National Assessment of Hurricane-Induced Coastal 

Erosion Hazards: Mid-Atlantic Coast

By Kara S. Doran, Hilary F. Stockdon, Kristin L. Sopkin, David M. Thompson, and Nathaniel G. Plant

Open-File Report 2013–1131

U.S. Department of the Interior

U.S. Geological Survey
ii

U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia 2013

This report and any updates to it are available online at:

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Suggested citation:

Doran, K.S., Stockdon, H.F., Sopkin, K.L., Thompson, D.M., and Plant, N.G., 2012, National assessment of hurricaneinduced coastal erosion hazards: Mid-Atlantic Coast: U.S. Geological Survey Open-File Report 2013–1131, 28 p. 

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endorsement by the U.S. Government.

Although this report is in the public domain, permission must be secured from the individual 

copyright owners to reproduce any copyrighted material contained within this report.
iii

Contents

1. Introduction................................................................................................................................................................ 1

1.1 Impacts of Hurricanes on Coastal Communities .................................................................................................. 1

1.2 Prediction of Hurricane-Induced Coastal Erosion................................................................................................. 3

1.3 Storm-Scaling Model............................................................................................................................................ 4

2. Methods..................................................................................................................................................................... 6

2.1 Lidar-Derived Beach Morphology........................................................................................................................ 6

2.2 Hurricane-Induced Water Levels.......................................................................................................................... 7

2.2.1 Tide and Storm Surge ................................................................................................................................... 8

2.2.2. Wave Height and Period..............................................................................................................................10

2.2.3 Wave Setup and Swash ...............................................................................................................................13

2.3 Probability of Coastal Change.............................................................................................................................14

3. Results......................................................................................................................................................................15

3.1 Coastal Morphology ............................................................................................................................................15

3.2 Hurricane-Induced Water Levels.........................................................................................................................20

3.3 Probability of Coastal Change.............................................................................................................................21

4. Discussion ................................................................................................................................................................23

4.1 Validity of Assumptions.......................................................................................................................................23

4.2 Relative Importance of Waves and Storm Surge ................................................................................................23

4.3 Assessment Updates ..........................................................................................................................................23

5. Conclusion................................................................................................................................................................24

6. Acknowledgments.....................................................................................................................................................24

References Cited..........................................................................................................................................................25

Tables...........................................................................................................................................................................27

Figures

1. Roads, boardwalks, and foundations in Seaside Heights, New Jersey, collapsed when the underlying dune 

was eroded during Hurricane Sandy. (AP Photo/Mike Groll) ............................................................................ 1

2. Waves and surge during Hurricane Sandy (October 2012) left an overwash deposit covering roads, parking 

areas, and homes in Mantoloking, New Jersey. ............................................................................................... 2

3. Storm surge, waves, and currents from Hurricane Sandy (October 2012) cut a breach across the road and 

bridge in Mantoloking, New Jersey. (AP Photo/Doug Mills).............................................................................. 3

4. Sketch defining the relevant morphologic and hydrodynamic parameters in the storm impact scaling model of 

Sallenger (2000)............................................................................................................................................... 4

5. Examples of post-storm conditions after collision (Nags Head, North Carolina; Isabel, 2003), overwash (Santa 

Rosa Island, Florida; Ivan, 2004), and inundation (Dauphin Island, Alabama; Katrina, 2005). ......................... 5

6. Cross-shore profile of gridded elevations indicating the locations of the dune crest (xc, zc), toe (xt, zt), 

shoreline (xsl,zsl), mean beach slope (m), mean high water (MHW), and high water line (HWL)..................... 7
iv

7. Sea, Lake, and Overland Surges from Hurricanes (SLOSH) category 1 modeled surge maximum of the 

maximum (MOM) for the Delaware Bay basin .................................................................................................. 9

8. The Simulating WAves Nearshore (SWAN) model computational grid............................................................11

9. Modeled significant wave height (Hs, top) and parameterized peak period (Tp, bottom) at the 20-meter isobath 

extending from Virginia (left) to New York (right) .............................................................................................12

10. Significant wave height (Hs) and peak (Tp) wave periods observed at National Data Buoy Center buoys.......13

11. Maximum shoreline water level (98) for a category 1 hurricane and raw and smoothed dune crest elevation 

(zc) for a 1-kilometer alongshore section .........................................................................................................15

12. Distributions of dune crest elevation (zc, top), dune toe elevation (zt, middle), and mean beach slope (m, 

bottom) for the mid-Atlantic sandy coastlines ..................................................................................................16

13. Mean dune crest elevation (zc) for 1-kilometer sections of coastline compared to the standard deviation 

(zc) in those section.......................................................................................................................................17

14. Distribution of dune crest (zc) and dune toe (zt) elevations for Virginia, Maryland, Delaware, New Jersey, and 

New York. Abbreviation: m, meter. ..................................................................................................................18

15. Dune crest (zc, red line) and dune toe (zt, blue line) elevations (top) and hurricane-induced mean (50; middle) 

and maximum (98; bottom) shoreline water levels extending from Virginia (left) to New York (right).............19

16. Distributions of mean beach slope (m) elevations for Virginia, Maryland, Delaware, New Jersey, and New 

York. ................................................................................................................................................................20

17. Probabilities of collision (inner strip), overwash (middle strip), and inundation (outer strip) during a category 1 

hurricane for Virginia, Maryland, Delaware, New Jersey, and New York.........................................................22

Tables

1. Mean elevation of dune crest (zc) and dune toe (zt) and mean beach slope (m) for the sandy beaches along 

the mid-Atlantic coast. .....................................................................................................................................27

2. Mean input wind speed, significant wave height (Hs), and wave period (Tp) and modeled setup (setup), runup 

(R2) and storm surge (surge) for category 1–4 hurricanes. ............................................................................27

3. Percentage of coast very likely (p>0.9) to experience erosion associated with collision, overwash, and 

inundation during category 1–4 hurricanes......................................................................................................28
v

Conversion Factors

SI to Inch/Pound

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

kilometer (km) 0.5400 mile, nautical (nmi) 

meter (m) 1.094 yard (yd) 

Flow rate

meter per second (m/s) 2.237 mile per hour (mi/h)

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 (NAVD 88).

