ABSTRACT
Title of Thesis: FACTORS REGULATING VARIABILITY IN WATER
QUALITY AND NET BIOGEOCHEMICAL FLUXES
IN THE PATUXENT RIVER ESTUARY
Jeremy Mark Testa, Master of Science, 2006
Thesis Directed By: Professor W. Michael Kemp
Marine-Estuarine-Environmental-Sciences
Net biogeochemical production and transport rates for several variables were
computed for the Patuxent River estuary from 1985 to 2003 using a box model.
Monthly rate estimates were analyzed for temporal patterns and variability in
response to climatic factors and nutrient management. The middle estuary was the
most productive estuarine region and was characterized by strong pelagic-benthic
coupling. Phytoplankton biomass in this region peaked in spring as fueled by
seaward nutrient inputs. Nutrients regenerated from decomposition of this spring
bloom were required to support summer productivity.
Improvements of sewage treatment in the watershed resulted in declining
point source nutrient loads to the estuary, but water quality did not improve in the
mesohaline estuary. Poor water quality in the middle estuary was maintained by
persistent non-point nutrient loads, while degrading water quality in the lower estuary
correlated with increasing DIN inputs from Chesapeake Bay, high river flow, and
declining herbivorous grazing.
FACTORS REGULATING VARIABILITY IN WATER QUALITY AND NET
BIOGEOCHEMICAL FLUXES IN THE PATUXENT RIVER ESTUARY
By
Jeremy Mark Testa
Thesis submitted to the Faculty of the Graduate School of the
University of Maryland, College Park, in partial fulfillment
of the requirements for the degree of
Master of Science
2006
Advisory Committee:
Professor W. Michael Kemp, Chair
Professor Walter R. Boynton
Professor Lawrence P. Sanford
Dr. James D. Hagy III
? Copyright by
Jeremy Mark Testa
2006
ACKNOWLEDGEMENTS
I would like to thank my advisor, Dr. W. Michael Kemp, for his support,
guidance, and insight throughout my graduate career. His devotion to my research
and open minded thinking often challenged me to think beyond my initial assertions
and conclusions. His honest criticism of my work and eagerness to engage in
conversation over the past three years were critical to my improvement as a scientist
and an intellectual. I would also like to thank Dr. Larry Sanford for helpful
suggestions about box modeling and its limitations and Jim Hagy for generously
helping me learn the box model technique and spending extra time evaluating my
interpretations and techniques. Dr. Walter Boynton provided much insight and data
for this thesis and answered my many questions throughout the years in a quick and
spirited manner - for this, I am grateful.
Dave Kimmel and Jude Apple generously provided information about
statistical techniques, while Shih-Nan Chen, Angie Hengst, Jennifer O?Keefe, and
Caroline Wicks made my fieldwork possible by volunteering to spend a day across
the bridge. Greg Kearns and Julie Bortz also helped facilitate fieldwork and data
analysis. I?d also like to thank Tom Fisher, Lou Codispoti, Vince Kelly, Anne
Gustafson, Larry Harding, and Dave Miller for providing me with data and support. I
am grateful to the Horn Point Laboratory for funding my salary, travel, and research,
and to the NOAA National Estuarine Research Reserve System for a Graduate
Research Fellowship.
Lastly, I would like to thank all my friends and colleagues at Horn Point
Laboratory, who helped make the last three years some of the best in my life. I must
also thank my family for their constant love and support. I will thank Jamie last,
whose confidence and words of encouragement, especially during the weeks before
my defense, were critical to my success.
ii
TABLE OF CONTENTS
List of Tables ............................................................................................................... v
List of Figures.............................................................................................................. vi
Background and Introduction ...................................................................................... 1
References............................................................................................................ 5
Chapter I: Spatial and temporal variability of biogeochemical processes in the
Patuxent River estuary: Inferences from water quality data....................... 7
Abstract................................................................................................................ 7
Introduction.......................................................................................................... 8
Methods.............................................................................................................. 12
Study site and data availability ................................................................ 12
Computing salt and water transport ......................................................... 13
Nutrient transport and production rates ................................................... 15
Stoichiometric calculations...................................................................... 20
Results................................................................................................................ 21
Seasonal variability in non-conservative rates......................................... 22
Stoichiometric calculations...................................................................... 24
Axial distribution of non-conservative rates............................................ 24
Pelagic-benthic coupling.......................................................................... 26
Nutrient transport rates ............................................................................ 27
Discussion.......................................................................................................... 28
Seasonal and regional variability in surface biogeochemistry................. 28
Seasonal and regional variability in bottom biogeochemistry................. 32
Assessing error in box model rates .......................................................... 35
Pelagic-benthic coupling.......................................................................... 36
Effect of freshwater input ........................................................................ 42
Summary and Conclusions ................................................................................ 44
References.......................................................................................................... 46
Sources of unpublished data .............................................................................. 55
Tables................................................................................................................. 57
Figures................................................................................................................ 61
Chapter II: Responses of water quality and biogeochemical fluxes to nutrient
management and freshwater inputs in the Patuxent River estuary .......... 75
Abstract.............................................................................................................. 75
Introduction........................................................................................................ 76
Methods.............................................................................................................. 79
Water quality data.................................................................................... 80
Transport and production of non-conservative variables......................... 80
Hypoxia.................................................................................................... 85
Nutrient load and freshwater flow data.................................................... 85
iii
Statistical analyses ................................................................................... 86
Results................................................................................................................ 86
Temporal trends in nutrient loading......................................................... 86
Temporal trends in water quality ............................................................. 88
Temporal trends in net biogeochemical fluxes ........................................ 90
Trends and controls on hypoxia............................................................... 92
Discussion.......................................................................................................... 92
Hypoxia.................................................................................................. 102
Summary and Conclusions .............................................................................. 103
References........................................................................................................ 104
Sources of unpublished data ............................................................................ 111
Tables............................................................................................................... 112
Figures.............................................................................................................. 114
Summary and Synthesis............................................................................................ 136
Appendix I: Relationships between chlorophyll a, total suspended solids, and
secchi depth along the estuarine axis of the Patuxent River................. 142
Figures.............................................................................................................. 144
Appendix II: Estimating denitrification using non-conservative fluxes of nitrogen
and phosphorus: Approach and comparison with different methods... 146
References........................................................................................................ 149
Sources of unpublished data ............................................................................ 149
Figures.............................................................................................................. 150
Complete Reference List........................................................................................... 152
iv
LIST OF TABLES
Table 1.1: Physical dimensions of all boxes in for the box model of Hagy et al.
(2000). Dimension information may be used to convert all box
model computed nutrient transports and production rates to the
desired units ..........................................................................................57
Table 1.2: Correlation coefficients and p values for the relationships (top panel
rate versus side panel rate) between selected surface and bottom water
biogeochemical rates and chlorophyll a in three regions (upper, middle,
lower estuary) of the Patuxent River estuary. Rates include net
biogeochemical production of bottom layer DSi, DIP, NH
4
+
, and O
2
as
computed with a box model, spring particulate organic carbon (POC)
sinking, and chlorophyll a. Box model computed rates are monthly
rates and chlorophyll a and POC sinking are annual means for the years
1985 to 2003 (O
2
data are annual means when related to these
variables).............................................................................................. 58
Table 1.3: Resulting F-values of one-way ANOVA to test for significant
differences between months for selected box model computed net
production rates and chlorophyll a. Associated p-values < 0.01
indicated by ** and p < 0.05 indicated by *. Monthly means calculated
for all data from 1985 to 2003 (n = 228) for the upper (Box 2),
middle (Box 4), and lower (Box 5) Patuxent River estuary. ............... 59
Table 1.4: Comparisons between non-conservative box model estimated rates
of bottom layer nutrient regeneration and oxygen demand in the
Patuxent river estuary with sediment-water oxygen and nutrient
exchange (SONE) rates measured in the Patuxent. All rates are in
units of mmol m
-2
d
-1
. SONE rates from Boynton and
Rohland (2001) .................................................................................... 60
Table 2.1: Summary of analytical methods used by the Chesapeake Bay
Program and in Flemer et al. (1970) to measure several water
quality variables................................................................................. 112
Table 2.2: Comparison of trend test results for 1985 to 2003 from linear
regression and Seasonal Kendall models. Significant p-values are
those less than 0.05 and are bold ....................................................... 113
v
LIST OF FIGURES
Figure 1.1: Map of the Patuxent River estuary with Chesapeake Bay (inset),
including box model boundaries (Hagy 1996), Chesapeake Bay
Program water quality monitoring stations (www.chesapeakebay.net),
and the location of Maryland Department of Natural Resources?
continuous water quality sensors (www.eyesonthebay.net).
Chesapeake Bay Program station codes are to the left of each station
and numbers at the right of box model boundaries indicate distance
from the mouth of the estuary (km). ................................................... 61
Figure 1.2: Schematic description of the box model structure (as seen in Hagy et
al. 2000). Included are box model boundaries, exchange coefficients,
and inputs. The estimated exchanges presented in this diagram are
seaward advection (Q
m
), landward advection (Q?
m
), vertical advection
(Q
vm
), vertical diffusive exchange (E
vm
), and horizontal dispersion
(Q
m,m+1
). Included inputs are the volume of each box and the salt
concentration (not included), river flow (Q
r
), the input of freshwater to
each box (Q
fm
), and the salinity at the seaward boundary (not
included). ............................................................................................ 62
Figure 1.3: Generalized depiction of two-layer non-conservative box model for
boxes 2-6. The non-advective exchange, E
m,m-1
(c
m
- c
m-1
), is part of the
calculation for Box 2 only. Notation is the same as in Figure 2 except
for box volume (V) and up estuary (m-1) and down estuary (m+1)
concentrations or water fluxes. Atmospheric inputs are included,
though the non-conservative flux of DIN is the only
rate where atmospheric inputs are included........................................ 63
Figure 1.4: Average diel percent oxygen saturation curve for the month of August
(2003 and 2004) in three regions of the Patuxent River estuary. The
data were used to correct Chesapeake Bay Program monitoring data
for the time of day sampled. Error bars represent one standard
deviation of the mean. Data are from continuous water quality sensors
maintained by the Maryland Department of Natural Resources (details
of station location, depth, and available data can be found at
www.eyesonthebay.net)...................................................................... 64
Figure 1.5: Contour plots of chlorophyll a (left panel) and dissolved
oxygen/salinity (right panel) in the Patuxent River estuary in the
winter, spring, and summer of 1995. Black lines represent salinity
contours in the right panel and red area represents hypoxic water
(O
2
< 2 mg l
-1
). Box model boundaries are indicated by white
lines. Salinity contours of 1, 5, and 13 are labeled ............................ 65
vi
Figure 1.6: Monthly mean rates of net biogeochemical production of surface and
bottom layer O
2
(surface rate corrected for air-sea exchange), DIN,
DIP, and DSi computed by the box model for the upper (Box 2),
middle (Box 4), and lower (Box 5) Patuxent River estuary. Monthly
means (? SE) were calculated for all years from 1985 to 2003.
Horizontal dashed lines are drawn at zero net production rates. Error
bars represent one standard error of the mean .................................... 66
Figure 1.7: Relationships between temperature and monthly rates of bottom
layer net production of DSi, DIP, and O
2
, computed by
the box model, in the middle region (Box 4) of the Patuxent
River estuary ....................................................................................... 67
Figure 1.8: Monthly mean rates of net biogeochemical production of O
2
(corrected
for air-sea exchange) and DIN computed for the surface layer by the
box model, as well as chlorophyll a in the surface layer of the middle
(Box 4) and lower (Box 5) regions of the Patuxent River estuary.
Monthly mean values (? SE) were calculated for years of above
average river flow (open shapes, flow > 20 year mean, n = 7) and
below average river flow (shaded shapes, flow < 20 year mean, n = 9).
Horizontal dashed lines are drawn at zero net production rates. Error
bars represent one standard error of the mean .................................... 68
Figure 1.9: Mean annual rates of net biogeochemical production of surface and
bottom layer DIN, DIP, and O
2
(surface rate corrected for air-sea
exchange) computed by the box model, as well as chlorophyll a along
the estuarine axis of the Patuxent River estuary. Annual means (? SE)
were calculated for years of above average river flow (squares, flow >
above 20 year mean + SE) and below average river flow (circles, flow
< 20 year mean - SE). Error bars represent one standard error of the
mean. .................................................................................................. 69
Figure 1.10: Mean annual rates of net biogeochemical production of surface and
bottom layer O
2
(top panel, corrected for air-sea exchange) and DIN
(bottom panel) along the estuarine axis of the Patuxent River estuary.
Annual means were calculated for years of above average river flow
(flow > above 20 year mean + SE) and below average river flow
(flow < 20 year mean - SE). The rates are total mass fluxes in
each layer in units of 10
8
mmol d
-1
. Dark bars are surface layer
rates and gray bars are bottom layer rates. Surface DIN
consumption and bottom O
2
consumption rates were multiplied
by -1 to simplify comparisons............................................................. 70
vii
Figure 1.11: Mean monthly surface layer particulate organic carbon (POC)
concentration and box model computed POC sinking (left panel),
and surface layer net diatom growth (NEP
Si
, right panel) and
chlorophyll a in the middle Patuxent River estuary (Box 3, 4).
Monthly means (? SE) were calculated from 1985 to 2003 data.
Horizontal dashed lines are drawn at zero. Error bars represent one
standard error of the mean. ................................................................. 71
Figure 1.12: Correlation of mean annual rates of box model computed bottom
layer O
2
consumption with mean annual surface chlorophyll a (top
panel), spring POC sinking (middle panel), and surface net O
2
production (bottom panel, corrected for air-sea exchange)
in the middle Patuxent River estuary. Data are annual means
for the years 1985 to 2003. ................................................................. 72
Figure 1.13: Correlation between mean annual box model computed spring POC
sinking and bottom layer DSi, DIP, and NH
4
+
production in the middle
region (Box 4) of the Patuxent River estuary. Data are
annual means for the years 1985 to 2003. .......................................... 73
Figure 1.14: (Top panel) Monthly mean net O
2
production in the middle (solid line)
and lower (dotted line) regions of the Patuxent River estuary. (Bottom
panels) Monthly mean total inputs of DIN from seaward sources
(squares) and vertical inputs from the bottom layer (circles) to the
middle (Box 4), and lower (Box 5) regions of the Patuxent River
estuary. Error bars represent one standard error
of the mean.......................................................................................... 74
Figure 2.1: Map of the Patuxent River estuary with Chesapeake Bay (inset),
including box model boundaries and Chesapeake Bay Program water
quality monitoring stations. Chesapeake Bay Program station codes
are to the left of each station and numbers at the right of box model
boundaries indicate distance from the mouth of the estuary (km).
Map based upon image in Hagy et al. 2000...................................... 114
Figure 2.2: Schematic description of the box model structure (as seen in Hagy et
al. 2000). Included are box model boundaries, exchange coefficients,
and inputs. The estimated exchanges presented in this diagram are
seaward advection (Q
m
), landward advection (Q?
m
), vertical
advection (Q
vm
), vertical diffusive exchange (E
vm
), and horizontal
dispersion (E
m+1,m
). Included inputs are the volume of each box
and the salt concentration (not included), river flow (Q
r
), the input
of freshwater to each box (Q
fm
), and the salinity at the seaward
boundary (not included).................................................................... 115
viii
Figure 2.3: Mean monthly inputs of total phosphorus (TP), total nitrogen (TN) and
water (discharge) from all sewage treatment facilities on the Patuxent
River from 1985 to 2003. Inputs are presented as discharges released
into waters above and below the fall line. Data are from the
Chesapeake Bay Program?s Point source Nutrient Database
(www.chesapeakebay.net). ............................................................... 116
Figure 2.4: Time series (1985 to 2003) of mean monthly river discharge (top
panel), total nitrogen and phosphorus concentrations (middle panel),
and total nitrogen and phosphorus loading at the USGS gauging
station at Bowie, MD (ww.usgs.gov)................................................ 117
Figure 2.5: Plot of mean monthly river flow and mean monthly advective total
nitrogen load to the Patuxent River estuary at the fall line (top panel)
and at the landward boundary of Box 2 (bottom panel). Data are from
the years 1985 to 2003 and were assembled from USGS river flow and
solute gauging at Bowie, MD (www.usgs.gov) and from box model
computed transports. Data are separated as months before BNR was
implemented and months after BNR. The linear fits were created
using all pre- and post-BNR data...................................................... 118
Figure 2.6: Time series (1985 to 1997) of non-point source total nitrogen (left
panel) and total phosphorus (right panel) loading to the Patuxent
River estuary, above and below Benedict Bridge, which is located
near the seaward boundary of Box 2. Solid black lines are the annual
averages of total load. Data are output from the Chesapeake Bay
Watershed Model for the Patuxent watershed (Linker et al. 1996). . 119
Figure 2.7: Box plots of temporal trends (1963 to 2003) of chlorophyll a (top
panel), nitrate (middle panel), and DIP (bottom panel) concentrations
in the upper and middle regions of the Patuxent River estuary. Data
are from the Chesapeake Bay Program Water Quality Monitoring
Program (1985 to 2003), The Department of Natural Resources
(1978), and Flemer et al. (1970) (1968 to 1974). Vertical dashed lines
indicate the beginning of BNR implementation (nitrate) and the
initiation of phosphorus removal (DIP) at sewage plants. The top of
the boxes indicates the 75
th
percentile, the bottom of the boxes are the
25
th
percentile, the line in the box is the median, and the error bars
are the 10
th
and 90
th
percentile .......................................................... 120
ix
Figure 2.8: Time series (1985 to 2003) of annual mean DIN (open circles) and
DIP (black diamonds) concentrations in the upper (Box 2), middle
(Box 4), and lower (Box 5) regions of the Patuxent River estuary.
Data are from the Chesapeake Bay Program Water Quality Monitoring
Program. Labels of the x-axis indicate the initiation of phosphorus
removal and BNR at sewage plants. ................................................. 121
Figure 2.9: Correlations between annual mean sewage total nitrogen load below
the fall line and annual mean surface layer dissolved inorganic
nitrogen in the upper (Box 2), middle (Box 4), and lower (Box 5)
Patuxent River estuary (left panel, 1985 to 2003). Size of circles
indicates the relative magnitude of annual mean river flow. Sewage
load data from the Chesapeake Bay Program nutrient input monitoring
data set (www.chesapeakebay.net). Time series of annual mean
freshwater input with circles around years in the wet mid-1990s
(1993, 1994, 1996, 1997) and the dry ?99-?02 (right panel). Dark
horizontal line represents 19-year average ....................................... 122
Figure 2.10: Time series (1985 to 2003) of annual mean chlorophyll a (left panel)
and secchi depth (right panel) in surface waters of the upper (Box 2),
middle (Box 4), and lower (Box 5) Patuxent River estuary. Data are
from the Chesapeake Bay Program and x-axis labels indicate the
beginning of phosphorus removal and BNR at sewage treatment plants
in the watershed. Trend lines are simple linear regressions and
correlation coefficient and p-value are indicated for each region
and variable....................................................................................... 123
Figure 2.11: Time series (1985 to 2003) of mean summer chlorophyll a (left panel)
and secchi depth (right panel) in surface waters the lower estuary.
Data are from the Chesapeake Bay Program. Trend lines are simple
linear regressions and correlation coefficient and p-value are
indicated for each region and variable.............................................. 124
Figure 2.12: Correlations between annual mean river flow and annual mean
chlorophyll a in the upper (Box 2), middle (Box 4), and
lower (Box 5) Patuxent River estuary (top panel, 1985 to 2003).
Time series (1985 to 2003) of residuals (observed ? predicted) of
chlorophyll a versus river flow relationship in the same three regions
as above (bottom panel). Dashed horizontal lines indicate the
zero value.......................................................................................... 125
x
Figure 2.13: Time series (1985 to 2003) of surface and bottom layer net O
2
production in the upper (Box 2), middle (Box 4), and lower (Box 5)
Patuxent River estuary. Data are annual means and surface layer net
O
2
production is calculated by adding an air-sea exchange flux to the
box model estimate of net O
2
production. Vertical dashed lines
indicate the beginning of BNR implementation and horizontal
dashed lines indicate net O
2
production of zero................................ 126
Figure 2.14: Correlations between annual mean river flow and annual mean net O
2
production in the upper (Box 2), middle (Box 4), and lower (Box 5)
Patuxent River estuary (top panel, 1985 to 2003). Time series (1985 to
2003) of residuals (observed ? predicted) of net O
2
production versus
river flow relationship in the same three regions as above (bottom
panel). Dashed horizontal lines indicate the zero value................... 127
Figure 2.15: Time series (1985 to 2003) of hypoxic volume days in the Patuxent
River estuary. The vertical dashed line indicates the initiation of
BNR. ................................................................................................. 128
Figure 2.16: Regression of annual hypoxia (hypoxia = O
2
< 2 mg l
-1
) to annual
mean freshwater inputs and February to May (Spring) freshwater
inputs................................................................................................. 129
Figure 2.17: Regression of hypoxic volume with June-August dissolved O
2
inputs
into Box 3 and 4 from landward advection and vertical diffusion
(1985 to 2003 data) ........................................................................... 130
Figure 2.18: Regression of total nitrogen load (non-point + septic + point loads)
above Benedict Bridge with net O
2
production in the surface layer of
Box 3 and Box 4 (middle estuary). Total nitrogen loads for the region
above Benedict Bridge are output from the Chesapeake Bay
Watershed Model for the Patuxent River. Data include the years 1985
to 1997 and are annual means. Trend lines are simple linear
regressions and correlation coefficient and p-value are indicated for
each region and variable ................................................................... 131
Figure 2.19: Time series (1985 to 2003) of annual mean total nitrogen (TN)
concentrations the upper (Box 2), middle (Box 4), and lower
(Box 5) regions of the Patuxent River estuary. Data are from the
Chesapeake Bay Program Water Quality Monitoring Program ....... 132
xi
Figure 2.20: Time series (1985 to 2003) of box model computed annual mean net
exchange of DIN between the Patuxent River estuary and mainstem
Chesapeake Bay (top panel). Positive values indicate net input
into the Patuxent River estuary. Time series (1985 to 2003) of the
ratio of vertical DIN inputs to horizontal DIN inputs from upstream to
the surface layer of Box 5 (bottom panel). Solid black line indicates a
ratio of one, where horizontal inputs are equal to vertical
inputs..................................................................................................133
Figure 2.21: Regression of annual mean net DIN exchange between the Patuxent
River estuary and mainstem Chesapeake Bay with annual mean net O
2
production in the surface layer of Box 5 (lower estuary). Trend lines
are simple linear regressions and correlation coefficient and p-value
are presented. .....................................................................................134
Figure 2.22: Time series (1985 to 2003) of mean summer (June to August)
Mnemiopsis leidyi biovolume (top panel) and adult Acartia tonsa
concentration (bottom panel) in the middle Patuxent River estuary
(Box 3, 4; Chesapeake Bay Monitoring Station LE1.1)....................135
Figure AI.1: Correlations between mean monthly secchi depth and total
suspended solids (TSS, left panel) and between secchi depth and
chlorophyll a (right panel) at six stations spanning the tidal fresh
(Station TF1.6) to mesohaline (Station LE1.4) regions of the
Patuxent River estuary (see Chapter I, Fig. 1.1 for station
location). Data for all months from 1985 to 2003 (n = 228) ........... 144
Figure AI.2: Correlations between mean monthly 1/secchi depth (~ k
d
, m
-1
) and
total suspended solids (TSS, left panel) and between 1/secchi depth
and chlorophyll a (right panel) at six stations spanning the tidal
fresh (Station TF1.6) to mesohaline (Station LE1.4) regions of
the Patuxent River estuary (see Chapter I, Fig. 1.1 for station
location). Data for all months from 1985 to 2003 (n = 228) ........... 145
Figure AII.1: Mean monthly integrated net denitrification (denitrification - nitrogen
fixation) computed by the box model in the upper (Box 2), middle
(Box 4), and lower (Box 5) Patuxent River estuary. Monthly
means (? SE) were calculated from 1985 to 2003 data. Horizontal
dashed lines are drawn at zero net denitrification. Error bars
represent one standard error of the mean.......................................... 150
xii
Figure AII.2: Correlations between mean annual box model computed net
denitrification and mean annual freshwater flow measured at the
fall line (top panel) and distribution of box model computed net
denitrification along the axis of the Patuxent river estuary (bottom
panel). Error bars in the lower panel represent one standard error
of the mean for all data from 1985 to 2003 ...................................... 151
xiii
BACKGROUND AND INTRODUCTION
Estuarine ecosystems are among of the most productive systems in the
biosphere. High estuarine productivity is due, in part, to the large fluxes of nutrients
and carbon to estuaries from adjacent terrestrial and riverine systems (Nixon 1995).
