Geology topics

Nathaniel G. Plant

Publications and source records attributed to Nathaniel G. Plant.

At least 37 records · Page 2Linked to original sources

USGS2018

Barrier-island and estuarine-wetland physical-change assessment after Hurricane Sandy

Introduction The Nation’s eastern coast is fringed by beaches, dunes, barrier islands, wetlands, and bluffs. These natural coastal barriers provide critical benefits and services, and can mitigate the impact of storms, erosion, and sea-level rise on our coastal communities. Waves and storm surge resulting from Hurricane Sandy, which made landfall along the New Jersey coast on October 29, 2012, impacted the U.S. coastline from North Carolina to Massachusetts, including Assateague Island, Maryland and Virginia, and the Delmarva coastal system. The storm impacts included changes in topography, coastal morphology, geology, hydrology, environmental quality, and ecosystems. In the immediate aftermath of the storm, light detection and ranging (lidar) surveys from North Carolina to New York documented storm impacts to coastal barriers, providing a baseline to assess vulnerability of the reconfigured coast. The focus of much of the existing coastal change assessment is along the ocean-facing coastline; however, much of the coastline affected by Hurricane Sandy includes the estuarine-facing coastlines of barrier-island systems. Specifically, the wetland and back-barrier shorelines experienced substantial change as a result of wave action and storm surge that occurred during Hurricane Sandy (see also USGS photograph, http://coastal.er.usgs.gov/hurricanes/sandy/photo-comparisons/virginia.php ). Assessing physical shoreline and wetland change (land loss as well as land gains) can help to determine the resiliency of wetland systems that protect adjacent habitat, shorelines, and communities. To address storm impacts to wetlands, a vulnerability assessment should describe both long-term (for example, several decades) and short-term (for example, Sandy’s landfall) extent and character of the interior wetlands and the back-barrier-shoreline changes. The objective of this report is to describe several new wetland vulnerability assessments based on the detailed physical changes estimated from observations. The scope includes understanding changes caused by both short- and long-term processes using both remotely sensed and in situ observations to characterize changes to the wetland in terms of accretion/expansion and erosion/contraction. Accretion may be due to net vertical and (or) horizontal deposition, including estuarine-shoreline change due to overwash. Wetland erosion may be due to elevated waves and water levels in the estuary itself. We included additional information based on wave runup and storm-surge elevations based on models and elevation data. We then developed a predictive assessment for wetland vulnerability that describes the likelihood of changes of the estuarine shoreline and the landward extent of sand overwash driven by processes occurring on the ocean-facing shoreline. This assessment is intended to be linked to the beach and dune vulnerability assessments that have been developed previously.

Open-File Report↗

USGS2018

The influence of bed friction variability due to land cover on storm-driven barrier island morphodynamics

Variations in bed friction due to land cover type have the potential to influence morphologic change during storm events; the importance of these variations can be studied through numerical simulation and experimentation at locations with sufficient observational data to initialize realistic scenarios, evaluate model accuracy and guide interpretations. Two-dimensional in the horizontal plane (2DH) morphodynamic (XBeach) simulations were conducted to assess morphodynamic sensitivity to spatially varying bed friction at Dauphin Island, AL using hurricanes Ivan (2004) and Katrina (2005) as experimental test cases. For each storm, three bed friction scenarios were simulated: (1) a constant Chezy coefficient across land and water, (2) a constant Chezy coefficient across land and depth-dependent Chezy coefficients across water, and (3) spatially varying Chezy coefficients across land based on land use/land cover (LULC) data and depth-dependent Chezy coefficients across water. Modeled post-storm bed elevations were compared qualitatively and quantitatively with post-storm lidar data. Results showed that implementing spatially varying bed friction influenced the ability of XBeach to accurately simulate morphologic change during both storms. Accounting for frictional effects due to large-scale variations in vegetation and development reduced cross-barrier sediment transport and captured overwash and breaching more accurately. Model output from the spatially varying friction scenarios was used to examine the need for an existing sediment transport limiter, the influence of pre-storm topography and the effects of water level gradients on storm-driven morphodynamics.

