Geology topics

Sean Vitousek

Publications and source records attributed to Sean Vitousek.

At least 37 records · Page 2Linked to original sources

USGS2024

Climate controls on longshore sediment transport and coastal morphology adjacent to engineered inlets

Coastal jetties are commonly used throughout the world to stabilize channels and improve navigation through inlets. These engineered structures form artificial boundaries to littoral cells by reducing wave-driven longshore sediment transport across inlet entrances. Consequently, beaches adjacent to engineered inlets are subject to large gradients in longshore transport rates and are highly sensitive to changes in wave climate. Here, we quantify annual beach and nearshore sediment volume changes over a 9-yr time period along 80 km of wave-dominated coastlines in the U.S. Pacific Northwest. Beach and nearshore monitoring during the study period (2014-2023) reveal spatially coherent, multi-annual patterns of erosion and deposition on opposing sides of two engineered inlets, indicating a regional reversal of longshore-transport direction. A numerical wave model coupled with a longshore transport predictor was calibrated and validated to explore the causes for the observed spatial and temporal patterns of erosion and deposition adjacent to the inlets. The model results indicate that subtle but important changes in wave direction on seasonal to multi-annual time scales were responsible for the reversal in the net longshore sediment transport direction and opposing patterns of morphology change. Changes in longshore transport direction coincided with a reversal in the Pacific Decadal Oscillation (PDO) climate index, suggesting large-scale, multi-decadal climate variability may influence patterns of waves and sediment dynamics at other sites throughout the Pacific basin.

Oregon, Washington↗

USGS2023

Monitoring interdecadal coastal change along dissipative beaches via satellite imagery at regional scale

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.

Oregon, Washington↗

USGS2023

Benchmarking satellite-derived shoreline mapping algorithms

Satellite remote sensing is becoming a widely used monitoring technique in coastal sciences. Yet, no benchmarking studies exist that compare the performance of popular satellite-derived shoreline mapping algorithms against standardized sets of inputs and validation data. Here we present a new benchmarking framework to evaluate the accuracy of shoreline change observations extracted from publicly available satellite imagery (Landsat and Sentinel-2). Accuracy and precision of five established shoreline mapping algorithms are evaluated at four sandy beaches with varying geologic and oceanographic conditions. Comparisons against long-term in situ beach surveys reveal that all algorithms provide horizontal accuracy on the order of 10 m at microtidal sites. However, accuracy deteriorates as the tidal range increases, to more than 20 m for a high-energy macrotidal beach (Truc Vert, France) with complex foreshore morphology. The goal of this open-source, collaborative benchmarking framework is to identify areas of improvement for present algorithms, while providing a stepping stone for testing future developments, and ensuring reproducibility of methods across various research groups and applications.

Communications Earth & Environment↗

USGS2023

A model integrating satellite-derived shoreline observations for predicting fine-scale shoreline response to waves and sea-level rise across large coastal regions

Satellite-derived shoreline observations combined with dynamic shoreline models enable fine-scale predictions of coastal change across large spatiotemporal scales. Here, we present a satellite-data-assimilated, “littoral-cell”-based, ensemble Kalman-filter shoreline model to predict coastal change and uncertainty due to waves, sea-level rise (SLR), and other natural and anthropogenic processes. We apply the developed ensemble model to the entire California coastline (approximately 1,760 km), much of which is sparsely monitored with traditional survey methods (e.g., Lidar/GPS). Water-level-corrected, satellite-derived shoreline observations (obtained from the CoastSat toolbox) offer a nearly unbiased representation of in situ surveyed shorelines (e.g., mean sea-level elevation contours) at Ocean Beach, San Francisco. We demonstrate that model calibration with satellite observations during a 20-year hindcast period (1995–2015) provides nearly equivalent model forecast accuracy during a validation period (2015–2020) compared to model calibration with monthly in situ observations at Ocean Beach. When comparing model-predicted shoreline positions to satellite-derived observations, the model achieves an accuracy of <10 m RMSE for nearly half of the entire California coastline for the validation period. The calibrated/validated model is then applied for multi-decadal simulations of shoreline change due to projected wave and sea-level conditions, while holding the model parameters fixed. By 2100, the model estimates that 24%–75% of California's beaches may become completely eroded due to SLR scenarios of 1.0–3.0 m, respectively. The satellite-data-assimilated modeling system presented here is generally applicable to a variety of coastal settings around the world owing to the global coverage of satellite imagery.

