1 The nitrogen budget of the Chesapeake Bay: results from a land-estuarine ocean biogeochemical modeling system To be submitted to Journal of Geophysical Research – Biogeosciences Yang Feng 1 , Marjorie A. M. Friedrichs 1 , John Wilkin 2 , Hanqin Tian 3 , Qichun Yang 3 , Eileen E. Hofmann 4 , Jerry D. Wiggert 5 and Raleigh R. Hood 6 1 Virginia Institute of Marine Science, College of William and Mary, Gloucester Point, Virginia 2 Institute of Marine and Coastal Sciences, Rutgers University, New Brunswick, New Jersey 3 International Center for Climate and Global Change Research and School of Forestry and Wildlife Sciences, Auburn University, Auburn, Alabama 4 Center for Coastal Physical Oceanography, Old Dominion University, Norfolk, Virginia 5 Department of Marine Science, University of Southern Mississippi, Stennis Space Center, Mississippi 6 Horn Point Laboratory, University of Maryland Center for Environmental Science, Cambridge, Maryland
63
Embed
The nitrogen budget of the Chesapeake Bay: results from a ... · Estuaries play an important role in global biogeochemistry by transferring and transforming nutrients between the
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
1
The nitrogen budget of the Chesapeake Bay:
results from a land-estuarine ocean biogeochemical modeling system
To be submitted to Journal of Geophysical Research – Biogeosciences
Yang Feng1, Marjorie A. M. Friedrichs1, John Wilkin2, Hanqin Tian3, Qichun Yang3, Eileen E.
Hofmann4, Jerry D. Wiggert5 and Raleigh R. Hood6
1Virginia Institute of Marine Science, College of William and Mary, Gloucester Point, Virginia 2Institute of Marine and Coastal Sciences, Rutgers University, New Brunswick, New Jersey 3International Center for Climate and Global Change Research and School of Forestry and Wildlife
Sciences, Auburn University, Auburn, Alabama 4Center for Coastal Physical Oceanography, Old Dominion University, Norfolk, Virginia 5Department of Marine Science, University of Southern Mississippi, Stennis Space Center, Mississippi 6Horn Point Laboratory, University of Maryland Center for Environmental Science, Cambridge,
Maryland
2
Abstract
Estuaries play an important role in global biogeochemistry by transferring and transforming
nutrients between the land and ocean, yet are often overly simplified in basin-scale
biogeochemical models. In this study, a new land-estuarine-ocean biogeochemical modeling
system (ROMS-ECB) is developed to: (1) quantify the nitrogen budget of the Chesapeake Bay,
and (2) investigate the importance of resolving these estuarine transformations on nutrient
delivery to the shelf. Using the Chesapeake Bay as a testbed, the model is evaluated with in situ
and satellite-derived data and shows significant skill in reproducing the variability of both
physical and biogeochemical fields. The modeling system is then used to estimate the function of
the bay in modifying riverine nitrogen inputs during the 2001 - 2005 time period. The results
suggest that of the nitrogen entering the Chesapeake Bay, ~35% is buried and ~20% is
denitrified. The ecosystem metabolism over this period is net autotrophic, resulting in a large
export of organic nitrogen to the shelf and negligible export of inorganic nitrogen, in agreement
with estimates derived from observations. Significant interannual variability is associated with
the modeled fluxes, especially nitrogen export to the shelf. Simulations derived from a lower
resolution continental shelf model without key estuarine biogeochemical processes results in
significantly lower burial and denitrification fluxes and unrealistically high exports of inorganic
nitrogen to the shelf. These results indicate that improving biogeochemical simulations on the
continental shelf will likely require the nesting of higher resolution estuarine models that
incorporate biogeochemical processes specifically relevant to estuaries.
