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Submarine Groundwater Discharge and Nutrient Loading to Feather Sound, Old Tampa Bay, Florida


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SUBMARINE GROUNDWATER DISCHARG E AND NUTRIENT LOADING TO FEATHER SOUND, OLD TAMPA BAY, FLORIDA By ERIC J. DAVIS A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2004

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Copyright 2004 by Eric J. Davis

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ACKNOWLEDGMENTS I would like to thank the United States Geological Survey for funding my research. I would also like to give particular thanks to my advisor, Dr. Jonathan Martin, for his guidance throughout my research. In addition, I would like to thank my committee members, Dr. Elizabeth Screaton and Dr. Michael Annable, for their assistance and suggestions towards my thesis. I would like to thank Dr. Peter Swarzenski, with the USGS, for his assistance in the field. I would also like to thank Dr. Dan Yobbi, USGS, for lending me valuable reports and maps. I would like to thank Dr. Jason Curtis for his assistance in the stable isotope lab. I would like to thank Howie Scher for his assistance with my radiogenic isotopes. I would like to thank William Kenny for assisting with nutrient analyses and for making his lab available to me. I would like to thank Dr. John Jaeger for his assistance with the Geotek Logger, and for assistance with GIS applications. I would also like to thank George Kamenov for his assistance in the Clean Lab, help with graphic imaging, and for discussions regarding oxygen and strontium isotopes. I would like to thank Jehangir Bhadha, my predecessor and office mate, for his guidance and allowing me to use his thesis as a model. I would like to thank Mike Hillesheim for assistance with graphic imaging. I would like to thank the faculty, staff, and all of my colleagues here at the Department of Geological Sciences for their help and support throughout my time in Gainesville. Finally, I would like to thank my mom for her patience and for supporting me for the past 3 years. iii

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TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iii LIST OF TABLES............................................................................................................vii LIST OF FIGURES.........................................................................................................viii ABSTRACT.........................................................................................................................x CHAPTER 1 INTRODUCTION........................................................................................................1 1.1 Statement of Problem.............................................................................................1 1.2 The Significance of Estuaries.................................................................................4 1.3 Eutrophication, The Nutrient Budget, and Nutrient Cycles...................................6 1.4 Previous Studies of SGD and Nutrient Loading.....................................................9 1.5 Study Area............................................................................................................14 1.6 Hypotheses............................................................................................................14 1.7 Local Geology and Hydrostratigraphy.................................................................16 1.8 Regional Climate..................................................................................................19 2 METHODS.................................................................................................................22 2.1 Work Plan.............................................................................................................22 2.2 Seepage Meter......................................................................................................23 2.2.1 Background.................................................................................................23 2.2.2 Seepage Meter Construction, Deployment and Seepage Measurements...25 2.3 Water Samples......................................................................................................26 2.3.1 Multisamplers and Pore water....................................................................27 2.3.1.1 Design...............................................................................................27 2.3.1.2 Deployment......................................................................................29 2.3.1.3 Sampling...........................................................................................29 2.3.2 Bay water....................................................................................................30 2.3.3 Analyses.....................................................................................................30 2.4 Sediment Cores.....................................................................................................32 2.4.1 Sampling.....................................................................................................32 2.4.2 Analysis......................................................................................................33 2.5 Groundwater Flow Models...................................................................................34 iv

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2.5.1 Water Budget..............................................................................................34 2.5.2 Flow Net.....................................................................................................36 2.5.3 Chloride Mixing Model..............................................................................38 3 RESULTS...................................................................................................................41 3.1 Physical Analyses.................................................................................................41 3.1.1 Seepage Meters...........................................................................................41 3.1.2 Sediment Cores...........................................................................................42 3.1.2.1 Lithology..........................................................................................42 3.1.2.2 Bulk density and porosity.................................................................46 3.2 Chemical Analyses...............................................................................................47 3.2.1 Tracers........................................................................................................47 3.2.1.1 Chloride and Salinity........................................................................47 3.2.1.2 Isotopes.............................................................................................49 3.2.2 Nutrients.....................................................................................................54 3.2.2.1 Ammonium.......................................................................................57 3.2.2.2 SRP (phosphate)...............................................................................58 3.2.2.3 Nutrient breakdown: TSN, TN, TSP, and TP..................................60 3.3 Groundwater Flow Models...................................................................................61 3.3.1 Water Budget..............................................................................................61 3.3.2 Flow Net.....................................................................................................61 3.3.3 Chloride Mixing Model (CMM)................................................................62 4 DISCUSSION.............................................................................................................64 4.1 Seepage Meters.....................................................................................................64 4.2 Comparison of Measured and Modeled Submarine Groundwater Discharge......67 4.3 Evaluating the Exchange of Bay Water and Pore Water Using Tracers..............68 4.3.1 Chloride, 18O, and Sr................................................................................68 4.3.2 The Chloride Mixing Model.......................................................................74 4.4 Nutrients and an Estimate of Nutrient Flux..........................................................76 4.4.1 Introduction................................................................................................76 4.4.2 Nutrient Loading and Flux.........................................................................78 5 CONCLUSIONS........................................................................................................83 5.1 The Importance of This Study..............................................................................83 5.2 The Conceptual Model.........................................................................................83 5.3 Future Work..........................................................................................................85 APPENDIX A WATER CHEMISTRY DATA..................................................................................87 B NUTRIENT BREAKDOWN: AVERAGE OF ALL LOCATIONS..........................90 v

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C CHLORIDE MIXING MODEL.................................................................................95 D CALCULATIONS FOR DERIVING NUTRIENT FLUX STOICHIOMETRICALLY......................................................................................111 LIST OF REFERENCES.................................................................................................115 BIOGRAPHICAL SKETCH...........................................................................................121 vi

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LIST OF TABLES Table page 1-1 Average historical (1916-2001) monthly, a nnual, and seasonal rainfall data at the St. Petersburg rainfall gauge....................................................................................20 2-1 Estimated precision of various solutes for water samples........................................31 2-2 Components and associated values of hydrologic equation.....................................35 3-1 The average, maximum, minimum, and st andard deviation of salinity, chloride, and other field measurements during the dry season...............................................48 3-2 The average, maximum, minimum, and st andard deviation of salinity, chloride, and other field measurements during the rainy season............................................48 3-3 Surficial a quifer fl ow net calculations. ...................................................................62 3-4 Co m parison of results from various groundwater seepage m easure m ent techniques.................................................................................................................63 4-1 Water column and pore water tracer concentration seasonal differences................73 4-2 Comparison of nutrient fluxes from two techniques. Units are gr/m2/year.............80 4-3 A comparison of water and nutrient flux data from this thesis to previous studies.......................................................................................................................8 1 vii

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LIST OF FIGURES Figure page 1-1 Location of Pinellas County, FL..............................................................................15 1-2 Generalized stratigraphic and hydrog eologic section, Pinellas County...................18 1-3 Conceptual, cross-sectiona l view of saltwater-freshwater relations in the Tampa Bay area, and flow paths of groundwater.................................................................19 1-4 Daily rainfall (cm) recorded at the St. Pete gauging station before and during each sampling event.................................................................................................21 2-1 The Tampa Bay basemap created from individual digital orthophoto quadrangle quarters (DOQQs) downloaded from the LABINS website...................................23 2-2 A depiction of a seepage meter placed in the sediment under the water column.....25 2-3 A satellite image of the st udy site and sampling stations.........................................27 2-4 Design of a multisampler.........................................................................................28 2-5 Digital photograph of vibracore assemb ly taken during the August sampling trip, from the deck of the USGS pontoon boat................................................................33 2-6 The outline of the surface and groundwater divides superimposed onto the Tampa Bay basemap created using GIS software................................................................36 2-7 The Surficial Aquifer flow net. R o m an numerals (I VI) represe n t d i scr e tized transmissivity zones.................................................................................................38 3-1 Submarine groundwater discharge magn itudes from seep meters at various locations within the sampling grid...........................................................................41 3-2 TB-9 core lithology, di gital photograph, and porosit y ...............................................44 3-3 TB-9A core lithology, digital photograph, and porosity..........................................45 3-4 Seasonal water column salinities from TB-1, 9B, 9, 10, 11, 12, 4, 9A....................48 3-5 Chloride concentration versus de pth below sediment-water interface.....................52 viii

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3-6 Water column and pore water Sr concentrations.....................................................53 3-7 18O concentration versus depth..............................................................................56 3-8 Ammonium concentrations versus depth below the sediment-water interface at sampling station TB-9..............................................................................................58 3-9 Phosphate concentrations versus depth at sampling station TB-9...........................59 4-1 A conceptual model showing mixing at the sediment-water interface due to bioturbation, wave action, or tidal set up.................................................................72 ix

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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science SUBMARINE GROUNDWATER DISCHARGE AND NUTRIENT LOADING TO FEATHER SOUND, OLD TAMPA BAY, FLORIDA By Eric J. Davis May 2004 Chair: Jonathan Martin Major Department: Geological Sciences Submarine groundwater discharge (SGD) and associated nutrient fluxes can be important components of the hydrologic and nutrient cycles in estuarine environments. Investigations of physical properties of sediments, chemical composition of the pore water and bay water, and local groundwater flow in and around Feather Sound, Tampa Bay, Florida, suggest that shallow sediments could be an important source of nutrients to the bay. This nutrient flux depends on (i) the rate and origin of groundwater discharge, (ii) the concentration of the nutrients in the discharged water, and (iii) the magnitude and frequency of mixing between the bay water and pore water. Submarine groundwater discharge can originate from continentally derived aquifer water, and thus have a similar chemical composition to local meteoric water or be modified by water-rock reactions as it flows through the aquifers. The meteoric water component of SGD can contribute pollutants and excess nutrients from the mainland. Alternatively, SGD can originate from bay water if mixing occurs with the shallow pore waters. Mixing, or recirculation, x

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of bay water through the shallow sediments is often a significant local source of nutrients because it enhances organic matter remineralization in the pore water, and releases the inorganic nutrient by-products back to the overlying water column. In this study seepage meters yielded an average groundwater discharge rate of ~51 ml/m2/min, while the groundwater flow models indicated groundwater discharge rates from 0.36 to 0.55 ml/m2/min. This difference indicates that the SGD at the study site may contain up to ~98 % recirculated seawater. In addition, Cl concentrations and Sr and oxygen isotope ratios are identical between shallow pore waters and overlying bay water regardless of changes in water column chemistry. Seasonal pore water concentration profiles of these tracers suggest mixing occurs to a depth of up to ~120 cm below the sediment-water interface. Sediment-released nutrient flux, facilitated by recirculation, ranges from 3.94 to 4.77 gr/m2/year for total phosphorus, and from 11.44 to 18.48 gr/m2/year for total nitrogen. In the case of total nitrogen, fluxes of 11.44 to 18.48 gr/m2/year equate to annual discharges of nitrogen from the sediment to the water column from ~2,460 to ~3,970 tons for the Old Tampa Bay segment of Tampa Bay, FL. A recent estimate of external loading of total nitrogen to Old Tampa Bay is approximately 485 tons per year, suggesting the sediment-released nutrient load may be up to over 8 times higher than all external sources combined. Organic matter remineralization and subsequent sediment release appear to be a significant component of the nutrient budget, and an important source of nutrients to Tampa Bay. xi

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CHAPTER 1 INTRODUCTION 1.1 Statement of Problem Marine scientists have recently focused on submarine groundwater discharge (SGD) as both an important part of the hydrologic cycle and the nutrient budget of nearshore marine environments. This process has been reported to be a significant flow path for nutrients, and other contaminants from agricultural lands, septic tanks, and other point and non-point sources directly into coastal zones (Johannes, 1980; Simmons, 1992; Weiskel and Howes, 1992; Gallagher et al., 1996; Martin et al., 2002; Burnett et al., 2002), with important ecological consequences for estuaries, lagoons, marshes, reefs, and other marginal marine ecosystems (Johannes, 1980; Emerson et al., 1984; Capone and Bautista, 1985; Zimmerman et al., 1985; Simmons, 1992; Gallagher et al., 1996; Rutkowski et al., 1999; Corbett et al., 2000; Martin et al., 2002). Sediments on the sea floor can also be a significant source of nutrients generated by catabolism of organic matter detritus by microbes (Pritchard and Schubel, 1981; Nixon, 1981). This process can be enhanced or accelerated if oxygenated groundwater is advected through the seafloor sediments. For example, a study by Wang et al. (1999) has shown that internal nutrient cycling and transport exceeded external loading for Tampa Bay for the period between 1985 and 1994. Submarine groundwater discharge (SGD) is defined as any flow across the seabed, regardless of mechanism or driving force (Burnett et al., 2002; Martin et al., 2003) Therefore, SGD encompasses diffuse, aquifer derived groundwater seepage, point-source 1

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2 discharge, such as submarine spring vents, and recirculated seawater. Diffuse groundwater seepage or submarine spring discharge can occur wherever an aquifer with a head greater than surface waters is connected to overlying surface waters through permeable bottom sediments (Johannes, 1980; Rutkowski et al., 1999). In such a case, the water would originate on continents as meteoric water, and flow laterally through aquifers from the continents to coastal areas. Recirculated seawater, on the other hand, is the exchange of large quantities of water across the sediment-water interface, and is controlled by at least three major categories of processes including wave and tidal pumping (Nielson, 1990; Shum, 1992, 1993; Li et al., 1999; Huettel and Webster, 2001), density driven flow (Rasmussen, 1998), and passive or active flow through structures produced by burrowing organisms (bioirrigation) (Aller, 1980; Smethie et al., 1981; Boudreau and Marinelli, 1994). Compared to other components of the hydrologic and nutrient cycles, SGD and internal nutrient loading are generally poorly constrained variables. The nature of mixing of fresh water and seawater, and the magnitudes of water and nutrient fluxes associated with SGD are difficult to evaluate because of the dispersed nature of the discharge, the slow rate of flow, the range of processes controlling the fluxes, and the diversity of techniques that have been used to evaluate these fluxes. Three basic methodologies have been applied for quantitative assessments of SGD: modeling, direct physical measurements, and tracer techniques (Burnett et al., 2001;2002). Both analytical and numerical models have been used, ranging in complexity from simple mass balance calculations to computer-based, numerical simulations. Direct physical measurements are limited to seepage flux meters and to tracer techniques measuring natural

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3 geochemical species, which are enriched in groundwater and behave conservatively in seawater. Comparing results of these techniques indicates that freshwater often only represents a fraction of SGD, with the remainder composed of admixed seawater (Bokuniewicz, 1992; Burnett et al., 2002; Martin et al., 2002). In one such study, Li et al. (1999), presented a theoretical model that showed that groundwater circulation and oscillating flow caused by recirculative forces may constitute up to 96 % of SGD compared with 4 % due to fresh groundwater discharge. One particular location that demonstrates this discovery is the Indian River Lagoon, FL. It has been the site of numerous hydrodynamic and hydrogeochemical studies over the past 15 years and various techniques have been used to quantify SGD. Numerical modeling of the lagoon-aquifer system (Pandit and El-Khazen, 1990) has yielded SGD values that are several orders of magnitude lower than the SGD values obtained from direct physical measurements and chemical tracer studies (Belanger and Walker, 1990; Martin et al., 2002). The discrepancy arises because numerical modeling only accounts for continentally derived aquifer water, whereas the other methods do not differentiate the components of the SGD, but rather measure an integrated total discharge. The difference has been assumed to be recirculated seawater, but the various studies were conducted at different times and at different places in the lagoon and thus are not directly comparable. Mixing could provide additional sources of nutrients to coastal regions by enhancing organic matter remineralization. Mixing would pump surface water that is near saturation with atmospheric oxygen into the shallow anoxic sediments, thus increasing the oxidation potential of the pore water, and ultimately the degradation of

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4 organic matter. Subsequently, the inorganic by-products of this process are advected back out into the water column and reintroduced into the food web. This phenomenon is known as enhanced nutrient loading, and can cause a feedback loop that can result in accelerated eutrophication. The purpose of this investigation is to quantify the exchange of water and solutes across the sediment-water interface using seepage meters, analytical groundwater flow models, and geochemical tracers. Rarely have all three been utilized in the same study, but such a study allows the various constituents of SGD to be determined and traced to their source or sources. The comparison between the direct physical measurements and model calculations is important to this study because it reveals any discrepancy between what is measured and what is predicted, which suggests other forces involved in SGD other than terrestrial hydraulic gradients. Without fully understanding the nature, origin, and driving forces of SGD it is impossible to quantify internal nutrient loading and characterize the hydrologic and nutrient budgets of coastal zones. Without characterizing these budgets, ecological impact of SGD to a particular system cannot be known. 1.2 The Significance of Estuaries The study site for this project is a portion of southwestern Old Tampa Bay, known as Feather Sound. Feather Sound is an estuary; it is semi-enclosed and coastal, has a free connection to the open sea, and has a salinity gradient caused by the dilution of seawater with freshwater from upland drainage and other external sources (Biggs and Cronin, 1981). Estuaries and other inshore marine waters typically are enriched in nutrients because of their position at the distal end of watersheds. Three major life forms of autotrophs are often intermixed in an estuary and play varying roles in maintaining a high

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5 gross production rate: phytoplankton, benthic microflora, and macroflora (large attached plants, including seaweed, submerged eelgrass, emergent marsh grasses, and, in the tropics, mangrove trees) (Odum, 1997). The high primary production that characterizes estuaries provides hatcheries for many commercial coastal shellfish and fish that are harvested not only in the estuary but offshore as well. Estuaries are thus vital to the marine foodweb, and consequently to humans. Estuaries rely on an influx of nutrients and fresh water from external sources to maintain healthy biological productivity because of a net loss of water and its associated nutrients to the oceans. Nutrients are also lost through burial to the sediment. Healthy estuaries maintain a delicate equilibrium between water and nutrient inputs and outputs. They can compensate for, and assimilate, large quantities of nutrients despite the large fluctuations that occur with variations in flow from tributaries, groundwater, and other inputs. Nutrients can be stored, incorporated in standing crops of plants, released, cycled and exported, and estuaries frequently achieve high production of plants and animals without creating any undesirable enrichment of nutrients (Cronin and Neilson, 1981). However, there is a nutrient level threshold beyond which the health of an estuary may suffer because of eutrophic conditions. Excessive enrichment commonly results from increasing human population, and associated development. According to the World Resources Institute, at least 60% of the planets human population lives within 100 km of the coast (Abel and McConnell, 2002). Coastal areas have the fastest growing populations, and more than half the worlds coastlines are at significant risk from development activities related to this population growth (Abel and McConnell, 2002). By the year 2010, 75 % of the U.S. population will

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6 live within 75 km of a coastline (Wang et al., 1999). Tampa Bay, one of Gulf of Mexicos largest estuaries, exemplifies the environmental stresses that U.S. coasts face. Eutrophication has ultimately resulted in a decline in eelgrass meadows (Wang et al., 1999), which are vital to aquatic animals for food and habitat. Without these habitats, levels of these animals would no longer support the fishing and tourist industries. 1.3 Eutrophication, The Nutrient Budget, and Nutrient Cycles Humans can accelerate eutrophication by artificially enriching water bodies with excess nutrients, and/or organic matter. One focus of this study is internal nutrient loading, but external loading ultimately drives eutrophication (Wang et al., 1999). Eutrophication has been broadly defined as high biological productivity resulting from excessive nutrient and organic matter concentrations. Enrichment in organic matter results from the addition to estuaries of dissolved and particulate organic carbon, organic nitrogen, and organic phosphorus that would not naturally be a source to estuaries, such as from sewage. Another component of the cycle is inorganic nutrient enrichment, which primarily is an increase in dissolved inorganic nitrogen and phosphorus, and originates from natural and anthropogenic processes. Both inorganic and organic nutrients lead to excessive phytoplankton (or algal) growth, which in turn leads to two things: 1) an increase in turbidity, which blocks sunlight vital to photosynthesis, and 2) a depletion of dissolved oxygen at depth because respiration associated with bacterial decomposition of organic matter consumes dissolved oxygen. Nitrogen and phosphorus are involved in biogeochemical cycling as essential components of living tissues of both plants and animals. Plants convert dissolved nitrogen and phosphorus in various forms into plant organic matter; some of which is eaten by animals and becomes animal organic matter. In forming organic matter, these

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7 nutrients are used by phytoplankton in definite ratios to carbon. An idealized marine ratio (Redfield Ratio) of the average composition of marine plankton is C106N16P1. Nitrogen, or phosphorus, can be the limiting nutrient in estuaries, but most commonly, nitrogen is limiting, which is the case for Tampa Bay (Wang et al., 1999). If concentrations of either nitrogen or phosphorus increases, biological productivity increases. The nitrogen cycle is complex, because nitrogen occurs as a variety of species in natural waters, and because of its abundance in the atmosphere. It enters natural waters through a variety of pathways and in a variety of forms. Nitrogen (N2) makes up about 80 % of the air mixture by volume, but nitrogen in this form is unreactive. The conversion of N2 into chemically reactive and biologically available compounds by the combination of nitrogen with hydrogen, carbon, and oxygen is called nitrogen fixation. Lightning, sunlight, chemical oxidation, and other processes facilitate these reactions in the atmosphere. Therefore, nitrogen can be transported from the atmosphere by way of rain and particulate fallout, processes known as wet and dry deposition, respectively. Nitrogen from atmospheric deposition is inorganic, and includes species such as NO3-, NO, NO2-, and NH4+ from NH3, and other gases. In general, the contribution of nitrogen by atmospheric deposition has not changed significantly over the years relative to contributions by sources such as sewage effluent, and stormwater runoff (Dreschel et al., 1990). However, a study conducted in the Panhandle of Florida data from various river gauging stations, ranging from Pensacola to Gainesville, revealed that atmospheric deposition appeared to be the principal source of nitrogen to local water bodies (Winchester et al., 1995).

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8 Another component of the nitrogen cycle is biological fixation. This mechanism occurs on land and in the marine environment, and it involves the uptake of nitrogen gas by terrestrial plants or cyanobacteria. These organisms convert the N2 into organic, nitrogen bearing compounds. Then, erosion and runoff from the land contribute organic nitrogen to the marine nitrogen budget. Organic nitrogen, regardless of its origin or form, feeds marine plants and algae, which eventually feed animals. When these organisms die they undergo bacterial decomposition in the water column, and sediments, which results in the liberation of ammonia to solution (ammoniafication). Ammonia remains in solution as ammonium where it can be oxidized to other forms of nitrogen (nitrification). Or, some ammonia can escape back to the atmosphere. In either case, some nitrogen is recycled back into the global nitrogen cycle, while some nitrogen-containing detritus makes its way to the seafloor. Organic nitrogen that is buried in sediment can be reintroduced to the food web by organic matter remineralization and subsequent diffusion or advection of porewater back to the water column. Remineralization involves the oxidation of organic matter by oxygen, but can occur at depth, in anoxic environments if other oxidants, such as MnO2, NO3-, and SO42-, are present. A chemical reaction can be written to describe one possible stoichiometry of organic matter oxidation by oxygen (e.g. Froelich et al., 1979). (CH2O)106 (NH3)16 (H3PO4) + 138 O2 106 CO2 + 16 HNO3 + H3PO4 + 122 H2O (1-1) Phosphorus has no stable gaseous phase in the atmosphere, and thus the phosphorus cycle is less complicated than the nitrogen cycle. Most phosphorus originates from weathering of rocks. Therefore, the most likely pathway for phosphorus to enter a marine system is via surface water runoff. Inorganic phosphate is in the form of orthophosphate

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9 anions. These nutrients follow a similar path as inorganic nitrogen species once in marine systems where they are incorporated into the food web. Nitrogen and phosphorus influxes are part of a natural, healthy ecosystem, but development around Tampa Bay has resulted in an increase of both elements. Nitrogen influxes have increased because of an increase in paved surfaces (resulting in higher storm water runoff), an increase in septic tanks and point-source discharge of partially treated sewage, a conversion of woodlands to agricultural use (resulting in the extensive application of fertilizers and manure, and erosion), and industrial, automotive, and power plant pollutants that can be fixed in the atmosphere, leading to dry and wet deposition. Humans have altered the phosphorus cycle by deforestation (leading to erosion of phosphorus containing sediments and rocks), the use of phosphorus fertilizers, and the production of industrial wastes, sewage, and detergents (Berner and Berner, 1996). Also, phosphorus mining is a major industry in the region surrounding Tampa. Mining inevitably leads to accelerated erosion and loss of phosphatic material to bay. 1.4 Previous Studies of SGD and Nutrient Loading Submarine groundwater discharge and internal nutrient loading have been extensively studied in coastal environments around the world, using a variety of methods. Many previous studies include only groundwater discharge rates without the associated nutrient fluxes. Some of the more extensively observed regions include both nutrient and groundwater fluxes. Most studies do not differentiate the components of seepage water, but some recent studies have shown recognition of the contribution of recirculated water. Using Lee-Type seepage meters Bokuniewicz (1980) calculated that SGD across the bay floor was about 27.8 ml/m2/min within 30 m of the shoreline, or 10-20 % of the total freshwater

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10 inflow including surface water runoff in the Great South Bay, New York. Subsequently, Bokuniewicz (1992) measured fluxes from the same area as great as 104 ml/m2/min, and suggested that SGD included some recirculation of salt water in his study area resulting from density driven convection. On the basis of Bokuniewiczs (1980) estimate of average daily SGD, and assuming 10 m nitrate near the sediment-water interface, Capone and Bautista (1985) calculated that SGD could account for at least 20 % of the nitrogen input from surface runoff. Recent work on SGD has taken place in the Indian River Lagoon System, FL. Submarine groundwater discharge and nutrient flux have been calculated for Indian River Lagoon using a variety of methods. Zimmerman et al. (1985) reported seepage meter derived seepage velocities from 6.65 8.89 cm/day. They also reported theoretical diffusive flux rates for dissolved reactive phosphorus (on the basis of Fickian type diffusion) of from 3 to 70 x 10-6 gr/m2/day. Pandit and El-Khazen (1990) employed numerical modeling to calculate SGD. They constructed a finite element model to calculate seepage rates based on a 2D idealized cross-section of the lagoon between the water table divide on the mainland and the ocean, assuming the confining Hawthorne Formation is not permeable and the groundwater source is from the Surficial Aquifer. Their model calculated a groundwater flux of 0.002 ml/m2/min. Martin et al. (2002), Lindenberg (2001), and Martin et al. (2003) measured SGD using seepage meters and natural radioisotope tracers. Their seepage meters yielded a flux of from 40 65 ml/m2/min, and their tracer tests (Rn, Ra) yielded similar results, 11 66 ml/m2/min. A numerical model only accounts for continentally derived water, while the tracer and seepage meter studies include all the water components in the

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11 seepage water. In part because of this discrepancy, Martin et al. (2002) suggested that only 2.5 % of groundwater discharge to the Indian River Lagoon originates from the underlying aquifers. Lindenberg (2001) cited the importance of SGD and associated nutrient influxes. Using a mass balance equation of chloride concentrations she found that fresh groundwater constitutes only 1 % to 4 % of seepage water discharging into the lagoon. In addition, on the basis of water samples collected using seepage meters, she concluded that nutrient loading of total nitrogen and total phosphorus was 11 to 17 times greater than the total nitrogen and 14 to 23 times greater than the total phosphorus of surface water discharge from drainage areas surrounding the lagoon. Several other sites around Florida have been investigated. Simmons (1992) measured SGD in the Florida Keys. Using seepage meters he determined groundwater flux to be from 3.75 to 6.1 ml/m2/min. Corbett et al. (2000) employed two analytical models for measuring meteoric groundwater discharge in Apalachicola Bay. One model was a flow net and the other was a simple water balance calculation. The independent approaches agreed with each other, with an estimated groundwater flux from the surficial aquifer to the bay between 1-9 x 106 m3/yr. Cable et al. (1996) used two naturally occurring trace gases, 222Rn and CH4, along with seepage meters, to quantify seepage rates, and to determine the components of the SGD near a submarine spring in the northeastern Gulf of Mexico. These gases are present in groundwater at concentrations that are elevated by several orders of magnitude relative to seawater. Their surface water samples displayed radon and methane concentrations inversely related to salinity and considerably greater than those found in surrounding waters. Calculated diffusive fluxes of 222Rn showed that the surface waters receive only a small contribution by diffusion.

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12 They concluded that advective processes must be contributing to the water column inventory, given that seepage meters yielded a discharge rate of ~90 ml/m2/min. Swarzenski et al. (2001) used a host of natural geochemical tracers, including salinity, strontium isotopes, 222Rn, CH4, and dissolved nitrogen to derive the origin of spring water ~3km off shore of Crescent Beach, FL in the Atlantic Ocean. With a vent water salinity about 17 % of open ocean values, strontium isotope ratios indicative of Floridan aquifer system groundwater, low concentrations of dissolved nitrogen species, and enriched concentrations of 222Rn and CH4 relative to seawater, they concluded that the water discharging at Crescent Beach Spring is not newly recycled seawater, but is geochemically similar to artesian groundwater present along the coast at Crescent Beach. These studies show diversity in application of various techniques to measure and characterize SGD in different hydrogeological settings. Another area of extensive SGD and nutrient loading research is the Chesapeake Bay. The Chesapeake Bay is similar to Tampa Bay because of the widespread human population and development around the bay, and because of the economys dependence on the bays resources. Like Tampa Bay, Chesapeake Bays health is at risk because of human intervention and exploitation. Research by Taft et al., (1978) indicated that regeneration and release of nutrients from sediments is several times larger than the inputs from two of the principal ultimate sources of nitrogen to the bay, the Susquehanna River and municipal sewage discharge. Also, the Chesapeake Bay has been cited as susceptible to groundwater pollution because of its unconfined groundwater system (Robinson et al., 1998). Gallagher et al. (1996) investigated the transport of land-applied nutrients and pesticides from the aquifers to tidal surface waters, and measured both SGD

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13 and nutrient flux along Virginias coastal plain. They found that submarine groundwater transport of both nutrients and pesticides does occur, and that SGD rates represent a mixture of fresh groundwater and seawater resulting from large scale interstitial recirculation patterns. The potential for this type of phenomenon exists in Feather Sound due to unconfined and semi-confined aquifer conditions, as well as the putative spring vent in the study site. Gallagher et al. (1996) reported a mean water discharge rate of 10.5 ml/m2/min on the basis of seepage meters. They also reported a mean measured nitrogen flux of 0.04 mg/m2/min, and a maximum of 0.5 mg/m2/min. Robinson et al. (1998) reported SGD measurements from two methods, maximum instantaneous discharge rates based on piezometer measurements, and seepage meters. Their calculations based on piezometers, gradients, and KZ assumption indicated SGD from 12.5 to 320 ml/m2/min, while their seepage meter measurements indicated SGD varied from 8.33 to 55 ml/m2/min. Both methods indicated discharge rates decreasing with distance from the shore. Measurements based on piezometer measurements were inversely correlated with tidal elevation thus leading to a decrease in rates away from the shoreline, while the seepage meter rate decreases correspond to offshore decreases in sediment hydraulic conductivity and potentiometric head differentials across the sediment-water interface. Robinson and Gallagher (1999) modeled the groundwater seepage process based on density dependant fluid flow, the water table and changing tidal boundary conditions. The model predicted SGD to be dependant on distance from the shoreline, and in the order of 0 to ~35 ml/m2/min (based on a visual interpretation of Figure 8 (Robinson and Gallagher, 1999)). This finding is in accord with results from Bokuniewicz (1980). Their model also predicted that fresh groundwater discharge rates

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14 were significantly less than the total groundwater discharge and constituted 6.2 % of the total discharge across the sediment-water interface. These previous studies are a sample of current, recent, and past research. They serve to demonstrate that SGD can be greater than what is expected from hydrologic models, the source of SGD can be traced using various techniques, SGD is often composed of recirculated seawater, and the associated nutrient flux can be a significant part of a nutrient budget. 1.5 Study Area The study area of this project is based around a putative spring vent in Feather Sound, a portion of Old Tampa Bay. The spring vent is located ~200 m offshore of Pinellas County at N27.913401 and W-82.660021. Water depth is variable, but generally less than 2 meters (Figure 1-1). 1.6 Hypotheses Through detailed field sampling and measurements, laboratory analyses, and analytical modeling, the following hypotheses were tested: A submarine spring may exist offshore in Feather Sound, and may be directly contributing nutrients and other pollutants to the bay from continentally derived, fresh aquifer water. The spring vent has previously been identified on the basis of aerial photographs, salinity differences in the vicinity of the springs purported vent (Swarzenski, 2001), and visual contrasts between bay floor vegetation in the region of the vent and distally. Diffuse SGD will be composed of mixed seawater and freshwater. Freshwater will constitute minor amounts of the SGD. Diffuse SGD (non-point) may be a source of nutrients to the bay as a result of the recirculation of oxygenated bay water through shallow sediments, thus facilitating enhanced organic matter remineralization. These hypotheses were tested using the following methods:

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15 1. Quantification of SGD using seepage meters, a simple mass balance flow calculation, an analytical groundwater flow model (flow net), and a two-end member chloride-mixing model. Seepage meters and the chloride-mixing model provide an estimate of total SGD, while the flow net and water budget models predict offshore flow of continentally derived meteoric water. 2. Differentiation of the components of the SGD using natural geochemical solutes in the water column and pore water in order to trace the SGD to its source or sources. Figure 1-1. Location of Pinellas County, FL. The black dot indicates the approximate location of the study site, and the black rectangle indicates the approximate location of the rainfall gauging station. This map was taken and modified from Zarbock et al, 1996.

