Spatial and Temporal Variability of Longshore Transport Along Gold Coast, Australia

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Author(s)
Splinter, KD
Golshani, A
Stuart, G
Tomlinson, R
Griffith University Author(s)
Year published
2010
Metadata
Show full item recordAbstract
Spatial and temporal variability of longshore transport potential for a 35-km stretch of sandy coastline on the east coast of Australia is examined using a 25-year data set. Six-hourly offshore wave data is binned into yearly wave classes using a global k-means algorithm that accounts for wave height, period, and direction simultaneously. Wave class estimates are shoaled into the nearshore using MIKE 21 Spectral Wave (SW) model. Longshore transport is calculated using the formulas of Kamphuis (1991; 2002) and Bayram et al. (2007) and show good agreement with previously published estimates for the Gold Coast, suggesting the ...
View more >Spatial and temporal variability of longshore transport potential for a 35-km stretch of sandy coastline on the east coast of Australia is examined using a 25-year data set. Six-hourly offshore wave data is binned into yearly wave classes using a global k-means algorithm that accounts for wave height, period, and direction simultaneously. Wave class estimates are shoaled into the nearshore using MIKE 21 Spectral Wave (SW) model. Longshore transport is calculated using the formulas of Kamphuis (1991; 2002) and Bayram et al. (2007) and show good agreement with previously published estimates for the Gold Coast, suggesting the wave classification scheme sufficiently represents the variability in yearly wave data. Results show large temporal and spatial variability of transport potential along the coastline. Spatial variation is attributed to shoreline orientation and wave exposure, while temporal variability is significantly correlated with variations in the Southern Oscillation Index.
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View more >Spatial and temporal variability of longshore transport potential for a 35-km stretch of sandy coastline on the east coast of Australia is examined using a 25-year data set. Six-hourly offshore wave data is binned into yearly wave classes using a global k-means algorithm that accounts for wave height, period, and direction simultaneously. Wave class estimates are shoaled into the nearshore using MIKE 21 Spectral Wave (SW) model. Longshore transport is calculated using the formulas of Kamphuis (1991; 2002) and Bayram et al. (2007) and show good agreement with previously published estimates for the Gold Coast, suggesting the wave classification scheme sufficiently represents the variability in yearly wave data. Results show large temporal and spatial variability of transport potential along the coastline. Spatial variation is attributed to shoreline orientation and wave exposure, while temporal variability is significantly correlated with variations in the Southern Oscillation Index.
View less >
Conference Title
Proceedings of the Coastal Engineering Conference
Copyright Statement
© The Author(s) 2010. This is an Open Access article distributed under the terms of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Subject
Ocean engineering