_ United States
Environmental Protection
\/Qfl Agency
Office of Research and
Development
Washington, DC 20460
EPA/600/R-07/021
March 2007
www.epa.gov
The Relationship Between Land-Based
Nitrogen Loading and Eelgrass Extent
for Embayments in Southern New England:
Initial Model Construction
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EPA/600/R-07/021
March 2007
The Relationship Between Land-Based Nitrogen Loading
and Eelgrass Extent for Embayments in Southern
New England: Initial Model Construction
James S. Latimer, Steven Rego, Giancarlo Cicchetti, Carol Pesch,
Edward H. Dettmann, and Richard McKinney
U. S. Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects Research Laboratory
Atlantic Ecology Division
Narragansett, RI 02882 USA
Michael Charpentier
Computer Sciences Corporation
Narragansett, RI 02882
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Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Disclaimer
This document has been reviewed in accordance with U.S. Environmental Protection Agency
policy and approved for publication. Mention of trade names or commercial products does not
constitute endorsement or recommendation for use.
Abstract
This report outlines research results of the US EPA Atlantic Ecology Division in fulfilling the
National Health and Environmental Effects Laboratory's Aquatic Stressors Nutrient Program's
charge to develop nutrient load-ecological response models useful in setting loading limits
protective of estuarine designated uses. The results reveal that eelgrass extent is significantly
related (r2 = 0.82, p<0.0001) to land-based nitrogen loading when estuarine volume and flushing
time are considered. Once this preliminary model is revised and validated, it can be used by local,
state and tribal resource managers as part of the weight of evidence required to set nitrogen loading
thresholds protective of eelgrass habitat for the class of estuaries defined as southern New England
shallow embayments.
Key Words: New England, eutrophication, estuary, shallow embayment, nitrogen, water quality
criteria, biocriteria, response variability, eelgrass, SAV, Zostera marina
Preferred Citation:
Latimer JS, Rego S, Cicchetti G, Pesch C, Dettmann EH, McKinney R, Charpentier M (2006)
The Relationship Between Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
in Southern New England: Initial Model Construction. Report No. AED-05-107, US EPA/ORD/
NHEERL/Atlantic Ecology Division, Narragansett, RI
AED contribution number AED-05-107.
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Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Table of Contents
Background 1
Aquatic Stressors Research Plan 1
Excess Nutrients as Aquatic Stressors 1
Ecological Response to Excess Nutrients 1
Methods 2
Overview 2
Study Systems 2
Estimation of Nitrogen Loading Rate 5
Flushing Time 9
Determination of Eelgrass Extent 9
Results and Discussion 10
Model Development and Refinement using Physical Characteristics of Estuaries 11
Model Functionality 11
Uncertainty Explained by the Models 12
Conclusions 14
Implementation Issues 15
Activities / Future Research 15
References 16
Appendix 1. Estimation of Flushing Time 19
Appendix 2. Method for Determination of the Spatial Extent of Submerged Vegetation 29
Appendix 3. Nitrogen Load-Eelgrass Extent Model: Assumptions and Limitations 35
iii
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Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Tables
Table 1. CMESC descriptors to define the class of estuarine embayment
for the study systems 3
Table 2. Characteristics of the eelgreass study estuarine embayments 4
Table 3. Nitrogen loading rate (kg N/yr) from watershed sources to each
of the study embayments 8
Table 4. Extent indicators of eelgrass for study systems 11
Table 5. Stastical summary values from a non-linear fit of a power law
function of the loading-response data using different physical variables 13
Figures
Figure 1. A conceptual diagram of some of the processes that affect
the health and extent of eelgrass 2
Figure 2. Study systems for the aerial eelgrass survey 2
Figure 3. Schematic diagram of the nitrogen loading model (NLM) 6
Figure 4. Listing of equations, variables, and magnitudes used in the NLM 7
Figure 5. Comparison of nitrogen loading rate calculated using
the NLM and SPARROW models 8
Figure 6. Relationship of eelgrass shoreline segment to bed area 10
Figure 7. Graphical representations of the relationships between nitrogen load and eelgrass extent
(sum of shoreline segments, m) 13
Figure 8. Graphical representations of the relationships between nitrogen load and eelgrass extent
(as % of total embayment perimeter) 14
iv
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Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Background
Aquatic Stressors Research Plan
The National Health and Environmental Effects
Research Laboratory's (NHEERL's) Aquatic
Stressors research implementation plans focus on
the effects on estuaries of four specific stressors:
habitat alteration, nutrients, suspended and bedded
sediments, and toxic chemicals (EPA 2002a).
This focus is consistent with recent scientific
consensus, recognizing that these stressors have
the greatest potential for causing adverse effects
to aquatic ecosystems (EPA 2002a). The ultimate
goal of the Aquatic Stressors research is to develop
scientifically valid approaches for protecting aquatic
ecosystems from the impacts of these stressors and
restoring those systems that have been degraded.
The immediate focus is to develop and improve
assessment methodologies, diagnostic capabilities,
and ecological criteria to guide management options
for protection, restoration and remediation efforts to
meet designated uses.
Excess Nutrients as Aquatic Stressors
It is well established that human activities have
dramatically changed the amounts, distributions,
and movements of major nutrient elements (nitrogen
and phosphorus) in the landscape and have increased
nutrient loading to receiving waters (Howarth et
ah 2002). Some of these changes affect use of the
nation's aquatic resources and pose risks to human
health and the environment (NRC 2000). EPA is
in the process of developing guidelines that states
and tribes can use to set nutrient criteria for the
nation's waters. For waters failing to meet water
quality standards, states and tribes are required to
develop total maximum daily loads (TMDLs) to
eliminate the causes of non-attainment. Our current
understanding of aquatic ecosystem function is
inadequate to allow extrapolation of relationships
between nutrient load and ecological effects for
systems with extensive data to prediction of adverse
impacts in those with more limited data. NHEERL
research will provide the load-response relationships
for classes of estuaries around the country that will
facilitate the translation of effects-based numeric
criteria to nutrient loading limits that are protective
of aquatic life.
Ecological Response to Excess
Nutrients
Ecological responses to excess nutrients generally
fall into two categories: primary and secondary
(Cloern 2001). The primary response is an increase
in algal production (or carbon supply as defined by
Nixon (1995)) and/or shifts in the algal community
composition at the base of the food web. Secondary
responses include increases in extent and duration
of hypoxia, losses of submerged aquatic vegetation
including eelgrass, and changes and losses of
biodiversity including changes in fish abundance
and species composition.
Seagrasses enhance estuarine habitats by acting
as food sources for consumers and by providing
sediment enrichment through decomposition,
stabilization and erosion control. Seagrasses are
highly valuable nurseries and forage grounds for
many kinds of fish, shellfish and wildlife, and are
considered one of the most productive ecosystems in
the world (Duarte 2001). They provide both food and
shelter from predators for a variety of shallow water
nekton (Orth et ah 1984). These habitats are areas
of high organic matter production, typically between
500-1000 g C m2/yr, and their plant morphology and
growth complexes create micro-habitats for nekton
(Hoss & Thayer 1993). Benthic and pelagic diversity
is also higher in seagrass habitats than in other habitat
types (Hughes et ah 2002). In recent literature
reviews, researchers found that fish abundance
increased with seagrass extent (Beck et ah 2001,
Heck Jr. et ah 2003). Seagrasses are an important
location for attachment of juvenile bivalves such as
the bay scallop (Argopecten irradians) and the hard
clam (Mercenaria mercenaria), and these habitats
provide protection for these juveniles (Orth et ah
1984). Although juvenile shellfish and fish species
can use other types of habitat, seagrasses provide the
bulk of shelter in many estuaries, and the loss of these
vegetated habitats may produce declines in juvenile
fish (Wyda et ah 2002). Fragmentation of vegetated
habitats can also have important implications for
species such as the bay scallop, since patchy seagrass
beds (with a high edge to interior ratio) can lead to
enhanced rates of predation on juvenile scallops
(Irlandi et al. 1995).
1
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Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
A
Nitrogen From
Watershed
^^^»Nitrogen From Light from
Atmosphere Sun
Nitrogen From
Sea
Sediment Processess
_ Nitrogen From
Atmosphere
Nutrien-
Re ge near at ion
Nitrogen From
Watershed
Figure 1A. Conceptual diagram of some of the
processes that affect the health and extent of eelgrass.
B. Simplified conceptual diagram utilized for the
comparative systems empirical approach.
ri i
AHR «
CT
I
BSR«
0 10 20 30 40
Kilometers
|.\j* KISM
Figure 2. Study systems for the 2002 aerial eelgrass
survey.
Anthropogenic loading of nutrients has been
implicated in the loss ofseagrasses( Short et ah 1995).
The major cause of seagrass decline is not thought
to be via nitrate toxicity, but rather through light
limitation by planktonic, macro-algal or epiphytic
algal shading (Duarte 1995, Hauxwell et ah 2001,
Hauxwell et ah 2003) (Figure 1A). Geochemical
processes that are associated with high organic
loading such as hypoxia and sulfide production in
the sediment have also been shown to affect the
health of seagrasses (Eldridge et ah 2004).
There is a strong consensus among estuarine
ecologists that excess nitrogen, not excess
phosphorus, is the main cause of eutrophication in
most estuaries (Howarth & Marino 2006). Therefore,
in this document we report preliminary relationships
between watershed-based nitrogen loading rate and
eelgrass {Zostera marina) extent in southern New
England shallow embayments. Additional reports
are forthcoming on the relationship between nitrogen
loading rate and primary productivity and benthic
condition in similar coastal embayments.
Methods
Overview
The development of empirical models that relate
nitrogen load to ecosystem response is based on a
comparative systems approach in which loading and
ecological responses are determined for a number
of study embayments along a nitrogen load gradient
within one class of estuary. The program is divided
into three major components: 1) determination of
nitrogen loading rate to coastal embayments from
the watershed and atmosphere; 2) assessment of
ecosystem response (in this case eelgrass extent)
and 3) application of normalizing factors to refine
the load-response models. Figure IB describes
the components of the conceptual model that
are measured or estimated using the empirical
comparative systems approach.
Study Systems
Sixteen (16) estuarine embayments, reflecting a
gradient of watershed-derived nitrogen loading in
southern New England, were evaluated in this study
(Figure 2).
2
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Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
The study estuaries are considered to be representa-
tive of a specific class, one that will be defined using
the Coastal and Marine Ecological Classification
Standard (CMECS) conceptual classification
scheme (Madden et al. 2005). The CMECS is an
ecosystem-oriented, science-based framework for
the identification, inventory, and description of
coastal and marine habitats and biodiversity. The
study systems fall into the following Level 1 and
2 CMECS Hierarchical Classification categories:
Level 1 Regime: Estuarine, Level 2 Formation-
Geoform: Embayment. Thus, at the highest class
levels of the CMECS, the study systems are defined
as estuarine embayments. In this context, estuaries
are defined as enclosed or semi-enclosed coastal
water bodies that are influenced by fresh water input
that reduces salinity to below 30 psu during at least
two months of the year. Moreover, an embayment is
an estuary that is partially enclosed or surrounded by
a landmass but that has a significant open connection
to the sea (Madden et al. 2005).
The CMECS scheme includes additional chemical,
physical, biological and biogeographic descriptors
to further refine the upper level class designations.
Table 1 contains the descriptors and the applicable
values for the study systems of this research
summary. Table 2 provides some additional specific
data for the individual embayments. For simplicity,
throughout this report, we will refer to the class
of estuaries under study as southern New England
shallow embayments. Nevertheless, the reader
should keep in mind the additional components of
the study systems noted in Table 1.
Table 1. CMECS descriptors to define the class of estuarine embayment for the study systems
(Madden et al. 2005).
Descriptors
Magnitudes
Chemical
Temperature Class:
Salinity Class:
Oxygen Class:
Turbidity Class:
Turbidity Type:
Turbidity Provenance:
Physical
Energy Type:
Energy Intensity:
Energy Direction:
Depth Class:
Tide Class:
Primary Water Source:
Enclosure Status:
Biological
Trophic Status:
Ecological
Region:
Additional1
Embayment Size:
Watershed Size:
Ecoregions:
Geographic:
Cold (0-10°C) to temperate (10-20°C)
Mesohaline (5-18 psu) to euhaline (30-40 psu)
Variable (anoxic, hypoxic, oxic, saturated, supersaturated)
Moderately turbid (2-4 m)
Mixed (chlorophyll, mineral, colloidal, dissolved color, detrital)
Mixed (allochthonous, autochthonous, resuspended, terrigenous, marine)
Wind/tide/current
Moderate (moderate currents and wave action, 2-4 kn)
Mixed
Very shallow (0-5 m)
Small (0.1-1 m) to moderate (1-5 m) tidal range
Watershed, local estuary, local marine (non-river dominated)
Partially-enclosed (50-75% area encircled by land)
Oligotrophic (<5 ug Chl-a/L) to eutrophic (>50 ug Chl-a/L)
Eight (8); Virginian Atlantic Region. The region extends along the eastern North
American continent from Cape Hattaras northward to Cape Cod. The region lies within
the temperate limatological zone, and is interposed between the east coast and the
Northern Gulf Stream Transition Region offshore (Region 9).
Small (0.1 km2) to medium (6 km2)
Small (0.5 km2) to medium (73 km2)
Northeastern Coastal Zone and Atlantic Coast Pine Barrens (Shirazi et al. 2003)
Southern New England Region (CT, RI and southeastern MA coastal)
'These descriptors are important in further refining the class of estuarine embayments included in this report.
3
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Table 2. Characteristics of the eelgrass study estuarine embayments.