3
1. Introduction
Located at the intersections between land and ocean, estuaries play an important role in
global carbon and biogeochemical cycles [Bauer et al., 2013; Bianchi and Bauer, 2011]. The
CO2 released from estuaries on a global scale is roughly equivalent to the CO2 taken up by
continental shelves even though estuaries make up only a small portion of coastal ocean area
[Cai et al., 2011]. In addition, nutrients and organic matter discharged from the land are
transformed or buried within estuaries before reaching continental shelves [Canuel et al., 2012].
Furthermore, estuaries are generally located within coastal watersheds where human populations
are high. As a result, the ecosystems there are sensitive and vulnerable to human-related
activities [Bricker et al., 2007].
The fate of riverine nutrients entering estuaries has been intensely studied over the past
several decades. This is particularly true for the Chesapeake Bay [Boynton et al., 1995; Fisher et
al., 1988; Kemp et al., 1997; Nixon et al., 1987; Smullen et al., 1982]. Based on data collected
from multiple sources prior to 1990, Boynton et al. [1995] estimated the annual mean nitrogen
budget of the entire Chesapeake Bay and revealed that inorganic nitrogen entering the estuary
was rapidly converted to an organic form, and there was a net seaward transport of total nitrogen.
Using much of the same data but focusing on the main stem of the Bay, Kemp et al. [1997] found
that due to the net autotrophic status of the Bay, the ratio of dissolved inorganic nitrogen to total
organic nitrogen at the Bay mouth was much smaller than that at the head of the Bay. These
previous nitrogen budget calculations using observational data are associated with some
uncertainties, which largely result from the relatively low temporal and spatial sampling
frequency of the observations, especially regarding resolving the interannual variability of the
hydrological conditions (i.e. wet versus dry years) caused by extreme weather events. In this
4
study, an estuarine-biogeochemistry model was first developed and coupled to a three-
dimensional (3D) hydrodynamic model. Then, the nitrogen budget of the Chesapeake Bay was
calculated from the coupled model simulations. Since the estuarine physical circulation fields
generated from 3D hydrodynamic models are typically capable of successfully simulating flow
fields on multiple scales and typically resolve both high frequency (tidal) processes as well as
variability on multiple time scales (annual to interannnual), nitrogen budgets computed using the
3D coupled hydrodynamic-biogeochemical model are complementary to those estimates derived
using observations.
Although a number of 3D coupled estuarine models have been previously implemented in
the Chesapeake Bay [Cerco et al., 2002; Li et al., 2009; Testa et al., 2014; Xu and Hood 2006;
Scully et al., 2010; 2013], these previous efforts have been limited to one or two specific aspects
of estuarine biogeochemistry associated with nitrogen cycling, such as phytoplankton biomass,
dissolved inorganic nutrients, or dissolved oxygen concentrations. To our knowledge, this is the
first time the entire nitrogen cycle in Chesapeake Bay has been investigated using a coupled
hydrodynamic-biogeochemical model. Another significant difference between previous
Chesapeake model implementations and the modeling effort described here, is that our river
forcing is provided by a process-based terrestrial ecosystem model. The advantage of linking our
estuarine-biogeochemistry model with a land model is that impacts of past changes in climate,
land use and land cover on estuarine nutrient cycling processes can be examined. In addition, the
effects of future changes in climate and land management practices can be evaluated.
Although 3-D models provide a useful tool for quantitative ocean biogeochemical studies
at multiple spatial and temporal scales, the reliability of such model results over these varying
scales is often difficult to assess due to the paucity of observational data at these multiple scales.
5
An advantage of implementing our estuarine-biogeochemistry model in the Chesapeake Bay is
that a variety of measurements, including both physical and biogeochemical variables, are
available to facilitate a full model evaluation over multiple years. As a result, quantitative model-
data comparisons are possible using multiple skill metrics [Jolliff, et al., 2009; Stow et al., 2009].
Such comparisons are critical, as they reveal the advantages and potential limitations of a
particular model, which must be carefully considered before using such a model as a tool for
scientific study or decision-making. The focus of this study is primarily on introducing our
model and evaluating its performance; therefore the simulation time selected is limited to a
contemporary period (2001-2005) when observations are available. However, past and future
scenario simulations will be conducted and discussed in follow-up studies. As a result, our linked
modeling system will likely not only benefit future estuarine scientific studies, but also support
management applications and future high stakes decision-making.
