Report of the special session of “Chesapeake Bay Ecological 

Forecasting: Moving ecosystem modeling from research to 

operation” of the 2010 Chesapeake Modeling Symposium

Raleigh Hood, Robert Wood, David Green, Xinsheng Zhang

September 2010

 NOAA Technical Memorandum NOS NCCOS 120 
_________________________________________________________________

REPORT AUTHORS

Raleigh Hood

University of Maryland Center for Environmental Science

Horn Point Laboratory

Robert Wood

NOAA National Ocean Service

National Centers for Coastal Ocean Sciences

Cooperative Oxford Laboratory

David Green

NOAA National Weather Service

Office of Climate, Water, and Weather Services

 

Xinsheng Zhang

NOAA National Ocean Service

National Centers for Coastal Ocean Sciences

Cooperative Oxford Laboratory

Acknowledgments: On behalf of the conveners, we would like to thank all of the presenters 

and participants for supporting the “Chesapeake Bay Ecological Forecasting: Moving ecosystem 

modeling from research to operation” session. In particular, we are grateful to Kevin Sellner, 

Raghu Murtugudde, Ed Martino, Lowell Bahner, Doug Wilson, Howard Townsend, Heath Kelsey, 

Ruth Kelty, Mark Monaco, Rob Magnien, Alan Lewitus and Kevin McMahon for their assistance 

in preparing this report and/or supporting Chesapeake Bay Ecological Forecasting in general, 

and to Ruth Kelty for her efforts to synthesize information for this report. Staff at the 

Chesapeake Research Consortium, Inc. (CRC) provided excellent facilities and support. We 

thank the NCBO, EPA/CBP and NSF for partial funding to support this symposium.

This report should be cited as: R. Hood, R. Wood, D. Green, X. Zhang. 2010. Report of the 

special session of “Chesapeake Bay Ecological Forecasting: Moving ecosystem modeling from 

research to operation” of the 2010 Chesapeake Modeling Symposium. Special Report. NOAA 

NCCOS CCEHBR. Charleston, SC. 35 pp. 

For more information contact: Xinsheng.Zhang@noaa.gov

 

Mention of trade names or commercial products does not constitute 

endorsement or recommendation for their use by the Unites States
Table of Contents

Executive Summary ............................................................................................................................. 1

Session Objectives, Scope and Product ............................................................................................ 4

Presenters List ...................................................................................................................................... 5

Abstracts and Representative Slides ................................................................................................ 6

References .......................................................................................................................................... 32
1 

Executive Summary

Moving ecosystem modeling from research to applications and operations has direct 

management relevance and will be integral to achieving the water quality and living resource 

goals of the 2010 Chesapeake Bay Executive Order. Yet despite decades of ecosystem 

modeling efforts of linking climate to water quality, plankton and fish, ecological models are 

rarely taken to the operational phase. In an effort to promote operational ecosystem modeling 

and ecological forecasting in Chesapeake Bay, a meeting was convened on this topic at the 

2010 Chesapeake Modeling Symposium (May, 10-11). These presentations show that 

tremendous progress has been made over the last five years toward the development of 

operational ecological forecasting models, and that efforts in Chesapeake Bay are leading the 

way nationally. 

Ecological forecasts predict the impacts of chemical, biological, and physical changes on 

ecosystems, ecosystem components, and people. They have great potential to educate and 

inform not only ecosystem management, but also the outlook and opinion of the general 

public, for whom we manage coastal ecosystems. In the context of the Chesapeake Bay 

Executive Order, ecological forecasting can be used to identify favorable restoration sites, 

predict which sites and species will be viable under various climate scenarios, and predict the 

impact of a restoration project on water quality. 

MAJOR FINDINGS

• Operational ecosystem modeling and ecological forecasting has great potential to facilitate 

and inform ecosystem research and management, and also help protect human health in 

Chesapeake Bay and other coastal systems. 

• Modeling efforts are most successful when a small number of parameters (e.g. salinity and 

temperature) can explain and predict complex ecological processes. The most developed 

Chesapeake Bay forecasts are empirical models based upon logistic regression, neural 

networks and other statistical approaches. Ultimately the goal is operational 

implementation of a wide variety of models that will include fully mechanistic ecosystem 

and biogeochemical models that are, in turn, used to force multi-parameter empirical 

models of high order biological phenomenon.

• Within the Bay, the sea nettle forecast is the most advanced ecological forecast. It has 

begun to provide operational products and services and is ready to be transferred to 

operations by the National Weather Service (NWS). The pathogen forecast is the next most 

advanced along the continuum from research to applications and operations. Pathogen 

forecast products are being tested, and partners are planning the transfer to application or 

routine operation of the forecast. Fish habitat quality and recruitment are linked to critical 

environmental factors (e.g. dissolved oxygen and nutrients), and these more complex 

forecasts range in maturity from development to calibration and validation.

• Within NOAA, ‘Operational Forecast’ denotes a level of commitment and operation. The 

NWS will likely house the forecasts, and a rigorous process is in place to move forecasts 

from research and development to deployment, maintenance, and assessment of a 24/7 

operational forecast. 
2 

• With the implementation of CBOFS2 and the Chesapeake Bay Sea Nettle forecasting model, 

Chesapeake Bay scientific and management communities will be uniquely positioned to lead 

the way nationally in the implementation of robust ecological forecasting models. 

TRANSITIONING TO OPERATIONS

While there are multiple pathways to moving a forecast from research to applications and 

operations, the NWS has offered their Operations and Service Improvement Process (OSIP) as a 

standard, systematic, disciplined process to evaluate ecosystem forecasting needs and 

opportunities in the Chesapeake Bay. OSIP promotes sustainability of an operational model 

through critical review and systematic science, technology, service and budget evaluation. The 

transition process involves five stages: 1) data collection and model validation; 2) operational 

model concept exploration and definition; 3) applied research and analysis; 4) operational 

development; and 5) deployment, maintenance and assessment. 