Direct
Total
Embayment
Flushing
WWTF
Embayment
Volume2
Watershed
Time
Embayment
State
ID
Larger Systems
Sources
Area1 (km2)
(trfx 1()•*)
Area (km2)3
Wf
Branford Flarbor
CT
BHC
Long Island Sound
Yes
1.41
1,450
71.3
3.27
CI arks Cove
MA
CCM
Buzzards Bay
No
2.79
10,700
7.08
4.11
Cuttyhunk Pond
MA
CPM
Buzzards Bay-Outer
No
0.40
622
0.50
2.15
Falmouth Inner Harbor
MA
FHM
Nantacket Sound
No
0.12
301
1.58
1.46
Katama Bay
MA
KBM
Atlantic Ocean
No
5.88
10,900
9.58
5.26
Lagoon Pond
MA
LPM
Nantucket Sound
No
2.12
7,690
12.6
3.75
Little Bay
MA
LTM
Buzzards Bay
No
0.88
710
10.4
2.80
Mattapoisett Harbor upper
MA
MFIM
Buzzards Bay
No
2.81
8,190
73.2
4.11
Menemsha Pond
MA
MPM
Vineyard Sound
No
2.76
6,130
4.20
4.09
Onset Bay
MA
OBM
Buzzards Bay
No
2.59
3,840
11.8
4.01
Tarpaulin Cove
MA
TCM
Vineyard Sound
No
0.78
3,420
1.88
2.69
Vineyard Haven-Inner
MA
YHM
Vineyard Sound/Harbor
No
0.18
673
0.84
1.66
Allen Harbor
RI
AHR
Narragansett Bay
No
0.31
913
4.62
1.99
Bonnet Shores
RI
BSR
Narragansett Bay
No
0.69
3,120
3.72
2.58
Easton Bay
RI
EBR
Narragansett Bay
No
2.00
8,130
15.6
3.68
Kickamuit River
RI
KRR
Narragansett Bay
No
2.24
4,380
20.1
3.82
WWTF = wastewater treatment facility
1 Determined using GIS analysis: Total Embayment Area: Existing coastline data were used as a starting point to delineate the embayments. For
each embayment, a bounding line was chosen to define the terminal extent of the embayment. For some embayments, a bounding line was also
chosen for the upstream extent of the embayment. Seaward boundary was defined using shoreline features and best professi onal judgment. The
area of water was summed within each embayment to arrive at the total embayment area. Additional metadata are available from first author.
2 Estimated using GIS analysis and bathymetry data (USGS Mylar 7.5 minute quad maps). Additional metadata are available from first author.
3 Determined using GIS analysis: Watershed area without embayment: Existing watershed data for each embayment were augmented by delineations
performed on-screen using USGS topographic sheets as backdrops. Once the data layers were completed, the Arclnfo software generated area
values. The area of the embayment itself was not included in this value. Additional metadata are available from first author.
4 See text and appendix 1 for methodology.
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Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Estimation of Nitrogen Loading Rate
Watershed and atmospheric derived nitrogen
loading rates were estimated for each of the study
embayments using a modification of a published
nitrogen loading model (NLM) (Valiela et al. 1997)
(Figure 3). As originally constructed, the model
predicts total dissolved nitrogen loads to shallow
embayments forrural and suburban watersheds where
the watersheds are underlain by unconsolidated
sands and groundwater flow is the dominant method
of transport. The original NLM includes nitrogen
inputs from wastewater (via septic systems, using
values for per capita contributions of nitrogen),
fertilizer use on turf and agriculture, and atmospheric
deposition (estimated from regional data). We
have modified the model to include inputs from
wastewater treatment facilities (WWTFs) for those
embayments when present. Equations in the NLM
depict attenuation of nonpoint source nitrogen during
passage through the different landcover types on a
watershed (i.e., natural vegetation, turf, agriculture,
and impervious surfaces) and losses during travel
through the soils, the vadose zone, and the aquifer
(Figure 3). WWTF inputs to the embayments are
not attenuated and are computed from point source
effluent monitoring data. The attenuation rates
used in each component of the watersheds were
from published empirical measurements (Valiela
et al. 1997). The estimates derived from the NLM
reflect the sum of the attenuated nitrogen loading
from each source (wastewater, fertilizer, and
atmospheric deposition) to produce an estimate of
the total dissolved nitrogen entering the receiving
embayment.
Figure 4 provides a listing of the assumptions,
calculations, and variables used in the model and
Table 3 provides the estimated nitrogen loading rate
to the study embayments used for this report. Ocean-
derived nitrogen loading rates were not included in
the estimates. Loads ranged from 1,050 - 42,000 kg
N/yr. Normalizing the loading to embayment area
yields a calculated loading range of 23.5 - 330 kg
N/ha/yr; this is similar to other shallow embayments
in the region (5.3 - 407 kg N/ha/yr (Flauxwell et
al. 2003)) and comparable to, although on the high
side of, estuaries nationwide (1.0 - 49.0 kg N/ha/yr
(Castro et al. 2003)).
The nitrogen loading estimates from the NLM were
compared to those derived from the USGS Spatially
Referenced Regression on Watersheds (SPARROW)
model (Moore etal. 2004) to assess correspondence.
The loading output from the NLM compared
favorably with output from the SPARROW model
when applied to a set of common study systems
(Figure 5A; r2 = 0.97, n = 17). Even when the
highest loaded system is excluded, the fit is quite
good (Figure 5B, r2 = 0.73, n = 16). The SPARROW
model calculates nitrogen and phosphorus delivery
to the coastal environment, in a very different way
from the NLM, by estimating the delivery of total
nitrogen and accounting for in-stream losses based
on the travel time and stream flow for each stream
reach within the continental United States (Smith
et al. 1997, Moore et al. 2004) So the fact that the
two models, each with very different conceptual
frameworks, provide comparable estimates suggests
that they may reflect something of the actual nitrogen
inputs to the embayments.
The land use model has been evaluated for the
uncertainty of estimated loading values (Collins
et al. 2000). Using parametric (propagation of
error estimates) and nonparametric (bootstrap and
enumeration of combinations) statistical techniques
on 8 of the 16 input variables, and testing them on one
estuarine system, it was estimated that the standard
error of the single value estimate (i.e., N load kg N/
yr) was ± 14% and the 95% confidence interval (CI)
was 73-127%. The authors recommend, however,
that a better assessment of the uncertainty of the
loading estimate is accomplished by estimating the
central tendency in an inferred population. This
will give the worst case estimate of uncertainty
and, according to the authors, better reflects the
heterogeneity of watershed characteristics. This
estimate was the standard deviation of the population
distribution. Its value was ± 38% with the 95% CI of
25-175%. These estimates of uncertainty in loading
should be considered by Regional, State and Tribal
authorities in the sphere of planning, management,
and risk assessment.
5
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N Source
Atmospheric Deposition (w+d)
N Source
Fertilizer Application
i
1
Watershed Surface ;
1
~ -
1
+ ¦
r i
1
_ j
r i
T
Natural Vegetation
65% retained in plants & soil
Turf
62% retained in plants & soil (atmos)
39% lost as gases (fertilizer)
Agriculture/Horticulture
62% retained in plants & soil (atmos)
39% lost as gases (fertilizer)
Impervious - roofs/driveways
62% retained in plants & soil
Impervious - roads/lots/runways
0% retained in plants & soil
35% tran
sported 38% tran
61 % transportec
sported (atmos)
(fertilizer) 6
38% tran
1 % transporte
sported (atmos) 38% tran
i (fertilizer)
sported 100% tran:
; ported
Watershed Subsurface
Vadose Zone
61% lost
39% tran sported
N Source
Wastewater
I
Aquifer Zone
35% lost
66% transported
Septic Tank/Leach Field
40% lost
60% tran .ported
^
Septic Plumes
34% lost
65% tran sported
WWTF
Marine Embayment
Figure 3. Schematic diagram of the nitrogen loading model (NLM) with WWTFs (wastewater treatment facility) included; modified from
(Valiela et al. 1997); W+D = wet and dry atmospheric deposition; WWTF data were derived from reported monitoring data (sources: CT,
Paul Stacey (CT DEP); RI, Scott Duerr (City of Westerly), Scott Nixon (Univ. RI); MA, Brian Friedman, Russell Isaac (MADEP)).
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Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Input Category
Included Landuse Types
Nitrogen Load
Atmospheric Deposition:
Natural Vegetation
forests, wetlands, natural lands
Atmos. Dep.1
(kg N ha/yr)
¦2 x Area
(ha)
Turf
lawns, golf courses
Atmos. Dep.
(kg Nha/vr)
x Area
(ha)
Agricultural Land
crop land
Atmos. Dep.
(kg N ha/yr)
x Area
(ha)
Impervious Surfaces: roofs,
driveways
roofs, driveways
ATMOS. DEP.
(kg N ha/vr)
x Area
(ha)
Impervious Surfaces: roads,
runways, parking lots
roads, runways, parking lots
Atmos. Dep.
(kg N ha/yr)
x Area
(ha)
Fertilizer Application: ^ ^
lawns, golf courses
Appl. Rate.3
(kg N ha/yr)
x Area x Fn
(ha) (fract)
Agricultural Land
crop land
Appl. Rate.
(kg N ha/yr)
x Area - N rem4
(ha)
Human Septic Wastewater5:
residential land
(kg N person/ yr) x (persons per
house) x (# of houses)
[Rainfall nitrate]:
[Rainfall ammonia]:
[Rainfall dissolved organic N]:
[TDN]:
Ave Annual Rainfall:
Wet to Total Deposition Factor:
% atmos N transported from Nat'l Veg Soils:
% atmos N transported from Turf Soils:
% atmos N transported from Agr. Soils:
Median Home Size:
No of stories/home:
House footprint area:
Average area of roof:
Average area of driveway:
% atmos N transported from Impervious Soils (roof/driveway):
Fertilizer N applied to lawns:
Fertilizer N applied to agriculture:
Fertilizer N applied to rec/golf courses:
Average lawn area:
% of homes that use fertilizer:
% of fertilizer N transported from Turf Soils:
% of fertilizer N transported from Agri Soils:
% of fertilizer N transported from Rec. Soils:
Per capita human N excretion rate:
People per house:
% waste transported from septic tank/leach fields:
% waste transported from septic plumes:
% watershed N transported from vadose zone:
% N transported from aquifer:
# of houses in high density residential areas:
# of houses in medium-high density residential areas:
# of houses in medium density residential areas:
# of houses in medium-low density residential areas:
# of houses in low density residential areas:
Figure 4. Listing of equations (top section), variables, and magnitudes (bottom section) used in the NLM.
Data sources: (Valiela el cd. 1997, Luo el cd. 2002). Example landuse categories are for RI. References/Notes:
'Uses the concentration of N03", NH4+, and DON in local precipitation and yearly rainfall totals to generate
the atmospheric deposition term. 2Model also includes dry deposition (i.e., from NOx, particles adhering to
leaves and impervious surfaces), which is adjustable as a proportion of wet deposition. 3Uses average fertilizer
addition rates for Cape Cod of 105 kg N ha/yr for lawns and 115 kg N ha/ yr for golf courses. FN = "0.34"
refers to the fraction of homeowners applying fertilizer (applies to lawns only). 4The term "N rem" allows
for nitrogen removed from the watershed as crops (i.e., consumed outside of the watershed); assumed zero
in current estimates. 5As published, the NLM applies to nonpoint sources; however, some of the estuaries
contain municipal point source inputs. The magnitude of these inputs was estimated from effluent monitoring
data. In these cases, if point source inputs were significantly greater than estimated nonpoint source inputs,
then nonpoint sources were not included.
270 ug N/L
920 ug N/L
180 ug N/L
1370 ug N/L
123.4 cm
125
35%
38%
38%
179 sq m
2
89 sq m
0.00996 ha
0.01254 ha
38%
104 kg N/ha
136 kg N/ha
115 kg N/ha
0.05 ha
34%
61%
61%
61%
4.8 kg N/pp/yr
2A
60%
66%
39%
65%
8
6
1.33
0.667
7
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Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Table 3. Nitrogen loading rate (kg N/yr) from watershed sources to each of the study embayments
(after attenuation through watershed).
Embayment
ID
Watershed
Wastewater
Atmo spheric F ertilizer
Point Atmospheric Total Nitrogen
Sources Deposition to Load to
(WWTF) Water Surface Embayment1
Branford Harbor
BHC
12,300
11,800
8,860
17,600
2,980
41,000
Clarks Cove
CCM
19,700
1,570
2,640
0
5,910
29,800
Cuttyhunk Pond
CPM
179
9
16
0
843
1,050
Falmouth Inner Harbor
FHM
3,060
418
388
0
264
4,130
Katama Bay
KBM
3,170
951
1,470
0
12,400
18,030
Lagoon Pond
LPM
3,460
1,430
1,520
0
4,490
10,900
Little Bay
LTM
2,230
1,460
1,280
0
1,850
6,820
Mattapoisett Harbor upper
MHM
6,740
10,700
5,680
0
5,930
29,050
Menemsha Pond
MPM
536
233
61
0
5,840
6,670
Onset Bay
OBM
9,100
1,620
1,050
0
5,480
17,200
Tarpaulin Cove
TCM
5
181
0
0
1,650
1,830
Vineyard Haven-Inner
VHM
573
120
81
0
389
1,160
Allen Harbor
AHR
1,440
529
1,570
0
664
4,210
Bonnet Shores
BSR
4,830
493
576
0
1,460
7,350
Easton Bay
EBR
29,130
3,460
5,340
0
4,230
42,200
Kickamuit River
KRR
12,870
3,220
7,720
0
4,730
28,500
WWTF = wastewater treatment facility (direct discharge to embayment)
'Total is the sum of all land-derived nitrogen sources. In the cases where WWTF inputs are significant (in bold), the
total wastewater input is considered solely from WWTFs; otherwise watershed wastewater inputs are used.
o
x
B)
450"
300-
250-
150-
y = 0.89UX + 7720 r = 0.967
100 150 200 250 300 350 400 450 500
120-
100-
80-
60-
40-
20-
0'
~
/f B
^ ~
*
y = 0.938x + 5820 r2 = 0.729*™
0
20
40
60
80
100
120
140
SPARROW (kg N/yr x 103)
Figure 5 A. Comparison of nitrogen loading rate calculated using the NLM and SPARROW models for
embayments in which both models provide estimates (includes additional systems from a larger study, open
symbols represent systems in this report). B. Is the comparison with the highest loaded system removed.
