Database Management and Environmental Modeling to
Characterize Sources and Effects of Natural Parameters and Anthropogenic
Contaminants
In
Coastal Ecosystems
A LU-CES State of Knowledge Project Final Report
Submitted to:
South Carolina Sea Grant Consortium
287 Meeting Street
Charleston, S.C. 29401
Submitted by:
Dwayne E. Porter1,2, Thomas C. Siewicki3, Jeff Allen4, Don Edwards1, and William K. Michener5
1
Belle W. Baruch Institute for Marine Biology and Coastal Research
2
School of Public Health
University of South Carolina
Columbia, SC
3
Center for Coastal Environmental Health and Biomolecular Research
National Oceanic and Atmospheric Administration
Charleston, SC
4
Strom Thurmond Institute of Government and Public Affairs
Clemson University
Clemson, SC
5
Joseph W. Jones Ecological Research Center
Newton, GA
14 November 1998
Table of Contents
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 03
Section 1. Design and implementation of a multi-participant
database management program . . . . . . . . . . . . . . . . . . . 08
1.1 Rationale for multi-participant data management initiatives . . . . . . . . . 08
1.2 Components of a multi-participant database management program . . . . . . . 09
1.3 Draft general guidelines for LU-CES data management and information dissemination . 14
1.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . 15
1.5 References . . . . . . . . . . . . . . . . . . . . . . . . 16
Section 2. Modeling of estuarine stressors . . . . . . . . . . . . . . . . . 17
2.1 Hydrodynamics . . . . . . . . . . . . . . . . . . . . . . 19
2.2 Sediments . . . . . . . . . . . . . . . . . . . . . . . . 20
2.3 Nutrients . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.4 Chemicals . . . . . . . . . . . . . . . . . . . . . . . . 21
2.5 Population dynamics . . . . . . . . . . . . . . . . . . . . . 24
2.6 Additional considerations . . . . . . . . . . . . . . . . . . . 25
2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . 25
2.8 References . . . . . . . . . . . . . . . . . . . . . . . . 27
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 34
Request for comments . . . . . . . . . . . . . . . . . . . . . . . 34
Introduction
The South Carolina and Georgia (SC&GA) coasts are projected to receive the greatest infusion of
human occupancy in the US over the next 20 years. It is estimated that 75% of the US population will
live within 50 miles of the coast (Vernberg et al. 1992). A result of dense urban development adjacent
to estuarine waterways is the unintentional discharge of chemical and microbial contaminants and
nutrients directly into those water bodies (NOAA National Status and Trends Program; Daskalakis and
O'Connor 1995; O'Connor 1996; Watkins and Burkhardt 1996; Puckett 1995; Cole et al. 1984). There
is extensive literature on the association of one or a group of contaminants to a land use(s). Nitrogen
and phosphorus (Puckett 1995), Clostridium perfringins and fecal coliforms (Burkhardt and Watkins
1992; USFDA National Shellfish Sanitation Program 1996) non-pyrogenic polycyclic aromatic
hydrocarbons (Lee et al. 1981) pyrogenic PAHs (Pruell and Quinn 1985), stable isotopes (Drimmie
and Frape 1995), metals (Buckley et al. 1995; Diamond and Ling-Lamprecht 1996), pesticides (Pereira
et al. 1996), nitrate (Hamilton et al. 1993) are examples of individual or groups of constituents linked
to land uses. Turf grass management for golf courses, parks and roadways, for example, will be
increasingly important along with its attendant pesticide runoff. Pait et al. (1992) showed that
southeastern US estuaries are already at greater risk from pesticides than other US regions. Scales of
these investigations are generally very large, however, and hardly amenable to defining sources within
estuarine systems characteristic of the southeastern coastal zone. Scale refinements will require
integration of multiple stressor and other environmental data into models that not only characterize
sources but potential impacts as well. In addition, most of these studies examined large, industrialized
urban centers that will not typify future development of the southeast coast.
Existing infrastructure and planning for anticipated residential and service industry development and
resultant impacts are either non-existent or wholly inadequate in the SC&GA coastal area that is
currently dominated by agriculture, tourism and small communities. Developmental changes must be
planned for and this requires that appropriate data, information, and technologies are available to
coastal resource planners and manager.
According to a 1995 NOAA report, the top priorities for coastal resource managers were to acquire 1.)
information on nonpoint sources of pollution and preventing wetland habitat loss; 2.) scientific data
linking development activity to adverse resource impacts; and 3.) techniques for managing
development impacts and mediating multiple use conflicts (NOAA Coastal Committee 1995).
The NOAA survey of coastal resource managers identified a need for the following products and
services to address the above priorities:
1. scientifically-supported information for addressing coastal development impacts,
2. some type of information clearinghouse, and
3. correlation/linkages between land-use practices, implementation of non-point source
coastal zone management strategies, and changes in water quality parameters.
A primary obstacle perceived by this user group to obtaining such services and products from federal
agencies was a lack of information on what technical assistance or data are available and no direct
mechanism to get usable information when it is needed. Much of the existing coastal data in the US are
stored by many different researchers, in different formats and on different media. Long time series of
coastal data will enhance understanding, assessment and prediction of global productivity; climate
change; nutrient cycles; population trends and cycles; trends in concentrations and impacts of marine
pollutants; impacts of different land-use strategies on coastal environments; and interactions between
coastal and deeper water regions.
The advent of database management programs, the Internet and the World Wide Web (WWW), and
Geographic Information Systems (GIS), particularly when coupled to statistical modeling, allow new
approaches to managing development of our coastal ecosystems. The South Atlantic Bight Land Use Coastal Ecosystems Study (LU-CES) will combine existing and newly gathered data into a single
(virtual) archive for use in forecasting impacts to coastal and estuarine ecology in the SC&GA region.
The project will then be able to devise alternative development strategies to minimize these impacts.
This is a noble goal but one, which is exceedingly complex and can only be done after very focused
research. Paramount among the initial requirements of the project is a clearly understood objective and
efficient approach to acquire that objective. We are proposing that a modeling approach to identifying
sources and impacts of stressors to these ecosystems is the principal objective. This will require that a
well-structured database management program accessing a repository of GIS and tabular data, when
obtainable, and links to data, when data must be maintained in the original repository, are absolutely
essential.
Geographic Information Processing, i.e., the integration of database management, remote sensing,
Global Positioning Systems (GPS), GIS, and statistical and spatial modeling, is being used by estuarine
resource managers to address ecosystem, landscape and global issues (Michener et al. 1989, Haddad
and Michener 1991; Jefferson et al. 1991; Dunlap and Porter 1993; Holland et al. 1993; Porter 1995;
Porter et al. 1995; Porter et al. 1997; Jensen et al. 1998). GIS allows the integration of both archived
and recent data from disparate sources. Data types include biology, chemistry, physical sciences,
socioeconomic, remote sensing, cartography, oceanography and image processing, among others.
These techniques are used to assess relationships among land uses, land cover, environmental
conditions and ecological effects (Porter et al. 1997). Strong academic and research programs in
Geographic Information Processing at both the University of South Carolina and the University of
Georgia have provided resource management agencies in these states opportunities to use state-of-theart tools and applications of GIS, data management, environmental modeling, remote sensing, and
environmetrics. This expertise has benefited the local resource management and planning agencies
also. A recent survey by the Berkeley-Charleston -Dorchester Council of Governments (1997)
identified over 35 groups using GIS for resource management in the three-county area.
This project addressed the following SOK report recommendation of the LU-CES RFP:
•
Document the state of knowledge on modern database design and assess it as it has been
used in the study regions. Particular emphasis should be placed on input/output, shared
use, data management and potential for use in modeling studies.
This State of Knowledge (SOK) final report examines coastal environmental database management
and modeling efforts and provide draft guidelines for data management and information dissemination
for use by the LU-CES project throughout its life. This report is divided into two sections. Section 1
discusses issues of environmental data management and information dissemination. Section 2
discusses the current status of environmental modeling of estuarine stressors. Both sections are
intended as standalone documents developed for future publication.
References
Berkeley-Charleston-Dorchester Council of Governments. 1997. 1997 Berkeley Charleston Dorchester
Regional GIS User Group Directory. North Charleston, SC. 79 pp.
Buckley, D.E., J.N. Smith and G.V. Winters. 1995. Accumulation of contaminant metals in marine
sediments of Halifax Harbor, Nova Scotia: Environmental factors and historical trends. Appl.
