Geology topics

Nathaniel G. Plant

Publications and source records attributed to Nathaniel G. Plant.

At least 55 records · Page 3Linked to original sources

USGS2015

How well can wave runup be predicted? comment on Laudier et al. (2011) and Stockdon et al. (2006)

Laudier et al. (2011) suggested that there may be a systematic bias error in runup predictions using a model developed by Stockdon et al. (2006). Laudier et al. tested cases that sampled beach and wave conditions that differed from those used to develop the Stockdon et al. model. Based on our re-analysis, we found that in two of the three Laudier et al. cases observed overtopping was actually consistent with the Stockdon et al. predictions. In these cases, the revised predictions indicated substantial overtopping with, in one case, a freeboard deficit of 1 m. In the third case, the revised prediction had a low likelihood of overtopping, which reflected a large uncertainty due to wave conditions that included a broad and bi-modal frequency distribution. The discrepancy between Laudier et al. results and our re-analysis appear to be due, in part, to simplifications made by Laudier et al. when they implemented a reduced version of the Stockdon et al. model.

Coastal Engineering↗

USGS2015

Modelling multi-hazard hurricane damages on an urbanized coast with a Bayesian Network approach

Hurricane flood impacts to residential buildings in coastal zones are caused by a number of hazards, such as inundation, overflow currents, erosion, and wave attack. However, traditional hurricane damage models typically make use of stage-damage functions, where the stage is related to flooding depth only. Moreover, these models are deterministic and do not consider the large amount of uncertainty associated with both the processes themselves and with the predictions. This uncertainty becomes increasingly important when multiple hazards (flooding, wave attack, erosion, etc.) are considered simultaneously. This paper focusses on establishing relationships between observed damage and multiple hazard indicators in order to make better probabilistic predictions. The concept consists of (1) determining Local Hazard Indicators (LHIs) from a hindcasted storm with use of a nearshore morphodynamic model, XBeach, and (2) coupling these LHIs and building characteristics to the observed damages. We chose a Bayesian Network approach in order to make this coupling and used the LHIs ‘Inundation depth’, ‘Flow velocity’, ‘Wave attack’, and ‘Scour depth’ to represent flooding, current, wave impacts, and erosion related hazards. The coupled hazard model was tested against four thousand damage observations from a case site at the Rockaway Peninsula, NY, that was impacted by Hurricane Sandy in late October, 2012. The model was able to accurately distinguish ‘Minor damage’ from all other outcomes 95% of the time and could distinguish areas that were affected by the storm, but not severely damaged, 68% of the time. For the most heavily damaged buildings (‘Major Damage’ and ‘Destroyed’), projections of the expected damage underestimated the observed damage. The model demonstrated that including multiple hazards doubled the prediction skill, with Log-Likelihood Ratio test (a measure of improved accuracy and reduction in uncertainty) scores between 0.02 and 0.17 when only one hazard is considered and a score of 0.37 when multiple hazards are considered simultaneously. The LHIs with the most predictive skill were ‘Inundation depth’ and ‘Wave attack’. The Bayesian Network approach has several advantages over the market-standard stage-damage functions: the predictive capacity of multiple indicators can be combined; probabilistic predictions can be obtained, which include uncertainty; and quantitative as well as descriptive information can be used simultaneously.

Coastal Engineering↗

USGS2015

Nearshore dynamics of artificial sand and oil agglomerates

Weathered oil can mix with sediment to form heavier-than-water sand and oil agglomerates (SOAs) that can cause beach re-oiling for years after a spill. Few studies have focused on the physical dynamics of SOAs. In this study, artificial SOAs (aSOAs) were created and deployed in the nearshore, and shear stress-based mobility formulations were assessed to predict SOA response. Prediction sensitivity to uncertainty in hydrodynamic conditions and shear stress parameterizations were explored. Critical stress estimates accounting for large particle exposure in a mixed bed gave the best predictions of mobility under shoaling and breaking waves. In the surf zone, the 10-cm aSOA was immobile and began to bury in the seafloor while smaller size classes dispersed alongshore. aSOAs up to 5 cm in diameter were frequently mobilized in the swash zone. The uncertainty in predicting aSOA dynamics reflects a broader uncertainty in applying mobility and transport formulations to cm-sized particles.

