Geology topics

Hilary F. Stockdon

Publications and source records attributed to Hilary F. Stockdon.

At least 19 recordsLinked to original sources

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↗

USGS2019

Hurricane Matthew: Predictions, observations, and an analysis of coastal change

Hurricane Matthew, the strongest Atlantic hurricane of the 2016 hurricane season, made land-fall south of McClellanville, S.C., around 1500 Coordinated Universal Time (UTC) on October 8, 2016. Hurricane Matthew affected the States of Florida, Georgia, South Carolina, and North Carolina along the U.S. Atlantic coastline. Numerous barrier islands were breached, and the erosion of beaches and dunes occurred along most of the South Atlantic coast. The U.S. Geological Survey (USGS) fore-casted potential coastal-change effects—including dune erosion and overwash that can threaten coastal resources and infrastructure—to assist with pre-storm management decisions. Following the storm, oblique aerial photography was collected, and lidar topographic survey missions were flown. These two datasets were used to document the changes that resulted from the storm and to validate coastal change forecasts. Comparisons of pre- and post-storm photographs were used to characterize the nature, extent, and spatial variability of hurricane-induced coastal changes. Analyses of pre- and post-storm lidar eleva-tions were used to quantify magnitudes of change in shoreline positions, dune elevations, and beach volumes. Erosion was observed along the coast from Florida to North Carolina; however, the coastal response exhibited extensive spatial variability, as would be expected over such a large region.

Florida, Georgia, South Carolina, North Carolina↗

USGS2019

Forecasts of coastal change hazards

Model predictions of severe storm impacts provide coastal residents, emergency managers, and partner organizations valuable predictive information for planning and response to extreme storm events. The foundation of this work is a USGS-developed numerical model to forecast storm-induced coastal water levels and expected coastal change, including dune erosion, overwash, and inundation. The model is operated in three modes: generalized scenarios, real-time storms, and an operational forecast, with each mode requiring slightly different water level inputs. To evaluate and improve the accuracy of the models, we collect data on water levels and coastal change. In particular, observations before, after, and during storm conditions are used to test the different model applications. Forecast validation for Hurricanes Matthew (2016) and Irma (2017) illustrate three cases with demonstrated forecast skill and three cases with poor skill, and reveal elements of the modeling and/or testing approach which require improvement.

Conference Paper↗

USGS2019

iCoast – Did the Coast Change?: Storm-impact model verification using citizen scientists

The USGS provides model predictions of severe storm impacts prior to landfall based on pre-storm morphology and predicted total water levels, including waves and surge. Presented in near real time on the USGS Coastal Change Hazard Portal, they provide coastal residents, scientists, and emergency managers valuable coastal response information. iCoast – Did the Coast Change?, an online tool for comparing pre- and post-storm photography, allows volunteer citizen scientist help researchers classify storm impacts through aerial photography. Using spatially matched pre- and post-storm image pairs from Hurricane Sandy, iCoast users identified the coastal processes seen in each image. These classifications were compared to the predictive model probabilities just prior to Sandy’s landfall. User classifications agreed with the model predictions with a high degree of confidence for dune erosion and overwash. However, for inundation, user classifications, limited to observations of island breaching, were less confident and should be used with caution

Conference Paper↗

USGS2019

The influence of shelf bathymetry and beach topography on extreme total water levels: Linking large-scale changes of the wave climate to local coastal hazards

Total water levels (TWLs) at the coast are driven by a combination of deterministic (e.g., tides) and stochastic (e.g., waves, storm surge, and sea level anomalies) processes. The contribution of each process to TWLs varies depending on regional differences in climate and framework geology, as well as local-scale variations in beach morphology, coastal orientation, and shelf bathymetry. Large-scale changes to the climate altering the frequency, direction, and intensity of storms, may therefore propagate to the nearshore differently, amplifying or suppressing local coastal hazards and changing the exposure of coastal communities to extreme TWLs. This study investigates the hydrodynamic and geomorphologic factors controlling local TWLs along high-energy United States coastlines where wave-influences dominate TWLs. Three study sites in the states of Washington, Oregon, and California are chosen to explore how regional and local differences in beach topography and wave transformation over shelf bathymetry drives variations in the magnitude and impacts of extreme TWLs. Results indicate that TWLs are most influenced by wave transformation processes in locations with steep beach slopes (which drive larger relative contributions of wave runup) and complex offshore bathymetry, while beach topography influences the severity of coastal impacts. Once the relative morphologic controls on TWLs are better understood, hypothetical future climate scenarios are explored to assess how changes to the average deepwater wave climate (height, period, and direction) may alter local TWLs when compared to estimates of likely sea level rise and future coastal management strategies. Changes to the wave climate are found to be as detrimental to the coastline as sea level rise in some locations, where small variations of the TWL drive large, nonlinear changes in hours of impact to the backshore beach. Overall, this study develops an approach for quantifying the range of hydrodynamic and morphologic controls on the magnitude of TWLs which will ultimately better prepare coastal communities for uncertain changes to the global climate.

