Estuarine turbidity maxima revisited

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Estuarine turbidity maxima revisited: Instrumental approaches, remote sensing, modeling studies, and new directions

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D.A. Jay*, S.A. Talke*, A. Hudson*, M. Twardowski† *Department of Civil and Environmental Engineering, Portland State University, Portland, Oregon, USA † Harbor Branch Oceanographic Institute, Florida Atlantic University, Fort Pierce, Florida, USA

2.1 INTRODUCTION 2.1.1 PURPOSE: TOWARD A NEW UNDERSTANDING We seek here to bring together recently developed observational techniques with analytical and numerical modeling results to evaluate the present understanding of estuarine turbidity maxima (ETM). This synthesis emphasizes gaps in our knowledge and possible future observational paths; thus, we formulate a series of research questions. ETM dynamics are important for several reasons. First, the ETM is often the locus of long-term deposition (e.g., in Chesapeake Bay (Sanford et al., 2001), and in the Ems Estuary (de Jonge et al., 2014)). Even in sand-bedded systems without longterm deposition in ETM channels, there may still be deposition in peripheral bays or on tidal flats adjacent to the ETM (Sherwood et al., 1990; Simenstad et al., 1992). ETM also interact with navigation in several ways. Most obviously, the turbid zone of an estuary usually requires frequent dredging. Harbor sedimentation, often caused by agriculture and deforestation, has been a problem for millennia (Garbrecht and Garbrecht, 2004), and Marriner (2007) recognized the role of human-induced turbidity and convergent sediment fluxes in ancient harbor siltation. Although we have found no mention of ETM in ancient harbors, they must have been present, just as they are now. Navigational development (channel deepening and reduction of width) may create an ETM or enhance an existing one by increasing salinity intrusion and stratification and altering tides, e.g., in the Ems (de Jonge et al., 2014). Also, many contaminants are particle bound, and the ETM can be a locus of sediment Developments in Sedimentology, Volume 68, ISSN 0070-4571, http://dx.doi.org/10.1016/B978-0-444-63529-7.00004-3 © 2015 Elsevier B.V. All rights reserved.

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contamination, as in San Francisco Bay (Schoellhamer et al., 2007). ETM processes may act to redistribute, as well as to trap, contaminants, a process thought to occur in the Passaic River estuary (Chant et al., 2011). Furthermore, ETM play a strong role in estuarine food webs, and are a site of important geochemical transformations (North and Houde, 2001; Simenstad et al., 1995). Finally, estuaries have often been viewed as a filter on the fine-grained material supplied from rivers to the ocean (Schubel and Carter, 1984), and the ETM is one locus (along with tidal flats) of the biogeochemical processes involved, with implications for the global carbon balance. Discussing ETM phenomena requires establishing some terminology. Here, “suspended sediment concentration—SSC,” denotes all particulate solids above the bed. Unfortunately, measured SSC values depend on the means of collection and determination. If SSC is determined by filtration, then particles smaller than the filter pore size are excluded. At the large end of the size spectrum, some objects are too large to collect, even if they conceptually belong to SSC. By the above definition, SSC is the same as total suspended solids or TSS, but this definition is not universal. The U.S. Geological Survey (USGS), for example, defines TSS using a full water column integrated sample, while SSC is defined from a subsample. Because “SSC” includes the word “concentration,” it is convenient to also use “suspended particulate matter” or “SPM,” with the distinction being grammatical. In the context of the dynamical equation for suspended sediment conservation, “C” is used to refer specifically to the volume concentration of SPM, whereas SSC is ambiguous and may refer to either volume or mass concentration. Regardless of its definition, SCC in the ETM environment is dominated by nonliving, particulate solids above the bed that can settle, i.e., those particulates (including detritus) have a positive settling velocity, WS.

2.1.2 WHAT IS AN ETM AND WHY DOES IT MATTER? ETM were apparently first measured and discussed by Glangeaud (1938) and Postma and Kalle (1955). Since then, a growing body of literature has shown that ETM influence estuarine morphology, nutrient cycling, contaminant fate and transport, water quality (e.g., oxygen depletion), algae blooms and macrofauna, and carbon export to the ocean (Chant et al., 2011; Dronkers, 1986a; de Swart et al., 2009; North and Houde, 2001; Schuttelaars et al., 2013; Talke et al., 2009a). But what is an ETM? The following description, from a numerical model tutorial, is indicative of the understanding of the general oceanographic community: “ETM’s (Estuarine Turbidity Maxima) are regions of high turbidity that are observed in most estuaries or tidal rivers. The ETM forms usually at the tip of the salt wedge (interface of salt marine water and fresh river water), and is a convergence point of fine sediment. The sediment concentrations may be several orders of magnitude higher than in the seawater or river water. This high concentration cloud is advected upstream and downstream with the tidal currents and the freshwater discharge” (http://oss.deltares.nl/web/ delft3d/community-wiki/, accessed 30 January 2015). Notably absent in this definition is any mention of the diverse processes that create and maintain the high sediment

2.1 Introduction

concentrations in ETM in various types of estuaries, and it is unclear whether ETM occur in “most” estuaries. Also, the focus on the “salt wedge” is misplaced—ETM occur in estuaries without salt wedges, and under weakly stratified conditions. In fact, Allen et al. (1980), Doxaran et al. (2009), and Talke et al. (2009b) point out that ETM are found in tidal rivers, far upstream of any salinity intrusion. In the absence of clear understanding within the oceanographic community of what an ETM is, we begin by providing a short description of ETM phenomena. The ETM is a zone of elevated SSC compared to background levels elsewhere in the same estuary, though the maximum ETM concentration in one estuary (e.g., 0.15–1 g L
1 in the Columbia River estuary; Fain et al., 2001; Jay and Musiak, 1994), may be small relative to background levels in another (e.g., 1–100 g L
1 in the Ems; Talke et al., 2009b). An ETM may be described in terms of the type of system within which it occurs (a coastal plain, a tidal river, or a salt wedge estuary), but it is more relevant to describe the type of “particle trapping”; i.e., the mechanisms that concentrate or trap SSC to form an ETM. Convergent SPM fluxes are almost always involved, such that the horizontal water column SPM flux (us C), has a near-bed maximum, either tidally or when averaged over a tidal cycle. (Here, us is along-channel velocity, with the channel oriented in the s direction.) The convergent fluxes may be related to the mean, tidal, or overtide flows (Burchard and Baumert, 1998; Festa and Hansen, 1978; Jay and Musiak, 1994). Figure 2.1 provides a 2D (x–z) conceptual view of tidal variations and the mean salinity and SPM distributions in a stratified ETM, emphasizing the relationship between the salinity and SPM fields—tidal variations in the salinity, velocity, and vertical mixing fields influence the fluxes that drive ETM formation. Note that the 2D view in Fig. 2.1 can be modified by lateral effects, and that both salinity and SSC stratification can alter the velocity field. While most studies have focused on tides and stratified flow processes in ETM formation, winds may also be an important factor (e.g., in the Mondovi Estuary in India; Kessarkar et al., 2009). The role of convergent fluxes has been summarized by a conveyor belt analogy (e.g., Small and Prahl, 2004). Near-bed transport moves SSC landward toward the head of the salinity intrusion, where further landward transport cannot occur because upstream, near-bed flow is absent (Festa and Hansen, 1978). River-estuaries frequently exhibit a turbidity maximum near the upstream limits of salinity intrusion, e.g., in the Fraser River estuary (Kostachuk et al., 1989), but the mechanisms are more complex than the “conveyer belt” analogy would imply. Thus, internal waves and time variable mixing/stratification combine in the Fraser River estuary to cause higher SSC concentrations during weakly stratified parts of the tidal cycle than during the stratified periods (Kostaschuk, 2002), while sand dunes influence the vertical mixing that helps to determine the intrusion extent of the salt wedge (Kostaschuk et al., 2010). Data suggest that during extreme high flows in the Fraser River estuary, elevated SSC levels still occurred throughout the tidal cycle, even on greater ebb flows when the conveyor belt is absent (Fig. 2.2). The systems described in the previous paragraph are at the high-energy end of the ETM spectrum—they have predominantly sand beds in their ETM channel reaches,

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Ocean

River

Mean

Ebb

Flood

Z X

FIGURE 2.1 Typical tidal average, ebb, and flood ETM properties (top to bottom). Salinity is shown as contours, SPM as shading, and currents by arrows.

and the SSC trapped in their ETM is not stored permanently on the bed of the ETM reach, although lateral trapping of fines in peripheral bays occurs in the Columbia River estuary (Fain et al., 2001; Jay et al, 1990). Particle trapping also occurs in less energetic systems, where much of the material in the ETM remains on, or near, the bed at least seasonally and becomes part of the fixed bed. In such systems, time-lags between tidal currents and particle erosion/settling play an important role in particle trapping, for example in Chesapeake Bay (Sanford et al., 2001). In such systems, maximum SSC concentrations during a tidal cycle do not coincide with a current maximum, but follow the maximum by a delay (by as much as an hour) that is variable between systems. This does not mean that convergent fluxes are unimportant, only that they are modified by nonlinear particle behaviour (e.g., in the Ems, Chernetsky et al., 2010), such that the time history of horizontal fluxes is different from what it would be in the absence of settling lags. Finally, SSC levels may be high enough under fluid mud conditions to contribute substantially to horizontal and vertical density gradients in the multiphase water–sediment mixture, altering currents and sediment transport (Talke et al., 2009b), as well as the WS for cohesive sediments (e.g., Pejrup, 1988). In seeking a broad overview of ETM phenomena, we will attempt to include the full spectrum of estuarine types that have ETM.

2.1 Introduction

FIGURE 2.2 (A) Aggregate and (B) fines concentrations with salinity contours in the Fraser River on an incoming tide during spring freshet conditions, as determined by a combined calibration procedure using both acoustic backscatter (ABS) from an acoustic Doppler current profiler and an optical backscatter (OBS) sensor. While aggregates are mostly confined to the saline water mass, fines are seen in advance of the incoming salt wedge.

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The discussion so far has been implicitly two-dimensional (2D) in the alongchannel and vertical directions, but lateral SSC variability and transport are also important. Transport to, and from, lateral shoals and flats has been implicated in ETM behaviour in the Tay estuary (Weir and McManus, 1987), Hudson River estuary (Geyer et al., 1998), and the Seine estuary, where wave resuspension from flats is a dominant process (Le Hir et al., 2001). Lateral gradients in salinity, the Coriolis effect, channel curvature, and tidal rectification influence whether sediment collects on one side of a channel or another (Huijts et al., 2006, 2009; Lerczak and Geyer, 2004; Scully et al., 2009). Lateral variations in salinity and SSC also influence residual flow patterns and transport in the along-channel direction, often producing inward flow in shallow tidal areas and outflow in the channels (Lerczak and Geyer, 2004; Li and O’Donnell, 1997). Channel topographic features such as sills, holes, and constrictions influence circulation and salinity intrusion, and, therefore, sediment fluxes and trapping locations. For example, for much of the year there are two ETM in the Columbia River estuary, each in a deep hole in laterally separated channels (Fain et al., 2001; Hudson, 2014). While these topographic lows are frequently near the saline limit, ETM position is far less variable than the position of the salt front. Particle behaviour and interactions are also a vital part of ETM formation, and most of the silt and clay-sized particles found in an ETM would be exported to the ocean without aggregation (van Leussen, 1988). ETM aggregation occurs by a number of mechanisms. In addition to “salting out” of particles at the salinity front via electrostatic interactions (Turner and Millward, 2002), microbes play a vital role in gluing particles together into aggregates via transparent exopolymer particles (TEP). TEP may occur in suspension (Sun et al., 2012), but often attach to other organic particles, where they facilitate adhesion of inorganic and organic particles, modifying particle density, and leading ultimately to aggregate formation (Crump et al., 1998; Jahmlich et al., 1999; Mari and Roberts, 2008; Zimmerman-Timm et al., 1998). These concentration-dependent aggregation processes are modified by turbulence, which both bring particles together and shear them apart (e.g., Winterwerp, 2002). Aggregates are also often assumed to be fractal (Hill et al., 2011; Kranenburg, 1994), so that their mass increases with diameter D at a rate less than D3 (as it would if they were solid bodies). In fluid muds, Manning and Bass (2006) and van Leussen (2011) have used an aggregation model based on the idea of “macroflocs” and “microflocs.” Macroflocs are made up of microflocs; microflocs are smaller, denser, and less easy to disrupt than the macroflocs that they assemble into. Recent advances in particle optics and acoustics, reviewed below, provide the basis for further improvements in our understanding of aggregation and particle behaviour in the ocean, but much of this knowledge has not yet been applied to the ETM environment. Vertical processes are also vital in ETM particle trapping. A steady 1D balance in the vertical (z) direction, the “Rouse” or “Rouse–Vanoni balance” (Rouse, 1939), is widely used in describing the behavior of SSC, in ETM and in boundary layers in general. The Rouse balance assumes that vertical settling (WSC) is locally balanced
 @C by vertical turbulent mixing Kv at each point in the water column (Kv is the @z

2.1 Introduction

vertical turbulent diffusivity of SSC). When this concept is valid, it is highly useful— the vertical distribution of SSC in a water column can be related to the bed immediately below, without consideration of bed or water column processes at adjacent locations. For example, the Rouse balance explains how salinity stratification that reduces vertical mixing increases SSC near the bed, where it can be more efficiently transported in the landward direction (Geyer, 1993). But this explanation embodies a contradiction. If net horizontal transport is occurring, time variations and/or spatial gradients in C must exist, a violation of the assumptions of the Rouse balance (cf. Cartwright et al., 2013; Fain et al., 2001; Jay et al., 1999; Nowacki et al., 2012). So is the Rouse balance valid? The answer is: “it depends.” In favorable circumstances, horizontal and time gradients are weak enough that the Rouse balance holds approximately, even as the SSC field evolves in time and space. We evaluate this issue more carefully by scaling the advection–diffusion equation in Section 2.4. ETM dynamics are inherently nonstationary, most obviously on tidal time scales. Continuous wavelet transform (CWT) analyses and short-term Fourier decompositions of ETM variables (salinity, SSC, and microbial productivity) show that ETM processes and properties have a strong tidal component, but are far more irregular than tidal currents or water levels (Flinchem and Jay, 2000; Van de Kreeke et al., 1997). ETM processes are highly variable and do not repeat exactly from tidal cycle to tidal cycle or day-to-day. The SPM distribution in many estuaries, like the salinity distribution (MacCready, 2007), probably adjusts on time scales longer than the neap-spring cycle in many estuaries, complicating understanding and analytical modeling of ETM. Because the tides and river flow that influence ETM dynamics vary over monthly, seasonal, and interannual time scales, an ETM evolves toward a moving target. In a similar vein, the relevance of floods and storm surges to sediment transport and sedimentology is well known (e.g., Donnelly, 2001), but little thought has been given to how ETM respond and adjust to storm surge and other types of nontidal variability (e.g., upwelling and MSL fluctuations). Summing up the above description, we offer the following definition of an ETM: an estuary turbidity maximum is a localized extreme of tidally averaged turbidity and SSC in an estuary or tidal river. It is created by a combination of sheared and convergent SPM transport, lags in deposition and erosion, tidally variable vertical mixing, and/or aggregate formation. The circulation mechanisms that drive ETM formation include river flow, tides (linear and nonlinear), gravitational circulation, internal asymmetry, wind, and wind waves. Processes that promote formation of an ETM may be vertical, lateral or both, and ETM are often found in, or near, topographic lows. While ETM often occur near the head of salinity intrusions, an ETM may occur anywhere in an estuary, including landward of a salinity intrusion, and some estuaries have multiple ETM with different mechanisms of formation.

2.1.3 SCOPE OF PAPER The remainder of this paper focuses on several important ingredients in development of a better understanding of ETM dynamics. We first describe the emergence of new in situ optical and acoustic measurement techniques that have greatly increased

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knowledge of particle properties and behavior (Section 2.2), mostly in non-ETM settings. These techniques are integral to new remote sensing methods, and their use can potentially improve our understanding of ETM dynamics. As images from highresolution satellites (e.g., RapidEye, Ikonos, and WorldView) become more widely available and new methods are developed to use such satellite imagery for analysis of narrow channels in inland waters, satellite oceanography will become more important in analyses of ETM, providing dense, and synoptic spatial coverage (Section 2.3). Drones, aircraft, and fixed observation platforms may also become logical tools for ETM observation, supplementing satellite data. Finally, analytical and numerical models provide insights that may be used (Section 2.4) with the above observational techniques to motivate a discussion of ETM dynamical questions (Section 2.5). A summary and conclusions are given in Section 2.6.

2.2 IN SITU MEASUREMENTS: RECENT ADVANCES This section summarizes recent results obtained using in situ acoustic and optical instrumentation that may be useful for future ETM studies. The importance of describing them herein is perhaps heightened, precisely because most were not applied to ETM, so that they may be less than familiar to estuarine and environmental scientists.

2.2.1 ACOUSTICAL MEASUREMENTS AND INSTRUMENTS Acoustic instrumentation has been widely used for estimation of SSC in ETM, despite a variety of difficulties. As discussed, there remains considerable scope for innovation.

2.2.1.1 Uses of the Acoustic Doppler Velocimeter Widespread adoption in the 1990s of acoustic instruments that use the Doppler shift of backscattered sound waves to measure 3D velocity led quickly to use of the magnitude of the acoustic backscatter (ABS), to determine particle concentrations. Acoustic sampling has several advantages, most obviously that it does not disrupt particles, as long as the acoustic energy levels remain low. Another advantage is that the entire water column can potentially be sampled by a single instrument. Finally, the particle size response of the instrument, and the size and location of the sampling volume, can be adjusted by varying instrument acoustic frequency and the spatial arrangement of the transducer(s). The two primary acoustic instruments in use are the acoustic Doppler velocimeter (ADV) and the acoustic Doppler current profiler (ADCP) or acoustic Doppler profiler (ADP). We consider first the ADV, as it is most likely more traditional single-point sampling of sediment properties by invasive instruments. An ADV typically consists of a central acoustic source and three radially arranged receivers at 120° angles to one another (Kraus et al., 1994), although other

2.2 In situ measurements: Recent advances

sensor configurations are also available. All transducers focus on a small sampling volume (<1–2 cm3) usually at a distance of 50–160 mm from the sensors, although profiling versions of the instrument that cover a vertical range of 30 mm have been developed (Zedel and Hay, 2010). Sampling rates of up to 200 Hz are available, and the frequencies used are typically in the 5–16 MHz range. Use of such high frequencies limits the range of the instrument due to absorption, while the short wavelengths (30–100 mm in seawater) allow a small sampling volume and provide sensitivity to SSC. Because of the turbulence capabilities and the small sampling volume, the ADV is used primarily in benthic or surface boundary layers (e.g. Anderson et al., 2007; Talke et al., 2013) and in laboratory settings, although it can also be profiled to sample a larger part of the water column (e.g., Fugate and Friedrichs, 2003; Kay and Jay, 2003a,b). For measuring turbulence while profiling (or from any moving platform), an integrated inertial motion unit (IMU) is needed to remove the motion of the platform (Kilcher et al., 2014; Thomson et al., 2015). SSC and particle properties can be determined from an ADV by calibrating ABS against conventional SSC samples (e.g., Kawanisi and Yokosi, 1997), at least for the fraction with D >  40 mm. WS determination assumes a local Rouse balance between the vertical turbulent transport of SSC, hw0 C0 i (h i indicates a time average of turbulent quantities, the primes indicate turbulent deviations from local means), 0 0 and particle settling (WSC). WS is determined as the slope of a plot of h wCC i versus C. Fugate and Friedrichs (2002) validated this type of ADV determination of WS by comparison to other methods. However, Jay et al. (1999) found several difficulties in ADV estimation of WS in the Columbia River estuary ETM. First, variations in particle sizes required four SSC versus ABS calibrations for different tidal phases. Furthermore, unrealistic values of WS were obtained at some stages of the tide, likely because horizontal transport or time variations rendered the Rouse balance unusable. Maa and Kwon (2007) also note that ambient vertical velocity may affect the determination of WS, especially in the vicinity of bathymetric features. Russo and Boss (2012) confirmed the utility of ADV determination of SSC using laboratory data. Cartwright et al. (2013) modified ADV determination of WS, solving on a pointby-point basis for WS in a muddy estuary, to better reflect the variability of estuarine processes. Cartwright et al. (2011) showed that ADV methods could be applied in a muddy environment with multiple particle sizes, but a washload concentration must be assumed. In summary, use of ADVs to simultaneously measure benthic boundary layer turbulence and sediment transport processes is a mature practice. Because an ADV provides information at only one point, ADV sampling needs to be combined with other approaches to obtain a system-level understanding. How to optimally achieve this depends on the system, and the art of ADV sampling continues to evolve.