Alabama↗

USGS2018

A framework for modeling scenario-based barrier island storm impacts

Methods for investigating the vulnerability of existing or proposed coastal features to storm impacts often rely on simplified parametric models or one-dimensional process-based modeling studies that focus on changes to a profile across a dune or barrier island. These simple studies tend to neglect the impacts to curvilinear or alongshore varying island planforms, influence of non-uniform nearshore hydrodynamics and sediment transport, irregular morphology of the offshore bathymetry, and impacts from low magnitude wave events (e.g. cold fronts). Presented here is a framework for simulating regionally specific, low and high magnitude scenario-based storm impacts to assess the alongshore variable vulnerabilities of a coastal feature. Storm scenarios based on historic hydrodynamic conditions were derived and simulated using the process-based morphologic evolution model XBeach. Model results show that the scenarios predicted similar patterns of erosion and overwash when compared to observed qualitative morphologic changes from recent storm events that were not included in the dataset used to build the scenarios. The framework model simulations were capable of predicting specific areas of vulnerability in the existing feature and the results illustrate how this storm vulnerability simulation framework could be used as a tool to help inform the decision-making process for scientists, engineers, and stakeholders involved in coastal zone management or restoration projects.

Chandeleur Islands↗

USGS2018

Rapid, remote assessment of Hurricane Matthew impacts using four-dimensional structure-from-motion photogrammetry

Timely assessment of coastal landforms and structures after storms is important for evaluating storm impacts, aiding emergency response and restoration, and initializing and assessing morphological models. Four-dimensional multiview photogrammetry, also known as structure from motion (4D SfM), provides a method for generating three-dimensional reconstructions of landscapes at two times (before and after events) using only photos and existing information for ground control points. Here, these techniques were applied using National Oceanic and Atmospheric Administration (NOAA)-obtained oblique aerial photos taken before (2015) and immediately after Hurricane Matthew (2016) to assess coastal changes near Matanzas, Florida. This work demonstrated that 3D digital elevation models can be constructed within 48 hours of postevent photo collection without on-site ground control measurements. One advantage of timely SfM elevation-change assessments is that they avoid confusion of storm impacts with changes that occur after the event but before LIDAR surveys can be performed. The accuracy and precision of the 4D SfM maps were assessed a posteriori using the first-available LIDAR data, which were collected more than a month after the hurricane, and 11 independent ground-truth survey points measured a week after the hurricane. Horizontal coordinates of the 4D SfM reconstruction were biased by an average of 0.79 m (0.83 m root-mean-square difference; RMSD) compared with the ground-truth points, but vertical elevations were more accurate. They were biased from the LIDAR by −0.09 to −0.25 m, with ∼0.20 m RMSD from both the LIDAR data and five ground-truth points with good vertical positioning and 0.25 m RMSD from LIDAR data along a 60-m stretch of pavement. This level of precision was sufficient to quantify geomorphological change that was often in excess of 1 m. The methodology is conducive for rapid assessment of changes along short stretches (tens of kilometers) of coast with modest resources and could be scaled up for larger regions.

Florida↗

USGS2017

Smartphone technologies and Bayesian networks to assess shorebird habitat selection

Understanding patterns of habitat selection across a species’ geographic distribution can be critical for adequately managing populations and planning for habitat loss and related threats. However, studies of habitat selection can be time consuming and expensive over broad spatial scales, and a lack of standardized monitoring targets or methods can impede the generalization of site-based studies. Our objective was to collaborate with natural resource managers to define available nesting habitat for piping plovers ( Charadrius melodus ) throughout their U.S. Atlantic coast distribution from Maine to North Carolina, with a goal of providing science that could inform habitat management in response to sea-level rise. We characterized a data collection and analysis approach as being effective if it provided low-cost collection of standardized habitat-selection data across the species’ breeding range within 1–2 nesting seasons and accurate nesting location predictions. In the method developed, >30 managers and conservation practitioners from government agencies and private organizations used a smartphone application, “iPlover,” to collect data on landcover characteristics at piping plover nest locations and random points on 83 beaches and barrier islands in 2014 and 2015. We analyzed these data with a Bayesian network that predicted the probability a specific combination of landcover variables would be associated with a nesting site. Although we focused on a shorebird, our approach can be modified for other taxa. Results showed that the Bayesian network performed well in predicting habitat availability and confirmed predicted habitat preferences across the Atlantic coast breeding range of the piping plover. We used the Bayesian network to map areas with a high probability of containing nesting habitat on the Rockaway Peninsula in New York, USA, as an example application. Our approach facilitated the collation of evidence-based information on habitat selection from many locations and sources, which can be used in management and decision-making applications.