California, Oregon, Washington↗

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

The relative contribution of waves, tides, and nontidal residuals to extreme total water levels on U.S. West Coast sandy beaches

To better understand how individual processes combine to cause flooding and erosion events, we investigate the relative contribution of tides, waves, and nontidal residuals to extreme total water levels (TWLs) at the shoreline of U.S. West Coast sandy beaches. Extreme TWLs, defined as the observed annual maximum event and the simulated 100 year return level event, peak in Washington, and are on average larger in Washington and Oregon than in California. The relative contribution of wave-induced and still water levels (SWL) to the 100 year TWL event is similar to that of the annual maximum event; however, the contribution of storm surge to the SWL doubles across events. Understanding the regional variability of TWLs will lead to a better understanding of how sea level rise, changes in storminess, and possible changes in the frequency of major El Niños may impact future coastal flooding and erosion along the U.S. West Coast and elsewhere.

California, Oregon, Washington↗

USGS2017

Testing model parameters for wave‐induced dune erosion using observations from Hurricane Sandy

Models of dune erosion depend on a set of assumptions that dictate the predicted evolution of dunes throughout the duration of a storm. Lidar observations made before and after Hurricane Sandy at over 800 profiles with diverse dune elevations, widths, and volumes are used to quantify specific dune erosion model parameters including the dune face slope, which controls dune avalanching, and the trajectory of the dune toe, which controls dune migration. Wave‐impact models of dune erosion assume a vertical dune face and erosion of the dune toe along the foreshore beach slope. Observations presented here show that these assumptions are not always valid and require additional testing if these models are to be used to predict coastal vulnerability for decision‐making purposes. Observed dune face slopes steepened by 43% yet did not become vertical faces, and only 50% of the dunes evolved along a trajectory similar to the foreshore beach slope. Observations also indicate that dune crests were lowered during dune erosion. Moreover, analysis showed a correspondence between dune lowering and narrower beaches, smaller dune volumes, and/or longer wave impact.

Maryland, New Jersey, New York↗

USGS2015

National assessment of nor’easter-induced coastal erosion hazards: mid- and northeast Atlantic coast

Beaches serve as a natural buffer between the ocean and inland communities, ecosystems, and natural resources. However, these dynamic environments move and change in response to winds, waves, and currents. During extreme storms, changes to beaches can be great, and the results are sometimes catastrophic. Lives may be lost, communities destroyed, and millions of dollars spent on rebuilding. During storms, large waves may erode beaches, and high storm surge may shift the erosive force of the waves higher on the beach. In some cases, the combined effects of waves and surge may cause overwash (when waves and surge overtop the dune, transporting sediment inland) or flooding. Buildings and infrastructure on or near a dune can be undermined during wave attack and subsequent erosion. A number of strong northeast storms—storms with winds tending to blow from the northeast direction—referred to as nor’easters, have hit the mid- and northeast Atlantic coast of the United States in recent years (February 2013 and January 2015). Waves from these storms caused severe erosion, flooding, and undermining of roads in many areas along the northeast Atlantic coast. Waves overtopping a dune can transport water and sand inland, covering roads and blocking evacuation routes or impeding emergency relief. If storm surge inundates barrier island dunes, currents flowing across the island can create a breach, or a new inlet, completely severing evacuation routes. Extreme coastal changes caused by hurricanes or nor’easters may increase the vulnerability of communities both during a storm and to future storms. For example, when sand dunes are substantially eroded, inland structures are exposed to storm surge and waves. On barrier islands, absent or low dunes allow water to flow inland across the island.

Delaware, Maine, Maryland, Massachusetts, New Hamp↗

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

Enhancing evaluation of post-storm morphologic response using aerial orthoimagery from Hurricane Sandy

Improved identification of morphological responses to storms is necessary for developing and maintaining predictive models of coastal change. Morphological responses to Hurricane Sandy were measured using lidar and orthophotos taken before and after the storm. Changes to dune features measured from lidar were compared to the occurrence of overwash deposits measured using orthophotos. Thresholds on morphologic change (e.g. overwash volume and dune height change) were defined to optimize agreement between the classification of lidar and orthophoto-derived dune erosion and overwash. A linear regression showed that overwash volume can be calculated from orthophoto-derived overwash extent.