Retention and recycling of these nutrients and carbon inputs within estuaries can
sustain high productivity in times of low exogenous inputs (Kemp and Boynton
1984), but nutrient cycling processes may be complex and involve non-linear
feedbacks (Kemp et al. 2005). Estuarine primary productivity provides fuel for upper
trophic levels, but productivity is also linked to water quality problems (e.g., hypoxia)
that may adversely affect upper trophic levels (Breitburg et al. 2003). Because of the
importance of primary productivity to food webs (which are ultimately harvested by
humans), a great deal of interest has been focused on understanding the factors
regulating productivity and nutrient cycling and how these factors change over time
and space.
Anthropogenic activities and climatic variability influence estuarine
productivity and nutrient cycling (Paerl et al. 2006). Perhaps the most notable of
anthropogenic influences on coastal systems is the widespread, elevated input of
nutrients since the mid-20
th
century (Nixon 1995). The resulting increase in
productivity has led to many ecosystem-level changes in estuaries, including changes
in phytoplankton species composition, elevated export of algal material to bottom
waters, and reduced water clarity (Paerl 1988, Cloern 2001). Fluctuations of
freshwater inputs cause responses similar to nutrient enrichment and are perhaps the
most direct climatic influence on estuarine ecosystems. In many systems, high
1
freshwater inputs are associated with reduced dissolved oxygen concentrations and
water clarity and increased phytoplankton biomass (Malone et al. 1988, Justic et al.
1996, Boynton and Kemp 2000). Assessing the interactions between nutrient
management and freshwater flow is important for improved understanding of
estuarine primary productivity and water quality.
Chesapeake Bay and its tidal tributaries have changed markedly during the
past several decades in response to nutrient enrichment (D?Elia et al. 2003, Kemp et
al. 2005). High inter-annual variability in freshwater inputs has occurred
contemporaneously with changes in nutrient loads (Kemp et al. 2005). In the
Patuxent River estuary, the sixth largest tributary of Chesapeake Bay, nutrient
enrichment has led to increased algal biomass, hypoxic volume, and decline of once-
abundant submerged aquatic vegetations beds (D?Elia et al. 2003, Stankelis et al.
2003). Towards the goal of reversing the negative effects of nutrient enrichment,
sewage treatment upgrades in the Patuxent River watershed have led to reductions in
point source phosphorus and nitrogen loads in the watershed, beginning in the 1980s.
In response to eutrophication, and in part to monitor the effects of nutrient
load reductions, an ambitious water quality monitoring program has been established
in Chesapeake Bay and its tributaries. The Chesapeake Bay Monitoring Program has
been measuring water quality (e.g., nutrient and oxygen concentrations, water clarity)
and ecosystem processes (e.g., primary production, sediment oxygen and nutrient
exchanges) at many stations in the Patuxent River estuary since 1985. The resulting
data sets present opportunities to analyze ecosystem level responses to climatic
variability and anthropogenic effects. Such data may also be utilized to develop
2
empirical models (Hagy 1996) and as baseline data to calibrate complex water quality
models (Lung and Bai 2003).
Empirical modeling, which involves building simple, direct relationships
between biological, chemical, or physical rates and the variables that drive them, can
be used as a first order method to predict the response of these rates to climatic and
anthropogenic forcing or internal variability. Such models were successfully
developed to predict the response of lakes to nutrient loading (Vollenweider 1976)
and were later developed for estuarine systems (e.g., Boynton and Kemp 2000).
Empirical methods are grounded in observations, yet observations may be too
infrequent or the driving forces to complex to accurately predict the rates using such
simple formulations. Alternatively, sophisticated numerical simulation models aim to
capture the detail in mechanisms that drive ecological rates. Complex models are
advantageous, as they may be used to capture fundamental ecological and
biogeochemical processes and can be calibrated with monitoring data (Lung and Bai
2003, Fisher et al. 2006). The disadvantages of these models are that they are often
complex and highly tuned, making their construction, implementation, and analysis
expensive in terms of time and resources. While there is value in both the empirical
and numerical approaches, the development of intermediate complexity models may
offer an alternative to traditional approaches.
An example of an intermediate complexity model includes the coupling of a
simple physical transport model (often called a ?box model?) to available nutrient and
carbon concentrations provided by water quality monitoring programs. The result is
the calculation of simple, empirical estimates and proxies for transformations of
3
oxygen, carbon, and nutrients in estuaries (Smith et al. 1991, Hagy 1996). Such an
approach can be mechanistic, in that the functional relationships between
biogeochemical rates and their driving variables can be explored; yet the model is
also empirical because the rates and relationships are derived from in situ
observations. Such rates, if comparable to direct measurements of similar processes,
may be used to assess ecological interactions in estuaries (e.g., pelagic-benthic
coupling, net ecosystem production), the seasonal variability in the processes, and to
evaluate their response to climatic variability and changes in nutrient loading.
Considering the widespread availability of hydrologic, hypsographic, and water
quality data in many of the nation?s coastal systems, box models provide the
opportunity to transform these abundant measurements into meaningful ecological
rates. The LOICZ program (Land-Ocean Interactions in the Coastal Zone) has begun
to make such calculations in many of the world?s estuaries.
The purpose of this thesis is to analyze a nineteen-year monitoring data set to
address important questions regarding estuarine ecological processes and the response
of these processes to external forcing. Box models are presented as useful tools to
transform routine monitoring data into regionally resolved rates of net ecosystem
production and net nutrient production and transport along the axis of the Patuxent
River estuary. The work in this thesis is an extension of the box model analysis of
Hagy (1996) and Hagy et al. (2000) and was based upon formulations originally
developed by Pritchard (1969) and Officer (1980). Chapter I includes an examination
and quantification of the spatial and temporal coupling of nutrient inputs to net
ecosystem production and nutrient regeneration along the axis of the estuary. In
4
Chapter II, a time series (1985 to 2003) of water quality measurements and box
model computed net production and transport rates are used to evaluate the response
of the Patuxent River estuary to nutrient management and to variability in river flow.
References
Boynton, W.R. and W.M. Kemp. 2000. Influence of river flow and nutrient loads on
selected ecosystem processes: A synthesis of Chesapeake Bay data, p. 269-298.
In J.E. Hobbie (ed.), Estuarine Science, A Synthetic Approach to Research and
Practice. Island Press, Washington DC.
Breitburg, D.L., A. Adamack, K.A. Rose, S.E. Kolesar, M.B. Decker, J.E. Purcell,
J.E. Keister, and J.H. Cowan, Jr. 2003. The pattern and influence of low
dissolved oxygen in the Patuxent River, a seasonally hypoxic estuary. Estuaries
26: 280-297.
Cloern, J.E. 2001. Our evolving conceptual model of the coastal eutrophication
problem. Marine Ecology Progress Series 210: 223-253.
D?Elia, C.F., W.R. Boynton, and J.G. Sanders. 2003. A watershed perspective on
nutrient enrichment, science, and policy in the Patuxent River, Maryland: 1960-
2000. Estuaries 26: 171-185.
Fisher, T.R., J.D. Hagy III, W.R. Boynton, and M.R. Williams. 2006. Cultural
eutrophication in the Choptank and Patuxent estuaries of Chesapeake Bay.
Limnology and Oceanography 51: 435-447.
Hagy, J.D. 1996. Residence times and net ecosystem processes in the Patuxent River
estuary. Masters Thesis, University of Maryland at College Park, College Park,
Maryland.
Hagy, J.D., L.P. Sanford, and W.R. Boynton. 2000. Estimation of net physical
transport and hydraulic residence times for a coastal plain estuary using box
models. Estuaries 23: 328-340.
Justic, D., N.N. Rabalais, and R.E. Turner. 1996. Effects of climate change on
hypoxia in coastal waters: A doubled CO
2
scenario for the northern Gulf of
Mexico. Limnology and Oceanography 41: 992-1003.
Kemp, W.M. and W.R. Boynton. 1984. Spatial and temporal coupling of nutrient
inputs to estuarine primary production: The role of particulate transport and
decomposition. Bulletin of Marine Science 35: 522-535.
5
Kemp, W.M., W.R. Boynton, J.E. Adolf, D.F. Boesch, W.C. Boicourt, G. Brush, J.C.
Cornwell, T.R. Fisher, P.M. Glibert, J.D. Hagy, L.W. Harding, E.D. Houde,
D.G. Kimmel, W.D. Miller, R.I.E. Newell, M.R. Roman, E.M. Smith, and J.C.
Stevenson. 2005. Eutrophication of Chesapeake Bay: Historical trends and
ecological interactions. Marine Ecology Progress Series 303: 1-29.
Lung, W. and S. Bai. 2003. A water quality model for the Patuxent estuary: Current
conditions and predictions under changing land-use scenarios. Estuaries 26:
267-279.
Malone, T.C., L.H. Crocker, S.E. Pike, and B.W. Wendler. 1988. Influence of river
flow on the dynamics of phytoplankton in a partially stratified estuary. Marine
Ecology Progress Series 48: 235-249.
Nixon, S.W. 1995. Coastal marine eutrophication: A definition, social causes, and
future concerns. Ophelia 41: 199-219.
Officer, C.B. 1980. Box models revisited, p. 65-114. In P. Hamilton and R.B.
Macdonald (eds.), Estuarine and Wetland Processes. Plenum Press, New York.
Paerl, H.W. 1988. Nuisance phytoplankton blooms in coastal, estuarine, and inland
waters. Limnology and Oceanography 33: 823-847.
Paerl, H.W., L.M. Valdes, B.L. Peierls, J.E. Adolf, and L.W. Harding, Jr. 2006.
Anthropogenic and climatic influences on the eutrophication of large estuarine
systems. Limnology and Oceanography 51: 448-462.
Pritchard, D.W. 1969. Dispersion and flushing of pollutants in estuaries. American
Society of Civil Engineers Journal of Hydraulics Division 95(HYI): 115-124.
Smith, S.V., J.T. Hollibaugh, S.J. Dollar, and S. Vink. 1991. Tomales Bay
metabolism C-N-P stoichiometry and ecosystem heterotrophy at the land-sea
interface. Estuarine, Coastal and Shelf Science 33: 223-257.
Stankelis, R.M., M.D. Naylor, and W.R. Boynton. 2003. Submerged aquatic
vegetation in the mesohaline region of the Patuxent estuary: Past, present, and
future status. Estuaries 26: 186-195.
Vollenweider, R.A. 1976. Advances in defining critical loading levels for phosphorus
in lake eutrophication. Memorie dell?Istituto Italiano di Idrobiologia 33: 53-83.
6
CHAPTER I
Spatial and temporal variability of biogeochemical processes in the Patuxent
River estuary: Inferences from water quality data
Abstract
Regional, seasonal, and inter-annual variations of nutrient inputs, net
ecosystem production, and pelagic-benthic interactions were examined in the
Patuxent River estuary, a tributary of Chesapeake Bay. Monthly rates of net
biogeochemical production and physical transport of carbon, oxygen (O
2
), and
nutrients were calculated for six estuarine regions using a data-constrained salt- and
water-balance model (box model) and a time series of water quality data. Assuming
fixed metabolic stoichiometry for O
2
, carbon, and silicate, we also derived estimates
of net carbon production, particulate organic carbon (POC) sinking, and net diatom
growth. Analyses of monthly mean rates revealed distinct regional and seasonal
patterns in net O
2
production, including late spring peaks in surface layer rates (80 to
100 mmol O
2
m
-2
d
-1
) and summer peaks in bottom layer rates (-100 to -200 mmol O
2
m
-2
d
-1
). Net O
2
production and chlorophyll a, which reached annual maxima in
spring when NO
3
-
inputs to the estuary peaked, were highest in the middle region of
the estuary and correlated with net DIN and DSi uptake. Rates of POC sinking (10 to
90 mmol C m
-2
d
-1
), which also peaked during the spring bloom, were correlated with
bottom layer nutrient regeneration and O
2
consumption at annual, but not monthly,
timescales. Correlations between surface layer carbon production/sinking and bottom
layer nutrient regeneration (i.e., pelagic-benthic coupling) were strongest in the
middle estuary, where rates were high, water depth was relatively shallow, and
7
interaction with adjacent landward and seaward sub-systems was minimal. The
magnitude of net O
2
production and nutrient uptake rates was enhanced by flow.
Rates of net O
2
production, POC sinking, and nutrient regeneration agree favorably
with previously measured rates in the estuary. This analysis demonstrates the
potential to infer patterns and regulating factors for biogeochemical processes using
box modeling and statistical analyses of basic water quality and hydrologic data.
Introduction
Estuarine ecosystems form the transition zone between adjacent terrestrial,
riverine, and oceanic regions (Smith et al. 1991). Biogeochemically reactive organic
and inorganic materials enter estuaries from surrounding watersheds and the
atmosphere and are processed within estuaries prior to transport to adjacent oceans
(Webster et al. 2000). Estuarine transformations of anthropogenic and terrestrially
derived materials are regulated by a balance between physical transport and
biogeochemical uptake and recycling (Kemp and Boynton 1984, Smith et al. 1991,
Howarth et al. 1996). Important transformations include both biological processes,
such as organic carbon production and nutrient uptake/regeneration (Kemp and
Boynton 1984), and physical-chemical reactions, such as flocculation and surface
sorption/desorption (e.g., Sholkovitz 1976). Understanding the nature and magnitude
of these transformation processes is essential for evaluating and managing estuarine
production and nutrient cycling.
Inter-annual variations in river flow exert strong control over biogeocheimical
transformation processes in estuaries. River flow may enhance phytoplankton
biomass and productivity in mid-estuarine regions of temperate systems via enhanced
8
nutrient inputs (Boynton and Kemp 2000), but flow may also reduce primary
production in some systems where increased inputs of suspended particles tend to
induce light-limited photosynthesis (Cloern et al. 1983, Howarth et al. 2000).
Although elevated nutrient inputs delivered with high flow may enhance
photosynthesis and associated net ecosystem production (D?Avanzo et al. 1996,
Caffrey 2004), higher inputs of labile organic carbon tend to increase respiration,
thereby decreasing net ecosystem production (Smith and Hollibaugh 1997). Higher
freshwater inputs may also increase particulate organic matter sinking, as well as
benthic respiration and nutrient regeneration (Boynton and Kemp 2000). Direct
denitrification may be enhanced by flow if NO
3
-
loading is elevated (Jorgensen and
Sorensen 1988, Kana et al. 1998), while coupled nitrification and denitrification may
be either enhanced with higher NH
4
+
recycling or depressed due to hypoxia
(Seitzinger 1988, Kemp et al. 1990). Although river flow is a key driver of
biogeochemical processes at decadal scales for whole estuaries, important variability
also occurs at shorter temporal and spatial scales.
Many biogeochemical processes in estuaries vary widely over regional scales
(Taft et al. 1978, Kemp et al. 1997, Harding et al. 2002). For example, phytoplankton
biomass and productivity maxima in estuaries often develop where turbidity is low
and nutrient limitation is relieved (Pennock and Sharp 1994). Regional variation in
net ecosystem production is also common in many estuarine systems, ranging from
net heterotrophy in landward waters to net autotrophy in seaward waters (Smith et al.
1991, Heath 1995, Howarth et al. 1996, Kemp et al. 1997). Benthic respiration and
nutrient regeneration display distinct patterns of variation along salinity and depth
9
gradients (Fisher et al. 1982, Boynton and Kemp 1985). Physical-chemical
processes, such as flocculation of organic and inorganic materials, occur throughout
estuaries, but tend to be concentrated in specific regions, such as the seawater-
freshwater mixing zone (Sholkovitz 1976). Denitrification also varies along estuarine
axes and is often influenced by gradients in nutrients and dissolved oxygen
(Henriksen and Kemp 1988, Kemp et al. 1990). Consequently, patterns of nutrient
uptake and production vary along estuarine salinity gradients (Fisher et al. 1988).
Estuarine biogeochemical processes also exhibit substantial seasonal
variation. Although peak phytoplankton biomass may occur either in summer (Smith
and Hollibaugh 1997) or spring (Harding et al. 2002), annual maxima in primary
productivity generally occur in summer for temperate estuaries (Boynton et al. 1982,
Malone et al. 1988). Net ecosystem production also varies seasonally, but the
seasonality of peaks vary, depending on the magnitude and timing of annual
hydrographs, terrestrial carbon inputs, and nutrient availability (Smith and Hollibaugh
1997, Kemp et al. 1997, Ram et al. 2003). Direct denitrification may peak during
spring with high nitrogen inputs, while coupled nitrification-denitrification is
characterized by summer minima in systems with bottom water hypoxia (Kemp et al.
1990). In systems where the water column is well oxidized throughout the year,
denitrification may also peak in summer and fall (Nowicki 1994, Jorgensen and
Sorensen 1988). Temperature maxima during summer often drive benthic nutrient
regeneration (Fisher et al. 1982, Cowan and Boynton 1996), but high spring supplies
of organic material may be regenerated prior to summer (Graf et al. 1982, Boynton
and Rohland 2001).
10
Seasonal and regional patterns in biogeochemical processes are mediated by
horizontal and vertical transport. Vertical exchanges of carbon and nutrients between
surface and bottom waters connect pelagic and benthic habitats in coastal ecosystems
(Kemp et al. 1999). Although the timing and magnitude of benthic respiration and
nutrient regeneration are strongly regulated by temperature (Cowan and Boynton
1996), these processes often respond rapidly to vertical sinking of labile organic
material (Graf et al. 1982). Elevated horizontal nutrient inputs fuel phytoplankton
biomass and sinking (Boynton and Kemp 2000), but strong horizontal transport
during high flow periods may cause regions of high productivity to be separated from
depositional areas (Hagy 2005). In addition, sediment nutrient regeneration
associated with benthic respiration of organic matter, which was deposited during
previous periods, may be transported vertically to surface waters and fuel summer
productivity (Kemp and Boynton 1984, Malone et al. 1988). Ultimately, the degree
of interaction between surface and bottom layers is dependent on depth, where
shallow systems exchange more material between surface and bottom water masses
than deep systems (Kemp et al. 1999).
Estuarine transformations of nutrients and organic carbon are ultimately
regulated by interactions between physical transport and biogeochemical processes.
For example, a conceptual model for the Patuxent River and Chesapeake Bay
suggests that large spring nutrient inputs are transformed from dissolved into
particulate forms in the upper estuary, which subsequently sink and are transported
seaward, where dissolved inorganic nutrients are regenerated via decomposition,
dissolution, and/or desorption to fuel summer peaks in phytoplankton productivity
11
(Kemp and Boynton 1984, Malone et al. 1988). Although many of the ecological,
biogeochemical, and physical transport processes relevant to this model have been
measured at seasonal and regional scales in estuaries (Fisher et al. 1982, Pennock and
Sharp 1994, Smith and Kemp 1995, Cowan and Boynton 1996, Roden et al. 1995),
none of these studies have been sufficiently comprehensive to support an integrated
assessment of this model. As a result, key questions remain to be addressed further in
estuarine ecosystems. How do biogeochemical processes vary over seasonal and
inter-annual time scales? How do these relationships vary regionally in the estuary?
How do surface and bottom layer biogeochemical rates relate to each other? How
does physical transport drive these processes and link adjacent regions?
The purpose of this chapter is to use a suite of integrated rates of net
biogeochemical production and physical transport for nutrients, oxygen, and organic
carbon to examine the spatial and temporal coupling of nutrient inputs and primary
production along the axis of the Patuxent River estuary. Rates were computed using
a previously developed salt- and water- balance ?box? model (Hagy et al. 2000)
applied to a 19-year water quality monitoring database. Net biogeochemical rates
were derived by computing residual changes in concentrations of non-conservative
materials after accounting for physical transport using net non-tidal velocities and
diffusivities (Taft et al. 1978, Smith et al. 1991).
Methods
Study site and data availability
The Patuxent River estuary is a tributary system of Chesapeake Bay (USA,
Fig. 1.1) that receives relatively high inorganic nutrient loads and that has been the
12
target of nutrient reduction strategies for the past twenty five years (D?Elia et al.
2003). The estuary is ~65 km long, has a mean low-water estuarine volume of 577 x
10
6
m
3
, and a surface area of 126 x 10
6
m
2
. It averages 2.2 km in width and 6.0 m in
depth over the most seaward 45 km of the estuary (Cronin and Pritchard 1975). The
mean tidal range is 0.4 m at 9 km from the estuary mouth and increases landward to
0.8 m above 40 km from the mouth (Boicourt and Sanford 1988). Two-layered
circulation occurs for most of the year in the lower estuary, with a seaward-flowing
surface layer and a landward-flowing bottom layer. The upper estuary (above km 46)
is vertically well mixed. Fall-line (99 km from mouth) freshwater discharge averaged
10.3 m
3
s
-1
from 1977 to 2003 (USGS 2005). Water quality has been monitored at 9
stations along the estuarine axis since 1985, including measurements of salinity,
temperature, O
2
, chlorophyll a, nutrients, and organic carbon (CBP 2005, Fig. 1.1).
In addition, a series of continuous water quality sensors (measurements include O
2
,
temperature, chlorophyll a) have been deployed from spring through fall at six
stations throughout the estuary (MD DNR 2005, ACT 2005).
