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SUBMARINE GROUNDWATER DISCHARGE AND NUTRIENT LOADING TO
FEATHER SOUND, OLD TAMPA BAY, FLORIDA
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
Eric J. Davis
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
ACKNOWLEDGMENT S ................. ................. iii.._._. ....
LI ST OF T ABLE S ........._..... .......... ............... vii...
LIST OF FIGURES ...._.._ ................ .......__. .........vi
AB S TRAC T ......_ ................. ............_........x
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
18.104.22.168 Design............... ...............27.
22.214.171.124 Deploy ment ............ ..... ._ ...............29..
126.96.36.199 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
188.8.131.52 Bulk density and porosity ....._.__._ ..... ... .__. .. ..._._. ......4
3.2 Chemical Analyses .............. ...............47....
3.2.1 Tracers .............. .. ....... ..... ............4
184.108.40.206 Chloride and Salinity............... ...............47
220.127.116.11 Isotopes............... ...............49
3.2.2 Nutrients .............. ...............54....
3.2.2. 1 Ammonium ................. ...............57...._ .__ ...
18.104.22.168 SRP (phosphate) ................. ....... ........ ... ..... ...............58...
22.214.171.124 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..
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
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
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
Eric J. Davis
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.
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
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
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,
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
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
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).
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.
Mexico ., \
Scale 1:350,000 0 10 km
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
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,
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
3 r* MIDDLE
Z ~EOCENE < I UNtT
8- 10 -LIMESTONE gggy,
I I I II I DOLDMITE
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)
-244 14n 00-i 19 00 Lclrd
FLOR DA AQUIFER
-see // // // LOWER C NFNINIG BED
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-
AVG RAINFALL (cm)
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)
l l I I I I I I Il l I I I I I I I I I
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S I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I
hh h h hh hh -,
hlhl d In
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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
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,
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
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
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
150 Cross Section
230 iii-r'hnt ra
Figure 2-4. Design of a multisampler (from Martin et al., 2003).
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.
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.
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
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 %
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
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.
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
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
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
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
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
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)
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
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
Fine to medium grained, pale orange
siliceous sand. Almost no shell
Real Irnage % Porosity
20 30 40 I
1 40 -
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.
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
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).
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.
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
126.96.36.199 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
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
188.8.131.52 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-
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.
delta O l8 (per mil)
delta O l8 (per mil) b
10 1.50 2.)0 2.50 3.
delta O l8 (per mil)
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.
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.
Figure 3-8. Ammonium concentrations versus depth below the sediment-water interface
at sampling station TB-9.
184.108.40.206 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.
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.
100 0.* 00 0.200
0.300 0.400 0.600 0.B00
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.
220.127.116.11 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
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
Water Budget Flow Net CMM Seepage Meters
0.55 0.36 2.21 51
56,900,000 41,600,000 226,450,000 5,177,000,000
m3/ea tostdy 2,230,000 1,460,000 8,870,000 202,800,000
m3/day to study 6104 4000 24,300 555,700
seepage velocity 0.08 0.05 0.39 7.62
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
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
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.
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.
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
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
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
Flux of Phosphate
Average Stoichiometry Based On
Pore Water Concentration WC oxygen
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
Study Site Water Flux/Velocity Nutrient Flux/Load from Sediment Citation
(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.
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.
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