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Abbreviations

CHARTS Compact Hydrographic Airborne Rapid Total Survey

CRM Coastal Relief Model

DEM Digital Elevation Model 

EAARL Experimental Advanced Airborne Research Lidar 

GPS Global Positioning System 

HWL High Water Line

Hz Hertz

IPCC Intergovernmental Panel on Climate Change

Lidar Light Detection and Ranging

MHW Mean High Water

MOM Maximum of the maximum

NAD83 North American Datum 1983

NAVD88 North American Vertical Datum 1988

NGDC National Geophysical Data Center

NGVD29 National Geodetic Vertical Datum 1929

NHC National Hurricane Center

NOAA National Oceanic and Atmospheric Administration

RMS Root-Mean-Square
vi

SLOSH Sea, Lake, and Overland Surges from Hurricanes

SWAN Simulating WAves Nearshore

USACE U.S. Army Corps of Engineers

USGS U.S. Geological Survey

WIS Wave Information Studies
1

National Assessment of Hurricane-Induced Coastal 

Erosion Hazards: Mid-Atlantic Coast

By Kara S. Doran, Hilary F. Stockdon, Kristin L. Sopkin, David M. Thompson, and Nathaniel G. Plant 

1. Introduction 

1.1 Impacts of Hurricanes on Coastal Communities

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 large, 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 shifts 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. Building and 

infrastructure on or near a dune can be undermined during wave attack and subsequent erosion. During 

Hurricane Ivan in 2004, a five-story condominium in Orange Beach, Alabama, collapsed after the sand 

dune supporting the foundation eroded. Hurricane Sandy, which made landfall as an extra-tropical 

cyclone on October 29, 2012, caused erosion and undermining that destroyed roads, boardwalks, and 

foundations in Seaside Heights, New Jersey (fig. 1). 

Figure 1. Roads, boardwalks, and foundations in Seaside Heights, New Jersey, collapsed when the underlying 

dune was eroded during Hurricane Sandy. (AP Photo/Mike Groll)
2

Waves overtopping a dune can transport sand inland, covering roads and blocking evacuation 

routes or emergency relief (fig. 2). If storm surge inundates barrier island dunes, currents flowing 

across the island can create a breach, or a new inlet, completely severing evacuation routes. Waves and 

surge during Hurricane Sandy, which made landfall on October 29, 2012, left a breach that cut the road 

and bridge to Mantoloking, N.J. (fig. 3).

 

Figure 2. Waves and surge during Hurricane Sandy (October 2012) left an overwash deposit covering roads, 

parking areas, and homes in Mantoloking, New Jersey.
3

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 on a barrier island are eroded

substantially, inland structures are exposed to storm surge and waves. Absent or low dunes also allow

water to flow inland across the island, potentially increasing storm surge in the back bay, on the soundside of the barrier, and on the mainland.

Figure 3. Storm surge, waves, and currents from Hurricane Sandy (October 2012) cut a breach across the road 

and bridge in Mantoloking, New Jersey. (AP Photo/Doug Mills)

1.2 Prediction of Hurricane-Induced Coastal Erosion

In the last decade, communities along the mid-Atlantic coast of the United States have been 

devastated by many powerful hurricanes including Isabel (2003), Irene (2011), and Sandy (2012). 

Waves and surge accompanying these storms resulted in widespread beach and dune erosion and 

extensive overwash (Morgan and Sallenger, 2009). A clear need exists to identify areas of our coastline 

that are likely to experience extreme erosion during a hurricane. This information can be used to 

determine vulnerability levels and the associated risk of building houses or infrastructure on land that 

shifts and moves with each storm landfall. A decade of research on storm-driven coastal change 

hazards within the U.S. Geological Survey (USGS) National Assessment of Coastal Change Hazards 

project has provided the data and modeling capabilities to produce the first regional assessment of the 

vulnerability of coastlines to extreme erosion during hurricane landfall. Vulnerability is defined in terms 

of the probability for coastal change, predicted using a USGS-developed storm-impact scale that 

compares predicted elevations of hurricane-induced water levels to measured elevations of coastal 

topography (Sallenger, 2000). This approach defines four coastal change regimes that describe the 

dominant interactions between beach morphology and storm processes and resulting modes of coastal 

change. Here, the focus is on the vulnerability of open-coast sandy beaches on the U.S. mid-Atlantic

shoreline. 
4

1.3 Storm-Scaling Model

During a storm, the combined effects of (1) the astronomical tide, (2) storm surge, and (3) wave 

runup (both setup, setup, and swash, S) move the erosive forces of the storm higher on the beach than 

during typical wave conditions. The total elevation of these three parameters defines two key metrics 

that characterize the nearshore hydrodynamic forcing of a storm: (1) the extreme high water level 

attained during a storm, defined here as the 98-percent exceedance level (98), and (2) the storm-induced 

mean water level (50), defined by only storm surge, tide, and wave setup. 

Figure 4. Sketch defining the relevant morphologic and hydrodynamic parameters in the storm impact scaling 

model of Sallenger (2000) (modified from Stockdon and others, 2009).