Geochem. 10(2):175-195.
Burkhardt, W. and W.D. Watkins. 1992. Clostridium perfringins provided the only reliable measure of
human contamination in the marine environment. In: Abstracts of the 92nd General Meeting of the
American Society for Microbiology. American Society for Microbiology, Washington, DC, p. 378.
Cole, R.H., R.E. Frederick, R.P. Healy and R.G. Rolan. 1984. Preliminary findings of the priority
pollutant monitoring project of the National Urban Runoff Program. J. Water Pollut. Control. Fed.
56:898-908.
Daskalakis, K.D. and T.P. O'Connor. 1995. Normalization and elemental sediment contamination in
the coastal United States. Environm. Sci. Technol. 29:470-477.
Diamond, M., H.W Ling-Lamprecht. 1996. Loadings, dynamics and response time of seven metals in
Hamilton Harbour: Results of a mass balance study. Water-Qual. Res. J. Can. 31(3):623-641.
Drimmie, R.J. and S.K. Frape. 1996. Stable chlorine isotopes in sediment pore waters of Lake Ontario
and Lake Erie. Isotopes in Water Resources Management, Vol. 1. Proceedings International Atomic
Energy Association Vienna, p. 141-155.
Dunlap, R.E. and D.E. Porter. 1993. Use of geographic information processing for the identification of
"indirect" impacts associated with regulatory permitting programs: For now, a conceptual model.
Proceedings Coastal Zone ‘93 1:79-93.
Haddad, K.D. and W.K. Michener. 1991. Design and implementation of a coastal resource Geographic
Information System: administration considerations. Proceedings, Coastal Zone ‘91, Vol. 3:1958-1967.
Hamilton, P.A., J.M. Denver, P.J. Phillips and R.J Sherlock. 1993. Water-quality assessment of the
Delmarva Peninsula, Delaware, Maryland, and Virginia - Effects of agriculture activities on, and
distribution of, nitrate and other inorganic constitutents in the surficial aquifer. U.S Geol Survey Open
File Reports, Denver, CO.
Holland, A.F., D.E. Porter, R.F. Van Dolah, R.H. Dunlap, G.H. Steele and S.M. Upchurch. 1993.
Environmental assessment for alternative dredged material disposal sites in Charleston Harbor. Tech.
Rep. 82, Marine Resource Division, South Carolina Dept. Natural Resources, Charleston, SC.
Jefferson, W.H., W.K. Michener, D.A. Karinshak, W. Anderson and D.E. Porter. 1991. Developing
GIS data layers for estuarine resource management. Proceedings GIS/LIS 91 1:331-342.
Jensen, J.R., D.E. Porter, C. Coombs, B. Jones, D. White and S. Schill. 1998. Extraction of smooth
cordgrass (Spartina alterniflora) biomass and leaf area index parameters from high resolution imagery.
Geocarto. In press.
Lee, M.L., M.V. Novotny and K.D. Bartle. 1981. Analytical Chemistry of Polycyclic Aromatic
Compounds. Academic Press, New York, NY, 462 pp.
Michener, W.K., D.J. Cowan and W.L. Shirley. 1989. Geographic Information Systems for coastal
research. In: Proceedings, Sixth Symposium on Coastal and Ocean Management, American Society of
Engineers, p. 4791-4805.
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the NOAA Coastal Committee.
O'Connor, T.P. 1996. Trends in chemical concentrations in mussels and oysters collected along the US
coast from 1996 to 1993. Mar. Environ. Res. 41:183-200.
Pait, A.S., A.E. De-Souza and D.R.G. Farrow. 1992. Agricultural pesticide use in coastal areas: A
national summary. National Ocean Service, Silver Spring, MD 113pp.
Pereira, W.E., J.L. Domagalski, F.D. Hostettler, L.R. Brown and J.B. Rapp. 1996. Occurrence and
accumulation of pesticides and organic contaminants in river sediment, water and clam tissues from
the San Joaquin River and tributaries, California. Environ. Toxicol. Chem. 15(2):172-180.
Porter, D.E. 1995. Use of Geographic Information Processing Technology to Model Cumulative
Impacts of Regulatory Permitting Programs on Coastal Wetlands: A South Carolina Perspective.
Doctoral dissertation. Columbia, SC: University of South Carolina. 203 pp.
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Information Processing to Compare the Rate of Wetlands Alterations in an Undeveloped and a
Managed Estuary. Proceedings, Third Thematic Conference on Remote Sensing for Marine and
Coastal Environments. Seattle, WA. Vol. II. pp. 496-507.
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Geographic Information Processing to Assess the Impacts of Urbanization on a Localized Coastal
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sediments. Est. Coast. Shelf Sci. 21:295-312.
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Library in Marine Science 20, University of South Carolina, Columbia, SC p. 241-263.
Section 1.
Design and implementation of a multi-participant database management program
A multi-participant environmental data management program is both a scientific and management
imperative for effective coastal resources research and management. The management of long-term
environmental monitoring data sets provides for baseline studies, trend analyses and impact assessment
of both natural and anthropomorphic phenomena. Advances in information technology are rapidly
changing the way research and resource management agencies can assimilate, manage, disseminate
and share the data and information pertinent to effective resource management (National Research
Council 1993). The technology revolution is providing organizations the opportunity to collect and
integrate digital data independently while providing cost-effective mechanisms for the exchange of
data and information among various state and federal resource agencies, scientific disciplines, industry
and jurisdictional areas.
Within the LU-CES study area several federal and state resource management agencies and academic
units have and are participating in multi-participant, multidisciplinary environmental monitoring and
resource assessment programs each having an emphasis on data and information exchange. These
programs include the National Science Foundation (NSF) funded Long-Term Ecological Research
(LTER) and Land-Margin Ecosystems Research (LMER) programs, and the National Oceanic and
Atmospheric Administration (NOAA) funded Urbanization and Southeastern Estuarine Systems
(USES) and National Estuarine Research Reserve System (NERRS) programs (Table 1.1). In addition,
discussions with state resource management agencies and research institutes within South Carolina and
Georgia reveal a variety of approaches towards and policies for data management and information
dissemination. Building on these experiences and discussions, this section addresses issues important
for consideration in the development and implementation of the LU-CES database management and
information dissemination program and provides draft guidelines for data management and
information dissemination.
1.1 Rationale for multi-participant database management initiatives
Acknowledging that it is difficult enough to develop and maintain a single participant (e.g., intraagency, intra-site) database management program, potential participants of a multi-participant program
must first come to grips with why they should participate. Two compelling reasons are as follows.
1.) Federal directives require federally funded organizations and projects to make their data and
information available to the public, and to coordinate database development. By Executive Order, the
National Spatial Data Infrastructure (NSDI) program calls for heads of agencies to submit to the Office
of Management and Budget (OMB) a schedule and coordinated funding plan to ensure a coordinated
and coherent Federal effort in database development. OMB Circular A-130 states in summary, as
policy, that agencies shall "...distribute information at the agency's initiative, rather than merely
responding when the public requests" (Anderson 1994).
2.) It makes sound financial and resource management sense. Environmental data are very valuable, as
collection efforts are expensive to implement and maintain. On average, the more expansive the
geographic area covered or the more detail required, the more expensive the data collection efforts. By
implementing a program fostering the exchange of good data and information among groups, spatial
overlap in data collection efforts can be eliminated potentially lowering data collection costs. By
providing managers with the best available data regardless of jurisdictional boundaries or agency
affiliation, more informed resource management decisions can be made and appropriate plans
implemented. This is especially true when dealing with resource management issues which do not
adhere to jurisdictional boundaries, therefore management plans that may have both environmental and
economic impacts within and across jurisdictions should be based on the most comprehensive data
available.
Table 1.1. Multi-participant database management activities.
Program Participants
Types of data collected
LTER
LTER sites
biological, chemical, meteorological, physical
LMER
LMER sites
biological, chemical, meteorological, physical
USES
Federal and state resource managers and
researchers
biological, chemical, land use/land cover,
physical, socioeconomic
NERRS NERRS reserves and state coastal zone
management agencies
biological, chemical, meteorological, physical,
land use/land cover
1.2 Components of a multi-participant database management program
A properly implemented database management program consists of several items including hardware
and software, personnel, data and documentation. More important to the overall success of maintaining
a usable database is the implementation of a database management strategy. In addition to obtaining
inter-administrative support, there are at least five key components for a successful implementation of
a multi-participant database management strategy:
A.) user needs assessment (UNA);
B.) data collection protocol;
C.) quality assurance/quality control (QA/QC) procedures;
D.) program documentation and metadata; and
E.) data and information dissemination hub.