2.2.1.2 ADCP methods An ADCP or ADP typically uses 3–5 beams to calculate 3D velocity throughout the water column. The depth capabilities of the instrument vary with frequency—lower frequencies can penetrate 100 s of meters, but require larger sampling bins, limiting

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their spatiotemporal resolution. High frequencies (up to 2.4 MHz) have small cell sizes, but provide only a few meters of range. Just as with the ADV, the ABS from an ADCP can be calibrated to provide SSC. The early uses of ADCPs for water column particulate measurement focused on zooplankton (e.g., Flagg and Smith, 1989), possibly because the wavelength of an ADCP (typically 0.5–20 mm) was more commensurate with zooplankton size than suspended sediment diameter. There are four “flavors” of commercial ADCPs: (1) Narrowband (NB) ADCPs use a Fourier transform to estimate the Doppler shift of the acoustic signal, from which 3D velocity is calculated. NB-ADCPs have superior range, but require larger bins and/or more pings to accurately determine velocity than broadband (BB) ADCPs. Spatial bin sizes are typically >0.5–1 m even for the highest frequency instruments, which is rather large for resolving the vertical sediment distribution. (2) Broadband (BB) ADCPs use a coded pulse to achieve improved accuracy and bin resolution, at the cost of sampling range. BB technology encompasses multiple signal coding approaches with different properties. Spatial resolution is 10s of centimeters for higher frequency instruments, and many BB-ADCPs can also function in NB mode or as pulse-coherent ADCPs. (3) Pulse Coherent (PC) ADCPs require that two successive pulses ensonify the same water parcel, and 3D velocity is determined from the acoustic phase difference. PC-ADCPs provide better accuracy and spatial resolution, but their use is restricted to short path lengths and slower flows. Because of its superior spatial resolution (a few 10 s of mm), a PC-ADCP can resolve SSC profiles on a scale more comparable to the scale of variability near the bed (e.g., Ha et al., 2011; Yu et al., 2011). Moreover, the limited range and small bin sizes of PC-ADCPs motivate their use in a downward-looking configuration within 1–2 m of the bed. (4) Horizontal (H) ADCPs usually have three beams arranged in a plane, to resolve 2D horizontal velocity. Most water bodies are wider than they are deep, requiring a long horizontal path and a low acoustic frequency. H-ADCPs have been used to estimate sediment discharge, either by use of an optical backscatter sensor (OBS) to determine SSC (Buschman et al., 2012) or by using H-ADCPs for both velocity and SSC (Moore et al., 2012). There are several ADCP deployment modes useful for estuarine studies. To provide spatial coverage, an ADCP can be mounted on, or be towed behind, a vessel in a downward-looking mode. Moored configurations for collection of time series include upward-looking from near the bed, downward-looking from near the surface, and downward-looking from a platform on the bed. The last configuration provides the best coverage of the benthic boundary layer, but requires a second, upwardlooking instrument to sample the rest of the flow. Wake effects from the platform legs remain a serious consideration for downward-looking deployments on benthic platforms.

2.2 In situ measurements: Recent advances

SSC measurement with an ADCP is not straight-forward, despite an ADCP’s spatial coverage. First, typical ADCP beam configurations (with beams at 20°–30° from the vertical) cause the spatial extent of the sampling volume to increase with distance from the instrument. While 3D velocity determination requires at least three beams, it is customary to determine sediment concentration from individual beams (e.g., Holdaway et al., 1999), because the acoustic properties of the transducers are diverse, and the spatial structure of SSC may be more “fine-grained” than the mean velocity field. However, Gartner (2004) found a better comparison to OBS data when the four ADCP beams were averaged, perhaps because the OBS sensors were not colocated with any of the beams. Still, the multiple beams of an ADCP have the potential to provide independent SSC values at multiple locations, although Jourdin et al. (2014) have emphasized that the beams may give diverse responses for the same ping for a variety of reasons. A second challenge stems from the fact that acoustic beam diameter increases with distance. Unless there is a vertical beam, “side-lobe” energy impinges on the bed, or free surface, before the energy that is traveling in the direction of the beam orientation. Because the bed is much more reflective than particles in the water, sidelobe reflection limits ADCP resolution of velocity, and backscatter close to the boundary. Also, there is a small “blanking” distance near the transducer, and a larger distance in which nonlinear acoustic effects related to the finite transducer diameter must be included in the ABS calculation. Thus, an ADCP cannot sample more than 80–90% of the water column, and most of the “missing” information is in the benthic and/or surface boundary layers. In an upward-looking configuration, it is also difficult to sample closer than 1 m from the bed, so the benthic boundary is still missed. The above complications in the use of ADCPs for velocity and SSC measurements, and the need for calibration of ABS, typically mean an ADCP is only part of a sediment measurement program. A third potential complication in the use of an ADCP for SSC determination in an ETM is that acoustic scintillation associated with density fine structure and turbulence may contribute to the ABS signal (DiIorio and Farmer, 1994; Farmer et al., 1987). It is still fairly rare in an estuarine context for backscatter from fine structure to be quantified a´ la Seim and Gregg (1994), at least in part because of the difficulty in separating the effects of fine structure and particulates. This issue has been little recognized in the geological literature, while it has been a larger concern to estuarine physicists, because an ETM often appears in conjunction with the salt front in strongly tidal salt wedge estuaries. Given the large overturns visible on echo sounders during frontal passage (e.g., Kostaschuk and Church, 1993; Talke et al., 2010), the backscatter may be related to both scintillation effects and SSC. More generally, the importance of scintillation will depend on the concentration and size of the particles present, the strength of the turbulence, and acoustic frequency. Multifrequency acoustic sampling might be a good way to untangle the various contributions to the ABS. Merckelbach (2006) emphasized a different problem associated with ABS estimation of SSC, that particles are not random phase scatterers, as usually assumed by

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acoustic theory. In the weakly stratified Western Wadden Sea with <100 mg kg
1 of SSC, Merckelbach (2006) found that fine structure backscatter was 10
6 weaker than particle ABS, but that the approach of Thorne and Hanes (2002) to ABS calibration still overestimated SSC by up to 60 , because turbulence can create patches of particles with a spatial separation such that the backscatter is coherent, not random phase. This effect is analogous to convergence of surface drifters by the Stokes drift of surface waves. An algorithm is provided for the coherent ABS regime that corrects the overestimation seen in this case, but it is necessary to parameterize the turbulent dissipation to use this approach. Thus, while the problem of anomalous ABS associated with turbulence is, in principle, solved, there remain practical difficulties. Furthermore, Reichel and Nachtnebel (1994) concluded that a single ADCP could not generally distinguish between changes in particle size and SSC. Very small particles cause attenuation due to viscous losses, while large particles side scatter much of the acoustic energy (Gartner, 2004; Gray and Gartner, 2009). In environments where fine materials are abundant, viscous attenuation can be 25% or more (Holdaway et al., 1999). In a typical vertical ADCP deployment, upward fining will increase viscous losses toward the surface while scattering increases toward the bed (Sassi et al., 2012), but the details of the size distribution matter, including the mean size, the sorting, and the type of size distribution function (normal, log normal, or bimodal). For sand-sized particles, Thorne and Meral (2008) and Moate and Thorne (2009) suggest that a change in sorting can cause differences in ABS of 3–10 . Finally, for ETM studies it is important that water column absorption is a weak function of salinity, an effect that is most important for ADCPs with acoustic frequencies below 1 MHz (Lee and Hanes, 1995). Like variations in particle size, variations in salinity can cause variations in backscatter that are indistinguishable from variations in SSC. This issue is most complicated for vertical deployments in stratified systems—even if salinity S is known at the ADCP head, it may vary with depth along the beam. Despite the above issues, a considerable number of studies have used a wide variety of tactics to derive SSC from ADCP–ABS. Here, we focus on innovative combinations of ADCP sampling with other instrumentation and other novel ABS analysis methods. Use of optical instruments (typically an OBS) to calibrate ADCP backscatter data dates back at least to Thorne et al. (1991) and Thevenot and Kraus (1993). Because an OBS is sensitive primarily to particles with D <  40 mm, while ADCP–ABS responds mostly to larger particles, there is an implicit assumption in use of an OBS to calibrate ABS that there is a fixed particle size distribution, such that the SSC of fines reflects the total SSC. Complications ensue, therefore, when the particle field varies in time (Jay et al., 1999) or space (Gartner, 2004). A related issue is that the ADCP acoustic wavelength typically exceeds the particle or aggregate diameter. A coarsening of the sediment toward the size of the ADCP maximum acoustic response causes an increase in apparent concentration. The opposite effect occurs for a fining (Gartner, 2004). There are several possible solutions. Particle sizes

2.2 In situ measurements: Recent advances

can be determined by gravimetric sampling and sizing or by settling tube analyses, but both methods cause disruption of particles if aggregates are present. Another tactic is to combine ADCP–ABS with data from a laser in situ scattering transmissometer (LISST; e.g., Fugate and Friedrichs, 2003; Gartner, 2004; Hoitink and Hoekstra, 2005; Shugar et al., 2010). The LISST (see below) uses light diffractometry to provide both a volume concentration and an equivalent spherical size distribution, but gravimetric data are still needed for the determination of mass concentration. Another useful tactic is to use ADV and ADCP together (e.g., Scully and Friedrichs, 2007), because the large frequency difference between the two can potentially render the distinction between changes in size and concentration clearer. The additional information needed to resolve the effect of variations in grain size can sometimes be obtained from water column data. A minimum of two samples in the vertical would seem to be the minimum necessary in an ETM, where there is typically a mixed particle population with sizes ranging from fine silt to aggregates. Sassi et al. (2012) used near-surface and near-bed water samples in a freshwater context to provide an ABS calibration that accounted for vertical variations in grain size. Sahin et al. (2013) employed a similar dual-OBS calibration method to determine SSC profiles in a muddy wave-current boundary layer, using the results to understand the role of aggregation on SSC profiles. It was found that the smaller average floc sizes led to reduced vertical variations in SSC levels, consistent with the slower settling of smaller aggregates, relative to larger aggregates present at other times. The spatial coverage of ADCPs makes use of acoustic theory and multiple ADCPs a potentially attractive approach to distinguishing changes in size from changes in concentration. Topping et al. (2007) employed three side-looking ADCPs (0.6, 1, and 2 MHz) in the Grand Canyon to measure sediment transport by size fraction. The silt and clay fraction was determined from beam attenuation, while ABS was used to infer sand concentration. A similar approach was followed by Moore et al. (2012) to determine sediment discharge in a river with mostly fine sediments, but the frequencies used were more relevant to ETM environments (0.3, 0.6, and 1.2 MHz), and the 0.3 MHz unit was oriented so that it looked diagonally upward, to provide vertical coverage. Closer to the ETM environment, Jourdin et al. (2014) used paired, vertically oriented ADCPs of different frequencies (300 and 500 kHz; and 300 and 1200 kHz) to estimate SSC during two deployments in a macrotidal coastal embayment. They recommend a 4  separation in the acoustic frequencies of the ADCPs employed. SSC and size distributions can also be determined throughout the water column without the need for a second ADCP, if a Rouse balance can be assumed and settling velocities are known. Based on Owen tube sampling and underwater video (Knowles and Wells, 1998; Reed and Donovan, 1994), Fain et al. (2001) decomposed the SSC distribution into four WS classes (“washload,” fine silt, coarse silt, and aggregates or sand), inferred friction velocity (U*) from a moored ADP, and fitted (by nonnegative linear regression) the corresponding Rouse profiles of the WS classes to ADPcalibrated ABS. ABS was calibrated to bulk SSC using OBS casts at the locations of the moorings. Figure 2.3 shows examples of typical fits to the ABS profile.

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CHAPTER 2 Estuarine turbidity maxima revisited

A

7 Cobserved C1:Ws = 0.01 mm s−1 C2:Ws = 3 mm s−1 C3:Ws = 15 mm s−1 C4:Ws = 45 mm s−1 C1 + C2 + C3 + C4

Height above bed (m)

6

5

4

3

2

1

B

0

20

40

60

80

C (mg L

−1)

100

120

140

7 Cobserved 6

C1: 0.01 mm s−1 C2: 3 mm s−1

Height above bed (m)

62

5

4

C3: 15 mm s−1 C4: 45 mm s−1 C1 + C2 + C3 + C4

3

2

1 0

20

40 60 SPM (mg L−1)

80

100

FIGURE 2.3 Two examples of fits of Rouse profiles for four WS classes to ABS profiles, following the procedures of Fain et al. (2001). Rapidly settling material (sand and flocs, WS ¼ 15 and 45 mm s
1) is in prominent (A), while washload (WS ¼ 0.01 mm s
1) is prominent in (B). While the Rouse profile fit is quite successful in both of these cases, advection and time evolution of the SSC field sometimes yield profiles that cannot be represented with the Rouse model.

2.2 In situ measurements: Recent advances

Reliance on a Rouse balance limits the applicability of the method as used to date, but a more general SPM balance and/or multiple instruments can be used to generalize this approach. In summary, ADCPs are, like ADVs, widely used in analyses of SSC and sediment transport, in combination with various other instruments to provide size information. However, progress in the use of both ADCPs and ADVs is hindered by the absence of a comprehensive theory of acoustic scattering in water that accounts for all the relevant effects: variations in grain size distribution, aggregate properties, and along-beam variations in salinity and turbulence.

2.2.1.3 Other acoustic methods As tools for SSC analysis, ADCPs suffer from their multiple missions—the need to extract Doppler shift information greatly limits time–space resolution and range. Single-beam ABS can provide vertical resolution better than 10 mm, and the temporal resolution is limited primarily by path length rather than the need to resolve the Doppler shift (e.g., Hamilton, 1998), if velocity measurement is not a consideration. In addition to determining SSC from backscatter, the bin resolution of such instruments is sufficient to measure changes in bed elevation in the millimeter range, sufficient for resolving seasonal and even tidal variations in deposition and erosion in some systems (Ganthy et al., 2013). Pioneering multifrequency acoustic sizing work by Holliday and others (Hay and Sheng, 1992; Holliday, 1977, 2009; Holliday et al., 2003; Thorne and Hardcastle, 1997) resulted in development of several Tracor Acoustic Profiling System (TAPS) models with 6 or 8 frequencies up to 3 MHz, capable of sizing particles as small as O(0.1 mm). Multifrequency instruments are now commercially available. Improved inversion algorithms to determine size and concentration have also been developed (Moate and Thorne, 2012; Thorne and Meral, 2008), but these analyses still do not include fines and aggregates. Recently, a new dual frequency form of ADV, known as the acoustic concentration and velocity profiler (ACVP) has been developed for the measurement of boundary layer turbulence, and sediment concentration and transport (Hurther et al., 2011). Thus, multifrequency ABS instruments have potential for analyses of ETM dynamics. But an acoustic scattering theory for aggregates will need to be elaborated if multifrequency acoustics are to reach their full potential for estuarine and ETM studies. Like multifrequency echo systems, multibeam echo sounders can be repurposed to investigate coherent flow structures and suspended sediment dynamics (Best et al., 2010; Simmons et al., 2010). Finally, side-scan sonar can be used to remotely map turbidity plumes in 3D, particularly for hydrothermal vent and shelf slope sediment plumes (Orr and Hess, 1978; Palmer, 1996; Rona et al., 1991). A challenge for ETM applications would be use of a high enough frequency to resolve SPM while still achieving a meaningful range. Turbidity is usually treated as an interference with sonar mapping of bathymetry and other underwater structures, but relatively weak signal returns from the water column should have promise in resolving ETM with the improving fidelity of present commercial systems.

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CHAPTER 2 Estuarine turbidity maxima revisited

2.2.2 OPTICAL MEASUREMENTS AND INSTRUMENTS We summarize here optical techniques with potential for in situ ETM measurements, in addition to their broader use in oceanic and pelagic environments for validation of ocean color remote sensing.

2.2.2.1 Optical backscatter sensors The OBS was originally developed for surf-zone measurements (Downing, 2006), but it is widely used in ETM and estuarine studies. An OBS measures the backscatter from suspended sediment at infrared wavelengths from 760 to 920 nm. It is rugged, easy to deploy, can observe a wide concentration range, and, for a given size of SSC, has a nearly linear response over a wide range of concentrations. According to Lorenz–Mie theory for spherical particles (Downing, 2006), the OBS response varies approximately as D
1 (e.g., Morel, 1973), and a 10  increase in particle size is accompanied by a 10  decrease in response, for D greater than about 20 mm. There is, however, an enhanced response (>D
1) for smaller particles. Particles with relatively high refractive index (e.g., minerals) backscatter more efficiently (10) than particles with low refractive index, such as living cells with cytoplasm of similar refractive index to the surrounding medium (Twardowski et al., 2001). Particle shape and aggregation state affect the OBS response due to changes in particle cross section and scattering efficiency, with disruption of aggregates increasing the response by markedly increasing the concentration of smaller particles. While Lorenz–Mie theory is not applicable, high concentrations of fluid mud can still be monitored by an OBS, using an empirical nonlinear calibration (Kineke and Sternberg, 1992). The practical importance of these aspects of the OBS response varies with the system and sampling protocol. For example, a relatively wide range of particle sizes present at most times allowed a single OBS calibration to be used in the Columbia River estuary ETM, although the regression coefficients varied between cruises (Reed and Donovan, 1994). On the other hand, sand had to be accounted for separately in the Fraser River estuary, to achieve a workable OBS calibration. OBS sensors are also susceptible to both biofouling and long-term sensor drift (Wright and Schoellhamer, 2005). Despite calibration difficulties, the OBS has been a mainstay of coastal sediment observation for more than three decades, often serving as a means for transferring a calibration derived from gravimetric samples to other optical and acoustic sensors. Thus, OBS used in ETM studies is here described primarily in conjunction with use of other, newer techniques.

2.2.2.2 The laser in situ scattering transmissometer The LISST-100X is widely used, but its output is complex and more difficult to interpret than that of an OBS, with which it is often paired. It measures forward scatter from a 670 nm laser beam source imaged onto a target with 32 concentric rings to determine concentration as a function of particle size—large particles scatter more light through smaller angles than small particles. There are presently two LISST

2.2 In situ measurements: Recent advances

models, Type-B and Type-C, which resolve particles with D between 1.25 and 250 mm (scattering angle from 0.1° to 20°), and D between 2.5 and 500 mm (scattering angle from 0.05° to 10°), respectively (Agrawal, 2005). Instruments for other size ranges with slightly different optics have also been developed. Total particle cross section and volume concentration can be determined from a concomitant beam attenuation measurement, as in a conventional transmissometer. The LISST thus provides an ability to measure the size distribution along with SSC, but volume concentration must be converted to mass concentration, as with any transmissometer. A variant of the LISST, the LISST-ST, has been developed that also measures WS via a settling tube (Agrawal and Pottsmith, 2000). There are a number of issues that complicate LISST use in the ETM environment. One difficulty arises in the least-squares inversion of the optical signal (intensity vs. angle for the 32 concentric rings) to volume concentration in 32 size classes using scattering theory. The inversion is based on a library of scattering functions conventionally computed from Lorenz–Mie theory (assuming spherical particles), each specific to 1 of the 32 logarithmically spaced size classes. These scattering functions can be fit to the measured scattering function in the bulk sample to yield volume concentrations for equivalent spherical particles in each size class. A benefit of the technique is the inversion is only weakly dependent on particle refractive index. Natural particles are, however, not spherical. Thus, the above inversion usually underestimates particle volume, as the ratio of cross section (the parameter actually measured) to volume is typically smaller for nonspherical particles. The inversion also underestimates the peak size and provides noisy results for larger sizes (Pedocchi and Garcı´a, 2006a). Multimodal size distributions, common in ETM environments with aggregated and disaggregated particles coexisting, complicate size determinations. Also, nonspherical particles exhibit a wider scattering pattern than spherical particles, sometimes with specific modal peaks being associated with the particle’s minimum and maximum axes (Karp-Boss et al., 2007). Pedocchi and Garcı´a (2006b) describe a tunable algorithm, applicable to nonaggregated sediments, that reduces noise, improves the resolution of multiple peaks, and better estimates the size(s) of the peak(s). Agrawal et al. (2008) provide an alternative, empirical inversion kernel that replaces the theoretical scattering distributions to better account for particle shapes typical of sediments. As discussed below, understanding the effects of aggregation on scattering is ongoing, but aggregates will not scatter light in the same way as solid particles of the same size and shape, and their density (closely linked to refractive index) is strongly correlated with scattering behavior. Density fine structure resulting from turbulence in a stratified flow can scatter light via a Schlieren effect (Mikkelsen et al., 2008), causing a sensor problem somewhat analogous to the impacts of acoustic scintillation on ABS measurements. Styles (2006) showed that, for a LISST set up to measure particles from 2.5 to 500 mm, data from the inner nine rings were corrupted by salinity differences of 2–10 psu across the sampling volume, limiting determinations of median diameter to size distributions with medians <128 mm. There is no known solution to this problem, which is a serious challenge to use of a LISST in the (usually) stratified ETM environment.

