted for due to the

finite acceptance angle (Boss et al., 2009a; Hill et al., 2011). The measured light intensity

is converted into the beam attenuation coefficient according to Equation 1 (van de Hulst,

1981),

where c is the beam attenuation coefficient, Io is the intensity of light in particle free water and Im is the intensity of light measured on a particle rich sample at the receiver optics across a length, L, away from the light source.

3.4 ac9 Data Treatment

The ac-9 used in this study is a nine wavelength (412, 440, 488, 510, 532, 555,

650, 676, 715 nm), 0.10 m path length, dual path reflective tube absorption meter and

transmissometer with a 0.93 degree acceptance angle (Pegau et al. 1997; Twardowski et

al. 1999). Practically, the measured beam attenuation coefficient does not equal the

theoretical beam attenuation coefficient since all light cannot be collected at the receiver

due to a finite acceptance angle. Therefore the acceptance angle can influence the

measured beam attenuation coefficient and should be noted when comparing beam

attenuation coefficients measured by different instruments (Boss et al. 2009b).

The basic design is a combination of a lamp, optical path, focusing optics, control

and signal conditioning hardware and software (Pegau et al., 1997; Twardowski et al.,

1999). Attenuation measurements were made by pulling water through a one mm mesh screen to the attenuation side of a WET Labs ac-9 spectrophotometer using a SBE3T pump. At the same time, absorption by dissolved organic matter, ag, was collected by pumping 0.2 µm filtered water though the ac9’s absorption side using SBE5T pump.

The beam attenuation coefficient (c) describes the loss of light transmitted through a water sample due to light scattering (b) and absorption (a) by dissolved and particulate materials (Equation 2)

(2) c = a + b.

The measured beam attenuation coefficient, cm, is a sum of the attenuation of water, cw, and particles plus dissolved substances, cpg (Equation 3):

(3) cm = cpg + cw.

Several corrections were made to cm in order to retrieve the attenuation by particles and dissolved matter. The attenuation by water, cw , which also incorporates detector drift, was removed prior to analyses using laboratory water calibration data. This data was collected before and after the deployment period. Drift corrections were applied to the data according to Twardowski et al.,(1999). The attenuation due to particles and dissolved substances, cpg, was deduced from the measured attenuation, cm, according to

Equation 4:

(4) cpg = cm - cw

In order to evaluate the attenuation by particles only, the attenuation by dissolved matter was subtracted using paired absorption data, ag. Since the scattering of dissolved matter is immeasurable using our methods (Twardowski et al., 1999) it is assumed to be

zero. The attenuation due to particles only is then attained by subtracting the absorption by dissolved substances, ag, from the attenuation due to particle plus dissolved

substances, cpg at each of the nine wavelengths (Equation 5):

(5) cp = cpg - ag.

cp and ag measured by the ac9 and calculated in this manner are denoted by cpac9 and agac9 ,

respectively. Temperature and salinity corrections were applied to cpac9 according to

Pegau et al., (1997).

Particulate attenuation at red wavelengths, for example, 650 and 670 nm, has been

used successfully to track the concentration of suspended particulate matter (SPM) in a

natural waters (e.g. Souza et al., 2001; Boss et al., 2009b).Generally, an increase in

cp(650) indicates an increase in the SPM concentration and is most sensitive to particles

smaller than 20 µm in size (Boss et al., 2004)

3.5 cp Spectra and Particle Size

The shape of the particulate attenuation spectrum decreases monotonically with

wavelength in coastal and oceanic water (Boss et al., 2004). This decrease in amplitude with wavelength follows a power law, primarily driven by the high proportional effects of scattering ( i.e b >> a ). The corrected particulate attenuation (cp (λ)) spectrum from 412

to 650 nm was fit to a power law using a least squares optimization program (courtesy of

E. Boss, http://misclab.umeoce.maine.edu/software.php) in the MATLAB computing

environment according to Equation 6

-γ (6) cpac9(λ) = cpac9(650)(λ/650)

where the parameter of interest, γ, is the spectral slope.

Assuming particles are perfect scatterers (no absorption) and that their size

distribution follows a Jungian type power law (Boss et al., 2001), the slope, γ, of the

attenuation spectrum cpac9 is related linearly to the slope of the particle size distribution

(Twardowski et al., 2001; Boss et al., 2001; Boss et al., 2004). Therefore, one can invert the scattering spectrum to estimate the shape of the in situ fine (<20 µm) particle size distribution by simply measuring the particulate attenuation at three or more wavelengths.

An interpretation of the inversion according to Boss et al., (2001) and

Twardowski et al., (2001) is as follows: an increase in γ implies a “steeper” particle size distribution and a lower mean particle size where an increase in γ indicates a “gentler”

particle size distribution and a lower mean particle size.

3.6 In Situ Particle Size Distributions

The Sequoia Scientific (Bellevue, WA) Laser In Situ Scattering Transmissometer

(LISST-100X type B, or LISST-B for short) measures the volume scattering function

(VSF) of particles at 32 log spaced intervals over the angular range in water from ~0.09

to ~15 degrees across a 5 cm path length (Agrawal and Pottsmith, 1994). The particulate

particle plus dissolved attenuation coefficient 670 nm, cpgLISST(670), was also collected

o with a 0.0269 acceptance angle. agac9(676) was subtracted from cpgLISST(670). to yield

cpLISST(670).The raw incident scattered laser power that falls on each ring is amplified,

converted to a voltage, digitized and stored on a built-in datalogger for later retrieval and analysis. Although light scattered at angles larger or smaller than the angles covered by

the ring detectors is not detected, particles outside the size range can still scatter light

onto the ring detectors, resulting in the observation of rising tails in the particle size

distributions (Agrawal and Pottsmith, 2000). Rising tails are often observed at the fine

end of the size spectrum (e.g. Mikkleson et al., 2006) and are a result of ‘subrange’

particles leaking into the size spectrum (Agrawal et al., 2008). Typically, the rising tails

are removed to prevent their false interpretation. Rising tails were not observed in this

study and so the entire size range was included in the analysis.

Upon retrieval of the instrumentations, LISST-B data was offloaded into LISST

SOP v4.5 processing software and inverted into particle size information. The inversion

used a kernel matrix to relate the VSF to a particle size distribution (PSD) (Agrawal and

Pottsmith, 2000). Since the laser diffraction method produces particle volume

concentrations derived from determinations of particle area, the method is sensitive to

particle shape. However, particles are rarely spherical in nature and Agrawal et al.,

(2008) predicts an underestimate in size of up to 25 % when assuming sphericity, since

random size grains are not isotropic with respect to size. A random shape inversion

kernel matrix (Agrawal et al., 2008) was not available at the time of data processing so

the final output from the unit is the spherical grain volume concentration distribution in

32 logarithmically spaced size bins from about 1.5 to 250 µm.

