Constructing Historical Long-Term Primary 

Production in the Chesapeake Bay for Use in 

Ecosystem-Based Fisheries Management 

Models 

Hongguang Ma, Howard Townsend, Madeline B. Sigrist, Villy Christensen, Carl 

Walters, Joe Buszowski, Josh Korman, and Dave Preikshot 

U.S. Department of Commerce 

National Oceanic and Atmospheric Administration 

National Marine Fisheries Service 

NOAA Technical Memorandum NMFS-F/SPO-105 

October 2009 

Constructing Historical Long-Term Primary 

Production in the Chesapeake Bay for Use in 

Ecosystem-Based Fisheries Management Models 

Hongguang Ma1, Howard Townsend2, Madeline B. Sigrist3, Villy Christensen4, Carl Walters4, Joe 

Buszowski5

, Josh Korman6, and Dave Preikshot7

1

NOAA Chesapeake Bay Office/Cooperative Oxford Laboratory/Versar, Inc., 904 South Morris Street, Oxford, MD 21654; 

email Hongguang.Ma@noaa.gov 

2

NOAA Chesapeake Bay Office/Cooperative Oxford Laboratory, 904 South Morris Street, Oxford, MD 2165; email 

Howard.Townsend@noaa.gov 

3

NOAA/Chesapeake Research Consortium, 410 Severn Avenue, Suite 107A, Annapolis, MD 20403 

4

Fisheries Centre, University of British Columbia, 2259 Lower Mall, Vancouver, British Columbia, Canada V6T 1Z4. 

Telephone 604-822-5751; fax 604-822 8934; email v.christensen@fisheries.ubc.ca 

5

Mountainsoft, 1028 Benchlands Tr., Canmore, Alberta, Canada T1W 3B6. Telephone 403-687-6000; email 

joe@mountainsoft.net 

6

Ecometric Research Inc., 3560 22nd Ave. W, Vancouver, British Columbia, Canada V6S 1J3. Telephone 604-737-8314; 

email jkorman@ecometric.com 

7

Pacific Biological Station, Department of Fisheries and Oceans, 3190 Hammond Bay Road, Nanaimo, British Columbia, 

Canada V9T 6N7 

NOAA Technical Memorandum NMFS-F/SPO-105 

October 2009

U.S. Department of Commerce 

Gary Locke, Secretary 

National Oceanic and Atmospheric Administration 

Jane Lubchenco, Ph.D., Under Secretary for Oceans and Atmosphere 

National Marine Fisheries Service 

James W. Balsiger, Ph.D., Acting Assistant Administrator for Fisheries
Suggested citation: 

Ma, Hongguang, and Howard Townsend, Madeline B. Sigrist, Villy Christensen, Carl Walters, Joe 

Buszowski, Josh Korman, and Dave Preikshot. 2009. Constructing Historical Long-Term Primary 

Production in the Chesapeake Bay for Use in Ecosystem-Based Fisheries Management Models. U.S. 

Dep. Commerce, NOAA Tech. Memo. NMFS-F/SPO-105, 51 p. 

A copy of this report may be obtained from: 

Howard Townsend 

NOAA Chesapeake Bay Office 

410 Severn Ave., Suite 107A 

Annapolis, MD 21403 

howard.townsend@noaa.gov 

Or online at: 

http://chesapeakebay.noaa.gov 
1 

Table of Contents 

Introduction..................................................................................................................................................2 

Methods........................................................................................................................................................5 

Results and Discussion ................................................................................................................................7 

Acknowledgments........................................................................................................................................9 

References..................................................................................................................................................10 

Tables.........................................................................................................................................................11 

Appendices.................................................................................................................................................14 

Figures........................................................................................................................................................22 
2 

Introduction 

Need for historical reconstruction of primary production 

A 2006 report by the National Research Council states that in order to deal with rapid changes in marine 

ecosystems brought about by human impacts, progress toward ecosystem-based management (or 

ecosystem approaches to management) is necessary (NRC 2006). Ecosystem-based management for 

fisheries requires a long-term historical perspective to enable policymakers to account for shifting 

baselines (sensu Pauly 1995). Such long-term perspective also fosters a better understanding of largescale, long-term ecosystem mechanisms and, therefore, supports a broader understanding of what to 

account for in the future. Consequently, extensive data are needed to model fisheries ecosystems 

especially for periods of many decades. Historical fish population data can be obtained from fish 

surveys, existing models, and catch records. Unfortunately, historical data on primary producers and 

lower trophic levels are often less available, collected discontinuously, or measured in ways not readily 

transferable to the context of a fisheries-based modeling perspective. We are thus often compelled to use 

historical models and reconstructions of primary producers and lower trophic levels.

Coupled physical-biological models such as Nutrients-Plankton-Zooplankton-Detritus models (e.g., 

Aksnes and Lie 1990, Xu and Hood 2006) can be used to estimate primary production; however, they 

simulate processes on fine spatial and temporal scales and cover relatively short time periods. This scale 

is not well suited for dynamic-modeling-based historical reconstruction of ecosystems, because 

computation time requirements to span a model for several decades would create prohibitively long 

simulation runs. In a more generalized fishery-ecosystem model, multiple simulation runs can be 

subjected to sensitivity analysis and thorough exploratory analyses. Such broader-scope approaches are 

recommended to account for wider ranges of ecosystem services and potential consequences as a result 

of fishing (NRC 2006). 

In this paper, we describe the development of a simple hydrodynamic model with a monthly time step to 

create a long-term forcing function for a fisheries ecosystem model of the Chesapeake Bay. Harding 

(1994) and Harding and Perry (1997) provided an analysis of historical and recent data on chlorophyll-a 

for the Chesapeake Bay. Such indices could be used as a forcing function for the fisheries model but the 

coverage for chlorophyll-a observations was sparse in the 1950s (though improved in the 1960s and 

1970s). Good coverage started after 1984 when a systematic monitoring program began. Using a 

dynamic model, instead of static indices, allows us to explore the potential impact of historical changes 

in factors such as nutrient loading and its influence on the fisheries ecosystem. To bridge the gap 

between coupled biological-physical models and fisheries models, in support of ecosystem-based 

management, we have developed a regional estuarine ecology model to reconstruct historical primary 

production in the Chesapeake Bay. This primary production reconstruction has been used as a forcing 

function for phytoplankton production rate in an ecosystem-based fisheries model in the Chesapeake 

Bay (Christensen et al. 2008, Ma et al. 2008). 

Chesapeake Bay Regional Estuarine Ecosystem Model (CBREEM) 

Plausible simulations of the Chesapeake Bay’s ecosystem history designed to improve the model’s fit to 

time-series data) require a method to emulate potential changes in productivity attributable to primary 

production. Previously, no indices existed to encapsulate 50 years of primary production in the 

Chesapeake Bay. Therefore, to create an index of primary productivity, we developed the Chesapeake 

Bay Regional Estuarine Ecosystem Model (CBREEM). 
3 

CBREEM is a simple, linearized, barotropic, two-layer hydrodynamic model; it uses climatologic, 

hydrologic, and nutrient loading data to estimate historical patterns in primary productivity for a 

regional estuary. The objective in developing this modeling system for the Chesapeake estuary was to 

integrate information on physical, chemical, and ecological processes into a model for predicting 

temporal and spatial changes in key indicators of interest to environmental managers. 

The processes included in CBREEM involve variables that can change on various temporal and spatial 

scales, from minutes and meters up to years and kilometers. When faced with such disparate scales in a 

complex system, modeling generally involves defining a space/time ‘window’ of primary interest. 