65

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CHAPTER 2 Estuarine turbidity maxima revisited

One possible strategy is to confine measurements to a neutrally stratified boundary layer, as verified by coincident CTD profiling. Despite the above difficulties, the LISST has been used in numerous studies in ETM environments. For example, Mikkelsen and Pejrup (2001) showed that LISST data and fractal arguments could be used to determine three important particle parameters: D, WS (from Stokes’ Law), and rS, as long as a calibration for LISST attenuation versus mass concentration is available. This approach is useful but does not take into account the importance of particle composition (e.g., organic vs. inorganic and the link to refractive index). Another issue is that the Kolmogorov scale acts as an effective upper limit on aggregate size (Fugate and Friedrichs, 2003), with maximum size Dmax  (n/E)¼, where n is viscosity and E is turbulent kinetic energy. Thus, the above fractal spectrum has a limited range of applicability in terms of size. Moreover, Mikkelsen et al. (2005) emphasize that a LISST will typically give different estimates of aggregate size than obtained by other instruments, e.g., a digital floc camera (DFC; discussed below). In particular, the upper limit (250–500 mm) of LISST size resolution is less than the aggregate size in many environments (causing an underestimation of mean size), while at the same time scattering from flocs too large to be resolved by the LISST increase estimates of particles in the larger size classes (causing an overestimation of concentration for the larger sizes). Thus, particle measurement and characterization in the ETM environment is an incomplete and evolving science. In particular, there is a need to incorporate recent particle optics results from studies in other environments, motivated primarily by the needs of remote sensing (below).

2.2.2.3 Holography and floc cameras Traditionally, oceanographers have sought to characterize particle size D, WS, and density rS. For isolated mineral particles, rS is typically known, and only one measurement (of either D or WS) is needed, although grain shape plays an important secondary role in setting WS. Aggregates are more complex, in that they are made up of a variety of primary particles, they trap interstitial fluid, and their density varies with size in a quasi-fractal manner (rS  D
a; Kranenburg, 1994). Aggregates are also very easily disturbed, and collection/removal from the ambient environment results in alteration of aggregates. Thus, determination of aggregate WS and rS typically requires direct, in situ measurement even if D is known, although theoretical work suggests that an indirect approach may be possible. Aggregate WS and rS may be measured directly using a DFC (Mikkelsen et al., 2005). A DFC illuminates a thin 40  40 mm2 slab of water with a 20 ms LED pulse. The pixel size is 15 mm, and nine pixels are needed to resolve a particle, yielding a nominal minimum D of 45 mm (Hill et al, 2011). Analysis of successive images can be used to determine WS. Kumar et al. (2009) developed a DFC with a nominal D range of 15 mm to several mm, but this device has not been used in estuarine studies. Additionally, the DFC has not yet reached the status of a commercially available, off-the-shelf instrument. Holographic imaging offers an alternative approach to in situ measurement of D and WS (Davies et al., 2011; Graham and Nimmo Smith, 2010; Sheng et al., 2006).

2.2 In situ measurements: Recent advances

The Graham and Nimmo Smith (2010) device has a field of view of 7.4  7.4 mm2 and obtains images at the rate of 25 frames/s using a 532 nm laser; it resolves particles with D from 20 mm to 7 mm. Another device described in Talapatra et al. (2013) used a 650 nm laser to measure in-water holographic images at 15 frames/s of particles in the size range of 3.9 mm to several millimeters. A key advantage of a holographic approach over DFC is the fact that in-focus particle images can be extracted from a 3D volume during reconstruction from a single holographic frame, providing an improved depth of field by several orders of magnitude. With the advent of digital reconstruction techniques, increased graphic processor unit processing speeds, and increased storage capabilities, holographic imaging is realizing its potential in characterizing undisturbed suspended particles. Even with these recent advances, in situ holographic imaging is not yet a practical tool for determining a full particle size distribution or for WS. Only particles with D >  40 mm, can be imaged reliably with a holographic system employing an inexpensive, stable continuous wave laser, as smaller particles blurred due to water movement. Pulsed lasers can provide detailed imaging for these small particles, but such lasers are far more expensive and more challenging to implement in an imaging system. A commercial holographic particle imaging system with a continuous wave laser is now available from Sequioa Scientific for marine use, although it does not directly provide WS. In the only estuarine use of holography that we have been able to find, Braithwaite et al. (2010) confirmed the fractal variation of rS with D in a combined LISST-100 and holographic study of the Tamar estuary. Measurements appear to have been taken on the seaward side of the ETM only and ETM dynamics were not examined.

2.2.2.4 Inherent optical property measurements and theoretical modeling of particle optics The need to interpret satellite remote sensing images has driven a revolution in optical oceanography with multispectral and hyperspectral sensors, but the results of this revolution have not been applied to the ETM environment, in part, because satellite images have lacked spatiotemporal resolution needed for estuarine use. Also, a stratified estuary, with a particle field concentrated near the bed, is not the most obvious place to employ optical imagery of the water surface. However, theoretical and technical advances in in situ particle optics, mostly focused on coastal and oceanic environments for ocean color remote sensing and naval applications, have great potential for use in estuarine oceanography. A number of companies offer in situ instruments measuring inherent optical properties (IOPs). This extensive work has been reviewed elsewhere (e.g., Bowers and Binding, 2006; Neukermans et al., 2012; Sosik, 2008; Twardowski et al., 2005), and only a brief summary is presented herein. The optical properties of estuarine waters are typically controlled by particles with D ranging from colloidal (submicron) to a few millimeters. Bowers and Binding (2006) summarize research regarding the optical properties of mineral particles as follows: (a) particle-specific absorption a* (¼absorption a divided by SSC) decreases linearly with increasing wavelength in the visible spectrum down to a

67

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CHAPTER 2 Estuarine turbidity maxima revisited

constant value in the infrared; (b) mineral particles scatter up to 10 more light than they absorb; (c) specific scattering b* (¼ scattering b at all angles divided by SSC) increases with increasing E because aggregate size decreases at high turbulence levels; and (d) the effects of particle composition on optical properties can be minimized by using ratios of properties at different wavelengths. Bowers et al. (2009) extended these ideas to aggregates, arguing that area-dependent scattering is dominant for coastal and estuarine mineral flocs in the red part of the spectrum, whereas absorption is also important for blue and green light. Measurements in the red and green parts of the spectrum can together be used to derive particle area and size (Boss et al., 2001). Further examination of particle behavior in shelf waters suggests that b* is (DrS)
1, with most of the variability due to variations in rS (Bowers et al., 2009). Neukermans et al. (2012) suggest that SSC is best predicted by side- or backscatter, while particle cross section is most closely related to c*. However, bb* (¼backscattering at all angles divided by SSC) varies by 3–4 due to composition, with inorganics having stronger backscatter. Hill et al. (2011) compared observations and scattering theory, pointing out that c* varies with size by only 2, and much less than suggested by theory. Twardowski et al. (2015) combine earlier work by Twardowski et al. (2001), Boss et al. (2001, 2009), and Khelifa and Hill (2006) to suggest a practical, multispectral observation approach that could be applied to estuaries to determine SSC in the presence of aggregates, based on commercially available IOP sensors that can be towed or profiled through the water column. They employed size-dependent models of optical attenuation c and backscattering bp, and defined a particle parameter space using the parameters c* and bp/c (the latter both at 650 nm), that appears to be applicable to a wide variety of environments (Fig. 2.4). The principle behind this parameter space is that bp/c depends largely on refractive index n (Twardowski et al., 2001), while c* depends primarily on the exponent a of a power law model describing the particle size distribution (Boss et al., 2001). Density rS is determined by the model from refractive index, but WS would still have to be determined as per Winterwerp (1998), or in some other way. Overall, the measurement requirement is for c at two wavelengths (to resolve size effects) and bp. While this approach models, rather than measures, a particle size distribution, it has been empirically validated. Thus, the analytical model could predict SSC as well as a direct empirical fit between SSC and measured c. The potential utility of such an analytical model is its general applicability for diverse waters with widely varying particle composition, whereas empirical fits to derive SSC from a single optical measurement must be determined for every candidate water mass. In very turbid waters, path length L is a concern with transmissometers, relative to backscatter devices like the OBS, as the smallest pathlengths available are typically L ¼ 100 mm, except for the LISST, which has L ¼ 50 mm. Measurements for c are optimized when L  1/c, so a device with L ¼ 100 mm is optimized for c  10 m
1 water (Hojerslev, 1994), which corresponds to an SSC of typically 10–30 g m
3 (Hill et al., 2011). For accurate transmissometry measurements equivalent to turbidity dynamic ranges of the OBS, pathlengths would need to be O(1 cm).

FIGURE 2.4 Particle optics model, with (A) field data (122 sites from 10 different global locations) overlying analytical model results (gray traces) for SPM as a function of optical properties and (B) scatter plot of modeled versus independently measured SPM. Note that the refractive index n (which depends strongly on rS) varies with the ratio of backscatter to attenuation at 650 nm, while the specific attenuation varies with the slope of particle distribution, set by exponent a. Adapted from Twardowski et al. (2015).

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CHAPTER 2 Estuarine turbidity maxima revisited

In summary, advances from optical oceanography, developed primarily for coastal and oceanic environments, have considerable potential for application in the ETM environment. In some cases, however, modifications of commercially available sensors are required, such as shortened pathlengths and reduced gains to widen the dynamic range on the high turbidity end. Such sensors are amenable to deployment on towed and autonomous underwater vehicles (e.g., Glenn et al., 2008), which could enable rapid assessment of ETM. Modeling work that considers aggregates should be directly applicable.

2.3 BUILDING AN INTEGRAL UNDERSTANDING OF ETM VIA REMOTE SENSING: POSSIBILITIES AND CHALLENGES Monitoring estuaries with satellite remote sensing have emerged as an extension of previous campaigns in the open ocean through advances in sensor technologies and development of algorithms linking water biogeochemical properties to surface reflectance (IOCCG, 2000; Mouw et al., 2015; Twardowski et al., 2005). Recent work emphasizes the potential of remote sensing for the study of estuarine and ETM environments. The primary advantage of using satellite data to study ETM is that measurements are synoptic and can resolve spatial features of the turbidity distribution that cannot be observed by either vessel-based or moored instruments (e.g., Fig. 2.5). This synoptic capability motivates qualitative interpretations of the particle trapping mechanisms and effects of topography and lateral transports described in

FIGURE 2.5 Turbidity distributions in the CRE derived from MODIS-based surface reflectance (Hudson, 2014). The turbidity is a function of both river flow and tidal range. Panels (A) and (B) represent conditions at a time of moderate tidal ranges (2.6 m) during low (A) and high (B) flow rates. Panels (C) and (D) illustrate neap (1.7 m tidal range) in (C) and spring (3.5 m in (D)) tidal conditions during low river flow. Panels (B) and (D) show ETM that are prominent during high flow and spring tide periods.

2.3 Building an integral understanding of ETM via remote sensing

Section 2.1. When coupled with hydrodynamic theory (Section 2.4), a quantitative understanding of these physical mechanisms can emerge. Moreover, the global nature of satellite measurements can potentially facilitate system intercomparisons and provide insight into trends and variability over the period of record. We focus here on satellite-based remote sensing, although we note that our overview is applicable to airplane- and land-based remote sensing surveys as well.

2.3.1 MEASURING TURBIDITY REMOTELY Satellite-based instruments capable of monitoring ETM (Table 2.1) measure reflectance at the top of the atmosphere. Reflectance at the surface of the water is obtained after application of atmospheric corrections. Surface reflectance is the ratio of water-leaving radiance to incident solar irradiance. Current instruments measure surface reflectance in discrete wavelength bands that allow discrimination of spectral structure in terms of optically significant components (OSCs), such as phytoplankton Table 2.1 Satellite-Based Instruments Utilized for Monitoring Coastal Water Bodies Record of Deployment

Spatial Resolution

Spectral Properties

Return Interval

Coastal Zone Color Scanner (CZCS)

1978–1986

825 m

Sea-viewing Wide Field of view Sensor (SeaWiFS)

1997–2010

700 m

Collected data only 2 h per day 1 day

Moderate Imaging Spectroradiometer (MODIS)

1999– present

Up to 250 m

Medium Resolution Imaging Spectrometer (MERIS)

2002– present

Up to 300 m

Landsat Return Beam Vidicon (RBV), Multispectral Scanner (MSS), and Thermatic Mapper (TM) Hyperspectral Imager for the Coastal Ocean (HICO)

1972– present

Up to 30 m

5 bands in visible spectrum at 20 nm bandwidth 8 bands in visible and near infrared (NIR) spectrum at 20 nm bandwidth 36 bands in visible and IR spectrum at 10–400 nm bandwidth 15 bands in visible and IR spectrum at 10–30 nm bandwidth 7 bands in visible and IR spectrum at 100 nm bandwidth

2009–2014

Up to 90 m

87 bands in visible and NIR at 6 nm bandwidth

3 days

Name

From Hudson (2014).

0.5 day

1 day

2 weeks

71

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CHAPTER 2 Estuarine turbidity maxima revisited

pigments. IOPs, such as absorption and backscatter, change with the relative concentrations of OSCs and directly determine the reflectance signature of the water body (Bukata et al., 1995; Twardowski et al., 2005; Werdell et al., 2013; Zaneveld et al., 2005). Thus, IOPs form the link between remotely sensed surface reflectance and the biogeochemical constituents of the water, allowing estimation of SSC and other OSCs. However, a challenge in coastal waters is that multiple OSCs (including mineral particles, detritus, aggregates, different types of chlorophyll, and dissolved organic matter) each contribute to the measured reflectance in a spectral band, potentially leading to an underconstrained problem, if an insufficient number of spectral bands are measured. Remote estimation methods for OSC concentrations fall into two broad categories: (a) semi-analytical algorithms based on modeling radiative transfer phenomena that rely on deconvolution of the measured reflectance spectrum of a water body into individual OSC concentrations and (b) empirical algorithms that rely on statistical correlations between remote and in situ data. Semi-analytical algorithms can be developed using nonlinear optimization, principal component analysis (PCA), neural networks, or algebraic matrix inversion, and have the potential advantage of estimating multiple OSC concentrations simultaneously (IOCCG, 2006; Maritorena et al., 2002; Werdell et al., 2013). They are particularly useful in complex coastal and nearshore waters where simple empirical algorithms break down. Empirical algorithms often take the form of simple band ratios and are most useful in conditions where the various OSCs covary, which is not likely in ETM environments. Nonetheless, regionally and seasonally specific empirical algorithms have been developed for coastal waters and estuaries (Astoreca et al., 2012; Babin et al., 2003; Siegel et al., 2005; Snyder et al., 2008).

2.3.2 LESSONS LEARNED FROM REMOTE MEASUREMENTS IN ESTUARIES Most applications of satellite ocean color data to estimate OSC concentrations in water bodies do not extend nearshore, but several studies have demonstrated the potential of remote measurements for studying estuarine processes (Aurin et al., 2010; Chen et al., 2006; Doxaran et al., 2003, 2005, 2006; Feng et al., 2014; Hu et al., 2004; Hudson, 2014; Martinez et al., 2009; Moore et al., 2014; Zhao et al., 2011). These studies have calibrated and validated algorithms for determining OSC concentrations in estuaries and have begun analyzing the causes and meaning of the resulting OSC distributions. Qualitative links have been established between physical forcing and ETM variability. Despite a disconnection in the literature between hydrodynamic processes and satellite observations of ETM, preliminary results support systematic study of ETM with satellite data. More specifically, recent studies show that ETM can be monitored through remote estimates of turbidity or suspended sediment, and that empirical algorithms can be time independent, at least within a system (Doxaran et al., 2003, 2005, 2006; Hu et al., 2004). This allows study of ETM responses to diverse forcing conditions

2.3 Building an integral understanding of ETM via remote sensing

over long time scales. For example, MODIS data collected in Tampa Bay, Florida showed that the ETM migrate the full length of the system during the transition between the wet and dry seasons, but ETM behavior also showed significant interannual variability related to wind forcing (Chen et al., 2006). Also, multiple ETM were observed in the Gironde estuary, France (as noted by Allen et al. (1980) from conventional observations), and coherent turbulent structures modulated the turbidity distributions—both phenomena likely being related to channel geometry (Doxaran et al., 2006, 2009). Connections between coastal processes and their underlying mechanisms can also be determined through ocean color remote sensing. For example, satellite data have been used to quantify trends in annual sediment transport volumes, which increased in the Amazon River (Martinez et al., 2009) and decreased in the Yangtze estuary (Feng et al., 2014) over the last decade, possibly due to the deforestation and the construction of the Three Gorges Dam, respectively. Furthermore, a study in the Mobile Bay estuary, Alabama, compared MODIS measurements to 3D hydrodynamic model results to demonstrate that resuspension and transport in response to a high-wind event failed to export sediment from the system (Zhao et al., 2011). Studies like these hint at the potential capabilities of remote sensing data to investigate ETM behavior, in that they provide quantitative interpretations of suspended sediment dynamics in non-ETM environments. MODIS measurements of the Columbia River estuary gathered over a decade demonstrated that North and South Channel ETM have distinct responses to hydrodynamic forcing (Hudson, 2014), supporting previous studies that suggested suspended sediment dynamics and the associated ecological communities are spatially variable (Jay and Musiak, 1994, 1996; Sherwood et al., 1990). MODIS data also confirmed that the South Channel ETM exhibited greater sensitivity to fluvial and tidal input. But despite large river flows and strong vertical mixing during spring tides, ETM were not observed seaward of river kilometer 15. Comparisons to a 2D (x–z) semi-analytical hydrodynamic model suggested that topographic lows served to enhance landward fluxes of sediment, thereby inhibiting seaward migration of the ETM. What additional gains can be made in our understanding of ETM, given the current state of remote sensing technologies? Many applications of hydrodynamic and sediment transport models suffer from substandard calibrations and inaccurate boundary conditions, due to the paucity of in situ measurements, an issue that can, in part, be addressed through use of satellite data. Another logical step is to connect remote sensing observations to near-bed processes using hydrodynamic and sediment transport models. This connection is important because satellite data represent integrated measurements of OSC concentrations only within about the first optical depth (1/K, where K is the diffuse attenuation coefficient for downwelling solar irradiance), and are, therefore, unable to resolve vertical features and processes. Satellite data are also limited by poor temporal resolution relative to ETM time scales. Combining satellite data and model results will, therefore, improve the utility of satellite data and support interpretations of ETM phenomena. Assimilation of satellite data (e.g., chlorophyll and water temperature) into estuarine models, as is presently

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done for global ocean models, could improve these models. In the future, the estuarine salinity gradient may be measureable remotely. The Aquarius mission has proven satellite-based instruments can precisely measure salinity (RMSE 0.3 PSU), but the spatial scale (150 km) is far too large for estuarine applications (Drucker and Riser, 2014). Despite such promising prospects, serious challenges remain in using remote sensing to analyze ETM processes. In addition to the complexity of coastal waters, these issues include: (a) irregular and incomplete temporal coverage of nonstationary, tidal systems by polar orbiting satellite imagers; (b) the inability of satellite optics to penetrate to the bed in the stratified ETM environment; (c) inadequate spatial resolution of satellite images for ETM studies; (d) poor atmospheric conditions and uncertain atmospheric corrections in nearshore environments; (e) difficulties in validating surface reflectance and algorithm retrievals in waters with high turbidity; and (f ) a lack of sufficiently quantitative algorithms for concentration and composition of suspended particles. As an illustration of some of these issues, consider a Landsat 5 image (30 m resolution) of New York Harbor taken about 2 weeks after Hurricane Irene in 2011 (Fig. 2.6). It shows considerable surface turbidity following the storm surge and flooding in the Hudson River and other tributaries. It resolves many features of the Hudson and Raritan River plumes but fails to capture features in smaller tributaries, e.g., those at the north end of Newark Bay, where flooding was also severe. Furthermore, the Landsat return period (16 days) makes coverage of this dynamic event incomplete (cf. Ralston et al., 2013 for more details of the event). Efforts are being made to resolve all of the above issues. Commercial satellite data remedy, in principal, problems with time–space resolution, but high cost of

FIGURE 2.6 Landsat 5 observations of New York Harbor on 9/11/2011, about 2 weeks following Hurricane Irene. A true color image (A) shows resuspension and transport following the storm. Measurements in band 3 at 630–690 nm (B) match the sediment distribution, suggesting data could be calibrated to in situ turbidity.