Water-level elevations are compared to the elevation of the toe (zt) and crest (zc) of the most 

seaward sand dune that defines the landward limits of the beach system and represent the first-line 

defense of a barrier island to an approaching storm. Using these parameters, four storm-impact regimes, 

or thresholds for coastal change, are defined to provide a framework for examining the general types 

and relative magnitudes of coastal change that are likely to occur during hurricanes (fig. 4) (Sallenger, 

2000; Stockdon and others, 2007a). 

 swash (98 < zt)

 collision (98 > zt)

 overwash (98 > zc) 

 inundation (50 > zc)
5

(Note: Following Plant and Stockdon (2012), our nomenclature differs from Sallenger (2000) to 

emphasize probabilistic definition of water levels and to clearly distinguish both the horizontal and 

vertical components of dune morphology.)

Figure 5. Examples of post-storm conditions after collision (Nags Head, North Carolina; Isabel, 2003), overwash 

(Santa Rosa Island, Florida; Ivan, 2004), and inundation (Dauphin Island, Alabama; Katrina, 2005).

The swash regime represents a range of relatively calm weather conditions, where water levels 

are confined to areas seaward of the dune base. Sand that is eroded from the beach during more 

energetic periods is generally transported offshore and may return to the beach during more quiescent 

conditions. The erosion and recovery cycle can occur over a time span on the order of weeks. When 

waves reach the base of the dune (collision regime), the front of the dune is expected to erode (fig. 5, 

left). Again, sand is transported seaward and then re-deposited on the beach or sandbar. In this case, the 

beach is likely to recover in the weeks and months following the storm. However, because aeolian 

processes are responsible for natural dune growth, recovery of the dune may take years. Some portion of

the total sand transport is directed alongshore, but the gradients are assumed to be small for purposes of 

these assessments.

In extreme cases, such as during stronger storms and for relatively lower dunes, waves and surge 

may exceed the dune crest elevation, resulting in overwash. Under these conditions, waves transport 

sand landward from the beach and dune (fig. 5, center). Impacts may be more long-lasting in this 

regime as sand is deposited inland, making it unavailable for natural recovery following a storm. 

During inundation of the beach (fig. 5, right), storm-induced mean water levels exceed the elevation of 

the crest of the primary dune or berm. Some of the most extreme coastal changes on barrier islands

occur within this regime; the beach system (dune crest and beach) is completely submerged, and net 

landward transport of sediment is likely to occur (Sallenger, 2000). Typically, larger magnitudes of 

shoreline retreat and beach erosion will occur when the beach is inundated as a result of sand transport

occurring under all storm-scale regimes (Stockdon and others, 2007a). On narrow barrier islands, 

inundation allows strong currents to cross the island and focus where dunes are low, thus carving 

breaches. 

The predictive accuracy of the storm-impact scaling model was tested by hindcasting the likely 

impacts of Hurricanes Bonnie (1998) and Floyd (1999) (Stockdon and others, 2007a) and of Hurricane 

Ivan (2004) (Stockdon and others, 2007b) and comparing these with observed morphologic changes. 

For Hurricane Bonnie, the overall hindcast accuracy of the model in predicting one of the four regime 

types was 51 percent, while the accuracy for Floyd was a slightly improved 58 percent. For Hurricane 
6

Ivan, the overall hindcast accuracy of the model in predicting one of the four regime types was 

68 percent. The accuracy of the model varied among regimes and was highest for the overwash 

conditions. Underprediction of the actual storm response was more likely than overprediction. Errors

were likely due to profile evolution of the low-lying barriers during the storm. As dune-crest elevation 

was lowered, the collision regime could proceed to overwash and then inundation.

With skillful hindcasting results, the model has also been applied in real time for landfalling

hurricanes (Plant and others, 2010; see also http://coastal.er.usgs.gov/hurricanes/sandy/coastalchange/). Using pre-storm measurements of dune elevations and real-time forecasts of storm surge and 

wave conditions, the USGS routinely reports the likelihood of beaches experiencing coastal change 

associated with collision, overwash, and inundation. These analyses are posted online 

(http://coastal.er.usgs.gov/hurricanes) and revised with updated hydrodynamic forecasts as a storm 

approaches landfall.

Using a methodology similar to the real-time forecasts, this report quantifies the likely impact of 

hypothetical hurricane landfalls on the U.S. mid-Atlantic coastline. The probabilities of hurricaneinduced coastal change are used to define the vulnerability of this region to extreme erosion from waves 

and storm surge associated with category 1–4 hurricanes. This report, along with similar analyses for 

the Gulf of Mexico shorelines (Stockdon and others, 2012) and southeast Atlantic shorelines (Stockdon 

and others, 2013), forms the basis of a national assessment of storm-induced coastal erosion hazards.

2. Methods

In order to use this model for a large-scale assessment of the potential for coastal change during 

future hurricane landfall, accurate estimates of (1) the dune parameters and (2) the expected hurricaneinduced water level for hypothetical storms are needed. Well-documented models of storm surge have 

been used to estimate worst-case scenarios of water-level elevations for category 1–4 hurricanes and can 

be used directly by our modeling approach. Simulations of corresponding wave conditions for category 

1–4 hurricane landfalls are more challenging, but given simplifying assumptions it is possible to 

determine worst-case scenario wave heights for each category. Numerical simulations of storm surge 

and wave heights support application of our approach to large stretches of coast. Light detection and 

ranging (lidar) topographic surveying has made it possible to accurately measure dune elevations along 

similarly long (hundreds of kilometers) lengths of coastline. The combination of high-resolution 

measurements and advanced hydrodynamic modeling makes it possible to estimate probabilities of 

hurricane-induced coastal change and to identify coastal erosion vulnerability at a national scale.