As of now, there are no operational forecasting models in the Chesapeake Bay. The sea nettle 

forecast is ready for transition, but has not yet gone through the rigorous approval and 

implementation process that NOAA requires. The term “prototype operational model” is 

therefore used in this report.

SESSION OVERVIEW

Presentations in this session ranged from reports from NOAA agencies on current efforts and 

prospects for implementing true, 24/7 operational ecological forecasting models in NOAA 

(Uccellini, Green and Brown), to prototype operational nowcast/forecast models (Friedrichs, 

Hood, Constantin de Magny, Jacobs, Wiggert and Long), and also new models that might be 

implemented operationally in the future (Zhang, Prasad, Evans, Bi, Martino, North and Kaushal). 

New developments include the transition of the Chesapeake Bay Sea Nettle forecast model to 

operational within NOAA (Brown), the formulation of new ecological forecasting models for 

pathogens like Vibrio cholerae and Vibrio vulnificus (Constantin de Magny and Jacobs) and 

emerging empirical and mechanistic models for forecasting biogeochemical properties like 

dissolved oxygen (Wiggert, Long, Prasad and Evans). 

Sea Nettles

Sea nettles in Chesapeake Bay are strongly constrained by temperature (>20°C) and salinity (12 

– 16 ppt). Temperature and salinity are calculated mechanistically using a Chesapeake Bay 

implementation of ROMS called ChesROMS (Decker, Brown et al. 2007, 

http://sourceforge.net/projects/chesroms/). The Chesapeake Bay Sea Nettle forecast model is 

an empirical logistic regression model that relates probability of occurrence of the jelly fish to 

temperature and salinity. The forecast has begun to provide operational products and services 

and is ready to be transferred to operations by the NWS.

 

Pathogens and Harmful Algal Blooms
3 

Many pathogens (e.g. Vibrio cholerae and Vibrio vulnificus) are also constrained by temperature 

and salinity, and the developing pathogen forecasts follow the same empirical modeling 

approach as the sea nettle forecasts (Constantin de Magny, Long et al. 2009; Jacobs, Rhodes et 

al. 2010). Brown and Ramers et al. (2010) take a slightly different approach to predict presence 

or absence of the harmful algal bloom species Karlodinium veneficum. Their empirical neural 

network model incorporates time of year as well as temperature and salinity as the forcing 

variables. Kaushal is developing more complex models using forecasts of watershed nutrient 

and contaminant loadings to model bacterial loading and turbidity of drinking water supplies 

for human health applications. He is applying novel techniques, such as computational social 

science and social network computations. New frameworks that facilitate the development 

and comparison of these models are now available (Friedrichs). 

Environmental Variables

Temperature and salinity are calculated mechanistically using a Chesapeake Bay 

implementation of ROMS called ChesROMS (Decker, Brown et al. 2007, 

http://sourceforge.net/projects/chesroms/). NOAA’s Marine Modeling and Analysis Branch is 

working with the NWS to transition a Chesapeake Bay implementation of ROMS (the 

Chesapeake Bay Operational Forecast System or CBOFS2) to operations. The Chesapeake Bay 

Operational Forecast System (CBOFS2) is a mechanistic hydrodynamic model designed to 

predict temperature and salinity for a wide variety of empirical ecological and biogeochemical 

forecasting models. It is a Chesapeake Bay version of ROMS and has been validated. 

Considerable effort has also been dedicated to developing models that can forecast oxygen 

concentrations because of its importance to pelagic and benthic species in Chesapeake Bay. 

Wiggert and Long are taking a mechanistic biogeochemical approach, predicting oxygen 

concentration by calculating air-sea exchange and biological oxygen production and 

consumption. This prognostic oxygen model is still being calibrated but looks promising. 

Prasad et al. (2010) take an empirical approach, using multiple linear regression models to 

predict oxygen concentration from temperature, salinity, and dissolved nutrients. The current 

assessment is that the empirical approaches are more mature and robust. However, 

operational mechanistic biogeochemical modeling holds great promise to provide short-term 

forecasts of not only oxygen, but additional biogeochemical and environmental properties, like 

nutrient and phytoplankton concentrations, and water clarity. 

Fish Habitat and Recruitment

Improvements in hydrographic and other environmental forecasts enable advances in forecasts 

of fish habitat quality and recruitment (Bi, Martino, North and Zhang). Improved capabilities to 

forecast fish recruitments are being evaluated, and include using down-scaled seasonal climate 

projections to forecast fish year-class strength one to three years prior to entering Atlantic 

coast fisheries (Murtugudde, 2009; Martino and Houde, 2010). A suite of habitat suitability and 

bioenergetics models for striped bass and forage fishes based on species/life-stage-specific 

physiological requirements and allometric scaling have been developed and employed 

(Costantini et al. 2008; Ludsin, Zhang et al. 2009). Input variables to these models include 

observed and modeled temperature, plankton, and oxygen.
4 

Session Objectives, Scope and Product

Session Lead: Raleigh Hood

Session Co-Lead(s): Robert Wood, David Green, Xinsheng Zhang

Date: May 11, 2010

Time: 8:30 am

Room: Coastal West

Session Objectives 

Despite decades of ecosystem modeling efforts, ecological models are rarely taken to the 

operational phase. As we endeavor to move towards ecosystem management it is essential 

that we acknowledge that, in the words of David Fluharty, "It is extremely important to avoid 

making perfect knowledge of the ecosystem the enemy of using the good knowledge we have." 

Operational modeling has great potential to educate and inform not only ecosystem 

management, but also the outlook and opinion of the general public, for whom we manage 

Chesapeake Bay and other coastal ecosystems. This session will focus on existing or developing 

ecological or ecosystem models that have great potential to become operational in the near 

future. We will also explore the challenges facing both scientists and managers in moving 

towards model operationalization and use.

Session Scope 

Introduction with classic short presentation session focusing on scientific and management 

approaches, followed by panel/audience discussion. 