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16 3. Use mass balance calculations, average pore water nutrient concentrations, and field measured SGD to estimate nutrient fluxes across the sediment-water interface within the bay. 4. Measure physical properties of sediment to correlate sediment type and groundwater discharge rate, and to measure porosity for groundwater and nutrient flux calculations. These tests: 1) quantified how much water is discharging out of the bay sediments; 2) delineate the origin or sources of the SGD; 3) tested the existence of a freshwater spring in the study area, and, 4) measured the nutrient load associated with the spring and from the recirculation-remineralization mechanism. 1.7 Local Geology and Hydrostratigraphy Pinellas County, the peninsular feature on the western flank of Tampa Bay, is underlain by a sequence of sedimentary rocks whose lithology and structure control the occurrence and movement of groundwater. Figure 1-2 shows the sequence of geologic formations and hydrogeologic units in Pinellas County. The principal rock types that underlie the county are: 1) unconsolidated sand, clay, and marl, and 2) limestone and dolomite. Sand, clay, and marl are the principal sediments in the upper part of the section in middle Miocene and younger rocks. Water in these deposits occurs in primary porosity. Limestone and dolomite are the dominant rock types in the lower part of the section in lower Miocene to upper Eocene rocks. Water in these rocks occurs and moves principally in secondary openings, including joints, openings along bedding planes, and pores that commonly have been enlarged from dissolution by groundwater (Causseaux, 1982). Groundwater in Pinellas County occurs both under unconfined and confined conditions. Two aquifer systems are present: the Surficial Aquifer System and the Floridan Aquifer System. The units are separated by the intermediate confining unit.

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17 Deposits of the surficial aquifer form a sand blanket that covers the area around and beneath the bay. The thickness of the aquifer ranges from 0 to ~40 m. The aquifer is as much as 40 m thick in the ridge of central Pinellas County where it is probably composed of dune remnants. Beneath Tampa Bay, the surficial aquifer is generally less than 12 m thick. Depth to the water table is generally less than 1.5 m below land surface, but is spatially variable. The upper confining bed separates the surficial aquifer from the Floridan Aquifer and is the principal lithologic unit that separates the bay and aquifer. It consists of relatively impermeable, fine-grained deposits within the Hawthorn Formation and possibly includes clay at the top of the Tampa Limestone. Thickness ranges from 0 to an average of about 7.6 m in Old Tampa Bay, to ~76 m in other parts of Tampa Bay. The Floridan Aquifer system is below the intermediate confining layer. The Floridan Aquifer system includes the Upper Floridan Aquifer, middle confining unit, and Lower Floridan Aquifer. The top of the Upper Floridan Aquifer is defined as the first occurrence of a persistent carbonate sequence. The base of the Upper Floridan Aquifer is defined as the first occurrence of interbedded gypsum in the carbonates below dark-brown, microcrystalline dolomite in the Avon Park Formation. In Pinellas County, the top of the persistent carbonate sequence coincides with the top of the Tampa Member of the Arcadia Formation of the Hawthorn Group. This study is concerned only with the uppermost producing zone of the Upper Floridan aquifer, the first ~60 m of the aquifer. Based on figure 1-3, from Hutchinson (1983) 61 m represents the approximate depth to the saltwater front in the vicinity of study area.

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18 Figure 1-2. Generalized stratigraphic and hydrogeologic section, Pinellas County. Taken and modified from Knockenmus and Thompson (1991), which was earlier modified from Hickey (1982).

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19 Figure 1-3. Conceptual, cross-sectional view of saltwater-freshwater relations in the Tampa Bay area, and flow paths of groundwater. The peninsula on the left represents Pinellas County. The view cuts across the study site. Taken and modified from Hutchinson (1983). 1.8 Regional Climate The subtropical climate of Tampa Bay is characterized by warm, humid summers, and mild, relatively dry winters. The average annual rainfall for 1916-2001 was ~100 cm at the Southwest Florida Water Management Districts (SWFMD) St. Petersburg gauging station at N274546.09 and W823752.34 (Figure 1-1). More than 75 % of the annual rainfall occurs during the wet season of June through September, usually in the form of convective thunderstorms. Evapotranspiration in Pinellas County is estimated to be 99 cm per year, and about 60 % occurs from May to October (Cherry et al., 1970). Rainfall was slightly elevated during 2002, relative to the historical mean, but 2002 rainfall was

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20 lower than the average of the last decade. Historical and 2002 rainfall data are provided in tables 1-1 and 1-4 a,b. Table 1-1. Average historical (1916-2001) monthly, annual, and seasonal rainfall data at the St. Petersburg rainfall gauge (location shown in Figure 1-1). The data is from the SWFWMD online database. The wet season denotes June-September. MONTH AVG RAINFALL (cm) JANUARY 4.85 FEBRUARY 1.37 MARCH 6.38 APRIL 1.55 MAY 0.84 JUNE 18.64 JULY 20.62 AUGUST 16.23 SEPTEMBER 25.73 OCTOBER 3.20 NOVEMBER 2.13 DECEMBER 1.73 ANNUAL 100.13 WET SEASON 77.93 DRY SEASON 22.50

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01234561/41/111/181/252/12/82/152/223/13/83/153/223/294/54/124/194/265/35/105/175/245/316/76/146/216/287/57/127/197/268/28/98/168/23Rainfall (cm)Date (2002) 21 Figure 1-4. Daily rainfall (cm) recorded at the St. Pete gauging station before and during each sampling event. The data is from the SWFWMD online database ( http://www.swfwmd.state.fl.us/ Last accessed, February 24, 2004)

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CHAPTER 2 METHODS 2.1 Work Plan Two separate sampling trips were made to the study site in 2002. The first was in April and the second in August. The sampling trips were timed to follow seasonal variations in rainfall; the first trip occurred during the typical dry season and the second trip occurred during the typical rainy season. During the trips, SGD rates were measured using seepage meters (a direct physical measurement), bay sediment pore waters and water column waters were collected for chemical analyses, and sediment cores were collected for lithological and hydrological interpretation. All samples were preserved in the field and brought back to the University of Florida for chemical and physical analysis. Analytical groundwater modeling followed chemical and physical analysis of water samples and sediment cores. Three types of analytical models, including a mass balance flow calculation, a flow net, and a two end-member chloride mixing model were used as comparative tools to the direct physical measurement of SGD. A sampling grid was designed around the vent of the putative spring to resolve proximal and distal changes in groundwater seepage and water chemistry. The grid (Figures 2-1, and 2-4) covers approximately 7,500,000 m2 of the bay floor. Most sampling locations are uniformly distributed around the approximate location of the discharge point of the spring and spaced approximately 600 meters apart, creating a grid that is approximately 3000 meters by 2500 meters. Two additional sampling locations 22

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23 (TB-9A and TB-9B) are located within the grid close to the putative spring. Access to the field site was made with the use of USGS boats in concert with USGS personnel. Figure 2-1. The Tampa Bay basemap created from individual digital orthophoto quadrangle quarters (DOQQs) downloaded from the LABINS website. The gray box represents the approximate location of the study area. The study area is not to scale. 2.2 Seepage Meter 2.2.1 Background Seepage meters have been used to study groundwater discharge into different bodies of water for over seven decades. Isrealson and Reeve (1944) devised the first

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24 seepage meter to measure seepage outflow from irrigation canals. Since then, seepage meters have been used to study groundwater discharge out of lakes (Lee, 1977; Downing and Peterka, 1978; Fellows and Brezonik, 1980; Connor and Belanger, 1981; Belanger and Mikutel, 1985; Cherkauer and Nader, 1989; Hirsch, 1998), and rivers, canals, and coastal regions (Bokuniewicz, 1980; Capone and Bautista, 1985; Simmons, 1992; Cable et al., 1996). The modern seepage meter, referred to as the Lee-type seepage meter (shown in figure 2-2) was described by Lee (1977). Although improvements have been made over the years, manual seepage meters still consist of the end of a standard 55-gallon drum with an open port placed near the rim that allows a plastic water collection bag to be attached. The volume of water that enters the bag over a known time and area yields the seepage rate (Lee, 1977; Lee et al, 1980; Shaw and Prepas, 1989; Cable et al., 1997) here reported as ml/m2/min. There are several benefits in using seepage meters to measure SGD. They have a rather simple and inexpensive design, they are relatively easy to deploy, and they provide a quick, direct physical measurement of SGD, which is otherwise determined through numerical or analytical modeling, and chemical tracers. Notwithstanding these benefits, seepage meter results have been questioned. For instance, Lee (1977) noted that seepage velocity in estuaries was significantly inversely correlated with water surface elevation, although Bokuniewicz (1980) found no correlation between seepage rates and tidal heights. Furthermore, Shaw and Prepas (1989) showed data indicating the presence of artifacts associated with a short-term, anomalous influx caused by a hydraulic gradient created when an empty bag is attached to the meter. This gradient appeared to cause seepage bags to fill more rapidly as the plastic bag expands and draws in water not

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25 Figure 2-2. A depiction of a seepage meter placed in the sediment under the water column. The receptacle bag is connected and the valve is open to allow flow through the port. Arrows indicate direction of groundwater flow. associated with seepage. The expansion appears to be related to mechanical properties of plastic bags, and can result in significant artifacts in calculated seepage rates (Cable et al., 1997). Another possible drawback of using seepage meters was pointed out by Shinn et al. (2002), who suggested that meters presenting positive relief on the sea floor are subject to the Bernoulli effect when placed in areas where there are waves and/or currents. In other words, the devices artificially advect shallow ground water (Shinn et al., 2002). Shinn et al. (2002) also claim that advection is not caused by flexing of the collection bags as reported by Shaw and Prepas (1989). 2.2.2 Seepage Meter Construction, Deployment and Seepage Measurements Seepage meters were constructed from the sawed off ends of 55-gallon steel drums. The drums were cut 15 cm in from the ends. Half-inch diameter ports were drilled into the flat top 6 cm from the edge of the drum. The meters were sanded and painted with

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26 two coats of two-part marine epoxy paint. A male garden hose fitting was inserted into the port and made watertight using rubber washers and silicon caulking. Rubber handles were screwed into the side of the meter using washers and silicon caulking (Lindenberg, 2001). Seepage meters were installed with the flat side up. The meters were inserted into the sediment so that the rim was completely buried to prevent bay water from flowing into the meter under the rim. The side with the port was tilted slightly upward to prevent gases from accumulating causing backpressure and possibly lifting the meter free of the sediment. Seepage meters were deployed at the stations depicted on figure 2-3. Seepage rates were measured in April. The method of deployment and sampling of the seepage meters followed procedures outlined in Cable (1997). Seepage meters were allowed to equilibrate for 24 hours prior to sampling. Once equilibrated, seepage rates were measured in triplicate. The 4-1 plastic collection bags were primed with 1000 ml of estuarine water prior to deployment to prevent artifacts as indicated by Shaw and Prepas (1989). Seepage flux was calculated by dividing the volume of water that flowed into the collection bag by the amount of time the bag was on the meter and by the area of the meter (0.28 m2). No control experiment was conducted. 2.3 Water Samples Pore water and bay water samples were collected in April and August. Pore water was collected using multiple level piezometers (multisamplers, Martin et al, 2003). Bay water was collected in a grab manner using a peristaltic pump and rubber tubing suspended 50 cm above the bay floor. Pore water and bay water were measured in the field for salinity, conductivity, temperature, dissolved oxygen, and pH, and preserved and

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27 returned to the laboratory for measurements of concentrations of solutes and nutrients including sulfate, chloride, TN, TSN, TP, and TSP. Depending on the results of the concentration measurements, a subset of pore water samples was selected for measurements of the 18O values and 87Sr/86Sr ratios. Figure 2-3. A satellite image of the study site and sampling stations. TB-9 is the location of the putative spring. The image was taken from the LABINS (Land Boundary Information System) website in the form of a digital orthophoto quadrangle quarter (DOQQ). 2.3.1 Multisamplers and Pore water 2.3.1.1 Design Multisamplers are 2 m long sections of PVC pipe with eight ports located at various distances along their lengths (Martin et al., 2003). The design of the

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28 multisampler consists of 2 ID schedule 80 PVC pipe with one-quarter inch ID (3/8 OD) PVC tubing fed through the interior of the pipe (figure 2-4). The PVC tubing is glued to ports in the pipes and each port is screened with a 250 m screening material (Nytex). The ports are separated by 10 to 40 cm with the closest spacing in the upper section, and increasing spacing with depth. This distribution allows higher resolution sampling of the pore water near the sediment-water interface where concentration gradients are likely to change more rapidly with depth because of diagenetic reactions (Martin et al., 2002). The multisamplers have 8 ports that are located at 10, 30, 50, 80, 110, 150, 190, and 230 cm from the top of the instrument. If fully inserted in the sediment, these values also represent the sampling depth below the sediment-water interface. The ports exit the device in a spiral fashion with each one located 90 offset from the ports above and below. The tubing is led outside the PVC pipe through a T-joint (Martin et al., 2003). The base of the multisampler is plugged with a solid point that enables installation. Figure 2-4. Design of a multisampler (from Martin et al., 2003).

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29 2.3.1.2 Deployment Multisamplers were deployed at five different locations (TB-1, TB-4, TB-9, TB-9A, and TB-9B), during each sampling trip. This sampling array allowed higher resolution sampling near the putative spring vent as well as near shore and further out in the bay. The multisamplers were driven into the sediment using a fence post driver. The fence post driver was repeatedly lifted and dropped on the top of the multisampler in conjunction with human force pushing down. The multisamplers were driven in to the base of the T-joint, which means they were fully inserted. 2.3.1.3 Sampling Sampling was done as soon as the multisamplers were fully driven into the sediment. The PVC tubing was brought to the boat, primed by mouth, and connected to a peristaltic pump. The pore water was pumped at a rate of approximately 1 ml/s into a small plastic bucket. The water was monitored until oxygen concentrations and temperature stabilized, at which time these parameters plus pH, and salinity were recorded and water samples were collected. Each port was sampled in succession from shallowest to deepest. Water was drawn from the bucket, after the stabilized bucket was emptied, using a 60 ml syringe and transferred into one of several HDPE bottles. One sample was unfiltered, another was filtered using a 0.45 m filter, the third sample was filtered and preserved using 50l of 16 N optima grade HCl and stored in a glass Qorpack bottle for supplemental isotopic analyses. All bottles were pre-labeled with the sampling station, port number, and the date. All bottles were immediately stored on ice after being filled. Some ports did not yield water when pumped due to clogging or low permeability sediment, but those that did would pump unlimited volumes of water.

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30 2.3.2 Bay water In addition to pore water, samples of the bay water were collected. Bay water was collected from 50 cm above the bay floor using a peristaltic pump. A small weight was attached to PVC tubing and lowered into the water column to the proper depth. 2.3.3 Analyses Pore water and bay water were brought back to the University of Florida for chemical analysis. Measurements of nutrients were done in the Land Use and Environmental Change Institute (LUECI) Laboratory; measurements of ions were done in the Hydrochemical Prep Lab, and isotopes were prepped, spiked, and measured in the Clean Lab, TIMS lab, or Stable Isotope Lab, all in the Department of Geological Sciences. Concentrations of PO4 and SiO2 were measured on the non-acidified filtered water samples, and NH4+ was measured on acidified filtered water samples using spectrophotometric techniques following procedures described in Clesceri et al. (1989). Nitrogen and phosphorus concentrations were measured following Kjeldahl digestion on a Technicron Autoanalyzer II for both filtered and non-filtered samples. The concentrations of these samples were reported as total nitrogen (TN) and total phosphorus (TP) concentrations for the non-filtered samples. Filtered samples were used to measure total soluble nitrogen (TSN), total soluble phosphorus (TSP), and NO3 concentrations, prior to Kjeldahl digestion of the sample. Precision of PO4 and NH4+ analyses were checked by analyzing a check standard every fourth sample, and calculating the coefficient of variation (standard deviation divided by mean) of the values measured for the check standard. Precision of the average of the differences in the duplicates of TSN,

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31 TN, TSP, TP, and NO3 concentrations were checked by analyzing duplicates every tenth sample (table 2-1). Table 2-1. Estimated precision of various solutes for water samples SOLUTE PRECISION % PO4 0.53 NH4 0.86 TSN 0.64 TN 0.67 TSP 0.62 TP 1.30 SiO2 0.44 Concentrations of chloride were measured using AgNO3 titration (Clesceri et al., 1989). Repeated measurements of three internal standards, St. Augustine Seawater (SAS), and two known concentrations of NaCl (553.377 mM and 553.668 mM) yield a reproducibility error of less than 0.8 %. These standards were measured approximately every fifth sample, or ~22 times per sampling event. Sulfate concentrations were measured from the filtered samples using an Automated Dionex model 500 Ion Chromatograph. Measurements of SAS every fifth sample yielded a reproducibility error of less than 0.8% for sulfate concentrations. Oxygen isotopes were measured using a CO2 equilibration technique on pore water from locations TB-1, TB-4, TB-9, TB-9A, and TB-9B from both sampling trips. Glass vials with 200 l of sample were capped with septum caps under CO2 atmosphere in a glovebag. Then the vials were placed in a heated Aluminum block at 45C to equilibrate for 12 hours. Once equilibrated, the CO2 was analyzed with an automatic multipreparation stage and H2O was removed from a water trap at C. Purified CO2 was analyzed on a Micromass Prism II gaseous source mass spectrometer. Results are reported relative to SMOW in standard delta notation. Samples were run in duplicate,

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32 and the value reported is the average for the duplicates. Estimated precision is 0.1 based on standards run during the analysis every fifth sample. Strontium isotope ratios and concentrations were measured only on pore water from location TB-9 from both sampling trips. In the Clean Lab, 300 ml water samples were spiked with a diluted RS95 spike, which is a solution with high concentrations of 84Sr and evaporated to dryness. The residue was acidified with 100 l 3.5N HNO3 for Sr separation. The Sr was separated from other cations with Sr Spec resin in a 3.5N HNO3 medium, and at the end Sr was collected in 1.5 ml 4xH2O and evaporated to dryness. After prepping, the samples were loaded on oxidized tungsten single filaments and were analyzed using a VG Micromass 54 spectrometer run in dynamic mode. Errors in measured 87Sr/86Sr are better than 0.000022 (2) based on long-term reproducibility of the NBS 987 standard. The laboratory value of the standard is 0.710240. 2.4 Sediment Cores 2.4.1 Sampling Two cores were taken during the August, 2002 sampling trip, one at TB-9, and the other at TB-9A. The cores were collected using a vibracoring technique. Vibracoring works on the principal of liquefaction in fine-grained sediments by displacing sediment to allow passage of the coring pipe (Smith, 1984). Coring was accomplished using approximately 2 m long sections of aluminum pipe as the core barrel having an internal diameter of 7.5 cm. The pipes were fitted with core catchers. The pipes were attached to a motor that generates vibrations (Figure 2-5). Simultaneous with the motor, human force was applied downward to help drive the pipe into the sediment. The pipe was driven as far as possible into the sediment.

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33 Figure 2-5. Digital photograph of vibracore assembly taken during the August sampling trip, from the deck of the USGS pontoon boat. The motor in the foreground turns a flexible rod that runs down the length of the cable. The cable is secured to the side of the aluminum-coring barrel. The rotation of the flexible rod is expressed as strong vibrations against the coring barrel that help drive it into the sediment. The pipes were then pulled out of the sediment using a winch and steel cable. No significant compaction was observed. The pipes were immediately capped on both ends, cut in half, and then stored in an upright position. 2.4.2 Analysis The two cores were stored in a walk in refrigerator in the Florida Institute of Paleoenvironmental Research (FLIPER) Laboratory, at the Department of Geological Sciences, University of Florida. In this lab, the cores were split lengthwise, described, photographed and sectioned within one month of sampling. One section was used to measure sediment bulk density (fractional porosity), and take high-resolution digital images (40 pixels/cm) of the entire core, using the Geotek Multi-sensor Core Logger

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34 (MSCL). Bulk density was determined using a standard aluminum density calibration piece (Weber et al., 1997). The other section was preserved for lithological description and future sediment analyses. 2.5 Groundwater Flow Models Modeling of groundwater flow was used as a comparative tool to the seepage meter measurements. The purpose of the water budget mass balance calculation is to determine the groundwater component of the local hydrologic system, and solve for the groundwater inflow to the study area. The flow net is used to support the findings from the water budget model using field-measured hydrogeological properties. The two-end member chloride-mixing model is a tool for deriving water flux from chemical mixing, and will be used to further corroborate the flux attained from the previous two models. In contrast to the two analytical models, the conceptualization of the chloride-mixing model (CMM) is different. Whereas the water budget and flow net only account for SGD of meteoric derived aquifer water, the CMM solves for a flux on the basis of re-circulating bay water. 2.5.1 Water Budget Modeling was initiated with a mass balance flow calculation. The first step in this model was to construct a base map that could be incorporated into GIS software and used to calculate distances and areas for the model calculations. The base map was constructed using digital orthophoto quadrangle quarters (DOQQs) from the LABINS (Land Boundary Information System) website. LABINS is a clearinghouse of satellite and aerial photographs in various projections for the state of Florida. The appropriate DOQQs were downloaded, organized, and uploaded into Global Mapper software. Global Mapper allows easy assembly of DOQQs and re-projection of the whole image

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35 into any projection needed. For this study, Global Mapper was only used to assemble the numerous DOQQs that comprised the entire Tampa Bay area. The original projection, Albers, was maintained. After the base map was assembled it was incorporated as a theme into ArcView GIS 3.2a. ArcView maintains topology and preserves real-world coordinates. ArcView was used in conjunction with several USGS reports to constrain a regional watershed and groundwater divide. On the basis of maps taken from Hutchinson (1983) and Yobbie et al. (1980), including potentiometric and surficial aquifer maps, lines on the base map were digitized to denote a surface water divide that was coincident with a groundwater divide (Figure 2-6). The area within the divide boundaries is considered the catchment area for groundwater discharge to Old Tampa Bay. Once the base map was completed, the hydrologic mass balance for the area was determined. Data for the model were extracted from USGS Water-Resources Investigations Report 84-4289 (Causseaux, 1982). Table 2-2 indicates values used for variables in the mass balance. Table 2-2. Components and associated values of hydrologic equation. Inputs Outputs Value P 140 cm/year ET 64 cm/year RS 15 cm/year EWT 36 cm/year Rbase 15 cm/year G ? Water input to the system is: average annual precipitation (P). Water losses from the system are: evapotranspiration (ET) from the land surface, stream runoff (RS), evaporation (EWT) from the water table, and stream baseflow (Rbase). The unknown output was groundwater (G) on the basis of USGS reports 84-4289 and 82-54

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36 (Hutchinson, 1983). The model was calculated assuming that all groundwater that does not contribute to stream baseflow within the basin flows into the bay. Figure 2-6. The outline of the surface and groundwater divides (derived from potentiometric and topographic maps from Causseaux, 1982; Hutchinson, 1983; Yobbi et al., 1980) superimposed onto the Tampa Bay basemap created using GIS software. 2.5.2 Flow Net In addition to the water budget model, a flow net of the surficial aquifer was constructed. This model represents a modification of the model presented in Hutchinson, 1983. In Hutchinson, 1983, a flow net of the Upper Floridan aquifer for Tampa Bay was constructed for two months, May and September of 1980. Hutchinson (1983) compared the potentiometric surface maps from both months and determined that there was not enough difference in the position of the equipotential lines to justify having two separate

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37 flow nets. Hutchinson (1983) averaged the discharge rates from these flow nets into one annual discharge rate. Hutchinsons (1983) flow net used Darcys formula Q = TIL whereby Q = discharge, T = transmissivity (ft2/d), I = potentiometric gradient (ft/mi), and L = length of flow zone (mi). For Hutchinson (1983) the area within the constructed groundwater basin, near the bay, was broken into flow zones. Flow zones were designated on the basis of transmissivity, hydraulic gradient, and length of flow zone. Hutchinson (1983) showed discharge rates only from the Upper Floridan aquifer, while the model here includes flow in the surficial aquifer on the assumption that not all precipitation infiltrates to the Upper Floridan, or becomes stream baseflow. The method of Hutchinson (1983) was used here, assuming that the water table position does not change enough over the course of the year to justify having seasonal flow nets. Consequently, a water table map from May 1980, described in Yobbi et al. (1980) was chosen for analysis. The map was digitized and discrete flow zones were constructed on the basis of transmissivity, hydraulic gradient, and length of flow zones (Figure 2-6). Transmissivity was determined based on saturated thickness of the surficial aquifer coupled with hydraulic conductivity obtained from Causseaux (1982). Saturated thickness was variable, ranging from 30 to 50 ft, and hydraulic conductivity was held constant at 180 ft/d. The potentiometric gradient was measured on the potentiometric surface map (Figure 2-7). Transmissivity for the eastern portion of the Old Tampa Bay was extrapolated from Pinellas county data. The results from the surficial flow net were added to the discharge calculated by Hutchinson (1983) for the Upper Floridan Aquifer.

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38 Figure 2-7. The Surficial Aquifer flow net. Roman numerals (I VI) represent discretized transmissivity zones. Arrows denote the general direction of groundwater flow. 2.5.3 Chloride Mixing Model A third model used here is a two-end member chloride-mixing model. The two-end member mixing equation is: X(WCA) + (1-X)(AP) = A (Equation 2-1)

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39 Where X is the fraction of bay water mixing into the shallow sediments; WCA is the August water column chloride concentration; AP is the April pore water chloride concentration at depth z, and A is the August pore water chloride concentration at depth z. The reason for using the two sampling dates in the mixing model is to determine the volume of August bay water necessary to change April porewater chloride concentrations to August porewater chloride concentrations. Locations TB-1, TB-4, TB-9, TB-9A, and TB-9B were considered. An exponential trendline was fit to the chloride depth profiles. An exponential curve was chosen because it satisfies both the conceptual model of decreasing bay water circulation into the sediment with depth, and because the raw data appear to have an exponential shape. Chloride concentrations were then estimated using the equations of each curve (profile) for every 2 cm of depth. From these chloride concentrations, the fraction of mixing between the August water column and April pore water was calculated using Equation 2-1 to the depth where the curves cross, which varies for each sampling location, and represents the depth below which mixing ceases. The mixing fraction, solved from the equation above, was multiplied by the measured porosity incrementally for every two centimeters below the sediment-water interface. When the product of these two variables is summed over the entire mixing depth the result is equal to the total volume (V) of bay water that must circulate through the sediment, from April to August, in order to achieve pore water concentrations equal to those measured in August. V = X (Equation 2-2) Where is the porosity, and X = mixing fraction

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40 Moreover, this volume of water, divided by a representative area, divided by time is the flux (J) into, and presumably out of, the sediment. J is reported in terms of ml/m2/min, where V is the volume of bay water that circulates through the sediment, A is the representative area, and t is the number of days between last day of sampling in April and first day of sampling in August. J = V-1t-1 (Equation 2-3)

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CHAPTER 3 RESULTS 3.1 Physical Analyses 3.1.1 Seepage Meters Seepage rates are randomly distributed throughout the study area (Figure 3-1). The minimum seepage flux of 16.0 6.0 ml/m2/min occurred at station TB-18. The maximum seepage flux of 92.6 28.2 ml/m2/min occurred at station TB-7. The average of all measured seepage fluxes is 50.5 ml/m2/min with 1 of 22.8 ml/m2/min. There was an 83% difference between the maximum and minimum seepage rates. Figure 3-1. Submarine groundwater discharge magnitudes from seep meters at various locations within the sampling grid. The relative diameter of a circle is proportional to the measured SGD at that location. 41

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42 The average seepage flux of 50.5 ml/m2/min is assumed to represent the average flux for the entire study area because the measured seepage rates were randomly distributed within the study area. Combining this flux with the area of the study site (7,637,843 m2), the total SGD is 555,698 m3/day, or 202,829,770 m3/year. If the average flow from the study area is similar across all of Old Tampa Bay a total of 14,183,464 m3 water /day, or 5,176,964,360 m3/year discharges from an area of ~194,946,360 m2. 3.1.2 Sediment Cores 3.1.2.1 Lithology The core recovered from TB-9 is approximately 188 cm in length (Figure 3-2). Examination of the core revealed it is composed of siliceous sand in the first ~30 cm, followed by a shell hash layer from about 30 cm to approximately 160 cm, with the remainder of the core consisting of siliceous sand. The core can be further subdivided into seven distinct zones. The uppermost 0.3 cm is composed of greenish-gray clay. This is exclusive to the top of the core. From 0.3 cm to 22 cm the core consists of fine-grained sand grading into medium grained, light siliceous sand with sparse dark siliceous silty-sand bands throughout. Some iron staining is present within this zone. Between 22 cm to 28 cm the core contains medium grained, light siliceous sand becoming increasingly darker in color with depth. This change in color might represent an increase in silt content. Small shell fragments (0.1 cm 0.5 cm) increase in concentration with depth. The shell hash layer begins at 28 cm and continues to a depth of 80 cm. The shells are mostly mollusks (bivalves and gastropods) and range in size from tiny fragments (<0.1 cm) to whole valves (2 cm 3 cm). The matrix of this layer is a dark, silty, siliceous sand. From 80 to 160cm the shell hash layer continues, but the matrix becomes a light siliceous sand. Shell size is variable as in the previous section. Some

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43 clay lenses are visible. Shell concentration decreases with depth. At 160 cm the shell hash layer grades into fine to medium grained, light siliceous sand to depths of 174 cm. Shell fragments are smaller than the previous section ranging from 1mm to 1cm in size. The bottom of the core (174 cm 188 cm) consists of fine to medium grained, pale orange, siliceous sand with very few shell fragments. The core recovered from TB-9A is 198 cm in length (Figure 3-3), with similar structure to the core from TB-9. However, the shell hash layer begins approximately 69 cm deeper than in the core from TB-9. TB-9A is generally composed of siliceous sand in the first ~97 cm, followed by a shell hash layer from about 97 cm to approximately 179 cm, with the remainder of the core consisting of siliceous sand. The core can be further subdivided into six distinct zones. The top of the core, the first 20 cm, consists of fine-grained, light colored siliceous sand. From 20 cm to 35 cm the core transitions into a fine-grained, light colored siliceous sand which grades into dark siliceous sand. From 35 cm to 97 cm the core is comprised of fine to medium-grained, dark siliceous, silty-sand with some very small (mms to 1cm) shell fragments interspersed. The shell hash layer begins at 97 cm and continues to a depth of 179 cm. This shell hash layer is identical to that found at TB-9. Between 179 cm and 195 cm the shell hash layer transitions into a fine-grained, gray, siliceous sand horizon with some very small shell fragments and clay lenses interspersed. The remainder of the core (195 cm 198 cm) is comprised of a very fine, light siliceous sand.

