Amelie Arias

Coastal water levels during storms result from interacting tides, storm surges, waves, currents, bathymetry, and coastal geometry, producing spatially heterogeneous responses that remain difficult to characterize from point observations alone. The Surface Water and Ocean Topography (SWOT) mission provides a new opportunity to investigate this complexity through wide-swath High-Resolution (HR) observations of sea surface height (SSH). Here, SWOT HR water levels are first evaluated against five tide gauges in the English Channel and subsequently analyzed at three contrasting sites, Cherbourg, Villers-sur-Mer, and Étretat, using complementary spatial and scale-resolved approaches. SWOT HR shows strong agreement with tide gauges at the best-performing sites, with RMSE values of approximately 0.11 m and correlations exceeding 0.99, while accuracy remains site-dependent. Beyond point-based validation, the two-dimensional HR fields reveal pronounced spatial heterogeneity, including localized and elongated SSH anomalies with distinct geometries and orientations. EOF decomposition shows that more than half of the variance remains beyond the first six modes at all three sites, highlighting the complexity of the HR spatial signal. The detrended SSH anomalies show organized spatial patterns, while their point-by-point linear correlations with radar backscatter (σ⁰) remain close to zero. Across the three investigated coastal domains and two storm events, the two-dimensional wavelet analysis identifies enhanced SSH variability within an energetic scale range of approximately 0.5–1.0 km, although its intensity, spatial distribution, and organization differ substantially among sites. Comparison with independent wave, surface-current, and bathymetric fields further places these observations within contrasting hydrodynamic and morphological environments, suggesting that the observed sub-kilometer SSH variability may be associated with local coastal processes, although their individual contributions remain unresolved. . Together, these analyses demonstrate the added value of SWOT HR for resolving the two-dimensional, multiscale complexity of storm-time coastal SSH, providing new spatial information for investigating coastal dynamics and supporting high-resolution model validation and data assimilation.

Coastal water levels during storms result from interacting tides, storm surges, waves, currents, bathymetry, and coastal geometry, producing spatially heterogeneous responses that remain difficult to characterize from point observations alone. The Surface Water and Ocean Topography (SWOT) mission provides a new opportunity to investigate this complexity through wide-swath High-Resolution (HR) observations of sea surface height (SSH). Here, SWOT HR water levels are first evaluated against five tide gauges in the English Channel and subsequently analyzed at three contrasting sites, Cherbourg, Villers-sur-Mer, and Étretat, using complementary spatial and scale-resolved approaches. SWOT HR shows strong agreement with tide gauges at the best-performing sites, with RMSE values of approximately 0.11 m and correlations exceeding 0.99, while accuracy remains site dependent. Beyond point-based validation, the two-dimensional HR fields reveal pronounced spatial heterogeneity, including localized and elongated SSH anomalies with distinct geometries and orientations. EOF decomposition shows that more than half of the variance remains beyond the first six modes at all three sites, highlighting the complexity of the HR spatial signal. These residual structures are spatially organized rather than random, while their point-by-point relationship with radar backscatter (σ⁰) remains negligible. Two-dimensional Morlet wavelet analysis identifies a recurrent energetic range of approximately 0.5–1.0 km across the three domains, showing strongly contrasting intensity, localization, geometry, and persistence across neighboring scales. Comparison with independent wave, surface-current, and bathymetric fields further shows that this variability occurs within markedly different hydrodynamic and morphological environments, consistent with the combined influence of storm forcing and local coastal configuration rather than a single controlling process. The complementarity of these analyses demonstrates the added value of SWOT HR for resolving the two-dimensional, multiscale complexity of storm-time coastal SSH, providing new spatial information for understanding coastal dynamics and for high-resolution model validation and data assimilation.

Modélisation de l'évolution du trait de côte à l'échelle globale sous l'effet des facteurs humains et climatiques: Comprendre l'évolution des littoraux sableux face aux changements globaux représente un enjeu majeur, tant environnemental que sociétal. Cette thèse développe GLOBCOASTS, un modèle de simulation de la ligne d'eau à l'échelle mondiale, fondé sur une formulation morphodynamique simplifiée intégrant la variabilité du niveau marin, le transport longitudinal des sédiments, les apports fluviaux, et l'effet de la houle sur la pente de plage. Le modèle repose uniquement sur des données accessibles (satellites, réanalyses climatiques), permettant une application globale, y compris dans les régions à faible densité d'observations. Les résultats montrent que GLOBCOASTS peut reproduire les grandes tendances d'évolution du trait de côte aux échelles saisonnières à pluriannuelles, avec des performances comparables à des approches locales plus complexes. L'étude évalue également l'impact des barrages et des infrastructures côtières sur la fragmentation des flux sédimentaires, révélant une réduction moyenne de 15% des apports fluviaux mondiaux en sable, et des déséquilibres critiques dans plusieurs régions deltaïques ou tropicales. Enfin, l'influence du forçage climatique (ENSO) est explorée, mettant en lumière des réorganisations morphologiques persistantes dans plusieurs bassins exposés. Ce travail propose un changement d'échelle et de perspective pour l'étude du littoral, combinant modélisation dynamique, données spatiales et diagnostic global. GLOBCOASTS offre un outil stratégique pour la prévision, la gestion et l'adaptation des littoraux face aux pressions anthropiques et climatiques du XXIe siècle.

The estimation of the land-sea interface, or waterline, variability due to high energetic events - i.e. potentially inducing extreme coastal level and erosion- constitutes an important component for the comprehensive risk assessment of the global coastal zone. The large spatial scales and the requirement for real-time coastal risk assessment pose the need for timely forecasts of the key driving processes, in conjunction with the human stakes, exposed population, infrastructures and properties, at risk. Moreover, the interplay between the involved physical processes necessitates the inclusion of a large number of plausible scenarios, useful for the involved decision makers. We present a flexible and low computational-cost framework for the real-time estimation of the global waterline change and associated coastal risk. This framework utilizes satellite-derived probabilistic water level data for the global ocean, combined with state of the art wave and hydrological numerical data and up-to-date satellite observations of the waterline. This information is integrated with high spatial resolution exposure data, providing in real-time the assessment of the imminent risk at the global coastal zone. The outcome of the proposed approach may serve as an additional forecast tool for a first-pass risk assessment, facilitating both short-term and long-term risk mitigation studies.

Grain size and beach slope are critical factors in coastal science and management. However, it is difficult to have information on their distribution everywhere in the world, as most of the coast has never been documented. For many applications, it is essential to have at least a rough estimate when local field measurements are not available. Here, we review the existing prediction formulas relating beach slope to grain size and wave conditions, using publicly available global datasets and comparing them with a benchmark dataset of ground measurements from different authors worldwide. Uncertainties arise from the input parameters, in particular coastal waves, a key parameter of all formulae, but also from empirical coefficients that are undocumented or inaccessible with the global dataset. Despite the recognized importance of tides, they are often overlooked in formulae relating beach slope to sediment grain size. We therefore present an improved formulation that incorporates tidal effects. Although satellites offer a promising alternative to predictive formulae for direct estimation of beach slope and grain size, the current accuracy and methodologies of satellite data are insufficient for global applications. Continued advances in satellite missions, including higher resolution and revisit frequency, as well as new sensors, are essential to improve predictive capabilities and facilitate wider implementation.