Session Product

Based upon the presentations, feedback, and panel discussions of this session, a document will 

be produced that outlines an effective strategy towards developing and implementing 

operational ecological models to support Chesapeake ecosystem based management.
5 

Presenters List

1. Louis W. Uccellini, NOAA/NWS/NCEP, Louis.Uccellini@noaa.gov

2. David Green, NOAA/NWS/OCWWS, david.green@noaa.gov

3. Carl Friedrichs, Virginia Institute of Marine Science, College of William & Mary, 

cfried@vims.edu

4. Raleigh Hood, Horn Point Laboratory, University of Maryland Center for Environmental 

Science, rhood@hpl.umces.edu

5. Christopher Brown, NOAA/NESDIS, Christopher.W.Brown@noaa.gov

6. John Jacobs, NOAA/NOS/NCCOS Cooperative Oxford Laboratory, John.Jacobs@noaa.gov

7. Xinsheng Zhang, NOAA/NOS/NCCOS Cooperative Oxford Laboratory,

Xinsheng.Zhang@noaa.gov

8. Jerry Wiggert, University of Southern Mississippi, Jerry.Wiggert@usm.edu

9. Wen Long, Horn Point Laboratory, University of Maryland Center for Environmental Science,

wenlong@hpl.umces.edu 

10. Mary Anne Evans, University of Michigan, mevans@umich.edu

11. M. Bala Krishna Prasad, Earth System Science Interdisciplinary Center, University of 

Maryland, mbkp@umd.edu

12. Hongsheng Bi, Chesapeake Biological Laboratory, University of Maryland Center for 

Environmental Science, hbi@cbl.umces.edu

13. Ed Martino, NOAA/NOS/NCCOS Cooperative Oxford Laboratory, Ed.Martino@noaa.gov

14. Elizabeth North, Horn Point Laboratory, University of Maryland Center for Environmental 

Science, enorth@hpl.umces.edu

15. Sujay Kaushal, Chesapeake Biological Laboratory, University of Maryland Center for 

Environmental Science, kaushal@cbl.umces.edu
6 

Abstracts and Representative Slides

NCEP SUPPORT FOR ECOLOGICAL FORECASTING

Uccellini, Louis

NOAA/NWS/NCEP, Louis.Uccellini@noaa.gov

This presentation will highlight how the National Centers for Environmental Prediction (NCEP) 

supply backbone capabilities for an operational ecological forecasting system. This includes 

support for applied research, technology transfer and implementation of a common modeling 

infrastructure. The NCEP is a recognized global leader delivering a seamless suite of operational 

environmental analysis, diagnostics and forecasts for a domain that now ranges from the sun to 

the sea, including weather, ocean, climate, water, and space weather prediction services. In 

order to address evolving user needs the NCEP is capitalizing on emerging scientific and 

technological advances and partnering with other agencies and institutions promoting 

ecosystem-based management. Recent efforts are targeting the transition of regional models 

and prototype ecological forecasts for the Chesapeake Bay and its tidal tributaries. The models 

rely on variables, including air temperature, wind speed, sea surface temperature, salinity, and 

solar radiation from NOAA forecasted data. The operational concept is to apply relevant 

observations and partner models to NCEP infrastructure and increase data availability, access 

and distribution of integrated environmental services through Weather Forecast Offices and 

other operational centers to state and local authorities. Specific challenges include scalability, 

data assimilation, and verification for short term forecasts, seasonal outlooks and climate 

projections.

Shared development, improvement in ocean and coastal models, hydrologic and land use 

models, and linkage with marine ecological and biogeochemical science are being advanced 

with an Earth System Modeling view. This presentation will focus on the continuing NCEP 

support for multidisciplinary activities required to transition ecosystem modeling from research 

to applications. 
7 

View presentation: http://www.chesapeakemeetings.com/ChesMS2010/sessions.php
8 

CHALLENGES AND PROGRESS TOWARD INTEGRATED ENVIRONMENTAL SERVICES

David Green

NOAA/NWS/NCWWS, david.green@noaa.gov

This presentation will identify the challenges and progress toward transitioning experimental 

model-based ecological forecasts into operational applications. NOAA has a long history of 

forecasting weather, tides, currents, floods, and fish stock through its various Line Offices and 

partnerships. In recent years the portfolio of experimental ecological models has matured and 

the infrastructure developed to tackle a broader suite of multidisciplinary and regional 

management issues. The lack of a robust framework and approach enabling transition of these 

models and derived forecasts, scenarios, and projections into sustained and durable 

environmental services limits the ability to meet user needs. The evaluation of requirements for 

service and operational improvement indicate that the integration of ecosystem and 

biogeochemistry models with existing climate, water and weather models is needed for 

ecosystem-based management and this shortfall presents unique development challenges. 

These include: understanding complex physical, biological, chemical, and behavioral 

interactions sufficiently to inform decision making over broad time and spatial scales; acquiring 

and assimilating adequate and representative observational and process data; testing and 

validating standard models; characterizing uncertainty in probabilistic forecasts; and providing 

adequate support to sustain operations. This presentation will provide examples of marine and 

coastal ecosystem model development and demonstration projects at NOAA and through 

regional partnerships that are contributing to a strengthened ecological forecast and warning 

system as a contribution to a broader integrated environmental service.
9 

View presentation: http://www.chesapeakemeetings.com/ChesMS2010/sessions.php
10

THE CHESAPEAKE FOCUS RESEARCH GROUP (FRG) OF THE COMMUNITY SURFACE DYNAMICS 

MODELING SYSTEM (CSDMS): A PATHWAY TO COMMUNITY OPERATION OF A MULTI-MODEL 

ECOLOGICAL FORECASTING TEST BED

Carl Friedrichs

Virginia Institute of Marine Science, College of William & Mary, cfried@vims.edu

The Community Surface Dynamics Modeling System (CSDMS, pronounced "Systems") is a 

national facility funded by the National Science Foundation to provide community access to 

high performance computing (HPC) facilities, model linking software, and HPC expertise for the 

purpose of advancing the application and evolution of freely available, open source models of 

earth surface processes, including coastal and estuarine hydrology, hydrodynamics and 

ecosystem function. The Chesapeake FRG is CSDMS's first geographically-focused research 

group and has the specific goal of assembling a collection of interchangeable models and model 

components to address environmental processes within the Chesapeake region. The 

Chesapeake FRG is a partnership between CSDMS and the Chesapeake Community Modeling 