A. User needs assessment
Within a multi-participant database management program, there will exist a variety of hardware
platforms, software preferences, data management practices and expectations. The
identification of existing and proposed database management programs, technology facilities
and support personnel, and system requirements and expectations comprise the first step in the
development and implementation of a multi-participant database management program. This is
accomplished through the administration of a UNA to all potential participants in the program.
The UNA addresses, at a minimum, five issues:
i.) perceived mission, goals and objectives;
ii.) type of data being collected;
iii.) amount of data collected and information generated;
iv.) sophistication; and
v.) infrastructure (Michener and Haddad, 1992).
Although several UNAs have been completed in recent years involving many of the same
groups associated with the LU-CES project, a current UNA should be administered. By
analyzing the results of the UNA, the development of the database management program
begins by identifying what is being done by whom, who has what, and what is expected. From
a synthesis of the obtained information, it is determined what is necessary and what can be
accomplished.
B. Data collection protocol
From the UNA comes an identification of what data are being collected and what data are
necessary for effective resource management. This forms the basis of the development of the
data collection protocol. Data collection protocol is the documented identification of what
parameters are to be measured, when they to be measured, and how they are to be measured.
These constitute your data standards. The data collection protocol combined with proper
documentation provides for consistency and ensures data collection procedures can be assessed
at a later date (e.g., years later after data collection personnel and data collection techniques
may have changed).
It is unreasonable to expect every participating group in a multi-participant database
management program will require the same data. An expectation that every group will or can
conform to consistent data collection techniques is most likely unrealistic. If the results of the
UNA indicate that one or more environmental parameters are consistently identified as
important, it may be possible to implement a data protocol that ensures consistency for key data
variables. For example, the 22 current NERRS Reserves collect disparate amounts of data often
dependent on facilities, personnel and current research projects. As part of the System-wide
Monitoring Program, NERRS Reserves are developing a nationwide database of baseline
environmental conditions in NERRS estuaries. By using consistent protocol for data acquisition
and QA/QC these data will provide for an inter-site comparison of water quality parameters
(Matthews et al. 1997).
C. Quality assurance / quality control procedures
Data required for effective resource management may be collected using a variety of
techniques. These include remote sensing, in situ sampling, and the use of automated data
loggers. Data entry may involve manual entry of data from handwritten field sheets,
downloading data directly from a data logger to a computer, or the use of sophisticated digital
image processing and GIS techniques. Collected data are of no use to researchers and resource
managers if the data do not accurately reflect measured conditions. The development,
implementation and consistent application of QA/QC procedures facilitate the detection of data
corrupted by errors caused by automated or human data collection and data entry techniques
(Michener and Haddad 1992). QA/QC procedures often entail multiple levels of data checking.
With some automated data collection equipment, downloading directly from data loggers to
computers reduces the potential for human-introduced errors. For non-automated field data
collection efforts, standardized data entry forms can help reduce the potential for humanintroduced errors. Visual verification performed during and after data collection and entry as
well as automated procedures for data entry verification help provide for higher quality data.
Another aspect of data QA/QC is temporal and spatial scale. This is particularly true when
dealing with GIS data layers and remotely sensed data as the spatial scale, and time and
conditions at the time of acquisition of the source data (usually satellite imagery, aerial
photographs, airplane-mounted sensors, hardcopy maps) limit the detail and accuracy of
information which can be extracted from the data. This important topic of scale is addressed in
a separate SOK report submitted by Cowen et al. (1998).
Data archival and protection are additional components of QA/QC procedures. When dealing
with the potential for loss of data (e.g., natural disaster, hard disk failure, fire), plan for the
worst. Implement and adhere to a data backup plan. Maintain multiple copies of all data sets. If
an off-site storage facility for data archival is not feasible, a secure, fireproof cabinet is a small
investment. In addition to data backup and archival, a database management strategy must
provide for data integrity and security. This is to ensure that data sets are not altered or
accessed improperly. Implemented QA/QC procedures, no matter how rigorous, are of no value
if not fully implemented and adhered to. As with all components of a multi-participant database
management program, documentation of the QA/QC process is a requirement.
D. Program documentation and metadata
The importance of system and data documentation cannot be overemphasized. Often times,
documentation procedures are disregarded for reasons such as:
- documentation takes extra time;
- no reason to write it down, it is all stored in my head;
- if anyone has questions, they can ask me; and
- it is my system and/or data, so I do not have to document it.
Proper documentation does take time and often the importance of documentation is not
recognized. As personnel changes occur, data collection procedures are altered, and data are
exchanged among groups, documentation is the key component linking everything together.
Procedures for system and data documentation must be budgeted for when designing the multiparticipant database management program.
Data documentation, currently referred to as metadata, are "data about data". Metadata provide
information as to the characteristics of a data set, lineage of a data set, and contacts for
accessing or acquiring a data set. Standardized metadata procedures provide a means to
document datasets within organizations, to contribute to and facilitate multi-participant data
exchange programs, and facilitate locating, understanding and utilizing existing data sets. The
choice of a database structure should not matter as long as the content and structure of the
metadata standard are adhered to by those responsible for data collection and management
(Federal Geographic Data Committee 1994).
In 1994, the President signed Executive Order 12906 titled Coordinating Geographic Data
Acquisition and Access: The National Spatial Data Infrastructure requiring federal agencies
and federally-funded projects to use the Federal Geographic Data Committee (FGDC) standard
to document data they produce beginning in 1995. The FGDC metadata standard was
developed from the perspective of "what a user needs to know about a data set." The standard
provides a common set of terminology and definitions, and supports common uses of metadata.
Through the FGDC, the federal government, in particular NOAA's Coastal Services Center
(CSC) in Charleston, SC, as well as state governments, are working to develop standardized
metadata procedures that are intended to improve metadata development efficiency and
expected to enhance the use of data, reduce redundancy in data collection activities, and
provide a means for determining the usefulness of specific data sets. A good description of
metadata and links to metadata development tools for both GIS data layers and tabular data sets
is available at the CSC's web presentation (http://www.csc.noaa.gov/metadata/#fgdc).
E. Data and information dissemination hub
In addition to the development of data protocol and procedures for QA/QC, a multi-participant
database management program can only be successful if participants and potential users have
access to the data being collected and information being generated. This is the issue of
connectivity and communication. Connectivity is the ability to access data and information
regardless of where the data are stored and on what type of machine. Advances in hardware,
software and telecommunications have eliminated the past bottleneck of communicating across
different hardware platforms. Communication is the process of utilizing the connectivity to
identify, provide, obtain and exchange data, ideas and information pertinent to effective
resource management.
The development of an information hub is one technique used for connectivity and
communication. The concept of an information hub should not be confused with the concept of
centralized computing. Centralized computing implies a single computer acting as a server to
one or more terminals, each of which is dependent upon the server. The role of an information
hub is to facilitate coordination and communication within a site and across any number of
sites. Although the information hub will be at one location interacting with numerous other
groups, the information hub does not take the place of or supersede the existing database
management programs of organizations involved in the multi-participant program. At a
minimum, the information center coordinates the exchange of data and metadata, develops and
disseminates standard products and provides documentation.
Data exchange or data dissemination should not be confused with information dissemination.
The Federal and state governments are working to define the public's right to access data and
information under Freedom of Information (FOI) laws. It is important to be cognizant of FOI
laws and their potential impact on a multi-participant database management program. The issue
of data disclaimers covering custodial liability must also be addressed. There are documented
cases of litigation resulting from decisions made by one party based in part or in whole on data
or information provided by another party. While the information hub may be set up as a central
repository for all collected data, it is not a requirement. Individual groups may be inclined to
not provide the information hub their data sets. Instead links are implemented so that when a
data request is made to the information hub, the transfer of requested data is transparent. No
matter what the distribution medium (e.g., CD-ROM, diskette, on-line transfer), always provide
associated metadata with data sets.
