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Multi-timescale analysis of the salinity and algal biomass of the Fraser River plume from repeated ferry… Halverson, Mark J. 2009

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Multi-timescale analysis of the salinity and algal biomass of the Fraser River plume from repeated ferry transects by Mark J. Halverson B.Sc., The University of Minnesota, 2000 M.Sc., The University of Wisconsin, 2002 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF Doctor of Philosophy in The Faculty of Graduate Studies (Oceanography)  The University Of British Columbia (Vancouver) August 2009 c Mark J. Halverson 2009  Abstract An instrumented ferry made eight transects per day across the Fraser River plume over the years 2003 – 2006 as part of the STRATOGEM program to study biophysical coupling in the Strait of Georgia. Water temperature, salinity, chlorophyll-a fluorescence, nitrate concentration, and dissolved oxygen were measured. This thesis utilizes salinity and chlorophyll-a fluorescence to study mixing in the plume, and the impact of the plume on algal biomass. First, the effects of river discharge and tides on plume salinity and surface area are quantified. Tidal fluctuations are caused by advection of the estuarine salt field, while fortnightly variations are caused by modulation of mixing in the estuary. Tidal and fortnightly variations are strongest at high river discharge and weakest at low discharge. Plume salinity decreases quasi-linearly with river discharge. Plume surface area increases with river discharge, from about 300 km2 at low river flow to about 1,200 km2 at high river flow, and can be predicted by scaling the river mouth deformation radius. Second, the plume fresh water flushing time is estimated and a salinity budget is constructed. Fresh water flushing time is 2.2 days, independent of river discharge. The quasi-steady budget predicts a vertical entrainment flux which varies with river discharge. The discharge-dependent vertical entrainment velocities in the estuary and plume implied by the entrainment flux are consistent with other methods. Flow speeds at the edge of the plume estimated from this method are too weak to maintain a plume front, suggesting fronts are transient and created on tidal time-scales. Third, a time series of surface and depth-integrated chlorophyll-a biomass is constructed. Chlorophyll-a fluorometer data are corrected for fluoresii  Abstract cence quenching with a parameterization specific to the region, and then calibrated with extracted samples. Instantaneous along-track differences in surface chlorophyll-a can be large, however, averaged over the whole time series, the distribution is nearly uniform. In contrast, depth-integrated values are about 35% lower on average in the plume compared to surrounding waters. Interannual variability in biomass is partly due to the magnitude and duration of the spring bloom, which is itself influenced by wind mixing and grazing.  iii  Table of Contents Abstract  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ii  Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . .  iv  List of Tables  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii  List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii Acknowledgments Dedication  . . . . . . . . . . . . . . . . . . . . . . . . . . .  x  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  xi  Statement of Co-Authorship . . . . . . . . . . . . . . . . . . . . . xii 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  1  1.1  Circulation and mixing in estuaries and river plumes  . . . .  3  1.2  Primary productivity and algal biomass in river plumes . . .  6  1.3  The Strait of Georgia and Fraser River  7  1.3.1  Circulation and mixing in the Fraser River estuary and river plume  1.3.2  . . . . . . . . . . . .  . . . . . . . . . . . . . . . . . . . . . . . .  11  STRATOGEM program . . . . . . . . . . . . . . . . . . . . .  13  1.4.1  Ferry sampling . . . . . . . . . . . . . . . . . . . . . .  13  1.4.2  Hydrographic sampling . . . . . . . . . . . . . . . . .  18  1.4.3  Remote sensing  . . . . . . . . . . . . . . . . . . . . .  19  1.4.4  Meteorological data . . . . . . . . . . . . . . . . . . .  20  Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  29  1.4  Biological studies in the lower Strait of Georgia  10  iv  Table of Contents 2 Estuarine forcing of a river plume 2.1  2.2  2.3  Introduction  . . . . . . . . . . . . . . .  37  . . . . . . . . . . . . . . . . . . . . . . . . . . .  37  2.1.1  The estuary - river plume system  2.1.2  Estuarine mixing and advection  Results  . . . . . . . . . . .  37  . . . . . . . . . . . .  38  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  40  2.2.1  Horizontal plume salinity structure  . . . . . . . . . .  40  2.2.2  Plume salinity . . . . . . . . . . . . . . . . . . . . . .  41  2.2.3  Plume surface area  . . . . . . . . . . . . . . . . . . .  45  . . . . . . . . . . . . . . . . . . . . . . . . . . . .  46  Discussion  . . . . . . . . . . . . . . .  46  . . . . . . . . . . . . . . . . .  53  . . . . . . . . . . . . . . . . . . . . . . . . . . . .  57  Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  69  2.4  2.3.1  Controls of plume salinity  2.3.2  Controls of plume area  Conclusion  3 Fresh water flushing time and entrainment in a river plume 74 3.1  Introduction  3.2  Results  3.3  3.4  . . . . . . . . . . . . . . . . . . . . . . . . . . .  74  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  77  3.2.1  Plume and Strait of Georgia water salinity  3.2.2  Plume fresh water fraction  3.2.3  Plume surface area  3.2.4  Plume fresh water volume and flushing time  . . . . .  79  3.2.5  Plume salinity budget . . . . . . . . . . . . . . . . . .  80  3.2.6  Entrainment flux  . . . . . . . . . . . . . . . . . . . .  82  3.2.7  Flow speed at the plume front . . . . . . . . . . . . .  84  Discussion  . . . . . .  77  . . . . . . . . . . . . . . .  77  . . . . . . . . . . . . . . . . . . .  78  . . . . . . . . . . . . . . . . . . . . . . . . . . . .  85  3.3.1  Curvature in entrainment flux  3.3.2  Entrainment velocity  3.3.3  Mixing at the plume front and frontogenesis  . . . . .  91  3.3.4  Fresh water flushing time . . . . . . . . . . . . . . . .  92  Conclusion  . . . . . . . . . . . . .  85  . . . . . . . . . . . . . . . . . .  87  . . . . . . . . . . . . . . . . . . . . . . . . . . . .  95  Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106  v  Table of Contents 4 Phytoplankton biomass and the Fraser River plume . . . . 110 4.1  Introduction  4.2  Methods  4.3  4.4  4.5  . . . . . . . . . . . . . . . . . . . . . . . . . . . 110  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112  4.2.1  Fluorescence quenching . . . . . . . . . . . . . . . . . 113  4.2.2  In situ and extracted chlorophyll-a  4.2.3  Depth-integrated biomass . . . . . . . . . . . . . . . . 118  Results  . . . . . . . . . . 116  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119  4.3.1  Continuous spatial variations . . . . . . . . . . . . . . 119  4.3.2  Plume and SoG water biomass time series  4.3.3  Seasonal excess specific growth rates  Discussion  . . . . . . 122  . . . . . . . . . 125  . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126  4.4.1  Fluorescence quenching and chlorophyll-a biomass . . 126  4.4.2  The plume and phytoplankton biomass . . . . . . . . 128  4.4.3  Interannual variability in chlorophyll-a biomass  Conclusion  . . . 132  . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137  Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 5 Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161  5.1  Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162  5.2  New contributions to oceanography  5.3  Future research  5.4  Comments on ferry sampling . . . . . . . . . . . . . . . . . . 168  . . . . . . . . . . . . . . 164  . . . . . . . . . . . . . . . . . . . . . . . . . 166  Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173  Appendices A Fraser discharge at the mouth . . . . . . . . . . . . . . . . . . 177 B MODIS plume area and ferry plume area  . . . . . . . . . . 179  vi  List of Tables 2.1  Comparison of plume salinity at various tidal frequencies with tidal elevation . . . . . . . . . . . . . . . . . . . . . . . . . . .  3.1  Parameters used to compute fresh water flushing time for a selection of river plumes . . . . . . . . . . . . . . . . . . . . .  4.1  59  97  Yearly breakdown of mean near-surface and depth-integrated chlorophyll-a biomass in the plume and in SoG water . . . . . 139  4.2  Seasonal mean surface and depth-integrated biomass in the plume and in SoG water . . . . . . . . . . . . . . . . . . . . . 140  4.3  Spring bloom characteristics during the exponential growth phase: 2003 – 2006 . . . . . . . . . . . . . . . . . . . . . . . . 141  vii  List of Figures 1.1  Strait of Georgia, ferry track and STRATOGEM CTD stations 22  1.2  Fraser discharge at its mouth and at Hope, BC . . . . . . . .  23  1.3  Wind speed at Sand Heads . . . . . . . . . . . . . . . . . . .  24  1.4  Hovm¨oller diagram for the 2005 ferry-measured temperature, salinity, and chlorophyll-a . . . . . . . . . . . . . . . . . . . .  25  1.5  Ferry sampling depth determination . . . . . . . . . . . . . .  26  1.6  Definition of the plume according to salinity . . . . . . . . . .  27  1.7  Comparison of MODIS radiance and ferry salinity . . . . . .  28  2.1  Along-track plume structure . . . . . . . . . . . . . . . . . . .  60  2.2  Plume and SoG water time series . . . . . . . . . . . . . . . .  61  2.3  Fraser discharge vs. plume and SoG salinity . . . . . . . . . .  62  2.4  Variance-conserving spectrum of plume salinity and Fraser discharge . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  63  2.5  Example of the tidal variation of plume salinity . . . . . . . .  64  2.6  Lomb-Scargle periodogram of plume salinity . . . . . . . . . .  65  2.7  Plume surface area time series . . . . . . . . . . . . . . . . . .  66  2.8  Fraser discharge vs. plume surface area . . . . . . . . . . . .  67  2.9  Seasonality of the plume salinity fortnightly cycle . . . . . . .  68  3.1  Fraser discharge vs. plume fresh water fraction . . . . . . . .  98  3.2  Time series of a) plume fresh water volume and Fraser discharge, and b) fresh water flushing time . . . . . . . . . . . .  3.3  99  Schematic diagram of plume used to visualize the salinity budget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100  3.4  Fraser discharge vs. entrainment flux . . . . . . . . . . . . . . 101 viii  List of Figures 3.5  Fraser discharge vs. flow velocity . . . . . . . . . . . . . . . . 102  3.6  Fraser discharge vs. a) entrainment velocity and b) salt flux . 103  3.7  Fraser discharge vs. Froude number at the plume front . . . . 104  3.8  River discharge vs. fresh water volume for various river plumes105  4.1  Time series of a) incident PAR and b) plume and SoG water PAR extinction coefficients . . . . . . . . . . . . . . . . . . . 142  4.2  Idealized daily cycle of fluorescence quenching . . . . . . . . . 143  4.3  Fluorescence quenching estimate from ferry data . . . . . . . 144  4.4  Comparison of raw ferry Chla to calibrated CTD Chla and ferry bottle extracted Chla . . . . . . . . . . . . . . . . . . . 145  4.5  2 m chlorophyll-a concentration vs. 0 – 20 m depth-integrated chlorophyll-a biomass  . . . . . . . . . . . . . . . . . . . . . . 146  4.6  Time-averaged transects of salinity and Chla . . . . . . . . . 147  4.7  Mean winter, freshet, and spring bloom transects of salinity, Chla, and nitrate . . . . . . . . . . . . . . . . . . . . . . . . . 148  4.8  Depth-integrated plume and SoG water chlorophyll-a biomass time series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149  4.9 Seasonal excess growth rates . . . . . . . . . . . . . . . . . . . 150 4.10 Example of weekly time-scale spatial and temporal variations in Chla . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 4.11 Mean profiles of salinity, PAR, and Chla in a) the plume and b) SoG water . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 4.12 Time series of the 2003 – 2006 spring blooms . . . . . . . . . . 153 4.13 Excess growth rate and wind mixing strength for the exponential growth phase of the spring blooms . . . . . . . . . . . 154 4.14 Comparison of excess growth rates and herbivorous zooplankton dry-weights . . . . . . . . . . . . . . . . . . . . . . . . . . 155 B.1 Ferry and satellite plume area comparison . . . . . . . . . . . 181  ix  Acknowledgments First I must thank my thesis advisor, Prof. Rich Pawlowicz, for helping me to shape a collection of interesting figures and ideas into a story, and for giving me the opportunity to work with the ferry dataset. Without his guidance my thesis would have essentially been a long methods and results section. Next, I would like to thank Susan Allen for her honest impression of my first thesis proposal. A substantial proportion of the revamped product eventually blossomed into this thesis. Evgeny Pakhomov and Philippe Tortell also deserve credit for their patience with my lack of any formal training in biology.  x  Dedication The people who are most deserved of this dedication are my parents. I would like to thank them for not pushing me into getting a real job and for providing support, financial and otherwise. Other individuals who I must thank are my grandfathers, who inspired me by demonstrating a lifelong passion for learning. Finally, I would like to recognize the enthusiasm of the science professors, instructors, and teachers from all stages of my education.  xi  Statement of Co-Authorship The major analysis in this thesis is contained in Chapters 2, 3, and 4. The authors of these chapters are myself and Rich Pawlowicz. I carried out all of the data analysis and writing, but Rich Pawlowicz contributed substantially by suggesting specialized analysis techniques, by helping to interpret the results, and by carefully editing the manuscript. The design of the ferry sampling system was largely the work of Rich Pawlowicz, but the maintenance of the system was done largely by myself. A version of Chapter 2 has been published in the Journal of Geophysical Research. Chapters 3 and 4 are planned for publication soon.  xii  Chapter 1  Introduction The purpose of this thesis is to describe how salinity varies in a buoyant river plume over a wide range of time-scales, and how algal biomass is impacted by its presence. The motivation for this project, in a broad sense, stems from questions about how river plumes affect the circulation [e.g. Lentz , 1995; Hickey et al., 1998] and biological productivity [e.g. Lohrenz et al., 1990; DeMaster et al., 1996] of the coastal ocean. Understanding the influence of river plumes on coastal oceanography is necessary, for example, in regions of dense human settlement because they will affect pollutant dispersal, sedimentation, and the local ecosystem. The analysis I describe was only made possible by the acquisition of a unique data set in the STRATOGEM project, which was established to study the coupling of biology and physics in the Strait of Georgia, BC, Canada (Sec. 1.4). This data set has the qualities necessary to characterize a river plume over a wide range of time-scales, a perspective uncommon in the literature. River plumes are influenced by a number of factors which operate over a wide range of time-scales, from hourly to annual. Tides vary over semidiurnal, diurnal, and fortnightly scales. Wind is generally strongest on daily to weekly scales, while river flow variations occur over scales of a few days to a year depending on, for example, the size and relief of the catchment [e. g. Geyer et al., 2000]. Biological time-scales span the same range, from diurnal growth patterns to the annual spring and summer blooms. Traditional oceanographic sampling methods (i. e. moorings and hydrographic surveys) often cannot simultaneously capture sufficient spatial and temporal resolution to fully characterize a river plume. Previous work on the physics and biology of river plumes may have missed important details due to these practical sampling considerations. High resolution observations are critical 1  Chapter 1. Introduction even if, for example, only seasonal descriptions are desirable, because of the potential coupling of short and long time-scale processes (i.e. through rectification) and aliasing. The Fraser River plume, the focus of this thesis, is an example of a river plume which experiences large changes in river discharge and tidal range, and should therefore show changes over a wide range of time-scales. The Fraser River discharges into the lower Strait of Georgia near the city of Vancouver (Fig. 1.1), and the buoyant plume it forms is, by one account, “the showpiece of the Strait of Georgia” [LeBlond, 1983]. The local salinity [Royer and Emery, 1982], primary productivity [Parsons, 1969], and currents [Crean et al., 1988] are modified by the plume’s presence. Understanding how it influences them is important because 10% of Canada’s population lives around the southern Strait of Georgia, making it a region of considerable human interest. Not only are studies of the plume important to scientific issues such as secondary production, but they may also support policies related to, for example, marine traffic (the Port of Vancouver is the largest and busiest in Canada). In the remainder of this chapter, I discuss circulation and mixing (Sec. 1.1), and primary productivity (Sec. 1.2), in estuaries and river plumes. The Strait of Georgia and the Fraser River are introduced in Sec. 1.3, followed by a brief literature review of the physics and biology of the Fraser River plume. In Sec. 1.4.1, the ferry sampling program is detailed, followed by a description of additional data sources used in this work. The major analysis of the thesis is contained in Chapters 3 - 5, each of which represent the contents of a published article or a manuscript in preparation for publication. Chapter 2 analyzes the full ferry time series of plume salinity and surface area, and identifies the dominant forcing for each over a range of time-scales. Chapter 3 determines the fresh water flushing time of the Fraser plume and develops a salinity budget to understand and quantify mixing in the estuary and plume. Chapter 4 identifies the role of the plume in setting the surface and depth-integrated chlorophyll-a biomass. A substantial part of this chapter involves corrections to the in situ chlorophyll-a fluorometer for fluorescence quenching and calibration to extracted chlorophyll-a. Chapter 2  Chapter 1. Introduction 5 summarizes this thesis by explicitly linking individual chapters together. It then concludes with a discussion on future research and some comments and advice on sampling with ferries.  1.1  Circulation and mixing in estuaries and river plumes  In this thesis, the term estuary refers specifically to the region where fresh and sea water initially mix. It is a hydrodynamic definition rather than a morphological definition. Due to the immensity of the estuarine mixing literature, this discussion will mostly be restricted to regions dynamically similar to the Fraser estuary. In the Fraser estuary, the initial contact of fresh and salt water generally occurs in a relatively narrow river channel upstream of the river mouth. The interface is highly stratified in the vertical, and the intrusion of salt water forms a wedge shape [Ages, 1979; Geyer and Farmer , 1989]. The flow is dominated by along-channel motion and bound by lateral barriers. A river plume is defined as the region away from the river mouth but still directly influenced by fresh or brackish water. Flow is not constricted by physical barriers. In some cases, described below, the plume is the primary location of mixing between fresh and salt water. The plume formed by Fraser outflow is very thin compared to the water depth within a short distance of the river mouth [Stronach, 1981], so it can be categorized as surface-advected as opposed to bottom-advected [Yankovsky and Chapman, 1997]. Rotation can be important over a time-scale of f −1 ∼ 3 hours. It is  dynamically useful to separate the plume into near- and far-fields, and a coastal current. However, the ferry used in this work is never more than 35 km from the mouth of the Fraser River (Fig. 1.1). Thus, the discussion will focus on near- and far-field plumes, and not buoyant coastal currents which may extend hundreds of kilometres or more [e.g. Garvine, 1995].  3  Chapter 1. Introduction  Estuary Basic estuarine circulation, a balance between the along-channel density gradient, bottom stress, and the sea-surface tilt [Hansen and Rattray, 1965], drives a shear flow with brackish water flowing seaward at the surface and saltier water flowing up-river at the bottom. In a salt-wedge estuary, waves may grow on the sharp interface between fresh and saline water. With sufficient shear, the waves may become unstable and break to cause mixing. In some cases, turbulence in the bottom boundary layer may also cause substantial mixing [e. g. Nash et al., 2009]. Tides have a tendency to advect the entire structure [e. g. Garvine, 1975], but also modulate the bottom stress and shear on the halocline. The shear is generally maximized during the ebb tide [Partch and Smith, 1978; Geyer and Farmer , 1989], though mixing may still be present over the whole tidal cycle [Peters, 1997; MacDonald and Horner-Devine, 2008]. Constrictions in the river channel may also accelerate the flow and cause mixing [Geyer and Farmer , 1989]. If shear across the pycnocline is sensitive to the strength of the ebb, then shear-driven mixing should vary on fortnightly scales from spring-neap and/or tropic-equatorial cycling [Geyer and Farmer , 1989]. Jay and Smith [1990] and Peters [1997] directly observe more intense mixing during spring tides than during neap tides in the Columbia and Hudson estuaries, respectively. If mixing in the estuary is to vary with the fortnightly cycle then the estuarine salt field must adjust faster than the forcing itself [MacCready, 1999]. However, adjustment times for the salt field in response to forcing vary with river discharge and the estuary dimensions [Kranenburg, 1986; Hetland and Geyer , 2004]. Highly stratified estuaries tend to adjust on shorter time-scales than fortnightly.  Near-field plume As the brackish estuary water enters the coastal waters, it will lift off the river bed because it is more buoyant than oceanic water. The lifting accelerates the plume and the flow becomes supercritical (i.e. U/c > 1, where U is the flow speed and c is the internal wave speed) [MacDonald, 2003]. Mixing 4  Chapter 1. Introduction in the near-field region is intense, characterized by a high vertical salt flux [Hetland, 2005]. The outflow velocity is set primarily by the spreading and thinning of the plume, and by entrainment of dense water from below [McCabe et al., 2008]. The seaward extent of the near-field plume is terminated by a salinity front. Here surface water converges and is forced downwards [Garvine, 1974] and mixing can again be strong [Orton and Jay, 2005]. The convergence causes downwelling velocities high enough to be measured directly [O’Donnell, 1998; Orton and Jay, 2005]. Seaward of the front, flow is subcritical, evident in at least one case by the release of internal waves [Nash and Moum, 2005].  Far-field plume There is no universal definition of what constitutes a far-field plume. Here the term will be used to describe the region of the plume which resides outside of the near-field, where the tidal release of water from the mouth has only weak effects. This definition resembles that given by Hetland [2005], who used it to describe the part of the plume where wind-mixing becomes important relative to inertial mixing (which dominates in the near-field). Others discuss the far-field as the last stage of a plume before it becomes indistinguishable from coastal ocean water [e. g. Horner-Devine et al., 2009]. In this case, the spatial scales can reach hundreds of kilometers if the plume originates from a relatively large river. Buoyancy, wind, and coastal currents largely dictate the dynamics. Classifying the entire region of the plume seaward of the near-field as the far-field is likely an oversimplification. For example, Horner-Devine et al. [2009] have divided the Columbia River plume into a near-field plume, bulge, and far-field plume because each is governed by a different set of dynamics. A bulge is a recirculating eddy which has been observed in some numerical models [Fong and Geyer , 2002; Hetland, 2005] and lab tank models [Avicola and Huq, 2003]. It has has been modeled as an anticyclonic eddy in cyclostrophic balance with an offshore extent equal to a few times the deformation radius [Yankovsky and Chapman, 1997]. Bulges are somewhat less  5  Chapter 1. Introduction ubiquitous in the ocean because of wind and ambient currents, but such a structure has been observed in the Hudson [Chant et al., 2007] and Columbia River plumes [Horner-Devine, 2009]. Regardless of the presence of a bulge, the far-field plume is a less transient feature than the tidally-pulsed nearfield plume [e.g. Yankovsky et al., 2001]. Mixing continues in the far-field, but it is characterized by a much smaller salt flux per unit area. However, it can be just as important as the estuary in an integral sense because it covers a larger area [Hetland, 2005]. For this thesis, the terms near- and far-field are defined as follows. When river discharge is high, the plume salinity correlates well with tidal elevation (Sec. 2.3.1), implying that much of the ferry transect consists of the near-field plume. As discussed in Chapters 2 and 3, this definition is likely consistent with dynamical definitions [e.g. Hetland, 2005]. When river discharge is low, tidal effects are weaker. At low flow, a ferry transect will contain less of the near-field plume than during high flow because the near-field plume is smaller or non-existent.  1.2  Primary productivity and algal biomass in river plumes  River plumes have competing attributes with respect to primary productivity. They provide fresh water (i.e. stratification) and sometimes nutrients, which increase productivity, but also suspended sediments which reduce photosynthetically active radiation (PAR) and thus decrease productivity. At times, productivity can be greatest at the edge of the plume, where an optimum balance of the required factors for growth is found. For example, productivity in the Amazon River plume is light limited near the river mouth and nutrient limited further offshore, and so productivity is maximized at the plume edge [Smith and DeMaster , 1996]. Flow convergence at the plume front may supply nutrients, which Harrison et al. [1991] liken to a “continuous flow culture.” Phytoplankton biomass, productivity, and community composition may  6  Chapter 1. Introduction or may not be affected by the presence of a river plume. Some plumes enhance productivity relative to their environment, while others do not. For example, the Mississippi river carries very high levels of limiting nutrients and discharges into oligotrophic waters. Wawrik and Paul [2004] calculate that 13% of the total primary productivity in the Gulf of Mexico occurs in the Mississippi River plume despite covering only 2.8% of its area. The Columbia River plume, however, lies in an entirely different setting. It discharges onto a mid-latitude shelf which is generally productive because of coastal upwelling [e.g. Hickey et al., 1998]. The river introduces very little nitrate, but entrainment of oceanic water into the plume from mixing in the estuary and near-field adds micro- and macro-nutrients [Lohan and Bruland, 2006]. Despite the increase in surface nutrients, the Columbia plume’s influence on chlorophyll-a biomass and community structure is generally weak, and depends on the details of the water into which it propagates [Frame and Lessard, 2009]. River plumes are dynamic in the sense that the water contained within them is replenished constantly by river discharge and entrainment. Fresh water residence time of the Mississippi River plume is short enough to limit the accumulation of phytoplankton biomass [Lohrenz et al., 1999]. A similar result was hypothesized in the Amazon plume, where the relatively short residence time limited the development of blooms, despite the presence of sufficient nutrients [DeMaster et al., 1996].  1.3  The Strait of Georgia and Fraser River  The Fraser River empties into the Strait of Georgia (hereafter SoG), a mid-latitude semi-enclosed coastal basin situated between mainland British Columbia and Vancouver Island. The Fraser River is the largest point source of fresh water for the SoG, making up 50 - 85% of the total discharge of fresh water [Waldichuk , 1957; Crean et al., 1988; Pawlowicz et al., 2007]. Figure 1.1 shows a map of the lower SoG with 551 nm reflectance measured using the Moderate Resolution Imaging Spectroradiometer (MODIS). The Fraser River plume is formed by discharge from each of its three arms. The south7  Chapter 1. Introduction ernmost arm, carrying 87% of the total river flow [Crean et al., 1988], is 15 km due south of Vancouver. The SoG is approximately 30 km wide here, and observations show the plume can span the SoG under some conditions [Tabata, 1972]. Before reaching the SoG, the river follows a breakwatered 9 km channel through tidal mud flats ending at the Sand Heads meteorological station. The channel is periodically dredged to a navigable depth of about 12 m but has a few pools of roughly 18 m depth. From the seaward extent of the mud flats the plume is detached from the bottom as the depth of the SoG increases quickly to greater than 100 m. Direct measurements of the Fraser River discharge at the mouth are not available, but an estimate is necessary for this thesis. Pawlowicz et al. [2007] have shown that the mouth discharge can be 20 to 100% higher than the Hope discharge, which is the location traditionally used to monitor the river despite being 120 km upstream. Appendix A details the method used in this thesis to account for the tributaries downstream of Hope. The result of the method is shown in Fig. 1.2 along with the measured discharge at Hope, BC. Discharge values in this thesis will always be given for the mouth unless stated otherwise. Fraser flow is uncontrolled and the annual discharge cycle is driven by early summer snow melt, providing freshet flows which peak in early June. Flows at the river mouth at this time ranged from 7,500 – 9,000 m3 s−1 over 2003 – 2006, which is 1,000 – 2000 m3 s−1 higher than the flow at Hope (Fig. 1.2). By late summer, the discharge reduces to 1,000 – 1,500 m3 s−1 and remains low (with the exception of fall 2004) until the following spring. The estimate shows the discharge at the mouth to be about 500 m3 s−1 higher than the discharge at Hope during normal winter flows. Winter rain storms often cause the discharge to more than double over the span of a few days, commonly from 1,500 – 3,000 m3 s−1 or higher during rare cases (e. g. October 2003). During rain storms the difference between the estimated mouth discharge and the measured Hope discharge can exceed 1,000 m3 s−1 . Winds in the SoG are heavily influenced by its mountainous orography, which cause them to align with its major axis. In summer, the North Pacific High generally sets up a down-strait flow (i.e. to the southeast), while in 8  Chapter 1. Introduction winter winds blow up-strait because of the Aleutian Low [Thomson, 1981]. Local topographical features, such as the Fraser Valley, may alter the larger scale patterns, particularly during polar air outbreaks in winter and sea breezes in summer [Thomson, 1981]. In Fig. 1.3 the measured wind speeds at Sand Heads are shown (data source described in Sec. 1.4.4). The time series shown matches the ferry data coverage, and has been smoothed with a 30-day moving average to emphasize the seasonal tendencies in wind speed and direction (hourly values can be substantially larger). Along-strait winds are generally stronger than cross-strait. Filtered wind speeds are highest in winter, reaching 2.0 – 4.5 m s−1 up-strait with a lesser component blowing to the southwest at about 1 m s−1 . Wind speeds are typically lower in summer. They blow to the northwest early in summer at 1.5 m s−1 , and switch to blowing down-strait at 1 m s−1 in July. Cross-strait winds at this time are light. The filtered time series greatly underestimates weather-band variability in wind direction and magnitude. Maximum instantaneous values in 2003 – 2006 reached 17 m s−1 in winter and 13 m s−1 in summer (not shown). Tides in the SoG are of the mixed type and characteristic of the temperate eastern Pacific. Spring/neap (i. e. lunar phase) and tropic/equatorial (i. e. lunar declination) cycling each produce a sizable fortnightly modulation, and together they will be generically referred to as the fortnightly cycle. The minimum daily tidal range over the study period was 1.9 m, while the maximum was 5.0 m. Along-strait ebb tide currents are typically 1 m s−1 in the narrow southern end of the SoG, near Tsawwassen, and decrease northwards as the SoG widens [see Fig. 1.19 of Crean et al., 1988]. In the vicinity of the plume, the maximum tidal excursion is estimated to be 10 km. Tidal heights used in this thesis were those predicted for Point Atkinson, BC, located 25 km north of the Fraser River mouth. Tidal records are available closer to the river mouth, but a time series uncontaminated by river stage was desirable.  9  Chapter 1. Introduction  1.3.1  Circulation and mixing in the Fraser River estuary and river plume  Estuary The Fraser River estuary is considered a salt wedge estuary [Stronach, 1981; Crean et al., 1988; Geyer and Farmer , 1989]. Mixing is primarily caused by shear instability in the salt wedge interface [Crean et al., 1988; Geyer and Farmer , 1989], while mixing caused by bottom stress is much weaker [MacDonald and Horner-Devine, 2008]. Mixing is most important during ebb tides, when shear is maximized, but still of measurable importance for much of the tidal cycle [MacDonald and Horner-Devine, 2008]. Because the magnitude of the shear depends on the tidal range, fortnightly variations in mixing have been suggested [Geyer and Farmer , 1989]. Spatially, mixing is concentrated where flow is accelerated by constrictions [Geyer and Farmer , 1989]. The location of the salt wedge changes substantially with variations in the tides and river flow [Crean et al., 1988; Geyer and Farmer , 1989; Kostaschuk and Atwood, 1990]. At low river flows (about 1,000 m3 s−1 at Hope), the head of the salt wedge will appear about 20 km upstream of the mouth at low tide and 35 km upstream at high tide. During the freshet (flows of about 8,000 m3 s−1 ), the salt wedge is at the river mouth at low tide and 18 km up-river at high tide. The majority of the studies discussed above were conducted when the river flow was greater than 3,000 m3 s−1 , and it is important to note that mixing has not been characterized at lower discharge. River plume Mixing and circulation in the region immediately seaward of the river mouth is heavily influenced by the tides [Crean et al., 1988]. At higher river flows the salt wedge is flushed out to the river mouth on large ebb tides [Stronach, 1981; MacDonald and Geyer , 2004]. The outflow is accelerated when it lifts off the bed and moves over salt water. The flow acceleration will force the  10  Chapter 1. Introduction flow towards the critical Froude number, causing mixing by shear instability [MacDonald and Geyer , 2004]. Water from beneath the plume is entrained, which provides up to half of the observed deceleration [Cordes et al., 1980]. As with the mixing and salinity structure of the estuary, the nature of the near-field plume has not been characterized at low river discharge. Recurring anticyclonic eddies of 8 - 12 km in size are formed near the edges of the buoyant outflow jet at relatively high discharge [Hodgins, 1994]. Drifter tracks initiated at the mouth during negligible winds delineate a clockwise eddy with a radius r = u/f [Crean et al., 1988], or about 10 km for a 1 m/s flow. Numerical models of the plume also predict eddies in the absence of wind [Stronach, 1981; Crean et al., 1988]. The eddies weaken as they are advected by along-strait tidal flows, and do not remain coherent for more than a tidal cycle [Hodgins, 1994]. On larger scales, Coriolis, wind stress, and buoyancy are the most important forces. Tabata [1972] notes rotational features in aerial photography which are large enough to span the SoG. Surface drifters released at the mouth often follow the wind direction independent of the Coriolis force and river discharge [Giovando and Tabata, 1970]. The plume may also be advected by the tidal streams in the SoG [Royer and Emery, 1982], which become increasingly significant where the SoG narrows to the south. Convergences and divergences in the surface flow may tear or pool the plume [Stronach, 1981].  1.3.2  Biological studies in the lower Strait of Georgia  The Fraser River plume has three important qualities which affect the growth of phytoplankton: fresh water, sediment, and nitrate. Fresh water from the river will increase stratification, with the greatest effect near the river mouth. This may allow the spring bloom to begin there before the less stratified regions of the SoG [Parsons, 1969; Yin et al., 1996]. Yin et al. [1997] argued that the spring bloom begins as a result of the increased stratification when the Fraser river discharge increases towards the early summer freshet. Stratification, however, is more sensitive to variations  11  Chapter 1. Introduction in wind mixing than variations in river flow [Collins et al., 2009]. Thus the timing of the bloom onset is much more sensitive to wind than river flow. River discharge is highest in summer, creating the strongest stratification. At this time, nitrate is the limiting factor for growth [Harrison et al., 1983]. Periodic wind events of sufficient strength to break the stratification and mix up nutrients can promote algal growth [St. John et al., 1993], although motile organisms like Myrionecta rubra, which has been observed in the SoG, may descend to depths where nitrate is found [Pawlowicz et al., 2009]. The Fraser River plume may contain higher nutrients in summer than Strait of Georgia surface water because of entrainment [Parsons, 1969; Harrison et al., 1991]. Nitrate can be entrained into the plume by mixing in the estuary [Yin et al., 1995a,b] or by wind mixing in the near-field plume [Yin et al., 1995c]. In the estuary, the amount of entrained nitrate increases with river discharge, and more nitrate is entrained during spring tides than during neap tides. Increased nutrients do not ensure increased productivity. Stockner et al. [1979] report that annually-averaged chlorophyll biomass, primary productivity, and specific growth rate increase with distance from the river mouth, along with a decrease in the light extinction coefficient. In a more detailed process study, Harrison et al. [1991] divide the plume into three salinity classes during a series of summer sampling trips. The freshest water mass, referred to as the riverine plume (i.e. the near-field plume), had lower productivity and chlorophyll-a biomass than the more saline parts of the plume, despite having a similar 0 – 15 m light extinction coefficient. Parsons [1969] found higher productivity in the region affected by the plume when compared to waters outside the plume from February to April. In this case the productivity difference was attributed to mixed layer depths. More importantly, however, the paper explicitly states that the Fraser River plume adds significant variability to measurements. This statement, and a similar one made by Harrison et al. [1991], emphasizes the need to resolve the spatial structure of the plume.  