U.S Atlantic Coast↗

USGS2017

Examples of storm impacts on barrier islands

This chapter focuses on the morphologic variability of barrier islands and on the differences in storm response. It describes different types of barrier island response to individual storms, as well as the integrated response of barrier islands to many storms. The chapter considers case study on the Chandeleur Island chain, where a decadal time series of island elevation measurements have documented a wide range of barrier island responses to storms and long-term processes that are representative of barrier island behaviour at many other locations. These islands are low elevation, extremely vulnerable to storms and exhibit a diversity of storm responses. Additionally, this location experiences a moderately high rate of relative sea-level rise, increasing its vulnerability to the combined impacts of storms and long-term erosional processes. Understanding how natural processes, including storm impacts and intervening recovery periods interact with man-made restoration processes is also broadly relevant to understand the natural and human response to future storms.

Book chapter↗

USGS2017

Correction of elevation offsets in multiple co-located lidar datasets

Introduction Topographic elevation data collected with airborne light detection and ranging (lidar) can be used to analyze short- and long-term changes to beach and dune systems. Analysis of multiple lidar datasets at Dauphin Island, Alabama, revealed systematic, island-wide elevation differences on the order of 10s of centimeters (cm) that were not attributable to real-world change and, therefore, were likely to represent systematic sampling offsets. These offsets vary between the datasets, but appear spatially consistent within a given survey. This report describes a method that was developed to identify and correct offsets between lidar datasets collected over the same site at different times so that true elevation changes over time, associated with sediment accumulation or erosion, can be analyzed.

Alabama↗

USGS2017

A methodology for modeling barrier island storm-impact scenarios

A methodology for developing a representative set of storm scenarios based on historical wave buoy and tide gauge data for a region at the Chandeleur Islands, Louisiana, was developed by the U.S. Geological Survey. The total water level was calculated for a 10-year period and analyzed against existing topographic data to identify when storm-induced wave action would affect island morphology. These events were categorized on the basis of the threshold of total water level and duration to create a set of storm scenarios that were simulated, using a high-fidelity, process-based, morphologic evolution model, on an idealized digital elevation model of the Chandeleur Islands. The simulated morphological changes resulting from these scenarios provide a range of impacts that can help coastal managers determine resiliency of proposed or existing coastal structures and identify vulnerable areas within those structures.

Louisiana↗

USGS2016

Probabilistic assessment of erosion and flooding risk in the northern Gulf of Mexico

We assess erosion and flooding risk in the northern Gulf of Mexico by identifying interdependencies among oceanographic drivers and probabilistically modeling the resulting potential for coastal change. Wave and water level observations are used to determine relationships between six hydrodynamic parameters that influence total water level and therefore erosion and flooding, through consideration of a wide range of univariate distribution functions and multivariate elliptical copulas. Using these relationships, we explore how different our interpretation of the present-day erosion/flooding risk could be if we had seen more or fewer extreme realizations of individual and combinations of parameters in the past by simulating 10,000 physically and statistically consistent sea-storm time series. We find that seasonal total water levels associated with the 100 year return period could be up to 3 m higher in summer and 0.6 m higher in winter relative to our best estimate based on the observational records. Impact hours of collision and overwash—where total water levels exceed the dune toe or dune crest elevations—could be on average 70% (collision) and 100% (overwash) larger than inferred from the observations. Our model accounts for non-stationarity in a straightforward, non-parametric way that can be applied (with little adjustments) to many other coastlines. The probabilistic model presented here, which accounts for observational uncertainty, can be applied to other coastlines where short record lengths limit the ability to identify the full range of possible wave and water level conditions that coastal mangers and planners must consider to develop sustainable management strategies.

