Marcan Graffin

The position of the coastal waterline—the interface between land and sea—varies over time due to sedimentary and hydrodynamic processes influenced by climate variability. While previous studies have mainly focused on local morphodynamic or storm‐driven shoreline changes, the large‐scale climatic controls on global waterline variability remain poorly understood. This study addresses this gap by analyzing how climate‐driven environmental factors govern coastal waterline dynamics across seasonal to interannual timescales. Using a globally consistent, satellite‐derived data set of coastal waterlines from 1999 to 2019, combined with reanalysis‐based estimates of sea‐level anomalies, wave setup and freshwater runoff, we use an empirical mode decomposition framework to disentangle the contributions of these drivers. This approach isolates the dominant temporal components of waterline variability and identifies regional relationships with environmental forcing. Our results reveal two main regimes of climate‐driven waterline variability: (a) a seasonal pattern that intensifies with latitude and is primarily linked to storm‐driven wave energy flux and sea‐level oscillations; and (b) an interannual pattern that peaks in the tropics and is strongly modulated by large‐scale climate oscillations, such as ENSO, and by variability in fluvial discharge. We demonstrate that regional sea‐level and freshwater anomalies can alter the waterline by amounts comparable to those caused by waves. This challenges the conventional view that wave‐driven morphodynamics dominate waterline change. By framing the waterline as a climate‐sensitive indicator of coastal change, this study establishes the first global‐scale, data‐driven framework for diagnosing and predicting waterline variability. The results provide a basis for developing climate‐informed waterline forecasts and improving assessments of coastal vulnerability in the context of ongoing sea‐level rise.

Observation de l'influence de la variabilité climatique sur la morphodynamique côtière à partir de l'imagerie satellitaire optique: L'évolution des côtes sableuses, soumises à une large variétés de pressions naturelles et anthropiques, demeure encore mal comprise à l'échelle globale. Parallèlement, l'essor de l'observation de la Terre par satellite offre un accès sans précédent à des données spatio-temporelles étendues, ouvrant de nouvelles perspectives pour l'analyse de la morphodynamique côtière.Dans ce contexte, cette thèse vise à mieux comprendre la dynamique des côtes sableuses à l'échelle mondiale à travers l'étude de l'évolution du trait de côte, ligne d'interface terre/mer, sur plusieurs décennies, en lien avec les modes de variabilité climatique. À cette fin, une chaîne de traitement automatisée a été développée en Python, pour extraire, traiter et analyser les images des missions Sentinel-2 et Landsat (5, 7, 8, 9). Combinant plusieurs indices spectraux et méthodes de seuillage, elle permet l'application systématique d'approches personnalisables.1) Une validation sur huit sites ateliers met en évidence une forte dépendance de la précision des mesures aux conditions environnementales locales. Les sites macro-tidaux, en particulier, présentent une sensibilité accrue aux méthodes utilisées, générant des séries temporelles de positions du trait de côte plus bruitées. Cette hétérogénéité a permis d'établir des lois empiriques liant l'erreur de détection à la pente de plage et l'amplitude de marée, offrant un cadre pour évaluer la fiabilité des observations dans des zones dépourvues de données de terrain.2) L'outil a ensuite été appliqué à la façade pacifique nord-américaine pour produire un jeu de données couvrant 25 ans (1997-2022). L'analyse met en évidence des variations saisonnières marquées selon la latitude, avec des excursions du trait de côte dépassant 25 m dans les états de Washington et Oregon, contre moins de 10 m au sud de la Californie. Ces fluctuations sont fortement corrélées à la puissance des vagues dans le nord de la région, tandis qu'en Basse-Californie, elles sont davantage liées aux variations du niveau de la mer. À l'échelle interannuelle, les réponses morphodynamiques varient selon les régions : au sud, les variations sont modérées et concentrées lors des épisodes ENSO (El Niño Southern Oscillation), alors qu'au nord, les réponses sont plus complexes et moins systématiques. Cette étude souligne l'influence spatiale différenciée d'ENSO, qui module la trajectoire et l'intensité des tempêtes hivernales, affectant les dynamiques d'érosion.3) Une étude complémentaire approfondit ensuite la compréhension du trait de côte en se basant sur des données de terrain sur quatre sites pour étudier l'évolution des profils de plage intertidaux à l'aide de méthodes statistiques. Cette étude permet d'identifier une dynamique structurées autour de deux mécanismes principaux : la translation du profil intertidal et l'évolution de sa pente. Cette étude intérroge la nature ambivalente du trait de côte, qui peut être appréhendé soit comme un indicateur purement morphologique - défini comme l'intersection entre une élévation donnée et le profil de plage -, soit comme un indicateur hydro-morphologique, dont la position intègre les variations spatio-temporelles du niveau d'eau liées aux forçages climatiques. Ces deux conceptions impliquent des dynamiques distinctes, compliquant davantage l'interprétation de l'évolution du trait de côte, qui elle même n'est qu'un indicateur de l'évolution globale de la plage.Enfin, cette thèse ouvre des perspectives concrètes pour la surveillance côtière à grande échelle, en proposant une méthodologie reproductible qui s'imbrique dans d'autres axes d'observations et de modélisations de la hydro-morphodynamique côtière, permettant de mieux comprendre et anticiper les évolutions littorales et d'appuyer la prise de décision dans un contexte de changement global.