12  Chapter 1. Introduction  1.4 1.4.1  STRATOGEM program Ferry sampling  The primary data set used in this work was acquired from a collection of sensors sampling the engine cooling seawater aboard the 130 m long M.V. Queen of New Westminster on a route from Tsawwassen to Duke Point (Fig. 1.1). The ferry was instrumented as part of the Strait of Georgia Ecosystem Monitoring (STRATOGEM) project to study coupled physics and biology in the Strait of Georgia. Ferry sampling was implemented in this program to capture spatial and temporal events too fine to be resolved by monthly hydrographic surveys. The advantage of this sampling platform is illustrated by Fig. 1.4, a Hovm¨oller diagram of ferry data highlighting the variability of temperature, salinity, and chlorophyll-a concentration in the vicinity of the Fraser River plume. Although 8,502 transects are available over the 4 year sampling period, only one year (∼2,100 transects) is shown to get a sense of the annual cycle. Along-track variations in temperature are generally weak, but are strong in salinity as the ferry traverses the plume. Chlorophyll-a shows some along-track structure, but has the largest changes in time. As will be discussed in this thesis, variability in these fields exists at time-scales finer than can be shown in Fig. 1.4. Ferry instrumentation, sampling depth, and data processing Nearly all of the ferry time series was obtained from instruments on the M.V. Queen of New Westminster. For a month in spring 2003, another vessel, the Queen of Alberni, serviced this route and was also instrumented, but in later years gaps in service were not filled by instrumented replacement vessels. The track essentially runs along-strait, oriented to the northwest/southeast. The ferry makes four round-trip sailings per day, the first of which departs Tsawwassen at 0515 local time, and the last departs Duke Point at 2345. A complete transect covers 70 km and takes two hours, yielding an average speed of 19 knots. Ferry data began on 13 January 2003, and continued until 29 October 2006. Annual refits introduce month-long data gaps beginning  13  Chapter 1. Introduction around November. Occasional instrumentation problems caused additional gaps in the data record, the longest being for a month in May 2004. Other gaps tend to last only for a few days and occur infrequently and sporadically through the time series. Routine visits were made every two weeks or less to download logged data, clean the sensors, and perform maintenance tasks. The instrument suite has chemistry-free sensors to measure temperature and salinity (Seabird SBE45), chlorophyll-a fluorescence (WetLabs WetStar), and location (GPS) at 2 - 10 second intervals. For shorter periods of time, dissolved oxygen (Aanderra Optode), and nitrate concentration (Satlantic ISUS) were also measured. The thermosalinograph manufacturer specifies that the temperature is accurate to ±0.002◦ C. Warming during the time between inflow and sensing  likely occurs to some degree but it was not possible to detect it by comparison  to profile data. Salinity is theoretically accurate to ±0.005, but regular  lab and factory calibrations revealed that fouling occasionally freshened the salinity by up to 0.6. Because the plume was identified by relative changes in salinity, and because the salinity can vary by 10 in a single transect, fouling was not corrected. The chlorophyll-a fluorometer presented a number of special problems during the ferry program. Early in the ferry program an error was found in a factory re-calibration, and by comparison with later models of the same instrument, it was found that it read substantially higher for part of the time series. A correction factor was determined in-house and applied to the data. A consequence of the factory miscalibration was that the instrument could saturate at high chlorophyll-a levels, though it appears this occurred during only a few transects of the 2004 spring bloom. In September 2004, the fluorometer was serviced by WETLabs to correct the faulty factory calibration. The raw instrument output appears to be consistent before (with the in-house correction) and after the factory servicing as a comparison of in situ fluorescence with bottle extracted fluorescence revealed no significant differences. The second difficulty was significant fouling of the fluorometer over a time-scale of about two weeks despite the use of an automated cleaner. During regular biweekly servicing, the degree of fouling was characterized by 14  Chapter 1. Introduction inserting a plastic cable tie in the instrument before and after cleaning. A time-dependent correction factor for the period between services was derived from the pre- and post-cleaning values. This multiplicative factor begins at 1 immediately after cleaning and then increases to a maximum generally between 1 and 2 at the time of the next service. The estimated uncertainty in chlorophyll-a fluorescence is about 15%. According to the manufacturer, nitrate readings are stable to ± 0.05 µM  from reading to reading, and accurate to within the larger of 2 µM or 2%. However, the calibration appears to drift substantially over a day due to  fouling of the optical components. As a result, the ISUS could not be run continuously. Instead, it was deployed over relatively brief periods (∼weeks) during which extra effort was made to keep the instrument clean. Even so, the instrument drifted beyond the manufacturer’s specifications. Routine bottle sampling could not successfully characterize the drift. Deploymentspecific calibrations were developed for the periods used in this thesis, and are discussed in detail in Section 4.3.1. Systematic errors of up to 3 µM after calibration can be expected, although smaller changes along an individual transect remain significant because of the instrument’s stability over a 1.5 hour transect. To minimize errors caused by fouling, only transects taken immediately after cleaning the instrument were used in this thesis. The ferry travels at roughly 20 knots, and thus the highest achievable spatial resolution is 40 m (for a 5 second sampling interval). However there is a delay of about 3 minutes between the time water enters a sea chest and the time it passes through the instruments. While in the sea chest, water can mix and this effectively decreases the spatial resolution. The 3 minute residence time implies a spatial resolution of 1.9 km. A ship’s hull disturbs surface waters as it travels, potentially altering the data. The water may be affected in two different ways. First, the hull may mix the top few meters of the water column, implying that the ferry is sampling a depth average. This explanation is favoured by, for example, the ferry program of Ensign and Paerl [2006], who assume a 0 – 1.8 m average. The second possibility is that the hull will deflect surface water downwards so that the intake depth is different than the source water depth [Hinatsu 15  Chapter 1. Introduction et al., 2004]. While the analysis about to be described can not conclusively select between the two possibilities, the deflection of water case is assumed because it is based on the more robust analysis of Hinatsu et al. [2004]. It also simplifies the analysis where vertical profiles are needed. On the M.V. Queen of New Westminster, engine cooling water is drawn from a depth of 3.5 m at mid-ship, though variations in payload may change the depth by up to 40 cm. An effective sampling depth can be determined by comparing the ferry salinity to depth profiles of salinity taken near the ferry track. Vertical profiles were taken as part of the hydrographic component of the STRATOGEM project (Sec. 1.4.2). Salinity is chosen for the comparison because it increases monotonically with depth, whereas temperature and chlorophyll-a can have subsurface maxima which tend to complicate the analysis. Ferry data falling within 1 km of the hydrographic stations S2-3, S3, and S4-1 (Fig. 1.1) were extracted and interpolated onto the CTD time grid. There were 31 – 33 CTD casts (depending on the station) which had data meeting the criteria. The ferry and CTD data were plotted pair-wise for each CTD depth from 1 to 6 meters (not shown). The effective sampling depth is the depth where the ferry salinity is nearest to the CTD salinity, quantified by a linear regression forced through the origin. In Fig. 1.5, the results are plotted in terms of the difference between the regressed slope and a slope of one as a function of depth. The zero crossing of a particular station is the best choice for sampling depth. S2-3 shows the largest range of slopes because it is closest to the river mouth and therefore strongly stratified, while S4-1 shows the smallest change in slopes. The curves cross zero at 1.6, 1.3, and 2.1 m at S2-3, S3 and S4-1, respectively. The 2σ errors in the regressed slopes, estimated from fits to boot-strap replicates at each depth, imply the sampling depth inferred from S2-3, for example, could vary between about 1.3 and 2 m. The cause of this variation is likely a combination of temporal and spatial differences between the ferry and CTD data, as well as the 40 cm variation in the ship’s draft from differences in payload. Considering the errors of each fit, an effective sampling depth of 2 m will be used. Using an integer depth also means that the (binned) CTD data will not need to be interpolated. 16  Chapter 1. Introduction Discrete samples of seawater were taken while the ferry was underway for comparison with raw instrument output during periods in April 2003, June 2003, spring 2004, spring 2005, and June 2005. Sample bottles were manually filled from a valved hose which split the source water before it passed through the sensors. In each sampling period, 10 to 20 samples were taken per transect between one and three times weekly. In total, 25 and 100 samples were taken during each week to month-long period. The water samples were analyzed for salinity, nutrients, total chlorophyll-a, and plankton identification, though in this thesis only extracted chlorophyll-a is used. Seawater for extracted chlorophyll-a analysis was collected in opaque 500 mL bottles and stored in a cooler for up to 3 hours before filtering. Between 100 to 200 mL of sample were vacuum filtered through 0.2 µm polycarbonate filters. The filters were bathed in 90% acetone for 18 to 36 hours to extract the pigments [Parsons et al., 1984]. Fluorescence was measured using a Turner Designs 10AU fluorometer. Comparison of extracted chlorophyll-a to the ferry in situ values is discussed in Sec. 4.2.2. Data quality control and processing includes discarding data obtained while in port (during this time seawater pumps are run intermittently) and during equipment servicing when the source water was turned off. The data was binned to 30 second intervals, and then gridded according to alongstrait distance by projecting the tracks onto a line running along the center of the SoG (see Fig. 1.1). High and low resolution time series of the data are used. The high resolution time series is irregularly spaced in time because of the ferry sailing schedule, and the lower resolution version is constructed by forming daily means of the high resolution time series. When regularly sampled data is required, as for some spectral analysis, data gaps were filled with a constant value. The value assigned each gap was the mean of the data four days on each side of it. Defining the plume It is often useful in this thesis to separate a transect into plume and nonplume waters. The spatial resolution allows the plume boundaries to be  17  Chapter 1. Introduction identified from a ferry transect according to the salinity record. Typical along-track transects of salinity, taken during a times of moderate and low river discharge (3,800 m3 s−1 and 1,600 m3 s−1 , respectively), are shown in Fig. 1.6. The x-axis is reversed because 0 km lies near Tsawwassen, on the eastern side of the SoG, and 55 km is on the western side. In the moderate river discharge transect (lower curves), as in the majority of transects, the plume is found in the southeastern half of the transect, and is apparent by the dip in salinity between 3 and 31 km. At greater along-strait distances (northwest of the plume, near Vancouver Island), the salinity is roughly constant. The plume is defined as the part of the transect which has a salinity S < Sref − Sof f set . The reference salinity is the spatial mean of  salinity between 45 and 50 km, which lies outside the plume (see Fig. 2.1).  This salinity is also referred to as SoG water salinity. It is calculated for each individual transect. Sof f set was chosen to be a linear function of the reference salinity: Sof f set = 2 − 0.13 ∗ Sref  (1.1)  This quantity varies between 0.4 to 1.8, and is largest when the reference salinity is low. If the salinity offset was too high during low flow periods, then much of the plume would be missed because the difference between plume and reference salinity is small. The broken line in Fig. 1.6 shows Sref − Sof f set for that particular transect. The salinity is low-pass filtered  in along-strait distance by a running mean filter having 10% of the width of the transect, and the plume boundary is then chosen as the point where Sref − Sof f set intersects the filtered salinity curve.  1.4.2  Hydrographic sampling  The primary field component of the STRATOGEM program was a collection of regular monthly surveys conducted in the central and southern SoG from April 2002 until June 2005. During spring blooms, and for a short period in summer 2003, cruises were held weekly or biweekly. Nine stations were sampled for a range of physical and biogeochemical parameters, of which three were located near the ferry track: S2-3 in the plume, S3 at the plume bound18  Chapter 1. Introduction ary, and S4-1 outside the plume (Fig. 1.1). Full-depth continuous profiles of a range of parameters were taken, along with bottle samples at a number of depths (see Pawlowicz et al. [2009] for details), but in this thesis only profiles of salinity, chlorophyll-a fluorescence, and PAR are used. Salinity is accurate to within ±0.01. In situ chlorophyll-a fluorescence was calibrated  to samples of extracted chlorophyll-a. Subtracting 0.8 µg Chla l−1 from the continuous profiles produces a good match to the extracted samples over all seasons [Pawlowicz et al., 2009]. Absolute values of PAR are unreliable but relative changes with depth are dependable. Vertical net tows for mesozooplankton enumeration were done at S4-1 over the depth ranges 0 - 100 m and 0 - 400 m with a SCOR-type net (57 cm mouth diameter, 236 µm mesh) equipped with a TSK flowmeter. The hauls were typically taken early in the afternoon local time. The net was towed vertically at about 1 m s−1 . The samples were preserved in 5% buffered formalin. The samples were analyzed for abundance and species composition under a dissecting microscope equipped with an ocular micrometer. Abundances were converted to dry-weight biomass equivalents using species-specific length to weight relationships from the marine zooplankton database maintained at the Institute of Ocean Sciences (Fisheries and Oceans Canada) in Sidney, B.C.  1.4.3  Remote sensing  A series of 92 narrow-band 1 km horizontal resolution images were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument aboard the Aqua satellite. The purpose of the images was to measure the surface area of the Fraser River plume (Chapter 2). Each image includes data at 9 wavebands in the visible spectrum, each with a bandwidth of about 10 nm. Although an image is available from MODIS at a given location every one or two days, only 92 images were both free of clouds and coincided with a time when the ferry was underway making measurements. The images are irregularly spaced in time, and cover the period 4 April 2003 to 24 April 2006. The time span is a few months shorter than  19  Chapter 1. Introduction the ferry time series because the images were obtained in cooperation with a study to ground-truth MODIS chlorophyll-a algorithms [Komick , 2007]. They had been processed into Level 2 products giving top of atmosphere leaving radiance, and spatially subsetted to include Straits of Georgia and Juan de Fuca by Komick [2007]. Every image was taken between 12:45 and 14:30 local time, which corresponds with the 12:45 sailing from Duke Point to Tsawwassen. River plumes are generally more reflective than sea water at visual wavelengths [Kirk , 1994; Li et al., 2003] because they contain a higher concentration of suspended sediments. The highest contrast between SoG water and Fraser River plume water was found at 551 nm. The plume was chosen in each image semi-automatically. More objective methods may be desirable [e. g. Thomas and Weatherbee 2006], but the variability in the plume spectral signature and the manageable number of files made it unnecessary. The method is justified because comparisons of ferry salinity and satellite radiance showed a good correlation, with high radiance corresponding to low salinity. The strength of the relationship means the ferry and satellite measure the same plume (i. e. the sediment and salinity plumes represent the same water mass). As an example, Fig. 1.7 shows the correlation for a single ferry transect on 5 July 2003. Points originating from within the plume, as it is defined in Sec. 1.4.1, show a near-linear relationship between salinity and radiance. Points originating from SoG water, on the other hand, have uniformly low radiance values over a small range in salinity.  1.4.4  Meteorological data  Hourly wind speeds at 10 m were measured at the Sand Heads meteorological station (Environment Canada station #6831), located at the mouth of the Fraser River. The coordinate frame was rotated 55◦ counterclockwise to match the along-channel orientation of the Strait (Fig. 1.1). Hourly downwelling shortwave irradiance was measured at the University of British Columbia’s climate station. For consistency with biological literature, measured shortwave flux in W m−2 is converted to PAR in µE m−2 s−1 by taking  20  Chapter 1. Introduction PAR to be 47% of shortwave [Papaioannou et al., 1993] and then converting to molar units with a quanta-to-Watt ratio of 2.5 x 1018 photons W−1 . Albedo was calculated from standard oceanographic utilities in the AIR-SEA MATLAB toolbox [Pawlowicz et al., 2001].  21  50  Chapter 1. Introduction  50  50  Strait of Georgia 18’  50  S4−1 Fe  50 km  50  12’  Vancouver rry  40 km  Tr  Sand Heads  ac  k  Fraser River  S3 30 km  6’ Duke Point Ferry Terminal  S2−3 20 km 10 km  49oN  50  0 km  50  50  50  48’  124oW  Tsawwassen Ferry Terminal  50  50  r ve ou nc nd Va Isla  54’  48’  36’  24’  12’  o 123 W  Figure 1.1: Map of the lower Strait of Georgia plotted on a MODIS 551 nm top of atmosphere 1 km resolution image. The image was taken 19 July 2005, while the river discharge was ∼6,000 m3 s−1 . The plume appears as dark shades signifying a high reflectance. The small white points are three months of sub-sampled GPS fixes from the ferry. Westbound tracks sail north of eastbound tracks. STRATOGEM hydrographic stations S2-3, S3, and S4-1 are labeled and marked by triangles.  22  Chapter 1. Introduction  10000 Fraser @ mouth Fraser @ Hope  volume discharge [m3 s−1]  8000  6000  4000  2000  0 Jan  Jul  2004  Jul  05  Jul  06  Jul  Figure 1.2: A time series of Fraser discharge at two locations, mouth (thin black) and Hope (thick gray). The discharge at the mouth has been estimated according to the method described in the Appendix A, whereas the Hope discharge is measured directly by the Water Survey of Canada.  23  Chapter 1. Introduction  along−strait (+ to northwest) cross−strait (+ to northeast)  4 3  wind speed [m s−1]  2 1 0 −1 −2 −3 −4 Jan  Jul  2004  Jul  05  Jul  06  Jul  Figure 1.3: Wind speeds measured at Sand Heads at the mouth of the Fraser River. Hourly data has been filtered with a 30-day running mean and rotated 55◦ counter-clockwise to align with the axis of the SoG.  24  Chapter 1. Introduction  Temperature [oC] 5  2005  10  15  Salinity 20  a)  10 14 18 22 26 30 b)  Chlorophyll−a [µg.l−1] 0.1  1  3 5 10 30  c)  2005  Feb  Feb  Mar  Mar  Apr  Apr  May  May  Jun  Jun  Jul  Jul  Aug  Aug  Sep  Sep  Oct  Oct  50 40 30 20 10 0  50 40 30 20 10 0 along−track distance [km]  50 40 30 20 10 0  Figure 1.4: Hovm¨oller diagram for the 2005 ferry-measured a) temperature, b) salinity, and c) chlorophyll-a. The black areas represent missing data. This figure illustrates the high temporal and spatial resolution, as well as the excellent temporal coverage of the ferry sampling program. 25  Chapter 1. Introduction  0.4  difference between 1:1 and fitted slope  S2−3  0.3  S3 S4−1  0.2  0.1  0  −0.1  −0.2  1  2  3  4  5  6  depth [m]  Figure 1.5: Shown is the difference between the 1:1 line and the slope from a regression of CTD vs. ferry salinity. The ferry sampling depth is the depth such that the difference is minimized. S3 crosses at 1.3 m, S2-3 at 1.6 m, and S4-1 at 2.1 m. Error bars are 2 standard deviations, and are based on fits to bootstrap replicates of the CTD vs. ferry regression.  26  Chapter 1. Introduction  30  low river discharge  28 26  moderate river discharge  salinity  24 22  Sref − Soffset  20 original salinity filtered salinity  18 16 14  50  40  30 20 along−track distance [km]  10  0  Figure 1.6: Shown are typical along-track salinity transects, taken during times of low and moderate river discharge (1,600 and 3,800 m3 s−1 , respectively). The thin gray lines are the original data and the heavy black lines were filtered with a sliding boxcar 1/10th the width of the transect. The plume is defined as part of the transect having a salinity lower than Sref S of f set , which lies between the solid circles in each curve.  27  Chapter 1. Introduction  3  551nm water−leaving radiance [mW cm−2 um−1 sr−1]  2.5  2  1.5  1 plume SoG water 0.5  0 10  15  20  25  ferry salinity  Figure 1.7: Comparison of water-leaving MODIS radiance at 551 nm and ferry salinity. Water-leaving radiance was obtained from top of atmosphere radiance by the darkest pixel subtraction technique. The image was taken on 5 July 2003 at 14:10 PDT, while the ferry departed Vancouver Island at 12:45 the same day. Points originating from the plume are denoted by filled circles, while points originating from SoG water are denoted by open circles.  28  Bibliography Ages, A. (1979), The salinity intrusion in the Fraser River: Salinity, temperature, and current observations, 1976, 1977, Pacific Marine Science Report 79-14, Institute of Ocean Sciences, Patricia Bay, Sidney, B.C. Avicola, G., and P. Huq (2003), The characteristics of the recirculating bulge region in coastal buoyant outflows, J. Mar. Res., 61, 435–463. Chant, R., S. Glenn, E. Hunter, J. 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Ser., 70, 291–304. Hetland, R. (2005), Relating river plume structure to vertical mixing, J. Phys. Oceanogr., 35 (9), 1667–1688. Hetland, R., and W. Geyer (2004), An idealized study of the structure of long, partially mixed estuaries, J. Phys. Oceanogr., 34 (12), 2677–2691. Hickey, B., L. Pietrafesa, D. Jay, and W. Boicourt (1998), The Columbia River plume study: Subtidal variability in the velocity and salinity fields, J. Geophys. Res., 103 (C5), 10,339–10,368, doi:10.1029/94JC00343. Hinatsu, M., Y. Tsukada, H. Tomita, and A. Harashima (2004), Study on estimation of original location of water sampled through inlet set on volunteer observing ship, J. Adv. Mar. Sci Tech. Soc., 9, 37–46. Hodgins, D. (1994), Remote sensing of surface currents in the Fraser River plume with the SeaSonde HF radar, Tech. rep., Environment Technology Centre (Canada), Emergencies Science Division. Horner-Devine, A. (2009), The bulge circulation in the Columbia River plume, Cont. Shelf Res., 29, doi:10.1016/j.csr.2007.12.012. Horner-Devine, A., D. Jay, P. Orton, and E. Spahn (2009), A conceptual model of the strongly tidal Columbia River plume, J. Marine Syst., doi: 10.1016/j.jmarsys.2008.11.025, in press. Jay, D., and J. Smith (1990), Circulation, density distribution and neapspring transitions in the Columbia River Estuary, Prog. Oceanogr., 25, 81–112. Kirk, J. (1994), Light and photosynthesis in aquatic ecosystems, 2nd ed., Cambridge University Press.  31  Bibliography Komick, N. (2007), Remote sensing chlorophyll-a in the Strait of Georgia, Master’s thesis, University of Victoria. Kostaschuk, R., and L. Atwood (1990), River discharge and tidal controls on salt-wedge position and implications for channel shoaling: Fraser River, British Columbia, Can. J. Civil Eng., 17 (3), 452–459. Kranenburg, C. (1986), A time scale for long-term salt intrusion in wellmixed estuaries, J. Phys. Oceanogr., 16, 1329–1331. LeBlond, P. (1983), The Strait of Georgia: functional anatomy of a coastal sea, Can. J. Fish. Aquat. Sci., 40, 1033–1063. Lentz, S. (1995), The Amazon River plume during AMASSEDS: Subtidal current variability and the importance of wind forcing, J. Geophys. Res., 100 (C2), 2377–2390, doi:10.1029/94JC00343. Li, R., Y. Kaufman, B. Gao, and C. Davis (2003), Remote sensing of suspended sediments and shallow coastal waters, IEEE T. Geosci. Remote, 41 (3), 559–566. Lohan, M., and K. Bruland (2006), Importance of vertical mixing for additional sources of nitrate and iron to surface waters of the Columbia River plume: Implications for biology, Mar. Chem., 98, 260–273, doi: 10.1016/j.marchem.2005.10.003. Lohrenz, S., M. Dagg, and T. Whitledge (1990), Enhanced primary production at the plume/oceanic interface of the Mississippi River, Cont. Shelf Res., 10 (7). Lohrenz, S., G. Fahnenstiel, D. Redalje, G. Lang, M. Dagg, T. Whitledge, and Q. Dortch (1999), Nutrients, irradiance, and mixing as factors regulating primary production in coastal waters impacted by the Mississippi River plume, Cont. Shelf Res., 19, 1113–1141. MacCready, P. (1999), Estuarine adjustment to changes in river flow and tidal mixing, J. Phys. Oceanogr., 29, 708–726. 32  Bibliography MacDonald, D. (2003), Mixing processes and hydraulic control in a highly stratified estuary, Ph.D. thesis, Massachusetts Institute of Technology. MacDonald, D., and W. Geyer (2004), Turbulent energy production and entrainment at a highly stratified estuarine front, J. Geophys. Res., 109, C05004, doi:10.1029/2003JC002094. MacDonald, D., and A. Horner-Devine (2008), Temporal and spatial variability of vertical salt flux in a highly stratified estuary, J. Geophys. Res., 113, C090220, doi:10.1029/2007JC004620. McCabe, R., B. Hickey, and P. MacCready (2008), Observational estimates of entrainment and vertial salt flux in the interior of a spreading river plume, J. Geophys. Res., 113, C08027, doi:10.1029/2007JC004361. Nash, J., and J. Moum (2005), River plumes as a source of large-amplitude internal waves, Nature, 437, 400–403, doi:10.1038/nature03936. Nash, J., L. Kilcher, and J. Moum (2009), Structure and composition of a strongly stratified, tidally pulsed river plume, J. Geophys. Res., doi: doi:10.1029/2008JC005036, in press. O’Donnell, J. (1998), Convergence and downwelling at a river plume front, J. Phys. Oceanogr., 28, 1481–1495. Orton, P., and D. Jay (2005), Observations at the tidal plume front of a high-volume river outflow, Geophys. Res. Lett., 32, L11605, doi:10.1029/ 2005GL022372. Papaioannou, G., N. Papanikolaou, and D. Retalis (1993), Relationships of photosynthetically active radiation and shortwave irradiance, Theor. Appl. Climatol., 48, 23–27. Parsons, T., Y. Maita, and C. Lalli (1984), A manual of chemical and biological methods for seawater analysis, 1st ed., Pergamon Press Canada Ltd.  33  Bibliography Parsons, T. R. (1969), Production studies in the Strait of Georgia. Part I. Primary production under the Fraser River plume, February to May, 1967, J. Exp. Mar. Biol. Ecol., 3, 27–38. Partch, E., and J. Smith (1978), Time dependent mixing in a salt wedge estuary, Estuar. Coast. Mar. Sci., 6, 3–19. Pawlowicz, R., R. Beardsley, S. Lentz, E. Dever, and A. Anis (2001), Software simplies air-sea data estimates, EOS Trans. Amer. Geophys. Union, 82 (1), 2–2. Pawlowicz, R., O. Riche, and M. Halverson (2007), The circulation and residence time of the Strait of Georgia using a simple mixing-box approach, Atmos. Ocean, 45 (4), 173–193, doi:10.3137/ao.450401. Pawlowicz, R., A. Sastri, S. Allen, D. Cassis, O. Riche, M. Halverson, and J. Dower (2009), Carbon flow and plankton ecology of the Strait of Georgia, British Columbia, in prep. Peters, H. (1997), Observations of stratified turbulent mixing in an estuary: neap-to-spring variations during high river flow, Estuar. Coast. Shelf Sci., 45, 69–88. Royer, L., and W. Emery (1982), Variations of the Fraser River plume and their relationship to forcing by tide, wind and discharge, Atmos. Ocean, 20, 357–372. Smith, W., Jr., and D. DeMaster (1996), Phytoplankton biomass and productivity in the Amazon River plume: correlation with seasonal river discharge, Cont. Shelf Res., 16 (3), 291–319. St. John, M., S. Marinone, J. Stronach, P. Harrison, J. Fyfe, and R. Beamish (1993), A horizontally resolving physical-biological model of nitrate concentration and primary productivity in the Strait of Georgia, Can. J. Fish. Aquat. Sci., 50, 1456–1466.  34  Bibliography Stockner, J., D. Cliff, and K. Shortreed (1979), Phytoplankton ecology of the Strait of Georgia, British Columbia, J. Fish. Res. Board Can., 36, 657–666. Stronach, J. (1981), The Fraser River plume, Strait of Georgia, Ocean Management, 6, 201–221. Tabata, S. (1972), The movement of the Fraser River-influenced water from a series of aerial photographs, Pacific marine report no. 72-6, Marine Sciences Branch, Pacific Region. Thomas, A., and R. Weatherbee (2006), Satellite-measured temporal variability of the Columbia River plume, Remote Sens. Environ., 100, 167–178. Thomson, R. (1981), Oceanography of the British Columbia coast, 291pp pp., Canadian Special Publications of Fisheries and Aquatic Sciences 56. Waldichuk, M. (1957), Physical oceanography of the Strait of Georgia, British Columbia, J. Fish. Res. Board Can., 14, 321–486. Wawrik, B., and J. Paul (2004), Phytoplankton community structure and productivity along the axis of the Mississippi River plume in the oligotrophic Gulf of Mexico waters, Aquat. Microb. Ecol., 35, 185–196. Yankovsky, A., and D. Chapman (1997), A simple theory for the fate of buoyant coastal discharges, J. Phys. Oceanogr., 27, 1386 – 1401. Yankovsky, A., B. Hickey, and A. M¨ unchow (2001), Impact of variable inflow on the dynamics of a coastal buoyant plume, J. Geophys. Res., 106 (C9), doi:0.1029/2001JC000792. Yin, K., P. Harrison, S. Pond, and R. Beamish (1995a), Entrainment of nitrate in the Fraser River estuary and its biological implications. I. Effects of the salt wedge, Estuar. Coast. Shelf Sci., 40, 505–528. Yin, K., P. Harrison, S. Pond, and R. Beamish (1995b), Entrainment of nitrate in the Fraser River estuary and its biological implications. II. Effects  35  Bibliography of spring vs. neap tides and river discharge, Estuar. Coast. Shelf Sci., 40, 529–544. Yin, K., P. Harrison, S. Pond, and R. Beamish (1995c), Entrainment of nitrate in the Fraser River estuary and its biological implications. III. Effects of winds, Estuar. Coast. Shelf Sci., 40, 545–558. Yin, K., P. Harrison, R. Goldblatt, and R. Beamish (1996), Spring bloom in the central Strait of Georgia: interactions of river discharge, winds and grazing, Mar. Ecol.-Prog. Ser., 138, 255–263. Yin, K., P. Harrison, , R. Goldblatt, M. St. John, and R. Beamish (1997), Factors controlling the timing of the spring bloom in the Strait of Georgia estuary, British Columbia, Canada, Can. J. Fish. Aquat. Sci., 54, 1985– 1995.  36  Chapter 2  Estuarine forcing of a river plume by river flow and tides1 2.1  Introduction  2.1.1  The estuary - river plume system  An estuary-river plume system may be broadly decomposed into three interacting components: the estuary, and the near- and far-field plumes [Hetland, 2005] (Sec. 1.1). Physically, the estuary and near-field plume are characterized by intense mixing of fresh and salt water. The far-field refers to the region away from the geometric influence of the river mouth, which we would observe as the surface advected river plume. To some degree, these categories also delineate regions dominated by different external forcing factors. Inertial shear mixing dominates in the estuary and near-field and is modulated by variations in river flow and tides, while wind forcing dominates the far-field. Much of the work on estuaries and river plumes implicitly treats them as separate entities. That is, work on estuaries is principally centered on river and tidal effects within the estuary [e. g. Hansen and Rattray, 1965; Partch and Smith, 1978; Geyer and Farmer , 1989; Hetland and Geyer , 2004; MacCready, 2007], and work on river plumes is concerned with the effects 1 A version of this chapter was published in: Halverson, M., and R. Pawlowicz (2008), Estuarine forcing of a river plume by river flow and tides, J. Geophys. Res., 113, C090330, doi:10.1029/2008JC0048440.  37  Chapter 2. Estuarine forcing of a river plume of, for example, wind stress and ambient flows [e. g. Chao, 1988; Lentz , 1995a; Hickey et al., 1998; Fong and Geyer , 2002; Hetland, 2005]. The connection has received little attention since the work of Garvine [1975] in the Connecticut River Estuary. Understanding the connection is important because momentum and buoyancy at the mouth will influence river plume dynamics further afield [Garvine, 1987], an issue especially important in numerical models of river plumes [e. g. Hetland, 2005]. Here we attempt to link the two by identifying the effects of estuarine mixing and advection on basic properties of a river plume. Tidal and river forcing introduce a wide range of time scales to the problem of estuarine dynamics. Tides operate on semi-diurnal, diurnal, and fortnightly time scales, while river forcing often works on annual time scales. The wide range of time scales is difficult to cover with field studies. Ship-based work cannot capture long time scales without aliasing higher frequency processes, moorings lack horizontal information, and satellites cannot measure the most important tracer, salinity. A ferry sampling program in the Strait of Georgia, BC, Canada, has provided a data-set capable of identifying the effects of estuary mixing on a river plume over all forcing time scales (Sec. 1.4.1). The ferry crosses the Fraser River plume, which originates from a strongly forced estuary. Maximum daily tidal range is 5 m, while the river flow varies by a factor of 10 over its annual cycle. Such long term sampling of a strongly forced system provides a unique opportunity to study the effects of estuary processes on the properties of a river plume.  2.1.2  Estuarine mixing and advection  River flow and tides are the primary external forces that introduce time dependence to estuarine salinity structure. River flow introduces stratification while tides have a tendency to break down stratification. Because these external forcing factors mix and advect water in an estuary, they should also affect basic properties of a river plume. Tidal asymmetry of estuarine flow favours mixing on ebb tides, when  38  Chapter 2. Estuarine forcing of a river plume shear is maximized at the interface of the salt-wedge and river water. In the Duwamish estuary, Partch and Smith [1978] observe that the vertical salt flux is ten times higher during ebbs than floods, and that nearly half of the total vertical salt flux occurs over a relatively short time during the ebb. In the Fraser estuary, Geyer and Farmer [1989] observe unstable waves on the salt-wedge halocline, and show that the tendency to mix across the interface is a function of the tidal phase. The shear across the interface increases during the ebb phase until the Richardson number reaches a critical value. Geyer and Farmer [1989] hypothesize that the estuarine structure should be sensitive to the fortnightly modulation of tidal amplitude because of the sensitivity of shear across the interface to the strength of the ebb tide. Variation of mixing over a spring-neap cycle has been observed in the Columbia [Jay and Smith, 1990] and Hudson [Peters, 1997] estuaries. The ability of the tides to drive mixing is a function of river discharge in some estuaries. In the Columbia estuary, river flow modulates the ability of the tides to break down stratification at the salt-wedge [Jay and Smith, 1990]. During high river flow, stratification is too strong to allow even the largest ebb tides to create enough shear to break down the salt-wedge. However, at low river flow, it is possible for the weakest tides to mix away the salt-wedge. Advection of the estuary salt field by tides has received less attention than mixing, but in anticipation of our results we note that the position of the salt-wedge with respect to the river mouth will likely affect plume properties. A salt-wedge may move a significant distance over a single tidal cycle [e. g. Garvine, 1975; Kostaschuk and Atwood, 1990], at times comparable to the length of the salt intrusion itself. River discharge also sets the position of the salt-wedge. An early theoretical investigation by Hansen and Rattray [1965] suggested that the steady state length of the salt intrusion should be inversely proportional to river flow. The inverse relation has been observed in number of estuaries [e. g. Abood, 1974; Kostaschuk and Atwood, 1990; Jay and Smith, 1990; Monismith et al., 2002]. A long estuary presents a greater surface area for mixing compared to a short estuary. 