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Synthesis of Long-Term Coastal Monitoring Datasets to Identify and Model Relationships between Land Cover, Coastal Ecosystem Change, Climate, and Weather

Principal Investigators

Andrew Tweel and Denise Sanger, South Carolina Department of Natural Resources

Project Information

2018-2020 Healthy Coastal Ecosystems

Project Number: R/ER-51

Research Description

As coastal populations continue to grow, increasing stress is placed on downstream ecosystems. These relationships between watershed land use and estuarine quality have been quantified at a variety of spatial scales and response variables. How these relationships will interact with predicted changes in climate and weather patterns, however, has not been identified. Long-term environmental monitoring programs provide an excellent opportunity for answering such questions. Gaining a more thorough understanding of the spatial and temporal relationships between coastal land use, estuarine quality, and changes in weather and climate will be critical toward ensuring a sustainable relationship between our growing coastal population, changing weather patterns, and coastal ecosystems. Coastal planners and stormwater managers can utilize this information to design best management practices that account for increases in stormwater runoff, install targeted retrofits of stormwater infrastructure to maximize benefit, and apply these models to predict and manage potential decreases in environmental quality before water bodies are classified as impaired.

The overall goal of the proposed effort is to develop models that relate land use to estuarine environmental quality by incorporating and analyzing data with regard to climate and weather (e.g., temperature and precipitation patterns) at a range of spatiotemporal scales. To meet this goal, the PI proposes the following objectives: (1) Quantify relationships between land use and adjacent water quality (temperature, salinity, DO, fecal coliform), SCECAP water quality index, sediment contaminants (effects range median quotient (ERMQ), SCECAP sediment quality index, and benthic index of biological integrity (B-IBI)) at multiple watershed spatial scales; (2) Examine temporal variability of the relationships listed in objective 1 under a range of weather and climate patterns, average salinity, and seasonal temperature scenarios to assess their effect on estuarine quality; and (3) Develop probabilistic models to predict environmental quality under the various scenarios in objective 2. Resultant information will support improved stormwater management and land-use decision-making to aid in the development of stormwater management plans by coastal communities as required by MS4 regulations.

Contact for Questions

Dr. Andrew Tweel (