Computing salt and water transport
In this study, we computed the Patuxent estuary?s time-dependent, seasonal
mean circulation using mean monthly salinity and freshwater input data. Salinity data
were acquired from the Chesapeake Bay Water Quality Monitoring Program (CBP
2005) and the freshwater input data (river flow and precipitation) were obtained from
the United States Geological Survey (USGS 2005) and the National Oceanographic
and Atmospheric Administration (NOAA 2005). This box modeling approach
13
computes advective and diffusive exchanges of water and salt between adjacent
control volumes and across end-member boundaries using the solution to non-steady
state equations balancing salt and water inputs, outputs, and storage changes
(Pritchard 1969, Officer 1980, Hagy et al. 2000). The control volumes, hereafter
referred to as ?boxes?, are assumed to be well mixed. Stratified estuarine regions are
represented by surface and bottom layers that capture the essential features of two-
layered estuarine circulation (Pritchard 1969). Boundaries separating adjacent boxes
were chosen based upon several factors: (1) data availability; (2) density
stratification; and (3) relatively uniform salinity gradients and water volumes among
boxes (Fig. 1.1).
The box model used in this analysis calculates advection and mixing between
eleven boxes in the Patuxent River estuary (6 surface boxes, 5 lower boxes, Fig. 1.2,
Hagy et al. 2000). The model computes lateral advective and diffusive exchanges in
two directions, vertical advective and diffusive exchanges, and freshwater input.
Thus, the salt balance for a surface layer box ?m? in the two-dimensional scheme is
described below (Fig. 1.2)
V
m
dt
ds
m
= Q
m-1
s
m-1
+ Q
vm
s?
m
- Q
m
s
m
+ E
vm
(s?
m
- s
m
)
+ [E
m-1,m
(s
m-1
- s
m
) + E
m,m+1
(s
m+1
- s
m)
] (1)
and the water balance is
dt
dV
m
= 0 = Q
m
? (Q
m-1
+ Q
vm
+ Q
fm
)
(2)
where V
m
is the volume of the box, Q
m
is the advective transport to the seaward box,
Q
m-1
is the advective transport from the landward box, Q
vm
is the vertical advective
input into the box, Q
fm
is the freshwater input directly into the box, E
m-1,m
is the
14
diffusive exchange with the landward box, E
m,m+1
is the diffusive exchange with the
seaward box, E
vm
is the vertical diffusive exchange, s
m
is the salinity in the upper
layer box, s
m-1
is the salinity in the landward box, s
m+1
is the salinity in the seaward
box, and s?
m
is the salinity in the lower layer box. The left hand side of Eq. 1 is
computed as the monthly salinity change (salinity distribution assumed to be uniform
in each box), while the left hand side of Eq. 2 is assumed to be zero at monthly time
scales.
In the case that all horizontal and vertical advective and non-advective terms
from Eq. 1 were included in the computation, there would be more unknown
exchange coefficients than equations and the system would not be solvable (Officer
1980, Hagy et al. 2000). To permit the system to be solvable, non-advective
exchanges (E
m-1,m
and
E
m,m+1
) were assumed to be negligible in the region of the
estuary with a consistent gravitational circulation (Boxes 2-6, Fig. 1.2, Hagy et al.
2000). Justification for this assumption and further detail of the box model is
described in Hagy et al. (2000). The box model equations are solved using two
equations at a time, allowing the derivation of closed expressions for the model
solution and avoiding the need for a matrix approach (Hagy et al. 2000).
Nutrient transport and production rates
We computed monthly, seasonal, and annual rates of transport and net
biogeochemical production of dissolved O
2
, nutrients, and carbon for six regions of
the Patuxent River estuary from 1985 to 2003. Physical transport rates for these non-
conservative biogeochemical variables were computed by multiplying the solute
15
concentration by the advective and non-advective fluxes (Q?s and E?s, respectively)
for each box and month.
In this analysis, we calculated transport and net production rates for the
following non-conservative variables: (1) dissolved inorganic nitrogen (DIN = NO
2
-
+
NO
3
-
+ NH
4
+
), (2) dissolved inorganic phosphorus (DIP = PO
4
3-
), (3) dissolved
silicate (DSi = SiO
3
2-
), (4) total organic carbon (TOC), and (5) dissolved O
2
.
Monthly mean values of salinity, nutrients, organic carbon, and dissolved O
2
were
computed for each box (and upstream and downstream boundaries) using water
quality monitoring data measured at 2-4 week intervals at 9 stations along the
Patuxent axis (Fig. 1.1). The resulting mean values were calculated using a simple
linear interpolation scheme with a grid of 477 cells spaced at 1 m vertical intervals,
1.85 km horizontal intervals, and spanning the width of the estuary (Hagy et al.
2000). Contour plots of the interpolated data were viewed to test for outlier
measurements.
Mass balance equation(s) (Eq. 1 and 2) of the resulting nutrient transports into
and out of each box, combined with the volume-weighted concentration change of the
variable, yield a residual term (P
m
) that represents the non-conservative net
production rate (production ? consumption) of nutrients, organic carbon, and O
2
.
For any surface layer Box m in the two-layer scheme of the box model, the mass
balance equation is
V
m
dt
dc
m
= Q
m-1
c
m-1
+ Q
vm
c?
m
+ E
vm
(c?
m
- c
m
)
+ E
m+1,m
(c
m+1
- c
m
)
- E
m,m-1
(c
m
- c
m-1
) - Q
m
c
m
+ P
m
(3)
16
which can be rearranged to calculate P
m
P
m
= V
m
dt
dc
m
- Q
m-1
c
m-1
- Q
vm
c?
m
- E
vm
(c?
m
- c
m
)
- E
m+1,m
(c
m+1
- c
m
)
+ E
m,m-1
(c
m
- c
m-1
) + Q
m
c
m
(4)
Note that E
m,m-1
= 0 for m ? 2, E
m,m+1
= 0 for m ? 1, and E
vm
= 0 and Q
vm
= 0 for m =
1 (Fig. 1.2, 1.3). For any bottom layer Box m, the mass balance expression is
V?
m
dt
dc
m
'
= Q?
m+1
c?
m+1
- Q
vm
c?
m
? Q?
m
c?
m
- E
vm
(c?
m
- c
m
)
+ P?
m
(5)
which can be rearranged to calculate bottom layer net production, P?
m
P?
m
= V?
m
dt
dc
m
'
- Q?
m+1
c?
m+1
+ Q
vm
c?
m
+ Q?
m
c?
m
+ E
vm
(c?
m
- c
m
)
(6)
The variables used in Eq. 3-6 include V?
m
, which represents the volume of bottom
layer boxes, where the subscript, m, is the box identifier numbered 1 to 6 from the
landward to the seaward ends, and prime notation indicates the bottom layer. In
addition, c
m
is the concentration of the non-conservative material, Q?
m
is the advective
fluxes to and from Box m in bottom layers, Q
vm
is the vertical advection from bottom
to surface layer, E
vm
is the vertical diffusive exchange between the surface and
bottom layers of Box m
,
and P?
m
is the net production (or consumption) rate in bottom
layers.
The non-conservative net production rates were calculated in units of mass per
time within the box volume (i.e., mass fluxes, mmol d
-1
). Rates were also computed
in volumetric units (mmol m
-3
d
-1
) by dividing mass fluxes by either the mean low
water volume for surface rates or the volume below the pycnocline for bottom rates.
(Table 1.1). Depth-integrated rates were computed by dividing mass fluxes by the
17
mean low-water surface area and vertical fluxes were calculated by dividing the mass
fluxes by the pycnocline area (Table 1.1).
An input term for wet atmospheric deposition of DIN to all surface layer
boxes was calculated using data for precipitation and concentrations of NO
3
-
and
NH
4
+
in precipitation. Mean annual nitrogen concentrations in precipitation were
acquired from the National Atmospheric Deposition Program (NADP 2005) and were
multiplied by monthly precipitation values, thus scaling the seasonal distribution of
wet nitrogen deposition to precipitation and estimating a monthly mass flux of
nitrogen to all surface layer boxes. This estimate of wet atmospheric deposition was
added as an input term to the surface layer dissolved inorganic nitrogen balance.
Although we did not include direct non-point nutrient inputs to each box, we
did test the effects of this omission for computing net production rates of DIN and
DIP. Monthly non-point DIN/DIP loads to each box were derived from the
Chesapeake Bay Watershed Model (Linker et al. 1996). We found that including
these estimates for nutrient inputs altered calculations for monthly net production
rates of DIN and DIP by less than 10% in Box 3-6 for DIN and in all boxes for DIP.
The net production rates for DIN declined by 5-40% in Box 1 and 2 for DIN when
direct non-point DIN loads were included. Although direct non-point source inputs
of nutrients are important in the upper regions of the estuary (especially Box 2), they
do not substantially alter the magnitude of rates computed for other regions of the
estuary.
Computing net production or consumption of dissolved O
2
required two
adjustments to the box model calculations: (1) a correction for diel variability relative
18
to time of sample and (2) a correction for air-sea O
2
gas transfer. The first step to
estimate net O
2
production with the non-conservative O
2
production rate is to correct
the discrete O
2
measurements from the monitoring program to equivalent diel mean
O
2
concentrations based on observed patterns of variation. Continuous dissolved O
2
data from moored sensors in the Patuxent?s surface layer reveal that concentrations
tend to vary consistently from 10-30% during each day due to effects of
photosynthesis, respiration, and exchange with the atmosphere or adjacent water
masses (Kemp and Boynton 1980). To make this correction, we first calculated
hourly mean O
2
values
(as % saturation) for each month of the year, using two years
of data. Data were taken from four continuous (sample every 15 minutes) water
quality sensors (Fig. 1.1) maintained by the Maryland Department of Natural
Resources (MD DNR 2005) and the Alliance for Coastal Technologies (ACT 2005,
Fig. 1.4) that span the estuarine axis. We then calculated a coefficient for each hour
of the day in each month at each station to correct the monitoring program
measurement. This unitless coefficient (kc
hr
) is equal to the mean daily % O
2
saturation value for each month and station (DOSAT
day
) divided by the mean hourly
% O
2
saturation value for each month and station (DOSAT
hour
).
hour
day
hr
DOSAT
DOSAT
kc = (7)
The corrected O
2
value was calculated by multiplying the measured monitoring O
2
concentration by the appropriate correction coefficient for time of day, month of year,
and nearest sampling station.
O
2
concentrations corrected for diel variability were used in the box model to
compute physical transport and net non-conservative production rates of dissolved
19
O
2
. Net O
2
production rates in surface boxes were corrected for air-sea exchange.
We computed the air-sea O
2
exchange on monthly time scales using O
2
values in the
top 0.5 m of the water column following Caffrey (2003):
)/ ? (1=
?
??
22 s
CC F
2
O-A
? (8)
where ? is the air-sea exchange coefficient (g O
2
m
-2
h
-1
), C
O2
is the adjusted daily
mean O
2
concentration at 0.5 m depth (g m
-3
), C
O2-S
is the O
2
saturation value (g m
-3
).
We used a value for ? of 0.5 g O
2
m
-2
h
-1
for all months, which is based on published
relationships between ? and wind speed (e.g., Hartman and Hammond 1984, Marino
and Howarth 1993, Caffrey 2003) and monthly mean wind speed observed at the
nearby Patuxent Naval Air Station. Analyses of the wind data suggested that there
were significant variations in wind velocity on daily to weekly scales, but there were
no significant monthly or seasonal trends.
Stoichiometric calculations
The net production rates computed with the box model were used to estimate
additional biogeochemical processes by assuming fixed stoichiometric relationships
between variables. Stoichiometric ratios used in this analysis were derived from
traditional relationships for carbon, O
2
, and DSi (?Redfield ratios?). We estimated
the contribution of diatom photosynthesis to total net organic carbon production rates
by applying a stoichiometric adjustment to the computed net rate of surface layer net
DSi production rate:
PC(Si)
m
= k
C:Si
(-P(Si)
m
)
(9)
20
where PC(Si)
m
is the net carbon production attributed to diatoms (mmol C m
-3
d
-1
),
k
C:Si
is the assumed carbon-silica ratio for diatoms of 6.625, and P(Si)
m
is the box
model computed surface net silica production rate (mmol Si m
-3
d
-1
) (Hagy 1996).
The net DSi production rate is multiplied by -1 because it is assumed that DSi uptake
is associated with net carbon production. We also estimated the sinking flux of
particulate organic carbon (S(POC)
m,
mmol C m
-2
d
-1
) across the pycnocline using
box model computed net production rate estimates of O
2
and carbon in the surface
layer in the stratified estuarine regions (Boxes 2-6) as follows:
S(POC)
m
= k
C
:
O
P(O
2
)
m
? P(TOC)
m
(10)
where P(O
2
)
m
is surface layer net O
2
production rate (mmol O
2
m
-2
d
-1
), and P(TOC)
m
is surface layer net production rate of total organic carbon (mmol C m
-2
d
-1
), and k
C
:
O
is the photosynthetic quotient (PQ = 1). This formulation assumes that, in the
absence of particulate carbon sinking, net O
2
production (converted to carbon units)
and total carbon production are equivalent.
Results
Seasonal changes in the concentration and distribution of chlorophyll a,
salinity, and dissolved O
2
in the estuary during the winter, spring, and summer of
1995 (a year of average freshwater inputs) are shown in Figure 1.5. The chlorophyll
a peak occurred in early spring during the period of maximum nitrate load (Kemp and
Boynton 1984) and migrated seaward during the following month, eventually sinking
in late spring in the middle regions of the estuary (Fig. 1.5). The peak extended to 7-
10 meters in depth and 10-20 kilometers along the axis of the middle estuary (Fig.
21
1.5). Salinity stratification is most intense in spring, relaxing by early summer.
Hypoxia (O
2
< 2 mg l
-1
) develops in the same region where the majority of
chlorophyll a sinking through the pycnocline occurred (Fig. 1.5). These dynamics
have been described previously (e.g., Malone et al. 1988, Boynton and Kemp 2000)
and are fundamental processes in estuarine ecology that link the terrestrial landscape
to the estuarine ecosystem. In the following section, we display the ability of box
models to assign rates to these processes and help quantify the interactions of surface
and bottom layer processes with nutrient transport, production, and consumption.
Seasonal variation in non-conservative rates
Seasonal cycles of non-conservative production of O
2
in the surface layer can
be summarized by spring-summer net heterotrophy in the upper estuary, giving way
to spring net autotrophy in the middle and lower estuary surface layers (Fig. 1.6).
Summer heterotrophy in the upper estuary (-80 mmol O
2
m
-2
d
-1
) corresponds with
reduced net DIN and DSi uptake (< 2 mmol m
-2
d
-1
) and net DIP production in
summer (Fig. 1.6). Surface net O
2
production (net autotrophy) peaked in late spring
(60-80 mmol m
-2
d
-1
) in the middle and lower estuary and is linked to net DSi
consumption (-5 mmol Si m
-2
d
-1
), though net DSi production (10-25 mmol Si m
-2
d
-1
)
is dominant in summer (Fig. 1.6). Peak annual net DIN consumption of -5 mmol N
m
-2
d
-1
lags 2-3 months behind peak net O
2
production (Fig. 1.6). Net DIP production
in the surface layer and consumption in the bottom layer correlate; peaks occur in
summer, though bottom production is 2-3 times higher than surface consumption per
m
-2
.
22
Bottom layer rates of DSi (8-20 mmol Si m
-2
d
-1
), DIN (4-20 mmol N m
-2
d
-1
),
and DIP (0.6-1.2 mmol P m
-2
d
-1
) production peak between May and September
throughout the estuary concomitant with peaks in O
2
consumption (Fig. 1.6). DIP,
NH
4
+
, and DSi regeneration and bottom O
2
consumption were significantly correlated
with temperature in the upper, middle, and lower estuary (Fig. 1.7). These
relationships are strongest in the middle estuary and are exponential throughout the
estuary (Fig. 1.7). The magnitudes of nutrient regeneration and O
2
consumption per
m
-2
are generally highest in the lower estuary (Fig. 1.9). Despite the significant
relationships, 50-80% of the variation is not explained by temperature.
We tested for significant differences between monthly means for the entire
data set of box model computed net production rates (n = 228) using a one-way
ANOVA with month as the independent variable. There was significant seasonal
variation between months for all variables except surface layer net O
2
production in
the middle and lower estuary (Table 1.3). The significant seasonal variation exists
despite high inter-annual variability in the box model computations (Chapter II).
Surface net O
2
production and surface nutrient consumption were generally
enhanced in wet years (mean annual river flow > 20-year average), relative to dry
years (mean annual river flow < 20-year average, Fig. 1.8). Surface net O
2
production
was 10-15 mmol m
-2
d
-1
higher throughout the summer (May to September) in the
middle and lower estuary and chlorophyll a was 10-15 ?g l
-1
higher (Fig. 1.8).
Consequently, summer DIN consumption was 0.3-1.5 mmol m
-3
d
-1
higher during wet
years and the summer peak persisted later in the year in the middle and lower estuary
(Fig. 1.8). Surface layer O
2
production, chlorophyll a, and DIN consumption were
23
significantly higher in the middle and lower estuary during wet years than dry years,
but bottom layer rates were not significantly affected by flow (ANOVA, significance
at p < 0.05).
Stoichiometric computations
Particulate organic carbon sinking (10-90 mmol C m
-2
d
-1
) and concentration
(200-250 mmol C m
-3
) peaked in late winter and spring (February to April)
throughout the estuary (Fig. 1.11). Chlorophyll a and net diatom growth (~5 mmol C
m
-3
d
-1
) peaked in the same time of year in these regions (Fig. 1.11). Particulate
organic carbon sinking and concentration and chlorophyll a were higher in the middle
region of the estuary than lower regions, though sinking estimates are not available
for the upper estuary (Fig. 1.11). Particulate organic carbon sinking was minimal
during June to August and increased to a fall peak of 10-30 mmol C m
-2
d
-1
(Fig.
1.11). Computed sinking rates (calculated as POC sinking flux divided by the POC
concentration) ranged from 0.4-0.6 m d
-1
during winter spring and 0.1-0.2 m d
-1
during summer (Fig. 1.11).
Axial distributions of non-conservative rates
Rates of net O
2
production reveal a gradient from net heterotrophy in
landward regions (Box 1, 2) to net autotrophy in seaward regions (Box 3-5, Fig. 1.9),
with peak net O
2
production occurring in the middle and lower estuary (40-100 mmol
O
2
m
-2
d
-1
). During wet years, chlorophyll a was elevated and the biomass peak
shifted 20 km seaward (Fig. 1.9). DIN and DIP consumption were 20-50% higher in
the middle estuary than the other regions and increased with river flow up to 30%
24
(Fig. 1.9). In fact, chlorophyll a, net O
2
production, and DIP consumption were
reduced in the most landward reach of the upper estuary during wet years (Fig. 1.9).
Mean bottom layer O
2
consumption, chlorophyll a, and DIP production were 5-30%
higher in wet years relative to dry years, but these differences were not significant
(Fig. 1.9). The axial distribution of rate magnitudes did not change with variation in
freshwater inputs (Fig. 1.9).
Differences between the magnitude of the total surface layer O
2
production
and bottom layer O
2
consumption changed in response to freshwater inputs (Fig.
1.10). Although freshwater flow resulted in 5-20% increases in net O
2
production in
the surface layer, bottom layer rates were generally unaffected by freshwater flow
(Fig. 1.10), resulting in higher differences between surface and bottom layer O
2
production in the middle estuary during periods of above average flow. Although
surface O
2
production was higher than bottom consumption during years with lower
freshwater flow in the middle estuary, surface and bottom layer rates were nearly
balanced in the lower estuary (Fig. 1.10). The same was true for net DIN uptake in
the surface layer and net DIN production in the bottom layer. Elsewhere in the
estuary, however, surface net DIN consumption was an order of magnitude higher
than bottom production in both wet and dry years (Fig. 1.10)
Pelagic-benthic coupling
The relationships between surface and bottom layer biogeochemical rates
illustrate the coupling between surface and bottom processes and how this coupling
varies along the axis of the estuary. We found significant (p < 0.05) positive
25
correlations between annual mean surface layer net O
2
production and bottom layer
O
2
consumption in the upper (r
2
= 0.33, p < 0.02), middle (r
2
= 0.43, p < 0.01), and
lower estuary (r
2
= 0.26, p < 0.05, Fig. 1.12). The correlation is strongest and the
surface and bottom layer rates were highest in the middle estuary (surface NEP = 10-
40 10
3
kg O
2
d
-1
, bottom O
2
consumption = 10-30 10
3
kg O
2
d
-1
), with slightly lower
rates in the lower estuary. Although the upper estuary was generally heterotrophic,
the most positive surface layer production rates do correspond with the highest
bottom layer consumption rates. In general, more O
2
is produced in the surface layer
of the middle estuary than is consumed in the bottom layer (Fig. 1.12).
Positive correlations also exist between surface chlorophyll a and bottom
layer O
2
consumption (Table 1.2, Fig. 1.12). The chlorophyll a versus O
2
consumption relationship is strongest in the middle estuary (r
2
= 0.40, p < 0.01) and is
also statistically significant in the upper estuary (Table 1.2, Fig. 1.12). Chlorophyll a
(in areal units) increases seaward, with highest chlorophyll in the lower and middle
estuary, respectively (20-100 mg m
-2
in lower, 20-80 mg m
-2
in middle). Particulate
organic carbon sinking is also positively correlated with bottom layer O
2
consumption
on an annual scale in the middle estuary (Table 1.2). Similarly, chlorophyll a (in
volumetric units) is significantly and positively correlated with box model computed
POC sinking in the middle estuary (Table 1.2). Particulate organic carbon sinking
during February to April was significantly correlated with bottom layer net NH
4
+
,
DIP, and DSi production in the middle estuary (Fig. 1.13) (NH
4
+
: r
2
= 0.25, p < 0.05;
DSi: r
2
= 0.32, p < 0.05; DIP: r
2
= 0.51, p < 0.01). We found that bottom layer net
NH
4
+
, DIP, and DSi production (i.e., bottom layer regeneration) were significantly
26
and positively correlated with bottom layer O
2
consumption in the middle and lower
estuary (Table 1.2). The correlation between NH
4
+
(r
2
= 0.39, p < 0.01), DIP (r
2
=
0.23, p < 0.05) regeneration and O
2
consumption is strongest in the lower estuary, but
significant relationships exist in both the middle and lower estuary for NH
4
+
, DIP,
and DSi (Table 1.2). The nutrient production versus O
2
consumption correlations
explained little variability and were not significant in the upper and lower estuary.
Nutrient transport rates
Rates of nutrient inputs to the surface layer of the middle estuary are
important for driving biomass accumulation, net O
2
production, and pelagic-benthic
coupling. DIN transport rates to the upper and lower estuary were dominated by
spring seaward inputs and the magnitude of seaward inputs increased in more down-
estuary boxes (Fig. 1.14). The magnitude of vertical transport of DIN to these waters
was similar to seaward advection from May to October (3 mmol N m
-2
d
-1
in the
middle, 5 mmol N m
-2
d
-1
in the lower). Conversely, DIN transport to the middle
estuary was dominated by seaward advection in spring, but vertical imports
dominated from May to October and were 50% higher than seaward inputs in the
middle estuary (Fig. 1.14). Spring DIN inputs from seaward advection were
sufficient to support spring net O
2
production, but vertical DIN inputs were required
to support summer rates of net O
2
production (Fig. 1.14).