Dashed line represents 1:1 correspondence between the two models. ***p<0.01.
8
-------
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Flushing Time
Flushing time is the time required to exchange
water out of an entire defined water body (Monsen
et al. 2002). We computed the flushing time for
each of the study systems using an empirical model
that used literature-derived flushing time data
and morphological characteristics for estuarine
embayments that are similar to those in this study
(see Appendix 1 for a detailed description of the
methodology). The morphological variables
evaluated include area, length, depth, and volume.
Both linear and nonlinear (power law) regressions
were used to assess which morphological
characteristic best fit the flushing time data. The
correlation of embayment area to flushing time
was better than for the other morphological
characteristics (see Appendix 1).
Flushing time is expected to be important in
increasing the variance explained by the derived
load-response models (Dettmann 2001, Kelly 2001,
Latimer & Kelly 2003). The calculated flushing
times for this study, derived by using the results of
the power law equations, ranged over a factor of 3.5,
from 1.5 - 5.3 days (Table 2). Finally, it should be
noted that the flushing times are approximations of
the average conditions - the actual flushing times
are time variant and are a function of a number of
factors (e.g., tidal forcing, wind, etc.) that are not
explicitly included. In addition, the approximations
can only be applied for systems that are similar to
those used to develop the algorithm.
Determination of Eelgrass Extent
The primary purpose of this study was to develop
an empirical relationship between the extent of
eelgrass (Zostera marina) and the amount of
nitrogen input to shallow estuarine embayments in
southern New England. During fall 2002, aerial
imagery was collected in 16 study embayments from
the Connecticut, Rhode Island, and Massachusetts
shoreline.
The intent of characterizing ecosystem response
using an empirical approach was to assign a single
number describing eelgrass extent for each of these
embayments. This differs from the usual goals of
seagrass habitat assessment, which are generally
focused on the scale of individual eelgrass beds
rather than on whole-system responses among many
embayments. Traditional habitat assessments look
for subtle changes in the health of seagrass beds
from one year to another, focusing on changes in bed
density or bed area. For this study, comparability
between whole embayments (over three states) is
more important than detailed assessments of any
one, or complex of beds, in a particular embayment.
In summary, rapid methods of data acquisition
and analysis are preferred because the purpose is
to characterize eelgrass extent for entire estuarine
embayments over a large geographic area. Thus our
methods are based on 1) rapid aerial data acquisition;
2) minimal image processing; 3) a metric of extent
based on linear adjacent-to-shoreline segment
length, rather than bed area; and 4) the reference of
image data to existing larger-scale georectifications
(as opposed to georectification of each individual
image). See Appendix 2 for additional details of the
methodology.
The original sample design included imagery
collection from 38 embayments; however some
systems could not be flown due to FAA restrictions,
and data from other systems were unattainable due
to technical shortcomings. Additional systems will
be included in future surveys.
A camera system was mounted downward-looking
through a port in a Cessna Skymaster fixed wing
aircraft. Target flight altitude was 1000 feet, and
speed was generally maintained at 100 knots. Flights
covered as much of the coastline as possible in
straight-line transects, since excessive maneuvering
of the aircraft resulted in added effort in post flight
image analysis. Aerial imagery was collected from
Oct 10th through Oct 31st 2002. As weather and
surface chop dictated flight times, some concessions
were made in desired conditions. Sampling and
analysis protocols followed the procedures outlined
byNOAA's Coastal Ocean Services Coastal Change
Analysis Program (Dobson et ah 1995, Finkbeiner
et al. 2001).
Images were acquired and analyzed using off-the-
shelf image software, reviewed for representa-
tiveness, tiled into mosaics, and analyzed using
GIS software. A set of existing digital orthophoto
9
-------
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
images was obtained as an aid in reference scaling
the flight-derived images (USGS NAPP 2003,
MassGIS 2005, RIGIS 2006). Eelgrass beds were
identified in images based on vegetative structure
and growth morphologies reported in the literature
(Costa 1988).
Eelgrass extent was delineated using a simplified
linear measure of the shoreline that had eelgrass
present, rather than the more traditional areal extent.
This method is useful when efficient assessment
of the total amount of eelgrass in a large number
of embayments is required. Under conditions
of excess nitrogen individual eelgrass beds may
lose area from the deepwater edge (Orth & Moore
1983) which may not be visible in aerial images.
In addition, embayments may first lose eelgrass
at the head of the estuary where nitrogen loading
is greatest (Costa 1988). Since the need is to
quantify vegetation extent at the scale of the entire
embayment, the shoreline segment measurement
is an appropriate ecosystem response measure for
eelgrass.
In order to validate our linear-eelgrass-extent
indicator, an existing digital map of eelgrass beds in
Massachusetts (MassGIS 2005) was used to examine
the relationship of the linear measurement (length
of shoreline adjacent to eelgrass beds, subsequently
called shoreline segment) to the more traditional
bed area measure in 23 Massachusetts embayments
(12 of the embayments were identical to the current
study systems, 11 additional embayments were
selected to increase sample size) (Pesch et ah
Submitted). Shoreline segment was measured and
summed by embayment. A significant relationship
between shoreline segment and area of eelgrass
beds was observed (r2 = 0.96). This relationship
followed a strong power law functionality (Figure
6). Therefore, we concluded that shoreline segment
provides a good first-order assessment of extent
of eelgrass for an entire embayment (Pesch et ah
Submitted).
Results and Discussion
Eelgrass extent results are reported in Table 4. In
these systems, eelgrass extent (eelgrass shoreline
segment) ranged from 55 m - 4500 m (excluding
non-detectable eelgrass). Expressed as a percentage
of shoreline, eelgrass extent ranged from < 0.5% to
95%.
ra
a>
ra
TS
a>
.Q
ra
u>
0)
LU
10,000,000
1,000,000
100,000
10,000
1,000
100
10
Ur$
¦ nfiti
n 1.32
y = 6.27x
r2= 0.96
10
100 1,000 10,000
Shoreline segment (m)
100,000
Figure 6. Relationship of eelgrass shoreline segment to bed area, for whole
embayments, for 23 Massachusetts embayments (Pesch et al. Submitted).
10
-------
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Table 4. Extent indicators of eelgrass for study systems.
Eelgrass Shoreline
Segment
System Name
Length, m
% of Shoreline
Historical Presence
Branford Harbor
55
0.3%
Yes
Clarks Cove
2,570
43%
Yes
Cuttyhunk Pond
1,880
55%
Yes
Falmouth Inner Harbor
228
9%
No data
Katama Bay
2,150
10%
Yes
Lagoon Pond
3,370
30%
Yes
Little Bay
327
4%
Yes
Mattapoisett Harbor
4,470
36%
Yes
Menemsha Pond
3,420
26%
Yes
Onset Bay
1,100
5%
Yes
Tarpaulin Cove
2,460
95%
Yes
Vineyard Haven - Inner
687
49%
Yes
Allen Harbor
0
0%
Yes
Bonnet Shores
0
0%
No
Easton Bay
0
0%
No
Kickamuit River
421
3%
Yes
'Based on assessment of historical maps and extant records C. Pesch EPA, personal communication.
Model Development and Refinement
using Physical Characteristics of
Estuaries
Empirical relationships (models) were developed
between eelgrass extent and derived nitrogen
loading rates using data from all embayments that
had observable eelgrass. The calculated annual
nitrogen loading rate was normalized using a suite
of physical variables. These variables (embayment
area, volume, and flushing time) were expected to
be important factors contributing to the sensitivity
of estuaries to anthropogenic nitrogen (Biggs et ah
1989, NRC 2000). We determined the extent to
which these variables could be used to explain and
reduce the variance in eelgrass response to nitrogen.
As expected, one result of this analysis shows
that there is little or no significant relationships
between eelgrass extent and simple annual nitrogen
loading rate (kg N/yr) to the embayments (Table
5, Figures 7A & 8A). However, eelgrass extent is
significantly related to nitrogen loading rate when
total embayment volumes or flushing times are
taken into consideration (Figures 7C, D and 8C,
D and Table 5). The extent of eelgrass, expressed
as a percentage of shoreline (Figure 8), was most
highly related (r2 = 0.82 p<0.0001) when both
flushing time and volume were used to normalize
nitrogen loading rate. (Figure 8 D). These results
are reasonable since the nitrogen load is likely to
be processed according to the volume and flushing
time of the embayments. Thus, those systems that
have greater volumes and/or shorter flushing times
will be able to either dilute, or export, the nitrogen
and have less ability to stimulate phytoplankton or
epiphytes which ultimately shade eelgrass.
Model Functionality
After exploring linear and non-linear functions, the
best-fit of the data was observed using the power
law functional relationship in Figure 8 D (nonlinear
curve fitting SAS PROC NLIN). This implies a
threshold-type behavior of eelgrass to loading rate.
The observed data reveal a precipitous drop in
eelgrass extent over a narrow loading window, above
which eelgrass drops off more slowly. Flauxwell
and others observed similar behavior for eelgrass
in the Waquoit Bay embayment system (Flauxwell
et al 2003). In that study, eelgrass bed area was
observed to drop off at a loading of 30 kg N/ha/
yr, with complete disappearance at 60 kg N/ha/yr.
These thresholds fall into a similar range as those for
our models (i.e., translated into volume and flushing
11
-------
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
time loading rates, the equivalent is estimated to be
between 13 and 24 mgN/nr). Thus, our observed
load-response models yield similar responses to
nitrogen, using more systems in the same class of
estuaries, but along a larger geographic area.
Uncertainty Explained by the Models
If eelgrass extent were solely, or mainly, related
to nitrogen loading rate from the watershed and
atmosphere, then all of the variance would be
explained by the constructed models. While the
variance explained by the model, in which nitrogen
is normalized to volume or flushing time, is striking,
there is still approximately 20% of the variance not
explained. This may be due to other factors that
affect eelgrass ecology that are unrelated to nitrogen
inputs, e.g., suitable substrate, lack of seed stock due
to historical wasting disease, wave action and current
conditions, depth, etc. In addition, the unexplained
variance can be caused by the variability inherent
in the estimation of the variables that comprise the
constructed models, i.e., eelgrass extent, nitrogen
loading rate, flushing time, and volume. For
example, the static nature of the nitrogen loading
rate estimations, which are based on landuse data
from the mid-to-late 1990s, may not be appropriately
compared to the direct measures of eelgrass extent
in 2002. Furthermore, oceanic inputs of nitrogen
were not considered at all. Explicit consideration of
light limitation may improve the variance explained
by the model. Further work needs to be done to
address these issues. Nevertheless, the relationship
is significant even with the assumptions and
limitations of the study.
While the best-fit power law model (Figure 8D)
explains approximately 80% of the variance in the
data, this statistic alone does not mean that all of
the uncertainty associated with the construction of
the model has been quantified. Appendix 3 catalogs
the assumptions and limitations of the model
components. It is important that anyone using the
preliminary model consider exactly how the model
was derived and the assumptions and limitations
upon which it was based. There are two aspects of
the model that need to be highlighted here: revision
and validation. The model will be revised based on
at least two more years worth of eelgrass response
data for the embayments noted in this report as
well as additional embayments in the same class.
In addition, the model will be validated by using
eelgrass data from embayments not used for the
construction of the empirical model. In this way,
the model may be assessed for its ability to predict
eelgrass extent for other southern New England
embayments in the same class.
There were three embayments - Allen Harbor, Bonnet
Shores, and Easton Bay - that had no detectable
eelgrass. We suspect that Allen Harbor is a system
that receives more nitrogen than the NLM depicts,
and thus is more degraded than would be expected
from the model predictions. The harbor has a large
number of floating docks and moorings that host a
great number of boats during the boating season.
These boats are potential sources of additional
nitrogen as well as direct carbon inputs from sewage.
In many parts of the harbor the sediment is highly
organic and suffers from severe periodic anoxia in
the summer (Cicchetti et ah 2006). These factors
suggest that Allen Harbor is too heavily loaded with
nitrogen to support eelgrass - however, this is not
known with certainty. Bonnet Shores and Easton
Bay are two other systems that had no detectable
eelgrass. Moreover, there is no evidence that these
systems ever had eelgrass present (Table 4). They
have the following characteristics: high wave action
due to their geographic orientation (SSE and S), high
mouth openness, and a hard-sand/rocky substrate
characterizes much of the benthic environment (G.
Cicchetti EPA, personal communication). For these
reasons it is understandable that eelgrass would
not be able to flourish. Therefore, Allen Harbor,
Bonnet Shores, and Easton Bay were not used in the
development of the load-response models.
12
-------
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Table 5. Statistical summary values (r2, a, and b) from a non-linear fit of a power law function of the load-
response data when loading is normalized using different physical variables.