2.1 Lidar-Derived Beach Morphology

The morphology of the beaches and dunes was mapped using airborne lidar topographic surveys 

conducted from 2009 to 2010 by the U.S. Army Corps of Engineers (USACE) Compact Hydrographic 

Airborne Rapid Total Survey (CHARTS) and the USGS Experimental Advanced Airborne Research 

Lidar (EAARL) systems. The combination of laser-based ranging with inertial and GPS-based 

navigation provides an efficient method for collecting high-resolution data of sub-aerial topography 

with sufficient accuracy (root-mean-square (RMS) vertical accuracy = 15 centimeters, cm; horizontal 

accuracy = 1–1.5 meters, m) to resolve the spatial details of sand-dune elevation and position (Sallenger 

and others, 2003). Three-dimensional lidar data were gridded using a fixed-scale interpolator (Plant and 

others, 2002), which allows for variability in cross-shore and alongshore resolution, here, 2.5 m and 
7

10 m, respectively. In addition to a gridded topographic surface, this method produces a corresponding 

grid of the RMS error, which provides a measure of noise in the data. A Hanning filter with a width 

equal to two times the grid resolution was chosen to minimize noise in the data associated with 

vegetation, alongshore variability, and other error sources while preserving distinct morphology. 

Analysis of cross-shore profiles of gridded data allows for automated extraction of dune crest (zc) and 

toe (zt), as well as shoreline position (xsl) and beach slope (m), at a regular alongshore interval, here, 10 

m (fig. 6). These features are ultimately used to estimate wave runup and the corresponding stormresponse regimes, as well as toquantify actual morphologic changes before and after storms. Detailed 

descriptions of the algorithm used to extract shoreline position, dune crest and toe, and beach slope can 

be found in the first report of this series: National Assessment of Hurricane-Induced Coastal Erosion 

Hazards: Gulf of Mexico (Stockdon and others, 2012) and references contained therein.

Figure 6. Cross-shore profile of gridded elevations indicating the locations of the dune crest (xc, zc), toe (xt, zt), 

shoreline (xsl,zsl), mean beach slope (m), mean high water (MHW), and high water line (HWL). Abbreviation: 

m, meter.

2.2 Hurricane-Induced Water Levels

During a hurricane, water levels at the shoreline include the combined effects of tide, storm 

surge, and local wave energy. Predictions of coastal change during hurricanes require estimates of both 

the mean and extreme water levels that can be expected for each category. The mean hurricane-induced 

water level, 50 , is defined as 

50 = tide + surge + setup (1)

where tide is the astronomical tide level, surge is storm surge, and setup is wave setup, the superelevation of the water surface at the shoreline due to wave breaking. The extreme water levels attained 

during the storm include wave swash, the time-varying component of wave energy on the beach, and are

defined as

98 = 50 + 1.1(S (2)
8

where S is the total swash excursion about the setup level and the 1.1 multiplier corrects for 

parameterization bias. It is important to note that both the mean and maximum water levels include a 

contribution from waves, which can increase water levels at the shoreline by the same magnitude as 

surge for category 1–3 hurricanes (Stockdon and others, 2007a).

2.2.1 Tide and Storm Surge

The predicted elevations of combined high tide and storm surge (tide + surge) for category 1–4

hurricanes were extracted from the National Oceanic and Atmospheric Administration (NOAA) SLOSH 

(Sea, Lake, and Overland Surges from Hurricanes) model, which has been used by NOAA in inundation 

risk studies and operational storm surge forecasting. The numerical model is based on linearized, depthintegrated equations of motion and continuity (Jarvinen and Lawrence, 1985). Storm surge is modeled 

by simulating the conditions of each category storm approaching the coast from different angles and at 

varying speeds. Changes in maximum surge elevations are forced by time-varying wind-stress and 

pressure-gradients that depend on the hurricane location, minimum pressure, and the radius of

maximum winds (Jarvinen and Lawrence, 1985).

Storm surge levels (tide + surge) are simulated for each storm category in each of eight model 

domains subdividing the mid-Atlantic region. These simulations represent the peak water levels in each 

domain forced by thousands of hypothetical storms of varying forward speed, size, and direction, under 

mean-higher-high water tide conditions. The SLOSH model does not simulate category 5 hurricanes 

north of the Pamlico Sound basin, so only categories 1–4 are included in this report. The maximum 

surge within each grid cell, or the maximum of the maximum (MOM), represents a worst-case, localized 

surge level that could occur for a nearby hurricane landfall (fig. 7). (Note that the MOM is a composite 

from many storms and does not represent water levels that would occur along the entire coast for a 

single storm.) The resulting spatial variations in maximum storm surge reflect local water depths, 

proximity to bays and rivers, and so on and are accurate to ±20 percent of the calculated value (NOAA, 

2007). Prediction errors in the SLOSH model can arise from differences between the parametric wind 

models, which force SLOSH, and the actual hurricane wind field (Houston and others, 1999) as well as 

discrepancies between the coarse model grid and the actual topography and bathymetry over which the 

storm will travel. Additional constraints were applied to address some practical implementation 

problems associated with our use of the MOMs and are described in detail in Stockdon and others 

(2012).
9

Figure 7. Sea, Lake, and Overland Surges from Hurricanes (SLOSH) category 1 modeled surge maximum of the 

maximum (MOM) for the Delaware Bay basin. Abbreviation: m, meter.
10

2.2.2. Wave Height and Period

Wave conditions vary spatially and temporally during hurricanes due to the same factors that 

cause variation in storm surge. Unfortunately, there is no equivalent of the MOMs that can be used to 

specify maximum wave height and period for category 1–4 hurricanes. Observations during hurricanes 

are incomplete as instrumented buoys tend to fail during conditions stronger than tropical storms. 

Long-term hindcasts of historical wave conditions (for example, USACE Wave Information Studies 

[WIS]) also underrepresent storm conditions. Therefore, wave conditions were estimated using the 

Simulating WAves Nearshore (SWAN) model, a spectral wave model that resolves random, shortcrested, wind-generated waves varying in time and space (Holthuijsen and others, 1993). 