39  Chapter 2. Estuarine forcing of a river plume With this cumulative understanding of mixing and advection processes in estuaries, we proceed to study their effects on the salinity and surface area of a river plume. As such this chapter is organized as follows: In Sec. 2.2 we describe the mean horizontal structure of the plume and analyze the time series of salinity and surface area. In Sec. 2.3 we interpret the time series in terms of estuarine mixing and advection processes, and compare the results to other systems. Finally, the chapter is summarized in Sec. 2.4.  2.2 2.2.1  Results Horizontal plume salinity structure  Visual examination of some of the 8,502 ferry transects reveals a large degree of variability, both in the along-strait salinity structure, and in a time series of salinity at any point in the transect. The mean plume structure is shown in Fig. 2.1, formed by averaging all of the ferry transects. The lowest mean salinity is 22, found at an along-track distance of 16 km, which is the closest point of the transect to the mouth of the Fraser River (see Fig. 1.1). The actual distance between the river mouth and the 16 km mark depends on whether the transect originates from a west- or east-bound transect. The west-bound transect is typically 5.5 km away from the river mouth, while the east-bound transect is 8.9 km away. This low salinity feature at 16 km will be referred to as the plume “core”. The average along-strait extent of the plume, bounded by the region of near-constant salinity at the western edge, is 45 km from the reference location and 33 km from the river mouth. Outside of the plume the mean salinity is 26.5. This water will be referred to as Strait of Georgia (SoG) surface water. The salinity gradient on both sides of the plume core is 0.22 km−1 . We note that the horizontal salinity gradient of the mean transect is much weaker than the gradient in any single transect. The plume front changes location based on the external forcing and averaging over many tracks lessens the gradient (note the sharp gradients in the single transect shown in Fig. 1.6). Deviations from the mean salinity transect can be substantial. The dis-  40  Chapter 2. Estuarine forcing of a river plume tribution of observed salinities at any location is not symmetric about the mean transect. Fresh anomalies have a greater excursion from the mean than saline anomalies, thus we present the 5, 25, 50, 75 and 95% percentiles in Fig. 2.1 to quantify the variability. The range of salinity values is widest at the core of the plume and narrows towards either end. At the core of the plume, only 5% of the observations had a salinity less than 12 and the minimum salinity at this point is near 5. Only 5% of the measurements at the core of the plume exceed 28, larger than the mean SoG water salinity, but smaller than instantaneous SoG surface water salinity for those transects. The SoG water salinity is rarely lower than 22 or greater than 29.5.  2.2.2  Plume salinity  The nature of the ferry track does not allow us to compute a true spatial mean of the plume salinity (e. g. the volume average of salinity within some isohaline). Instead we define a mean salinity equal to the spatial average over the section of the ferry transect within the plume (discussed in Sec. 1.4.1). We expect this definition to be a meaningful proxy for the true mean plume salinity based on comparisons with 48 CTD profiles taken during the STRATOGEM hydrographic program over the years 2002 - 2005 (Sec. 1.4.2). The profiles were taken semi-regularly at station S2-3, located about 4.5 km from Sand Heads (Fig. 1.1). They show that the plume depth, defined as the base of the strongly stratified surface layer, ranges from 4 8 m. As the ferry samples at about 2 m (i. e. a depth less than or half of the plume thickness), and the salinity profiles are linearly stratified, the ferry approximately measures the vertical mean. The potential error introduced by this assumption is relatively small. For example, if the salinity linearly increases from 20 to 30 over a layer thickness of 10 m, the 2 m salinity is only 10% smaller than the mean salinity. Sub-tidal variations Figure 2.2 shows the complete daily-binned time series of plume and SoG water salinity. Each panel shows one year of data. We anticipate the Fraser 41  Chapter 2. Estuarine forcing of a river plume River to be important in setting the salinity and thus include a time series of discharge at the mouth as the shaded area. Three trends immediately stand out: the strong co-variance of the plume and SoG water salinity with river discharge, the 10 - 20 day fluctuations superimposed on the seasonal changes, and the correlation of SoG water salinity with plume salinity. We include 25-day low-pass filtered curves of salinity in Fig. 2.2 (thick lines) to illustrate seasonal changes. The freshest water occurs early in June during the freshet. At this time the SoG water salinity can be as low as 20 and the plume salinity can be as low as 10. Other significant discharge events, such as in October 2003 and January 2005, can drive the SoG water and plume salinity to freshet levels. During low flow periods (e. g. late fall) the SoG water salinity is roughly 28 and the plume salinity is near 25. The time series of plume salinity suggests that the Fraser River correlates with plume and SoG water salinity, but only on time scales greater than about 15 days. Figure 2.3 shows the SoG water salinity and plume salinity as a function of Fraser River discharge at the mouth. Each time series has been filtered with a 25-day Hamming window and sub-sampled to remove autocorrelation. The solid lines are calculated with LOESS, a nonparametric data smoother [Cleveland, 1979]. The salinity of each decreases quasi-linearly with increasing discharge, the plume salinity decreasing more sharply. The time series of plume salinity (Fig. 2.2) appears to contain a periodic component on 10 - 20 day scales superimposed on the Fraser discharge signal. To examine this in more detail, Fig. 2.4 shows a variance-conserving power spectrum of the daily mean plume salinity. The spectrum was computed with the Welch method [e. g. Percival and Walden, 1993] using a 200-day Hamming window with a 50% overlap yielding 26 effective degrees of freedom. All gaps in the time series were filled with gap-specific constant values. Each value was calculated by taking the mean of the 4 days on both sides of the gap. The 2σ intervals around the spectrum were derived from the distribution of Monte-Carlo realizations. Also shown is the power spectrum of the Fraser River discharge calculated with the same window parameters. Notable peaks in plume salinity appear at ∼35, 14 and 10.7 days. The broad 42  Chapter 2. Estuarine forcing of a river plume peak at 35 days is caused by the occasional 30 - 40 day-long gaps in the time series which appear when the ferry is removed from service for its annual re-fit. Changing the gap filling procedure had some effect on the 35 day peak, however the 14 and 10.7 day peaks were robust. The physical cause of the peak at 10.7 days is unclear. If spectra are computed with one year of data, instead of the full four years, the peak at 10.7 days only occurs in 2005 (whereas the peak at or near 14 days occurs each year). The peak in salinity spectral energy at 14 days and the lack of a similar cycle in the Fraser spectrum suggest that the plume salinity can be influenced by fortnightly cycling in tides, and will be investigated further in Sec. 2.3.1. Tidal variations To determine the importance of tides on daily time scales we make use of the high resolution ferry time series. A time series illustrating the importance of individual ebb and flood cycles on plume salinity is shown in Fig. 2.5. Panel (a) plots the ferry salinity and the tidal height at Point Atkinson over a period of 10 days in June 2006, while panel (b) shows a period of 10 days in February 2004. In the summer, plume salinity has a tendency to vary in phase with the tides. Within a diurnal tidal cycle, the freshest water is generally found at lower-low water, while the most saline water occurs at higher-high water. The change of salinity over a single large ebb, from higher-low water to lower-low water, can reach 5 salinity units. The tidal nature of the plume salinity appears to be seasonally modulated, as Fig. 2.5b shows less variation over a tidal cycle. The salinity at lower-low water can be up to 2 units lower than during other times in the tidal cycle. We make use of the Lomb-Scargle method [Lomb, 1976; Scargle, 1982] to study the frequency composition of the high resolution plume salinity time series. A standard spectral estimator could not be used because the ferry sailings are not spaced at regular intervals. The oversampling factor was set to 4 (corresponding to about 1/2 hour), and we use the full time series to gain the highest possible frequency resolution, ∆f = 1.8038 x 10−4 cycles/day. The resulting periodogram is shown in Fig. 2.6, but the frequency axis is  43  Chapter 2. Estuarine forcing of a river plume limited to focus on the semi-diurnal (panel a) and diurnal (panel b) tidal frequencies. The 99% significance level for spectral lines is shown by the horizontal broken line. Statistically significant peaks tend to cluster around frequencies of one, two, four, and five times daily, fortnightly, semi-annually, and annually. Annual and semi-annual peaks are caused by the river freshet, while the fortnightly peaks are expected based on the 14-day cycle observed in the Welch periodogram (Fig. 2.4) of daily-binned salinity. The cycles at four and five per day are caused by fact that the eastbound and westbound ferry tracks are not exactly colocated (Fig. 1.1) such that the ferry’s westbound track lies closer to the Fraser River mouth than the east-bound track. The proximity causes the west-bound tracks to have a lower salinity (average of 1 over the whole time series). Because the west-bound sailing departs every five hours, a cycle is created at 4 – 5 day−1 . Every statistically significant diurnal and semi-diurnal peak in the plume salinity occurs at a known tidal frequency except for the peak at 0.996 day−1 (24.092 hr). Here we define the known tidal frequencies to be the 67 constituents which would be used in a harmonic analysis of a one year time series with the MATLAB fitting software, T_TIDE [Pawlowicz et al., 2002]. However, there is not a significant cycle in salinity at the frequency of every tidal constituent. The line heights relative to M2 of all statistically significant peaks are listed in Table 2.1 along with the elevation amplitude of the respective tidal constituent. We also include constituents with elevation amplitudes exceeding 5% of M2 , though their line heights in salinity were not significant. Within the diurnal band, significant spectral power was found at the frequencies of K1 , P1 , O1 , ψ1 , and π1 . The peak at Q1 was not significant. In the semi-diurnal band, the power at M2 , K2 , and T2 is significant, while the power at S2 and N2 is not. In the salinity spectrum, K1 is three times higher than M2 , and is the strongest line in the semi-diurnal and diurnal bands. In terms of elevation, K1 is 95% of M2 . In salinity, the line heights at P1 and O1 are 139% and 78% of M2 , which are proportionately higher than their constituent amplitudes in elevation. Only Q1 is smaller in salinity than elevation. Somewhat unexpectedly, spectral energy density appears in salinity at the frequencies 44  Chapter 2. Estuarine forcing of a river plume of ψ1 and π1 , which are negligible in the elevation spectrum. In the semidiurnal band, the plume salinity line height at S2 and N2 are 8% and 3%, respectively, of M2 , and are below the 99% significance level, despite their importance in tidal elevation. The spectral energy density in salinity at K2 is 78% of M2 , though it is only 7% of M2 in elevation. Finally, the spectral energy density in salinity at T2 is 62% of M2 , even though it is a very small constituent in elevation (2% of M2 ).  2.2.3  Plume surface area  It is possible to estimate the surface area of the plume with a combination of ferry data and satellite images. The ferry track cuts a straight line through the plume. A simple estimate of area can be made by assuming the ferry measures the diameter of a semi-circular plume [Pawlowicz et al., 2007]. However, visual inspection of the MODIS images and previous studies [i. e. Tabata, 1972] show that the plume can take on a myriad of shapes. Even so, the length of the ferry track inside the plume (squared) should be proportional to the plume surface area. Instead of assuming a particular geometry, the ferry time series of squared length was compared to a time series of area obtained from a series of 92 MODIS images (Sec. 1.4.3). A regression between the time series of low-pass filtered ferry squared length and satellite measured area was obtained by determining the principal eigenvector of a singular value decomposition analysis (detailed in App. B). It is this regression that determines the plume geometry and thus the scale factor to apply to the ferry data. For our study, the result of the regression, including the boot-strapped 95% confidence intervals, is that: Aplume = (−730 ± 710) + (1.4 ± 0.7)L2f erry  (2.1)  where Lf erry is the length of the track within the plume measured by the ferry. The final ferry time series, scaled by Eq. (2.1), appears in Fig. 2.7, along with the satellite area and Fraser River discharge. The most outstanding feature is the variability in the plume area. For example, in September 2003, 45  Chapter 2. Estuarine forcing of a river plume the estimated area varies from < 0 to 1000 km2 (note that the plume can have a negative area - this is just a by-product of the regression with satellite area which was allowed an offset). We note that some of the variability in estimated plume area is likely caused by the ability of our algorithm to properly find the plume within the transect given the variability in the alongtrack structure (Fig. 2.1) and a geometrical scaling factor which likely varies in time. Also, we note that the plume can be advected by wind and tides relative to the ferry track, altering our estimate of plume surface area. A low-pass filtered time series (25-day Hamming window) is presented along with the original time series to remove the variability. Plume area varies from 400 km2 or less during low flow periods to as much as 1,700 km2 during strong freshets. Variability in the time series, quantified with ± 2 standard error curves centered on the low-pass time series in Fig. 2.7, is such that the  effects of small fluctuations in river discharge are unclear. Only freshets and other large discharge events (e. g. those in October 2003 and January 2005) cause statistically significant changes in surface area. The time series of low-pass filtered plume surface area reveals that the Fraser River appears to set the seasonality in the plume area. To investigate the effect of the Fraser River directly, we plot the plume area as a function of the river discharge (Fig. 2.8). Instead of plotting the original time series, 25-day Hamming window filtered and decimated versions of each are shown. The surface area increases with increasing river discharge, but unlike salinity, which varies quasi-linearly with discharge (Fig. 2.3), the non-parametric fit suggests the area varies proportionately less at high discharge than at low discharge.  2.3 2.3.1  Discussion Controls of plume salinity  River discharge On time scales greater than about 15 days, both the plume salinity and SoG water salinity decrease linearly with increasing river discharge. Plume salin46  Chapter 2. Estuarine forcing of a river plume ity decreases at 1.4 per 1000 m3 s−1 , while the SoG water salinity decreases at half that rate, 0.7 per 1000 m3 s−1 . Because their respective slopes differ, the salinity gradient between them increases with discharge. The plume salinity is 2 fresher than the SoG water salinity at minimum river discharges, and 8 fresher at maximum flow. The direct comparison of a spatial mean of plume salinity to river flow presented in Fig. 2.3 is the first to the author’s knowledge. The mean salinity has a special significance because, with some assumptions, it is essentially a measure of the fresh water content in the plume. If the depth of the plume does not vary greatly with river flow, and if the ferry transect is representative of any transect through the plume, our linear track mean becomes a volume mean. With these assumptions, we conclude the Fraser plume contains a higher proportion of fresh water relative to SoG water during high river flow than during low river flow. Furthermore, the fresh water flushing time, defined as the total volume of fresh water divided by input of river flow, is roughly independent of river discharge because the surface area increases with increasing river discharge. Chapter 3 further explores the implications of the variation of percent fresh water, fresh water volume, and fresh water flushing time with river discharge. Royer and Emery [1982] observe a correlation between Fraser river discharge and surface salinity on ferry routes north and south of our ferry route. They measure a lag of up to three days between river discharge and salinity. We do not observe a lag in our daily binned data, presumably because we sample closer to the river mouth. A comparison of the Royer and Emery [1982] salinity time series with Fig. 2.2 suggests that their plume is our SoG water because the range in salinity is similar. Other systems show a freshening with increasing river discharge. For example, Figure 6 of Geyer et al. [2000] shows an inverse relation between the salinity 12 km from the river mouth in the Eel River plume and the river discharge. The surface salinity in Amazon River plume shows a seasonal signal, as the low salinity contours are pushed seaward during winter-spring high flows compared to summer-fall low flows [Lentz , 1995b]. Finally, we address the correlation between river discharge and SoG wa47  Chapter 2. Estuarine forcing of a river plume ter salinity (Fig. 2.3). Two factors are responsible for the correlation. First, SoG water salinity also depends on fresh water from other rivers, and they may have a discharge cycle similar to the Fraser. This fact was exploited by Pawlowicz et al. [2007] to scale the Fraser discharge to the total fresh water input into the SoG. Second, the plume must lose water through the plume front to balance input from the river and entrainment through the base (Sec. 3.2.5). Water that has been fluxed through the plume front joins the SoG surface water. Because the Fraser river flow constitutes at least half of the total fresh water input into the SoG, there will be significant volume of fresh water fluxed from the plume to influence SoG water salinity. There will only be a small phase difference between river discharge and SoG salinity because, as we shall see later, the fresh water flushing time is 2.2 days (Sec. 3.2.4). Tides: fortnightly cycle The 14-day cycle in plume salinity (Fig. 2.4) is particularly interesting because it is an important sub-tidal time scale for estuaries. To quantify the relationship between the fortnightly tidal cycle and the plume salinity, a daily measure of the strength of the tide was needed. We chose to use the maximum daily tidal range, or the difference in height between higher-high water and lower-low water. The squared coherence and phase between tidal range and filtered plume salinity was computed by using spectra and cross spectra obtained with Welch’s method with a 100-day Hamming window. When the full ferry time series was considered, the maximum squared coherence was 0.34 at 14 days, significant above the 95% level. The 14-day peak was broad, reaching zero coherence at 10 and 20 days. The phase shift was not discernible from zero so that local maxima in salinity were in phase with maxima in daily tidal height. Visual inspection of a time series of maximum daily range and plume salinity suggest that the relationship is stronger in the summer than in other seasons. To quantify the seasonal change in the coherence between fortnightly tidal cycling and plume salinity, the full time series was split  48  Chapter 2. Estuarine forcing of a river plume into 50% overlapping, 184 day segments. The coherence band-averaging used 46-day Hamming windows overlapped by 50%. Figure 2.9 plots the coherence in each segment as a time series along with the Fraser discharge. The squared coherence is 0.4 or higher and statistically significant in and around the freshet, but is low and statistically insignificant during low river flows. The change in Fraser plume salinity as a function of the fortnightly tidal cycle may be expected based on the results of Geyer and Farmer [1989]. In the Fraser estuary, the intensity of mixing at the salt-wedge is a function of the tides. During the flood tide, the salt-wedge advances upriver and is stably stratified. During an ebb tide, shear increases on the salt-wedge halocline until instability mixes fresh water downwards and salt water upwards. Geyer and Farmer [1989] hypothesize that the amount of mixing should be proportional to the strength of the ebb, and therefore to fortnightly cycling. Strong ebbs, characteristic of spring tides, would more effectively break down the salt-wedge, increasing the upward flux of salt. In other estuaries, the fortnightly cycle in mixing has been observed directly. In the Hudson River estuary, Peters [1997] finds an insignificant vertical turbulent salt flux across the halocline during neap period ebbs, but a significant flux during spring period ebbs. In the Columbia River Estuary, Jay and Smith [1990] find the stability of the salt-wedge to be sensitive to spring/neap cycling, and add that the sensitivity is a function of the river flow. At the highest river flows, the stratification is strong enough to withstand shearing, but the salt-wedge is broken down during most ebb tides during low flow periods. MacCready et al. [2002] present a highly simplified conceptual model of the fortnightly cycle of estuarine structure, useful in the present case to elucidate the mechanism behind the fortnightly cycle in plume salinity. During the well-mixed state (spring tides), relatively high salinity water exits the estuary at the surface, while during the stratified state (neap tides), relatively fresh water exits at the surface. The Fraser Estuary, however, re-stratifies much faster than the MacCready et al. [2002] model, and during spring tides it does not remain fully mixed during all phases of the tide. 49  Chapter 2. Estuarine forcing of a river plume Based on the studies in the Fraser, Columbia, and Hudson River estuaries, we suggest that the 14-day cycle in plume salinity observed in the Fraser plume is caused by the fortnightly cycle of mixing in the estuary. Ebb tides during times of maximum tidal range in the fortnightly cycle would create a higher upward salt flux than weak ebb tides. The increased upward flux exports higher salinity water out of the estuary in the seaward upper layer. When ebb tides are weak, the shear is not strong enough to break down stratification. Thus upper layer water glides over the salt-wedge, exits the estuary with relatively little mixing, and becomes the plume. Visual inspection of the time series suggests that the fortnightly modulation of mixing can cause fluctuations in the plume salinity of about 1 to 2. This is relatively small compared to the observed effects of ebb/flood cycles and changes in river flow, whose combined effects can cause changes of 5 to 15. Tides: ebb/flood cycles The Lomb-Scargle periodogram (Fig. 2.6) illustrates the tidal nature of the plume. The periodogram had two important features which are not necessarily intuitive: First, the relative strength of the diurnal lines compared to the semi-diurnal lines, and second, the existence of lines in the salinity spectrum at frequencies of minor tidal constituents. Both of these features are caused by tidal and/or river discharge effects on the estuarine salt field. To describe the amplification of the diurnal lines, we will consider the effects of tides on the Fraser River estuary salt field. We will assume that lateral advection of the estuarine horizontal salinity gradient is the dominant process setting the plume salinity, which appears consistent with mooring data in the region immediately seaward of the river mouth [Crean et al., 1988]. A simple explanation for the amplification is based on estimating how far each tidal constituent would advect the estuarine salt field. The excursion can be estimated roughly by the product of each constituent’s velocity with half of its tidal period. Tidal velocities are not available in the Fraser estuary,  50  Chapter 2. Estuarine forcing of a river plume but a rough estimate can be made from the model of Foreman et al. [1995]. At Tsawwassen (gauge 20), the ratio K1 /M2 is 1.03 in terms of elevation. Off-shore of Tsawwassen in mid-strait (station d), the ratio K1 /M2 is 0.68 in terms of velocity. Therefore, in terms of an excursion, K1 /M2 is 1.36 because K1 has twice the period of M2 . In the Lomb-Scargle periodogram of salinity, the ratio K1 /M2 is 2.89. However, we note that the Lomb-Scargle periodogram returns the frequency content of a signal in terms of a spectral energy density, so the square root is the relevant quantity here. The square root of the K1 /M2 salinity spectral energy is 1.70, which is reasonably close to the estimated K1 /M2 excursion ratio of 1.36 given the crudeness of the model. The amplification of diurnal lines was also found farther away from the Fraser river mouth in the ferry time series of Royer and Emery [1982]. As the tide ebbs the southward streaming currents advect the plume southward into the southern ferry track (see their Fig. 1 for a map of the route). The decrease in salinity by advection is enhanced by the increase of river discharge caused by the ebbing tide. Qualitatively our results are consistent. However, our ferry route is measuring changes of salinity within the plume itself whereas Royer and Emery [1982] observe changes as the plume is advected into the ferry track. The second interesting feature in the Lomb-Scargle periodogram of plume salinity was the existence of lines at the frequencies of tidal constituents with very small or negligible amplitudes (ψ1 , π1 , and T2 ). These lines are caused by the Fraser River’s strong annual effect on plume salinity. They can be understood in terms of the effects of modulating a high frequency signal (the “carrier” - here the larger diurnal and semi-diurnal peaks), by a low frequency signal (here the Fraser annual cycle). When the modulation is by an annual cycle, the sidebands will lie ∆f = 1/365 days−1 on each side of the carrier, potentially interfering with peaks of true astronomical origin which are also annual modulations. Astronomically, T2 is the major elliptic wave of S2 , ψ1 is the elliptic wave of K1 , and π1 is the elliptic wave of P1 [e. g. Melchior , 1966]. To investigate the effect of an annual modulation on diurnal and semi51  Chapter 2. Estuarine forcing of a river plume diurnal cycles in plume salinity, Fig. 2.6 contains markers identifying the locations where annual sidebands should occur for the lines at the M2 , K2 , O1 , P1 , and K1 frequencies. The markers are centered about the main peaks and denoted by small dots with vertical lines. Each tidal frequency marked with 1 year modulations shows sidebands, but not all sidebands are statistically significant. In the diurnal band, the significant spectral energy at ψ1 , and π1 (and the non-tidal 24.00 hr line) are sidebands of K1 and P1 . In the semi-diurnal band, the T2 line height is significant. Spectral energy appearing at T2 could be an annual sideband of S2 , but the spectral energy density at S2 is only 8% of M2 . Thus it is not clear why the line height of T2 is as high as it is. Seasonality of tidal effects Figures 2.5 and 2.9, and the annual modulations just discussed, reveal that the effect of tides on the Fraser plume salinity is a function of river discharge. Tidal variations in salinity are maximized during high river flow and minimized during low flow. To explain this observation, the effects of river discharge on the estuarine salt field must be considered. Utilizing the results for the structure of an estuary based on the work of Hansen and Rattray [1965]; Chatwin [1976] along with the work specific to the Fraser estuary by Stronach [1981]; Crean et al. [1988]; Geyer and Farmer [1989]; Kostaschuk and Atwood [1990], we describe a conceptual model for the combined effects of tides and river flow on the plume salinity. The model will be discussed in terms of the differences between winter and summer flows. In winter, an empirical relationship from Kostaschuk and Atwood [1990] predicts that the tip of the salt wedge will be located 36 km up-river at high water and 21 km up-river at low water, meaning the salt-wedge is always upstream of the river mouth. This suggests that water at the river mouth will always be mixed because most mixing occurs at the salt wedge. Water mixed into the upper layer of the estuary will have a relatively high salinity because the salt wedge salinity is higher in winter than summer [Stronach,  52  Chapter 2. Estuarine forcing of a river plume 1981]. The effects of tides on plume salinity should be minimal, because at the river mouth, tidal motion will be advecting mixed water while the stronger salinity gradient remains upstream. Fortnightly changes in the strength of mixing on the ebb tide may still occur, but again those effects will be strongest well upstream of the river mouth and plume. In summer, the salt wedge will lie 18 km upstream from the river mouth at high water, and at the river mouth at low water [Kostaschuk and Atwood, 1990]. Thus, in the region of the mouth, the tidal excursion will be moving water with a strong salinity gradient characteristic of the salt wedge. The salinity of the salt wedge is slightly lower in summer than in winter [Stronach, 1981], so that the mixed water leaving at the surface will be slightly fresher compared to winter. During strong ebbs in the fortnightly cycle, mixing is enhanced, and the plume salinity will be, on the average, a few salinity units higher than during weak ebbs.  2.3.2  Controls of plume area  River discharge Considerable variability characterizes the daily plume surface area time series (Fig. 2.7), most of which lies in a frequency band not associated with physical forcing. Much of this variability is likely caused by the shortcomings of using a 1-D ferry transect or by limitations of the plume finding algorithm. Previous estimates of surface area in other systems have also contained significant errors. Garvine [1975] estimates the Connecticut River plume surface area with a series a 1-D transects, and while it is clearly correlated with river flow, significant unexplained variability remains. However, after removing variations with periods less than 25 days, the surface area of the Fraser River plume clearly increases with river flow from 400 km2 to 1,400 km2 . The surface area varies proportionately less at high river flow than at low river flow according to the non-parametric fit in Fig. 2.8. The suitable theory for determining the relationship between surface area and river discharge will depend in part on the dynamics of the plume at the mouth. The mouth Rossby number, Ro = U/f Lmouth , where U is the 53  Chapter 2. Estuarine forcing of a river plume river inflow velocity and Lmouth is the width of the river mouth, calculated using the mean annual fresh water outflow and mouth dimensions in Geyer and Farmer [1989], is 4.4. The mouth Kelvin number, Kmouth = Lmouth /Ld [Garvine, 1987], where Ld is the deformation radius (∼ 7 km), is 0.13. Both numbers indicate rotation will be unimportant at the river mouth. The effects of buoyancy relative to momentum will become evident at a distance lm = M 3/4 B −1/2 from the river mouth, where M and B are the initial fluxes of momentum and buoyancy, respectively [Fischer et al., 1979]. To estimate this distance, the momentum flux is written as U Q, and the buoyancy flux is written as g′ Q, where Q is the volume flux of fresh water, and g′ is reduced gravity. After substitution, the length scale becomes: lm =  U 3/4 1/4 Q . g′1/2  (2.2)  In estimating lm we use the observed minimum and maximum river discharge, measurements of the outflow velocity at or near the river mouth from Ages [1979] and MacDonald and Geyer [2004], and assume g′ ranges from 0.1 - 0.2 m s−2 . In the Fraser plume, lm ranges from 20 m (winter) to 36 m (summer). In winter, the river discharges brackish water, while Q in Eq. (2.2) refers specifically to fresh water. In this case, however, the discrepancy is of no consequence. lm would have to be on the order of a few kilometres to impact the plume on the length scale sampled by the ferry, which is a factor of 50 higher than our estimate. Such an increase would require an even larger change in U , g′ , or Q because lm is relatively insensitive to them. This very short distance suggests that buoyancy forcing is always important, and that water issuing from the mouth will not behave as a pure jet. Farther away from the mouth, rotational effects are evident in aerial photographs [Tabata, 1972] and drifter tracks [Crean et al., 1988]. Idealizing a river plume as an anticyclonic rotating bulge in cyclostrophic balance,  54  Chapter 2. Estuarine forcing of a river plume without rotation, Yankovsky and Chapman [1997] find a bulge radius rs = Rd  (3 + F 2 ) , (2 + F 2 )1/2  (2.3)  where Rd is the outflow internal deformation radius, (g′ ho )1/2 /f , F is the outflow Froude number, U/(g′ ho )1/2 , ho is the outflow depth, and g′ is the reduced gravity. When the inflow is weak or the density difference large (F ≪ 1), the bulge radius is 4.24Rd , while for strong flows or small density differences (F ≫ 1), the bulge radius is U f −1 .  From the plume area time series (Fig. 2.7), the length scale of the plume,  assuming it is a semi-circle for simplicity, ranges from 16 km (winter) to 32 km (summer). If F ≫ 1 for the Fraser outflow, then rs would range from  10 km (U = 1 m s−1 , f =10−4 ) to 20 km (U = 2 m s−1 ), smaller than the observations. If F ≪ 1, rs ranges from 19 km (winter: ∆S = 5, ho = 5 m)  to 32 km (summer: ∆S = 15, ho = 5 m), in closer agreement to our observations. The low Froude number limit is likely more appropriate even though  the observed outflow is supercritical at the mouth [MacDonald, 2003] because dissipative processes and mixing in the near-field plume [Cordes et al., 1980; MacDonald et al., 2007], not accounted for in the theory, quickly reduce the flow speeds. Taking the reduced gravity to be proportional to river discharge (as was shown in Fig. 2.3), we find that the plume area then varies linearly with discharge and outflow depth, A ∝ Qho . A straight line  may reasonably approximate the non-parametric fit in Fig. 2.8, suggesting  the Yankovsky and Chapman [1997] model is appropriate, but we note the distinct curvature in the non-parametric fit more closely follows a Q1/2 dependence. This latter scaling was also found by Warrick and Fong [2004] for other plumes, but this was derived by squaring the buoyant jet length scale (Eq. 2.2), which is not appropriate in this case because lm is so small. It is possible that variations in the outflow depth ho can account for this difference but we cannot easily test this. However it should be noted that the Fraser plume, with its short fresh water flushing time (Sec. 3.2.4), is not really in steady state [see also Hodgins, 1994], so the rotating bulge ideal-  55  Chapter 2. Estuarine forcing of a river plume ization may not be as useful as it is in other situations [e. g. Chant et al., 2007]. Tides The time series of plume area (Fig. 2.7) appears to contain a large periodic signal superimposed on the seasonal Fraser cycle. A power spectrum of the plume surface area (not shown), computed with the same spectral method and windowing parameters used to estimate the periodogram of plume salinity (Fig. 2.4), shows that the spectral energy is primarily concentrated in the 8 - 15 day range, too wide to be caused solely by fortnightly tidal cycling (the fortnightly cycle in salinity peaks sharply at 14 days). If there is a fortnightly cycle it has been distributed to higher frequencies. We also note that the Fraser has little spectral energy in the 8 - 15 day band, and thus it is not likely causing the 8 - 15 day variance in plume area. Advection of the plume by wind relative to the ferry track may complicate the surface area time series. Estimates of plume area on weekly time-scales or shorter would likely be affected. At the semi-diurnal and diurnal scales, a Lomb-Scargle periodogram of track-by-track surface area reveals a noisy red spectrum. There are no significant cycles at any of the main tidal constituents. Intuitively, the plume surface area should be larger at low water because the river discharges more water than at high tide. However, it is likely that any significant cycle in area is buried in noise due to physical variability, sampling errors, and the 2.2 day fresh water flushing time which implies it takes a few tidal cycles for signals at the mouth to reach the edges of the plume. Tidal effects are evident in the along-strait location of the plume. Advection by tides in the SoG are on the order of 10 km, so we would expect to see changes in the offshore extent of the plume by tidal advection. A Lomb-Scargle periodogram of the position of the offshore extent shows tidal lines, though they are somewhat weaker than those of plume salinity. At longer scales, a Welch PSD of daily averaged off-shore extent shows broad (∆t ≈ 10 days) peak at 15 days, and the effect appears stronger in summer. 56  Chapter 2. Estuarine forcing of a river plume  2.4  Conclusion  With a novel data set based on an instrumented ferry we have been able to identify how river discharge and tides set the near-field salinity of a river plume, and how river discharge sets the plume surface area. The temporal resolution is fine enough to resolve tidal effects in the diurnal and semidiurnal bands, and the length is of sufficient duration to identify seasonal changes. Plume salinity is a quasi-linear function of river discharge on time scales of 25 days or more, and ranges from 25 at 1000 m3 s−1 to 15 at 8000 m3 s−1 . SoG water salinity varies in the same manner but with a weaker dependence on river flow. It decreases from 28 to 23 over the same range of discharge. The tides operate on the plume salinity over three time-scales, semidiurnal, diurnal, and fortnightly. The plume is fresher at lower-low water than higher-high water. The low salinity water originates from the river as the discharge increases during the ebb cycle. On a single large ebb during the summer, the mean plume salinity can drop as much as 5. In winter during low river flows, the amplitude of tidal signal is reduced and changes of about 1 or 2 at the end of a large ebb tide are more typical. On fortnightly scales, particularly during summer, the plume salinity fluctuates in phase with the daily maximum tidal range, being fresher during neap tides and saltier during spring tides by a few units. Both the ebb/flood and fortnightly tidal cycles are superimposed on the river flow seasonal cycle, and the magnitude of their effects increases with increasing river discharge. We were able to scale ferry estimates of surface area to estimates from remote sensing data. Errors inherent in using the ferry to estimate surface area likely dominate natural high frequency variations.  