4.5 Particle Size
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 in
GEOLOGY at
CALIFORNIA STATE UNIVERSITY, SACRAMENTO
SUMMER
2011
© 2011
John Franco Saraceno
ALL RIGHTS RESERVED ii
OPTICAL OBSERVATIONS OF SUSPENDED PARTICLE DYNAMICS IN A TIDAL
WETLAND
A Thesis by
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.
Department of Geology
___________________
Date iv
Abstract of
OPTICAL OBSERVATIONS OF SUSPENDED PARTICLE DYNAMICS IN A TIDAL
WETLAND by
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,
, 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
m flocculated into larger particle aggregates.
The largest aggregates reached a maximum size of 150
m 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
Chapter
1.1 The Ecological and Societal Importance of Estuaries and Tidal Wetlands ............ 12
viii
ix
LIST OF TABLES
Page
1.
2.
3.
x
LIST OF FIGURES
Page
1.
2.
3.
Figure 3 Sacramento River discharge and Mallard Island turbidity ........................... 62
4.
Figure 4 Browns Island hydrodynamics, wind velocity and turbidity ........................ 63
5.
6.
Figure 6A Hydrodynamics, turbidity, attenuation and volume concentration............ 65
7.
8.
Figure 7 Hydrodynamics and particle volume concentration of three size bins ......... 67
9.
Figure 8 Particle size and hydrodynamics .................................................................. 68
10.
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
13 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
m, microflocs (loosely bound groups of component particles) as having diameters greater than 33
m but less than 133
m and macroflocs (groups of microflocs) having diameters greater than 133
m (Manning, 2004; Mikkleson et al. 2006).
1.2.2 Flocculation
15
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
16 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
m.
17
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
m in diameter
contributed nearly 60 % of the total scattering across a large size range (2 to 500
m).
18
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.
19
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
20 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.,
21
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,
22 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 km 2 . 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.
23
Browns Island is a natural tidal wetland in the San Francisco Estuary. The 2.8 km
2 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
24
Island in the main channel approximately 15 m apart for a three week period in the winter from January 11 th
to February 2 nd
, 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
25
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 13 th
to February 2 nd
, 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
26 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),
(1)𝑐 =
1 𝑙
ln (
𝐼𝑜
𝐼𝑚
) 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
27 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, a g
, was collected by pumping 0.2
m 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, c m
, is a sum of the attenuation of water, c w
, and particles plus dissolved substances, c pg
(Equation 3):
(3) c m
= c pg
+ c w
.
Several corrections were made to c m
in order to retrieve the attenuation by particles and dissolved matter. The attenuation by water, c w
, 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, c pg
, was deduced from the measured attenuation, c m
, according to
Equation 4:
(4) c pg
= c m
- c w
In order to evaluate the attenuation by particles only, the attenuation by dissolved matter was subtracted using paired absorption data, a g
. 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
28 by dissolved substances, a g
, from the attenuation due to particle plus dissolved substances, c pg at each of the nine wavelengths (Equation 5):
(5) c p
= c pg
- a g
. c p
and a g
measured by the ac9 and calculated in this manner are denoted by c pac9 and a gac9
, respectively. Temperature and salinity corrections were applied to c pac9
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 c p
(650) indicates an increase in the SPM concentration and is most sensitive to particles smaller than 20
m 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 (c p
(
)) 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
(6) c pac9
(
) = c pac9
(650)(
/650)
-
where the parameter of interest,
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,
, of the
29 attenuation spectrum c pac9
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
m) 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
implies a “steeper” particle size distribution and a lower mean particle size where an increase in
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, c pgLISST
(670), was also collected with a 0.0269
o acceptance angle. a gac9
(676) was subtracted from c pgLISST
(670). to yield c pLISST
(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’
30 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
m.
The open path LISST-B collected data every 30 minutes in sync with the pumped system from the afternoon on January 25 th until midday on February 2 nd . 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 D
50
, or median volume diameter (selected from the 50 th 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).
31
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 precombusted 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
32 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.