One approach to calculating storm-wave parameters would be to simulate individual idealized 

hurricanes in SWAN, much like was done with SLOSH in order to build a MOM-consistent dataset. 

However, an attempt to cover the entire coastline with idealized hurricane tracks, and include a 

sufficient sampling of storm parameter space (wind speed, radius of maximum winds, forward speed, 

and so on), would require a massive computational effort. Furthermore, because we require wave 

information at a specific shallow water depth (20 m), the height of hurricane waves will tend to be 

limited by dissipation due to breaking, white-capping, and friction. Consequently, the sensitivity of 

parameterized shoreline water levels to wave parameter errors is expected to be limited as well. 

Therefore, we apply a simple approach of generating stationary waves (in time) using SWAN for 

the maximum wind speed that defines each hurricane category over the entire mid-Atlantic basin. For 

determination of the contribution of waves to the total hurricane-induced water levels, the maximum 

wave-height value from an ensemble of simulations of the maximum wind speed was selected as the 

representative value for each category, very similar to the MOM product from the SLOSH model. As 

with the MOMs, these results do not represent an individual storm but rather a composite, worst-case 

estimate at each location from many storm scenarios.

For our analysis, SWAN version 40.85 was used. The model was run in third-generation mode 

using the Westhuysen formulation for white-capping and Yan formulation for wind input (GEN3 

WESTH). Bottom friction was included using default values, and all other model parameters were left 

as default. The resolution of the SWAN computational grid was 1.5 kilometers (km) x 1.5 km (fig. 8). 

Bathymetry was interpolated from the National Geophysical Data Center (NGDC) Coastal Relief Model 

(CRM). SWAN was run 24 times for each wind speed with direction varying from 0° to 345° at 15°

increments (fig. 9). For each wind speed, the model results from the 24 wind direction runs were 

combined into a single grid by retaining the wave parameters from the wind direction that generated the 

largest significant wave height, Hs in each grid cell.

Simulated Hs at the 20-m isobaths for category 1–4 hurricanes was relatively uniform along the 

mid-Atlantic coast (fig. 9). The resulting maximum wave heights at the 20-m isobaths typically ranged 

from about 7 m to about 9 m. To put these model results in context, we compared the modeled wave 

heights to observed wave heights measured over 20-year periods at several buoys located in different 

water depths throughout the mid-Atlantic. Maximum observed wave heights were between 8 and10 m 

at relatively shallow locations (that is, 15 to 30 m water depths). The simulated wave heights are 

representative of maximum possible wave heights for each hurricane category. 

The swash parameterization requires estimates of peak period, which were not reliably estimated 

due to our simplified wind field. Consistent peak-period estimates were obtained from an analysis of 

20 years of NOAA National Data Buoy Center data for six shallow-water buoys operating in the midAtlantic. The analysis yielded a parameterized relation between significant wave height, obtained from 

the SWAN model, and peak wave periods (fig. 10). 

Tp = b0 + b1*Hs + b2*Hs

2 + b3h, (3)
11

where Tp is peak wave period, Hs is significant wave height, and h is the depth corresponding to each 

wave period and height. Data from all buoys in water depths less than 30 m were used in a linear 

regression to estimate the model coefficients, bn, resulting in 

b0 = 6.4817 (0.0425)

b1 = 0.8462 (0.0219) 

b2 = 0.0037(0.0033) 

b3 = -0.0068 (0.0009)

This parameterization was used to compute peak wave periods for each hurricane category that were 

consistent with mid-Atlantic observations, using the simulated significant wave heights and the 20-m 

depth as input. 

Figure 8. The Simulating WAves Nearshore (SWAN) model computational grid. Longitudinal and latitudinal 

resolution is 1.5 kilometers. The 20-meter isobath along the U.S. coastline is shown in red. Abbreviation: 

m, meter.
12

Figure 9. Modeled significant wave height (Hs, top) and parameterized peak period (Tp, bottom) at the 20-meter isobath extending from Virginia 

(left) to New York (right) for category 1-4 hurricanes. State boundaries are indicated by vertical lines. Abbreviations: m, meter; km, kilometer; 

s, second. 
13

Figure 10. Significant wave height (Hs) and peak (Tp) wave periods observed at National Data Buoy Center buoys. 

The black dots are from the shallow water buoys and three additional buoys south of Long Island, NY. The 

curve represents the weighted regression based on the buoy data. Abbreviations: m, meter; s, second.

2.2.3 Wave Setup and Swash

Swash and setup, the wave-induced components of total shoreline water levels, are 

parameterized using modeled wave conditions and measured beach slope (Stockdon and others, 2006). 

Setup, required in calculation of both mean (50) and extreme (98) hurricane-induced water levels, is 

parameterized as 

 

1/2

0 0 0.35   setup m

 H L

(4)

where L0 = gTp

2

/2 Wave swash, S, the time-varying component of water levels at the shoreline and 

part of the calculation of extreme (98) hurricane-induced water levels, is parameterized as

 

1/2 2

0 0 0.563 0.005 m

S H L     

 

(5)

Combining equations (4) and (5) with modeled tide and surge provided estimates of hurricane-induced

mean and maximum water levels (equations 1 and 2).
14

2.3 Probability of Coastal Change

Probabilities of coastal change are based on estimating the likelihood that the beach system will 

experience erosion and deposition patterns consistent with collision, overwash, or inundation regimes. 

Uncertainties that were incorporated in the probability estimates arise from that associated with 

measurements of topographic elevation (for example, lidar positional and interpolation uncertainties) 

and that associated with predicting wave runup elevations.