Marine Pollution Bulletin↗

USGS2015

Field observations of artificial sand and oil agglomerates

Oil that comes into the surf zone following spills, such as occurred during the 2010 Deepwater Horizon ( DWH ) blowout, can mix with local sediment to form heavier-than-water sand and oil agglomerates ( SOAs ), at times in the form of mats a few centimeters thick and tens of meters long. Smaller agglomerates that form in situ or pieces that break off of larger mats, sometimes referred to as surface residual balls ( SRBs ), range in size from sand-sized grains to patty-shaped pieces several centimeters ( cm ) in diameter. These mobile SOAs can cause beach oiling for extended periods following the spill, on the scale of years as in the case of DWH . Limited research, including a prior effort by the U.S. Geological Survey ( USGS ) investigating SOA mobility, alongshore transport, and seafloor interaction using numerical model output, focused on the physical dynamics of SOAs . To address this data gap, we constructed artificial sand and oil agglomerates ( aSOAs ) with sand and paraffin wax to mimic the size and density of genuine SOAs . These aSOAs were deployed in the nearshore off the coast of St. Petersburg, Florida, during a field experiment to investigate their movement and seafloor interaction. This report presents the methodology for constructing aSOAs and describes the field experiment. Data acquired during the field campaign, including videos and images of aSOA movement in the nearshore (1.5-meter and 0.5-meter water depth) and in the swash zone, are also presented in this report.

Florida↗

USGS2014

Inundation of a barrier island (Chandeleur Islands, Louisiana, USA) during a hurricane: Observed water-level gradients and modeled seaward sand transport

Large geomorphic changes to barrier islands may occur during inundation, when storm surge exceeds island elevation. Inundation occurs episodically and under energetic conditions that make quantitative observations difficult. We measured water levels on both sides of a barrier island in the northern Chandeleur Islands during inundation by Hurricane Isaac. Wind patterns caused the water levels to slope from the bay side to the ocean side for much of the storm. Modeled geomorphic changes during the storm were very sensitive to the cross-island slopes imposed by water-level boundary conditions. Simulations with equal or landward sloping water levels produced the characteristic barrier island storm response of overwash deposits or displaced berms with smoother final topography. Simulations using the observed seaward sloping water levels produced cross-barrier channels and deposits of sand on the ocean side, consistent with poststorm observations. This sensitivity indicates that accurate water-level boundary conditions must be applied on both sides of a barrier to correctly represent the geomorphic response to inundation events. More broadly, the consequence of seaward transport is that it alters the relationship between storm intensity and volume of landward transport. Sand transported to the ocean side may move downdrift, or aid poststorm recovery by moving onto the beach face or closing recent breaches, but it does not contribute to island transgression or appear as an overwash deposit in the back-barrier stratigraphic record. The high vulnerability of the Chandeleur Islands allowed us to observe processes that are infrequent but may be important at other barrier islands.

Louisiana↗

USGS2014

Scaling coastal dune elevation changes across storm-impact regimes

Extreme storms drive change in coastal areas, including destruction of dune systems that protect coastal populations. Data from four extreme storms impacting four geomorphically diverse barrier islands are used to quantify dune elevation change. This change is compared to storm characteristics to identify variability in dune response, improve understanding of morphological interactions, and provide estimates of scaling parameters applicable for future prediction. Locations where total water levels did not exceed the dune crest experienced elevation change of less than 10%. Regions where wave-induced water levels exceeded the dune crest exhibited a positive linear relationship between the height of water over the dune and the dune elevation change. In contrast, a negative relationship was observed when surge exceeded the dune crest. Results indicate that maximum dune elevation, and therefore future vulnerability, may be more impacted from lower total water levels where waves drive sediment over the dune rather than surge-dominated flooding events.