An information hub can facilitate the exchange of ideas and foster interaction by developing a
series of standard products providing for some level of inter-site comparison. The availability
and dissemination of standard products can provide an effective tool for facilitating inter-site
interaction and communication. The standard products may be as simple as a chart comparing
total catch from a two-seam shrimp net tow by geographical region to the development of an
on-line interactive GIS model for predicting land use change. Standard products, regardless of
complexity, should provide information important to the overall goal of effective coastal
resource management. Documentation, as mentioned previously, is the important link that ties
everything together. The information hub should be responsible for acquiring, maintaining and
disseminating metadata with all data request.
One effective tool for the development of an information hub is the World Wide Web (WWW).
Using hypermedia browsers, the WWW provides a hypermedia interface to the various
protocols, data formats and information archives used on the Internet. It provides powerful new
methods for visualizing, exchanging, and using data and information. A strength of the WWW
for the development of multi-participant data and information dissemination programs is that it
can be accessed from a variety of platforms including Macintoshes, Windows and Unix-based
systems. In the development of a Web-based data and information dissemination hub, several
issues must be addressed including access to the Internet and the WWW. While Internet access
is becoming more and more common, many local resource management and planning agencies
(potential users of the LU-CES data and information) have no or limited access to the Internet.
The user needs assessment should help to identify the level of access available to the Internet
and the WWW.
In the LU-CES study area, several examples exist of Web-based coastal environmental data and
information management hubs. These include:
NOAA NERRS Centralized Data Management Office - http://www.baruch.sc.edu/cdmohome.html
NOAA Coastal Services Center - http://www.csc.noaa.gov/
NOAA-funded Urbanization and Southeastern Estuarine Systems Program –
http://www.baruch.sc.edu/usesweb/useshome.html
SC Department of Natural Resources - http://water.dnr.state.sc.us/gisdata/index.html
A prototype Web-based data and information server has been developed for the LU-CES
project. Located at http://inlet.geol.sc.edu/luces2/luces/LUCES_1.HTML, this Web presentation is
intended to provide an initial framework demonstrating Web-based capabilities for data and
metadata assimilation and dissemination, linkages to data sets stored at offsite locations,
information dissemination, and providing access to on-line land-use planning tools such as the
prototype Coastal Data Mapper.
The Coastal Data Mapper web-based planning tool is a prototype example of a system
providing coastal spatial data as well as mapping products via the Internet. At present a large
gap exists between the amount of data and information available and the ability of the scientific
community to make that information useful and put it into the hands of decision makers and
personnel interacting with the general public. The prototype shows several examples of coastal
data/information products and how they can be displayed over the Internet. The prototype is not
an interactive system; just an example of how the system might look when implemented. The
following text describes the project team's vision for the Coastal Data Mapper.
Because of rapid growth in digital information, there is a critical need for "information
managers", those who would provide a value-added filtering process in sifting and managing
information to make it meaningful to the rest of society. The proposed Coastal Data Mapper
project would implement a Geographic Information System (GIS) accessible through the
internet and focusing on meeting the information needs of coastal resource managers, scientists,
planners and citizens who are sometimes restricted in gaining access to these types of
technologies.
Digital cartographic information is no different than other types of information. Presently, there
are volumes of digital data sets (many at no or very low cost) available to the public. The
creation of the TIGER database by the US Census Bureau and the US Geological Survey offers
the opportunity to visually analyze census data in urban and rural areas throughout the US. Not
only are the data consistent in coverage, i.e., available for any area of the US, the scale of the
data at 1:100,000 makes the cartographic data useable for some planning applications. The
ability for local planners to tie demographic data to digital maps of the road network and census
tracts/blocks empowers the planner to perform more meaningful types of analyses. Though
these data has been available for several years, technologically underserved areas have been
very slow gaining access to these data and utilizing them in productive ways.
Other types of data include political boundaries, congressional districts, demographic data
tagged to geographic coordinates, transportation networks, utility and infrastructure networks,
hydrology, land use and land cover, agricultural data, zoning, special interest districts and many
more. Most of these data are easily obtainable, but unless the requesting party has access to GIS
software, they generally cannot use the data. In addition, unless the computer user is trained in
GIS, the data are often not in a form that can be quickly and easily accessed or interpreted. As
the LU-CES project matures, additional data sets developed through this project can be
included in the Coastal Data Mapper.
The question or challenge is how to wade through all of the data to get useful information in an
easy, time efficient manner. The project team envisions a simple front-end interface for this
data that can be accessed through the Internet. In a sense, this interface acts as the "information
manager", and allows even the novice user the capability to produce meaningful maps. The
Coastal Data Mapper system, when developed, will permit anyone with access to the Internet
and the WWW to browse an assortment of map layers and data types associated with a
particular data set at the county and regional scale for coastal South Carolina and Georgia. An
individual will query the data by selecting an area and data layer of interest. Then a script will
be executed using GIS software (a batch file running in the background for one to two minutes
depending on the complexity of the question asked) to create a file which is sent to the user's
computer screen, printer or fax machine. The proposed Coastal Data Mapper program has the
potential to give decision-makers almost instantaneous access to information that would
normally take days or even weeks to produce.
The extent of benefits of the Coastal Data Mapper will be able to provide its users are almost
limitless. Many questions that are now asked and answered with traditional GIS could also be
handled by Coastal Data Mapper. Users of the Coastal Data Mapper interface will not need to
be experts in GIS yet they will have access to information and the ability to create tabular and
map graphics normally only available to highly trained GIS technicians. Those interested in
biological and agricultural questions will have access to a wide array of data including
information on estuarine systems, coastal plants and animals, crops, domestic animals, soils,
pesticides, etc. Individuals needing information about population characteristics will be able to
determine where certain segments of citizenry are located and in what concentrations. Those
interested in facility and service location will be able to visualize how those relate to population
and infrastructure distribution. Natural resource managers and individuals interested in land
development will be able to query data sets for land use classifications and relate them to
conservation or development planning. The Coastal Data Mapper could give decision-makers
almost instantaneous access to information that would normally take days or even weeks to
produce.
The Coastal Data Mapper could provide a utility for making information accessible to people
and areas traditionally underserved by the technological community. With Coastal Data
Mapper implemented, users of the technology will not be required to be as sophisticated in the
technical aspects of spatial information gathering as the typical GIS technician, or those having
access to such. Standardizing the most common analyses through menu systems, removing the
burden of building and cleaning up the geographic data, and enhancing the information delivery
process as well as the training programs could all lead to tremendous gains in GIS
implementation for all those interested in coastal information for South Carolina and Georgia.
1.3 Draft general guidelines for LU-CES data management and information
dissemination
It is expected that each LU-CES investigator / project will 1.) adhere to data management and
information dissemination guidelines currently implemented within his/her organization, or 2.) develop
data management and information dissemination guidelines in consultation with key investigators and
associated parent organizations, and 3.) that these policies, whether existing or new, must conform to
current policies for data management and information dissemination of the National Oceanic and
Atmospheric Administration.
The following provides general guidelines for data management and information dissemination, but
each investigator / project should be prepared to defend its own policy. These guidelines have been
modeled after those developed for the NOAA National Estuarine Research Reserve System Systemwide Monitoring Program and the National Science Foundation Long-term Ecological Research
Program.
General guidelines
Data management and information dissemination policy should include provisions that assure that:
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Prior to approval, each proposed project must submit an approved plan for data
management.
Investigators utilize software (GIS, data management, spreadsheet, etc.) capable of
exporting data in the most commonly utilized data exchange formats.
Investigators have a reasonable opportunity to have first use of data they collected.
The timely availability of data, information, and developed products to the scientific,
resource management, and education communities.
Investigators / projects contributing data to LU-CES databases receive adequate
acknowledgement for the use of their data by other researchers and that investigators
receive copies of any publication using that data.
For each data set, metadata, or data documentation, is developed in accordance with
current FGDC guidelines.
Investigators and their organizations are not liable for unauthorized use, misuse, or
misinterpretation of LU-CES data, metadata, or developed products.
Data and metadata must continue to be available even if an investigator leaves the
project through transfer or death.
Standards of quality assurance and quality control are documented in the metadata and
adhered to.
Long-term archival storage of data and metadata is maintained.
Researchers have an obligation both to contribute data collected with LU-CES funding to
the LU-CES database and to publish the data in the open literature in a timely fashion.