The open path LISST-B collected data every 30 minutes in sync with the pumped system from the afternoon on January 25th until midday on February 2nd. The LISST

operated in moored mode and automatically averaged 25 samples at 1 Hz ten times over a

span of four minutes every 30 minutes to an internal datalogger. The ten 25 sample bursts

were further reduced using averaging. The D50, or median volume diameter (selected

from the 50th percentile of the cumulative size distribution), and the mean volume diameter for each scan was calculated using the “compute_mean.m” MATLAB script

from Sequoia Scientific (Hill et al., 2000; Mikkleson et al., 2006).

3.7 Discrete Measurements

3.7.1 Sample Collection

During the sampling period, discrete samples were collected using a depth

integrated water sampler on an hourly schedule for a 22 hour period on February 2, 2006

(See Downing et al., 2009 for details).

3.7.2 SPM Concentration

Samples were collected using a van Dorn water sampler, transferred to pre-

combusted amber glass bottles and transported to the laboratory on ice. Suspended

particulate matter concentrations were determined gravimetrically on the filters (Clesceri,

1998) at the USGS Marina Lab.

3.7.3 Particle Organic Content

After dry weighing, the filter trapped materials were combusted at 500 degrees C

for 24 hours. Combusted and weighed filters yielded the amount of material lost on

ignitions (LOI). Assuming the loss of water from hydrous clays during this step is

negligible, the amount LOI is linearly related to the fraction of organic fraction of SPM,

particulate organic matter. The ratio of POM to SPM is the organic content. Particle

organic content data is presented in Table 2.

3.8 Time Series Analysis

Time series analysis was carried out in MATLAB (The Mathworks, Nantick,

MA) (release 2008b) using the MATLAB Signal Processing Toolbox Finite (Fast)

Fourier Transform (FFT) function. The signal mean was subtracted from the time series to remove any long term trend that would appear as low frequency noise in the analysis.

The signal was then converted to the frequency domain and dominant signal frequencies

(in cycles per day, d-1) were identified using a Fourier transform. The signal power at each frequency was calculated by summing the squares of the amplitude coefficients at each frequency. To aid in comparing the frequency spectra of different parameters, each power spectrum was normalized by its maximum value.

Chapter 4

RESULTS

Different periods were chosen to focus on different SPM forcing mechanisms across a range of time scales at Browns Island. The study focused on the period from

January 13th to February 2nd in order to identify SPM concentration variability associated

with random events and the multi-day spring neap cycle. An intense study of SPM

concentration and size was carried over a shorter period from January 25th to February 2nd

to understand the relation of sediment dynamics with the mixed semi-diurnal tides.

Following heavy December precipitation discharge at Rio Vista peaked to 350 kcfs on January 3 2006 (Figure 3). Base turbidity levels were low and exhibited diurnal fluctuations around a mean of about 20 FNU prior to December 25th.On January 1st, a

sudden increase in turbidity occurred nearly coincident, but slightly lagged, the rise in

discharge at Rio Vista. Within a few days, turbidity peaked to nearly 350 FNU on

January 4th and remained elevated through at least February 2nd, 2006.

4.1 Hydrodynamics

A continuous time series of water depth and velocity was generated for several weeks in the main channel at Browns Island. This data identified many modes of variability (Figure 4, top). Water levels exhibited a semi-monthly spring neap cycle as well as shorter diurnal (once per day) and semi-diurnal (twice per day) cycle. A single mixed tidal cycle contains two high flood tides and two low ebb tides. These are segmented into a higher high (HH) water, a lower high (LH) water, a higher low (HL) and a lower low (LL) component. Maximum, HH, (4.75 m) and minimum (3.25 m) daily

water levels occurred on the spring tide (i.e. from 1/23- 2/2), while the maximum and

minimum daily water levels were similar on the neap tide (1/18 to 1/25). The 21 day

record from January 13th to February 2nd had a maximum tidal range of 1.5 m. This

difference was highest on the spring tide and lowest on the neap tide. Time series analysis

identified many components in the water level signal. Major signal components had

frequencies of 1.02 and 1.90 d-1 with minor components at 0.88 and 2.04 d-1 (Table 3).

Channel velocity varied according to local water level conditions throughout the

study period (Figure 4, top). Water velocity was zero at slack high and low tides, and maximum midway between the slack tides. The water velocity was highest on the spring tide and lowest on the neap tides (0.6 m s-1 vs. 0.2 m s-1, respectively). HH ebb velocities

were larger in magnitude than the ebb LL velocities by up to 60% at times. However, this

difference was much smaller in a neap tide where flood velocities, at times, exceeded ebb

velocities. During a typical tidal cycle, channel velocity was higher in ebb (off-island)

tides (e.g. -0.6 m s-1), than flood (on island) tides (0.4 m s-1).

4.2 Wind Velocity

Previous studies have shown wind speed to be a dominant forcing variable on

sediment dynamics in shallow marine environments (e.g. Fettweis et al., 2010; Baeye et

al., 2011), especially in the broad and shallow San Francisco Estuary (Schoelhammer,

2002). Wind speed and direction at Suisun meteorological station varied considerably

during the study period (Figure 4, middle). Four periods (1/14, 1/18, 1/23, 1/30) exhibited

elevated wind speeds above 8 m s-1. Moderate to high wind velocity out of the south by

southwest direction dominated the record (Figure 5), though a few significant strong

events blew out of the northerly direction. On January 23rd, winds (11 m s-1) blew out of

the north by north east direction for nearly 24 hours, the highest recorded during the period of the study (Figure 4, bottom; Figure 5).

4.3 SPM Concentration Surrogates

4.3.1 Turbidity

At Browns Island, turbidity exhibited both periodic and aperiodic variability

(Figure 4, bottom; Figure 6B). In terms of periodic variability, turbidity varied in concert with the tides. Baseline turbidity values varied around 20 FNU. Lowest turbidity values coincided with the neap tide while the highest values coincided with the spring tide. The highest turbidity occurred as a sharp peak on January 23rd (160 FNU). The asymmetric peak rose quickly but fell gradually over the course of a few tidal cycles. On the recession, two smaller peaks were superimposed on the receding limb. All of these peaks correspond with peaks in wind velocity related to the wind event that started on January

22nd.