The content of this paper is organized into six sections. Secion 1 is the introduction.
Section 2 and the associated appendix present a complete description of our land-estuarine-
biogeochemistry modeling system. In Section 3, the model skill is evaluated relative to extensive
data from the Chesapeake Bay Program as well as satellite ocean color data. In Section 4, a
nitrogen budget for the Bay was estimated using our modeling tool and compared with previous
estimates using observational data [Boynton et al., 1995; Kemp et al., 1997]. In Section 5,
nitrogen fluxes from the estuarine implementation are compared to model results using a lower
resolution continental shelf model which ignores key estuarine biogeochemical processes.
Implications of these results within the broader context of estuarine biogeochemistry are
discussed in Section 6.
6
2. Model Description
2.1 Hydrodynamic Model
The physical component of the coupled model is based on the Regional Ocean Modeling
System (ROMS) [Shchepetkin and McWilliams, 2005] version 3.6. The model domain and
horizontal grid follows the Chesapeake Bay community implementation of the ROMS
(ChesROMS) [Brown et al., 2013; Xu et al., 2012]. The domain spans the region from 77.2°W to
75.0°W and from 36°N to 40°N, covering the main stem and primary tributaries of the
Chesapeake Bay, as well as part of the mid-Atlantic Bight (Fig. 1). The horizontal grid spacing
varies with highest resolution (430 m) in the northern Bay near the Chesapeake and Delaware
Canal and lowest resolution (~10 km) in the southern end of the mid-Atlantic Bight, and an
average grid-spacing within the Chesapeake Bay of 1.7 km. As in ChesROMS, the model has 20
terrain-following vertical layers with higher resolution near the surface and bottom boundaries.
However, unlike ChesROMS, the vertical s-coordinate function follows Shchepetkin and
Williams [2009], and stretching parameters at the surface and bottom are set to 6.0 and 4.0,
respectively. The bottom topography is also slightly smoothed as in Scully [2013] to avoid
pressure gradient errors caused by steep bathymetry.
The model is forced with spatially uniform but temporally varying winds, measured
every hour at the Thomas Point Light Buoy (-76.4°W, 38.9°N). These observed winds are used
rather than other wind products such as those derived from the North American Regional
Reanalysis (NARR), since the latter underestimates the observed summer winds by roughly 30%,
and does not show the strong directional asymmetry that may play a key role in modulating the
strength of vertical mixing [Scully, 2013]. Other atmospheric forcing, including air temperature,
relative humidity, pressure, precipitation, long and shortwave radiation were obtained from
7
NARR with the 3-hr time resolution. The NARR shortwave radiation was found to be
systematically higher than adjacent buoy observations, and therefore it was reduced by 80%
[Wang et al., 2012]. At the open boundary, the model is forced by open ocean tides and non-tidal
water levels as in ChesROMS [Xu et al., 2012].
The model is configured to use the recursive MPDATA 3D advection scheme for tracers,
3rd-order upstream advection scheme for 3D horizontal momentum and 4th-order centered
difference for 3D momentum in the vertical. The Generic Length-Scale vertical turbulent mixing
scheme [Warner et al., 2005b] is implemented with the stability functions of Kantha and
Clayson [1994], and background mixing coefficients for both momentum and tracers are set to
10-5 m2 s-1 as in Scully [2010].
2.2 Biogeochemical Model
The biogeochemical model (Fig. 2b) describes a simplified nitrogen cycle with eleven
state variables: nitrate ([NO3]), ammonium ([NH4]), phytoplankton (P), zooplankton (Z), small
and large detritus (DS and DL), semi-labile and refractory dissolved organic nitrogen ([DON]SL
and [DON]RF), inorganic suspended solids ([ISS]), chlorophyll ([Chl]) and oxygen ([O2]).