27
Discussion
Seasonal and regional variability in surface layer biogeochemistry
Net production rates for surface layer O
2
, DIN, DIP, and DSi all exhibit
distinct and significant seasonal cycles and regional distributions in the estuary (Fig.
1.6). Net negative rates of O
2
production (i.e., net consumption) are maintained in the
upper estuary during spring and summer by the region?s characteristic high turbidity
(mean TSS = 70 mg l
-1
, secchi depth = 0.4-0.6) and large allocthonous carbon inputs
(annual mean = 125 mmol C m
-2
d
-1
). This pattern is common in temperate estuarine
systems (Howarth et al. 1992, Hopkinson and Vallino 1995), where turbidity favors
net O
2
consumption by reducing light for photosynthesis (Appendix I, Cloern et al.
1983), while allochthonous inputs of organic matter fuel respiration (e.g., Smith and
Kemp 1995, Smith and Hollibaugh 1997). A transition from net heterotrophy in the
upper estuary to autotrophy in the middle and lower estuary is also a common feature
of temperature estuaries (Fig. 1.8, e.g., Kemp et al. 1997) and is consistent with the
?river continuum? concept (Vannote et al. 1980).
Whereas phytoplankton production tends to peak in summer in the middle and
lower estuary (data not shown) and in Chesapeake Bay (Harding et al. 2002) and
other coastal systems (Radach et al. 1990, Paerl et al. 1998), seasonal maxima for net
O
2
production coincide with spring chlorophyll a peaks in the Patuxent estuary (Fig.
1.6). Positive rates for net O
2
production in late spring and early summer, which have
been observed in many temperate estuaries (Kenney et al. 1988, Hoppema 1991)
including Chesapeake Bay (Smith and Kemp 1995), occur when diatom blooms
dominate (Malone et al. 1988). With the onset of warm summer temperatures, net O
2
28
production declines as respiration increases (Howarth et al. 1992, Smith and Kemp
1995), though high pelagic respiration has been measured in cooler periods when
phytoplankton biomass is high (L.M. Jensen et al. 1990). The seasonality and
regional variation in box model estimates of net O
2
production compare favorably
with similar computations for Chesapeake Bay (Kemp et al. 1997), while integrated
rates of net O
2
production between -0.5 and 2.6 g O
2
m
-2
d
-1
are comparable to rates
reported for Chesapeake Bay (Kemp et al. 1997).
We chose to quantify net ecosystem production (i.e., total system
photosynthesis minus community respiration; Smith et al. 1991, Kemp et al. 1997)
based on the non-conservative net production or consumption of O
2
. An alternative
method, which applies fixed stoichiometric assumptions to convert the net non-
conservative DIP production rate into carbon units, has been widely used (e.g., Smith
et al. 1991, LOICZ; Gordon et al. 1996). We were concerned that, for estuaries like
the Patuxent, that DIP biogeochemistry is controlled by non-biological processes,
including physical sorption-desorption (Jitts 1959, Pomeroy et al. 1965, Gunnars and
Blomqvist 1997) and flocculation with Fe and Mn oxides and hydroxides (Sholkovitz
1976, Sundby et al. 1992), especially at low salinity. Given the limitations of using
DIP to calculate net ecosystem production, O
2
appeared to be a more appropriate
measure.
We were able to address directly two potential problems associated with using
net O
2
production as a measure of net ecosystem production. We corrected
instantaneous measurements of O
2
for systematic diel variations associated with
photosynthesis and respiration (Fig. 1.4), but found that this correction altered the O
2
29
concentration by < 5%. We also corrected surface layer net O
2
production rates using
conventional expressions for air-sea exchange and a measured exchange coefficient
(? ); we found that variations in ? of ? 50% resulted in relatively small changes in net
O
2
production, 5-10% in the middle and lower estuary (Box 3-6) and 5-15% in the
upper estuary (Box 1, 2). In any case, box model estimates of net ecosystem
production based on net O
2
production agree well with estimates using different
techniques (Smith and Kemp 1995, Kemp et al. 1997).
Seasonal variations in net O
2
production appear to be linked to the annual DSi
cycle. Because DSi does not react in chemical and physical sorption or precipitation
processes at concentrations measured in Chesapeake Bay (Kamatani and Riley 1979)
and because DSi dissolution is low at temperatures common during spring (< 13
o
C,
Yamada and D?Elia 1984), the net DSi production rate provides a useful indicator of
net diatom DSi uptake. Spring peaks in net O
2
production and net DSi consumption
in the middle and lower estuary are coincident with the typical timing of diatom
spring blooms (Fig. 1.6, 1.11; Malone et al. 1988, Fisher et al. 1988). Converting net
DSi uptake to equivalent carbon units (C:Si = 6.625) suggests diatoms comprises 50-
80% of net O
2
production in the middle and lower estuary during spring (Fig. 1.6).
Fall peaks in DSi uptake are consistent with fall peaks in the abundance of diatoms
(Skeletonema costatum, Cyclotella spp., and Thalassiosira spp.) in the Patuxent River
estuary (Lacouture et al. 1993). During summer, a shift to net DSi production in all
regions of the estuary indicates remineralization of DSi, which is a primarily
chemical process that likely occurs in the shallow sediments contained in the surface
layer box (D?Elia et al. 1983), not in the water column.
30
Strong seasonal patterns of surface layer DIN uptake (i.e., net negative
production) rates were observed throughout the estuary, including spring peaks. DIN
limitation has been found in the mesohaline region of the Patuxent River estuary in
previous studies (e.g., D?Elia et al. 1986), and net O
2
production (i.e., net nutrient
uptake) accounted for 80% of DIN uptake in the middle and lower estuary during
February to March (O
2
:N = 6.625), but less than 50% in May and June. Excess DIN
uptake in spring and summer is attributed to denitrification rates of 2-4 mmol N m
-2
d
-
1
, or 83-125 ?mol N m
-2
h
-1
, which agree favorably with estimates obtained using
15
N
tracer methods (Jenkins and Kemp 1984). In fact, box model computations of water
column integrated net denitrification (Appendix II) suggest spring/ early summer
peaks of 50-150 ?mol N m
-2
h
-1
, which is comparable to these measurements. This
surface layer denitrification is likely occurring in shallow water sediments along the
flanks of the river, which are in contact with 75% of the surface layer (Table 1.1).
Coupling between net O
2
production and DIP production was less direct and
consistent in the middle and lower estuary surface layer. Although net uptake of DIP
would be expected to correlate with net O
2
production in autotrophic regions, the
observed DIP uptake in the surface layer lagged behind O
2
-based net production by 3-
4 months in the middle and lower Patuxent estuary (Fig. 1.6). Surface layer DIP
consumption rates of 0.1-0.4 mmol P m
-2
d
-1
in August of the lower estuary approach
the expected uptake due to equivalent net O
2
production of 0.15-0.22 mmol P m
-2
d
-1
(assuming O
2
:DIP = 106), but DIP and O
2
rates do not match during spring, winter, or
fall.
31
These weak correlations suggest that net DIP production in the surface layer
may also be controlled by physical or chemical processes. DIP can attach to organic
and inorganic particles (Carpenter and Smith 1984) and sink out of the surface layer.
Stratification and increased residence time (Hagy et al. 2000) during summer may
allow for DIP scavenged onto particles to sink to the lower layer and be regenerated,
but without quickly returning to the surface, as is true in many lakes (Jahnke 1992).
Such a mechanism may allow for the large DIP net uptake rates observed during
summer, which are larger than rates estimated for net DIN uptake using biological
stoichiometry. DIP is also involved in diagenetic and sorption-desorption reactions in
systems with strong O
2
and salinity gradients (Krom and Berner 1981, Fox et al.
1985), such as the Patuxent. Biological and chemical processes are likely interact to
control net DIP consumption during summer.
Seasonal variability in bottom layer biogeochemistry
Summer peaks in bottom layer processes indicate the role of temperature in
respiration and nutrient recycling. Correspondence between net rates of nutrient
production and net O
2
consumption in bottom layers during summer (Table 1.2) is
consistent with diagentic coupling between organic matter decomposition and
nutrient regeneration, as is often measured in flux cores and benthic chambers
(Cowan and Boynton 1996) and in mesocosm experiments (Kelly et al. 1985).
Although it appears that a large proportion of fresh organic matter is delivered to
sediments during spring (Fig. 1.11, Kanneworff and Christensen 1986, Boynton and
Kemp 2000), O
2
consumption and DIN/DIP production do not peak until late spring
32
and summer when temperature increases bacterial respiration rates and enzyme
activity (Fig. 1.6, 1.7; Fisher et al. 1982, Shiah and Ducklow 1994). Summer peaks
in aerobic respiration and nutrient regeneration are common in other temperate
estuaries (Kamp-Nielsen 1992, Yoon and Benner 1992, Cowan et al. 1996). General
agreement between box model estimated bottom layer net production rates and
benthic chamber flux measurements in the Patuxent River estuary (Table 1.4,
Boynton and Rohland 2001) indicate that benthic processes tend to dominate these
seasonal patterns.
Although O
2
consumption and nutrient regeneration in the bottom layer are
positively related with water temperature (Fig.1.7), temperature affects each nutrient
via different chemical, physical, and biological mechanisms. For example, whereas
temperature stimulation of organic matter hydrolysis and release of DIN and DIP
occurs through effects of enzyme catalyzed biochemical reactions (Cowan and
Boynton 1996), temperature enhancement of biogenic silica remineralization is
attributable primarily to effects of physical-chemical dissolution (Yamada and D?Elia
1984, Chauvaud et al. 2000). Although DIP is remineralized initially via biochemical
decomposition, physical-chemical processes tend to regulate DIP release from
sediments to overlying water, including sorption to particles and flocculation with
metal oxy-hydroxides. These processes are, in turn, regulated by seasonal variations
in redox conditions within these sediment systems (Fisher et al. 1982, Cowan and
Boynton 1996). A fraction of the net O
2
consumption in bottom layers is attributable
to aerobic respiration; however, a large fraction of this O
2
uptake may be due to
oxidation of sulfide (produced from sulfate reduction) via both chemical and
33
microbial processes (Roden 1990, Roden et al. 1995). In Chesapeake Bay, Roden
(1990) concluded that sulfate reduction could account for approximately 75% of
summer organic matter oxidation in sediments. Under conditions of anoxic bottom
water, a large fraction of the reduced sulfur (as hydrogen sulfide) diffuses vertically
through the water column until it is oxidized near the pycnocline where free O
2
is
abundant.
Temperature is not the only factor causing seasonal lags between surface
nutrient uptake and bottom nutrient regeneration. A late spring O
2
consumption peak
found throughout the estuary precedes the temperature maximum by 1-2 months,
suggesting the respiration of recently deposited labile material (Fisher et al. 1982,
Graf et al. 1982). Rates of sediment O
2
consumption measured in the Patuxent using
benthic chambers also peaked in late spring and early summer before the seasonal
temperature maxima (Boynton and Rohland 2001). Multiple regressions explained
40% more of the variability in bottom layer O
2
consumption using contemporaneous
temperature and the previous month?s bottom layer chlorophyll a than when using
temperature alone (Hagy 1996). These relationships suggest the importance of labile
organic matter deposition to bottom layer respiration and indicate that this vertical
coupling is not instantaneous (Kanneworff and Christensen 1986, Kamp-Nielsen
1992, Kemp et al. 1999).
Assessing error in box model rates
The monitoring data used to compute mean concentrations for a given box are
collected from mid-channel stations at 2-4 week intervals (Fig. 1.1). The boxes span
34
the width of the estuary, containing both deep, two-layer regions near the estuarine
channel and shallow (< 4 m) vertically mixed areas flanking the channel. These areas
may have different water quality conditions (e.g., Ward et al. 1984), which could
produce errors in computing monthly mean concentrations for an entire box from
only the mid-channel data. Comparisons of point measurements in the channel with
similar measurements from shallow water sensors in 2004 and 2005 (MD DNR 2005)
indicate significant correlations between salinity (r
2
= 0.91-0.98), O
2
(r
2
= 0.72-0.95),
and chlorophyll a (r
2
= 0.36-0.90, n = 8-20) with regression slopes ranging from 0.88-
1.3. Similar agreements were found in comparing monthly means of the mid-channel
and shallow water concentrations throughout Chesapeake Bay, where parallel
measurements at mid-channel and adjacent nearshore areas were statistically
indistinguishable 90% of the time when stations were < 2 km apart (Kemp et al.
2004).
In addition, relatively short-term (1-3 weeks) variability in nutrient
concentrations may not be captured by the monthly and fortnightly monitoring
program samples. To determine the potential error imposed by computing monthly
means with data that does not capture short-term variability, we examined time series
of continuously monitored nutrient data over 30-day periods in adjacent Chesapeake
Bay tributaries (Choptank and Pocomoke Rivers, NAS-2E nutrient monitoring
systems, L. Codispoti and V. Kelly, unpublished data). These analyses revealed that
seasonal variability (monthly time scale) is greater than diel variability over 90% of
the data set. Because seasons are the time scales of interest in this analysis, it appears
that the shorter-term variability has little impact on seasonal trends, as long-term
35
monthly averages reveal consistent seasonal nutrient cycles (Hagy 1996). Although
episodic spikes in nutrient concentration observed in association with storm events
could cause misrepresentation of the monthly mean nutrient concentrations, the actual
sampling protocols generally preclude such problems.
Pelagic-benthic coupling
The contrast between O
2
production and nutrient uptake in the surface layer
versus the bottom layer illustrates the dominance of autotrophy in the surface layer
and heterotrophy in the bottom layer. Implicit in these patterns is a vertical
connection of surface and bottom waters via particle sinking and vertical advection
and diffusion. Such pelagic-benthic coupling has been described in many systems
(e.g., Graf et al. 1982, Kamp-Nielsen 1992, Kemp et al. 1999) and includes a series of
processes that is relevant to coastal zone management.
Box model computed POC sinking rates (per pycnocline area) of 20-90 mmol
C m
-2
d
-1
in the middle estuary and 5-80 mmol C m
-2
d
-1
in the lower estuary (Fig.
1.11) are approximately the same magnitude as measured POC deposition rates of 10-
150 mmol C m
-2
d
-1
measured at nearby Chesapeake Bay mesohaline sites (Roden et
al. 1995) and in the lower Patuxent River (Kemp and Boynton 1984). POC sinking
thus provides a mechanism to transport surface layer production to bottom layers
(Graf et al. 1982, Kanneworff and Christensen 1986, Kemp et al. 1999). Coincident
spring peaks of POC sinking, chlorophyll a, and net diatom growth support the view
that diatom blooms comprise most of the spring vertical particle flux (Malone et al.
1988) and that a large fraction of the spring bloom is ungrazed (Peinert et al. 1982,
36
Kanneworff and Christensen 1986). Box model computed POC sinking rates during
winter-spring (0.4-0.6 m d
-1
) agree with measured sinking rates of larger
phytoplankton cells, such as diatoms (Bienfang 1981). Studies in different coastal
systems observed a spring sinking peak, suggesting that sinking, not grazing, is the
dominant loss term for spring phytoplankton blooms (Smetacek et al. 1978, Smetacek
1980, Peinert et al. 1982, Keller and Riebesell 1989).
Examination of annual mean rates of surface layer net O
2
production, bottom
layer O
2
consumption, chlorophyll a, and POC sinking indicated that pelagic and
benthic processes are tightly coupled in the middle region of the Patuxent River
estuary, but more weakly connected in other regions. This is the case because the
middle estuary is characterized by relatively shallow depths (Kemp et al. 1999, Bailey
2005), moderate residence times (Hagy et al. 2000), and low interaction with adjacent
systems relative to other regions of the Patuxent (Sanford and Boicourt 1990,
Gallegos et al. 1992, Fisher et al. 2006). Because we observed positive net O
2
production in the surface layer (i.e., production > respiration) and we equate O
2
production to carbon production, we expect this excess production to be exported to
and respired in adjacent regions (Kemp et al. 1997). Significant correlations between
surface net O
2
production and bottom net O
2
consumption (kg d
-1
) suggest that net
production tends to sink to the bottom layer (Fig. 1.12). Correlations between both
surface chlorophyll a and POC sinking with bottom layer O
2
consumption in the
middle estuary provide further evidence of direct pelagic-benthic coupling (Table 1.2,
Fig. 1.12), which has been found in other temperate estuaries (Kamp-Nielsen 1992,
Vidal et al. 1992, Yoon and Benner 1992). Correlations between POC sinking and
37
surface chlorophyll a in the middle estuary indicate that much of the POC sinking is
linked to plankton biomass (Table 1.2; Lignell et al. 1993, Hagy 2002, 2005). Such
correlations are qualitative, however, and do not quantify how much of the bottom
layer respiration is accounted for by organic matter sinking.
Independent estimates of rates of POC sinking and bottom layer respiration
may be used to estimate what fraction of bottom layer respiration is due to POC
sinking from the surface layer. We addressed the importance of annual mean POC
sinking by comparing sinking rates to bottom layer O
2
consumption rates in the
middle estuary (O
2
converted to carbon using RQ = 1 e.g., Hopkinson 1985).
Because the surface layer overlies both the lower layer of deep channel water and
bottom sediments in the shallow flanks, it is wider than the bottom layer. Thus, all of
the sinking POC from the surface layer may not reach the central bottom layer. If we
assume that POC sinking occurs uniformly throughout the surface layer and that
carbon sinking to the bottom layer only occurs where the surface and bottom layer
overlap (~25% of the total area of Boxes 3-5 at MLW, Table 1), POC sinking would
account for 20-50% of bottom respiration. If we assume, on the other hand, that the
entire POC flux from the surface layer was transported to the bottom layer within a
year, POC sinking would account for 50 to > 100% of bottom respiration. The latter
assumption requires that the majority of organic particles settling over the flanks are
transported laterally down the slope toward the adjacent channel?s lower layer (e.g.,
Kemp et al. 1997). This latter assumption is supported by measurements of plankton
and benthic photosynthesis and respiration in Chesapeake Bay (Kemp, unpublished
data), revealing net autotrophy in shallow water and net heterotrophy in deep water.
38
Distances between the shallow flanks and the main channel are shorter in the
Patuxent (1-3 km wide) compared to the mainstem Chesapeake (10-15 km wide),
suggesting that lateral carbon transport is likely. Previous studies have found that
carbon sedimentation can account for a large fraction of sediment respiration (Kamp-
Nielsen 1992, Cowan and Boynton 1996) and that respiration is elevated in regions
where more organic material is present (Vidal et al. 1992, Yoon and Benner 1992).
Our calculations indicate that POC sinking from the surface layer will often,
but not always provide the carbon necessary to support bottom layer respiration.
Carbon deficits have also been found in other systems (e.g., 75% of bottom
respiration unaccounted for in Kiel Bight) and were attributed to transport and benthic
photosynthesis (Graf et al. 1982). Alternative sources of carbon to account for the
additional respiration in the Patuxent could include landward carbon transport via
gravitational circulation (Kemp et al. 1997). In fact, total organic carbon transport to
the middle and lower estuary in the bottom layer is 30-70 10
3
kg d
-1
, which is 3-4
times higher than the organic carbon sinking flux (10-25 10
3
kg C d
-1
). A large
fraction (up to 95%) of this horizontally imported carbon is exported from the region,
however, and the net organic carbon inputs via advection and diffusion are 0.2-5.0
10
3
kg C d
-1
, enough to satisfy the excess respiratory demand in some years, but not
all. Carbon advected through the bottom layer likely originates as surface layer
carbon from Chesapeake Bay and the lower Patuxent River estuary. By the time this
material reaches the middle estuary, residual compounds may be less labile than
locally produced surface carbon. POC sinking is thus the dominant carbon source to
39
bottom waters, as suggested by the tight pelagic-benthic coupling in the middle
estuary.
Estimated spring vertical transport of organic carbon to the bottom layer of the
middle estuary is proportional to annual bottom layer net nutrient regeneration (Fig.
1.13). We use annual rates to represent the fact that regeneration may respond rapidly
to carbon inputs (M.H. Jensen et al. 1990), or may lag with temperature effects
(Kanneworff and Christensen 1986). Net bottom layer DSi production is correlated
with both surface chlorophyll a (r
2
= 0.61) and spring POC sinking (r
2
= 0.35),
indicating the role of sinking diatoms as a source of biogenic silica (Yamada and
D?Elia 1984) and a link between surface productivity and DSi regeneration (Cowan
and Boynton 1996). Correlations between both chlorophyll a and POC sinking with
net bottom layer NH
4
+
and DIP production (Table 1.2) also support the link between
nutrient remineralization and surface phytoplankton biomass (Nixon 1981, Cowan et
al. 1996). In fact, POC sinking can account for 50-100% of NH
4
+
, DIP, and DSi
regeneration, while a smaller fraction of POC sinking is lost to long-term burial (e.g.,
Fisher et al. 1982). NH
4
+
regeneration has been correlated with phytoplankton
productivity and sinking in Chesapeake Bay (Boynton and Kemp 2000), Danish
coastal waters (M.H. Jensen et al. 1990), and in several other estuarine and coastal
systems (Nixon 1981). These results suggest that organic matter deposition to the
bottom layer affects the magnitude of nutrient regeneration, while temperature and O
2
consumption influence the timing of regeneration (Cowan and Boynton 1996, Cowan
et al. 1996).
40
Bottom layer nutrient regeneration is an important source of nutrients for
productivity during summer in many temperate estuaries, but not in spring (Kemp and
Boynton 1984, Dollar et al. 1991). Although seaward DIN transport fuels ?new?
phytoplankton production (i.e., high chlorophyll a and net O
2
production) in the
middle and lower estuary during spring (Fig 1.14, Malone et al. 1988, Magnien et al.
1992) vertical inputs of DIN from the bottom to the surface layer are large enough to
satisfy 70-80% of summer surface DIN uptake (Fig. 1.14, Kemp and Boynton 1984).
Approximately two-thirds of this vertically transported nitrogen is NH
4
+
(Hagy 1996),
and more than half of this NH
4
+
was derived from bottom layer regeneration. Similar
contributions of sediment nutrient regeneration, particularly NH
4
+
, to summer
phytoplankton productivity have been found in other temperate estuaries (Christian et
al. 1991, Fisher et al. 1992, Malone et al. 1988). The additional 20-30% of N inputs
during summer are probably derived from upstream sources, atmospheric inputs, and
internal pelagic recycling processes (Nixon 1981, Paerl 1985). In the lower estuary,
over 100% of the surface layer?s net DIN demand could be supported by seaward
DIN (both NO
3
-
and NH
4
+
) transport in all seasons. Seaward DIN transport may be
comparable to vertical imports in the lower estuary because horizontal transport is
amplified in a seaward direction in systems with two-layer circulation (Hagy et al.
