Longshore variability of beach states and bar types in a microtidal, storm-influenced, low-energy environment

•We examine large spatial beach morphology in a low-energy microtidal environment.•We highlight the utility of 3D LiDAR bathymetry, grain size and waves in beach studies.•We propose a 3D morphodynamic classification of microtidal low-energy beaches.•Microtidal beach types include non-barred dissipative and rock platform-constrained beaches.•Beach state transitions may be gradual on long beaches or marked by rocky capes.•Subtidal sediment fall velocity improves predictive skill of the beach parameter Ω.

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 meso-tidal 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 the Mediterranean consists of quasi-continuous sandy beaches with a well-developed double sandbar system. All the reported classical beach states were observed on this coast, from reflective to dissipative, along with two more unusual states: the rock platform-constrained beach state which is associated with bedrock outcrops, and the non-barred dissipative beach state which is more 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.