Although analogous carbon state variables are included in the model as well (dissolved organic
carbon, detrital carbon and dissolved inorganic carbon), these will be described and analyzed in a
separate publication.
All state variables are horizontally and vertically advected and diffused along with the
physical circulation variables. The biological source/sink terms, functions and parameter values
are presented in Appendix A. The model structure was based on Druon et al. [2010], which was
originally derived from Fennel et al. [2006], with modifications similar to those described by
Hofmann et al. [2008, 2011] and Friedrichs et al. [2014, this issue]. However, these models were
8
all designed for coastal applications. To adapt the model to an estuarine application in the
Chesapeake Bay, a number of model formulations were modified, as described below.
2.2.1 Refractory and Semi-labile DON
Since dissolved organic nitrogen plays a critical role in estuarine nitrogen cycling
processes [Keller and Hood, 2011], semi-labile and refractory DON components are included as
separate state variables in the model. Although [DON]RF does not participate actively in any
biological processes, it is input from the rivers, and transported via advection and diffusion
throughout the model domain and reduces the light intensity. The [DON]SL is derived from
phytoplankton exudation, sloppy feeding and detrital solubilization, and is remineralized into
NH4 (Appendix A).
2.2.2 Inorganic Suspended Solids
As the refractory DON, inorganic suspended solids (ISS) do not participate in the
nitrogen cycling directly, but play an important role in reducing the light intensity in the northern
Chesapeake Bay. The ISS formulation and related parameters follow Xu and Hood [2006].
Specifically, ISS is introduced as an additional state variable, which is decreased by water
column sinking and increased by bottom resuspension (Appendix A).
2.2.3 Light Attenuation
The photosynthetic available radiation decreases exponentially with water depth:
where I0 is the light just below the sea surface, PARfrac is the fraction of light that is available
for photosynthesis, KD is the diffuse attenuation coefficient, and z is depth. Xu et al. [2005] used
chlorophyll, total suspended solids (TSS), and surface salinity to specify KD for the Chesapeake
Bay, where salinity was used as a proxy for chromophoric dissolved organic matter (CDOM),
I(z) = I0 ⋅PARfrac ⋅e−zKD
9
since CDOM is generally inversely related to salinity. To avoid KD becoming negative in high
salinity regimes, Xu et al. [2005] identified empirical relations for high (≥ 15 psu) and low (≤ 15
psu) salinity regimes, respectively. They found that their model successfully explained 70% of
the observed KD variability in the Chesapeake Bay. However, their empirical relationship was
based on Chesapeake Bay Program observations from 1995 and 1996, which is outside the more
recent study period used in this analysis. Therefore, as a part of this analysis their method was
repeated using observations from 2000-2005, and resulted in the following empirical
relationship:
where TSS [in mg L-1] represents total suspended solids, including both the inorganic suspended
solids (section 2.1.2.1) and organic suspended solids (defined here as particulate organic nitrogen
(PON) including P, Z, DS, and DL). This relationship was found to explain 76% of the observed
variability in KD. Chlorophyll was excluded from the relationship, as it did not successfully
explain any significant additional variability [Feng et al., in prep.]. With this single relationship,
KD is positive when salinity is less than ~24 PSU, which covers almost the entire Chesapeake
Bay. In high salinity regions of the model domain (close to the Bay mouth and on the Mid-
Atlantic Bight shelf) it is possible for the right hand side of the above equation to become
negative. To prevent this, the configuration of KD used for the U.S East Coast shelf model
[Friedrichs et al., 2014, this issue], is used in high salinity regimes:
If , then
where [DON] represents total DON, i.e. the sum of both refractory and semi-labile components.