33
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 13 th
to February 2 nd
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 25 th to February 2 nd 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 25 th
.On January 1 st
, 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 4 th
and remained elevated through at least February 2 nd
, 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
34 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 13 th
to February 2 nd
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
35 events blew out of the northerly direction. On January 23 rd
, 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 23 rd
(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 25 th
to February 2 nd
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,
36 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 c pac9
(650) and c pLISST
(670) signals (collectively referred to as c p
) 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 c pLISST
(670) to c pac9
(650) (c p
ratio) was usually near one (median = 1.012, data not shown). The maximum and minimum daily tidal values of c pLISST
(670) and c pac9
(650) increased and decreased with the spring tide, with the maximum daily range in values observed on January 29 th ; coincident with peak maximum water levels (Figure 7). As with turbidity and component particles VC, c p
maxima (c pac9
(650) = 17 m
-1
, c pLISST
(670)
= 17 m
-1
*) were coincident with the HH flood tide while c p
minima were coincident with the LL ebb tide (c pac9
(650), c pLISST
(670) = 5 m
-1
). Time series analysis identified the dominant spectral power lay in diurnal periodicity for both signals (Table 3). Two excursions in c pLISST
(670) from the tidal pattern and agreement with c pac9
(650) occurred
37 late on both January 27 th
and January 30 th
. Within these restricted periods c pLISST
(670) did not track with water level, and instead spiked to 40 m -1 , the highest observed c p values. At these times, c p
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
L L
-1 to greater than 1500
L L
-1
.As with beam attenuation and turbidity, VC was many times higher on high tides (~250
L L -1 ) than on low tides (~15
L 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 27 th
and January 30 th
. These two events, which exceeded VC of 500
L 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
L 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
m) varied with both diurnal and semi-diurnal periodicity (Figure 7C). Microflocs concentration was nearly double in concentration of the component particles with
38 maximum values (~200
L L -1 ) coincident with the HH tide. Microfloc VC minima (~ 20
L L -1 ) were of the same magnitude as component particles and the timing was coincident with the ebb low tide. Macroflocs (D>133
m) 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
L 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, c p
, and volume concentration. Although the optical surrogates increased in concert with SPM concentration, there was a poor correlation between SPM and turbidity, c p
and VC (r 2 <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).
39
4.5 Particle Size
4.5.1 c p
Slope
The particulate beam attenuation (c pac9
) slope,
, is both theoretically and empirically related to bulk particle size (Boss et al., 2004). Since
is most sensitive to particles smaller than 20
m, once can expect good agreement with component particles
(D<33
m) (Boss et al., 2004).
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
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 (D
50
) 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). D
50
was lower on flood tides than on ebb tides.
D
50 varied from 25 to 30
m on flood tides to 75 to 150
m 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
maxima (Figures 8C-
D).
4.5.3 Particle Size Distributions
Example particle size distribution (PSD) spectra displayed multi-modal particle
40 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
m (10
L L
-1
, component particles) and 110
m
(17
L 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
m (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
m (10
L L
-1
) and 110
m (25
L 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
m with VC often exceeding 100
L L -1
41
(Figure 9, bottom). The occurrences of the high concentrations of large particles coincide with D
50
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
42 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 31 st
, 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
43 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,
44
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 semidiurnal 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
m. 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
45 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
m 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
m 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).
46
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).
revealed qualitative changes in the fine particle pool not evident in the particle size spectra. Trends in
indicate that the particle population ebbing from the island was more concentrated in small particles than water that flowed on to the island.
behaved oppositely than other particle size indices, the mean and median (D
50
) particle size, because the ac9 is sensitive to a smaller particle fraction.
data suggests the coarser particles in the particle size range <20
m 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 (D
50
) 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. c p
measured by the ac9 and LISST-B agreed with one another except when D
50
exceeded 100
m; probably due to the LISST-B’s smaller
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 c p
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
47 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
48 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
m and the production and export of larger, organic enriched particle aggregates (>150
m) 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
49 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
50 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.
51
6.2 Conclusions
- Particles smaller than 33
m 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.
52
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.
53
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
54 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,
360 o
, East = 90 o
) at Suisun meteorological station ( courtesy CIMIS ) (middle) over one spring neap cycle from January 13 th
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 13 th
until February 2 nd
, 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.