A closer inspection of periodic turbidity variability during a spring tide from

January 25th to February 2nd shows coincident timing with high and low tides (Figures

6A-B). Turbidity minima and maxima correlated with water level minima and maxima,

respectively (Figure 6B). During this restricted period of study, turbidity was higher at

higher high water (35 FNU), lower low water (10 FNU), a similar range observed outside

the island at Mallard Island. At times, however, the timing of turbidity and water level

were not as clearly coupled. For example, the gradual increase flood tide corresponded

with a sharp increase in turbidity and during the HH flood slack tide. At other times,

turbidity stayed elevated through the HH slack tide. A minor observation is that turbidity

dipped a few FNU at each HH slack tide, before increasing to maximum values just prior

to the ebb (Figures 6A-B). At point a turbulence and bed shear stress are at a minimum at

slack tide. At this point in time, turbidity is low, but increases as soon as water begins to

flood on the island and water velocities peak, turbidity also peaks to a maximal value,

suggesting channel resuspension. Turbidity drops as SPM settles, then peaks again at

peak ebb velocities, indicating channel resuspension, pattern previously observed by

Ganju et al., (2005). However, as velocity begins to drop again , d, turbidity falls.

4.3.2 Particulate Beam Attenuation

Both cpac9(650) and cpLISST(670) signals (collectively referred to as cp) were both

characterized by semi-diurnal variability (Figure 6 C). Though the ac9 was pumped and

screened and the LISST-B sampled with an open path, in general, values of the two

parameters were remarkably similar in magnitude and timing (Figure 7 B-C). The ratio of

cpLISST(670) to cpac9(650) (cp ratio) was usually near one (median = 1.012, data not

shown). The maximum and minimum daily tidal values of cpLISST(670) and cpac9(650)

increased and decreased with the spring tide, with the maximum daily range in values

observed on January 29th; coincident with peak maximum water levels (Figure 7). As

-1 with turbidity and component particles VC, cp maxima (cpac9(650) = 17 m , cpLISST(670)

-1 = 17 m *) were coincident with the HH flood tide while cp minima were coincident with

-1 the LL ebb tide (cpac9(650), cpLISST(670) = 5 m ). Time series analysis identified the dominant spectral power lay in diurnal periodicity for both signals (Table 3). Two excursions in cpLISST(670) from the tidal pattern and agreement with cpac9(650) occurred

th th late on both January 27 and January 30 . Within these restricted periods cpLISST(670) did

-1 not track with water level, and instead spiked to 40 m , the highest observed cp values. At

these times, cp ratio departed from the median value of near 1, indicative of perfect

agreement, to a range of 0.2 to 2.

4.3.3 Volume Concentration

Total (cumulative) particle volume concentration (VC) for all size bins is

presented in Figure 6D (Note: broken axis). VC varied over a large range from less than 2

µL L-1 to greater than 1500 µL L-1 .As with beam attenuation and turbidity, VC was many

times higher on high tides (~250 µL L-1) than on low tides (~15 µL L-1). Further, VC

varied with semi-diurnal periodicity as it was higher on HH than LH tides and lower on

LL than HL tides, respectively (Figure 6D).Two events characterized by anomalously high VC broke from this pattern late on January 27th and January 30th. These two events,

which exceeded VC of 500 µL L-1, were coincident with the LL tide.

The particle volume concentration are segmented onto three VC fractions and

plotted with hydrographical data in Figure 7. VC of the component particle fraction, D<

33um (component) varied with clear semi-diurnal periodicity (Figure 7B), where nearly equal minima and maxima occurred twice daily.VC of component particles varied between 20 and ~100 µL L-1 with maxima coincident with flooding higher high tide and minima coincident with ebbing low tide (LL and LH). VC of the microflocs (33 >D> 133

µm) varied with both diurnal and semi-diurnal periodicity (Figure 7C). Microflocs concentration was nearly double in concentration of the component particles with

maximum values (~200 µL L-1) coincident with the HH tide. Microfloc VC minima (~ 20

µL L-1) were of the same magnitude as component particles and the timing was

coincident with the ebb low tide. Macroflocs (D>133 µm) varied only with diurnal

periodicity and comprised a small percentage of the total volume except during two

positive excursions on the eves of 1/27 and 1/30 (Figure 7D). With exception to the

excursion, typical peak concentration was no more than 50 µL L-1. Aside from these two

excursions, component particles and microflocs accounted on average, from 75 % to 90

% of the total particle volume. On average, component particles dominated the particle

fraction (50%) over the study period. Although, it is important to note that, this does not

necessarily translate into dominance of the particle mass concentration (e.g. Curran et al.,

2007). Though a minor component, a small dip in VC occurs in both component particles

and microflocs at slack HH tide (Figure 7B-D).

4.4 SPM, POM Concentration and Organic Content

SPM and POM concentration collected on discrete samples varied from 15 to 50

mg L-1 and 1.8 to 5 mg L-1, respectively, with lowest values observed at HH flood tide and highest values observed at LL ebb tide (Table 2). Though restricted to only one tidal cycle, in general terms, these results are consistent with optical surrogates for SPM concentration, turbidity, cp, and volume concentration. Although the optical surrogates increased in concert with SPM concentration, there was a poor correlation between SPM

2 and turbidity, cp and VC (r <0.5, data not shown). Organic particle content was lowest

(9%) with the flood tide, but rose to over 12% with the ebb tide (Table 2).

4.5 Particle Size

4.5.1 cp Slope

The particulate beam attenuation (cpac9) slope, γ, is both theoretically and

empirically related to bulk particle size (Boss et al., 2004). Since γ is most sensitive to

particles smaller than 20 µm, once can expect good agreement with component particles

(D<33 µm) (Boss et al., 2004). γ varied smoothly and inversely with the tides (Figure 8

A-B). The shallowest values of gamma (0.8 nm-1) (largest mean component particle size)

were observed at HH slack tide while the steepest values (1.4 nm-1) (smaller mean

component particle size) were coincident with LL slack tide (Figure 8 A,C) The variability in γ within the one week time window was semi-diurnal in character, as indicated by time series analysis (Figure 8B; Table 3).