Table 2: Comparison of Chesapeake Bay Nitrogen Fluxes (1×109g-N y-1) Historical
estimates (1975-1990)
ROMS-ECB Model1 (2001-2005)
Total Nitrogen input from river 134a DIN - 96 ± 50
TON - 58 ± 22 Burial Stem + Trib 53b 46 ± 10
Stem 21c 22 ± 4 Denitrificationd
Stem + Trib 40b 34 ± 10 Stem 23c 22 ± 9
Net ecosystem metabolisme 54c 74 ± 23 Total Nitrogen export to ocean
DIN 3c 8 ± 8 TON 78c 91 ± 36
aFrom this study bFrom data derived estimates of Boynton et al. [1995] cFrom data derived estimates of Kemp et al. [1997] dIncludes both water column and sediment denitrification eCalculated from TON budget
40
Table 3: Comparison of Chesapeake Bay Nitrogen Fluxes (1×109g-N y-1) Obtained Using Estuarine Model (ROMS-ECB; this study) and Shelf Model (USECoS; Friedrichs et al., this issue]. ROMS-ECB
(2001-2005) USECoS (2004-2008)
river input DIN 96.4 ± 49.5 82.4 ± 16.1 river input TON 58.1 ± 22.2 74.5 ± 16 Burial 45.7 ± 9.5 5.1 ± 0.6 Denitrification3 33.5 ± 9.6 6.0 ± 0.5 DIN export to ocean 7.9 ± 7.7 49.0 ± 10.4 TON export to ocean 90.5 ± 35.7 55.9 ± 10.9
41
Table A1: State Variable Equations Including All Biogeochemical Source and Sink Terms Variable (Symbol)
Processes Time rate of change in each term
Phytoplankton (P)
Change per unit time = +Primary production ([NH4]+ [NO3]) -‐Exudation ([DON]SL) -‐Exudation ([NH4]) -‐Grazing assimilation (Z) -‐Fecal pellets from grazing on P ( DL)
+ Primary production ([NH4] + [NO3] P) + C excess based production ([CO2]P) -‐ Nitrification ([NH4] [NO3]) -‐ Exudation (P [NH4]) -‐ Excretion (Z [NH4])
Figure 4: Observed and simulated mean monthly depth-averaged temperature from
2001-2005 in the upper (a), middle (b), and lower (c) Bay. Vertical bars represent ±1
standard deviation.
50
Figure 5: Taylor (a) and Target (b) diagrams illustrating model skill for hydrodynamic
and biogeochemical fields. Squares represent temporal model skill and circles represent
spatial model skill.
Figure 6: As in Figure 3, except for salinity.
Figure 7: Observed and simulated monthly salinity from 2001-2005 in the upper (a),
middle (b), and lower (c) Bay. Error bars are ±1 standard deviations.
Figure 8: Observed and simulated climatological (average over five years)
biogeochemical fields from 2001-2005. Panels from top to bottom: NO2 + NO3, NH4,
PON, DON, chlorophyll, and oxygen. Left panels: concentrations along the trench with
background color representing the simulation and circles showing the observations. Right
panels: vertically integrated observed and simulated concentrations at stations shown in
Figure 1b with error bars showing ±1 standard deviation relative to the 5-year mean.
Gray dashed lines in (a) denote the boundaries of the upper, middle and lower Bay.
Figure 9: Observed and simulated vertically integrated monthly biogeochemical fields
averaged over 2001-2005. Error bars are ±1 standard deviations. Panels from left to right:
NO2 + NO3, NH4, PON, DON, chlorophyll and oxygen. Panels from top to bottom:
upper, middle and lower Bay.
51
Figure 10: Comparison between five-year (2001-2005) averaged sea surface chlorophyll
from (a) SeaWiFS and (b) model simulation. Skill assessment is illustrated by (c)
unbiased RMSD, and (d) Willmott skill together with histograms of (e) unbiased RMSD
and (f) Willmott skill.
Figure 11: The nitrogen budget for 2001-2005 in the Chesapeake Bay from our modeling
system (unit: 1 × 109 g-N y-1). The exchange of DIN/PON between the internal Bay and
exterior ocean was estimated using the mean velocity and DIN/PON concentration fields
averaged daily at a cross section of Bay mouth (red line in Figure 1a). Net ecosystem
metabolism (NEM) was estimated from the TON budget as in Kemp et al. [1997].