2000). This analysis provides quantitative support for the concept that high spring
DIN inputs generate net organic production that sinks to the lower layer, where
organic N is remineralized in summer to support primary productivity (Kemp and
Boynton 1984, Malone et al. 1988).
41
Effects of freshwater input
In many estuaries, high river flow supports increased phytoplankton
productivity and biomass, due to elevated nutrient inputs (Malone et al. 1988, Paerl et
al. 2006). This occurs in the lower and middle Patuxent estuary (Fig. 1.8, 1.9) where
elevated summer chlorophyll a and net O
2
production during high flow years suggests
that flow relieves nutrient limitation later into the year. Increased DIN consumption
in wet years illustrates the nutrient demand of increased phytoplankton productivity
in the middle and lower estuary (Fig. 1.8). Thus, a logical hypothesis is that elevated
carbon production and nutrient uptake in years of high flow would lead to elevated
bottom layer respiration and nutrient regeneration, as was found in Chesapeake Bay
(Boynton and Kemp 2000).
Despite the significant positive effects of river flow on surface biomass and
productivity, available data suggest that bottom layer respiration and regeneration are
less affected (Fig. 1.9, 1.10). Particulate organic carbon sinking is positively, but not
significantly related to flow (r
2
= 0.15, p > 0.1), while surface and bottom layer
chlorophyll a increased significantly with flow (Fig. 1.8). These correlations suggest
that flow does in fact lead to more deposition of recent phytoplankton biomass to the
bottom layer. Previous studies in Chesapeake Bay have identified increased
chlorophyll a deposition with elevated flow (Boynton and Kemp 2000, Hagy 2005)
and increased NH
4
+
regeneration from sediments with elevated flow (Boynton and
Kemp 2000, Boynton and Rohland 2001). In addition, surface net O
2
production and
chlorophyll a are significantly correlated, thus we expect an increase in surface rates
42
to cause an increase in bottom rates. Thus, why were rates of bottom layer O
2
consumption and nutrient regeneration not significantly enhanced with flow?
To begin to answer this question, we examined surface and bottom rates of net
O
2
and DIN production/consumption in units of kg d
-1
for each box. The magnitudes
of surface layer and bottom layer net production rates in each region were not always
equivalent (Fig. 1.10), suggesting that there may be an important mechanism for
nutrient and carbon export. Horizontal transport is a large component of nitrogen and
O
2
budgets in the boxes of the middle estuary (data not shown). The fact that surface
layer O
2
production and DIN consumption exceed O
2
respiration and DIN
regeneration in the bottom layer of the middle estuary suggest that some fraction of
surface materials are transported out of the region represented by the box (Fig. 1.10).
Unlike the middle estuary, total masses of net O
2
and DIN production and
consumption in surface and bottom layers are nearly equivalent in the lower estuary,
especially during low flow periods. This suggests very little horizontal export out of
the region (Fig. 1.10). Discrepancies between surface and bottom rates in the lower
estuary are higher during high flow, much like the middle estuary, suggesting
horizontal export seaward, but where does this material ultimately go? It does not
appear that material potentially exported from the middle estuary sinks in the lower
estuary, where sinking rates are low and surface and bottom layer O
2
and DIN
production and consumption rates nearly match (Fig. 1.11). Boynton et al. (in prep)
and Boynton et al. (1995) estimated net export of total nitrogen from the Patuxent
estuary to Chesapeake Bay (0.21 10
6
kg N yr
-1
), suggesting that production in the
middle and lower estuary may be exported from the system altogether. Analyses of
43
chlorophyll a deposition to Chesapeake Bay sediments suggest that particles are
transported seaward from where they are produced during high flow periods (Hagy
2005). Although physical transport may cause export of particulates from the middle
Patuxent estuary during high flow, the large scale of our regional analysis (5-10 km)
might aggregate this process into a single box. Limited sediment chlorophyll data in
the Patuxent River suggest that the highest levels are in the middle region of the
estuary (Boynton and Rohland 1998), supporting the idea that regional maxima in
surface biomass sink to the bottom layer locally.
Summary and Conclusions
This analysis leads to several important conclusions regarding factors
regulating organic production and nutrient recycling. The following statements
summarize the major processes: (1) The majority of ?new? nutrients are delivered to
the estuary during late winter and spring. (2) ?New? nutrient inputs, most
importantly NO
3
-
, fuel a spring phytoplankton bloom that subsequently sinks across
the pycnocline. (3) Organic material exported to the bottom layer is regenerated in
late spring and summer in quantities generally proportional to those deposited. (4)
POC sinking likely accounts for 50-100% of bottom layer respiration in the middle
estuary and deficits are probably accounted for by the labile portion of organic carbon
delivered in landward flowing water masses. (5) Bottom layer regeneration of
particulate materials is necessary to support rates of net O
2
production and nutrient
uptake in surface layers during summer. (6) Pelagic and benthic processes are most
tightly linked in the middle estuary, which is highly productive and does not interact
44
substantially with adjacent systems. (7) Elevated freshwater flow generally enhances
surface layer processes more than bottom layer processes, indicating both vertical
pelagic-benthic coupling and seaward transport of organic production under high flow
conditions. These results generally agree with previously described conceptual
models and are likely applicable to other temperate estuarine systems.
We therefore propose a refinement to the conceptual model of spatial and
temporal coupling of nutrient inputs to net production in the Patuxent River estuary
(Kemp and Boynton 1984). Although we do not propose that transformation of N
and P to particulate forms in the oligohaline estuary during spring is unimportant, we
suggest that seaward advection of inorganic nutrients is high enough during spring to
support large phytoplankton blooms in the mesohaline estuary. These blooms are
dominated by diatoms and sink to the lower layer following the senescence of the
bloom. Deposited phytoplankton biomass is respired and regenerated in the bottom
layer during late spring and summer, which allows export of NH
4
+
to surface waters
to support summer productivity. Strong correlations between spring-dominated
carbon sinking and summer-dominated nutrient regeneration support this assertion,
and suggest that regeneration of particulate materials from oligohaline waters may not
be as important as previously thought in the mesohaline regions.
Although net biogeochemical production rates estimated in this study are
necessarily averaged over relatively large scales of months to decades and 10-30 km,
significant regional and seasonal patterns were clearly evident. In addition, inter-
annual variability in key biogeochemical processes was significantly related to
changes in river flow and horizontal transport. Significant correlations between
45
processes in vertically connected surface and bottom layers emphasize the importance
of pelagic-benthic coupling at these scales. Using box modeling methods to interpret
water quality monitoring data in terms of physical and biogeochemical rates provides
a valuable tool to help understand large-scale processes and controls for estuaries
such as the Patuxent, especially those with two-layered circulation. This technique
has potential to be an important research and management tool in the growing number
of well-monitored estuarine systems throughout the world, as it has already been
applied in many systems (e.g., Chesapeake Bay; Taft et al. 1978, Baltic Sea; Wulff
and Stigebrandt 1989, Tomales Bay; Smith et al. 1991, Patuxent River estuary; this
study, the Scheldt Estuary; Gazeau et al. 2005).
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55
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Virginia.
56
Table 1.1: Physical dimensions of all boxes in for the box model of Hagy et al.
(2000). Dimension information may be used to convert all box model computed
nutrient transports and production rates to the desired units.
57
Table 1.2: Correlation coefficients and p values for the relationships (top panel rate
versus side panel rate) between selected surface and bottom water biogeochemical
rates and chlorophyll a in three regions (upper, middle, lower estuary) of the Patuxent
River estuary. Rates include net biogeochemical production of bottom layer DSi,
DIP, NH
4
+
, and O
2
as computed with a box model, spring particulate organic carbon
(POC) sinking, and chlorophyll a. Box model computed rates are monthly rates and
chlorophyll a and POC sinking are annual means for the years 1985 to 2003 (O
2
data
are annual means when related to these variables).
58
Table 1.3: Resulting F-values of one-way ANOVA to test for significant differences
between months for selected box model computed net production rates and
chlorophyll a. Associated p-values < 0.01 indicated by ** and p < 0.05 indicated by
*. Monthly means calculated for all data from 1985 to 2003 (n = 228) for the upper
(Box 2), middle (Box 4), and lower (Box 5) Patuxent River estuary.
59
Table 1.4: Comparisons between non-conservative box model estimated rates of
bottom layer nutrient regeneration and oxygen demand in the Patuxent river estuary
with sediment-water oxygen and nutrient exchange (SONE) rates measured in the
Patuxent. All rates are in units of mmol m
-2
d
-1
. SONE rates from Boynton and
Rohland (2001).
60
Figure 1.1: Map of the Patuxent River estuary with Chesapeake Bay (inset), including
box model boundaries (Hagy 1996), Chesapeake Bay Program water quality
monitoring stations (www.chesapeakebay.net), and the location of Maryland
Department of Natural Resources? continuous water quality sensors
(www.eyesonthebay.net). Chesapeake Bay Program station codes are to the left of
each station and numbers at the right of box model boundaries indicate distance from
the mouth of the estuary (km).
61
Figure 1.2: Schematic description of the box model structure (as seen in Hagy et al.
2000). Included are box model boundaries, exchange coefficients, and inputs. The
estimated exchanges presented in this diagram are seaward advection (Q
m
), landward
advection (Q?
m
), vertical advection (Q
vm
), vertical diffusive exchange (E
vm
), and
horizontal dispersion (E
m,m+1
). Included inputs are the volume of each box and the
salt concentration (not included), river flow (Q
r
), the input of freshwater to each box
(Q
fm
), and the salinity at the seaward boundary (not included).
62
Figure 1.3: Generalized depiction of two-layer non-conservative box model for boxes
2-6. The non-advective exchange, E
m,m-1
(c
m
- c
m-1
), is part of the calculation for Box
2 only. Notation is the same as in Figure 2 except for box volume (V) and up estuary
(m-1) and down estuary (m+1) concentrations or water fluxes. Atmospheric inputs
are included, though the non-conservative flux of DIN is the only rate where
atmospheric inputs are included.
63
Figure 1.4: Average diel percent oxygen saturation curve for the month of August
(2003 and 2004) in three regions of the Patuxent River estuary. The data were used
to correct Chesapeake Bay Program monitoring data for the time of day sampled.
Error bars represent one standard deviation of the mean. Data are from continuous
water quality sensors maintained by the Maryland Department of Natural Resources
(details of station location, depth, and available data can be found at
www.eyesonthebay.net).
64
Figure 1.5: Contour plots of chlorophyll a (left panel) and dissolved oxygen/salinity
(right panel) in the Patuxent River estuary in the winter, spring, and summer of 1995.
Black lines represent salinity contours in the right panel and red area represents
hypoxic water (O
2
< 2 mg l
-1
). Box model boundaries are indicated by white lines.
Salinity contours of 1, 5, and 13 are labeled.
65
Figure 1.6: Monthly mean rates of net biogeochemical production of surface and
bottom layer O
2
(surface rate corrected for air-sea exchange), DIN, DIP, and DSi
computed by the box model for the upper (Box 2), middle (Box 4), and lower (Box 5)
Patuxent River estuary. Monthly means (? SE) were calculated for all years from
1985 to 2003. Horizontal dashed lines are drawn at zero net production rates. Error
bars represent one standard error of the mean.
66
Figure 1.7: Relationships between temperature and monthly rates of bottom layer net
production of DSi, DIP, and O
2
, computed by the box model, in the middle region
(Box 4) of the Patuxent River estuary.
67
Figure 1.8: Monthly mean rates of net biogeochemical production of O
2
(corrected for
air-sea exchange) and DIN computed for the surface layer by the box model, as well
as chlorophyll a in the surface layer of the middle (Box 4) and lower (Box 5) regions
of the Patuxent River estuary. Monthly mean values (? SE) were calculated for years
of above average river flow (open shapes, flow > 20 year mean, n = 7) and below
average river flow (shaded shapes, flow < 20 year mean, n = 9). Horizontal dashed
lines are drawn at zero net production rates. Error bars represent one standard error
of the mean.
68
Figure 1.9: Mean annual rates of net biogeochemical production of surface and
bottom layer DIN, DIP, and O
2
(surface rate corrected for air-sea exchange)
computed by the box model, as well as chlorophyll a along the estuarine axis of the
Patuxent River estuary. Annual means (? SE) were calculated for years of above
average river flow (squares, flow > above 20 year mean + SE) and below average
river flow (circles, flow < 20 year mean - SE). Error bars represent one standard error
of the mean.
69
Figure 1.10: Mean annual rates of net biogeochemical production of surface and
bottom layer O
2
(top panel, corrected for air-sea exchange) and DIN (bottom panel)
along the estuarine axis of the Patuxent River estuary. Annual means were calculated
for years of above average river flow (flow > above 20 year mean + SE) and below
average river flow (flow < 20 year mean - SE). The rates are total mass fluxes in
each layer in units of 10
8
mmol d
-1
. Dark bars are surface layer rates and gray bars
are bottom layer rates. Surface DIN consumption and bottom O
2
consumption rates
were multiplied by -1 to simplify comparisons.
70
Figure 1.11: Mean monthly surface layer particulate organic carbon (POC)
concentration and box model computed POC sinking (left panel), and surface layer
net diatom growth (NEP
Si
, right panel) and net surface layer chlorophyll a in the
middle Patuxent River estuary (Box 3, 4). Monthly means (? SE) were calculated
from 1985 to 2003 data. Horizontal dashed lines are drawn at zero. Error bars
represent one standard error of the mean.
71
Figure 1.12: Correlation of mean annual rates of box model computed bottom layer
O
2
consumption with mean annual surface chlorophyll a (top panel), spring POC
sinking (middle panel), and surface net O
2
production (bottom panel, corrected for
air-sea exchange) in the middle Patuxent River estuary. Data are annual means for
the years 1985 to 2003.
72
Figure 1.13: Correlation between mean annual box model compute spring POC
sinking and bottom layer DSi, DIP, and NH
4
+
production in the middle region (Box 4)
of the Patuxent River estuary. Data are annual means for the years 1985 to 2003.
73
Figure 1.14: (Top panel) Monthly mean net O
2
production in the middle (solid line)
and lower (dotted line) regions of the Patuxent River estuary. (Bottom panels)
Monthly mean total inputs of DIN from seaward sources (squares) and vertical inputs
from the bottom layer (circles) to the middle (Box 4) and lower (Box 5) regions of the
Patuxent River estuary. Error bars represent one standard error of the mean.
74
CHAPTER II
Responses of water quality and biogeochemical fluxes to nutrient
management and freshwater inputs in the Patuxent River estuary
Abstract
We conducted a quantitative assessment of estuarine ecosystem responses to
variability in freshwater inputs and reduced phosphorus and nitrogen loading from
sewage treatment facilities in the Patuxent River estuary. We analyzed a 19-year data
set of climatic forcing, nutrient loading, and water quality conditions for six estuarine
regions to compute monthly rates of net biogeochemical production and physical
transport of dissolved oxygen (O
2
), dissolved inorganic nitrogen (DIN), dissolved
inorganic phosphorus (DIP), and dissolved silicate (DSi) using a salt- and water-
balance model. Chlorophyll a, DIN and DIP concentration, surface net O
2
production, and bottom layer O
2
respiration were positively correlated with river flow
on annual and seasonal time scales. Point source rates of DIN and DIP loading to the
estuary, which declined by 40-60% following upgrades to sewage treatment plants,
correlated with decreasing DIN and DIP throughout the Patuxent and declines in
primary productivity and phytoplankton biomass in the tidal fresh region of the
estuary. No clear trends in water quality and net O
2
production were apparent in the
middle estuary, which appears to be due to persistently high nutrient loads from non-
point sources. Despite declining seaward N and P transport to the region, chlorophyll
a and surface net O
2
production have increased and water clarity has decreased in the
lower estuary. Elevated chlorophyll a concentrations and net O
2
production rates in
the lower estuary appear to be linked to above-average river flow in the 1990s, as
75
well as increasing net inputs of DIN into the estuary from Chesapeake Bay. In
addition, significantly reduced grazing pressure from copepods during the time period
favored increases in phytoplankton biomass and productivity. Thus, unexpected
changes in external and internal factors have obscured the effects of nutrient
management on water quality in the Patuxent River estuary.
Introduction
Effects of eutrophication are becoming increasingly evident in Chesapeake
Bay (Kemp et al. 2005) and other coastal systems worldwide (Cloern 2001). Coastal
eutrophication is driven by elevated inputs of key nutrients to ecosystems via
anthropogenic sewage discharge, agricultural runoff, and atmospheric deposition
(Nixon 1995, Jickells 2005). The structure and function of coastal ecosystems can
change dramatically with eutrophication and responses include reduced cover of
submerged aquatic vegetation (Duarte 1995), decreasing dissolved oxygen
concentrations (Diaz 2001), increased frequency of toxic algae blooms (Paerl 1988),
and food web shifts (Larsson et al. 1985, Smetacek et al. 1991). Effective
management of coastal systems will require improved scientific understanding of
ecological responses to changes in nutrient inputs (Cloern 2001).
Nutrient load reductions have been mandated for many estuaries and coastal
systems in order to improve water quality conditions, including nutrient
concentrations, phytoplankton biomass, water clarity, and dissolved oxygen
concentrations (Conley et al. 2002, Kemp et al. 2005, Paerl et al. 2006). In general,
eutrophication abatement has targeted phosphorus for most freshwater systems
(Edmondson 1970, Schindler 1978), while more recent restoration efforts have
76
emphasized both phosphorus and nitrogen in estuarine and coastal marine systems
(Kemp et al. 2005, Paerl et al. 2006). In estuaries throughout the world where
nutrient reduction mandates have been made, water quality monitoring programs are
currently in place, in part to evaluate the response of the system to nutrient load
reductions (Cloern 2001). Previous studies have used such data to demonstrate the
recovery of water quality in coastal systems following nutrient load reduction (Smith
et al. 1981, Lewis et al. 1998, Carstensen et al. 2006), while other studies have
yielded less conclusive results, emphasizing the role of complicating physical and
ecological factors (Kemp et al. 2005, Paerl et al. 2006).
Climatic variability also exerts dramatic influence on estuarine ecological
processes. Fluctuations of freshwater inputs are perhaps the most notable
consequence of climate and may mask the response of a system to nutrient
management (Kimmerer 2002). Freshwater input affects residence time, salinity
distribution, stratification, turbidity, and nutrient loads, all of which influence water
quality and key ecosystem processes. In many estuaries, high freshwater inputs are
associated with reduced dissolved oxygen concentrations and water clarity and
increased phytoplankton biomass and nutrient loads (Malone et al. 1988, Justic et al.
1996, Boynton and Kemp 2000). Elevated freshwater flow can cause reduced
phytoplankton productivity and biomass in other systems by inducing light limitation
or reducing residence time (Cloern et al. 1983, Howarth et al. 2000). River flow
tends to enhance hypoxia directly via increases in vertical stratification and indirectly
via nutrient delivery and stimulation of primary production (Hagy et al. 2004).
Assessing the interactions of nutrient management and freshwater flow is important
77
for improved understanding of estuarine ecology and water quality for effective
eutrophication management in coastal waters.
In one major tributary of Chesapeake Bay, the Patuxent River estuary, point
source nitrogen and phosphorus loads have decreased substantially during the past
two decades (D?Elia et al. 2003). Phosphorus load reductions began with a statewide
ban on phosphate detergents in 1984, with subsequent upgrading of the eight major
sewage treatments facilities in the Patuxent River basin to include phosphorus
removal (Lung and Bai 2003). Reductions in nitrogen loads from sewage treatment
plants in the watershed began in 1990 with the installation of biological nitrogen
removal systems (BNR), in which the final nitrogen transformation is denitrification
(Lung and Bai 2003). Freshwater flow to the estuary has been highly variable during
the last two decades, including several sequential years with large differences in flow
(Lung and Bai 2003). A monitoring program has produced spatially and temporally
resolved water quality data in the estuary since 1985, in addition to measurements and
estimates of nutrient loads and freshwater inflows. The Patuxent River estuary
therefore provides a unique opportunity to evaluate the estuarine system response to
long and short-term changes in water quality in response to nutrient loading and
freshwater inputs (D?Elia et al. 2003, Jordan et al. 2003). Despite the available data
sets, most previous studies have focused on particular aspects of the Patuxent River
estuary?s response to nutrient load reductions (Stankelis et al. 2003, Fisher et al.
2006). A comprehensive analysis of estuarine ecological responses to declines in
nitrogen and phosphorus loading and to variability in freshwater flow remains to be
achieved.
78
The purpose of this chapter is to analyze a multi-decade data set of water
quality variables for the Patuxent River estuary to evaluate temporal trends in relation
to contemporaneous nutrient management and to variations in freshwater flow.
Consequently, this study will discern human impacts on water quality from
hydrologic forcing. We hypothesize that point source nitrogen and phosphorus
management has yielded generally improved water quality conditions in the Patuxent
River (i.e., reduced phytoplankton biomass/productivity, nutrient concentrations, and
respiration, but increased water clarity and dissolved oxygen concentrations), despite
variability in freshwater flow.
Methods
We analyzed data for key water quality variables from stations along the
Patuxent River estuarine salinity gradient for the periods 1963-1970, 1978, 1981, and
1985 to 2003. These data were assembled from unpublished technical reports and the
Chesapeake Bay Monitoring Program. We analyzed the monitoring data (1985 to
2003) to estimate monthly and regional rates of net production and transport of
nutrients, dissolved oxygen (O
2
), and other water quality variables, using a simple
model of salt- and water-balances. We examined relationships between nutrients, O
2
concentrations, and net production rates versus river flow and nutrient loading rates to
evaluate the interacting roles of nutrient management and hydrologic variability in
controlling water quality conditions and ecosystem processes.
79
Water Quality Data
We compiled water quality variables from the Chesapeake Bay Monitoring
Program for the period 1985 to 2003, including inorganic and organic nutrients (NO
3
-
,
NH
4
+
, PO
4
-3
, TN, TP, mg l
-1
), dissolved O
2
(mg l
-1
), chlorophyll a (?g l
-1
), secchi
depth (m), and salinity. NO
3
-
and NO
2
-
are commonly reported as a sum value, with
NO
2
-
usually comprising a minor fraction of the sum; thus we hereafter report the sum
as NO
3
-
. Water samples were obtained from a submersible pump, filtered
immediately, and stored for later analysis. The methods used for chemical analysis
(Table 2.1) undergo routine robust QA/QC reviews. Unpublished data from technical
reports and personal communications were also compiled (Flemer et al. 1970, MD
DNR 1980, Boynton et al. 1981). With a few exceptions, similar methods for field
sampling and chemical analysis were used to generate these data (Table 2.1). The
sampling locations spanned the estuarine axis of the Patuxent River (Fig. 2.1).