4.5.2 Mean and Median Particle Size

The mean and median (D50) particle size exhibited strong diurnal variability

(Figure 8C; Table 3) and in general tracked one another. The mean size was similar in magnitude to the median size during flood tides but was consistently lower than the median size during ebb tides (Figure 8C). D50 was lower on flood tides than on ebb tides.

D50 varied from 25 to 30 µm on flood tides to 75 to 150 µm during ebb tides. Peak particle diameters occurred at LL tide, just following peak ebb velocity (Figure 8 top, bottom). Particle diameters maxima were coincident with positive γ maxima (Figures 8C-

D).

4.5.3 Particle Size Distributions

Example particle size distribution (PSD) spectra displayed multi-modal particle

population (Figure 9). The modality and concentration of particles varied markedly in

size and concentration during different times of the hydrograph. Examination of the HH

(peak flood slack), and LL (peak ebb slack) tides revealed differences between SFE and

Browns Island end-member waters. Peak flood slack tide (SFE end member) was more concentrated in particles in nearly every size bin than at ebb slack tide (Browns Island end member) (Figure 9, top). At peak flood slack tide, the particle spectra is bimodal,

with peaks centered at just less than 2.5 µm (10 µL L-1, component particles) and 110 µm

(17 µL L-1, flocs), with a gently sloping continuum between the peaks (Figure 9,top, blue

pointed line). The peak ebb slack tide water was reduced in VC across the size range in

comparison to flood slack tide (Figures 6D, 9. LL water particle size spectra were especially reduced in component particles and microflocs. Coincident with reductions in concentration of smaller particle bins, the large particles increased in size from 110 to

150 µm (Figure 9, top, black starred line).

Further analysis of the differences in particle sizes of water masses coming onto

and leaving from the island was carried out by comparing PSD’s grouped by the two

types of tides, ebbing and flooding for one full 24 hour tidal cycle (1/29 – 1/30, n=48).

PSD’s of particles in water flooded onto the island were low in volume concentration and

bimodal with peaks centered at 2.5 µm (10 µL L-1) and 110 µm (25 µL L-1), respectively

(Figure 9, middle). Ebbing tides were on average many times higher in volume

concentration than flooding tides and were characterized by unimodal distributions

skewed to particle aggregates larger than 200 µm with VC often exceeding 100 µL L-1

(Figure 9, bottom). The occurrences of the high concentrations of large particles coincide

with D50 excursions at LL ebb tide just after peak ebb water velocity. (Figure 8; Figure 9, bottom)

Chapter 5

INTERPRETATION

Suspended particulate matter (SPM) plays an important role in the

biogeochemistry of estuaries and wetlands. Recent wetland restoration efforts have

targeted tidal wetlands; yet, many questions pertaining to SPM dynamics remain since

these environments operate on rapid timescales and are difficult to observe. A paired in

situ optical and acoustic sensor package was deployed for several weeks during the

winter in the main channel at Browns Island, CA to assess sediment size and

concentration dynamics. In addition to gaining information about tidal wetland sediment

properties, the in situ measurements enabled the identification of processes controlling

estuarine and marsh sediment dynamics

5.1 Hydrodynamics

Sacramento River discharge at Rio Vista peaked to over twenty times that of

baseflow levels in late December as a result of heavy precipitation and landscape runoff

in the watershed. The precipitation event resulted in the largest sediment runoff event to

the SFE of the water year (Buchanan and Lionberger, 2009). Browns Island main channel

water depth and velocity were controlled by regional tidal forces. Therefore, island water

levels and periods of marsh inundation were influenced by spring- neap, semi-diurnal lunar and solar tides. The spring tide, which peaked on January 31st, was the most

energetic period of the study period resulting in maximum inundation and exchange of

water between the island and estuary.

5.2 Drivers of SPM Concentration

Previous work throughout the SFE as well as Browns Island has shown optical properties such as turbidity to be appropriate predictors of suspended sediment concentration (Schoelhammer et al., 2002; Ganju et al., 2005; Buchanan and Lionberger,

2009). This evidence, combined with many examples of the successful application of optical measurements in the assessment of SPM concentration in the literature, supports their use as optical surrogates in this study (e.g. Boss et al., 2009b). Though turbidity, attenuation and volume concentration were not strongly correlated to SPM concentration, this study interprets a general relationship between them as follows: an increase in the optical measurements data indicates an increase in SPM concentration. Over the range of conditions presented here, a one to one relation between optical property and SPM concentration is assumed. However, significant changes in sediment quality coincident with changes in concentration, such as particle size, may confound this relationship.

5.2.1 Precipitation

Fluvial inputs are the primary source of sediment to the San Francisco Estuary especially during the winter months (Schoelhammer, 2002).The Sacramento River discharge event that occurred in early January, 2006 was nearly coincident with a pulse in turbidity at Mallard Island, indicating that strong precipitation resulted in a watershed sediment runoff event. This event provided fresh riverine sediment to the estuary.

5.2.2 Wind

Though winds were predominately out of the west due to the prevailing

westerly’s, winds out of this direction did not influence SPM levels at Browns Island.

However, an episodic northerly wind event characterized by 15 minute average wind

speeds greater than 11 m s-1 was coincident with the highest measured turbidity values

measured during the study at Browns Island, indicating the important role of both wind

speed and direction in the resuspension of bottom sediments (e.g. Schoelhammer,

2002).The likely source of re-suspended bottom sediments was recently deposited fluvial

sediments associated with the precipitation runoff event.

5.2.3 Tidal forcing

On tidal time scales, all SPM concentration surrogates showed significant semi-

diurnal and diurnal variability, indicating that particle dynamics were linked to local hydrodynamics, as previously noted by others (Schoelhammer, 2002; Ganju et al., 2005;

Wright and Schoelhammer, 2005; Bergamaschi et al., 2011). Turbidity, particulate attenuation, and VC (component, micro flocs) maxima, were coincident with the HH tide, while minima were coincident with LL ebb tide, suggesting that tidally driven fluctuations in water level were the dominate forcing variable on SPM concentration.