Costs of making data and metadata available should be recovered directly or by
reciprocal sharing and collaborative research.
Once provided, LU-CES data sets not be resold or distributed by the recipient.
1.4 Summary
The ability to exchange data and information is critical to the success of the LU-CES project and to
SC&GA resource managers and the private sector for effective management of coastal resources. A
multi-participant database management program can enhance the efforts of resource managers
responsible regulating the use of coastal resources. By making available data and information not
feasible to collect by individual groups, researchers and resource managers will have an increased
knowledge base for use in their decision making process. The successful development and
implementation of a multi-participant database management program must be supported at the
appropriate administrative level of participating organizations. A UNA is a first step to identify
existing capabilities, infrastructure, needs and expectations of participants. From an assessment of the
results of the UNA, program implementation begins with the development of data protocol, procedures
for QA/QC, and the design of an information hub that facilitates connectivity and communication
through the exchange of data and information. Most important, standardized procedures for system and
data documentation must be implemented and adhered to.
1.5 References
Anderson, D. 1994. Developing a Common Strategy for Managing SCS Strategic Databases.
Grassclippings. Winter 1994. Vol. 7. No. 3.
Cowen, D.J., J.R. Jensen and M. Hodgson. 1998. State of Knowledge on GIS Databases and Land
Use/Cover Patterns: South Carolina. Submitted to South Carolina Sea Grant Consortium. Charleston,
SC.
Federal Geographic Data Committee. 1994. The 1994 Plan for the National Spatial Data
Infrastructure - Building the Foundation of an Information Based Society. FGDC. Reston, VA. 14 pp.
Michener, W.K. and K. Haddad. 1992. Chapter 1 - Database Administration. In Proceedings,
Symposium on Data Management for Inland and Coastal Field Stations. J.H. Lauff, J.J. Alberts and
J.B. Lorenz (eds.). W.K. Kellogg Biological Station, Michigan State University.
Matthews, G.O., W. Jefferson, M.E. Crane and D.E. Porter. 1997. CDMO Operations Manual, Version
3.0. NERRS Centralized Data Management Office, Georgetown, SC.
National Research Council. 1993. Toward a Coordinated Spatial Data Infrastructure for the Nation.
NRC. Mapping Science Committee. National Academy Press. 170 pp.
Section 2. Modeling of estuarine stressors
A model is an abstraction or simplification of a system and modeling is an extension of scientific
analysis (Hall and Day 1977). Models allow theory to be developed that better explains processes at
work. Models, whether they are complex or simplistic, contain basic components including elements,
function and prediction (Wheeler 1988). Elements of a model are the variables that are used in the
analysis. Function describes the relationships between elements. And, prediction is the measure of how
accurately the model depicts reality.
Environmental models are generally developed to allow complex ecosystem relationships to be better
understood (Jorgensen 1986). Historically, the complex and dynamic nature of coastal and estuarine
areas has inhibited the ability to model such areas to a high degree of success. Fortunately, the
advancement of computer and information technologies is allowing researchers to incorporate
multidisciplinary scientific expertise and data into newly developed integrated ecosystem models. This
is a review of models used to predict sources, fates and effects of stressors in southeastern US
estuaries. The focus of this review is models either already used or thought applicable to estuaries in
the geographic area approximately bounded by Cape Fear, North Carolina and Cape Canaveral, Florida
and typified by vast Spartina marshes, digitated creeks and channels, and barrier islands. Brief
descriptions of recently used models are provided including how they were developed and used. The
information is provided for model users in the southeastern US. Both riverine and non-riverine
estuaries, impacted by a wide range of freshwater flows, are found in this region. Thus, models are
discussed that are applicable to such a variety of conditions. Because of the rapid development of
modeling technology, approaches developed within the past ten years are emphasized.
Thomann (1998) described three historical stages to model development. The first was from 19251980 when stressor sources were external to the models with only point sources linked directly. In the
second phase, 1980-1990, sediment models were coupled to water column and
hydrodynamic/watershed models. In the current third phase, air sheds and other watershed aspects
(e.g., land use) are being incorporated. Future improvements will be from improvements in
understanding ecosystems and also from developing consensus between land- and estuary-use
managers (Thomann 1998).
Several reviews on different aspects of environmental modeling have been done. Jorgensen et al.
(1995) provides a recent description of environmental and ecological models. A general review by
Chang (1992) describes the variety of aquatic models that are available: river basin, estuary, lake,
reservoir, nonpoint source pollution, storm water management, groundwater, potable water and
wastewater models. A more specific review of estuarine flow and water quality models is also
available and provides example results from the Manawquan Estuary, Passaic River and ChesapeakeDelaware Canal (Najarian and Harleman 1989). A recent review of modeling of nonpoint source
pollution in the vadose zone using GIS is available (Corwin et al. 1997). Finally, the EPA lists linear
regression models in use in 1992 and using urban watershed data (United States Environmental
Protection Agency 1991; 1992).
An excellent overview of general modeling considerations is provided by Lung (1989). Some of the
most significant stressors identified in estuarine modeling include salinity changes, suspended solids,
microbes, dissolved oxygen (BOD, CBOD, etc.), nutrients, and toxic chemicals (both acute and
chronic). Model types include both hydrodynamic and water quality. Several models are specific to
waste load allocations. Fate and transport of toxic chemicals are modeled according to their kinetic
properties. Interphase exchanges including: adsorption/desorption, bioaccumulation and
bioconcentration (active and passive), and air-water exchanges (volatilization, aerosolization) are
incorporated. Chemical reactions that are typically accounted for include: chemical species,
biotransformation (alteration, degradation), photolysis, hydrolysis, abiotic processes (dissolution,
precipitation) and sediment diagenesis (advection, dispersion, settling, stratification, salinity, tide).
Smith (1992) prepared a report on the usage of computer models of estuaries by eastern coastal states.
This report suggests which models are most often used and why. State agency modelers have different
concerns and uses than do federal, private or academic modelers. Model selection approaches used by
eastern US coastal state agencies include: 1) identify the problem, 2) generate lists of possible models,
3) make a selection from these lists subject to data, time and resource constraints. These agencies often
use models based upon the EPA's WASP4 or QUAL2E because they often receive EPA funding
support. Models used in the Southeast include: WASP4, CE-QUAL-W2, HAR-03, Georgia Estuary
Model, MIT Transient Water Quality Network, QUAL-2E, Chesapeake Bay Water Quality Model,
HSPF, SPAM, AWEST, DEM-DYNDEL, TOXIWASP, Transient Salinity Intrusion Model,
CORMIX, NCWQAP, BLTM, CWQM, the EPA Simplified Math Model, AUTOSS, HEM, JRWQM,
SIM, TPM, VMP, HSPF, PEM, TAM. The Chesapeake Bay Water Quality Model (CBWQ) was
considered quite advanced; however, most of the Southeast state agencies considered it too complex
for general use (Smith1992). Table 2.1 lists the models used by southeastern state agencies according
to Smith (1992).
Table 2.1. Models used in southeastern United States estuaries by state agencies.
Model Type
Dissolved
Oxygen/Eutrophication
Model Name
AutoQUAL
NCWQAP
QUAL-2E
GAEST
HEM
James River Water Quality
Simplified Math
Tidal Prism
BLTM
DEM-DYNDEL
MIT Model
PEM
TAM
CWQM
SPAM
AWEST
HAR-03
WASP4
CE-QUAL-W2
CBWQ
3-D Hydrodynamic
Toxic Chemical
CORMIX
TOXIWASP
VMP
Nonpoint Source
HSPF
Salinity
Salinity Intrusion
Transient Salinity
Appropriate model selection should depend upon: objectives of the study, level of detail required and
model criteria (DeCoursey 1985). Models help identify areas where science is lacking particularly
regarding coupling of processes, facilitate sensitivity analysis of important processes, enable
examination of dominant scales of processes, and in analysis of water resource management
alternatives for implementation (Najarian and Harleman 1989). It is hoped that this review will assist
users in selecting appropriate models.
2.1 Hydrodynamics
Hydrodynamic models are used to describe circulation patterns and tidal flows in estuaries, thus
impacting transport of stressors. For example, the TRIM_3D three-dimensional model is used to
simulate shallow water flow and tidal circulation in estuaries (Casulli and Bertolazzi 1993). The
TABS-2 model developed by the US Army Engineer Waterways Experiment Station is also used to
model shallow water flow in estuaries (Jones and Richards 1992). Lowery (1998) describes a model
used in Gulf of Mexico estuaries to characterize freshwater retention and flushing over tidal cycles.
