Geology topics

Patrick L. Barnard

Publications and source records attributed to Patrick L. Barnard.

At least 37 records · Page 2Linked to original sources

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

Relative contributions of water-level components to extreme water levels along the US Southeast Atlantic Coast from a regional-scale water-level hindcast

A 38-year hindcast water level product is developed for the U.S. Southeast Atlantic coastline from the entrance of Chesapeake Bay to the southeast tip of Florida. The water level modelling framework utilized in this study combines a global-scale hydrodynamic model (Global Tide and Surge Model, GTSM-ERA5), a novel ensemble-based tide model, a parameterized wave setup model, and statistical corrections applied to improve modelled water level components. Corrected water level data are found to be skillful, with an RMSE of 13 cm, when compared to observed water level measurement at tide gauge locations. The largest errors in the hindcast are location-based and typically found in the tidal component of the model. Extreme water levels across the region are driven by compound events, in this case referring to combined surge, tide, and wave forcing. However, the relative importance of water level components varies spatially, such that tides are found to be more important in the center of the study region, non-tidal residual water levels to the north, and wave setup in the north and south. Hurricanes drive the most extreme water level events within the study area, but non-hurricane events define the low to mid-level recurrence interval water level events. This study presents a robust analysis of the complex oceanographic factors that drive coastal flood events. This dataset will support a variety of critical coastal research goals including research related to coastal hazards, landscape change, and community risk assessments.

Florida, Georgia, North Carolina, South Carolina, ↗

USGS2023

Rapid modeling of compound flooding across broad coastal regions and the necessity to include rainfall driven processes: A case study of Hurricane Florence (2018)

In this work, we show that large-scale compound flood models developed for North and South Carolina, USA, can skillfully simulate multiple drivers of coastal flooding as confirmed by measurements collected during Hurricane Florence (2018). Besides the accuracy of representing observed water levels, the importance of individual processes was investigated. We demonstrate that across the area of interest, it is necessary to include marine, pluvial, and fluvial forcing and the processes of wind stress and infiltration to correctly model water levels along the coast and further inland. This work highlights the need to include these processes in modeling coastal compound flooding. By using high-resolution topo-bathymetry that is incorporated via subgrid derived tables in the Super-Fast INundation of CoastS (SFINCS) model, we improved the skill of the model at efficiently simulating flooding across large-scale domains with locally relevant results.

North Carolina, South Carolina↗

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

The influence of vegetated marshes on wave transformation in sheltered estuaries

Assessing the influence of marshes on mitigating flooding along estuarine shorelines under the pressures of sea level rise requires understanding wave transformation across the marsh. A numerical model was applied to investigate how vegetated marshes influence wave transformation. XBeach non-hydrostatic (XB-NH) was calibrated and validated with high frequency pressure data from the marsh at China Camp State Park in San Pablo Bay, California (USA). The model was used to examine how marsh and hydrodynamic characteristics change the potential for marshes to mitigate wave driven flooding. Model results demonstrate that hydrodynamics, vegetation, and marsh width influence wave transformation most, while marsh morphology parameters such as elevation and slope had least effect. Results suggest that in the range of settings explored here (incident wave heights ranging from 0.5 to 3 m and water levels ranging from current mean higher high water to 3 m above current mean higher high water), in comparison to wave propagation over an unvegetated mudflat, marsh vegetation reduces runup by a median of 40 cm and wave height by a median of 35 cm. Results illustrate how marshes can be strategically utilized to provide flood reduction benefits.

Coastal Engineering↗

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

An integrated approach for physical, economic, and demographic evaluation of coastal flood hazard adaptation in Santa Monica Bay, California

The increased risk of coastal flooding associated with climate-change driven sea level rise threatens to displace communities and cause substantial damage to infrastructure. Site-specific adaptation planning is necessary to mitigate the negative impacts of flooding on coastal residents and the built environment. Cost-benefit analyses used to evaluate coastal adaption strategies have traditionally focused on economic considerations, often overlooking potential demographic impacts that can directly influence vulnerability in coastal communities. Here, we present a transferable framework that couples hydrodynamic modeling of flooding driven by sea level rise and storm scenarios with site-specific building stock and census block-level demographic data. We assess the efficacy of multiple coastal adaptation strategies at reducing flooding, economic damages, and impacts to the local population. We apply this framework to evaluate a range of engineered, nature-based, and hybrid adaptation strategies for a portion of Santa Monica Bay, California. Overall, we find that dual approaches that provide protection along beaches using dunes or seawalls and along inlets using sluice gates perform best at reducing or eliminating flooding, damages, and population impacts. Adaptation strategies that include a sluice gate and partial or no protection along the beach are effective at reducing flooding around inlets but can exacerbate flooding elsewhere, leading to unintended impacts on residents. Our results also indicate trade-offs between economic and social risk-reduction priorities. The proposed framework allows for a comprehensive evaluation of coastal protection strategies across multiple objectives. Understanding how coastal adaptation strategies affect hydrodynamic, economic, and social factors at a local scale can enable more effective and equitable planning approaches.