2.3 Building an integral understanding of ETM via remote sensing

images has limited their use to date. New generations of OSC retrieval algorithms will also improve our ability to answer more in-depth questions about temporal and spatial variability of ETM. Despite the growing use of regionally and temporally specific empirical algorithms to monitor estuaries, there is considerable potential for semi-analytical algorithms to provide more accurate retrievals of OSCs in coastal water bodies (IOCCG, 2006), and efforts to increase their utility are underway (Aurin et al., 2010). Practical implementation of such algorithms is becoming simpler with consolidated software packages such as GIOP, which allows direct algorithm evaluation, regional tuning, and ensemble inversion modeling (Werdell et al., 2013). Furthermore, globally valid semi-analytical algorithms to remotely measure turbidity are emerging, allowing more accurate OSC retrievals despite geographic variability in IOPs (Dogliotti et al., 2015). Such advances will promote the quantitative synoptic mapping of ETM and comparison of different estuary types. The future of other remote sensors is promising as well. Sensors with spatial resolution of 100 m or less and spectral resolution of less than 5 nm (e.g., HICO aboard the International Space Station and HSI with the EnMAP mission, planned for launch in 2018) will allow finer scale detection of ETM features and the composition of suspended particles. A comparison of various semi-analytical and empirical algorithms suggested the higher spatial and spectral resolution of HICO improves OSC retrievals over those obtained using data from the MODIS or MERIS sensors (Ryan et al., 2014). Furthermore, geostationary sensors like Korea’s GOCI (Geostationary Ocean Colour Imager), and GOCCI-2, also planned for launch in 2018, can sample every 15 min over a single location, addressing the need to resolve ETM features on tidal time scales. Plans for a U.S. geostationary imager called GEO-CAPE are being reviewed, and its mission focus will be U.S. estuaries. The next U.S. polar orbiting imager (PACE), to be launched in the 2022–2023 timeframe, will have hyperspectral capability and extended spectral range into the UV, both key to development of better algorithms to derive OSCs from space. PACE will also have a polarimeter, to detect water-leaving, multiangle polarized scattering. Such a sensor would allow better characterization of particle composition than is possible from remote measurements of reflectance alone, and introduces the possibility of developing algorithms that integrate measurements from both sensors. Another development is the emergence of hyperspectral imagers deployed on aircraft, such as the NASA PRISM imager developed by JPL (http://airbornescience. jpl.nasa.gov/instruments/prism) and the European Space Agency APEX. Flown from aircraft, these imagers have meter scale resolution. NASA supports open competitions to use PRISM for coastal research. In the near future, drones may also become an appropriate platform for optical studies of ETM phenomena. Lidar (light detection and ranging) sensors, deployed from aircraft for decades, have the potential to be used in ETM sensing. Lidar provides remote profiles of particle backscattering and attenuation in the water column along the flight track (Churnside, 2014; Churnside et al., 2014; Vasilkov et al., 2001), and there is interest in making routine lidar measurements from space (Behrenfeld et al., 2013). But lidar

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can also be operated from ships, resolving 2D ribbon maps of particle distributions while underway. As an active form of remote sensing, this use of lidar is similar in concept to ADCP signal returns, but for the smaller particle size classes that strongly impact turbidity. Depth range for lidar systems can be several tens of meters, even in relatively turbid waters. In the past, lidar held significant potential, but was expensive and challenging to implement. Recent advances in lidar technology suggest that it can become a practical ocean-observing tool (Zimmerman et al., 2013). In summary, it is uncertain what new insights into ETM behavior lie ahead, but the vital role of remote sensors in such discoveries is clear. The synoptic nature of remotely derived data promotes system scale interpretations and, when combined with models and in situ monitoring networks, will help build an integral understanding of ETM.

2.4 ETM DYNAMIC: INSIGHTS FROM THEORY, MODELING AND OBSERVATIONS 2.4.1 ESTUARINE CIRCULATION AND ETM FORMATION The formation of ETM is fascinating and nonintuitive, in that sediment is trapped and concentrated, despite dilution by seawater and dispersion (Dorrestein and Otto, 1960; Fischer, 1973; Hansen and Rattray, 1965; Monismith et al., 2002). Oscillatory tides and stratification spread particles by shear dispersion (Fischer et al., 1979), and the interaction of tidal flows with large-scale geometric features produce “chaotic dispersion,” in which particles that are initially close together spread apart due to differing paths around sand-bars and other geometric features (Banas and Hickey, 2005; de Swart et al., 1997; Zimmerman, 1986). On smaller spatiotemporal scales, near-bed turbulence erodes sediment, spreads it in the direction of the prevailing flow, and interacts with larger time/length scales. How does the process of particle trapping work, given these dispersive processes. In an ideal 2D world, the SSC mass balance for an infinitesimal control volume is: dCðx,z, tÞ @ @ ¼
fCðx,z, tÞU ðx,z, tÞg
fCðx, z,tÞðW ðx,z, tÞ
ws Þg dt @x @z

(2.1)

where U and W are the horizontal and vertical velocities and turbulent fluctuations have not yet been separated from the mean flow. Here, we have assumed laterally homogenous conditions for simplicity, the z-axis points upward from the water surface, and x is 0 at the estuary/ocean boundary and is positive landward. The complexity of sediment transport processes in an estuary are readily apparent: horizontal and vertical variability caused by tides and river flow affect both the velocity and sediment concentration C(x,z,t), and hence the sediment balance. Theoretical analyses usually simplify Eq. (2.1), by assuming steady river flow and that equilibrium conditions are established over time scales that are long

2.4 ETM dynamic: Insights from theory, modeling and observations

compared to a tidal cycle, but short compared to time scales over which morphology changes. This stationarity is a theoretical construct, never attained in practice. River discharge varies, and tides are affected by weather time scales of 3–7 days (Kukulka and Jay, 2003a; Moftakhari et al., 2013), and by the spring-neap cycle. Nevertheless, we assume that first-order processes can be described by such hypothetical tidally averaged conditions, and that stochastic nonstationary effects can be treated as dispersive processes (but see below). We decompose the SSC and velocity into tidally averaged, tidally fluctuating, and turbulent components: C ¼ c + ce + c0 u ¼ u + ue + u0 e + w0 Ws ¼ ws + w fs + w0 w¼w +w

(2.2)

where the overbar denotes a tidal average, a tilde denotes a tidally fluctuating term, a prime denotes a turbulent fluctuation, and variable dependences (x,z,t) have been dropped for simplicity. Because cohesive sediments with tidally varying properties are found in ETM, ETM processes are influenced by lags, and tidal asymmetries in settling velocities (Winterwerp, 2002, 2011), which must be included in a tidally averaged mass balance. Combining Eqs. (2.1) and (2.2) and averaging over a time scale that is large compared to turbulence, but small relative to the tidal time scale (Reynolds averaging), yields correlation terms that can be represented as a Fickian dðc + e cÞ ðc + ec Þ dispersion, e.g., c0 u0 ¼
Kh dx and c0 w0 ¼
Kz dz , where Kh and Kz are eddy diffusion coefficients in the horizontal and vertical directions. For simplicity, we neglect turbulent fluctuations in WS. Tidal averaging then yields: ( ) ( ) @ dðc + ceÞ @ dðc + ceÞ e
cew es + ceue + + cew cu
Kh c ðw
ws Þ
Kz 0¼ @x dx @z dz (2.3) e , ceue, and cew e s are produced by The tidally (and spatially) variable correlations cew tidal fluctuations in velocity and sediment concentration around the tidal averaged mean, and (implicitly) include any residual affect produced by nonstationary trends in the averaged quantities. A simple assumption is that the resulting turbulent diffusion and tidal correlation terms can both be represented by Fickian dispersion, i.e., dðc + c~Þ dc ee
Kh dðcdx+ c~Þ + ceue ¼
Kh dc dx and
Kz dz + c w ¼
Kz dz . Kh and Kz are tidally averaged horizontal and vertical dispersion coefficients (which are different from Kh and dc Kz because they represent multiple processes), and dc dz and dx are tidally averaged SSC gradients. The problems and assumptions implicit in this tidally averaged approach are discussed by Stacey et al. (2010). Nevertheless, an analogous assumption is made in tidally averaged salinity models (MacCready, 2004, 2007). Considering only noncohesive sediment with a constant WS, we recover the advection–diffusion equation presented in Talke et al. (2009b).

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2.4.2 THE TRADITIONAL MODEL The formation of a turbidity maximum, like tidally averaged estuarine circulation, was traditionally considered to be caused primarily by salinity (density) gradients and river flow. In this transport model, upstream flow and sediment transport caused by gravitational circulation (Hansen and Rattray, 1965) are balanced by seaward transport caused by river flow, as first modeled numerically by Festa and Hansen (1978). An analytical version of the traditional model was developed by Talke et al. (2008, 2009b) to investigate the effects of along-channel density gradients caused by turbidity. This analysis does not consider, however, the impacts of tidal straining (Simpson et al., 1990), internal asymmetry in the tidal and overtide velocity profiles, and ebb–flood differences in mixing (Geyer, 1993; Jay, 2010; Jay and Musiak, 1994, 1996) in creating residual circulation and distinct flood and ebb velocity profiles, all known to contribute to ETM formation (Burchard and Baumert, 1998; and see the discussion below). The analytical model derived herein follows Talke et al. (2009b) and provides insight into the factors that produce a turbidity maximum and determine its shape. We also highlight the assumptions made, providing a point of departure for future investigations. Under idealized conditions, Eq. (2.3) is dominated by the vertical divergence terms, allowing for separation of vertical and horizontal terms, and a simple solution for the SPM profile. Next, we impose a condition of morphodynamic equilibrium, a concept developed in Schuttelaars and de Swart (1996) and Friedrichs et al. (1998). This condition specifies that no net erosion or deposition (i.e., morphological change) is allowed within the model, yielding a steady-state turbidity maximum. A rigid lid approximation is applied, for simplicity. The resulting tidally averaged dimensional mass balance equation for C is: 0¼


@
@ @ dc @ dc fcðw
ws Þg + Kh + Kz fcug + @x @z @x dx @x dz

(2.4)

Overbars have been dropped for simplicity, and we lump all time-varying terms into the dispersion coefficients. Talke et al. (2009b) nondimensionalized Eq. (2.4) using typical depth scales (H ¼ 10 m), tidally averaged velocity scales (U ¼ 0.01 m s
1, W ¼ 10
5 m s
1), a length scale LS over which salinity varies (10 km), a fixed WS (0.001 m s
1), and values of C (0.5–1 g L
1) found in the Ems estuary. Horizontal dispersion and tidally averaged vertical diffusivity were scaled as 100 and 0.001 m2 s
1, respectively. This nondimensionalization allows Eq. (2.4) to be separated into leading order and order E terms. At the lowest order, SSC conservation balances time-averaged settling against upward turbulent diffusion and other processes represented by Kv; in dimensional variables:   @ @ cðx, zÞ Kv ¼0 (2.5) fcðx, zÞWS g + @z @z dz The assumptions underlying the separation in Eq. (2.5) of the horizontal and vertical terms are less suitable in estuaries with smaller horizontal length scales (set, for

2.4 ETM dynamic: Insights from theory, modeling and observations

example, by topography), in deeper or more stratified estuaries with less vertical mixing (smaller Kv), or in estuaries with strong horizontal advection. Nonetheless, assuming that SSC values are set only by vertical processes, SSC profiles like the Rouse profile can be attained using analytical representations of eddy diffusivity. The analogy to the usual Rouse balance is purely formal, in that the Rouse balance is averaged only over turbulent time scales, whereas both tidal and turbulent processes are averaged here. A simple assumption regarding the mixing coefficient Kv is needed, and we take it as constant. Vertically integrating Eq. (2.5), and assuming equilibrium conditions in which there is no net erosion or deposition, the constant of integration is zero. Integrating again yields an exponential profile of SSC in the vertical direction:
ws

cðzÞ ¼ cb ðxÞe Kv

ð H + zÞ

,

(2.6)

in which Cb(x) is the bottom concentration as a function of x and H + z ¼ 0 at the bed. While simplistic, the exponential profile is consistent with the observation that SSC is preferentially found near the seabed and reasonably approximates field measurements in the Ems estuary (Talke et al., 2009a). To define the horizontal SPM distribution, we now consider also the O(e) part of the SSC balance, and impose a morphodynamic equilibrium condition in which there is no tidally averaged flow or SPM flux through the top and bottom boundaries (at z ¼ 0 and
H). At the upstream boundary at x ¼ L, we further assume that the vertically integrated flux of sediment (sediment transport) is an arbitrary constant, B2. After integration with respect to x, the balance is in the horizontal: Z 0 @c uc + KH dz ¼ B2 (2.7) @x
H The constant of integration, B2, was assumed by Talke et al. (2009a) to be equal to zero, an appropriate assumption for estuaries like the Ems in which incoming sediment load is much smaller than in situ concentrations. In that case, Eq. (2.7) states that the ETM created by convergent along-channel advection is balanced by downgradient horizontal diffusion. For a system with a substantial supply (B2 > 0) from the upstream or the ocean, this balance is modified by supply. Equation (2.7) can be solved to determine an expression for the horizontal SSC distribution, if u is known. Using the usual x-invariant velocity profile for gravitational circulation and river flow in a channel of constant depth (Hansen and Rattray, 1965) produces the following expression for SSC at the bed (Talke et al., 2009a):    1 gbH 3 3Q x (2.8) Ts sðx Þ
T Q Cb ðxÞ ¼ A1 exp
48r0 Av TK Kh 2bH where s(x) is the along-channel salinity distribution, Q in m3 s
1 is the river discharge, A1 is a constant that is constrained by the total amount of sediment available for resuspension, Av is the tidally averaged vertical eddy diffusivity, and the constants TK, Ts, and Tq are defined in Talke et al. (2008, 2009a) and depend on the

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sediment Peclet number, defined as Pev ¼ wsH/Kv. The sediment Peclet number is a measure of the relative strength of settling and vertical mixing, and determines whether sediment is well mixed or concentrated near the bed. Hence, the values of the constants TK, Ts, and Tq influence the spread of sediment, through their dependence on Pev. If the conventional boundary layer value of Kv ¼ kU*z is used, then the sediment Peclet number reduces to the Rouse number. A similar expression to Eq. (2.8) was derived by Talke et al. (2008, 2009b) for an exponentially converging estuary. The model described by Eqs. (2.6) and (2.8) neglects important processes such as internal asymmetry and tidally varying stratification, which numerous studies have shown to be more important than gravitational circulation in producing upstream sediment flux in many ETM (Burchard and Baumert, 1998; Geyer, 1993; Hudson, 2014; Jay and Musiak, 1994, 1996). However, internal asymmetry produces a circulation pattern with a similar vertical structure as gravitational circulation and that is similarly dependent on dS/dx and H (Hudson, 2014; Jay, 2010). Thus, while Eq. (2.8) is not fully predictive, it can be used as a proxy for how estuary turbidity zones work in relation to a diagnostic (prescribed) salinity variation. Other parameters held equal, Eq. (2.8) predicts that the circulation pattern produced by gravitational circulation and/or internal asymmetry cause the following ETM behaviors, conceptually illustrated in Fig. 2.7; an ETM: • • • • • •

Moves upstream as river discharge decreases and Cb(x) increases (Fig. 2.7A and F). Moves strongly upstream as depth is increased, e.g., by dredging (Fig. 2.7E). Moves upstream when WS or Pev is increased (Fig. 2.7C). Moves downstream when KV is increased (Fig. 2.7D). Is strongly dependent on the salinity intrusion length (Fig. 2.7H) and the sharpness of the salinity gradient (Fig. 2.7G). Becomes more spread out as KH increases (Fig. 2.7B).

These theoretical results have been confirmed by observational studies (e.g., de Jonge et al., 2014). The position of the ETM center has maximal SSC and dC/dx ¼ 0, and horizontal dispersion is absent. Model sensitivity studies show that the ETM center typically occurs within the brackish water zone (salinity of 1–2 psu), but can occur at any point landward of the maximum salinity gradient (Talke et al., 2009b). At the ETM center point, the upstream-directed sediment fluxes from tidally averaged circulation and the downstream-directed fluxes from river discharge are balanced (see Eq. 2.7): Z 0 uc dz ¼ 0 (2.9)
H

On either side of the ETM, dispersive fluxes spread SSC away from the maximum. Seaward of the ETM, sediment fluxes related to the salinity distribution are much stronger than river-induced fluxes, and the length scale over which SSC is elevated (the e-folding length scale of SSC) is similar to the salinity intrusion length, LS.

2.4 ETM dynamic: Insights from theory, modeling and observations

FIGURE 2.7 Sensitivity study showing the variation of near-bottom SSC in an exponentially converging estuary (following Talke et al., 2008) as a function of: (A) average sediment concentration c*, (B) dispersion coefficient Kh, (C) settling velocity ws, (D) eddy diffusivity Av, (E) depth H, (F) freshwater discharge Q, (G) length scale of salinity gradient xL, and (H) location of the maximum salinity gradient xc. Panels (A)–(C) are normalized by the concentration at each turbidity maximum, while other results are presented in kg m
3.

Landward of the ETM, river discharge balances upstream flux by sediment dispersion, producing a different functional dependence and e-folding (decay) length scale (Talke et al., 2008). Together, these differing functional dependencies produce an asymmetric sediment distribution, in which the spread of sediment upriver is typically larger than the SSC spread in the seaward direction. Equation (2.9) also implies that estuarine turbidity minima can exist seaward of the maximum salinity gradient (Talke et al., 2009b). However, an ETM can also form in association with the convergent fronts and topography near the ocean entrance (Fain et al., 2001), an issue not included in the above model. Finally, high river discharge can overwhelm the upstream transport. In this situation, no maximum is formed and sediment is discharged into the ocean. As discussed in the next section, estuaries in which this often occurs (e.g., the Fraser River estuary) are marked by relatively low-SSC values in relation to their sediment supply, whereas high-SSC values occur in estuaries in which flushing flows rarely occur (e.g., the Ems–Dollard estuary and Gironde estuary). The statistical model of Uncles et al. (2002) also validates this assertion.

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The observed dependence of the turbidity distribution on WS in Fig. 2.7C is further explained by considering the fates of large and small particles. Large, rapidly settling particles are found near the bed, and are thus preferentially moved upstream by near-bed currents. Small, slowly settling particles are found throughout the water column, and are, therefore, much more likely be transported seaward. Below a threshold WS (or equivalently, a small Pev), an ETM cannot form, since sediment is exported to sea. For this reason, flocculation is essential to the trapping of fine sediments; by increasing WS to O(0.001 m s
1), it makes sediment trapping in an ETM possible (e.g., Winterwerp, 2002). The difference between the trapping location of different particle sizes also provides a mechanism for horizontal sorting of sediment size classes within an estuary (Talke et al., 2008).

2.4.3 MORE COMPLEX MODELS More complex models such as Chernetsky et al. (2010) and Hudson (2014) directly represent tidal fluxes and tidal variations in surface elevation; then the morphodynamic equilibrium condition is: Z z   @c ðu + ueÞ  ðc + ceÞ + KH (2.10) dz ¼ B2 , @x
H where the free surface z is allowed to vary and brackets h i denote tidal averaging. Equation (2.10) can, in principle, be modified to include variations in cross-sectional width, variable depth, or lateral circulation. Chernetsky et al. (2010) develop a model in which barotropic tidal asymmetry (the phasing of the M4 overtide with the M2 tide) produce larger flood than ebb velocities, causing net upstream transport of SPM. Including tidal variations also allows for nondensiometric sources of tidally averaged circulation to be represented in the model, such as the Stokes drift. Tidal asymmetry (i.e., barotropic overtides) produces a tendency for currents to remain slow for a longer period at high water than low water, producing more settling of sediment at the end of the flood tide than at the end of the ebb (Groen, 1967). This temporal settling lag was especially effective at moving SPM with a low WS (0.2 mm s
1) upstream, and is quite sensitive to changes in bed friction and depth (Chernetsky et al., 2010). In contrast to the model of Talke et al. (2009b), the primary balance producing an ETM at the head of the salinity intrusion was upstream transport by tidal asymmetry (at the M2 frequency) and downstream river flow transport. Chernetsky et al. (2010) also modeled a second ETM far upstream of salinity intrusion, concluding that this second ETM was influenced by upstream sediment transport by the combination of the M2 and M4 tides. On the other hand, Burchard and Baumert (1998) emphasize the role of internal asymmetry, as described by Jay and Musiak (1994, 1996). The diverse processes that influence the SPM balance (Eq. 2.10) suggest the complexity of ETM physics and the difficulty in providing a universally applicable explanation of the mechanisms involved. In Chernetsky et al. (2010), including just the M2 and M4 tidal constituents resulted in more than a dozen transport terms, even though the model did not consider transport by internal asymmetry (Jay and Musiak, 1994,

2.4 ETM dynamic: Insights from theory, modeling and observations

1996), or the other sources of along-channel residual circulation and transport produced by lateral variations in bathymetry and density (e.g., Lerczak and Geyer, 2004; Li and O’Donnell, 1997; Scully et al., 2009). Variations in flood/ebb mixing produced by the straining of salinity (Jay, 1991a; Scully and Friedrichs, 2007) or by sediment stratification (Winterwerp, 2011) cause “tidal pumping” (a net tidal flux) that moves sediment upstream (cf., Jay, 1991b, Geyer, 1993). The same internal asymmetry produces flood/ebb differences in WS, both through tidal differences in hindered settling and floc sizes (Scully and Friedrichs, 2007; Winterwerp, 2002, 2011). Lags between u and C due to scour, erosion, erosion threshold, and settling can also cause landward transport and ETM trapping of particles (Dronkers, 1986b; Dyer, 1995; Groen, 1967; Hoitink et al., 2003; Postma, 1961; van Straaten and Kuenen, 1958). Width convergence (Friedrichs et al., 1998) and bathymetric features (Hudson, 2014) also alter ETM characteristics, and other aspects of tidal dynamics add additional layers of complexity. For example, the interaction of diurnal (D1) and semidiurnal (D2) tides in mixed D1/D2 estuaries produces a linear tidal asymmetry, which on the U.S. West Coast results in a much stronger ebb than flood tide (Hoitink et al., 2003; Nidzieko, 2010) on the largest tides. In the Ems estuary, Van de Kreeke et al. (1997) found that transport at the M2 frequency dominated near the bed. However, net sediment fluxes at the M4, M6, M8, and M10 frequencies equaled those from M2 higher in the water column. Analysis over longer time scales might allow resolution of correlations between SSC and velocity at other tidal frequencies, but frequency resolution would be limited by nonstationarity. It is sobering that, in the Ems estuary, Winterwerp (2011), Chernetsky et al. (2010), and Talke et al. (2009b) each highlights the vital role of different transport mechanisms. Thus, our understanding of ETM is still evolving, and more field measurements and model studies are required to identify the important elements in the transport process. Numerical models avoid some simplifications used in analytical models, such as a linearized bed stress. Nonetheless, even numerical models use parameterizations of uncertain validity for erosion, settling, and eddy viscosity (e.g., Krone, 1962; Partheniades, 1962). Moreover, numerical models necessarily assume homogeneity over a grid cell area. As with analytical models, all unresolved processes are modeled by horizontal and vertical dispersion and diffusion coefficients. Despite these abstractions, actual sediment erosion occurs through the interaction of coherent turbulent structures with dunes, ripples, and other bed features (Best, 2005; Kostaschuk and Church, 1993; Talke et al., 2013). While the assumption that the processes averaged are stochastic and diffusive often yields reasonable answers, this is a largely untested hypothesis. Combined with numerical diffusion caused by computational advection schemes, many numerical models smooth regions with strong gradients, thus failing to represent small-scale variability that is essential to forming the macroscopic environment.