Variable used to
Normalize Load
Loading Rate
Units
Figure
Eelgrass Shoreline Segment length (m)
No normalization
kg N/yr
0.008
1070
-0.056
7A
Embayment area
kg N/ha/yr
0.230
10900
-0.440
7B
Embayment volume
g N/mVyr
0.346
2650
-0.389
7C
Embayment flushing time/volume
mg N/m3
0.176
4120
-0.260
7D
Eelgrass as % of shoreline
No normalization
kg N/yr
0.415
1690
-0.473
8A
Embayment area
kg N/ha/yr
0.331
552
-0.742
8B
Embayment volume
g N/mVyr
0.688*
52.5
-0.817
8C
Embayment flushing time/volume
mg N/m3
0.819*
280
-0.793
8D
*significant at p<0.001
cn
w
CD
_D)
(D
HI
A
-
~
~
~
1 1 1
y = 1070.1x"° 0559
r2= 0.008
~
~ ~
1 1 1 1
~
~
t*c , , ,
~
1 1 1 i
0
10 15 20 25 30 35
Loading Rate (un-normalized, kg N/yrx103)
40
45 0
5,000-
4,500-
4,000-
3,500-
3,000-
2,500-
2,000-
1,500-
1,000-
500-
0-
~
~
~
B
2651,8x
0.346
5 10 15 20 25
Loading Rate (normalized to estuarine volume, g N/m3/yr)
~
D
y = 10935x
r2= 0.230
~
~
~
~
~
-hO—0-h-
100 150
~
~
y = 4118.4x
r2= 0.176
~
0 50 100 150 200 250 300
Loading Rate (normalized to estuarine area, kg N ha/yr)
350 o
-i0-
100 125 150 175 200 225 250 275
Loading Rate (normalized to estuarine volume and flushing time, mg N/m3)
Figure 7. Graphical representations of the relationships between nitrogen load, normalized with important
physical variables, and eelgrass extent (sum of shoreline segments, m): A un-normalized load, B normalized
to embayment area, C normalized to embayment volume, and D normalized to flushing time/volume. Open
symbols indicate embayments with no detectable eelgrass (these were not included to derive equations).
13
-------
Land-Based Nitrogen Loading and Eeigrass Extent for Embayments
100
90-
80-
70
60-
50
40
30-
20-
10
A
y= 1692.6 x"u
r2= 0.415
~
~
o O1-^-
~
4>
A,
y = 52.5139 x"u
r2= 0.688
~
~
~
. J—
~
0
10 15 20 25 30 35
Loading Rate (un-normalized, kg N/yrx103)
40
45 0
5 10 15 20 25
Loading Rate (normalized to estuarine volume, g N/m3/yr)
-0.7418
551.9 x
r - 0.331
D
y = 280.1 x"L
r2= 0.819
0 50 100 150 200 250 300
Loading Rate (normalized to estuarine area, kg N/ha/yr)
350 0 25 50 75 100 125 150 175 200 225 250 275
Loading Rate (normalized to estuarine volume and flushing time, mg N/m3)
Figure 8. Graphical representations of the relationships between nitrogen load, normalized with important
physical variables, and eeigrass extent (as % of total embayment perimeter): A un-normalized load, B nor-
malized to embayment area, C normalized to embayment volume, and D normalized to flushing time/volume.
Open symbols indicate embayments with no detectable eeigrass (these were not included to derive equations)
only C and D were significant, p<0.001.
Conclusions
The derived relationships, or models, presented
in this report provide evidence to environmental
managers that eeigrass extent is quantitatively related
to nitrogen loading inputs (properly normalized),
and can aid in the development of critical nitrogen
load rate limits protective of eeigrass habitat. The
nitrogen load-eelgrass response model needs to
be considered preliminary, because it is based on
data obtained in only one year. We plan to collect
data over multiple years to improve the robustness
of the model and to assess inter-annual and inter-
system variability. In addition, we are planning
validation steps to determine the ability of the model
to predict eeigrass extent for embayments not used
for model development. Nevertheless, the model is
compelling. Moreover, it is similar to that found
for embayments of Waquoit Bay on Cape Cod,
Massachusetts (Hauxwell et ah 2003) but greatly
extends the geographic area of applicability.
Our results provide the following insights relevant
to state and national efforts to derive biocriteria and
nitrogen load limits for coastal embayments:
~ Data for system-level ecological response
variables (e.g., eeigrass extent), can yield
useful nitrogen load-response models through
an empirical multiple-system comparative
approach when important physical variables
are incorporated as normalizing factors (e.g.,
volume, flushing time).
~ The load-response model derived in this study
corroborates eeigrass response behavior reported
elsewhere in the literature.
~ Eeigrass extent, measured as length along the
shoreline and expressed as percent of shoreline,
is a useful system-wide metric for derivation
of nitrogen loading rate limits across multiple
embayments within the same estuarine class.
14
-------
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
~ Eelgrass extent for shallow embayments along the
southern New England coast is highly sensitive
to land-derived inputs of nitrogen (including
atmospheric inputs).
~ Load-response models can be used in a
management context to derive critical loading
limits protective of estuarine water quality.
Implementation Issues
The preliminary nitrogen load-eelgrass extent model
derived in this study should be understood as a model
that estimates what nitrogen levels are necessary
to produce water quality conditions favorable for
eelgrass. The model is based on empirical data
(measured eelgrass extent) and associated estuarine
nitrogen inputs and normalization factors (volume
and flushing time). Further, the model is based on
a system-level metric of existing eelgrass extent
in relation to nitrogen inputs; it does not provide
information about the trajectory of loss, or about
eelgrass recovery. Eelgrass recovery may follow
a different trajectory than loss, because eelgrass
creates an environment favoring its growth, while
the lack of eelgrass may create an environment that
is less hospitable. Eelgrass extent may not increase
even with the estimated nitrogen load reductions
due to water quality factors other than nitrogen
loading (e.g., temperature effects), sediment quality,
lack of seed availability, or rhizome expansion, and
more.
We emphasize that the eelgrass segment indicator is
method that is only useful at the whole embayment
scale (i.e., a system-level metric); assessment of
individual beds using this indicator is inappropriate.
The assessment of eelgrass extent using this metric
is not meant to be used for overall state eelgrass
habitat assessment, rather only in the context of
predictions based on the empirical load-response
model. Management officials should be aware
that eelgrass segment based biocriteria may be less
sensitive to changes in nitrogen inputs than other
more traditional assessment methods (bed area)
or newer assessment methods (bed density). The
eelgrass segment method is suitable, at the system
level, to determine improvement or degradation as
predicted from the empirical model.
Activities I Future
Research
The following are a list of additional activities that
will refine and improve the current model:
~ Continued ground-truthing activities for eelgrass
and other submerged aquatic vegetation;
~ Additional compilation of data on eelgrass
extent to evaluate inter-annual and inter- system
variation;
~ Evaluation of other classification factors for use
in minimizing variance in the load-response (e.g.,
light limitation as expressed as area or volume of
embayment above a critical depth (2-3 m));
~ Evaluation of othervariables that affect vegetation
distributions such as habitat characteristics;
~ Evaluation of other system level eelgrass
indicators (e.g., aerially derived eelgrass
density);
~ Evaluation of the magnitude and effect of
nitrogen loading from oceanic sources;
~ Validation of the load-response model using
embayments not used to develop the preliminary
model;
~ Development of similar models for other classes
of estuary.
15
-------
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
References
Beck M.W., Heck Jr. K.L., Able K.W., Childers
D.L., Eggleston D.B., Gillanders B.M., Halpern
B., Hays C.G., Hoshino K., Minello T.J., Orth
R.J., Sheridan P.F., Weinstein M.P. (2001) The
identification, conservation, and management
of estuarine and marine nurseries for fish and
invertebrates. Biosci 51:633-641.
Biggs R.B., DeMoss T.B., Carter M.M., Beasley
E.L. (1989) Susceptibility of U.S. estuaries to
pollution. Rev Aquat Sci 1:189-207.
Castro M.S., Driscoll C.T., Jordan T.E., Reay W.G.,
Boynton W.R. (2003) Source of nitrogen to
estuaries in the United States. Estuaries 26:803
- 814.
Cicchetti G., Latimer J.S., Rego S.A., Nelson W.G.,
Bergen B.J., Coiro L.L. (2006) Relationships
between near-bottom dissolved oxygen regimes
and sediment profile camera measures. J Marine
Syst 62:124-141.
Cloern J.E. (2001) Our evolving conceptual model
of the coastal eutrophication problem. Mar Ecol
Prog Ser 210:223-253.
Collins G. , Kremer J.M., Valiela I. (2000)
Assessming uncertainly in estimates of nitrogen
loading to estuarines for research, planning, and
risk assessment. Environ Manage 25:635-645
Costa J.E. (1988) Eelgrass in Buzzards Bay:
Distribution, Production, and Historical Changes
in Abundance. Report No. EPA 503/4-88-
002, U.S. Environmental Protection Agency,
Washington DC.
Dettmann E.H. (2001) Effect of water residence
time on annual export and denitrification of
nitrogen in estuaries: a model analysis. Estuaries
24(4):481-490.
Dobson J.E., Bright E.A., Ferguson R.L., Field
D.W., Wood L.L., Haddad K.D., III H.I., Jensen
J.R., Klemas V.V., Orth R.J., Thomas J.P (1995)
NOAA Coastal Change Analysis Program (C-
CAP): Guidance for Regional Implementation.
Report No. NOAA Technical Report NMFS
123, Department of Commerce, NOAA.
Duarte C.M. (1995) Submerged aquatic vegetation
in relation to different nutrient regimes. Ophelia
41:87 - 112.
Duarte C.M. (2001) The future of seagrass meadows.
Environmental Conservation 29:192-206.
Eldridge P.M., Kaldy J.E., Burd A.B. (2004)
Stress response model for the tropical seagrass
Thalassia testudinum: the interactions of light,
temperature, sedimentation, and geochemistry.
Estuaries 27:923-937.
EPA (2002a) Aquatic Stressors Framework and
Implementation Plan for Effects Research.
Report No. EPA 600/R-02/074, US EPA, Office
of Research and Development, National Health
and Environmental Effects Research Laboratory,
Research Triangle Park, NC.
Finkbeiner M., Stevenson B., Seaman R. (2001)
Guidance for Benthic Habitat Mapping: an
Aerial Photographic Approach. Report No.
NOAA/CSC/20117-PUB, U.S. NOAA Coastal
Services Center, Charleston, SC.
Hauxwell J., Cebrian J., Furlong C., Valiela I. (2001)
Macroalgal canopies contribute to eelgrass
(Zostera marina) decline in temperate estuarine
ecosystems. Ecol 82:1007-1022.
Hauxwell J., Cebrian J., Valiela I. (2003) Eelgrass
Zostera marina loss in temperate estuaries:
relationship to land-derived nitrogen loads and
effect of light limitation imposed by algae. Mar
Ecol Prog Ser 247:59-73.
Heck Jr. K.L., Hays G., R.J. O (2003) Critical
evaluation of the nursery role hypothesis for
seagrass meadows. MarEcolProg Ser 253:123-
136.
16
-------
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Hoss D.E., Thayer G.W. (1993) The importance
of habitat to the early life history of estuarine
dependent fishes. American Fisheries Society
Symposium 14:147-158.
Howarth R.W., Marino R. (2006) Nitrogen as the
limiting nutrient for eutrophication in coastal
marine ecosystems: evolving views over three
decades. Limn Ocean 51:364-376.
Howarth R.W., Sharpley A., Walker D. (2002)
Sources of nutrient pollution to coastal waters
in the United States: implications for achieving
coastal water quality goals. Estuaries 25:656-
676.
Hughes J.E., Deegan L.A., Wyda J.C., Weaver M.J.,
Wright A. (2002) The effects of eelgrass habitat
loss on estuarine fish communities of southern
New England. Estuaries 25:235-249.
Irlandi E., Ambrose W.J., Orlando B. (1995)
Landscape ecology and the marine environment:
How spatial configuration of seagrass habitat
influences growth and survival of the bay scallop
Oikos 72:304-313.
Kelly J.R. (2001) Nitrogen Effects on Coastal Marine
Ecosystems. In: Follett R.F., Hatfield J.L. (eds)
Nitrogen in the Environment: Sources, Problems
and Management. Elsevier, p 207-251
Latimer J.S., Kelly J.R. (2003) Proposed
Classification Scheme for Predicting Sensitivity
of Coastal Receiving Waters to Effects of
Nutrients. Report No. AED-03-04-001, US
EPA/ORD/NHEERL.
Luo Y., Yang X., Carley R.J., Perkins C. (2002)
Atmospheric deposition of nitrogen along the
Connecticut coastline of Long Island Sound:
a decade of measurements. Atmospheric
Environment 36:4517-4528.
Madden C.J., Grossman D.H., Goodin KL. (2005)
Coastal and Marine Systems of North America:
Framework for an Ecological Classification
Standard: Version II, NatureServe, Arlington,
VA.
MassGIS (2005) 1:5,000 Color Ortho Imagery.
Monsen N.E., Cloern J.E., Lucas L.V., Monismith
S.G. (2002) A comment on the use of flushing
time, residence time, and age as transport time
scales. Limn Ocean 47:1545 - 1553.
Moore R.B., Johnston C.M., Robinson K.W., Deacon
J.R. (2004) Estimation of Total Nitrogen and
Phosphorus in New England Streams Using
Spatially Referenced Regression Models.
Report No. Scientific Investigations Report
2004-5012, United States Geological Survey.
Nixon S.W. (1995) Coastal marine eutrophication:
a definition, social causes, and future concerns.
Ophelia 41:199-219.
NRC (2000) Clean Coastal Waters: Understanding
and Reducing the Effects of Nutrient Pollution,
Vol. National Academy Press, National
Academy of Sciences, Committee on the Causes
and Management of Eutrophication, National
Research Council, Washington, DC.
Orth R.J., Kenneth L., Heck J., Montfrans J.V.
(1984) Faunal communities in seagrass beds: a
review of the influence of plant structure and prey
characteristics on predator: prey relationships.
Estuaries 7:339-350.
Orth R.J., Moore K.A. (1983) Chesapeake Bay: an
unprecedented decline in submerged aquatic
vegetation. Science 222:51-52.
Pesch C.E., McGovern D.G., Rego S., Cicchetti
G., Latimer J.S. (Submitted) Proposed use of
shoreline length adjacent to Eelgrass (Zostera
marina) beds as a first order measure of Eelgrass
extent at the embayment scale in New England.
Environ Manage.
RIGIS (2006) Rhode Island Geographic Information
System.