Dynamic variation that is very fast compared to this window is modeled in terms of time-varying 

equilibria and averages, and variation that is very slow is represented through constant ‘parameters.’ The 

window of primary interest for CBREEM is a seasonal variation on spatial scales of one to two 

kilometers. A model time step of one month was used by the model to capture this seasonal variation. 

Based on this window, we elected to treat most physical and chemical processes, such as diurnal 

variation in wind-driven currents and associated chemical concentration fields, which come to 

equilibrium on time scales of hours, by calculating equilibrium spatial fields of these variables and then 

averaging the equilibria over monthly time steps. Longer-term variations, such as decadal trends in sealevel heights, are treated as constant parameters for individual runs of the model that focus on the 

window of primary ecological concern.

CBREEM solves for equilibrium velocity fields on a Richardson grid i.e., Arakawa E-grid in a monthly 

time step. It then solves for wind forcing and water forcing to determine water mass flow, which is used 

for making chemical mass-balanced calculations in the grid. The chemical part of the model is thus very 

simple, though the developers aimed initially to use non-steady-state hydrodynamic models to drive the 

chemical part of the CBREEM. The more complex representation was not used because the velocity 

fields failed to satisfy mass-balance. Instead, a linearized hydrodynamic model was used for most 

calculations. 

The model uses grids placed over the entire tidal portion of the Chesapeake Bay. Freshwater inputs were 

added with a one-month time step. Wind was used to drive surface flows. Monthly hydrodynamic 

velocity fields were calculated in two layers: shallow and deep water. Layer thickness changes due to 

baroclinic effects. Transport patterns produced by this model feed the equilibrium chemical model. 

The equilibrium chemical model solves partial differential equations, setting ∂t

∂ (concentration) = 0. It 

was assumed that the equilibrium concentration was equivalent to the monthly average. This was tested 

in Tampa Bay and seemed to fit these chemical patterns well (unpublished). This methodology was first 

developed by John Hunter in Australia in the 1980s, and the programming code from Hunter 

(unpublished) was used and carried forward to present-day models. 

The approach for hydrodynamic modeling is similar to that used by Wright et al. (1986) for the Gulf of 

Maine. It first solves for total flows and then solves for vertical structure, based on Hunter and Hearn’s 

(1991) methodology, 

x h

fv g t

u

b

x

s

x

ρ

τ − τ

+

∂

∂ζ − = − ∂

∂
4 

y h

fu g t

v

b

y

s

y

ρ

τ − τ

+

∂

∂ζ + = − ∂∂

(hv) 0

y

(hu)

t x = ∂

∂

+

∂

∂

+

∂

∂ζ

where u and v are the x- and y-components of non-tidal velocity, x and y are cross-shelf and along-shelf 

directions, t is time, s b τ τ y y & are surface and bottom stresses in y direction, s b τx x &τ are surface and 

bottom stresses in x direction, h is the water depth in the absence of motion, ξ is the surface elevation 

above z = 0, g is the acceleration due to gravity, and ρ is the seawater density. 

The model then performs chemical calculations to set partial differential equations for spatial and 

temporal change. The model is subdivided into physical and chemical submodels that work in 

combination to generate monthly time series of ecological parameters of concern. 

a. Physical submodel 

The hydrographic/physical submodel computes the equilibrium wind-driven horizontal currents and 

upwelling velocities between the two layers that will develop after a few hours of steady winds over the 

model grid, if baroclinic current effects are not large. Coriolis force is ignored in the model. The model 

is parameterized so that the modeler/user can directly enter desired average current velocities expected 

in open water from wind stress effects in both west to east and north to south directions. Sea-surface 

height anomalies can be set at the boundaries of the grid. The ratio of interface to bottom friction makes 

the model behave as a single-layer system (surface and bottom velocities equal) when set to low values, 

and makes the surface layer move much faster than the deep layer if when set to large values. 

The horizontal and vertical mixing-rate parameters define how much water movement and mixing will 

occur in addition to the directional movement created by wind-driven currents. The chemical 

distribution simulations assume that all input chemicals (from point or freshwater sources) are initially 

delivered to the surface water layer via freshwater buoyancy effects. The vertical mixing rate parameters 

along with particulate organic sinking and upwelling/downwelling rates provide the modeled 

mechanisms by which chemicals may reach deep water near the input. 

b. Chemistry submodel 

Equilibrium chemical concentrations for each month are computed by setting up and solving the sparse 

linear equation system that results when the derivative of concentration is set to zero in the differential 

equation, defining rate of concentration change in each model grid cell as a (linear) function of inputs 

(velocities · input concentrations), outputs (velocities · concentration in cell), and internal gain/loss rates 

(e.g., decomposition = constant · concentration). For phytoplankton, each cell is assigned a growth rate 

equal to a maximum rate times phytoplankton concentration times a Michaelis-Menten function of 

available nitrogen concentration in the cell (which in turn is computed as total nitrogen minus nitrogen 

already in phytoplankton in the cell). 

c. Ecology submodel 

The purpose of the ecology submodel is to simulate monthly changes in sea grass and epiphyte 

populations over several years or decades and to examine cumulative impacts of the physical and 

chemistry regimes predicted by the model. Also computed in this submodel are seasonal changes in 

photoperiod, water temperature, and light penetration to the sea bottom over the grid. Light penetration 
5 

patterns are computed from epiphyte and phytoplankton concentrations and also are also influenced by 

suspended particulate concentrations. The submodel for sea grass and epiphyte still needs to be 

improved because of lack of accurate estimates for some parameters or input data. 

Phytoplankton growth rates, used to calculated steady-state monthly chlorophyll-a concentrations, are 

computed based on the maximum doubling rate, reduced by a factor calculated from a MichaelisMenten relationship (the Michaelis constant is the concentration of nitrogen resulting in a 50% reduction 

in the maximum growth rate). Epiphytic algae growth is also controlled by Michaelis-Menten kinetics in 

addition to a loss rate that represents physical erosion as well as constant grazing. Sea grass growth is 

computed based on a maximum potential rate, adjusted by the difference between photosynthetic gains 

less respiration losses, driven by monthly changes in temperature and light. Sea grass biomass 

accumulation changes based on current biomass and salinity. Sea grass biomass is calculated as a 

function of the previous month’s biomass plus the total new growth in the current month less losses due 

to leaf turnover. 

Currently, we only focus on the phytoplankton component in CBREEM, which is driven by nutrient. In 

the fisheries ecosystem model, the production rate is forced by long-term output (for ~ 50 years) from 

CBREEM for phytoplankton, not for sea grass. 

Methods 

Precipitation

Monthly total precipitation data were found at the NOAA National Climate Data Center, specifically, 

the section for cooperative precipitation stations (www.ncdc.noaa.gov/oa/climate/online/coopprecip.html). Data were available for Chestertown, Conowingo Dam, Solomons, Salisbury, Wicomico, 

Annapolis (by combining figures from the United States Naval Academy and the Annapolis police 

barracks), and Unionville, Maryland; and Norfolk, Fredericksburg, and Warsaw, Virginia. 

The time span covered for each station varies, and not all cover the period of interest for the reference 

data needed for the Chesapeake Bay spatial model (1950-2002). Thus, the stations listed above were 

chosen as representative of the geographic extent of the Chesapeake Bay region, while being adequately 

stocked with data to cover the time period of interest. If data points were missing, then values were 

inserted equal to the average for that month from the previous two and the following two years. 

