Longshore variability of beach states and bar types in a microtidal, storm-influenced, low-energy environment
Résumé
Beach classification models are widely used in the literature to describe beach states in response to environmental
conditions. These models were essentially developed for sandy barred to barless beaches in micro- to mesotidal
environments subject to moderate to high wave energy conditions and have been based on field studies
over limited stretches of coast. Here,we further interrogate the performance of the Australian beach classification
scheme by analysing beach states and corresponding bar types on a regional scale in a storm-influenced, low
wave-energy, microtidal environment, using a large and unique spatial and temporal dataset of supra- and
subtidal beach morphology and sedimentology. The 200 km-long coast of the Gulf of Lions in theMediterranean
consists of quasi-continuous sandy beaches with a well-developed double sandbar system. All the reported classical
beach stateswere observed on this coast, from reflective to dissipative, alongwith two more unusual states:
the rock platform-constrained beach state which is associated with bedrock outcrops, and the non-barred dissipative
beach statewhich ismore commonly found in large tidal-range settings. LiDAR bathymetry shows that the
transitions between beach state zones are marked mainly headlands but transitions also occur progressively
along stretches of continuous sandy beach. The longshore distribution of beach states and associated bar types
on a regional scale can be related to the variability of hydrodynamic conditions (wave incidence and energy)
and sediment characteristics (particle size). However, the influence of these parameters on beach state seems
to be largely controlled by the geological context such as the presence of a river mouth, headland or rock platform.
Finally, we assessed the ability of the parameter Ω, commonly used to characterise beach states, which
combines wave characteristics and sediment fall velocity, to predict the observed beach states and bar types
using a very large set of hydrodynamic and sedimentary data. Our results, based on high frequency spatial sampling,
show that the fall velocity of the subtidal sediment coupled with wave statistics one month prior the observed
beach state strongly improved the predictive power of the parameter Ω.