Program, which, in turn, is supported by the NOAA Chesapeake Bay Office, the EPA Chesapeake 

Bay Office, and the member institutions of the Chesapeake Research Consortium. This 

presentation provides additional background regarding the CSDMS Chesapeake FRG, its 

activities to date, and its short and long-term goals. At present, the Chesapeake FRG is seeking 

funding to establish a community accessible test bed at CSDMS to link, operate and interchange 

existing open-source Chesapeake Bay models for (1) land-use/hydrology, (2) hydrodynamics, 

and (3) water quality/ecosystem function. As part of this project, multiple existing open-source 

models would be interchanged and assessed against existing data sets. Ideally, a process would 

be developed by which multiple scientifically-vetted, open-source model suites could be 

formally recommended for ensemble operational use.
11

View presentation: http://www.chesapeakemeetings.com/ChesMS2010/sessions.php
12

CBEFS: THE CHESAPEAKE BAY ECOLOGICAL FORECASTING SYSTEM

Raleigh Hood

Horn Point Laboratory, University of Maryland Center for Environmental Science,

rhood@hpl.umces.edu

The Chesapeake Bay is an important recreational and economic resource that has been 

subjected to increasing stress due to anthropogenic impacts. These impacts include 

eutrophication and they have contributed to increased frequency of noxious bloom events that 

can negatively impact human health. In this presentation we give an introductory overview of 

the Chesapeake Bay Ecological Forecasting System (CBEFS), which provides prototype ecological 

nowcasts and shortterm forecasts of physical and biogeochemical properties as well as blooms 

of jellyfish, harmful algae and pathogenic microbes in the Bay.

View presentation: http://www.chesapeakemeetings.com/ChesMS2010/sessions.php
13

TRANSITIONING A CHESAPEAKE BAY ECOLOGICAL PREDICTION SYSTEM PRODUCT TO 

OPERATIONS

Christopher Brown

NOAA/NESDIS, Christopher.W.Brown@noaa.gov

NOAA is transitioning a prototype ecological system that predicts the probability of 

encountering sea nettles (Chrysaora quinquecirrha), a stinging jellyfish, in the Chesapeake Bay 

and its tidal tributaries, to operations. These jellyfish can negatively impact activities in the bay, 

and knowing where and when this biotic nuisance occurs may help to alleviate its effects. The 

bay-wide nowcasts and three-day forecasts of sea nettle likelihood are generated daily by 

forcing an empirical habitat model (that predicts the probability of sea nettles) with real-time

and 3-day forecasts of sea-surface temperature (SST) and salinity (SSS). Importantly, this 

prediction system can be easily modified to predict the probability of other important target 

organisms, such as harmful algal blooms and water-borne pathogens, in the Chesapeake Bay. In 

the operational system, the SST and SSS fields will be generated by the Chesapeake Bay 

Operational Forecast System (CBOFS2), a 3-dimensional hydrodynamic model developed and 

operated by NOAA's Ocean Service and run at the NWS's National Centers for Environmental 

Prediction. It is anticipated that the operational forecasts will be disseminated and delivered 

through existing methods and portals, e.g. as digital images available via the World Wide Web 

and text-based messages included within NWS marine weather forecast products. This activity 

represents the first effort to leverage and strengthen NOAA-wide capabilities in order to 

transition a prototype ecological forecast product to NWS operations. The steps involved are 

consistent with NOAA Research to Operations Policy and adhere to the NWS Operations and 

Services Improvement Process (OSIP), the accepted requirements management process for 

bringing research results into the NWS operational environment. Consequently, in addition to 

operationalizing the sea nettle forecasts, the exercise will develop a framework for transitioning 

other ecological forecasts, promote an enterprise architecture and earth system management 

infrastructure, and improve delivery of knowledge-based products for integrated 

environmental decision support services.
14

View presentation: http://www.chesapeakemeetings.com/ChesMS2010/sessions.php
15

PREDICTING THE DISTRIBUTION OF VIBRIO VULNIFICUS IN CHESAPEAKE BAY

John Jacobs

NOAA/NOS/NCCOS Cooperative Oxford Laboratory, John.Jacobs@noaa.gov

Vibrio vulnificus is a gramnegative pathogenic bacterium endemic to coastal waters worldwide, 

and a leading cause of seafood related mortality. Because of human health concerns, 

understanding the ecology of the species and potentially predicting its distribution is of great 

importance. We evaluated and applied a previously published qPCR assay to water samples (n = 

235) collected from the main-stem of the Chesapeake Bay (2007- 2008) by Maryland and 

Virginia State water quality monitoring programs. Results confirmed strong relationships 

between the likelihood of Vibrio vulnificus presence and both temperature and salinity that 

were used to develop a logistic regression model. The habitat model demonstrated a high 

degree of concordance (93%), and robustness as subsequent bootstrapping (n=1000) did not 

change model output (p >0.05). We forced this empirical habitat model with temperature and 

salinity predictions generated by a regional hydrodynamic modeling system to demonstrate its 

utility in future pathogen forecasting efforts in the Chesapeake Bay.