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44 Figure 3-2. TB-9 core lithology, digital photograph, and porosity

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45 Figure 3-3. TB-9A core lithology, digital photograph, and porosity

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46 3.1.2.2 Bulk density and porosity Bulk density of the sediment was measured using the Geotek MSCL (Multi Sensor Core Logger) at 0.5 cm increments throughout the length of the cores. Bulk density occasionally reflects the type of sediment present, for example, shelly zones show higher bulk density than soft, sandy or clayey zones. At TB-9 the bulk density values ranged from 1.76 to 2.24 gm/cc, with an average of 2.04 gm/cc. The density was highest from about 30 cm to 100 cm, which probably reflects the portion of the shell hash layer with the highest concentration of large, intact shells. At TB-9A bulk density ranged from 1.71 to 2.3 gm/cc, with an average of 2.03 gm/cc. Again, the density was highest in the portion of the core containing a high concentration of large, intact shells (from about 130 cm to 180 cm). Figures depicting changes in bulk density throughout the cores were omitted because bulk density is the inverse (mirror image) of fractional porosity, which is shown on figures 3-3 and 3-4. Porosity of the sediment is intrinsically related to the bulk density by assuming two-end member combination of the water and solid with a constant density. Values for mineral grain density (MGD), and fluid phase density (WD) are used to calculate the fractional porosity (FP) by: FP = (MGD GD1) / (MGD WD) (Equation 3-1) Where GD1 is the gamma density as determined by the gamma density-processing panel. For Tampa Bay calculations, MGD is assumed to = 2.65 gr/cm3 (quartz), and WD = 1.024 gr/cm3 (average seawater). At TB-9, porosity ranges between 17.43 49.49 % with an average of ~ 30.4 % (Figure 3-2). A zone of lower porosity occurs around 30 cm to about 100 cm, which is roughly the depth of the shell hash layer, and is consistent with the poor sorting there. At TB-9A, the porosity ranges between 17.87 52.8 % with an

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47 average of 31.2 % (Figure 3-3). Similarly to TB-9, a zone of lower porosity occurs within the zone of the shell hash layer (130 cm to 180 cm). It is worth noting that the density of quartz was applied to the entire lengths of both cores to solve for porosity, although the shells are clearly composed of CaCO3. The percent difference in density of quartz and calcite is approximately 2 %, based on a density of calcite of 2.7 gr/cm3. This difference could be a source of error in these porosity calculations. 3.2 Chemical Analyses 3.2.1 Tracers For this study, tracers were conservative, naturally occurring solutes in the water column and pore water. Tracers included chloride and salinity, and strontium and oxygen isotopes. All average, minimum, maximum and standard deviations for tracer concentrations in the water column are posted in tables 3-1 and 3-2 for the dry and rainy seasons, and plots of tracer concentration versus depth are provided for pore water (below). 3.2.1.1 Chloride and Salinity Water column chloride concentrations and salinity were measured in water collected from both sampling trips at stations TB-1, TB-4, TB-9, TB-9A, and TB-9B. During the dry season, the average water column chloride concentration was 461 mM with 1 of 4.29 mM (Table 3-1). During the rainy season, the average water column chloride concentration was 395 mM with 1 of 5.9 mM (Table 3-2). There was a decrease of 14.3% in the average water column chloride concentration from April to August. During the dry season, the average water column salinity was 27.99 with 1 of 0.25 (Table 3-1). During the rainy season, the average water column salinity was 23.65 with 1 of 0.1 (Table 3-2). There was a decrease of 15.5 % in average water

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48 column salinity from April to August. Water column salinity was plotted versus sampling stations to determine spatial variability (Figure 3-4). All stations where salinity was measured in the field are listed; some sta tions were not re-measured in August. Table 3-1. The average, maximum, minimu m, and standard deviation of salinity, chloride, and other field measur ements during the dry season. Salinity Cond. temp. DO pH Cl() (mS/cm)0C (mg/L) (mM) Avg. Water column 27.99 43.34 28.04 7.40 7.84 461 Max 28.3 43.8 29.3 8.32 8.04 465 Min 27.7 42.9 27.1 5.81 7.76 455 1 0.25 0.29 0.96 0.83 0.10 4.29 Table 3-2. The average, maximum, minimu m, and standard deviation of salinity, chloride, and other field measur ements during the rainy season. Salinity Cond. temp. DO pH Cl() (mS/cm)0C (mg/L) (mM) Avg. Water column 23.65 37.30 29.55 6.16 8.06 395 Max 23.8 37.7 30.4 7.66 8.22 400 Min 23.6 37 28.7 4.83 7.91 386 1 0.10 0.27 0.83 1.24 0.16 5.90 28.1 27.727.7 27.6 27.7 27.9 28.328.3 23.623.623.6 23.8 21 22 23 24 25 26 27 28 29 TB-1TB-9BTB-9 TB-10TB-11TB-12TB-4TB-9A LocationSalinity (per-mil) Dry Season Wet Season Figure 3-4. Seasonal water column salin ities from TB-1, 9B, 9, 10, 11, 12, 4, 9A.

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49 Pore water chloride concentrations were measured at field stations TB-1, TB-4, TB-9, TB-9A, and TB-9B during both sampling trips. In general, pore water chloride concentrations decrease with depth below the sediment-water interface in April, and increase with depth below the sediment-water interface in August (Figure 3-5a-e). The concentration profiles appear to increase or decrease exponentially with depth below the sediment-water interface. The August and April profiles also converge at a specific depth below the sediment-water interface that varies from station to station. For TB-1, the convergence depth is at approximately 80 cmbsf (cm below sea floor); for TB-4, ~ 100 cmbsf; for TB-9, ~ 50 cmbsf; for TB-9A, ~ 75 cmbsf, and for TB-9B, ~ 150 cmbsf. 3.2.1.2 Isotopes Water column and pore water Sr concentrations and 87Sr/86Sr were measured at field station TB-9 during both sampling trips (Figure 3-6a,b). During the dry season, the water column Sr concentration was 8.328 ppm 0.164 %. The 87Sr/86Sr was 0.709140. During the rainy season, the water column Sr concentration was 7.2378 0.141 %. The 87Sr/86Sr was 0.70912 0.0000010. There was a decrease of 13.8 % in water column Sr concentrations from April to August, but no seasonal change in water column 87Sr/86Sr. Pore water Sr concentrations and 87Sr/86Sr versus depth are depicted below. Strontium concentrations decrease with depth in April, but increase with depth in August. The concentrations converge at around 50 cmbsf, consistent with the chloride profiles at TB-9. 87Sr/86Sr remains constant from the water column to greater than 100 cmbsf, with a value similar to the 87Sr/86Sr of modern seawater, 0.70906 0.00003.

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50 460454453450435425400.00405.00407.00405.00435.00433.00-50050100150380400420440460Concentration (mM)Depth (cm) April August 463464461457452438431390.36392.32404.13403.81412.33450.71448.75-50050100150380400420440460Concentration (mM)Depth (cm) April August a b

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51 464457452451451448449445386.42400.20414.96447.11440.54425.46-50050100150380400420440460Concentration (mM)Depth (cm) April August 455459445426415399.21390.36411.35437.26424.47-50050100150380400420440460Concentration (mM)Depth (cm) April August c d

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52 457456454456451448449453441435396.92374.61391.01408.07414.30426.11431.03-50050100150380400420440460Concentration (mM)Depth (cm) April August e Figure 3-5. Chloride concentration versus depth below sediment-water interface (a) TB 1, (b) TB-4, (c) TB-9A, (d) TB-9, (e) TB-9B. The axis is the water column.

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53 -500501001502007.00007.20007.40007.60007.80008.00008.20008.4000Sr Concentration (ppm)Depth April August -500501001502000.70905000.70910000.70915000.70920000.70925000.70930000.7093500Sr 87/88Depth April August a b Figure 3-6. Water column and pore water Sr concentrations (a), water column and pore water 87Sr/86Sr (b). The axis represents the water column.

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54 Water column and pore water 18O were measured at stations TB-9, TB-1, and TB-4 during both sampling trips (Figure 3-7a-c). During the dry season, the average water column 18O was 2.02 and the range was from 1.87 2.16 During the rainy season, the average water column 18O was 1.52 and the range was from 1.45 1.62 18O profiles have a similar shape to chloride and Sr profiles. They increase with depth during the dry season, and decrease with depth during the wet season, and the curves converge at a given depth. The convergence depth for TB-1 is ~80 cmbsf; for TB-4 it is ~150 cmbsf, and for TB-9 it is ~50 cmbsf. The convergence depths for TB-1 and TB-9 appear to be similar to those from the chloride profiles, and within ~50 cm for TB-4. 3.2.2 Nutrients This study includes measurements of nitrite, nitrate, ammonium, TSN, TN, SRP, TSP, TP, and biogenic silica. TSN (total soluble nitrogen) is measured on filtered samples and includes both dissolved inorganic nitrogen (DIN) and dissolved organic nitrogen (DON) species. DIN species include nitrate, nitrite, and ammonium. DON is not directly measured, but is the difference between TSN (which is directly measured) and the measured DIN species. Common DON species include urea, amino acids, proteins, purines, and pyrimidines. TN (total nitrogen) is measured on unfiltered samples and thus includes particulate nitrogen (PN) and TSN. Particulate nitrogen concentration is thus determined by subtracting TSN from TN. The term SRP (soluble reactive phosphorus) is a measurement of DIP (dissolved inorganic phosphorus). In this study phosphate comprises most of the DIP, so SRP is a measure of phosphate. TSP (total soluble phosphorus) is directly measured from filtered samples, and is the sum of all dissolved organic phosphorus (DOP) and SRP.

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55 -500501001502001.001.502.002.503.00delta O 18 (per mil)Depth (cm) April August -500501001502001.001.502.002.503.00delta O 18 (per mil)Depth (cm) April August a b

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56 -500501001502001.001.502.002.503.00delta O 18 (per mil)Depth (cm) April August c Figure 3-7. 18O concentration versus depth, (a) TB-1, (b) TB-9, (c) TB-4. DOP can thus be calculated by subtracting SRP from TSP. Common DOP species include proteins and sugars. Particulate phosphorus (PP) can be calculated by subtracting TSP from TP. The focus of this study is on ammonium, TSN, TN, SRP, TSP, and TP. Nitrate and nitrite are excluded since they are almost completely reduced to ammonium. A complete nutrient breakdown and analysis is included in Appendix B.

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57 3.2.2.1 Ammonium Ammonium (NH4+) is the predominant inorganic nitrogen species in the nutrient budget of the study site. NH4+ was analyzed at stations TB-1, TB-4, TB-9, TB-9B, and TB-9A for both sampling trips. Dry season measurements indicate an average water column concentration of 0.026 mg/L with a range of 0.011 0.042 mg/L. The maximum water column concentration occurred at station TB-1, while the minimum occurred at station TB-4. The average dry season pore water concentration for the study site was 0.657 mg/L with 1 of 0.33 mg/L. The maximum pore water average was 1.00 mg/L, which occurred at station TB-1, and the minimum average was 0.613 mg/L, which occurred at station TB-4. Rainy season measurements indicate an average NH4+ water column concentration of 0.0022 mg/L with a range of 0.001 0.004 mg/L. The maximum water column concentration occurred at station TB-1, while the minimum occurred at TB-9B. The average pore water concentration for the study site was 0.750 mg/L with 1 of 0.78 mg/L. The maximum pore water average was 1.299 mg/L, which occurred at station TB-1, and the minimum average was 0.321, which occurred at TB-4. Average water column ammonium decreased by 92 % from April to August, while average pore water ammonium increased by 14 % from April to August. Ammonium profile plots depict scattered data and indicate no concentration gradient upwards to or downwards from the sediment-water interface. Figure 3-7 shows data from TB-9 and is representative of the scatter seen throughout the entire study area.

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58 -500501001500.000.501.001.50Concentration (ppm)Depth (cm) April August Figure 3-8. Ammonium concentrations versus depth below the sediment-water interface at sampling station TB-9. 3.2.2.2 SRP (phosphate) PO4+ was analyzed at stations TB-1, TB-4, TB-9, TB-9B, and TB-9A for both sampling trips. Dry season measurements indicate an average water column concentration of 0.069 mg/L with a range of 0.065 0.073 mg/L. The maximum water column concentration occurred at station TB-1, while the minimum occurred at station TB-9A. The average pore water concentration for the study site was 0.263 mg/L with 1 of 0.11 mg/L. The maximum pore water average was 0.395 mg/L, which occurred at station TB-1, and the minimum average was 0.145 mg/L, which occurred at station TB-9.

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59 Rainy season measurements indicate an average water column concentration of 0.037 mg/L with a range of 0.045 0.033 mg/L. The maximum water column concentration occurred at station TB-1, while the minimum occurred at TB-9B. The average pore water concentration for the study site was 0.199 mg/L with 1 of 0.11 mg/L. The maximum pore water average was 0.247 mg/L, which occurred at station TB-1, and the minimum average was 0.125, which occurred at TB-4. Average water column phosphate decreased by 42 % from April to August, while average pore water phosphate decreased by 24 % from April to August. -500501001500.0000.1000.2000.3000.4000.5000.600Concentration (ppm)Depth (cm) April August Figure 3-9. Phosphate concentrations versus depth at sampling station TB-9.

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60 Phosphate profile plots depict scattered data and indicate no concentration gradient upwards to or downwards from the sediment-water interface. Figure 3-9 shows data from TB-9 and is representative of the entire study area. 3.2.2.3 Nutrient breakdown: TSN, TN, TSP, and TP The remainder of the nutrient data simply allows one to quantify bulk dissolved organic nitrogen or phosphorus concentrations, and particulate (insoluble) nitrogen or phosphorus concentrations. The nutrient breakdown in Appendix B contains detailed data regarding these constituents. During the dry season, the water column TSN was comprised of 7 % dissolved inorganic nitrogen, and 93 % dissolved organic nitrogen. The PN concentration was 0.079 mg/L, which is 16 % of the TN concentration. The TSP was comprised of 88 % DIP and 12 % DOP. The PP concentration is 0.028 mg/L, which is 6 % of the TP. During the dry season, the average pore water TSN concentration was comprised of 58 % DIN, and 42 % DON. The PN concentration was 0.297 mg/L, which is 20 % of the TN concentration. The average pore water TSP was comprised of 100 % DIP. The PP concentration was 0.073 mg/L, which is 5 % of the TP. During the rainy season, the water column TSN was comprised of 1 % dissolved inorganic nitrogen, and 99 % dissolved organic nitrogen. The PN concentration was 0.0594 mg/L, which is 15 % of the TN concentration. The TSP was comprised of 88 % DIP and 12 % DOP. The PP concentration was 0.0212 mg/L, which is 33 % of the TP. During the rainy season, the average pore water TSN concentration was comprised of 68 % DIN, and 32 % DON. The PN concentration was 0.147 mg/L, which is 12 % of the TN concentration. The average pore water TSP was comprised of 95 % DIP, and 5 % DOP. The PP concentration was 0.066 mg/L, which is 24 % of the TP.

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61 Compositionally, the water column remained relatively constant from April to August, although DIN dropped slightly and DON increased slightly. Also, PP increased 5-fold. As for the pore water, it too remained relatively constant with time, but with a slight increase in DIN and a slight decrease in DON. Also, PP increased 5-fold. 3.3 Groundwater Flow Models 3.3.1 Water Budget On the basis of the groundwater divide and hydrologic equation, and assuming inputs equal outputs, approximately 0.1016 m of precipitation per year infiltrates into the local aquifer system and is discharged into the bay as groundwater. Given a drainage basin area of 755 km2, and the area of Old Tampa Bay, 195 km2, the area of land upon which precipitation falls is approximately 560 km2. This area multiplied by 0.1016 m of groundwater equals ~56,900,000 m3/year of groundwater that moves through the system and discharges into the bay. If distributed evenly across the bay, approximately 2,230,000 m3/year or 6104 m3/day of groundwater would discharge from the study area. This discharge volume equals a flux of ~0.55 ml/m2/min, and a seepage velocity of ~0.08 cm/day. Table 3-4 provides a comparison of results from this model, the flow net analysis, and the chloride-mixing model along with the seepage meters. 3.3.2 Flow Net The flow net of the Upper Floridan Aquifer indicates a discharge of ~31,000,000 m3/year. The Surficial Aquifer flow net yielded a discharge of ~10,600,000 m3/year. Combining these two models results in a total discharge of ~41,600,000 m3/year from the local aquifer system. If distributed evenly across the basin, approximately 4,000 m3/day discharges from the study area. This volume equals a flux of ~0.36 ml/m2/min, and a

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62 seepage velocity of ~0.05 cm/day (table 3-4). Table 3-3 contains the discharge rates through each flow zone (See also figure 2-7). Table 3-3. Surficial Aquifer flow net calculations. 1Discharge rates through each flow zone were computed by Darcys formula: Q = TIL. Flow Zone (T) Transmissivity (ft2/d) (I) Potentiometric gradient (ft/mi) (L) Length of flow zone (mi) (Q) Discharge rate1(gal/d) I 9000 2.2 5 740,520 II 5400 3.2 5 646,272 III 5400 6.4 11 2,843,597 IV 9000 2.6 8 1,400,256 V 5400 4.4 7 1,244,073 VI 5400 6.4 3 775,526 Total 7,650,244 3.3.3 Chloride Mixing Model (CMM) The results of this model represent a minimum discharge since it is not known how often the pore water was replaced by bay water between April and August of 2002. Calculated groundwater discharge represents only a fraction of the seepage meter groundwater discharge (Appendix C), similar to the mass balance and flow net analysis. The CMM was applied to chloride data from sites TB-1, TB-4, TB-9, TB-9A, and TB-9B. The model yielded information not only dealing with fluxes of groundwater, but also dealing with depth to which mixing occurs. This depth varied from station to station and therefore fluxes range from station to station because water flux is based, in part, on the total volume of water that can enter and exit the sediment. The mixing depths calculated from the CMM are different than the mixing depths observed in figure 3-7 because the CMM smoothed the chloride data and fit them to exponential curves. For TB-1, the CMM indicated a mixing depth down to 112 cmbsf. Based on the model, SGD at TB-1 was 2.11 ml/m2/min. For TB-4, the CMM indicated a mixing depth down to 110 cmbsf.

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63 The groundwater flux at this station was 2.04 ml/m2/min. TB-9 had a mixing depth down to 54 cmbsf. The groundwater flux at this location was 1.00 ml/m2/min. TB-9A had a mixing depth down to 134 cmbsf. The groundwater flux at this location was 2.55 ml/m2/min. TB-9B had a mixing depth down to 182 cmbsf. The groundwater flux at this station was 3.32 ml/m2/min. The average SGD of all stations from this model was 2.21 ml/m2/min, with 1 of 0.84 ml/m2/min. The maximum flux was 3.31 ml/m2/min, calculated at TB-9B, and the minimum was 1.00 ml/m2/min, calculated at TB-9. Based on these fluxes, seepage velocities would range between 0.29 cm/day and 0.48 cm/day. Table 3-4. Comparison of results from various groundwater seepage measurement techniques. Water Budget Flow Net CMM Seepage Meters ml/m2/min from study area 0.55 0.36 2.21 51 m3/year to O.T.B 56,900,000 41,600,000 226,450,000 5,177,000,000 m3/year to study site 2,230,000 1,460,000 8,870,000 202,800,000 m3/day to study site 6104 4000 24,300 555,700 seepage velocity 0.08 0.05 0.39 7.62

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CHAPTER 4 DISCUSSION 4.1 Seepage Meters The results from the seepage meters in this study indicate a range of values from ~16 ml/m2/min to ~93 ml/m2/min, with an average groundwater discharge of ~51 ml/m2/min. Seepage meters were used during the April sampling trip, which corresponds to the end of the dry season, therefore seasonal variations were not examined. Rainfall data from April is included in figure 1-4, and denotes only trace rainfall during the April sampling event. Appreciable rainfall during, or just prior to a sampling event could potentially increase hydraulic head, and increase the SGD from the bay floor causing aquifer derived water discharge to increase. Rainfall in Tampa Bay during the months from January to April was normal. Submarine groundwater discharge may also depend on the season. August precipitation data, figure 1-4, denotes much greater rainfall than in April. Also, Tampa Bay generally receives ~3.5 times (~55 cm) as much rainfall during the rainy seasons than the dry season (Table 1-1). Lindenberg (2001) observed temporal differences in SGD in the Indian River Lagoon. In the northern area of her study site she measured a 58 % increase in average seepage rates from the dry to the rainy season. The average seepage meter discharge during the dry season was 39.91 .66 ml/m2/min, while it was 63.08 .99 ml/m2/min during the rainy season. Lindenberg (2001) demonstrated a significant difference with 95 % confidence in the distributions of seepage rates between the two seasons using a Wilcoxon signed rank test. Lindenberg (2001) suggested the temporal variation could be caused by an increase 64

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65 in discharge from the surficial aquifer. Based on findings from Lindenberg (2001), and precipitation data from this study, rainy season seepage rates may show significant variation to dry season rates in Tampa Bay. There is no clear pattern of seepage rates among the various sampling stations (figure 3-1). Rates of equal or greater seepage magnitude occur offshore, and east and west of the putative spring vent (TB-9), such as TB-19, TB-12, and TB-4. Previous work shows that seepage meter measured SGD decreases roughly exponentially with distance from the shore (e.g. Bokuniewicz, 1980), although this trend does not occur here. The idea that seepage is affected by distance from shore is related to tidal heights and potentiometric surfaces, neither of which would play a major role in this study if SGD were mostly recirculated water. Other factors could cause variation of seepage rates, including the spatial heterogeneity of hydraulic properties and composition of the aquifers/aquitards that lie below the study site; the composition of shallow sediment at each station; hydraulic conductivity; the presence of benthic dwelling organisms; and possible sampling artifacts associated with seepage meters. If SGD were mostly recirculated seawater (~98 %), characteristics of and processes occurring in the shallow sediments would likely affect seepage rates, and not properties of deeper rocks and sediment. Variations in aquifer properties are likely to occur within the study site, but probably do not significantly influence the distribution of seepage rates here. The variation of composition of the uppermost 2 m of sediment such as grain size may affect the spatial distribution of seepage rates if there is recirculation. The two cores collected for this study reveal similar lithologies and porosities, and consist mostly of quartz sand, with shell-hash horizons. The porosity of the cores varies throughout, but

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66 both cores have an average porosity of ~31 %, and both show a trend of decreasing porosity with depth. In general, the shell-hash layers exhibit an overall lower porosity than the sands, possibly due to poor sorting and variable grain sizes. However, there are layers of porosity elevated over those of the sands within the shell-hash layers of both cores (Figures 3-2 and 3-3). A correlation exists relating discharge rates to the depth and thickness of the shell-hash horizon. The shell-hash zone in the TB-9 core (Figure 3-2) begins ~30 cmbsf and extends to about 130 cmbsf. The shell-hash zone in the TB-9A core (figure 3-3) begins ~100 cm bsf and extends another 90 cm below that. TB-9 has a higher discharge rate (60.5 .5 ml/m2/min) than TB-9A (54.3 .3 ml/m2/min). This observation suggests the thickness and depth of the shell-hash layer may exert control on SGD. The presence of benthic dwelling organisms in sediments may affect seepage rates by altering sediment characteristics. Sediments are altered by organisms through bioturbation, biodeposition, and production of cementing by-products such as shells and mucous (Day et al., 1989). Bioturbation often results in the formation of burrows which can act as conduits for water, which change the hydraulic properties of sediments by increasing porosity and permeability. Although these types of structures are likely, they were not observed in either of the two cores via visual inspection. In addition to burrows, benthic animals leave behind feces and bacterial mucous in sediments. The mucous and feces act as cementing agents and bind sediment particles together (Day et al., 1989). Cemented particles reduce porosity and permeability in the sediments. In contrast to burrows, these processes would reduce water discharge rates.

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67 Seepage meter results from Feather Sound are typical of seepage meter studies in terms of the magnitude of measured flux (eg Bokuniewicz, 1980;1992; Martin et al., 2002; Cable et al., 1996). Seepage rates are randomly distributed throughout the study area, and demonstrate no evidence of point source discharge and no correlation to distance from the shore, but are probably dependant on the characteristics of the shallow sediment, and the presence of organisms in the shallow sediment. Seepage meters can reflect seasonal changes in weather, but are unlikely to do so in Feather Sound since aquifer derived water appears to constitute on a small fraction of the net SGD. Seepage meter data may be erroneous due to the possibility of sampling artifacts and malfunction. If seepage rates are multiplied by pore water nutrient concentrations a flux of nutrients to the water column may be obtained. The resultant flux would not represent a new source of nutrients to the bay if recirculation accounted for the majority of the net seepage water, but the source would be internal. 4.2 Comparison of Measured and Modeled Submarine Groundwater Discharge Mass balance calculations provide a technique to estimate the volume of continentally derived aquifer water flowing into the bay. Assuming a fraction of the total rainfall falling on the land adjacent to the study site eventually flows to Tampa Bay, this model provides an average annual value for continentally derived SGD. Seepage meters yield an average integrated discharge rate of ~51 ml/m2/min, while the water budget model yields a result of ~0.60 ml/m2/min. Like the water budget calculation, the flow net analysis only measures fresh SGD, by using field-measured values for the hydraulic properties of the aquifers, aquifer thicknesses, the water table, and potentiometric surface elevations. For analysis here, the intermediate confining unit is assumed to be permeable with full hydraulic connection

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68 between the Upper Floridan and Surficial Aquifers. By combining the expected discharge from the Upper Floridan and Surficial Aquifers into one seepage rate, the flow net model yields a rate of ~0.35 ml/m2/min, which is the same order of magnitude as the water budget calculation, and suggests that most seepage water originates from some source other than the underlying aquifers. The difference in discharge rates between the models and the seepage meters indicate that freshwater may constitute approximately 1 to 2 % of the seepage water, with ~98 % of the seepage water originating from the bay via recirculative forces. Given the relatively porous and permeable nature of the shallow sediments, the shallow water column (which subjects the bay floor to advective forces acting within the water column), and potential bioirrigation, recirculation of bay water may provide the necessary flux of water to explain the discrepancy. 4.3 Evaluating the Exchange of Bay Water and Pore Water Using Tracers 4.3.1 Chloride, 18O, and Sr Variations in pore water concentrations of Cl, 18O, and Sr and 87Sr/86Sr with depth and through time reflect mixing of surface water and pore water, and can be used to separate different sources of water, including bay water and meteoric water from aquifer water. Interestingly, the tracer concentrations in the pore water and bay water are similar (Figures 3-5, 3-6, & 3-7). Chloride is conservative in most diagenetic reactions other than evaporite mineral precipitation and dissolution or during hydration reactions, which makes it a useful element for observations of mixing between different sources of water (Martin et al., in press). Oxygen isotope fractionation in the water column is controlled by evaporation and precipitation, similar to Cl concentrations, but provide signals for aquifer and seawater that are unique from Cl concentrations. Strontium isotope ratios

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69 are not influenced by evaporation and precipitation (although the Sr concentration is), but these ratios in aquifer water differ from those in seawater. Sr isotope ratios can be strongly altered by carbonate mineral dissolution, and therefore assume the characteristics of the carbonate rocks they flow through. Chloride, 18O values, and Sr concentrations in pore water and the water column decrease between the dry and rainy seasons in the study area (e.g. chloride figure 3-5). Rainfall has a low chloride concentration, generally 0.03 mM 0.3 mM (Berner and Berner, 1996), and thus precipitation falling directly on the Bay would dilute the tracer concentration of the water during the rainy season, while evaporation during the dry season would increase concentrations. Recharge from surface runoff would also increase during the rainy season also diluting the bay water. The similarity in pore water to bay water compositions supports the conclusion, made on the basis of the difference in measured and modeled flow rates, that bay water circulates through the shallow sediments. The shapes of the depth profiles also support recirculation of bay water rather than flux of new aquifer water. At each sampling location, dry and rainy season pore water concentration profiles converged at variable depths below the sediment-water interface. This trend is seen in the raw chloride data as well as the smoothed data calculated in the CMM (Figure 3-5 and Appendix C). The average convergence depth was calculated to be ~120 cmbsf in the CMM (discussed below), after the data was smoothed. Below the convergence point concentrations remain approximately constant with time indicating that temporal changes are restricted to shallow sediment. This depth is likely to be controlled by the sediment properties, the

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70 strength of the forces (tides, waves, density differences) causing exchange, the presence of benthic organisms, and the depth to which these organisms dwell. The shapes of the Cl concentration profiles with depth reflect bay water recirculation through the shallow sediments. For example, average data from 10 cmbsf suggest that pore water is re-mixed bay water. At this depth, the average pore water chloride concentration is 457 mM while the average water column chloride concentration is 462 mM. Similarly, the average pore water chloride concentration from all sampling location during August is 392 mM from 10 cmbsf while the water column is 394 mM. These differences fall within the measurement error of ~5 mM, and indicate there is not a significant difference in their values. If continentally derived meteoric aquifer water flowed to the pore spaces chloride concentrations should be lower than the water column. Diffusion can be responsible for changes in chemical concentrations. The effect of diffusion on geochemical tracer profiles, in a similar hydrogeological setting, was tested in Martin et al. (in press) using a general diagenetic equation (Berner, 1980; Boudreau, 1997; 2000). The process of diffusion was shown to be too slow to account for the change in chloride concentrations that occurred between a May sampling event and an August sampling event in the Banana River Lagoon, FL. With aquifer derived water constituting less than 5 % of the SGD, and a diffusion model shown to be too slow to generate profiles similar to the observed data, changes in tracer concentration profiles were attributed to advection. Based on the findings of Martin et al. (in press), diffusion does not appear to control the concentration changes observed in geochemical data from this study.

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71 The convergence of chloride concentrations to constant values at depth suggests that the upper portion of the pore water is the location where most mixing occurs. Considering that mechanisms for recirculation may diminish with depth, it would be expected that bay water would have the greatest influence on the pore water near the sediment-water interface. This concept is the basis for the CMM that fits chloride data to an exponential model to smooth the data. Mixing of bay water into the sediments to depths of ~120 cmbsf is greater than expected either from bioirrigation or by wave pumping (e.g. Shum, 1993; Boudreau, 1998). Shum (1993) relates depth of penetration of bay water into sediments from wave-induced flow to the shape and height of ripples on the seafloor, wave height, depth of water, current velocity, and other variables. Shum (1993) shows that depth of mixing of water column with pore water is generally proportional to the height of these ripples. Although no ripple measurements were made in Tampa Bay, visual observations indicate that in Feather Sound ripples are only a few centimeters high. Most likely wave action alone does not control the mixing depth. Boudreau (1998) reported that the activities of deposit-feeding organisms are restricted to a narrow zone of marine sediments, with a worldwide mean of 9.8 cm, restricting bioirrigation to these depths. The cause of the relatively deep penetration of recirculated water in the study area is unknown, but may reflect several of these mechanisms combined with the sandy and probably highly permeable sediment. Figure 4-1 depicts a conceptual model of the proposed mechanisms responsible for mixing in the shallow pore spaces. The oxygen isotope ratios from this study indicate enrichment in 18O relative to SMOW at all depths in the sediment; all water samples had positive values for 18O.