2.4.4 INTEGRAL ANALYSIS OF A CHANNELIZED ETM The foregoing discussion emphasizes that a wide variety of processes influence ETM formation across the spectrum of estuaries, and that processes known to be important (e.g., internal asymmetry, effects of variable stratification, spring-neap variability,

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and flocculation asymmetry) have not been included in existing theoretical analyses, because of the mathematical difficulties involved. Clearly, a more general framework is needed. Jay et al. (2007a,b) present an integral framework based on the generalized Lagrangian mean (GLM) theory or wave–mean flow interaction (Andrews and McIntyre, 1978; Jay and Musiak, 1994). GLM theory facilitates understanding the horizontal fluxes that drive particle trapping in an ETM, the effects of nonstationarity, and a wide variety of other ETM processes. GLM theory has fewer assumptions than the analytical theories discussed in the previous section, and can be used to develop scaling relationships to interpret estuarine field data that are independent of any particular treatment of the SPM and velocity profiles. While not considered by Jay et al. (2007a,b), lateral exchange with tidal flats is included here. Also, to allow consideration of aggregation effects, three separate populations of particles are included: estuarine aggregates, estuarine fines, and fluvial fines. Aggregation and disaggregation are assumed to occur primarily between estuarine aggregates and estuarine fines. The nondimensional 2D (x–z) SSC conservation equation can be written, after lateral and vertical integration and tidal averaging: Z @ x2 BH hfCe gidx ¼SR ½Qr hfCe gixx12 I @t x1 " !#x1 n X + A HB fhuV ihCVe ig + fhuVi CVei ig Z

i¼1 x2

Z

x2 x2

+F

B Aggregation dx + G B Disaggregation dx x x Z 1 x2 Z x12 +O B Deposition dx + C B Erosion dx x x1 Z 1x2 +F DH Lateral Flux dx x1

(2.11) The parameters and scaling used in Eq. (2.11) are, with nondimensional variables on the right: Ce0 ¼ CECe Cf0 ¼ CFCf, Cr0 ¼ CRCr QR0 ¼ QRQr t0 ¼ t/o x0 ¼ Lxx, z0 ¼ Hz o, o ¼ 1.4  10
4 s
1, 10
5 s
1 0 u [x,z,t] ¼ DU u[x,z,t] UT, UR, Uflat B H, DH

Estuarine aggregate volume concentration Estuarine and fluvial volume concentrations of fines River flow Time The horizontal and vertical coordinates Tidal frequency, 1/neap-spring period Velocity (scaled by the flood–ebb difference) Tidal, river flow, and tidal flat exchange velocity scales Channel width Channel depth and depth at channel lateral boundary

2.4 ETM dynamic: Insights from theory, modeling and observations

Lx Kv0 ¼ kU*HK[x,z,t] k P ¼ WSE/(kU*) E ¼ CE/CR A ¼ PDU/UT SR ¼ PUR/UT F, G I ¼ EPHo/WSE SR ¼ PUR/UT F ¼ PDH/H Uflat/UT C, O

SPM horizontal scale length Turbulent SPM diffusivity von Ka´rma´n constant Ratio of settling to vertical mixing (Rouse number) ETM trapping efficiency Advection number Supply number Aggregation and disaggregation numbers Inventory number Supply number Lateral transport number Erosion and deposition numbers

Brackets { } indicate a vertical average, braces h i indicate a tidal average, and subscript V indicates a vertical deviation from a vertical average. The subscript i ¼ 1, n indicates the n tidal constituents and overtides that may transport SPM via wave fluxes, and primes indicate dimensional variables. The along-channel integral is from points x1 to x2, downstream and upstream of the ETM, respectively. Lateral fluxes are assumed to occur over a shallow depth DH along the lateral margins of the ETM. Lateral variations in u and Ce have been neglected for simplicity. A 3D treatment is possible, but it would result in many additional terms. The above scaling assumes that SPM is supplied primarily by the river; a marine source would require minor changes. The details of the aggregation/disaggregation and erosion/deposition scaling and models are given in Jay et al. (2007a). Because all horizontal transports are represented explicitly as wave fluxes, there is no KH term. The scaling in Eq. (2.11) produces insights that are not otherwise easily accessible. For example, only shear fluxes (involving correlated vertical variations in C and u) in the mean and tidal flows can move SSC landward to balance export by the river flow. Thus, the existence of an ETM in a 2D (x–z) estuary requires that there be correlated variations (in time and vertical) of uVi and CVei; i.e., a vertically uniform SPM component (washload) cannot be trapped. Because SSC is concentrated near the bed where the mean flow is landward, the mean flow typically transports SSC landward (Jay and Musiak, 1994). Similarly, the wave (tidal and overtide) fluxes will usually transport SPM landward (Jay and Musiak, 1994, 1996) if the bed is flat. If the above analysis were extended to include lateral variations, then correlated shear and SSC variations could trap a vertically uniform SPM component, and this may happen in some weakly stratified estuaries. Similar GLM reasoning shows that salinity intrusion into an estuary requires shear fluxes of salt, in the tidal and/or mean flows (Jay, 1991b). That is, a 2D (x–z) neutrally stratified estuary cannot exist, although as with SPM, lateral variations can bring salt into neutrally stratified estuary. The total ETM inventory stored in the water column and scaled by nondimensional Inventory number, I, appears at left in Eq. (2.12). This inventory is controlled by the seven processes on the right, each with its own nondimensional scaling

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number. Considering the ratios of aggregation/disaggregation and erosion/deposition (F/G and C/O, respectively), reduces to five the number of processes involved. Moreover, if morphodynamic equilibrium conditions prevail, then there will be no net deposition or erosion, aggregation and disaggregation will balance, and there will be no net transport to, or from, the tidal flats. In this case, the inventory change term on the left of Eq. (2.12) vanishes, and the supply term (scaled by Supply number SR) and the shear-flux term (scaled by advection number A) must balance. On this basis, Jay et al. (2007b) provided a qualitative interpretation of Columbia estuary ETM phenomena in terms of four nondimensional numbers, A, E, P, and SR. E was used rather than I ¼ EP Ho/WSE, because E is the controlling factor in I, it is easier to monitor on a seasonal basis than I, and it influences F and G. It is useful to describe the distribution of fluxes and SSC in a steady ETM (Fig. 2.8A and B), before considering the behavior of A, E, P, and SR. We define x1 and x2 such that

FIGURE 2.8 Conceptual sketch (A) of the spatial distribution of estuarine aggregate concentration hCei and horizontal advective fluxes of hCei (arrows) for a steady ETM, without net (tidally averaged) particle alteration or net deposition/erosion. The maximum in hCei defines the mid-ETM point. The seaward fluvial SPM flux QR hCei is shown at the top (black arrows), while the landward SPM shear fluxes caused by the tides and mean flow are shown at the bottom (gray arrows). The mid-ETM point is shown here at the mean upstream limit of intrusion of salinity hSi, but may be found at other locations. Conceptual sketch (B) of the spatial distribution of estuarine aggregate concentration hCei and estuarine fines concentration hCfi for a steady ETM, without net (tidally averaged) deposition or erosion. In this case, aggregation of fines during passage through the ETM increases hCei at the seaward boundary (at x1) relative to the landward boundary (at x2). As before, the ETM is assumed to be located at the tidal-mean upstream limit of salinity intrusion. Panels (A) and (B) are from Jay et al. (2007a,b).

2.4 ETM dynamic: Insights from theory, modeling and observations

{hCei} has the same value at the two points. Then river flow transport QR{hCei}, scaled by SR, brings in the same amount of material (the “Supply”) from upriver (at x1) as it exports at x2. The river flow transport is maximal at mid-ETM (Fig. 2.8A), because QR is constant with x, and {hCei} is maximal in mid-ETM, by definition. For a steady state to pertain, the landward shear fluxes due to the mean and tidal flows (scaled by A) must be everywhere equal to and opposite to the seaward river flow transport. If the sole source of fines is from upriver, fines will typically decrease seaward due to aggregation in mesohaline waters (Fig. 2.8B), although supply from the bed or from lateral areas could prevent this decrease from being observed. The SSC inventory (scaled by I) is not constant, as illustrated by neap-to-spring changes during a period of steady river flow (Fig. 2.9). Salinity intrusion in the Columbia River estuary is maximal on the neap tides. As tidal range increases, stratification and salinity intrusion decrease. This exposes fines accumulated on the bed to erosion and increases the water column inventory of SSC. SSC values increase at the seaward end of the ETM, increasing {hCei} and QR{hCei}. The result is export of SSC on the spring tides. As tidal range decreases after the spring tide, salinity intrusion increases, and aggregates accumulate on the bed again. If river flow varies, the ETM will move up or downriver, and {hCei} will change, due to altered supply and trapping processes, including changes in the salinity intrusion length. These springneap adjustments are especially clear in high-energy estuaries like the Columbia and Fraser Rivers, because there is only a few days lag between tidal range and ETM processes. ETM adjustments in systems with slower time scales are more complex and not well understood. Jay et al. (2007b) estimated time series of A, E, P, and SR from ADP estimates of ABS (calibrated by OBS data and gravimetric samples) at four moorings in the Columbia River estuary. An important finding was that P averaged about 0.5–0.8 x1

x2

〈Cspring 〉

〈Cneap 〉 〈Sneap 〉 〈Sspring 〉 x2

FIGURE 2.9 Conceptual sketch of the spatial distribution of estuarine aggregate concentration hCei during a neap-to-spring tide transition that results in significant erosion and export of ETM material. Concentrations hCneapi (hCei on a neap tide) at points x1 and x2 on either side of the ETM are the same, but spring tide hCei (h{Cspring}i) increases at x1 due to reduced salinity intrusion and stratification, erosion, and reduced landward shear fluxes. Taken together, these factors result in an export event. From Jay et al. (2007a).

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FIGURE 2.10 Trapping efficiency E versus Supply number SR for 15 selected estuaries; data sources and estuaries are provided in table 2 of Jay et al. (2007b). From Jay et al. (2007b).

(depending on location) and was relatively constant in the ETM (between about 0.4–1) when E was maximal and A was small (i.e., when the ETM was centered at a mooring). To generalize, the relationship between E and SR was determined from literature data for 15 estuaries (Fig. 2.10). The relationship between E ¼ CE/CR and SR ¼ PUR/UT was found to be: E ¼ 0:71SR
0:77 R2 ¼ 0:79:

(2.12)

Equation (2.12) states that the trapping efficiency decreases as river inflow increases and estuary length decreases, even although a short estuary with high river inflow may have a strong two-layer flow. Conversely, stronger tides relative to river flow and an increased P lead to stronger trapping. This finding is consistent with the results of Burchard and Baumert (1998), that gravitational circulation (Hansen and Rattray, 1965) and internal asymmetry (Jay and Musiak, 1996) are important mechanisms for ETM particle trapping. Also, Fig. 2.10 and the direct relationship between E and F demonstrated by Jay et al. (2007b) suggest that “aggregation should be systematically more important to ETM processes in estuaries with weak river flow relative to tidal currents.” As noted above, the variations of WS and U* are linked, such that P is not highly variable in most ETM, whereas the UR/UT ratio varies by several orders of magnitude. Taking P ¼ 0.7 as typical, the following scaling pertains to a channelized ETM:

2.4 ETM dynamic: Insights from theory, modeling and observations


0:77 DU UR UT A  0:7 SR  0:7 E  0:93 UT UR UT
0:77
0:77 UT H UT oH I  6:1  10
6 s
1 ¼ 0:93 : UR UR WSE KU

(2.13a)

(2.13b)

Together with Eq. (2.12), Eq. (2.13a) suggests that increasing tidal currents and tidal variations in shear increase trapping efficiency E. Equation (2.13b) suggests that I oH is related to the ratio WSE/H, or alternatively to a subtidal Ianniello number kU  (cf. Burchard et al., 2011). The scalings in Eqs. (2.13a) and (2.13b) also suggest that there is a dynamical “sweet spot” that optimally traps particles (Jay et al., 2000; Fig. 2.11), although no parameter space study has been conducted to test this idea. Conceptually, the Rouse number P (on the horizontal axis) cannot be too large, or material cannot be suspended. If P is too small, then the SSC is washload and cannot be trapped by the shear fluxes in Eq. (2.12), scaled by A. The advection number A (on the vertical axis) is also important. If there is too much advection in a shallow system, then there will be no stratification and shear to trap particles. However, if there is too little advection in a system that is too deep, there will be no suspension at all. The “sweet spot” idea also suggests that the particles trapped in an ETM need to be adjusted to the energy level of the system, which has implications for the estuarine foodweb.

Too shallow

A Washload limit

Bedload limit

Contours of E Too deep

P

FIGURE 2.11 The “sweet spot” for particle trapping, reinterpreted from Jay et al. (2000). Trapping efficiency E (the ratio of SPM concentration in an ETM relative to source water concentration) as a function of Rouse number P ¼ WS/(kU*) and Advection number A ¼ PDU/UT; k is the von Ka´rman constant, DU is the flood–ebb difference in near-bed velocity, and UT is the tidal velocity. The most effective trapping occurs at a sediment size matched to the strength of particle trapping in the estuary.

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Specifically, the microbes that glue aggregates together and the epibenthic fauna that consume ETM particles must be adjusted to the type of particles that can be trapped. The energy level is, in turn, often affected by the sedimentation that occurs in an ETM, which can change bed depth. In summary, an integral formulation of the ETM SSC balance shows that trapping efficiency E varies almost linearly with UT/UR and allows development of quasiuniversal ETM scalings. P is relatively constant in mid-ETM, at least for ETM found near the head of the salinity intrusion. Further theoretical analyses are needed that specify the shear fluxes that trap particles. Essentially, this requires parameterizing the shear fluxes in the GLM model (Eq. 2.11) using a profile model like Eq. (2.11). As discussed in Section 2.5, available theory and observations do not suffice to answer many relevant dynamical and practical questions.

2.5 DISCUSSION: TOWARD A MORE COMPLETE UNDERSTANDING OF ETM DYNAMICS 2.5.1 MAKING USE OF NEW IN SITU AND REMOTE SENSING CAPABILITIES Sections 2.2–2.4 show that in situ acoustic and optical instrumentation and remote sensing methods have been developed that have not routinely been applied to understanding ETM dynamics. These offer good possibilities for addressing outstanding dynamical questions, but there are real logistical difficulties, and some potential sampling approaches remain expensive. In the acoustic realm, use of ADCP/ADP profilers seems to provide the most practical approach to sampling the 3D variability of ETM environments, but calibration of the ABS signal to SSC and dealing with contamination by fine structure and turbulence effects remains a problem. For ABS calibration in situations with time–space variable size distributions, Jourdin et al. (2014) recommended the use of two profilers separated in frequency by a factor of four. OBS sensors are often used for calibration of ABS, and deployment of multiple OBS sensors (or OBS profiling from a vessel) can be used instead of, or along with, multiple ADCP/ADP profilers. Moreover, multifrequency profilers and new beam configurations are now available, with up to nine beams of several frequencies being available, although these have not yet been used in the ETM environment. Combining a low frequency (250–600 kHz) H-ADCP/ADP with a vertically oriented profiler would also seem to offer advantages that have not been explored. Also, the acoustic response of aggregates is not fully known, and further work on calibration of ABS in turbulent and stratified environments is needed. Calibration of SSC to gravimetric samples remains essential, and ABS estimates of SSC do not provide estimates of effective diameter D, sediment density rS or WS, which must be obtained with other instrumentation. Fortunately, a combination of moored instruments to determine SSC and periodic calibration cruises using a wider variety of instruments should be able to provide the requisite information in most cases.

2.5 Discussion

Optical sampling with combinations of OBS and multispectral optical devices has the capacity to allow rapid sampling of the ETM environment, using a towed profiler approach. The limited dynamic range of transmissometers remains a problem, however. Although LISST sensors are very useful, inversion algorithms for nonspherical particles and aggregates provide a challenge, and there is a little prospect that the Schlieren problem related to fine structure will be resolved. Thus, a LISST is generally best used in a selective manner, near the bed and the surface, where the Schlieren problem is most likely minimal. In the ETM environment, all optical equipment will suffer from biofouling difficulties during lengthy deployments. Short moored experiments, perhaps with a LISST-ST (a LISST with a settling tube), or with a holographic sampler or floc camera are attractive, although logistically complex and expensive. As always, the OBS provides added value at low cost. At a system scale, the increased spatial resolution of private remote sensing platforms provides an opportunity to extend remote sensing of particles to the ETM particle field. This approach will have to be combined with modeling and/ or field observations to connect the surface observations to the near-bottom ETM field. In particular, large ETM aggregates are unlikely to be visible from the surface. Also, images from high-resolution private satellites remain, for the moment, expensive. Drones may provide an excellent remote sensing platform in some environments and ship-borne lidar may also prove useful. In summary, sensors and our understanding of how to use them are evolving rapidly. How they should best be used is to some extent a system-specific problem, because of the diversity of ETM environments. In logistical terms, the largest distinction is probably between high-energy ETM systems with sand beds, lower energy environments with mud beds but without fluid mud, and systems with extensive fluid mud. Each requires different logistics and raises distinct dynamical questions.

2.5.2 DYNAMICAL QUESTIONS The theoretical analyses in Section 2.4 provide a framework in which to resolve dynamical questions, for example, the relative importance of mean flow versus wave transport of SPM in ETM particle trapping. However, these analyses cannot alone resolve such issues, or predict what mechanisms will be important in different systems. The numerical studies of Burchard and Baumert (1998) do quantify the fluxes involved, but for a limited parameter space. Other questions, such as the dynamics of lateral particle trapping and how to determine which systems exhibit predominantly lateral versus vertical trapping mechanisms, have not been addressed. Here, we highlight some important open questions.

2.5.2.1 Trapping mechanisms and the material trapped The 2D theories outlined in Section 2.4 consider channelized ETM, focusing on particle trapping associated with the upstream limits of salinity intrusion. Thus, these analyses fail to address the larger question of what kind of particle trapping will occur in different types of estuaries. Even for systems in which trapping at the head of the salinity intrusion is the primary mechanism, the roles of settling lags versus

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convergent SSC transport cannot be predicted from external forcing and geometry. It is also not possible to definitely state which systems will have trapping driven primarily by mean flow SSC transport versus tidal and overtide SSC fluxes. We are left with the generality that, the stronger advection is in a narrow, channelized system and the higher the energy level, the more important advection will be relative to settling lags, and the more important tidal fluxes will be. Internal asymmetry plays a prominent role in the wave and mean fluxes of some estuaries (Burchard and Baumert, 1998; Jay and Musiak, 1994), but our knowledge of what fluxes occur in which types of estuaries remains limited. Also, what we do know is focused on vertical, not lateral SSC flux variability. Theoretical and numerical works have examined the lateral variability of the mean flow, salinity and salt transport in horizontally extensive channels, focusing on the balance between friction, the Coriolis force, and acceleration (Burchard et al., 2011; ValleLevinson et al., 2003), but the effects of channel curvature, vital in many estuaries, have been neglected. This can be an important factor in even relatively narrow systems (Chant et al., 2011). Moreover, systematic analysis of SSC fluxes, analogous to those for salt and water, have not been conducted. Thus, the balance of lateral trapping (driven by exchanges with tidal flats or shallow areas adjacent to deeper channels) and mechanisms associated with vertical shear and stratification in the deeper channels is a vital question that has not been addressed, although lateral fluxes have been quantified in specific systems. Finally, while wind and wave forcings are known to be important in laterally extensive systems, wind forcing has not been included in the parameter space(s) used in the discussion of ETM phenomena. Other aspects of system geometry are also important. First, multiple ETM occur in a number of systems. The Columbia River has, for example, three ETM. Two are in the expected places, near the head of the salinity intrusion in each of the two major channels (Fain et al., 2001; Jay and Musiak, 1994). There is a third ETM, however, associated with fronts seen in the narrow ocean entrance as it opens up into a broader lower estuary. All three are centered, for much of the year, in topographic lows. The more seaward ETM appears, however, to be less biologically important, perhaps because it is more transient (Simenstad et al., 1995). Similarly, the Gironde estuary has multiple ETM. In this case, the more seaward ETM occurs near the head of salinity intrusion, whereas the more landward ETM is related to tidal/overtidal fluxes of SPM in the more landward tidal river. These two cases are not isolated examples, though they are dynamically quite different. The possible occurrence of multiple ETM and their relevance to estuarine ecosystems has not been investigated in a holistic manner—the literature provides only an incomplete catalog of diverse special cases. Finally, ETM occur in both sand-bedded and mud-bedded systems, with the difference in bed material reflecting the strength of external forcing. It is less obvious, however, whether there is some fundamental difference in the types of ETM associated with these two types of system, physically or ecologically. The above problems can all be addressed, at least in part, via either numerical or theoretical studies. Both of these approaches suffer, however, from ongoing difficulties in quantifying erosion and deposition for cohesive sediments, a fundamental problem that is beyond the scope of this review.