View presentation: http://www.chesapeakemeetings.com/ChesMS2010/sessions.php
16

CHESAPEAKE BAY STRIPED BASS HABITAT SUITABILITY FORECASTING: MOVING ECOSYSTEM 

MODELING FROM RESEARCH TO OPERATION

Xinsheng Zhang

NOAA/NOS/NCCOS Cooperative Oxford Laboratory, Xinsheng.Zhang@noaa.gov

Operational modeling offers the potential to educate and inform ecosystem management and 

the outlook and opinion of the general public for whom we manage coastal ecosystems. Striped 

bass (Morone saxatilis) supports important commercial and recreational fisheries in 

Chesapeake Bay. Improved understanding and forecasting of striped bass habitat suitability, 

and the interactions of striped bass and its prey are important from both scientific and resource 

management perspectives. We have developed a suite of life-stage-specific, striped bass 

habitat suitability models that can be used to evaluate and forecast how hydro-climate 

variability drives variability in habitat quality and quantity. Particular emphasis was placed on 

summer conditions, when the striped bass population is vulnerable to stress from warm 

temperatures and low dissolved oxygen. Model results suggest that Coutant's temperatureoxygen "squeeze" could affect striped bass in Chesapeake Bay through predator-prey habitat 

overlap/separation and encounter rates, and vulnerability to pathogens. These models have 

been integrated into the Chesapeake Bay Ecological Forecasting Modeling System, a joint 

NOAA-University of Maryland Earth System Science Interdisciplinary Center effort that links 

atmospheric, hydrodynamic, water quality, and living resources sub-models to produce 

operational and accessible models relevant to Bay restoration efforts. One ultimate objective is 

to provide managers with decision support tools for planning ecosystem-based fisheries 

management.
17

View presentation: http://www.chesapeakemeetings.com/ChesMS2010/sessions.php
18

ASSESSMENT OF A COUPLED PHYSICALBIOGEOCHEMICAL MODEL DEVELOPED FOR 

ECOLOGICAL FORECAST USE IN CHESAPEAKE BAY

Jerry Wiggert

University of Southern Mississippi, Jerry.Wiggert@usm.edu

The Chesapeake Bay is a valuable recreational, ecological and economic resource that is 

commonly subject to harmful algal bloom (HAB) outbreaks that threaten its continued viability. 

With expanding knowledge of the conditions likely to promote HAB occurrence, forecasting 

these events is becoming ever more tenable. HAB triggers include both physical and 

biogeochemical environmental properties; therefore a fully coupled physical-biogeochemical 

numerical model that accurately simulates, and forecasts, the Bay's environment is well-suited 

for application as a means of generating nowcasts or forecasts of HAB occurrence. Attaining 

this technological capability has been a primary motivation for the development of the 

biogeochemical version of the Chesapeake Bay Regional Ocean Modeling System (ChesROMS), 

which incorporates Rutgers University's Regional Ocean Modeling System (ROMS) as the 

physical modeling engine. The ecosystem model has been modified from the standard Fashamtype formulation incorporated within the ROMS distribution to include components that 

explicitly accommodate the impact of river borne sediments, inorganic nutrients and dissolved 

organic matter. Nutrient inputs from diffuse land sources as well as those from atmospheric 

deposition are also taken into account. In addition, dynamic simulation of dissolved oxygen has 

been implemented, with the intent of resolving seasonally developing hypoxic conditions within 

the Bay and the accompanying transition to denitrification within the water column and 

underlying benthos. Here, simulations for the year 1999 will be presented and characterized 

with respect to in situ observations made available by the Chesapeake Bay Program. These 

results will highlight and assess the realism of model simulated phytoplankton bloom dynamics 

and the seasonal evolution of dissolved oxygen distributions in the Bay.
19

View presentation: http://www.chesapeakemeetings.com/ChesMS2010/sessions.php
20

CHESROMS: AN NPZD-BASED CHESAPEAKE BAY BIOGEOCHEMICAL PREDICTION MODEL 

IMPLEMENTATION AND DEMONSTRATION

Wen Long

Horn Point Laboratory, University of Maryland Center for Environmental Science,

wenlong@hpl.umces.edu

The presentation will provide an introduction to ChesROMS v1.2, an open source prediction 

model of the Chesapeake Bay that is based on the 3-D primitive equation numerical ocean 

circulation model ROMS (Regional Ocean Modeling System). The initial hydrodynamic 

implementation of ChesROMS has been employed to provide input to empirical harmful algal 

bloom (HAB) predictive models. Recently, ChesROMS has been extended to include a fully 

coupled Nitrogen-Phytoplankton-Zooplankton-Detritus (NPZD) type dynamic lower trophic level 

ecological component that simulates the Bay's biogeochemical cycles. The model framework 

provides necessary components for retrospective and near real time data acquisition and preand post- processing, which make it suitable for hindcasting, nowcasting and short term 

forecasting of bay-wide physical, ecological and water quality conditions. A web based GIS 

visualization system has also been developed for dissemination. Key aspects of the 

biogeochemical model include point and non-point (diffusive) nutrient inputs, atmospheric 

nutrient deposition, light attenuation by particulate and dissolved components, and 

mechanistic implementation of water column and benthic denitrification. The Bay's open 

boundary is relaxed to climatological nutrient and biomass concentrations and physical 

properties. The simulated biogeochemical state variables include nitrate, ammonia, 

phytoplankton, chlorophyll a, zooplankton, dissolved organic nitrogen, detritus, dissolved 

oxygen and inorganic suspended solids. An overview of the model components, strategies and 

implementation specifics will be presented. The system is initially deployed at the NOAA 

Chesapeake Bay Office for nowcasting and three-day forecasting with a daily updating cycle. An 

initial assessment of results from re-forecasting years 2008 and 2009 will be presented.
21