Journal of Geophysical Research C: Oceans↗

USGS2016

Tidal hydrodynamics under future sea level rise and coastal morphology in the Northern Gulf of Mexico

This study examines the integrated influence of sea level rise (SLR) and future morphology on tidal hydrodynamics along the Northern Gulf of Mexico (NGOM) coast including seven embayments and three ecologically and economically significant estuaries. A large-domain hydrodynamic model was used to simulate astronomic tides for present and future conditions (circa 2050 and 2100). Future conditions were simulated by imposing four SLR scenarios to alter hydrodynamic boundary conditions and updating shoreline position and dune heights using a probabilistic model that is coupled to SLR. Under the highest SLR scenario, tidal amplitudes within the bays increased as much as 67% (10.0 cm) because of increases in the inlet cross-sectional area. Changes in harmonic constituent phases indicated that tidal propagation was faster in the future scenarios within most of the bays. Maximum tidal velocities increased in all of the bays, especially in Grand Bay where velocities doubled under the highest SLR scenario. In addition, the ratio of the maximum flood to maximum ebb velocity decreased in the future scenarios (i.e., currents became more ebb dominant) by as much as 26% and 39% in Weeks Bay and Apalachicola, respectively. In Grand Bay, the flood-ebb ratio increased (i.e., currents became more flood dominant) by 25% under the lower SLR scenarios, but decreased by 16% under the higher SLR as a result of the offshore barrier islands being overtopped, which altered the tidal prism. Results from this study can inform future storm surge and ecological assessments of SLR, and improve monitoring and management decisions within the NGOM.

Florida, Louisiana↗

USGS2016

Evaluation of dynamic coastal response to sea-level rise modifies inundation likelihood

Sea-level rise (SLR) poses a range of threats to natural and built environments1, 2, making assessments of SLR-induced hazards essential for informed decision making3. We develop a probabilistic model that evaluates the likelihood that an area will inundate (flood) or dynamically respond (adapt) to SLR. The broad-area applicability of the approach is demonstrated by producing 30 × 30 m resolution predictions for more than 38,000 km2 of diverse coastal landscape in the northeastern United States. Probabilistic SLR projections, coastal elevation and vertical land movement are used to estimate likely future inundation levels. Then, conditioned on future inundation levels and the current land-cover type, we evaluate the likelihood of dynamic response versus inundation. We find that nearly 70% of this coastal landscape has some capacity to respond dynamically to SLR, and we show that inundation models over-predict land likely to submerge. This approach is well suited to guiding coastal resource management decisions that weigh future SLR impacts and uncertainty against ecological targets and economic constraints.

Nature Climate Change↗

USGS2016

Decoupling processes and scales of shoreline morphodynamics

Behavior of coastal systems on time scales ranging from single storm events to years and decades is controlled by both small-scale sediment transport processes and large-scale geologic, oceanographic, and morphologic processes. Improved understanding of coastal behavior at multiple time scales is required for refining models that predict potential erosion hazards and for coastal management planning and decision-making. Here we investigate the primary controls on shoreline response along a geologically-variable barrier island on time scales resolving extreme storms and decadal variations over a period of nearly one century. An empirical orthogonal function analysis is applied to a time series of shoreline positions at Fire Island, NY to identify patterns of shoreline variance along the length of the island. We establish that there are separable patterns of shoreline behavior that represent response to oceanographic forcing as well as patterns that are not explained by this forcing. The dominant shoreline behavior occurs over large length scales in the form of alternating episodes of shoreline retreat and advance, presumably in response to storms cycles. Two secondary responses include long-term response that is correlated to known geologic variations of the island and the other reflects geomorphic patterns with medium length scale. Our study also includes the response to Hurricane Sandy and a period of post-storm recovery. It was expected that the impacts from Hurricane Sandy would disrupt long-term trends and spatial patterns. We found that the response to Sandy at Fire Island is not notable or distinguishable from several other large storms of the prior decade.