2.5 Discussion

Finally, there is the issue of what material is trapped in diverse types of ETM across a broad spectrum of estuaries. The fact that the Rouse number P exhibits rather little variability across the spectrum of ETM is an important hint, but why this is true is not a purely physical oceanographic problem—the nature of the organic and inorganic materials supplied to the system and the biogeochemical transformations occurring in the ETM are vital. Recognition that the material trapped is that which can be trapped emphasizes the physics of the problems, but does not explain how such material is reliably produced in diverse estuaries. In this case, both observations and numerical studies are needed. The discussion of this subsection can be summarized in terms of the following questions: Q1 What is the balance of lateral versus vertical trapping mechanisms across the spectrum of estuaries that have ETM? How does this balance vary with geometry (depth and variations in depth, convergence rate, bed friction, and channel curvature), bed type (sand vs. mud), and external forcing (tides, winds, and river flow)? Q2 How can we predict whether an estuary will have a single versus multiple ETM? Q3 What are the relevant biogeophysical processes that determine what material is trapped in an ETM, and how is it that such a wide variety of estuaries are able to trap SPM with such modest variations in Rouse number?

2.5.2.2 Nonstationary aspects of ETM It has long been recognized that the estuarine salinity distribution is nonstationary, and that the adjustment time may be longer than the length of the neap-spring cycle. While the vertical SSC distribution adjusts rapidly on tidal time scales, this is usually not the case for the ETM horizontal position, but we know little regarding adjustment of ETM to changes in flow, wind, and tidal range. Also, it is unclear what role extreme events (e.g., storms, storm surges, and floods) have in ETM dynamics. In the Columbia River estuary, for example, Fain et al. (2001) argued that sediment supplied to lateral peripheral areas during a winter flood continued to be a source of material to the channelized ETM until mid-summer, half a year later. These considerations motivate the following questions: Q4 How does the ETM adjustment time vary in different types of estuaries, and does nonstationarity play a role in multiple ETM in some systems? Q5 How do extreme events affect ETM dynamics and the ETM ecosystem, and how do the impacts of such events endure? What effects are there beyond the immediate adjustment to altered flows and water levels, and are changes to the ETM bed important in this regard?

2.5.2.3 Distinguishing human and climatic impacts on ETM dynamics and ecosystems Projecting future ETM ecosystem change is difficult, in part, because many of the important changes that impact estuarine ecosystems (with feedbacks onto sediment transport) are difficult to simulate. Predicting the impacts of climate on ETM has a

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modeling component, like hydrologic projections. Global climate models can be downscaled to drive regional hydrologic and landscape models to predict future fluvial water and sediment supply and water quality (e.g., dissolved oxygen (DO) and temperature). These forcings can be used to drive estuarine circulation and sediment transport modeling for future scenarios. Determining future coastal boundary conditions is challenging, but still within the realm of contemporary climate modeling practice. Thus, for example, modeling might project that reduced precipitation and increased temperatures in a river basin would lead to increased salinity intrusion and lower DO levels. Reduced flows might lead to reduced sediment supply, but at least a temporary increase in sediment supply might occur due to deglaciation, changes in vegetation, or fires. Decreased flows in a convergent estuary would lead to a more landward ETM, to the extent that ETM position is related to salinity intrusion. This would lead, in most systems, to reduce ETM surface area and volume, likely with higher temperatures and lower DO. However, the consequences for bed aggradation/degradation and the ETM ecosystem are not easily predicted. Moreover, engineering alterations of diverse sorts have, to date, more strongly affected many estuaries than climate (e.g., de Jonge et al., 2014; Jay and Naik, 2011; Naik and Jay, 2011; Talke and de Swart, 2006; Winterwerp, 2011). Whether this will continue, as MSL rise accelerates, is unclear. Moreover, future engineering changes, such as storm-surge gates and other barriers, may render any simulation that did not include such (unknown) future engineering structures irrelevant. These considerations motivate the following questions: Q6 How will ETM circulation and sedimentation processes respond to plausible climatic impacts such as changes in water and sediment inputs, increased water temperatures, reduced DO levels, and increased MSL? Q7 How will climate change affect the balance of aggradation and erosion to the ETM bed, and what altered feedbacks affecting circulation and sedimentation processes from ecosystem processes can be anticipated? Q8 How will ETM dynamics and ecosystems respond to likely future engineering changes such as increased water diversion and storm-surge gates?

2.5.2.4 ETM dynamics and contaminants Hydrophobic contaminants including metals, PCBs, dioxins, and DDT adhere to fine particles, typically with D < 20 mm (Uncles et al., 2002). These fines are then incorporated into ETM aggregates and/or the estuarine bed. Thus, contaminant trapping is a frequent occurrence in ETM (e.g., Baugh et al., 2013; Chant et al., 2011; Schoellhamer et al., 2007). Because cleanup efforts are complex and difficult, the association between particle trapping and ETM dynamics is a vital question, about which surprisingly little is known. In the Passaic River estuary, for which cleanup costs are estimated to be several billion dollars, trapping conditions are highly variable, and have changed over time as the mean depth of the estuary has varied. Salinity intrusion is almost expelled from the system during extreme floods, while reaching >16 km into the system at other times (Chant et al., 2011). Thus, even

2.6 Summary and conclusions

although the normal tidal excursion of the Passaic River ETM is only a few kilometers, its position is highly variable, and modeling studies suggest that multiple ETM may be present (Sea Engineering and HDR, 2011). The Passaic River is, moreover, narrow and sinuous, and localized high levels of contamination are found throughout the entire salinity intruded reach, and up to the head of the tide. The result is an irregular distribution of contaminants with several maxima, related in part to point bar deposits. Clearly, 2D (x and z) ETM theory is insufficient to explain the contaminant distribution in systems where contamination is associated with the ETM. While numerical modeling can provide additional insights regarding specific systems, better dynamical understanding is also needed. These considerations motivate the following questions: Q9 How do ETM water column concentrations relate to bed deposition, and are contaminants, usually associated with the fine fractions of material found in an ETM, deposited in a different pattern than other ETM suspended sediments? Q10 How does deposition rate vary along an ETM in a river estuary, and how is this related to channel convergence, sinuosity, and other geometric factors?

2.6 SUMMARY AND CONCLUSIONS This paper has described recent advances in in situ instrumentation and remote sensing relevant to ETM and estuaries in general. The use of ABS to measure SSC is well established, yet fraught with difficulties. The most serious issues are the role of turbulence and fine structure in influencing ABS, and the ability of the output from a single acoustic transducer to distinguish between changes in particle size distribution and changes in concentration. Advances have been made in dealing with both of these issues, and most long-term monitoring of SSC in estuarine/ETM environments will likely include monitoring of ABS as a component, in part, because of the ability of ADCPs and ADPs to provide simultaneous estimates of velocity and SSC, facilitating SSC flux estimates. Additional instrumentation and gravimetric samples are needed, however, for calibration, and to determine other important SSC factors, such as the individual particle diameter D and size distribution of aggregates, settling velocity WS, and density rS. There are a number of optical instruments relevant to in situ observations of ETM properties. These include the OBS for estimation of SSC, LISSTs for observation of the size distribution, holographic imagers and floc cameras for aggregate properties and WS, and multispectral scanners for determination of OBS, transmission and (indirectly) SSC. Like estimates of SSC from ABS, LISST estimates of the size distribution suffer from several problems, including a limited size range that can be observed, and interference from Schlieren effects. Optical instruments (the OBS aside) are also difficult to use in highly turbid environments, because their finite optical path length implies a limited dynamic range in SSC. Long-duration deployments of optical instruments in estuaries also suffer from sensor fouling. Thus, the

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best uses of optical sensors for ETM studies would appear to be short-term deployments and vessel surveys in systems that are not extremely turbid, where they can provide a very high level of detail regarding many suspended sediment properties. Remote sensing is an emerging technology for studies of ETM dynamics, as images from private satellites with high spatial resolution (a few meters rather than kilometers) become available. There is, however, a need for system-specific empirical and/or more universal semi-analytical calibration algorithms, cost remains an inhibiting factor, time resolution is limited, and the ETM bottom boundary layer, where most ETM sediments reside, is not directly susceptible to satellite remote sensing. Thus, remote sensing will need to be used in conjunction with models and other observations to provide context for the remote sensing images. Theoretical and numerical models have provided important insights into ETM dynamics, primarily for ETM occurring at, or near, the upstream limits of salinity intrusion. We know much less about other types of ETM, e.g., those associated with lateral processes, wind, and waves. The role of nonstationarity in ETM dynamics and how ETM will respond to climate change (including MSL rise) and altered human management are also important topics. These issues were encapsulated in a series of 10 research questions that are vital for progressing knowledge of ETM, and can be addressed by theoretical, numerical, and observational studies.

ACKNOWLEDGMENTS Internal Portland State University funding provided support for D.A.J. for preparation of this review. Office of Naval Research Young Investigator Award (N00014-13-1-0084) provided funding for A.H. and S.A.T. for paper preparation. Tierra Solutions, Inc. provided funding to D.A.J. and S.A.T. for revisions. Discussions with A. Nayak improved this work, and support for M.T. was provided by the Harbor Branch Oceanographic Institute Foundation. Thanks to Philip Orton, Stevens Institute of Technology for providing updated versions of Figs. 2.1, 2.3, and 2.10.

REFERENCES Agrawal, Y.C., 2005. The optical volume scattering function: temporal and vertical variability in the water column off the New Jersey Coast. Limnol. Oceanogr. 50, 1787–1794. Agrawal, Y.C., Pottsmith, H.C., 2000. Instruments for particle size and settling velocity observations in sediment transport. Mar. Geol. 168, 89–114. http://dx.doi.org/10.1016/ S0025-3227(00)00044-X. Agrawal, Y.C., Whitmire, A., Mikkelsen, O.A., Pottsmith, H.C., 2008. Light scattering by random shaped particles and consequences on measuring suspended sediments by laser diffraction. J. Geophys. Res. 113, C04023. http://dx.doi.org/10.1029/2007JC004403. Allen, G.P., Salomon, J.C., Bassoullet, P., De Penhoat, Y., De Grandpre, C., 1980. Effects of tides on mixing and suspended sediment transport in macrotidal estuaries. Sediment. Geol. 26, 69–90.

References

Andrews, D.G., McIntyre, M.E., 1978. An exact theory of nonlinear waves on a Lagrangianmean flow. J. Fluid Mech. 89, 609–646. http://dx.doi.org/10.1017/S0022112078002773. Astoreca, R., Doxaran, D., Ruddick, K., Rousseau, V., Lancelot, C., 2012. Influence of suspended particle concentration, composition and size on the variability of inherent optical properties of the Southern North Sea. Cont. Shelf Res. 35, 117–128. http://dx.doi.org/ 10.1016/j.csr.2012.01.007. Anderson, T.J., Fredsoe, J., Pejrup, M., 2007. In situ estimation of erosion and deposition thresholds by Acoustic Doppler Velocimeter (ADV). Estuar. Coast. Shelf Sci. 75, 327–336. Aurin, D., Dierssen, H.M., Twardowski, M.S., Roesler, C.S., 2010. Optical complexity in Long Island Sound and implications for coastal ocean color remote sensing. J. Geophys. Res. 115, C07011. http://dx.doi.org/10.1029/2009JC005837. Babin, M., Stramski, D., Ferrari, G.M., Ferrari, G.M., Ferrari, G.M., Claustre, H., Bricaud, A., Obolensky, G., Hoepffner, N., 2003. Variations in the light absorption coefficients of phytoplankton, nonalgal particles, and dissolved organic matter in coastal waters around Europe. J. Geophys. Res. 108, 3211. http://dx.doi.org/10.1029/2001JC000882. Baugh, J., Feates, N., Littlewood, M., Spearman, J., 2013. The fine sediment regime of the Thames Estuary—a clearer understanding. Ocean Coast. Manag. 79, 10–19. http://dx. doi.org/10.1016/j.ocecoaman.2012.05.012. Banas, N.S., Hickey, B.M., 2005. Mapping exchange and residence time in a model of Willapa Bay, Washington, a branching, macrotidal estuary. J. Geophys. Res. 110, C11011. http:// dx.doi.org/10.1029/2005JC002950. Behrenfeld, M.J., Hu, Y., Hostetler, C.A., Dall’Olmo, G., Rodier, S.D., Hair, J.W., Trepte, C.R., 2013. Space-based lidar measurements of global ocean carbon stocks. Geophys. Res. Lett. 40, 4355–4360. http://dx.doi.org/10.1002/grl.50816. Best, J., 2005. The fluid dynamics of river dunes: a review and some future research directions. J. Geophys. Res. 110, F04S02. http://dx.doi.org/10.1029/2004JF000218. Best, J., Simmons, S., Parsons, D., Oberg, K., Czuba, J., Malzone, C., 2010. A new methodology for the quantitative visualization of coherent flow structures in alluvial channels using multibeam echo-sounding (MBES). Geophys. Res. Lett. 37, L06405. http://dx. doi.org/10.1029/2009GL041852. Boss, E., Slade, W., Hill, P.S., 2009. Effect of particulate aggregation in aquatic environments on the beam attenuation and its utility as a proxy for particulate mass. Opt. Express 17, 9408–9420. Boss, E., Twardowski, M.S., Herring, S., 2001. Shape of the particulate beam attenuation spectrum and its inversion to obtain the shape of the particulate size distribution. Appl. Opt. 40, 4885–4893. Bowers, D.G., Binding, C.E., 2006. The optical properties of mineral suspended particles: a review and synthesis. Estuar. Coast. Shelf Sci. 67, 219–230. http://dx.doi.org/10.1016/j. ecss.2005.11.010. Bowers, D.G., Braithwaite, K.M., Nimmo-Smith, W.A.M., Graham, G.W., 2009. Light scattering by particles suspended in the sea: the role of particle size and density. Cont. Shelf Res. 29, 1748–1755. Braithwaite, K.M., Bowers, D.G., Nimmo Smith, W.A.M., Graham, G.W., Agrawal, Y.C., Mikkelsen, O.A., 2010. Observations of particle density and scattering in the Tamar Estuary. Mar. Geol. 277, 1–10. Bukata, R.P., Jerome, J.H., Kondratyev, K.Y., Pozdnyakov, D.V., 1995. Optical Properties and Remote Sensing of Inland and Coastal Waters. CRC Press, p. 362.

97

98

CHAPTER 2 Estuarine turbidity maxima revisited

Burchard, H., Baumert, H., 1998. The formation of estuarine turbidity maxima due to density effects in the salt wedge: a hydrodynamic process study. J. Phys. Oceanogr. 28, 309–321. Burchard, H., Hetland, R.D., Schulz, E., Schuttelaars, H.M., 2011. Drivers of residual estuarine circulation in tidally energetic estuaries: straight and irrotational channels with parabolic cross section. J. Phys. Oceanogr. 41, 548–570. Buschman, F.A., Hoitink, A.J.F., de Jong, S.M., Hoekstra, P., Hidayat, H., Sassi, M.G., 2012. Hydrol. Earth Syst. Sci. 16, 4191–4204. http://dx.doi.org/10.5194/hess-16-4191-2012. www.hydrol-earth-syst-sci.net/16/4191/2012/. Cartwright, G.M., Friedrichs, C.T., Sanford, L.P., 2011. In situ characterization of estuarine suspended sediment in the presence of muddy flocs and pellets. In: Wang, P., Rosati, J.D., Roberts, T.M. (Eds.), Proceedings of Coastal Sediments 2011. World Scientific, Miami, FL, pp. 642–655. http://dx.doi.org/10.1142/9789814355537_0049. Cartwright, G.M., Friedrichs, C.T., Smith, S.J., 2013. A test of the ADV-based Reynolds flux method for in situ estimation of sediment settling velocity in a muddy estuary. Geo-Mar. Lett. 33, 477–484. http://dx.doi.org/10.1007/s00367-013-0340-4. Chant, R.J., Fugate, D., Garvey, E., 2011. The shaping of an estuarine superfund site: roles of evolving dynamics and geomorphology. Estuar. Coasts 34, 90–105. Chen, Z., Hu, C., Muller-Karger, F., 2006. Monitoring turbidity in Tampa Bay using MODIS/ Aqua 250-m imagery. Remote Sens. Environ. 109, 207–220. http://dx.doi.org/10.1016/j. rse.2006.12.019. Chernetsky, A.S., Schuttelaars, H.M., Talke, S.A., 2010. The effect of tidal asymmetry and temporal settling lag on sediment trapping in tidal estuaries. Ocean Dyn. 60, 1219–1241. Churnside, J., 2014. Review of profiling oceanographic lidar. Opt. Eng. 53, 051405. Churnside, J., Sullivan, J., Twardowski, M., 2014. Lidar extinction-to-backscatter ratio of the ocean. Opt. Express 22, 18698–18706. Crump, B.C., Baross, J.A., Simenstad, C.A., 1998. Dominance of particle-attached bacteria in the Columbia River estuary. Aquat. Microbiol. Ecol. 14, 7–18. Davies, E.J., Nimmo Smith, W.A.M., Agrawal, Y.C., Souza, A.J., 2011. Scattering signatures of suspended particles: an integrated system for combining digital holography and laser diffraction. Opt. Express 19, 25488–25499. http://dx.doi.org/10.1364/OE.19.025488. de Jonge, V.N., Schuttelaars, H.M., van Beusekom, J.E.E., Talke, S.A., de Swart, H.E., 2014. The influence of channel deepening on estuarine turbidity dynamics, as exemplified by the Ems estuary. Estuar. Coast. Shelf Sci. 139, 46–59. de Swart, H.E., De Jonge, V.N., Vosbeek, M., 1997. Application of the Tidal Random Walk Model to calculate water dispersion coefficients in the Ems estuary. Estuar. Coast. Shelf Sci. 45 (1), 123. de Swart, H.E., Schuttelaars, H.M., Talke, S.A., 2009. Phytoplankton growth in turbid estuaries: a simple model. Cont. Shelf Res. 29, 136–147. http://dx.doi.org/10.1016/j. csr.2007.09.006. DiIorio, D., Farmer, D.M., 1994. Path-averaged turbulent dissipation measurements using high frequency acoustical scintillation analysis. J. Acoust. Soc. Am. 96, 1056–1069. Dogliotti, A.I., Ruddick, K.G., Nechad, B., Doxaran, D., Knaeps, E., 2015. A single algorithm to retrieve turbidity from remotely-sensed data in all coastal and estuarine waters. Remote Sens. Environ. 156, 157–158. http://dx.doi.org/10.1016/j.rse.2014.09.020. Donnelly, J.P., 2001. 700 yr sedimentary record of intense hurricane landfalls in southern New England. Geol. Soc. Am. Bull. 113, 714–727. Dorrestein, R., Otto, L., 1960. On the mixing and flushing of the water in the Ems-estuary. In: Voorthuysen, J.H.v., Kuenen, P.H. (Eds.), Das Ems-Estuarium (Nordsee). In: Verh. K. Ned. Geol.-Mijnb.k.Genoot. (Geol. Ser.), vol. 19, pp. 83–102.