Two stations did not have complete data sets (Solomons and Unionville, Maryland). Solomons data 

from 1983 onward was generated by correlation to rainfall at Warsaw, Virginia (which had the highest 

correlation to historic Solomon's data of all other stations in the analysis r2 = 0.529). For Unionville, the 

1997 data were based on correlation analysis with Conowingo data (r2 = 0.563). 

The rainfall data were available from 1950 to the end of 1997. The data for 1998-2002 were generated 

via an estimation technique. A reference index was calculated based on multiplier values of rainfall 

measured in Washington, D.C. For each month (from 1998-2002), the rainfall measurement was 

divided by the average for that month over the five-year period. The resulting yearly and monthly 

(January 1998-December 2002) values were then multiplied by the average for each station for that 

month to fill in values from January 1998 to December 2002. This assumes a rough correlation between 
6 

rainfall in Washington and the other stations of the region, which was supported by a graphic analysis of 

the time-series data for the 1950 to 1997 data for Washington and all other stations. 

Wind 

Wind data were available on a daily basis from 1985 to 2002 at the NOAA National Buoy Data Buoy 

Center web site (www.ndbc.noaa.gov/cwind.shtml). 

Only two stations were available for the Chesapeake Bay region; the one selected was Station TPLM2-

Thomas Point, MD 38.90 N 76.44 W (38°53'54" N 76°26'12" W). The data were reported as wind speed 

(m s-1) averaged over an eight-minute period once for each hour of the day. Gaps in data were common 

and were dealt with according to three rules, depending on the length of time lost. For up to 12 hours of 

missing data, the average of the two blocks of equal time, both before and after the missing values, were 

inserted. The assumption being, that at so short a time scale, the daily weather behavior around that time 

block would accurately predict the missing values. Correlation analysis showed that for the 1985 data, 

the prediction method compared to the actual data resulted in an r2

 value of 0.81. For missing time 

periods between 12 hours and 9 days 23 hours, the data were deleted, and average monthly data 

calculated without that time block. For missing time periods 10 days or greater, the average for that date 

and time over the five previous and five succeeding years were inserted, on the assumption that climatic 

mean data would account for this longer-term missing data. After 1996, the data set was very poor (more 

than 200 errors per year in data records, though never in long time spreads), so all missing data was 

simply deleted for this time period. 

Sine and cosine functions were used to convert each hourly wind direction and speed to an x and y 

vector, which in turn were used to find average monthly wind directions. The average velocity of the 

wind, however, was generated by simply averaging the wind speed scalar values for each month. The 

average monthly direction of the wind was calculated by determining the average x and y vector for 

each month and converting that to a radians scalar. Thus, the final x and y vectors are combinations of 

the average of the wind speeds for any given month and the average direction for the x and y 

components for each month. 

The historical average monthly winds for 1950 to 1985 were estimated by taking the average wind 

vectors for that month (1950-1985). The historic estimated winds were simply the average monthly 

absolute velocity scalar value applied to the average monthly direction values. One problem with this 

method was the inability to predict extreme events. That is, the predicted values have diminished 

amplitude compared to real values. This should be considered during model simulations, because it 

suggests that any anomalous wind events (i.e., those of durations on the order of two weeks that would 

generate higher or lower amplitude behavior in monthly averaged data) between 1950 and 1985 will not 

be captured by the model. 

River Flow

River-flow data, recorded as average flow on a daily basis in ft3 s-1 were found on the USGS web site for 

surface water statistics on Maryland and Virginia (http://waterdata.usgs.gov/md/nwis/sw and 

http://waterdata.usgs.gov/va/nwis/sw). The daily values were averaged over each month to derive time 

series for the Choptank, James, Nanticoke, Patapsco, Patuxent, Potomac, Rappahannock, Susquehanna, 

and York Rivers. River gage data exists for numerous locations on all these rivers, but much of it starts 

after the period of interest, ends before 2003, or is discontinuous. Therefore, gages were chosen to be as 
7 

close as possible to the river mouth, while maximizing the percentage of time coverage for the purposes 

of the model. 

The only missing data in the time series were for the Patapsco, Patuxent, and Susquehanna Rivers. In 

each case, a correlation analysis was done to find the average monthly river flows (as an x value) from 

each of the other six rivers compared to predicted monthly averages for the three incomplete time series 

(as the resultant y value). For the Patapsco River, missing values were based on the Potomac River (Y = 

0.0125 · X + 40.78; r2

 = 0.439). For the Patuxent River, missing values were based on the 

Rappahannock River (Y = 0.01375 · X + 127.83; r2

 = 0.725). For the Susquehanna River, missing values 

were derived from the Potomac River (Y = 2.2859 · X + 12229; r2 = 0.664). The flow for the “York” 

River is a result of adding flows from its two tributaries, the Pamunkey and Mattaponi Rivers. 

By tracking flows for these rivers we also account for the vast majority of the fresh-water input to the 

Chesapeake Bay. According to a January 6, 2004, USGS press release 

(http://md.water.usgs.gov/outreach/), of the rivers described in this data set, three (the Susquehanna, 

Potomac, and James) account for approximately 85% of the total fresh-water input to the Chesapeake 

Bay. Using long-term medians of the data at hand, we can therefore estimate that the remaining six 

account for a further 10% of Chesapeake Bay fresh water. This implies that the flow time series here 

account for approximately 95% of all the fresh-water input to the Bay. 

Total Nitrogen 

Time series of total nitrogen were available for nine major rivers as well as for 40 stations in the 

Chesapeake Bay itself (Figure 1). All of these data sets were obtained from the online water quality 

database for the Chesapeake Bay Program (http://www.chesapeakebay.net). Monitoring data for all 

stations began in 1984 on a roughly biweekly basis and continues to the present. For the Chesapeake 

Bay stations, data were grouped by month and into two sets, above and below the pycnocline. All river 

stations were chosen to be representative of the river mouth (in some cases a so-called ‘lower estuary’ 

station had to be used). 

Total nitrogen time-series data for Susquehanna River before 1984 were estimated by using an index 

derived by Hagy et al. (2004) and a scaling factor to enable the end point of the Hagy data to meet the 

start point of the Chesapeake Bay Program data. The nitrogen time series for two other major rivers 

(Potomac and James) before 1984 were estimated from the Susquehanna data. The scale factors were 

acquired by minimizing the sum of squared differences between the monthly time series for each river 

(after 1984) and the product of the Susquehanna index and the scale factor. The values are 1.682 for 

Susquehanna River, 0.734 for Potomac River, and 0.510 for James River. These factors were then 

applied to Hagy’s Susquehanna index for the period before 1984 to derive probable nitrogen 

concentrations and interannual trends for all these three rivers from 1950 to 1984. For other rivers, the 

nitrogen monthly time series before 1984 were the monthly average after 1984. 