This is a common observation in tidally dominated estuaries (e.g. Stumpf, 1983; Wells and Kim, 1991). Since turbidity and attenuation covary closely with the concentration of component particles, they likely reflect the behavior of SPM smaller than 33 µm. SPM was reduced in waters returning to the estuary - a direct result of the interaction between the wetland and riverine and estuarine Reduction in the component particle and microfloc

populations between HH and LL tides, are especially clear in particle size spectra (Figure

9, top). These reductions are coincident with reductions in turbidity and attenuation, providing additional evidence that turbidity and attenuation are due to silt and clay sized particles. Particle in these size fraction should be a especially considered in remote sensing and water clarity studies in the SFE as they are the dominate source of light scattering and attenuation, which is consistent with theory and the literature. Since particles smaller than 133 µm dominated the particle pool by volume (90% on average) reductions of SPM in ebb tidewaters is due to the loss of fine particles, possibly via deposition. However, settling velocities are in general to low for fine particles to explain settling in the water column. If waters overtopped the banks and inundated the marsh surface, then particles could have been prevented from returning to the main channel and passing past the sensors on ebb tide. Once on the marsh surface, a higher residence time could lead to marsh deposition. Deposition on the marsh surface can result from a reduction in water velocities and turbulence due to vegetation induced drag on the marsh surface (Kadlec, 1990; Yang et al., 2008). Christiansen et al., (2001) found that 70-80 % of particles larger than 15 µm to flocculate and settle out on the creek bank following high tide. Flocculation of finer particles into larger ones would increase settling velocity and promote settling. A back of the envelope calculation requires settling rates to be on the order of 0.5 to 1 mm s-1 for the entire water column to settle (4 m) within the period

of the slack tide (2 h), well within the reported values of estuarine flocs (Shi and Zhou,

2004).

5.3 Drivers of SPM Size

In situ measurements indicated strong variability in particle size across the range

of sizes studied (Figures 7B-D, 9). γ revealed qualitative changes in the fine particle pool not evident in the particle size spectra. Trends in γ indicate that the particle population ebbing from the island was more concentrated in small particles than water that flowed on to the island. γ behaved oppositely than other particle size indices, the mean and median (D50) particle size, because the ac9 is sensitive to a smaller particle fraction.

γ data suggests the coarser particles in the particle size range <20 µm were deposited or

incorporated into flocs, while the smaller particles in that fraction remained suspended

and returned to the estuary with ebbing waters. The lack of a robust consistent

relationship between turbidity and SPM at Browns Island, (here and in Ganju et al., 2005)

may be in part due to fine particle size changes coincident with changes in concentration

(Conner and Visser, 1992; Downing et al., 2006). Future studies that wish to develop a

strong proxy for SPM should incorporate size variance into the model.

Ebb water mean and median (D50) particle sizes were dominated by the presence

of flocs. Ebb water flocs were on average an order of magnitude larger than flood water

particles due to flocculation during slack flood and ebb tides (Figure 8C). The largest

differences between mean and median particle size was coincident with the periods of

elevated floc size; indicating that the particle pool was characterized by high variance

following high ebb currents. cp measured by the ac9 and LISST-B agreed with one

another except when D50 exceeded 100 µm; probably due to the LISST-B’s smaller

47 acceptance angle and its dependence on particle size (Hill et al., 2011) or due to the different sampling configurations (open vs. pumped). Slade et al., (2011) observed attenuation values of aggregates subject to shear to be lower than cp measured on unaltered samples by a factor of 2- many times greater than the difference between measurements observed here, suggesting the difference was due to different acceptance angles was to blame. This finding supports the importance of stating instrument specifications such as the acceptance angle when comparing similar measurements of attenuation between instruments.

Particle size distribution spectra illustrate the complexity and dynamic nature of the estuarine particle pool (Figure 9). Flood water particle spectra are tri-modal, characterized by the presence of three distinct particle fractions: component particles, microflocs and macroflocs, and are in comparable concentrations. Macroflocs are the dominate fraction of particles present in ebbing waters, reflecting the conversion of smaller particles into macroflocs by way of flocculation during ebb tide. Macroflocs are efficient scavengers and may have amassed smaller particles via differential settling at slack tide when turbulence and shear velocities were low (Droppo, 2001; Burd and

Jackson, 2009). Peak diameters and peak VC in macroflocs coincided with one another and occurred once a day just after maximum water depth (inundation) and maximum ebb tide velocity. Since the macroflocs are in highest concentration following the peak ebb velocity, tidal resuspension is a consideration for the appearance of these large particles

(Ganju et al., 2005). However, ebb velocities were only slightly greater than flood velocities, which were not correlated with elevated particle sizes or their concentration,

suggesting velocity and resuspension did not play an important role here as in Ganju et

al., (2005). Another possible explanation for the occurrence of flocs with the ebb tide is

the presence of organic matter, which is relatively high at Brown Island due to organic

enriched peat dominated soils. Downing et al., (2009) observed the highest concentration

of organic matter (as dissolved organic carbon) in ebbing waters on the spring tide,

following marsh inundation and draining of the peat dominated marsh surface. The

timing of peak floc size observed here is coincident with peak organic matter

concentration indication that organic matter and hydrology are important drivers of

flocculation.

Organic content data on grab samples indicates the flocs were enriched in organic

matter by 2-3 percent, supporting the interpretation that organic matter played a role in

flocculation as has been observed in experimental studies (e.g. Verney et al. 2009).

Determination of the relative importance of dissolved organic versus particulate organic matter in this process was not possible but both organic matter pools likely play a part in flocculation.

The interaction of Browns Island with estuarine waters results in the loss of particles less than 133 µm and the production and export of larger, organic enriched particle aggregates (>150 µm) to the estuary. Bergamaschi et al., (2011) observed that the

Island was a net source of particulate MeHg to the estuary over the period studied here.

Although this study did not evaluate the mercury content of particles, similar timing of macrofloc and MeHg suggests the process of flocculation could play a key role in the transport of particulate MeHg to the estuary.

5.4 Conceptual Model

A close inspection of the data reveals a tight coupling of hydrology and particle dynamics and multiple lines of evidence suggest that the process of flocculation contributes to variations in particle size at Browns Island in the winter. A conceptual model is presented here in order to explain the observations of co-variation in SPM concentration, size, and hydrodynamics. Refer to Figures 6B for and Figure 8 for an explanation of the text. In Figure 6B, a two day record of variations in hydrology and turbidity, a surrogate of SPM concentration, is provided to better explain the processes controlling variations in SPM concentration at Browns Island. As water moves into and out of the island, velocity and thus shear stress increase, causing local resuspension of sediment in the water column. As velocities fall on the ebb tide, resuspended fine sediment interacts with organic matter which has peaked at this moment due to island drainage and increases in size, likely due to the process of flocculation. As velocities begin to pick back up, particle size quickly drops, possibly due to floc break up due to turbulence.