JGR Earth Surface↗

USGS2023

Multiscale assessment of shoreline evolution in the US Pacific Northwest via a process-based model

Prediction of shoreline evolution in coastal environments is critical to aid adaptation strategy planning for coastal communities. To perform reliable predictions, process-based shoreline change models have recently gained popularity in many applications. The study region here, Tillamook County, Oregon, on the US Pacific Northwest coast, has recently been experiencing elevated shoreline erosion rates. The inherent uncertainties driving coastal change, e.g., sea-level rise and changing patterns of storminess, emphasize the need for robust shoreline evolution predictions in this region. To this end, we applied CoSMoS-COAST, an ensemble data-assimilated shoreline model that simulates short- and long-term shoreline change processes. We calibrated and validated the model using the hindcasted wave time series and observed shoreline positions and found a strong correlation between the number of observed shoreline positions and the model’s hindcasting skill. Moreover, results revealed an alongshore close-to-uniform shoreline change rate in the past several years, mainly driven by short-term, wave-driven processes. So far CoSMoS-COAST has performed satisfactorily with the spatially sparse supply of quality historical shoreline positions in our application. Moreover, this model resolves various components (e.g., short- and long-term processes) in the shoreline change sufficiently. This study represents a starting point in the long-term projection of shoreline evolution throughout the entire PNW (Oregon and Washington) coastline since Tillamook County features the majority of the coastal settings present in other coastal regions in the PNW.

Oregon↗

USGS2023

Earth science looks to outer space

Satellite data are revolutionizing coastal science. A study revealing how the El Niño/Southern Oscillation impacts coastal erosion around the Pacific Rim shows what is possible.

Nature Geoscience↗

USGS2023

Flushing time variability in a short, low-inflow estuary

Flushing time, the time scale for exchange and mixing between embayed and oceanic waters in an estuary, plays an integral role in determining water quality and aquatic ecosystem health. Here, we investigated the spatiotemporal variability of flushing times throughout Morro Bay, a short, low-inflow estuary (LIE) on the California coast, using a calibrated and validated hydrodynamic model (Delft3D). Morro Bay has historically supported an extensive eelgrass ( Zostera marina ) habitat, which declined substantially from 139 to 5.4 ha during 2007–2017. Eelgrass decline motivated the current research into the role of changing bed roughness and oceanic drivers (i.e., tide and sea-level rise) on estuarine hydrodynamics and flushing times. We found that tidal variability exerts the strongest control on flushing times compared to other effects, i.e., bed roughness or sea-level rise. Additionally, we found that increasing sea level and decreasing bed roughness (associated with declining seagrass coverage) yielded higher rates of mixing (lower flushing times). We detected a strong correspondence between areas having shorter flushing times (e.g., near the estuary mouth) and areas occupied by resilient eelgrass populations in Morro Bay. Our findings further indicated that flushing times in short LIEs are particularly sensitive to several factors (e.g., bed roughness, sea level) that are susceptible to anthropogenic disturbance and future climate change.

California↗

USGS2023

Advances in morphodynamic modeling of coastal barriers: A review

As scientific understanding of barrier morphodynamics has improved, so has the ability to reproduce observed phenomena and predict future barrier states using mathematical models. To use existing models effectively and improve them, it is important to understand the current state of morphodynamic modeling and the progress that has been made in the field. This manuscript offers a review of the literature regarding advancements in morphodynamic modeling of coastal barrier systems and summarizes current modeling abilities and limitations. Broadly, this review covers both event-scale and long-term morphodynamics. Each of these sections begins with an overview of commonly modeled phenomena and processes, followed by a review of modeling developments. After summarizing the advancements toward the stated modeling goals, we identify research gaps and suggestions for future research under the broad categories of improving our abilities to acquire and access data, furthering our scientific understanding of relevant processes, and advancing our modeling frameworks and approaches.