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72 Dansgaard (1964) developed an equation of a line that relates 18O of precipitation to mean annual air temperature. Based on work from Dansgaard (1964), if mean annual air temperature in St. Petersburg, FL is ~23C then 18O of precipitation, and therefore aquifer water, should be ~2.4 relative to SMOW. Figure 4-1. A conceptual model showing mixing at the sediment-water interface due to bioturbation, wave action, or tidal set up. B.W.=Bay Water; SGD=Submarine Groundwater Discharge; P.W.=Pore Water; B=Worms burrowing into sediment and ingesting sediment particles. Modified from Bhadha, 2003. Therefore, the 18O of aquifer water and rainwater should be greater than both the average water column (1.77 ) and average pore water (Figure 3-7). Consequently, oxygen isotopes are a poor indicator of the source of the pore water since all possible sources of water in the Tampa Bay area may be enriched with respect to 18O. However, oxygen isoptope data can be used to corroborate the mixing phenomenon suggested by the chloride concentration profiles since the concentration of 18O changes with respect to

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73 depth and time in the sediment. Oxygen isotope concentrations in the water column are influenced by evaporation, and this signal is carried into the pore water. The naturally occurring isotopes of strontium provide additional information on potential flow paths or mixing regimes of various water masses. The isotopic composition of 87Sr/86Sr is 0.70906.0003 in the oceans, and ranges from 0.70775 to 0.70790 in Upper Floridan aquifer host rock (McNutt, 2000). Water column and pore water strontium isotope data from TB-9 are both similar to modern seawater, and differ considerably from Upper Floridan groundwater (87Sr/86Sr=0.708909-0.709027; Martin et al., 2002). Similar to the other tracers in this study, the 87Sr/86Sr of the water column is almost identical to that of the pore water (Figure 3-6). The graph exhibits coinciding straight lines, which means that Sr isotope ratios of pore water and the water column do not change with time. This means that the chloride, oxygen, and strontium concentrations must change as result of precipitation and evaporation and not from a change in sources of water. It is this observation that supports the mixing hypothesis and demonstrates that there are not additional sources of water, such as meteoric water, in the aquifer. Table 4-1 contains Sr isotope data. Table 4-1. Water column and pore water tracer concentration seasonal differences. April August Water Column 87Sr/86Sr 0.709140 0.709120 Average Pore Water 87Sr/86Sr 0.709110 .000009 (1) 0.709167 .000085 (1) Water Column Sr Concentration 8.328 ppm 7.2378 ppm Sr Concentration at 10 cm bsf 8.16 ppm 7.40 ppm AvgerageWater Column Chloride 461 4.29 (1) mM 395 5.90 (1) mM Average Water Column 18O 2.02 (1.87-2.16) 1.52 (1.45-1.62)

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74 4.3.2 The Chloride Mixing Model A two-end member chloride-mixing model was used to determine a mixing fraction between the water column and pore water and converted to a flux of seepage water assuming bay water is recirculated through the shallow sediments, mixing of bay water and pore water decreases exponentially with depth below the sediment-water interface, and the rate at which mixing occurs is equivalent to the interval of time between sampling events (April to August). The results from the CMM are presented in Appendix C. The average mixing depth (the arithmetic mean of the mixing depth from each sample station) is 120 cmbsf. The average flux (the arithmetic mean of the flux from each sample station) predicted from the CMM is ~2.2 .84 ml/m2/min with a minimum flux of 1.01 ml/m2/min from TB-9, and a maximum flux of 3.31 ml/m2/min from TB-9B. Although these discharge rates represent ~4.5 % of the average discharge rate measured with seepage meters, the total volume of bay water that circulated through the sediments between April and August is unknown. An upper limit for recirculation time can be constrained by setting the equation from the CMM to the discharge rate from the seepage meters. Using TB-1 as an example calculation for recirculation time, the flux calculated using the CMM is 2.11 ml/m2/min. This location contains 34 cm3 of water over the depth that mixing occurs (112 cm). Dividing this volume by 1m2, and time (t), which is the new variable, it should equal the average seepage meter flux of 51 ml/m2/min. Solving for (t) indicates complete mixing should occur in ~4.6 days. The result from this calculation suggests that if the seepage rate of 51 ml/m2/min is caused by recirculation, then the pore water should be replaced every 4.6 days rather than 112 days, which is the interval between sampling events.

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75 This study produces two results for SGD that account for recirculated water, one from the seepage meters and one from the CMM. The result from the seepage meters is an order of magnitude greater than the result from the CMM. It is difficult to determine which measurement is correct. If the actual SGD rate is 51 ml/m2/min then the seepage meters are correct. If the actual rate is 2.2 ml/m2/min then the CMM is correct. If the seepage meters are correct then more than 1 pore volume mixes between April and August, but the residence time of the pore water is unknown. If the CMM is assumed to be correct then only 1 pore volume mixes between April and August, but the residence time is the period between sampling events. Additionally, if the CMM is correct then the seepage meters are wrong. Section 2-2 discusses some of the possible drawbacks and shortcomings of seepage meters. When equation 2-3 is set equal to the flux from the seepage meters the residence time of the pore water is shown to be ~4.5 days, and this result demonstrates that sampling every 3-4 months is not ideal. This comparison is made bearing in mind that either flux could be wrong. The CMM results, supported by 18O, Sr and 87Sr/86Sr data, suggests that most, if not all, of the seepage water is seawater, and the tracer data also corroborate evidence from the groundwater flow models. The changes in 18O, and Sr concentrations with depth and through time support the magnitude and frequency of recirculation indicated by the CMM, and the groundwater flow models suggest that only a small fraction of the net seepage water is continentally derived. The data indicate no significant spatial relationship between water column chemistry and the location of the putative spring. For example, the water column Cl concentrations at TB-9 during August reflects one of the

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76 highest chloride measurements in the group, and salinity measurements that are similar to sampling points far away from the putative vent (Figures 3-4, 3-5). 4.4 Nutrients and an Estimate of Nutrient Flux 4.4.1 Introduction Water column and pore water nutrient concentrations are essential to determine nutrient cycling in an estuarine system. In general, these data can be used in flux calculations that, in turn, help to quantify nutrient loading to a system. A comparison of water column and pore water concentrations can be used to calculate enrichments in the pore water. Often, the shape of the nutrient pore water profiles may be used to determine advective and diffusive fluxes of solutes to the overlying water column (Aller, 1980; Berner, 1980). Here, nutrient profiles preclude shape modeling as a method to determine advective and diffusive fluxes of solutes to the overlying water column because the data are scattered, and indicate no concentration gradient either upward to or downward from the sediment-water interface (eg figures 3-8, 3-9). Data from this study suggest that continentally derived, meteoric aquifer water constitutes only 1-2 % of the net SGD, and thus nutrient contributions from aquifer discharge should be small. Nutrients can also be sourced to bay water by direct discharge of sewage effluent, surface runoff, atmospheric deposition, regeneration of organic matter in the water column, and remineralization of organic matter in bay sediments. Furthermore, recirculation may enhance remineralization of nutrients in the sediment by introducing oxygenated water to the sediments and facilitating aerobic microbial activity. In the water column of marine environments, the concentration of organic nitrogen and phosphorus species are usually higher than inorganic nitrogen and phosphorus in the water column, while the converse is true in the sediment pore water because of bacterial

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77 remineralization of organic nitrogen and phosphorus in the pore waters (Treyfry et al., 1992; Herbert, 1999). When inorganic nutrients flow back to the water column, they are assimilated back into the food web, and drive part of the overall nutrient cycle. Results from this study indicate that the average water column TSN during the dry season is composed of 7 % DIN (dissolved inorganic nitrogen) and 93 % DON (dissolved organic nitrogen) [Appendix B]. The average water column organic and inorganic total soluble nitrogen both drop slightly during the rainy season. This might be due to dilution effects, or most likely as a result of increased primary production that creates more organic matter and utilizes available inorganic matter. The average water column TSP breaks down into 88 % inorganic phosphate and 12 % organic phosphate for both sampling events, opposite of the inorganic and organic fractions of nitrogen. Organic phosphorus should be more abundant in the water column than inorganic phosphorus. Inorganic phosphorus may be elevated in the water column from recirculation of the bay water or from excess P from apatite deposits. The recirculated water would increase the flux of inorganic byproducts of organic decay to the water column. Another explanation is that the bay is N limited, and thus excess inorganic phosphorus goes unused by the photosynthesizing organisms. Water column TN decreased from 0.487 mg/L to 0.39 mg/L from the dry to rainy season, while total phosphorus decreased from 0.107 mg/L to 0.08 mg/L. The decrease in both totals suggests dilution effects due to increased rainfall. During the dry season average pore water total soluble nitrogen was composed of 58 % dissolved inorganic nitrogen and 42 % dissolved organic nitrogen, and these numbers changed to 68 % and 32 %, respectfully, during rainy season. Dry season average pore water total soluble

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78 phosphorus is composed entirely of dissolved inorganic phosphorus, while this number drops to 95 % during the rainy season. These data also support the recirculation and enhanced nutrient loading hypotheses because pore water should contain more inorganic phosphorus if bay water is actively pumped into the sediment, and bacterially mediated decomposition is occurring below the sediment-water interface, converting organic material into inorganic material. In Feather Sound, ammonium is the most prevalent inorganic form of nitrogen in both the water column and pore water throughout the year, with trace nitrite or nitrate. Decaying organic matter is converted to ammonia via ammoniafication, and is transformed later to ammonium. In Feather Sound pore water oxygen generally decreases to trace concentrations below the sediment water interface suggesting that microbes heavily utilize oxygen as a source of energy to facilitate metabolism of organic matter. Nitrate concentrations are also low in the pore water, suggesting that microbes may also utilize nitrates as a source of energy. Unlike oxygen, however, nitrates are not replenished in the bay water rapidly, like oxygen, when the pore water flows back into the water column. Changes in oxygen and nitrate concentrations with depth further support the recirculation hypothesis. 4.4.2 Nutrient Loading and Flux Depending on the rate that pore water mixes with bay water, and amount of remineralized carbon, submarine groundwater discharge may provide a considerable amount of nutrients to the bay. Some dissolved nutrients in the pore water would be carried into the shallow sediments along with the circulating bay water, and thus net contributions of nutrients from pore water to bay water would be the total concentration less the concentration in the water column. Nutrient fluxes are calculated using two

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79 methods. One method involves the average seepage meter flux and the average nutrient pore water concentration (either NH4+ for N or SRP for P) at all depths and locations, for each sampling event. The average pore water ammonium concentration from both April and August is 0.001 gr/L .0003. For phosphorus, average concentrations were 0.00019 gr/L from April, and 0.00016 gr/L from August. Appendix B shows the range of values. When these concentrations (the average of the two seasons) are multiplied by the average seepage meter flux of 51 ml/m2/min, the calculated NH4+ flux is 18.48 gr/m2/year (10.10 26.81 ml/m2/min) and the PO4 flux is 4.77 gr/m2/year (2.62 6.92 ml/m2/min). Nutrient fluxes were also calculated using the measured water column oxygen concentrations along with the stoichiometry of equation 1-1, assuming that all of the water column oxygen is consumed in the oxidation of organic matter. Generally, the oxygen concentrations of all pore water samples are an order of magnitude less than the overlying water column. Although the non-zero concentrations of oxygen could indicate that there is not complete microbial reduction of the oxygen in the pore water, the measured oxygen may originate from atmospheric contamination during sampling. The lack of NO3in the pore water suggests that oxygen is depleted; otherwise the microbes would not reduce the NO3-. The flux of oxygen is calculated by multiplying the measured oxygen concentration of the water by seepage meter fluxes. Ammonium and phosphate fluxes are proportional to oxygen flux according to equation 1-1. The process for stoichiometrically calculating nutrient flux is described in Appendix D. Table 4-2 is a comparison of results from both techniques.

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80 Table 4-2. Comparison of nutrient fluxes from two techniques. Units are gr/m2/year. Flux of Ammonium Average Stoichiometry Based On Pore Water Concentration WC oxygen 18.48 11.44 Flux of Phosphate Average Stoichiometry Based On Pore Water Concentration WC oxygen 4.77 3.94 Assuming that the seepage meter flux and the average nutrient concentrations represent the entire study area, and using the areas reported in section 3.1.1, the annual NH4+ load from the sediments within the study area ranges from ~1.4 x 105 kg (average pore water nutrient concentration) to ~8.7 x 104 kg (stoichiometry of equation 1-1). The annual NH4+ load for all of Old Tampa Bay ranges from ~3.6 x 106 kg to ~2.2 x 106 kg, assuming that flux is equal throughout the bay. The annual phosphate load for the study area ranges from ~3 x 104 kg to 3.6 x 104 kg, depending on the technique. The annual phosphate load for Old Tampa Bay ranges from ~7.7 x 105 kg to ~9.3 x 105, depending on the method, and assuming equal flux throughout the bay. These data are compared with other results of nutrient loads and SGD (Table 4-3). All data are reported with identical units, unless otherwise noted. Also, the results are reported in terms of ammonium and phosphate, however these compounds are representative of the nitrogen phosphorus load, respectively, in the study area because these were the only measured species of nitrogen and phosphorus in the water. Wang et al. (1999) created a water quality model using Water Analysis Simulation Program (WASP) to simulate and evaluate the relationship between external nutrient loading and water quality of Tampa Bay. The model quantifies processes governing

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81 internal nutrient cycling and phytoplankton growth, and part of the model estimates internal nitrogen and phosphorus loading. Wang et al. (1999) suggests the major sources of inorganic nitrogen are benthic microbial processes which transform organic nitrogen to inorganic nitrogen and release it to the water column. Table 4-3. A comparison of water and nutrient flux data from this thesis to previous studies. According their model, which estimates benthic nutrient release from a mass balance calculation of the total bay nutrient budget, benthic ammonia release from the sediment contributes from 4-15 % of the ammonia in the nitrogen budget, whereas all external loads only contribute from 0-7 %. The other source of ammonia in the model is mineralization of organic matter in the water column (28-44 %). Sinks include phytoplankton growth, which utilizes 36-47 % of the available ammonia; nitrification of ammonia (3-6 %); and dispersion (0-6 %). According to their model, internal loads of ammonia released from bottom sediments exceeded the total external load for the entire bay. Wang et al (1999) indicate annual sediment release of ammonia in Old Tampa Bay

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82 is ~12.82 gr/m2/year, similar to results from this study. Although Wang et al. (1999) do not specify the mechanisms forcing nutrient release from the sediment the coherence of their results with this study suggest that much of the loading may be from recirculated bay water. Their study also indicates that internal loads due to benthic releases of phosphorus exceeded all external loadings combined but do not provide sufficient graphical data to estimate an annual sediment release of phosphorus.

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CHAPTER 5 CONCLUSIONS 5.1 The Importance of This Study The importance of this study is two-fold: 1) quantiyfing and characterizing the SGD and associated nutrient flux in Feather Sound enables a more accurate assessment of the hydrologic and nutrient budgets of Old Tampa Bay, and, 2) the results suggest that the shallow sediments are a source for nutrients to Old Tampa Bay. If SGD (regardless of the source of water) is low or diffuse, or necessary oxidizers are not present below the sediment-water interface, organic matter will be buried in the sediment thus removing it from the bay. In these scenarios the sediment is capable of sequestering excess nutrients from an increase in external nutrient loading. This study also demonstrates that the putative spring in the vicinity of Feather Sound was non-flowing during the sampling events in 2002, or does not exist, and that continentally derived aquifer water does not contribute significantly to SGD. These two findings suggest that Tampa Bay is not susceptible to new groundwater pollution such as demonstrated in Chesapeake Bay (eg. Gallagher et al. 1996; Robinson et al., 1998). 5.2 The Conceptual Model Submarine groundwater discharge has been shown to be an integral component of the marine hydrologic budget and can have a profound effect on diagenetic reactions near the sediment-water interface. Pore water concentration profiles of the geochemical tracers, along with seepage meter measurements and groundwater flow modeling suggest significant mixing between the shallow pore water and the overlying bay water in Old 83

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84 Tampa Bay which drives organic matter remineralization in the sediment. Seepage meters indicate an average groundwater discharge rate of 50.5 ml/m2/min with 1 of 22.8 ml/m2/min, while groundwater flow models, which only measure aquifer-derived groundwater, indicate discharges are approximately two orders of magnitude less. In addition, a two-end member chloride-mixing model suggests that complete mixing can occur in a matter of days. The differences between analytical models and direct measurements suggest that mixing may constitute up to 99 % of the submarine groundwater discharge. Bay water is mixed with pore water to an average depth of ~120 cm below the sediment-water interface. This depth is deeper than previously seen for mixing which is important because more of the sediment column will be altered by diagenetic reactions with the water. The exact physical mechanism or mechanisms that drive this mixing are unknown, but probably include some combination of advective forces such as density driven flow, wave action, tidal setup, and bioirrigation. Nutrient concentration data indicate that organic matter is remineralized in the shallow sediments of the bay and may be enhanced by mixing, as oxygen-saturated bay water flows through the shallow pore space. Inorganic nitrates (ammonium) and phosphates (phosphorus) are the by-products of organic matter degradation, and upon remineralization are reintroduced into the water column. Depending on the method of calculation, nitrogen flux ranges from 11.44 to 18.48 gr/m2/yr and phosphorus flux ranges from 3.94 to 4.77 gr/m2/yr. Nitrogen flux from this study agrees within an order of magnitude with nitrogen flux from Wang et al. (1999), which was calculated by a numerical model. Wang et al. (1999) calculated sediment nitrogen loading to be ~12.82 gr/m2/yr, which constitutes from 4 15 % of the total nitrogen budget for Old Tampa

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85 Bay. Nitrogen fluxes from this study, 11.44 to 18.48 gr/m2/year, equate to annual discharges of nitrogen from the sediment to the water column from ~2,460 to ~3,970 tons for the Old Tampa Bay segment of Tampa Bay, FL. Zarbock et al. (1996) recently estimated all external loads of nitrogen to Old Tampa Bay to be approximately 485 tons per year. This estimate includes non-point sources, domestic point sources, industrial point sources, and atmospheric deposition. Therefore, the sediment-released nutrient load appears to be up to over 8 times higher than all external sources combined. Sediment-released nutrients appear to be a significant component of the nutrient cycle, and an important internal source of nutrients to the bay, and should be considered in further ecological investigations into the overall health of Tampa Bay. The submarine spring, originally thought to exist on the basis of early reconnaissance, either does not exist or was not flowing during the time of sampling. It is possible that drought conditions over the years leading up to sampling had reduced its flow. Both point and non-point discharge of aquifer-derived water across the sediment water interface were negligible during the time of sampling, but it may be that both types of discharge occur ephemerally, during periods of intense rainfall. 5.3 Future Work This research provides preliminary information about Tampa Bay such as the physical properties of the sediment, rates of submarine groundwater discharge, nutrient fluxes to bay from sediment release, and the origin of the SGD. Future work should be designed to refine in both space and in time the apparent advective mixing of water in the shallow sediments that was observed during the first year of sampling. Work should include discrete time series measurements of pore water and water column solutes to determine what effects tidal fluctuations have on SGD. A dye-tracing test could be

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86 conducted to determine the exact flow path of continentally derived aquifer water. The SGD rate obtained from this method would be a useful comparison to the groundwater flow models employed in this study. Also, groundwater monitoring wells and piezometers on shore and off shore would be useful for several reasons. They would provide local aquifer water composition that could be compared to the pore water and bay water, and piezometers would provide useful hydraulic head data that would refine the data used in the flow net. Future work should also include seasonal seepage meter deployment to verify the affect that climate has on SGD. Duplicate deployment should be utilized to detect error associated with the meters.

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APPENDIX A WATER CHEMISTRY DATA

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A p ril Water Chemistr y Data 88

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89 Au g ust Water Chemistr y Data

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APPENDIX B NUTRIENT BREAKDOWN: AVERAGE OF ALL LOCATIONS

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91 Nutrient Breakdown: A p ril: Avera g e of all Locations. Units are m g /L.

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92

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93 Nutrient Breakdown: Au g ust: Avera g e of all Locations. Units are m g /L.

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94

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APPENDIX C CHLORIDE MIXING MODEL

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Raw Data TB-1 96

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97 Curve Matching, TB-1 TB-1 Mixing Model Curvey = 2E+21e-0.1023xR2 = 0.6258y = 3E-15e0.0881xR2 = 0.5636050100150200250350400450Chloride Concentration (mmol)Depth (cm) April August Expon.(April) Expon.(August)

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98 Curve Matching Data, TB-1

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99 Raw Data TB4

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100 Curve Matching, TB-4 TB-4 Mixing Model Curvey = 2E+21e-0.1008xR2 = 0.5547y = 8E-09e0.0531xR2 = 0.5845050100150200250300350400450Chloride Concentration (mmol)Depth (cm) April August Expon.(April) Expon.(August)

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101 Curve Matching Data, Tb-4

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102 Raw Data TB9

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103 Curve Matching, TB-9 TB-9 Mixing Model Curvey = 2E+16e-0.0786xR2 = 0.6519y = 1E-14e0.085xR2 = 0.621205101520253035404550556065707580859095100105110115120125130135140145150155160165170175180185190350400450500Chloride Concentration (mmol)Depth (cm) April August Expon.(April) Expon.(August)

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104 Curve Matching Data, TB-9

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105 Raw Data TB9A

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106 Curve Matching, TB-9A TB-9A Mixing Model Curvey = 2E+48e-0.2388xR2 = 0.9718y = 3E-13e0.0763xR2 = 0.880905101520253035404550556065707580859095100105110115120125130135140145350400450Chloride Concentration (mmol)Depth (cm) April August Expon.(April) Expon.(August)

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107 Curve Matching Data, TB-9A

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108 Raw Data TB9A

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109 Curve Matching, TB-9B TB-9B Mixing Model Curvey = 2E+32e-0.1571xR2 = 0.5195y = 1E-09e0.0589xR2 = 0.4525050100150200250300350400350400450Chloride Concentration (mmol)Depth (cm) April August Expon.(April) Expon.(August)

PAGE 121

110 Curve Matching Data, TB-9B

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APPENDIX D CALCULATIONS FOR DERIVING NUTRIENT FLUX STOICHIOMETRICALLY

PAGE 123

A p ril Nitro g en Flux 112

PAGE 124

113 Au g ust Nitro g en Flux

PAGE 125

114 Methodology For Deriving Nutrient Flux From Equation 1-1 Example: April Tb-1 Nitrogen Flux Water Column Oxygen Concentration = 8.32 mg/L Step 1: Convert Oxygen Concentration to mmol/L (8.32 mg/L)/(16 mg/mmol) = 0.52 mmol/L Step 2: Multiply Molar Concentration of Oxygen by Seepage Meter Flux (0.52 mmol/L) x (0.051 L/m2/min) = 0.027 mmol O/m2/min = 7.4 mol O2/m2/year Step 3: Divide Molar Flux of O2 by 138 Moles of O2 (per equation 1-1) to Obtain Moles of NH4+ (7.4 mol O2/m2/yr)/(138 moles O2) = 0.05 moles NH4+ Step 4: Multiply moles of NH4+ by 16 moles of NH4+ (per equation 1-1) (0.05 mol NH4+) x (16 moles NH4+) = 0.8 moles NH4+ Step 5: Convert Moles of NH4+ to Grams Using Molecular Weight of NH4+ (0.8 mol NH4+) x (18 gr) = 14.4 grams NH4+ Final Answer: Flux of Nitrogen per m2 per year = 14.4 gr NH4+/m2/year

PAGE 126

LIST OF REFERENCES Abel D.C. and McConnell R.L. (2002) Environmental Issues in Oceanography. Prentice-Hall, Inc., Upper Saddle River, NJ. Aller R.C. (1980) Quantifying Solute Distributions in the Bioturbated Zone of Marine Sediments by Defining an Average Micro-Environment. Geochim. Cosmochim. Acta 44, 1955-1965. Belanger T.V., Mikutel D.F. (1985) On the Use of Seepage Meters to Estimate Groundwater. Journal of Hydrology 27, 155-167. Belanger T.V., Walker R.B. (1990) Ground Water Seepage in the Indian River Lagoon, Florida. Tropical Hydrology and Caribean Water Resources. Proceedings on the International Symposium of the American Water Resources Association, 367-375. Berner R. (1980) Early Diagenesis, A Theoretical Approach. Princeton University Press, Princeton, NJ. Berner E.K. and Berner R.A. (1996) Global Environment: Water, Air and Geochemical Cylces. Prentice-Hall, Inc., Upper Saddle River, NJ. Bhadha J. (2003) Chemical Tracing and Analytical and Mass-Balance Modes of Pore Water Circulation in the Banana River Lagoon, Florida. University of Florida, M.S. thesis, Gainesville. Biggs R.B. and Cronin E.L. (1981). Special Characteristics of Estuaries. In Estuaries and Nutrients, ed. B.J. Neilson and L.E. Cronin, pp. 3-21. Humana Press, Clifton, NJ. Bokuniewicz H. (1980) Groundwater Seepage into Great South Bay, New York. Estuarine and Coastal Marine Science 10, 437-444. Bokuniewicz H. (1992) Analytical Descriptions of Subaqueous Groundwater Seepage. Estuaries 15, 458-464. Boudreau B.P., Marinelli R.L. (1994) A Modeling Study of Discontinuous Biological Irrigation. Journal of Marine Research 42, 947-968. Boudreau B.P. (1997) Diagenetic Models and Their Implementation. Springer-Verlag, New York, NY. 115

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116 Boudreau B.P. (1998) Mean Mixed Depth of Sediments: The Wherefore and the Why. Limnology and Oceanography 43, 524-526. Boudreau B.P. (2000) The Mathematics of Early Diagenesis: From Worms to Waves. Reviews of Geophysics 38, 389-416. Burnett W.C., Taniguchi M., Oberdorfer J. (2001) Measurement and Significance of the Direct Discharge of Groundwater into the Coastal Zone. Journal of Sea Research 46, 109-116. Burnett B., Chanton J., Christoff J., Kontar E., Krupa S., Lambert M., Moore W., ORourke D., Paulsen R., Smith C., Smith L., Taniguchi M. (2002) Assessing Methodologies for Measuring Groundwater Discharge to the Ocean: EOS 83, 117-123. Cable J.E., Bugna G.C., Burnett W.C., Chanton J.P. (1996) Application of 222Rn and CH4 for Assessment of Groundwater Discharge to the Coastal Ocean. Limnology and Oceanography 41(6), 1347-1353. Cable J.E., Burnett W.C., Chanton J.P., Corbett D.R., Cable P.H. (1997) Field Evaluation of Seepage Meters in the Coastal Marine Envrionment. Estuarine, Coastal and Shelf Science 45, 367-375. Capone D.G., Bautista M.F. (1985) A Groundwater Source of Nitrate in Nearshore Marine Sediments. Nature, 313, 214-216. Causseaux K.W. (1982) The Surficial Aquifer in Pinellas County, Florida. Water-Resources Investigation 84-4289. USGS. Cherkauer D.S., Nader D.C. (1989) Distribution of Groundwater Seepage to Large Surface-Water Bodies: The Effect of Hydraulic Heterogeneities. Journal of Hydrology 109, 151-165 Cherry R.N., Stewart, J.W., Mann J.A. (1970) General Hydrology of the Middle Gulf Area, Florida. Report of Investigation 56. USGS. Clersceri L.H., Greenberg A., Trussel R.R., Franson M.A. (1989) Standard Methods for the Examination of Water and Wastewater. American Public Health Association, American Water Works Association, Water Pollution Control Federation, Washington DC, 1289. Connor J.N., Belanger T.M. (1981) Ground Water Seepage in Lake Washington and the Upper St. Johns River Basin, Florida. Water Resources Bulletin 17 (5), 779-805. Corbett D.R., Dillon K.S., Burnett W.C. (2000) Tracing Ground Water Flow on a Barrier Island in the North-East Gulf of Mexico. Estuarine, Coastal, and Shelf Science 51, 227-242.

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117 Cronin E.L. and Neilson B.J. (1981) Preface. In Estuaries and Nutrients, ed. B.J. Neilson and L.E. Cronin, pp. ix,x. Humana Press, Clifton, NJ. Dansgaard, W. (1964) Stable Isotopes and Precipitation. Tellus 16 (4), 436-468. Day J.W., Hall C.A., Kemp W.M., Yanez-Arancibia A. (1989) Estuarine Ecology. John Wiley & Sons, New York, NY. Downing J.A., Peterka J.J. (1978) Relationship of Rainfall and Lake Groundwater Seepage. Limnology and Oceanography 23, 821-825. Dreschel T.W., Madsen B.C., Maull L.A., Hinkle C.R., Knott W.M. (1990) Precipitation Chemistry: Atmospheric Loadings to the Surface Waters of the Indian River Lagoon Basin by Rainfall. Environmental Chemistry 3, 185-187. Emerson S., Jahnke R., Heggie D. (1984) Sediment-Water Exchange in Shallow Estuarine Sediments. Journal of Marine Research 42, 709-730. Faure G. (1994) Principles of Isotope Geology. John Wiley and Sons, New York, NY. Fellows C.R., Brezonik P.L. (1980) Seepage Flow into Florida Lakes. Water Resources Bulletin 16 (4), 635-641. Froelich P.N., Klinkhammer G.P., Bender M.L., Luedtke N.A., Heath G.R. (1979) Early Oxidation of Organic Matter in Pelagic Sediments of the Eastern Equatorial Atlantic: Suboxic Diagenesis. Geochim. Cosmochim. Acta 43, 1075-1090. Gallagher D.L., Dietrich A.M., Reay W.G., Hayes M.C., Simmons G.M. (1996) Groundwater Discharge of Agricultural Pesticides and Nutrients to Estuarine Surface Water. Groundwater Monitoring and Remediation 5, 118-129. Hirsch J.D. (1998) Characterization of Hydraulic Seepage Within Brooklyn Lake, Keystone Heights, Florida. Masters Thesis, University of Florida, Gainesville. Huettel M. and Webster L.T. (2001) Porewater Flow in Permeable Sediments. In B.P. Boudreau and B.B. Jorgenson (eds.), The Benthic Boundary Layer Transport Processes and Biogeochemistry, 144-179. Oxford University Press, New York. Hutchinson C.B. (1983). Assessment of the Interconnection Between Tampa Bay and the Floridan Aquifer, Florida. Water-Resources Investigation 82-54. USGS. Isrealson O.W., Reeve R.C. (1944) Canal Lining Experiments in the Delta Area, Utah. Utah Agr. Exp. Sta. Tech. Bull. 313, 52. Johanes R.E. (1980) The Ecological Significance of the Submarine Discharge of Groundwater. Marine Ecological Program 3, 365-373.