Results and Discussion 

River flow and relative nutrient loading 

Susquehanna River, Potomac River, and James River were the top three in terms of river flow, with 

average monthly rates of 38685, 11629, and 6815 ft3

 s-1 for years 1945 to 2001 (Figure 2). The average 

rates for Rappahannock River and York River were around 1500 ft3

 s-1. The average rates for the other 
8 

four rivers (Choptank River, Nanticoke River, Patapsco River, Patuxent River) were less than 400 ft3

 s1

(Figure 2). Based on the monitoring data from 1984 to 2003, the mean (standard deviation) of monthly 

average nitrogen concentration was 0.9105 (0.4163) mg l-1 for Choptank River, 0.4788 (0.1329) mg l-1

for James River, 1.4868 (0.7645) mg l-1 for Nanticoke River, 1.3054 (0.3741) mg l-1 for Patapsco River, 

0.7234 (0.1935) mg l-1 for Patuxent River, 0.6886 (0.1825) mg l-1 for Potomac River, 0.5399 (0.1272) 

mg l-1 for Rappahannock River, 1.5988 (0.3459) mg l-1 Susquehanna River, and 0.4967 (0.1055) mg l-1

for York River. The average nutrient concentration for the Bay (1984 to 2003) was 0.9126 mg l-1. The 

relative monthly concentration (to the average concentration 0.9126 mg l-1) at each river was used as 

relative nutrient loading in the input file. 

Time series of chlorophyll-a and total nitrogen 

Figure 3 shows the monthly chlorophyll-a from 1945 to 2001. The values are the ratios to the mean of 

monthly chlorophyll-a concentrations during the period (1945-2001). The values for the beginning 

months are low due to initialization problem (the phytoplankton biomass starts from 0 in 1945). Figure 4 

represents the ratios of chlorophyll-a to the value in 1950 for years 1950-2001. This time series has been 

used as a forcing function for primary production rate in the Chesapeake Bay Fisheries Ecosystem 

Model. Figure 5 demonstrates the January-May total nitrogen load at Conowingo, Maryland alongside 

the chlorophyll-a time series. Susquehanna River nitrogen accounts for about 80% of the aggregate 

nitrogen inputs to the main stem of the Chesapeake Bay (Scavia et al. 2006). It appears that chlorophylla was driven by total nitrogen loads. Chlorophyll-a and nitrogen loads are highly correlated (R2

=0.522, 

P=1.48·10-9) 

At most monitoring stations, the correlation between predicted and observed nutrient concentrations was 

significant (Table 1). At station CB 1.1, total nutrient values from model output were much higher than 

observations (Figure 6, Table 1). The model output and observation values were consistent at stations 

CB 2.1, 2.2, 3.1-3.3 (Figures 7-11, Table 1). However, at other stations (CB 4.1, 4.2, 4.3, 4.4, 5.1-5.5, 

6.1-6.4, 7.1-7.4) the model outputs showed larger oscillations (Figures 12-16). 

Except for station CB1.1, the model output for chlorophyll-a was not significantly correlated with 

observational data (Table 2). Spatial chlorophyll-a data was from Chesapeake Bay Program monitoring. 

The data were averaged to get daily values at each monitoring station. For each station, the values were 

then averaged to monthly estimates. The constructed monthly chlorophyll-a index from Harding and 

Perry 1997 is shown in Figure 27. The data were from 1950-1994. The chlorophyll-a was first averaged 

to get daily average within areas (six areas; see Harding and Perry 1997 for area definition) values. The 

values then averaged to daily (across six areas) and monthly (across all days within a month) values. The 

comparison between the model prediction and the reference time series appears unfavorable; however, 

there are many years with missing data in the latter. 

Effect of nutrient reduction 

The 1987 Chesapeake Bay Agreement set a goal to reduce nutrient input by 40% by 2000 through the 

“combined tributary strategy.” The Chesapeake Bay Program’s Water Quality Model (WQM) scenario 

runs showed decreases in chlorophyll-a due to nutrient reduction in the period modeled, and the 

maximum decrease (percentage) from CBREEM was similar to the results from the WQM (Figure 28). 

Note that the amplitude of change within a year is larger for the WQM which has finer (hourly) time 

steps than does the CBREEM. In WQM, production by phytoplankton is determined by the intensity of 

light, by the availability of nutrient, and by the ambient temperature (Cerco and Noel 2004). In addition, 
9 

the WQM has predators feeding on primary producers whereas CBREEM has no predators for 

phytoplankton, and chlorophyll-a changes would experience more control from nutrient input. 

Therefore, CBREEM may more accurately emulate extreme impacts of nutrient reduction on 

phytoplankton production. 

Sensitivities 

The results are not sensitive to changes in wind, evaporation rate, open-ocean wind, temperature, 

salinity, and vertical diffusion rate. Sensitivity was assessed for the following parameters/inputs: 

a. MM coefficient 

A value of 0.025 was used for Michaelis Menten (MM) coefficient, based on the CBP Water Quality 

Model (Cerco and Noel 2004). Changing the value by ±50% results in no significant change in 

chlorophyll-a output (Table 3). 

b. Precipitation 

Precipitation after 1997 was generated via the estimation techniques described above. However, even if 

the precipitation values are assumed to be zero for these years, the maximum chlorophyll-a decrease was 

8%. 

c. Bottom interface stress coefficients 

Changing the coefficient from 0.7 to 3, i.e. making the surface layer move much faster than the deep 

layer, result in significant changes in chlorophyll-a output. As seen in Figure 29, the magnitude of 

change predicted for the 1990s is larger. 

d. Mixing diffusion rate 

Changing the horizontal mixing diffusion rate by ±50% results in minor changes in chlorophyll-a (Table 

3). The result is not sensitive to the change in vertical diffusion rate. 

e. Surface layer depth 

A depth of 10 m was used for the surface layer. Changing the value for surface depth leads to some 

changes in chlorophyll-a output (Table 3). However, the depth change in surface layer does not the 

general temporal pattern much (Figure 30). 

In summary, the modeled nitrogen concentrations from CBREEM were similar to the observed data for 

most monitoring stations. The phytoplankton production in CBREEM appeared to be driven by nutrient 

load. The simulation for a nutrient management strategy showed a similar decrease pattern (percentage) 

as WQM. Additional efforts may be needed to incorporate the impacts of other factors such as water 

temperature on phytoplankton production. 

Acknowledgements 

Special thanks to J. Johnson for providing nutrient and chlorophyll-a data from CBP’s monitoring 

program; J Hagy for nitrogen load data; L. Bahner for Visual Basic programming for part of CBREEM; 

H. Kelsey for S-plus programming; T. Fisher for discussion about the historical nitrogen trends for 

rivers; and Chris Kinkade and Kim Couranz for comments on an early draft. 
10 

References 

Aksnes, D.L. and U. Lie. 1990. A coupled physical-biological pelagic model of a shallow sill fjord. 

Estuarine, Coastal, and Shelf Science 31: 459-486.

Baliles, G., W. Schaefer, R. Casey, L. Thomas, M. Barry, K. Cole. 1987. Chesapeake Bay Agreement. 

U.S. Environmental Protection Agency Chesapeake Bay Program, Annapolis, MD, USA. 

Cerco, C.F. and M. R. Noel. 2004. The 2002 Chesapeake Bay Eutrophication Model. EPA 903-R-04-

004, Chesapeake Bay Program Office, U.S. Environmental Protection Agency, Annapolis, MD, USA, 

349 pp. 

Christensen, V., A. Beattie, C. Buchanan, H. Ma, S.J.D. Martell, R.J. Latour, D. Preikshot, M. Sigrist, 

J.H. Uphoff, C.J. Walters, R.J. Wood, H. Townsend. Submitted. Fisheries ecosystem model of the 

Chesapeake Bay: Methodology, parameterization and model exploration. NOAA Tech. Memo. 

Hagy, J. D., W.R. Boynton, C.W. Keefe, and K.V. Wood. 2004. Hypoxia in Chesapeake Bay, 1950-

2001: Long-term changes in relation to nutrient loading and river flow. Estuaries 27: 634-658. 