On longer  time-scales, plume surface area is proportional to the river discharge at the mouth. Based on the low-pass filtered time series, the plume area ranges from 200 - 500 km2 at low river flow to 1000 - 1500 km2 during freshets. Rotation and buoyancy appear to govern the plume surface area according to the Yankovsky and Chapman [1997] surface advected plume formulation. Inertial effects appear to be irrelevant in the larger scale plume structure 57  Chapter 2. Estuarine forcing of a river plume because the high outflow Froude number limit of Eq. (2.3) substantially under-predicts the surface area. Although the low outflow Froude number limit of the Yankovsky and Chapman [1997] estimates the magnitude of the plume surface area well, it predicts a linear relationship, while there is a weak curvature in the data. The strong modulation of tidal effects by river flow complicates the idea of categorizing the section of the plume sampled by the ferry as near-field. Enhanced tidal effects at high river flow suggest the ferry samples the nearfield plume, while in winter it appears that the near-field plume remains much closer to the river mouth (or does not exist) because observed tidal effects are weak. The changing character of the estuary and river plume with river flow resembles the results of Garvine [1975] in the Connecticut River Estuary, where the sharpest salinity gradients move out of the estuary at high flows. Likewise we suggest that the dependence on river flow of the location of the salt-wedge with respect to the river mouth causes the winter/summer differences in the Fraser Estuary and plume. The Fraser River estuary and near-field plume appear to be close relatives of the Columbia estuary and near-field in a few respects. First, the systems are very strongly forced; they share a similar tidal range, mouth width, and river discharge (though much of the seasonality of the Columbia has been diminished by dams). Both exhibit a low mouth Kelvin number, signifying rotation is of secondary importance in defining the dynamics at the river mouth. Second, both systems exhibit a change in mixing at the salt-wedge as a function of the fortnightly tidal cycle. The analogy between the systems likely ends much farther from the river mouth, where wind is the dominant external force. The Fraser lies within a nearly enclosed system while the Columbia discharges onto a shelf. This may affect the far-field dynamics, where wind and rotation dominate, in two respects. First, the Fraser plume may span the SoG and reach a lateral barrier. Second, the Ekman flows driven by wind are very important in coastal shelf systems, whereas wind driven circulation in the deeper SoG will likely respond differently.  58  Chapter 2. Estuarine forcing of a river plume  Table 2.1: Comparison of line heights at various tidal frequencies in the salinity spectrum with the constituent elevation at Point Atkinson, BC. All quantities are given as ratios to M2 . Constituent  Elevation at Point Atkinson  Salinity Line Height  M2  1.00  1.00  S2  0.25  0.08  N2  0.20  0.03  K2  0.07  0.78  T2  0.02  0.62  K1  0.94  2.89  O1  0.53  0.78  P1  0.29  1.39  Q1  0.08  0.04  ψ1  0.02  0.99  π1  0.02  1.31  †  † †  †  Not significant at the 99% significance level.  59  Chapter 2. Estuarine forcing of a river plume  30 95% 75% 50% mean  25  salinity  25%  20 5% Sand Heads  15  10  50  40  30 20 along−track distance [km]  10  0  Figure 2.1: Average horizontal plume salinity structure (thick line) with 5, 25, 50, 75, and 95% percentiles (thin lines) as functions of along-track distance. The vertical dashed line marks the along-strait distance closest to the Fraser River mouth.  60  Chapter 2. Estuarine forcing of a river plume  30  10  a) 2003  8 25  6 plume salinity  20  4  reference salinity  2  Fraser discharge  15 D 30  J  F  M  A  M  J  J  A  S  O  N  0 D 10  b) 2004  8 25  6 4  20  salinity  30  J  F  M  A  M  J  J  A  S  O  N  0 D 10  c) 2005  8 25  Fraser [103 m3s−1]  2 15 D  6 4  20  2 15 D 30  J  F  M  A  M  J  J  A  S  O  N  0 D 10  d) 2006  8 25  6 4  20  2 15 D  J  F  M  A  M  J  J  A  S  O  N  0 D  Figure 2.2: Full time series of mean daily plume salinity (black line) and SoG water salinity (gray line). The heavy lines are daily salinities filtered with a 25-day Hamming window. The shaded curve is the Fraser flow at the mouth. Each panel is a year of data, from 2003 (panel a) to 2006 (panel d).  61  Chapter 2. Estuarine forcing of a river plume  30  salinity  25  20  mean plume salinity SoG water salinity  15 1000  2000  3000  4000 5000 6000 Fraser discharge [m3s−1]  7000  8000  Figure 2.3: Fraser plume salinity (filled circles) and SoG water salinity (open circles) as functions of Fraser River discharge. The regression is between the 25-day filtered time series of salinity and the 25-day filtered time series of river discharge. The solid lines illustrate the regressions and were estimated by the LOESS algorithm.  62  Chapter 2. Estuarine forcing of a river plume  0.2  plume salinity Fraser discharge  power * variance−1  0.15  0.1  0.05  0  50 40  30  20  14  10 9 8 7 6 days/cycle  5  4  3  2  Figure 2.4: Variance conserving spectrum of the mean plume salinity (thick solid line) and the Fraser River discharge (thin solid line) with their respective 2σ confidence intervals. Both spectra were calculated using the Welch method with a 200-day window and 50% overlap, yielding 26 effective degrees of freedom.  63  Chapter 2. Estuarine forcing of a river plume  25  5  22  4  19  3  16  2  13  1  10 11  12  13  14  15  plume salinity  16 17 June 2006  18  19  20  tidal prediction  30  0 21 5  b) winter  27  4  24  3  21  2  18  1  15 1  2  3  4  5 6 7 February 2004  8  9  10  tidal height [m]  salinity  a) summer  0 11  Figure 2.5: Time series of plume salinity (filled circles and solid dark line) and predicted tidal height at Point Atkinson (solid gray line). Each panel displays 10 days of ferry data. Panel (a) shows 10 days in June 2006, during high river discharge (∼ 8,500 m3 s−1 ), and panel (b) shows 10 days in February 2004, during low river discharge (∼ 1,300 m3 s−1 ). Note the smaller variance in plume salinity during winter, and the lower sensitivity to the large diurnal ebb tides.  64  Chapter 2. Estuarine forcing of a river plume  80  12.00 (S2)  a) semi−diurnal  spectral power density [psu2 day]  60 40  11.97 (K2)  12.42 (M2)  12.66 (N2)  12.02 (T2)  20 0 1.9  1.92  1.94  1.96  1.98  b) diurnal  60 25.82 (O1)  26.87 (Q1)  2.02  23.93 (K1) 24.07 (P1) 23.87 24.13 (ψ ) 1 π ( 1)  80  40  2  20 0 0.9  0.92  0.94 0.96 −1 frequency [day ]  0.98  1  1.02  Figure 2.6: Lomb-Scargle periodogram of plume salinity focused on (a) the semi-diurnal band and (b) the diurnal band. The period of each constituent, in hours, is given above its name. Small dots with the vertical solid lines show the sidebands produced by a yearly modulation about the peak on which they are centered. The dashed line shows the 99% significance level. Spectral energy density at K1 extends well beyond the y-axis upper limit.  65  Chapter 2. Estuarine forcing of a river plume  2500  8  1500  6  1000  4  500  2  0 D 2500  J  F  M  A  M  J  J  A  S  O  N  0 D 10  b) 2004  2000  8  1500  6  1000  4  500  2  0 D 2500  J  F  M  A  M  J  J  A  S  O  N  0 D 10  c) 2005  2000  8  1500  6  1000  4  500  2  0 D  J  F  M  A  M  J  J  A  S  O  N  Fraser [103 m3s−1]  plume surface area [km2]  10  a) 2003  2000  0 D  ferry area  2500  25 day filtered  10  2000  satellite area  8  1500  Fraser discharge  6  d) 2006  1000  4  500  2  0 D  J  F  M  A  M  J  J  A  S  O  N  0 D  Figure 2.7: Each panel is a time series of daily mean Fraser River plume surface area, from 2003 (panel a) to 2006 (panel d). The surface area has been scaled by a linear regression to the satellite area time series (filled circles). The thin line is the daily mean area, while the heavy line is the area filtered with a 25-day Hamming window. The thin broken curves surrounding the filtered time series are the ±2 standard error bounds. The Fraser River discharge at the mouth is shown in solid gray.  66  Chapter 2. Estuarine forcing of a river plume  1600 1400  plume area [km2]  1200 1000 800 600 400 non−parametric fit γQ1/2  200 0 1000  2000  3000  4000 5000 Q [m3s−1]  6000  7000  8000  Figure 2.8: Relationship between 25-day filtered plume surface area and 25-day filtered Fraser River discharge, Q, (filled circles). A LOESS nonparametric fit has been made to emphasize the slight curvature (thick solid line), and is bracketed by 1σ bounds on the fit from boot-strapped replicates (thin solid lines). A fit of γQ1/2 , where γ=11.1, is used in Chapter 3 (thick dashed line).  67  Chapter 2. Estuarine forcing of a river plume  1  10 coherence 95% significance  9  0.8  8  0.7  7  0.6  6  0.5  5  0.4  4  0.3  3  0.2  2  0.1  1  0 Jan  Jul  2004  Jul  05  Jul  06  Jul  Fraser discharge [103 m3s−1]  squared coherence @ 14 days  0.9  0  Figure 2.9: Time series of squared coherence at 14 days between maximum tidal range and plume salinity (solid line and circles). The dashed lines show the 95% significance level from the coherence analysis. Each point is plotted at the center of a 184-day segment over which the coherence was computed. Adjacent segments were overlapped by 50%, introducing some auto-correlation. The shaded area shows the Fraser River discharge at the mouth.  68  Bibliography Abood, K. (1974), Circulation in the Hudson Estuary: Hudson River Colloquium, in Annals of the New York Academy of Science, vol. 250, edited by O. Roels, pp. 38–111, NY Academy of Science. Ages, A. (1979), The salinity intrusion in the Fraser River: Salinity, temperature, and current observations, 1976, 1977, Pacific Marine Science Report 79-14, Institute of Ocean Sciences, Patricia Bay, Sidney, B.C. Chant, R., S. Glenn, E. Hunter, J. Kohut, R. Chen, R. Houghton, J. Bosch, and O. Schofield (2007), Bulge formation of a buoyant river outflow, J. Geophys. Res., 113, doi:10.1029/2007JC004100. Chao, S.-Y. (1988), Wind-driven motion of estuarine plumes, J. Phys. Oceanogr., 18, 1144–1166. Chatwin, P. (1976), Some remarks on the maintenance of the salinity distribution in estuaries, Estuar. Coast. Mar. Sci., 4, 555–566. Cleveland, W. (1979), Robust locally weighted regression and smoothing scatterplots, J. Am. Stat. Assoc., 74, 829–836. Cordes, R., S. Pond, B. de Lange Boom, and P. LeBlond (1980), Estimates of entrainment in the Fraser River plume, British Columbia, Atmos. Ocean, 18, 15–26. Crean, P., T. Murty, and J. Stronach (1988), Mathematical Modelling of Tides and Estuarine Circulation: The Coastal Seas of Southern British Columbia and Washington State, no. 30 in Lecture Notes on Coastal and Estuarine Studies, Springer-Verlag, New York. 69  Bibliography Fischer, H., E. List, R. Koh, J. Imberger, and N. Brooks (1979), Mixing in Inland and Coastal Waters, 1st ed., Academic Press, Inc. Fong, D., and W. Geyer (2002), The alongshore transport of freshwater in a surface-trapped river plume, J. Phys. Oceanogr., 32, 957–972, doi: 10.1029/2000JC900134. Foreman, M., R. Walters, R. Henry, C. Keller, and A. Dolling (1995), A tidal model for Juan-de-Fuca Strait and the Southern Strait of Georgia, J. Geophys. Res., 100 (C1), 721–740, doi:10.1029/94JC02721. Garvine, R. (1975), The distribution of salinity and temperature in the Connecticut River Estuary, J. Geophys. Res., 80 (9), 1176–1183. Garvine, R. (1987), Estuary plumes and fronts in shelf waters: a layer model, J. Phys. Oceanogr., 17, 1877–1896. 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(1994), Remote sensing of surface currents in the Fraser River plume with the SeaSonde HF radar, Tech. rep., Environment Technology Centre (Canada), Emergencies Science Division. 70  Bibliography Jay, D., and J. Smith (1990), Circulation, density distribution and neapspring transitions in the Columbia River Estuary, Prog. Oceanogr., 25, 81–112. Kostaschuk, R., and L. Atwood (1990), River discharge and tidal controls on salt-wedge position and implications for channel shoaling: Fraser River, British Columbia, Can. J. Civil Eng., 17 (3), 452–459. Lentz, S. (1995a), The Amazon River plume during AMASSEDS: Subtidal current variability and the importance of wind forcing, J. Geophys. Res., 100 (C2), 2377–2390, doi:10.1029/94JC00343. Lentz, S. (1995b), Seasonal variations in the horizontal structure of the Amazon Plume inferred from historical hydrographic data, J. Geophys. Res., 100 (C2), 2391–2400, doi:10.1029/94JC01847. Lomb, N. (1976), Least-squares frequency analysis of unequally spaced data, Astrophys. Space Sci., 39 (2), 447–462. MacCready, P. (2007), Estuarine adjustment, J. Phys. Oceanogr., 37 (8), 2133–2145. MacCready, P., R. Hetland, and W. Geyer (2002), Long-term isohaline salt balance in an estuary, Cont. Shelf Res., 22, 1591–1601. MacDonald, D. (2003), Mixing processes and hydraulic control in a highly stratified estuary, Ph.D. thesis, Massachusetts Institute of Technology. MacDonald, D., and W. Geyer (2004), Turbulent energy production and entrainment at a highly stratified estuarine front, J. Geophys. Res., 109, C05004, doi:10.1029/2003JC002094. MacDonald, D., L. Goodman, and R. Hetland (2007), Turbulent dissipation in a near-field river plume: A comparison of control volume and microstructure observations with a numerical model, J. Geophys. Res., 112, C07026, doi:10.1029/2006JC004075. Melchior, P. (1966), The Earth Tides, Pergamon Press Ltd., Oxford. 71  Bibliography Monismith, S., W. Kimmerer, J. Burau, and M. Stacey (2002), Structure and flow-induced variability of the subtidal salinity field in Northern San Francisco Bay, J. Phys. Oceanogr., 32, 3003–3019. Partch, E., and J. Smith (1978), Time dependent mixing in a salt wedge estuary, Estuar. Coast. Mar. Sci., 6, 3–19. Pawlowicz, R., B. Beardsley, and S. Lentz (2002), Classical tidal harmonic analysis including error estimates in MATLAB using T TIDE, Comput. Geosci., 28, 929–937. Pawlowicz, R., O. Riche, and M. Halverson (2007), The circulation and residence time of the Strait of Georgia using a simple mixing-box approach, Atmos. Ocean, 45 (4), 173–193, doi:10.3137/ao.450401. Percival, D., and A. Walden (1993), Spectral Analysis for Physical Applications: Multitaper and Conventional Univariate Techniques, Cambridge University Press. Peters, H. (1997), Observations of stratified turbulent mixing in an estuary: neap-to-spring variations during high river flow, Estuar. Coast. Shelf Sci., 45, 69–88. Royer, L., and W. Emery (1982), Variations of the Fraser River plume and their relationship to forcing by tide, wind and discharge, Atmos. Ocean, 20, 357–372. Scargle, J. (1982), Studies in astronomical time series analysis. II. Statistical aspects of spectral analysis of unevenly spaced data, Astrophys. J., 263, 835–853. Stronach, J. (1981), The Fraser River plume, Strait of Georgia, Ocean Management, 6, 201–221. Tabata, S. (1972), The movement of the Fraser River-influenced water from a series of aerial photographs, Pacific marine report no. 72-6, Marine Sciences Branch, Pacific Region. 72  Bibliography Warrick, J., and D. Fong (2004), Dispersal scaling from the world’s rivers, Geophys. Res. Lett., 31, L04301, doi:10.1029/2003GL019114. Yankovsky, A., and D. Chapman (1997), A simple theory for the fate of buoyant coastal discharges, J. Phys. Oceanogr., 27, 1386 – 1401.  73  Chapter 3  Fresh water flushing time and entrainment in a river plume2 3.1  Introduction  One driver of primary production in deep estuaries and estuarine-like systems is the upwelling flux of nutrient-rich water [e.g. Pawlowicz et al., 2007]. During entrainment in these systems, deeper water is brought to the surface, which often brings nutrients to sunlit waters for algal growth. In a river plume, this upwelling flux of deep water may significantly augment the nutrients brought by the river itself [DeMaster and Pope, 1996]. In low nitrate river systems, entrainment into the plume is the primary source of nitrate [Harrison et al., 1991; Lohan and Bruland, 2006]. Quantifying the upwelling entrainment velocity is important in, for example, biophysical models [e.g. Collins et al., 2009], but it is generally too small to measure directly and therefore must be inferred from other measurements. Entrainment occurs throughout the estuary/river plume system but varies in nature and intensity [Hetland, 2005]. In a salt-wedge estuary and a nearfield plume, entrainment is caused by a strong vertical shear of the horizontal velocity. In an estuary, the shear is established by estuarine circulation [Hansen and Rattray, 1965], but modified by tidal variations, and is generally maximized at some point during the ebb [Partch and Smith, 1978; Geyer and 2  A version of this chapter will be submitted for publication. Halverson, M., and R. Pawlowicz (2009), Fresh water flushing time and entrainment in a river plume.  74  Chapter 3. Fresh water flushing time and entrainment in a river plume Farmer , 1989]. In some cases, entrainment is localized to channel constrictions or bathymetric changes which accelerate the flow [Geyer and Farmer , 1989]. Turbulent dissipation is potentially high, perhaps the highest among oceanic shear flows [MacDonald and Geyer , 2004]. The mixing vertically redistributes salt and momentum and thus the outflow characteristics of the water which becomes the plume [Hetland, 2005]. Mixing continues farther from the estuary and near-field plume, but at lower intensities. Shear and stratification are weaker, and wind becomes the dominant driver of mixing. Mixing intensity (i. e. vertical salt flux) is lower, but the rate of work due by mixing can be higher because it occurs over a larger area [Hetland, 2005]. Tracking fresh water provides a means of quantifying the relative importance of mixing and advection processes. MacCready et al. [2002] present a framework to quantify advection and mixing by analyzing the volume of water between isohalines. The advantage of this formulation is that the processes which create water of a particular salinity are transparent. The pitfall of the isohaline budget is that it requires detailed knowledge of the salinity field, and to date has been applied only to numerical studies. Monsen et al. [2002] summarize three quantities used to characterize mixing and advection in a semi-enclosed system: residence time, age, and flushing time. Residence time is the time it takes for a water parcel to exit a system, while age is the amount of time a water parcel has spent since entering the system. They are both quantities assigned to individual water parcels within a domain, and quite often they will vary throughout the domain because of spatially variable mixing and advection. By their nature, residence time and age are difficult to evaluate with field measurements. Flushing time, however, is a simple bulk estimate of the exchange characteristics of the domain [Monsen et al., 2002]. While it obfuscates physical processes, it is generally straight forward to compute. In the case of a river feeding a river plume of fresh water volume Vf w at a rate Q, the fresh water flushing time is: τf w =  Vf w Q  (3.1)  A relatively small flushing time, for example, implies fresh water is mixed 75  Chapter 3. Fresh water flushing time and entrainment in a river plume or advected away quickly. The appropriate choice for volume is somewhat arbitrary for a river plume because it lacks solid boundaries. One meaningful definition could be to define the volume with surfaces important for mixing and entrainment, or specific isohalines [e.g. MacCready et al., 2002]. River plumes are generally strongly stratified, and the base of the stratified layer is expected to be a dynamically meaningful surface because mixing and entrainment may be important here. The seaward extent of a plume is often bound by a zone of strong flow convergence resulting in a salinity front. Here, mixing is important as plume water reaches the front and is forced downwards [Garvine, 1974]. This simple configuration, with entrainment from below, and loss through the front, suggests that a volume and salt budget can be constructed for the plume within a volume defined by sharp salinity gradients. Such a budget is in some ways analogous to a budget formulated for a system enclosed mostly by solid boundaries (i.e. a coastal sea), except that in this case water is allowed to move through the boundaries. Salt and volume budgets can supplement some of the information lost by characterizing a system with flushing time. For example, vertical entrainment velocities, which are often too small to be measured directly, can be estimated from a salinity budget [e. g. Cordes et al., 1980]. In this chapter the fresh water flushing time of the Fraser plume is estimated with the ferry data set (Sec. 1.4.1). A salinity budget is then developed and used to estimate the key unknown parameters maintaining the salinity balance. These analyses provide insight into the mixing processes which maintain the salinity of the plume. The plume salinity and surface area time series are used extensively in this chapter. As such, it begins with a brief summary of them in Sec. 3.2. Next, the flushing time and salinity budget equations are developed and applied to the ferry data. Section 3.3 contains a discussion of the quantities derived from the budget: entrainment flux, entrainment velocity, vertical salt flux, and flow speed. Next, the implications of the fresh water flushing time are discussed and compared to other systems. The work is summarized in Sec. 3.4.  76  Chapter 3. Fresh water flushing time and entrainment in a river plume  3.2 3.2.1  Results Plume and Strait of Georgia water salinity  A time series of mean plume salinity along with Fraser discharge at the mouth is shown in Fig. 2.2.  Mean salinity is defined here as the spa-  tial average of the section of a ferry transect determined to be the plume (Sec. 1.4.1). Tidal variations are removed by binning the time series into daily means. The daily-binned time series shows that plume salinity decreases with increasing river discharge, and that fluctuations around the long time scale behaviour are larger during high river discharge compared to low river discharge. The fortnightly modulation of mixing in the estuary is partly responsible for the fluctuations (Sec. 2.3.1). When the fortnightly cycle is removed, mean plume salinity decreases quasi-linearly at 1.4 per 1000 m3 s−1 (Fig. 2.3). Strait of Georgia (SoG) surface water, defined as the spatial average of salinity at along-strait distances between 45 and 50 km (Fig. 1.1), is less variable than plume salinity. SoG water salinity varies from 17 to 30, and, like plume salinity, exhibits relatively high frequency fluctuations. These fluctuations are smaller in magnitude than those of plume salinity, and are not correlated with river discharge. After low-pass filtering, SoG salinity decreases quasi-linearly with river flow at 0.7 per 1000 m3 s−1 (Fig. 2.3), or half the rate observed in the plume.  3.2.2  Plume fresh water fraction  The first step in estimating a fresh water flushing time is to estimate the fraction of the plume volume occupied by fresh water. This quantity is given by: f =1−  Sp So  (3.2)  where Sp is the volume-mean plume salinity and So is a reference salinity. In this chapter, the reference salinity is taken to be the salinity of SoG water as it was defined in Sec. 1.4.1. To estimate the volume-averaged plume salinity, the plume is selected from a ferry transect according to salinity. A spatial 77  Chapter 3. Fresh water flushing time and entrainment in a river plume average of the points within the plume forms the mean 2 m salinity, and is equivalent to what has been simply called plume salinity until now. Next, the 2 m salinity is adjusted to represent the depth-averaged salinity: Sp =  3 7 S(z = 2) + So 10 10  (3.3)  where Sp now represents the volume-averaged salinity needed in Eq. (3.2). In deriving Eq. (3.3), the plume was taken to be a 7 m thick, linearly stratified layer, which appears to be a reasonable approximation based on the mean salinity profile at S2-3 (Fig. 4.11a). At 7 m, the salinity is equal to the SoG water salinity. Figure 3.1 shows the fraction of the plume volume occupied by fresh water relative to SoG water as function of river discharge for the 25-day low-pass filtered time series. At the lowest river flows, the plume is only about 5% fresh water, while at 8000 m3 s−1 , the plume contains 25% fresh water. The increase is very close to linear, changing at 2.9% per 1,000 m3 s−1 .  3.2.3  Plume surface area  The next step in estimating the fresh water flushing time is to estimate the total volume of the plume. In this case, a simple geometry will be assumed so the volume can be written as the product of depth and surface area. Depth will be held constant at 7 m. The surface area was estimated and discussed in Chapter 2. To summarize, the variance of plume surface area is dominated by high frequency energy in the 8 to 15 day band (Sec. 2.3.2 and Fig. 2.7). It is not clear what forces these frequencies, but it was hypothesized to be a combination of advection of the plume relative to the ferry track by wind and tides, and errors in the plume-finding algorithm (Sec. 1.4.1). Smoothing with a 25-day Hamming window removes the high frequency variance. The remaining variance is forced mostly by river discharge. Surface area increases with increasing river discharge. At low flows, surface area varies between 200 to 500 km2 while at high flows the area varies between 1,000 to 1,500 km2 (Fig. 2.8). In the case of the salinity budget, a time series of surface area is not nec78  Chapter 3. Fresh water flushing time and entrainment in a river plume essary. Instead, a parameterization of surface area as a function of river discharge is sufficient. Non-parametric smoothing of the relationship between river flow and plume surface area by the LOESS algorithm [Cleveland, 1979] reveals that the surface area varies proportionately less with increasing flow (Fig. 2.8). Scaling relationships derived by Warrick and Fong [2004] suggest that the area should scale like Q1/2 . Such a function fits the non-parametric curve well, though in Sec. 2.3.2 it is argued the justification provided by Warrick and Fong [2004] does not apply for the larger spatial scales of the Fraser. Despite the apparent difference in physics, the Q1/2 parameterization will be used simply because it fits the observations well (Fig. 2.8). Fitting the data yields the following empirical relation: A = γ Q1/2  (3.4)  where γ is 11.1 ± 0.5 km2 (m3 s−1 )−1/2 .  3.2.4  Plume fresh water volume and flushing time  Surface area, percent fresh water, and plume depth can be combined to estimate the total volume of fresh water in the plume. Formally, fresh water volume is the volume integral of fresh water fraction: Vf w (t) =  D  1−  S(x, y, z, t) So (t)  dV,  (3.5)  where S(x, y, z, t) is salinity, and So is a reference salinity. Equation (3.5) must be simplified in order to obtain an expression which can be evaluated with ferry data. A simplified form, written in terms of the plume fresh water fraction, surface area, and depth is: Vf w (t) ≃ A(t)Df (t)  (3.6)  where D is the plume depth. Inserting the ferry measurements, and taking the depth to be a constant 7 m, yields a time series of fresh water volume (Fig. 3.2a). The fresh water volume, as with the plume area and salinity, 79  Chapter 3. Fresh water flushing time and entrainment in a river plume shows a high degree of variability, primarily caused by the 8 – 15 day variance in surface area (Fig. 2.7), so a 25-day Hamming window was used to smooth the time series. Fresh water volume is large during times of high river discharge and low during times of low river discharge. Dividing the plume fresh water volume by the river discharge yields the fresh water flushing time, τf w (Fig. 3.2b). As with the fresh water volume, the flushing time is very noisy, so it is smoothed with a 25-day Hamming window. The smoothed flushing time varies from less than 1 to just over 4 days, with a mean of 2.2 days. Plotting fresh water flushing time versus river discharge (not shown) suggests there can be a weak relationship between them. However, the 95% confidence interval about the smoothed flushing time is 1.2 days in width throughout the time series, so that any dependence on river flow would be weak on statistical grounds. Thus, for the remainder of the thesis, it will be taken as a constant 2.2 days.  3.2.5  Plume salinity budget  Model and framework The salinity budget is based on a single layer slab plume with surface area, A, and thickness, D, illustrated by the schematic diagram in Fig. 3.3. Both fresh water input by the river, Q, and entrainment of sea water at the base of the plume add volume, while brackish plume water is entrained out of the plume front [Garvine, 1974], removing water at a rate Ff . The volume flux of sea water entrained into the plume is written as the product of the plume surface area A, and an entrainment velocity, we . The volume encompassing the plume is filled with a uniform salinity, Sp . The time evolution of plume volume, Vp , and total salt mass, ρVp Sp , for this model are: dVp dt d(ρp Sp Vp ) dt  = Q + we A − Ff  (3.7)  = ρo we ASo − ρp Ff Sp  (3.8)  80  Chapter 3. Fresh water flushing time and entrainment in a river plume where So is the salinity of the water entrained into the plume from below and ρ is density. It is assumed that the entrained water has a salinity equal to the SoG water salinity. The first term on the r.h.s of Eq. (3.7) is the addition of river water, the second is the addition of salty water from below by entrainment, and the third term is the loss of water through the plume front. In Eq. (3.8), salt is gained from below by term 1 on the r.h.s and lost through the plume front by the second term. The density difference between the plume and SoG water is small and drops out of the equation. Eliminating the plume front term from the equations, writing the volume as the product of surface area and thickness, and expanding the l.h.s of Eq. (3.8) with the chain rule gives the balance for plume salinity. dSp Q we =− Sp + (So − Sp ) dt AD D  (3.9)  The derivative term can be conceptualized and referred to as a storage term, which is important when the r.h.s. is unbalanced. The first term on the r.h.s of Eq. (3.9) is always negative and is therefore a sink, while the second term is always positive because So > Sp , and is therefore a source of salinity. Steady balance Rapid changes in river discharge and fortnightly cycling in tides may cause plume salinity to decrease by as much as 4 day−1 or increase by as much as 6 day−1 (Chapter 2). Wind may cause mixing, but it can also change the estimate of plume salinity if it advects the plume relative to the ferry track. The plume salinity balance (Eq. 3.9) does not explicitly account for tides and wind, though in principle their effects can be parameterized into we . Alternatively, the time series can be filtered to focus only on the effects of varying river discharge, and the time mean of tidal and wind mixing will be absorbed into we . An important consequence of smoothing the terms in Eq. (3.9) is that a steady balance for plume salinity is achieved, and the steady solution allows a very straightforward computation of entrainment flux. In Chapter 2, a 25-day Hamming window was applied to salinity to remove tidal and wind 81  Chapter 3. Fresh water flushing time and entrainment in a river plume variations. Here, the same filter length is justified as a suitable time-scale for achieving a quasi-steady plume salinity balance. On appropriately long time-scales, the sink and source terms of Eq. (3.9) will balance and keep the salinity in a quasi-steady state. The storage and sink terms are readily evaluated with existing data, however the source term contains we , which is unknown. To evaluate the sink term, Q and Sp are filtered with a 25-day Hamming window, A is parameterized with Eq. (3.4), and D is fixed at 7 m. The result is that it varies from -1.8 to -0.9 d−1 , and has a mean value of -1.3 day−1 . The storage term is comparatively small after smoothing. Only 25% of the storage time series has an absolute value exceeding 0.13 day−1 (10% of the mean sink), and the extremes (-0.4 to +0.3 day−1 ) are less than half of the minimum sink. Thus, the plume salinity is quasi-steady at time-scales of 25 days, and the form of Eq. (3.9) is valid in the sense that all variations in salinity can be modeled with varying river discharge and entrainment from below. Processes which act on shorter time-scales have a net effect that is incorporated into we . In the quasi-steady limit of Eq. (3.9), the sink and source terms balance each other, yielding a Knudsen relation for the upward flux of salt water driven by river flow: So Q = we A −1 (3.10) Sp The steady balance in this form allows the entrainment flux, we A, to be readily determined from salinity and river flow.  3.2.6  Entrainment flux  Equation (3.10) may be rearranged to solve for we A given the known quantities, Q, Sp , and So : we A =  So Sp  Q −1  (3.11)  Figure 3.4 shows the entrainment flux as a function of river discharge. While we A shows a fair amount of scatter, it is clear that it begins at about 17,000 m3 s−1 , and increases with river flow until reaching a maximum in  82  Chapter 3. Fresh water flushing time and entrainment in a river plume the range of 25,000 to 35,000 m3 s−1 at higher flows. Fitting a LOESS nonparametric curve clarifies the relationship, and suggests that the entrainment flux peaks at 28,000 m3 s−1 at a river discharge of about 5,000 m3 s−1 . The 2σ boot-strapped error bounds on the LOESS curve are wide enough such that the existence of a maximum in we A is questionable, but it is clear that it initially rises before reaching a plateau at about 4,500 m3 s−1 . Plume and SoG water salinity were needed to estimate the entrainment flux, meaning Eq. (3.11) is not prognostic because, intuitively, plume salinity itself should depend on the entrainment flux. Plume and SoG salinity are each correlated with river flow (Fig 2.3), and if they can be parameterized with river discharge then an empirical expression can be derived in which entrainment flux depends only on river discharge. Plume and SoG water salinity will be parameterized in terms of the fresh water fraction (Eq. 3.2). The denominator of Eq. (3.11) can be written as f (1 − f )−1 . Equation (3.11) then becomes we A = Q  1 −1 f  (3.12)  This form is particularly useful because f is very well approximated by a linear function of river discharge. A fit of α + βQ to the fresh water fraction, with boot-strapped 95% confidence intervals, yields α = 2.97 ± 0.63×10−2  and β = 2.49 ± 0.21×10−5 (m3 s−1 )−1 (Fig. 3.1). Strictly speaking, the  linear model is unrealistic because it allows f > 1 for large Q. An example  of a more realistic model is 1 − exp(−(α + βQ)). However, in practice, the  models fit the data equally well over the range of observed f . Inserting the linear parameterization into Eq. (3.12) yields an expression for entrainment flux which is a function of river flow only: we A = Q  1 −1 α + βQ  (3.13)  The curve predicted by this parameterization is included in Fig. 3.4. It matches the LOESS curve very well. It initially increases with discharge from 17,500 to 27,500 m3 s−1 over the range 1,000 to 5,000 m3 s−1 . At higher 83  Chapter 3. Fresh water flushing time and entrainment in a river plume flows, it decreases very slightly before reaching 27,000 m3 s−1 at a river discharge of 8,000 m3 s−1 . An alternative to parameterizing f is to rely on the individual relationships of plume and SoG salinity to river flow (Fig. 2.3). However, although both plume and SoG salinity are nearly linear, their ratio is not and this small deviation leads to a fit for we A which is not useful. For example, the entrainment flux is under-predicted by about 1,500 m3 s−1 at low river flow.  3.2.7  Flow speed at the plume front  Quantifying we A means that the volume budget (Eq. 3.7) can be closed when the plume volume is in a quasi-steady state. The total volume entering the plume is the sum of the river flow and the entrainment flux, which ranges in magnitude from 18,000 to 35,000 m3 s−1 . The total volume flux in must equal the volume flux out through the front (Eq. 3.7). The cross-front velocity required to keep the plume volume in steady state may be estimated by expressing the flux through the front into the product of a velocity and a surface area: Ff = U Df Cf  (3.14)  where U is the mean velocity normal to the plume front surface area. The surface area of the front is written as the product of the plume circumference, Cf , and the plume thickness at the front, Df . If the plume is taken to be semi-circular, then Cf can re-written in terms of the surface area measured by the ferry. Solving for U , and employing the steady limit of Eq. (3.7) yields: U (Q) =  we A + Q √ Df 2πA  (3.15)  where the entrainment flux is given by Eq. (3.11) and the surface area is given by Eq. (3.4). The velocity at the front versus river discharge is plotted in Fig. 3.5 for a range of Df between 2 and 7 m. As with the entrainment flux, the scatter in velocity for each thickness is large enough that changes with river discharge are perhaps only weakly significant. To clarify the behaviour, U was also calculated by relying on the parameterization of we A (Eq. 3.13). 84  Chapter 3. Fresh water flushing time and entrainment in a river plume The curves show that the velocity increases by 15% from a minimum at 1,000 m3 s−1 to a very broad maximum at 3,500 m3 s−1 . It then declines by a lesser amount over the remaining range of river flow. For example, the 5 m curve begins at 7.8 cm s−1 , increases to a maximum of 9.3 cm s−1 , and then decreases to 8.7 cm s−1 .  3.3  Discussion  The basic salt and volume conservation equations, and the fresh water flushing time series, have produced a number of tools which, with some manipulation, shed light on entrainment and circulation in the plume.  