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118 Knockenmus L.A. and Thompson T.H. (1991) Hydrogeology and Simulated Development of the Brackish Ground-Water Resources in Pinellas County, Florida. Water-Resources Investigations Report 91-4026. USGS. Lee D.R. (1977) A Device for Measuring Seepage Flux in Lakes and Estuaries. Limnology and Oceanography 22, 140-147. Lee D.R., Cherry J.A., Pickens J.F. (1980) Groundwater Transport of a Salt Tracer Through a Sandy Lakebed. Limnology and Oceanography 25 (1), 45-61. Li L., Barry D.A., Stagnitti F., Parlange J.Y. (1999) Submarine Ground Water Discharge and Associated Chemical Input to a Coastal Sea. Water Resources Research 35, 3253-3259. Lindenberg M.K. (2001) The Quantity, Characteristics, Source and Nutrient Input of Ground Water Seepage into the Indian River Lagoon, FL. University of Florida, M.S. Thesis, Gainesville. Martin J.B., Cable J.E., Swarzenski P.W. (2002) Quantification of Ground Water Discharge and Nutrient Loading to the Indian River Lagoon. St. Johns Water Management District. Martin J.B., Hartl K.M., Corbett D.R., Swarzenski P.W., Cable J.E. (2003) A Multi-Level Pore-Water Sampler For Permeable Sediments. Journal of Sedimentary Research. Nielson P. (1990) Tidal Dynamics of the Water Table in Beaches. Water Resources Research 26, 2127-2134. Nixon S.W. (1981) Remineralization and Nutrient Cycling in Coastal Marine Ecosystems. In Estuaries and Nutrients, ed. B.J. Neilson and L.E. Cronin, pp. 111-138. Humana Press, Clifton, NJ. Odum E.P. (1997) Ecology: A Bridge Between Science and Society. Sinauer Associates, Inc. Publishers, Sunderland, MA. Pandit A., El-Khazen C.C. (1990) Ground Water Seepage into the Indian River Lagoon at Port St. Lucie. Florida Scientist 53, 169-179. Pritchard D.W. and Schubel J.R. (1981) Pysical and Geological Processes Controlling Nutrient Levels in Estuaries. In Estuaries and Nutrients, ed. B.J. Neilson and L.E. Cronin, pp. 47-68. Humana Press, Clifton, NJ. Rasmussen L.L. (1998) Groundwater Flow, Tidal Mixing and Haline Convection in Coastal Sediments: Tallahassee, Florida State University, MS, 119. Robinson M., Gallagher D. (1999) A Model of Ground Water Discharge from an Unconfined Coastal Aquifer. Ground Water 37, 80-87.

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119 Robinson M.A., Gallagher D.L., Reay W.G. (1998) Field Observations of Tidal and Seasonal Variations in Ground Water Discharge to Tidal Estuarine Surface Water. Ground Water Monitoring and Remediation 18, 83-92. Rutkowski C.M, Burnett W.C., Iverson R.L., Chanton J.P. (1999) The Effect of Ground Water Seepage on Nutrient Delivery and Sea-Grass Distribution in the Northeastern Gulf of Mexico. Estuaries 22, 1033-1040. Shaw R.D., Prepas E.E. (1989) Anomolous, Short-Term Influx of Water into Seepage Meters. Limnology and Oceanography 34 (7), 1343-1351. Shum K.T. (1992) Wave-Induced Advective Transport Below a Rippled Water-Sediment Interface. Journal of Geophysical Rearch 97, 789-808. Shum K.T. (1993) The Effects of Wave Induced Pore Water Circulation on the Transport of Reactive Solutes Below a Rippled Sediment Bed. Journal of Geophysical Research 98, 10,289-10,301. Simmons G.M., Jr. (1992) Importance of Submarine Ground Water Discharge (SGWD) and Seawater Cycling to Material Flux Across Sediment/Water Interfaces in Marine Environment. Marine Ecological Program Series 84, 173-184. Smethie W.M., Nittrouer C.A., Self R.F.L. (1981) The use of Radon-222 as a Tracer of Sediment Irrigation and Mixing on the Washington Continental Shelf: Marine Geology 42, 173-200. Smith D.G. (1984) Vibracoring Fluvial and Deltaic Sediments: Tips on Improving Penetration and Recovery. Journal of Sedimentary Petroluem 54 (2), 660-663. Swarzenski P.W., Reich C.D., Spechler R.M., Kindinger J.L., Moore W.S. (2001) Using Multiple Geochemical Tracers to Characterize the Hydrogeology of the Submarine Spring off Crescent Beach, Florida. Chemical Geology 179 (1-4), 187-202. Wang P.F., Martin J., Morrison G. (1999) Water Quality and Eutrophication in Tampa Bay, Florida. Estuarine, Coastal and Shelf Science 49, 1-20. Weiskel P.K., Howes B.L. (1992) Differential Transport of Sewage-Derived Nitrogen and Phosphorus Through a Coastal Watershed. Environmental Science and Technology 26, 352-360. Winchester J.W., Escalona L., Fu J., Furbish D.J. (1995) Atmospheric Deposition and Hydrogeologic Flow of Nitrogen in Northern Florida Watersheds. Geochimica et Cosmochimica Acta 59 (11), 2215-2222. Yobbi D.K., Mills L.R., Woodham W.M. (1980) Groundwater Levels in Selected Well Fields and in West-Central Florida, May 1980. Open-File Report 80-1001. USGS.

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120 Zarbock H.W., Janicki A.J., Janicki S.S. (1996) Estimates of Total Nitrogen, Total Phosphorus, and Total Suspended Solids to Tampa Bay, Florida. Tampa Bay National Estuary Program. Zimmerman C.F., Montgomery J.R., Carlson P.R. (1985) Variability of Dissolved Reactive Phosphorus Flux Rates in Near-Shore Estuarine Sediments: Effects of Ground Water Flow. Estuaries 8, 228-236.

PAGE 132

BIOGRAPHICAL SKETCH I graduated from h igh s chool in 1996, in A tlanta, GA. From t here I attended the University of Georgia, in Athens, GA. As a freshman I began taking classes in the premedicine track, but after two quarters of sitting in 350-person lectures I decided to deviate from the normal canon of science classes. Geology seemed like an interesting major, and part of the requirement for gr aduating was attending a summer field camp based in Colorado. I had always been intere sted in Earth science, stemming all the way back to elementary school when I learned about plate tectonics, and also during family trips to the western U.S. and Canadian Rockies. I enrolled in my first geology course to take a break from the impersonal lectures of the pre-med track. I soon discovered a passion for the subject, the field trips, and the intimate class structure, and quickly learned how to study for the first time in my life. The challenge of applying physical, chemical, and mathematical principles into a tangible, often hands -on discipline forced me to go beyond memorizing equations and facts, and forced me to think abstractly and geometrically. I struggled through a course fo r the first time in my life and I think that challenge is why I continued with geology to the graduate level. I became a geology major, I graduated in the summer of 2000 with a Bachelor of Science degree, and I went on to work for a year in the private sector as an Environmental Consultant in New Orleans, LA. After a year I decided to return to academia to further my education. I joined the Department of Geological Sciences at the University of Florida in 2001, and I 121

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122 graduated in the spring of 2004 with a Master of Science degree, and a minor in environmental engineering.


Permanent Link: http://ufdc.ufl.edu/UFE0004261/00001

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Title: Submarine Groundwater Discharge and Nutrient Loading to Feather Sound, Old Tampa Bay, Florida
Physical Description: Mixed Material
Copyright Date: 2008

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SUBMARINE GROUNDWATER DISCHARGE AND NUTRIENT LOADING TO
FEATHER SOUND, OLD TAMPA BAY, FLORIDA















By

ERIC J. DAVIS


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA


2004

































Copyright 2004

by

Eric J. Davis
















ACKNOWLEDGMENTS

I would like to thank the United States Geological Survey for funding my research.

I would also like to give particular thanks to my advisor, Dr. Jonathan Martin, for his

guidance throughout my research. In addition, I would like to thank my committee

members, Dr. Elizabeth Screaton and Dr. Michael Annable, for their assistance and

suggestions towards my thesis. I would like to thank Dr. Peter Swarzenski, with the

USGS, for his assistance in the field. I would also like to thank Dr. Dan Yobbi, USGS,

for lending me valuable reports and maps. I would like to thank Dr. Jason Curtis for his

assistance in the stable isotope lab. I would like to thank Howie Scher for his assistance

with my radiogenic isotopes. I would like to thank William Kenny for assisting with

nutrient analyses and for making his lab available to me. I would like to thank Dr. John

Jaeger for his assistance with the Geotek Logger, and for assistance with GIS

applications. I would also like to thank George Kamenov for his assistance in the Clean

Lab, help with graphic imaging, and for discussions regarding oxygen and strontium

isotopes. I would like to thank Jehangir Bhadha, my predecessor and office mate, for his

guidance and allowing me to use his thesis as a model. I would like to thank Mike

Hillesheim for assistance with graphic imaging. I would like to thank the faculty, staff,

and all of my colleagues here at the Department of Geological Sciences for their help and

support throughout my time in Gainesville. Finally, I would like to thank my mom for

her patience and for supporting me for the past 3 years.





















TABLE OF CONTENTS


page


ACKNOWLEDGMENT S ................. ................. iii.._._. ....


LI ST OF T ABLE S ........._..... .......... ............... vii...


LIST OF FIGURES ...._.._ ................ .......__. .........vi


AB S TRAC T ......_ ................. ............_........x


CHAPTER


1 INTRODUCTION ................. ...............1.......... ......


1.1 Statement of Problem .............. ...............1.....
1.2 The Significance of Estuaries ................ .. ........... ......... ......... ...... 4
1.3 Eutrophication, The Nutrient Budget, and Nutrient Cycles .............. ..................6
1.4 Previous Studies of SGD and Nutrient Loading ........................... ...............9
1.5 Study Area ................ ...............14........... ...
1.6 Hypotheses.................... ......___.. .. ...............14.....
1.7 Local Geology and Hydrostratigraphy .............. ...............16....
1.8 Regional Climate ................. ...............19.......... ....


2 M ETHODS .............. ...............22....


2. 1 W ork Plan ................. ...............22.......... ....
2.2 Seepage M eter .............. ...............23....
2.2. 1 Background ................. .. .......... ........ .. .. ........2
2.2.2 Seepage Meter Construction, Deployment and Seepage Measurements ...25
2.3 W ater Samples ............... ... .. .......... ...............26......
2.3.1 Multisamplers and Pore water ................. ...._ ...............27
2.3.1.1 Design............... ...............27.
2.3.1.2 Deploy ment ............ ..... ._ ...............29..
2.3.1.3 Sampling............... ...............29
2.3.2 Bay water............... ...............3 0.
2.3.3 Analyses .............. ...............30....
2.4 Sediment Cores ............ _. ..... ...............32..
2.4. 1 Sampling ............ _. .... ...............32...
2.4.2 Analy si s ................. ...............3 3..
2.5 Groundwater Flow Models ................. ...............34........... ...












2.5.1 W ater Bud get ................. ...............34........... ..
2.5.2 Flow N et ................... ........... ...............36.....
2.5.3 Chloride Mixing Model .............. ...............38............. ..


3 RE SULT S .............. ...............41....


3.1 Physical Analyses ........._.__........_. ...............41...
3.1.1 Seepage Meters............... ...............41.
3.1.2 Sediment Cores. ......_......._......_.__.. ......._.. ................42
3.1.2. 1 Lithology ......_.__._ .... .._._. ....__. ....___.. ........._.....42
3.1.2.2 Bulk density and porosity ....._.__._ ..... ... .__. .. ..._._. ......4
3.2 Chemical Analyses .............. ...............47....
3.2.1 Tracers .............. .. ....... ..... ............4
3.2.1.1 Chloride and Salinity............... ...............47
3.2.1.2 Isotopes............... ...............49
3.2.2 Nutrients .............. ...............54....
3.2.2. 1 Ammonium ................. ...............57...._ .__ ...
3.2.2.2 SRP (phosphate) ................. ....... ........ ... ..... ...............58...
3.2.2.3 Nutrient breakdown: TSN, TN, TSP, and TP .............. ..................60
3.3 Groundwater Flow Models ........._._... .....__ ...............61..
3.3.1 Water Bud get ........._.___..... .___ ...............61..
3.3.2 Flow Net ........._._.......... ...._._........._. ...........6
3.3.3 Chloride Mixing Model (CMM) .............. ...............62....


4 DI SCUS SSION ................. ...............64................


4. 1 Seepage Meters ................. .. ................ ............. ..... .. .... .......6
4.2 Comparison of Measured and Modeled Submarine Groundwater Discharge......67
4.3 Evaluating the Exchange of Bay Water and Pore Water Using Tracers ..............68
4.3.1 Chloride, 618O, and Sr .............. ...............68....
4.3.2 The Chloride Mixing Model ................. ...............74..............
4.4 Nutrients and an Estimate of Nutrient Flux .....__.___ ..... ... ._ ..........._..._.76
4.4.1 Introduction .............. ... ...............76...
4.4.2 Nutrient Loading and Flux .............. ...............78....


5 CONCLUSIONS .............. ...............83....


5.1 The Importance of This Study .............. ...............83....
5.2 The Conceptual Model .............. ...............83....
5.3 Future Work............... ...............85..


APPENDIX


A WATER CHEMISTRY DATA ................. ...._.._ ......._ ............8


B NUTRIENT BREAKDOWN: AVERAGE OF ALL LOCATIONS..........................90












C CHLORIDE MIXING MODEL ................ ...............95................


D CALCULATIONS FOR DERIVING NUTRIENT FLUX
STOICHIOMETRICALLY ................. ...............111................


LIST OF REFERENCES ................. ...............115................


BIOGRAPHICAL SKETCH ................. ...............121......... ......

















LIST OF TABLES


Table pg

1-1 Average historical (1916-2001) monthly, annual, and seasonal rainfall data at the
St. Petersburg rainfall gauge. ............. ...............20.....

2-1 Estimated precision of various solutes for water samples. ................. ............_...3 1

2-2 Components and associated values of hydrologic equation. ........._..... ...............3 5

3-1 The average, maximum, minimum, and standard deviation of salinity, chloride,
and other field measurements during the dry season. ............. .....................4

3-2 The average, maximum, minimum, and standard deviation of salinity, chloride,
and other field measurements during the rainy season. ............. .....................4

3-3 Surficial aquifer flow net calculations. ............. ...............62.....

3-4 Comparison of results from various groundwater seepage measurement
techniques ................. ...............63.................

4-1 Water column and pore water tracer concentration seasonal differences. ...............73

4-2 Comparison of nutrient fluxes from two techniques. Units are gr/m2/year. ............80

4-3 A comparison of water and nutrient flux data from this thesis to previous
studies ................ .............81..................

















LIST OF FIGURES


Figure pg

1-1 Location of Pinellas County, FL. ................ ...............15...............

1-2 Generalized stratigraphic and hydrogeologic section, Pinellas County. ..................1 8

1-3 Conceptual, cross-sectional view of saltwater-freshwater relations in the Tampa
Bay area, and flow paths of groundwater ................. ...............19........... ..

1-4 Daily rainfall (cm) recorded at the St. Pete gauging station before and during
each sampling event. ............. ...............21.....

2-1 The Tampa Bay basemap created from individual digital orthophoto quadrangle
quarters (DOQQ's) downloaded from the LABINS website. ................ ...............23

2-2 A depiction of a seepage meter placed in the sediment under the water column.....25

2-3 A satellite image of the study site and sampling stations. .............. ............._..27

2-4 Design of a multi sampler. ............. ...............28.....

2-5 Digital photograph of vibracore assembly taken during the August sampling trip,
from the deck of the USGS pontoon boat. ................ ...............33..............

2-6 The outline of the surface and groundwater divides superimposed onto the Tampa
Bay basemap created using GIS software. ............. ...............36.....

2-7 The Surficial Aquifer flow net. Roman numerals (I VI) represent discretized
transmissivity zones. ............. ...............38.....

3-1 Submarine groundwater discharge magnitudes from seep meters at various
locations within the sampling grid.. ............ ...............41.....

3-2 TB-9 core lithology, digital photograph, and porosity ................. ............ .........44

3-3 TB-9A core lithology, digital photograph, and porosity ........._..._.... ......_.._.......45

3-4 Seasonal water column salinities from TB-1, 9B, 9, 10, 11, 12, 4, 9A. ...................48

3-5 Chloride concentration versus depth below sediment-water interface. ........._.._.......52










3-6 Water column and pore water Sr concentrations. ............. .....................5

3-7 6 IsO concentration versus depth. ...._. ......_._._ .......__. ..........5

3-8 Ammonium concentrations versus depth below the sediment-water interface at
sampling station TB-9. ............. ...............58.....

3-9 Phosphate concentrations versus depth at sampling station TB-9. ..........................59

4-1 A conceptual model showing mixing at the sediment-water interface due to
bioturbation, wave action, or tidal set up .............. ...............72....
















Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

SUBMARINE GROUNDWATER DISCHARGE AND NUTRIENT LOADING TO
FEATHER SOUND, OLD TAMPA BAY, FLORIDA

By

Eric J. Davis

May 2004

Chair: Jonathan Martin
Major Department: Geological Sciences

Submarine groundwater discharge (SGD) and associated nutrient fluxes can be

important components of the hydrologic and nutrient cycles in estuarine environments.

Investigations of physical properties of sediments, chemical composition of the pore

water and bay water, and local groundwater flow in and around Feather Sound, Tampa

Bay, Florida, suggest that shallow sediments could be an important source of nutrients to

the bay. This nutrient flux depends on (i) the rate and origin of groundwater discharge,

(ii) the concentration of the nutrients in the discharged water, and (iii) the magnitude and

frequency of mixing between the bay water and pore water. Submarine groundwater

discharge can originate from continentally derived aquifer water, and thus have a similar

chemical composition to local meteoric water or be modified by water-rock reactions as it

flows through the aquifers. The meteoric water component of SGD can contribute

pollutants and excess nutrients from the mainland. Alternatively, SGD can originate

from bay water if mixing occurs with the shallow pore waters. Mixing, or recirculation,










of bay water through the shallow sediments is often a significant local source of nutrients

because it enhances organic matter remineralization in the pore water, and releases the

inorganic nutrient by-products back to the overlying water column.

In this study seepage meters yielded an average groundwater discharge rate of

~51 ml/m2/min, while the groundwater flow models indicated groundwater discharge

rates from 0.36 to 0.55 ml/m2/min. This difference indicates that the SGD at the study

site may contain up to ~98 % recirculated seawater. In addition, Cl- concentrations and

Sr and oxygen isotope ratios are identical between shallow pore waters and overlying bay

water regardless of changes in water column chemistry. Seasonal pore water

concentration profiles of these tracers suggest mixing occurs to a depth of up to~-120 cm

below the sediment-water interface.

Sediment-released nutrient flux, facilitated by recirculation, ranges from 3.94 to

4.77 gr/m2/year for total phosphorus, and from 11.44 to 18.48 gr/m2/year for total

nitrogen. In the case of total nitrogen, fluxes of 1 1.44 to 18.48 gr/m2/year equate to

annual discharges of nitrogen from the sediment to the water column from ~2,460 to

~3,970 tons for the Old Tampa Bay segment of Tampa Bay, FL. A recent estimate of

external loading of total nitrogen to Old Tampa Bay is approximately 485 tons per year,

suggesting the sediment-released nutrient load may be up to over 8 times higher than all

external sources combined. Organic matter remineralization and subsequent sediment

release appear to be a significant component of the nutrient budget, and an important

source of nutrients to Tampa Bay.















CHAPTER 1
INTTRODUCTION

1.1 Statement of Problem

Marine scientists have recently focused on submarine groundwater discharge

(SGD) as both an important part of the hydrologic cycle and the nutrient budget of

nearshore marine environments. This process has been reported to be a significant flow

path for nutrients, and other contaminants from agricultural lands, septic tanks, and other

point and non-point sources directly into coastal zones (Johannes, 1980; Simmons, 1992;

Weiskel and Howes, 1992; Gallagher et al., 1996; Martin et al., 2002; Burnett et al.,

2002), with important ecological consequences for estuaries, lagoons, marshes, reefs, and

other marginal marine ecosystems (Johannes, 1980; Emerson et al., 1984; Capone and

Bautista, 1985; Zimmerman et al., 1985; Simmons, 1992; Gallagher et al., 1996;

Rutkowski et al., 1999; Corbett et al., 2000; Martin et al., 2002). Sediments on the sea

floor can also be a significant source of nutrients generated by catabolism of organic

matter detritus by microbes (Pritchard and Schubel, 1981; Nixon, 1981). This process

can be enhanced or accelerated if oxygenated groundwater is advected through the

seafloor sediments. For example, a study by Wang et al. (1999) has shown that internal

nutrient cycling and transport exceeded external loading for Tampa Bay for the period

between 1985 and 1994.

Submarine groundwater discharge (SGD) is defined as any flow across the seabed,

regardless of mechanism or driving force (Burnett et al., 2002; Martin et al., 2003)

Therefore, SGD encompasses diffuse, aquifer derived groundwater seepage, point-source









discharge, such as submarine spring vents, and recirculated seawater. Diffuse

groundwater seepage or submarine spring discharge can occur wherever an aquifer with a

head greater than surface waters is connected to overlying surface waters through

permeable bottom sediments (Johannes, 1980; Rutkowski et al., 1999). In such a case,

the water would originate on continents as meteoric water, and flow laterally through

aquifers from the continents to coastal areas. Recirculated seawater, on the other hand, is

the exchange of large quantities of water across the sediment-water interface, and is

controlled by at least three maj or categories of processes including wave and tidal

pumping (Nielson, 1990; Shum, 1992, 1993; Li et al., 1999; Huettel and Webster, 2001),

density driven flow (Rasmussen, 1998), and passive or active flow through structures

produced by burrowing organisms (bioirrigation) (Aller, 1980; Smethie et al., 1981;

Boudreau and Marinelli, 1994).

Compared to other components of the hydrologic and nutrient cycles, SGD and

internal nutrient loading are generally poorly constrained variables. The nature of mixing

of fresh water and seawater, and the magnitudes of water and nutrient fluxes associated

with SGD are difficult to evaluate because of the dispersed nature of the discharge, the

slow rate of flow, the range of processes controlling the fluxes, and the diversity of

techniques that have been used to evaluate these fluxes. Three basic methodologies have

been applied for quantitative assessments of SGD: modeling, direct physical

measurements, and tracer techniques (Burnett et al., 2001;2002). Both analytical and

numerical models have been used, ranging in complexity from simple mass balance

calculations to computer-based, numerical simulations. Direct physical measurements

are limited to seepage flux meters and to tracer techniques measuring natural










geochemical species, which are enriched in groundwater and behave conservatively in

seawater.

Comparing results of these techniques indicates that freshwater often only

represents a fraction of SGD, with the remainder composed of admired seawater

(Bokuniewicz, 1992; Burnett et al., 2002; Martin et al., 2002). In one such study, Li et al.

(1999), presented a theoretical model that showed that groundwater circulation and

oscillating flow caused by recirculative forces may constitute up to 96 % of SGD

compared with 4 % due to fresh groundwater discharge. One particular location that

demonstrates this discovery is the Indian River Lagoon, FL. It has been the site of

numerous hydrodynamic and hydrogeochemical studies over the past 15 years and

various techniques have been used to quantify SGD. Numerical modeling of the lagoon-

aquifer system (Pandit and El-Khazen, 1990) has yielded SGD values that are several

orders of magnitude lower than the SGD values obtained from direct physical

measurements and chemical tracer studies (Belanger and Walker, 1990; Martin et al.,

2002). The discrepancy arises because numerical modeling only accounts for

continentally derived aquifer water, whereas the other methods do not differentiate the

components of the SGD, but rather measure an integrated total discharge. The difference

has been assumed to be recirculated seawater, but the various studies were conducted at

different times and at different places in the lagoon and thus are not directly comparable.

Mixing could provide additional sources of nutrients to coastal regions by

enhancing organic matter remineralization. Mixing would pump surface water that is

near saturation with atmospheric oxygen into the shallow anoxic sediments, thus

increasing the oxidation potential of the pore water, and ultimately the degradation of










organic matter. Subsequently, the inorganic by-products of this process are advected

back out into the water column and reintroduced into the food web. This phenomenon is

known as enhanced nutrient loading, and can cause a feedback loop that can result in

accelerated eutrophication.

The purpose of this investigation is to quantify the exchange of water and solutes

across the sediment-water interface using seepage meters, analytical groundwater flow

models, and geochemical tracers. Rarely have all three been utilized in the same study,

but such a study allows the various constituents of SGD to be determined and traced to

their source or sources. The comparison between the direct physical measurements and

model calculations is important to this study because it reveals any discrepancy between

what is measured and what is predicted, which suggests other forces involved in SGD

other than terrestrial hydraulic gradients. Without fully understanding the nature, origin,

and driving forces of SGD it is impossible to quantify internal nutrient loading and

characterize the hydrologic and nutrient budgets of coastal zones. Without characterizing

these budgets, ecological impact of SGD to a particular system cannot be known.

1.2 The Significance of Estuaries

The study site for this proj ect is a portion of southwestern Old Tampa Bay, known

as Feather Sound. Feather Sound is an estuary; it is semi-enclosed and coastal, has a free

connection to the open sea, and has a salinity gradient caused by the dilution of seawater

with freshwater from upland drainage and other external sources (Biggs and Cronin,

1981).

Estuaries and other inshore marine waters typically are enriched in nutrients

because of their position at the distal end of watersheds. Three major life forms of

autotrophs are often intermixed in an estuary and play varying roles in maintaining a high










gross production rate: phytoplankton, benthic microflora, and macroflora (large attached

plants, including seaweed, submerged eelgrass, emergent marsh grasses, and, in the

tropics, mangrove trees) (Odum, 1997). The high primary production that characterizes

estuaries provides hatcheries for many commercial coastal shellfish and fish that are

harvested not only in the estuary but offshore as well. Estuaries are thus vital to the

marine foodweb, and consequently to humans.

Estuaries rely on an influx of nutrients and fresh water from external sources to

maintain healthy biological productivity because of a net loss of water and its associated

nutrients to the oceans. Nutrients are also lost through burial to the sediment. Healthy

estuaries maintain a delicate equilibrium between water and nutrient inputs and outputs.

They can compensate for, and assimilate, large quantities of nutrients despite the large

fluctuations that occur with variations in flow from tributaries, groundwater, and other

inputs. Nutrients can be stored, incorporated in standing crops of plants, released, cycled

and exported, and estuaries frequently achieve high production of plants and animals

without creating any undesirable enrichment of nutrients (Cronin and Neilson, 1981).

However, there is a nutrient level threshold beyond which the health of an estuary may

suffer because of eutrophic conditions.

Excessive enrichment commonly results from increasing human population, and

associated development. According to the World Resources Institute, at least 60% of the

planet' s human population lives within 100 km of the coast (Abel and McConnell, 2002).

Coastal areas have the fastest growing populations, and more than half the world' s

coastlines are at significant risk from development activities related to this population

growth (Abel and McConnell, 2002). By the year 2010, 75 % of the U. S. population will









live within 75 km of a coastline (Wang et al., 1999). Tampa Bay, one of Gulf of

Mexico's largest estuaries, exemplifies the environmental stresses that U.S. coasts face.

Eutrophication has ultimately resulted in a decline in eelgrass meadows (Wang et al.,

1999), which are vital to aquatic animals for food and habitat. Without these habitats,

levels of these animals would no longer support the fishing and tourist industries.

1.3 Eutrophication, The Nutrient Budget, and Nutrient Cycles

Humans can accelerate eutrophication by artificially enriching water bodies with

excess nutrients, and/or organic matter. One focus of this study is internal nutrient

loading, but external loading ultimately drives eutrophication (Wang et al., 1999).

Eutrophication has been broadly defined as high biological productivity resulting from

excessive nutrient and organic matter concentrations. Enrichment in organic matter

results from the addition to estuaries of dissolved and particulate organic carbon, organic

nitrogen, and organic phosphorus that would not naturally be a source to estuaries, such

as from sewage. Another component of the cycle is inorganic nutrient enrichment, which

primarily is an increase in dissolved inorganic nitrogen and phosphorus, and originates

from natural and anthropogenic processes. Both inorganic and organic nutrients lead to

excessive phytoplankton (or algal) growth, which in turn leads to two things: 1) an

increase in turbidity, which blocks sunlight vital to photosynthesis, and 2) a depletion of

dissolved oxygen at depth because respiration associated with bacterial decomposition of

organic matter consumes dissolved oxygen.

Nitrogen and phosphorus are involved in biogeochemical cycling as essential

components of living tissues of both plants and animals. Plants convert dissolved

nitrogen and phosphorus in various forms into plant organic matter; some of which is

eaten by animals and becomes animal organic matter. In forming organic matter, these










nutrients are used by phytoplankton in definite ratios to carbon. An idealized marine

ratio (Redfield Ratio) of the average composition of marine plankton is C106N16P1.

Nitrogen, or phosphorus, can be the limiting nutrient in estuaries, but most commonly,

nitrogen is limiting, which is the case for Tampa Bay (Wang et al., 1999). If

concentrations of either nitrogen or phosphorus increases, biological productivity

increases.

The nitrogen cycle is complex, because nitrogen occurs as a variety of species in

natural waters, and because of its abundance in the atmosphere. It enters natural waters

through a variety of pathways and in a variety of forms. Nitrogen (N2) makes up about

80 % of the air mixture by volume, but nitrogen in this form is unreactive. The

conversion of N2 into chemically reactive and biologically available compounds by the

combination of nitrogen with hydrogen, carbon, and oxygen is called nitrogen fixation.

Lightning, sunlight, chemical oxidation, and other processes facilitate these reactions in

the atmosphere. Therefore, nitrogen can be transported from the atmosphere by way of

rain and particulate fallout, processes known as wet and dry deposition, respectively.

Nitrogen from atmospheric deposition is inorganic, and includes species such as NO3 ,

NO, NO2-, and NH4+ fTOm NH3, and other gases. In general, the contribution of nitrogen

by atmospheric deposition has not changed significantly over the years relative to

contributions by sources such as sewage effluent, and stormwater runoff(Dreschel et al.,

1990). However, a study conducted in the Panhandle of Florida data from various river

gauging stations, ranging from Pensacola to Gainesville, revealed that atmospheric

deposition appeared to be the principal source of nitrogen to local water bodies

(Winchester et al., 1995).









Another component of the nitrogen cycle is biological fixation. This mechanism

occurs on land and in the marine environment, and it involves the uptake of nitrogen gas

by terrestrial plants or cyanobacteria. These organisms convert the N2 into organic,

nitrogen bearing compounds. Then, erosion and runoff from the land contribute organic

nitrogen to the marine nitrogen budget. Organic nitrogen, regardless of its origin or form,

feeds marine plants and algae, which eventually feed animals. When these organisms die

they undergo bacterial decomposition in the water column, and sediments, which results

in the liberation of ammonia to solution (ammoniafication). Ammonia remains in

solution as ammonium where it can be oxidized to other forms of nitrogen (nitrification).

Or, some ammonia can escape back to the atmosphere. In either case, some nitrogen is

recycled back into the global nitrogen cycle, while some nitrogen-containing detritus

makes its way to the seafloor. Organic nitrogen that is buried in sediment can be

reintroduced to the food web by organic matter remineralization and subsequent diffusion

or advection of porewater back to the water column. Remineralization involves the

oxidation of organic matter by oxygen, but can occur at depth, in anoxic environments if

other oxidants, such as MnO2, NO3-, and SO42-, are present. A chemical reaction can be

written to describe one possible stoichiometry of organic matter oxidation by oxygen

(e.g. Froelich et al., 1979).