Chapter 6

CONCLUSIONS

6.1 Summary

In situ particle concentration and size dynamics were observed in a tidal wetland channel for several weeks during the winter. These observations represent the first in situ measurement of particle size in a tidal wetland and aided in the understanding of the role of tidal wetlands in the cycling of estuarine particles. Tidal action was a driving force of particle dynamics at Browns Island, as tidal water levels controlled the area of estuarine water inundation. Particle volume concentration, attenuation and turbidity data were maximum at the height of the higher high (HH) flood tide and minimum at the lower low

(LL) tide, consistent with the loss of SPM through deposition and/or flocculation following the interaction of estuarine water with the island.

Following HH slack tide, fine component and microfloc particle fractions underwent flocculation, reaching maximum size just prior to the LL slack tide. The controlling mechanism for flocculation was likely particle to particle collisions due to differential settling in the presence of elevated organic matter concentrations sourced from draining pore waters (Downing et al., 2009). Following elevated ebb current velocity, flocs exported out to the estuary. Export of organic rich flocs could be an important mechanism for the export of contaminants and organic matter off the island.

6.2 Conclusions

- Particles smaller than 33 µm dominated particle scattering and beam attenuation.

-Tidal currents deposit fine, inorganic particles onto the island for either temporary or permanent storage which helps maintain island elevation.

-High organic matter concentration levels facilitated the flocculation and growth of smaller particles into large particle aggregates.

-Following strong ebb current velocities, the island was a source of large, organic enriched aggregates to the estuary.

6.3 Recommendations for Future Work

Exploring particle characteristics synoptically around the island, such as further upstream at the lake, on the marsh surface during inundation and near the channel banks could help isolate the conditions and key processes controlling flocculation on the island.

Since aggregates were enriched in organic matter, the deployment of optical sensors suitable for evaluating particulate organic matter at high frequencies could help that effort. Evaluating flow conditions across the marsh and the deployment of sediment traps on the marsh surface could assess the role of vegetation in trapping fine particulate matter on the island.

The export of large organic rich aggregates in response to tidal forcing is consistent with the dominant flux of carbon and particulate MeHg from Browns Island during the winter season (Bergamaschi et al., 2011). Future work should focus on understanding the relationship between flocculated particles and Hg cycling in tidal wetlands.

The density of particles at Browns Island should be measured directly in order to understand the significance of particle import and export on a mass basis, rather than volume basis as measured in the present study. These data could be combined with particle size data to provide settling velocities in support of sediment and contaminate transport models.

Figure captions

Table 1 Nomenclature

Table 2 Suspended particulate matter organic properties

Table 3 Time series analysis results

Results for several hydrodynamic and optical propery variables. All data are normalized to each signals maximum value to ease signal intercomparability. Frequencies are presented in units of cycles per day. Frequencies observed to have significant signal power are highlighted in bold text.

Figure 1 Study area location map

Map of Browns Island tidal wetland (from Ganju et al., 2005). Browns Island is located in the eastern San Francisco Bay Delta at the confluence of the Sacramento and San

Joaquin Rivers. The study site was located at the main channel site marker (X).

Figure 2 Instrumentation package

The above picture shows the instrumentation cage used in this study (from Downing et al., 2009). The stainless steel instrumentation cage contained a Seabird 37 MicroCat

CTD, a WETlabs ac9 spectrophotometer and DH4 datalogger, two Seabird pumps and filter tower, a custom solid state relay power switch, a Hydro labs DH4 multi parameter water quality sonde, a Sequoia Scientific LISST 100-X type B particle sizer (not shown) and an 80 amp-hr Deep Sea Power and Light sealed lead acid battery.

Figure 3 Sacramento River discharge and Mallard Island turbidity

Daily mean discharge for the Sacramento River, at Rio Vista, CA (USGS station

#11455420, retrieved from NWIS Web), and bottom turbidity at Mallard Island

(Buchanan and Lionberger, 2009) .The Sacramento is the dominate input of freshwater

and fluvial sediments to the San Francisco Bay. Discharge and turbidity peaked on

January 4, 2006 to 325 thousand cubic feet per second (kcfs) and 360 FNU, respectively,

due to significant December rains and operation of the Yolo bypass flood control

structure.

Figure 4 Browns Island Hydrodynamics, wind velocity and turbidity

Water depth (m) , velocity (m s-1) (top), and turbidity (FNU) (bottom) at Browns Island

in the main channel (top, bottom) as well as wind speed (m s-1) and direction (North = 0,

360o, East = 90o) at Suisun meteorological station (courtesy CIMIS) (middle) over one

spring neap cycle from January 13th to February 2, 2006.

Figure 5 Suisun wind velocity rose plot

Wind rose plot of wind vector data collected at Suisun meteorological station from

January 13th until February 2nd , 2006.Wind speed and direction varied over the period

with the majority of high winds wind out of the west by south west and east by south east

directions. High wind speeds (>10 m s-1) occasionally occurred out of the north by north east direction.

Figure 6A Hydrodynamics, turbidity and particle volume concentration

One week time series of water depth (m) (A, red line) and water velocity (m s-1) (A, black

dashed), turbidity (FNU) (B), attenuation (m-1) coefficients as measured by the ac9 (C,

thick green) and LISST-B (C, thin black), respectively, and VC of all particle sizes (D)

during the spring tide in the main channel at Browns Island.

Figure 6B Hydrodynamics, turbidity inset

Time series inset of Figure 6A of water depth (m) (A, red line) and water velocity (m s-1)

(A, black dashed) and turbidity (FNU) (B) during the spring tide in the main channel at

Browns Island from 1/28 to 1/30/2006. See text for the description of markers a-e, but

they are briefly mentioned here. Marker a: mid to low turbidity at slack tide and zero

water velocity, markers b and c, high turbidity corresponds to periods of peak flood and

ebb water velocity, indicating resuspension, respectively. During ebb, turbidity drops in

response to zero velocity, likely resulting in deposition. The cycle of resuspension and

deposition repeats every tidal cycle (e).

Figure 7 Hydrodynamics and particle volume concentration of three size bins

One week long time series of water depth (m) (solid red), water velocity (m s-1) (black

dashed) and volume concentration of component particles (B) and microflocs (C)

(bottom) and macroflocs (D) during the spring tide in the main channel at Browns Island.