Figure 12: Interannual variability of nitrogen fluxes computed for the 2001-2005
analysis period.
Figure 1: Chesapeake Bay (a) model grid and bathymetry, and (b) map illustrating location of riverine inputs (magenta dots), Thomas Point Light Buoy used for wind forcing (yellow triangle) and EPA Chesapeake Bay Program Water Quality Monitoring Stations in the upper (red circles), middle (green circles), and lower (blue circles) Bay. The black line in (a) denotes the edge of the Bay interior, over which the Bay-wide budget numbers are computed. The black line in (b) shows the stations along the trench of the Bay used in Figs. 3, 6, 8.
77.5 77 76.5 76 75.5 7536
36.5
37
37.5
38
38.5
39
39.5
40
longitude(oW)
latit
ude(
o N)
a
10m 20m 30m
77.5 77 76.5 76 75.5 7536
36.5
37
37.5
38
38.5
39
39.5
40
longitude(oW)
latit
ude(
o N)
b
uppermiddlelower
Figure 2: Schematic illustrating the nitrogen component of ROMS-ECB.
[DON]SL(
[DON]SL(
[NO3]( [NH4](
P(
Z(
DS(
DL(
PON(
[NO3]( [NH4](
Water(Column(
[O2](
ISS(
[DON]RF(
N2(Sediment(
Figure 3: Observed and simulated seasonal temperature from 2001-2005. Left panels: temperature along the trench with background color representing the simulation and circles showing the observations. Right panels: modeled vs. observed temperature at coincident times and locations. Panels from top to bottom: winter (Dec-Feb); spring (Mar-May); summer (Jun-Aug) and fall (Sep-Nov). Gray dashed lines in (a) denote the boundaries of the upper, middle and lower Bay. Stations from upper Bay to lower Bay are: CB2.1, CB2.2, CB3.1, CB3.2, CB3.3C, CB4.1C, CB4.2C, CB4.3C, CB5.1, CB5.2, CB5.3, CB5.4, CB5.5, CB6.1, CB6.2, CB6.3, CB7.3, CB7.4.
−30−25−20−15−10−5
Upper Middle Lower
dept
h(m
)
a−10
0
10
20
30
40
−10
0
10
20
30
40
Mod
el (o C)
b
−30−25−20−15−10−5
dept
h(m
)
c−10
0
10
20
30
40
−10
0
10
20
30
40
Mod
el (o C)
d
−30−25−20−15−10−5
dept
h(m
)
e−10
0
10
20
30
40
−10
0
10
20
30
40
Mod
el (o C)
f
300250200150100500
−30−25−20−15−10−5
distance (km) Upper Bay −−> Lower Bay
dept
h(m
)
g−10
0
10
20
30
40
−10 0 10 20 30 40−10
0
10
20
30
40
Observation (oC)
Mod
el (o C)
h
Figure 4: Observed and simulated mean monthly depth-averaged temperature from 2001-2005 in the upper (a), middle (b), and lower (c) Bay. Vertical bars represent ±1 standard deviation.
Figure 5: Taylor (a) and Target (b) diagrams illustrating model skill for hydrodynamic and biogeochemical fields. Squares represent temporal model skill and circles represent spatial model skill.
0 0.5 1 1.5 2
−0.99
−0.9
−0.7
−0.5
−0.3−0.1 0.1
0.3
0.5
0.7
0.9
0.99
Standard deviation
Cor re lat ion Coef f ic ient
O
a
ubRMSD
Bias
−1.5 −1 −0.5 0 0.5 1 1.5
−1.5
−1
−0.5
0
0.5
1
1.5b
Temperature (spatial)Salinity (spatial)NO2 + NO3 (spatial)
Figure 7: Observed and simulated monthly salinity from 2001-2005 in the upper (a), middle (b), and lower (c) Bay. Error bars are ±1 standard deviations.