Geophysical Research Letters↗

USGS2014

Predictions of barrier island berm evolution in a time-varying storm climatology

Low-lying barrier islands are ubiquitous features of the world's coastlines, and the processes responsible for their formation, maintenance, and destruction are related to the evolution of smaller, superimposed features including sand dunes, beach berms, and sandbars. The barrier island and its superimposed features interact with oceanographic forces (e.g., overwash) and exchange sediment with each other and other parts of the barrier island system. These interactions are modulated by changes in storminess. An opportunity to study these interactions resulted from the placement and subsequent evolution of a 2 m high sand berm constructed along the northern Chandeleur Islands, LA. We show that observed berm length evolution is well predicted by a model that was fit to the observations by estimating two parameters describing the rate of berm length change. The model evaluates the probability and duration of berm overwash to predict episodic berm erosion. A constant berm length change rate is also predicted that persists even when there is no overwash. The analysis is extended to a 16 year time series that includes both intraannual and interannual variability of overwash events. This analysis predicts that as many as 10 or as few as 1 day of overwash conditions would be expected each year. And an increase in berm elevation from 2 m to 3.5 m above mean sea level would reduce the expected frequency of overwash events from 4 to just 0.5 event-days per year. This approach can be applied to understanding barrier island and berm evolution at other locations using past and future storm climatologies.

Louisiana↗

USGS2014

Evaluating coastal landscape response to sea-level rise in the northeastern United States: approach and methods

The U.S. Geological Survey is examining effects of future sea-level rise on the coastal landscape from Maine to Virginia by producing spatially explicit, probabilistic predictions using sea-level projections, vertical land movement rates (due to isostacy), elevation data, and land-cover data. Sea-level-rise scenarios used as model inputs are generated by using multiple sources of information, including Coupled Model Intercomparison Project Phase 5 models following representative concentration pathways 4.5 and 8.5 in the Intergovernmental Panel on Climate Change Fifth Assessment Report. A Bayesian network is used to develop a predictive coastal response model that integrates the sea-level, elevation, and land-cover data with assigned probabilities that account for interactions with coastal geomorphology as well as the corresponding ecological and societal systems it supports. The effects of sea-level rise are presented as (1) level of landscape submergence and (2) coastal response type characterized as either static (that is, inundation) or dynamic (that is, landform or landscape change). Results are produced at a spatial scale of 30 meters for four decades (the 2020s, 2030s, 2050s, and 2080s). The probabilistic predictions can be applied to landscape management decisions based on sea-level-rise effects as well as on assessments of the prediction uncertainty and need for improved data or fundamental understanding. This report describes the methods used to produce predictions, including information on input datasets; the modeling approach; model outputs; data-quality-control procedures; and information on how to access the data and metadata online.

Open-File Report↗

USGS2014

A probabilistic method for constructing wave time-series at inshore locations using model scenarios

Continuous time-series of wave characteristics (height, period, and direction) are constructed using a base set of model scenarios and simple probabilistic methods. This approach utilizes an archive of computationally intensive, highly spatially resolved numerical wave model output to develop time-series of historical or future wave conditions without performing additional, continuous numerical simulations. The archive of model output contains wave simulations from a set of model scenarios derived from an offshore wave climatology. Time-series of wave height, period, direction, and associated uncertainties are constructed at locations included in the numerical model domain. The confidence limits are derived using statistical variability of oceanographic parameters contained in the wave model scenarios. The method was applied to a region in the northern Gulf of Mexico and assessed using wave observations at 12 m and 30 m water depths. Prediction skill for significant wave height is 0.58 and 0.67 at the 12 m and 30 m locations, respectively, with similar performance for wave period and direction. The skill of this simplified, probabilistic time-series construction method is comparable to existing large-scale, high-fidelity operational wave models but provides higher spatial resolution output at low computational expense. The constructed time-series can be developed to support a variety of applications including climate studies and other situations where a comprehensive survey of wave impacts on the coastal area is of interest.

Coastal Engineering↗

USGS2014

Evaluation of wave runup predictions from numerical and parametric models

Wave runup during storms is a primary driver of coastal evolution, including shoreline and dune erosion and barrier island overwash. Runup and its components, setup and swash, can be predicted from a parameterized model that was developed by comparing runup observations to offshore wave height, wave period, and local beach slope. Because observations during extreme storms are often unavailable, a numerical model is used to simulate the storm-driven runup to compare to the parameterized model and then develop an approach to improve the accuracy of the parameterization. Numerically simulated and parameterized runup were compared to observations to evaluate model accuracies. The analysis demonstrated that setup was accurately predicted by both the parameterized model and numerical simulations. Infragravity swash heights were most accurately predicted by the parameterized model. The numerical model suffered from bias and gain errors that depended on whether a one-dimensional or two-dimensional spatial domain was used. Nonetheless, all of the predictions were significantly correlated to the observations, implying that the systematic errors can be corrected. The numerical simulations did not resolve the incident-band swash motions, as expected, and the parameterized model performed best at predicting incident-band swash heights. An assimilated prediction using a weighted average of the parameterized model and the numerical simulations resulted in a reduction in prediction error variance. Finally, the numerical simulations were extended to include storm conditions that have not been previously observed. These results indicated that the parameterized predictions of setup may need modification for extreme conditions; numerical simulations can be used to extend the validity of the parameterized predictions of infragravity swash; and numerical simulations systematically underpredict incident swash, which is relatively unimportant under extreme conditions.