Figure 8 Particle size and hydrodynamics

Depth (m) (A, red) and water velocity (m s-1 )(A, black dashed), spectral slope of the particulate beam attenuation spectrum (B) and mean and median particle diameter over

the period January 25th through February 2nd with close ups. The one day close up period

occurred on the maximum spring tide, from noon on January 30th to noon on January 31st

and is indicated by a gray rectangle. Lower γ values, indicative of lower mean particle

size in the component particle fraction, were coincident with lower low slack ebb tide,

while higher γ were coincident with the higher high flood slack tide. Mean and median

56 particle size was driven by the presence of flocculated particles in ebbing waters. The circular markers indicate dynamic periods of particle transformation and are explained in the text (see discussion).

Figure 9 Example particle size distributions

Particle size volume concentration distributions measured in situ by the LISST-B at different points on the hydrograph on January 30th and 31st.These selected PSDs correspond to peak flood (blue line with dot marker) and peak slack tide at 3 PM and 10

PM on January 30th , respectively (black dashed line with star marker, top), higher high at

3 PM on January 30th (dotted black line, middle) and lower high tide 4AM on January

31st (solid blue line, middle) flooding and full ebbing cycle from 3AM until noon on

January 30th (solid black line, bottom). Note different y-axis scales.

Table 1 Nomenclature

Abbreviation Units Definition a m-1 Absorption coefficient -1 ag m Absoprtion coefficient of dissolved materials b m-1 Scattering coefficient c m-1 Attenuation coefficient -1 cm m Measured attenuation coefficient -1 cg m Attenuation coefficient of dissolved materials -1 cp m Particulate beam attenuation -1 cpac9 m Particulate beam attenuation, measured by the ac9 -1 cp gLI SST m Attenuation of dissolved and particulate substances, measured by the LISST-B FNU FNU For mazin nephelometric units γ nm-1 Slope of the particulate attenuation spectrum LISST-B n/a Laser In Situ Scattering Transmissometer B model by Sequoia Scientific ac9 n/a Nine wavelength visible spetctrophotometer by WETLabs SPM mg L-1 Suspended particulate matter POM mg L-1 Particulate organic matter

D50 µm Median aggregate volume diameter PSD um Particle volume size distributon VC µL L-1 Particle volume concentration HH m Higher high water level HL m Higher low water level LH m Lower high water level LL m Lower low water level

Table 2 Suspended particulate matter organic content

Depth (m) SPM (mg L-1) LOI (mg L-1) POM (mg L-1) % OC 3.02 16.2 12.4 2.0 11.0 3.26 19.3 9.5 1.8 8.7 3.59 24.3 10.1 2.5 9.2 3.84 22.3 10.2 2.3 9.3 3.98 28.1 9.2 2.6 8.4 4.03 31.5 9.8 3.1 8.9 3.93 35.7 9.8 3.5 8.9 3.66 30.6 11.4 3.5 10.2 3.39 42.3 11.2 4.7 10.1 3.17 26.7 11.4 3.0 10.2 2.97 20.8 13.8 2.9 12.1 2.78 17.9 14.1 2.5 12.4 2.64 15.5 13.6 2.1 12.0 2.66 33.4 12.6 4.2 11.2 2.99 37.8 11.1 4.2 10.0 3.38 34.4 9.8 3.4 8.9 3.62 38.1 10.6 4.0 9.6 3.76 32.9 9.9 3.3 9.0 3.82 29.6 9.9 2.9 9.0 3.74 48.3 10.2 4.9 9.3 3.52 29.8 9.8 2.9 8.9 3.28 22.2 10.4 2.3 9.4

Table 3 Time series analysis results

-1 Frequency (d ) Depth cpac9 cpLISST VC γ Primary Micro Mac ro 0.15 0.03 0.05 0.05 0.47 0.06 0.11 0.30 0.16 0.29 0.02 0.02 0.03 0.75 0.03 0.05 0.05 0.45 0.44 0.02 0.03 0.03 0.08 0.04 0.02 0.06 0.02 0.58 0.01 0.00 0.00 0.19 0.03 0.01 0.04 0.09 0.73 0.04 0.00 0.00 0.49 0.01 0.02 0.06 0.32 0.88 0.38 0.09 0.09 1.00 0.06 0.16 0.50 0.57 1.02 0.79 0.13 0.14 0.85 0.19 0.12 0.92 1.00 1.17 0.03 0.02 0.02 0.08 0.03 0.03 0.05 0.02 1.31 0.01 0.01 0.01 0.33 0.04 0.04 0.06 0.25 1.46 0.01 0.01 0.01 0.14 0.05 0.02 0.00 0.01 1.60 0.02 0.00 0.00 0.35 0.01 0.01 0.01 0.24 1.75 0.04 0.01 0.01 0.04 0.01 0.00 0.02 0.02 1.90 1.00 1.00 1.00 0.49 1.00 1.00 1.00 0.23 2.04 0.36 0.13 0.11 0.04 0.12 0.48 0.75 0.06 2.19 0.05 0.01 0.00 0.09 0.01 0.03 0.03 0.05 2.33 0.02 0.01 0.00 0.24 0.02 0.03 0.02 0.20 2.48 0.01 0.01 0.01 0.16 0.02 0.02 0.02 0.03 2.63 0.01 0.01 0.01 0.10 0.01 0.01 0.01 0.09 2.77 0.01 0.00 0.00 0.09 0.00 0.00 0.02 0.03 2.92 0.07 0.16 0.16 0.08 0.13 0.14 0.27 0.11 3.36 0.00 0.00 0.00 0.08 0.00 0.02 0.02 0.11 3.50 0.00 0.00 0.00 0.07 0.00 0.01 0.01 0.02 3.94 0.01 0.05 0.06 0.01 0.05 0.09 0.12 0.01 4.09 0.00 0.01 0.00 0.03 0.01 0.04 0.05 0.09 4.23 0.00 0.01 0.01 0.09 0.02 0.01 0.01 0.03 4.96 0.00 0.01 0.01 0.07 0.01 0.01 0.03 0.05 5.11 0.00 0.00 0.01 0.01 0.00 0.00 0.00 0.02

Results for several hydrodynamic, water quality and optical propery variables. All data are normalized to each signals maximum value to ease intercomparability. Frequencies are presented in units of cycles per day and frequencies with significant power are highlighted in bold text.

Figure 1 Study area location map Map of Browns Island tidal wetland, which is located in the eastern San Francisco Bay Delta at the confluence of the Sacramento and San Joaquin Rivers (from Ganju et al., 2005). Two main channels can be seen entering from the western end of the island. The study site was located at the main channel site marker (X).

