Geology topics

Sean Vitousek

Publications and source records attributed to Sean Vitousek.

At least 19 recordsLinked to original sources

USGS2026

Significant coastal dune loss challenges California's climate resilience and biodiversity goals

Coastal sand dunes support unique biodiversity and buffer beaches and communities against storm impacts. However, these sensitive and dynamic ecosystems are increasingly threatened by erosion, sea-level rise (SLR), and encroaching coastal development. Restoration projects designed to re-establish the eco-geomorphic functions of degraded dunes and associated ecosystem services present a viable pathway to relieve these stressors. Yet, even for populated coasts like California, insufficient information on the location of dunes and patterns of change over time constrain regional efforts to identify suitable restoration/conservation sites, and forecast future impacts on vulnerable beach–dune systems. Here, we present a comprehensive assessment of coastal dune extent and loss, spanning 165 years and 9.5° of latitude across California. Areas and drivers of change were quantified by comparing historical and current dune extents generated using historical surveys, high-resolution aerial imagery, and LiDAR, combined with a machine-learning tool. Coastal dunes currently extend along 352 km (22%) of California's 1,625 km coastline, covering almost 300 km 2 . This represents a 60% (442 km 2 ) decrease in area since the mid-1800s with the loss, degradation, and fragmentation of many dune ecosystems attributed to urban development, altered land-uses, and erosion. Strong regional patterns of dune loss were evident, reflecting population demographics and land uses. Densely populated areas of southern California experienced the greatest dune losses (95%, 108 km 2 ), although substantial reductions were also found in central California (60%, 331 km 2 ). Our results highlight the magnitude of coastal change in California over time and can inform future conservation, restoration, and resilience-planning efforts and priorities.

California↗

USGS2026

Morphodynamic controls on the performance of dune-based coastal flood mitigation under sea-level rise

Dune-based adaptation is increasingly used as a nature-based solution for coastal flood mitigation, yet many assessments rely on hydrodynamic-only approaches that treat beach and dune morphology as static, neglecting storm-driven erosion that can degrade dune performance during extreme events. This study develops a process-based modeling framework coupling extreme value analysis of regional wave climate with XBeach surfbeat simulations to evaluate coastal flooding, erosion, and dune performance across three time horizons (2025, 2055, 2085) and four storm return periods (5, 20, 50, and 100 years) at two contrasting sites in Santa Cruz County, California. At Santa Cruz Beach, flood extent increases at an accelerating rate with sea-level rise (SLR) for a given return period, while higher return period storms exhibit diminishing incremental increases under fixed SLR conditions. Storm-scale morphodynamic feedbacks reduce predicted flood extent by up to 32.4% through profile adjustment and nearshore bar formation. Dune effectiveness is strongly regime-dependent, reducing flooding by up to 60% under collision-regime conditions but declining rapidly as the system transitions toward overwash and inundation under stronger storms and late-century SLR. Comparison of static and erodible dune representations shows that flood estimates are not consistently conservative, with differences reaching up to 54.3% depending on storm intensity and time horizon. At Capitola Beach, limited accommodation space and water-level-dominated dynamics collectively constrain morphodynamic adjustment, render the site near its inundation capacity under present-day conditions, and result in near-complete dune collapse and limited flood mitigation across all scenarios. These findings highlight the importance of site-specific regime evaluation, process-based morphodynamic modeling, and adaptive management when assessing dune-based adaptation under rising sea levels.

California↗

USGS2026

Resolving uncertainty in satellite-derived shorelines of a reef-lined beach using high spatiotemporal resolution topographic surveys

