Field and numerical investigations of the morpho-hydrodynamic processes of the tidal inlet at Shippagan Gully, New Brunswick, Canada.

Saved in:
Bibliographic Details
Title: Field and numerical investigations of the morpho-hydrodynamic processes of the tidal inlet at Shippagan Gully, New Brunswick, Canada.
Authors: Provan, Mitchel1 mitchel.provan@nrc-cnrc.gc.ca, Logan, Seth2, Nistor, Ioan3, Cornett, Andrew1, Drouin, Alain4
Source: Coastal Engineering Journal. Dec2018, Vol. 60 Issue 4, p400-422. 23p.
Subjects: Hydrodynamics, Sediment transport, Numerical analysis, Tides
Geographic Terms: New Brunswick
Abstract: This paper presents the result of a multiyear study consisting of field measurements and numerical modeling of a highly dynamic tidal inlet located on the west side of the Gulf of Saint Lawrence near Le Goulet, New Brunswick, Canada. Due to a lack of human intervention in the past few decades, natural processes have taken hold of this tidal inlet and large volumes of sediment have been deposited within the inlet. The Shippagan Gully inlet transects the Acadian Peninsula and is therefore subject to tidal forcing from both ends. An appreciable phase lag present in the two open ocean boundaries results in ebb flows through Shippagan Gully, which regularly exceed 2 m/s, to be twice as strong as the flood flows. This flow imbalance along with a long history of human intervention has created a complex and highly dynamic tidal inlet. The purpose of the study was to develop a numerical model of the hydrodynamic and morphological processes that occur at Shippagan Gully and to use the model to assess alternative engineering measures that could be implemented to promote a safe and navigable channel through the inlet. [ABSTRACT FROM AUTHOR]
Copyright of Coastal Engineering Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Be the first to leave a comment!
You must be logged in first