References

Downing, J., 2006. Twenty-five years with OBS sensors: the good, the bad, and the ugly. Cont. Shelf Res. 26, 2299–2318. Doxaran, D., Friodefond, J.-M., Castaing, P., 2003. Remote-sensing reflectance of turbid sediment-dominated waters. Reduction of sediment type variations and changing illumination conditions effects by the use of reflectance ratios. Appl. Opt. 42, 2623–2634. Doxaran, D., Cherukuru, R.C.N., Lavender, S.J., 2005. Use of reflectance band ratios to estimate suspended and dissolved matter concentrations in estuarine waters. Int. J. Remote Sens. 26, 1763–1769. http://dx.doi.org/10.1080/01431160512331314092. Doxaran, D., Castaing, P., Lavender, S.J., 2006. Monitoring the maximum turbidity zone and detecting fine-scale turbidity features in the Gironde estuary using high spatial resolution satellite sensor (SPOT HRV, Landsat ETM +) data. Int. J. Remote Sens. 27, 2303–2321. Doxaran, D., Froidefond, J.-M., Castaing, P., Babin, M., 2009. Dynamics of the turbidity maximum zone in a macrotidal estuary (the Gironde, France): observations from field and MODIS satellite data. Estuar. Coast. Shelf Sci. 81, 321–332. Dronkers, J., 1986a. Tidal asymmetry and estuarine morphology. Neth. J. Sea Res. 20, 117–131. Dronkers, J., 1986b. Tide-induced residual transport of fine sediment. In: van de Kreeke, J. (Ed.), Physics of Shallow Estuaries and Bays. Springer-Verlag, New York, NY, pp. 228–258. Drucker, R., Riser, S.C., 2014. Validation of Aquarius sea surface salinity with Argo: analysis of error due to depth of measurement and vertical salinity stratification. J. Geophys. Res. Oceans 119, 4626–4637. http://dx.doi.org/10.1002/2014JC010045. Dyer, K.R., 1995. Sediment transport processes in estuaries. In: Perillo, G.M.E. (Ed.), Geomorphology and Sedimentology of Estuaries, Developmental Sedimentology, vol. 53. Elsevier Science, New York, NY, pp. 423–449 (Chapter 14). Fain, A.M.V., Jay, D.A., Wilson, D.J., Orton, P.M., Baptista, A.M., 2001. Seasonal, monthly and tidal patterns of particulate matter dynamics in the Columbia River estuary. Estuaries 24, 770–786. Farmer, D.M., Clifford, S.F., Verrall, J.A., 1987. Scintillation structure of a turbulent tidal flow. J. Geophys. Res. 92, 5369–5382. Flagg, C.N., Smith, S.L., 1989. On the use of the acoustic Doppler current profiler to measure zooplankton abundance. Deep-Sea Res. 36, 455–474. Feng, L., Hu, C., Chen, X., Song, Q., 2014. Influences of the Three Gorges Dam on total suspended materials in the Yangtze Estuary and its adjacent coastal waters: observations from MODIS. Remote Sens. Environ. 140, 779–788. http://dx.doi.org/10.1016/j.rse.2016. 10.002. Festa, J.F., Hansen, D.V., 1978. Turbidity maxima in partially mixed estuaries—a twodimensional numerical model. Estuar. Coast. Shelf Sci. 7, 347–359. Flinchem, E.P., Jay, D.A., 2000. An introduction to wavelet transform tidal analysis methods. Estuar. Coast. Shelf Sci. 51, 177–200. Fischer, H.B., 1973. Longitudinal dispersion and turbulent mixing in open channel flow. Annu. Rev. Fluid Mech. 5, 59–78. Annual Reviews. Fischer, H.B., List, E.G., Koh, R.C.Y., Imberger, J., Brooks, N.H., 1979. Mixing in Inland and Coastal Waters. Academic Press, New York, NY. Friedrichs, C.T., Armbrust, B.D., de Swart, H.E., 1998. Hydrodynamics and equilibrium sediment dynamics of shallow, funnel-shaped tidal estuaries. In: Dronkers, A., Scheffers, A. (Eds.), Physics of Estuaries and Coastal Seas. Balkema, Rotterdam, The Netherlands, pp. 315–327.

99

100

CHAPTER 2 Estuarine turbidity maxima revisited

Fugate, D.C., Friedrichs, C.T., 2002. Determining concentration and fall velocity of estuarine particle populations using ADV, OBS and LISST. Cont. Shelf Res. 2, 1867–1886. Fugate, D.C., Friedrichs, C.T., 2003. Controls on suspended aggregate size in partially mixed estuaries. Estuar. Coast. Shelf Sci. 58, 389–404. Ganthy, F., Sottolichio, A., Verney, R., 2013. Seasonal modification of tidal flat sediment dynamics by seagrass meadows of Zostera noltii (Bassin d’Arcachon, France). J. Mar. Syst. 109–110, S233–S240. Garbrecht, J., Garbrecht, G., 2004. Sedimentation of harbors and counter-measures in the Greek and Roman Era. Water Resour. Environ. Hist. 11–20. http://dx.doi.org/10.1061/ 40738(140)3. Gartner, J.W., 2004. Estimating suspended solids concentrations from backscatter intensity measured by acoustic Doppler current profiler in San Francisco Bay, California. Mar. Geol. 211, 169–187. http://dx.doi.org/10.1016/j.margeo.2004.07.001. Geyer, W.R., 1993. The importance of suppression of turbulence by stratification on the estuarine turbidity maximum. Estuaries 16, 113–125. Geyer, W.R., Signell, R.P., Kineke, G.C., 1998. Lateral trapping of sediment in a partially mixed estuary. In: 8th International Biennial Conference on Physics of Estuaries and Coastal Seas, 1996. A. A. Balkema, Rotterdam, The Netherlands. Glangeaud, L., 1938. Transport et sedimentation dans l’es tuaire et a I’embouchure de la Gironde. Caracteres Petrographiques des Formations Fluviatiles, Saumatres, Littordes, et Nkritiques. Bull. Geol. Soc. France 8, 599–630. Glenn, S., Jones, C., Twardowski, M., Bowers, L., Kerfoot, J., Kohut, J., Webb, D., Schofield, O., 2008. Glider observations of sediment resuspension in a Middle Atlantic Bight fall transition storm. Limnol. Oceanogr. 53 (5, Pt. II), 2180–2196. Graham, G.W., Nimmo Smith, W.A.M., 2010. The application of holography to the analysis of size and settling velocity of suspended cohesive sediments. Limnol. Oceanogr. Methods 8, 1–15. http://dx.doi.org/10.4319/lom.2010.8.1. Gray, J.R., Gartner, J.W., 2009. Technological advances in suspended-sediment surrogate monitoring. Water Resour. Res. 45, W00D29. http://dx.doi.org/10.1029/ 2008WR007063. Groen, P., 1967. On the residual transport of suspended matter by an alternating tidal current. Neth. J. Sea Res. 3 (4), 564–574. Ha, H.K., Maa, J.P.-Y., Park, K., Kim, Y.H., 2011. Estimation of high-resolution sediment concentration profiles in bottom boundary layer using pulse-coherent acoustic Doppler current profilers. Mar. Geol. 279, 199–209. http://dx.doi.org/10.1016/j.margeo. 2010.11.002. Hamilton, L.J., 1998. Calibration and interpretation of acoustic backscatter measurements of suspended sediment concentration profiles in Sydney Harbour. Acoust. Aust. 26, 87–93. Hansen, D.V., Rattray Jr., M., 1965. Gravitational circulation in straits and estuaries. J. Mar. Res. 23, 104–122. Hay, A.E., Sheng, J., 1992. Vertical profiles of suspended sand concentration and size from multifrequency acoustic backscatter. J. Geophys. Res. 97, 15661–15677. Hill, P.S., Boss, E., Newgard, J.P., Law, B.A., Milligan, T.G., 2011. Observations of the sensitivity of beam attenuation to particle size in a coastal bottom boundary layer. J. Geophys. Res. 116, C02023. http://dx.doi.org/10.1029/2010JC006539. Hoitink, A.J.F., Hoekstra, P., 2005. Observations of suspended sediment from ADCP and OBS measurements in a mud-dominated environment. Coast. Eng. 52, 103–118.

References

Hoitink, A.J.F., Hoekstra, P., van Maren, D.S., 2003. Flow asymmetry associated with astronomical tides: implications for the residual transport of sediment. J. Geophys. Res. 108, 3315. http://dx.doi.org/10.1029/2002JC001539. Hojerslev, N., 1994. A history of early optical oceanographic instrument design in Scandinavia. In: Spinrad, R.W. et al., (Ed.), Ocean Optics. Oxford University Press, New York, NY, pp. 118–147. Holdaway, G.P., Thorne, P.D., Flatt, D., Jones, S.E., Prandle, D., 1999. Comparison between ADCP and transmissometer measurements of suspended sediment concentration. Cont. Shelf Res. 19, 421–441. Holliday, D.V., 1977. Extracting bio-physical information from the acoustic signatures of marine organisms. In: In: Anderson, N.R., Zahuranec, B.J. (Eds.), Ocean Sound Scattering Prediction, Marine Science Series, vol. 5. Plenum Press, New York, NY, pp. 619–624. Holliday, D.V., 2009. Technology for evaluating marine ecosystems in the early 21st century. In: Beamish, R.J., Rothschild, B.J. (Eds.), The Future of Fisheries Science in North America. Fish and Fisheries Series, vol. 31. Springer, New York, NY, pp. 283–311. Holliday, D.V., Donaghay, P.L., Greenlaw, C.F., McGehee, D.E., McManus, M.M., Sullivan, J.M., Miksis, J.L., 2003. Advances in defining fine- and micro-scale pattern in marine plankton. Aquat. Living Resour. 16 (3), 131–136. Hu, C., Chen, Z., Clayton, T.D., Swarzenski, P., Brock, J.C., Juller Karger, F.E., 2004. Assessment of estuarine water-quality indicators using MODIS medium-resolution bands: initial results from Tampa Bay, FL. Remote Sens. Environ. 93, 423–441. http://dx.doi.org/ 10.1016/j.res.2004.08.007. Hudson, A.S., 2014. Applications of remote sensing to the study of estuarine physics: suspended sediment dynamics in the columbia river estuary. Portland State University. Master’s thesis. Huijts, K.M.H., Schuttelaars, H.M., de Swart, H.E., Valle-Levinson, A., 2006. Lateral entrapment of sediment in tidal estuaries: an idealized model study. J. Geophys. Res. 111, C12016. http://dx.doi.org/10.1029/2006JC003615. Huijts, K.M.H., Schuttelaars, H.M., de Swart, H.E., Friedrichs, C.T., 2009. Analytical study of the transverse distribution of along-channel and transverse residual flows in tidal estuaries. Cont. Shelf Res. 29, 89–100. Hurther, D., Thorne, P.D., Bricault, M., Lemmin, U., Barnoud, J.-M., 2011. A multi-frequency acoustic concentration and velocity profiler (ACVP) for boundary layer measurements of fine-scale flow and sediment transport processes. Coast. Eng. 58, 594–605. IOCCG, 2000. Remote sensing of ocean colour in coastal, and other optically-complex, waters. In: Sathyendranath, S. (Ed.), Reports of the International Ocean-Colour Coordinating Group, No. 3. IOCCG, Dartmouth, Canada. IOCCG, 2006. Remote sensing of inherent optical properties: fundamentals, tests of algorithms, and applications. In: Lee, Z.-P. (Ed.), Reports of the International Ocean-Colour Coordinating Group, No. 5. IOCCG, Dartmouth, Canada. Jahmlich, S., Thomsen, L., Graf, G., 1999. Factors controlling aggregate formation in the benthic boundary layer of the Mecklenburg Bight (western Baltic Sea). J. Sea Res. 41, 245–254. Jay, D.A., 1991a. Residual circulation in shallow estuaries: shear, stratification and transport processes. In: Cheng, R.T. (Ed.), Residual Currents and Long-Term Transport. Coastal and Estuarine Studies, vol. 38. Elsevier, New York, pp. 49–63.

101

102

CHAPTER 2 Estuarine turbidity maxima revisited

Jay, D.A., 1991b. Estuarine salt conservation: a Lagrangian approach. Estuar. Coast. Shelf Sci. 32, 547–565. Jay, D.A., 2010. Estuarine variability. In: Valle-Levinson, A. (Ed.), Contemporary Issues in Estuarine Physics. Cambridge University Press, New York, pp. 62–99. Jay, D.A., Geyer, W.R., Montgomery, D.R., 2000. An ecological perspective on estuarine classification. In: Hobbie, J.E. (Ed.), Estuarine Science, a Synthetic Approach to Research and Practice. Island Press, Washington, D.C., pp. 149–175. Jay, D.A., Giese, B.S., Sherwood, C.R., 1990. Energetics and sedimentary processes in the Columbia River estuary. Prog. Oceanogr. 25, 157–174. Jay, D.A., Orton, P., Kay, D.J., Fain, A., Baptista, A.M., 1999. Acoustic determination of sediment concentrations, settling velocities, horizontal transports and vertical fluxes in estuaries. In: Anderson, S.P., Terray, E.A., Rizzoli White, J.A., Williams III, A.J. (Eds.), Proceedings of the IEEE Sixth Working Conference on Current Measurement, pp. 258–263. Jay, D.A., Musiak, J.D., 1994. Particle trapping in estuarine turbidity maxima. J. Geophys. Res. 99, 20446–20461. Jay, D.A., Musiak, J.D., 1996. Internal tidal asymmetry in channel flows: origins and consequences. In: Pattiaratchi, C. (Ed.), Mixing Processes in Estuaries and Coastal Seas, an American Geophysical Union Coastal and Estuarine Sciences Monograph, AGU, Washington, D.C., pp. 219–258. Jay, D.A., Naik, P., 2011. Distinguishing human and climate influences on hydrological disturbance processes in the Columbia River, USA. J. Hydrol. Sci. 56, 1186–1209. Jay, D.A., Orton, P.M., Chisholm, T., Wilson, D.J., Fain, A.M.V., 2007a. Particle trapping in stratified estuaries—I consequences of mass conservation and explorations of a parameter space. Estuar. Coasts 30, 1095–1105. Jay, D.A., Orton, P.M., Chisholm, T., Wilson, D.J., Fain, A.M.V., 2007b. Particle trapping in stratified estuaries—II application to observations. Estuar. Coasts 30, 1106–1125. Jourdin, F., Tessier, C., Le Hir, P., Verney, R., Lunven, M., Loyer, S., Lusven, A., Filipot, J.-F., Lepesqueur, J., 2014. Dual-frequency ADCPs measuring turbidity. Geo-Mar. Lett. 34 (4), 381–397. http://dx.doi.org/10.1007/s00367-014-0366-2. Karp-Boss, L., Azevedo, L., Boss, E., 2007. LISST-100 measurements of phytoplankton size distribution: evaluation of the effects of cell shape. Limnol. Oceanogr. Meth. 5, 396–406. Kawanisi, K., Yokosi, S., 1997. Characteristics of suspended sediment and turbulence in a tidal boundary layer. Cont. Shelf Res. 36, 69–75. Kay, D.J., Jay, D.A., 2003a. Interfacial mixing in a highly-stratified estuary. 1. Characteristics of mixing. J. Geophys. Res. 108, 3072. http://dx.doi.org/10.1029/2002 JC000252. Kay, D.J., Jay, D.A., 2003b. Interfacial mixing in a highly-stratified estuary. 2. A “method of constrained differences” approach for the determination of the momentum and mass balances and the energy of mixing. J. Geophys. Res. 108, 3073. http://dx.doi.org/ 10.1029/2002 JC000253. Kessarkar, P.M., Rao, V.P., Shynu, R., Ahmad, I.M., Mehra, P., Michael, G.S., Sundar, D., 2009. Wind-driven estuarine turbidity maxima in Mandovi Estuary, central west coast of India. J. Earth Syst. Sci. 118, 369–377. Khelifa, A., Hill, P.S., 2006. Models for effective density and settling velocity of flocs. J. Hydraul. Res. 44, 390–401. Kilcher, L.F., Thomson, J., Colby, J., 2014. Determining the spatial coherence of turbulence at MHK sites. In: Proceedings of the 2nd Marine Energy Technology Symposium. Kineke, G.C., Sternberg, R.W., 1992. Measurements of high-concentration suspended sediments using the optical backscatter sensor. Mar. Geol. 108, 253–258.

References

Kostaschuk, R., 2002. Flow and sediment dynamics in migrating salinity intrusions: Fraser River estuary, Canada. Estuaries 25, 197–203. http://dx.doi.org/10.1007/BF02691307. Kostaschuk, R., Best, J., Villard, P.V., 2010. The influence of dunes on mixing in a migrating salt-wedge: Fraser River estuary, Canada. Earth Surf. Process. Landf. 35, 460–465. http:// dx.doi.org/10.1002/esp.1928. Kostaschuk, R.A., Church, M.A., 1993. Macroturbulence generated by dunes—Fraser River, Canada. Sediment. Geol. 85 (1–4), 25–37. Kostachuk, R.A., Lutenauer, J.L., Church, M.A., 1989. Suspended sediment hysteresis in a salt-wedge estuary, Fraser River, Canada. Mar. Geol. 87, 273–285. Knowles, S.C., Wells, J.T., 1998. In situ aggregate analysis camera (ISAAC): a quantitative tool for analyzing fine-grained suspended material. Limnol. Oceanogr. 43, 1954–1962. Kranenburg, C., 1994. The fractal structure of cohesive sediment aggregates. Estuar. Coast. Shelf Sci. 39, 451–460. Kraus, N., Lohrmann, A., Cabrera, A., 1994. New acoustic meter for measuring 3D laboratory flows. J. Hydraul. Eng. 120, 407–420. Krone, R.B., 1962. Flume Studies of the Transport of Sediments in Estuarial Shoaling Processes. Hydraulic Engineering Laboratory and Sanitary Engineering Research Laboratory, University of California, Berkeley. Kukulka, T., Jay, D.A., 2003a. Impacts of Columbia River discharge on salmonid habitat I. a non-stationary fluvial tide model. J. Geophys. Res. 108, 3293. http://dx.doi.org/ 10.1029/2002JC001382. Kumar, R., Ruiz, A., Strom, K., 2009. A digital floc camera for nonintrusive measurement of floc parameters. In: World Environmental and Water Resources Congress, pp. 1–6. http:// dx.doi.org/10.1061/41036(342)330. Lee, H.L., Hanes, D.M., 1995. Direct inversion method to measure the concentration profile of suspended particles using backscattered sound. J. Geophys. Res. 100, 2649–2657. Le Hir, P., Ficht, A., Jacinto, R.S., Lesueur, P., Dupont, J.-P., Lafite, R., Brenon, I., Thouvenin, B., Cugier, P., 2001. Fine sediment transport and accumulations at the mouth of the Seine Estuary (France). Estuaries 24, 950–963. Lerczak, J.A., Geyer, W.R., 2004. Modeling the lateral circulation in straight, stratified estuaries. J. Phys. Oceanogr. 34, 1410–1428. Li, C., O’Donnell, J., 1997. Tidally driven residual circulation in shallow estuaries with lateral depth variation. J. Geophys. Res. 102 (C13), 27915–27929. http://dx.doi.org/ 10.1029/97JC02330. Maa, J.P.-Y., Kwon, J.-I., 2007. Using ADV for cohesive sediment settling velocity measurements. Estuar. Coast. Shelf Sci. 73 (2007), 351–354. MacCready, P., 2004. Toward a unified theory of tidally averaged estuarine salinity structure. Estuaries 27, 561–570. MacCready, P., 2007. Estuarine adjustment. J. Phys. Oceanogr. 37, 2133–2145. Manning, A.J., Bass, S.J., 2006. Variability in cohesive sediment settling fluxes: observations under different estuarine tidal conditions. Mar. Geol. 235, 177–192. Mari, X., Robert, M., 2008. Metal induced variations of TEP sticking properties in the southwestern lagoon of New Caledonia. Mar. Chem. 1102, 98–108. http://dx.doi.org/10.1016/j. marchem.2008.02.012. Maritorena, S., Siegel, D.A., Peterson, A.R., 2002. Optimization of semianalytical ocean color model for global-scale applications. Appl. Opt. 41, 2705–2714. Marriner, N., 2007. Geoarchaeology of Phoenicia’s Buried Harbours: Beirut, Sidon and Tyre, 5000 Years of Human–Environment Interactions. HAL Archives, Geomorphology, Universit_e de Provence—Aix-Marseille I.