3.3.1  Curvature in entrainment flux  The entrainment flux increases rapidly with river flow until it plateaus at a river flow of about 4,500 m3 s−1 (Fig. 3.4). While this observed variation of entrainment flux is statistically significant, it is important to note that the magnitude and shape of the curve are each sensitive to the details of plume and SoG salinity. This can be tested by varying the empirical α and β coefficients in Eq. (3.13). For example, increasing (decreasing) α by 20% of its best fit value of 2.97×10−2 (approximately the 95% confidence interval), while keeping β fixed at its original, empirically determined value of 2.49×10−5 (m3 s−1 )−1 , will increase (decrease) the entrainment flux by about 2,000 m3 s−1 over all values of river flow. Increasing (decreasing) β by 10% (approximately the 95% confidence interval), increases (decreases) the entrainment flux by 1,000 m3 s−1 at low river flow to 2,500 m3 s−1 at high river flow. Thus, varying α and β within their estimated confidence intervals can change the magnitude of the entrainment flux. However, the increase in entrainment flux until a river discharge of about 5,000 m3 s−1 , is robust. The general shape of the curve shows an increase in entrainment flux with discharge at low flows. The flux should be zero at zero discharge and extrapolation of the data does tend to agree with this assertion. The  85  Chapter 3. Fresh water flushing time and entrainment in a river plume implication of this with respect to Eq. 3.12 is that f must remain a finite value at low flow, so that α > 0 in Eq. (3.13). That is, even a small plume must have some non-zero fresh water fraction. At sufficiently high discharge the relationship between entrainment flux and discharge will depend on the fresh water fraction which should (eventually) reach a maximum. If the maximum is one, the entrainment flux would again be zero and there would necessarily be a maximum entrainment at some intermediate discharge. If f reaches some asymptotic value slightly less than one then the entrainment flux would approach a value equal to some small fraction of the river flow. In this case there may or may not be an entrainment maximum at intermediate flow. Quantitatively, if α < βQ, then the flux must decrease from some intermediate maximum. The data do not make a definitive statement on an eventual decrease of entrainment flux because extrapolating it beyond Q = 8,000 m3 s−1 cannot be done with any confidence. The LOESS parameterization begins to decline at the highest river flows sampled, but the trend is only very weakly significant because of the width of the 2σ bounds. The limiting cases of α = 0 and α = 1 imply a curved entrainment flux. There are additional physical arguments for a river flow dependent entrainment flux. The entrainment flux plateaus at a river flow of about 4,500 m3 s−1 , which is approximately the flow required to push the salt wedge to the mouth at low tide. According to Kostaschuk and Atwood [1990, Eq. 3], the minimum discharge required to flush the salt wedge out of the river at low tide is 3,500 m3 s−1 at Hope. This is 500 to 1,000 m3 s−1 lower than the flow at the mouth (Fig. 1.2), implying the salt wedge is flushed out of the estuary at a mouth discharge of 4,000 to 4,500 m3 s−1 . Section 2.3.1 discussed evidence that the location of the salt wedge influences plume salinity, and it is likely that the river flow affects the location at which entrainment is strongest [Geyer and Farmer , 1989; MacDonald and Geyer , 2004]. Thus, a change in the entrainment flux is not unexpected, but the mechanisms which would tend to influence its observed variation with river discharge are not understood. While it is apparent there are some limitations in using the ferry data 86  Chapter 3. Fresh water flushing time and entrainment in a river plume and the steady salinity budget to estimate an entrainment flux, it is clear that we A ≃ 2.5×104 m3 s−1 to within 30% for a wide range in river flow.  Furthermore, as shown in the next section, the consistency with previous work supports the quantitative estimate of entrainment flux made here.  3.3.2  Entrainment velocity  While it is not clear why entrainment flux becomes independent of river discharge at high A, the shape and magnitude of the curve do have implications for entrainment velocity. Entrainment velocity is a more fundamental quantity than entrainment flux because it does not depend on the dimensions of the system (i.e. surface area). It is determined by the local stratification and shear, and can be linked to a number of important turbulence parameters. Entrainment velocity can be estimated from entrainment flux by dividing it by the surface area across which entrainment takes place: we (Q) =  we A Ae  (3.16)  With this formulation, entrainment velocity will generally depend on river discharge because the surface area for entrainment and the entrainment flux each depend on river discharge. Entrainment is active over the entire estuary and river plume system, so that choosing an Ae is not straightforward. Therefore, two idealized models will be considered. The first assumes that entrainment occurs exclusively in the plume, and the second assumes that entrainment occurs only in the estuary and near-field plume. The plausibility of each model is then discussed by comparing the entrainment parameters implied by each to literature values. Entrainment in the plume In this model the river discharges fresh water (i.e. no entrainment occurs upstream of the river mouth), and entrainment occurs uniformly over the river plume (near-field and beyond). The surface area for entrainment will be taken as the surface area of the plume as measured by the ferry, param87  Chapter 3. Fresh water flushing time and entrainment in a river plume eterized as a function of river discharge according to Eq. (3.4). The entrainment velocity is therefore a function of river discharge only, decreasing quasi-linearly from about 4.5 m day−1 (5.2×10−2 mm s−1 ) at 1,000 m3 s−1 to 2.2 m day−1 at 8,000 m3 s−1 (Fig. 3.6a). The difference between plume and SoG salinity becomes larger with increasing river flow, so the smaller entrainment velocity at high discharge could reflect a tendency for increased stratification to inhibit entrainment. The entrainment velocity of O(10−2 ) mm s−1 is at least on order of magnitude smaller than previous estimates in the Fraser plume. Cordes et al. [1980] estimate a dimensionless entrainment coefficient, we /U , of 2.4×10−4 , which would be a velocity of 2.4 ×10−1 mm s−1 for a 1 m s−1 flow typical  of their measurements. More recently, MacDonald and Geyer [2004] estimate a vertical entrainment velocity of 4 to 8 mm s−1 . The factor of 10 more difference between the estimate in Fig. 3.6a and these literature values arises from differences in the location of sampling with respect to the river mouth. The Cordes et al. [1980] study was conducted within about 10 km of the river mouth, while the MacDonald and Geyer [2004] measurements were only a few kilometres downstream of the river mouth. Flows here are somewhat more energetic than those expected for the whole plume, and thus entrainment velocities would be higher. The Cordes et al. [1980] entrainment coefficient can be used to make an independent estimate of the entrainment velocity. At 10 cm s −1 , typical of the flow at the plume front (Fig. 3.5), the entrainment velocity in the plume is 2.4 ×10−2 mm s−1 , which  is on the order of those estimated independently in Fig. 3.6.  Part of the usefulness of quantifying the entrainment velocity is that it can be used to compute turbulent flux parameters, such as the upward vertical salt flux, we So , and the turbulent salt diffusivity, Ks . The upward salt flux can be computed as a function of river flow because reference salinity is correlated with river flow. As with the entrainment velocity, the salt flux decreases with river discharge (Fig. 3.6b), from 1.5 ×10−3 m s−1 at high  flow to 0.6 ×10−3 m s−1 at low flow. A turbulent diffusivity for salt can be  estimated with the expression we So = −Ks ∂S ∂z [e.g. McCabe et al. 2008].  From the mean salinity profile at S2-3 shown in Fig. 4.11a, stratification in 88  Chapter 3. Fresh water flushing time and entrainment in a river plume the plume can be reasonably approximated as a linear increase of salinity with depth from S(z = 2 m) (i.e. the ferry salinity with no depth correction) to S(z = 7 m) ≈ So . Turbulent salt diffusivity decreases with increasing river  discharge in a similar manner to entrainment velocity and salt flux, although the dependence is stronger. At 1,000 m3 s−1 , it is about 3.0×10−3 m2 s−1 , and at 8,000 m3 s−1 , it is 0.5×10−3 m2 s−1 . This is only slightly lower than the value of 3 – 4×10−3 m2 s−1 measured by MacDonald and Geyer [2004] in the near-field of the Fraser River plume. Entrainment in the estuary and near-field plume Since very low salinity water is generally not seen in the plume, a more realistic model is that entrainment mostly takes place in the estuary at low flow, and in the near-field plume at high river flow. If entrainment takes place in the estuary, then we will be larger than the estimate for entrainment in the plume because the surface area is smaller. At low discharge, the entrainment flux is 17,000 m3 s−1 (Fig. 3.4) and, while the tip of the salt wedge may reach 30 km upstream [Kostaschuk and Atwood, 1990], the actual length of the salt wedge (and thus the area for entrainment) is likely shorter because water at the mouth is brackish and less stratified. Hydrographic measurements in the estuary at low discharge [Ages, 1979] suggest 15 km is a better length. Using a mean river width of 0.5 km, the surface area for entrainment is 7.5 km2 . The entrainment velocity is thus 2.3 mm s−1 (200 m day−1 ). It increases with river discharge, because the length of the salt intrusion decreases with increasing river discharge [Hansen and Rattray, 1965; Kostaschuk and Atwood, 1990], and because the entrainment flux increases over low to moderate river discharge (Fig. 3.4). While there are no known estimates of we in the estuary at low discharge for comparison, this value is similar to the MacDonald and Geyer [2004] estimates at high flow in the Fraser near-field plume. During high river flow the salt intrusion is flushed from the mouth at low tide, and entrainment primarily takes place in the near-field plume. A surface area for entrainment is more difficult to estimate in this case because  89  Chapter 3. Fresh water flushing time and entrainment in a river plume the near-field plume spreads as it leaves the mouth and its seaward extent is not known. Salinities as low as 10 have been recorded where the ferry track passes within 6 km of the river mouth (Fig. 1.1). This distance will taken as a reasonable estimate of the extent of the near-field plume. The width is assumed to be 3 km to allow for spreading (the mouth is about 1 km wide). The surface area for entrainment is 18 km2 , and the entrainment velocity at the observed peak entrainment flux of 27,000 m3 s−1 is then 1.6 mm s−1 (130 m day−1 ). These entrainment rates agree well with MacDonald and Geyer [2004], who estimate vertical velocities of 4 - 8 mm s−1 in the Fraser near-field plume during the ebb at high river discharge. Discrepancies between MacDonald and Geyer [2004] and this work are well within the crudeness of the area estimates. Furthermore, the MacDonald and Geyer [2004] velocities are expected to be higher because mixing tends to be very localized in the Fraser River estuary [MacDonald and Horner-Devine, 2008], which would tend to reduce the surface area for entrainment. Note also that the estuary and near-field plume entrainment velocities are about the same, which might be expected if mixing is due to shear instability driven by inertial flows. Where does entrainment occur? Mixing is a continuous process from the estuary to the far-field plume, but the intensity is not uniformly distributed [Hetland, 2005]. Assuming that it takes place only in specific parts of the estuary and river plume, as was done in the budget analysis, is artificial, but it produces entrainment velocities of the right magnitude. The entrainment velocities for the estuary and near-field plume match previous estimates well, which means that all of the entrainment necessary to produce the observed plume salinity can be accounted for without recourse to entrainment in the whole plume. If the entrainment velocity for the estuary or near-field plume were applied to the entire plume surface area, the total entrainment flux would be a factor of ∼50  times larger than that estimated in Fig. 3.4. Thus the most likely scenario  is that most of the entrainment occurs in the estuary and in the near-field,  90  Chapter 3. Fresh water flushing time and entrainment in a river plume while only minimal entrainment occurs outside the near-field. Attributing most of the entrainment to the estuary is also consistent with one of the main points of Chapter 2, which was that the plume salinity is influenced by estuarine processes.  3.3.3  Mixing at the plume front and frontogenesis  In Sec. 3.2.7, the horizontal velocity of fluid normal to the front was calculated by assuming the plume volume was in steady state. The dynamically important quantity, however, is the speed of the fluid relative to the speed of the front. A plume front is maintained if the flow within the plume travels faster than the front itself, i.e. the flow must be supercritical. This tendency is quantified with a Froude number: Fr =  U 2g′ Df  where U is the flow speed in the plume and  (3.17) 2g′ Df is the front propagation  speed for inviscid flow with no mixing [Benjamin, 1968]. Mixing at the front √ increases the factor multiplying the phase speed (the 2 factor in Eq. (3.17) - c.f. O’Donnell, 1993), so that the Froude number estimates made here will be upper limits. Equation (3.17) can be computed as function of river discharge by substituting Eq. (3.15) for U and by using plume and SoG salinity to estimate g′ . Figure 3.7 shows the Froude number for selected depths between 2 and 7 m. For a fixed plume front thickness, the Froude number decreases with increasing river flow. It is inversely proportional to depth, and the maximum is about 1 for a 2 m plume front and a river flow of 1,000 m3 s−1 . Despite first-hand observations of a well-defined front at the edge of the Fraser plume [especially in summer, c. f. Harrison et al., 1991], the Froude numbers for all of the curves are less than 1 except for the 2 m front at low flow (Fig. 3.7). This means that a front would not form at a radius from the river mouth derived from the plume surface area if the plume flow was driven solely by the requirement to conserve volume on long time-scales. Another  91  Chapter 3. Fresh water flushing time and entrainment in a river plume interpretation is that a front bounding the plume could only be sustained until the plume reaches a radius such that F r = 1, which is smaller than the radius observed by the ferry. This implies that fronts are formed by shorter time-scale processes which can drive higher velocities, such as tides. There is no single, ubiquitous front, only transient ones forced with each release of water from the river mouth. Why is a front observed at such a distance given the small Froude numbers? One explanation is based on the observations of the buoyant, tidally pulsed outflow from the Leschenault Estuary [Luketina and Imberger , 1987]. This particular plume contains multiple “subfronts” within the main front bounding the plume. The subfronts are formed by variations in the mouth Froude number, which is itself caused by seiches in the estuary. Observations show that the subfronts propagate faster than the main front. When they catch the main front, the cross-front salinity gradient is strengthened. It is possible that the Fraser plume contains subfronts, although the generation mechanism might differ from that in the Leschenault Estuary. In the Fraser estuary, the subfrontal formation would be caused by the tidal release of water from the river mouth. It would take about 17 hours for a gravity current moving at 50 cm s−1 to move 30 km, a typical length scale for the Fraser plume. Thus, there is time for a subfront to form on the next ebb tide before the main front dissipates. If the subfront propagates faster than the main front, it could perhaps reinforce the strength of the main front, allowing the main front to propagate beyond the maximum distance suggested by the volume budget analysis.  3.3.4  Fresh water flushing time  Flushing time is a relatively basic property of a river plume, and quantifying it is useful for multiple reasons. Lohrenz et al. [1990] suggest the relatively short flushing time of the Mississippi plume limits biomass, and it is likely a factor in the Fraser plume because they have similar flushing times. For example, cells growing at 0.6 d−1 (measured in Sec. 4.4.3) will approximately triple in 2.2 days. Chlorophyll-a concentrations in the plume can sometimes  92  Chapter 3. Fresh water flushing time and entrainment in a river plume exceed 80 µg l−1 (Fig. 4.12), and the short residence time implies that this can only occur if water having more than 25 µg Chla l−1 is entrained. Flushing time is likely an important consideration in sedimentation by a river plume, related to the accumulation rate and particle size class distribution observed offshore of a river mouth [e. g. Geyer et al., 2000]. For example, floc settling at 1 mm s−1 [e. g. Geyer et al., 2004] would settle out of the plume in 2 hours. Therefore, if sediment is observed at the outer edge of the plume, then it must have been suspended by turbulence or settle at a slower rate. The flushing time of the Fraser plume is twice the period of the diurnal tides, the dominant tidal force in the estuary and near-field plume (Sec. 2.3.1). This implies that the plume is essentially the product of two tidal pulses of water. The similar forcing and flushing times explain why the plume exhibits large fluctuations in salinity. The estuarine signal in plume salinity is not smoothed because the plume does not accumulate fresh water (i. e. the plume is not a low-pass filter). Furthermore, the strong correlation between river discharge and SoG salinity, with little or no apparent lag (Fig. 2.2), exists because fresh water spends about 2 days within the plume before it is replaced. Presumably this water escapes through the plume front and influences SoG surface water. Dynamically, the flushing time is long enough to let rotation influence the momentum balance. However, because the plume is tidally pulsed on a similar time-scale, it lacks a coherent structure, such as the rotating bulge ubiquitous in some models [e. g. Yankovsky and Chapman, 1997; Fong and Geyer , 2002] and observations [Chant et al., 2007; Horner-Devine, 2009]. If the flushing time was much longer than the tidal forcing, the plume itself would be buffered from high frequency forcing [Yankovsky et al., 2001]. An individual pulse of water from the mouth would make less of an impact, and a more coherent structure might be expected. Instead, the Fraser plume does not maintain a coherent structure for more than a day [Hodgins, 1994]. Estimates of fresh water flushing time have been reported for only a few river plumes. Lohrenz et al. [1990] estimate a fresh water flushing time of 2 days in the Mississippi River plume, during a time of anomalously low river 93  Chapter 3. Fresh water flushing time and entrainment in a river plume flow. In the Eel River plume during floods, Geyer et al. [2000] estimate fresh water flushing times ranging between 2.3 and 12 hours. The bulge of the Columbia River plume has a fresh water flushing time of 3 to 4 days [HornerDevine et al., 2009]. The fresh water flushing time of other plumes may be estimated from published values and/or assumptions of discharge, surface area, plume depth, and salinity according to Eq. (3.6). A compilation of these parameters, along with their respective references appears in Table 3.1. In all cases it was possible to estimate τf w for high and low river flow. The flushing times vary from a few hours in the Connecticut plume, to 10 days in the Columbia plume. The flushing times for the Amazon and Mississippi plumes range from about 1.5 to 5 days. Figure 3.8 illustrates the data in Table 3.1 graphically by plotting river discharge versus fresh water volume. Two important points are raised by the figure. First, flushing time between various plumes differs by a factor of 102 , while discharge changes by a factor of 103 . Second, for an individual plume, river discharge causes a change in fresh water volume such that the flushing time stays about the same. For example, the fresh water flushing time of the Eel River plume varies by less than a factor of 2, despite a factor of 5.5 change in river discharge. The Amazon plume does not appear to follow this trend, but it also had the most crude estimate of fresh water volume. It is likely that τf w varies less than the estimates compiled in Table 3.1. The Connecticut and Columbia flushing times differ significantly from the Eel, Fraser, Mississippi, and Amazon, but are easily brought to within the range of the others in light of the accuracy of their fresh water volume estimates. Garvine [1975] measures the depth and surface area of the Connecticut River plume inside the S = 20 isohaline. If the depth and surface area were referenced to 25, for example, the depth could easily increase to 5 m, which would cause its flushing time at high river discharge to increase to 0.5 days. The volume of the Columbia is referenced to an unknown salinity in Hickey et al. [1998]. If referenced to a salinity near that of the ocean, the fresh water volume, and therefore τf w , would be large. Horner-Devine et al. [2009] state that the flushing time of the bulge region in the Columbia is 3 94  Chapter 3. Fresh water flushing time and entrainment in a river plume to 4 days. The bulge is a region more dynamically consistent with the other plumes. Unfortunately this particular estimate could not be included in Table 3.1 because the reference provides no information on the river discharge or dimensions of the plume. If τf w is approximately the same for a number of river plumes, then the way in which plumes maintain fresh water cannot depend very strongly on their size or on the details of how they are forced. The plumes in Table 3.1 are formed by river flows which span three orders of magnitude, yet the fresh water volume increases proportionately so that the flushing time varies much less. They range greatly in geography, meaning each is forced by a unique combination of tides, wind, or ambient currents. For example, the Amazon is the largest river in the world by discharge. It empties onto a shallow continental shelf at the equator, and tides are minimal. The Fraser River, on the other hand, is a mid-latitude river with a substantially smaller discharge and large tides. It discharges into a deep, semi-enclosed coastal sea. Yet, their fresh water flushing times are about the same.  3.4  Conclusion  The fresh water flushing time and salinity balance of a river plume have been quantified with a data set covering a wide range in time scales. While monthly or longer changes have been emphasized here, the high time resolution is necessary to provide data which is not aliased by high frequency processes such as tides. A simple salinity budget was constructed for the Fraser plume consisting of input from the river, input from entrainment, and loss through mixing at the front. In the steady salinity limit, the entrainment flux of salty water increases with river discharge until reaching a river flow of about 4,500 m3 s−1 , which is approximately the value necessary to force salt wedge out of the estuary at low tide. Entrainment velocities implied by the entrainment flux for the estuary and near-field are consistent with previous estimates made by different techniques. This implies that entrainment in the near-field is sufficient to account for the observed plume salinity, and that little further 95  Chapter 3. Fresh water flushing time and entrainment in a river plume entrainment is required in the far-field. Although not required by the budget, entrainment likely continues in the far-field plume, but it must do so at a much lower intensity. By closing the volume budget, a cross-front velocity was estimated and compared to the theoretical front propagation speed to form a Froude number. The Froude number was found to be almost always subcritical, implying that other factors important on short scales must be maintaining the front at its observed distance. The tidal modulation of river flow is especially important in the structure of the plume. With a mean fresh water flushing time of 2.2 days, the plume is the product of only 2 tidal releases of fresh or mixed water from the mouth. Therefore, the plume will have nearly lost its coherent structure after 2 diurnal tides. The short flushing time allows the plume salinity to vary substantially as conditions in the estuary change. Fresh water flushing times for a number of plumes vary by a factor of 102  (but probably less), despite a factor of 103 or more difference in river  flow. The notion of a weakly varying flushing time over a range of river flow applies to individual systems as well. It suggests a common picture of the mechanisms maintaining fresh water in a river plume – perhaps unexpected considering the variety of environments in which they are found and their difference in structure.  96  Chapter 3. Fresh water flushing time and entrainment in a river plume  Table 3.1: Compilation of river discharge (Qf w ), surface area (A), mean salinity (Sp ), depth (D), and fresh water volume (Vf w ) used in Fig. 3.8. Estimates for each parameter are made at low and high river discharge. Fresh water volume is calculated with Eq. (3.6) if it is not given directly in the reference. Qf w  A  [m3 s−1 ]  [km2 ]  D  Vf w  τf w  [m]  [km3 ]  [days]  8,500  1,500  15  7  2.50  3.40  1,000  450  24  7  0.10  1.12  10,000  -  -  -  1.30  0.1  1,800  -  -  -  0.30  0.2  27,700  4,200  26  10  6.77  2.83  17,200  1,840  26  10  2.97  2.00  17,000  -  26  -  17.7  12.1  3,000  -  26  -  3.2  12.4  200,000  33,700  25  10  206  5.97  150,000  18,750  25  10  68.8  2.65  3,050  80  20  1  40×10−3  0.10  1  5.0×10−3  0.04  Fraser  Eel  Mississippi  Columbia  Amazon  Connecticut  1,100  10  Sp  20  reference Chapter 21 Geyer et al. [2000]2 Walker [1996]3 Hickey et al. [1998]4 Lentz [1995]5 Garvine [1975]6  1 Q f w and Vf w are representative values from this work; A and Sp from reference. 2 Q, V , and τ fw f w from Table 1 of reference. τf w in Table 1 does not match Fig. 3.8 because it was  for a smaller A. 3 Minimum and maximum A from Table 2 of reference. Salinity assumed. 4 The total volume of the plume (fresh and salt water), is quoted as 20 - 110 km3 . Salinity assumed. 5 Plume dimensions from Figs. 5 and 6 of reference. Plume approximately triangular with W = 150  km and 450 km (low and high flow), and L = 150 km. Salinity assumed. 6 A within S = 20 isohaline. D estimated in reference.  97  Chapter 3. Fresh water flushing time and entrainment in a river plume  0.25  plume fresh water fraction, f  0.2  LOESS α+βQ  0.15  0.1  0.05  0 0  1000  2000  3000  4000 5000 Q [m3 s−1]  6000  7000  8000  Figure 3.1: Fraction of the plume composed of fresh water versus river discharge. The reference salinity was taken to be the salinity of SoG water, defined as water outside of the plume near the northwestern edge of the ferry track.  98  Chapter 3. Fresh water flushing time and entrainment in a river plume  4  a)  6 2 4 1  2  0 Jan 6  Jul  2004  Jul  05  Jul  06  Jul  Jul  2004  Jul  05  Jul  06  Jul  Q [103 m3s−1]  8  Fraser  3 Vfw [km3]  10  Vfw  0  b)  τfw [days]  95%  4  2  0 Jan  Figure 3.2: Panel (a) is a time series of plume fresh water volume (dots) along with Fraser River discharge (gray). The thick line is the fresh water volume but filtered with a 25-day Hamming window. Panel (b) shows the fresh water flushing time, formed by dividing the river discharge into the fresh water volume. Dots are unfiltered while the heavy curve is filtered with a 25-day Hamming window.  99  Chapter 3. Fresh water flushing time and entrainment in a river plume  Figure 3.3: Schematic diagram of a plume used to derive the volume (Eq. 3.7) and total salt (Eq. 3.8) budgets for a plume of surface area A and thickness D. Q is the river flow, we A is the upward flux of SoG water, and Ff is the volume flux lost through the plume front. With respect to the salt budget, we A brings in salty water So , and Ff exports plume salinity Sp . The river has zero salinity.  100  Chapter 3. Fresh water flushing time and entrainment in a river plume  4  4  x 10  weA [m3 s−1]  3.5  3  2.5  2  1.5 0  fresh water fit, α = 0.03; β = 2.49e−05 LOESS α = 0; β = 2.49e−05  1000  2000  3000  4000 5000 Q [m3 s−1]  6000  7000  8000  Figure 3.4: Entrainment flux as a function of river discharge. The circles are direct estimates made with Eq. (3.11) and the solid line uses the fresh water fraction parameterization of Eq. (3.13). The parameterization compares well with the LOESS smoothed curve (thick dashed line) and the 2σ error bounds (thin dashed lines). The thin solid line represents the case where α = 0.  101  Chapter 3. Fresh water flushing time and entrainment in a river plume  30  25  2m  U [cm s−1]  20  3m  15  10  5m 7m  5 fresh water fit  0 1000  2000  3000  4000 5000 Q [m3 s−1]  6000  7000  8000  Figure 3.5: Outward flow velocity of water at the plume front as a function of river discharge for a range in plume front thickness (Eq. 3.15). The solid lines are the velocity but with entrainment flux parameterized by the fresh water fraction (Eq. 3.13), and are included to clarify the relationship between the flow speed and river discharge.  102  Chapter 3. Fresh water flushing time and entrainment in a river plume  5  a)  we [m d−1]  4.5 4 3.5 3 2.5 2 1000  weSo [10−3 m s−1]  1.7  2000  3000  4000 5000 Q [m3 s−1]  6000  7000  8000  2000  3000  4000 5000 Q [m3 s−1]  6000  7000  8000  b)  1.5 1.3 1.1 0.9 0.7 0.5 1000  Figure 3.6: Panel (a) shows the vertical entrainment velocity as a function of river discharge calculated with Eq. (3.16). The solid line is the entrainment velocity derived from the parameterized plume fresh water fraction. Entrainment was assumed to occur in the river plume. Panel (b) is the vertical salt flux, So we . The solid line uses the we parameterization from panel (a) multiplied by the SoG salinity LOESS parameterization of Fig. 2.3.  103  Chapter 3. Fresh water flushing time and entrainment in a river plume  1.2  1  Fr  0.8  0.6  2m  0.4 3m  0.2  5m 7m  0 1000  2000  3000  4000 5000 Q [m3 s−1]  6000  7000  8000  Figure 3.7: Froude number for the front as a function of river discharge for a range in plume front thickness (Eq. 3.17).  104  Chapter 3. Fresh water flushing time and entrainment in a river plume  3  10  2  10  Amazon 1  Columbia  Vfw [km3]  10  Mississippi 0  10  Fraser τfw [days]  10  High Flow Low Flow  2 1 0.5  −2  10  Connecticut  0.2 0.1  −3  10  Eel  10 5  −1  2  10  3  10  4  10 Q [m3s−1]  5  10  6  10  Figure 3.8: Comparison of fresh water volume versus river discharge in a number of plumes. The data sources are summarized in Table 3.1. High discharge is marked with triangles while low discharge marked with circles. Lines of constant fresh water flushing time are included. The flushing time of the Eel differs with Table 3.1 because Geyer et al. [2000] calculate τf w for a smaller portion of the plume than Vf w .  105  Bibliography Ages, A. (1979), The salinity intrusion in the Fraser River: Salinity, temperature, and current observations, 1976, 1977, Pacific Marine Science Report 79-14, Institute of Ocean Sciences, Patricia Bay, Sidney, B.C. Benjamin, T. (1968), Gravity currents and related phenomena, J. Fluid Mech., 31, 209–243. Chant, R., S. Glenn, E. Hunter, J. Kohut, R. Chen, R. Houghton, J. Bosch, and O. Schofield (2007), Bulge formation of a buoyant river outflow, J. Geophys. Res., 113, doi:10.1029/2007JC004100. Cleveland, W. (1979), Robust locally weighted regression and smoothing scatterplots, J. Am. Stat. Assoc., 74, 829–836. Collins, A. K., S. E. Allen, and R. Pawlowicz (2009), The role of wind in determining the timing of the spring bloom in the Strait of Georgia, Can. J. Fish. Aquat. Sci., in press. Cordes, R., S. Pond, B. de Lange Boom, and P. LeBlond (1980), Estimates of entrainment in the Fraser River plume, British Columbia, Atmos. Ocean, 18, 15–26. DeMaster, D., and R. Pope (1996), Nutrient dynamics in Amazon shelf waters: results from AMASSEDS, Cont. Shelf Res., 16 (3), 263–289. Fong, D., and W. Geyer (2002), The alongshore transport of freshwater in a surface-trapped river plume, J. Phys. Oceanogr., 32, 957–972, doi: 10.1029/2000JC900134.  106  Bibliography Garvine, R. (1974), Dynamics of small-scale oceanic fronts, J. Phys. Oceanogr., 4, 557–569. Garvine, R. (1975), The distribution of salinity and temperature in the Connecticut River Estuary, J. Geophys. Res., 80 (9), 1176–1183. Geyer, W., and D. Farmer (1989), Tide-induced variation of the dynamics of a salt wedge estuary, J. Phys. Oceanogr., 19, 1060–1072. Geyer, W., P. Hill, T. Milligan, and P. Traykovski (2000), The structure of the Eel River plume during floods, Cont. Shelf Res., 20, 2067–2093. Geyer, W., P. Hill, and G. Kineke (2004), The transport, transformation and dispersal of sediment by buoyant coastal flows, Cont. Shelf Res., 24, 927–949. Hansen, D., and M. Rattray (1965), Gravitational circulation in straits and estuaries, J. Mar. Res., 23, 104–122. Harrison, P., P. Clifford, W. Cochlan, K. Yin, M. St. John, P. Thompson, M. Sibbald, and L. Albright (1991), Nutrient and phytoplankton dynamics in the Fraser River plume, Strait of Georgia, British Columbia, Mar. Ecol.Prog. Ser., 70, 291–304. Hetland, R. (2005), Relating river plume structure to vertical mixing, J. Phys. Oceanogr., 35 (9), 1667–1688. Hickey, B., L. Pietrafesa, D. Jay, and W. Boicourt (1998), The Columbia River plume study: Subtidal variability in the velocity and salinity fields, J. Geophys. Res., 103 (C5), 10,339–10,368, doi:10.1029/94JC00343. Hodgins, D. (1994), Remote sensing of surface currents in the Fraser River plume with the SeaSonde HF radar, Tech. rep., Environment Technology Centre (Canada), Emergencies Science Division. Horner-Devine, A. (2009), The bulge circulation in the Columbia River plume, Cont. Shelf Res., 29, doi:10.1016/j.csr.2007.12.012.  107  Bibliography Horner-Devine, A., D. Jay, P. Orton, and E. Spahn (2009), A conceptual model of the strongly tidal Columbia River plume, J. Marine Syst., doi: 10.1016/j.jmarsys.2008.11.025, in press. Kostaschuk, R., and L. Atwood (1990), River discharge and tidal controls on salt-wedge position and implications for channel shoaling: Fraser River, British Columbia, Can. J. Civil Eng., 17 (3), 452–459. Lentz, S. (1995), Seasonal variations in the horizontal structure of the Amazon Plume inferred from historical hydrographic data, J. Geophys. Res., 100 (C2), 2391–2400, doi:10.1029/94JC01847. Lohan, M., and K. Bruland (2006), Importance of vertical mixing for additional sources of nitrate and iron to surface waters of the Columbia River plume: Implications for biology, Mar. Chem., 98, 260–273, doi: 10.1016/j.marchem.2005.10.003. Lohrenz, S., M. Dagg, and T. Whitledge (1990), Enhanced primary production at the plume/oceanic interface of the Mississippi River, Cont. Shelf Res., 10 (7). Luketina, D., and J. Imberger (1987), Characteristics of a surface buoyant jet, J. Geophys. Res., 92 (C5), 5435–5447. MacCready, P., R. Hetland, and W. Geyer (2002), Long-term isohaline salt balance in an estuary, Cont. Shelf Res., 22, 1591–1601. MacDonald, D., and W. Geyer (2004), Turbulent energy production and entrainment at a highly stratified estuarine front, J. Geophys. Res., 109, C05004, doi:10.1029/2003JC002094. MacDonald, D., and A. Horner-Devine (2008), Temporal and spatial variability of vertical salt flux in a highly stratified estuary, J. Geophys. Res., 113, C090220, doi:10.1029/2007JC004620. McCabe, R., B. Hickey, and P. MacCready (2008), Observational estimates of entrainment and vertial salt flux in the interior of a spreading river plume, J. Geophys. Res., 113, C08027, doi:10.1029/2007JC004361. 108  Bibliography Monsen, N., J. Cloern, and L. Lucas (2002), A comment on the use of flushing time, residence time, and age as transport time scales, Limnol. Oceanogr., 47, 1545–1553. O’Donnell, J. (1993), Surface fronts in estuaries: A review, Estuaries, 16 (1), 12–39. Partch, E., and J. Smith (1978), Time dependent mixing in a salt wedge estuary, Estuar. Coast. Mar. Sci., 6, 3–19. Pawlowicz, R., O. Riche, and M. Halverson (2007), The circulation and residence time of the Strait of Georgia using a simple mixing-box approach, Atmos. Ocean, 45 (4), 173–193, doi:10.3137/ao.450401. Walker, N. (1996), Satellite assessment of Mississippi River plume variability: causes and predictability, Remote Sens. Environ., 58, 21–35. Warrick, J., and D. Fong (2004), Dispersal scaling from the world’s rivers, Geophys. Res. Lett., 31, L04301, doi:10.1029/2003GL019114. Yankovsky, A., and D. Chapman (1997), A simple theory for the fate of buoyant coastal discharges, J. Phys. Oceanogr., 27, 1386 – 1401. Yankovsky, A., B. Hickey, and A. M¨ unchow (2001), Impact of variable inflow on the dynamics of a coastal buoyant plume, J. Geophys. Res., 106 (C9), doi:0.1029/2001JC000792.  109  Chapter 4  Daily to interannual variability of chlorophyll-a biomass in the Fraser River plume3 4.1  Introduction  The coastal ocean is a very dynamic system. As such it is often undersampled, with good resolution in time traded off against poor spatial resolution (i. e. moorings) or inversely, good resolution in space for time (i. e. CTD transects). If both are achieved, then longer-term temporal coverage may be poor. One such contributor to the high degree of variability is river plumes. A river plume may undergo forcing on tidal to annual time-scales by tides, wind, and river flow [Garvine, 1975]. Growth conditions for phytoplankton are modified by the tidal and riverine inputs of buoyancy, nutrients, and sediment. These inputs have competing attributes with respect to primary productivity. Fresh water provides a stratified water column and sometimes nutrients, but suspended sediments reduce light. Nutrients in the plume can be enhanced or diluted relative to the coastal water. For example, in the Mississippi River plume, nutrient concentrations are higher than in the Gulf of Mexico [e. g. Lohrenz et al., 1999], while in the Fraser river plume, nutrients can be higher or lower than in the Strait of Georgia depending on 3 A version of this chapter will be submitted for publication. Halverson, M., and R. Pawlowicz (2009), Daily to interannual variability in chlorophyll-a biomass from an instrumented ferry: Influence of the Fraser River plume.  