Harding Jr., L. W. 1994. Long-term trends in the distribution of phytoplankton in Chesapeake Bay: roles 

of light, nutrients and streamflow. Marine Ecology Progress Series 104: 267-291. 

Harding Jr., L.W. and E.S. Perry. 1997. Long-term increase of phytoplankton biomass in Chesapeake 

Bay, 1950-1994. Marine Ecology Progress Series 157: 39-52. 

Hunter, J. R. and C. J. Hearn. 1991. On obtaining the steady-state solutions of the linearized threedimensional hydrodynamic equations. Applied Mathematical Modeling 16: 200-208. 

Ma, H., H. Townsend, X. Zhang, M. Sigrist, and V. Christensen (in revision). Linking the Chesapeake 

Bay fisheries ecosystem model with a water quality model to explore trophic and habitat impacts on 

fisheries stocks. Ecological Modelling

National Research Council. 2006. Dynamic changes in marine ecosystems: fishing, food webs, and 

future options. National Academies Press, Washington, D.C. 154 pp. 

Pauly, D. 1995. Anecdotes and the shifting baseline syndrome of fisheries. Trends in Ecology and 

Evolution 10(10):430. 

Preikshot, D., V. Christensen, R. Beamish and D. Pauly. 2004. An examination of ecosystem dynamics 

across different spatial scales reflected by climate and fisheries effects in North Pacific Oceanic 

ecosystems. Poster presented at the IOC/SCOR International Symposium on ecosystem indicators, Paris, 

30 March – 3 April. (http://www.ecosystemindicators.org/Posters/Preikshot.pdf). 

Scavia, D., E. L. A. Kelly, and J. D. Hagy. 2006. A simple model for forecasting the effects of nitrogen 

loads on Chesapeake Bay hypoxia. Estuaries and Coasts 29: 674-684. 

Wright D. G., D. A. Greenberg, J. W. Loder, and P. C. Smith. 1986. The steady-state barotrophic 

responses to surface wind stress. Journal of Physical Oceanography 16: 947-966. 

Xu, J. and R. R. Hood. 2006. Modeling biogeochemical cycles in Chesapeake Bay with a coupled 

physical-biological model. Estuarine, Coastal and Shelf Science 69: 19-46. 
11 

Tables 

Table 1. Correlations between predicted and observed nutrient concentrations (S=surface, B=bottom, 

df=degree of freedom, R2=percentage of the variance explained by the linear relation between predicted 

and observed concentrations, P=probability) 

________________________________________________________ 

Station Depth df R2 P 

CB1.1 S 209 0.007143 0.2215 

CB2.1 S 209 0.3184 0.0 

CB2.2 S 209 0.5318 0.0 

CB3.1 S 209 0.4932 0.0 

CB3.2 S 209 0.4341 0.0 

CB3.3C S 209 0.4151 0.0 

CB3.3E S 209 0.3361 0.0 

 B 209 0.1575 0.0 

CB4.1C S 209 0.3205 0.0 

 B 209 0.1246 0.0 

CB4.1W S 209 0.1763 0.0 

CB4.2C S 209 0.1527 0.0 

 B 209 0.1809 0.0 

CB4.2E S 209 0.0976 0.0 

CB4.2W S 209 0.0533 0.0007 

CB4.3C S 209 0.07541 0.0 

 B 209 0.2053 0.0 

CB4.3E S 209 0.0633 0.0002 

 B 209 0.1562 0.0 

CB4.3W S 209 0.05197 0.0009 

CB4.4 S 209 0.03863 0.0042 

 B 209 0.135 0.0 

CB5.1 S 209 0.02576 0.0197 

 B 209 0.09947 0.0 

CB5.1W S 209 0.03296 0.0082 

CB5.2 S 209 0.0136 0.0005 

 B 209 0.1342 0.0 

CB5.3 S 209 0.04485 0.0002 

 B 209 0.1009 0.0 

CB5.4 S 209 0.0735 0.0001 

 B 209 0.1117 0.0 

CB5.5 S 209 0.05049 0.001 

 B 209 0.0976 0.0 

CB6.1 S 209 0.0349 0.0065 

 B 209 0.05614 0.0005 

CB6.2 S 209 0.00845 0.1836 

CB6.3 S 209 0.0168 0.0002 

 B 209 0.0226 0.029 

CB6.4 S 209 0.0048 0.3158 
12 

CB7.1 S 209 0.07884 0.0 

 B 209 0.1017 0.0 

CB7.1N S 209 0.0625 0.0002 

CB7.1S S 209 0.08005 0.0 

 B 209 0.02758 0.01574 

CB7.2 S 209 0.08321 0.0 

 B 209 0.01694 0.05913 

CB7.2E S 209 0.07418 0.0001 

 B 209 0.0545 0.0006 

CB7.3 S 209 0.0149 0.0843 

 B 209 0.447 

CB7.3E S 209 0.01566 0.06966 

CB7.4 S 209 0.000598 0.724 

 B 209 0.001736 0.5473 

CB8.1 S 209 0.006162 0.2563 

CB8.1E S 209 0.005705 0.2748 

 B 209 0.00049 0.3118 

EE3.5 S 209 0.05763 0.0004 

 B 209 0.03435 0.006946 
13 

Table 2. Correlations between predicted and observed chlorophyll-a concentrations (S=surface, 

B=bottom) 

_______________________________________________________________ 

Station Depth df R2 P 

CB1.1 S 113 0.07694 0.002689 

 B 45 

CB2.2 S 180 0.00007367 0.9084 

 B 49 

CB3.3C S 185 0.0008296 0.6956 

 B 124 

CB4.3C S 185 0.001126 0.6484 

 B 124 0.002184 0.6033 

CB5.2 S 166 0.002836 0.4929 

 B 123 0.01039 0.2581 

CB6.1 S 166 0.003959 0.4178 

 B 167 0.01104 0.1739 

CB6.4 S 193 0.02459 0.02857 

 B 195 

CB7.3E S 184 0.002784 0.4744 

 B 185 

CB7.4 S 193 0.004809 0.3354 

 B 194 0.001505 0.5892 

Table 3. Parameter values and sensitivity tests 

_________________________________________________________ 

Parameter (value) Changes Changes in chlorophyll-a 

-------------------------------------------------------------------------------------- 

Michaelis Menten constant (0.025) +50% -4.3% to 3.9% 

 -50% -2.9% to 4.0% 

Horizontal mixing diffusion rate (2) +50% -8.8% to 16.6% 

 -50% -19.6% to 14.8% 

Bottom interface stress coeff (0.7) to 3 -22.7% to 53.2% 

Surface layer depth (10) +50% -9.3% to 25.2% 

 -50% -23.6% to 44.8% 

_________________________________________________________ 
14 

Appendix A: Abbreviations Used in the Text 

CBREEM: Chesapeake Bay Regional Estuarine Ecosystem Model 

CBFEM: Chesapeake Bay Fisheries Ecosystem Model 

CBP: Chesapeake Bay Program 

NOAA: National Oceanic and Atmospheric Administration 

USGS: U.S. Geological Survey 

WQM: Water Quality Model 

Appendix B: User’s Guide to CBREEM 

1.0 Introduction 

This guide provides introductory information for people wanting to run the CBREEM simulation model. 

We describe how to install and run the program to construct policy scenarios and view model results. 