New York↗

USGS2015

Land-cover types, shoreline positions, and sand extents derived From Landsat satellite imagery, Assateague Island to Metompkin Island, Maryland and Virginia, 1984 to 2014

The U.S. Geological Survey has a long history of responding to and documenting the impacts of storms along the Nation’s coasts and incorporating these data into storm impact and coastal change vulnerability assessments. These studies, however, have traditionally focused on sandy shorelines and sandy barrier-island systems, without consideration of impacts to coastal wetlands. The goal of the Barrier Island and Estuarine Wetland Physical Change Assessment project is to integrate a wetland-change assessment with existing coastal-change assessments for the adjacent sandy dunes and beaches, initially focusing on Assateague Island along the Maryland and Virginia coastline. Assateague Island was impacted by waves and storm surge associated with the passage of Hurricane Sandy in October 2012, including erosion and overwash along the ocean-facing sandy shoreline as well as erosion and overwash deposition in the back-barrier and estuarine bay environments. This report serves as an archive of data that were derived from Landsat 5 and Landsat 8 imagery from 1984 to 2014, including wetland and terrestrial habitat extents; open-ocean, back-barrier, and estuarine mainland shoreline positions; and sand-line positions along the estuarine mainland and barrier shorelines from Assateague Island, Maryland to Metompkin Island, Virginia. The geographic information system data files with accompanying formal Federal Geographic Data Committee metadata can be downloaded from the Data Downloads page.

Maryland, Virginia↗

USGS2015

Using a Bayesian network to predict barrier island geomorphologic characteristics

Quantifying geomorphic variability of coastal environments is important for understanding and describing the vulnerability of coastal topography, infrastructure, and ecosystems to future storms and sea level rise. Here we use a Bayesian network (BN) to test the importance of multiple interactions between barrier island geomorphic variables. This approach models complex interactions and handles uncertainty, which is intrinsic to future sea level rise, storminess, or anthropogenic processes (e.g., beach nourishment and other forms of coastal management). The BN was developed and tested at Assateague Island, Maryland/Virginia, USA, a barrier island with sufficient geomorphic and temporal variability to evaluate our approach. We tested the ability to predict dune height, beach width, and beach height variables using inputs that included longer-term, larger-scale, or external variables (historical shoreline change rates, distances to inlets, barrier width, mean barrier elevation, and anthropogenic modification). Data sets from three different years spanning nearly a decade sampled substantial temporal variability and serve as a proxy for analysis of future conditions. We show that distinct geomorphic conditions are associated with different long-term shoreline change rates and that the most skillful predictions of dune height, beach width, and beach height depend on including multiple input variables simultaneously. The predictive relationships are robust to variations in the amount of input data and to variations in model complexity. The resulting model can be used to evaluate scenarios related to coastal management plans and/or future scenarios where shoreline change rates may differ from those observed historically.

Maryland and Virginia↗

USGS2015

Archive of bathymetry data collected at Cape Canaveral, Florida, 2014

Remotely sensed, geographically referenced elevation measurements of the sea floor, acquired by boat- and aircraft-based survey systems, were produced by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida, for the area at Cape Canaveral. The work was conducted as part of a study to describe an updated bathymetric dataset collected in 2014 and compare it to previous data sets. The updated data focus on the bathymetric features and sediment transport pathways that connect the offshore regions to the shoreline and, therefore, are related to the protection of other portions of the coastal environment, such as dunes, that support infrastructure and ecosystems. Cape Canaveral Coastal System (CCCS) is a prominent feature along the Southeast U.S. coastline and is the only large cape south of Cape Fear, North Carolina. Most of the CCCS lies within the Merritt Island National Wildlife Refuge and included within its boundaries are the Cape Canaveral Air Force Station (CCAFS), NASA’s Kennedy Space Center (KSC), and a large portion of Canaveral National Seashore. The actual promontory of the modern cape falls within the jurisdictional boundaries of the CCAFS. Hydrographic survey data were collected August 18-20, 2014 ( USGS Field Activity Number 2014-324-FA ). The study covered a 20 kilometer (km) section of shoreline extending from Port Canaveral, Fla., to the northern end of the KSC property, and from the shoreline to about 2.5 km offshore. Data were acquired using both sound navigation and ranging (sonar) and light detection and ranging (lidar) systems. Two jet skis and a 17-foot (ft) outboard motor boat equipped with the USGS SANDS (System for Accurate Nearshore Depth Surveying) hydrographic system collected precision sonar data. The USGS airborne EAARL-B mapping system flown in a twin engine airplane was used to collect lidar data. The missions were synchronized so that there was temporal and spatial overlap between the sonar and lidar operations. Additional data were collected to evaluate water clarity to verify the ability of lidar to receive bathymetric returns. Both systems used differential Global Positioning System GPS and utilized the National Oceanic and Atmospheric Administration/National Geodetic Survey (NOAA/NGS) Continuously Operating Reference Station (CORS) station located at CCAFS was used as the reference station. This data series serves as an archive of processed single-beam sonar and lidar bathymetry data. Graphical Information System (GIS) data products include XYZ point bathymetry data files, a color coded bathymetry map, and interpolated bathymetry grid surface. Additional information includes an error analysis and formal Federal Geographic Data Committee (FGDC) metadata. For more information about similar projects, please visit the Barrier Island Evolution Web site.