(CH20)106 (rT3)16 (H3PO4) + 138 02 + 106 CO2 + 16 HNO3 + H3PO4 + 122 H20 (1-1)

Phosphorus has no stable gaseous phase in the atmosphere, and thus the phosphorus

cycle is less complicated than the nitrogen cycle. Most phosphorus originates from

weathering of rocks. Therefore, the most likely pathway for phosphorus to enter a marine

system is via surface water runoff. Inorganic phosphate is in the form of orthophosphate









anions. These nutrients follow a similar path as inorganic nitrogen species once in

marine systems where they are incorporated into the food web.

Nitrogen and phosphorus influxes are part of a natural, healthy ecosystem, but

development around Tampa Bay has resulted in an increase of both elements. Nitrogen

influxes have increased because of an increase in paved surfaces (resulting in higher

storm water runoff), an increase in septic tanks and point-source discharge of partially

treated sewage, a conversion of woodlands to agricultural use (resulting in the extensive

application of fertilizers and manure, and erosion), and industrial, automotive, and power

plant pollutants that can be "fixed" in the atmosphere, leading to dry and wet deposition.

Humans have altered the phosphorus cycle by deforestation (leading to erosion of

phosphorus containing sediments and rocks), the use of phosphorus fertilizers, and the

production of industrial wastes, sewage, and detergents (Berner and Berner, 1996). Also,

phosphorus mining is a major industry in the region surrounding Tampa. Mining

inevitably leads to accelerated erosion and loss of phosphatic material to bay.

1.4 Previous Studies of SGD and Nutrient Loading

Submarine groundwater discharge and internal nutrient loading have been

extensively studied in coastal environments around the world, using a variety of methods.

Many previous studies include only groundwater discharge rates without the associated

nutrient fluxes. Some of the more extensively observed regions include both nutrient and

groundwater fluxes.

Most studies do not differentiate the components of seepage water, but some recent

studies have shown recognition of the contribution of recirculated water. Using Lee-

Type seepage meters Bokuniewicz (1980) calculated that SGD across the bay floor was

about 27.8 ml/m2/min Within 30O m of the shoreline, or 10-20 % of the total freshwater









inflow including surface water runoff in the Great South Bay, New York. Subsequently,

Bokuniewicz (1992) measured fluxes from the same area as great as

104 ml/m2/min, and suggested that SGD included some recirculation of salt water in his

study area resulting from density driven convection. On the basis of Bokuniewicz's

(1980) estimate of average daily SGD, and assuming 10 Cpm nitrate near the sediment-

water interface, Capone and Bautista (1985) calculated that SGD could account for at

least 20 % of the nitrogen input from surface runoff

Recent work on SGD has taken place in the Indian River Lagoon System, FL.

Submarine groundwater discharge and nutrient flux have been calculated for Indian River

Lagoon using a variety of methods. Zimmerman et al. (1985) reported seepage meter

derived seepage velocities from 6.65 8.89 cm/day. They also reported theoretical

diffusive flux rates for dissolved reactive phosphorus (on the basis of Fickian type

diffusion) of from 3 to 70 x 10-6 gr/m2/day. Pandit and El-Khazen (1990) employed

numerical modeling to calculate SGD. They constructed a finite element model to

calculate seepage rates based on a 2D idealized cross-section of the lagoon between the

water table divide on the mainland and the ocean, assuming the confining Hawthorne

Formation is not permeable and the groundwater source is from the Surficial Aquifer.

Their model calculated a groundwater flux of 0.002 ml/m2/min. Martin et al. (2002),

Lindenberg (2001), and Martin et al. (2003) measured SGD using seepage meters and

natural radioisotope tracers. Their seepage meters yielded a flux of from 40 65

ml/m2/min, and their tracer tests (Rn, Ra) yielded similar results,

11 66 ml/m2/min. A numerical model only accounts for continentally derived water,

while the tracer and seepage meter studies include all the water components in the









seepage water. In part because of this discrepancy, Martin et al. (2002) suggested that

only 2.5 % of groundwater discharge to the Indian River Lagoon originates from the

underlying aquifers. Lindenberg (2001) cited the importance of SGD and associated

nutrient influxes. Using a mass balance equation of chloride concentrations she found

that fresh groundwater constitutes only 1 % to 4 % of seepage water discharging into the

lagoon. In addition, on the basis of water samples collected using seepage meters, she

concluded that nutrient loading of total nitrogen and total phosphorus was 11 to 17 times

greater than the total nitrogen and 14 to 23 times greater than the total phosphorus of

surface water discharge from drainage areas surrounding the lagoon.

Several other sites around Florida have been investigated. Simmons (1992)

measured SGD in the Florida Keys. Using seepage meters he determined groundwater

flux to be from 3.75 to 6.1 ml/m2/min. COrbett et al. (2000) employed two analytical

models for measuring meteoric groundwater discharge in Apalachicola Bay. One model

was a flow net and the other was a simple water balance calculation. The independent

approaches agreed with each other, with an estimated groundwater flux from the surficial

aquifer to the bay between 1-9 x 106 m3/yr. Cable et al. (1996) used two naturally

occurring trace gases, 222Rn and CH4, alOng with seepage meters, to quantify seepage

rates, and to determine the components of the SGD near a submarine spring in the

northeastern Gulf of Mexico. These gases are present in groundwater at concentrations

that are elevated by several orders of magnitude relative to seawater. Their surface water

samples displayed radon and methane concentrations inversely related to salinity and

considerably greater than those found in surrounding waters. Calculated diffusive fluxes

of 222Rn showed that the surface waters receive only a small contribution by diffusion.









They concluded that advective processes must be contributing to the water column

inventory, given that seepage meters yielded a discharge rate of ~90 ml/m2/min.

Swarzenski et al. (2001) used a host of natural geochemical tracers, including

salinity, strontium isotopes, 222Rn, CH4, and dissolved nitrogen to derive the origin of

spring water ~3km off shore of Crescent Beach, FL in the Atlantic Ocean. With a vent

water salinity about 17 % of open ocean values, strontium isotope ratios indicative of

Floridan aquifer system groundwater, low concentrations of dissolved nitrogen species,

and enriched concentrations of 222Rn and CH4 relative to seawater, they concluded that

the water discharging at Crescent Beach Spring is not newly recycled seawater, but is

geochemically similar to artesian groundwater present along the coast at Crescent Beach.

These studies show diversity in application of various techniques to measure and

characterize SGD in different hydrogeological settings.

Another area of extensive SGD and nutrient loading research is the Chesapeake

Bay. The Chesapeake Bay is similar to Tampa Bay because of the widespread human

population and development around the bay, and because of the economy's dependence

on the bays resources. Like Tampa Bay, Chesapeake Bay's health is at risk because of

human intervention and exploitation. Research by Taft et al., (1978) indicated that

regeneration and release of nutrients from sediments is several times larger than the

inputs from two of the principal ultimate sources of nitrogen to the bay, the Susquehanna

River and municipal sewage discharge. Also, the Chesapeake Bay has been cited as

susceptible to groundwater pollution because of its unconfined groundwater system

(Robinson et al., 1998). Gallagher et al. (1996) investigated the transport of land-applied

nutrients and pesticides from the aquifers to tidal surface waters, and measured both SGD









and nutrient flux along Virginia's coastal plain. They found that submarine groundwater

transport of both nutrients and pesticides does occur, and that SGD rates represent a

mixture of fresh groundwater and seawater resulting from large scale interstitial

recirculation patterns. The potential for this type of phenomenon exists in Feather Sound

due to unconfined and semi-confined aquifer conditions, as well as the putative spring

vent in the study site. Gallagher et al. (1996) reported a mean water discharge rate of

10.5 ml/m2/min On the basis of seepage meters. They also reported a mean measured

nitrogen flux of 0.04 mg/m2/min, and a maximum of 0.5 mg/m2/min. Robinson et al.

(1998) reported SGD measurements from two methods, maximum instantaneous

discharge rates based on piezometer measurements, and seepage meters. Their

calculations based on piezometers, gradients, and Kz assumption indicated SGD from

12.5 to 320 ml/m2/min, While their seepage meter measurements indicated SGD varied

from 8.33 to 55 ml/m2/min. Both methods indicated discharge rates decreasing with

distance from the shore. Measurements based on piezometer measurements were

inversely correlated with tidal elevation thus leading to a decrease in rates away from the

shoreline, while the seepage meter rate decreases correspond to offshore decreases in

sediment hydraulic conductivity and potentiometric head differentials across the

sediment-water interface. Robinson and Gallagher (1999) modeled the groundwater

seepage process based on density dependant fluid flow, the water table and changing tidal

boundary conditions. The model predicted SGD to be dependant on distance from the

shoreline, and in the order of 0 to ~35 ml/m2/min (based on a visual interpretation of

Figure 8 (Robinson and Gallagher, 1999)). This finding is in accord with results from

Bokuniewicz (1980). Their model also predicted that fresh groundwater discharge rates









were significantly less than the total groundwater discharge and constituted 6.2 % of the

total discharge across the sediment-water interface.

These previous studies are a sample of current, recent, and past research. They

serve to demonstrate that SGD can be greater than what is expected from hydrologic

models, the source of SGD can be traced using various techniques, SGD is often

composed of recirculated seawater, and the associated nutrient flux can be a significant

part of a nutrient budget.

1.5 Study Area

The study area of this proj ect is based around a putative spring vent in Feather

Sound, a portion of Old Tampa Bay. The spring vent is located ~200 m offshore of

Pinellas County at N27.9134010 and W-82.6600210. Water depth is variable, but

generally less than 2 meters (Figure 1-1).

1.6 Hypotheses

Through detailed field sampling and measurements, laboratory analyses, and

analytical modeling, the following hypotheses were tested:

* A submarine spring may exist offshore in Feather Sound, and may be directly
contributing nutrients and other pollutants to the bay from continentally derived,
fresh aquifer water. The spring vent has previously been identified on the basis of
aerial photographs, salinity differences in the vicinity of the spring' s purported vent
(Swarzenski, 2001), and visual contrasts between bay floor vegetation in the region
of the vent and distally.

* Diffuse SGD will be composed of mixed seawater and freshwater. Freshwater will
constitute minor amounts of the SGD.

* Diffuse SGD (non-point) may be a source of nutrients to the bay as a result of the
recirculation of oxygenated bay water through shallow sediments, thus facilitating
enhanced organic matter remineralization.

These hypotheses were tested using the following methods:










1. Quantification of SGD using seepage meters, a simple mass balance flow
calculation, an analytical groundwater flow model (flow net), and a two-end
member chloride-mixing model. Seepage meters and the chloride-mixing model
provide an estimate of total SGD, while the flow net and water budget models
predict offshore flow of continentally derived meteoric water.

2. Differentiation of the components of the SGD using natural geochemical solutes in
the water column and pore water in order to trace the SGD to its source or sources.



















'Pinellas :k
SCounty




Gulf of
Mexico ., \








Scale 1:350,000 0 10 km
Projection UTMl
Datum NAD 27


Figure 1-1. Location of Pinellas County, FL. The black dot indicates the approximate
location of the study site, and the black rectangle indicates the approximate
location of the rainfall gauging station. This map was taken and modified
from Zarbock et al, 1996.










3. Use mass balance calculations, average pore water nutrient concentrations, and
field measured SGD to estimate nutrient fluxes across the sediment-water interface
within the bay.

4. Measure physical properties of sediment to correlate sediment type and
groundwater discharge rate, and to measure porosity for groundwater and nutrient
flux calculations.

These tests: 1) quantified how much water is discharging out of the bay sediments;

2) delineate the origin or sources of the SGD; 3) tested the existence of a freshwater

spring in the study area, and, 4) measured the nutrient load associated with the spring and

from the recirculation-remineralization mechanism.

1.7 Local Geology and Hydrostratigraphy

Pinellas County, the peninsular feature on the western flank of Tampa Bay, is

underlain by a sequence of sedimentary rocks whose lithology and structure control the

occurrence and movement of groundwater. Figure 1-2 shows the sequence of geologic

formations and hydrogeologic units in Pinellas County. The principal rock types that

underlie the county are: 1) unconsolidated sand, clay, and marl, and 2) limestone and

dolomite. Sand, clay, and marl are the principal sediments in the upper part of the section

in middle Miocene and younger rocks. Water in these deposits occurs in primary

porosity. Limestone and dolomite are the dominant rock types in the lower part of the

section in lower Miocene to upper Eocene rocks. Water in these rocks occurs and moves

principally in secondary openings, including joints, openings along bedding planes, and

pores that commonly have been enlarged from dissolution by groundwater (Causseaux,

1982).

Groundwater in Pinellas County occurs both under unconfined and confined

conditions. Two aquifer systems are present: the Surficial Aquifer System and the

Floridan Aquifer System. The units are separated by the intermediate confining unit.










Deposits of the surfieial aquifer form a sand blanket that covers the area around and

beneath the bay. The thickness of the aquifer ranges from 0 to ~40 m. The aquifer is as

much as 40 m thick in the ridge of central Pinellas County where it is probably composed

of dune remnants. Beneath Tampa Bay, the surfieial aquifer is generally less than 12 m

thick. Depth to the water table is generally less than 1.5 m below land surface, but is

spatially variable. The upper confining bed separates the surficial aquifer from the

Floridan Aquifer and is the principal lithologic unit that separates the bay and aquifer. It

consists of relatively impermeable, fine-grained deposits within the Hawthorn Formation

and possibly includes clay at the top of the Tampa Limestone. Thickness ranges from 0

to an average of about 7.6 m in Old Tampa Bay, to ~76 m in other parts of Tampa Bay.

The Floridan Aquifer system is below the intermediate confining layer. The Floridan

Aquifer system includes the Upper Floridan Aquifer, middle confining unit, and Lower

Floridan Aquifer. The top of the Upper Floridan Aquifer is defined as the first

occurrence of a persistent carbonate sequence. The base of the Upper Floridan Aquifer is

defined as the first occurrence ofinterbedded gypsum in the carbonates below dark-

brown, microcrystalline dolomite in the Avon Park Formation. In Pinellas County, the

top of the persistent carbonate sequence coincides with the top of the Tampa Member of

the Arcadia Formation of the Hawthorn Group. This study is concerned only with the

uppermost producing zone of the Upper Floridan aquifer, the first ~60 m of the aquifer.

Based on figure 1-3, from Hutchinson (1983) 61 m represents the approximate depth to

the saltwater front in the vicinity of study area.






























CD I I AQUIFER



a 305C DOLOMrfE



381 -21.000
AVON PARK
FORMATION

457.
3 r* MIDDLE
CONFINING
Z ~EOCENE < I UNtT
S533-


O GYPSIFEAOUS
8- 10 -LIMESTONE gggy,
m AND
I I I II I DOLDMITE





762 -LOWER
OLDSMARFLORI(DAN
L)MESTONIE UFE







991-




Figure 1-2. Generalized stratigraphic and hydrogeologic section, Pinellas County. Taken
and modified from Knockenmus and Thompson (1991), which was earlier
modified from Hickey (1982).












i a 16 km


61 3 wefe S potentiometric
a. 1041* srur ee
g -- SURFICIAL AUFER -
OT es IFgn-lr-I. --- <:~ UPPER CONFINI G BED




-183 .SFRIP~ l~aX'Z'1. FRESHWATERa
<25 mg/L chlorlde)
AL WATER
-244 14n 00-i 19 00 Lclrd
FLOR DA AQUIFER
-305 '

-see // // // LOWER C NFNINIG BED

-427(olek
VERTICAL SCALE GREATLY EXAGGERATED
Figure 1-3. Conceptual, cross-sectional view of saltwater-freshwater relations in the
Tampa Bay area, and flow paths of groundwater. The peninsula on the left
represents Pinellas County. The view cuts across the study site. Taken and
modified from Hutchinson (1983).

1.8 Regional Climate

The subtropical climate of Tampa Bay is characterized by warm, humid summers,

and mild, relatively dry winters. The average annual rainfall for 1916-2001 was ~100 cm

at the Southwest Florida Water Management District' s (SWFMD) St. Petersburg gauging

station at N274546.090 and W823752.340 (Figure 1-1). More than 75 % of the annual

rainfall occurs during the wet season of June through September, usually in the form of

convective thunderstorms. Evapotranspiration in Pinellas County is estimated to be 99

cm per year, and about 60 % occurs from May to October (Cherry et al., 1970). Rainfall

was slightly elevated during 2002, relative to the historical mean, but 2002 rainfall was










lower than the average of the last decade. Historical and 2002 rainfall data are provided

in tables 1-1 and 1-4 a,b.

Table 1-1. Average historical (1916-2001) monthly, annual, and seasonal rainfall data at
the St. Petersburg rainfall gauge (location shown in Figure 1-1). The data is
from the SWFWMD online database. The wet season denotes June-
September.


MONTH
JANUARY
FEBRUARY
MARCH
APRIL
MAY
JUNE
JULY
AUGUST
SEPTEMBER
OCTOBER
NOVEMBER
DECEMBER

ANNUAL
WET SEASON
DRY SEASON


AVG RAINFALL (cm)
4.85
1.37
6.38
1.55
0.84
18.64
20.62
16.23
25.73
3.20
2.13
1.73


100.13
77.93
22.50



























































Date (2002)

Figure 1-4. Daily rainfall (cm) recorded at the St. Pete gauging station before and during each sampling event. The data is from the
SWFWMD online database (http://www.swfwmd. state.fl.us/ Last accessed, February 24, 2004)


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CHAPTER 2
METHOD S

2.1 Work Plan

Two separate sampling trips were made to the study site in 2002. The first was in

April and the second in August. The sampling trips were timed to follow seasonal

variations in rainfall; the first trip occurred during the typical dry season and the second

trip occurred during the typical rainy season. During the trips, SGD rates were measured

using seepage meters (a direct physical measurement), bay sediment pore waters and

water column waters were collected for chemical analyses, and sediment cores were

collected for lithological and hydrological interpretation. All samples were preserved in

the field and brought back to the University of Florida for chemical and physical analysis.

Analytical groundwater modeling followed chemical and physical analysis of water

samples and sediment cores. Three types of analytical models, including a mass balance

flow calculation, a flow net, and a two end-member chloride mixing model were used as

comparative tools to the direct physical measurement of SGD.

A sampling grid was designed around the vent of the putative spring to resolve

proximal and distal changes in groundwater seepage and water chemistry. The grid

(Figures 2-1, and 2-4) covers approximately 7,500,000 m2 Of the bay floor. Most

sampling locations are uniformly distributed around the approximate location of the

discharge point of the spring and spaced approximately 600 meters apart, creating a grid

that is approximately 3000 meters by 2500 meters. Two additional sampling locations










(TB-9A and TB-9B) are located within the grid close to the putative spring. Access to

the field site was made with the use of USGS boats in concert with USGS personnel.


Scale: 1 : 299,785 meters


Figure 2-1. The Tampa Bay basemap created from individual digital orthophoto
quadrangle quarters (DOQQ's) downloaded from the LABINS website. The
gray box represents the approximate location of the study area. The study
area is not to scale.

2.2 Seepage Meter

2.2.1 Background

Seepage meters have been used to study groundwater discharge into different

bodies of water for over seven decades. Isrealson and Reeve (1944) devised the first










seepage meter to measure seepage outflow from irrigation canals. Since then, seepage

meters have been used to study groundwater discharge out of lakes (Lee, 1977; Downing

and Peterka, 1978; Fellows and Brezonik, 1980; Connor and Belanger, 1981; Belanger

and Mikutel, 1985; Cherkauer and Nader, 1989; Hirsch, 1998), and rivers, canals, and

coastal regions (Bokuniewicz, 1980; Capone and Bautista, 1985; Simmons, 1992; Cable

et al., 1996). The modern seepage meter, referred to as the Lee-type seepage meter

(shown in figure 2-2) was described by Lee (1977). Although improvements have been

made over the years, manual seepage meters still consist of the end of a standard 55-

gallon drum with an open port placed near the rim that allows a plastic water collection

bag to be attached. The volume of water that enters the bag over a known time and area

yields the seepage rate (Lee, 1977; Lee et al, 1980; Shaw and Prepas, 1989; Cable et al.,

1997) here reported as ml/m2/min.

There are several benefits in using seepage meters to measure SGD. They have a

rather simple and inexpensive design, they are relatively easy to deploy, and they provide

a quick, direct physical measurement of SGD, which is otherwise determined through

numerical or analytical modeling, and chemical tracers. Notwithstanding these benefits,

seepage meter results have been questioned. For instance, Lee (1977) noted that seepage

velocity in estuaries was significantly inversely correlated with water surface elevation,

although Bokuniewicz (1980) found no correlation between seepage rates and tidal

heights. Furthermore, Shaw and Prepas (1989) showed data indicating the presence of

artifacts associated with a short-term, anomalous influx caused by a hydraulic gradient

created when an empty bag is attached to the meter. This gradient appeared to cause

seepage bags to fill more rapidly as the plastic bag expands and draws in water not
































Figure 2-2. A depiction of a seepage meter placed in the sediment under the water
column. The receptacle bag is connected and the valve is open to allow flow
through the port. Arrows indicate direction of groundwater flow.

associated with seepage. The expansion appears to be related to mechanical properties of

plastic bags, and can result in significant artifacts in calculated seepage rates (Cable et al.,

1997). Another possible drawback of using seepage meters was pointed out by Shinn et

al. (2002), who suggested that meters presenting positive relief on the sea floor are

subject to the Bernoulli effect when placed in areas where there are waves and/or

currents. In other words, the devices artificially advect shallow ground water (Shinn et

al., 2002). Shinn et al. (2002) also claim that advection is not caused by flexing of the

collection bags as reported by Shaw and Prepas (1989).

2.2.2 Seepage Meter Construction, Deployment and Seepage Measurements

Seepage meters were constructed from the sawed off ends of 55-gallon steel drums.

The drums were cut 15 cm in from the ends. Half-inch diameter ports were drilled into

the flat top 6 cm from the edge of the drum. The meters were sanded and painted with









two coats of two-part marine epoxy paint. A male garden hose fitting was inserted into

the port and made watertight using rubber washers and silicon caulking. Rubber handles

were screwed into the side of the meter using washers and silicon caulking (Lindenberg,

2001).

Seepage meters were installed with the flat side up. The meters were inserted into

the sediment so that the rim was completely buried to prevent bay water from flowing

into the meter under the rim. The side with the port was tilted slightly upward to prevent

gases from accumulating causing backpressure and possibly lifting the meter free of the

sediment.

Seepage meters were deployed at the stations depicted on figure 2-3. Seepage rates

were measured in April. The method of deployment and sampling of the seepage meters

followed procedures outlined in Cable (1997). Seepage meters were allowed to

equilibrate for 24 hours prior to sampling. Once equilibrated, seepage rates were

measured in triplicate. The 4-1 plastic collection bags were primed with 1000 ml of

estuarine water prior to deployment to prevent artifacts as indicated by Shaw and Prepas

(1989). Seepage flux was calculated by dividing the volume of water that flowed into the

collection bag by the amount of time the bag was on the meter and by the area of the

meter (0.28 m2). No control experiment was conducted.

2.3 Water Samples

Pore water and bay water samples were collected in April and August. Pore water

was collected using multiple level piezometers (multisamplers, Martin et al, 2003). Bay

water was collected in a "grab" manner using a peristaltic pump and rubber tubing

suspended 50 cm above the bay floor. Pore water and bay water were measured in the

field for salinity, conductivity, temperature, dissolved oxygen, and pH, and preserved and










returned to the laboratory for measurements of concentrations of solutes and nutrients

including sulfate, chloride, TN, TSN, TP, and TSP. Depending on the results of the

concentration measurements, a subset of pore water samples was selected for

measurements of the 618O values and s7Sr/86Sr ratios.


Scale: 1 : 4,423
0 km 0.37 km


Figure 2-3. A satellite image of the study site and sampling stations. TB-9 is the location
of the putative spring. The image was taken from the LABINS (Land
Boundary Information System) website in the form of a digital orthophoto
quadrangle quarter (DOQQ).

2.3.1 Multisamplers and Pore water

2.3.1.1 Design

Multisamplers are 2 m long sections of PVC pipe with eight ports located at

various distances along their lengths (Martin et al., 2003). The design of the










multisampler consists of 2" ID schedule 80 PVC pipe with one-quarter inch ID (3/8" OD)

PVC tubing fed through the interior of the pipe (figure 2-4). The PVC tubing is glued to

ports in the pipes and each port is screened with a 250 Cpm screening material (Nytex).

The ports are separated by 10 to 40 cm with the closest spacing in the upper section, and

increasing spacing with depth. This distribution allows higher resolution sampling of the

pore water near the sediment-water interface where concentration gradients are likely to

change more rapidly with depth because of diagenetic reactions (Martin et al., 2002).

The multisamplers have 8 ports that are located at 10, 30, 50, 80, 110, 150, 190, and 230

cm from the top of the instrument. If fully inserted in the sediment, these values also

represent the sampling depth below the sediment-water interface. The ports exit the

device in a spiral fashion with each one located 900 offset from the ports above and

below. The tubing is led outside the PVC pipe through a T-j oint (Martin et al., 2003).

The base of the multisampler is plugged with a solid point that enables installation.


i 7 surface




Sedarnent-Water
10 FTInterface
S30




110 Screened
133crn2
150 Cross Section
Screening
190 ,*Mtra

230 iii-r'hnt ra
PVC Pipe
Figure 2-4. Design of a multisampler (from Martin et al., 2003).









2.3.1.2 Deployment

Multisamplers were deployed at five different locations (TB-1, TB-4, TB-9, TB-

9A, and TB-9B), during each sampling trip. This sampling array allowed higher

resolution sampling near the putative spring vent as well as near shore and further out in

the bay. The multisamplers were driven into the sediment using a fence post driver. The

fence post driver was repeatedly lifted and dropped on the top of the multisampler in

conjunction with human force pushing down. The multisamplers were driven in to the

base of the T-j oint, which means they were fully inserted.

2.3.1.3 Sampling

Sampling was done as soon as the multisamplers were fully driven into the

sediment. The PVC tubing was brought to the boat, primed by mouth, and connected to a

peristaltic pump. The pore water was pumped at a rate of approximately 1 ml/s into a

small plastic bucket. The water was monitored until oxygen concentrations and

temperature stabilized, at which time these parameters plus pH, and salinity were

recorded and water samples were collected. Each port was sampled in succession from

shallowest to deepest. Water was drawn from the bucket, after the stabilized bucket was

emptied, using a 60 ml syringe and transferred into one of several HDPE bottles. One

sample was unfiltered, another was filtered using a 0.45 Clm filter, the third sample was

filtered and preserved using 50Cl1 of 16 N optima grade HCI and stored in a glass Qorpack

bottle for supplemental isotopic analyses. All bottles were pre-labeled with the sampling

station, port number, and the date. All bottles were immediately stored on ice after being

filled. Some ports did not yield water when pumped due to clogging or low permeability

sediment, but those that did would pump unlimited volumes of water.









2.3.2 Bay water

In addition to pore water, samples of the bay water were collected. Bay water was

collected from 50 cm above the bay floor using a peristaltic pump. A small weight was

attached to PVC tubing and lowered into the water column to the proper depth.

2.3.3 Analyses

Pore water and bay water were brought back to the University of Florida for

chemical analysis. Measurements of nutrients were done in the Land Use and

Environmental Change Institute (LUECI) Laboratory; measurements of ions were done in

the Hydrochemical Prep Lab, and isotopes were prepped, spiked, and measured in the

Clean Lab, TIMS lab, or Stable Isotope Lab, all in the Department of Geological

Sciences.

Concentrations of PO4 and Sio2 Were meaSured on the non-acidified filtered water

samples, and NH4' WAS measured on acidified filtered water samples using

spectrophotometric techniques following procedures described in Clesceri et al. (1989).

Nitrogen and phosphorus concentrations were measured following Kj eldahl digestion on

a Technicron Autoanalyzer II for both filtered and non-filtered samples. The

concentrations of these samples were reported as total nitrogen (TN) and total phosphorus

(TP) concentrations for the non-filtered samples. Filtered samples were used to measure

total soluble nitrogen (TSN), total soluble phosphorus (TSP), and NO3 COncentrations,

prior to Kjeldahl digestion of the sample. Precision of PO4 and NH4+ analySCS WeTO

checked by analyzing a check standard every fourth sample, and calculating the

coefficient of variation (standard deviation divided by mean) of the values measured for

the check standard. Precision of the average of the differences in the duplicates of TSN,










TN, TSP, TP, and NO3 COncentrations were checked by analyzing duplicates every tenth

sample (table 2-1).

Table 2-1. Estimated precision of various solutes for water samples
SOLUTE PRECISION %
PO4 0.53
NH4 0.86
TSN 0.64
TN 0.67
TSP 0.62
TP 1.30
SiO2 0.44

Concentrations of chloride were measured using AgNO3 titration (Clesceri et al.,

1989). Repeated measurements of three internal standards, St. Augustine Seawater

(SAS), and two known concentrations of NaCl (553.377 mM and 553.668 mM) yield a

reproducibility error of less than & 0.8 %. These standards were measured approximately

every fifth sample, or ~22 times per sampling event. Sulfate concentrations were

measured from the filtered samples using an Automated Dionex model 500 lon

Chromatograph. Measurements of SAS every fifth sample yielded a reproducibility error

of less than & 0.8% for sulfate concentrations.

Oxygen isotopes were measured using a CO2 equilibration technique on pore water

from locations TB-1, TB-4, TB-9, TB-9A, and TB-9B from both sampling trips. Glass

vials with 200 Cl1 of sample were capped with septum caps under CO2 atmosphere in a

glovebag. Then the vials were placed in a heated Aluminum block at 450C to equilibrate

for 12 hours. Once equilibrated, the CO2 WAS analyzed with an automatic

multipreparation stage and H20 was removed from a water trap at -900C. Purified CO2

was analyzed on a Micromass Prism II gaseous source mass spectrometer. Results are

reported relative to SMOW in standard delta notation. Samples were run in duplicate,









and the value reported is the average for the duplicates. Estimated precision is 0.1 %o

based on standards run during the analysis every fifth sample.

Strontium isotope ratios and concentrations were measured only on pore water

from location TB-9 from both sampling trips. In the Clean Lab, 300 ml water samples

were spiked with a diluted RS95 spike, which is a solution with high concentrations of

84Sr and evaporated to dryness. The residue was acidified with 100 Cl1 3.5N HNO3 for Sr

separation. The Sr was separated from other cations with Sr Spec resin in a 3.5N HNO3

medium, and at the end Sr was collected in 1.5 ml 4xH20 and evaporated to dryness.

After prepping, the samples were loaded on oxidized tungsten single filaments and were

analyzed using a VG Micromass 54 spectrometer run in dynamic mode. Errors in

measured s7Sr/86Sr are better than 0.000022 (20) based on long-term reproducibility of

the NBS 987 standard. The laboratory value of the standard is 0.710240.

2.4 Sediment Cores

2.4.1 Sampling

Two cores were taken during the August, 2002 sampling trip, one at TB-9, and the

other at TB-9A. The cores were collected using a vibracoring technique. Vibracoring

works on the principal of liquefaction in fine-grained sediments by displacing sediment to

allow passage of the coring pipe (Smith, 1984). Coring was accomplished using

approximately 2 m long sections of aluminum pipe as the core barrel having an internal

diameter of 7.5 cm. The pipes were fitted with core catchers. The pipes were attached to

a motor that generates vibrations (Figure 2-5). Simultaneous with the motor, human force

was applied downward to help drive the pipe into the sediment. The pipe was driven as

far as possible into the sediment.
