The probabilities of collision (dune erosion), overwash, and inundation were calculated using 

distributions of morphologic and hydrodynamic parameters extracted from 1-km sections of coast. 

Hydrodynamic and morphologic data were co-located alongshore using a common reference line (the 

20-yr high water line (HWL) shoreline). Morphologic features, zc, zt, and m, were interpolated to the 

reference line and smoothed using a Hanning window with a full width of 2 km. Each interpolated, 

smoothed value of (xc,zc), (xt,zt), and m was assigned a RMS error calculated from the scatter of the data 

in the smoothing window (fig. 11, top). The variables were represented with a normal distribution of 

values at the location of each 1-km section of coast using the interpolated value as the mean and the 

RMS error as the standard deviation (fig. 11, bottom). This analysis produced mean and standard 

deviations for both the hydrodynamic and the morphologic variables. 

Using the statistical distribution of the input values at each alongshore location, the probability, 

p, that the total water level exceeds the dune crest or toe elevation threshold for a particular storm 

regime is calculated from the normal cumulative distribution function

 

2

2

2

0

1

2

t

p e dt





 

  

 

(6)

where μ is the mean difference between either the mean (inundation) or extreme (collision, overwash) 

water levels and either the dune toe (collision) or dune crest (overwash, inundationregimes). The 

variance of the difference, σ

2

, is the sum of the variances of the inputs. Thus, the probabilities of each 

storm-impact regime are calculated as: 

 collision: pc = probability ([98 - zt] > 0)

 overwash: po = probability ([98 - zc] > 0)

 inundation: pi = probability ([50 - zc] > 0)

For example, figure 11 shows the cumulative distribution of (98 -xc) for a 1-km section of coast

for a category 1 hurricane. The probability that this value exceeds zero defines the likelihood that 

overwash will occur at this location.
15

Figure 11. Maximum shoreline water level (98) for a category 1 hurricane and raw and smoothed dune crest 

elevation (zc) for a 1-kilometer alongshore section (top). Shaded areas indicate the RMS error about the mean 

value within the section. Cumulative probability distribution, p(98-zc) where the sum of p over the range 

(98-zc)>0 defines the probability of overwash (bottom). Abbreviation: m, meter.

3. Results

3.1 Coastal Morphology

Dune morphologies along the mid-Atlantic coast vary extensively over both short and long 

spatial scales (table 1). Mean dune crest elevation, zc, for sandy beaches in this region is 4.39 m, and 

the standard deviation, , is 2.04 m, indicating spatial variability high enough that low elevations ( –

2) are just above the HWL (fig. 12, top). Elevations range from 1 to 30 m. Dune toe elevations are 

consistently low with less spatial variability (fig. 12, middle; zt = 2.63 m; zc = 0.87 m). Local spatial 

variability tends to scale with dune elevation; locations with higher mean elevations often exhibit 

greater spatial variability (fig. 13).
16

Figure 12. Distributions of dune crest elevation (zc, top), dune toe elevation (zt, middle), and mean beach slope 

(m, bottom) for the mid-Atlantic sandy coastlines. Abbreviation: m, meter.
17

Figure 13. Mean dune crest elevation (zc) for 1-kilometer sections of coastline compared to the standard 

deviation (zc) in those sections. The red line indicates the best fit line between the two variables. r

2 = 0.31, N 

= 674. Note: Data are plotted on a log-log scale. Abbreviation: m, meter.

The highest dunes (zc in excess of 10 m up to 30 m) are located at the eastern end of Long 

Island, N.Y. (figs. 14–15); however, variability is high for the State of New York (zc 1.95 m; zc = 

5.50 m). The lowest elevations are found in Virginia, where zc = 2.96 m, and zc = 1.89 m.
18

Figure 14. Distribution of dune crest (zc) and dune toe (zt) elevations for Virginia, Maryland, Delaware, New Jersey, 

and New York. Abbreviation: m, meter.
19

Figure 15. Dune crest (zc, red line) and dune toe (zt, blue line) elevations (top) and hurricane-induced mean (50; middle) and maximum (98; 

bottom) shoreline water levels extending from Virginia (left) to New York (right). State boundaries are indicated by vertical lines. Abbreviation: m, 

meter; km, kilometer.
20

Although also spatially variable, mean beach slope is generally low (fig. 16), leading to dissipative 

conditions during storms. The lowest slopes are located along the Virginia coast ( = 0.038;  = 

0.024). Steeper slopes are present along the Maryland coast ( = 0.065); however, the standard deviation 

was also greatest along the Maryland coast ( = 0.030). 

Figure 16. Distributions of mean beach slope (m) elevations for Virginia, Maryland, Delaware, New Jersey, and 

New York.

 

3.2 Hurricane-Induced Water Levels

The variability of hurricane-induced water levels can be attributed to both hydrodynamics and 

beach morphology. Variability of Hs at h = 20 m within a single category, due to local bathymetry and 

dissipation from depth-limited breaking, white-capping, and bottom friction, leads to corresponding 

alongshore differences in 98 and 50. The largest waves were located offshore of Long Island, N.Y.,

where a narrow shelf allows larger waves to make it to the nearshore (fig. 8), as wave breaking and, to a 

lesser extent, bottom dissipation act over very short distances before reaching the 20-m isobath. 
21

However, for the sandy beaches used in this analysis, variability of Hs along the shoreline for a single 

category is minimal; standard deviation of Hs ranges from 84 cm for a category 1 storm to 51 cm for a 

category 4 storm (table 2).

Smaller scale spatial variability of 98 is dominated by alongshore variations in m (fig. 15), as the 

wave-driven components of shoreline water levels, setup and S, are dependent on both input wave 

conditions and local slopes (equations 4 and 5). For similar input wave conditions, steeper beach slopes 

result in higher total runup elevations (R2 = 1.1[setup + S/2]). For example, category 3 R2 is 0.5 m

higher for Maryland than Virginia, despite the fact that wave heights are 0.5 m lower for Maryland. The 

higher wave runup is due to the steeper beach slope in Maryland (m = 0.064) compared to the beach 

slope in Virginia (m = 0.038). 