Sandy coasts are highly dynamic environments shaped by a myriad of hydro-sedimentary processes operating across various spatio-temporal scales. From seasonal to centennial timescales, sandy beach dynamics are strongly influenced by climate variability expressed in various forms, including seasonal cycles, climate modes (e.g., El Niño Southern Oscillation (ENSO)), and global warming. Satellite-based methods enable the observation of coastlines with unprecedented spatial and temporal coverage. However, over the past decade, as these methods have matured, coastal change studies have predominantly focused on long-term trends rather than on climate-driven cycles of shoreline change. In this study, we utilize a large dataset of monthly-sampled time series of waterline positions along the Pacific Coast of the United States and Baja California, Mexico. From these time series, we derived information on long-term trends and seasonal cycles of waterline change. Our analysis reveals clear contrasts in seasonal patterns of waterline variability across the study area, with greater amplitudes observed at higher latitudes. A shift in the dominant dynamics is evident in Southern Baja California, where shoreline variability is more influenced by intertropical dynamics and sea-level anomalies than by wave energy modulation, which dominates further north. ENSO is found to induce contrasting responses of the waterline along the study area. During El Niño events, shoreline retreat is observed in subtropical regions, while La Niña winters drive waterline retreat in higher latitudes, with southern regions either remaining stable or advancing.

Recurrent massive strandings of pelagic Sargassum have severely impacted Caribbean and Gulf of Mexico coastlines over the past decade, generating major environmental, sanitary, and socioeconomic consequences. Accurate monitoring of Sargassum dynamics in nearshore waters remains challenging, as most existing satellite products rely on moderate-resolution sensors that inadequately resolve coastal processes. Here, we present a high-spatial- and -temporal-resolution Sargassum detection dataset derived from the VENµS (Vegetation and Environment New Micro-Satellite) mission, providing daily observations at 4 m resolution for five coastal zones in Guadeloupe, Martinique, and the Yucatán Peninsula over the 2022–2024 period. VENµS imagery consists of 12 multispectral bands, and the analysis specifically uses the red, the red-edge/near-infrared and the short-wave infrared bands. Detection is based on the Alternative Floating Algae Index (AFAI), combined with land and cloud masking, background estimation, and adaptive thresholding. We demonstrate the capability of this dataset to resolve fine-scale Sargassum raft dynamics, characterize the seasonal influx of Sargassum along the coastline, and assess exposure across different coastal typologies. By offering the highest combined spatial and temporal resolution currently available for these regions, this dataset provides a novel resource for coastal impact assessment, nearshore drift analysis, and validation of Sargassum transport and stranding models.

Sandy beaches act as buffers against various coastal hazards but are vulnerable to episodic (seasonal) and chronic (interannual) erosion. Understanding the variation of shoreline position, a key metric in coastal morphology, over a wide spectrum of time scales is therefore crucial in assessing hazard vulnerability. Long-standing research has investigated the role of El Niño-Southern Oscillation (ENSO), the dominant mode of climate variability in the Pacific Basin, in seasonal shoreline position variability. Yet, ENSO's chronic influence–and that of another significant Pacific climate mode, the Pacific Decadal Oscillation (PDO)–on shoreline anomalies remains poorly understood. Here, we examine the variability of sandy beaches in the U.S. Pacific Northwest, a ∼750 km long diverse coastal region on the U.S. West Coast. We leverage 40 years (1984-2024) of shoreline position data from publicly available Earth-observing (Landsat) satellite imagery at a high spatial resolution (>10,000 shore-normal transects at 50-m alongshore spacing) and employ Convergent Cross Mapping (CCM), a novel methodology for inferring causality in dynamical systems. We discover that strong El Niño years are signified by erosion (75.1% of transects), and strong La Niña years largely exhibit accretional behavior (73.4% of transects). Furthermore, we establish, for the first time, that both ENSO and PDO exert a statistically significant driving control on interannual shoreline position variability, particularly on the alongshore component (in 95% and 100% of littoral cells, respectively), with water level fluctuations also playing a critical role. This effort advances our understanding of the complex seasonal-to-interannual interactions between Pacific Basin climate variability and the PNW's coastal morphodynamics, with implications for sediment management and coastal adaptation.