Edinger et al. (1990) described a three dimensional hydrodynamic and transport model on distribution
of discharges into deep estuaries as applied to the Gastineau Channel, Alaska outfall. This latter model
might be applicable to some deep channels of the Southeast. The Inlet Model (Duever 1988) was
derived to model hydrology of wetlands based upon atmospheric and vegetation moisture, surface
water, vadose and groundwater. One innovative study coupled a pollution runoff model, the
Agricultural Nonpoint Source (AGNPS), and a hydrodynamic model, Simulation of Water Resources
for Rural Basin (SWRRB), and applied them to Charleston Harbor, SC in an attempt to better
characterize nutrient loading in a southeast estuary. This study used GIS to integrate detailed spatial
data; particularly land uses, with the models for predicting nitrogen loads (Choi 1996). Cheng and
Smith (1993) provide a review of improvements in estuarine hydrodynamic modeling. The paper
emphasizes formulation, numerical methods, spatial and temporal resolution and computational
efficiency.
2.2 Sediments
Several models are available to predict sediment sources, transport and fate. The CREAMS sediment
model is used for determining both sediment loads and degradation (Williams and Nicks 1981).
Benninger and Wells (1993) used potassium/aluminum ratios and zinc (Zn) and copper (Cu) levels in
sediment to determine the source of sediment and applied the technique to a southeastern estuary,
finding that fine sediment in the Neuse River (North Carolina) is from Pamlico Sound. Brunk (1997)
was successful describing turbulent coagulation of sediment. He used phenanthrene, kaolin and
bacterial polymers as estuary surrogate components (Brunk 1997). Some models simulate vertical
particle movement. The rate of sediment resuspension and deposition appears to control the particle
association and removal from the water (Baskaran and Santschi 1993). The SOR3D model was
designed to assess the influence of point source pollution on temperature, dissolved oxygen and
suspended solids (Yearsley 1993). The trend is to incorporate sediment models with other models to
predict transport and fate of associated stressors (see section below on chemicals). Lung (1989)
provides a good overview of important considerations for sediment modeling.
2.3 Nutrients
Numerous regression-based models of nutrient transport and fate, and eutrophication, have been
developed with varying applicability to the Southeast coastal zone. In Narragansett Bay, Keller (1989)
compared empirical techniques (basically principal component regressions) using temperature, light
and nutrients as independent variables and mechanistic formulations using the same variables as major
limiting factors. These techniques were used to assess primary productivity. An empirical model,
derived in the Potomac River for calculating area loadings of pollution from both urban and
agricultural areas, was developed (Smullen et al. 1978). Runoff samples were analyzed for total
nitrogen, total and dissolved kjeldahl nitrogen, nitrite, nitrate, total and dissolved phosphorus, orthophosphorus, COD, and both biological and chemical contaminants. Smullen et al. (1978) used simple
linear regression models based upon rainfall, impervious surface area and land use. More recently, the
Little River (undeveloped river) and Webhannet River (extensively developed), were compared using a
simple, finite element model (Holden 1997). In this study, urban land use produced nitrogen and
phosphorus loadings 1.4 and 2.5 times higher, respectively, in the developed compared to the
undeveloped estuary. The model was more accurate for the undeveloped estuary but only predicted 3983% of observed nitrogen in the developed estuary. It was concluded that the difference was because
of small streams and ground water draining developed land and a shorter path to the estuary
diminishing the effectiveness of the model. These are common complications for estuaries of the
southeastern US.
Efforts have been made to develop criteria and methods for nutrient modeling useful to coastal
planners. Persson and Wallin (1994) describe methods to characterize environmental sensitivity based
upon coastal morphometry, water exchange and bottom dynamics. A simulation model by Hopkinson
and Vallino (1993) was used to explore effects of anthropogenic impacts in watersheds on spatial
patterns of production and respiration in a generalized estuarine system. Effects of variations in ratios
of inorganic and organic nitrogen loading, residence time of water in the estuary, degradability of
allochthonous organic matter and ratios of dissolved to particulate organic matter inputs were
incorporated. They showed total organic carbon levels in rivers have increased three- to five-fold. Big
factors are channelization and damming. A two-dimensional, real-time, integrated hydrodynamiceutrophication model was developed by Shen (1996). The hydrodynamic model provided fields for the
eutrophication model. The eutrophication model simulates phytoplankton, organic nitrogen, ammonia
nitrogen, nitrite-nitrate nitrogen, organic phosphorus, inorganic phosphorus, CBOD and DO levels and
was used in the Rappahannock River (Shen 1996). A model of the Chesapeake Bay was used to
examine pollution-reduction strategies coupling a water quality model to a 3-D hydrodynamic model,
thus coupling a water column model to sediment oxygen demand and a nutrient flux model on an
intertidal scale (Cerco et al. 1995).
Some researchers have developed surrogates for hard to measure parameters in nutrient modeling.
Lowery (1996) developed methods for sub-watershed nitrogen loading surrogates, flushing estimates
and eutrophication modeling. An estuary-level eutrophication model was used for nitrogen loadings,
nutrient ratios and salinity stratification in a multinomial logistic regression (Lowery 1996). Similarly,
Costanza (1996) conducted sub-basin eutrophication modeling including: sub-watershed nitrogen
loading, sub-basin flushing estimates, eutrophication models for estuaries and human population
estimation methods. He also reaggregated county population data to hydrologic cataloging units as
nitrogen loading surrogates, these agreed to census block compilations 85 percent of the time. Subbasin estimates were better and more useful than total estuary estimates (Costanza 1996).
Widely distributed models have been improved to better apply them to broader circumstances. The
QUAL2E model has been used to analyze dissolved oxygen in streams and rivers. Barnwell et al.
(1987) created an expert advisor QUAL2E and reviews aspects pertinent to expert systems. The
estuarine (Northern CA) new math model, DSM2-QUAL, was developed to incorporate new routines
for decay, growth and interactions among water quality parameters in the original QUAL model and it
simulates primary production, DO, phytoplankton, nutrients and temperature (Rajbhandari 1995). The
Pigeon River Allocation Model (PRAM) runs in concert with the QUAL2E model to represent carbon,
oxygen and nutrient dynamics in rivers (Brown and Barnwell 1987). The AGNPS model simulates
generation, transport and deposition of water, sediment and nutrients in receiving waters due to farm
management decisions (Young et al. 1987). The RBM10 model uses nutrient, plankton, suspended
solids, macrophyte and resident fauna data to conduct risk analyses in rivers where runoff causes
changes in dissolved oxygen, suspended solids and eutrophication (Bowler et al. 1992).
2.4 Chemicals
With an increasing interest in managing toxic chemical pollution has been a corresponding increase in
chemical modeling. Most models of chemical contaminants in estuaries are concerned with distribution
and fate of toxic chemicals and most are biogeochemical in nature, i.e., predict mass transport.
Chemical models differ from other ecological models by including chemical attributes (e.g., solubility)
and some models include measures of possible effects. Chemical ecotoxicological models can be food
chain/trophic level-based or mass flow-based. Models that predict the fates of chemicals may or may
not include risk assessment components. Many are linked to measures of sedimentation. When
contaminants are sorbed to sediment, the sediment is treated as either a sink or a suspended toxic
source when it is stirred-up and reenters the water column.
Hwang (1995) linked models to produce a hydrodynamic-sediment transport contaminant model. The
hydrodynamic model generates velocity measures, the sediment model predicts movements and
concentrations of suspended sediment in estuaries and coastal waters, and the toxic model quantifies
interactions between sediments and chemicals and impacts on biota (adsorption, desorption, exchange).
It was used on PCBs in New Bedford Harbor (Hwang 1995). Zhang (1994) used a mechanistic model
based upon Thomann et al. (1989). He found the Thomann point estimates gave reasonable water
column PCB concentrations but not for sediment. He further found that settling and resuspension are
controlling processes in the Hudson River (Zhang 1994). Nichols (1990) described the fate of Kepone
in the James River through fine-sediment dispersal to sediment sinks. Using his models he estimated
42-90% of all Kepone input remained in this system by entrapment in estuarine circulation and
seasonal refluxing, with the ultimate fate becoming its burial in mid-estuary bed sediment (Nichols
1990). The SIMPLESINGH and SIMPLERIVER models, a general environment model and a general
river model, were developed to predict fate of both organic and inorganic chemicals (Singh et al.