The modeling exercise demonstrated the relative importance of waves with respect to storm surge. 

For a category 1 storm, wave-driven components represent 57 percent of the total hurricane-induced 

water levels; the remaining 43 percent is attributed to tides and surge (table 2). As the intensity of the 

storm increases, the relative contribution of wave-driven components decreases. In a category 4 hurricane,

surge dominates the signal, contributing 66 percent to the total water-level elevation. Wave height does 

not grow as rapidly due to dissipation through white-capping and breaking; however, surge continues to 

grow, approximately 1 m with each category.

3.3 Probability of Coastal Change

The probabilities of collision, overwash, and inundation indicate whether a specified coastal 

change regime is very likely (probability >90 percent), likely (>66 percent), about as likely as not (34 to 

66 percent), unlikely (<33 percent), and very unlikely (<10 percent) given the local landfall of each 

hypothetical storm scenario. (Range descriptions are based on guidance from the Intergovernmental Panel 

on Climate Change (IPCC) (Le Treut and others, 2007).) Probabilities of coastal change for the lowest 

category hurricane show the vulnerability of beaches along the mid-Atlantic coast to dune erosion and 

overwash (fig. 17). For direct landfall of the lowest category hurricane, 89 percent of sandy beaches are

very likely to experience dune erosion due to collision, and 25 percent of the coastal areas are vulnerable 

to overwash (table 3). Inundation of the beach and dune system is expected along 6 percent of the 

coastline, typically near areas with low elevation beach berms (Virginia eastern shore, fig. 17). For a 

category 4 hurricane landfall, 92 percent of mid-Atlantic beaches are very likely to experience overwash 

and associated beach and dune erosion (table 3), and the percentage of beaches that are very likely to be

inundated increases to 66 percent. 

Spatial variability exists over smaller spatial scales on the order of kilometers in areas with 

complex dune fields or extensive three-dimensional beach morphology. For example, in Assateague 

Island (Virginia and Maryland), the mix of low and high dunes leads to a more complicated picture of 

hurricane-induced erosion hazards (fig. 17). The smaller scale variability (alongshore spacing of 1 km) 

for a specific area can be examined using interactive maps that are available online. The probability of 

each mode of coastal change for category 1–4 landfalls, as well as supporting morphology and water level 

data, are available online (http://coastal.er.usgs.gov/hurricanes/erosionhazards/). 
22

Figure 17. Probabilities of collision (inner strip), overwash (middle strip), and inundation (outer strip) during a category 1 hurricane for Virginia,

Maryland, Delaware, New Jersey, and New York. Where the dune is identified as a berm, the collision regime is undefined.
23

4. Discussion

4.1 Validity of Assumptions

This analysis is based on an estimate of a worst-case scenario with respect to storm surge levels

and wave heights associated with each hurricane category. The implied assumption is that each location 

along the coast experiences the right-front quadrant of the hurricane at landfall, which results in onshoredirected winds and the combined effects of both the circulation and the forward motion of the storm. 

Waves and surge associated with an actual storm may be significantly different than model results. 

The SLOSH results are grouped by Saffir-Simpson categories, which are based on wind speeds. It is 

recognized that wind-based Saffir-Simpson categories are not always an accurate indicator of expected 

storm surge (Irish and others, 2008, 2009); however, they do provide a convenient way to represent the 

data based on storm intensity, allowing users to examine a range of conditions in a small number of 

scenarios. Real-time analysis of individual landfalling hurricanes includes more realistic updates of 

surge, tides, and waves (http://coastal.er.usgs.gov/hurricanes/). 

This assessment also assumes that the existing lidar-surveyed topography is up to date and 

accurately and synchronously reflects dune and beach morphology. Significant changes to beach and 

dune morphology between the survey date and dates of future hurricane landfall may affect the 

probabilities of coastal change. We assume that dune elevations change relatively slowly under nonstorm conditions. Changes in beach slope, which will in turn affect the elevation of wave runup, may 

occur more frequently as the foreshore profile adjusts to the daily wave climate. However, use of a mean 

beach slope (fig. 6) in this analysis provides a more temporally stable estimate of slope and hence a more 

consistent measure of storm-induced wave runup.

4.2 Relative Importance of Waves and Storm Surge

Hurricane-induced coastal changes are caused by waves and surge and the interaction of these 

processes with coastal morphology. Waves (setup and swash) were shown to increase water levels at the 

shoreline by 130 percent for a category 1 storm when compared to considering storm surge alone. Using 

a regional average of wave height and period for a category 1 hurricane (table 2), the predicted wavedriven component of shoreline water levels, R2, was 2.64 m, high enough to reach the mean dune toe 

elevation (zt = 2.63 ), even without surge. For the category 2 conditions modeled in this analysis, the 

wave-driven and storm surge components were of similar magnitudes (2.71 m and 3.23 m, respectively). 

During a category 4 storm, the magnitude of surge was almost two times that of the wave component; 

however, the magnitude of runup was 3 m, exceeding the dune crest height in some areas (table 1). This 

indicates that the waves alone are large enough to make dunes in this area vulnerable to extreme erosion. 