California↗

USGS2022

Measuring and attributing sedimentary and geomorphic responses to modern climate change: Challenges and opportunities

Today, climate change is affecting virtually all terrestrial and nearshore settings. This commentary discusses the challenges of measuring climate-driven physical landscape responses to modern global warming: short and incomplete data records, land use and seismicity masking climatic effects, biases in data availability and resolution, and signal attenuation in sedimentary systems. We identify opportunities to learn from historical and paleo data, select especially sensitive study sites, and report null results to better characterize the extent and nuances of climate-change effects. We then discuss efforts to improve attribution practices, which will lead to better predictive capabilities. We encourage the earth-science community to prioritize scientific research on climate-driven physical landscape changes so that societies will be better prepared to manage the effects on health and safety, infrastructure, water–food–energy security, economics, and ecosystems that follow from climate-driven physical landscape change.

Earth's Future↗

USGS2022

Characterizing storm-induced coastal change hazards along the United States West Coast

Traditional methods to assess the probability of storm-induced erosion and flooding from extreme water levels have limited use along the U.S. West Coast where swell dominates erosion and storm surge is limited. This effort presents methodology to assess the probability of erosion and flooding for the U.S. West Coast from extreme total water levels (TWLs), but the approach is applicable to coastal settings worldwide. TWLs were derived from 61 years of wave and water level data at shore-perpendicular transects every 100-m along open coast shorelines. At each location, wave data from the Global Ocean Waves model were downscaled to the nearshore and used to empirically calculate wave run-up. Tides were simulated using the Oregon State University’s tidal data inversion model and non-tidal residuals were calculated from sea-surface temperature and pressure anomalies. Wave run-up was combined with still water levels to generate hourly TWL estimates and extreme TWLs for multiple return periods. Extremes were compared to onshore morphology to determine erosion hazards and define the probability of collision, overwash, and inundation.

California, Oregon, Washington↗

USGS2022

Global and regional sea level rise scenarios for the United States

This report and accompanying datasets from the U.S. Sea Level Rise and Coastal Flood Hazard Scenarios and Tools Interagency Task Force provide 1) sea level rise scenarios to 2150 by decade that include estimates of vertical land motion and 2) a set of extreme water level probabilities for various heights along the U.S. coastline. These data are available at 1-degree grids along the U.S. coastline and downscaled specifically at NOAA tide-gauge locations. Estimates of flood exposure are assessed using contemporary U.S. coastal flood-severity thresholds for current conditions (e.g., sea levels and infrastructure footprint) and for the next 30 years (out to year 2050), assuming no additional risk reduction measures are enacted. This effort builds upon the 2017 Task Force report (Sweet et al., 2017). In particular, the set of global mean sea level rise scenarios from that report are updated and downscaled with output directly from the United Nations Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6; IPCC, 2021a), through the efforts of the NASA Sea Level Change Team; updates include adjustments to the temporal trajectories and exceedance probabilities of these scenarios based upon end-of-century global temperatures. As with the 2017 report, these global mean sea level rise scenarios are regionalized for the U.S. coastline. In addition, methodology supporting the U.S. Department of Defense Regional Sea Level (DRSL) database (Hall et al., 2016) is adapted for the extreme water level dataset newly developed for this report. This report will be a key technical input for the Fifth National Climate Assessment (NCA5). These data and information are being incorporated into current and planned agency tools and services, such as NOAA’s Sea Level Rise Viewer and Inundation Dashboard, NASA’s Sea Level Change Portal, and others. Although the intent of this report is not to provide authoritative guidance or design specifications for a specific project, it is intended to help inform Federal agencies, state and local governments, and stakeholders in coastal communities about current and future sea level rise to help contextualize its effects for decision-making purposes.