View presentation: http://www.chesapeakemeetings.com/ChesMS2010/sessions.php
22

SIMPLE MODEL FOR UNCERTAINTY AND REGIME SHIFT ANALYSIS OF CHESAPEAKE BAY 

HYPOXIA

Mary Anne Evans

University of Michigan, mevans@umich.edu

Because all models are simplifications of natural systems it is desirable to use a variety of model 

constructs for any given natural system. More confidence can be placed in a result, such as the 

nutrient loading decreases needed to achieve a given reduction in hypoxic area, if this result is 

supported by multiple modeling approaches. We contribute to this diversity of modeling 

approaches with an implementation of the Streeter-Phelps river model for the bottom waters 

of Chesapeake Bay. This model is structurally simple, such that it can be parameterized with a 

minimum of field data, but retains enough mechanistic detail to allow validation against 

measured rate processes in the bay. Additional strengths of this model include explicit handling 

of parameter uncertainty and incorporation of multiple data types in parameterization and 

forecasting through Bayesian inference and Monte Carlo simulation. Results to date show (1) 

that Chesapeake Bay hypoxia is strongly influenced by total nitrogen (TN) loading but that the 

conversion efficiency of nitrogen to hypoxia varies from year to year, (2) specifically that this 

conversion efficiency has increased since 1980s, (3) that due to this increase in efficiency, TN 

load reductions of 35%, that previously would have returned the bay to hypoxic areas typical of 

the 1950s-1970s, would now be inadequate to return the bay even to the 1980s and 1990s 

hypoxic areas.
23

View presentation: http://www.chesapeakemeetings.com/ChesMS2010/sessions.php
24

ECOLOGICAL PREDICTION OF DISSOLVED OXYGEN IN THE CHESAPEAKE BAY

M. Bala Krishna Prasad

Earth System Science Interdisciplinary Center, University of Maryland, mbkp@umd.edu

The structure and dynamics of aquatic ecosystem are a function of set of biogeochemical 

properties and forecast of these biogeochemical properties is a critical step in the sustainable

management of the aquatic ecosystems. Dissolved oxygen (DO) is a key parameter in the 

aquatic science research which not only controls the biological productivity but also regulated 

by the biological productivity. Nutrient pollution resulted from the increasing human activities 

alters the oxygen dynamics by reducing its concentrations to a critical level where the 

ecosystem cannot support the biological systems. The Chesapeake Bay is a largest estuary in 

the North America and is experiencing summer hypoxia/anoxia events due to the cultural 

eutrophication. Statistical step-wise multiple regression approach is applied to develop an 

empirical model to predict DO in Chesapeake Bay by using a longterm data (1990-2006) of 

water temperature, salinity, dissolved nutrients (N and P). The predicted DO concentrations 

correlated well with the observed values. The predicted DO values depict the current status of 

the Bay and are useful to develop several linked products by integrating results from other 

hydrodynamic physical models for the ecosystem conservation and management.

View presentation: http://www.chesapeakemeetings.com/ChesMS2010/sessions.php
25

COPEPODS AND SALMON: FORECASTING SALMON SURVIVAL OFF WASHINGTON AND 

OREGON

Hongsheng Bi

Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science, 

hbi@cbl.umces.edu

The abundance of yearling Chinook (Oncorhynchus tshawytscha) and coho salmon (O. kisutch) 

was sampled concurrently with physical (temperature, salinity, water depth) and biological 

variables (chlorophyll a concentration and copepod abundance) along Washington and Oregon 

coast in June 1998-2006. Copepods were divided into four different groups by their water-type 

affinities: cold neritic, subarctic, warm neritic and warm oceanic, and each group was used an 

independent environmental variable. Data collected in 1998- 2008 were used to perform 

nonparametric correlation and negative binomial loglinear mixed regression with a spatial 

random factor, and data collected in 2006 was used to validate the models. Yearling Chinook 

abundance was negatively related with temperature, and positively related to the density of 

cold neritic copepod, chlorophyll a concentration, and salinity. Yearling coho abundance was 

positively related with temperature, and negatively related with warm oceanic copepods, warm 

neritic copepods, and water depth. The two salmon species also showed different spatial 

patterns in most years. Yearling Chinook abundance showed significant spatial autocorrelations 

in 1999, 2000, 2002, 2004, and 2005, and yearling coho abundance showed significant spatial 

autocorrelations in 2000- 2002. The spatial random factor in the negative binomial loglinear 

mixed model was positively correlated with juvenile salmon abundance and showed similar 

spatial cluster patterns as juvenile salmon abundance for both species. Thus the occurrence of 

spatial autocorrelation could be attributed to the spatial random. Both annual mean abundance 

of yearling Chinook and the spring Chinook jack counts, a measurement of local population 

success, were positively correlated with cold neritic copepods. The annual mean abundance of 

yearling coho and the ocean survival rate were negatively correlated with warm oceanic 

copepods. The differences in how each species mapped onto habitat variable might be 

explained by the fact that yearling coho has relatively wider coastal distribution, and could feed 

on a relatively wider range of prey items than yearling Chinook. To address the spatial 

autocorrelation and the difference between two species, information on other physical 

processes, such as large scale transports, eddies and fronts, and biological processes such as 

prey and predator may be necessary.
26

View presentation: http://www.chesapeakemeetings.com/ChesMS2010/sessions.php
27

PROJECTING RECRUITMENT OF CHESAPEAKE BAY STRIPED BASS BASED ON EARLY-LIFE 

HABITAT QUALITY

Ed Martino

NOAA/NOS/NCCOS Cooperative Oxford Laboratory, Ed.Martino@noaa.gov

Ecosystem-based approaches to fishery management require improved understanding of 

habitat requirements and how environmental variability affects stock dynamics. High variability 

(>20 fold) in age-0 juvenile recruitment is a common feature of Chesapeake

Bay striped bass population dynamics. Spatio-temporal variability in zooplankton prey for 

striped bass larvae, controlled by synoptic-scale climatology and hydrological conditions, 

explains strong environmental controls of recruitment. In two spawning-nursery locations, 

Ricker stock-recruitment models were fit using stock biomass (SSB) alone and with freshwater 

flow to partition effects of SSB and hydrology on age-0 recruitments for years 1989-2008. 

Models including flow explained more variability in recruitments than models with SSB alone 

(AIC=507 to AIC=273). Regression models including freshwater flow and temperature explain a 

high proportion of variability (r2

=0.63) in age-0 recruitment. Modeling results indicate that 

environmental variability controls larval survival and the level of age-0 recruitment. Estimates 

of zooplankton abundance and distribution based on net and acoustic sampling coarsely depicts 

annual differences in prey availability, and indicates feeding conditions and potential survival of 

striped bass larvae. We also develop indicators of habitat quality and quantity based on 

isohaline location, and assess their ability to predict recruitment. After the larval stage, 

regulation occurs via density-dependent growth and size-selective overwintering mortality of 

age-0 juveniles. Age-1 juvenile abundance can be predicted in a regression model (r2

=0.82, 

p<0.001) using age-0 juvenile abundance, age-0 length attained, and winter temperature. 