Automated satellite-derived shoreline (SDS) detection is increasingly used for coastal monitoring, yet few high spatiotemporal resolution datasets exist for detailed error analysis. This study compares SDS from Landsat 7, Landsat 8, Sentinel-2, and PlanetScope against 3.5 years of weekly orthomosaic and digital elevation model surveys of a 500 m reef-lined beach in Waikīkī, Hawai‘i ( n = 128 surveys). We evaluated error mitigation strategies including image co-registration, outlier removal, and water-level corrections (tides and wave set-up). SDS accuracy improved substantially after these corrections, with root mean square errors (RMSE) ranging from 2.83 m (PlanetScope) to 5.54 m (Sentinel-2). Our analysis showed that satellite-derived waterlines (SDWs) do not consistently align with the same geomorphic feature (e.g., waterline, low water mark). Mid-resolution satellites aligned with the waterline in only ~ 50% of cases, whereas higher-resolution PlanetScope imagery (3.7 m) aligned consistently in 97% of cases. These findings reveal that SDS can represent varying shoreline proxies, introducing an under-recognized uncertainty that higher-resolution imagery can help resolve. We demonstrate that high-resolution imagery and robust error mitigation are useful for improving the reliability of automated shoreline detection.

Hawaii↗

USGS2026

Unravelling equilibrium shoreline response to waves and sea-level rise: Numerical modelling of laboratory experiments

Knowledge gaps in the physics of shoreline response to the combined action of waves and sea-level rise (SLR) make long-term shoreline projections uncertain. The lack of sufficiently long-term shoreline data partly hinders a better understanding of shoreline change driven by SLR. Thereby, existing formulations related to the equilibrium approach, which states that a beach profile shape equilibrates with its local wave and sea-level conditions, have serious limitations. Recent physical modelling studies provided data of beach profile evolution under changing sea levels in controlled laboratory settings. Here, we bring together and analyze laboratory data from three different physical modelling studies to better understand and predict shoreline response to SLR in the context of equilibrium concept. The data indicate a clear relationship among the analyzed variables, highlighting the importance of accounting for interactions between SLR and incident wave power. To further understand these interactions, and given the limited range of conditions tested in the laboratory, we implement a quasi-2D shoreline change model to generate additional synthetic data. We first calibrate and validate the model to emulate the existing laboratory experiments. The model reproduces multiple laboratory experiments that cover a range of settings, providing confidence in its accuracy. Further work will include the generation of synthetic data obtained by forcing the model with new combinations of SLR and wave conditions to better capture the dependency of shoreline recession on SLR and wave conditions.

Conference Paper↗

USGS2026

Climate-driven waterline variability along the North American West Coast

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.

Baja California, California, Oregon, Washington↗

USGS2026

Vulnerability of key sea turtle nesting beaches to future erosion and sea level rise

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.

Earth's Future↗

USGS2026

Net widening of Southern California beaches

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.

California↗

USGS2026

ENSO and PDO drive shoreline position anomalies in the U.S. Pacific Northwest

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 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 variability. Yet, ENSO’s chronic influence—and that of another Pacific climate mode, the Pacific Decadal Oscillation (PDO)—on shoreline anomalies remains poorly understood. Here, we examine the variability of sandy beaches in the US Pacific Northwest, a ∼750 km long coastal region on the US West Coast. We leverage 40 years (1984–2024) of shoreline 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 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 exhibit accretional behavior (73.4% of transects). Furthermore, we establish, for the first time, that both ENSO and PDO exert a statistically significant control on interannual shoreline variability, particularly on the alongshore component (in 95 and 100% of littoral cells, respectively), with water level fluctuations playing a critical role. This effort advances our understanding of the seasonal-to-interannual interactions between Pacific Basin climate variability and the PNW’s coastal morphodynamics, with implications for sediment management and coastal adaptation.

California, Oregon, Washington↗

USGS2025

Benchmarking shoreline prediction models over multi-decadal timescales

Robust predictions of shoreline change are critical for sustainable coastal management. Despite advancements in shoreline models, objective benchmarking remains limited. Here we present results from ShoreShop2.0, an international collaborative benchmarking workshop, where 34 groups submitted shoreline change predictions in a blind competition. Subsets of shoreline observations at an undisclosed site (BeachX) over short (5-year) and medium (50-year) periods were withheld from modelers and used for model benchmarking. Using satellite-derived shoreline datasets for calibration and evaluation, the best performing models achieved prediction accuracies on the order of 10 m, comparable to the accuracy of the satellite shoreline data, indicating that certain beaches can be modelled nearly as well as they can be remotely observed. The outcomes from this collaborative benchmarking competition critically review the present state-of-the-art in shoreline change prediction as well as reveal model limitations, facilitate improvements, and offer insights for advancing shoreline-prediction capabilities.