110  Chapter 4. Phytoplankton biomass and the Fraser River plume the season. In the southern/central Strait of Georgia, flow from the strongly seasonal Fraser river forms a large buoyant plume which covers up to 1400 km2 (Sec. 2.2.3). It introduces a high degree of spatial and temporal variability to the surface water hydrography [Sec. 2.2.2; Royer and Emery, 1982]. Plume salinity and surface area fluctuate over time-scales from less than a day to a year (Chapter 2). Interannual changes in summertime plume salinity are evident in response to interannual variations in the freshet discharge (Fig. 2.2). Biological processes in the vicinity of the Fraser River plume are affected on the same temporal and spatial scales as physical processes [Parsons, 1969; Stockner et al., 1979; Harrison et al., 1991]. Some of the short time-scale factors, such as tides, wind, and rapid changes in river flow, have been linked to changes in primary productivity in the plume [Yin et al., 1995a,b,c]. Aspects of the annual cycle have been reasonably well characterized [Parsons, 1969; Stockner et al., 1979; Pawlowicz et al., 2009b], but the observations in these studies have usually compromised high time resolution for good total coverage. The lack of high time resolution sampling programs operating consecutively over a number of years is a significant gap in identifying the effects of physical variability on primary production and the potential consequences on fluctuations in higher trophic level organisms such as copepods [Bornhold, 2000] and salmon [Beamish et al., 2004]. Such observations are necessary to accurately quantify interannual variability, which is important when, for example, identifying the effects of decadal (or longer) climate change over the scale of a few years. Characterizing a system over a broad range of time-scales while still retaining spatial coverage requires a different approach to sampling than the traditional oceanographic cruise. Ship-of-opportunity systems are one way to overcome the sampling difficulties. Traditionally, these observations were based on open ocean merchant vessels, beginning 80 years ago with the Continuous Plankton Recorder [e. g. Reid et al., 2003]. More recently, passenger ferries have become an effective sampling platform, and have been implemented world-wide, including eastern North America [Buzzelli et al., 111  Chapter 4. Phytoplankton biomass and the Fraser River plume 2003; Balch et al., 2004] and northern Europe [Rantaj¨ arvi et al., 1998; Petersen et al., 2008]. Ferry sampling can provide a very detailed picture of the marine system. Some make multiple daily transects of the same body of water and operate in all seasons. The range of seawater properties which can be measured is quite extensive. For example, recent programs have produced reliable measurements of O2 , pH, optical properties, and nutrients [e. g. Buzzelli et al., 2003; Balch et al., 2004; Ensign and Paerl, 2006; Petersen et al., 2008]. In this chapter, data from an instrumented ferry is used to study the effect of a river plume on near-surface and depth-integrated chlorophyll-a biomass over a wide range of time-scales. The ferry made repeated transects of the Fraser River plume in the Strait of Georgia for nearly four years. The good spatial resolution allows the plume to be identified by salinity, while the high temporal resolution and total coverage can resolve diurnal to interannual changes. In Sec. 4.2, limitations of chlorophyll-a fluorescence as a proxy for algal biomass are addressed, and a relationship between nearsurface concentrations and depth-averaged biomass is developed. In Sec. 4.3, the data set is analyzed to provide a new look at the influence of the Fraser River plume on near-surface and water column biomass. In Sec. 4.4, the differences between plume and SoG water are discussed, and the data are broken down into annual means to discuss interannual variability. The discussion concludes with a more detailed study of variability in spring blooms.  4.2  Methods  In this chapter, algal biomass will be proxied by in situ chlorophyll-a fluorescence. However, the raw fluorometer output must be modified to obtain an accurate measurement because of the inherent limitations of in situ fluorescence measurements. Unless otherwise stated, biomass refers to the concentration of chlorophyll-a per unit volume or unit area, and not carbon biomass. Raw, in situ instrument output will be denoted symbolically by fChla. It has nominal units of µg Chla l−1 . The first step in improving fChla is to correct for fluorescence quenching (Sec. 4.2.1), the result of which 112  Chapter 4. Phytoplankton biomass and the Fraser River plume will be called qfChla. Next, qfChla is compared to extracted chlorophyll-a samples collected from the ferry, denoted by xChla (Sec. 4.2.2). The final product is the best estimate for chlorophyll-a concentration, and is simply denoted by Chla. Lastly, near-surface chlorophyll-a concentration will be converted to depth-integrated chlorophyll-a biomass.  4.2.1  Fluorescence quenching  Because the ferry sails day and night a diurnal signal in fluorescence is expected as cells photoadapt to changes in irradiance by a process known as fluorescence quenching [e.g. M¨ uller et al., 2001]. To quantify this effect in the SoG, a time series of photosynthetically active radiation (PAR) in the plume and in SoG water at 2 m was needed. Because no direct measurements were available, 2 m PAR was estimated using measured shortwave flux from the STRATOGEM project, calculated albedo (a), and estimates of the PAR extinction coefficient (kz ) according to: PAR(z) = (1 − a)PAR(0) exp [−kz (z)z]  (4.1)  The first step was to use vertical profiles to regress kz with either salinity (for points in the plume) or chlorophyll-a (in SoG water). With the regression, ferry salinity and chlorophyll-a can be transformed into a kz appropriate for points in the plume and in SoG water. The regressions were developed from vertical profiles at S2-3 and S4-1 (Fig. 1.1). At S2-3, suspended sediment originating from the Fraser River is the dominant factor in light attenuation, and it is correlated with salinity (Sec. 1.4.3). The PAR extinction coefficient at 1 and 2 m was estimated by taking consecutive depth differences of log(PAR), and then regressed with salinity at the same depths, (n = 95, r 2 = 0.31). The regression is applied to the daily-binned ferry salinity, and combined with albedo and PAR above the water surface to produce a time series of PAR at 2 m. In SoG water (S41), chlorophyll-a is assumed to be the dominant factor for light attenuation. The PAR extinction coefficient was computed by fitting PAR profiles to an exponential decay model, and then regressed with chlorophyll-a fluorescence 113  Chapter 4. Phytoplankton biomass and the Fraser River plume at 2 m (n = 45, r 2 = 0.58). The regression was applied to ferry chlorophyll-a to form a high resolution time series of the extinction coefficient. The resulting time series of plume and SoG water kz are shown in Fig. 4.1, along with a time series of measured incident shortwave irradiation (in molar units of µE m−2 s−1 , see Sec. 1.4.4) for reference. The mean plume extinction coefficient is 1.1 m−1 , with variations ranging from 0.5 – 2.0 m−1 . It is highest during the Fraser River freshet, when the lowest salinities are observed. Seasonal trends are more evident in the low-pass filtered (30day Hamming window) time series, which varies from about 0.6 – 1.8 m−1 . In SoG water, the mean extinction coefficient is 0.27 m−1 , with variations between 0.2 and 0.8 m−1 . It is highest during the spring phytoplankton bloom. The difference between the plume and SoG water extinction coefficients causes significant differences in PAR at 2 m. The mean and maximum of PAR in the plume are 90 and 320 µE m−2 s−1 , respectively. In SoG water PAR is substantially higher; the mean and maximum are 470 and 1,020 µE m−2 s−1 , respectively. Fluorescence measurements in the absence of light represent chlorophylla concentrations multiplied by some scale factor. When light is present, the true fluorescence, i.e. that which depends only on the amount of chlorophylla, is reduced by nonphotochemical quenching. The true daytime fluorescence in the absence of quenching can be estimated by linearly interpolating between dawn and dusk measurements. The ratio of the observed daytime fluorescence to the interpolated daytime value is then a measure of quenching effects. Figure 4.2 shows an idealized one day time series of fluorescence to illustrate the method. Before sunrise, observed and true fluorescence are equal, i.e. the observed fluorescence reflects the true biomass. As PAR increases towards midday, the observed fluorescence will decrease relative to the true fluorescence by an amount which increases with PAR. As dusk approaches, the difference between observed and true fluorescence returns to zero. The decline in observed fluorescence may produce the illusion that biomass decreases during midday if the quenching effect is strong. In this example, biomass increased linearly throughout the day to illustrate the linear interpolation performed on the observations. While the diel 114  Chapter 4. Phytoplankton biomass and the Fraser River plume cycle in biomass in the SoG is approximately sinusoidal Pawlowicz et al. [2009a], a linear change over daylight hours appears to be a reasonable approximation. If non-linear biomass trends are important, they are expected to be most apparent when PAR is low and quenching effects are weak. Figure 4.3 shows the ratio of the observed daytime fluorescence to the estimated true daytime fluorescence as a function of PAR at 2 m for 1,081 days of ferry data near S4-1. The ratio decreases with increasing irradiance, which is especially evident by the non-parametrically smoothed LOESS curve [Cleveland, 1979]. The smoothed fit shows that fluorescence per unit chlorophyll-a decreases from 1.3 at low irradiance to 0.57 at 1,100 µE m−2 s−1 . The ratio equals one at 170 µE m−2 s−1 , which is called the threshold irradiance. A ratio >1 is not an effect of quenching. Rather, it may be caused by non-linear growth, which may be noticeable when quenching effects are absent. At S2-3, 51% of the PAR estimates at 2 m exceed the threshold irradiance, while at S4-1 78% of the data exceeds the threshold, indicating that corrections should be made. The form of the correction is based on an empirical function used by Cullen and Lewis [1995] to fit the ratio of observed to true fluorescence as a function of irradiance: f Chla −(E − Et ) = A + (1 − A) exp B C  (4.2)  where f Chla is raw fluorescence in µg l−1 , B is the true chlorophyll-a biomass in µg l−1 , and E is the measured scalar downwelling irradiance. Physically, A is the asymptotic ratio at high irradiance, Et is the threshold irradiance for quenching, and C determines how quickly the ratio drops with increasing irradiance. The best fit coefficients and 1σ bounds for the ferry data are 0.56 ± 0.07, 182 ± 27 µE m−2 s−1 , and 304 ± 139 µE m−2 s−1 for A, Et , C,  respectively.  Quenching corrections to chlorophyll-a biomass were made with Eq. (4.2) when the estimated 2 m PAR was greater than the threshold irradiance. No correction was applied when the irradiance was below Et . When analyzing continuous along-track data, the transects were first divided into regions of 115  Chapter 4. Phytoplankton biomass and the Fraser River plume plume and SoG water according to Sec. 1.4.1, and data within the respective regions were corrected with the appropriate time series of 2 m PAR. CTD profiles of fChla at S2-3 and S4-1 (Fig. 4.11) were also corrected, except that kz was estimated directly from the CTD PAR profiles.  4.2.2  In situ and extracted chlorophyll-a  Bottle samples were taken periodically while the ferry was underway and analyzed for extracted chlorophyll-a (Sec. 1.4.1). Sampling was conducted during the following times (with number of samples and time span): June 2003 (N = 103, 11 days), August 2003 (N = 27, 1 day), spring 2004 (N = 70, 4 weeks), spring 2005 (N = 100, 8 weeks), and June 2005 (N = 30, 2 weeks). Figure 4.4 shows a direct comparison of ferry qfChla to bottle xChla (small dots). While noisy, the LOESS non-parametric curve (solid line) suggests bottle xChla and ferry qfChla match well when below 5 µg l−1 . Bottle xChla begins to vary proportionately less with ferry qfChla above 5 µg l−1 , and eventually peaks at 28 µg l−1 , whereas ferry qfChla reaches 63 µg l−1 . The significant difference between bottle xChla and ferry qfChla is most evident in the high qfChla values measured during the 2004 and 2005 spring blooms. For example, there are 25 qfChla samples above 30 µg Chla l−1 from these periods, but only 2 xChla samples above the same threshold. In spring 2005, 7 samples exceeded 40 µg Chla l−1 , while none of the associated bottle xChla exceeded even 30 µg Chla l−1 . The bottle xChla is consistently lower than ferry qfChla before and after the September 2004 fluorometer re-calibration (Sec. 1.4.1). The bottle sampling results contradict a similar comparison made in the SoG by Pawlowicz et al. [2009b], who found a much simpler relationship between in situ CTD chlorophyll-a and extracted chlorophyll-a. As an independent check of the ferry bottle sampling, ferry qfChla was compared to 2 m CTD chlorophyll-a in a similar fashion to the way in which sampling depth was determined (Sec. 1.4.1). The CTD chlorophyll-a itself was corrected for quenching, and adjusted to extracted samples by subtracting 0.8 µg Chla l−1 [Pawlowicz et al., 2009b] so that the comparison could be  116  Chapter 4. Phytoplankton biomass and the Fraser River plume included in Fig. 4.4. Ferry data within 2 km of S2-3, S3, and S4-1 (Fig. 1.1) were extracted from the track. The resulting three time series were interpolated in time to match the CTD profiles. The relationship between the interpolated ferry qfChla and the CTD Chla is shown along with the bottle sampling results in Fig. 4.4 (open diamonds and broken line). When qfChla > 1 µg Chla l−1 , CTD Chla reads higher by a nearly constant factor (i.e. offset in log space). Because it varies by only a small amount over a large range, a simple scale factor was obtained by a least squares regression to adjust the ferry fChla to the CTD Chla: Chlaf erry = 1.5 qf Chlaf erry − 0.8 µgChla l−1  (4.3)  where the 0.8 factor is based on the comparison of in situ fluorescence to extracted chlorophyll-a in Pawlowicz et al. [2009b]. Summarizing the comparisons, bottle xChla indicates ferry qfChla is too high by as much as a factor of 2, and CTD Chla suggests ferry qfChla is too low by a factor of 1.5. These results are inconsistent. However, the CTD comparison is likely more accurate because the CTD itself was calibrated by more extracted chlorophyll-a samples (969) spread out uniformly over the time series. In addition, the ferry fluorometer is expected to read low because fluorescence decreases with increased flow rate through the instrument [WET Labs, 2007]. Flow rates in the ferry system are at least 6 times faster than the rate used for factory calibration, which could easily account for the 33% reduction in fluorescence. This implies that the bottle sampling results are unreliable, though it is unclear if this was caused by the extraction procedure, or something unique to ferry sampling (e.g. high flow rates, high pressure). A drawback of calibrating ferry qfChla according to Eq. (4.3) is that it produces a few very high readings (up to 94 µg l−1 in spring 2005), which are somewhat larger than might be expected based on the common ob−1 [Mei et al., 2005, servation that 1 µM NO− 3 tends to produce 1 µg Chla l  and references therein]. Surface nitrate in the SoG reaches a maximum of about 30 µM in winter [Pawlowicz et al., 2007], so that the highest observed 117  Chapter 4. Phytoplankton biomass and the Fraser River plume chlorophyll-a concentration should be about 30 µg Chla l−1 . Near-surface flow convergences might increase nutrient supplies in some locations. The calibration is reasonable, however, in the sense that the very large values are not frequent enough to distort annual means, which appear reasonable based on previous estimates (Sec. 4.4.3).  4.2.3  Depth-integrated biomass  A weakness of the ferry is that it samples at a single depth, while integrated water column biomass is a more meaningful quantity for ecosystem studies. However, a depth-integrated biomass may be estimated from surface concentration if a scale factor can be found to relate them: ChlaS2−3 (z)dz = γ ChlaS2−3 (2 m)  (4.4)  ChlaS4−1 (z)dz = δ ChlaS4−1 (2 m)  (4.5)  where the subscripts and the γ and δ scale factors reflect the anticipated difference between plume and SoG water. The subscripts S2-3 and S4-1 indicate the location of the CTD profiles used to represent plume and SoG water, respectively. To estimate the scale factors, chlorophyll-a concentration at the ferry sampling depth, 2 m, is regressed with the 0 – 20 m depth integral to relate near-surface concentration to depth-integrated biomass (Fig. 4.5). The results of the fit, with 95% confidence intervals, are γ = 8.7 ± 1.3 m and  δ = 12.6 ± 2.9 m.  The fact that γ and δ in Eqs. (4.4) and (4.5) are different shows that  the scaling of near-surface to water column biomass varies with position along the track. With only three stations along the ferry track, it was not practical to develop scaling for every point in the transect by interpolation. Thus, for the part of the result section concerned with temporal variability (Sec. 4.3.2), the transect is divided into plume and SoG water according to salinity (Sec. 1.4.1). Near-surface concentration is then scaled to depthintegrated biomass with Eqs. (4.4) and (4.5).  118  Chapter 4. Phytoplankton biomass and the Fraser River plume  4.3  Results  4.3.1  Continuous spatial variations  Salinity and chlorophyll-a Figure 4.6 presents the mean chlorophyll-a and salinity transects computed from 8,502 crossings. The plume is evident as the low salinity feature between the along-strait distances 0 and 43 km (Fig. 4.6). The mean minimum plume salinity is 22, found at kilometer 16, which is the closest point of the transect to the river mouth. This feature will be referred to as the plume core. On both sides of the core, salinity increases at 0.22 km−1 (Sec. 2.2.1). The horizontal salinity gradient of the mean transect is much weaker than those found in individual transects because they can vary in position along the track. Averaging over many tracks smooths the variations. A histogram reveals that chlorophyll-a biomass is roughly log-normally distributed in time (not shown). Thus, log-normal statistics (transformed back to linear space for plotting) are used to characterize the typical transect. The mean log-normal transect, which closely resembles the mode of the untransformed data, is presented in Fig. 4.6. The mean value of the log-normal track is 3.0 µg Chla l−1 , and shows very little structure. The one standard error interval about the log-mean transect is 1.3 µg Chla l−1 in width, which is much larger than the along-track variations. This implies that the along-strait structure is highly variable, and that any randomly chosen transect will not likely resemble the log-normal mean. The standard arithmetic mean is also included for comparison. Like the log-normal transect, it has very little along-track structure in comparison to the standard error interval. The average of the mean transect is 6.4 µg Chla l−1 , which is 3.4 µg Chla l−1 higher than the log-normal transect average. It is higher because of the rare, but very high, values which occur during blooms. Nitrate Data coverage with the ISUS nitrate sensor is much more sparse than salinity and chlorophyll-a because of difficulties keeping the instrument clean 119  Chapter 4. Phytoplankton biomass and the Fraser River plume (Sec. 1.4.1). To minimize potential errors caused by fouling, the ISUS transects presented here were obtained immediately after cleaning the instrument. While perhaps a total of 100 reliable nitrate transects are available over the 4 year ferry time series, they are concentrated into relatively brief deployments during which extra effort was made to keep the instrument clean. Therefore, it was not possible to form a long-term picture of the alongtrack structure of nitrate (analogous to chlorophyll-a in Fig. 4.6) without introducing a strong seasonal bias. Instead, mean transects of nitrate were formed for particular ISUS deployments, creating event-scale pictures for three periods. Figure 4.7 shows transects of chlorophyll-a, salinity, and nitrate from a) winter 2005/06, b) the 2005 freshet, and c) the 2005 spring bloom. In each period, there are many more chlorophyll-a and salinity transects available than nitrate. However, mean chlorophyll-a and salinity transects were constructed with data coincident with the nitrate transects because the goal is to investigate correlations of nitrate with salinity and chlorophyll-a. If a short-lived event (i.e. a bloom or river discharge spike) was missed by the ISUS, it would potentially distort any correlation. In general, however, mean transects of chlorophylla or salinity with all available data over the ISUS deployments resembled those made only with transects having ISUS data. The only exception is spring 2005 when the chlorophyll-a transects were highly variable in a short amount of time. The winter transects are the mean of 9 crossings taken between 6 December 2005 and 15 February 2006. Bottle samples were not obtained in winter, so raw nitrate output from the instrument is used. Although drifts are possible, this does not result in gross errors because fouling in winter is also minimized. The freshet transects are the mean of 23 crossings taken from 13 June 2005 to 26 June 2005. The internal calibration of the ISUS drifted during this period despite regular cleaning, and comparisons to bottle sampling could not correct for it. However, in nearly all of the freshet transects, raw nitrate numbers are spatially constant outside of the plume, increasing over the sampling period from - 1 µM to 5 µM. It is hypothesized that the only spatially constant value possible is 0 µM, because salinity and 120  Chapter 4. Phytoplankton biomass and the Fraser River plume nitrate show little correlation. In other words, phytoplankton have utilized all of the nutrients, otherwise nitrate should vary with salinity. Subtracting the constant value from the entire transect supplants calibrating with bottle samples. The spring bloom transects are composed of 8 crossings from the period 27 February 2005 to 10 March 2005. While many more transects are available over a wider span of the spring bloom, these were specifically chosen during the time of peak biomass. As with the freshet data, the internal calibration drifted, and correcting the ISUS with track by track bottle sampling produced inconsistent results. Therefore, raw instrument output is plotted. In winter (Fig. 4.7a), salinity and nitrate are relatively uniform compared to other seasons, while chlorophyll-a shows some variability. Plume salinity ranges from 24 at the core to 27.5 at the edge. Chlorophyll-a is inversely correlated with the plume, peaking in the core at 1.1 µg Chla l−1 , and decreasing to 0.2 µg Chla l−1 in SoG water. Nitrate, however, co-varies with salinity. It is lowest at the plume core at 20 µM, and highest in SoG water at 22.5 µM. Fluctuations away from the mean are relatively small for salinity and nitrate despite the 3 month span of the data. The along-track standard deviation of nitrate is about 1.5 µM across the transect. Chlorophyll-a is most variable; the standard deviation of the mean chlorophyll-a transect is approximately as large as the mean itself. During the freshet deployment (Fig. 4.7b), plume salinity is substantially lower than in winter because of higher river flow (Fig. 2.2). Salinity increases from 13 in the plume core to 22.5 in SoG water. The relationship between salinity and chlorophyll-a is more complex than in winter. In the plume, chlorophyll-a peaks at 5.5 µg Chla l−1 along the salinity gradient northwest (i.e. at greater along-track distances) of the plume core. The minimum value in the plume, 2.5 µg Chla l−1 , occurs at the core. The minimum value in the transect, 2 µg Chla l−1 , is in SoG water. Nitrate is highest in the plume core at 5.5 µM. Northwest of the plume core, along the salinity gradient, it drops steadily before reaching 0 µM in SoG water (as it must because of the way the ISUS was calibrated in summer). Variations from the mean transect are relatively small for salinity, nitrate, and chlorophyll-a. For example, the 121  Chapter 4. Phytoplankton biomass and the Fraser River plume along-track standard deviation of nitrate ranges from 0.5 µM in SoG water to a maximum of 2 µM in the plume core. During the peak spring bloom transects, plume salinity at the core is 22, while SoG salinity is 26.5 (Fig. 4.7c). The spring bloom shows the greatest variations in chlorophyll-a biomass. Chlorophyll-a is lowest at the eastern edge at 10 µg Chla l−1 . It increases to a maximum of 55 µg Chla l−1 in the plume, and then drops to about 37 µg Chla l−1 at the western edge of the track. Nitrate is highest at the eastern edge at 13 µM. It drops to nearly zero in the plume, coincident with the maximum in chlorophyll-a, and remains below 2 µM for the rest of the transect. The standard deviation of both the nitrate and chlorophyll-a transects are nearly as large as their respective means, while for salinity it is much smaller, varying from 1 – 2. This indicates that, even over this relatively brief period changes are rapid, and a randomly selected transect will probably not resemble the mean. Because variations in salinity are relatively small compared to those in chlorophyll-a and nitrate, the variability in nitrate is likely due to phytoplankton processes. Although large spatial variations appear in both chlorophyll-a and nitrate, recall that the long-term mean chlorophyll-a transect is less variable (Fig. 4.6), so the correlation between the two suggests that a long-term nitrate transect can be more uniform than those in Fig. 4.7.  4.3.2  Plume and SoG water biomass time series  To identify changes in time, the transects will be simplified into time series of plume and SoG water chlorophyll-a. The plume is chosen according to salinity (Sec. 1.4.1). Plume chlorophyll-a is the spatial arithmetic average of the points identified as the plume, while SoG chlorophyll-a is the spatial average of the points in SoG water. Applying the γ and δ factors from Eqs. (4.4) and (4.5) to 2 m chlorophyll-a produces depth-integrated biomass (Fig. 4.8). Depth integrated chlorophyll-a from the profiles are included in the figure for comparison, and also to demonstrate the information lost with coarse time resolution. Two trends are persistent in the whole time series: the co-variance of  122  Chapter 4. Phytoplankton biomass and the Fraser River plume plume and SoG water chlorophyll-a, and the systematically higher chlorophylla in the SoG water arising from Eqs. (4.4) and (4.5). Annual and seasonal means are summarized in Tables 4.1 and 4.2, respectively. Details of the time series will be discussed in terms of the seasons. Winter Phytoplankton chlorophyll-a biomass in December, January, and February is consistently lower than in any other season. Excluding the last two weeks of February 2005 (the only year in the time series when the spring bloom began before March 1; Sec. 4.4.3), the mean water column biomass in the plume is 9.7 ± 1.2 mg Chla m−2 , while the mean SoG water biomass is  11.9 ± 2.3 mg Chla m−2 . Fluctuations away from the winter mean can be up  to 100% of the mean levels (but are not apparent in Fig. 4.8 because of the scale). These fluctuations are higher in SoG water than in plume water. Finally, of the four years, the 05/06 winter has the lowest biomass by a factor  of about two. However, the difference is relatively small compared to the size of the fluctuations, and lies within the uncertainty of the fluorometer recalibration in September 2004 (Sec. 1.4.1). Spring Chlorophyll-a from March 1 to May 1 is highly variable but on the average higher than any other season because of the annual spring bloom. Mean plume depth-integrated biomass is 120 mg Chla m−2 , while the mean SoG water biomass is 150 mg Chla m−2 . While the mean is lower in the plume than in SoG water, the high degree of variability means it is possible for the plume biomass to exceed SoG water biomass for brief periods. The central feature of this period is the annual spring bloom, signified by the rapid accumulation of biomass beginning late February or March. The chlorophyll-a levels reached in this period are the highest found in the year. Following the initial bloom, elevated levels continue as a series of peaks and troughs until late April or early May, when chlorophyll-a briefly drops to very low levels, but then rebounds again (except 2003). The character of 123  Chapter 4. Phytoplankton biomass and the Fraser River plume the spring bloom varies from year to year. For example, the 2005 spring bloom began in the third week of February, the earliest bloom onset in the time series. It also took the least amount of time to reach peak biomass, 850 mg Chla m−2 in SoG water and 600 mg Chla m−2 in the plume. The 2003, 2004, and 2006 blooms began in early March, and reached levels of 250 to 450 mg Chla m−2 in the plume, and 400 to 700 mg Chla m−2 in SoG water. The spring bloom is the dominant annual event, and because of its significant biomass it influences the annual mean. Bloom timing and magnitude has potential implications for higher trophic levels, and will be analyzed in more detail in Sec. 4.4.3. Summer June 1 through August 1 is characterized by irregular spurts of biomass which are typically shorter and lower in magnitude than spring blooms. The mean plume chlorophyll-a biomass for these months is 43 mg Chla m−2 , while the mean SoG water biomass is 69 mg Chla m−2 . In general, summer blooms are larger in SoG water than in the plume. They typically persist for a week, and reach 100 - 200 mg Chla m−2 in SoG water and 50 100 mg Chla m−2 in the plume. An exception was a major bloom in late July and early August 2003, when SoG water chlorophyll-a reached about 450 mg Chla m−2 , or three times larger than that seen in the plume. This particular bloom was also unusual in that it lasted for nearly one month. Interestingly, it was missed entirely by the hydrographic surveys. Fall Chlorophyll-a biomass from September 1 to November 1 is generally lower than in spring and summer but higher than winter. Mean plume chlorophylla biomass is 33 mg Chla m−2 , while the mean SoG water chlorophyll-a biomass is 45 mg Chla m−2 . Blooms still occur, but tend to be smaller than in other seasons, and are generally higher in SoG water than in plume water. The largest fall bloom occurred in 2004, when SoG water and plume biomass 124  Chapter 4. Phytoplankton biomass and the Fraser River plume reached 225 mg Chla m−2 and 150 mg Chla m−2 , respectively, or about double the biomass of other fall blooms. The return to winter levels occurs at about the time the ferry is taken out of service for its annual re-fit.  4.3.3  Seasonal excess specific growth rates  The biomass time series demonstrates that the phasing of significant blooms varies between the individual years. To investigate timing of seasonal blooms, biomass is plotted versus year-day for each of the four years (Fig. 4.9a). The data was track-averaged, converted from 2 m to depth-integrated biomass, and filtered with a 30-day Hamming window to emphasize seasonal trends. The four-year mean shows a prominent biomass peak in late March, although this peak can vary by more than a month depending on the year. It is relatively flat into summer, with a very weak peak in July, which is due, in part, to differences in the phasing of blooms in the individual years. An alternative view is to cast the biomass time series in terms of a specific growth rate to normalize for biomass. A rate can be directly calculated from the ferry data. In this case, an exponential model of growth will be used [e.g. Parsons et al., 1977]: d(Chla) = µChla dt  (4.6)  where µ is defined here as the “excess” specific growth rate. When µ is calculated simply from a biomass time series, it implicitly encapsulates all growth and loss terms (e.g. respiration and grazing). The resulting excess specific growth rates are plotted versus month in Fig. 4.9b. Beginning in January, excess growth is quite small, varying between ±0.03 day−1 . The largest positive excess growth rates are associated  with the February or March spring bloom. Individual spring bloom excess growth rates vary between 0.08 day−1 in 2003 to 0.18 day−1 in 2005. While these rates are large compared to the rest of the year, they are low compared to other estimates of specific growth rates in the SoG [Pawlowicz et al., 2009a] because of the substantial averaging and because the rates calculated here include respiration, exudation, sinking, and grazing. The magnitude 125  Chapter 4. Phytoplankton biomass and the Fraser River plume and phasing of the peak spring bloom rates differ between years. Every spring bloom is followed by a period of negative excess growth ranging from -0.05 day−1 to -0.13 day−1 , signifying the loss of biomass from the surface. In summer, excess growth rates vary between ±0.05 d−1 , which are about half  the magnitude of spring bloom rates. The phasing of summer blooms is not  consistent from year to year, causing the mean specific excess growth rate to remain close to zero. Fall is generally characterized by negative excess growth except for a few notable blooms in 2003 and 2004. Annual growth patterns vary by an amount such that any particular year will deviate significantly from the mean trend. The net effect is to make the 4 year mean excess growth lower in magnitude than the individual years. The 4 year mean curve begins to rise in mid-January and peaks in March at 0.07 day−1 . The differences in bloom timing make the seasonal mean unrepresentative of actual rates, which can be up to 0.18 day−1 . By April, biomass declines, and in May is lost (“negative growth”) at a rate of -0.07 day−1 . The spring bloom, and the subsequent large loss in late spring, constitute the most significant and regular features common to every year. Positive growth rates, though much smaller than spring, again occur in late June. The positive rates become losses of 0.02 day−1 by early August. Into the fall, the mean excess growth rate is slightly negative, though each year independently experienced a short period of positive growth.  