Figure 2-5. Digital photograph of vibracore assembly taken during the August sampling
trip, from the deck of the USGS pontoon boat. The motor in the foreground
turns a flexible rod that runs down the length of the cable. The cable is
secured to the side of the aluminum-coring barrel. The rotation of the flexible
rod is expressed as strong vibrations against the coring barrel that help drive it
into the sediment.

The pipes were then pulled out of the sediment using a winch and steel cable. No

significant compaction was observed. The pipes were immediately capped on both ends,

cut in half, and then stored in an upright position.

2.4.2 Analysis

The two cores were stored in a walk in refrigerator in the Florida Institute of

Paleoenvironmental Research (FLIPER) Laboratory, at the Department of Geological

Sciences, University of Florida. In this lab, the cores were split lengthwise, described,

photographed and sectioned within one month of sampling. One section was used to

measure sediment bulk density (fractional porosity), and take high-resolution digital

images (40 pixels/cm) of the entire core, using the Geotek Multi-sensor Core Logger










(MSCL). Bulk density was determined using a standard aluminum density calibration

piece (Weber et al., 1997). The other section was preserved for lithological description

and future sediment analyses.

2.5 Groundwater Flow Models

Modeling of groundwater flow was used as a comparative tool to the seepage meter

measurements. The purpose of the water budget mass balance calculation is to determine

the groundwater component of the local hydrologic system, and solve for the

groundwater inflow to the study area. The flow net is used to support the Eindings from

the water budget model using Hield-measured hydrogeological properties. The two-end

member chloride-mixing model is a tool for deriving water flux from chemical mixing,

and will be used to further corroborate the flux attained from the previous two models. In

contrast to the two analytical models, the conceptualization of the chloride-mixing model

(CMM) is different. Whereas the water budget and flow net only account for SGD of

meteoric derived aquifer water, the CMM solves for a flux on the basis of re-circulating

bay water.

2.5.1 Water Budget

Modeling was initiated with a mass balance flow calculation. The first step in this

model was to construct a base map that could be incorporated into GIS software and used

to calculate distances and areas for the model calculations. The base map was

constructed using digital orthophoto quadrangle quarters (DOQQ's) from the LABINS

(Land Boundary Information System) website. LABINS is a clearinghouse of satellite

and aerial photographs in various projections for the state of Florida. The appropriate

DOQQ's were downloaded, organized, and uploaded into Global Mapper software.

Global Mapper allows easy assembly of DOQQ's and re-proj section of the whole image









into any proj section needed. For this study, Global Mapper was only used to assemble the

numerous DOQQ's that comprised the entire Tampa Bay area. The original projection,

Albers, was maintained. After the base map was assembled it was incorporated as a

theme into ArcView GIS 3.2a. ArcView maintains topology and preserves real-world

coordinates. ArcView was used in conjunction with several USGS reports to constrain a

regional watershed and groundwater divide. On the basis of maps taken from Hutchinson

(1983) and Yobbie et al. (1980), including potentiometric and surficial aquifer maps,

lines on the base map were digitized to denote a surface water divide that was coincident

with a groundwater divide (Figure 2-6). The area within the divide boundaries is

considered the catchment area for groundwater discharge to Old Tampa Bay.

Once the base map was completed, the hydrologic mass balance for the area was

determined. Data for the model were extracted from USGS Water-Resources

Investigations Report 84-4289 (Causseaux, 1982). Table 2-2 indicates values used for

variables in the mass balance.

Table 2-2. Components and associated values of hydrologic equation.
Inputs Outputs Value

P 140 cm/year
ET 64 cm/year
Rs 15 cm/year
EwT 36 cm/year
Rbase 15 cm/year
G ?

Water input to the system is: average annual precipitation (P). Water losses from

the system are: evapotranspiration (ET) fTOm the land surface, stream runoff(Rs),

evaporation (EwT) fTOm the water table, and stream baseflow (Rbase). The unknown

output was groundwater (G) on the basis of USGS reports 84-4289 and 82-54










(Hutchinson, 1983). The model was calculated assuming that all groundwater that does

not contribute to stream baseflow within the basin flows into the bay.


Scale: 1 :299,785
1.1lr
Figure 2-6. The outline of the surface and groundwater divides (derived from
potentiometric and topographic maps from Causseaux, 1982; Hutchinson,
1983; Yobbi et al., 1980) superimposed onto the Tampa Bay basemap created
using GIS software.

2.5.2 Flow Net

In addition to the water budget model, a flow net of the surficial aquifer was

constructed. This model represents a modification of the model presented in Hutchinson,

1983. In Hutchinson, 1983, a flow net of the Upper Floridan aquifer for Tampa Bay was

constructed for two months, May and September of 1980. Hutchinson (1983) compared

the potentiometric surface maps from both months and determined that there was not

enough difference in the position of the equipotential lines to justify having two separate









flow nets. Hutchinson (1983) averaged the discharge rates from these flow nets into one

annual discharge rate. Hutchinson's (1983) flow net used Darcy's formula Q = TIL

whereby Q = discharge, T = transmissivity (ft2/d), I = potentiometric gradient (ft/mi), and

L = length of flow zone (mi). For Hutchinson (1983) the area within the constructed

groundwater basin, near the bay, was broken into flow zones. Flow zones were

designated on the basis of transmissivity, hydraulic gradient, and length of flow zone.

Hutchinson (1983) showed discharge rates only from the Upper Floridan aquifer,

while the model here includes flow in the surficial aquifer on the assumption that not all

precipitation infiltrates to the Upper Floridan, or becomes stream baseflow. The method

of Hutchinson (1983) was used here, assuming that the water table position does not

change enough over the course of the year to justify having seasonal flow nets.

Consequently, a water table map from May 1980, described in Yobbi et al. (1980) was

chosen for analysis. The map was digitized and discrete flow zones were constructed on

the basis of transmissivity, hydraulic gradient, and length of flow zones (Figure 2-6).

Transmissivity was determined based on saturated thickness of the surficial aquifer

coupled with hydraulic conductivity obtained from Causseaux (1982). Saturated

thickness was variable, ranging from 30 to 50 ft, and hydraulic conductivity was held

constant at 180 ft/d. The potentiometric gradient was measured on the potentiometric

surface map (Figure 2-7). Transmissivity for the eastern portion of the Old Tampa Bay

was extrapolated from Pinellas county data. The results from the surficial flow net were

added to the discharge calculated by Hutchinson (1983) for the Upper Floridan Aquifer.















































Figure 2-7. The Surficial Aquifer flow net. Roman numerals (I VI) represent
discretized transmissivity zones. Arrows denote the general direction of
groundwater flow.

2.5.3 Chloride Mixing Model

A third model used here is a two-end member chloride-mixing model. The two-

end member mixing equation is:


X(WCA) + (1-X)(AP) = A (Equation 2-1)









Where "X" is the fraction of bay water mixing into the shallow sediments; "WCA" is the

August water column chloride concentration; (A"A is the April pore water chloride

concentration at depth z, and "A" is the August pore water chloride concentration at

depth z. The reason for using the two sampling dates in the mixing model is to determine

the volume of August bay water necessary to change April porewater chloride

concentrations to August porewater chloride concentrations.

Locations TB-1, TB-4, TB-9, TB-9A, and TB-9B were considered. An exponential

trendline was fit to the chloride depth profiles. An exponential curve was chosen because

it satisfies both the conceptual model of decreasing bay water circulation into the

sediment with depth, and because the raw data appear to have an exponential shape.

Chloride concentrations were then estimated using the equations of each curve (profile)

for every 2 cm of depth. From these chloride concentrations, the fraction of mixing

between the August water column and April pore water was calculated using Equation 2-

1 to the depth where the curves cross, which varies for each sampling location, and

represents the depth below which mixing ceases.

The mixing fraction, solved from the equation above, was multiplied by the

measured porosity incrementally for every two centimeters below the sediment-water

interface. When the product of these two variables is summed over the entire mixing

depth the result is equal to the total volume (V) of bay water that must circulate through

the sediment, from April to August, in order to achieve pore water concentrations equal

to those measured in August.

V = E OX (Equation 2-2)

Where "O" is the porosity, and "X" = mixing fraction






40


Moreover, this volume of water, divided by a representative area, divided by time

is the flux (J) into, and presumably out of, the sediment. "J" is reported in terms of

ml/m2/min, Where "V" is the volume of bay water that circulates through the sediment,

"(A" is the representative area, and "t" is the number of days between last day of

sampling in April and first day of sampling in August.

J = Vt f(Equation 2-3)















CHAPTER 3
RESULTS

3.1 Physical Analyses

3.1.1 Seepage Meters

Seepage rates are randomly distributed throughout the study area (Figure 3-1). The

minimum seepage flux of 16.0 & 6.0 ml/m2/min Occurred at station TB-18. The

maximum seepage flux of 92.6 & 28.2 ml/m2/min Occurred at station TB-7. The average

of all measured seepage fluxes is 50.5 ml/m2/min With 10 of 22.8 ml/m2/min. There was

an 83% difference between the maximum and minimum seepage rates.


Scale: 1 :9,990
Skm 0 25 km

Figure 3-1. Submarine groundwater discharge magnitudes from seep meters at various
locations within the sampling grid. The relative diameter of a circle is
proportional to the measured SGD at that location.









The average seepage flux of 50.5 ml/m2/min is assumed to represent the average

flux for the entire study area because the measured seepage rates were randomly

distributed within the study area. Combining this flux with the area of the study site

(7,637,843 m2), the total SGD is 555,698 m3/day, or 202,829,770 m3/year. If the average

flow from the study area is similar across all of Old Tampa Bay a total of 14, 183,464 m3

water /day, or 5,176,964,3 60 m3/year discharges from an area of ~194,946,3 60 m2.

3.1.2 Sediment Cores

3.1.2.1 Lithology

The core recovered from TB-9 is approximately 188 cm in length (Figure 3-2).

Examination of the core revealed it is composed of siliceous sand in the first ~30 cm,

followed by a shell hash layer from about 30 cm to approximately 160 cm, with the

remainder of the core consisting of siliceous sand. The core can be further subdivided

into seven distinct zones. The uppermost 0.3 cm is composed of greenish-gray clay.

This is exclusive to the top of the core. From 0.3 cm to 22 cm the core consists of fine-

grained sand grading into medium grained, light siliceous sand with sparse dark siliceous

silty-sand bands throughout. Some iron staining is present within this zone. Between 22

cm to 28 cm the core contains medium grained, light siliceous sand becoming

increasingly darker in color with depth. This change in color might represent an increase

in silt content. Small shell fragments (0.1 cm 0.5 cm) increase in concentration with

depth. The shell hash layer begins at 28 cm and continues to a depth of 80 cm. The

shells are mostly mollusks (bivalves and gastropods) and range in size from tiny

fragments (<0.1 cm) to whole valves (2 cm 3 cm). The matrix of this layer is a dark,

silty, siliceous sand. From 80 to 160cm the shell hash layer continues, but the matrix

becomes a light siliceous sand. Shell size is variable as in the previous section. Some










clay lenses are visible. Shell concentration decreases with depth. At 160 cm the shell

hash layer grades into fine to medium grained, light siliceous sand to depths of 174 cm.

Shell fragments are smaller than the previous section ranging from 1mm to 1cm in size.

The bottom of the core (174 cm 188 cm) consists of fine to medium grained, pale

orange, siliceous sand with very few shell fragments.

The core recovered from TB-9A is 198 cm in length (Figure 3-3), with similar

structure to the core from TB-9. However, the shell hash layer begins approximately 69

cm deeper than in the core from TB-9. TB-9A is generally composed of siliceous sand in

the first ~97 cm, followed by a shell hash layer from about 97 cm to approximately 179

cm, with the remainder of the core consisting of siliceous sand. The core can be further

subdivided into six distinct zones. The top of the core, the first 20 cm, consists of fine-

grained, light colored siliceous sand. From 20 cm to 35 cm the core transitions into a

fine-grained, light colored siliceous sand which grades into dark siliceous sand. From 35

cm to 97 cm the core is comprised of fine to medium-grained, dark siliceous, silty-sand

with some very small mm'ss to 1cm) shell fragments interspersed. The shell hash layer

begins at 97 cm and continues to a depth of 179 cm. This shell hash layer is identical to

that found at TB-9. Between 179 cm and 195 cm the shell hash layer transitions into a

fine-grained, gray, siliceous sand horizon with some very small shell fragments and clay

lenses interspersed. The remainder of the core (195 cm 198 cm) is comprised of a very

fine, light siliceous sand.



































































Shell hash grading back into light
siliceous sand, fine to medium
grained. Shell fragments of variable
size (<1mm-1cm).

Fine to medium grained, pale orange
siliceous sand. Almost no shell
fragments.


Core Lithology


Real Irnage % Porosity
20 30 40 I


0-





20





40 -





60-





80 -





S100-





120-





1 40 -





160





180-


Clav iareenish-arav}


Fine grading into medium grained
light siliceous sand with sparse dark
silty-sand bands throughout. Also,
some iron staining visible.

Similar to previous section, but with
v. small shell f raaments (1mm-0.5 cm


Calcareous shell hash layer. Shells
ranging in size from < 1mm to whole
intact valves (2-3 cm). The shell
hash layer matrix is a dark, silty,
siliceous sand. The shell sizes are
variable throughout section.


80





100





120


Calcareous shell has layer continued,
but the matrix is a light siliceous sand
Shell size is variable like previous
section, but with decreasing
abundance with depth. The matrix
remains constant over entire section.
Some clay lenses are present.


Figure 3-2. TB-9 core lithology, digital photograph, and











Core Lithology


Real Image


% Porosity


Fine grained, light siliceous sand.


Fine grained, light siliceous sand
grading into dark siliceous silty- sand.


Fine to medium grained, dark
siliceous, silty sand with some
v. small shell fragments mm'ss cm).


100


Calcareous shell hash layer. Contain:
tiny fractured shells grading into
larger intact shells at around 110 cm.
Intact shells (2-3 cm) throughout
remainder of layer.

From 97 to 110 cm's depth, this
section becomes fine to medium
grained, dark silty-sand, grading into
a lighter sand matrix.

From 110 to 179 cm's depth, this
section is similar as above, but with
a light siliceous sand matrix.


125






150-






175


Fine grained, gray siliceous sand
with some v small shell fragments.
Some clay lenses are visible.


00 very fine, light siliceous sand.

Figure 3-3. TB-9A core lithology, digital photograph, and porosity









3.1.2.2 Bulk density and porosity

Bulk density of the sediment was measured using the Geotek MSCL (Multi Sensor

Core Logger) at 0.5 cm increments throughout the length of the cores. Bulk density

occasionally reflects the type of sediment present, for example, shelly zones show higher

bulk density than soft, sandy or clayey zones. At TB-9 the bulk density values ranged

from 1.76 to 2.24 gm/cc, with an average of 2.04 gm/cc. The density was highest from

about 30 cm to 100 cm, which probably reflects the portion of the shell hash layer with

the highest concentration of large, intact shells. At TB-9A bulk density ranged from 1.71

to 2.3 gm/cc, with an average of 2.03 gm/cc. Again, the density was highest in the

portion of the core containing a high concentration of large, intact shells (from about 130

cm to 180 cm). Figures depicting changes in bulk density throughout the cores were

omitted because bulk density is the inverse (mirror image) of fractional porosity, which is

shown on figures 3-3 and 3-4.

Porosity of the sediment is intrinsically related to the bulk density by assuming

two-end member combination of the water and solid with a constant density. Values for

mineral grain density (MGD), and fluid phase density (WD) are used to calculate the

fractional porosity (FP) by:

FP = (MGD GDI) / (MGD WD) (Equation 3-1)

Where "GD1" is the gamma density as determined by the gamma density-processing

panel. For Tampa Bay calculations, MGD is assumed to = 2.65 gr/cm3 (quartz), and WD

= 1.024 gr/cm3 (average seawater). At TB-9, porosity ranges between 17.43 49.49 %

with an average of ~ 30.4 % (Figure 3-2). A zone of lower porosity occurs around 30 cm

to about 100 cm, which is roughly the depth of the shell hash layer, and is consistent with

the poor sorting there. At TB-9A, the porosity ranges between 17.87 52.8 % with an









average of 31.2 % (Figure 3-3). Similarly to TB-9, a zone of lower porosity occurs

within the zone of the shell hash layer (130 cm to 180 cm). It is worth noting that the

density of quartz was applied to the entire lengths of both cores to solve for porosity,

although the shells are clearly composed of CaCO3. The percent difference in density of

quartz and calcite is approximately 2 %, based on a density of calcite of 2.7 gr/cm3. This

difference could be a source of error in these porosity calculations.

3.2 Chemical Analyses

3.2.1 Tracers

For this study, tracers were conservative, naturally occurring solutes in the water

column and pore water. Tracers included chloride and salinity, and strontium and oxygen

isotopes. All average, minimum, maximum and standard deviations for tracer

concentrations in the water column are posted in tables 3-1 and 3-2 for the dry and rainy

seasons, and plots of tracer concentration versus depth are provided for pore water

(below).

3.2.1.1 Chloride and Salinity

Water column chloride concentrations and salinity were measured in water

collected from both sampling trips at stations TB-1, TB-4, TB-9, TB-9A, and TB-9B.

During the dry season, the average water column chloride concentration was 461 mM

with 10 of 4.29 mM (Table 3-1). During the rainy season, the average water column

chloride concentration was 395 mM with 10 of 5.9 mM (Table 3-2). There was a

decrease of 14.3% in the average water column chloride concentration from April to

August. During the dry season, the average water column salinity was 27.99 %o with 10

of 0.25 %o (Table 3-1). During the rainy season, the average water column salinity was

23.65 %o with 10 of 0. 1 %o (Table 3-2). There was a decrease of 15.5 % in average water









Water column and pore water 6 O0 were measured at stations TB-9, TB-1, and TB-

4 during both sampling trips (Figure 3-7a-c). During the dry season, the average water

column 680 Owas 2.02 %o, and the range was from 1.87 2.16 %o. During the rainy

season, the average water column 680 Owas 1.52 %o, and the range was from 1.45 1.62

%o. 68 O profiles have a similar shape to chloride and Sr profiles. They increase with

depth during the dry season, and decrease with depth during the wet season, and the

curves converge at a given depth. The convergence depth for TB-1 is ~80 cmbsf; for TB-

4 it is ~150 cmbsf, and for TB-9 it is ~50 cmbsf. The convergence depths for TB-1 and

TB-9 appear to be similar to those from the chloride profiles, and within ~50 cm for TB-



3.2.2 Nutrients

This study includes measurements of nitrite, nitrate, ammonium, TSN, TN, SRP,

TSP, TP, and biogenic silica. TSN (total soluble nitrogen) is measured on filtered

samples and includes both dissolved inorganic nitrogen (DIN) and dissolved organic

nitrogen (DON) species. DIN species include nitrate, nitrite, and ammonium. DON is

not directly measured, but is the difference between TSN (which is directly measured)

and the measured DIN species. Common DON species include urea, amino acids,

proteins, purines, and pyrimidines. TN (total nitrogen) is measured on unfiltered samples

and thus includes particulate nitrogen (PN) and TSN. Particulate nitrogen concentration

is thus determined by subtracting TSN from TN. The term SRP (soluble reactive

phosphorus) is a measurement of DIP (dissolved inorganic phosphorus). In this study

phosphate comprises most of the DIP, so SRP is a measure of phosphate. TSP (total

soluble phosphorus) is directly measured from filtered samples, and is the sum of all

dissolved organic phosphorus (DOP) and SRP.



















, ,


H *


go


a *



a









=


m





a


delta O l8 (per mil)


delta O l8 (per mil) b


-50





1.
0






50


E


$ 100






150


-50




1.1
0






50


E
+ April
SAugust E
o 100






150


50 2.


50 2.


* April
SAugust



















10 1.50 2.)0 2.50 3.




me

a *



a *








=


delta O l8 (per mil)


C


















* April
SAugust


-50




1.







50






B 100





150





200


Figure 3-7. 6 IsO concentration versus depth, (a) TB-1, (b) TB-9, (c) TB-4.



DOP can thus be calculated by subtracting SRP from TSP. Common DOP species


include proteins and sugars. Particulate phosphorus (PP) can be calculated by subtracting

TSP from TP. The focus of this study is on ammonium, TSN, TN, SRP, TSP, and TP.


Nitrate and nitrite are excluded since they are almost completely reduced to ammonium.

A complete nutrient breakdown and analysis is included in Appendix B.









3.2.2.1 Ammonium

Ammonium (NH4 ) is the predominant inorganic nitrogen species in the nutrient

budget of the study site. NH4+ WAS analyzed at stations TB-1, TB-4, TB-9, TB-9B, and

TB-9A for both sampling trips. Dry season measurements indicate an average water

column concentration of 0.026 mg/L with a range of 0.01 1 0.042 mg/L. The maximum

water column concentration occurred at station TB-1, while the minimum occurred at

station TB-4. The average dry season pore water concentration for the study site was

0.657 mg/L with 10 of 0.33 mg/L. The maximum pore water average was 1.00 mg/L,

which occurred at station TB-1, and the minimum average was 0.613 mg/L, which

occurred at station TB-4.

Rainy season measurements indicate an average NH4+ water column concentration

of 0.0022 mg/L with a range of 0.001 0.004 mg/L. The maximum water column

concentration occurred at station TB-1, while the minimum occurred at TB-9B. The

average pore water concentration for the study site was 0.750 mg/L with 10 of 0.78

mg/L. The maximum pore water average was 1.299 mg/L, which occurred at station TB-

1, and the minimum average was 0.321, which occurred at TB-4. Average water column

ammonium decreased by 92 % from April to August, while average pore water

ammonium increased by 14 % from April to August.

Ammonium profile plots depict scattered data and indicate no concentration

gradient upwards to or downwards from the sediment-water interface. Figure 3-7 shows

data from TB-9 and is representative of the scatter seen throughout the entire study area.


















10 0.50 1.)0 1.




sor


58



Concentration (ppm)


* April
HAugust


Figure 3-8. Ammonium concentrations versus depth below the sediment-water interface
at sampling station TB-9.

3.2.2.2 SRP (phosphate)

PO4+ WAS analyzed at stations TB-1, TB-4, TB-9, TB-9B, and TB-9A for both

sampling trips. Dry season measurements indicate an average water column

concentration of 0.069 mg/L with a range of 0.065 0.073 mg/L. The maximum water

column concentration occurred at station TB-1, while the minimum occurred at station

TB-9A. The average pore water concentration for the study site was 0.263 mg/L with 10

of 0. 11 mg/L. The maximum pore water average was 0.395 mg/L, which occurred at

station TB-1, and the minimum average was 0.145 mg/L, which occurred at station TB-9.










































II 4

*






) 11


Rainy season measurements indicate an average water column concentration of

0.037 mg/L with a range of 0.045 0.033 mg/L. The maximum water column

concentration occurred at station TB-1, while the minimum occurred at TB-9B. The

average pore water concentration for the study site was 0. 199 mg/L with 10 of 0. 11

mg/L. The maximum pore water average was 0.247 mg/L, which occurred at station TB-

1, and the minimum average was 0.125, which occurred at TB-4. Average water column

phosphate decreased by 42 % from April to August, while average pore water phosphate

decreased by 24 % from April to August.


concentration (ppm)


100 0.* 00 0.200


0.300 0.400 0.600 0.B00


* April
IIAugust


Figure 3-9. Phosphate concentrations versus depth at sampling station TB-9.










Phosphate profile plots depict scattered data and indicate no concentration gradient

upwards to or downwards from the sediment-water interface. Figure 3-9 shows data from

TB-9 and is representative of the entire study area.

3.2.2.3 Nutrient breakdown: TSN, TN, TSP, and TP

The remainder of the nutrient data simply allows one to quantify bulk dissolved

organic nitrogen or phosphorus concentrations, and particulate (insoluble) nitrogen or

phosphorus concentrations. The nutrient breakdown in Appendix B contains detailed

data regarding these constituents.

During the dry season, the water column TSN was comprised of 7 % dissolved

inorganic nitrogen, and 93 % dissolved organic nitrogen. The PN concentration was

0.079 mg/L, which is 16 % of the TN concentration. The TSP was comprised of 88 %

DIP and 12 % DOP. The PP concentration is 0.028 mg/L, which is 6 % of the TP.

During the dry season, the average pore water TSN concentration was comprised of 58 %

DIN, and 42 % DON. The PN concentration was 0.297 mg/L, which is 20 % of the TN

concentration. The average pore water TSP was comprised of 100 % DIP. The PP

concentration was 0.073 mg/L, which is 5 % of the TP.

During the rainy season, the water column TSN was comprised of 1 % dissolved

inorganic nitrogen, and 99 % dissolved organic nitrogen. The PN concentration was

0.0594 mg/L, which is 15 % of the TN concentration. The TSP was comprised of 88 %

DIP and 12 % DOP. The PP concentration was 0.0212 mg/L, which is 33 % of the TP.

During the rainy season, the average pore water TSN concentration was comprised of 68

% DIN, and 32 % DON. The PN concentration was 0.147 mg/L, which is 12 % of the

TN concentration. The average pore water TSP was comprised of 95 % DIP, and 5 %

DOP. The PP concentration was 0.066 mg/L, which is 24 % of the TP.









Compositionally, the water column remained relatively constant from April to

August, although DIN dropped slightly and DON increased slightly. Also, PP increased

5-fold. As for the pore water, it too remained relatively constant with time, but with a

slight increase in DIN and a slight decrease in DON. Also, PP increased 5-fold.

3.3 Groundwater Flow Models

3.3.1 Water Budget

On the basis of the groundwater divide and hydrologic equation, and assuming

inputs equal outputs, approximately 0. 1016 m of precipitation per year infiltrates into the

local aquifer system and is discharged into the bay as groundwater. Given a drainage

basin area of 755 km2, and the area of Old Tampa Bay, 195 km2, the area of land upon

which precipitation falls is approximately 560 km2. This area multiplied by 0.1016 m of

groundwater equals ~56,900,000 m3/year of groundwater that moves through the system

and discharges into the bay. If distributed evenly across the bay, approximately

2,230,000 m3/year or 6104 m3/day of groundwater would discharge from the study area.

This discharge volume equals a flux of ~0.55 ml/m2/min, and a seepage velocity of ~0.08

cm/day. Table 3-4 provides a comparison of results from this model, the flow net

analysis, and the chloride-mixing model along with the seepage meters.

3.3.2 Flow Net

The flow net of the Upper Floridan Aquifer indicates a discharge of ~3 1,000,000

m3/year. The Surficial Aquifer flow net yielded a discharge of ~10,600,000 m3/er

Combining these two models results in a total discharge of ~41,600,000 m3/year from the

local aquifer system. If distributed evenly across the basin, approximately 4,000 m3/day

discharges from the study area. This volume equals a flux of ~0.36 ml/m2/min, and a










seepage velocity of ~0.05 cm/day (table 3-4). Table 3-3 contains the discharge rates

through each flow zone (See also figure 2-7).

Table 3-3. Surficial Aquifer flow net calculations. 1Discharge rates through each flow
zone were computed by Darcy's formula: Q = TIL.

Flow (T) Transmissivity (1) Potentiometric (L) Length of flow (Q) Discharge rate
Zone (ft2/d) gradient (ft/mi) zone (mi) (gal/d)

1 9000 2.2 5 740,520
II 5400 3.2 5 646,272
Ill 5400 6.4 11 2,843,597
IV 9000 2.6 8 1,400,256
V 5400 4.4 7 1,244,073
VI 5400 6.4 3 775,526

Total 7,650,244

3.3.3 Chloride Mixing Model (CMM)

The results of this model represent a minimum discharge since it is not known how

often the pore water was replaced by bay water between April and August of 2002.

Calculated groundwater discharge represents only a fraction of the seepage meter

groundwater discharge (Appendix C), similar to the mass balance and flow net analysis.

The CMM was applied to chloride data from sites TB-1, TB-4, TB-9, TB-9A, and TB-

9B. The model yielded information not only dealing with fluxes of groundwater, but also

dealing with depth to which mixing occurs. This depth varied from station to station and

therefore fluxes range from station to station because water flux is based, in part, on the

total volume of water that can enter and exit the sediment. The mixing depths calculated

from the CMM are different than the mixing depths observed in figure 3-7 because the

CMM smoothed the chloride data and fit them to exponential curves. For TB-1, the

CMM indicated a mixing depth down to 112 cmbsf. Based on the model, SGD at TB-1

was 2. 11 ml/m2/min. For TB-4, the CMM indicated a mixing depth down to 110 cmbsf.









The groundwater flux at this station was 2.04 ml/m2/min. TB-9 had a mixing depth down

to 54 cmbsf. The groundwater flux at this location was 1.00 ml/m2/min. TB-9A had a

mixing depth down to 134 cmbsf. The groundwater flux at this location was 2.55

ml/m2/min. TB-9B had a mixing depth down to 182 cmbsf. The groundwater flux at this

station was 3.32 ml/m2/m111.

The average SGD of all stations from this model was 2.21 ml/m2/min, With 1 o of

0.84 ml/m2/min. The maximum flux was 3.31 ml/m2/min, calculated at TB-9B, and the

minimum was 1.00 ml/m2/min, calculated at TB-9. Based on these fluxes, seepage

velocities would range between 0.29 cm/day and 0.48 cm/day.

Table 3-4. Comparison of results from various groundwater seepage measurement
techniques.

Water Budget Flow Net CMM Seepage Meters

ml/m2/m111 fTOm
0.55 0.36 2.21 51
study area
m3/year to
56,900,000 41,600,000 226,450,000 5,177,000,000
0.T.B

m3/ea tostdy 2,230,000 1,460,000 8,870,000 202,800,000
site
m3/day to study 6104 4000 24,300 555,700
site

seepage velocity 0.08 0.05 0.39 7.62















CHAPTER 4
DISCUSSION

4.1 Seepage Meters

The results from the seepage meters in this study indicate a range of values from

~16 ml/m2/min to~-93 ml/m2/min, With an average groundwater discharge of ~51

ml/m2/min. Seepage meters were used during the April sampling trip, which corresponds

to the end of the dry season, therefore seasonal variations were not examined. Rainfall

data from April is included in figure 1-4, and denotes only trace rainfall during the April

sampling event. Appreciable rainfall during, or just prior to a sampling event could

potentially increase hydraulic head, and increase the SGD from the bay floor causing

aquifer derived water discharge to increase. Rainfall in Tampa Bay during the months

from January to April was normal. Submarine groundwater discharge may also depend

on the season. August precipitation data, figure 1-4, denotes much greater rainfall than in

April. Also, Tampa Bay generally receives ~3.5 times (~55 cm) as much rainfall during

the rainy seasons than the dry season (Table 1-1). Lindenberg (2001) observed temporal

differences in SGD in the Indian River Lagoon. In the northern area of her study site she

measured a 58 % increase in average seepage rates from the dry to the rainy season. The

average seepage meter discharge during the dry season was

39.91 +21.66 ml/m2/min, While it was 63.08 +30.99 ml/m2/min during the rainy season.

Lindenberg (2001) demonstrated a significant difference with 95 % confidence in

the distributions of seepage rates between the two seasons using a Wilcoxon signed rank

test. Lindenberg (2001) suggested the temporal variation could be caused by an increase









in discharge from the surficial aquifer. Based on findings from Lindenberg (2001), and

precipitation data from this study, rainy season seepage rates may show significant

variation to dry season rates in Tampa Bay.