The objective in developing a modeling system for the Chesapeake Bay was to integrate information on 

physical, chemical, and ecological processes into a model for predicting temporal and spatial changes in 

key indicators of interest to environmental managers. Over the course of two workshops focused on 

ecosystem changes in another bay (Florida Bay), workshop participants identified the window of 

primary interest as involving seasonal variation on spatial scales of 1-2 km2

. Therefore, we elected to 

treat most physical and chemical processes, which come to equilibrium on time scales of hours, by 

calculating equilibrium spatial fields of these variables and then averaging the equilibria over monthly 

time steps. Longer-term variation, such as decadal trends in sea-level heights, are treated as constant 

parameters for model runs that focus on primary ecological concerns. The modeling system can thus be 

seen as having three basic parts: 

1) Routines to manage information on basic parameters (constants) such as bathymetry, freshwater input 

volume and concentrations of nutrients and particulates, and ecological productivity coefficients. 

2) Routines to compute short-term equilibrium advection and chemical concentration fields ("field" = 

spatial pattern over a grid of cells). 

3) Routines to simulate seasonal and interannual changes in some ecological variables such as sea grass 

biomass that accumulate on time scales of seasons and years. 

2.0 File Structure 

It is not necessary to understand the function of all the files copied onto your hard disk during the 

installation process. “CEM.exe” is the executable program file; double-clicking on it opens the interface. 

Beyond this, the files can be classified into four types, only the first of which (model parameter/scenario 

files) requires understanding to use the program efficiently. The other three types of files are described 

for those interested in more detail. 

Model Parameter/Scenario Files 

CBDefault.hyp: ASCII text files that contain default values for parameters used in Ecology, Chemistry, 

and Physics models (rate constants, parameters defining functional relationships, etc.). 
15 

CB1000m.evm and CB2000m.evm: ASCII text files that contain the base maps for the 1 km2 and 2 km2

grids, respectively. 

CB2000m.adv: ASCII text files that contain the advection fields for the 2 km2

 grids respectively. The 

fields consist of east-west and north-south velocity vectors for each cell in the grid, and there are two 

advection fields (surface and deep layers) associated with each month. 

default.mde: An ASCII text file containing information that controls the legends used to display the 

base maps and model output. 

*.vid files: Binary files that are used to store results from previous model runs. None were provided on 

the installation diskettes, but these files can be created by using the ‘record’ option described in Section 

3. 

Data Files 

CBForce.txt: The file contains inflow locations and monthly data for wind, rainfall, river flow, and 

nutrient loading. 

CB_WQ.mdb: The table CB2000m in the database contains observed data for nitrogen, phytoplankton, 

and others. After CEM interface appears, you need to push the “Initialize Data” button to create the data 

array for comparison between model output and observation. After the model run is completed, you can 

push the last button “Print Obs and Pred Data” to write five files (to a directory) for observed and 

predicted nitrogen, phytoplankton, and other variables. 

Source Code 

CEM.vbp: The Visual Basic project file. If you have Visual Basic 4.0 installed on your machine, you 

can view and edit the source code by double-clicking on this file from the Windows Explorer. 

* .frm and *.bas files: These are the graphics and code modules that make up the program. 

Runtime Libraries 

*.ocx and *.dll files: Support runtime libraries that are copied to the WINDOWS\SYSTEM directory of 

your computer during the installation procedure. 

3.0 Running the Model 

Double-click on the file “CEM.exe” in Windows Explorer to start the program. All the controls that are 

needed to run the model are located at the bottom of the main program window in a separate gray 

colored form. This form works like a set of tabs in a card catalog; clicking on any one of the tabs 

(labeled “simulation controls,” “playback results,” and “current files”) shows you all the controls 

associated with that tab. The majority of controls you need to operate the model are associated with the 

“simulation controls” tab. 

You can change the year when the model begins by editing the “Start Calendar Year” text box and 

hitting the enter key. The start calendar year determines which years of input data (wind, rainfall, 

inflows) are used to drive model calculations. If you change the start calendar year (and hit the enter 
16 

key) you will notice that the “Years to Simulate” text box is updated so that the model will finish its 

computations the last available year of input data. 

To begin a simulation, click on the “Start” button. When you do this, you will see that the “current year” 

and “current month” yellow text boxes and red progress bar will update, reflecting the time step 

currently being computed by the model. When the model is running, the caption of the “Start” button 

will change to “Interrupt.” Clicking on the button when it says “Interrupt” will suspend execution, 

allowing you to change model parameters or the graphics setup (e.g., which maps are displayed). When 

the model has been interrupted, the caption on the button will change back to “Start.” To resume the 

simulation from the point where you interrupted, click on the “Continue” button located immediately 

below the “Start” button. To restart a simulation from the beginning (as defined by the “Start Calendar 

Year”), click on the “Start” button rather than the “Continue” button. 

If you wish to have a time series of indicators as part of the model output (see Section 4), the overlay 

feature is a good way to compare results from successive model runs. After you have run a simulation, 

click on the “overlay” check box. Change parameters or inputs for the next run and then click on the 

“Start” button. By activating the overlay feature, the time series of results from the original will remain 

on the graphs, and the new results will be overlaid on top of them. As long as the overlay feature is 

active (the overlay box is checked), results from subsequent runs will be shown together on the time 

series graphs, rather than cleared. To turn the overlay feature off, click on the overlay box and the check 

mark will be removed. 

The model can be run using the historical time series of monthly rainfall, freshwater inputs, and wind 

speed and direction as driving inputs, or using interannual average monthly values over the entire time 

period. By default, the program runs using the historic conditions, but if you want to change this to 

average monthly conditions (to eliminate the effects of interannual variability) select the “Average 

Monthly Inputs” option on the “Current Files” tab of the gray form at the bottom of the main window. If 

you do not use historic conditions, the start calendar year becomes irrelevant. 

Long-term simulations can take considerable time especially when the 1 km2

 resolution grid is used. Use 

the record option if you want to replay the results in a subsequent session at a faster rate so that longterm dynamics are easier to visualize. To use the record option, prepare for a simulation by editing 

parameter values and base maps or loading previously saved parameter files (described below). Click on 

the record box at the bottom of the main program window on the “Current Files” tab. When you do this, 

a dialogue box will appear where you can select which monthly results you want to record. By default 

all months are selected, but if you want to record only a subset of the months in every year to reduce the 

size of the file produced by recording, or to speed up the rate of playback, toggle the months on and off 

by clicking on the cells in the table with ‘X’ marks. You will then be prompted to specify a name for the 

file where the results will be saved. 

Click on the “Start” button to begin recording and let the run go to completion or interrupt at any time. 

To play back the results, click on the “Playback Results” tab at the bottom of the main program window. 

Click on the button marked “Load Video File” to load the file containing the results you want to play 

back. To start the playback, click on the button that has the video camera icon. Stop the playback at 

anytime by clicking on the button with the hand icon. You can move to any point in the recording by 

dragging the slider along the bar to the right of the play/stop buttons, or by typing in a value in the white 
17 

text box to the right of the bar labeled time step and hitting the enter key. To restart the recording from 

the beginning, drag the slider to the left-most part of the bar and click on the button with the video 

camera icon. 

4.0 Viewing Model Results 

There are three types of results that summarize model output: 

1) A within-year sequence of maps for a single indicator (e.g. sea grass biomass), 

2) Monthly maps of up to six indicators that are cleared and updated each month as the model 

progresses, and 

3) A time series of model indicators that represents conditions at some “monitored” cells. 