Virginia↗

USGS2023

Understanding uncertainties in contemporary and future extreme wave events for broad-scale impact and adaptation planning

Understanding uncertainties in extreme wind-wave events is essential for offshore/coastal risk and adaptation estimates. Despite this, uncertainties in contemporary extreme wave events have not been assessed, and projections are still limited. Here, we quantify, at global scale, the uncertainties in contemporary extreme wave estimates across an ensemble of widely used global wave reanalyses/hindcasts supported by observations. We find that contemporary uncertainties in 50-year return period wave heights ( H s 50 "> H 50 s 50 ) reach (on average) ~2.5 m in regions adjacent to coastlines and are primarily driven by atmospheric forcing. Furthermore, we show that uncertainties in contemporary H s 50 "> H 50 s 50 estimates dominate projected 21st-century changes in H s 50 "> H 50 s 50 across ~80% of global ocean and coastlines. When translated into broad-scale coastal risk analysis, these uncertainties are comparable to those from storm surges and projected sea level rise. Thus, uncertainties in contemporary extreme wave events need to be combined with those of projections to fully assess potential impacts.

Science Advances↗

USGS2022

The future of coastal monitoring through satellite remote sensing

Satellite remote sensing is transforming coastal science from a “data-poor” field into a “data-rich” field. Sandy beaches are dynamic landscapes that change in response to long-term pressures, short-term pulses, and anthropogenic interventions. Until recently, the rate and breadth of beach change have outpaced our ability to monitor those changes, due to the spatiotemporal limitations of our observational capacity. Over the past several decades, only a handful of beaches worldwide have been regularly monitored with accurate yet expensive in situ surveys. The long-term coastal-change data of these few well-monitored beaches have led to in-depth understanding of many site-specific coastal processes. However, because the best-monitored beaches are not representative of all beaches, much remains unknown about the processes and fate of the other >99% of unmonitored beaches worldwide. The fleet of Earth-observing satellites has enabled multiscale monitoring of beaches, for the very first time, by providing imagery with global coverage and up to daily frequency. The long-standing and ever-expanding archive of satellite imagery will enable coastal scientists to investigate coastal change at sites vulnerable to future sea-level rise, that is, (almost) everywhere. In the past decade, our capability to observe coastal change from space has grown substantially with computing and algorithmic power. Yet, further advances are needed in automating monitoring using machine learning, deep learning, and computer vision to fully leverage this massive treasure trove of data. Extensive monitoring and investigation of the causes and effects of coastal change at the requisite spatiotemporal scales will provide coastal managers with additional, valuable information to evaluate problems and solutions, addressing the potential for widespread beach loss due to accelerated sea-level rise, development, and reduced sediment supply. Monitoring from Earth-observing satellites is currently the only means of providing seamless data with high spatiotemporal resolution at the global scale of the impending impacts of climate change on coastal systems.

Cambridge Prisms: Coastal Futures↗

USGS2022

Fire (plus) flood (equals) beach: Coastal response to an exceptional river sediment discharge event

Wildfire and post-fire rainfall have resounding effects on hillslope processes and sediment yields of mountainous landscapes. Yet, it remains unclear how fire–flood sequences influence downstream coastal littoral systems. It is timely to examine terrestrial–coastal connections because climate change is increasing the frequency, size, and intensity of wildfires, altering precipitation rates, and accelerating sea-level rise; and these factors can be understood as contrasting accretionary and erosive agents for coastal systems. Here we provide new satellite-derived shoreline measurements of Big Sur, California and show how river sediment discharge significantly influenced shoreline positions during the past several decades. A 2016 wildfire followed by record precipitation increased sediment discharge in the Big Sur River and resulted in almost half of the total river sediment load of the past 50 years (~ 2.2 of ~ 4.8 Mt). Roughly 30% of this river sediment was inferred to be littoral-grade sand and was incorporated into the littoral cell, causing the widest beaches in the 37-year satellite record and spreading downcoast over timescales of years. Hence, the impact of fire–flood events on coastal sediment budgets may be substantial, and these impacts may increase with time considering projected intensification of wildfires and extreme rain events under global warming.