Figure 8: Observed and simulated climatological (average over five years) biogeochemical fields from 2001-2005. Panels from top to bottom: NO2 + NO3, NH4, PON, DON, chlorophyll, and oxygen. Left panels: concentrations along the trench with background color representing the simulation and circles showing the observations. Right panels: vertically integrated observed and simulated concentrations at stations shown in Figure 1b with error bars showing ±1 standard deviation relative to the 5-year mean. Gray dashed lines in (a) denote the boundaries of the upper, middle and lower Bay.
300250200150100500
−30
−20
−10
Upper Middle Lower
dept
h(m
)
a
mm
ole−
N m−3
0
20
40
60
80
0 1 2 30
1
2
3
Observation (mole−N/m2)
Mod
el(m
ole−
N/m
2 )
b
300250200150100500
−30
−20
−10
dept
h(m
)
c
mm
ole−
N m−3
0
5
10
0 0.5 1 1.50
0.5
1
1.5
Observation (mole−N/m2)
Mod
el(m
ole−
N/m
2 )
d
300250200150100500
−30
−20
−10
dept
h(m
)
em
mol
e−N
m−3
0
5
10
15
20
25
0 0.5 1 1.50
0.5
1
1.5
Observation (mole−N/m2)
Mod
el(m
ole−
N/m
2 )
f
300250200150100500
−30
−20
−10
dept
h(m
)
g
mm
ole−
N m−3
0
10
20
30
0 0.5 1 1.50
0.5
1
1.5
Observation (mole−N/m2)
Mod
el(m
ole−
N/m
2 )
h
300250200150100500
−30
−20
−10
dept
h(m
)
i
mg−
Chl m
−3
0.1
1
10
100
101 102 103 104101
102
103
104
Observation (mg−Chl/m2)
Mod
el(m
g−Ch
l/m2 )
j
300250200150100500
−30
−20
−10
distance (km) Upper Bay −−> Lower Bay
dept
h(m
)
k
mm
ole−
O2 m
−3
0
100
200
300
0 5 10 15 200
5
10
15
20
Observation (mole−O2/m2)
Mod
el(m
ole−
O2/m
2 )
l
Figure 9: Observed and simulated vertically integrated monthly biogeochemical fields averaged over 2001-2005. Error bars are ±1 standard deviations. Panels from left to right: NO2 + NO3, NH4, PON, DON, chlorophyll and oxygen. Panels from top to bottom: upper, middle and lower Bay.
00.5
11.5
22.5 a
UPPE
R BA
Ym
ole−
N/m
2
NO2 + NO3
b
NH4
c
PON
d
DON
0
0.4
0.8
1.2
g−Ch
l/m2
e
CHLA
05
10152025
mol
e−O
2/m2
f
O2
obsmod
00.5
11.5
22.5 g
MID
DLE
BAY
mol
e−N/
m2
h i j
0
0.4
0.8
1.2
g−Ch
l/m2
k
05
10152025
mol
e−O
2/m2
l
J A J O0
0.51
1.52
2.5 m
LOW
ER B
AYm
ole−
N/m
2
J A J O
n
J A J O
o
J A J O
p
J A J O0
0.4
0.8
1.2
g−Ch
l/m2
q
J A J O05
10152025
mol
e−O
2/m2
r
Figure 10: Comparison between five-year (2001-2005) averaged sea surface chlorophyll from (a) SeaWiFS and (b) model simulation. Skill assessment is illustrated by (c) unbiased RMSD, and (d) Willmott skill together with histograms of (e) unbiased RMSD and (f) Willmott skill.
Figure 11: The nitrogen budget for 2001-2005 in the Chesapeake Bay from our modeling system (unit: 1 × 109 g-N y-1). The exchange of DIN/PON between the internal Bay and exterior ocean was estimated using the mean velocity and DIN/PON concentration fields averaged daily at a cross section of Bay mouth (red line in Figure 1a). Net ecosystem metabolism (NEM) was estimated from the TON budget as in Kemp et al. [1997].
Figure 12: Interannual variability of nitrogen fluxes computed for the 2001-2005 analysis period.