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Data Series 937

Prepared in cooperation with the New Jersey Department of Environmental Protection

Marine Geophysical Data Collected in a Shallow Back-Barrier Estuary, Barnegat Bay, New Jersey

By Brian D. Andrews, Jennifer L. Miselis, William W. Danforth, Barry J. Irwin, Charles R. Worley, Emile M. Bergeron, and Dann S. Blackwood

Thumbnail of and link to report PDF (3.53 MB)Abstract

In 2011, the U.S. Geological Survey, in cooperation with the New Jersey Department of Environmental Protection, began a multidisciplinary research project to better understand the water quality in Barnegat Bay, New Jersey. This back-barrier estuary is experiencing degraded water quality, algal blooms, loss of seagrass, and increases in oxygen stress, macroalgae, stinging nettles, and brown tide. The spatial scale of the estuary and the scope of challenges within it necessitate a multidisciplinary approach that includes establishing the regional geology and the estuary’s physical characteristics and modeling how the estuary’s morphology interacts to affect its water quality. This report presents the data collected during this project for use in understanding the morphology and the distribution of sea-floor and sub-sea-floor sediments within Barnegat Bay, describes the methods used to collect and process those data, and includes links to the final processed datasets. These data can be used by scientists to understand the links between geomorphology, geologic framework, sediment transport, and estuarine water quality and circulation.

First posted June 26, 2015

Revised September 8, 2015

For additional information, contact:
Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543–1598

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Suggested citation:

Andrews, B.D., Miselis, J.L., Danforth, W.W., Irwin, B.J., Worley, C.R., Bergeron, E.M., and Blackwood, D.S., 2016, Marine geophysical data collected in a shallow back-barrier estuary, Barnegat Bay, New Jersey (ver. 1.1, September 2016): U.S. Geological Survey Data Series 937, 15 p.,

ISSN 2327-638X (online)




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