Coastal Engineering↗

USGS2014

A Bayesian network approach to predicting nest presence of thefederally-threatened piping plover ( Charadrius melodus ) using barrier island features

Sea-level rise and human development pose significant threats to shorebirds, particularly for species that utilize barrier island habitat. The piping plover (Charadrius melodus) is a federally-listed shorebird that nests on barrier islands and rapidly responds to changes in its physical environment, making it an excellent species with which to model how shorebird species may respond to habitat change related to sea-level rise and human development. The uncertainty and complexity in predicting sea-level rise, the responses of barrier island habitats to sea-level rise, and the responses of species to sea-level rise and human development necessitate a modelling approach that can link species to the physical habitat features that will be altered by changes in sea level and human development. We used a Bayesian network framework to develop a model that links piping plover nest presence to the physical features of their nesting habitat on a barrier island that is impacted by sea-level rise and human development, using three years of data (1999, 2002, and 2008) from Assateague Island National Seashore in Maryland. Our model performance results showed that we were able to successfully predict nest presence given a wide range of physical conditions within the model’s dataset. We found that model predictions were more successful when the range of physical conditions included in model development was varied rather than when those physical conditions were narrow. We also found that all model predictions had fewer false negatives (nests predicted to be absent when they were actually present in the dataset) than false positives (nests predicted to be present when they were actually absent in the dataset), indicating that our model correctly predicted nest presence better than nest absence. These results indicated that our approach of using a Bayesian network to link specific physical features to nest presence will be useful for modelling impacts of sea-level rise- or human-related habitat change on barrier islands. We recommend that potential users of this method utilize multiple years of data that represent a wide range of physical conditions in model development, because the model performed less well when constructed using a narrow range of physical conditions. Further, given that there will always be some uncertainty in predictions of future physical habitat conditions related to sea-level rise and/or human development, predictive models will perform best when developed using multiple, varied years of data input.

Maryl↗

USGS2014

A cross-validation package driving Netica with python

Bayesian networks (BNs) are powerful tools for probabilistically simulating natural systems and emulating process models. Cross validation is a technique to avoid overfitting resulting from overly complex BNs. Overfitting reduces predictive skill. Cross-validation for BNs is known but rarely implemented due partly to a lack of software tools designed to work with available BN packages. CVNetica is open-source, written in Python, and extends the Netica software package to perform cross-validation and read, rebuild, and learn BNs from data. Insights gained from cross-validation and implications on prediction versus description are illustrated with: a data-driven oceanographic application; and a model-emulation application. These examples show that overfitting occurs when BNs become more complex than allowed by supporting data and overfitting incurs computational costs as well as causing a reduction in prediction skill. CVNetica evaluates overfitting using several complexity metrics (we used level of discretization) and its impact on performance metrics (we used skill).

Environmental Modelling and Software↗

USGS2014

Assessing mobility and redistribution patterns of sand and oil agglomerates in the surf zone

Heavier-than-water sand and oil agglomerates that formed in the surf zone following the Deepwater Horizon oil spill continued to cause beach re-oiling 3 years after initial stranding. To understand this phenomena and inform operational response now and for future spills, a numerical method to assess the mobility and alongshore movement of these “surface residual balls” (SRBs) was developed and applied to the Alabama and western Florida coasts. Alongshore flow and SRB mobility and potential flux were used to identify likely patterns of transport and deposition. Results indicate that under typical calm conditions, cm-size SRBs are unlikely to move alongshore, whereas mobility and transport is likely during storms. The greater mobility of sand compared to SRBs makes burial and exhumation of SRBs likely, and inlets were identified as probable SRB traps. Analysis of field data supports these model results.