Shirazi M. A., Johnson C.B., Omernik J.M., White D.,
Haggerty P.K., Griffith G.E. (2003) Quantitative
Soil Descriptions for Ecoregions of the United
States. J Environ Qual 32:550-561.
17
-------
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Short F.T., Burdick D.M., Kaldy J.E. (1995)
Mesocosm experiments quantify the effects
of eutrophication on eelgrass, Zostera marina.
Limn Ocean 40:740-749.
Smith R.A., Schwarz G.E., Alexander R.B. (1997)
Regional interpretation of water-quality
monitoring data. Water Resour Res 33:2781-
2798.
USGS NAPP (2003) National Aerial Photography
Program. United States Geological Survey.
Valiela I., Collins G., Kremer J., Lajtha K., Geist
M., Seely B., Brawley J., Sham C.H. (1997)
Nitrogen loading from coastal watersheds to
receiving estuaries: new method and application.
Ecol Appl 7:358-380.
Wyda J.C., Deegan L.A., Hughes J.E., Weaver M.J.
(2002) The response of fishes to submerged
aquatic vegetation complexity in two ecoregions
of the Mid-Atlantic Bight: Buzzards Bay and
Chesapeake Bay. Estuaries 25:86-100.
18
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Appendix 1
Estimation of Flushing Time
Empirical Estimation of Water Residence Times
of Non-Riverine Embayments
Edward H. Dettmann
December, 2002
Revised July 2006
U.S. Environmental Protection Agency
Office of Research and Development
National Health and Environmental Effects Research Laboratory
Atlantic Ecology Division
27 Tarzwell Drive
Narragansett, Rhode Island 02882
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Introduction
This study is a component of the Aquatic Stressors
Nutrient Program at the Atlantic Ecology Division
of the National Health and Environmental Effects
Research Laboratory of the U.S. Environmental
Protection Agency's Office of Research and
Development. This program is developing response
relationships between nutrient loading to estuaries,
and ecological responses such as reductions in
dissolved oxygen concentrations, loss of seagrasses,
and shifts in the composition or functioning of food
webs. Preliminary consideration of factors that
affect estuarine response to nutrient loading has
determined that water residence time in estuaries
plays a crucial role in this regard.
The purpose of this study is to develop a method
for empirically estimating water residence times for
small to medium-sized estuaries in southern New
England, and to use this method to estimate the
residence time of the estuaries being studied by the
Nutrient Project.
The approach taken in this study was suggested
by an analysis of data for a group of eleven North
American and European estuaries. Regression
of mean annual freshwater residence times versus
estuary area for these systems showed that there
appears to be a power-law relationship between
these two variables (Figure Al-1). Symbols that
identify the estuaries in Figure Al-1, and data
sources, are given in Table Al-1. While there is a
strong relationship between estuary area and water
residence time, note that the residence times for six
of the eleven estuaries differ by more than a factor
of two from that given by the regression line. Note
that the residence times of many of these estuaries
vary with freshwater inflows; the residence times
given in Table Al-1, and used in Figure Al-1, are
mean annual values.
One might expect that freshwater residence time
would increase with system size since the amount
of time that it takes fresh water to travel through
an estuary depends on the distance that it must
travel. Similarly, flushing of the system by tidal
action might also be expected to be less efficient
for larger estuaries with linear dimensions that are
large compared with the mean tidal excursion than
for small ones. Perhaps the most surprising aspect
of this relationship is that it is as good as it is, given
the number of factors that can affect flushing. The
sample of estuaries used in the above analysis had a
wide range of sizes, tidal ranges, and depths.
Somewhat analogous approaches have been
used elsewhere for other system types. System
morphology has been used to estimate water
residence times inmicrotidal embayments bordering
19
-------
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
_ 1000
C/)
§ 100
E
y = 0.0564x0554
r2 = 0.76
10 100 1,000 ' ' 10,000 ' 100,000 1,000,000
Area (km2)
Figure Al-l. Water residence time vs. surface area for 11 North American and European Estuaries.
Symbols identifying estuaries are defined in Table Al-l. The solid line is the regression line; the dashed
lines give 0.5 and 2 times the value given by the regression line.
Table Al-l. Estuaries appearing in Figure Al-l, with identifying symbols and data sources.
Estuary
Symbol
Data Source
Norsminde Fjord
(Denmark)
NF
Area: Nielsen et al. (1995); Residence time: Nielsen et al.
(1995) and K. Nielsen (personal communication)
Ochlockonee Bay
OB
Seitzinger (1987)
Boston Harbor
BH
Signell (1992), Signell and Butman (1992)
Narragansett Bay
NB
Pilson (1985)
Guadalupe Estuary
GE
D. Brock (personal communication), Residence time is
average of values for 1984 and 1987.
Westerschelde
(Belgium, Netherlands)
WS
Soetaert and Herman (1995)
North Adriatic Sea
NA
Degobbis et al. (1986)
Delaware Estuary
DE
Area: NOAA (1985), Residence time: Polis and
Kup ferman (1973)
Potomac Estuary
PE
Area: Boynton et al. (1995), Residence time: estimate by
W. Boicourt, (personal communication)
Chesapeake Bay
CB
Area:Lippson et al. (1973), Residence time: Nixon et al. (1996)
Baltic Sea
BS
Wulff and Stigebrandt (1989)
20
-------
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
the Baltic Sea. For instance, Hakanson et al (1986)
developed empirical models for systems with areas
1-100 km2 based on embayment morphometry.
Their best model depended on the mean width of
the embayment (W) and the topographic openness
(or exposure) of the embayment (E) to calculate the
residence time of the surface water. They calculated
the topographical openness from the relationship
between the width of the seaward boundary of the
embayment at five water depths and the length of
the contour line at each of these depths.
Persson et al (1994) developed models for
embayments with areas 1-150 km2 that had good
predictive capabilities, and that depend only on
the topographic openness, defined in this case as
the ratio of the cross-sectional area at the seaward
boundary and the bottom area.
Data Sources and Methods
It was decided to continue this analysis with data
for systems that are more similar in size and other
characteristics to those for which we wish to estimate
residence times. The method employed is similar
to that described above. A sample of New England
estuaries for which there have been determinations
of residence time was assembled, and regression
relationships obtained between residence time and
estuary area.
The embayments used to develop relationships
between area and water residence time are listed in
order of increasing surface area in Table Al -2. These
data were obtained from the papers and reports cited
in the last column of the table. The symbols defined
in the second column of the table are used to identify
the embayments associated with data points in some
figures. Surface areas of these embayments are in
the range 0.23-328 km2. Residence times are given
in units of both days and months, and are between 2
and 26 days.
The water residence times listed in Table Al-2 were
determined by various methods. The freshwater
replacement method was used to determine long-
term average residence times in Greenwich Cove
and Greenwich Bay (Granger et al, 2000). The
freshwater replacement method was also used to
determine freshwater residence time in Narragansett
Bay as a function of freshwater inflow rate (Pilson,
1985). The freshwater residence time inNarragansett
Bay varies between approximately 10 days at high
inflow rates to approximately 40 days at low inflow
rates. The value of 26 days given in Table Al-2 is for
the long-term mean inflow flow rate (105 m3 s1). The
residence time for Boston Harbor was determined by
a model analysis (Signell, 1992; Signell and Butman,
1992), and represents the e-folding time, the time
required for the mass of a uniformly-distributed
conservative tracer in the estuary to decrease to 37%
(e1) of its original amount. The residence time was
determined to be variable, depending on tidal and
wind influences, with 10 days being the midpoint
of the range 8-12 days determined to be the best
estimate. The residence time for Broad Cove, Sunset
Cove, Allen's Pond, and Little Bay were determined
using dye studies and the freshwater replacement
method (Geyer et al 1997). Determinations using
dye studies approximate the estuary residence
time, while the calculations using the freshwater
replacement method give the freshwater residence
time. The results of the two methods agreed to within
0-13 percent for Broad Cove, Sunset Cove, Onset
Bay, and Allen's Pond; for Little Bay, the dye study
gave the larger of the two values, which exceeded
the average of the two values by 48 percent (Geyer
et al 1997). The average of the results of the two
methods is given in Table Al-2.
Residence times were available for a few more local
estuaries, but were not included in this analysis,
either because they are microtidal or very shallow,
and therefore judged to be sufficiently different from
the estuaries in our study that flushing may well be
governed by different processes.
The embayments for which residence times are to
be determined are listed in Table Al-3 with values
for morphological parameters: areas, maximum
lengths, mean depths, and volumes. The values
of the morphological parameters were determined
by Michael Charpentier (Computer Sciences
Corporation) using GIS techniques, based on system
boundaries determined by members of the Nitrogen
Project Team (Mohamed Abdelrhman, Giancarlo
Cicchetti, Edward Dettmann, and Jim Latimer).
The surface areas of these estuaries vary between
0.2 km2 (Fort Wetherill Coves) to 605 km2 (Buzzards
Bay).
21
-------
Table Al-2. Embayments used to develop area-residence time relationships.
Surface Area Residence Time Reference for
Embayment Symbol (km2) (days) (months) Residence Time
Broad Cove (MA) BC 0.23 2.0 0.0657 Geyer et al. (1997)
Sunset Cove (MA) SC 0.34 1.8 0.0591 Geyer et al. (1997)
Allen's Pond (MA) AP 0.64 2.95 0.0969 Geyer et al. (1997)
Little Bay (MA) LB 0.74 1.45 0.0476 Geyer et al. (1997)
Mumford Cove (CT) MC 1.0 3.5 0.115 French et al. (1989)
Greenwich Cove (RI) GC 1.0 3.9 0.128 Granger et al. (2000)
Onset Bay (MA) OB 2.1 3.85 0.126 Geyer et al. (1997)
Greenwich Bay (RI) GB 11.8 7 0.23 Granger e?«/. (2000)
Boston Harbor (MA) BH 125 10 0.33 Signell (1992),
Signell and Butman (1992)
Narragansett Bay (RI) NB 328* 26 0.854 Pilson(1985)
* The area determined by Pilson (1985) does not include the Sakonnet River, and the
Sakonnet River was not included in his calculations of residence time.
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Measurements have been made of residence times
in a number of these embayments: Acushnet River
(New Bedford Inner Harbor, Apponaug Cove,
Greenwich Bay, Greenwich Cove, Narragansett
Bay, Providence-Seekonk River, and Warwick
Cove. The Providence River is a riverine estuary
for which the mechanisms governing flushing are
probably significantly different from those for most
of the other systems. The same is probably true for
the Pawcatuck River.
Results
Both linear and nonlinear regression was used with
the data for local embayments listed in Table Al-2.
The results of nonlinear regression with a power law
function are shown in Figure Al-2. The symbols
identifying embayments are defined in Table Al-2.
Overall most points are close to the regression line,
even the data point for Little Bay, the one farthest
from the line, is less that a factor of two distant. The
line in Figure Al-3 shows the linear regression to
the data. The curvature of the line is attributable
to the fact that the abscissa is logarithmic, in order
to show all data points legibly in the plot. All
data points except that for Little Bay are within a
factor of two of the linear regression line. Overall,
both regressions seem to represent the data well.
However, the linear regression has a minimum value
(y intercept) of 3.02 days. This seems unrealistic,
since some small systems would be expected to have
shorter residence times (Broad Cove, Sunset Cove,
and Allen's Pond, and Little Bay do).
Both regressions were used to calculate residence
times for the Nitrogen Project's study systems.
Residence times calculated with the power law and
linear equations are listed in Table Al-4, as well as
the average of these two values, and the difference
between the two values. Also listed in Table Al-4
are measured values for the few systems for which
they exist.
22
-------
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Table Al-3. Estuaries for which residence times are to be determined.
Depth (m)
Total Estuarine
Maximum
(Area Weighted
Estuarine
Embayment
State
Area (km2)
Length (m)
Mean)
Volume (nV)
Black Rock Harbor
CT
1.10
3,330
2.4
2,592,842
Branford Harbor
CT
1.41
4,475
1.0
1,451,460
Greenwich Cove
CT
2.21
3,304
1.5
3,365,874
Hammonasset R/Clinton Harbor
CT
2.73
5,457
0.6
1,635,647
Niantic River
CT
3.29
5,712
1.8
5,865,096
Pawcatuck River Estuary
CT
2.64
7,901
4.5
11,921,409
Acushnet River (NB inner harbor)
MA
4.06
7,141
3.4
13,647,557
Buzzards Bay
MA
605
50,195
10.4
6,267,250,920
Clarks Cove
MA
2.79
2,352
3.8
10,730,342
Cuttvhunk Pond
MA
0.40
949
1.6
621,857
Falmouth Inner Harbor
MA
0.12
1,113
2.4
301,163
Hadley Inner Harbor
MA
0.08
471
2.3
188,494
Katama Bay
MA
5.88
6,393
1.8
10,886,181
Lagoon Pond
MA
2.12
3,694
3.6
7,687,689
Lewis Bay
MA
4.77
3,217
1.4
6,481,274
Little Bay
MA
0.88
2,068
0.8
710,154
Mattapoisett Harbor upper
MA
2.81
2,710
2.9
8,192,927
Megansett Harbor
MA
2.26
3,170
2.1
4,675,521
Menemsha Pond
MA
2.76
3,144
3.9
10,656,051
Onset Bay
MA
2.59
4,091
1.5
3,838,143
Phinneys Harbor
MA
1.87
2,178
2.6
4,813,594
Sippican Harbor u&l
MA
7.28
6,139
3.0
21,481,455
Tarpaulin Cove
MA
0.78
822
4.4
3,422,848
Vineyard Haven-Inner
MA
0.18
343
3.7
673,019
Allen Harbor
RI
0.31
819
2.9
913,344
Apponaug Cove
RI
0.43
1,627
1.2
533,421
Bonnet Shores
RI
0.69
854
4.5
3,123,927
Bristol Harbor
RI
2.06
2,399
4.6
9,446,200
Coggeshall Point Harbor
RI
0.05
281
3.5
183,693
Easton Bay
RI
2.00
1,538
4.1
8,131,343
Fort Wetherill Cove - West
RI
0.02
232
7.8
138,452
Fort Wetherill Cove-Unnamed
RI
0.02
285
7.0
171,992
Great Salt Pond
RI
2.52
3,469
3.6
9,109,860
Greenwich Bay*
RI
12.0
5,174
2.5
29,747,801
Greenwich Cove
RI
0.75
2,577
1.8
1,360,725
Kickamuit River
RI
2.24
4,248
2.0
4,384,402
Mackerel Cove
RI
0.86
1,605
5.2
4,509,450
Mt. Hope Bay
RI
51.1
32,975
4.6
236,347,166
Narragansett Bay
RI
411
54,066
8.3
3,423,467,008
Old Harbor
RI
0.08
256
1.5
122,260
Potter Cove
RI
0.40
876
2.3
917,918
Providence-Seekonk River
RI
23.8
19,733
4.2
98,926,231
Sakonnet Harbor
RI
0.10
357
2.1
202,803
Warwick Cove
RI
0.56
2,203
1.5
824,373
* Area Includes Apponaug, Greenwich and Warwick Coves.