4.4 4.4.1  Discussion Fluorescence quenching and chlorophyll-a biomass  Nonphotochemical fluorescence quenching is one strategy used by phytoplankton to dissipate excess light energy which may otherwise damage the cell. It manifests as a reduction of fluorescence per unit chlorophyll with increasing irradiance [M¨ uller et al., 2001], which implies that in situ measurements will underestimate the true chlorophyll-a biomass in the presence of light. Along with production it constitutes a large proportion of diurnal chlorophyll-a fluorescence variability [Binder and DuRand, 2002]. The ef-  126  Chapter 4. Phytoplankton biomass and the Fraser River plume fect has been observed in a range of natural assemblages, from the equator [Cullen and Lewis, 1995] to the subarctic [Stramska and Dickey, 1992; Laney et al., 2005]. As a fundamental physiological process it has received significant attention [e.g. Laney et al., 2005], in part motivated by its effects on algal biomass derived from satellite-measured fluorescence [Cullen and Lewis, 1995]. As a routine step to improve in situ chlorophyll-a biomass estimates, however, it apparently receives little attention. In the case of ferry sampling, fluorescence quenching is likely important in many cases because sample water is drawn from a few meters under the surface. Yet, there is no mention of it in, for example Buzzelli et al. [2003], Balch et al. [2004], and Petersen et al. [2008]. Quenching may be overlooked because it (ideally) requires simultaneous measurements of fluorescence and true chlorophyll-a biomass, but this chapter suggests daytime and nighttime measurements of chlorophyll-a fluorescence can be a relatively simple way to estimate the effect. The present work demonstrates that the instantaneous correction can be large. Without it, near-surface chlorophyll-a biomass would be underestimated by nearly a factor of two at the highest light levels (Fig. 4.3). Averaged over longer periods, the correction is smaller but still important. For example, had the correction been omitted, the summer mean SoG 2 m chlorophyll-a concentration would have been 4.8 µg l−1 instead of 5.5 µg l−1 , a decrease of 13%. The onset of quenching occurs at only 182 µE m−2 s−1 , which is a typical mid-latitude irradiance above the water surface. The empirically determined coefficients describing the exponential fit in Eq. (4.2) differ from those found by Cullen and Lewis [1995]. For an equatorial Pacific assemblage, they determine A, Et , and C, to be 0.39, 199 µE m−2 s−1 , and 542 µE m−2 s−1 , respectively. In the SoG, the coefficients were found to be 0.56, 182 µE m−2 s−1 , and 304 µE m−2 s−1 . The SoG A and Et coefficients are approximately one standard deviation lower than those in Cullen and Lewis [1995], while C is approximately two standard deviations smaller. Thus the differences are only weakly significant except for C, which influences the rate of decrease of fluorescence with increasing PAR. The small difference in the quenching parameterization coefficients be127  Chapter 4. Phytoplankton biomass and the Fraser River plume tween Cullen and Lewis [1995] and this work may have been caused by methodological differences. For example, to account for secular daily growth (or loss), linear changes in biomass were removed. At low irradiance, quenching is weak, and the results are more sensitive to the shape of the daily change in biomass. Actual diel cycling of autotrophic biomass is sinusoidal in the SoG [Pawlowicz et al., 2009a], although during daylight hours the increase is approximately linear. Assuming that the daily growth cycle was sufficiently removed, a difference between the Cullen and Lewis [1995] coefficients and those derived here would suggest that phytoplankton in the SoG are more sensitive to irradiance than an equatorial assemblage. For example, fluorescence relative to biomass decreases by 44% at 1,100 µE m−2 s−1 for the SoG assemblage, whereas in the equatorial assemblage the decrease is 32% for the same PAR.  4.4.2  The plume and phytoplankton biomass  Near-surface The 2 m mean log-normal chlorophyll-a transect (Fig. 4.6) shows that the plume has very little influence when averaged over many transects, despite, on the average, having a larger kz than SoG water. The variability around this transect is much larger than any along-track variations. This does not imply that any randomly selected track will be featureless, only that its shape will differ from the mean. The nearly uniform along-track distribution is caused by averaging many (potentially non-uniform) tracks. Instantaneous differences between plume and SoG water near-surface biomass can be large. An example of how the mean along-track distribution belies the true variability is shown in Fig. 4.10a. Within three week period shown, peak biomass shifted from the plume boundary to SoG water, and then was low across much of the transect. The individual transects making up Fig. 4.10a are plotted in Fig. 4.10b along with the mean transect. In this particular case, the mean transect is slightly higher in SoG water, but most tracks differed significantly in structure. While tracks with high SoG water chlorophyll-a are less common in this example, they are large enough to 128  Chapter 4. Phytoplankton biomass and the Fraser River plume increase the average by a few µg Chla l−1 relative to the plume. The relatively uniform along-track 2 m statistics may be unexpected given the seasonally changing relationship between near-surface nitrate and the plume (Fig. 4.7a). In winter, nitrate and chlorophyll-a both co-vary with salinity. Nitrate is 1 µM lower in the plume than in SoG water, which may in part be caused by dilution of SoG water by the Fraser River, and uptake by phytoplankton. The Fraser River carries ∼14 µM in winter [Drinnan and  Clark , 1980], and SoG water has 22.5 µM. Using the corresponding plume core and SoG salinity of 24 and 27.5, respectively, implies that the plume is about 10% river water and 90% SoG water. This mixture would produce a plume nitrate of 20.5 µM, in agreement with the observations. Chlorophyll-a is higher in the plume than in SoG water by 1 µg Chla l−1 , which can also reduce nitrate. While the relative importance of each process is not clear, the correlation of both nitrate and chlorophyll-a with salinity indicates the distribution of 2 m chlorophyll-a is sensitive to the plume in winter. At the peak of the spring bloom, the nitrate and plume salinity are not closely related (Fig. 4.7c). Instead, nitrate and chlorophyll-a are linearly related such that high levels of one correspond to low values of the other. The effects of uptake by phytoplankton must overwhelm fluxes of nitrate from mixing since nitrate and salinity show little correlation. The freshet transects (Fig. 4.7b) demonstrate a more complex distribution than the winter or spring bloom peak transects, though some caution is necessary when generalizing the results to all of summer because the transects only span two weeks, a time-scale whereby along-track differences can be important (Fig. 4.10). The highest nitrate concentration is found at the plume core (5 µM), indicating that the entrainment of deeper water is the source of nutrients since the river contains about 2 µM in summer [Drinnan and Clark , 1980]. Nitrate at 2 m is generally very low in SoG surface water. Throughout most of the transect, nitrate correlates more strongly with salinity than with chlorophyll-a. Since nitrate decreases with increasing salinity, plume water must be mixing with nutrient-depleted water. Uptake may contribute some of the decrease in nutrients, but high chlorophyll-a correlates with low nitrate only between between 12 and 25 km. Thus there is some ambiguity 129  Chapter 4. Phytoplankton biomass and the Fraser River plume here as to whether mixing or chlorophyll-a is more important for the nitrate distribution. It is also possible that the depth distribution of chlorophyll-a becomes important as the plume and SoG water have different PAR profiles (Fig. 4.11), thus affecting the 2 m results. That the 2 m chlorophyll-a can be as high in the plume as out (Fig. 4.6) is surprising in light of the relatively short 2.2 day fresh water flushing time (Sec. 3.2.4), a factor which contributes to low standing stocks in some river plumes [Lohrenz et al., 1990; DeMaster and Pope, 1996]. At the maximum observed excess growth rate of 0.6 day−1 (Sec. 4.4.3), biomass in the plume can increase by a factor of 3.7 over one fresh water flushing time. However, excess growth rates are generally much lower, meaning the increase in biomass possible during a flushing time will be lower. This implies that the river contributes a substantial amount of chlorophyll-a, and/or that the water mixed into the plume must itself be enhanced in chlorophyll-a. The former possibility is not easily testable without river chlorophyll-a data, but Harrison et al. [1983] note that fresh water species are generally a small part of the phytoplankton community, and summertime vertical profiles of fluorescence 6 km upstream of the river mouth show low levels in the upper fresh layer [Yin et al., 1995a]. If the river chlorophyll-a is low, and local growth cannot account for the high 2 m chlorophyll-a, then entrainment of water already high in chlorophyll-a must be responsible. The 2 m plume chlorophyll-a should contain a strong signal from entrained water because the majority of the plume is SoG water (Fig. 3.1), but is entrainment of high chlorophyll-a water realistic, and is it important if mixing largely takes place in the estuary and near-field (Sec. 3.3.2)? Within the estuary, Yin et al. [1995a] describe a mechanism by which cells from a mid-depth chlorophyll-a maximum are mixed upwards into the fresh upper layer and advected seaward into the plume. There is potential for a recirculation of cells as they sink from the plume, and are brought landward by estuarine circulation. Farther from the river mouth, water column profiles at S2-3 (Fig. 4.11) show that the average chlorophyll-a concentration is only 40% lower at 7 m (taken to be the base of the plume in Chapter 3) than at 2 m, meaning there is a significant reserve of cells to be entrained upwards. 130  Chapter 4. Phytoplankton biomass and the Fraser River plume If a significant fraction of plume chlorophyll-a originates from SoG water, then a more appropriate estimate of the time available for algal growth may be the salt water flushing time, as opposed to the fresh water flushing time used in, for example, Lohrenz et al. [1990] and DeMaster et al. [1996]. A salt water flushing time can be defined as Vsw /(we A), where Vsw is the volume of SoG water in the plume (i.e. the total volume multiplied by the fraction occupied by SoG water), and we A is the entrainment flux of SoG water into the plume (Eq. 3.10). With this definition, the mean salt water flushing time is 1.8 days, with variations from 1.4 to 2.4 days (not shown). This is only 10 hours less than the fresh water flushing time of 2.2 days (Fig. 3.2). Therefore, at least in the case of the Fraser River plume, the distinction between salt and fresh water flushing times is probably not significant, and either quantity provides a useful estimate of the amount of growing time for algal biomass in the plume. Water column In Sec. 4.2.3, scale factors were obtained from CTD profiles to estimate depth-integrated chlorophyll-a biomass from 2 m chlorophyll-a. This was done to provide a more ecologically meaningful metric, and also to facilitate comparison with previous estimates of algal biomass. The scatter in the scale factors occurs because the depth distribution of chlorophyll-a changes seasonally [Parsons et al., 1970]. However, the difference between the plume and SoG water scale factors was large enough to conclude that the plume and SoG water depth-integrated biomass differ. Because the scale factors were obtained from CTD profiles taken during all seasons, this is expected to be true on approximately annual time-scales, while on seasonal or shorter timescales the details of the chlorophyll-a depth distribution can be important. Unlike 2 m chlorophyll-a, depth-integrated biomass is strongly influenced by the plume on long time-scales. Annual mean biomass is 25 - 62% lower in the plume than in SoG water (Table 4.1). Instantaneous differences between plume and SoG water chlorophyll-a biomass may reach, or even favour the plume by 250 mg m−2 , reflecting the variability noted by Parsons et al.  131  Chapter 4. Phytoplankton biomass and the Fraser River plume [1970]. The substantially lower biomass in the plume compared to SoG water agrees with Stockner et al. [1979], who found that depth-integrated biomass and productivity increase by a factor of two or more over a transect beginning at the Fraser River mouth and ending at the western edge of the SoG. There is a some consensus that the relatively low primary productivity in the Fraser plume relative to SoG water is caused by increased light attenuation [Parsons, 1969; Stockner et al., 1979], though some studies were inconclusive [Harrison et al., 1991]. High sediment loads in other river plumes have been documented to reduce productivity. Lohrenz et al. [1990] and DeMaster and Pope [1996] have found that suspended sediment reduces PAR to growth-limiting levels in the Mississippi and Amazon River plumes, respectively. Primary productivity was not measured in the STRATOGEM project, but reduced light and increased stratification in the plume relative to SoG water appear to keep biomass lower in the plume. Figure 4.11 presents mean profiles of salinity, PAR (normalized to surface), and chlorophyll-a (normalized to profile maximum) at S2-3 and S4-1. The chlorophyll-a maximum is narrower in profile at S2-3 than at S4-1, which causes depth-integrated biomass to be lower. The depth of the subsurface maximum is closer to the surface at S2-3, which explains why 2 m chlorophyll-a is approximately equal in the plume and SoG water (Fig. 4.6). On average, the ferry samples the chlorophyll-a maximum directly in the plume, but samples above the maximum outside the plume.  4.4.3  Interannual variability in chlorophyll-a biomass  Annual plume and SoG water biomass means were computed for both nearsurface concentration and depth-integrated biomass (Table 4.1). The four year mean plume chlorophyll-a biomass is 54.2 mg m−2 , but individual years vary from 42.4 mg m−2 to 70.3 mg m−2 . biomass is  73.8 mg m−2 ,  Mean SoG water chlorophyll-a  but individual years vary from 60.7 mg m−2 to  98.0 mg m−2 . Depth-integrated biomass in SoG water exceeds plume biomass  132  Chapter 4. Phytoplankton biomass and the Fraser River plume in the annual averages, despite roughly equal near-surface concentrations, because of the different scale factors relating 2 m to depth-integrated values Eqs. (4.4) and (4.5). Generally speaking, the plume and SoG water annual means co-vary, though in 2003 a very large summer bloom, which only occurred in SoG water, increased its mean disproportionately. To compare annual chlorophyll-a biomass means with historical measurements in the SoG, the depth-integrated chlorophyll-a biomass values in Table 4.1 are converted into carbon units with a C:Chla ratio of 40 gC(gChla)−1 [Pawlowicz et al., 2009b], and averaged over the four years. Mean plume and SoG water biomass are 2.17 gC m−2 and 2.95 gC m−2 , respectively. The mean SoG water value is 20% larger than Pawlowicz et al. [2009b], who find a strait-wide average of 2.5 gC m−2 from the STRATOGEM hydrographic data. It is more than 70% larger than earlier estimates of 1.7 gC m−2 and 1.8 gC m−2 found by Parsons et al. [1970] and Stockner et al. [1979], respectively. The ferry-based estimate is considered consistent with Pawlowicz et al. [2009b], based on the estimated 15% uncertainty in the fluorometer calibration. Discrepancies may also arise from the differences in sampling frequency and region, and the slightly differing time-span of the respective measurements. In Sec. 4.3.1, chlorophyll-a was stated to be approximately log-normally distributed. The effect is that the straight forward time mean of chlorophylla is larger than the log-normal mean because of the high values in the tail of the distribution. These values occur during the spring bloom. This implies that the annual mean biomass values are impacted by significant blooms, with the spring bloom being the most influential. For this reason, and for ecological reasons like the importance of the phasing of the spring bloom with the ontological migration of Neocalanus plumchrus [Yin et al., 1996], the spring blooms will be discussed in some detail. Variability in spring blooms: The rising phase Figure 4.12 presents a time series of 2 m track-averaged chlorophyll-a for the period of February 15 to May 1 for each year. As with excess specific growth  133  Chapter 4. Phytoplankton biomass and the Fraser River plume rates, track averaging is used because chlorophyll-a will be used to estimate specific growth rates. The earliest bloom in the ferry record peaked late in February 2005, while the latest bloom peaked in mid-April 2003, a difference of nearly two months. The maximum track-averaged concentration occurred in 2005 (∼ 80 µg l−1 ), and the lowest most likely occurred in 2003 (∼ 35 µg l−1 ). Bloom duration, defined as the number of days between the beginning of the exponential growth phase and the date when peak biomass was reached, varied from 26 days in 2003 to only 6 days in 2005. The results are summarized in Table 4.3. In a 1-D numerical study, Collins et al. [2009] correlated the cubed root of the mean December to February wind speed cubed to the arrival date of the spring bloom onset. The interpretation was that high wind speeds reduce stratification, which in turn reduces the average light experienced by phytoplankton compared to when winds are light. Variations of incident irradiance, proxied by cloud cover, are approximately half as important as wind with respect to bloom onset, while variations in fresh water flow from the Fraser River had essentially no impact. Wind continues to act on the bloom after it is underway. For example, periodic short-lived wind events (∼days) can disrupt its progression [Yin et al., 1996]. If wind variations persist for longer periods, the average growth rate over the exponential growth phase may be affected. Wind speeds during the exponential growth phase varied amongst the years (Fig. 4.12), and this variation is correlated with the rate of increase of chlorophyll-a over the same period. To quantify this relationship, specific excess growth rates were measured by a linear fit to log-scaled 2 m chlorophyll-a over the exponential growth phase. The bloom start is defined as the beginning of exponential growth in the ferry chlorophyll-a signal and the bloom end is the time when maximum biomass is attained. The highest excess growth rate occurred in 2005 at 0.59 day−1 , while the lowest occurred in 2003 at 0.09 day−1 (Fig. 4.13). The 2004 and 2006 blooms progressed at 0.13 day−1 and 0.19 day−1 , respectively. The strength of wind mixing, quantified as the cubed root of the mean wind speed cubed over the exponential growth period, was smallest in 2005 at 3.4 m s−1 . In all other years, wind mixing 134  Chapter 4. Phytoplankton biomass and the Fraser River plume was more than twice as strong, and growth rates were three to six times smaller. The strength of wind mixing in the years 2003, 2004, and 2006, varied by only a few percent, while the specific excess growth rates were more variable. If the results of Collins et al. [2009] are extrapolated to the present analysis (instead of bloom timing), then factors such as wind and Fraser River discharge should have some effect on the specific excess growth rate. In the case of light, mean PAR above the water surface over the exponential growth periods was 90 µE m−2 s−1 in 2003, and about 120 µE m−2 s−1 in 2004, 2005, and 2005. Thus, some of the variance in the bloom excess specific growth rates can be explained by the mean light levels, because 2003 had the lowest PAR and the lowest growth rate despite experiencing a similar wind mixing strength to that in 2004 and 2006. Another factor which may curb growth is grazing, which is capable of disrupting the progression of a spring bloom [Yin et al., 1996]. More grazers were present in 2003 than in the other years (Fig. 4.14), which will be discussed further in the context of the decay phase of a spring bloom. Variability in spring blooms: The decay phase In each of the four years, the spring bloom crashed at (negative) rates which were some of the highest of the year after reaching peak biomass (Fig. 4.9). Depending on the year, these losses peaked between mid-March and early May: earliest in 2005, and latest in 2003. The maximum negative excess rate occurred in 2003 at -0.13 day−1 , while the others ranged from -0.08 to -0.04 day−1 . In 2004 and 2005, chlorophyll-a showed a secondary crash shortly after it began to accumulate after the primary crash. No secondary crash occurred in 2003 and 2006, which were the years with a relatively late bloom peak. Grazing in spring has traditionally been dominated by Neocalanus plumchrus, which rises to the surface in March/April after overwintering at depth [Harrison et al., 1983]. Neocalanus appears independent of the timing of the spring bloom, and can exert enough grazing pressure to curb growth in the  135  Chapter 4. Phytoplankton biomass and the Fraser River plume bloom’s later stages [Yin et al., 1996]. Net tows from the STRATOGEM hydrographic surveys at S4-1 (Sec. 1.4.2) show that large abundances of herbivorous zooplankton appear in spring (Fig. 4.14). Unlike past field observations in spring [e.g. Parsons, 1969; Harrison et al., 1983; Yin et al., 1996], Neocalanus plumchrus was not necessarily the dominant zooplankton. In 2003, for example, the pteropod Limacina helicina dominated 0 – 100 m zooplankton biomass at 14 gC m−2 , compared to Neocalanus plumchrus at about 2 gC m−2 . L. helicina is an omnivorous zooplankter which appears to have received no attention in the SoG [e.g. Harrison et al., 1983]. It is capable of ingesting chlorophyll-a at rates reaching 5,000 µg(pig)d−1 ind−1 [Hunt et al., 2008], which is comparable to N. plumchrus [Dagg and Wyman, 1983]. A simple estimate shows that L. helicina can make a noticeable impact on the late stages of a spring phytoplankton bloom when its abundance is very high. The highest abundance of L. helicina recorded during the hydrographic surveys was 230 ind m−3 in 2003. If every individual grazed at 5,000 ng(pig)d−1 ind−1 , chlorophyll-a would decline at about 1 µg Chla l−1 d−1 , which equates to a specific rate of 0.1 d−1 for 10 µg Chla l−1 . This is nearly the same as the observed loss in May 2003 of 0.13 d−1 , with the caveat that the excess specific growth rates in Fig. 4.9 are roughly monthly means, and daily values are expected to be higher. Even so, the calculation demonstrates that Limacina helicina could impact the late stages of a bloom when chlorophyll-a levels are relatively high, although it probably could not singlehandedly cause a crash. Other additional factors, such as grazing by microzooplankton, which annually averaged is less important than macrozooplankton grazing [Pawlowicz et al., 2009b], and sinking from nutrient exhaustion [Waite et al., 1992], may also contribute to the observed crash. The bloom crash approximately corresponds with the beginning of the annual Fraser River freshet. The freshet carries with it an increase in suspended sediments [Milliman, 1980], which would reduce light and thus productivity (if nutrients were abundant). The freshet will also enhance stratification, which would tend to enhance productivity. On the other hand, if growth was nutrient limited, the increased fresh water would make it more difficult for wind to mix nitrates to the surface [St. John et al., 1993]. 136  Chapter 4. Phytoplankton biomass and the Fraser River plume Flushing times in the plume do not vary substantially (Fig. 3.2; Sec. 4.4.2), implying time is not a limiting factor. Thus the freshet has effects which would both enhance and reduce productivity, which complicate a simple interpretation of its effects. A close look at the ferry data also shows that in 2005, the initial crash led the freshet by more than a month, indicating that the Fraser River freshet alone cannot cause the crash.  4.5  Conclusion  In this chapter the ferry chlorophyll-a data was characterized over a range of time scales, with an emphasis on the spatial impact of the Fraser plume. Seasonal and interannual variations were quantified, and a focused discussion was presented on factors which may mitigate algal growth rates during the spring bloom. The advantageous sampling qualities of the ferry, including high time and space resolution, as well as good total temporal coverage, make a strong case for continued monitoring with this type of system. The case is easily justified by comparing the time series of depth-integrated biomass from the ferry with the hydrographic surveys (e.g. Fig. 4.8). Measuring algal biomass with ferry sampling has several weaknesses, however. For example, one limitation is that it samples a single depth of a stratified water column. Using concurrent CTD profiles, however, does allow for an estimate of depthintegrated chlorophyll-a from near-surface measurements. Chlorophyll-a fluorescence is a convenient proxy of chlorophyll-a biomass but it is potentially inaccurate. To make it more robust, the effects of quenching were corrected by applying a calibration derived specifically for the SoG. Comparison of ferry qfChla to a calibrated CTD fluorometer effectively standardized ferry qfChla to bottle xChla. In order to convert to carbon units, C:Chla must still be measured. While highly variable in general [de Jonge, 1980, and references therein], Pawlowicz et al. [2009b] have shown that it mostly varies between 25 and 100 gC(gChla)−1 in the Strait of Georgia. The spatial coverage of the ferry has shown that the Fraser River plume 137  Chapter 4. Phytoplankton biomass and the Fraser River plume affects the distribution of chlorophyll-a biomass and nitrate. In terms of near-surface concentrations, chlorophyll-a may show large differences with respect to the plume, but in a long term average sense, chlorophyll-a levels are remarkably insensitive to its presence. Depth-averaged biomass is smaller in the plume than in SoG water, at least in part because of increased light attenuation and the transient nature of the plume. The alongtrack nitrate distribution varies seasonally. At the peak of the spring bloom chlorophyll-a controls the distribution, independent of the plume, but during winter and the freshet the plume alters the distribution of both nitrate and chlorophyll-a. Excess growth rates show substantial variability from year to year, though two seasonal trends are persistent. First, excess growth rates are always positive in late February and March, and range from 0.1 to 0.2 day−1 (heavily filtered). Second, negative growth rates are consistently observed in April and May, which coincides with the presence of relatively large quantities of various herbivorous mesozooplankton species. In summer and fall, blooms are sporadic, meaning growth rates fluctuate and no consistent seasonal trend is clear. The spring bloom is an important event in the annual cycle of algal biomass. Interannual variations in the characteristics of the spring bloom are impacted significantly by wind mixing and grazing. Zooplankton which have been historically neglected, such as Limacina helicina, can dominate grazer biomass and may, at times, constitute the main grazing pressure on algal biomass. Secular climate change may impact spring blooms if winter and spring wind speeds change, or if water characteristics change in such a way as to favour zooplankton species other than the historically dominant community.  138  Chapter 4. Phytoplankton biomass and the Fraser River plume  Table 4.1: Yearly means of depth-integrated and 2 m biomass as inferred from the ferry record.  2003  2004  2005 20061 1  SoG  plume  SoG - plume  plume (% of SoG)  68.6  42.4  26.2  62%  5.49  4.86  0.63  89%  Integrated  67.9  56.4  11.5  83%  2m  5.43  6.46  -1.03  119%  Integrated  98.0  70.3  27.7  72%  2m  8.05  7.84  0.21  97%  Integrated  60.7  47.7  13  79%  2m  4.86  5.46  -0.60  112%  Chla [mg m−2 ] Chla(2 m)  [µg l−1 ]  Possible overestimate because there are no measurements in Nov/Dec.  139  Chapter 4. Phytoplankton biomass and the Fraser River plume  Table 4.2: Seasonal means of depth-integrated and 2 m chlorophyll-a biomass from the ferry record.  Winter1  SoG  plume  SoG - plume  plume (% of SoG)  11.9  9.7  2.2  82%  1.0  1.1  -0.1  110%  Integrated  150  120  30  80%  2m  12.3  13.6  -1.3  111%  Integrated  69  43  26  62%  2m  5.5  4.9  0.6  89%  Integrated  45  33  12  73%  2m  3.6  3.8  -0.2  106%  Chla [mg m−2 ] Chla(2 m)  Spring2 Summer3 Fall4  [µg l−1 ]  1 Dec 1 - Feb 28. Excludes 22 – 28 Feb 2005 because the bloom commenced early. 2 Mar 1 - May 31. Includes 22 – 28 Feb 2005 because the bloom commenced early. 3 Jun 1 - Aug 31 4 Sep 1 - Nov 31  140  Chapter 4. Phytoplankton biomass and the Fraser River plume  Table 4.3: Spring bloom characteristics during the exponential growth phase: 2003 – 2006 3 Start Duration Max 2 m Date max Excess growth 3 Uwind date  [days]  Chla [µg l−1 ]  reached  rate [day−1 ]  [m s−1 ]  20031  Mar 02  26  33  Mar 28  0.09  7.5  20042  Mar 02  19  58  Mar 21  0.13  7.4  2005  Feb 22  6  80  Mar 04  0.59  3.4  2006  Mar 05  23  65  Mar 31  0.19  7.7  1 Missing data during the periods 11 – 17 Mar and 29 Mar – 10 Apr 2 Fluorometer saturated, Max Chla value is a minimum  141  Chapter 4. Phytoplankton biomass and the Fraser River plume  PAR (0m) [µmol m−2s−1]  2000 a)  1500  1000  500  0 Jan  Jul  2004  Jul  05  Jul  06  Jul  Jul  06  Jul  plume SoG water  2 b)  kz [m−1]  1.5  1  0.5  0 Jan  Jul  2004  Jul  05  Figure 4.1: Time series of a) the incident PAR and b) the PAR extinction coefficients for the plume (black curves) and for SoG water (gray curves). The thin lines are made from daily estimates, while the heavy curves are created with a 30-day moving average (Hamming window). The extinction coefficients are based on regressions between salinity and kz from vertical profiles at S2-3, representing the plume, and chlorophyll-a and kz at S4-1, representing SoG water.  142  Chapter 4. Phytoplankton biomass and the Fraser River plume  Figure 4.2: Idealized daily change of biomass. In this example, true biomass increases linearly throughout the day, but the biomass inferred from in situ fluorescence appears to decease relative to the true biomass because of fluorescence quenching. Black dots represent the true fluorescence (i.e. chlorophyll-a biomass), and gray dots are the observed in situ fluorescence. PAR irradiance is shown by the shaded area.  143  Chapter 4. Phytoplankton biomass and the Fraser River plume  2  ratio of observed to estimated fChla  LOESS exponential fit Cullen and Lewis (1995)  1.5  1  0.5  0 0  100  200  300 400 500 600 700 800 noon PAR @ 2 meters [µmol m−2s−1]  900 1000 1100  Figure 4.3: Ratio of ferry daytime fluorescence to interpolations of nighttime fluorescence as a function of daytime PAR at 2 m near S4-1 (small dots and solid black line). Also shown are the best fit parameterization of Eq. 4.2 to the data (thick dashed line) and the Cullen and Lewis [1995] result for an equatorial Pacific assemblage (thin solid line).  144  Chapter 4. Phytoplankton biomass and the Fraser River plume  2  ferry xChla or CTD Chla [µg l−1]  10  CTD Chla CTD Chla (LOESS) ferry bottle xChla ferry bottle xChla (LOESS)  1  10  0  10  −1  10  −1  10  0  1  10  10  2  10  −1  ferry qfChla [µg l ]  Figure 4.4: Comparison of quenching-corrected ferry chlorophyll-a (qfChla) to calibrated CTD chlorophyll-a biomass and ferry bottle extracted chlorophyll-a (xChla). Large dots denote the CTD comparison, while the broken line is the associated LOESS fit. Small dots show the bottle calibration, and the solid line is the LOESS curve. The sudden decrease of CTD chlorophyll-a biomass is from the 0.8 offset, which has not been applied to ferry qfChla in this figure.  145  Chapter 4. Phytoplankton biomass and the Fraser River plume  3  depth−integrated Chla [mg m−2]  10  2  10  1  10  S4−1 S4−1 fit S2−3 0  S2−3 fit  10  −1  10  0  1  10  10  2  10  −1  2 meter Chla [µg l ]  Figure 4.5: Relationship between near-surface chlorophyll-a biomass and 0 20 m depth-integrated chlorophyll-a biomass at S4-1 (filled circles, solid line) and S2-3 (open circles, broken line). The regression lines are were obtained from a least squares fit forced through the origin, and define the scale factors γ and δ in Eqs. 4.4 and 4.5.  146  Chapter 4. Phytoplankton biomass and the Fraser River plume  7  28  6  Sand Heads  5 mean Chla mean Chla (log−normal) salinity  4  24  salinity  Chla [µg l−1]  26  22 3  2  50  40  30 20 along−strait distance [km]  10  0  20  Figure 4.6: Mean salinity transect (solid gray line) and mean chlorophyll-a biomass transects (log-normal: solid black, standard mean: dashed black) for the full ferry data set. The vertical dashed line is closest point of transect to the river mouth. Variability in the along-track chlorophyll-a biomass is quantified by the 68% confidence interval (thin solid) about the log-normal mean.  147  Chapter 4. Phytoplankton biomass and the Fraser River plume  Chla  0.8 0.6 0.4  nitrate  1  24  30  18  25  12  salinity  1.2  20  6  15  0  0  10  6  24  30  18  25  0.2  2  12  salinity  3  nitrate  4  salinity NO 3  Chla  50  40 30 20 along−track distance [km]  10  0  40 30 20 along−track distance [km]  10  0  10  0  b) freshet  5 Chla  a) winter  20  6  15  0  0  10  70  24  30  18  25  1  50  40 30 20  nitrate  Chla  50  12  salinity  60  20 c) spring bloom  6  15  0  10  10 0  50  40 30 20 along−track distance [km]  Figure 4.7: Mean transects of salinity (thick gray), chlorophyll-a (thick black), and nitrate (thin black) in a) winter (6 Dec 2005 - 15 Feb 2006), b) summer (13 - 26 Jun 2005), and c) spring (27 Feb 2005 - 10 Mar 2005). The vertical dashed line is the closest point of the track to Sand Heads. Note that the salinity and nitrate axes limits are the same between panels, while the chlorophyll-a limits varies.  148  Chapter 4. Phytoplankton biomass and the Fraser River plume  800  inside plume a) 2003  outside plume  600  S2−3  400  S4−1  200 0 Dec 2003 Feb Mar 800  Apr May Jun  Jul  Aug Sep Oct  Nov  Apr May Jun  Jul  Aug Sep Oct  Nov  Apr May Jun  Jul  Aug Sep Oct  Nov  Apr May Jun  Jul  Aug Sep Oct  Nov  b) 2004  depth−integrated Chla [mg m−2]  600 400 200 0 Dec 2004 Feb Mar 800  c) 2005  600 400 200 0 Dec 2005 Feb Mar 800  d) 2006  600 400 200 0 Dec 2006 Feb Mar  Figure 4.8: Time series of depth-integrated chlorophyll-a biomass in the plume (black line) and in SoG water (gray line). Depth-integrated biomass was estimated by scaling the ferry near-surface chlorophyll-a to depthintegrated chlorophyll-a biomass (Fig. 4.5). Markers are 0 - 20 m depthintegrated biomass from CTD profiles at S2-3 (black dots) and S4-1 (gray dots).  149  excess growth rate [day−1]  Chla biomass [mgChla m−2]  Chapter 4. Phytoplankton biomass and the Fraser River plume  300 250  2003 a)  2004 2005  200  2006  150  mean  100 50 0  Dec Jan Feb Mar Apr May Jun  Jul  Aug Sep Oct Nov Dec  Jul  Aug Sep Oct Nov Dec  0.2 b)  0.1 0 −0.1 −0.2  Dec Jan Feb Mar Apr May Jun  Figure 4.9: a) Depth-integrated chlorophyll-a biomass and b) excess specific growth rated plotted versus month. The chlorophyll-a biomass time series has been filtered with a 30-day Hamming window. The heavy solid lines are the four-year means.  150  Chapter 4. Phytoplankton biomass and the Fraser River plume  −1  Chla [µg.l ] 10 a)  8  4  6  6  4 8  July 2005  2 10 0 12  14  16  18  20  Chla [µgl−1]  30 b)  20  mean transect  10 0  50  40  30 20 along−track distance [km]  10  0  Figure 4.10: a) Hovm¨oller diagram of 2 m chlorophyll-a concentration during 18 days in July 2005 as a function of time and along-track distance. Each horizontal stripe is a single ferry transect. The western edge of the plume is marked by the black line. b) Line plot of the individual tracks in a) illustrating the variability and mean (thick black).  151  Chapter 4. Phytoplankton biomass and the Fraser River plume  salinity 25  20 0  30  depth [m]  a) S2−3  2  4  4  6  6  8  8  10  10  12  12  14  14  18 20 0 10  30  b) S4−1  2  16  salinity 25  20 0  mean salinity mean normalized Chla mean normalized PAR  1  10 % of surface PAR  2  10  16 18 20 0 10  1  10 % of surface PAR  2  10  Figure 4.11: Mean profiles of a) plume and b) SoG water salinity (thick line, upper linear scale), chlorophyll-a biomass normalized to profile maximum (thin line, linear, no scale), and PAR normalized to 0 m (dashed line, lower logarithmic scale). The chlorophyll-a profiles have been corrected for fluorescence quenching according to 4.2.  152  Chapter 4. Phytoplankton biomass and the Fraser River plume  100 30  20 a) 2003  15  10 3  10  1  5  0.3 Feb 15  25  Mar 7  17  27  Apr 6  16  26  100 30  20 b) 2004  15  3  10  1  5  0.3 Feb 15  25  Mar 7  17  27  Apr 6  16  26  100  20  30 c) 2005  15  wind speed [m s−1]  Chla [µg l−1]  10  10 3  10  1  5  0.3 Feb 15  25  Mar 7  17  27  Apr 6  16  26  100  20  30 d) 2006  15  10 3  10  1  5  0.3 Feb 15  25  Mar 7  17  27  Apr 6  16  26  Figure 4.12: Time series of track-averaged, log-scaled chlorophyll-a for the 2003 – 2006 spring blooms (dots). Each panel covers February 15 until May 1. The solid lines are linear fits to log-transformed biomass over the exponential growth phase of the spring bloom. 24-hour smoothed wind speed in m s−1 is shown by the thin solid line.  153  Chapter 4. Phytoplankton biomass and the Fraser River plume  0.7  8  7 0.5 6  0.4  excess growth rate wind mixing  0.3  5  wind mixing [ms−1]  specific excess growth rate [d−1]  0.6  0.2 4 0.1  0  2003  2004  2005  2006  3  Figure 4.13: Time series of excess growth rate and wind mixing strength measured over the exponential growth period of the spring bloom. Wind mixing is defined as the cubed root of the mean of the wind speed cubed.  154  Chapter 4. Phytoplankton biomass and the Fraser River plume  0.2  18 N. plumchrus M. pacifica L. helicina growth rate  15 12  0.1  9 6  0.05 3 0  0  −0.05  dry weight [g m−2]  specific excess growth rate [day−1]  0.15  −0.1 −0.15 −0.2  Jan  Apr  Jul  Oct  2004  Apr  Jul  Oct  05  Apr  Jul  Figure 4.14: 30-day filtered excess specific growth rate (solid line) from 2003 to mid-2005. The bars show the dry weight in g m−2 of the dominant herbivorous species from vertical 100 m net tows. Gray periods are the months of April and May.  155  Bibliography Balch, W., D. Drapeau, B. Bowler, E. Booth, J. Goes, A. Ashe, and J. Frye (2004), A multi-year record of hydrographic and bio-optical properties in the Gulf of Maine: I. Spatial and temporal variability, Prog. Oceanogr., 63, 57–98, doi:10.1016/j.pocean.2004.09.003. Beamish, R., A. Benson, R. Sweeting, and C. Neville (2004), Regimes and the history of the major fisheries off Canada’s west coast, Prog. Oceanogr., 60, 355–385. Binder, B., and M. DuRand (2002), Diel cycles in surface waters of the equatorial Pacific, Deep-Sea Res. Part II, 49, 2601–2617. Bornhold, E. (2000), Interannual and interdecadal patterns in timing and abundance of phytoplankton and zooplankton in the central Strait of Georgia, B.C.: with special reference to Neocalanus plumchrus, Master’s thesis, University of British Columbia. Buzzelli, C. P., J. Ramus, and H. W. Paerl (2003), Ferry-based monitoring of surface water quality in North Carolina estuaries, Estuaries, 26 (4), 975– 984, doi:10.1007/BF02803356. Cleveland, W. (1979), Robust locally weighted regression and smoothing scatterplots, J. Am. Stat. Assoc., 74, 829–836. Collins, A. K., S. E. Allen, and R. Pawlowicz (2009), The role of wind in determining the timing of the spring bloom in the Strait of Georgia, Can. J. Fish. Aquat. Sci., in press.  156  Bibliography Cullen, J., and M. Lewis (1995), Biological processes and optical measurements near the sea surface: Some issues relevant to remote sensing, J. Geophys. Res., 100 (C7), 767–784. Dagg, M., and K. Wyman (1983), Natural ingestion rates of the copepods Neocalanus plumchrus and N. cristatus calculated from gut contents, Mar. Ecol.-Prog. Ser., 13, 37–46. de Jonge, V. (1980), Fluctuations in the organic carbon to chlorophyll a ratios for estuarine benthic diatom populations, Mar. Ecol.-Prog. Ser., 2, 345–353. DeMaster, D., and R. Pope (1996), Nutrient dynamics in Amazon shelf waters: results from AMASSEDS, Cont. Shelf Res., 16 (3), 263–289. DeMaster, D., W. Smith, D. Nelson, and J. Aller (1996), Biogeochemical processes in Amazon shelf waters: chemical distributions and uptake rates of silicon, carbon, and nitrogen, Cont. Shelf Res., 16 (5/6), 617–643. Drinnan, R., and M. Clark (1980), Water chemistry: 1970 - 1978. Fraser River Estuary study, Tech. rep., Water Quality Working Group, Ministry of Environment, Parliament Buildings, Victoria, BC. Ensign, S., and H. Paerl (2006), Development of an unattended estuarine nutrient monitoring program using ferries as data-collection platforms, Limnol. Oceanogr.-Meth., 4, 399–405. Garvine, R. (1975), The distribution of salinity and temperature in the Connecticut River Estuary, J. Geophys. Res., 80 (9), 1176–1183. Harrison, P., J. Fulton, F. Taylor, and T. Parsons (1983), Review of the biological oceanography of the Strait of Georgia: the pelagic environment, Can. J. Fish. Aquat. Sci., 40, 1064–1094. Harrison, P., P. Clifford, W. Cochlan, K. Yin, M. St. John, P. Thompson, M. Sibbald, and L. Albright (1991), Nutrient and phytoplankton dynamics in the Fraser River plume, Strait of Georgia, British Columbia, Mar. Ecol.Prog. Ser., 70, 291–304. 157  Bibliography Hunt, B., E. Pakhomov, G. Hosie, V. Siegel, P. Ward, and K. Bernard (2008), Pteropods in Southern Ocean ecosystems, Prog. Oceanogr., 78, 193– 221. Laney, S., R. Letelier, and M. Abbot (2005), Parameterizing the natural fluorescence kinetics of Thalassiosira weissflogii, Limnol. Oceanogr., 50 (5), 1499–1510. Lohrenz, S., M. Dagg, and T. Whitledge (1990), Enhanced primary production at the plume/oceanic interface of the Mississippi River, Cont. Shelf Res., 10 (7). Lohrenz, S., G. Fahnenstiel, D. Redalje, G. Lang, M. Dagg, T. Whitledge, and Q. Dortch (1999), Nutrients, irradiance, and mixing as factors regulating primary production in coastal waters impacted by the Mississippi River plume, Cont. Shelf Res., 19, 1113–1141. Mei, Z.-P., L. Legendre, J.