55
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 25 th through February 2 nd with close ups. The one day close up period occurred on the maximum spring tide, from noon on January 30 th
to noon on January 31 st and is indicated by a gray rectangle. Lower
values, indicative of lower mean particle size in the component particle fraction, were coincident with lower low slack ebb tide, while higher
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 30 th
and 31 st
.These selected PSDs correspond to peak flood (blue line with dot marker) and peak slack tide at 3 PM and 10
PM on January 30 th , respectively (black dashed line with star marker, top), higher high at
3 PM on January 30 th
(dotted black line, middle) and lower high tide 4AM on January
31 st
(solid blue line, middle) flooding and full ebbing cycle from 3AM until noon on
January 30 th
(solid black line, bottom). Note different y-axis scales.
57
Table 1 Nomenclature
Abbreviation Units Definition a a g m
-1 m
-1
Absorption coefficient
Absoprtion coefficient of dissolved materials c b c m m
-1 m
-1 m
-1
Scattering coefficient
Attenuation coefficient
Measured attenuation coefficient c g m
-1
Attenuation coefficient of dissolved materials c p m
-1
Particulate beam attenuation c pac9 m
-1
Particulate beam attenuation, measured by the ac9 c pgLISST m
-1
Attenuation of dissolved and particulate substances, measured by the LISST-B
FNU
LISST-B ac9
SPM
POM
D
50
PSD
FNU Formazin nephelometric units nm
-1
Slope of the particulate attenuation spectrum n/a n/a
Laser In Situ Scattering Transmissometer B model by Sequoia Scientific
Nine wavelength visible spetctrophotometer by WETLabs mg L
-1
Suspended particulate matter mg L
-1
Particulate organic matter
m Median aggregate volume diameter
VC
HH
HL um Particle volume size distributon
L L
-1
Particle volume concentration m Higher high water level m Higher low water level
LH
LL m Lower high water level 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
3.26
16.2
19.3
12.4
9.5
2.0
1.8
11.0
8.7
3.59
3.84
3.98
4.03
24.3
22.3
28.1
31.5
10.1
10.2
9.2
9.8
2.5
2.3
2.6
3.1
9.2
9.3
8.4
8.9
3.93
3.66
3.39
3.17
2.97
2.78
2.64
2.66
35.7
30.6
42.3
26.7
20.8
17.9
15.5
33.4
9.8
11.4
11.2
11.4
13.8
14.1
13.6
12.6
3.5
3.5
4.7
3.0
2.9
2.5
2.1
4.2
8.9
10.2
10.1
10.2
12.1
12.4
12.0
11.2
2.99
3.38
3.62
3.76
3.82
3.74
3.52
3.28
37.8
34.4
38.1
32.9
29.6
48.3
29.8
22.2
11.1
9.8
10.6
9.9
9.9
10.2
9.8
10.4
4.2
3.4
4.0
3.3
2.9
4.9
2.9
2.3
10.0
8.9
9.6
9.0
9.0
9.3
8.9
9.4
58
59
Table 3 Time series analysis results
Frequency (d
-1
)
2.77
2.92
3.36
3.50
3.94
4.09
1.90
2.04
2.19
2.33
2.48
2.63
4.23
4.96
5.11
0.88
1.02
1.17
1.31
1.46
1.60
1.75
0.15
0.29
0.44
0.58
0.73
0.02
0.27
0.02
0.01
0.12
0.05
1.00
0.75
0.03
0.02
0.02
0.01
Micro
0.30
0.05
0.06
0.04
0.06
0.50
0.92
0.05
0.06
0.00
0.01
0.02
0.01
0.03
0.00
0.00
0.14
0.02
0.01
0.09
0.04
1.00
0.48
0.03
0.03
0.02
0.01
Primary
0.11
0.05
0.02
0.01
0.02
0.16
0.12
0.03
0.04
0.02
0.01
0.00
0.01
0.01
0.00
0.00
0.13
0.00
0.00
0.05
0.01
1.00
0.12
0.01
0.02
0.02