California↗

USGS2021

Reinterpreting the Bruun Rule in the context of equilibrium shoreline models

Long-term (>decades) coastal recession due to sea-level rise (SLR) has been estimated using the Bruun Rule for nearly six decades. Equilibrium-based shoreline models have been shown to skillfully predict short-term wave-driven shoreline change on time scales of hours to decades. Both the Bruun Rule and equilibrium shoreline models rely on the equilibrium beach theory, which states that the beach profile shape equilibrates with its local wave and sea-level conditions. Integrating these two models into a unified framework can improve our understanding and predictive skill of future shoreline behavior. However, given that both models account for wave action, but over different time scales, a critical re-examination of the SLR-driven recession process is needed. We present a novel physical interpretation of the beach response to sea-level rise, identifying two main contributing processes: passive flooding and increased wave-driven erosion efficiency. Using this new concept, we analyze the integration of SLR-driven recession into equilibrium shoreline models and, with an idealized test case, show that the physical mechanisms underpinning the Bruun Rule are explicitly described within our integrated model. Finally, we discuss the possible advantages of integrating SLR-driven recession models within equilibrium-based models with dynamic feedbacks and the broader implications for coupling with hybrid shoreline models.

Journal of Marine Science and Engineering↗

USGS2021

Global-scale changes to extreme ocean wave events due to anthropogenic warming

Extreme surface ocean waves are often primary drivers of coastal flooding and erosion over various time scales. Hence, understanding future changes in extreme wave events owing to global warming is of socio-economic and environmental significance. However, our current knowledge of potential changes in high-frequency (defined here as having return periods of less than 1 year) extreme wave events are largely unknown, despite being strongly linked to coastal hazards across time scales relevant to coastal management. Here, we present global climate-modeling evidence, based on the most comprehensive multi-method, multi-model wave ensemble, of projected changes in a core set of extreme wave indices describing high-frequency, extra-tropical storm-driven waves. We find changes in high-frequency extreme wave events of up to ∼50%–100% under RCP8.5 high-emission scenario; which is nearly double the expected changes for RCP4.5 scenario, when globally integrated. The projected changes exhibit strong inter-hemispheric asymmetry, with strong increases in extreme wave activity across the tropics and high latitudes of the Southern Hemisphere region, and a widespread decrease across most of the Northern Hemisphere. We find that the patterns of projected increase across these extreme wave events over the Southern Hemisphere region resemble their historical response to the positive anomaly of the Southern Annular Mode. Our findings highlight that many countries with low-adaptive capacity are likely to face increasing exposure to much more frequent extreme wave events in the future.

Environmental Research Letters↗

USGS2021

The application of ensemble wave forcing to quantify uncertainty of shoreline change predictions

Reliable predictions and accompanying uncertainty estimates of coastal evolution on decadal to centennial time scales are increasingly sought. So far, most coastal change projections rely on a single, deterministic realization of the unknown future wave climate, often derived from a global climate model. Yet, deterministic projections do not account for the stochastic nature of future wave conditions across a variety of temporal scales (e.g., daily, weekly, seasonally, and interannually). Here, we present an ensemble Kalman filter shoreline change model to predict coastal erosion and uncertainty due to waves at a variety of time scales. We compare shoreline change projections, simulated with and without ensemble wave forcing conditions by applying ensemble wave time series produced by a computationally efficient statistical downscaling method. We demonstrate a sizable (site-dependent) increase in model uncertainty compared with the unrealistic case of model projections based on a single, deterministic realization (e.g., a single time series) of the wave forcing. We support model-derived uncertainty estimates with a novel mathematical analysis of ensembles of idealized process models. Here, the developed ensemble modeling approach is applied to a well-monitored beach in Tairua, New Zealand. However, the model and uncertainty quantification techniques derived here are generally applicable to a variety of coastal settings around the world.

