Rafael Almar

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.

Since the 1960s, research on sea-level rise (SLR) and land subsidence has grown significantly; however, comprehensive syntheses remain limited. This study presents a systematic review of 2171 publications spanning 1964–2025, combining a global perspective with a regional focus on the Gulf of Guinea, a critically underrepresented region within the African continent. The results show a steady increase in publications, exceeding 80 per year since 2015. A combined bibliometric and content analysis approach was adopted, integrating large-scale metadata analysis with an in-depth evaluation of 166 full-text studies corresponding to 311 study sites. Bibliometric analyses highlight four main themes: (1) factors driving SLR and subsidence, including climate, geophysical, and human effects; (2) monitoring methods such as tide gauges, GPS, and InSAR-based land motion tracking; (3) impacts on coastal communities, and ecosystems; and (4) strategies for adaptation and mitigation. A comparative assessment of global research output and Low-Elevation Coastal Zone (LECZ) exposure reveals a marked spatial mismatch, with critically vulnerable regions, such as the Gulf of Guinea, remaining significantly underrepresented (44 studies). The synthesis identifies key conceptual, methodological, and practical research gaps. Addressing these gaps requires holistic frameworks that integrate SLR and subsidence, long-term monitoring networks, advanced modeling, and evidence-based adaptation strategies. By linking bibliometric evidence with the interpretation of research trends and gaps, this study provides an analytical basis for supporting monitoring strategies, coastal planning, and adaptive responses. Additionally, the results highlight priority directions for future research directions in the Gulf of Guinea region.

Sandy shoreline–sandbar systems exhibit complex variability arising from the interplay between hydrodynamic forcing and intrinsic morphological feedbacks. Using long-term satellite-derived shoreline and sandbar observations, we applied global polynomial modeling to reconstruct low-dimensional deterministic dynamics for 4 contrasting coastal sites. The resulting autonomous models reproduce key morphodynamic features, including self-sustained shoreline oscillations, shoreline–sandbar coupling, and intermittent transitions between quasi-stable configurations. Nonlinear stability analyses reveal that these systems behave as chaotic oscillators, characterized by locally divergent yet globally bounded trajectories. Energetic episodes correspond to rapid shoreline–sandbar exchanges, whereas long low-energy states reflect stable attractor confinement. Together, these results demonstrate that sandy coasts are governed by deterministic but chaotic dynamics, in which internal coupling and self-organization control both variability and finite predictability. The proposed framework offers a physically consistent and data-driven approach to characterize and compare coastal morphodynamics within a unified nonlinear dynamical perspective.

Cette thèse présente une étude de la dynamique des plages à l'échelle événementielle (ou "échelle des tempêtes"). Même si cette dynamique est essentielle, elle est restée principalement méconnue jusqu'à ce jour du fait du manque d'outils d'observation adaptés à son étude. Les nouvelles possibilités offertes par l'imagerie vidéo, notamment l'observation à haute fréquence, sont très novatrices. Dans cette thèse, un outil vidéo est présenté qui, à partir de la mesure des caractéristiques hydrodynamiques de surface, permet d'estimer avec précision la topographie littorale sur une large zone (km) et à haute fréquence (jour). Ce travail montre que les différentes structures sableuses littorales interagissent et qu'elles ne peuvent pas être étudiées de manière isolée. La dynamique des structures sableuses peut être fortement non-uniforme dans la direction parallèle à la plage, même en conditions de fortes vagues. De plus, la dynamique est cruciale car elle contribue aux transferts de sédiment entre le large et la plage. Dans un système à deux barres, plus que la hauteur des vagues, c'est le marnage qui influence majoritairement la dynamique de la barre intertidale en conditions de tempête. Nos résultats suggèrent qu'une grande part de la variabilité temporelle de la plage se situe à cette échelle court terme.

Sandy shoreline–sandbar systems exhibit complex variability arising from the interplay between hydrodynamic forcing and intrinsic morphological feedbacks. Using long-term satellite-derived shoreline and sandbar observations, we applied global polynomial modeling to reconstruct low-dimensional deterministic dynamics for four contrasting coastal sites. The resulting autonomous models reproduce key morphodynamic features, including self-sustained shoreline oscillations, shoreline–sandbar coupling, and intermittent transitions between quasi-stable configurations. Nonlinear stability analyses reveal that these systems behave as chaotic oscillators, characterized by locally divergent yet globally bounded trajectories. Energetic episodes correspond to rapid shoreline–sandbar exchanges, whereas long low-energy states reflect stable attractor confinement. Together, these results demonstrate that sandy coasts are governed by deterministic but chaotic dynamics, in which internal coupling and self-organization control both variability and finite predictability. The proposed framework offers a physically consistent and data-driven approach to characterize and compare coastal morphodynamics within a unified nonlinear dynamical perspective.

Shoreline variability is commonly interpreted through linear links with external forcings, yet many coastal systems display oscillatory modes whose origin cannot be traced to any single forcing frequency. Here, we show that a robust and regionally recurrent quasi-biennial (20–30 month) mode of shoreline variability emerges from a nonlinear triadic resonance between the semi-annual (≈6 month) components of wave energy and the corresponding delayed shoreline response. Using satellite-derived shoreline time series (1993–2019), a set of known environmental drivers, and an iterative cross-EOF method to remove all linear forcing contributions, we identify a persistent residual peak centered at 24–26 months that is absent from the forcing spectra themselves. This peak arises from resonance between two near semi-annual frequencies, specifically through excitation of the shoreline by semi-annual wave forcing that produces a phase-shifted (lagged) near semi-annual shoreline response. The semi-annual wave forcing originates from two mechanisms: (1) phase opposition between local wind-sea and remote swell along eastern boundary systems, and (2) asymmetric annual wind forcing in monsoonal regions and semi-enclosed basins. A global triadic phase-coupling analysis confirms that these near semi-annual components interact nonlinearly to inject energy at the difference frequency, producing the emergent ≈24-month mode, which represents, on average, 15 % of the total dataset's shoreline variance in resonance-prone regions. These results establish nonlinear resonance as a fundamental and previously overlooked mechanism shaping shoreline variability at regional scales, with implications for understanding and predicting coastal response to changes in storm seasonality and wave-climate asymmetry.

