Wave runup is known to depend on offshore wave conditions and coastal morphology. While most field studies on wave runup have focused on low‐to‐mild‐sloping sandy beaches, runup measurements on steep and irregular rocky cliff profiles are still very scarce. Here we investigate the physical processes controlling wave runup in such environments and the range of applicability of empirical runup formula. This study focuses on the steep rocky cliffs (0.1 < tanβ < 0.4) of Banneg Island, a small island located in the Molène archipelago, Brittany, France, occasionally flooded during extreme water level events. A statistical parameter for extreme runup is derived from the measurements of pressure sensors deployed in the intertidal zone. Deep water wave parameters are used to force a high‐resolution wave model, and nearshore wave parameters and high‐resolution topographic data are analyzed concurrently with runup time series in order to assess the dependence of the runup on hydrodynamic conditions and morphological parameters. The wave runup is shown to be strongly related to the square root of the offshore significant wave height times the offshore wavelength. The measurements also reveal the depth dependence of the runup, which is mainly attributed to the curvature of the foreshore profile. In comparison to empirical relation obtained for a mild‐sloping beach, the present data show a significant reduction in normalized wave runup, that is attributed to enhanced bottom friction over the rocky bottom.

Hydro-sedimentary numerical models have been widely employed to derive suspended particulate matter (SPM) concentrations in coastal and estuarine waters. These hydro-sedimentary models are computationally and technically expensive in nature. Here we have used a computationally less-expensive, well-established methodology of self-organizing maps (SOMs) along with a hidden Markov model (HMM) to derive profiles of suspended particulate inorganic matter (SPIM). The concept of the proposed work is to benefit from all available data sets through the use of fusion methods and machine learning approaches that are able to process a growing amount of available data. This approach is applied to two different data sets entitled “Hidden” and “Observable”. The hidden data are composed of 15 months (27 September 2007 to 30 December 2008) of hourly SPIM profiles extracted from the Regional Ocean Modeling System (ROMS). The observable data include forcing parameter variables such as significant wave heights ( $Hs$ and $Hs50$ (50 days)) from the Wavewatch 3-HOMERE database and barotropic currents ( $Ubar$ and $Vbar$ ) from the Iberian–Biscay–Irish (IBI) reanalysis data. These observable data integrate hourly surface samples from 1 February 2002 to 31 December 2012. The time-series profiles of the SPIM have been derived from four different stations in the English Channel by considering 15 months of output hidden data from the ROMS as a statistical representation of the ocean for ≈11 years. The derived SPIM profiles clearly show seasonal and tidal fluctuations in accordance with the parent numerical model output. The surface SPIM concentrations of the derived model have been validated with satellite remote sensing data. The time series of the modeled SPIM and satellite-derived SPIM show similar seasonal fluctuations. The ranges of concentrations for the four stations are also in good agreement with the corresponding satellite data. The high accuracy of the estimated 25 h average surface SPIM concentrations (normalized root-mean-square error— $NRMSE$ of less than 16%) is the first step in demonstrating the robustness of the method.

This paper presents a novel method for obtaining the probability wave of breaking Pb of deep water, dominant wind-sea waves (that is, waves made of the energy within +-30% of the peak wave frequency) derived from Gaussian wave field theory. For a given input wave spectrum we demonstrate how it is possible to derive a joint probability density function between wave phase speed (c) and horizontal orbital velocity at wave crest (u) from which a model for Pb can be obtained. A non-linear kinematic wave breaking criterion consistent with the Gaussian framework is further proposed. Our model would allow, therefore, for application of the classical wave breaking criterion (that is, wave breaking occurs if u/c > 1) in spectral wave models which, to the authors’ knowledge, has not been done to date. Our results show that the proposed theoretical model has errors in the same order of magnitude as six other historical models when assessed using three field datasets. With optimization of the proposed model’s single free parameter, it can become the best performing model for specific datasets. Although our results are promising, additional, more complete wave breaking datasets collected in the field are needed to comprehensively assess the present model, especially in regards to the dependence on phenomena such as direct wind forcing, long wave modulation and wave directionality.

The comparison of a morphodynamic model results with observations is an essential part to establish its credibility. In the past multiple model were validated against observations for sand banks, coastlines or estuarine environments. Some modelling have studied the marine dunes migrations but are generally limited to a two-dimensional study. In the present study, a three-dimensional morphodynamic model were setup on an area where highly dynamic dunes are present. The modelling results were analysed and compared to in-situ observations either using a 2D and a 3D method. The vertical and horizontal differences with observations were then assessed using the known method and, based on these results, an updated validation method were proposed to overcome some issues that could interfere with the process.