0.01
0.06
0.03
0.04
0.03
0.01
0.06
0.19
0.03
0.04
0.05
0.01
0.01
0.02
0.01
0.00
0.09
0.08
0.08
0.07
0.01
0.03
0.49
0.04
0.09
0.24
0.16
0.10
VC
0.47
0.75
0.08
0.19
0.49
1.00
0.85
0.08
0.33
0.14
0.35
0.04
0.09
0.07
0.01
0.00
0.16
0.00
0.00
0.06
0.00
1.00
0.11
0.00
0.00
0.01
0.01
c pLISST
0.05
0.03
0.03
0.00
0.00
0.09
0.14
0.02
0.01
0.01
0.00
0.01
0.01
0.01
0.01
0.00
0.16
0.00
0.00
0.05
0.01
1.00
0.13
0.01
0.01
0.01
0.01
c pac9
0.05
0.02
0.03
0.00
0.00
0.09
0.13
0.02
0.01
0.01
0.00
0.01
0.01
0.01
0.00
0.01
0.07
0.00
0.00
0.01
0.00
1.00
0.36
0.05
0.02
0.01
0.01
Depth
0.03
0.02
0.02
0.01
0.04
0.38
0.79
0.03
0.01
0.01
0.02
0.04
0.00
0.00
0.00
0.03
0.11
0.11
0.02
0.01
0.09
0.23
0.06
0.05
0.20
0.03
0.09
Macro
0.16
0.45
0.02
0.09
0.32
0.57
1.00
0.02
0.25
0.01
0.24
0.02
0.03
0.05
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.
60
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).
61
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
300
Sac R. Discharge at Rio Vista
Bottom Turbidity at Mallard Island
400
62
300
200
100
200
100
0
12/15 12/22 12/29 1/5 1/12
2005 - 2006 Date
1/19 1/26 2/2
0
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
4
3
01/13
16
12
8
4
0
01/13
200
150
100
50
0
01/13
01/20
01/20
01/20
Depth
Velocity
01/27
01/27
01/27
0.75
0
-0.75
02/02
360
Wind Speed
Wind Direction 270
180
90
0
02/02
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,
360 o
, East = 90 o
) at Suisun meteorological station ( courtesy CIMIS ) (middle) over one spring neap cycle from January 13 th
to February 2, 2006.
63
64
2%
2%
4%
6%
4%
8%
Speed (m s
-1
)
11 - 12
10 - 11
9 - 10
8 - 9
7 - 8
Figure 5 Suisun wind velocity distribution
Wind rose plot of wind vector data collected at Suisun meteorological station from
January 13 th
until February 2 nd
, 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.
65
5.5
5.0
4.5
4.0
3.5
3.0
A)
40
30
20
10
0
B)
30
C)
20
10
Depth
Velocity
c pac9
(650) c pLISST
(670)
0
1600
1400
1200
1000
500
250
0
1/25
D)
1/26 1/27 1/28 1/29
2006 Date
1/30 1/31 2/1 2/2
0.8
0.4
0.0
-0.4
-0.8
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.
66
5.5
5.0
4.5
4.0
3.5
A)
3.0
Depth
Velocity a e
0.8
-0.4
-0.8
0.4
0.0
40
30
20 c
B)
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 ).
67
5.5
5.0
4.5
4.0
3.5
3.0
A)
250
200
150
100
50
0
B)
250
200
150
100
50
0
C)
Depth
Velocity
0.8
0.4
0.0
-0.4
-0.8
1500
1000
500
150
100
50
0
1/25
D)
1/26 1/27 1/28 1/29
2006 Date
1/30 1/31 2/1 2/2
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
5.0
4.5
4.0
3.5
3.0
A)
1.5
B)
1.0
Depth
Velocity a b
0.5
200
150
100
C)
50
0
1/25 1/26
D50
Mean
1/27 1/28 1/29 1/30
2006 Date
Figure 8 Particle size and hydrodynamics
1/31 2/1 d f c
2/2 e
12 6 12 6 12
Jan 30th - 31st Hour
0.8
0.4
0.0
-0.4
-0.8
68
20
10
20
15
10
5
0
10
0
30
0
10
0
400
300
200
100
0
10
0
10
1
10
1
10
1
Particle Size (
m)
Figure 9 Example particle size distributions
Peak Flood Slack
Peak Ebb Slack
LH Flooding tide
HH Flooding tide
Ebbing tide
10
2
10
2
10
2
69
70
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