New South Wales↗

USGS2025

Are equilibrium shoreline models just convolutions?

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.

JGR Earth Surface↗

USGS2025

Waterline responses to climate forcing along the North American West Coast

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.

North American West Coast↗

USGS2025

Observations reveal changing coastal storm extremes around the United States

Understanding extreme storm surge events that threaten low-lying coastal communities is key to effective flood mitigation/adaptation measures. However, observational estimates are sparse and highly uncertain along most coastal regions with a lack of observational evidence about long-term underlying trends and their contribution to overall extreme sea-level changes. Here, using a spatiotemporal Bayesian hierarchical framework, we analyse US tide gauge record for 1950–2020 and find that observational estimates have underestimated likelihoods of storm surge extremes at 85% of tide gauge sites nationwide. Additionally, and contrary to prevailing beliefs, storm surge extremes show spatially coherent trends along many widespread coastal areas, providing evidence of changing coastal storm intensity in the historical monitoring period. Several hotspots exist with regionally significant storm surge trends that are comparable to trends in mean sea-level rise and its key components. Our findings challenge traditional coastal design/planning practices that rely on estimates from discrete observations and assume stationarity in surge extremes.

Nature Climate Change↗

USGS2025

Evaluating five shoreline change models against 40 years of field survey data at an embayed sandy beach

Robust and reliable models are needed to understand how coastlines will evolve over the coming decades, driven by both natural variability and climate change. This study evaluated how accurately five popular ‘reduced-complexity’ models replicate multi-decadal shoreline change at Narrabeen-Collaroy Beach, a sandy embayment in Sydney, Australia. Measured shoreline positions derived from approximately monthly field surveys were used for 20-year calibration and 20-year validation periods. The models performed similarly on average but with large variability between transects. The set-up of several models was modified to compensate for their sensitivity to imperfect input wave data, and further site-specific improvements were identified. Capturing interannual to decadal-scale variability in cross-shore and longshore dynamics at this site was challenging for all five models. Models appeared to aggregate key processes at this timescale into parameter values rather than representing them directly. This suggests time-varying parameters or changes to model structure may be necessary for decadal-scale simulations.

Coastal Engineering↗

USGS2025

Wave driven cross shore and alongshore transport reveal more extreme projections of shoreline change in island environments

Coastal erosion, intensified by sea level rise, poses significant threats to coastal communities in Hawaiʻi and similar island communities. This study projects long-term shoreline change on the Hawaiian Island of O‘ahu using the data-assimilated CoSMoS-COAST shoreline change model. CoSMoS-COAST models four key shoreline processes: (1) Alongshore transport, (2) Recession due to sea level rise, (3) Cross-shore transport due to waves, and (4) Residual processes represented by a linear trend term. This study marks the first application of CoSMoS-COAST for an oceanic equatorial island with narrow beaches and a dynamic wave climate. The model is informed with a novel combination of shoreline data derived from high-resolution imagery from Planet, Sentinel-2, and Landsat satellites, wave-climate hindcasts specific to Hawai‘i, and regional beach-slope surveys. On a dynamic northern Oʻahu beach, the model achieved a root mean square error of 9.4 m between observations and model output. CoSMoS-COAST predicts that 81% of O‘ahu’s sandy beach coastline could experience beach loss by 2100; with 39.8% of this loss happening by 2030. This represents an increase, 43.3%, in net landward shoreline change compared to previous erosion forecasts, for 0.3 m of sea level rise (2050). Additionally, dynamic processes such as cross-shore equilibrium processes and alongshore sediment transport, play a large contribution to gross shoreline change within the next decade, particularly on O ‘ahu’s north and west shores. In the long term, we find that recession due to sea level rise and residual processes dominate, but dynamic, wave-driven processes (longshore and cross-shore transport) still account for 34% of shoreline change between present and 2100. We assert dynamic, wave-driven processes are a crucial addition for accurate modeling of island sandy beach environments. These findings have implications for O‘ahu’s coastal planning and development, suggesting updates to shoreline policies that rely upon erosion forecasting, and highlights the importance of incorporating wave and alongshore transport in erosion models for other Pacific islands.

