Sandy shorelines are constantly evolving, threatening frequently human assets such as buildings or transport infrastructure. In these environments, sea-level rise will exacerbate coastal erosion to an amount which remains uncertain. Sandy shoreline change projections inherit the uncertainties of future mean sea-level changes, of vertical ground motions, and of other natural and anthropogenic processes affecting shoreline change variability and trends. Furthermore, the erosive impact of sea-level rise itself can be quantified using two fundamentally different models. Here, we show that this latter source of uncertainty, which has been little quantified so far, can account for 20 to 40% of the variance of shoreline projections by 2100 and beyond. This is demonstrated for four contrasting sandy beaches that are relatively unaffected by human interventions in southwestern France, where a variance-based global sensitivity analysis of shoreline projection uncertainties can be performed owing to previous observations of beach profile and shoreline changes. This means that sustained coastal observations and efforts to develop sea-level rise impact models are needed to understand and eventually reduce uncertainties of shoreline change projections, in order to ultimately support coastal land-use planning and adaptation.

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.

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This study proposes a new geomorphological typology of cliff-foreshore continuum in Charente-Maritime, by integrating foreshore characteristics into a statistical approach. The aim is to assess the contribution of often-neglected components to the characterization of rocky cliff coasts. Accordingly, the study replaces expert-based typologies with an objective methodology combining Factor Analysis of Mixed Data (FAMD) and Hierarchical Clustering Analysis (HCA). The analysis is based on a systematic sampling strategy, with 490 points spaced at 100 m intervals, and multi-source datasets including field work, LiDAR data, CHARM database and Litto3D database. Seven key variables were considered: lithology, topography (including height, slope, and aspect), structure (dip angle) of the cliffs and foreshore properties (nature and slope). The results identify five distinct groups of cliff-foreshore continuum. Four variables account for 80 % of the cumulative explained variance: lithology (32 %), nature of foreshore (26 %), cliff slope (16 %), and dip angle (8 %). The resulting typology displays strong spatial coherence, with each cliff group showing a contiguous geographical distribution. Comparison with a previous typology using a confusion matrix indicates 75 % similarity, partially validating the approach, although some discrepancies persist, notably regarding the identification of a potential sixth group. Overall, this study demonstrates the relevance of a systemic cliff-foreshore and statistically based approach to the characterization of rocky coasts.