Fire Island and New Jersey shore↗

USGS2015

Dune management challenges on developed coasts

From October 26-28, 2015, nearly 100 members of the coastal management and research communities met in Kitty Hawk, NC, USA to bridge the apparent gap between the coastal dune research of scientists and engineers and the needs of coastal management practitioners. The workshop aimed to identify the challenges involved in building and managing dunes on developed coasts, assess the extent to which scientific knowledge can be applied to the management community, and identify approaches to provide means to bridge the gap between needs and potential solutions.

Conference Paper↗

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

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

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

Hurricane Sandy: observations and analysis of coastal change

Hurricane Sandy, the largest Atlantic hurricane on record, made landfall on October 29, 2012, and impacted a long swath of the U.S. Atlantic coastline. The barrier islands were breached in a number of places and beach and dune erosion occurred along most of the Mid-Atlantic coast. As a part of the National Assessment of Coastal Change Hazards project, the U.S. Geological Survey collected post-Hurricane Sandy oblique aerial photography and lidar topographic surveys to document the changes that occurred as a result of the storm. Comparisons of post-storm photographs to those collected prior to Sandy’s landfall were used to characterize the nature, magnitude, and spatial variability of hurricane-induced coastal changes. Analysis of pre- and post-storm lidar elevations was used to quantify magnitudes of change in shoreline position, dune elevation, and beach width. Erosion was observed along the coast from North Carolina to New York; however, as would be expected over such a large region, extensive spatial variability in storm response was observed.

Open-File Report↗

USGS2014

National assessment of hurricane-induced coastal erosion hazards: Northeast Atlantic Coast

Beaches serve as a natural buffer between the ocean and inland communities, ecosystems, and natural resources. However, these dynamic environments move and change in response to winds, waves, and currents. During extreme storms, changes to beaches can be great, and the results are sometimes catastrophic. Lives may be lost, communities destroyed, and millions of dollars spent on rebuilding. During storms, large waves may erode beaches, and high storm surge may shift the erosive force of the waves higher on the beach. In some cases, the combined effects of waves and surge may cause overwash (when waves and surge overtop the dune, transporting sand inland) or flooding. Buildings and infrastructure on or near a dune can be undermined during wave attack and subsequent erosion. Hurricanes Irene and Sandy made landfall along the Eastern United States in August 2011 and October 2012, respectively. Although these hurricanes made landfall south and west of the northeast Atlantic coast, waves from these storms caused severe erosion and undermining, destroying roads in some areas of the coast (for example Westport, Massachusetts). Waves overtopping a dune can transport water and sand inland, covering roads and blocking evacuation routes or impeding emergency relief. If storm surge inundates barrier island dunes, currents flowing across the island can create a breach, or a new inlet, completely severing evacuation routes. Extreme coastal changes caused by hurricanes may increase the vulnerability of communities both during a storm and to future storms. For example, when sand dunes are substantially eroded, inland structures are exposed to storm surge and waves. On barrier islands, absent or low dunes allow water to flow inland across the island, potentially increasing storm surge in the back bay, on the sound-side of the barrier, and on the mainland.

Connecticut, Maine, Massachusetts, New Hampshire, ↗

USGS2014

Probabilistic estimation of dune retreat on the Gold Coast, Australia

Sand dunes are an important natural buffer between storm impacts and development backing the beach on the Gold Coast of Queensland, Australia. The ability to forecast dune erosion at a prediction horizon of days to a week would allow efficient and timely response to dune erosion in this highly populated area. Towards this goal, we modified an existing probabilistic dune erosion model for use on the Gold Coast. The original model was trained using observations of dune response from Hurricane Ivan on Santa Rosa Island, Florida, USA (Plant and Stockdon 2012. Probabilistic prediction of barrier-island response to hurricanes, Journal of Geophysical Research, 117(F3), F03015). The model relates dune position change to pre-storm dune elevations, dune widths, and beach widths, along with storm surge and run-up using a Bayesian network. The Bayesian approach captures the uncertainty of inputs and predictions through the conditional probabilities between variables. Three versions of the barrier island response Bayesian network were tested for use on the Gold Coast. One network has the same structure as the original and was trained with the Santa Rosa Island data. The second network has a modified design and was trained using only pre- and post-storm data from 1988-2009 for the Gold Coast. The third version of the network has the same design as the second version of the network and was trained with the combined data from the Gold Coast and Santa Rosa Island. The two networks modified for use on the Gold Coast hindcast dune retreat with equal accuracy. Both networks explained 60% of the observed dune retreat variance, which is comparable to the skill observed by Plant and Stockdon (2012) in the initial Bayesian network application at Santa Rosa Island. The new networks improved predictions relative to application of the original network on the Gold Coast. Dune width was the most important morphologic variable in hindcasting dune retreat, while hydrodynamic variables, surge and run-up elevation, were also important

Queensland↗