Monitoring sandy shoreline evolution from years to decades is critical to understand the past and predict the future of our coasts. Optical satellite imagery can now infer such datasets globally, but sometimes with large uncertainties, poor spatial resolution, and thus debatable outcomes. Here we validate and analyse satellite-derived-shoreline positions (1984–2021) along the Atlantic coast of Europe using a moving-averaged approach based on coastline characteristics, indicating conservative uncertainties of long-term trends around 0.4 m/year and a potential bias towards accretion. We show that west-facing open coasts are more prone to long-term erosion, whereas relatively closed coasts favor accretion, although most of computed trends fall within the range of uncertainty. Interannual shoreline variability is influenced by regionally dominant atmospheric climate indices. Quasi-straight open coastlines typically show the strongest and more alongshore-uniform links, while embayed coastlines, especially those not exposed to the dominant wave climate, show weaker and more variable correlation with the indices. Our results provide a spatial continuum between previous local-scale studies, while emphasizing the necessity to further reduce satellite-derived shoreline trend uncertainties. They also call for applications based on a relevant averaging approach and the inclusion of coastal setting parameters to unravel the forcing-response spectrum of sandy shorelines globally.

Observations from wave basin experiments and wave-resolving numerical simulations demonstrate the effect of wave-current interaction on shear stress around a sandy mound. Observations from the wave basin show that the mound deformation rate and morphological patterns depend on the mixture of waves and currents in the incident flow conditions. A SWASH nonhydrostatic numerical model was used to expand the parameter space of wave-current conditions observed in the flume and characterize the response of the near-bed shear stress to the mound. The model was validated with observations from wave-alone, current-alone, and wave-current flume tests and then ran for a suite of numerical flow conditions which isolate the impact of the ratio of wave-current energy on the bed shear stress. Results show how the current-to-wave ratio impacts the spatial heterogeneity of shear stress across the mound, with the region of shear stress intensification around the mound and the location of the peak shear stress becoming asymmetric with more mixed wave-current flows. These results show the nonlinear response of shear stress patterns to combined wave-current flows and how these patterns may impact eventual sediment transport and mound evolution.

A Bayesian network (BN) approach is used to model and predict shore-break-related injuries and rip-current drowning incidents based on detailed environmental conditions (wave, tide, weather, beach morphology) on the high-energy Gironde coast, southwest France. Six years (2011-2017) of boreal summer (15 June-15 September) surf zone injuries (SZIs) were analysed, comprising 442 (fatal and non-fatal) drownings caused by rip currents and 715 injuries caused by shore-break waves. Environmental conditions at the time of the SZIs were used to train two separate Bayesian networks (BNs), one for rip-current drownings and the other one for shore-break wave injuries. Each BN included two so-called "hidden"exposure and hazard variables, which are not observed yet interact with several of the observed (environmental) variables, which in turn limit the number of BN edges. Both BNs were tested for varying complexity using K-fold cross-validation based on multiple performance metrics. Results show a poor to fair predictive ability of the models according to the different metrics. Shore-break-related injuries appear more predictable than rip-current drowning incidents using the selected predictors within a BN, as the shore-break BN systematically performed better than the rip-current BN. Sensitivity and scenario analyses were performed to address the influence of environmental data variables and their interactions on exposure, hazard and resulting life risk. Most of our findings are in line with earlier SZI and physical hazard-based work; that is, more SZIs are observed for warm sunny days with light winds; long-period waves, with specifically more shore-break-related injuries at high tide and for steep beach profiles; and more rip-current drownings near low tide with near-shore-normal wave incidence and strongly alongshore non-uniform surf zone morphology. The BNs also provided fresh insight, showing that rip-current drowning risk is approximately equally distributed between exposure (variance reduction VrCombining double low line14.4ĝ€¯%) and hazard (VrCombining double low line17.4ĝ€¯%), while exposure of water user to shore-break waves is much more important (VrCombining double low line23.5ĝ€¯%) than the hazard (VrCombining double low line10.9ĝ€¯%). Large surf is found to decrease beachgoer exposure to shore-break hazard, while this is not observed for rip currents. Rapid change in tide elevation during days with large tidal range was also found to result in more drowning incidents. We advocate that such BNs, providing a better understanding of hazard, exposure and life risk, can be developed to improve public safety awareness campaigns, in parallel with the development of more skilful risk predictors to anticipate high-life-risk days.

Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer time-scales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for Tairua beach, New Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. In general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999–2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014–2017), both approaches showed a decrease in models’ capability to predict the shoreline position. This was more evident for some of the machine learning algorithms. A model ensemble performed better than individual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models.

Infragravity (hereafter IG) waves are surface ocean waves with frequencies below those of wind-generated “short waves” (typically below 0.04 Hz). Here we focus on the most common type of IG waves, those induced by the presence of groups in incident short waves. Three related mechanisms explain their generation: (1) the development, shoaling and release of waves bound to the short-wave group envelopes (2) the modulation by these envelopes of the location where short waves break, and (3) the merging of bores (breaking wave front, resembling to a hydraulic jump) inside the surfzone. When reaching shallow water (O(1–10 m)), IG waves can transfer part of their energy back to higher frequencies, a process which is highly dependent on beach slope. On gently sloping beaches, IG waves can dissipate a substantial amount of energy through depth-limited breaking. When the bottom is very rough, such as in coral reef environments, a substantial amount of energy can be dissipated through bottom friction. IG wave energy that is not dissipated is reflected seaward, predominantly for the lowest IG frequencies and on steep bottom slopes. This reflection of the lowest IG frequencies can result in the development of standing (also known as stationary) waves. Reflected IG waves can be refractively trapped so that quasi-periodic along-shore patterns, also referred to as edge waves, can develop. IG waves have a large range of implications in the hydro-sedimentary dynamics of coastal zones. For example, they can modulate current velocities in rip channels and strongly influence cross-shore and longshore mixing. On sandy beaches, IG waves can strongly impact the water table and associated groundwater flows. On gently sloping beaches and especially under storm conditions, IG waves can dominate cross-shore sediment transport, generally promoting offshore transport inside the surfzone. Under storm conditions, IG waves can also induce overwash and eventually promote dune erosion and barrier breaching. In tidal inlets, IG waves can propagate into the back-barrier lagoon during the flood phase and induce large modulations of currents and sediment transport. Their effect appears to be smaller during the ebb phase, due to blocking by countercurrents, particularly in shallow systems. On coral and rocky reefs, IG waves can dominate over short-waves and control the hydro-sedimentary dynamics over the reef flat and in the lagoon. In harbors and semi-enclosed basins, free IG waves can be amplified by resonance and induce large seiches (resonant oscillations). Lastly, free IG waves that are generated in the nearshore can cross oceans and they can also explain the development of the Earth's “hum” (background free oscillations of the solid earth).