-------
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Discussion
Overall, both linear and nonlinear regression gave
fits to the data for 10 systems for which there were
measured residence times. The data used to derive
these regressions included measurements of both
freshwater residence time and estuary residence
time, and the results should not be interpreted as
distinguishing between these two measures. It
should be noted, however, that measurements for five
of these systems (Broad Cove, Sunset Cove, Onset
Bay, Little Bay, and Allen's Pond) were measured
with two techniques that would be expected to yield
the freshwater residence time, and that these two
values were usually in close agreement.
The linear regression equation gives a minimum
residence time of approximately 3 days, and
is therefore probably not reliable for very
small systems. When the linear and power-law
regressions were used to calculate residence times
for the Nitrogen Project's study systems, the results
given by the two methods were generally in good
agreement. There are two notable exceptions:
Buzzards Bay (25 vs. 45 days), and Narragansett
Bay (22 vs. 32 days). Interestingly, the average of
the two values for Narragansett Bay was close to
the measured value. The area of Buzzards Bay is
outside the range of values used in the regression.
The Acushnet River (New Bedford Inner Harbor)
was not used to develop the regressions. This is
because one determination of this value (3.2 days)
by Abdelrhman (2002) was for river inflow rates
that are known to exceed the mean river flow rates.
The other reason is that a second study conducted
under contract to USEPA Region 1 has produced
some values that exceed this by a factor of about
five. These results are still under review, and have
not been made available to us yet for detailed study.
In view of this confusing picture, New Bedford
Harbor was not included in the base data set used
to develop the regressions. The regression results
(4.65 and 3.30 days) are close to the value of 3.2
days found by Abdelrhman (2002).
There are three approaches that we can take in
applying these results. We could use either of the
methods, or we could use the average of the two.
Given the fact that both equations give good fits
overall, and the fact that the linear equation gives a
minimum result of about 3 days, I think it advisable
to use the use the power law approach, at least for
smaller systems.
24
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Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Table Al-4. Study systems with calculated residence times, using both linear and nonlinear formulas. Differences
between selected total estuarine areas given in this table and in Table 2 are attributable to differences in choice of
estuary boundaries.
Total
Residence
Residence
Residence
Measured
Estuarine
Time
Time
Time
Residence
Area
Power Law
Linear
Average
Difference
Time
Embayment
State
(km2)
(days)
(days)
(days)
(days)
(days)
Black Rock Harbor
CT
1.10
3.02
3.10
3.06
-0.08
n.a.
Branford Harbor
CT
1.41
3.27
3.12
3.20
0.15
n.a.
Greenwich Cove - CT
CT
2.21
3.80
3.18
3.49
0.63
n.a.
Hammonasset R./Clinton Hbr.
CT
2.73
4.07
3.21
3.64
0.86
n.a.
Niantic River
CT
3.29
4.34
3.25
3.79
1.09
n.a.
Pawcatuck River Estuary
CT
2.64
4.03
3.21
3.62
0.83
n.a.
Acushnet River (NB inner hbr)a
MA
4.06
4.65
3.30
3.98
1.35
3.20
Buzzards Bay
MA
605
24.50
45.37
34.93
-20.86
n.a.
Clarks Cove
MA
2.79
4.11
3.22
3.66
0.89
n.a.
Cuttyhunk Pond
MA
0.40
2.15
3.05
2.60
-0.90
n.a.
Falmouth Inner Harbor
MA
0.12
1.46
3.03
2.25
-1.57
n.a.
Hadley Inner Harbor
MA
0.08
1.27
3.03
2.15
-1.75
n.a.
Katama Bay
MA
5.88
5.26
3.43
4.35
1.83
n.a.
Lagoon Pond
MA
2.12
3.75
3.17
3.46
0.58
n.a.
Lewis Bay
MA
4.77
4.90
3.35
4.13
1.55
n.a.
Little Bayb
MA
0.88
2.80
3.08
2.94
-0.29
1.45
Mattapoisett Harbor upper
MA
2.81
4.11
3.22
3.66
0.90
n.a.
Megansett Harbor
MA
2.26
3.83
3.18
3.50
0.65
n.a.
Menemsha Pond
MA
2.76
4.09
3.21
3.65
0.88
n.a.
Onset Bayb
MA
2.59
4.01
3.20
3.60
0.81
3.85
Phinneys Harbor
MA
1.87
3.59
3.15
3.37
0.44
n.a.
Sippican Harbor u&l
MA
7.28
5.65
3.53
4.59
2.12
n.a.
Tarpaulin Cove
MA
0.78
2.69
3.07
2.88
-0.39
n.a.
Vineyard Haven - Inner
MA
0.18
1.66
3.03
2.35
-1.37
n.a.
Allen Harbor
RI
0.31
1.99
3.04
2.51
-1.05
n.a.
Apponaug Covec
RI
0.43
2.20
3.05
2.63
-0.85
0.7
Bonnet Shores
RI
0.69
2.58
3.07
2.82
-0.49
n.a.
Bristol Harbor
RI
2.06
3.71
3.16
3.44
0.55
n.a.
Coggeshall Point Harbor
RI
0.05
1.10
3.02
2.06
-1.93
n.a.
Easton Bay
RI
2.00
3.68
3.16
3.42
0.52
n.a.
Fort Wetherill Cove - West
RI
0.02
0.77
3.02
1.89
-2.25
n.a.
Fort Wetherill Cove - Unnamed
RI
0.02
0.85
3.02
1.94
-2.17
n.a.
Great Salt Pond
RI
2.52
3.97
3.20
3.58
0.77
n.a.
Greenwich Bay"'
RI
12.0
6.67
3.86
5.27
2.81
7
Greenwich Cove-RI
RI
0.75
2.66
3.07
2.86
-0.42
3.9
Kickamuit River
RI
2.24
3.82
3.18
3.50
0.64
n.a.
Mackerel Cove
RI
0.86
2.78
3.08
2.93
-0.30
n.a.
Mt. Hope Bay
RI
51.1
10.78
6.59
8.69
4.19
n.a.
Narragansett Bay
RI
411
21.56
31.82
26.69
-10.27
26
Old Harbor
RI
0.08
1.27
3.03
2.15
-1.76
n.a.
Potter Cove
RI
0.40
2.15
3.05
2.60
-0.90
n.a.
Providence-Seekonk River6
RI
23.8
8.37
4.69
6.53
3.68
3.6
Sakonnet Harbor
RI
0.10
1.35
3.03
2.19
-1.67
n.a.
Warwick Covec
RI
0.56
2.42
3.06
2.74
-0.64
4.2
a Source: (Abdelrhman, 2002) d Area includes Apponaug, Greenwich, and Warwick Coves.
b Source: (Geyer et al., 1997) c The Providence-Seekonk River estuary is a river-dominated
c Measured freshwater residence time from system; this calculation method is not appropriate for this estuary.
Granger et al. (2000). Measured residence time from Asselin and Spaulding (1993).
25
-------
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Residence Time vs. Area
C/)
JZ
c
o
E,
0
j| o.M
0
O
C
0
"O
w
0
cr
0.01
Onb
GC y
AP 4
y = 0.0958x0332(months)
r2 = 0.89
y = 2.92x0332 (days)
Area (km2)
Figure Al-2. Water residence time vs. surface area for the 10 local estuaries listed in Table Al-2.
Symbols are defined in Table Al-2. The line is the power law fit to the data using nonlinear regression.
The regression equation is expressed in both monthly and daily time units.
Residence Time vs. Area
C/)
JZ
c
o
0
0
O
c
0
"O
w
0
cr
0.9
0.8-
0.7-
0.6-
0.5-
0.4-
0.3-
0.2-
0.1-
0
Onb
y = 0.0023x +0.0991 (months)
r2 = 0.9612
y = 0.070x + 3.02 (days)
GB
BH
S9 JL O >
DO I
°A
CL>
<
bc^OOlb^vic
i I I I i i i i |
i I I I i i i i |
i I I I i i i i ¦
i I I I i i i i
0.1
10
Area (km2)
100
1,000
Figure Al-3. Water residence time vs. surface area for the 10 local estuaries listed in Table Al-2. Symbols
are defined in Table Al-2. The line is the linear regression to the data. The regression equation is expressed
in both monthly and daily time units.
26
-------
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
References
Abdelrhman, M. 2002. Modeling how a hurricane
barrier in New Bedford Harbor, Massachusetts,
Affects the hydrodynamics and residence times.
Estuaries 25(2):177-196.
Asselin, S. and Spaulding M.L. . 1993. Flushing
times for the Providence River based on tracer
experiments. Estuaries 16(4):830—839.
Boynton, W. R., Garber J. H., Summers R., and
Kemp W. M. 1995. Inputs, transformations
and transport of nitrogen and phosphorus
in Chesapeake Bay and selected tributaries.
Estuaries 18(1B):285-314.
Degobbis, D., Gilmartin M., and Revelante N.
1986. An annotated nitrogen budget calculation
for the northern Adriatic Sea. Marine Chemistry
20:159-177.
French, D., Flarlin M., Gundlach E., Pratt S., Rines
H., Jayko K., Turner C., and Puckett S. 1989.
Mumford Cove water quality: 1988 monitoring
study and assessment of historical trends.
Prepared for Mumford Cove Restoration
Committee, Groton, CT. Applied Science
Associates, Inc., Narragansett RI, 126 pp. +
Appendices. January, 1989.
Geyer, W. R., Dragos P., et al. 1997. Flushing
Studies of Three Buzzards Bay Harbors: Onset
Bay, Little Bay, and Allen's Pond. Prepared for
Buzzards Bay Project: 39 pp.
Granger, S., Brush M., Buckley B., Traber M.,
Richardson M., and Nixon S.W. 2000. An
assessment of eutrophication in Greenwich Bay.
Paper No. 1 in: M. Schwartz (ed.) Restoring
water quality in Greenwich Bay: A Whitepaper
Series. Rhode Island Sea Grant, Narragansett,
R.I. 20pp.
Hakanson, L., Kvarnas H., and Karlsson B. 1986.
Coastal morphometry as a regulator of water
exchange-a Swedish example. Estuarine,
Coastal and Shelf Science 23: 873-887.
Lippson, A.J. (ed.). 1973. The Chesapeake Bay
in Maryland: An Atlas of Natural Resources.
Johns Hopkins University Press. Baltimore.
National Oceanic and Atmospheric Administration
(NOAA). 1985. National Estuarine Inventory,
Data Atlas, Vol. 1: Physical and Hydrological
Characteristics. U.S. Department of
Commerce, National Oceanic and Atmospheric
Administration, National Ocean Service,
Rockville, Maryland.
Nielsen, K., NielsenL. P., andRasmussenP. 1995.
Estuarine nitrogen retention independently
estimated by the denitrification rate and mass
balance methods: a study of Norsminde Fjord,
Denmark. Marine Ecology Progress Series
119:275-283.
Nixon, S. W., Ammerman J.W., Atkinson L.P,
V. Berounsky M., Billen G., Boicourt W.C.,
Boynton W. R., Church T. M., DiToro D. M.,
Elmgren R., Garber J. H., Giblin A. E., Jahnke
R. A., Owens N. J. P., Pilson M. E. Q., and
Seitzinger S. P. 1996. The fate of nitrogen and
phosphorus at the land-sea margin of the North
Atlantic Ocean. Biogeochemistry 35:141-180.
Jahnke, N.J.P, Owens M.E., Pilson Q. and Seitzinger
S.P 1996. The fate of nitrogen and phosphorus
at the land-sea margin of the North Atlantic
Ocean. Biogeochemistry 35:141-180.
Persson, J., Hakanson L., and Pilesjo P. 1994.
Prediction of surface water turnover time
in coastal waters using digital bathymetric
information. Environmetrics 5: 433-449.
Pilson, M. E. Q. 1985. On the residence time of
water in Narragansett Bay. Estuaries 8(1):2-14.
Polis, D. F. and Kupferman S. L. 1973. Physical
oceanography, p. 1-170. In D. F. Polis (ed.),
Delaware Bay Report Series, Volume 4. College
of Marine Studies, University of Delaware,
Newark.
27
-------
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Seitzinger, S. P. 1987. Nitrogen biogeochemistry in
an unpolluted estuary: the importance of benthic
denitrification. Marine Ecology Progress Series
41:177-186.
Signell, R. P. 1992. Tide- and wind-driven
flushing of Boston Harbor, Massachusetts, p.
594-606. In: M. L. Spaulding (ed.), Estuarine
and Coastal Modeling, Proceedings of the 2nd
International Conference. American Society of
Civil Engineers, New York.