Shoreline variability is commonly interpreted through linear links with external forcings, yet many coastal systems display oscillatory modes whose origin cannot be traced to any single forcing frequency. Here, we show that a robust and regionally recurrent quasi-biennial (20–30 month) mode of shoreline variability emerges from a nonlinear triadic resonance between the semi-annual (≈6 month) components of wave energy and the corresponding delayed shoreline response. Using satellite-derived shoreline time series (1993–2019), a set of known environmental drivers, and an iterative cross-EOF method to remove all linear forcing contributions, we identify a persistent residual peak centered at 24–26 months that is absent from the forcing spectra themselves. This peak arises from resonance between two near semi-annual frequencies, specifically through excitation of the shoreline by semi-annual wave forcing that produces a phase-shifted (lagged) near semi-annual shoreline response. The semi-annual wave forcing originates from two mechanisms: (1) phase opposition between local wind-sea and remote swell along eastern boundary systems, and (2) asymmetric annual wind forcing in monsoonal regions and semi-enclosed basins. A global triadic phase-coupling analysis confirms that these near semi-annual components interact nonlinearly to inject energy at the difference frequency, producing the emergent ≈24-month mode, which represents, on average, 15 % of the total dataset's shoreline variance in resonance-prone regions. These results establish nonlinear resonance as a fundamental and previously overlooked mechanism shaping shoreline variability at regional scales, with implications for understanding and predicting coastal response to changes in storm seasonality and wave-climate asymmetry.

Shoreline variability is commonly interpreted through linear links with external forcings, yet many coastal systems display oscillatory modes whose origin cannot be traced to any single forcing frequency. Here, we show that a robust and regionally recurrent quasi-biennial (20–30 month) mode of shoreline variability emerges from a nonlinear triadic resonance between the semi-annual (≈6 month) components of wave energy and the corresponding delayed shoreline response. Using satellite-derived shoreline time series (1993–2019), a set of known environmental drivers, and an iterative cross-EOF method to remove all linear forcing contributions, we identify a persistent residual peak centered at 24–26 months that is absent from the forcing spectra themselves. This peak arises from resonance between two near semi-annual frequencies, specifically through excitation of the shoreline by semi-annual wave forcing that produces a phase-shifted (lagged) near semi-annual shoreline response. The semi-annual wave forcing originates from two mechanisms: (1) phase opposition between local wind-sea and remote swell along eastern boundary systems, and (2) asymmetric annual wind forcing in monsoonal regions and semi-enclosed basins. A global triadic phase-coupling analysis confirms that these near semi-annual components interact nonlinearly to inject energy at the difference frequency, producing the emergent ≈24-month mode, which represents, on average, 15 % of the total dataset's shoreline variance in resonance-prone regions. These results establish nonlinear resonance as a fundamental and previously overlooked mechanism shaping shoreline variability at regional scales, with implications for understanding and predicting coastal response to changes in storm seasonality and wave-climate asymmetry.

Coastal storms represent a major environmental issue and constitute an important challenge for coastal flood management. This study analyzes the frequency and characteristics of storms on the Senegalese coast between 1993 and 2023, focusing on four coastal cities: Dakar, Saint-Louis, Mbour, and Cap-Skring. The analysis is based on wave data from the ERA5 model and on meteorological and oceanographic data from different models. Storms were detected using the Peak Over Threshold (POT) method, based on the 95th percentile and fitted to a generalized Pareto distribution (GPD). The results reveal a contrasted spatial distribution of coastal storms, with a higher occurrence in Dakar and Saint-Louis. An apparent increase in the frequency of storms is observed in Saint-Louis, Mbour, and Cap-Skring, while an apparent decrease is noted in Dakar; however, these trends are not statistically significant. Extreme coastal water levels (ECWL) associated with storms show an opposite evolution, with an apparent decrease in the first three regions and an apparent increase in Dakar. The most intense and longest storms, in terms of energy content (Es), are mainly observed in Dakar and Saint-Louis. A linear relationship is highlighted between the duration and intensity of storms. Storm occurrence shows a strong seasonal modulation, with a predominance during the dry season (November to May). The most energetic storms are mostly generated by waves from the west to west-northwest direction in Dakar and Saint-Louis, while Mbour and Cap-Skring present a wider directional window. This first analysis at the scale of the Senegalese coast provides essential elements for understanding the risk of coastal storms and constitutes support for coastal flood management in a context of climate change.

Machine learning has revolutionized scientific modeling, providing breakthroughs in fields ranging from weather prediction to protein folding. However, its adoption in physics-based domains remains limited due to the lack of interpretability in traditional black-box models. In environmental sciences, where understanding underlying mechanisms is critical, symbolic regression offers an alternative by discovering transparent mathematical expressions that can represent physical principles. In this work, we demonstrate the application of symbolic regression to physical modeling through shoreline prediction, a critical area for understanding coastal evolution under climate change and human influence. Unlike traditional physics-based models, which rely on assumptions that may not generalize across diverse coastal environments, our approach evolves a population of interpretable models directly from global observational data. By optimizing both predictive accuracy and model complexity, we uncover region-specific formulations that reveal the dominant physical drivers of shoreline change. This methodology enables data-driven discovery while maintaining alignment with physical intuition, providing new insights into physical dynamics across multiple spatial and temporal scales.