Alabama;Florida↗

USGS2014

Topographic lidar survey of Dauphin Island, Alabama and Chandeleur, Stake, Grand Gosier and Breton Islands, Louisiana, July 12-14, 2013

This Data Series Report contains lidar elevation data collected on July 12 and 14, 2013, for Dauphin Island, Alabama, and Chandeleur, Stake, Grand Gosier and Breton Islands, Louisiana. Classified point cloud data—data points described in three dimensions—in lidar data exchange format (LAS) and bare earth digital elevation models (DEMs) in ERDAS Imagine raster format (IMG) are available as downloadable files. Photo Science, Inc., was contracted by the U.S. Geological Survey (USGS) to collect and process these data. The lidar data were acquired at a horizontal spacing (or nominal pulse spacing) of 1 meter (m) or less. The USGS surveyed points within the project area from July 14–23, 2013, for use in ground control and accuracy assessment. Photo Science, Inc., calculated a vertical root mean square error (RMSEz) of 0.012 m by comparing 10 surveyed points to an interpolated elevation surface of unclassified lidar data. The USGS also checked the data using 80 surveyed points and unclassified lidar point elevation data and found an RMSEz of 0.073 m. The project specified an RMSEz of 0.0925 m or less. The lidar survey was acquired to document the short- and long-term changes of several different barrier island systems. Specifically, this survey supports detailed studies of Chandeleur and Dauphin Islands that resolve annual changes in beaches, berms and dunes associated with processes driven by storms, sea-level rise, and even human restoration activities. These lidar data are available to Federal, State and local governments, emergency-response officials, resource managers, and the general public.

Alabama;Louisiana↗

USGS2014

Topographic lidar survey of the Alabama, Mississippi, and Southeast Louisiana Barrier Islands, from September 5 to October 11, 2012

This Data Series Report contains lidar elevation data collected from September 5 to October 11, 2012, for the barrier islands of Alabama, Mississippi and southeast Louisiana, including the coast near Port Fourchon. Most of the data were collected September 5–10, 2012, with a reflight conducted on October 11, 2012, to increase point density in some areas. Point cloud data—data points described in three dimensions—in lidar data exchange format (LAS), and bare earth digital elevation models (DEMs) in ERDAS Imagine raster format (IMG), are available as downloadable files. The point cloud data were processed to extract bare earth data; therefore, the point cloud data are organized into four classes: 1-unclassified, 2-ground, 7-noise and 9-water. Aero-Metric, Inc., was contracted by the U.S. Geological Survey (USGS) to collect and process these data. The lidar data were acquired at a horizontal spacing (or nominal pulse spacing) of 1 meter (m) or less. The USGS conducted two ground surveys in a small area on Chandeleur Island on September 6, 2012, one on bare earth and the other in both bare earth and vegetated areas. The USGS calculated a vertical root mean square error (RMSEz) of 0.072 m and an offset of 0.007 m using interpolated 2-m by 2-m resolution grid surfaces made from the lidar bare-earth data and the combined USGS ground surveys. Aero-Metric, Inc., calculated an RMSEz of 0.025 m by comparing the USGS bare earth ground survey point data to the closest lidar points. The USGS also conducted a terrestrial lidar survey on Dauphin Island, Louisiana, on September 3, 2012. The USGS calculated a RMSEz of 0.32 m and an offset of 0.27 m, meaning the lidar data were 0.27 m higher than the ground truth (Guy and others, 2013), using interpolated 2-m by 2-m resolution grid surfaces from the airborne lidar bare-earth data and the terrestrial lidar survey. This lidar survey was acquired to document the changes of several different barrier island systems resulting from Hurricane Isaac (Guy and others, 2013). The survey supports detailed studies of Louisiana, Mississippi and Alabama barrier islands that resolve annual and episodic changes in beaches, berms and dunes associated with processes driven by storms, sea-level rise, and even human restoration activities. These lidar data are available to Federal, State and local governments, emergency-response officials, resource managers, and the general public.