Signell, R. P. and Butman B. 1992. Modeling
tidal exchange and dispersion in Boston
Harbor. Journal of Geophysical Research
47(C10):15,591-15,606.
Soetaert, K. and Herman P. M. J. 1995. Estimating
estuarine residence times in the Westerschelde
(The Netherlands) using a box model with
fixed dispersion coefficients. Hydrobiologia
311:215-224.
WulffF. and StigebrandtA. 1989. Atime-dependent
budget model for nutrients in the Baltic Sea.
Global Biogeochemical Cycles 3(1 ):63—78.
Sources Of Unpublished Materials
Boicourt, W., Horn Point Laboratory, University
of Maryland, P.O. Box 775, Cambridge, MD
21613.
Brock, D. A., Environmental Section, Texas Water
Development Board, P.O. Box 13231, Austin,
TX 78711-3231, USA.
Nielsen, K., Ministry of Environment and Energy,
National Environmental Research Institute,
Silkeborg, Denmark.
28
-------
Appendix 2
Method for Determination of the
Spatial Extent of Submerged Vegetation
Steven Rego
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Our primary goal in characterizing the extent of
submerged vegetation is to assign a single number
describing seagrass abundance in shallow well-
flushed embayments in southern New England. Our
seagrass goal differs from the usual goals of seagrass
assessment, which are often focused on single beds
rather than on entire systems, and which often look
for subtle changes in seagrass health from one year
to another, focusing on changes in bed density or
bed area. For our purposes, comparability among
systems across states is more important than is detail
of assessment in any one bed or system. In fact, a
method insensitive to subtle year-to-year variability
would be preferable for our purposes. Since our
needs are to characterize seagrass in entire systems
over a large geographic area, rapid methods of data
acquisition and analysis are greatly preferable. For
these reasons, our methods are based on 1) rapid
aerial data acquisition; 2) minimal image processing;
3) linear adjacent-to-shoreline (segment length)
habitat analysis rather than bed areal analysis; and
4) reference of image data to existing larger-scale
georectifications (as opposed to georectification of
each individual image.) We feel that this approach
will deliver excellent data that will meet our needs,
will be appropriately rapid to use, and will be easily
exported to other researchers with comparable
goals.
Camera system:
The camera system consisted of a 1.4 megapixel
progressive scan (Sony® DFW-SX900), color
camera mounted downward through a port on the
aircraft. The camera was outfitted with a rectilinear
wide-angle (90 degree) Schneider® lens. Imagery
was collected with approximately 30-60% image
overlap using a 0.5 FPS collection rate. Data was
streamed through a firewire port to an integrated
laptop (Sony® Vaio GR270) and stored on two
external 150GB hard drives connected via IEEE-
1394 firewire hub. Imagery was collected using
camera control and image acquisition software
(Streampix® SpectraServices Inc.). Approximate
file storage sizes ranged from 75-3 50MB / system
and total images per system ranged 345-1193.
Imagery collected using streampix was stored in a
proprietary format (SEQ files). Individual frames
were then viewed and exported individually or
played in sequence during image processing.
Imagery pixel scale was approximately 0.15 meters
(6 inches). Figure A2-1 shows an example image
showing submerged vegetation.
Aerial survey:
Each flight was characterized by a 'pre-flight'
review in which sampling systems were reviewed
by the pilot for "best flight capability". Our camera
system was mounted on Cessna Skymaster® aircraft.
Optimal flight altitude was 1000 ft., as flying any
lower raises concerns security concerns in our
region and permitting issues with the FAA. Speed
was generally maintained at 100 knots. Flights were
scheduled during the biomass season (early fall) but
did not occur until October 10th through 31 st during
a 5 day sampling window. As weather and surface
chop dictate flight times some concessions must
be made to desired conditions. Our flight / system
conditions are listed below (Finkbeiner, 2001):
~ Desired 30% min. image end lap
~ Desired sun angle between 30 and 45 degrees
~ Desired wind less than 10 knots
~ Desired tide stage within approximately 2 hours
of low tide.
~ Desired cloud cover less than 5%.
Image processing and analysis:
Images acquired using the Streampix® software
system was stored in a native file format (SEQ).
This format stores individual flight frames (0.5 /
sec) for viewing later either in video or frame by
frame format. Images were reviewed directly on the
Sony® Vaio GR270 laptop using the Streampix®
software. This initial review was performed to
identify only images taken of coastal areas of the
systems in question, other frames, inland and over-
water imagery taken during aircraft maneuvering
29
-------
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
were ignored (See Figure A2-1). Images of interest
were identified by frame number during review and
later retrieved for export to a lossless generic image
format (TIFF - tagged image file format) (Figure A2-
2). These images, due to the great deal of overlap,
were easily tiled into mosaics to speed analysis. For
measurement purposes only, a second set of imagery
(1995 orthophotography) was obtained for each area
where available from state agencies. Image analysis
and measurement was performed using Arc View®
ESRI version 3.2 and Photoshop VI0. Scaling
coefficients were established for imagery using the
following process:
(1) a clearly identified feature occurring in both
images was identified and measured on the
orthophoto (m) and in the collected imagery (pu
- page units).
(2) a scale ratio (total length in meters of target / total
length in pu of target) was calculated to develop
a coefficient for length between the registered
and un-registered imagery.
Submerged aquatic vegetation (SAV) and other
vegetative features were identified in the image
mosaics. SAV was delineated along the coastline
and measured in page units in Photoshop®. Lengths
were calculated in tables using the scale coefficients.
This technique was checked for accuracy using
identifiable ground control features in each image.
Imagery suffering from distortion due to severe
axial tilt was not used in the final analysis.
Vegetation Identification:
A brief literature review of seagrass identification
techniques showed that visual identification from
aerial imagery was vague. Image interpretation
depended on the interpreter's experience and image
quality. Our guidance document provided the basis
for our interpretative analysis and was drawn from
Eelgrass in Buzzards Bay: Distribution, Production
and Historical Changes in Abundance (Costa 1988).
Although a guide, the report does not provide
hard factual features to assist with SAV or botanic
identification; rather, they state the photographic
images of habitat features vary in ways that cannot
readily be modeled, described or communicated.
This is in part due to the temporal and geographic
variations seen in vegetative beds.
Tiered System for Identification of
Submerged Aquatic Vegetation:
A tiered approach was used for the identification of
submerged aquatic vegetation (Table A-l). The first
tier represents the initial assessment of the digital
image, if the factors were not definitive, factors
noted in the second tier were evaluated to obtain
vegetative identification (see decision rules noted
below). Figure A2-3 shows an example data sheet
for a single embayment.
The following decision rules were applied to the
tiers:
~ In the absence of any contradictory features,
any Tier 1 identifier alone constitutes a habitat
identification.
~ In the absence of any contradictory features,
any two Tier 2 identifiers constitute a habitat
identification.
~ If contradictory features are found, this may
possibly be due to 1) several visual signals
coming from two or more mixed habitat types in
the same area; 2) an unusual arrangement of a
single habitat; or 3) artifacts causing problems of
interpretation.
~ In some cases, further clues will lead to
resolution of these contradictory features - - for
instance, heavily epiphitized seagrass can have
visual features in common with both seagrass
and macroalgae. Where seagrass is mixed with
another vegetated habitat such as epiphytic
algae, macroalgae, or rockweed, the amalgam is
categorized as a "mixed" bed descriptor.
~ Where conflicting evidence exists, contradictory
features in Tier 2 can be cancelled against each
other and Decision Rules 1 or 2 above applied to
the balance of features to classify habitat types. If
contradictory features or lack of evidence result
in classification information such that none of the
Decision Rules are met, and this appears not to be
due to habitat mixing, then the habitat is classified
as "Unidentifiable Vegetation" or "Unidentifiable
Habitat".
30
-------
CO
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Isi
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CD
Descriptor
Target Species
Tier
Abbreviation
Identifying Remark
Description
Eelgrass
The following image features were used to identify dark subtidal photographic features as seagrass.
I
PPS
Propeller scars
long thin light colored scar lines on a dark background
MCS
Mooring chain scars
circular light colored scar areas on a dark background around moorings.
where swing of the mooring chain has scoured circular area
II
CGF
Circular Growth Form
patches circular in shape
GTX
Grainy texture
large grains seen in above
SEG
Sharp edges
beds without the blurry smeared edges not uncommon in macroalgal habitat
STG
Subtidal growth pattern
very little growth in the intertidal or very shallow subtidal and clear "band"
of no vegetation in the intertidal area
BGC
Blue-green color
typical coloration of Zostera in digital imagery
ADJ
Adjacent image
conclusive identification of seagrass habitat in the adjacent image
Drifting Macroalgae
The following image features were used to identify subtitdal photographic features as macroalgae.
I
IWU
hitertidal wash-up
seen at the water's edge and wrack line - drift algae pushed up onto shore
or very shallow areas
II
WFP
Wave formed patterns
clearly visible as parallel wavy bands in near shore areas
RVP
Rivulet patterns formed
where flowing water has shaped algal patterns
UNI
Uniform texture
reflecting a dense bed that completely covers substrate
BSE
Blurry smeared edges
patterns of drift algae grading into imvegetated habitat
MBC
Color
muddy brown color
MGC
Color
muddy green color
DGC
Color
dark green color
Rockweeds
The following image features were used to identify dark tidal/subtidal photographic features as rockweed.
I
RKH
Rock halos
dark rings around light intertidal rocks
IDB
hitertidal growth
growth in intertidal rocky areas with dark brownish color typical of Ascophyllum and Fucus
II
MGP
Mottled growth patterns
reflecting variation of the substrate elevation and zonation patterns
RCK
Bedrock
ridge patterns seen as parallel bands where algae grows on exposed bedrock
COB
Cobble
patterns seen as very high-contrast grainy texture where algae grows on cobble substrate
ITG
hitertidal growth
predominantly intertidal growth pattern
RBC
Color
reddish brown color
DBC
Color
very dark brown color
RDC
Color
reddish color
ADJ
Adjacent Image
conclusive identification of rockweed habitat in an adjacent image
-------
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Figure A2-1. Example of 2002 aerial image depicting vegetation targets.
"wi" H7 ot a jit rr? r*r < ir rr-'ir? a* 7P,:s,4""A ?r
]¦•¦> tc 5- 3T v.- nrwariv -v w lva' .vwivi' r. • sv n.-> or«v
H fiimV'H.NK £4=. MA
i1'2D'4.rN TD'ffiTZ'W
IS!: 2.C 1-7 N 7U' 51' S5f W St MAPTECH, INC.
Figure A2-2: Aerial image of an embayment divided into segments for analysis.
32
-------
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Lb
¦¦/"l
1
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E
Kl
y.
k
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i
Y
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r fl
i
U.
o <
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v..
It ^
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uJ-'Vi ^ ,j j." 'i !£j <
i > i H-y- « .
¦ij -.j \_s '-J >-ri ^ "-
v ~ -1
'•/" ^ **¦. "I '4- "3, 0
\j h,:-j v ^ ^,
- - I ri
^ >; x > s; > >¦ ^ >;
.: "< Zl Vn -1 rl \,r, '¦-'J
v.
¦i| 0
ci Q
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Figure A2-3: Example aerial imagery datasheet.
References
Costa J.E. (1988) Eelgrass in Buzzards Bay:
Distribution, Production, and Historical Changes
in Abundance. Report No. EPA 503/4-88-
002, U.S. Environmental Protection Agency,
Washington DC.
Finkbeiner M., Stevenson B., Seaman R. (2001)
Guidance for Benthic Habitat Mapping: an
Aerial Photographic Approach. Report No.
NOAA/CSC/20117-PUB, U.S. NOAA Coastal
Services Center, Charleston, SC.
33
-------
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
This page is blank intentionally
34
-------
Appendix 3. Nitrogen Load-Eelgrass Extent Model:
Assumptions and Limitations
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Four (4) sub-components make up the construction
of the empirically derived multiple-system nitrogen
load - eelgrass extent model detailed in this research
summary:
(1) Nitrogen loading model
(2) Water flushing time model
(3) Eelgrass extent sampling and analysis
(4) Estuarine morphological characteristics
Each of these components is subject to a unique set of
assumptions and limitations. The combination of all
of the limitations will apply to the overall empirical
model. The major assumptions and limitations of
these components are listed below, as well as the
overarching set of assumptions and limitations.
Loading Sub-model
Nitrogen loading rate values calculated for the
study embayments are based on application of the
published Nitrogen Loading Model (NLM) (Valiela
etal. 1997). The reader should refer to this publication
for details; however, the major assumptions of this
model are noted below.
The NLM estimates nitrogen loading to watersheds
and receiving waters (see Figure 3, in the main body
of this report, for additional details). It considers
diffuse, non-point source inputs and attempts to
estimate losses in various compartments of the
watershed. The model was developed for Waquoit
Bay, MA, but "...with inputs for local conditions it
is applicable to other rural to suburban watersheds
underlain by unconsolidated sandy sediments." The
authors include two major categories of nitrogen
inputs to the watershed: i) nitrogen to watershed
surfaces, which includes atmospheric deposition to
4 landuse types (natural vegetation, turf, agricultural
land, and impervious surfaces) and fertilizer
application to 2 landuse types (turf and agricultural
land); and ii) septic wastewater nitrogen.
Assumptions:
1. Table A3-1 contains the input categories for the
NLM and represents the total watershed derived
and atmospherically derived nitrogen to the
estuary. In addition, the algebraic expression used
to calculate the nitrogen inputs to the watersheds
are listed.
2. Table A3-2 contains the loss and transport
coefficients applied to each type of land use
category. The nitrogen that comes from the three
sources (Table A3-1) and is deposited on the
watershed is lost, or attenuated, according to the
coefficients in the table.
3. Table A3-3 lists the data required in order to
use the NLM to compute nitrogen inputs to the
watersheds and surfaces of the study estuaries. It
should be noted that because estimates of loading
are largely based on landuse data the values
represent long-term annual averages.