Transport and production of non-conservative variables
We calculated net non-conservative production rates of dissolved O
2
,
dissolved inorganic phosphorus (DIP = PO
4
3-
), dissolved inorganic nitrogen (DIN =
NO
3
-
+ NH
4
+
), and dissolved silicate (DSi = SiO
3
2-
) in six regions of the estuary for
each month from 1985 to 2003 using a modification of a previously described salt-
and water-balance model, or ?box model? (Pritchard 1969, Officer 1980). The
boundaries of the estuarine regions, or ?boxes? span the estuarine axis and were
chosen to enclose at least one monitoring station (used to characterize the box) and to
include similar volumes and areas for each box (Fig. 2.1). We analyzed and extended
80
a previously constructed box model (Hagy et al. 2000) to calculate monthly mean
non-conservative fluxes of nutrients and dissolved O
2
in the Patuxent River for the
period 1985 to 2003. This box modeling approach allowed us to compute the time-
dependent, monthly mean physical water transports between regions and across
boundaries for the estuary using data for salinity and freshwater input. The box
model for the Patuxent River estuary calculates advection and mixing between eleven
control volumes, or ?boxes? in the estuary, where the five most seaward boxes
include a surface and a bottom layer (Fig. 2.2). Previous analyses of box models for
this estuary indicate good agreement between computed velocities and mean values
observed as direct measurements from a moored platform (Hagy 1996, Hagy et al.
2000).
The water and salt balance equations follow the general form for box ?m? in a
two-layered, estuarine region (Fig. 2.2). The possible salt exchanges include axial
advective and diffusive exchanges in two directions, vertical advective and diffusive
exchanges, and freshwater input. Thus, the salt balance is described below
V
m
dt
ds
m
= Q
m-1
s
m-1
+ Q
vm
s?
m
? Q
m
s
m
+ E
vm
(s?
m
- s
m
)
+ [E
m-1,m
(s
m-1
- s
m
) ? E
m,m+1
(s
m+1
- s
m
)] (1)
and the water balance is
dt
dV
m
= 0 = Q
m
? (Q
m-1
+ Q
vm
+ Q
fm
) (2)
where V
m
is the volume of the box, Q
m
is the advective transport to the seaward box,
Q
m-1
is the advective transport from the landward box, Q
vm
is the vertical advective
input into the box, Q
fm
is the freshwater input into the box, E
m-1,m
is the diffusive
81
exchange with the landward box, E
m,m+1
is the diffusive exchange with the seaward
box, E
vm
is the vertical diffusive exchange, s
m
is the salinity in the surface layer box,
s
m-1
is the salinity in the landward box, s
m+1
is the salinity in the seaward box, and s?
m
is the salinity in the bottom layer box. The left hand side of equation 1 is computed
as the monthly salinity change, while the left hand side of equation 2 is assumed to be
zero at monthly time scales. Provided a box model with a total of n boxes, the total of
the salt- and water-balances yields 2n equations. To limit the number of unknown
exchanges to 2n, horizontal diffusive exchanges were assumed to be zero, except
between Box 1 and 2 (Officer 1980, Hagy et al. 2000).
The equations used to estimate the advective and non-advective transports for
non-conservative variables (i.e., DIN, DIP, DSi, and O
2
) are similar to the salt
balance equations except salinity is replaced by a non-conservative variable. For
non-conservative variables, the mass balance equations also must include a residual
term. This residual term provides a measure of the net production or consumption
rate (P
m
) of the non-conservative variable. For any surface layer box m in the two-
layer scheme of the box model, the equation is as below.
V
m
dt
dc
m
= Q
m-1
c
m-1
+ Q
vm
c?
m
+ E
vm
(c?
m
- c
m
)
+ E
m+1,m
(c
m+1
- c
m
)
+ E
m,m-1
(c
m
- c
m-1
) - Q
m
c
m
+ P
m
(3)
This above equation is rearranged to calculate the net production rate in the box (P
m
).
P
m
= V
m
dt
dc
m
- Q
m-1
c
m-1
- Q
vm
c?
m
- E
vm
(c?
m
- c
m
)
- E
m+1,m
(c
m+1
- c
m
)
- E
m,m-1
(c
m
- c
m-1
) + Q
m
c
m
(4)
82
Note that E
m,m-1
= 0 for m ? 2, E
m+1,m
= 0 for m ? 1, and E
vm
= 0 and Q
vm
= 0 for m =
1 (Fig. 2.2, 2.3). For any bottom layer box m, the mass balance expression is
V?
m
dt
dc
m
'
= Q?
m+1
c?
m+1
- Q
vm
c?
m
? Q?
m
c?
m
- E
vm
(c?
m
- c
m
)
+ P?
m
(5)
The above equation can be rearranged to calculate P?
m
P?
m
= V?
m
dt
dc
m
'
- Q?
m+1
c?
m+1
+ Q
vm
c?
m
+ Q?
m
c?
m
+ E
vm
(c?
m
- c
m
)
(6)
The variables used in equation 3 through 6 include V?
m
, which represent the
volume of the bottom layer boxes, where the subscript, m, is the box identifier
numbered 1-6 from the landward to the seaward ends, and prime notation indicates
the bottom layer. In addition, c?
m
is the concentration of the non-conservative
material, Q?
m
is the advective fluxes to and from box m in bottom layers, Q
vm
is the
vertical advection from bottom to surface layer, E
vm
is the vertical diffusive exchange
between the surface and bottom layers of box m
,
and
P?
m
is the net production (or
consumption) rate in bottom layers.
O
2
concentrations measured at varying times within the day were adjusted to
daily mean estimates using patterns of diel variability based on continuous sensor
observations at nearby sites (ACT 2005, MD DNR 2005). These estimates of daily
mean O
2
were used in the box model to compute physical transport and net non-
conservative production rates of dissolved O
2
, the latter of which were corrected
(surface layer only) for air-sea exchange. Organic carbon and nutrients do not
exchange significantly with the atmosphere, so similar adjustments do not have to be
made for those variables. We estimated the air-sea O
2
exchange on monthly time
83
scales using the estimated daily mean O
2
values in the top 0.5 m of the water column
following Caffrey (2003):
)/CC - (1 = F
S-
OOO-A
222
? (7)
where ? is the air-sea exchange coefficient (g O
2
m
-2
h
-1
), C
O2
is the adjusted daily
mean O
2
concentration at 0.5 m depth (g m
-3
), C
O2-S
is the O
2
saturation value (g m
-3
).
We used a value for ? of 0.5 g O
2
m
-2
h
-1
, which is a median value measured for
Chesapeake Bay and its tributaries (Kemp and Boynton 1980) and based on published
relationships with wind speed exchange (e.g., Hartman and Hammond 1984, Marino
and Howarth 1993, Caffrey 2003). Analysis of annual variations in wind speed and
direction (observed at the nearby Patuxent Naval Air Station) suggested that, while
there were significant variations in wind velocity on daily to weekly scales, there
were no significant monthly or seasonal trends.
The sum of net O
2
production in the surface and bottom layers provides an
estimate of net ecosystem production (NEP = total system carbon production ? total
system carbon respiration (see Hagy 1996, Howarth et al. 1996). We tested the
sensitivity of calculated surface layer net O
2
production rates to increases and
decreases in ? of ? 50%. Rates varied by 5-10% in the middle and lower estuary
(Box 3-6), where net O
2
production is highest, and by 5-15% in the upper estuary
(Box 1, 2).
84
Hypoxia
We calculated hypoxic volume as the volume of water in the estuary with a
dissolved O
2
concentration less than 2 mg l
-1
. Vertical dissolved O
2
profiles at
monitoring stations (Fig. 2.1) were interpolated to 1-meter intervals and then
extrapolated horizontally at constant depth. The resulting 2-dimensional interpolated
grid (1-meter x 1-nautical mile) was coupled to cross-sectional volumes along the
axis of the Patuxent River (Cronin and Pritchard 1975) to yield volumes of 477 cells
within the estuary. For each sampling date from 1985 to 2003, hypoxic volume was
calculated by summing the volume of the cells with O
2
less than 2 mg l
-1
. The
integrated area under the time series of hypoxic volume for each year is equivalent to
hypoxic volume days, a time-volume integrated value that represents hypoxia (m
3
d
yr
-1
).
Nutrient load and freshwater flow data
We assembled data for daily river flow and total nitrogen (TN) and
phosphorus (TP) inputs to the estuary from a stream gauge (Bowie, MD; USGS 2005)
for the period 1985 to 2003. Monthly averages were computed from daily rates of
river flow and total nutrient inputs to match the time scale of the box model rates and
water quality variables. Data for inputs of TN, TP, and water from sewage treatment
plants were obtained from the Chesapeake Bay Program nutrient input monitoring
program from 1985 to 2003. In addition, we also obtained estimates non-point TN
and TP loads to the Patuxent River above and below Box 2 (Fig. 2.1) produced from
the Chesapeake Bay Watershed Model for the period 1985 to 1997 (Linker et al.
1996).
85
Statistical analyses
We examined temporal trends in water quality data and computed nutrient
production rates from 1985 to 2003 using two approaches. First, we used Model I
linear regressions for the annual means of chlorophyll a, secchi depth, DIN and DIP
concentrations, and net O
2
production. We also performed trend analyses on the
monthly means of the same water quality variables using a Seasonal Kendall test.
The Seasonal Kendall test accounts for seasonality in the data and determines if the
slope of the trend lines was significantly different from zero. We reported Kendall?s
tau values (similar to correlation coefficient), slopes of trend lines, and p-values for
all trends calculated with the Seasonal Kendall tests. We consider significant slopes
to occur when the p-value is < 0.01. Lastly, we removed the effect of river flow from
time series of annual mean chlorophyll a and surface layer net O
2
production in the
upper, middle, and lower estuary by fitting linear regressions to the river flow versus
chlorophyll a and river flow versus net O
2
production relationships, and then analyzed
the residual values of the temporal trends.
Results
Temporal trends in nutrient loading
Point source discharges of total nitrogen (TN) declined by up to 50% (0.75
10
3
kg d
-1
decline above the fall line, 0.5 10
3
kg d
-1
below) after the incorporation of
BNR at sewage treatment facilities in 1990 (Fig. 2.3). Similarly, point source
discharge of total phosphorus (TP) declined sharply in 1986 (> 50% decline) after the
statewide phosphate ban from detergents, as well as sewage treatment plant upgrades.
86
Winter discharges of TN from sewage plants remained high because of seasonally
varying treatment protocols, which was especially evident below the fall line (Fig.
2.3). Total water discharge from all sewage treatment facilities in the watershed
increased steadily since 1985, concomitant with population increases in the watershed
(Fig. 2.3, D?Elia et al. 2003). In the upper estuary, point source loads comprised 50-
60% of total nitrogen loads before BNR, but now comprise only 20-30%. Following
sewage treatment upgrades, declining trends in TN and TP concentrations were
significant (p < 0.01) in the non-tidal freshwater region of the river through 2002
(Fig. 2.4). Elevated TP concentrations in 2003 were associated with sustained high
river flow (Fig. 2.4). Total nitrogen loads from upstream waters into the mesohaline
estuary declined significantly (p < 0.01) after BNR installation. Average declines
approached 100 kg N d
-1
from 1985-2002, but loads are elevated during periods of
high river flow (Fig. 2.4, 2.5). Total phosphorus loads from upstream waters into the
mesohaline estuary declined significantly (p < 0.01) after phosphate removal.
Average declines approached 116 kg P d
-1
from 1985-2002, but loads are elevated
during periods of high river flow (Fig. 2.4). River flow has been higher on average
during the 15 years after BNR (19.2 ? 2.3 m
3
s
-1
) than in the mid to late 1980s (14.4 ?
1.8 m
3
s
-1
) when phosphorus loads were higher (Fig. 2.4). Despite reduced mean
inputs of total nitrogen into the estuary per unit river flow after BNR, the highest flow
and load periods on record occurred episodically after BNR (Fig. 2.5). Total nitrogen
and phosphorus loads from non-point sources were elevated in the 1990s (3.0 ? 0.37
10
3
kg d
-1
) relative to the 1980s (2.0 ? 0.26 10
3
kg d
-1
; Fig. 2.6). Following BNR,
non-point TN and TP loads are similar or higher than point loads to the lower estuary.
87
Temporal trends in water quality
By combining data from individual studies between 1970 and 1980 with the
Chesapeake Bay Monitoring Program data (1985 to 2003 data, CBP 2005), we
analyzed long-term trends in water quality. This analysis suggests that DIP
concentrations increased by 50% and NO
3
-
increased by 10-20% between 1970 and
1980 in the upper and middle estuary, with concentrations observed in the late 1970s
being similar to those in 1985 (Fig. 2.7). Since BNR was established, NO
3
-
concentrations in the middle estuary have been reduced to levels observed in the
1960s, but concentrations in the upper estuary have remained elevated (Fig. 2.7).
Recent (2001-2003) concentrations of NO
3
-
and DIP have not returned to levels
observed in the 1960s in the upper estuary following sewage treatment upgrades (Fig.
2.7). Chlorophyll a values were lower in the 1960s than in the years after 1970 and
annual variability in chlorophyll a was also lower in the 1960s than any other decade
(Fig. 2.7). Trends in chlorophyll a are not significant.
Analyses of data from 1985 to 2003 reveal significant declines (Table 2.2) in
annual mean DIN concentration throughout the estuary (upper, middle, and lower
regions). Mean DIN concentration was 30-50% lower after BNR than before (Fig.
2.8). DIP concentrations also declined in all regions of the estuary in the mid 1980s,
following the phosphate detergent ban and sewage treatment upgrades (Table 2.2,
Fig. 2.8). Declining trends in DIN and DIP concentrations over time were significant
using both Model I linear regressions and the Seasonal Kendall test (Table 2.2).
Although DIN concentrations were elevated to pre-BNR levels during the high river
flow years of the mid-1990s and DIN was positively correlated with river flow in the
88
upper estuary, but not the lower and middle (r
2
= 0.1 - 0.2, p > 0.1; Fig. 2.8), DIP
responded negatively and insignificantly to river flow (p > 0.1; Fig. 2.8). We found a
significant correlation between total sewage nitrogen load and DIN concentration in
the upper estuary (p < 0.01), but not in the middle and lower estuary (Fig. 2.9).
During four mid-1990s years with high river flow (1993, 1994, 1996, 1997), DIN
concentrations in the middle and lower estuary were as high or higher than before
sewage upgrades. DIN concentrations, however, were lower than all previous years
during four years of below average flow (1999 to 2002, Fig. 2.9). For the remaining
years, DIN concentrations in the middle and lower estuary were significantly related
to sewage TN load (p < 0.01).
Chlorophyll a in the lower estuary was generally higher after the
implementation of BNR than before BNR, and two statistical tests indicated positive
trends in all regions of the estuary (Fig. 2.10). Positive trends were significant in the
upper and middle estuary based on the Seasonal Kendall test, but not for the linear
regression (Table 2.2). Lower estuary trends in chlorophyll a were significant at p <
0.1 for the Model I linear regression and p < 0.01 for the Seasonal Kendall test in the
middle and lower estuary (Table 2.2). Trends in mean summer (June to August)
chlorophyll a in the lower estuary were significantly (p < 0.05) positive from 1985 to
2003 (Fig. 2.11). Chlorophyll a was also significantly correlated with river flow in
the middle (r
2
= 0.35, p < 0.01) and lower (r
2
= 0.53, p < 0.01) estuary (Fig. 2.12).
An examination of the time series of the chlorophyll a versus river flow residuals,
which indicates factors controlling chlorophyll a aside from river flow, reveals
concave curves for the middle and lower estuary, with a negative trend occurring
89
during the first 8-9 years of the data set (Fig. 2.12). An increasing trend in the
residuals (i.e., more chlorophyll a than expected from flow) occurred from 1998 to
2003 in all regions of the estuary (Fig. 2.12).
Annual mean secchi depth decreased over time (i.e., water clarity declined) in
all regions of the estuary, which corresponds with the increasing chlorophyll a trends
(Fig. 2.10, see Appendix I). The trends are significant for both the Model I linear
regression and the Seasonal Kendall test in the middle estuary (Table 2.2).
Computations of k
d
, made using an empirical light model for mesohaline-polyhaline
of Chesapeake Bay water (Wu et al. 2005), suggest that chlorophyll contributes more
to light attenuation (20% of total k
d
) than TSS (3 %) in the lower Patuxent estuary.
Trends in mean summer (June-August) secchi depth in the lower estuary were also
significantly negative from 1985 to 2003 (Fig. 2.12). In general, the significance of
trends calculated with the Seasonal Kendall test for monthly mean chlorophyll a and
secchi depth generally agree with significance of the simple linear regression
computed for the annual means of chlorophyll a and secchi depth. We did not
perform a residual analysis for secchi depth because only weak relationships existed
between secchi depth and river flow.
Temporal trends in net O
2
production and biogeochemical fluxes
We found no clear trends of declining surface layer net O
2
production over the
period 1985 to 2003 in the upper (Box 2) and middle (Box 4) estuary. Net O
2
production was 50% higher after BNR than before in the lower estuary (Box 5; Fig.
2.13). Surface net O
2
production appeared to increase in the middle and lower estuary
90
over the 19-year record; however, only the trend calculated with a Model I linear
regression for the lower estuary was significant (Table 2.2, Fig. 2.13). Inter-annual
variability in river flow influenced net O
2
production, with annual mean river flow
explaining 72% of the variability in surface net O
2
production in the upper estuary (p
< 0.01). Relationships were weaker for the middle and lower estuary, as river flow
explained 15% (p = 0.09) and 36% (p < 0.01) of variability in net O
2
production,
respectively (Fig. 2.14). There was no clear trend in the residuals of the relationship
between net O
2
production and river flow for any regions of the estuary, but positive
residuals (i.e., more net O
2
production than expected from river flow) are more
frequent in the lower estuary after BNR than before (Fig. 2.14). Bottom layer O
2
consumption was generally lower (i.e., less negative) in the upper estuary during
years following BNR, but no significant differences were found between the periods.
Bottom layer O
2
consumption was generally higher (i.e., more negative) in the middle
and lower estuary in the post-BNR years, but no significant differences between the
periods were found (Fig. 2.13). Surface layer net O
2
production and bottom layer O
2
consumption were significantly correlated on annual time scales in Box 4 (r
2
= 0.43, p
< 0.01).
No clear trends in annual rates of net production of DIN, DIP, and DSi were
evident from 1985 to 2003 for the surface or bottom layer in all estuarine regions.
Rates of net DSi production in the bottom layer of the middle and lower estuary
appear to be higher in the mid 1990s when freshwater inputs were high and river flow
explained 40% (middle, p < 0.01) and 27% (lower, p = 0.02) of inter-annual
variability. Annual mean rates of surface DIN and DIP net consumption are
91
significantly correlated with annual mean river flow in the middle estuary (r
2
= 0.35,
p < 0.01 and r
2
= 0.49, p < 0.01, respectively).
Trends and controls on Hypoxia
Bottom waters of the Patuxent River estuary often experience hypoxia
between May and September. The extent and severity of hypoxia varied among years
(1985 to 2003) with no trend over time (Fig. 2.15). Temporal integrals for the
volume of hypoxia in bottom waters were significantly correlated with river flow
(Fig. 2.16) for both annual mean flow (r
2
= 0.35, p < 0.01) and spring (February to
May) flow (r
2
= 0.49, p < 0.01). Net bottom layer O
2
consumption rates were also
higher in the hypoxic region of the estuary (Box 3-5) than other regions (Fig. 2.13).
There was no significant response of hypoxia to point source nutrient management, as
hypoxic volumes remained high following sewage treatment upgrades (Fig. 2.15).
Hypoxia correlated with box model-computed physical O
2
inputs (horizontal
advection, vertical diffusion) to the hypoxic region (Box 4, Fig. 2.17).
Discussion
Water quality responses to point source nutrient load reductions have varied in
different regions of the Patuxent estuary. Definitive declines in DIN and DIP
concentrations in all regions of the estuary correspond to declines in sewage plant
nutrient loads (Fig. 2.3, 2.8). The return of nutrient concentrations to near historical
(1965 to 1970) levels in the middle estuary (Fig. 2.7) can also be attributed to
effective nutrient management of point sources. There is evidence that chlorophyll a
and plankton productivity have declined in the tidal fresh region of the river (data not
92
shown), indicating that regions of the estuary in close proximity to point sources are
recovering faster than seaward regions. Submerged aquatic plants in the oligohaline
and tidal fresh Patuxent River increased dramatically after BNR was initiated,
suggesting that water clarity or epiphytic algal biomass declined following sewage
treatment upgrades (Kemp et al. 2005, Fisher et al. 2006).
Similar water quality improvements in response to point source nutrient load
reductions have been reported for other estuarine systems. In the tidal upper Potomac
River, Carter and Rybicki (1986) found increased spatial extent of submerged
macrophytes less than a decade after phosphorus load reductions occurred
concomitant with increased water clarity, although other factors may have also
contributed to improved water clarity (Phelps 1994). In the Neuse River estuary,
phosphorus load reductions to the estuary resulted in measurable declines in annual
mean chlorophyll a in upstream sections of the estuary, but without concomitant
nitrogen load reductions, water quality remained poor in more saline regions of the
system (Paerl et al. 2004). Nutrient management has also been successful in Tampa
Bay, where water quality improvements following nitrogen load reductions allowed
the seagrass population to recover from historical declines (Lewis et al. 1998).
Similar success in achieving nutrient concentration reductions through point source
management (Smith et al. 1981, Carstensen et al. 2006) suggests that this
management tool can be quite effective.
Despite reductions in point source loads to the estuary, nutrient
concentrations and seaward transport rates in the Patuxent were higher during several
years after point source load reductions (Fig. 2.8). High concentrations during 1993,
93
1994, 1996, and 1997 correspond to above-average freshwater flow and associated
elevated non-point nutrient loads (Fig. 2.4, 2.6). In addition, high point source loads
below the fall line in winter probably contributed to high nutrient concentrations (Fig.
2.3). Higher nutrient concentrations are often caused by increased freshwater flow
and the resulting delivery of nutrients to estuarine waters (Boynton and Kemp 2000,
Paerl et al. 2006). High flow contributes to elevated nutrient loads to the Patuxent
River from non-point sources, particularly because non-point loads were 2 to 3 times
higher than point source loads in the 1990s (Fig. 2.7, Boynton et al., in prep). In
addition to elevated non-point loads, BNR is not currently activated in winter months
and consequently, rates of nitrogen loading to the middle and lower estuary in the
1990s and 2000s were higher than before BNR (Fig. 2.3).
Increases in loads from other direct sources of nutrients do not, however, fully
explain high nutrient concentrations during the mid 1990s. Trends in atmospheric
inputs of nitrogen have been stable since the 1980s (NADP 2005), suggesting this
source is not contributing to the observed persistently poor water quality. Although
contributions of groundwater NO
3
-
are significant for some coastal regions (e.g.,
Charette et al. 2001, Pearl 1997), analyses in Chesapeake Bay and its tributaries
suggest that direct inputs of NO
3
-
and freshwater from groundwater are less
significant, ranging from < 5-10% of total inputs (Hussain et al. 1999, Charette and
Buesseler 2004).