Alabama; Louisiana; Mississippi↗

USGS2014

Topographic lidar survey of the Chandeleur Islands, Louisiana, February 6, 2012

This Data Series Report contains lidar elevation data collected February 6, 2012, for Chandeleur Islands, Louisiana. Point cloud data in lidar data exchange format (LAS) and bare earth digital elevation models (DEMs) in ERDAS Imagine raster format (IMG) are available as downloadable files. The point cloud data—data points described in three dimensions—were processed to extract bare earth data; therefore, the point cloud data are organized into the following classes: 1– and 17–unclassified, 2–ground, 9–water, and 10–breakline proximity. Digital Aerial Solutions, LLC, (DAS) was contracted by the U.S. Geological Survey (USGS) to collect and process these data. The lidar data were acquired at a horizontal spacing (or nominal pulse spacing) of 0.5 meters (m) or less. The USGS conducted two ground surveys in small areas on the Chandeleur Islands on February 5, 2012. DAS calculated a root mean square error (RMSEz) of 0.034 m by comparing the USGS ground survey point data to triangulated irregular network (TIN) models built from the lidar elevation data. This lidar survey was conducted to document the topography and topographic change of the Chandeleur Islands. The survey supports detailed studies of Louisiana, Mississippi and Alabama barrier islands that resolve annual and episodic changes in beaches, berms and dunes associated with processes driven by storms, sea-level rise, and even human restoration activities. These lidar data are available to Federal, State and local governments, emergency-response officials, resource managers, and the general public.

Louisiana↗

USGS2014

Change in the length of the southern section of the Chandeleur Islands oil berm, January 13, 2011, through September 3, 2012

On April 20, 2010, an explosion on the Deepwater Horizon oil rig drilling at the Macondo Prospect site in the Gulf of Mexico resulted in a marine oil spill that continued to flow through July 15, 2010. One of the affected areas was the Breton National Wildlife Refuge, which consists of a chain of low-lying islands, including Breton Island and the Chandeleur Islands, and their surrounding waters. The island chain is located approximately 115–150 kilometers (km) north-northwest of the spill site. A sand berm was constructed seaward of, and on, the island chain. Construction began at the northern end of Chandeleur Islands in June 2010 and ended in April 2011 after 14 km of berm had been constructed. The berm consisted of three distinct sections based on where the berm was placed relative to the islands. The northern section of the berm was built in open water on a submerged portion of the Chandeleur Islands platform. The middle section was built approximately 70–90 meters (m) seaward of the Chandeleur Islands. The southern section was built on the islands’ beaches. Repeated Landsat and SPOT satellite imagery and airborne light detection and ranging (lidar) were used to observe the disintegration of the berm over time. The methods used to analyze the remotely sensed data and the resulting, derived data for the southern section are reported.

Breton Island;Chandeleur Island↗

USGS2014

Using a Bayesian Network to predict shore-line change vulnerability to sea-level rise for the coasts of the United States

Sea-level rise is an ongoing phenomenon that is expected to continue and is projected to have a wide range of effects on coastal environments and infrastructure during the 21st century and beyond. Consequently, there is a need to assemble relevant datasets and to develop modeling or other analytical approaches to evaluate the likelihood of particular sea-level rise impacts, such as coastal erosion, and to inform coastal management decisions with this information. This report builds on previous work that compiled oceanographic and geomorphic data as part of the U.S. Geological Survey’s Coastal Vulnerability Index (CVI) for the U.S. Atlantic coast, and developed a Bayesian Network to predict shoreline-change rates based on sea-level rise plus variables that describe the hydrodynamic and geologic setting. This report extends the previous analysis to include the Gulf and Pacific coasts of the continental United States and Alaska and Hawaii, which required using methods applied to the USGS CVI dataset to extract data for these regions. The Bayesian Network converts inputs that include observations of local rates of relative sea-level change, mean wave height, mean tide range, a geomorphic classification, coastal slope, and observed shoreline-change rates to calculate the probability of the shoreline-erosion rate exceeding a threshold level of 1 meter per year for the coasts of the United States. The calculated probabilities were compared to the historical observations of shoreline change to evaluate the hindcast success rate of the most likely probability of shoreline change. Highest accuracy was determined for the coast of Hawaii (98 percent success rate) and lowest accuracy was determined for the Gulf of Mexico (34 percent success rate). The minimum success rate rose to nearly 80 percent (Atlantic and Gulf coasts) when success included shoreline-change outcomes that were adjacent to the most likely outcome. Additionally, the probabilistic approach determines the confidence in calculated outcomes as the probability of the most likely outcome. The confidence was highest along the Pacific coast and it was lowest along the Alaskan coast.

Open-File Report↗