Requirements of ecosystem-based fishery management are addressed by evaluating the 

potential to apply these findings to determine indicators and reference points of habitat 

suitability and reproductive success.
28

View presentation: http://www.chesapeakemeetings.com/ChesMS2010/sessions.php
23

View presentation: http://www.chesapeakemeetings.com/ChesMS2010/sessions.php
24

ECOLOGICAL PREDICTION OF DISSOLVED OXYGEN IN THE CHESAPEAKE BAY

M. Bala Krishna Prasad

Earth System Science Interdisciplinary Center, University of Maryland, mbkp@umd.edu

The structure and dynamics of aquatic ecosystem are a function of set of biogeochemical 

properties and forecast of these biogeochemical properties is a critical step in the sustainable

management of the aquatic ecosystems. Dissolved oxygen (DO) is a key parameter in the 

aquatic science research which not only controls the biological productivity but also regulated 

by the biological productivity. Nutrient pollution resulted from the increasing human activities 

alters the oxygen dynamics by reducing its concentrations to a critical level where the 

ecosystem cannot support the biological systems. The Chesapeake Bay is a largest estuary in 

the North America and is experiencing summer hypoxia/anoxia events due to the cultural 

eutrophication. Statistical step-wise multiple regression approach is applied to develop an 

empirical model to predict DO in Chesapeake Bay by using a longterm data (1990-2006) of 

water temperature, salinity, dissolved nutrients (N and P). The predicted DO concentrations 

correlated well with the observed values. The predicted DO values depict the current status of 

the Bay and are useful to develop several linked products by integrating results from other 

hydrodynamic physical models for the ecosystem conservation and management.

View presentation: http://www.chesapeakemeetings.com/ChesMS2010/sessions.php
25

COPEPODS AND SALMON: FORECASTING SALMON SURVIVAL OFF WASHINGTON AND 

OREGON

Hongsheng Bi

Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science, 

hbi@cbl.umces.edu

The abundance of yearling Chinook (Oncorhynchus tshawytscha) and coho salmon (O. kisutch) 

was sampled concurrently with physical (temperature, salinity, water depth) and biological 

variables (chlorophyll a concentration and copepod abundance) along Washington and Oregon 

coast in June 1998-2006. Copepods were divided into four different groups by their water-type 

affinities: cold neritic, subarctic, warm neritic and warm oceanic, and each group was used an 

independent environmental variable. Data collected in 1998- 2008 were used to perform 

nonparametric correlation and negative binomial loglinear mixed regression with a spatial 

random factor, and data collected in 2006 was used to validate the models. Yearling Chinook 

abundance was negatively related with temperature, and positively related to the density of 

cold neritic copepod, chlorophyll a concentration, and salinity. Yearling coho abundance was 

positively related with temperature, and negatively related with warm oceanic copepods, warm 

neritic copepods, and water depth. The two salmon species also showed different spatial 

patterns in most years. Yearling Chinook abundance showed significant spatial autocorrelations 

in 1999, 2000, 2002, 2004, and 2005, and yearling coho abundance showed significant spatial 

autocorrelations in 2000- 2002. The spatial random factor in the negative binomial loglinear 

mixed model was positively correlated with juvenile salmon abundance and showed similar 

spatial cluster patterns as juvenile salmon abundance for both species. Thus the occurrence of 

spatial autocorrelation could be attributed to the spatial random. Both annual mean abundance 

of yearling Chinook and the spring Chinook jack counts, a measurement of local population 

success, were positively correlated with cold neritic copepods. The annual mean abundance of 

yearling coho and the ocean survival rate were negatively correlated with warm oceanic 

copepods. The differences in how each species mapped onto habitat variable might be 

explained by the fact that yearling coho has relatively wider coastal distribution, and could feed 

on a relatively wider range of prey items than yearling Chinook. To address the spatial 

autocorrelation and the difference between two species, information on other physical 

processes, such as large scale transports, eddies and fronts, and biological processes such as 

prey and predator may be necessary.
26

View presentation: http://www.chesapeakemeetings.com/ChesMS2010/sessions.php
27

PROJECTING RECRUITMENT OF CHESAPEAKE BAY STRIPED BASS BASED ON EARLY-LIFE 

HABITAT QUALITY

Ed Martino

NOAA/NOS/NCCOS Cooperative Oxford Laboratory, Ed.Martino@noaa.gov

Ecosystem-based approaches to fishery management require improved understanding of 

habitat requirements and how environmental variability affects stock dynamics. High variability 

(>20 fold) in age-0 juvenile recruitment is a common feature of Chesapeake

Bay striped bass population dynamics. Spatio-temporal variability in zooplankton prey for 

striped bass larvae, controlled by synoptic-scale climatology and hydrological conditions, 

explains strong environmental controls of recruitment. In two spawning-nursery locations, 

Ricker stock-recruitment models were fit using stock biomass (SSB) alone and with freshwater 

flow to partition effects of SSB and hydrology on age-0 recruitments for years 1989-2008. 

Models including flow explained more variability in recruitments than models with SSB alone 

(AIC=507 to AIC=273). Regression models including freshwater flow and temperature explain a 

high proportion of variability (r2

=0.63) in age-0 recruitment. Modeling results indicate that 

environmental variability controls larval survival and the level of age-0 recruitment. Estimates 

of zooplankton abundance and distribution based on net and acoustic sampling coarsely depicts 

annual differences in prey availability, and indicates feeding conditions and potential survival of 

striped bass larvae. We also develop indicators of habitat quality and quantity based on 

isohaline location, and assess their ability to predict recruitment. After the larval stage, 

regulation occurs via density-dependent growth and size-selective overwintering mortality of 

age-0 juveniles. Age-1 juvenile abundance can be predicted in a regression model (r2

=0.82, 

p<0.001) using age-0 juvenile abundance, age-0 length attained, and winter temperature. 