1989).
Models of inorganic chemicals are generally somewhat simpler than those for organic chemicals. Most
metals, for example, occur at only one or two oxidation states or dominant chemical forms in nature.
Normally, metals are affected by a limited number of environmental processes. Metal contaminant fate
often corresponds to sedimentation in an estuary. A two-dimensional model of copper distribution and
fate in San Francisco Bay found that important processes include advection, dispersion, partitioning
with suspended particles, settling and resuspension of copper (Chen et al. 1996). Cuthbert and Kalff
(1993) found aluminum (Al), iron (Fe), manganese (Mn), zinc (Zn) and copper (Cu) measured in
Ontario and Quebec showed relationships with suspended particulate, turbidity, color, temperature and
hydrology. These variables explained large portions of the system variation better for some metals than
others: Al (90%), Fe (85%), Mn (57%) and Zn (37%). Metals were most strongly associated with
suspended particulate concentrations. Independent variables included average areal runoff, average
suspended particulate matter for most metals, and conductivity and water color for dissolved Fe
(Cuthbert and Kalff 1993). Wood (1993) described a spatially and temporally explicit model for fate
and transport of nonconservative metals in estuaries, elaMET that incorporates advection, dispersion
and transformation. It was used to characterize adsorption kinetics and distribution of Cu, Cd and Zn in
South San Francisco Bay (Wood 1993). A two-dimensional model of fate and transport of toxic heavy
metals associated with cohesive sediments was also developed. It too was tested in South San
Francisco Bay. Nickel (Ni) was found in places where high sedimentation occurred (Shrestha and
Orlob 1993). Chen et al. (1996) used a two-dimensional estuary model designed to track total
dissolved solids, BOD, DO, algae and nutrient dynamics. It was modified to include fate and transport
of Cu and sediment and applied to San Francisco Bay (Chen et al. 1996). Regnier and Wollast (1993)
examined the distribution of nickel (Ni), cobalt (Co), chromium (Cr), Zn, Cu, Cd, lead (Pb) and Mn in
suspended matter and sediment in the Scheldt estuary (France, Belgium, Netherlands). Trace metals
normalized to Al allowed characterization of the origin of the solids and evaluation of their degree of
contamination. Large portions of the metal carried by the estuary were bound to suspended matter,
mainly in fine grains, and accumulated in areas of low salinity (Regnier and Wollast 1993). The ECoS
model simulated the distribution of salinity, turbidity and Cd contamination of the Gironde estuary,
France (Pham et al. 1997). This one-dimensional model accurately predicted Cd distribution and
residence time in the mixing zone.
Salinity can be the dominant process for determining fate of chemicals (Yeats 1993). Aluminum, Mn,
Fe, Co, Ni, Cu, Zn distributions can be described by simple relationships with salinity (Yeats 1993).
However, application of the model breaks down as oceanography becomes more complex.
Many models are specific to fate of organic chemicals only. Pesticides must often be treated differently
since they are intentionally applied. Unlike other chemical contaminants, the quantity of pesticide
introduced, is often known. The semi-empirical model, SWAT, was developed to predict
concentrations of agricultural pesticides moving to surface water based upon hydrologic
characteristics, soil type and amount of water moving to streams from rainfall (Brown and Hollis
1996). Developed in England, the model used soil parameters, pesticide chemical parameters and
rainfall as the independent variables (Brown and Hollis 1996). The Groundwater Loading Effects of
Agricultural Management Systems (GLEAMS) simulation model links climate, soil, topography,
nutrient and pesticide data to assess land management practices (Leonard et al. 1987). Di Guardo et al.
(1994) developed an equilibrium model to predict pesticide concentrations in agricultural runoff based
upon chemical fugacity. Ambrose (1987) linked the chemical transport and fate model, TOXIWASP,
to the hydrodynamic model, DYNHYD, to calculate upstream migration of seven organic
contaminants: chloroform, 1,2 dichloroethane, 1,2 dichloropropane, dimethoxymethane, methylene,
perchloroethylene, trichloroethylene. Tested in the Delaware estuary, it used published chemical
property data. Volatilization was the predominant loss mechanism (Ambrose 1987). The Rate Constant
Model of Chemical Fate in Lakes Model (Mackay 1994) was developed to predict fate of organic
chemicals in Lake Ontario by integrating lake dimensions, chemical properties, discharge rates and
atmospheric concentrations. This model might be applicable to estuarine systems and includes a food
chain bioaccumulation component. Siewicki (1997) used the EXAMS-II model (Burns et al. 1982),
first developed for modeling transport, fate and persistence of dissolved chemicals, to predict
environmental fate and exposure of oysters to particulate-bound fluoranthene. The model suggested
land-uses and boating had the greatest influence on exposure and can be used to predict human health
risks.
Land-use characteristics are now being considered in modeling chemical transport and fate. An
empirical model was derived in the Potomac River for calculating area loadings of pollution from both
urban and agricultural areas. Runoff samples were analyzed for nutrients, COD, fecal coliforms, Zn,
Pb, Cd, Cu, Fe, Cr and Mn. Simple linear regression models were used based upon rainfall, impervious
surface area and land use (Smullen et al. 1978). Maslia et al. (1994) describe environmental assessment
and site remediation studies of spatial and temporal chemical distributions that use GIS, simulation
models and demographic databases to automate exposure assessments. They applied SLAM (Steady
Layered Aquifer Model) to simulate ground-water flow and CLAM (Contaminant transport in Layered
Aquifer Media) to simulate aquifer contamination then used digital census data and GIS to determine
spatial distributions of human population (Maslia et al. 1994). Porter et al. (1997) found simple linear
regression equations to predict oyster tissue fluoranthene concentrations in Murrells Inlet, SC based
upon landscape features and surrogate pollution measures. Incorporating land-use characteristics via
GIS with contaminant modeling is technically complex and only beginning to be used. A shell-based
approach to modeling ameliorates this problem by providing an overall framework consisting of
graphics interface, GIS, data management tools, gridding software input and output interfaces, and
process models (Spaulding and Howlett 1995). This package was utilized to predict oil transport, fate,
dispersion and bottom deposition, sewage discharges, and water quality impacts from combined sewer
overflows (Spaulding and Howlett 1995).
Thomann et al. (1991) modeled fate and bioaccumulation of PCB homologues in striped bass of the
Hudson River. They found, of that discharged into the estuary since 1947, 66% was volatilized, 6%
stored into sediment, and the remainder lost to dredging and boundary transport. More than 90% of
PCBs in fish resulted from food-chain transfer (Thomann et al. 1991). Rowan and Rasmussen (1992)
reviewed empirical models of contaminants in the Great Lakes. They found that researchers were using
log-linear multiple regressions to link tissue concentrations to water and sediment characteristics as
well as basin-specific ecological attributes. Important factors for determining tissue concentrations of
PCBs and DDT were tissue lipid, trophic level and trophic structure of the food chain explaining 59
(DDT) to 72 (PCBs) percent of the variability of 25 species (Rowan and Rasmussen 1992). The Oyster
Bioaccumulation Model was developed to estimate radionuclide uptake by oysters near nuclear power
plants (Rose 1989). The model is based upon oyster uptake and growth parameters and designed
primarily for suggesting sampling plans. ECOFATE (Gobas 1992) estimates the amount of organic
chemical in water, sediment and aquatic organisms tissue and has been tested in Howe Sound, a marine
setting. However, many other lake and river models may be useful for estuaries given appropriate
adaptation and assumption. TOXFATE, for instance, was developed and tested in large lakes to predict
fate of organic chemicals (Halfon 1990).
Toxicity has also been modeled. Combined toxic effects of chemical mixtures on microorganisms were
predicted using the Quantitative Structure-Activity Relationship (QSAR) (Nirmalakhandan et al.
1994). QSAR was used to predict concentrations that cause 50 percent inhibitions and the investigators
found that predicted values agree with measured values with an R2=0.8 (Nirmalakhandan et al. 1994).