Boynton et al. (in prep) computed nitrogen and phosphorus loads to the
estuary from all sources (atmospheric, non-point, point) during 1985 to 1997 and
determined that total annual N loads to the upper estuary were stable and total
94
phosphorus loads had increased over time. This suggests that nutrient input
reductions from point sources had been replaced by non-point nutrient loading during
recent years of above average freshwater inputs (1993 to 1997). Despite these stable
trends, DIP and DIN concentrations throughout the estuary declined substantially by
the late 1990s relative to the 1980s (Fig. 2.8), concomitant with declining nutrient
transport to the middle (Fig. 2.5) and lower estuary (Seaward DIN transport: 2.7 ? 0.3
10
3
kg N d
-1
(1985 to 1990) and 1.7 ? 0.4 10
3
kg N d
-1
(1992 to 2003).
Persistent or increasing non-point loads of total nitrogen and phosphorus may
be responsible for the relative stability of net O
2
production, chlorophyll a, and secchi
depth in the middle estuary after the implementation of BNR at sewage treatment
plants (Fig. 2.6, 2.10, 2.13, Boynton et al., in prep). The magnitude of non-point total
nitrogen load to the upper estuary (above Box 2, ~1500 kg d
-1
) is indeed comparable
to point source loads before BNR and is two times higher than point source loads
after BNR (Fig. 2.4, 2.6). In two regions of the middle estuary (Box 3 and 4), surface
net O
2
production is correlated with total nitrogen loads (point + non-point, 1985 to
1997 data), suggesting that net O
2
production in this region is sensitive to both point
and non-point loads (Fig. 2.18). Similar regressions are not strong in the upper and
lower estuary, indicating that other factors tend to regulate net O
2
production. Spring
(February to April) chlorophyll a also correlates with non-point loads in the middle
estuary (Box 4; r
2
= 0.38, p < 0.05). Because secchi depth is correlated with
chlorophyll a in many regions of the Patuxent and Chesapeake Bay (Appendix I, Xu
et al. 2005) and other systems (e.g., Sanden and Hakansson 1996, Conley et al. 2002),
we would expect parallel responses of these two variables to changes in nutrient load.
95
Non-point source nitrogen and phosphorus management likely needs to improve in
order to allow further improvements in water quality to occur in this estuary.
In many estuaries, freshwater flow indirectly drives plankton productivity by
delivering nutrients and suspended materials and altering residence times (Cloern et
al. 1983, Malone et al. 1988, Paerl et al. 2006). For the Patuxent, a 20% increase in
mean annual river flow (10 m
3
s
-1
)
tends to increase nitrogen delivery to the estuary
by an amount equal to the N removal achieved by full-scale BNR at all sewage
treatment plants in the estuary (Fig. 2.5). As a result, annual mean levels of
phytoplankton biomass and net productivity in the middle and lower Patuxent estuary
correlate strongly with river flow (Fig. 2.12, 2.14). Similar relationships have been
reported for Chesapeake Bay and other temperate estuaries (Sin et al. 1999, Boynton
and Kemp 2000, Paerl et al. 2006). Positive relationships between flow and net O
2
production suggest that flow tends to enhance productivity more than respiration,
which is likely due to higher nutrient inputs (D?Avanzo 1996). In addition, lower
water temperatures, which are often associated with high flow years, tend to reduce
respiration rates (Smith and Kemp 1995, Howarth et al. 1996, Fisher et al. 2006).
Thus, the unexpected increases in chlorophyll a, light attenuation, and net O
2
production that we found in the mesohaline Patuxent estuary must, in part, be driven
by the unusually high freshwater flow in the 1990s, compared to the previous decade
(Fig. 2.4, 2.11, 2.14).
Conversely, flow generally reduced biomass and productivity in the tidal fresh
region of the estuary. Chlorophyll a and spring river flow were negatively correlated
(r
2
= 0.26, p < 0.05) in the tidal fresh region, because flow generally produces
96
increased turbidity and flushing rates, which is the case in upper regions of most
coastal plain estuaries (Kemp et al. 1997, Hagy et al. 2000, Howarth et al. 2000). For
the upper Patuxent, secchi depth was negatively correlated with flow (r
2
= 0.22, p <
0.1) because of higher inputs of suspended material (Appendix I).
Removing the effects of freshwater flow from chlorophyll a and net O
2
production rates permits an analysis of how other forcing variables affect these
ecosystem properties. Initial declines in chlorophyll a residuals from 1985 to 1997 in
all regions of the estuary support the assertion that point source nutrient reductions
did indeed reduce phytoplankton biomass per unit freshwater input (Fig. 2.12). A
distinct reversal of this trend toward more biomass per unit flow from 1998 to 2003 in
the middle and lower estuary suggests, however, that nutrient loading was increasing,
accepting that the phytoplankton community is nutrient limited (D?Elia et al. 1986,
Fisher et al. 2006). Positive residuals in the lower estuary from 1994 to 2003 suggest
other factors, such as increases in local nutrient sources or increased grazing were
sustaining higher phytoplankton biomass than expected from flow. In the middle
estuary, increases in non-point N and P inputs since the mid-1990s may account for
this increasing trend in chlorophyll a and net O
2
production in this region and time
period (Fig. 2.18). Increases in non-point nutrient inputs are not likely to be
important in the lower estuary because the sub-watershed of this region is small (~50
km
2
) compared to the upper estuary (~180 km
2
) and because seaward N and P
transports to the lower estuary were the lowest on record from 1998-2002.
Given the fact that watershed nitrogen inputs to the lower estuary have
generally declined since 1991, it is difficult to explain the contemporaneous increases
97
in chlorophyll a and net O
2
production in this region and time period. Alternative
macronutrients, primarily DIP and DSi, have been declining since 1985 in the lower
estuary (Fig. 2.8). Furthermore, DIN/DSi ratios < 1 and DSi/DIN ratios > 16 in all
seasons (data not shown) suggest that DSi is not limiting phytoplankton growth
(D?Elia et al. 1983, Conley and Malone 1992). DIN/DIP ratios suggest P-limitation
during early spring and N-limitation during summer (D?Elia et al. 1986, Fisher et al.
1992, Fisher et al. 1999). Because the summer is the period of most substantial
chlorophyll a increases since 1985, nitrogen is the relevant nutrient supporting the
phytoplankton biomass increases.
Trends of increasing chlorophyll a and net O
2
production are most significant
in the lower estuary and are not correlated with N or P inputs from point, non-point,
or atmospheric sources (Fig. 2.3, 2.6, 2.10, 2.13). The lower estuary is situated
adjacent to Chesapeake Bay, which is a large and nutrient-enriched system (Kemp et
al. 2005). As computed by the box model, the net input of DIN to the Patuxent from
Chesapeake Bay, has nearly tripled since 1990 and has increased steadily since 1985
(Fig. 2.20). From 1991 to 2003, seaward N transport to the lower estuary declined by
2.5 10
3
kg N d
-1
, while net DIN input from Chesapeake Bay increased by 1.0-1.5 10
3
kg, N d
-1
. DIN inputs from Chesapeake Bay, which enter the Patuxent in the bottom
layer, are transported to surface waters via vertical upwelling and diffusive exchange
to support plankton production. Indeed, vertical DIN inputs to surface waters were
the dominant (or co-dominant) source of DIN to the lower estuary in the mid to late
1990s when seaward inputs were declining and vertical inputs were increasing (Fig.
2.20). During this period, increases in net DIN inputs from Chesapeake Bay co-
98
occurred with increases in net O
2
production and chlorophyll a in the lower estuary
surface layer (Fig. 2.20). Stoichiometric conversions of the upwelling DIN flux to O
2
units (O
2
:C using PQ = 1.0, C:N = 6.625) in the surface layer indicate that vertical
DIN transport was adequate to support 80-100% of net O
2
production in the lower
estuary.
In the lower estuary, annual mean net O
2
production and annual mean net DIN
from Chesapeake Bay were significantly correlated (r
2
= 0.53, p < 0.01), suggesting
that DIN exchange with Chesapeake Bay could be influencing Patuxent water quality
(Fig. 2.21). In addition, summer mean chlorophyll a and mean annual net DIN inputs
from the bay were also strongly correlated in this region (r
2
= 0.50, p < 0.01; Fig.
2.14). Most of the DIN entering the Patuxent River from Chesapeake Bay is
delivered in during May, June, and July (Fisher et al. 2006), and trends of increasing
summer mean chlorophyll a and secchi depth from 1985 to 2003 were more
pronounced than the corresponding trends for annual means (Fig. 2.10, 2.12). In
general, DIN is most limiting for phytoplankton biomass and production in summer
months (Fisher et al. 1992), suggesting the seasonal importance of DIN supplied by
Chesapeake Bay. The importance of nitrogen import from Chesapeake Bay was also
inferred for phytoplankton blooms in other tributary estuaries (Jordan et al. 1991, Sin
et al. 1999), and such results underscore the need to resolve nutrient loads at regional
scales (D?Elia et al. 2003, Paerl et al. 2006).
A number of environmental factors may have contributed to the observed
temporal trends of increasing net input of DIN from Chesapeake Bay. Two potential
mechanisms were considered: (1) increased net DIN advection to the Patuxent from
99
the Bay via increased gravitation circulation and (2) increased net DIN advection to
the Patuxent from the Bay due to an increased DIN concentration gradient from the
Bay to the Patuxent. Although there was no significant trend in advective water
transport from the Bay to the Patuxent during the time period (1991-2003), the
normalized difference in DIN concentration between the Bay and the Patuxent
(?normalized? = DIN
BAY
-DIN
PAX
/(DIN
BAY
+DIN
PAX
)/2) was significantly higher from 1992
to 2003 (0.21 ? 0.02 mg N l
-1
) than from 1985 to 1990 (0.13 ? 0.01 mg N l
-1
) (t = 2.8,
p = 0.013). The net exchange of DIN at the estuary mouth was, in fact, significantly
correlated (r
2
= 0.52, p < 0.05) with this normalized DIN concentration difference
from 1985 to 2003. Furthermore, the non-normalized difference between Bay and
Patuxent DIN was significantly correlated with annual Patuxent River flow (1985 to
2003; r
2
= 0.71, p < 0.01), suggesting an effect of river flow on DIN concentration
gradient and net exchange.
If net inputs of DIN from Chesapeake Bay are contributing to increasing
phytoplankton productivity and biomass in the lower Patuxent estuary, how could
DIN concentrations be declining? Despite declining DIN concentrations and seaward
total nitrogen (TN) transport to the lower estuary, TN concentrations have remained
stable in this region (Fig. 2.19). Given the stable TN concentrations over the last two
decades, declining DIN indicates that concentrations of dissolved and/or particulate
organic nitrogen (DON, PON) must have been increasing. Because DON has been
declining from 1985 to 2003 (slope = -0.0001, p < 0.01), PON concentrations must
have been increasing over this period in the lower estuary. This inferred PON
increase corresponds with observed chlorophyll a increases from 1985 to 2003 (Fig.
100
2.10, 2.11). Thus, trends of decreasing DIN inputs from the watershed and increasing
net DIN inputs from the Bay resulted in a stable pattern of TN concentration in the
lower estuary. The contemporaneous trends of decreasing DIN and increasing PON,
however, require further explanation.
One possible explanation for this shift in the partitioning of TN from DIN to
PON would involve a decrease in grazing pressure on phytoplankton biomass.
Reduced grazing pressure would decrease phytoplankton mortality in the lower
estuary, allowing algal cells to assimilate more DIN. In fact, recent analyses in
Chesapeake Bay, the Patuxent estuary, and other tributaries suggest that the
abundance of the planktivorous ctenophore (Mnemiopsis leidyi) has increased during
the last decade (Purcell and Decker 2005, Breitburg and Fulford, in prep). It appears
that this trend may have, in turn, led to decreased abundance of the herbivorous
copepod, Acartia tonsa, in Chesapeake Bay (Pucell and Decker 2005).
To the extent that these food web changes have occurred in the Patuxent,
increased ctenophore grazing on copepods could have caused a top-down cascade that
favors elevated phytoplankton biomass. Data from a station in the mesohaline
Patuxent River estuary (Box 3, 4; CBP 2005) reveal a 5-fold increase in Mnemiopsis
abundance and biovolume during June, July, and August since 1994, concomitant
with a 5-fold decline in Acartia tonsa concentration (Fig. 2.22). Chlorophyll a has
been stable during June-August at this station and seaward regions despite nutrient
input declines, suggesting that a release of top-down control on phytoplankton could
have occurred. Assuming similar food web changes have occurred in the nearby
lower estuary, reduced grazing may explain why chlorophyll a in the Patuxent has
101
increased more in the months of May, June, and July, the time of year when grazing
is an important control on phytoplankton (White and Roman 1992) and when
gelatinous zooplankton are abundant (Purcell et al. 1994).
To quantify the potential effect of reduced Acartia tonsa abundance on
phytoplankton biomass, we multiplied measured clearance rates (14.5 ml copepod
-1
d
-
1
, Reaugh 2005) by summer copepod concentrations for all years from 1992 to 2003
(CBP 2005). These computations indicate that A. tonsa filtration declined from 15-
20% of the water column per day in summer prior to 1995 to < 1% between 1997 and
2002. At the higher filtration rates, copepods could substantially impact summer
phytoplankton abundance in Chesapeake Bay (Sellner and Kachur 1987). Although
copepods also prey on microzooplankton in summer (White and Roman 1992,
Reaugh 2005) and summer phytoplankton communities are numerically dominated by
phytoflagellates (Marshall and Alden 1990), larger, more edible, phytoplankton
species (e.g., Thalassiosira sp., Gymnodinium sp., and Cyclotella sp.) are also
abundant (CBP 2005).
Hypoxia
A common goal of coastal nutrient management is the elevation of summer O
2
concentrations in bottom waters (e.g., Diaz 2001, Kemp et al. 2005). This goal is
motivated by the fact that hypoxic waters cause physiological stress, growth
reduction, and mortality for many estuarine organisms (e.g., Breitburg et al. 2003).
Previous analyses reported a slightly shortened period of summer anoxic conditions
in the Patuxent River estuary following point source nutrient management (Magnien
102
1999). Our analysis indicates that nutrient management (BNR) has not relieved total
hypoxia in the Patuxent (Fig. 2.15) and Fisher et al. (2006) found no trend in bottom
water dissolved O
2
concentrations in the mesohaline Patuxent. Provided the results of
our analysis of phytoplankton and water quality dynamics over the past two decades,
these trends are not surprising. First, significant correlations between freshwater flow
and hypoxia (Fig. 2.16) imply that the high river flow of the 1990s increased hypoxia
via elevated stratification and nutrient delivery (Boicourt 1992, Hagy et al. 2004).
Secondly, hypoxia was fueled by organic matter derived from stable or elevated
phytoplankton biomass and net O
2
production in the middle and lower estuary, the
regions where bottom water hypoxia occurs (Fig. 2.10, 2.11, 2.13). Lastly, hypoxia
was correlated with physical O
2
inputs (landward advection and vertical diffusion)
from 1985 to 2003 (Fig. 2.17), illustrating how environmental controls can override
management effects on hypoxia (Breitburg 1990, Fisher et al. 2006).
Summary and Conclusions
Our analysis of a time series water quality data and net O
2
production reveals
different responses to nutrient management and climate variability in different regions
of the estuary. Water quality in the upper estuary (above Benedict Bridge) was
generally stable, and in some cases, improved, while water quality conditions
appeared to be stable in middle estuary and degrading in the lower estuary. Thus,
nutrient load reductions have led to improved water quality in regions that are closely
coupled to watershed nutrient inputs, but not in regions that may be influenced by
nutrient inputs from other sources.
103
The degrading trends in water quality that occurred in the lower estuary
provide insight into the interaction of internal and external forces in controlling water
quality. Food web changes may have allowed for increased phytoplankton biomass
via increased predation on copepods by a gelatinous predator. Periods of high
freshwater input to the estuary induced substantial fluctuations in algal biomass and
nutrient concentrations and may obscure the expected benefits of nutrient
management. Net nitrogen inputs to the Patuxent estuary from Chesapeake Bay are
currently similar in magnitude to seaward DIN inputs to the lower estuary and
underscore the need for whole ecosystem restoration and water quality management.
Although the results of this study may not be encouraging to managers
interested in controlling N and P inputs to estuaries, water quality in this system
would surely be degraded beyond what is currently observed if no nutrient
management was in place. This analysis displays the utility of box models to
compute net O
2
production rates and advective and diffusive nutrient transports.
Such rates are quite useful for management-related research and provided critical
information to this analysis.
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Sin, Y., R.L. Wetzel, and I.C. Anderson. 1999. Spatial and temporal characteristics of
nutrient and phytoplankton dynamics in the York River estuary, Virginia:
Analyses of long-term data. Estuaries 22: 260-275.
Smetacek, V., U. Bathmann, E.-M. Nothig, and R. Scharek. 1991. Coastal
eutrophication: Causes and consequences, p. 251-279. In R.C.F. Mantoura, J.-
M. Martin, and R. Wollast (eds.), Ocean Margin Processes in Global Change.
John Wiley and Sons Ltd., New York.
Smith, S.V., W.J. Kimmerer, E.A. Laws, R.E. Brock, and T.W. Walsh. 1981.
Kaneohe Bay sewage diversion experiment: Perspectives on ecosystem
responses to nutritional perturbation. Pacific Science 35: 279-395.
Smith, E.M. and W.M. Kemp. 1995. Seasonal and regional variations in plankton
community production and respiration for Chesapeake Bay. Marine Ecology
Progress Series 116: 217-231.
Stankelis, R.M., M.D. Naylor, and W.R. Boynton. 2003. Submerged aquatic
vegetation in the mesohaline region of the Patuxent estuary: Past, present, and
future status. Estuaries 26: 186-195.
Strickland, J.D.H. and T.R. Parsons. 1968. A practical handbook of sea-water
analysis. Fisheries Research Board of Canada, Bulletin 167. 311 pp.
White, J.R. and M.R. Roman. 1992. Seasonal study of grazing by metazoan
zooplankton in the mesohaline Chesapeake Bay. Marine Ecology Progress
Series 86: 251-261.
Xu, J., R.R. Hood, and S.-Y. Chao. 2005. A simple empirical optical model for
simulating light attenuation variability in a partially mixed estuary. Estuaries 28:
572-580.
110
Sources of Unpublished Data
CBP. 2005. U.S. Environmental Protection Agency. Unpublished data. Chesapeake
Bay Program Office, Chesapeake Bay Water Quality Monitoring Program.
Annapolis, Maryland.
MD DNR. 1980. Maryland Department of Natural Resources. Unpublished data.
Water Quality Monitoring Program. Annapolis, Maryland.
MD DNR. 2005. Maryland Department of Natural Resources. Unpublished data.
Shallow Water Monitoring Program. Annapolis, Maryland.
NADP. 2005. National Atmospheric Deposition Program. Unpublished data. NADP
Program Office. Illinois State Water Survey. Champaign, Illinois.
USGS. 2005. U.S. Geological Survey. Unpublished data. Surface Water Data. Reston,
Virginia.
111
Chesapeake Bay Program Flemer et al. 1970
Variable Analytical Methods Analytical Methods
Dissolved Oxygen
(O
2
)
Determined by a YSI calibrated
periodically with Winkler titrations
Not measured
Ammonium
(NH
4
+
)
Determined by the alkaline phenol
hypochlorite method (EPA 350.1 or
equivalent) using an autoanalyzer
Determined as NO
2
-
after ammonia is
oxidized by alkaline hypochlorite and
excess oxidant destroyed by arsenite
Nitrate plus Nitrite
(NO
2
-
+ NO
3
-
)
Determined as NO
2
-
using the diazo
method with an autoanalyzer (EPA
Method 353.2) with NO
3
-
reduced to
NO
2
-
with cadmium
Determined colorimetrically as NO
2
-
by
diazotizing with sulphanilamide and
coupling with N-(1-napthyl) -
ethylenediamine
Orthophosphate
(PO
4
3-
)
Determined as an antimony-phospho-
molybdate complex, which turns blue
after reacting with ascorbic acid and is
measured colorimetrically (EPA Method
365.1)
Composite reagent method (Strickland
and Parsons 1968)
Active Chlorophyll-a Determined by acetone extraction from a
ground filter followed by
spectrophotometric analysis before and
after acidification
Determined by extraction from a
ground filter followed by flurometric
analysis before and after acidification
(Flurometer was periodically calibrated
with a spectrophotometer)
Table 2.1: Summary of analytical methods used by the Chesapeake Bay Program and
in Flemer et al. (1970) to measure several water quality variables.
112
Method Region Statistics
Regression r
2
Slope p value
DIN Upper 0.47 -0.014 < 0.01
Middle 0.31 -0.008 < 0.01
Lower 0.31 -0.007 < 0.01
DIP Upper 0.51 -0.001 < 0.01
Middle 0.55 -0.001 < 0.01
Lower 0.49 -0.001 < 0.01
Chlorophyll a Upper 0.14 0.359 0.11
Middle 0.08 0.306 0.23
Lower 0.18 0.312 0.07
Secchi Depth Upper 0.27 -0.008 < 0.05
Middle 0.34 -0.017 < 0.01
Lower 0.38 -0.019 < 0.01
Net O
2
production Upper 0.01 -0.080 0.68
Middle 0.06 0.148 0.30
Lower 0.15 0.197 0.10
Seasonal Kendall Tau Slope p value
DIN Upper -0.23 -0.009 < 0.01
Middle -0.36 -0.006 < 0.01
Lower -0.38 -0.007 < 0.01
DIP Upper -0.33 -0.001 < 0.01
Middle -0.34 0.000 < 0.01
Lower -0.34 0.000 < 0.01
Chlorophyll a Upper 0.17 0.276 < 0.01
Middle 0.13 0.227 < 0.01
Lower 0.15 0.175 < 0.01
Secchi Depth Upper -0.17 -0.006 < 0.01
Middle -0.17 -0.013 < 0.01
Lower -0.16 -0.014 < 0.01
Net O
2
production Upper -0.02 -0.102 0.74
Middle 0.02 0.096 0.69
Lower 0.04 0.155 0.37
Maximum carbon fixation Upper -0.16 -1.241 < 0.01
Middle -0.07 -0.943 0.12
Table 2.2: Comparison of trend test results for 1985 to 2003 from linear regression
and Seasonal Kendall models. Significant p-values are those less than 0.05 and are
bold.
113
Figure 2.1: Map of the Patuxent River estuary with Chesapeake Bay (inset), including
box model boundaries and Chesapeake Bay Program water quality monitoring
stations. Chesapeake Bay Program station codes are to the left of each station and
numbers at the right of box model boundaries indicate distance from the mouth of the
estuary (km). Map based upon image in Hagy et al. 2000.
114
Figure 2.2: Schematic description of the box model structure (as seen in Hagy et al.
2000). Included are box model boundaries, exchange coefficients, and inputs. The
estimated exchanges presented in this diagram are seaward advection (Q
m
), landward
advection (Q?
m
), vertical advection (Q
vm
), vertical diffusive exchange (E
vm
), and
horizontal dispersion (E
m+1,m
). Included inputs are the volume of each box and the
salt concentration (not included), river flow (Q
r
), the input of freshwater to each box
(Q
fm
), and the salinity at the seaward boundary (not included).