Understanding waterline variability at seasonal to interannual timescales is crucial for predicting coastal responses to climate forcing. However, relationships between large-scale climate variability and coastal morphodynamics remain underexplored beyond intensively monitored sites. This study leverages a newly developed 25-year (1997–2022) satellite-derived waterline dataset along the North American West Coast. Our results reveal distinct latitudinal patterns in seasonal waterline change, with excursions exceeding 25 m in the Pacific Northwest, decreasing to less than 10 m in Southern California and farther south. Waterline fluctuations strongly follow wave power in the Pacific Northwest (R = −0.78), northern California (R = −0.75), and Baja California (R = −0.62), while Baja California Sur aligns more with sea-level variations (R = −0.42). Interannually, waterline change exhibits latitudinal dependence: south of southern California, variability is low, with major erosion confined to strong El Niño-Southern Oscillation (ENSO) events, while northern regions show mixed responses. ENSO-driven storm track shifts modulate winter wave climate, resulting in enhanced (attenuated) erosion from southern California to Baja California Sur during El Niño (La Niña). However, further north, ENSO impacts are less consistent, reflecting a complex interplay of storm track displacement and intensification. These findings highlight the spatial complexity of ENSO-driven morphodynamics and provide a framework for assessing climate-induced coastal vulnerability.

Satellite-based observations now play a key role in monitoring the morphological evolution of coasts, enabling the extraction of diverse change indicators. While satellite-based methods offer significant advantages for large-scale coastal monitoring, there is a limited body of research that assesses their robustness. Each step in the waterline extraction process introduces potential sources of error, and the diverse characteristics of coastlines, including their nature, tidal range, and wave climate, pose additional challenges that can significantly affect the accuracy of the detection. In this study, we evaluate the performance of a standardized satellite-derived waterline extraction method by validating its results using ground truth data from a dozen of sites around the world. We investigate the impact of pixel indexing on the performances of the process. Additionally, we establish links between beach characteristics and the accuracy of waterline detection.

Beach morphology can be observed over large spatio-temporal scales, and future shoreline positions can be predicted and coastal risk indicators can be derived by measuring satellite-derived instantaneous waterlines. Long-term satellite missions, such as Landsat and Sentinel-2, provide decades of freely available, high-resolution optical measurement datasets, enabling large-scale data collection and relatively high-frequency monitoring of sandy beaches. Satellite-Derived Shoreline (SDS) extraction methods are emerging and are increasingly being applied over large spatio-temporal scales. SDS generally consists of two steps: a mathematical relationship is applied to obtain a ratio index or pixel classification by machine-learning algorithms, and the land/sea boundary is then determined by edge detection. Indexes from lake waterline detection, such as AWEI or NDWI, are often transferred towards the shore without taking into account that these indexes are inherently affected by wave breaking. This can be overcome by using pixel classification to filter the indices, but this comes at a computational cost. In this paper, we carry out a thorough evaluation of the relationship between scene-dependent variables and waterline extraction accuracy, as well as a robust and efficient thresholding method for coastal land–water classification that optimises the index to satellite radiometry. The method developed for sandy beaches combines a new purpose-built multispectral index (SCoWI) with a refinement method of Otsu’s threshold to derive sub-pixel waterline positions. Secondly, we present a waterline extraction pipeline, called Shoreliner, which combines the SCoWI index and the extraction steps to produce standardised outputs. Implemented on the CNES High Performance Cluster (HPC), Shoreliner has been quantitatively validated at Duck, NC, USA, using simultaneous Sentinel-2 acquisitions and in situ beach surveys over a 3-year period. Out of six dates that have a satellite acquisition and an in situ survey, five dates have a sub-pixel RMS error of less than 10 m. This sub-pixel performance of the extraction processing demonstrates the ability of the proposed SDS extraction method to extract reliable, instantaneous and stable waterlines. In addition, preliminary work demonstrates the transferability of the method, initially developed for Sentinel-2 Level1C imagery, to Landsat imagery. When evaluated at Duck on the same day, Sentinel-2 and Landsat imagery several minutes apart provide similar results for the detected waterline, within the method’s precision. Future work includes global validation using Landsat’s 40 years of data in combination with the higher resolution Sentinel-2 data at different locations around the world.

Coastal morphological changes can be assessed using shoreline position observations from space. However, satellite-derived waterline (SDW) and shoreline (SDS; SDW corrected for hydrodynamic contributions and outliers) detection methods are subject to several sources of uncertainty and inaccuracy. We extracted high-spatiotemporal-resolution (~50 m-monthly) time series of mean high water shoreline position along the Columbia River Littoral Cell (CRLC), located on the US Pacific Northwest coast, from Landsat missions (1984–2021). We examined the accuracy of the SDS time series along the mesotidal, mildly sloping, high-energy wave climate and dissipative beaches of the CRLC by validating them against 20 years of quarterly in situ beach elevation profiles. We found that the accuracy of the SDS time series heavily depends on the capability to identify and remove outliers and correct the biases stemming from tides and wave runup. However, we show that only correcting the SDW data for outliers is sufficient to accurately measure shoreline change trends along the CRLC. Ultimately, the SDS change trends show strong agreement with in situ data, facilitating the spatiotemporal analysis of coastal change and highlighting an overall accretion signal along the CRLC during the past four decades.