Selecting the “Graphics - Setup Graphics” menu choice brings up a dialogue box that allows you to 

control how this information is displayed. The Setup Graphics dialogue box consists of two tabs: one 

controls the maps and time-series plots displayed (“Visible Maps and Graphs” tab), and the other 

controls the formatting of the time-series plots (“Format Graphs” tab). 

Each type of result (within-year map sequence, monthly updated maps, and time series) is displayed in 

its own Graph Pane. These panes can be toggled on and off by clicking on the check boxes in the upper 

left frame (entitled “Display Panes”) on the “Visible Maps and Graphs” tab. By default, the time-series 

pane is turned off, and only the within-year annual sequence and monthly map panes are shown. 

Increasing the number of panes reduces the size of all the maps and time-series plots, so there is a tradeoff between displaying the maximum amount of information versus its size and legibility. 

There can only be one within-year annual sequence indicator displayed at a time, and the indicator is 

selected by clicking on the dropdown list box within the “Within-Year Annual Map Sequence” frame. 

Sea grass biomass is the default indicator shown in the within-year annual sequence maps. There can be 

up to six monthly updated maps shown at any time as determined by the setup in the “Monthly Updated 

Maps” frame. By default, five maps are visible, displaying predictions of light, phosphorous and 

chlorophyll a concentration, salinity, and observed water color patterns. To change the number of 

monthly maps shown, click on the “Visible” check boxes (a check mark means the map will be shown, 

i.e., it is visible). Reducing the number of maps increases the size of the remaining ones which will be 

displayed. For each map that will be displayed (visible), select the desired indicator to plot in the 

individual map pane using the dropdown list boxes. The selection of time-series indicators is done in 

exactly the same way within the “Time Series Graphs” frame. 

Time-series results can be presented graphically for monitoring stations where observation data are 

available. To select monitoring stations, click Base Maps/Review Available WQ Data, then select the 

variable from dropdown button and click on the stations marked on the map. The time series of 

predicted and observed values for the selected variable/station will be shown on a plot. 

Viewing and Editing Map Legends 

To view the color legend associated with any map or time-series graph, double click on the map or 

graphs displayed on the main program window. For the maps, the legend shows the range of values 
18 

assigned to each color. When the legend is visible, you can single click on the map at any location to 

show the value for the specific cell that you clicked on. 

Map legends can be edited by selecting the “Base Maps - Edit Legend...” menu choice. Select a legend 

to edit by clicking on the dropdown list of map layers in the upper left corner of the Edit Legend 

dialogue box. If the number of categories for the currently selected legend is adequate, edit the break 

points and their associated category labels. To change colors for any category, click on the color to the 

right of the break point text box and then select (click) on a color in the color palette on the left hand 

side of the dialogue box. If you want to change the number of categories for the legend you are editing, 

specify the number in the “maximum number of categories” text box and hit the enter key. The available 

categories to be edited will then be changed. 

Changes made to the legend will not be reflected in model output until a simulation is started. Changes 

to the legend are not automatically saved to file, and you must use the “Base Maps - Save Legend to 

File...” menu choice to do this (see Section 7 for details). 

5.0 Viewing/Editing Base Maps 

The base map editor, used to view and edit the map layers that are input to the model (rather than 

predicted by it), can be accessed by selecting the “Base Maps - Edit/View Base Maps...” menu choice. 

The four base maps are water depth, point source locations and discharge volume, freshwater inflow 

locations, and bottom type. 

Use the dropdown list box in the upper right corner of the Base Maps dialogue box to move between the 

different base maps. To see the value for any cell in the grid, click on the cell with the mouse. The 

current row and column and value for that cell will be shown in white text boxes on the right side of the 

dialog box. To change the value of an individual cell, click or double click on it and then edit the current 

value in the white text box labeled “Selected Cell Value.” You must hit the enter key after making the 

change for it to take effect. You can navigate around the map using the arrow keys. To edit more than 

one cell at a time, check the Edit Mode box and paint an area on the map by holding down the left 

mouse button as you drag the mouse across a portion of the map. Selected cells will be highlighted in 

white. Enter a value that will be assigned to all these cells in the “Selected Cell Value” text box and hit 

the enter key. Turn off the Edit Mode selection when you are done editing. 

The point source map shows the location and discharge volumes for point sources of freshwater such as 

those discharged from major outfalls. The concentrations of nitrogen, salt, phytoplankton, and 

suspended particulates from point sources are set on the first line of the “Chemical Parameters” dialogue 

box accessed via the “Chemistry” menu choice. The concentrations of nutrient, salt, phytoplankton, and 

suspended particulates from freshwater inflow sources are set on the third line of the “Chemical 

Parameters” dialogue box. 

The bottom type map shows the distribution of sand, sea grass, and unknown bottom types. The bottom 

type affects the degree of resuspension due to wind-driven mixing. Changes made to the base map will 

be saved in memory and effect subsequent simulations. The changes will not be saved to a file unless 

you select the “Base Maps - Save Base Maps to File...” menu choice. If you save the revised base maps 

you can reload them in subsequent sessions using the “Base Maps - Restore Base Maps from File...” 

menu choice. Base maps are saved to files with a *.evm extension. Three sets of base maps were 
19 

distributed with this program. CB2000m.evm is the default base map file loaded at startup. It consists of 

a matrix of 157 rows by 79 columns of 2 km2

 cells. CB1000m.evm is a matrix of 314 rows by 158 

columns of 1 km2

 cells. The high resolution grid (1 km2) provides a more explicit spatial picture of the 

Bay’s features (coastline, basin shapes) at the cost of considerably more computational time to run a 

simulation. 

6.0 Viewing/Editing Model Parameters 

The CBREEM model consists of three submodels: 

1) A physical submodel, which computes current speed and direction for surface and deep layers for 

each month of the simulation, 

2) A steady-state submodel, which computes the concentration of nitrogen, salt, phytoplankton, and 

suspended particulates each month, and 

3) A dynamic ecological submodel, which simulates the growth and mortality of sea grass and epiphytic 

algae. Parameters controlling these submodels can be examined and edited from dialogue boxes 

accessed by selecting the “Physics,” “Chemistry,” and “Ecology” menu choices. 

Physical Submodel Parameters 

The physical submodel computes the equilibrium wind-driven and sea-surface pressure (height) 

anomaly-driven horizontal currents (and upwelling velocities between the two layers). You may also set 

sea surface height anomalies at the boundaries of the grid. For example, setting the surface height to 0.5 

m and 0 m at the north-west and south-west corners of the grid will cause the model to simulate a 

southerly current along the western boundary. The ratio of interface to bottom friction makes the model 

behave as either a single-layer system (surface and bottom velocities equal) if it is set to a low value or 

makes the surface layer move much faster than the deep layer if it is set to a large value (3-10). 

Selecting the “Edit Physical Parameters...” submenu choice below the “Physics” main menu item brings 

up a dialogue box showing the parameters controlling the physical submodel (Figure B.1). While there 

are many parameters within this box, the critical ones are the horizontal and vertical mixing rates (km 

day-1) for surface and deep water. These parameters define how much water movement and mixing will 

occur in addition to the directional movement created by wind driven currents. 