Figure 2 Instrumentation cage Results for several hydrodynamic and optical propery variables. All data are normalized to each signals maximum value to ease signal intercomparability. Frequencies are presented in units of cycles per day. Frequencies observed to have significant signal power are highlighted in bold text.

400 400

Sac R. Discharge at Rio Vista Bottom Turbidity at Mallard Island

300 300

200 200 Turbidity (FNU) Turbidity Discharge (kcfs) Discharge

100 100

0 0 12/15 12/22 12/29 1/5 1/12 1/19 1/26 2/2 2005 - 2006 Date

Figure 3 Sacramento River discharge and Mallard Island turbidity Daily mean discharge for the Sacramento River, at Rio Vista, CA (USGS station #11455420, retrieved from NWIS Web), and bottom turbidity at Mallard Island (Buchanan and Lionberger, 2009) .The Sacramento is the dominate input of freshwater and fluvial sediments to the San Francisco Bay. Discharge and turbidity peaked on January 4, 2006 to 325 thousand cubic feet per second (kcfs) and 360 FNU, respectively, due to significant December rains Yolo bypass flood control structure releases.

5 0.75 Depth )

Velocity -1

4 0 Depth (m) Velocity (m s (m Velocity

3 -0.75 01/13 01/20 01/27 02/02

16 360 ) ) ) °

-1 Wind Speed 12 Wind Direction 270

8 180

4 90 Wind Direction ( Direction Wind Wind Speed (m s (m Speed Wind 0 0 01/13 01/20 01/27 02/02

200

150

100

50 Turbidity (FNU) Turbidity

0 01/13 01/20 01/27 02/02 2006 Date

Figure 4 Browns Island hydrodynamics, wind velocity and turbidity Water depth (m) , velocity (m s-1) (top), and turbidity (FNU) (bottom) at Browns Island in the main channel (top, bottom) as well as wind speed (m s-1) and direction (North = 0, 360o, East = 90o) at Suisun meteorological station (courtesy CIMIS) (middle) over one spring neap cycle from January 13th to February 2, 2006.

NORTH

10%6%

4% 6%

4% 2% Speed (m s-1) 2% 11 - 12

WEST EAST 10 - 11

9 - 10

8 - 9

7 - 8

306 - -7 35

255 - -6 30

204 - -5 25

153 - -4 20

102 - -3 15

51 - 102 SOUTH

Figure 5 Suisun wind velocity distribution Wind rose plot of wind vector data collected at Suisun meteorological station from January 13th until February 2nd , 2006.Wind speed and direction varied over the period with the majority of high winds wind out of the west by south west and east by south east directions. High wind speeds (>10 m s-1) occasionally occurred out of the north by north east direction.

5.5 0.8

Depth )

A) Velocity -1 5.0 0.4 4.5 0.0 4.0 Depth(m) 3.5 -0.4 Velocity (m s (m Velocity 3.0 -0.8

40 B) 30

10 Turbidty (FNU) Turbidty 0

30 c (650) C) pac9

) c (670) 20 pLISST -1 (m p

c 10

1600 1400 D) ) -1 1200

L 1000 µ 500

VC ( 250 0 1/25 1/26 1/27 1/28 1/29 1/30 1/31 2/1 2/2 2006 Date

Figure 6A Hydrodynamics, attenuation, turbidity and volume concentration One week time series of water depth (m) (A, red line) and water velocity (m s-1) (A, black dashed), turbidity (FNU) (B), attenuation (m-1) coefficients as measured by the ac9 (C, thick green) and LISST-B (C, thin black), respectively, and VC of all particle sizes (D) during the spring tide in the main channel at Browns Island.

5.5 0.8

Depth 5.0 Velocity 0.4 )

e -1 4.5 a 0.0 4.0 Depth(m)

A) s (m Velocity -0.4 3.5

3.0 -0.8

20 c B) Turbidty (FNU) Turbidty 10 d 0

1/28 12:00 1/28 06:00 1/28 12:00 1/28 06:00 1/29 12:00 1/29 06:00 1/29 12:00 1/29 06:00 1/30 12:00 2006 Date Figure 6B Hydrodynamics, and turbidity inset Time series inset of Figure 6A of water depth (m) (A, red line) and water velocity (m s-1) (A, black dashed) and turbidity (FNU) (B) during the spring tide in the main channel at Browns Island from 1/28 to 1/30/2006. See text for the description of markers a-e, but they are briefly mentioned here. Marker a: mid to low turbidity at slack tide and zero water velocity, markers b and c, high turbidity corresponds to periods of peak flood and ebb water velocity, indicating resuspension, respectively. During ebb, turbidity drops in response to zero velocity, likely resulting in deposition. The cycle of resuspension and deposition repeats every tidal cycle (e).

5.5 0.8 Depth )

A) Velocity -1 5.0 0.4 4.5 0.0 4.0 Depth(m) 3.5 -0.4 Velocity (m s (m Velocity 3.0 -0.8

) 250 -1 200 B) L L L µ 150 100 50

VC comp ( comp VC 0

) 250 -1 200

L L L C) µ 150 100 50

VC micro( VC 0

) 1500 -1 D) L L L 1000 µ 500 150 100 50

VC macro ( macro VC 0 1/25 1/26 1/27 1/28 1/29 1/30 1/31 2/1 2/2 2006 Date

Figure 7 Hydrodynamics and particle volume concentration of three size bins One week long time series of water depth (m) (solid red), water velocity (m s-1) (black dashed) and volume concentration of component particles (B) and microflocs (C) (bottom) and macroflocs (D) during the spring tide in the main channel at Browns Island.

5.5 0.8 Depth A) ) Velocity -1 5.0 0.4 4.5 0.0 4.0

Depth(m) -0.4

3.5 (mVelocity s 3.0 -0.8 b 1.5 B) a ) -1 1.0 (nm γ c 0.5 f 200 d D50

m) C)

µ 150 Mean

100

Diameter ( Diameter e 0 1/25 1/26 1/27 1/28 1/29 1/30 1/31 2/1 2/2 12 6 12 6 12 2006 Date Jan 30th - 31st Hour Figure 8 Particle size and hydrodynamics

Peak Flood Slack 15 Peak Ebb Slack

0 0 1 2

) 10 10 10 -1 L

µ 30

LH Flooding tide HH Flooding tide 20

0 0 1 2 10 10 10 Volume Concentration ( ( Concentration Volume 400

Ebbing tide 300

200

100

0 0 1 2 10 10 10 Particle Size (µm)

Figure 9 Example particle size distributions

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