Hawaii↗

USGS2025

Shoreline seasonality of California’s beaches

We report on remote sensing techniques developed to characterize seasonal shoreline cycles from satellite-derived shoreline measurements. These techniques are applied to 22-yr of shoreline measurements for over 777 km of beach along California's 1,700-km coast, for which the general understanding is that shorelines exhibit winter-narrow and summer-recovery seasonality. We find that approximately 90% of beach transects exhibit significant and recurring seasonal cycles in the shoreline position. Seasonal shoreline excursions are twice as large in northern and central California (17.5–32.2 m) than southern California (7.3–15.9 m; interquartile ranges). Clustering analyses were effective at characterizing the temporal patterns of the seasonality, revealing that ∼459 km of beach (59%) exhibit winter-narrow conditions, whereas ∼189 km (24%) and ∼50 km (6.4%) exhibit spring-narrow and summer-narrow conditions, respectively. These spring- and summer-narrow conditions are most common in southern California, where they represent over half of the total length of beach shoreline. Multivariate analyses reveal that wave climate and geomorphic setting are significantly related to the magnitude and timing of shoreline seasonal cycles. Combinations of these variables explain 44% of the seasonality variance of the complete data set and 85% of the variance for a subset of 93 long (>1 km) continuous beaches. We conclude that diversity in waves and geomorphic setting along California cause a broad range of seasonal patterns in the shoreline. Combined, this indicates that the overly generalized “winter-narrow/summer-recovery” conventions for California beaches are not expressed universally and that shoreline seasonality is far more diverse than these simple canonical rules.

California↗

USGS2024

Projections of multiple climate-related coastal hazards for the US Southeast Atlantic

Faced with accelerating sea level rise and changing ocean storm conditions, coastal communities require comprehensive assessments of climate-driven hazard impacts to inform adaptation measures. Previous studies have focused on flooding but rarely on other climate-related coastal hazards, such as subsidence, beach erosion and groundwater. Here, we project societal exposure to multiple hazards along the Southeast Atlantic coast of the United States. Assuming 1 m of sea level rise, more than 70% of the coastal residents and US \ $ 1 trillion in property are in areas projected to experience shallow and emerging groundwater, 15 times higher than daily flooding. Storms increase flooding exposure by an order of magnitude over daily flooding, which could impact up to ~50% of all coastal residents and US \ $ 770 billion in property value. The loss of up to ~80% of present-day beaches and high subsidence rates that currently affect over 1 million residents will exacerbate flooding and groundwater hazard risks.

Florida, Georgia, North Carolina, South Carolina, ↗

USGS2024

Scalable, data-assimilated models predict large-scale shoreline response to waves and sea-level rise

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.

North Carolina↗

USGS2024

The projected exposure and response of a natural barrier island system to climate-driven coastal hazards

Accelerating sea level rise (SLR) and changing storm patterns will increasingly expose barrier islands to coastal hazards, including flooding, erosion, and rising groundwater tables. We assess the exposure of Cape Lookout National Seashore, a barrier island system in North Carolina (USA), to projected SLR and storm hazards over the twenty-first century. We estimate that with 0.5 m of SLR, 47% of current subaerial barrier island area would be flooded daily, and the 1-year return period storm would flood 74%. For 20-year return period storms, over 85% is projected to be flooded for any SLR. The modelled groundwater table is already shallow (< 2 m deep), and while projected to shoal to the land surface with SLR, marine flooding is projected to overtake areas with emergent groundwater. Projected shoreline retreat reaches an average of 178 m with 1 m of SLR and no interventions, which is over 60% of the current island width at narrower locations. Compounding these hazards is subsidence, with one-third of the study area currently lowering at > 2 mm/yr. Our results demonstrate the difficulty of managing natural barrier systems such as those managed by federal park systems tasked with maintaining natural ecosystems and protecting cultural resources.

North Carolina↗