115
Figure 2.3: Mean monthly inputs of total phosphorus (TP), total nitrogen (TN) and
water (discharge) from all sewage treatment facilities on the Patuxent River from
1985 to 2003. Inputs are presented as discharges released into waters above and
below the fall line. Data are from the Chesapeake Bay Program?s Point Source
Nutrient Database (www.chesapeakebay.net).
116
Figure 2.4: Time series (1985 to 2003) of mean monthly river discharge (top panel),
total nitrogen and phosphorus concentrations (middle panel), and total nitrogen and
phosphorus loading at the USGS gauging station at Bowie, MD (ww.usgs.gov).
117
Figure 2.5: Plot of mean monthly river flow and mean monthly advective total
nitrogen load to the Patuxent River estuary at the fall line (top panel) and at the
landward boundary of Box 2 (bottom panel). Data are from the years 1985 to 2003
and were assembled from USGS river flow and solute gauging at Bowie, MD
(www.usgs.gov) and from box model computed transports. Data are separated as
months before BNR was implemented and months after BNR. The linear fits were
created using all pre- and post-BNR data.
118
Figure 2.6: Time series (1985 to 1997) of non-point source total nitrogen (left panel)
and total phosphorus (right panel) loading to the Patuxent River estuary, above and
below Benedict Bridge, which is located at the seaward boundary of Box 2. Solid
black lines are the annual averages of total load. Data are output from the
Chesapeake Bay Watershed Model for the Patuxent watershed (Linker et al. 1996).
119
Figure 2.7: Box plots of temporal trends (1963 to 2003) of chlorophyll a (top panel),
nitrate (middle panel), and DIP (bottom panel) concentrations in the upper and middle
regions of the Patuxent River estuary. Data are from the Chesapeake Bay Program
Water Quality Monitoring Program (1985 to 2003), The Department of Natural
Resources (1978), and Flemer et al. (1970) (1968 to 1974). Vertical dashed lines
indicate the beginning of BNR implementation (nitrate) and the initiation of
phosphorus removal (DIP) at sewage plants. The top of the boxes indicates the 75
th
percentile, the bottom of the boxes are the 25
th
percentile, the line in the box is the
median, and the error bars are the 10
th
and 90
th
percentile.
120
Figure 2.8: Time series (1985 to 2003) of annual mean DIN (open circles) and DIP
(black diamonds) concentrations in the upper (Box 2), middle (Box 4), and lower
(Box 5) regions of the Patuxent River estuary. Data are from the Chesapeake Bay
Program Water Quality Monitoring Program. Labels of the x-axis indicate the
initiation of phosphorus removal and BNR at sewage plants.
121
Figure 2.9: Correlations between annual mean sewage total nitrogen load below the
fall line and annual mean surface layer dissolved inorganic nitrogen in the upper (Box
2), middle (Box 4), and lower (Box 5) Patuxent River estuary (left panel, 1985 to
2003). Size of circles indicates the relative magnitude of annual mean river flow.
Sewage load data from the Chesapeake Bay Program nutrient input monitoring data
set (www.chesapeakebay.net). Time series of annual mean freshwater input with
circles around years in the wet mid-1990s (1993, 1994, 1996, 1997) and the dry ?99-
?02 (right panel). Dark horizontal line represents 19-year average.
122
Figure 2.10: Time series (1985 to 2003) of annual mean chlorophyll a (left panel) and
secchi depth (right panel) in surface waters of the upper (Box 2), middle (Box 4), and
lower (Box 5) Patuxent River estuary. Data are from the Chesapeake Bay Program
and x-axis labels indicate the beginning of phosphorus removal and BNR at sewage
treatment plants in the watershed. Trend lines are simple linear regressions and
correlation coefficient and p-value are indicated for each region and variable.
123
Figure 2.11: Time series (1985 to 2003) of mean summer chlorophyll a (left panel)
and secchi depth (right panel) in surface waters the lower estuary. Data are from the
Chesapeake Bay Program. Trend lines are simple linear regressions and correlation
coefficient and p-value are indicated for each region and variable.
124
Figure 2.12: Correlations between annual mean river flow and annual mean
chlorophyll a biomass in the upper (Box 2), middle (Box 4), and lower (Box 5)
Patuxent River estuary (top panel, 1985 to 2003). Time series (1985 to 2003) of
residuals (observed ? predicted) of chlorophyll a versus river flow relationship in the
same three regions as above (bottom panel). Dashed horizontal lines indicate the zero
value.
125
Figure 2.13: Time series (1985 to 2003) of surface and bottom layer net O
2
production in the upper (Box 2), middle (Box 4), and lower (Box 5) Patuxent River
estuary. Data are annual means and surface layer net O
2
production is calculated by
adding an air-sea exchange flux to the box model estimate of net O
2
production.
Vertical dashed lines indicate the beginning of BNR implementation and horizontal
dashed lines indicate net O
2
production of zero.
126
Figure 2.14: Correlations between annual mean river flow and annual mean net O
2
production in the upper (Box 2), middle (Box 4), and lower (Box 5) Patuxent River
estuary (top panel, 1985 to 2003). Time series (1985 to 2003) of residuals (observed
? predicted) of net O
2
production versus river flow relationship in the same three
regions as above (bottom panel). Dashed horizontal lines indicate the zero value.
127
Figure 2.15: Time series (1985 to 2003) of hypoxic volume days in the Patuxent
River estuary. The vertical dashed line indicates the initiation of BNR.
128
Figure 2.16: Regression of annual hypoxia (hypoxia = O
2
< 2 mg l
-1
) to annual mean
freshwater inputs and February to May (Spring) freshwater inputs.
129
Figure 2.17: Regression of hypoxic volume with June-August dissolved O
2
inputs
into Box 3 and 4 from landward advection and vertical diffusion (1985 to 2003 data).
130
Figure 2.18: Regression of total nitrogen load (non-point + septic + point loads)
above Benedict Bridge with net O
2
production in the surface layer of Box 3 and Box 4
(middle estuary). Total nitrogen loads for the region above Benedict Bridge are
output from the Chesapeake Bay Watershed Model for the Patuxent River. Data
include the years 1985 to 1997 and are annual means. Trend lines are simple linear
regressions and correlation coefficient and p-value are indicated for each region and
variable.
131
Figure 2.19: Time series (1985 to 2003) of annual mean total nitrogen (TN)
concentrations the upper (Box 2), middle (Box 4), and lower (Box 5) regions of the
Patuxent River estuary. Data are from the Chesapeake Bay Program Water Quality
Monitoring Program.
132
Figure 2.20: Time series (1985 to 2003) of box model computed annual mean net
exchange of DIN between the Patuxent River estuary and mainstem Chesapeake Bay
(top panel). Positive values indicate net input into the Patuxent River estuary. Time
series (1985 to 2003) of the ratio of vertical DIN inputs to horizontal DIN inputs from
upstream to the surface layer of Box 5 (bottom panel). Solid black line indicates a
ratio of one, where horizontal inputs are equal to vertical inputs.
133
Figure 2.21: Regression of annual mean net DIN exchange between the Patuxent
River estuary and mainstem Chesapeake Bay with annual mean net O
2
production in
the surface layer of Box 5 (lower estuary). Trend lines are simple linear regressions
and correlation coefficient and p-value are presented.
134
Figure 2.22: Time series (1991 to 2003) of mean summer (June to August)
Mnemiopsis leidyi biovolume (top panel) and adult Acartia tonsa concentration
(bottom panel) in the middle Patuxent River estuary (Box 3, 4; Chesapeake Bay
Monitoring Station LE1.1).
135
SUMMARY AND SYNTHESIS
The Patuxent River estuary is a well-studied and well-monitored estuarine
system, for which rich water quality databases and numerous biogeochemical rate
measurements over the last 2-3 decades permit the analyses presented in this thesis.
Although monitoring data may be used directly to evaluate trends and make
comparisons between regions and years (Chapter II), I extended the work of Hagy et
al. (2000) to apply a box-modeling approach for transforming data on solute
concentrations into quantitative rates of net biogeochemical production and physical
transport at regional and seasonal scales (Chapter I, II). I use this approach to address
both fundamental scientific questions concerning coupling of ecological interactions
(Chapter I) and applied science questions on ecological responses to nutrient
management (Chapter II). This thesis demonstrates that box models provide a readily
accessible tool that can be used to examine relationships between physical transport
and biogeochemical processing of nutrients, organic carbon, and other non-
conservative substances for the growing number of well-monitored estuarine systems
worldwide.
This approach, however, is not without limitations. The ecological rates one
can calculate with a box model are strictly ?net? rates, that is, they provide an
aggregated sum of many biogeochemical processes into a single rate. In this study of
the partially stratified Patuxent River estuary, a two-layered box model provides
separate rates for surface and bottom layers, where surface rates in the euphotic zone
are generally positive for production of O
2
and negative for production of dissolved
inorganic nutrients, while the opposite is true for aphotic bottom layers.
136
Consequently, the sum of these surface and bottom rates (absolute values) provides a
sense of ?gross? behavior for some biogeochemical processes. In the case of a single
box without vertical separation of layers, however, production and consumption
processes may be of similar magnitude, making the net rates approach zero and
difficult to interpret. A second limitation to the box model approach is the inherent
difficulty in estimating error. Complex box models typically average point
measurements of concentrations over large space and time scales and also calculate
numerous transport rates using the sparse input data. The potential for error in such a
computation may be large, but the general lack of finer scale observations make it
difficult to quantify. In the case of a well-monitored system, like the Patuxent, error
evaluation can be performed for some processes (Chapter I).
On the other hand, the scales at which box models provide computations are
appropriate to address many relevant ecological questions. The key variability for
several important biogeochemical processes (e.g., phytoplankton productivity,
hypoxia, nutrient regeneration) operates at seasonal and regional scales, permitting
the use of box models to explore the controlling factors for these processes. Issues of
ecological responses to climatic forcing and nutrient pollution may also be addressed,
and box models provide a simple and accessible tool for managers to investigate
nutrient transport and exchange.
In Chapter I, a conceptual model of temporal and spatial coupling of nutrient
cycles and primary productivity developed for the Patuxent River estuary (Kemp and
Boynton 1984) was expanded to include how horizontal and vertical transport are
necessary to produce the spring phytoplankton bloom, regenerate the material from
137
the bloom in summer, and deliver regenerated nutrients back to the surface layer.
Longitudinal peaks of phytoplankton biomass in the middle estuary coincide with
similar peaks in nutrient uptake, particulate sinking, nutrient regeneration, and
vertical export to this middle region of the estuary. Kemp and Boynton (1984)
described a Patuxent estuary with a predominantly summer biomass peak, which is
true in the tidal fresh and oligohaline estuary, but waters seaward of river kilometer
40 are characterized by a spring bloom. Thus, despite the transformation of inorganic
nutrients into particulate forms in the oligohaline estuary during spring (as noted by
Kemp and Boynton 1984), large quantities of inorganic nitrogen are still transported
to the middle and lower estuary in spring. Summer productivity is dependent on
nutrient regeneration and export from the bottom layer of the middle and lower
estuary and this regeneration is coupled to the particulate organic carbon deposited to
the bottom layer following the breakup of the spring bloom.
In Chapter II, I explain inter-annual variability in water quality and net O
2
production and the response of the Patuxent estuary to nutrient load reductions from
sewage treatment plants. Although chlorophyll a declined in the tidal fresh region of
the river and nutrient concentrations declined throughout the estuary, chlorophyll a,
net O
2
production, and water turbidity were stable or increased in the mesohaline
estuary. Persistently poor water quality was due, in part, to elevated freshwater inputs
during the latter period of the data set and associated high non-point nutrient loads
from the upper watershed. Degrading water quality in the lower estuary also
correlated with trends of increasing net inputs of DIN from Chesapeake Bay and
declining herbivorous grazing. The practical primary conclusion of this chapter is
138
that further nutrient load reductions will be required (e.g., winter operation of BNR,
improved non-point source management) before substantial water quality
improvements will occur in the mesohaline reaches of the system. Additionally, this
study illustrates that water quality benefits of nutrient management may be masked in
tributary estuaries because of nutrient exchange with nutrient-rich seaward waters.
Fortunately, nutrient load reductions are being pursued throughout the Chesapeake
Bay watershed, which would lead to nutrient declines in the mainstem and the
tributaries and reduce the importance of the Bay as a nutrient source for upper
tributaries.
As was shown in both Chapters I and II, freshwater inputs are an important
forcing function for the Patuxent, as well as other estuarine ecosystems. High
freshwater inputs have many of the same effects of nutrient enrichment, such as
elevated productivity and reductions in water clarity and bottom water O
2
. The
implications of these effects are as follows; (1) the prevailing climatic conditions
must be considered during short term ecological studies in systems such as the
Patuxent estuary to include flow effects in data interpretation, (2) high variability in
freshwater input trends obscure the interpretation of water quality trends attributable
to nutrient management, and (3) predicted increases in precipitation and flow in the
Mid-Atlantic region associated with global atmospheric changes during the next
century might counteract ambitious nutrient management plans.
This thesis has answered many questions concerning the biogeochemistry of
the Patuxent River estuary, but many new questions have been raised in the process.
Tight links were found between the magnitude of surface productivity and bottom
139
respiration in the middle estuary, but why is this vertical coupling not as strong in the
upper and lower estuary? What transport mechanisms decouple surface and bottom
layer processes? Is the sinking of unrespired surface carbon production in the middle
estuary the reason that hypoxia develops in this region each year, or is landward
bottom layer delivery of low O
2
water and labile carbon also important? In respect to
Chapter II, an unequivocal explanation for the increasing phytoplankton biomass in
the lower estuary was not established. In addition, the relative importance of
increased algal biomass (via reduced grazing) causing net import of DIN from the
Bay into the Patuxent versus the net nutrient import causing the algal biomass
increase is unclear. It appears that river flow and physical O
2
inputs control inter-
annual variations in hypoxia more than do changes in phytoplankton biomass or net
O
2
production. Do physical forces control the extent and duration of hypoxia or has
the stable phytoplankton biomass in the middle and lower estuary caused the
persistent hypoxia? Further studies including analyses of box model computations
should help resolve these important questions.
The potential application of the box model technique to aid in the
management of coastal ecosystems has been implied throughout the text of this thesis.
One utility of box models is the conversion of routine hydrologic and water quality
monitoring data to nutrient transport and exchange rates. Such rates permit managers
to measure the extent to which nutrient load reductions in the watershed translate into
nutrient transport reductions along the axis of the estuary (Chapter II). A second and
perhaps more useful approach to box modeling is to estimate box model transport and
production rates needed to meet criteria for total maximum daily load (TMDL)
140
mandates. Such an approach might involve (1) developing empirical relationships
between nutrient loads and box model rates; (2) developing relationships between box
model rates and water quality criteria (e.g., bottom layer O
2
consumption and
hypoxia, Chapter II); and (3) using theses empirical relationships to identify the
maximum nutrient loading needed to maintain water quality conditions within criteria
or standards. These analyses, once developed, have the potential to provide important
information to managers at minimal cost.
141
APPENDIX I
Relationships between chlorophyll a, total suspended solids, and secchi depth
along the estuarine axis of the Patuxent River
We analyzed water quality monitoring data from the Chesapeake Bay
Program (CBP 2005) to evaluate the causative factors driving secchi depth (or light
extinction) in the Patuxent River estuary, MD. We developed relationships between
secchi depth and both total suspended solids (TSS) and chlorophyll a at six stations in
the oligohaline and mesohaline regions of the estuary (Fig. AI.1). We also developed
relationships between 1/secchi depth (~light extinction coefficient = k
d
, m
-1
) and both
total suspended solids (TSS) and chlorophyll a (Fig. AI.2). Station TF1.6 is the most
upstream station, while station LE1.4 is closest to Chesapeake Bay.
We found that a significant fraction (p < 0.05) of the variability in secchi
depth and light extinction is explained by TSS in the oligohaline regions of the
estuary (Fig. AI.1: TF1.6: r
2
= 0.29, TF1.7: r
2
= 0.44, and RET1.1: r
2
= 0.38, and Fig.
AI.2: TF1.6: r
2
= 0.13, TF1.7: r
2
= 0.16, and RET1.1: r
2
= 0.14), but not by
chlorophyll a. Conversely, we found that more of the variability in secchi depth and
light extinction is explained by chlorophyll a in the mesohaline region of the estuary
(Fig. AI.1: LE1.3: r
2
= 0.32, LE1.4: r
2
= 0.34, and Fig. AI.2: LE1.3: r
2
= 0.37, LE1.4:
r
2
= 0.39) than by TSS. The secchi depth versus chlorophyll a relationship in the
lower estuary is negative, which indicates that plankton biomass is attenuating light
more than inorganic/organic solids.
Our results indicate that light attenuation is driven by different factors in the
oligohaline estuary than in the mesohaline estuary. Strong correlations between TSS
142
and secchi depth in the tidal fresh/oligohaline estuary indicate that characteristically
high inorganic solid concentrations in this region are the most important factor
driving light attenuation. As inorganic solid concentrations are lower in the
mesohaline region relative to the oligohaline, plankton biomass is more important in
attenuating light in the mesohaline estuary. The significant correlation between
chlorophyll a and secchi depth in the mesohaline estuary indicates that the temporal
trends in these variables we found in Chapter II are related to each other.
143
Figure AI.1: Correlations between mean monthly secchi depth and total suspended
solids (TSS, left panel) and between secchi depth and chlorophyll a (right panel) at
six stations spanning the tidal fresh (Station TF1.6) to mesohaline (Station LE1.4)
regions of the Patuxent River estuary (see Chapter I, Fig. 1.1 for station location).
Data for all months from 1985 to 2003 (n = 228).
144
Figure AI.2: Correlations between mean monthly 1/secchi depth (~ k
d
, m
-1
) and total
suspended solids (TSS, left panel) and between 1/secchi depth and chlorophyll a
(right panel) at six stations spanning the tidal fresh (Station TF1.6) to mesohaline
(Station LE1.4) regions of the Patuxent River estuary (see Chapter I, Fig. 1.1 for
station location). Data for all months from 1985 to 2003 (n = 228).
145
APPENDIX II
Estimating denitrification using non-conservative fluxes of nitrogen and
phosphorus: Approach and comparison with different methods
Nitrogen cycling is an important component of estuarine biogeochemical
dynamics in that nitrogen is limiting to phytoplankton growth in many coastal
systems (D?Elia et al. 1986). Denitrification is a process where NO
3
-
is used as a
terminal electron acceptor by denitrifying bacteria, resulting in the production of
gaseous forms of nitrogen (N
2
, N
2
O). Because nitrogenous gases cannot be
assimilated by most species of estuarine phytoplankton, denitrification provides an
important sink for excess nitrogen in coastal marine ecosystems (Kemp et al. 1990).
We estimated net denitrification (denitrification ? nitrogen fixation) by
quantifying the deviation of the net TDN production rate from the net TDP
production rate:
)P (P - )P (P k P
DONmDINmDOPmDIPmTDP:TDNmN
2
++=
(1)
where P
N2m
is net denitrification (mmol m
-3
d
-1
), k
TDN:TDP
is the assumed nitrogen-
phosphorus molar ratio of 16, and P
DIPm
, P
DOPm
, P
DINm
, and P
DONm
are the box model
computed net production rates (mmol m
-3
d
-1
) for dissolved inorganic and organic
phosphorus and nitrogen, respectively (Smith et al. 1991). The computation of net
denitrification using the difference between net production rates of TDN and TDP has
been used in previous studies (e.g., Smith et al. 1991) and is specified for
denitrification calculations as part of LOICZ biogeochemical budgets (Land-Ocean
Interactions in the Coastal Zone, Webster et al. 2000). If the amount of nitrogen
released from organic matter oxidation is less than that expected from phosphorus
146
releases, the ?missing? quantity of nitrogen is attributable to loss via denitrification.
Positive values indicate a nitrogen sink, such as denitrification, while negative values
indicate nitrogen fixation. Net denitrification rates are expressed below in units of
?mol N m
-2
h
-1
to simplify comparisons with rate measurements.
Seasonal patterns in box model computed net denitrification rates vary along
the estuarine axis (Fig. AII.1). Net denitrification peaks in early spring and later in
summer in the upper estuary (~150 ?mol N m
-2
h
-1
), but March-May peaks were
found in the middle and lower estuary. Seasonal peak rates of net denitrification were
highest in the upper and middle estuary (Fig. AII.1: 100-180 ?mol m
-2
h
-1
, annual
mean = 75-125 ?mol m
-2
h
-1
). Net denitrification reached seasonal minima of 0-50
?mol N m
-2
h
-1
in July-September in the middle and lower estuary (Fig. AII.1). Mean
integrated net denitrification, averaged over Box 2 to Box 6, was significantly and
positively correlated with mean annual river flow (r
2
= 0.66, p < 0.01, n = 19).
Box model computed rates of net denitrification compare favorably with rate
measurements made in the Patuxent and adjacent Chesapeake Bay (Jenkins and
Kemp 1984, Kemp et al. 1990, Greene 2005). Significant correlations between
denitrification and mean annual river flow suggest that the large amounts of NO
3
-
delivered from terrestrial systems to the estuary during high flow fuels direct
denitrification throughout the estuary (Fig. AII.2) and coupled denitrification in the
middle and lower estuary associated with increased organic matter deposition to
sediments (Nielsen et al. 1995, Kana et al. 1998, Cornwell et al. 1999). River flow,
however, may decrease denitrification by causing reduced bottom water O
2
concentrations (see Chapter II), which might limit coupled nitrification-denitrification
147
(Cornwell et al. 1999). Perhaps the positive effects of NO
3
-
loading on denitrification
offset the negative effects of hypoxia. Seasonal rates of denitrification in the middle
and lower estuary indicate peaks in March-May when river flow and NO
3
-
concentration are high (Kemp et al. 1990, Nielsen et al. 1995) and seasonal minima in
summer when hypoxia develops in the region and low O
2
restricts coupled
nitrification-denitrification (Kemp et al. 1990). Net denitrification remains high in
summer in the upper estuary, where seasonal hypoxia does not develop. Such trends
agree with seasonal measurements of NO
3
-
, denitrification, and dissolved O
2
concentrations in Chesapeake Bay, where low O
2
inhibits nitrification, preventing the
buildup of NO
3
-
substrate for denitrification (Kemp et al. 1990). Rates of
denitrification are also highest in the upper and middle region of the estuary, where
NO
3
-
concentrations are high relative to the lower estuary and where organic matter
sinking to the bottom layer is highest (Fig. AII.2, see Chapter I). Thus, high carbon
sinking in the middle estuary may enhance water column denitrification (Cornwell et
al. 1999).
148
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Figure AII.1: Mean monthly integrated net denitrification (denitrification- nitrogen
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150
Figure AII.2: Correlations between mean annual box model computed net
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standard error of the mean for all data from 1985 to 2003.
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