Technical Report↗

USGS2021

Projecting climate dependent coastal flood risk with a hybrid statistical dynamical model

Numerical models for tides, storm surge, and wave runup have demonstrated ability to accurately define spatially varying flood surfaces. However these models are typically too computationally expensive to dynamically simulate the full parameter space of future oceanographic, atmospheric, and hydrologic conditions that will constructively compound in the nearshore to cause both extreme event and nuisance flooding during the 21st century. A surrogate modeling framework of waves, winds, and tides is developed in this study to efficiently predict spatially varying nearshore and estuarine water levels contingent on any combination of offshore forcing conditions. The surrogate models are coupled with a time-dependent stochastic climate emulator that provides efficient downscaling for hypothetical iterations of offshore conditions. Together, the hybrid statistical-dynamical framework can assess present day and future coastal flood risk, including the chronological characteristics of individual flood and wave-induced dune overtopping events and their changes into the future. The framework is demonstrated at Naval Base Coronado in San Diego, CA, utilizing the regional Coastal Storm Modeling System (CoSMoS; composed of Delft3D and XBeach) as the dynamic simulator and Gaussian process regression as the surrogate modeling tool. Validation of the framework uses both in-situ tide gauge observations within San Diego Bay, and a nearshore cross-shore array deployment of pressure sensors in the open beach surf zone. The framework reveals the relative influence of large-scale climate variability on future coastal flood resilience metrics relevant to the management of an open coast artificial berm, as well as the stochastic nature of future total water levels.

California↗

USGS2021

Changes in liquefaction severity in the San Francisco Bay Area with sea-level rise

This paper studies the impacts of sea-level rise on liquefaction triggering and severity around the San Francisco Bay Area, California, for the M 7.0 “HayWired” earthquake scenario along the Hayward fault. This work emerged from stakeholder engagement for the US Geological Survey releases of the HayWired earthquake scenario and the Coastal Storm Modeling System projects, in which local planners and engineers asked where, why, and by how much liquefaction hazards may change due to sea-level rise in the future. We assess the impacts of sea-level rise on liquefaction by computing changes in liquefaction potential index (LPI) for over 400 cone penetration test (CPT) soundings around the San Francisco Bay for groundwater table models developed for current and increased sea levels of up to 5 m. For the M 7.0 HayWired earthquake scenario, we find that while the majority of sites are insensitive to sea-level changes of less than 1 m, some sites are highly sensitive to small changes in water levels. We then repeat these analyses for a uniform shaking scenario to isolate the effects of sea-level rise and we find similar patterns of change. For both earthquake scenarios, modest changes in overall LPI are expected for increases in sea level, but individual sites may see significant increases in liquefaction likelihood and severity.

Conference Paper↗

USGS2021

Multiple climate change-driven tipping points for coastal systems

As the climate evolves over the next century, the interaction of accelerating sea level rise (SLR) and storms, combined with confining development and infrastructure, will place greater stresses on physical, ecological, and human systems along the ocean-land margin. Many of these valued coastal systems could reach “tipping points,” at which hazard exposure substantially increases and threatens the present-day form, function, and viability of communities, infrastructure, and ecosystems. Determining the timing and nature of these tipping points is essential for effective climate adaptation planning. Here we present a multidisciplinary case study from Santa Barbara, California (USA), to identify potential climate change-related tipping points for various coastal systems. This study integrates numerical and statistical models of the climate, ocean water levels, beach and cliff evolution, and two soft sediment ecosystems, sandy beaches and tidal wetlands. We find that tipping points for beaches and wetlands could be reached with just 0.25 m or less of SLR (~ 2050), with > 50% subsequent habitat loss that would degrade overall biodiversity and ecosystem function. In contrast, the largest projected changes in socioeconomic exposure to flooding for five communities in this region are not anticipated until SLR exceeds 0.75 m for daily flooding and 1.5 m for storm-driven flooding (~ 2100 or later). These changes are less acute relative to community totals and do not qualify as tipping points given the adaptive capacity of communities. Nonetheless, the natural and human built systems are interconnected such that the loss of natural system function could negatively impact the quality of life of residents and disrupt the local economy, resulting in indirect socioeconomic impacts long before built infrastructure is directly impacted by flooding.

California↗

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↗

USGS2021

Twenty-first-century projections of shoreline change along inlet-interrupted coastlines

Sandy coastlines adjacent to tidal inlets are highly dynamic and widespread landforms, where large changes are expected due to climatic and anthropogenic influences. To adequately assess these important changes, both oceanic (e.g., sea-level rise) and terrestrial (e.g., fluvial sediment supply) processes that govern the local sediment budget must be considered. Here, we present novel projections of shoreline change adjacent to 41 tidal inlets around the world, using a probabilistic, reduced complexity, system-based model that considers catchment-estuary-coastal systems in a holistic way. Under the RCP 8.5 scenario, retreat dominates (90% of cases) over the twenty-first century, with projections exceeding 100 m of retreat in two-thirds of cases. However, the remaining systems are projected to accrete under the same scenario, reflecting fluvial influence. This diverse range of response compared to earlier methods implies that erosion hazards at inlet-interrupted coasts have been inadequately characterised to date. The methods used here need to be applied widely to support evidence-based coastal adaptation.