If historic monthly inputs are used to drive the model (as determined on the “Current Files” tab at the 

bottom of the main window), monthly values of wind speed and direction from 1945 to 2001 will be 

used to compute advection patterns for each month of the simulation. If average monthly inputs are 

used, wind speed and open-sea velocities for each month can be specified in the table in the upper right 

of the ‘Physics Parameter’ dialogue box. In this case, specify the number of fields that need to be 

computed at the top of the dialog box (in a text box labeled number of fields) and hit the enter key, fill in 

the wind speeds and open-sea velocities in the table for each field, and assign a field code for each 

calendar month. The model will run faster if average monthly inputs are used because the physical 

submodel only has to be run once for each field when you hit the “compute” button at the bottom left of 

the dialog box. Note that if you make changes to the physical parameters that influence predicted current 

speeds and direction and are running the model based on average monthly conditions, you must 

recompute the advection fields by clicking on the button marked “compute.” 
20 

To view or edit the advection fields predicted by the physics submodel, select the “View/Edit Advection 

Fields...” submenu choice below the “Physics” main menu item. To see the current speed and direction 

in any cell simply click on it and edit the value in the text boxes labeled “West - East” or “North - 

South” velocity. Current speed and direction in each cell are represented by a line originating from the 

center of the selected cell which is marked by a yellow box when you click on the grid. To expand or 

reduce the size of the line representing the current speed and direction, change the scaling value in the 

text box labeled “scale vectors” and hit the enter key. This only changes how current speeds used in the 

model computations are displayed in the grid. Physical advection fields can be saved to files with *.adv 

extensions using the “Save Advection Fields to File...” submenu choice. These sequences can be 

restored in subsequent sessions using the “Restore Advection Fields from File...” sub-menu choice. By 

default the model loads the advection file “CB2000m.evm,” which contains the advection fields for 12 

months of the year for the 2 km2

 resolution grid. If the model runs using historic monthly inputs (which 

it does by default), then these fields are not used because they are recomputed each model month based 

on the historic driving inputs. 

Chemistry Submodel Parameters 

The main purpose of the “Chemistry Parameters” dialogue box is to set up chemical concentration 

parameters for point sources, major freshwater sources, and ocean boundary concentrations. 

Decomposition and sinking rates for phytoplankton and organics as well as concentrations of nitrogen 

and particulates resuspended from the bottom due to wind can also be edited (Figure B.2). Units for 

concentration within each of the four chemical types (nitrogen, salt, phytoplankton, and suspended 

particulates) must be consistent within each type. Note that the phytoplankton growth calculation 

(parameters defined in the “Ecology Parameters” dialog box) is designed to use phytoplankton 

chlorophyll a concentrations in ug l-1, and nitrogen concentrations in mg l-1. 

Ecological Submodel Parameters 

The growth parameters for sea grass, epiphyte, and phytoplankton can be entered in “Ecology Model 

Parameters” dialog box (Figure B.3). Phytoplankton growth rates are computed based on the maximum 

doubling rate, reduced by a factor calculated from a Michaelis-Menten relationship. Epiphytic algae 

growth is also controlled by Michaelis-Menten kinetics in addition to a loss rate that represents physical 

erosion as well as constant grazing. Sea grass growth is computed based on a maximum potential rate 

adjusted by the difference between photosynthetic gains less respiration losses driven by monthly 

changes in temperature and light. Sea grass biomass is calculated as a function of the previous month’s 

biomass plus the total new growth in the current month less losses due to leaf turnover. 

7.0 Managing Files 

A considerable amount of time can be spent developing alternate scenarios using the model. These 

scenarios are defined by changing parameters that control model dynamics and input data, and are 

displayed by formatting model outputs. Consequently, it is useful to save these conditions to files so that 

they can be repeated in the future by reloading the files and rerunning the model. 

All the parameters in the Physics, Chemistry, and Ecology dialog boxes (accessed from the physics, 

chemistry and ecology menu choices) are saved in files with the extension *.hyp. To save a parameter 

set to a .hyp file, select the “Files - Save Parameter File...” menu choice. You have the option of 

overwriting the currently loaded parameter file (CBDefault.hyp is the default file loaded at startup) or 
21 

entering a new file name. We recommend that you do not overwrite the parameter files CBDefault.hyp. 

If you want to restore a previously saved parameter file, select the “Files - Restore Parameter File...” 

menu choice. To see the parameter file currently in use, click on the “Current Files” tab in the lower 

gray-tabbed window at the bottom of the main program window. As a quick alternative to loading a 

parameter file (or any other type of file), double click on the yellow text box showing the current 

parameter file and a dialogue box will appear where you can select the parameter file you want to load. 

All the base map information (water depth, location of point source and freshwater inputs, bottom type) 

used as input for the model is stored in files with the extension *.evm. If you have edited the base maps 

(see Section 5.0) and want to save these changes to a file, select the “Base Maps - Save Base Maps to 

File...” menu choice. To restore a previously saved base map, select the “Base Maps - Restore Base 

Maps from File...” menu choice. Alternatively, a base map file can be quickly restored by doubleclicking on the yellow text box showing the currently loaded base map on the “Current Files” tab at the 

bottom of the main window. 

The legend used to categorize and display map information can be altered using the legend editor as 

described in Section 4. For example, monthly maps of salinity can be shown at four levels with a unique 

color for each level. Legends are also used to control the display of base map information. If you have 

edited the default legend pattern and wish to save it to a file, select the “Base Maps - Save Map Legend 

to file...” menu choice. The default legend file loaded at startup is “default.mde” and when saving a 

legend setup you can either overwrite this default file or save the legend to a new file name (with an 

“*.mde” extension). To reload a previously saved legend file, select the “Base Maps - Restore Legend 

from File...” menu choice or double click on the yellow text box showing the currently loaded legend 

file on the “Current Files” tab at the bottom of the main window. 
22 

Figures 

List of Figures 

Figure 1. Chesapeake Bay monitoring stations used for total nitrogen parameterization including 40 

station (red dots) in the main stem and stations near the mouths (colored dots) of nine major rivers. The 

stations in the tributaries (small yellow dots) were not used. 

Figure 2. River flow rates (ft3

 s-1) at (a) Susquehanna River (green), Potomac River (blue), and James 

River (red); (b) Rappahannock River (blue) and York River (green); (c) Choptank River, Nanticoke 

River, Patapsco River, and Patuxent River in 1945-2001. 

Figure 3. Chlorophyll-a output from the Chesapeake Bay Regional Estuarine Ecology Model. The 

chlorophyll-a values are the ratios of chlorophyll-a each month to the monthly average from 1945-2001. 

Figure 4. The forcing function used in Ecopath and Ecosim model for primary production rate in 

Chesapeake Bay (chlorophyll-a ratio to the value in 1950). 

Figure 5. The total nitrogen load (January-May) at Conowingo, Maryland (with solid markers), and the 

forcing function for primary production in Ecopath and Ecosim. The nitrogen time-series data were from 

Dr. Haggy (Scavia et al., 2006). 

Figures 6- 26. The simulated nitrogen (red lines) and observed nitrogen (blue dots) at stations CB1.1, 

CB2.1-2.2, CB3.1-3.3, CB4.1-4.4, CB5.1-5.5, CB6.1-6.4, and CB7.3-7.4. 

Figure 27. Chlorophyll-a index from Harding and Perry 1997 (line) and forcing function used in Ecopath 

and Ecosim (line with solid markers). 

Figure 28. The chlorophyll-a changes in CBREEM (line) and WQM (line with solid markers) under a 

management strategy by cutting nutrient input by 40%. The change is the ratio of change relative to the 

baseline chlorophyll-a. 

Figure 29. The chlorophyll-a outputs with different values (0.7-line with solid marker, 3-line without 

markers) in CBREEM. 

Figure B.1. The dialog box for physical parameters. 

Figure B.2. The dialog box for chemical parameters. 

Figure B.3. The dialog box for ecological parameters. 
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