Sean Vitousek

Threatened sea turtles rely on sandy beaches for nesting, linking their long‐term survival to global beach availability. However, beaches worldwide are increasingly threatened by anthropogenic stressors and sea level rise (SLR). Reliable vulnerability assessments require understanding beach dynamics across multiple time scales, informed by long‐term coastal change records. While many nesting beaches lie in remote, data‐poor environments, recent advances in coastal remote sensing now allow us to monitor coastal change worldwide. Here, we combine satellite‐derived shorelines (CoastSat), shoreline modeling (CoSMoS‐COAST), and global data sets to investigate shoreline evolution and future vulnerability at nine globally important sea turtle nesting sites. We investigate seasonal and long‐term shoreline change, hindcast (1980–2024) and forecast (2025–2100) shoreline positions under various SLR scenarios, and quantify available accommodation space based on backbeach elevation and infrastructure footprints. We find that shoreline evolution and vulnerability vary considerably, with three sites showing historical accretion trends and four sites showing erosion. This demonstrates that the previously widely applied bathtub approach—adding SLR to a static beach profile—is not suitable to assess the vulnerability of sea turtle nesting beaches to erosion. Three eroding beaches emerge as particularly vulnerable due to projected shoreline retreat coupled with limited accommodation space. Despite significant uncertainties arising from long‐term shoreline projections, our results provide important insights into seasonal and long‐term morphodynamics, identify vulnerable nesting sites, and offer a comprehensive, transferable framework for assessing shoreline evolution and relative erosion vulnerability at other sites. Understanding these dynamics is crucial to inform conservation and management strategies to future‐proof these critical nesting habitats.

Human impacts from dams reduce river sediment fluxes and are primary causes of coastal erosion worldwide. Here we provide new satellite-derived shoreline observation techniques to examine beach area trends across the diverse coastal settings of California. Contrary to global trends, these data reveal that the most heavily urbanized and dammed region of southern California experienced net beach growth of over 2 million m ² during 1984-2024. While several beaches experienced severe erosion, overall widening is explained by sufficient sediment supply and concentrated widening from longshore transport captured at coastal structures and in littoral convergence zones. These results indicate that adequate sediment sources exist in this human-modified landscape to mitigate coastal erosion, but that this sediment is not effectively distributed to vulnerable beaches. This highlights the critical role that longshore sediment transport plays in long-term beach trends and illuminates management opportunities for coastal sustainability at the regional scale.

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.

Yes. Equilibrium shoreline models, which simulate wave‐driven cross‐shore erosion and accretion, are mathematically equivalent to a discrete convolution (i.e., a weighted, moving average) of a time series of wave‐forcing conditions with a parameterized memory‐decay kernel function. The direct equivalence between equilibrium shoreline models and convolutions reveals key theoretical aspects of equilibrium behavior. Convolutions (representing quasi‐low‐pass filter operations) provide an intuitive theoretical description of shoreline erosion and accretion behavior in response to waves: that is, shoreline position often mirrors the weighted moving average of wave time series. Model‐convolution equivalence also provides a conceptual basis to interpret, evaluate, and construct data‐driven Machine‐Learning/Deep‐Learning (ML/DL) models that use convolutions to extract features from data and then apply them for prediction (e.g., Convolutional Neural Networks (CNNs)). Finally, our findings provide a methodological pathway (based on Fourier transforms) for future understanding of wave‐driven shoreline change, which can be used to interpret the coherence between the frequency spectrum of the processes of waves and shoreline change and construct more computationally efficient and effective shoreline‐modeling approaches.

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.

As the coastal population continues to expand, the risk of experiencing social and economic losses due to the effects of a changing climate also increases. Although considerable advances have been achieved in terms of developing numerical process-based models for shoreline change, ensuring reliable predictions remains a formidable task. We propose an innovative approach by implementing Convolutional Neural Networks (CNNs) to predict the evolution of shorelines in response to wave forcings. While the use of these data-driven model frameworks in coastal research is still in its early stages, it holds promise in capturing the strong autocorrelation and memory/storage effects involved in shoreline evolution and may therefore offer a viable alternative to process-based models, at least in some locations. Our models were put to the test at two distinct beach locations: Ocean Beach in California, characterized by its seasonal patterns, and Duck in North Carolina, which lacks a strong seasonal signal. We offer an assessment of the models' performance through the use of absolute- value error metrics. The findings presented shed light on the potential of Deep Learning in forecasting shoreline change.

Sandy beaches are highly vulnerable to short-term and long-term erosion due to waves and sea-level rise (SLR), respectively. As global SLR is accelerating due to climate change, reliable projections of shoreline change on long time scales (greater than decadal) are critical for coastal adaptation planning. Some shoreline models that address such long- term projections rely on the equilibrium beach theory to estimate the effects of waves and SLR. However, so far, the effects of interactions between SLR and wave action are not resolved explicitly. In this work, we present a novel physical interpretation of the beach response to SLR in the context of equilibrium beach theory. Using this concept, we analyze the integration of SLR effects into wave-driven equilibrium shoreline models, while accounting for the interactions between SLR and incident waves.

Coastal change is a complex combination of multi-scale processes (e.g., wave-driven cross-shore and longshore transport; dune, bluff, and cliff erosion; overwash; fluvial and inlet sediment supply; and sea-level-driven recession). Historical sea-level-driven coastal recession on open ocean coasts is often outpaced by wave-driven change. However, future sea-level-driven coastal recession is expected to increase significantly in tandem with accelerating rates of global sea-level rise. Few models of coastal sediment transport can resolve the multitude of coastal-change processes at a given beach, and fewer still are computationally efficient enough to achieve large-scale, long-term simulations, while accounting for historical behavior and uncertainties in future climate. Here, we show that a scalable, data-assimilated shoreline-change model can achieve realistic simulations of long-term coastal change and uncertainty across large coastal regions. As part of the modeling case study of the U.S. South Atlantic Coast (Miami, Florida to Delaware Bay) presented here, we apply historical, satellite-derived observations of shoreline position combined with daily hindcasted and projected wave and sea-level conditions to estimate long-term coastal change by 2100. We find that 63 to 94% of the shorelines on the U.S. South Atlantic Coast are projected to retreat past the present-day extent of sandy beach under 1.0 to 2.0 m of sea-level rise, respectively, without large-scale interventions.

With climate change and accelerating sea-level rise (SLR), long-term predictions of shoreline evolution are crucial to assess climate adaptation plans in coastal areas. Meanwhile, knowledge gaps in the physics of shoreline response to the combined action of waves and SLR hinder making reliable long-term shoreline projections. Equilibrium beach theory states that a beach profile shape equilibrates with its local wave and sea-level conditions. In the context of equilibrium beach theory, we present a physical interpretation of beach response to SLR that leads to the identification of two main contributing processes: passive flooding and increased wave-driven erosion efficiency (i.e. wave reshaping effect). Using this new interpretation, we analyze data from existing laboratory studies that measured the equilibrium beach response to controlled SLR and wave conditions. The data indicate a clear relationship among the analyzed variables, corroborating the potential of the proposed concept, and improving the understanding of shoreline response to SLR. Our results support the integration of SLR-driven recession into equilibrium shoreline models for future studies, making a step towards the development of a generalized equilibrium shoreline model.