JGR Earth Surface↗

USGS2020

Sediment connectivity: A framework for analyzing coastal sediment transport pathways

Connectivity provides a framework for analyzing coastal sediment transport pathways, building on conceptual advances in graph theory from other scientific disciplines. Connectivity schematizes sediment pathways as a directed graph (i.e., a set of nodes and links). This study presents a novel application of graph theory and connectivity metrics like modularity and centrality to coastal sediment dynamics, exemplified here using Ameland Inlet in the Netherlands. We divide the study site into geomorphic cells (i.e., nodes) and then quantify sediment transport between these cells (i.e., links) using a numerical model. The system of cells and fluxes between them is then schematized in a network described by an adjacency matrix. Network metrics like link density, asymmetry, and modularity quantify system‐wide connectivity. The degree, strength, and centrality of individual nodes identify key locations and pathways throughout the system. For instance, these metrics indicate that under strictly tidal forcing, sand originating near shore predominantly bypasses Ameland Inlet via the inlet channels, whereas sand on the deeper foreshore mainly bypasses the inlet via the outer delta shoals. Connectivity analysis can also inform practical management decisions about where to place sand nourishments, the fate of nourishment sand, or how to monitor locations vulnerable to perturbations. There are still open challenges associated with quantifying connectivity at varying space and time scales and the development of connectivity metrics specific to coastal systems. Nonetheless, connectivity provides a promising technique for predicting the response of our coasts to climate change and the human adaptations it provokes.

Ameland Inlet↗

USGS2020

Sea-level rise exponentially increases coastal flood frequency

Sea-level rise will radically redefine the coastline of the 21 st century. For many coastal regions, projections of global sea-level rise by the year 2100 (e.g., 0.5–2 meters) are comparable in magnitude to today’s extreme but short-lived increases in water level due to storms. Thus, the 21 st century will see significant changes to coastal flooding regimes (where present-day, extreme-but-rare events become common), which poses a major risk to the safety and sustainability of coastal communities worldwide. So far, estimates of future coastal flooding frequency focus on endpoint scenarios, such as the increase in flooding by 2050 or 2100. Here, we investigate the continuous shift in coastal flooding regimes by quantifying continuous rates of increase in the occurrence of extreme water-level events due to sea-level rise. We find that the odds of exceeding critical water-level thresholds increases exponentially with sea-level rise, meaning that fixed amounts of sea-level rise of only ~1–10 cm in areas with a narrow range of present-day extreme water levels can double the odds of flooding. Combining these growth rates with established sea-level rise projections, we find that the odds of extreme flooding double approximately every 5 years into the future. Further, we find that the present-day 50-year extreme water level (i.e., 2% annual chance of exceedance, based on historical records) will be exceeded annually before 2050 for most (i.e., 70%) of the coastal regions in the United States. Looking even farther into the future, the present-day 50-year extreme water level will be exceeded almost every day during peak tide (i.e., daily mean higher high water) before the end of the 21 st century for 90% of the U.S. coast. Our findings underscore the need for immediate planning and adaptation to mitigate the societal impacts of future flooding.

Scientific Reports↗

USGS2020

Steps to develop early warning systems and future scenarios of wave-driven flooding along coral reef-lined coasts

Tropical coral reef-lined coasts are exposed to storm wave-driven flooding. In the future, flood events during storms are expected to occur more frequently and to be more severe due to sea-level rise, changes in wind and weather patterns, and the deterioration of coral reefs. Hence, disaster managers and coastal planners are in urgent need of decision-support tools. In the short-term, these tools can be applied in Early Warning Systems (EWS) that can help to prepare for and respond to impending storm-driven flood events. In the long-term, future scenarios of flooding events enable coastal communities and managers to plan and implement adequate risk-reduction strategies. Modeling tools that are used in currently available coastal flood EWS and future scenarios have been developed for open-coast sandy shorelines, which have only limited applicability for coral reef-lined shorelines. The tools need to be able to predict local sea levels, offshore waves, as well as their nearshore transformation over the reefs, and translate this information to onshore flood levels. In addition, future scenarios require long-term projections of coral reef growth, reef composition, and shoreline change. To address these challenges, we have formed the UFORiC (Understanding Flooding of Reef-lined Coasts) working group that outlines its perspectives on data and model requirements to develop EWS for storms and scenarios specific to coral reef-lined coastlines. It reviews the state-of-the-art methods that can currently be incorporated in such systems and provides an outlook on future improvements as new data sources and enhanced methods become available.

Frontiers in Marine Science↗

USGS2020

Blind testing of shoreline evolution models

Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer time-scales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for Tairua beach, New Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. In general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999–2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014–2017), both approaches showed a decrease in models’ capability to predict the shoreline position. This was more evident for some of the machine learning algorithms. A model ensemble performed better than individual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models.

Scientific Reports↗