There is no clear pattern of seepage rates among the various sampling stations

(figure 3-1). Rates of equal or greater seepage magnitude occur offshore, and east and

west of the putative spring vent (TB-9), such as TB-19, TB-12, and TB-4. Previous work

shows that seepage meter measured SGD decreases roughly exponentially with distance

from the shore (e.g. Bokuniewicz, 1980), although this trend does not occur here. The

idea that seepage is affected by distance from shore is related to tidal heights and

potentiometric surfaces, neither of which would play a maj or role in this study if SGD

were mostly recirculated water. Other factors could cause variation of seepage rates,

including the spatial heterogeneity of hydraulic properties and composition of the

aquifers/aquitards that lie below the study site; the composition of shallow sediment at

each station; hydraulic conductivity; the presence of benthic dwelling organisms; and

possible sampling artifacts associated with seepage meters. If SGD were mostly

recirculated seawater (~98 %), characteristics of and processes occurring in the shallow

sediments would likely affect seepage rates, and not properties of deeper rocks and

sediment. Variations in aquifer properties are likely to occur within the study site, but

probably do not significantly influence the distribution of seepage rates here.

The variation of composition of the uppermost 2 m of sediment such as grain size

may affect the spatial distribution of seepage rates if there is recirculation. The two cores

collected for this study reveal similar lithologies and porosities, and consist mostly of

quartz sand, with shell-hash horizons. The porosity of the cores varies throughout, but









both cores have an average porosity of ~3 1 %, and both show a trend of decreasing

porosity with depth. In general, the shell-hash layers exhibit an overall lower porosity

than the sands, possibly due to poor sorting and variable grain sizes. However, there are

layers of porosity elevated over those of the sands within the shell-hash layers of both

cores (Figures 3-2 and 3-3). A correlation exists relating discharge rates to the depth and

thickness of the shell-hash horizon. The shell-hash zone in the TB-9 core (Figure 3-2)

begins ~30 cmbsf and extends to about 130 cmbsf. The shell-hash zone in the TB-9A

core (figure 3-3) begins ~100 cm bsf and extends another 90 cm below that. TB-9 has a

higher discharge rate (60.5 122.5 ml/m2/min) than TB-9A (54.3 129.3 ml/m2/min). This

observation suggests the thickness and depth of the shell-hash layer may exert control on

SGD.

The presence of benthic dwelling organisms in sediments may affect seepage rates

by altering sediment characteristics. Sediments are altered by organisms through

bioturbation, biodeposition, and production of cementing by-products such as shells and

mucous (Day et al., 1989). Bioturbation often results in the formation of burrows which

can act as conduits for water, which change the hydraulic properties of sediments by

increasing porosity and permeability. Although these types of structures are likely, they

were not observed in either of the two cores via visual inspection. In addition to burrows,

benthic animals leave behind feces and bacterial mucous in sediments. The mucous and

feces act as cementing agents and bind sediment particles together (Day et al., 1989).

Cemented particles reduce porosity and permeability in the sediments. In contrast to

burrows, these processes would reduce water discharge rates.










Seepage meter results from Feather Sound are typical of seepage meter studies in

terms of the magnitude of measured flux (eg Bokuniewicz, 1980; 1992; Martin et al.,

2002; Cable et al., 1996). Seepage rates are randomly distributed throughout the study

area, and demonstrate no evidence of point source discharge and no correlation to

distance from the shore, but are probably dependant on the characteristics of the shallow

sediment, and the presence of organisms in the shallow sediment. Seepage meters can

reflect seasonal changes in weather, but are unlikely to do so in Feather Sound since

aquifer derived water appears to constitute on a small fraction of the net SGD. Seepage

meter data may be erroneous due to the possibility of sampling artifacts and malfunction.

If seepage rates are multiplied by pore water nutrient concentrations a flux of nutrients to

the water column may be obtained. The resultant flux would not represent a new source

of nutrients to the bay if recirculation accounted for the maj ority of the net seepage water,

but the source would be internal.

4.2 Comparison of Measured and Modeled Submarine Groundwater Discharge

Mass balance calculations provide a technique to estimate the volume of

continentally derived aquifer water flowing into the bay. Assuming a fraction of the total

rainfall falling on the land adj acent to the study site eventually flows to Tampa Bay, this

model provides an average annual value for continentally derived SGD. Seepage meters

yield an average integrated discharge rate of ~51 ml/m2/min, While the water budget

model yields a result of ~0.60 ml/m2/min.

Like the water budget calculation, the flow net analysis only measures fresh SGD,

by using field-measured values for the hydraulic properties of the aquifers, aquifer

thicknesses, the water table, and potentiometric surface elevations. For analysis here, the

intermediate confining unit is assumed to be permeable with full hydraulic connection









between the Upper Floridan and Surficial Aquifers. By combining the expected

discharge from the Upper Floridan and Surficial Aquifers into one seepage rate, the flow

net model yields a rate of ~0.3 5 ml/m2/min, Which is the same order of magnitude as the

water budget calculation, and suggests that most seepage water originates from some

source other than the underlying aquifers. The difference in discharge rates between the

models and the seepage meters indicate that freshwater may constitute approximately 1 to

2 % of the seepage water, with ~98 % of the seepage water originating from the bay via

recirculative forces. Given the relatively porous and permeable nature of the shallow

sediments, the shallow water column (which subj ects the bay floor to advective forces

acting within the water column), and potential bioirrigation, recirculation of bay water

may provide the necessary flux of water to explain the discrepancy.

4.3 Evaluating the Exchange of Bay Water and Pore Water Using Tracers

4.3.1 Chloride, 6s"O, and Sr



Variations in pore water concentrations of Cl inO0, and Sr and s7Sr/86Sr with depth and

through time reflect mixing of surface water and pore water, and can be used to separate

different sources of water, including bay water and meteoric water from aquifer water.

Interestingly, the tracer concentrations in the pore water and bay water are similar

(Figures 3-5, 3-6, & 3-7). Chloride is conservative in most diagenetic reactions other

than evaporite mineral precipitation and dissolution or during hydration reactions, which

makes it a useful element for observations of mixing between different sources of water

(Martin et al., in press). Oxygen isotope fractionation in the water column is controlled

by evaporation and precipitation, similar to Cl- concentrations, but provide signals for

aquifer and seawater that are unique from Cl- concentrations. Strontium isotope ratios









are not influenced by evaporation and precipitation (although the Sr concentration is), but

these ratios in aquifer water differ from those in seawater. Sr isotope ratios can be

strongly altered by carbonate mineral dissolution, and therefore assume the

characteristics of the carbonate rocks they flow through.

Chloride, 618O values, and Sr concentrations in pore water and the water column

decrease between the dry and rainy seasons in the study area (e.g. chloride Eigure 3-5).

Rainfall has a low chloride concentration, generally 0.03 mM 0.3 mM (Berner and

Berner, 1996), and thus precipitation falling directly on the Bay would dilute the tracer

concentration of the water during the rainy season, while evaporation during the dry

season would increase concentrations. Recharge from surface runoffwould also increase

during the rainy season also diluting the bay water.

The similarity in pore water to bay water compositions supports the conclusion,

made on the basis of the difference in measured and modeled flow rates, that bay water

circulates through the shallow sediments. The shapes of the depth profies also support

recirculation of bay water rather than flux of new aquifer water. At each sampling

location, dry and rainy season pore water concentration profies converged at variable

depths below the sediment-water interface. This trend is seen in the raw chloride data as

well as the smoothed data calculated in the CMM (Figure 3-5 and Appendix C). The

average convergence depth was calculated to be ~120 cmbsf in the CMM (discussed

below), after the data was smoothed. Below the convergence point concentrations remain

approximately constant with time indicating that temporal changes are restricted to

shallow sediment. This depth is likely to be controlled by the sediment properties, the









strength of the forces (tides, waves, density differences) causing exchange, the presence

of benthic organisms, and the depth to which these organisms dwell.

The shapes of the Cl- concentration profiles with depth reflect bay water

recirculation through the shallow sediments. For example, average data from 10 cmbsf

suggest that pore water is re-mixed bay water. At this depth, the average pore water

chloride concentration is 457 mM while the average water column chloride concentration

is 462 mM. Similarly, the average pore water chloride concentration from all sampling

location during August is 392 mM from 10 cmbsf while the water column is 394 mM.

These differences fall within the measurement error of ~5 mM, and indicate there is not a

significant difference in their values. If continentally derived meteoric aquifer water

flowed to the pore spaces chloride concentrations should be lower than the water column.

Diffusion can be responsible for changes in chemical concentrations. The effect of

diffusion on geochemical tracer profiles, in a similar hydrogeological setting, was tested

in Martin et al. (in press) using a general diagenetic equation (Berner, 1980; Boudreau,

1997; 2000). The process of diffusion was shown to be too slow to account for the

change in chloride concentrations that occurred between a May sampling event and an

August sampling event in the Banana River Lagoon, FL. With aquifer derived water

constituting less than 5 % of the SGD, and a diffusion model shown to be too slow to

generate profiles similar to the observed data, changes in tracer concentration profiles

were attributed to advection. Based on the findings of Martin et al. (in press), diffusion

does not appear to control the concentration changes observed in geochemical data from

this study.









The convergence of chloride concentrations to constant values at depth suggests

that the upper portion of the pore water is the location where most mixing occurs.

Considering that mechanisms for recirculation may diminish with depth, it would be

expected that bay water would have the greatest influence on the pore water near the

sediment-water interface. This concept is the basis for the CMM that fits chloride data to

an exponential model to smooth the data.

Mixing of bay water into the sediments to depths of ~120 cmbsf is greater than

expected either from bioirrigation or by wave pumping (e.g. Shum, 1993; Boudreau,

1998). Shum (1993) relates depth of penetration of bay water into sediments from wave-

induced flow to the shape and height of ripples on the seafloor, wave height, depth of

water, current velocity, and other variables. Shum (1993) shows that depth of mixing of

water column with pore water is generally proportional to the height of these ripples.

Although no ripple measurements were made in Tampa Bay, visual observations indicate

that in Feather Sound ripples are only a few centimeters high. Most likely wave action

alone does not control the mixing depth. Boudreau (1998) reported that the activities of

deposit-feeding organisms are restricted to a narrow zone of marine sediments, with a

worldwide mean of 9.8 cm, restricting bioirrigation to these depths. The cause of the

relatively deep penetration of recirculated water in the study area is unknown, but may

reflect several of these mechanisms combined with the sandy and probably highly

permeable sediment. Figure 4-1 depicts a conceptual model of the proposed mechanisms

responsible for mixing in the shallow pore spaces.

The oxygen isotope ratios from this study indicate enrichment in IsO relative to

SMOW at all depths in the sediment; all water samples had positive values for 618O.











Dansgaard (1964) developed an equation of a line that relates 6 O0 of precipitation to

mean annual air temperature. Based on work from Dansgaard (1964), if mean annual air


temperature in St. Petersburg, FL is ~230C then 68 O of precipitation, and therefore

aquifer water, should be ~2.4 %o relative to SMOW.






B.W.








Bay weler
recirculation

pumpmng circulation duec




Figre -1.A oncptul ode shwin mxin atth seietw ter binterfacedet









oxyguen41 isoope ae tal pooridicto sofn the n sorco the poe ietwater since all possible





sourcgeso water ioun the7 Tamp Bay avreag may e entrice wFithrespc to Howqevery,




oxygen isoptope data can be used to corroborate the mixing phenomenon suggested by

the chloride concentration profiles since the concentration of IO changes with respect to










depth and time in the sediment. Oxygen isotope concentrations in the water column are

influenced by evaporation, and this signal is carried into the pore water.

The naturally occurring isotopes of strontium provide additional information on

potential flow paths or mixing regimes of various water masses. The isotopic

composition of s7Sr/86Sr is 0.70906+0.0003 in the oceans, and ranges from 0.70775 to

0.70790 in Upper Floridan aquifer host rock (McNutt, 2000). Water column and pore

water strontium isotope data from TB-9 are both similar to modern seawater, and differ

considerably from Upper Floridan groundwater (s7Sr/86Sr=0.708909-0.709027; Martin et

al., 2002). Similar to the other tracers in this study, the s7Sr/86Sr of the water column is

almost identical to that of the pore water (Figure 3 -6). The graph exhibits coinciding

straight lines, which means that Sr isotope ratios of pore water and the water column do

not change with time. This means that the chloride, oxygen, and strontium

concentrations must change as result of precipitation and evaporation and not from a

change in sources of water. It is this observation that supports the mixing hypothesis and

demonstrates that there are not additional sources of water, such as meteoric water, in the

aquifer. Table 4-1 contains Sr isotope data.

Table 4-1. Water column and pore water tracer concentration seasonal differences.
April August
Water Column* 8Sr/86Sr 0.709140 0.709120
Average Pore Water 8 Sr/86Sr 0.709110 f0.000009 (10) 0.709167 10.000085 (10)
Water Column Sr Concentration 8.328 ppm 7.2378 ppm
Sr Concentration at 10 cm bsf 8.16 ppm 7.40 ppm
AvgerageWater Column Chloride 461 & 4.29 (10) mM 395 & 5.90 (10) mM
Average Water Column 680" 2.02 %o (1.87-2.16) 1.52 %o (1.45-1.62)









4.3.2 The Chloride Mixing Model

A two-end member chloride-mixing model was used to determine a mixing fraction

between the water column and pore water and converted to a flux of seepage water

assuming bay water is recirculated through the shallow sediments, mixing of bay water

and pore water decreases exponentially with depth below the sediment-water interface,

and the rate at which mixing occurs is equivalent to the interval of time between

sampling events (April to August). The results from the CMM are presented in Appendix

C. The average mixing depth (the arithmetic mean of the mixing depth from each sample

station) is 120 146 cmbsf. The average flux (the arithmetic mean of the flux from each

sample station) predicted from the CMM is ~2.2 10.84 ml/m2/min With a minimum flux

of 1.01 ml/m2/min ffOm TB-9, and a maximum flux of 3.31 ml/m2/min ffOm TB-9B.

Although these discharge rates represent ~4.5 % of the average discharge rate measured

with seepage meters, the total volume of bay water that circulated through the sediments

between April and August is unknown. An upper limit for recirculation time can be

constrained by setting the equation from the CMM to the discharge rate from the seepage

meters. Using TB-1 as an example calculation for recirculation time, the flux calculated

using the CMM is 2. 11 ml/m2/min. This location contains 34 cm3 Of water over the depth

that mixing occurs (112 cm). Dividing this volume by 1m2, and time (t), which is the

new variable, it should equal the average seepage meter flux of 51 ml/m2/min. Solving

for (t) indicates complete mixing should occur in ~4.6 days. The result from this

calculation suggests that if the seepage rate of 5 1 ml/m2/min is caused by recirculation,

then the pore water should be replaced every 4.6 days rather than 112 days, which is the

interval between sampling events.









This study produces two results for SGD that account for recirculated water, one

from the seepage meters and one from the CMM. The result from the seepage meters is

an order of magnitude greater than the result from the CMM. It is difficult to determine

which measurement is correct. If the actual SGD rate is 5 1 ml/m2/min then the seepage

meters are correct. If the actual rate is 2.2 ml/m2/min then the CMM is correct. If the

seepage meters are correct then more than 1 pore volume mixes between April and

August, but the residence time of the pore water is unknown. If the CMM is assumed to

be correct then only 1 pore volume mixes between April and August, but the residence

time is the period between sampling events. Additionally, if the CMM is correct then the

seepage meters are wrong. Section 2-2 discusses some of the possible drawbacks and

shortcomings of seepage meters. When equation 2-3 is set equal to the flux from the

seepage meters the residence time of the pore water is shown to be ~4.5 days, and this

result demonstrates that sampling every 3-4 months is not ideal. This comparison is

made bearing in mind that either flux could be wrong.

The CMM results, supported by 618O, Sr and s7Sr/86Sr data, suggests that most, if

not all, of the seepage water is seawater, and the tracer data also corroborate evidence

from the groundwater flow models. The changes in 68"O, and Sr concentrations with

depth and through time support the magnitude and frequency of recirculation indicated by

the CMM, and the groundwater flow models suggest that only a small fraction of the net

seepage water is continentally derived. The data indicate no significant spatial

relationship between water column chemistry and the location of the putative spring. For

example, the water column Cl- concentrations at TB-9 during August reflects one of the










highest chloride measurements in the group, and salinity measurements that are similar to

sampling points far away from the putative vent (Figures 3-4, 3-5).

4.4 Nutrients and an Estimate of Nutrient Flux

4.4.1 Introduction

Water column and pore water nutrient concentrations are essential to determine

nutrient cycling in an estuarine system. In general, these data can be used in flux

calculations that, in turn, help to quantify nutrient loading to a system. A comparison of

water column and pore water concentrations can be used to calculate enrichments in the

pore water. Often, the shape of the nutrient pore water profiles may be used to determine

advective and diffusive fluxes of solutes to the overlying water column (Aller, 1980;

Berner, 1980). Here, nutrient profiles preclude shape modeling as a method to determine

advective and diffusive fluxes of solutes to the overlying water column because the data

are scattered, and indicate no concentration gradient either upward to or downward from

the sediment-water interface (eg figures 3-8, 3-9).

Data from this study suggest that continentally derived, meteoric aquifer water

constitutes only 1-2 % of the net SGD, and thus nutrient contributions from aquifer

discharge should be small. Nutrients can also be sourced to bay water by direct discharge

of sewage effluent, surface runoff, atmospheric deposition, regeneration of organic matter

in the water column, and remineralization of organic matter in bay sediments.

Furthermore, recirculation may enhance remineralization of nutrients in the sediment by

introducing oxygenated water to the sediments and facilitating aerobic microbial activity.

In the water column of marine environments, the concentration of organic nitrogen

and phosphorus species are usually higher than inorganic nitrogen and phosphorus in the

water column, while the converse is true in the sediment pore water because of bacterial









remineralization of organic nitrogen and phosphorus in the pore waters (Treyfry et al.,

1992; Herbert, 1999). When inorganic nutrients flow back to the water column, they are

assimilated back into the food web, and drive part of the overall nutrient cycle.

Results from this study indicate that the average water column TSN during the dry

season is composed of 7 % DIN (dissolved inorganic nitrogen) and 93 % DON (dissolved

organic nitrogen) [Appendix B]. The average water column organic and inorganic total

soluble nitrogen both drop slightly during the rainy season. This might be due to dilution

effects, or most likely as a result of increased primary production that creates more

organic matter and utilizes available inorganic matter. The average water column TSP

breaks down into 88 % inorganic phosphate and 12 % organic phosphate for both

sampling events, opposite of the inorganic and organic fractions of nitrogen. Organic

phosphorus should be more abundant in the water column than inorganic phosphorus.

Inorganic phosphorus may be elevated in the water column from recirculation of the bay

water or from excess P from apatite deposits. The recirculated water would increase the

flux of inorganic byproducts of organic decay to the water column. Another explanation

is that the bay is N limited, and thus excess inorganic phosphorus goes unused by the

photosynthesizing organisms.

Water column TN decreased from 0.487 mg/L to 0.39 mg/L from the dry to rainy

season, while total phosphorus decreased from 0.107 mg/L to 0.08 mg/L. The decrease

in both totals suggests dilution effects due to increased rainfall. During the dry season

average pore water total soluble nitrogen was composed of 58 % dissolved inorganic

nitrogen and 42 % dissolved organic nitrogen, and these numbers changed to 68 % and

32 %, respectfully, during rainy season. Dry season average pore water total soluble










phosphorus is composed entirely of dissolved inorganic phosphorus, while this number

drops to 95 % during the rainy season. These data also support the recirculation and

enhanced nutrient loading hypotheses because pore water should contain more inorganic

phosphorus if bay water is actively pumped into the sediment, and bacterially mediated

decomposition is occurring below the sediment-water interface, converting organic

material into inorganic material.

In Feather Sound, ammonium is the most prevalent inorganic form of nitrogen in

both the water column and pore water throughout the year, with trace nitrite or nitrate.

Decaying organic matter is converted to ammonia via ammoniafication, and is

transformed later to ammonium. In Feather Sound pore water oxygen generally

decreases to trace concentrations below the sediment water interface suggesting that

microbes heavily utilize oxygen as a source of energy to facilitate metabolism of organic

matter. Nitrate concentrations are also low in the pore water, suggesting that microbes

may also utilize nitrates as a source of energy. Unlike oxygen, however, nitrates are not

replenished in the bay water rapidly, like oxygen, when the pore water flows back into

the water column. Changes in oxygen and nitrate concentrations with depth further

support the recirculation hypothesis.

4.4.2 Nutrient Loading and Flux

Depending on the rate that pore water mixes with bay water, and amount of

remineralized carbon, submarine groundwater discharge may provide a considerable

amount of nutrients to the bay. Some dissolved nutrients in the pore water would be

carried into the shallow sediments along with the circulating bay water, and thus net

contributions of nutrients from pore water to bay water would be the total concentration

less the concentration in the water column. Nutrient fluxes are calculated using two









methods. One method involves the average seepage meter flux and the average nutrient

pore water concentration (either NH4' for N or SRP for P) at all depths and locations, for

each sampling event. The average pore water ammonium concentration from both April

and August is 0.001 gr/L 10.0003. For phosphorus, average concentrations were 0.00019

gr/L from April, and 0.00016 gr/L from August. Appendix B shows the range of values.

When these concentrations (the average of the two seasons) are multiplied by the average

seepage meter flux of 51 123 ml/m2/min, the calculated NH4+ flUX is 18.48 gr/m2/ya

(10.10 26.81 ml/m2/min) and the PO4 flUX is 4.77 gr/m2/year (2.62 6.92 ml/m2/min).

Nutrient fluxes were also calculated using the measured water column oxygen

concentrations along with the stoichiometry of equation 1-1, assuming that all of the

water column oxygen is consumed in the oxidation of organic matter. Generally, the

oxygen concentrations of all pore water samples are an order of magnitude less than the

overlying water column. Although the non-zero concentrations of oxygen could indicate

that there is not complete microbial reduction of the oxygen in the pore water, the

measured oxygen may originate from atmospheric contamination during sampling. The

lack of NO3- in the pore water suggests that oxygen is depleted; otherwise the microbes

would not reduce the NO3-. The flux of oxygen is calculated by multiplying the

measured oxygen concentration of the water by seepage meter fluxes. Ammonium and

phosphate fluxes are proportional to oxygen flux according to equation 1-1. The process

for stoichiometrically calculating nutrient flux is described in Appendix D. Table 4-2 is a

comparison of results from both techniques.










Table 4-2. Comparison of nutrient fluxes from two techniques. Units are gr/m2/er
Flux of Ammonium
Average Stoichiometry Based On
Pore Water Concentration WC oxygen
18.48 11.44

Flux of Phosphate
Average Stoichiometry Based On
Pore Water Concentration WC oxygen
4.77 3.94


Assuming that the seepage meter flux and the average nutrient concentrations

represent the entire study area, and using the areas reported in section 3.1.1, the annual

NH4+ l0ad from the sediments within the study area ranges from ~1.4 x 105 kg (average

pore water nutrient concentration) to ~8.7 x 104 kg (stoichiometry of equation 1-1). The

annual NH4+ l0ad for all of Old Tampa Bay ranges from ~3.6 x 106 kg to ~2.2 x 106 kg,

assuming that flux is equal throughout the bay. The annual phosphate load for the study

area ranges from ~3 x 104 kg to 3.6 x 104 kg, depending on the technique. The annual

phosphate load for Old Tampa Bay ranges from ~7.7 x 105 kg to ~9.3 x 105, depending

on the method, and assuming equal flux throughout the bay. These data are compared

with other results of nutrient loads and SGD (Table 4-3). All data are reported with

identical units, unless otherwise noted. Also, the results are reported in terms of

ammonium and phosphate, however these compounds are representative of the nitrogen

phosphorus load, respectively, in the study area because these were the only measured

species of nitrogen and phosphorus in the water.

Wang et al. (1999) created a water quality model using Water Analysis Simulation

Program (WASP) to simulate and evaluate the relationship between external nutrient

loading and water quality of Tampa Bay. The model quantifies processes governing











internal nutrient cycling and phytoplankton growth, and part of the model estimates


internal nitrogen and phosphorus loading. Wang et al. (1999) suggests the major sources


of inorganic nitrogen are benthic microbial processes which transform organic nitrogen to


inorganic nitrogen and release it to the water column.


Table 4-3. A comparison of water and nutrient flux data from this thesis to previous
studies.
Study Site Water Flux/Velocity Nutrient Flux/Load from Sediment Citation
Nitrogen Phosphorus

(mvrrimn) (grirrear) (gr/m /year)
Great South Bay. NY 27.8 --Boukuniewicz. 1980

Great South Bay. NY 104 --Boukuniewicz. 1992

Indian River Lagoon, FL 6.65 8 89 (cm/day) 1.1 x 10 2 6 x10 Zimmermal et al.. 1985

Indian River Lagoon, FL 40 65 artin etal., 2000
Indian River Lagoon, FL 28 39 45.22 71 48 7 86 3 72 Lindenberg. 2001

Chesepeakte Bay 10.5 21.7- 262.8- Gallagher et al., 1996

Chesepeakte Bay 8.33- 55- Robinsoni et al., 1998

Florida Keys 3.75 1 -Sirnrons. 1992
Gulf of Mexico 90 -Cable et al., 1996

OkI Tampa Bay -12.82 -Wang et al., 1999

Old Tampa Bay 51 11.44 -18.48 3.94 -4.77 this study

Old Tampa Bay 7.3 (crn/day) this study


According their model, which estimates benthic nutrient release from a mass balance


calculation of the total bay nutrient budget, benthic ammonia release from the sediment


contributes from 4-15 % of the ammonia in the nitrogen budget, whereas all external


loads only contribute from 0-7 %. The other source of ammonia in the model is


mineralization of organic matter in the water column (28-44 %). Sinks include


phytoplankton growth, which utilizes 36-47 % of the available ammonia; nitrification of

ammonia (3-6 %); and dispersion (0-6 %). According to their model, internal loads of


ammonia released from bottom sediments exceeded the total external load for the entire


bay. Wang et al (1999) indicate annual sediment release of ammonia in Old Tampa Bay









is ~12.82 gr/m2/year, similar to results from this study. Although Wang et al. (1999) do

not specify the mechanisms forcing nutrient release from the sediment the coherence of

their results with this study suggest that much of the loading may be from recirculated

bay water. Their study also indicates that internal loads due to benthic releases of

phosphorus exceeded all external loadings combined but do not provide sufficient

graphical data to estimate an annual sediment release of phosphorus.















CHAPTER 5
CONCLUSIONS

5.1 The Importance of This Study

The importance of this study is two-fold: 1) quantiyfing and characterizing the

SGD and associated nutrient flux in Feather Sound enables a more accurate assessment of

the hydrologic and nutrient budgets of Old Tampa Bay, and, 2) the results suggest that

the shallow sediments are a source for nutrients to Old Tampa Bay. If SGD (regardless

of the source of water) is low or diffuse, or necessary oxidizers are not present below the

sediment-water interface, organic matter will be buried in the sediment thus removing it

from the bay. In these scenarios the sediment is capable of sequestering excess nutrients

from an increase in external nutrient loading. This study also demonstrates that the

putative spring in the vicinity of Feather Sound was non-flowing during the sampling

events in 2002, or does not exist, and that continentally derived aquifer water does not

contribute significantly to SGD. These two findings suggest that Tampa Bay is not

susceptible to new groundwater pollution such as demonstrated in Chesapeake Bay

(eg. Gallagher et al. 1996; Robinson et al., 1998).

5.2 The Conceptual Model

Submarine groundwater discharge has been shown to be an integral component of

the marine hydrologic budget and can have a profound effect on diagenetic reactions near

the sediment-water interface. Pore water concentration profiles of the geochemical

tracers, along with seepage meter measurements and groundwater flow modeling suggest

significant mixing between the shallow pore water and the overlying bay water in Old









Tampa Bay which drives organic matter remineralization in the sediment. Seepage

meters indicate an average groundwater discharge rate of 50.5 ml/m2/min With 10 of 22.8

ml/m2/min, While groundwater flow models, which only measure aquifer-derived

groundwater, indicate discharges are approximately two orders of magnitude less. In

addition, a two-end member chloride-mixing model suggests that complete mixing can

occur in a matter of days. The differences between analytical models and direct

measurements suggest that mixing may constitute up to 99 % of the submarine

groundwater discharge. Bay water is mixed with pore water to an average depth of ~120

cm below the sediment-water interface. This depth is deeper than previously seen for

mixing which is important because more of the sediment column will be altered by

diagenetic reactions with the water. The exact physical mechanism or mechanisms that

drive this mixing are unknown, but probably include some combination of advective

forces such as density driven flow, wave action, tidal setup, and bioirrigation.

Nutrient concentration data indicate that organic matter is remineralized in the

shallow sediments of the bay and may be enhanced by mixing, as oxygen-saturated bay

water flows through the shallow pore space. Inorganic nitrates (ammonium) and

phosphates (phosphorus) are the by-products of organic matter degradation, and upon

remineralization are reintroduced into the water column. Depending on the method of

calculation, nitrogen flux ranges from 11.44 to 18.48 gr/m2/yr and phosphorus flux

ranges from 3.94 to 4.77 gr/m2/yr. Nitrogen flux from this study agrees within an order

of magnitude with nitrogen flux from Wang et al. (1999), which was calculated by a

numerical model. Wang et al. (1999) calculated sediment nitrogen loading to be ~12.82

gr/m2/yr, which constitutes from 4 15 % of the total nitrogen budget for Old Tampa










Bay. Nitrogen fluxes from this study, 11.44 to 18.48 gr/m2/year, equate to annual

discharges of nitrogen from the sediment to the water column from ~2,460 to ~3,970 tons

for the Old Tampa Bay segment of Tampa Bay, FL. Zarbock et al. (1996) recently

estimated all external loads of nitrogen to Old Tampa Bay to be approximately 485 tons

per year. This estimate includes non-point sources, domestic point sources, industrial

point sources, and atmospheric deposition. Therefore, the sediment-released nutrient

load appears to be up to over 8 times higher than all external sources combined.

Sediment-released nutrients appear to be a significant component of the nutrient cycle,

and an important internal source of nutrients to the bay, and should be considered in

further ecological investigations into the overall health of Tampa Bay.

The submarine spring, originally thought to exist on the basis of early

reconnaissance, either does not exist or was not flowing during the time of sampling. It is

possible that drought conditions over the years leading up to sampling had reduced its

flow. Both point and non-point discharge of aquifer-derived water across the sediment

water interface were negligible during the time of sampling, but it may be that both types

of discharge occur ephemerally, during periods of intense rainfall.

5.3 Future Work

This research provides preliminary information about Tampa Bay such as the

physical properties of the sediment, rates of submarine groundwater discharge, nutrient

fluxes to bay from sediment release, and the origin of the SGD. Future work should be

designed to refine in both space and in time the apparent advective mixing of water in the

shallow sediments that was observed during the first year of sampling. Work should

include discrete time series measurements of pore water and water column solutes to

determine what effects tidal fluctuations have on SGD. A dye-tracing test could be










conducted to determine the exact flow path of continentally derived aquifer water. The

SGD rate obtained from this method would be a useful comparison to the groundwater

flow models employed in this study. Also, groundwater monitoring wells and

piezometers on shore and off shore would be useful for several reasons. They would

provide local aquifer water composition that could be compared to the pore water and bay

water, and piezometers would provide useful hydraulic head data that would refine the

data used in the flow net. Future work should also include seasonal seepage meter

deployment to verify the affect that climate has on SGD. Duplicate deployment should

be utilized to detect error associated with the meters.














APPENDIX A
WATER CHEMISTRY DATA
















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