Nature--Scientific Reports↗

USGS2021

Assessment of flood forecast products for a coupled tributary-Coastal model

Compound flooding, resulting from a combination of riverine and coastal processes, is a complex but important hazard to resolve along urbanized shorelines in the vicinity of river mouths. However, inland flooding models rarely consider oceanographic conditions, and vice versa for coastal flood models. Here, we describe the development of an operational, integrated coastal-watershed flooding model to address this issue of compound flooding in a highly urbanized estuarine environment, San Francisco Bay (CA, USA), where the surrounding communities are susceptible to flooding along the bay shoreline and inland rivers and creeks that drain to the bay. The integrated tributary-coastal forecast model (Hydro-Coastal Storm Modeling System, or Hydro-CoSMoS) was developed to provide water managers and other users with flood forecast information beyond what is currently available. Results presented here are focused on the interaction of the Napa River watershed and the San Pablo Bay at the northern end of San Francisco Bay. This paper describes the modeling setup, the scenario used in a tabletop exercise (TTE), and the assessment of the various flood forecast information products. Hydro-CoSMoS successfully demonstrated the capability to provide watershed and coastal flood information at scales and locations where no such information is currently available and was also successful in showing how tributary flows could be used to inform the coastal storm model during a flooding scenario. The TTE provided valuable feedback on how to guide continued model development and to inform what model outputs and formats are most useful to end-users.

California↗

USGS2021

Drivers of extreme water levels in a large, urban, high-energy coastal estuary – A case study of the San Francisco Bay

Reliable and long-term hindcast data of water levels are essential in quantifying return period and values of extreme water levels. In order to inform design decisions on a local flood control district level, process-based numerical modeling has proven an essential tool to provide the needed temporal and spatial coverage for different extreme value analysis methods. To determine the importance of different physical processes to the extreme water levels we developed a process-based numerical model (Delft3D Flexible Mesh) and applied it to simulate a large, urban, high-energy coastal estuary (the San Francisco Bay). The unstructured grid with 1D/2DH model elements, allows for efficient model simulations and therefore it was possible to simulate over 70 years between 1950 and 2019. Results show significant skill in reproducing observations for the entire modeled time period with an average root-mean-square error of 8.0 cm. A process-based modeling approach allows for the explicit in- and exclusion of different physical processes to quantify their importance to the extremes. For the 100-year still water level (SWL), tide (70%) and non-tidal residual (NTR) (25%) explain the majority of the simulated high water levels in the Bay relative to Mean Higher High Water (MHHW). However, closer to the Delta, local fluvial inflow increases in importance. For longer return periods, the importance of tide decreases and the importance of remote NTRs and fluvial inflow increases.

California↗

USGS2021

The impacts of the 2015/2016 El Niño on California's sandy beaches

The El Niño Southern Oscillation is the most dominant mode of interannual climate variability in the Pacific. The 2015/2016 El Niño event was one of the strongest of the last 145 years, resulting in anomalously high wave energy across the U.S. West Coast, and record coastal erosion for many California beaches. To better manage coastal resources, it is critical to understand the impacts of both short-term climate variability and long-term climate impacts across the varied coastal settings of California. This study is the first to quantify the coastal response for one of the strongest El Niño events in the historical record across the coast of California through the analysis of nearshore wave conditions and seasonal beach changes for 8000 shore-normal transects. Through the analysis of pre- and post- El Niño LiDAR, we find that that central and northern California experienced the most sandy beach shoreline retreat/erosion during the El Niño winter, with a mean of 45.7 m of erosion (96% of beaches) in central California, a mean of 25.5 m of erosion (89% of beaches) in northern California, and a mean of 9.7 m of erosion (79% of beaches) in southern California. These patterns are compared to LiDAR and satellite-derived long-term shoreline change rates, in which southern California and central California beaches are moderately accreting, while northern California is eroding at an average of 79 cm per year. A significant correlation is found between cumulative wave energy flux and shoreline change during the El Niño winter across the state of California. Although local beach response during the El Niño winter was highly variable, heightened erosion was observed at river mouths and on the southern side of structures impeding littoral drift, with accretion observed on the northern side of these structures. These erosional patterns, driven by a northerly wave direction anomaly, contrast those of classic El Niño events such as the 1982–.83 and 1997–98 events, where more southerly storm tracks and southerly wave directions were key factors controlling shoreline behavior, and may indicate a shift in El Niño storm patterns driven by climate change.

California↗