Florida↗

USGS2015

Analysis of bathymetric surveys to identify coastal vulnerabilities at Cape Canaveral, Florida

Cape Canaveral, Florida, is a prominent feature along the Southeast U.S. coastline. The region includes Merritt Island National Wildlife Refuge, Cape Canaveral Air Force Station, NASA’s Kennedy Space Center, and a large portion of Canaveral National Seashore. The actual promontory of the modern Cape falls within the jurisdictional boundaries of Cape Canaveral Air Force Station. Erosion hazards result from winter and tropical storms, changes in sand resources, sediment budgets, and sea-level rise. Previous work by the USGS has focused on the vulnerability of the dunes to storms, where updated bathymetry and topography have been used for modeling efforts. Existing research indicates that submerged shoals, ridges, and sandbars affect patterns of wave refraction and height, coastal currents, and control sediment transport. These seabed anomalies indicate the availability and movement of sand within the nearshore environment, which may be directly related to the stability of the Cape Canaveral shoreline. Understanding the complex dynamics of the offshore bathymetry and associated sediment pathways can help identify current and future erosion vulnerabilities due to short-term (for example, hurricane and other extreme storms) and long-term (for example, sea-level rise) hazards. The purpose of this work is to describe an updated bathymetric dataset collected in 2014 and compare it to previous datasets. The updated data focus on the bathymetric features and sediment transport pathways that connect the offshore regions to the shoreline and, therefore, are related to the protection of other portions of the coastal environment, such as dunes, that support infrastructure and ecosystems. Previous survey data include National Oceanic and Atmospheric Administration’s (NOAA) National Ocean Service (NOS) hydrographic survey from 1956 and a USGS survey from 2010 that is augmented with NOS surveys from 2006 and 2007. The primary result of this analysis is documentation and quantification of the nature and rates of bathymetric changes that are near (within about 2.5 km) the current Cape Canaveral shoreline and interpretation of the impact of these changes on future erosion vulnerability.

Florida↗

USGS2015

Changes in erosion and flooding risk due to long-term and cyclic oceanographic trends

We assess temporal variations in waves and sea level, which are driving factors for beach erosion and coastal flooding in the northern Gulf of Mexico. We find that long-term trends in the relevant variables have caused an increase of ~30% in the erosion/flooding risk since the 1980s. Changes in the wave climate—which have often been ignored in earlier assessments—were at least as important as sea level rise (SLR). In the next decades, SLR will likely become the dominating driver and may in combination with ongoing changes in the wave climate (and depending on the emission scenario) escalate the erosion/flooding risk by up to 300% over the next 30 years. We also find significant changes in the seasonal cycles of sea level and significant wave height, which have in combination caused a considerable increase of the erosion/flooding risk in summer and decrease in winter relative to long-term trends.

Alabama↗

USGS2015

Morphodynamic data assimilation used to understand changing coasts

Morphodynamic data assimilation blends observations with model predictions and comes in many forms, including linear regression, Kalman filter, brute-force parameter estimation, variational assimilation, and Bayesian analysis. Importantly, data assimilation can be used to identify sources of prediction errors that lead to improved fundamental understanding. Overall, models incorporating data assimilation yield better information to the people who must make decisions impacting safety and wellbeing in coastal regions that experience hazards due to storms, sea-level rise, and erosion. We present examples of data assimilation associated with morphologic change. We conclude that enough morphodynamic predictive capability is available now to be useful to people, and that we will increase our understanding and the level of detail of our predictions through assimilation of observations and numerical-statistical models.

Conference Paper↗