103

104

CHAPTER 2 Estuarine turbidity maxima revisited

Martinez, J.-M., Jean-Loup, G., Filizola, N., Sondag, F., 2009. Increase in suspended sediment discharge of the Amazon River assessed by monitoring network and satellite data. CATENA 79, 257–264. http://dx.doi.org/10.1016/j.catena.2009.05.011. Merckelbach, L.M., 2006. A model for high-frequency acoustic Doppler current profiler backscatter from suspended sediment in strong currents. Cont. Shelf Res. 26, 1316–1335. Mikkelsen, O.A.P., Hill, S., Milligan, T.G., Chant, R.J., 2005. In situ particle size distributions and volume concentrations from a LISST-100 laser particle sizer and a digital floc camera. Cont. Shelf Res. 25, 1959–1978. Mikkelsen, O.A., Milligan, T.G., Hill, P.S., Chant, R.J., Jago, C.F., Jones, S.E., Krivtsov, V., Mitchelson-Jacob, G., 2008. The influence of schlieren on in situ optical measurements used for particle characterization. Limnol. Oceanogr. Methods 6, 133–143. Mikkelsen, O.A., Pejrup, M., 2001. The use of a LISST-100 laser particle sizer for in-situ estimates of floc size, density and settling velocity. Geo-Mar. Lett. 20, 187–195. http:// dx.doi.org/10.1007/s003670100064. Moate, B.D., Thorne, P.D., 2009. Measurements and inversion of acoustic scattering from suspensions having broad size distributions. J. Acoust. Soc. Am. 126, 2905–2917. Moate, B.D., Thorne, P.D., 2012. Interpreting acoustic backscatter from suspended sediments of different and mixed mineralogical composition. Cont. Shelf Res. 46, 67–82. Moftakhari, H.R., Jay, D.A., Talke, S.A., Kukulka, T., Bromirski, P.D., 2013. A novel approach to flow estimation in Tidal Rivers. Water Resour. Res. 49, http://dx.doi.org/ 10.1002/wrcr.20363. Morel, A., 1973. Diffusion de la lumiere par les eaux de mer. Resultats experimentaux et approche theorique. In: Optics of the Sea. AGARD Lecture Series, vol. 61. NATO, pp. 3.1.1–3.1.76. Mouw, C., Greb, S., Aurin, D., DiGiacomo, P., Lee, Z.-P., Twardowski, M., Binding, C., Hu, C., Ma, R., Moore, T., Moses, W., Craig, S., 2015. Aquatic color radiometry remote sensing of coastal and inland waters: challenges and recommendations for future satellite missions. Remote Sens. Environ. 160, 15–30. http://dx.doi.org/10.1016/j.rse.2015.02.001. Monismith, S.G., Kimmerer, W., Burau, J.R., Stacey, M.T., 2002. Structure and flow-induced variability of the subtidal salinity field in Northern San Francisco Bay. J. Phys. Oceanogr. 32, 3003–3019. Moore, S.A., Le Coz, J., Hurther, D., Paquier, A., 2012. On the application of horizontal ADCPs to suspended sediment transport surveys in rivers. Cont. Shelf Res. 46, 50–63. http://dx.doi.org/10.1016/j.csr.2011.10.013. Moore, T.S., Dowell, M.D., Bradt, S., Ruiz Verdu, A., 2014. An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters. Remote Sens. Environ. 143, 97–111. Naik, P., Jay, D.A., 2011. Distinguishing human and climate impacts on Columbia River: changes in mean flow and sediment transport. J. Hydrol. 404, 259–277. Neukermans, G., Loisel, H., Meriaux, X., Astoreca, R., McKee, D., 2012. In situ variability of mass-specific beam attenuation and backscattering of marine particles with respect to particle size, density, and composition. Limnol. Oceanogr. 57, 124–144. http://dx.doi.org/ 10.4319/lo.2011.57.1.0124. Nidzieko, N.J., 2010. Tidal asymmetry in estuaries with mixed semidiurnal/diurnal tides. J. Geophys. Res. 115, C08006. http://dx.doi.org/10.1029/2009JC005864. North, E.W., Houde, E.D., 2001. Retention of white perch and striped bass larvae: biological– physical interactions in Chesapeake Bay estuarine turbidity maximum. Estuaries 24, 756–769.

References

Nowacki, D.J., Horner-Devine, A.R., Nash, J.D., Jay, D.A., 2012. Rapid sediment removal from the Columbia River Plume near field. Cont. Shelf Res. 35, 16–28. Orr, M.H., Hess, F.R., 1978. Remote acoustic monitoring of natural suspensate distributions, active suspensate resuspension and slope/shelf water intrusions. J. Geophys. Res. 83, 4062–4068. Palmer, D.R., 1996. Rayleigh scattering from nonspherical particles. J. Acoust. Soc. Am. 99, 1901–1912. Partheniades, E., 1962. A study of erosion and deposition of cohesive soils in salt water. University of California at Berkeley. PhD thesis, 182 pp. Pedocchi, F., Garcı´a, M.H., 2006a. Evaluation of the LISST-ST instrument for suspended particle size distribution and settling velocity measurements. Cont. Shelf Res. 26, 943–958. Pedocchi, F., Garcı´a, M.H., 2006b. Noise-resolution trade-off in projection algorithms for laser diffraction particle sizing. Appl. Opt. 45, 3620–3628. Pejrup, M., 1988. Flocculated suspended sediment in a micro-tidal environment. Sediment. Geol. 57, 249–256. Postma, H., Kalle, K., 1955. Die Entstehung von Tru¨bungszonen im Unterlauf der Flu¨sse, speziell im Hinblick auf die Verha¨ltnisse in der Unterelbe. Deutsche Hydrograph. Zeit. 8, 138–144. Postma, H., 1961. Transport and accumulation of suspended matter in the Dutch Wadden Sea, Neth. J. Sea Res. 1, 148–190. Ralston, D.K., Warner, J.C., Geyer, W.R., Wall, G.R., 2013. Sediment transport due to extreme events: the Hudson River estuary after tropical storms Irene and Lee. Geophys. Res. Lett. 40, 5451–5455. http://dx.doi.org/10.1002/2013GL057906. Reed, D.J., Donovan, J., 1994. The character and composition of the Columbia River estuarine turbidity maximum. In: Dyer, K.R., Orth, R.J. (Eds.), Changes in Fluxes in Estuaries. Olsen and Olsen, Fredensborg, Denmark, pp. 445–450. Reichel, G., Nachtnebel, H.P., 1994. Suspended sediment monitoring in a fluvial environment: advantages and limitations applying an acoustic Doppler current profiler. Water Res. 28, 751–761. http://dx.doi.org/10.1016/0043-1354(94)90083-3. Rona, P.A., Palmer, D.R., Jones, C., Chayes, D.A., Czarnecki, M., Carey, E.W., Guerrero, J., 1991. Acoustic imaging of hydrothermal plumes, East Pacific Rise, 21°N, 109°W. Geophys. Res. Lett. 18, 2233–2236. Rouse, H., 1939. Experiments on the mechanics of sediment suspension. In: Proceedings 5th International Congress on Applied Mechanics, Cambridge, MA, pp. 550–554. Russo, C.R., Boss, E.S., 2012. An evaluation of acoustic Doppler velocimeters as sensors to obtain the concentration of suspended mass in water. J. Atmos. Ocean. Technol. 29, 755–761. http://dx.doi.org/10.1175/JTECH-D-11-00074.1. Ryan, J.P., Davis, C.O., Tufillaro, N.B., Kudela, R.M., Gao, B.-C., 2014. Application of the hyperspectral imager for the coastal ocean to phytoplankton ecology studies in Monterey Bay, CA, USA. Remote Sens. 6, 1007–1025. http://dx.doi.org/10.3390/rs6021007. Sassi, M., Hoitink, A., Vermeulen, B., 2012. Impact of sound attenuation by suspended sediment on ADCP backscatter calibrations. Water Resour. Res. 48, W09520. http://dx.doi. org/10.1029/2012WR012008. Sahin, C., Safak, I., Hsu, T.-J., Sheremet, A., 2013. Observations of suspended sediment stratification from acoustic backscatter in muddy environments. Mar. Geol. 336, 24–32. Sanford, L.P., Suttles, S.E., Halka, J.P., 2001. Reconsidering the physics of the Chesapeake Bay estuarine turbidity maximum. Estuaries 24, 655–669.

105

106

CHAPTER 2 Estuarine turbidity maxima revisited

Schoellhamer, D.H., Mumley, T.E., Leatherbarrow, J.E., 2007. Suspended sediment and sediment-associated contaminants in San Francisco Bay. Environ. Res. 105, 119–131. Schuttelaars, H.M., de Jonge, V.N., Chernetsky, A., 2013. Improving the predictive power when modelling physical effects of human interventions in estuarine systems. Ocean Coast. Manag. 79, 70–82. Schubel, J.R., Carter, H.H., 1984. Estuary as a filter for fine-grained suspended sediment. In: Kennedy, V.S. (Ed.), The Estuary as a Filter. Academic Press, Orlando, FL, pp. 81–105. Schuttelaars, H.M., de Swart, H.E., 1996. An idealized long-term morphodynamic model of a tidal embayment. Eur. J. Mech. B Fluids 15, 55–80. Scully, M.E., Friedrichs, C.T., 2007. Sediment pumping by tidal asymmetry in a partially mixed estuary. J. Geophys. Res. 112, C07028. http://dx.doi.org/10.1029/2006JC003784. Scully, M.E., Geyer, W.R., Lerczak, J.A., 2009. The influence of lateral advection on the residual estuarine circulation: a numerical modeling study of the Hudson River estuary. J. Phys. Oceanogr. 39, 107–124. Sea Engineering and HDR, 2011. Lower Passaic River System Understanding of Sediment Transport. Sea Engineering, Inc., Santa Cruz, 66 pp. Seim, H.E., Gregg, M.C., 1994. Detailed observations of a naturally occurring shear instability. J. Geophys. Res. 99, 10049–10073. Sheng, J., Malkiel, E., Katz, J., 2006. Digital holographic microscope for measuring threedimensional particle distributions and motions. Appl. Opt. 45, 3893–3901. Sherwood, C.R., Jay, D.A., Harvey, R.B., Hamilton, P., Simenstad, C.A., 1990. Historical changes in the Columbia River estuary. Prog. Oceanogr. 25, 299–352. Shugar, D.H., Kostaschuk, R., Best, J.L., Parsons, D.R., Lane, S.N., Orfeo, S., Hardy, R.J., 2010. On the relationship between flow and suspended sediment transport over the crest of a sand dune, Rı´o Parana´, Argentina. Sedimentology 57, 252–272. http://dx.doi.org/ 10.1111/j.1365-3091.2009.01110.x. Simenstad, C.A., Jay, D.A., Sherwood, C.R., 1992. Impacts of watershed management on landmargin ecosystems: the Columbia River estuary as a case study. In: Naimen, R. (Ed.), New Perspectives for Watershed Management—Balancing Long-term Sustainability with Cumulative Environmental Change. Springer-Verlag, New York, NY, pp. 266–306. Siegel, D.A., Maritorena, S., Nelson, N.B., Behrenfeld, M.J., McClain, C.R., 2005. Colored dissolved organic matter and its influence on the satellite-based characterization of the ocean biosphere. Geophys. Res. Lett. 32, L20605. http://dx.doi.org/10.1029/2005GL024310. Simenstad, C.A., Reed, D., Jay, D.A., Prahl, F., Small, L., Baross, J.A., 1995. LMER in the Columbia River estuary: an interdisciplinary approach to investigating couplings between hydrological, geochemical and ecological processes. In: Dyer, K.R., Orth, R.J. (Eds.), Changing Particle Fluxes in Estuaries: Implications from Science to Management. Olsen and Olsen, Fredensborg, pp. 437–444. Simmons, S.M., Parsons, D.R., Best, J.L., Orfeo, O., Lane, S.N., Kostaschuk, R., Hardy, R.J., West, G., Malzone, C., Marcus, J., Pocwiardowski, P., 2010. Monitoring suspended sediment dynamics using MBES. J. Hydraul. Eng. 136, 45–49. Simpson, J.H., Brown, J., Matthews, J.P., Allen, G., 1990. Tidal straining, density currents and stirring in the estuarine stratification. Estuaries 13, 125–132. Small, L.F., Prahl, F.G., 2004. A particle conveyor belt process in the Columbia River estuary: evidence from chlorophyll a and particulate organic carbon. Estuaries 27, 993–1013. Snyder, W.A., Arnone, R.A., Davis, C.O., Goode, W., Gould, R.W., Ladner, S., Lamela, G., Rhea, W.J., Stavn, R., Sydor, M., Weidemann, A., 2008. Optical scattering and backscattering by organic and inorganic particulates in U.S. coastal waters. Appl. Opt. 47, 666–677.

References

Sosik, H.M., 2008. Characterizing seawater constituents from optical properties. In: Babin, M., Roesler, C.S., Cullen, J.J. (Eds.), Real-Time Coastal Observing Systems for Ecosystem Dynamics and Harmful Algal Blooms. UNESCO Publishing, Paris. Stacey, M.T., Brennan, M.L., Burau, J.R., Monismith, S.G., 2010. The tidally averaged momentum balance in a partially and periodically stratified estuary. J. Phys. Oceanogr. 40, 2418–2434. http://dx.doi.org/10.1175/2010JPO4389.1. Styles, R., 2006. Laboratory evaluation of the LISST in a stratified fluid. Mar. Geol. 227, 151–162. Sun, C.-C., Wang, Y.-S., Li, Q.P., Yue, W.-Z., Wang, Y.-T., Sun, F.-L., Peng, Y.-L., 2012. Distribution characteristics of transparent exopolymer particles in the Pearl River estuary, China. J. Geophys. Res. 117, G00N17. http://dx.doi.org/10.1029/2012JG001951. Talapatra, S., Hong, J., McFarland, M., Nayak, A.R., Zhang, C., Katz, J., Sullivan, J., Twardowski, M., Rines, J., Donaghay, P., 2013. Characterization of biophysical interactions in the water column using in situ digital holography. Mar. Ecol. Progr. Ser. 473, 29–51. http://dx.doi.org/10.3354/meps10049. Talke, S.A., de Swart, H.E., 2006. Hydrodynamics and morphology in the Ems/Dollard estuary: review of models, measurements, scientific literature, and the effects of changing conditions. Ministry of Public Works, The Netherlands. Prepared for Rijkswaterstaat, IMAU Report R-01-06, 78 pp. Talke, S.A., de Swart, H.E., Schuttelaars, H.M., 2008. An analytical model of the equilibrium distribution of suspended sediment in an estuary. In: Dohmen-Janssen, H. (Ed.), River, Coastal and Estuarine Morphodynamics. Taylor & Francis, London, pp. 403–411. Talke, S.A., de Swart, H.E., de Jonge, V.N., 2009a. An idealized model and systematic process study of oxygen depletion in highly turbid estuaries. Estuar. Coasts 32 (4), 602–620. http:// dx.doi.org/10.1007/s12237-009-9171-y. Talke, S.A., De Swart, H.E., Schuttelaars, H.M., 2009b. Feedback between residual circulation and sediment distribution in highly turbid estuaries: an analytical model. Cont. Shelf Res. 29 (1), 119–135. http://dx.doi.org/10.1016/j.csr.2007.09.002. Talke, S.A., Horner-Devine, A.R., Chickadel, C.C., 2010. Mixing layer dynamics in separated flow over an estuarine sill with variable stratification. J. Geophys. Res. 115, C09004. http://dx.doi.org/10.1029/2009JC005467. Talke, S.A., Horner-Devine, A.R., Chickadel, C.C., Jessup, A.T., 2013. Turbulent kinetic energy and coherent structures in a tidal river. J. Geophys. Res. 118 (12), 6965–6981. http://dx.doi.org/10.1002/2012JC008103. Thevenot, M.M., Kraus, N.C., 1993. Comparison of acoustical and optical measurements of suspended material in the Chesapeake Estuary. J. Mar. Environ. Eng. 1, 65–79. Thomson, J., Talbert, J., de Klerk, A., Zippel, S., Guerra, M., Kilcher, L., 2015. Turbulence measurements from moving platforms. In: IEEE/OES Currents, Waves, and Turbulence Measurements Workshop, St. Petersburg, FL. Thorne, P.D., Hanes, D.M., 2002. A review of acoustic measurement of small-scale sediment processes. Cont. Shelf Res. 22, 603–632. Thorne, P.D., Hardcastle, P.J., 1997. Acoustic measurements of suspended sediments in turbulent currents and comparison with in-situ samples. J. Acoust. Soc. Am. 101, 2603–2614. Thorne, P.D., Meral, R., 2008. Formulations for the scattering properties of suspended sandy sediments for use in the application of acoustics to sediment transport processes. Cont. Shelf Res. 28, 309–317.

107

108

CHAPTER 2 Estuarine turbidity maxima revisited

Thorne, P.D., Vincent, C.E., Harcastle, P.J., Rehman, S., Pearson, N., 1991. Measuring suspended sediment concentrations using acoustic backscatter devices. Mar. Geol. 98, 7–16. http://dx.doi.org/10.1016/0025-3227(91) 90031-X. Topping, D.J., Wright, S.A., Melis, T.S., Rubin, D.M., 2007. High resolution measurement of suspended-sediment concentrations and grain size in the Colorado River in Grand Canyon using a multi-frequency acoustic system. In: Presented at 10th International Symposium on River Sedimentation, World Association for Sedimentological and Erosion Research, Moscow, Russia. Turner, A., Millward, G.E., 2002. Suspended particles: their role in estuarine biogeochemical cycles. Estuar. Coast. Shelf Sci. 55, 857–883. http://dx.doi.org/10.1006/ecss.2002.1033. Twardowski, M.S., Boss, E., Macdonald, J.B., Pegau, W.S., Barnard, A.H., Zaneveld, J.R.V., 2001. A model for estimating bulk refractive index from the optical backscattering ratio and the implications for understanding particle composition in Case I and Case II waters. J. Geophys. Res. 106, 14129–14142. Twardowski, M.S., Lewis, M.R., Barnard, A.H., Zaneveld, J.R.V., 2005. In-water instrumentation and platforms for ocean color remote sensing applications. In: Miller, R.L., Del Castillo, C.E., McKee, B.A. (Eds.), Remote Sensing of Coastal Aquatic Environments. Kluwer Academic, Dordrecht, The Netherlands, pp. 69–100 (Chapter 4). Twardowski, M.S., Sullivan, J., Stockley, N., 2015. Analytical model to derive SPM from inversion of attenuation and backscattering. In: Battelle Sediments Conference, New Orleans. Uncles, R.J., Stephens, J.A., Smith, R.E., 2002. The dependence of estuarine turbidity on tidal intrusion length, tidal range and residence time. Cont. Shelf Res. 22, 1835–1856. Van de Kreeke, J., Day, C.M., Mulder, H.P.J., 1997. Tidal variations in suspended sediment concentration in the Ems estuary: origin and resulting sediment flux. J. Sea Res. 38, 1–16. van Leussen, W., 1988. Aggregation of particles, settling velocity of mud flocs: a review. In: Dronkers, J., van Leussen, W. (Eds.), Physical Processes in Estuaries. Springer-Verlag, New York, NY, pp. 347–403. van Leussen, W., 2011. Macroflocs, fine-grained sediment transports, and their longitudinal variations in the Ems estuary. Ocean Dyn. 61, 387–401. http://dx.doi.org/10.1007/s10236011-0384-9. Van Straaten, L.M.J.U., Kuenen, Ph.H., 1958. Tidal action as a cause of clay accumulation. J. Sed. Petrol. 28 (4), 406–413. Valle-Levinson, A., Reyes, C., Sanay, R., 2003. Effects of bathymetry, friction and Earth’s rotation on estuary/ocean exchange. J. Phys. Oceanogr. 33, 2375–2393. Vasilkov, A.P., Goldin, Y.A., Gureev, B.A., Hoe, F.E., Swift, R.N., Wright, C.W., 2001. Airborne polarized lidar detection of scattering layers in the ocean. Appl. Opt. 40, 4353–4364. Weir, D.J., McManus, J., 1987. The role of wind in generating turbidity maxima in the Tay estuary. Cont. Shelf Res. 7, 1315–1318. Werdell, J.P., Franz, B.A., Bailey, S.W., Feldman, G.C., Boss, E., Brando, V.E., Dowell, M., Hirata, T., Lavender, S.J., Lee, Z.P., Loisel, H., Maritorena, S., Me´lin, F., Moore, T.S., Smyth, T.J., Antoine, D., Devred, E., Odile, H.F., d’Andon, O.H., Mangin, A., 2013. Generalized ocean color inversion model for retrieving marine inherent optical properties. Appl. Opt. 52, 2019–2037. Winterwerp, J., 1998. A simple model for turbulence induced flocculation of cohesive sediments. J. Hydraul. Res. 36, 309–326. Winterwerp, J.C., 2002. On the flocculation and settling velocity of estuarine mud. Cont. Shelf Res. 22 (9), 1339–1360.

References

Winterwerp, J.C., 2011. Fine sediment transport by tidal asymmetry in the high-concentrated Ems River: indications for a regime shift in response to channel deepening. Ocean Dynam. http://dx.doi.org/10.1007/sl0236-010-0332-0. Wright, S.A., Schoellhamer, D.H., 2005. Estimating sediment budgets at the interface between rivers and estuaries with application to the Sacramento–San Joaquin River Delta. Water Resour. Res. 41, W09428. http://dx.doi.org/10.1029/2004WR003753. Yu, J., Jiang, J., Gong, D., Li, S., Xu, Y., 2011. Determining suspended sediment concentration and settling velocity from PC-ADP measurements in the Beibu Gulf, China. Chinese J. Oceanol. Limnol. 29, 691–701. Zaneveld, J.R.V., Twardowski, M.S., Lewis, M., Barnard, A., 2005. Radiative transfer and remote sensing. In: Miller, R., Del-Castillo, C. (Eds.), Remote Sensing of Coastal Aquatic Waters. Springer-Kluwer Publishing, pp. 1–20. Zedel, L., Hay, A., 2010. Turbulence measurements in a jet: comparing the Vectrino I and Vectrino II. In: Proceedings of the IEEE/OES/CWTM Tenth Working Conference on Current Measurement Technology, pp. 173–178. Zhao, H.H., Chen, Q., Walker, N.D., Zheng, Q., MacIntyre, H.L., 2011. A study of sediment transport in a shallow estuary using MODIS imagery and particle tracking simulation. Int. J. Remote Sens. 32 (21), 6653–6671. http://dx.doi.org/10.1080/01431161.2010.512938. Zimmerman, J.T.F., 1986. The tidal whirlpool: a review of horizontal dispersion by tidal and residual currents. Neth. J. Sea Res. 20, 133–154. Zimmerman, R., Sukenik, C., Hill, V.J., 2013. Subsea LIDAR systems. In: Watson, J., Zeilinski, O. (Eds.), Subsea Optics and Imaging. Woodhead Publishing Series in Electronic and Optical Materials 471–487. Zimmerman-Timm, H., Holst, H., Muller, S., 1998. Seasonal dynamics of aggregates and their typical biocoenosis in the Elbe Estuary. Estuaries 21, 613–621.

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