Limitations:
1. The model is applicable to estuaries dominated
by nonpoint sources of nitrogen underlain by
unconsolidated sandy soils. However, it is here
applied to other estuaries in so far as the variance
explained by the nitrogen load-eutrophication
response model is statistically significant.
2. Does not include other direct nitrogen inputs
(e.g., from boats)
3. Does not include nitrogen loading from oceanic
sources
4. Does not include nitrogen loading from
regeneration in the estuary
5. The model cannot be used to assess temporal
trends and variation in loading
35
-------
Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Table A3- 1. Input categories for the Nitrogen Loading Model.
Input Category Included Landuse Types Nitrogen Load
Atmospheric Deposition:
Natural Vegetation
Turf
Agricultural Land
Impervious Surfaces: roofs,
driveways
Impervious Surfaces: roads,
roads, runways, parking lots
forests, wetlands, natural lands
lawns, golf courses
crop land
roofs, driveways
roads, runways, parking lots
ATMOS. DEP.1-2 x AREA
(kg N ha/ yr) (ha)
ATMOS. DEP. x AREA
(kg N ha/ yr) (ha)
ATMOS. DEP. x AREA
(kg N ha/ yr) (ha)
ATMOS. DEP. x AREA
(kg N ha/yr) (ha)
ATMOS. DEP. x AREA
(kg N ha/yr) (ha)
Fertilizer Application:
Turf
Agricultural Land
lawns, golf courses
crop land
APPL. RATE.3 x AREA x Fn
(kg N ha/ yr) (ha) (fract)
APPL. RATE, x AREA - N rem4
(kg N ha/yr) (ha)
Human Septic Wastewater5:
residential land
(kg N person/ yr) x (persons per
house) x (# of houses)
1 Uses the concentration of N03-, NH4+, and DON in local precipitation and yearly rainfall totals to enerate the
atmospheric deposition term.2 Model also includes dry deposition (i.e., from NO x. particles adhering to leaves and
impervious surfaces), which is adjustable as a proportion of wet deposition.3 Uses average fertilizer addition rates
for Cape Cod of 105 kg N ha/yr for lawns and 115 kg N ha/ yr for golf courses. FN = "0.34" refers to the fraction
of homeowners applying fertilizer (applies to lawns only).4 The term "N rem" allows for nitrogen removed from
the watershed as crops (i.e., consumed outside of the watershed); assumed zero in current estimates.5 As originally
published, the loading model applies to nonpoint sources; however, some of the estuaries contained municipal point
source inputs. The magnitude of these inputs was estimated from effluent monitoring data. In these cases, if point source
inputs were significantly greater than estimated nonpoint source inputs, then nonpoint sources were not included.
Table A3- 2. Loss and transport percentages used in the Nitrogen Loading Model.
Landuse Type In Situ Loss Transport
Atmospheric Deposition:
Natural Vegetation
65%, through retention in plants and soil
61% in vadose zone
35% in aquifer
Turf
62%, through retention in plants and soil
61% in vadose zone
35% in aquifer
Agricultural Land
62%, through retention in plants and soil
61% in vadose zone
35% in aquifer
Impervious Surfaces: roofs,
62%, through retention in plants and soil
61% in vadose zone
driveways1
35% in aquifer
Impervious Surfaces: roads,
0%
61% in vadose zone
runways, parking lots2
35% in aquifer
Fertilizer Application:
Turf
39% lost as gases
61% in vadose zone
35% in aquifer
Agricultural Land
39% lost as gases
61% in vadose zone
35% in aquifer
Human Septic Wastewater:
40% in septic tanks and leach fields
35% in aquifer
34% in plumes
'Assumes that precipitation falling on roofs and driveways subsequently runs off to lawns and natural
lands where losses may occur. 2Assumes that precipitation falling on roads, runways, parking
lots is collected in catchment basins and delivered directly to the vadose zone.
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Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Table A3- 3. Other data used to compute watershed loading by the Nitrogen Loading Model.
Data Required Source/Reference
Concentration of N03, NH4+, DON in regional precipitation
Yearly rainfall totals
Fertilizer application rates for lawns and golf courses
Average lawn size
Fertilizer application rates for farms
(Luo et al. 2002)
NCDC (http://www.ncdc.noaa.gov)
(Valiela et al. 1997)
(Valiela et al. 1997)
(Valiela et al. 1997)
Average roof and driveway areas
Annual N release per person
Number of houses
Occupancy rate for houses
Direct municipal wastewater treatment facility inputs
(Valiela et al. 1997)
(Valiela et al. 1997)
residential landuse (see landuse data below)
residential landuse (see landuse data below)
CT: Paul Stacey (CT DEP)*
RI: Scott Duerr (Westerly) Scott Nixon (URI) *
MA: Brien Friedman, Russell Isaac (MA DEP) *
Landuse areas for: natural vegetation, golf courses, crop land,
commercial and industrial development, and residential land
State of Connecticut1
State of Rhode Island2
State of Massachusetts3
'Developed from 30-meter Landsat thematic mapper (TM) data acquired by the Multi-resolution Land
Characterization (MRLC) Consortium. The base data set was leaves-off Landsat TM data, nominal-1992
acquisitions. Collection date: 1992. Collection platform: Landsat satellite.2Originally interpreted from 1988
aerial photography and updated from 1992 - 1995 orthos. Intended scale 1:24,000. Collection date: Originally
in 1988, updated with 1992 - 1995 data. Collection platform: Aerial photography. 'Interpreted from 1:25,000
aerial photography. Collection date: 1999. Collection platform: Aerial photography.*personal communication.
Flushing Time Sub-model
The flushing time (the time for fresh or estuarine
water to travel through an estuary) for each of
the study embayments is based on an empirical
relationship between published flushing times and
estuarine area (see Appendix 1). An average of
values generated by a power law and linear equation
were used to calculate flushing times for this study.
Assumptions (see Appendix 1)
1. Flushing time for study estuaries can be based on
empirical data from 10 estuaries of similar size
and in a similar biogeographic region.
2. Fresh water and estuarine flushing times are not
distinguished and are grouped as simply "water"
flushing time. This is because of the similarity of
results from literature values.
3. Morphological factors other than length and
width (e.g., depth, circulation restrictions, tides,
and shape) are not primary factors in predicting
flushing times within similar estuarine classes.
Limitations (see Appendix 1)
The model is applicable to embayment type estuaries
with the characteristics (Madden et ah 2005)
contained in Table A3-4.
Eelgrass Extent Sampling and Analysis
The eelgrass extent (length along the shoreline)
for each of the study estuaries was derived from
airplane digital photography, image processing,
image analysis, and vegetation identification. The
details of each of these components can be found in
Appendix 2.
Assumptions (see Appendix 2):
1. That airplane derived images, processed and
analyzed for submerged vegetation, represent
an indicator of extent of submerged aquatic
vegetation during the time of flights.
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Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Table A3- 4. List of CMECS descriptors to define the class of estuarine embayments for the study systems
Temperature Class:
Salinity Class:
Oxygen Class:
Turbidity Class:
Turbidity Type:
Cold (0-10°C) to temperate (10-20°C)
Mesohaline (5-18 psu) to euhaline (30-40 psu)
Variable (anoxic, hypoxic, oxic, saturated, supersaturated)
Moderately turbid (2-4 m)
Mixed (chlorophyll, mineral, colloidal, dissolved color, detrital)
Turbidity Provenance:
Energy Type:
Energy Intensity:
Energy Direction:
Depth Class:
Mixed (allochthonous, autochthonous, resuspended, terrigenous, marine)
Wind/tide/current
Moderate (moderate currents and wave action, 2-4 kn)
Mixed
Very shallow (0-5 m)
Tide Class:
Primary Water Source:
Enclosure Status:
Trophic Status:
Region:
Small (0.1-1 m) to moderate (1-5 m) tidal range
Watershed, local estuary, local marine (non-river dominated)
Partially-enclosed (50-75% area encircled by land)
Oligotrophic (<5 ug Chl-a/L) to eutrophic (>50 ug Chl-a/L)
Eight (8); Virginian Atlantic Region. The region extends along the eastern
North American continent from Cape Hattaras northward to Cape Cod. The region
lies within the temperate climatological zone, and is interposed between
the east coast and the Northern Gulf Stream Transition Region offshore (Region 9).
Embayment Size:
Watershed Size:
Ecoregions:
Geographic:
Small (0.1 km2) to medium (6 km2)
Small (0.5 km2) to medium (73 km2)
Northeastern Coastal Zone and Atlantic Coast Pine Barrens (Shirazi et al. 2003)
Southern New England Region (CT, RI and southeastern MA coastal)
2. Thatthesegmentindicatorisareasonablyaccurate
index of eelgrass extent for study estuaries and
compares favorably with areal estimates (Pesch
et al. Submitted).
3. That bottom substrate was likely only of major
importance for those systems in which no eelgrass
was detected.
Limitations (see Appendix 2):
1. Derived eelgrass extent indices represent the SAV
extent for 2002 only; additional data are needed
to assess temporal variability.
2. No point-specific groundtruthing during this
period took place; however, comparisons with
other studies for some of the study systems were
favorable.
3. Track-lines were not optimal for efficient
analysis.
Estuarine Morphological
Characteristics
Assumptions:
That data derived from GIS coverages give a
reasonably accurate depiction of major system
morphological characteristics (e.g., depth, volume,
area, etc.)
Limitations:
Subject to limitations of data availability, accuracy,
precision of the underlying data.
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Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
Overall Assumptions and Limitations
of the Nitrogen load - Eelgrass Extent
Model
Assumptions:
1. The model is subject to the assumptions of
the constituent components of the empirical
model (i.e., load model, residence time model,
eelgrass sampling, analysis, and morphological
characteristics). See above.
2. Nitrogen loading from sources outside of the
loading model are not primary drivers (e.g.,
oceanic inputs, direct carbon inputs, direct
nitrogen inputs).
3. Annual average nitrogen inputs (long-term)
provided by the loading model can be successfully
related to annual seagrass extent assessments.
Limitations:
The model is subject to the limitations of the
constituent components that make up the empirical
model (i.e., load model, residence time model,
eelgrass sampling, analysis, and morphological
characteristics). See above. Therefore the nitrogen
load-eelgrass response model can be applied to
the estuarine embayments with the characteristics
contained in Table A3-5.
Table A3- 5. Characteristics of estuarine embayments for which the nitrogen load-eelgrass extent model
can apply.
Temperature Class:
Salinity Class:
Oxygen Class:
Turbidity Class:
Turbidity Type:
Cold (0-10°C) to temperate (10-20°C)
Mesohaline (5-18 psu) to euhaline (30-40 psu)
Variable (anoxic, hypoxic, oxic, saturated, supersaturated)
Moderately turbid (2-4 m)
Mixed (chlorophyll, mineral, colloidal, dissolved color, detrital)
Turbidity Provenance:
Energy Type:
Energy Intensity:
Energy Direction:
Depth Class:
Mixed (allochthonous, autochthonous, resuspended, terrigenous, marine)
Wind/tide/current
Moderate (moderate currents and wave action, 2-4 kn)
Mixed
Very shallow (0-5 m)
Tide Class:
Primary Water Source:
Enclosure Status:
Trophic Status:
Region:
Small (0.1-1 m) to moderate (1-5 m) tidal range
Watershed, local estuary, local marine (non-river dominated)
Partially-enclosed (50-75% area encircled by land)
Oligotrophic (<5 ug Chl-a/L) to eutrophic (>50 ug Chl-a/L)
Eight (8); Virginian Atlantic Region. The region extends along the eastern North American
continent from Cape Hattaras northward to Cape Cod. The region lies within the temperate
climatological zone, and is interposed between the east coast and the Northern Gulf
Stream Transition Region offshore (Region 9).
Embayment Size:
Watershed Size:
Ecoregions:
Geographic:
Habitat Suitability:
Small (0.1 km2) to medium (6 km2)
Small (0.5 km2) to medium (73 km2)
Northeastern Coastal Zone and Atlantic Coast Pine Barrens (Shirazi el al. 2003)
Southern New England Region (CT, RI and southeastern MA coastal)
Estuaries that have suitable habitat characteristics that support eelgrass (in this report,
historical presence is used as a proxy)
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Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
References
Luo Y., Yang X., Carley R.J., Perkins C. (2002)
Atmospheric deposition of nitrogen along the
Connecticut coastline of Long Island Sound:
a decade of measurements. Atmospheric
Environment 36:4517-4528.
Madden C.J., Grossman D.H., Goodin K.L. (2005)
Coastal and Marine Systems of North America:
Framework for an Ecological Classification
Standard: Version II, NatureServe, Arlington,
VA.
NCDC (U.S. DOC, NOAA National Climate Data
Center) http://www.ncdc.noaa.gov.
Pesch C.E., McGovern D.G., Rego S., Cicchetti
G., Latimer J.S. (Submitted) Proposed use of
shoreline length adjacent to Eelgrass (Zostera
marina) beds as a first order measure of Eelgrass
extent at the embayment scale in New England.
Environ Manage.
Shirazi M. A., Johnson C.B., Omernik J.M., White D.,
Haggerty P.K., Griffith G.E. (2003) Quantitative
Soil Descriptions for Ecoregions of the United
States. J Environ Qual 32:550-561.
Valiela I., Collins G., Kremer J., Lajtha K., Geist
M., Seely B., Brawley J., Sham C.H. (1997)
Nitrogen loading from coastal watersheds to
receiving estuaries: new method and application.
Ecol Appl 7:358-380.
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Land-Based Nitrogen Loading and Eelgrass Extent for Embayments
SEPA
United States
Environmental Protection
Agency
Office of Research and Development
National Health and Environmental
Effects Research Laboratory
Narragansett, Rl 02882
Official Business
Penalty for Private Use
$300
EPA/600/R-07-021
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