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.

Modeling studies addressing daily to interannual coastal evolution typically relate shoreline change with waves, currents and sediment transport through complex processes and feedbacks. For wave-dominated environments, the main driver (waves) is controlled by the regional atmospheric circulation. Here a simple weather regime-driven shoreline model is developed for a 15-year shoreline dataset (2000–2014) collected at Truc Vert beach, Bay of Biscay, SW France. In all, 16 weather regimes (four per season) are considered. The centroids and occurrences are computed using the ERA-40 and ERA-Interim reanalyses, applying k-means and EOF methods to the anomalies of the 500-hPa geopotential height over the North Atlantic Basin. The weather regime-driven shoreline model explains 70% of the observed interannual shoreline variability. The application of a proven wave-driven equilibrium shoreline model to the same period shows that both models have similar skills at the interannual scale. Relation between the weather regimes and the wave climate in the Bay of Biscay is investigated and the primary weather regimes impacting shoreline change are identified. For instance, the winter zonal regime characterized by a strengthening of the pressure gradient between the Iceland low and the Azores high is associated with high-energy wave conditions and is found to drive an increase in the shoreline erosion rate. The study demonstrates the predictability of interannual shoreline change from a limited number of weather regimes, which opens new perspectives for shoreline change modeling and encourages long-term shoreline monitoring programs.

Building shoreline position databases able to capture event- to centennial-scale coastal changes is critical for scientists to improve knowledge of past coastal dynamics and predict future changes. Thanks to the commissioning of several satellites acquiring recurrent high-resolution optical images over coastal areas, coastal scientists have developed methods for detecting the shoreline position from satellite images in most parts of the world. These methods use image band analyses to delineate the waterline and require post-processing to produce time-consistent satellite-derived shorelines. However, the detection accuracy generally decreases with increasing tidal range. This work investigates an alternative approach for meso- and macrotidal coasts, which relies on the delineation of the boundary between dry and wet sand surfaces. The method was applied to the high-energy meso-macrotidal km-scale Cap Ferret sand spit, SW France, which has undergone large and contrasted shoreline changes over the last decades. Comparisons with topographic surveys conducted at Cap Ferret between 2014 and 2020 have shown that the raw satellite-derived wet/dry line reproduces well the mean high water shoreline, with an overall bias of 1.7 m, RMSE of 20.2 m, and R2 of 0.86. Building on this, the shoreline variability at Cap Ferret was investigated over the 1984–2021 period. Results have evidenced an alongshore gradient in the dominant modes of variability in the last 2 km of the sand spit. Near the tip, the shoreline has chronically retreated on the decadal scale at about 8.4 m/year and has been strongly affected on the interannual scale by the onset and migration of shoreline undulations having a wavelength of 500–1200 m and a cross-shore amplitude of 100–200 m. Some 3 km away from the sand spit extremity, the shoreline has been relatively stable in the long term, with a dominance of seasonal and interannual variability. This work brings new arguments for using the wet/dry line to monitor shoreline changes from spatial imagery at meso- and macrotidal sandy coasts.

Robinet, A.; Castelle, B.; Idier, D.; D'Anna, M., and Le Cozannet, G., 2020. Simulating the impact of sea-level rise and offshore bathymetry on embayment shoreline changes. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1263–1267. Coconut Creek (Florida), ISSN 0749-0208.LX-Shore is a reduced-complexity shoreline change model driven by cross-shore and longshore processes which can account for man-made or natural non-erodible areas such as groynes and headlands. Here we describe and further test the implementation of two recent developments allowing to account for (i) real and non-erodible offshore bathymetric features such as rocky outcrops and canyons affecting offshore wave transformation and, in turn, shoreline variability and (ii) shoreline retreat due sea-level rise. After a description of the numerical developments, the benefits of these new developments are demonstrated with the application of LX-Shore to an idealized embayed beach exposed to real wave climate during a 10-yr period. Three simulations are conducted to test the impact of an outcrop in the middle of the embayment and of a gradual 1-m sea-level rise on shoreline spatial and temporal modes of variability. Results show that the equilibrium planview shoreline and the shoreline variability are strongly impacted by only slightly modifying the bathymetry and varying the mean sea level. These results show the potential of LX-Shore to better understand and further predict shoreline change along real coasts exhibiting mixed (sandy/rocky) and complex seabed morphologies and undergoing sea-level rise.