-E. Tremblay, L. Miller, Y. Gratton, C. Lovejoy, P. Yager, and M. Gosselin (2005), Carbon to nitrogen (C:N) stoichiometry of the spring-summer phytoplankton bloom in the North Water Polynya (NOW), Deep Sea Research I, 52, 2301–2314. Milliman, J. (1980), Sedimentation in the Fraser River and its estuary, southwestern British Columbia (Canada), Estuar. Coast. Mar. Sci., 10, 609–633. M¨ uller, P., X.-P. Li, and K. Niyogi (2001), Non-photochemical quenching. A response to excess light energy, Plant Phys., 125, 1558–1566. Parsons, T., R. LeBrasseur, and W. Barraclough (1970), Levels of production in the pelagic environment of the Strait of Georgia, British Columbia: a review, J. Fish. Res. Board Can., 27 (7), 1251–1264. Parsons, T., M. Takahashi, and B. Hargrave (1977), Biological Oceanographic Processes, 2nd ed., Pergamon Press.  158  Bibliography Parsons, T. R. (1969), Production studies in the Strait of Georgia. Part I. Primary production under the Fraser River plume, February to May, 1967, J. Exp. Mar. Biol. Ecol., 3, 27–38. Pawlowicz, R., O. Riche, and M. Halverson (2007), The circulation and residence time of the Strait of Georgia using a simple mixing-box approach, Atmos. Ocean, 45 (4), 173–193, doi:10.3137/ao.450401. Pawlowicz, R., S. Allen, and M. Halverson (2009a), In-situ community productivity and respiration estimates derived from continuous ferry-based O2 measurements, Cont. Shelf Res., submitted. Pawlowicz, R., A. Sastri, S. Allen, D. Cassis, O. Riche, M. Halverson, and J. Dower (2009b), Carbon flow and plankton ecology of the Strait of Georgia, British Columbia, in prep. Petersen, W., H. Wehde, H. Krasemann, F. Colijn, and F. Schroeder (2008), FerryBox and MERIS - Assessment of coastal and shelf sea ecosystems by combining in situ and remotely sensed data, Estuarine Coastal Shelf Sci., 77, 296–307. Rantaj¨arvi, E., R. Olsonen, S. H¨allfors, J.-M. Lepp¨anen, and M. Raateoja (1998), Effect of sampling frequency on detection of natural variability in phytoplankton: unattended high-frequency measurements on board ferries in the Baltic Sea, J. Mar. Sci., 55, 697–704. Reid, P., J. Colebrook, J. Matthews, J. Aiken, and C. P. R. Team (2003), The Continuous Plankton Recorder: concepts and history, from Plankton Indicator to undulating recorders, Prog. Oceanogr., 58, 117–173, doi:10. 1016/j.pocean.2003.08.002. Royer, L., and W. Emery (1982), Variations of the Fraser River plume and their relationship to forcing by tide, wind and discharge, Atmos. Ocean, 20, 357–372. St. John, M., S. Marinone, J. Stronach, P. Harrison, J. Fyfe, and R. Beamish (1993), A horizontally resolving physical-biological model of 159  Bibliography nitrate concentration and primary productivity in the Strait of Georgia, Can. J. Fish. Aquat. Sci., 50, 1456–1466. Stockner, J., D. Cliff, and K. Shortreed (1979), Phytoplankton ecology of the Strait of Georgia, British Columbia, J. Fish. Res. Board Can., 36, 657–666. Stramska, M., and T. Dickey (1992), Variability of bio-optical properties of the upper ocean associated with diel cycles in phytoplankton population, J. Geophys. Res., 97 (C11), 17,873–17,887. Waite, A., P. Bienfang, and P. Harrison (1992), Spring bloom sedimentation in a sub-artic ecosystem: 1. Nutrients and sinking, Mar. Biol., 114 (1), 119– 129. WET Labs (2007), WETStar User’s Guide, WET Labs, Inc., Philomath, OR, Revision N. Yin, K., P. Harrison, S. Pond, and R. Beamish (1995a), Entrainment of nitrate in the Fraser River estuary and its biological implications. I. Effects of the salt wedge, Estuar. Coast. Shelf Sci., 40, 505–528. Yin, K., P. Harrison, S. Pond, and R. Beamish (1995b), Entrainment of nitrate in the Fraser River estuary and its biological implications. II. Effects of spring vs. neap tides and river discharge, Estuar. Coast. Shelf Sci., 40, 529–544. Yin, K., P. Harrison, S. Pond, and R. Beamish (1995c), Entrainment of nitrate in the Fraser River estuary and its biological implications. III. Effects of winds, Estuar. Coast. Shelf Sci., 40, 545–558. Yin, K., P. Harrison, R. Goldblatt, and R. Beamish (1996), Spring bloom in the central Strait of Georgia: interactions of river discharge, winds and grazing, Mar. Ecol.-Prog. Ser., 138, 255–263.  160  Chapter 5  Conclusion In this thesis three chapters were presented dealing with the physics and biology of the Fraser River plume. Each chapter took a different perspective. Chapter 2 examined the change of plume salinity and surface area from tidal to annual time-scales, and identified the forcing important on each scale. Chapter 3 quantified the fresh water flushing time, and developed a steady salinity budget which was used to study mixing in the estuary and river plume. Chapter 4 characterized algal biomass over a range of time-scales and quantified the effect of the Fraser plume on its spatial distribution. Relying on a ferry-mounted chlorophyll-a fluorometer presented special problems and understanding these were a significant fraction of the chapter. The general theme uniting the individual chapters of this thesis is the importance of sampling a system like the Fraser plume over a range of timescales. High time resolution observations are important even if longer timescale processes are the primary focus. For example, a large change in river flow has about twice the effect on plume salinity as a large ebb tide. If the goal is to identify seasonal river flow effects, it is important to resolve and average tidal fluctuations. Phytoplankton dynamics vary on the same time-scales as salinity and surface area. Diurnal variations in fluorescence, caused by photoadaptation [Laney et al., 2005] and productivity [Pawlowicz et al., 2009], warrant measurements throughout the daily irradiance cycle. The annual spring bloom, and other blooms, require sampling for up to a year, while the observed interannual variations in, for example, spring bloom characteristics (Table 4.3), suggest multi-year observations are necessary. The purpose of this conclusion is to tie the individual chapters together and suggest directions for future work. Section 5.1 contains a brief discussion 161  Chapter 5. Conclusion highlighting the links between some of the individual parts of the thesis. Section 5.2 will explicitly detail the new contributions to oceanography it has made. Outstanding questions will be addressed in Sec. 5.3, and Sec. 5.4 concludes the chapter with a discussion of some technical issues related to ferry sampling.  5.1  Summary  The sampling characteristics of the ferry have made it possible to study the plume over a wide range of time-scales. Descriptions of this sort are generally absent in studies of the Fraser estuary and plume as well as in other systems. Physical and biological process studies in the Fraser have largely focused on tidal variations, both on ebb/flood cycles [Stronach, 1977; Crean et al., 1988; Geyer and Farmer , 1989; Yin et al., 1995a; MacDonald, 2003] and fortnightly cycles [Harrison et al., 1991; Yin et al., 1995b], and typically only during relatively high river discharge. The same is true for other systems, such as the Columbia River plume and estuary [Jay and Smith, 1990; Orton and Jay, 2005; Lohan and Bruland, 2006; McCabe et al., 2008]. The only paper known by the author to describe both tidal and seasonal scale variability is Garvine [1975] in the Connecticut River estuary and plume. Chapter 2 focused on the relative importance of tides and river discharge in setting the plume salinity. Evidence was presented for the coupling of short and long time-scale processes, specifically for how river discharge modulates the tidal signal in mean plume salinity. On tidal time-scales, large variations of plume salinity are observed during high river discharge, and small variations in salinity are observed at low discharge (Fig. 2.5). The daily-binned time series of salinity (Fig. 2.2) showed large daily and seasonal variations in both plume and SoG water salinity, with no apparent phase lag with river forcing. When tidal and fortnightly salinity variations are removed with filtering, plume salinity decreases quasi-linearly with river discharge (Fig. 2.3). A suitable filter length was taken to be 25 days, which also successfully created a quasi-steady salinity budget (Sec. 3.2.5). Fi162  Chapter 5. Conclusion nally, the plume surface area was quantified, which allowed for an estimate of entrainment velocity, vertical turbulent salt flux, and vertical turbulent diffusivity (Sec. 3.3.2). The relatively short fresh water flushing time of 2.2 days (Sec. 3.2.4) explains how plume salinity may be quite variable. Tidal variations in plume salinity are primarily diurnal (Sec. 2.2.2) despite mixed tides in the SoG. If the flushing time were less than one day the plume would have no consistent signature because fresh water would be mixed away before the next tidal pulse. However, since it is 2.2 days, there is a brief amount of time for fresh water to accumulate and cause a persistent salinity signature. The short flushing time compared to the forcing means that the plume does not integrate the estuary signal, and thus salinity will change noticeably as mixing in the estuary changes. Strait of Georgia (SoG) water salinity is also correlated with Fraser discharge (Fig. 2.3). SoG water, as it is defined in Sec. 1.4.1, resides adjacent to the perimeter of the plume. Since water must be leaving the plume to conserve volume (Sec. 3.2.7), and because the plume fresh water flushing time is only twice the resolution of daily data, SoG salinity should be very nearly in phase with Fraser flow. The SoG salinity fluctuations should be weaker than those in the plume, however, because the plume water fluxed from fronts mixes with SoG water. Filtering salinity for 25 days was found to sufficiently remove daily and fortnightly tidal variations in plume salinity. In terms of a salinity budget driven only by river flow and entrainment, 25 days is a sufficient scale for a quasi-steady balance (Sec. 3.2.5). In this case the river is the only controlling factor, setting the surface area, fresh water volume, and entrainment flux. Tidal processes and wind may have residual effects, but they are absorbed into the entrainment term, we . From the budget equations, the mean horizontal flow speed of water at a radius where the plume boundary is found is relatively slow (Sec. 3.2.7). It is not fast enough to overtake a gravity current, implying that a persistent plume front is not maintained. Because plume fronts are observed in the Fraser plume, they must be formed on shorter time-scales and should be regarded as transient features. 163  Chapter 5. Conclusion A number of physical factors discussed in chapters 2 and 3 were useful for understanding chlorophyll-a biomass in the plume. Chlorophyll-a only has a few days to grow in the plume because of the short flushing times. On the average, 2 m plume chlorophyll-a is equal to SoG water chlorophyll-a (Sec. 4.4.2). This was resolved by noting that the plume is 75% or more SoG water (Fig. 3.1). It also implies that water entrained into the plume must already be high in chlorophyll-a. Light is attenuated faster with depth in the plume than in SoG water, which likely suppresses depth-integrated productivity (Fig. 4.11).  5.2  New contributions to oceanography  Ferry sampling programs are generally billed as monitoring platforms [e. g. Buzzelli et al., 2003; Balch et al., 2004; Petersen et al., 2008], a characterization which perhaps limits the scope of possible science. This thesis, on the other hand, has demonstrated that basic research is possible with a ferry data set. The value in monitoring, however, should not be downplayed. Indeed, the ferry sampling was funded as part of the STRATOGEM project which primarily relied on regular hydrographic monitoring in the Strait of Georgia (Sec. 1.4.2). In the spirit of monitoring, Chapter 4 developed a very basic time series of depth-integrated chlorophyll-a biomass. This is the first data set in the SoG to sample scales shorter than a day while still resolving the plume. Past work has acknowledged a need for data with these characteristics [Harrison et al., 1991; Yin et al., 1997]. The ferry data has provided a rich new view of the variability of autotrophic biomass, and will be of value to researchers interested in secondary and tertiary production in the Strait of Georgia. Developing an accurate chlorophyll-a biomass time series required some care to correct for the limitations of monitoring with in situ fluorescence. The effects of fluorescence quenching on chlorophyll-a biomass were evaluated in the SoG (Sec. 4.2.1). There is some evidence the local phytoplankton assemblage is more sensitive to light than, for example, an equatorial assemblage [Cullen and Lewis, 1995], but the methodological differences preclude 164  Chapter 5. Conclusion a definite statement. The effects of quenching on chlorophyll-a biomass have received a fair amount of attention, primarily for remote sensing purposes [Cullen and Lewis, 1995]. Making the correction to in situ estimates of chlorophyll-a concentration, however, is apparently rare, despite the fact that chlorophyll-a can be underestimated by up to 44% in the case of the SoG (Sec. 4.2.1). Chapters 2 and 3 were primarily focused on how tides and river discharge drive salinity variability in the Fraser River plume. The novelty and timescale coverage of the data permitted a unique approach to this problem. The spatial coverage made it possible to estimate the surface area of the plume, a measurement rare or non-existent in river plume literature but for two exceptions, Garvine [1975] and Walker [1996]. Furthermore, the ferry estimates of surface area were scaled to those made from satellite imagery. Quantifying the surface area was essential to Chapter 3, where the idea of fresh water flushing time as a conserved quantity for river plumes was proposed. Using a simplified salt and volume budget tailored specifically for river plumes provided a framework for estimating entrainment flux, entrainment velocity, salt flux, turbulent salt diffusivity, and Froude number from relatively simple measurements of river discharge and salinity (Chapter 3). The steady salinity approximation (Eq. 3.10) is identical to the Knudsen relations for an estuary. The difference is that the Knudsen relations are generally applied to a physical basin or estuary, whereas in the case of the river plume, the volume was defined as the water within threshold salinity. This threshold was allowed to float as the salinity of the receiving waters changed. Choosing the plume in this way generally seemed to define it according to a sharp gradient, which was expected to be dynamically meaningful.  165  Chapter 5. Conclusion  5.3  Future research  Improvements to surface area measurements The surface area of the plume was an important measurement in this thesis. Both the ferry (Fig. 2.7) and the remote sensing estimates (not shown) have significant unexplained variance. Variability in the ferry time series was likely caused by limitations of the plume-finding algorithm (Sec. 1.4.1), and advection of the plume by wind and tide relative to the ferry track. The along-track structure of salinity can be quite complicated, and multiple strong gradients are evident in some transects. This is again a result of the short flushing time. Improvements to the algorithm would presumably reduce the error in area and expose high frequency variability caused by wind and tides, although the linear nature of the ferry track may be the limiting factor. With respect to the remote sensing measurements, some of the variance could have been caused by aliasing of the tides because the images were taken within the same 2 hour window each day. The tides are important because they modulate the release of water from the river mouth, and because they affect the area of (reflective) mud flats.  Wind effects on the plume: advection and mixing There is some indication that the surface area seen by the ferry depends on wind speed and direction (not shown), but in general wind received little attention in the physics chapters of this thesis. A previous ferry study in the Strait of Georgia identified changes in along-track salinity because of advection of the plume by wind [Royer and Emery, 1982]. The ferry route in that study was further from the river mouth, and the plume was intermittently forced into the ferry transect resulting in a large salinity change. The ferry track used in this thesis is closer to the river mouth, and most of the transects pass through the plume (Fig. 4.6). Wind likely causes measurable mixing, but, at least in summer, much of the plume sampled by the ferry might be considered near-field where inertial mixing dominates wind mixing [Hetland, 2005]. 166  Chapter 5. Conclusion  Fresh water flushing time In Table 3.1, the surface area, depth, and salinity of a number of river plumes was tabulated and used to estimate the fresh water flushing time (Fig. 3.8). The information came from published journal articles, but the values were anecdotal, poorly documented, or, in one case, based on a crude estimate made from a surface salinity map. It was argued that if the thickness and surface area of plumes were measured in a consistent way (i.e. by salinity gradients or isohalines), then the fresh water volume would be dynamically meaningful. Accurately measuring fresh water volume with field data is difficult. Numerical modeling, combined with the isohaline analysis of MacCready et al. [2002], is one avenue which may answer the question of what sets fresh water flushing time. The possibility that fresh water flushing time is invariant among all river plumes (if a consistent dynamical region of the plume was chosen), or at least over a range of discharge for a single plume, suggests a common mechanism for maintaining fresh water over a wide range of forcing.  Seasonality in tidal effects on plume salinity In Chapters 2 and 3 it was postulated that the mouth discharges higher salinity water during low river flow than at high river flow. It was argued that mixing must shift from the estuary to the near-field plume as river discharge increases. Studies of mixing at the salt wedge at high discharge are plentiful [Stronach, 1981; Crean et al., 1988; Geyer and Farmer , 1989; MacDonald and Geyer , 2004; MacDonald and Horner-Devine, 2008], though at low discharge there is only a single data report presenting the salinity field in the estuary [Ages, 1979]. A characterization of the hydrographic structure of the estuary at low river flow would have helped in determining the cause of the seasonality of the tidal effects on the plume. Observations of the surface salinity at the mouth would have been particularly helpful. In addition to hydrography, it was important to have an estimate of flow speed at the mouth at low discharge (Sec. 2.3.2). Again velocity measurements are available at high discharge, but at low discharge it was simply assumed 167  Chapter 5. Conclusion that flow at the river mouth was supercritical.  Plume boundary and chlorophyll-a biomass Discussing the chlorophyll-a biomass in and out of the plume, as was done in Sec. 4.3.2, is potentially an oversimplification because it neglects the observation that biomass can be enhanced at the plume boundary (Fig. 4.10). In a long-term average (Fig. 4.6), however, the along-track distribution is not strongly affected by the plume boundary because it may occupy any portion of the ferry transect. It would be worthwhile to develop a method to account for the position of the boundary when averaging many transects. For example, a coordinate frame relative to the boundary could be defined instead of distance along the track.  5.4  Comments on ferry sampling  Ferry sampling is a relatively uncommon strategy, and the technology and methods behind it are unique to each system. Some systems, particularly those established specifically to monitor ecosystems and seawater properties, measure a wide range of parameters [Balch et al., 2004; Ensign and Paerl, 2006; Petersen et al., 2008]. The complexity of constructing and maintaining a system increases with the number of sensors, because each sensor operates under different principles (e. g. optical vs chemical), and may require differing water pressures, flow rates, and valving. The system used in this thesis was relatively simple. Instrumentation was minimal - only temperature, salinity, and chlorophyll-a fluorescence were routinely measured with oxygen and nitrate sensors used for short periods (Sec. 1.4.1). All of the sensors were chemical-free, so maintenance was generally straightforward, and there was no need to introduce extra plumbing to draw discrete samples for water chemistry. Individual sensors could be added, removed, or replaced in a few hours. This was particularly important when the ferry was removed from its normal service route and placed onto another, or removed for its annual re-fit.  168  Chapter 5. Conclusion  Engineering and science considerations for a ferry system Developing and maintaining the ferry sampling system used in this thesis was largely by trial-and-error with little guidance from previous efforts. Here some of the lessons learned are documented so that future programs can benefit and avoid time-consuming and potentially costly mistakes. Ferry data was logged onto a PC located on board the vessel, and was not relayed onshore. A telemetry system was not implemented because the data was downloaded during the biweekly maintenance trips. The pitfall of this approach was that if an instrument failed then data was lost until the next regular service. A real-time data transfer would have made it possible to quickly discover and fix instrument problems. Another benefit is that a targeted hydrographic survey could be arranged in response to, for example, the onset of a spring bloom. Sampling in early morning and late night turned out to be very important. Fluorescence quenching effects were quantified purely with ferry in situ fluorescence, under the assumption that nighttime fluorescence represented the true biomass (Fig. 4.3). With dissolved oxygen measurements, nighttime data allows an estimate of the total respiration rate [Pawlowicz et al., 2009]. From this, other important growth parameters controlling the flow of organic carbon have been quantified over diel to annual scales. It was important to know the precise ferry sampling depth (Sec. 1.4.1), because both the plume and SoG water are stratified throughout the year. The stratification causes strong property gradients in the top few meters of the water column (Fig. 4.11). In some regions, such as the Baltic sea, surface water stratification is weak or non-existent, so knowing the sampling depth is not critical [Rantaj¨ arvi et al., 1998]. The fresh water fraction (Sec. 3.2.2), and the scale factor converting surface to depth-integrated biomass (Fig. 4.5) made use of the sampling depth. It became apparent when comparing ferry and CTD profiles that the ferry was sampling nearer to the surface than implied by the depth of the water intake. Hinatsu et al. [2004] have found the same result for an instrumented ferry by simulating the hydrodynamics around the ship hull.  169  Chapter 5. Conclusion Multiple passenger ferries on the Horseshoe Bay – Departure Bay transect (essentially Pt. Atkinson to Duke Point in Fig. 1.1) in the Strait of Georgia were instrumented from March 2003 until June 2005. Three different ferries were retrofit for sampling because the vessels were occasionally switched to other routes. They were the M.V. Queens of Alberni, Cowichan, and Coquitlam. Data from all of these vessels, however, was unusable because of fouling within the system. On these vessels, the outlet from the sensors re-entered the cooling water system downstream of the ship’s engines. Occasionally water temperatures would reach 50◦ C, and fouling was quite severe. The excessive fouling and warm water was attributed to occasional flow reversals such that warm water, originating downstream of the engines, would flow back into the sensors. The plumbing configuration on the M.V. Queen of New Westminster was favourable because fouling was much less of a problem. On this vessel water was discharged directly overboard after it passed through the sensors instead of reconnecting with the engine cooling water. Thus there was no tepid water available to enter the system. This configuration appeared to maintain the correct pressure gradient so that water flow was unidirectional. The route serviced by this vessel required it to sail for up to 20 hours per day, longer than the other vessels on the Horseshoe Bay – Departure Bay route. Consequently the engine cooling pumps were run longer, which kept water flowing through the instruments for a larger fraction of the day. Fouling was primarily due to a unknown, possibly organic, compound thought to contain iron, which would slowly coat the interior of all components in the sampling system. The two optical sensors were affected most. The signal from the WETStar chlorophyll-a fluorometer would gradually decay by a significant amount within about a week after cleaning. Data from the Satlantic ISUS, however, was noticeably affected after a few hours and unusable in less than a day. Biological fouling was observed in spring and summer. Hydrochloric and oxalic acid were both effective in removing the organic iron residue and the biological fouling. A small number of barnacles settled inside the small flow-through box housing the Aanderaa oxygen sensor, presumably because flow speeds were lowest there. 170  Chapter 5. Conclusion Flow rates were much higher (∼10 L min−1 ) than that recommended for the WETStar chlorophyll-a fluorometer [WET Labs, 2007], which has a flowdependent response. A calibration curve is provided by the manufacturer, but flow rates on the ferry were nearly 10 times larger, well beyond the calibration curve. The flow rate was not precisely recorded, though when checked during service trips it appeared to vary by as much as a factor of two. Monitoring the flow rate would have made it possible to correct the fluorescence, though doing so would require the manufacturer to extend their calibration to higher flow rates.  Instrumentation Nitrate measurements with the ISUS were problematic. The ISUS nitrate sensor was very sensitive to fouling, and the data was unreliable within hours of cleaning the instrument. As such it was impossible to amass a time series with the same time-scale characteristics as the other instruments. Running the ISUS long-term would require cleaning the instrument roughly every transect with oxalic or hydrochloric acid. With this in mind the ISUS was deployed for relatively brief periods (2 – 4 weeks) throughout the ferry program. Maintenance trips were made 2 or 3 times weekly during these periods. The 2004, 2005, and 2006 spring blooms all have a series of reliable nitrate transects. Other deployments include a two week period in June 2005, and weekly transects in winter 05/06. Some use was made of these deployments (Sec. 4.3.1), but a detailed analysis of the entire ISUS data set did not fit the theme of this thesis. As an example of the possible science, frequent transects were taken during the 2005 Fraser River freshet to investigate whether fortnightly tidal cycling in the estuary affected nitrate in the plume, as suggested by Yin et al. [1995b]. An Aanderaa optode dissolved oxygen sensor was added to the ferry instrument suite in January 2006. Together with air-sea gas transfer, dissolved oxygen has been used to study net community production from the ferry [Pawlowicz et al., 2009]. While the specific excess growth rates calculated and discussed in Sec. 4.3.3 offer insights into productivity and mortal-  171  Chapter 5. Conclusion ity, their relative importance could only be hypothesized based on studies which quantify certain rate parameters. In other words dissolved oxygen provides a much more complete picture of autotrophic production. Finally, a flow-through transmissometer to measure visible light attenuation would have been valuable. For example, in Sec. 4.2.1, a time series of PAR at 2 m was required to evaluate fluorescence quenching effects. One ingredient needed to estimate it was a visible light extinction coefficient. While it was possible to make an estimate from the vertical profiles, the process required a number of assumptions. Measuring the light attenuation directly would have also opened more research avenues in, for example, sediment dispersion by the plume or optics and remote sensing.  172  Bibliography Ages, A. (1979), The salinity intrusion in the Fraser River: Salinity, temperature, and current observations, 1976, 1977, Pacific Marine Science Report 79-14, Institute of Ocean Sciences, Patricia Bay, Sidney, B.C. Balch, W., D. Drapeau, B. Bowler, E. Booth, J. Goes, A. Ashe, and J. Frye (2004), A multi-year record of hydrographic and bio-optical properties in the Gulf of Maine: I. Spatial and temporal variability, Prog. Oceanogr., 63, 57–98, doi:10.1016/j.pocean.2004.09.003. Buzzelli, C. P., J. Ramus, and H. W. Paerl (2003), Ferry-based monitoring of surface water quality in North Carolina estuaries, Estuaries, 26 (4), 975– 984, doi:10.1007/BF02803356. Crean, P., T. Murty, and J. Stronach (1988), Mathematical Modelling of Tides and Estuarine Circulation: The Coastal Seas of Southern British Columbia and Washington State, no. 30 in Lecture Notes on Coastal and Estuarine Studies, Springer-Verlag, New York. Cullen, J., and M. Lewis (1995), Biological processes and optical measurements near the sea surface: Some issues relevant to remote sensing, J. Geophys. Res., 100 (C7), 767–784. Ensign, S., and H. Paerl (2006), Development of an unattended estuarine nutrient monitoring program using ferries as data-collection platforms, Limnol. Oceanogr.-Meth., 4, 399–405. Garvine, R. (1975), The distribution of salinity and temperature in the Connecticut River Estuary, J. Geophys. Res., 80 (9), 1176–1183.  173  Bibliography Geyer, W., and D. Farmer (1989), Tide-induced variation of the dynamics of a salt wedge estuary, J. Phys. Oceanogr., 19, 1060–1072. Harrison, P., P. Clifford, W. Cochlan, K. Yin, M. St. John, P. Thompson, M. Sibbald, and L. Albright (1991), Nutrient and phytoplankton dynamics in the Fraser River plume, Strait of Georgia, British Columbia, Mar. Ecol.Prog. Ser., 70, 291–304. Hetland, R. (2005), Relating river plume structure to vertical mixing, J. Phys. Oceanogr., 35 (9), 1667–1688. Hinatsu, M., Y. Tsukada, H. Tomita, and A. Harashima (2004), Study on estimation of original location of water sampled through inlet set on volunteer observing ship, J. Adv. Mar. Sci Tech. Soc., 9, 37–46. Jay, D., and J. Smith (1990), Circulation, density distribution and neapspring transitions in the Columbia River Estuary, Prog. Oceanogr., 25, 81–112. Laney, S., R. Letelier, and M. Abbot (2005), Parameterizing the natural fluorescence kinetics of Thalassiosira weissflogii, Limnol. Oceanogr., 50 (5), 1499–1510. Lohan, M., and K. Bruland (2006), Importance of vertical mixing for additional sources of nitrate and iron to surface waters of the Columbia River plume: Implications for biology, Mar. Chem., 98, 260–273, doi: 10.1016/j.marchem.2005.10.003. MacCready, P., R. Hetland, and W. Geyer (2002), Long-term isohaline salt balance in an estuary, Cont. Shelf Res., 22, 1591–1601. MacDonald, D. (2003), Mixing processes and hydraulic control in a highly stratified estuary, Ph.D. thesis, Massachusetts Institute of Technology. MacDonald, D., and W. Geyer (2004), Turbulent energy production and entrainment at a highly stratified estuarine front, J. Geophys. Res., 109, C05004, doi:10.1029/2003JC002094. 174  Bibliography MacDonald, D., and A. Horner-Devine (2008), Temporal and spatial variability of vertical salt flux in a highly stratified estuary, J. Geophys. Res., 113, C090220, doi:10.1029/2007JC004620. McCabe, R., B. Hickey, and P. MacCready (2008), Observational estimates of entrainment and vertial salt flux in the interior of a spreading river plume, J. Geophys. Res., 113, C08027, doi:10.1029/2007JC004361. Orton, P., and D. Jay (2005), Observations at the tidal plume front of a high-volume river outflow, Geophys. Res. Lett., 32, L11605, doi:10.1029/ 2005GL022372. Pawlowicz, R., S. Allen, and M. Halverson (2009), In-situ community productivity and respiration estimates derived from continuous ferry-based O2 measurements, Cont. Shelf Res., submitted. Petersen, W., H. Wehde, H. Krasemann, F. Colijn, and F. Schroeder (2008), FerryBox and MERIS - Assessment of coastal and shelf sea ecosystems by combining in situ and remotely sensed data, Estuarine Coastal Shelf Sci., 77, 296–307. Rantaj¨arvi, E., R. Olsonen, S. H¨allfors, J.-M. Lepp¨anen, and M. Raateoja (1998), Effect of sampling frequency on detection of natural variability in phytoplankton: unattended high-frequency measurements on board ferries in the Baltic Sea, J. Mar. Sci., 55, 697–704. Royer, L., and W. Emery (1982), Variations of the Fraser River plume and their relationship to forcing by tide, wind and discharge, Atmos. Ocean, 20, 357–372. Stronach, J. (1977), Observational and modelling studies of the Fraser River plume, Ph.D. thesis, University of British Columbia, 221 pp. Stronach, J. (1981), The Fraser River plume, Strait of Georgia, Ocean Management, 6, 201–221. Walker, N. (1996), Satellite assessment of Mississippi River plume variability: causes and predictability, Remote Sens. Environ., 58, 21–35. 175  Bibliography WET Labs (2007), WETStar User’s Guide, WET Labs, Inc., Philomath, OR, Revision N. Yin, K., P. Harrison, S. Pond, and R. Beamish (1995a), Entrainment of nitrate in the Fraser River estuary and its biological implications. I. Effects of the salt wedge, Estuar. Coast. Shelf Sci., 40, 505–528. Yin, K., P. Harrison, S. Pond, and R. Beamish (1995b), Entrainment of nitrate in the Fraser River estuary and its biological implications. II. Effects of spring vs. neap tides and river discharge, Estuar. Coast. Shelf Sci., 40, 529–544. Yin, K., P. Harrison, , R. Goldblatt, M. St. John, and R. Beamish (1997), Factors controlling the timing of the spring bloom in the Strait of Georgia estuary, British Columbia, Canada, Can. J. Fish. Aquat. Sci., 54, 1985– 1995.  176  Appendix A  Fraser discharge at the mouth Most references to the Fraser River flow refer to the volume discharge measured at the Water Survey of Canada’s Hope, BC, gauge station (ID #08MF005). It lies 120 km upstream of the mouth and is the nearest active station to the mouth that does not experience tidal fluctuations. The closest station to the river mouth with discharge data is Port Mann, which is 35 km upstream of the mouth and downstream of all major tributaries. As discussed in Sec. 1.3, the river discharge at Port Mann can be 20 to 100% higher than at Hope. Here we describe a method to estimate a time series of daily river discharge at Port Mann. Strictly speaking, discharge is not measured at Port Mann, though daily data from a model run by the Water Survey of Canada is available for limited times. Model output at Port Mann is not available for the years 2003 - 2006. To estimate the river discharge at Port Mann for the years 2003 - 2006, we developed a relationship between discharge at Port Mann and Hope from historical data and then apply the results to the Hope discharge from 2003 2006. To account for the contribution of ungauged small rivers, a regression was used to scale the Chehalis River (#08MG001) to represent the sum of all the small drainage rivers and creeks that join the Fraser downstream of Hope (the Chehalis is a relatively small river with a gross drainage area of 383 km2 ). The Fraser flow at Hope and the Harrison River flow were subtracted from the Fraser flow at Port Mann (the Harrison River, #08MG013, is the largest tributary of the Fraser downstream of Hope). This quantity  177  Appendix A. Fraser discharge at the mouth represents the amount of water which needs to be added by small, ungauged rivers. It was then regressed with the Chehalis River flow. Thus, the flow at Port Mann would be represented by: QP ortM ann = QHope + QHarrison + a + bQChehalis  (A.1)  where Q represents volume discharge and a and b were determined by a least squares regression to be 112 and 7.71, respectively. A ten year span, from 1983 to 1993, of daily discharge was used in the analysis. The Chehalis time series was lagged by one day, reducing the standard deviation of the residuals by 10% compared to the original fit. The result of the fit is a straight line with about 500 m3 s−1 of scatter, while only a small fraction of points miss by a larger margin.  178  Appendix B  MODIS plume area and ferry plume area A ferry salinity transect through the plume provides a measure of its horizontal extent. This was exploited in Chapters 2 and 3 by squaring it to provide a measure of the plume surface area. Instead of applying a geometrical correction to the squared length to compute an area (i.e. π/2 for a semi-circle), the squared length was compared directly to measurements of surface area from MODIS imagery. In this way, a geometric factor was derived without making any assumptions about the plume geometry. To derive a correction factor from the MODIS imagery, the daily-binned ferry time series of squared length was smoothed with a 25 day Hamming window to remove the high degree of unexplained variance in the 8 - 15 day band (Sec. 2.3.2). Figure B.1 shows the direct comparison between the MODIS area estimate and the ferry squared length. The regression used to adjust the ferry squared length to the MODIS area was based on the first eigenvector of a singular value decomposition (SVD) of the area anomalies. An SVD analysis was chosen because, unlike a simple least squares solution, SVD allows for variance in both the MODIS surface area and the ferry squared length. The variance in the ferry squared length has been discussed in Sec. 2.3.2, while the variance in the MODIS surface area has been described in Sec. 5.3. The resulting regression, including the boot-strapped 95% confidence interval is: Aplume = (−730 ± 710) + (1.4 ± 0.7)L2f erry  (B.1)  where Aplume has units of km2 , and Lf erry has units of km. The first eigen179  Appendix B. MODIS plume area and ferry plume area vector accounts for 74% of the variance.  180  Appendix B. MODIS plume area and ferry plume area  1800 1600  MODIS surface area [km2]  1400 1200 1000 800 600 400 200 0 0  200  400  600 800 1000 1200 1400 ferry plume length squared [km2]  1600  1800  Figure B.1: Direct comparison between the Fraser River plume surface area as measured by the satellite versus the ferry measured squared length. The straight line fit is the first eigenvector from a singular value decomposition, and explains 74% of the variance.  181  

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