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High‐efficiency gravel longshore sediment transport and headland bypassing over an extreme wave event
Author(s) -
McCarroll R.J.,
Masselink G.,
Wiggins M.,
Scott T.,
Billson O.,
Conley D.C.,
Valiente N.G.
Publication year - 2019
Publication title -
earth surface processes and landforms
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.294
H-Index - 127
eISSN - 1096-9837
pISSN - 0197-9337
DOI - 10.1002/esp.4692
Subject(s) - headland , sediment transport , geology , shore , longshore drift , storm , flux (metallurgy) , energy flux , sediment , sedimentary budget , geomorphology , hydrology (agriculture) , oceanography , geotechnical engineering , materials science , physics , astronomy , metallurgy
Gravel beaches are common throughout the high latitudes, but few studies have examined gravel transport rates, in particular at high energy levels, and no studies have quantified gravel transport around headlands. Here, we present the first complete sediment budget, including supra‐, inter‐ and sub‐tidal regions of the beach, across multiple headland‐separated gravel embayments, combined with hydrodynamic observations, over an extreme storm sequence, representing at least a 1‐in‐50‐year event. Unprecedented erosion was observed (~400 m 3 m −1 , −6 m vertical), with alongshore flux of 2 × 10 5 m 3 , equivalent to annual rates. Total system volume change was determined to the depth of closure and then used to calculate alongshore flux rates. Alongshore wave power was obtained from a wave transformation model. For an open section of coastline, we derive a transport coefficient (CERC formula) of K Hs = 0.255 ± 0.05, exceeding estimates in lower‐energy conditions by a factor of 5 or more. We apply this coefficient to rocky segments of the shoreline, determining rates of headland bypass from 0 to 31% of potential flux, controlled by headland extent and toe depth. Our results support the hypothesis that gravel is transported more efficiently at higher energy levels and that a variable rate or threshold approach may be required. Complete coverage and varying morphology make this dataset uniquely suited to improving model predictions of gravel shoreline change. © 2019 John Wiley & Sons, Ltd. © 2019 John Wiley & Sons, Ltd.