Goudey (1987) developed a model to simulate toxic effects of metals on phytoplankton growth. The
WASP4, a one-dimensional hydrodynamic-water quality model, was used to predict toxicity in fathead
minnows in Delaware River (Fikslin 1994). Growth was selected as the most sensitive measure of
toxicity. Sensitivity analyses indicated the model was most sensitive to loading from point sources,
model boundaries and decay rates. It was least sensitive to freshwater inflows, tidal phase and
dispersion coefficients. The model was used to examine dissolved Cu, Pb and Zn, with Cu and Zn joint
actions seeming to account for toxicity (Fikslin 1994). Another model based upon the WASP models is
the Partitioning, Mass Balance and Bioaccumulation Model for Hydrophobic Organic Chemicals in
Lake Ontario (Endicott and Cook 1994). This model is a spreadsheet used to predict organic chemical
concentrations in water, sediment and biota. It is based upon hydrology, particle transport, chemical
property, physicochemistry, toxicity and bioenergetic data.
2.5 Population dynamics
Models of biomass productivity, distribution and impacts on their ecosystems will be coupled to
stressor source, transport, fate and toxicity models in the future. Thus, a few examples of biomass
models are provided. The Estuarine Phytoplankton Model (Cloern 1991) is used to assess
phytoplankton blooms in estuaries based largely upon zooplankton and macrofauna grazing, sinking
and turbulence data. Solidoro et al. (1993) created a model of macroalgae phytoplankton growth based
upon nutrient and meteorological conditions and it has been tested in estuaries. The Dynamic System
Model of Plankton Growth and Nutrient Uptake (Kumar 1991) predicts phytoplankton growth by
classes of organisms using detailed plankton-nutrient interactions in seawater. Madden and Kemp
(1996) describe a dynamic simulation model developed to elucidate mechanisms responsible for
decline of Chesapeake Bay submersed aquatic plants. The model calculates biomass pools and
biogeochemical rate processes. Their simulations investigated the influence of phytoplankton and
epiphytes on under water light, balance of limiting resources on growth and productivity, and
conditions needed for restoration (Madden and Kemp 1996). Jensen et al. (1998) used non-intrusive
remote sensing and digital image processing techniques to model above ground biomass for entire
estuaries in South Carolina. When compared to in situ biomass measurements, near-infrared and
middle-infrared bands as well as several vegetation indices were highly correlated. Kiefer et al. (1996)
describe a pelagic fish population model using satellite ocean color and thermal imagery, scientific
surveys and fish distribution data. Batchelder and Miller (1989) created a model called POPCYCLE to
track growth, development, reproduction and mortality of individual marine organisms. It was first
used on copepods but may be extended to other prey species. Another population dynamics model was
developed for striped bass to improve predictions of recruitment and other population parameters
(Rose and Cowan 1993). Many of the population dynamics models will eventually be coupled with
exposure and toxicity models to assess impacts of stressors on communities and ecosystems. Li (1994)
described the differential dynamic programming (DDP) method used to determine optimum freshwater
inflows into bays and estuaries of Texas to maximize fisheries.
Dabrowski (1989) developed a bioenergetic model to simulate young fish growth and survival based
upon density, prey type, temperature and feeding duration and is applicable to lakes and marine
systems. Some models were developed for lakes only but might be applicable to estuarine systems.
The Sustainability of Intensively Managed Populations in Lake Ecosystems (SIMPLE) model (Jones et
al. 1993) was developed to investigate the effects of harvesting, stocking rates and other fishery
management approaches on salmonid populations in lakes. Another lake model uses fishery, climate,
food and water quality data to suggest management options for fisheries (Jorgensen et al. 1992).
Coupling of models to predict ecosystem effects of both vegetation and fish populations as well as
effects on stressor levels and impacts is just evolving.
2.6 Additional considerations
The use of GIS in environmental modeling incorporates computer technology with spatial data to
provide powerful analytical tools for modeling complex systems (Dangermond 1987). GIS modeling is
evolving from increased computational capabilities and refined analytical and modeling techniques
(Parent and Church 1987; King and Kraemer 1993). Coastal researchers and resource managers to
address ecosystem and landscape issues (Michener et al. 1989; Dunlap and Porter 1993; Porter 1995;
Porter et al. 1997) are increasingly adopting the use of GIS. The nature of GIS is such that it can lend
itself to, and incorporates the components of modeling. Recent work has demonstrated the utility of
developing GIS-based assessment models for addressing coastal zone issues (Porter 1995; Porter et al.
1995, Porter et al. 1997; Siewicki 1997; Corbett et al. 1997). However, this is a very new technology
as applied to coastal development issues and few other specific examples of demonstrated GIS-based
estuarine/marine modeling are available.
The geostatistical technique known as "kriging" has been used to develop surface data layers for GIS
analysis of parameters that are expensive or time-consuming to measure in large numbers over entire
study areas (Porter et al. 1997). Burrough (1987) gives a very readable review of kriging aimed at
ecologists. For the technical details of the process and mathematical background refer to the texts by
Journel and Huijbregts (1978) written for geologists, and Cressie (1983) written for statisticians.
Because some measurements will always be made at only a limited number of locations, kriging (in
some evolved form), in conjunction with GIS, will likely become even more common for estuarine
management. Current research on applications of kriging to estuarine research includes explorations
into specialized distance measures specific to estuaries (Rathbun 1996; Little et al. 1997) and the use
of kriging to validate or enhance traditional data analytic techniques such as analysis of variance
(Edwards et al. in prep).
Sensitivity analysis is used to identify model parameters exerting greatest influence on model results.
Hamby (1994) reviewed more than a dozen sensitivity analysis techniques. Likewise, Monte et al.
(1996) described principles of Empirically Based Uncertainty Analysis based upon agreement between
models and sets of independent empirical data.
Marine models tend to be more complex descriptions of hydrodynamics compared to river or lake
models due to tides and because they are open systems. However, most marine models are not very
sophisticated ecological descriptions for the same reasons. It is likely that ecological considerations of
marine models will continue to be a rapidly growing area of research.
2.7 Summary
Models help explain complex relationships. A current trend is to link models that explain different
environmental processes and seek consensus among users on model development and application.
Hydrodynamic models help describe circulation patterns, tidal flows and freshwater retention and are
being coupled to pollution runoff models. Sedimentation models predict sources, transport, deposition,
resuspension and fate of particulate. These processes often dictate the fate of adhering chemical
contaminants. Nutrient runoff models are being coupled with hydrodynamic models at sub-watershed
levels and frequently incorporate novel surrogates of difficult to measure environmental parameters.
The current trend for chemical contaminant modeling is to link chemical process, hydrodynamic,
salinity and sedimentation models to predict sources, transport, fate and toxicity. Land-use
characteristics are increasingly included, particularly in urban settings. Geographic Information
Processing will become increasingly important in estuarine modeling of all types. Linking stressor
models with biomass productivity is just beginning. Finally, development of models specific to marine
and estuarine conditions lag freshwater systems. However, research is progressing that links
hydrodynamics, sedimentation, nutrients, chemical contaminants, GIS and other land-use techniques to
predict effects of stressors on saltwater systems.
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Acknowledgements
This work was funded by a grant (P/M-2RS2) from the South Carolina Sea Grant Consortium. The
investigators wish to acknowledge the support of Dr. Gary Kleppel and Mr. Rick DeVoe of the SC Sea
Grant office. This work could not have been accomplished without the assistance of several staff
members and graduate students at the University of South Carolina and Clemson University including
Ben Jones, Michel Gielazyn, David White, Jeff Jefferson, Robert Mateja, Kang Shou Lu, and Patrick
Harris. We also appreciate the time and effort contributed by other SOK investigators and data
management personnel at research and resource management agencies in South Carolina and Georgia.
Request for comments
The authors are interested in receiving comments on the materials discussed in both Section 1 and
Section 2. Please forward comments on Section 1 to Dr. Dwayne E. Porter at [email protected]. Comments
on Section 2 should be directed to Dr. Thomas C. Siewicki at [email protected].
Neither the funding agency for this project or the home organizations of the authors approve, recommend, or endorse any
proprietary product or material mentioned in this report. No reference shall be made to these organizations, or to this
publication, in any advertising or sales promotion which would indicate or imply that these organizations approve,
recommend, or endorse any proprietary product or proprietary material mentioned herein or which has as its purpose any
intent to cause directly or indirectly the advertised product to be used or purchased because of publication.