Requirements of ecosystem-based fishery management are addressed by evaluating the 

potential to apply these findings to determine indicators and reference points of habitat 

suitability and reproductive success.
28

View presentation: http://www.chesapeakemeetings.com/ChesMS2010/sessions.php
29

LARVAL TRANSPORT, HABITAT VOLUME, AND MARINE PROTECTED AREA OPTIMIZATION 

MODELS: FROM DEVELOPMENT TO OPERATIONAL USE

Elizabeth North

Horn Point Laboratory, University of Maryland Center for Environmental Science,

enorth@hpl.umces.edu

Coupled bio-physical models are increasingly being used to inform fisheries and ecosystem 

management and restoration programs. We describe three types of coupled biophysical models 

and present case studies for three Chesapeake Bay species: 1) a larval transport model for blue 

crab (Callinectes sapidus), 2) a habitat volume model for juvenile Atlantic sturgeon (Acipenser 

oxyrinchus oxyrinchus), and 3) a marine protected area optimization model for oysters 

(Crassostrea virginica). The differences and similarities in these modeling approaches will be 

highlighted and used to illustrate the information needs and challenges in moving these types 

of models towards operational use.

View presentation: http://www.chesapeakemeetings.com/ChesMS2010/sessions.php
30

FORECASTING WATERSHED NUTRIENT AND CONTAMINANT LOADS TO DRINKING WATER IN 

BALTIMORE, MARYLAND: A PILOT APPLICATION OF THE CHESAPEAKE BAY FORECASTING 

SYSTEM

Sujay Kaushal

Chesapeake Biological Laboratory, University of Maryland Center for Environmental Science,

kaushal@cbl.umces.edu

There have been increasing interactive effects of land use and climate change on water 

resources in Maryland. These interactive disturbances may have important effects on nutrient 

and contaminant loads to municipal drinking water supplies. Here, we develop and test a pilot 

application of the Chesapeake Bay Forecasting System regarding loadings of nitrogen, 

phosphorus, and road salt in the North Branch of the Patapsco River. The North Branch of the 

Patapsco River serves as a critical source of water to Liberty Reservoir, one of 3 major reservoirs 

operated by Baltimore City Department of Public Works that distribute an average of 265 

million gallons of water per day to 1.8 million people in the Baltimore region. Analysis of longterm trends in the North Branch of the Patapsco River and nearby watersheds show increasing 

salinization due to road salt use, pronounced effects of record drought and wet years on 

nitrogen and phosphorus loads, and rising stream and river water temperatures. The objective 

of the pilot application involves collaboration with Baltimore City Department of Public Works 

to investigate and model the effects of extreme weather events and precipitation variability on 

the temporal distribution and magnitude of storm discharge rates and contaminant loadings. 

This information may be useful for informing watershed restoration strategies to increase 

resilience in ecosystem nutrient retention functions in response to extremes in precipitation, 

and as a potential support tool for predicting the quality of water withdrawals across different 

weather conditions. Application of the Chesapeake Bay Forecasting System may be useful in 

assisting efforts to predict and protect quantity and quality of water, and preliminary challenges 

and/or results from the pilot application will be discussed.
31

View presentation: http://www.chesapeakemeetings.com/ChesMS2010/sessions.php
32

References

Brown, C. W., D. L. Ramers, et al. (Need all the authors). 2010. Predicting the abundance of the 

dinoflagellate Karlodinium veneficum in the Chesapeake Bay. Harmful Algae (in preparation).

Constantin de Magny, G., W. Long, C.W. Brown, R.R. Hood, A. Huq, R. Murtugudde and 

R.R. Colwell. 2009. Predicting the distribution of Vibrio spp. in the Chesapeake Bay: A Vibrio 

cholerae case study. Ecohealth 6: 378-389.

Costantini, M., S.A. Ludsin, D.M. Mason, X. Zhang, W.C. Boicourt and S.B. Brandt. 2008. Effect of 

hypoxia on habitat quality of striped bass Morone saxatilis (Walbaum) in Chesapeake Bay. Can. 

J. Fish. Aquat. Sci. 65(5): 989-1002.

Decker, M. B., C. W. Brown, R.R. Hood, J.E. Purcell, T.F. Gross, J.C. Matanoski, R.O. Bannon and 

E.M. Setzler-Hamilton. 2007. Predicting the distribution of the scyphomedusa, Chrysaora 

quinquecirrha, in Chesapeake Bay. Marine Ecology Progress Series 329: 99-113.

Jacobs, J. M., M. Rhodes, C.W. Brown, R.R. Hood, A. Leight, W. Long and R. Wood. 2010. 

Predicting the distribution of Vibrio vulnificus in Chesapeake Bay. NOAA Technical 

Memorandum NOS NCCOS. 112. 12 pp.

Ludsin, S.A., X. Zhang, S.B. Brandt,M.R. Roman, W.C. Boicourt, D.M. Mason and M. Costantini. 

2009. Hypoxia avoidance by planktivorous fish in Chesapeake Bay: implications for food web 

interactions and their recruitment. J. Exp. Mar. Biol. Ecol. 381: S121-S131.

Martino, E. J. and E.D. Houde. 2010. Recruitment of striped bass in Chesapeake Bay: spatial and 

temporal environmental variability and availability of zooplankton prey. Marine Ecology 

Progress Series 409: 213-228. 

Murtugudde, R. 2009. Regional earth system prediction: a decision making tool for 

sustainability? Current Opinion in Environmental Sustainability 1: 37-45.

Prasad M.B.K., L. Wen, X. Zhang, R. J. Wood, R. Murtugudde. 2010. Prediction of dissolved 

oxygen in the Chesapeake Bay. Aquatic Sciences (Submitted).