4.3 Assessment Updates

The vulnerability of sandy beaches can be expected to change in the future due to variations in 

storminess, sea-level rise, and human engineering efforts that alter beach configurations. As new 

observations and storm predictions become available, assessments may be revised to provide updated 

probabilities as well as a synthesis of how coastal vulnerability to storms changes in the future 

(http://coastal.er.usgs.gov/hurricanes/erosionhazards/sea). Specifically, coastal topography will be 

updated to account for actual storm-driven, or even engineered, changes to the coast associated with 

hurricane recovery, coastal restoration, and mitigation. This updating process is underway nearly 

continuously as USGS, USACE, State agencies, and other entities utilize increasingly effective lidar 

capabilities in acquiring coastal coverage for a variety of needs. Examples of the updates associated with 
24

major hurricane landfall can be found at http://coastal.er.usgs.gov/hurricanes/. In addition, as modeling 

capabilities advance, such as improvements to include storm size (Irish and others, 2008), surge inputs to 

coastal change analyses can be updated accordingly.

5. Conclusion

This assessment quantifies the probabilities of dune erosion, dune overwash, and beach/dune 

inundation during the landfall of category 1–4 hurricanes. The probabilities were calculated by 

comparing beach/dune elevations to modeled estimates of the hurricane-induced total water level at the 

shoreline, including contributions from both waves and storm surge. The beaches, coastal infrastructure, 

and habitat of the mid-Atlantic coast are vulnerable to extreme coastal changes during landfall of even 

category 1 hurricanes. The severity of these changes increases as the intensity of the storm-driven surge 

and waves increases and as the height of protective dunes decreases. Citizens and coastal managers who 

need to understand, plan for, and adapt to different levels of vulnerability will benefit from guidance on 

the likelihood of encountering mild, moderate, or severe erosion and deposition (overwash) associated 

with different storm intensities.

By including wave-driven setup and swash in addition to storm surge, we have identified the 

relative importance of waves in terms of their impact on erosion vulnerability. For category 1 hurricanes 

in this region, modeled wave runup is of greater magnitude than storm surge. As storm intensity 

increases, the relative importance of storm surge grows at a fast rate as nearshore wave height is limited 

due to dissipation by white-capping and breaking. Analyses that ignore the wave component of this 

problem will underestimate erosion vulnerability, particularly for lower category storms (or even weak 

tropical storms or cold fronts) . 

The combination of large waves and surges along a region with low coastal elevations makes the 

entire mid-Atlantic vulnerable to significant coastal erosion during storms. In the direct landfall of a

category 1 hurricane, 89 percent of dune-backed beaches along the mid-Atlantic coast are very likely 

(p>90 percent) to experience dune erosion during the collision regime. Overwash during a category 1 

landfall is very likely along 25 percent of the coast. During category 4 conditions, 92 percent of the midAtlantic beaches are very likely to experience overwash and associated erosion, and 66 percent of the 

beaches and dunes are very likely to be vulnerable to erosion due to inundation.

6. Acknowledgments

The USGS National Assessment of Coastal Change Hazards Project Extreme Storms and 

Hurricanes group thanks the many scientists and research assistants who have contributed to the research 

and data collections that made this assessment possible. We would especially like to acknowledge Abby 

Sallenger, who was instrumental in the creation and development of this project. Extensive data 

processing and mapping support were provided by Kathryn Smith, Kristy Guy, and Jolene Gittens. We 

also thank the EAARL and CHARTS programs as well as USGS field support and survey teams for the 

comprehensive lidar dataset that formed the basis of this analysis. Peter Howd and Kathy Weber

provided thorough and thoughtful reviews. This research has been supported by the USGS Coastal and

Marine Geology Program. 
25

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27

Tables

Table 1. Mean elevation of dune crest (zc) and dune toe (zt) and mean beach slope (m) for the sandy beaches 

along the mid-Atlantic coast. [Standard deviation is given in parentheses. m, meter]

State/region zc [m] zt [m] m Survey date 

[month/year]

U.S. mid-Atlantic coast 4.39 (2.04) 2.63 (0.87) 0.048 (0.027)

Virginia 2.96 (1.89) 2.15 (0.66) 0.038 (0.024) 11/2009, 7/2010

Maryland 3.81 (1.11) 2.40 (0.68) 0.064 (0.030) 9/2010

Delaware 5.04 (1.92) 2.57 (0.48) 0.042 (0.016) 9/2010

New Jersey 4.88 (1.61) 2.47 (0.77) 0.047 (0.029) 8/2010

New York 5.50 (1.93) 3.14 (0.93) 0.051 (0.022) 8/2010

Table 2. Mean input wind speed, significant wave height (Hs), and wave period (Tp) and modeled setup (setup), 

runup (R2) and storm surge (surge) for category 1–4 hurricanes. [Standard deviation is given in parentheses. m, 

meter; m/s, meters per second; s, second]

Hurricane 

intensity 

category

Wind

speed [m/s]

Hs [m] Tp [s] setup [m] R2 [m] surge [m]

1 42.5 7.67 (0.84) 13.05(0.75) 0.74 (0.34) 2.58 (0.61) 1.98 (0.46)

2 49.2 7.88 (0.71) 13.24 (0.63) 0.77 (0.35) 2.69 (0.63) 3.23 (0.66)

3 58.1 8.20 (0.58) 13.53 (0.52) 0.81 (0.37) 2.83 (0.66) 4.52 (0.83)

4 69.3 8.75 (0.51) 14.03 (0.46) 0.85 (0.40) 2.99 (0.71) 5.77 (0.98)
28

Table 3. Percentage of coast very likely (p>0.9) to experience erosion associated with collision, overwash, and 

inundation during category 1–4 hurricanes. 

Hurricane intensity category

1 2 3 4

Collision 

 U.S. mid-Atlantic coast 89 100 100 100

 Virginia 97 100 100 100

 Maryland 90 100 100 100

Delaware 83 100 100 100

 New Jersey 96 100 100 100

 New York 76 99 99 100

Overwash

 U.S. mid-Atlantic coast 25 45 77 92

 Virginia 62 71 83 90

 Maryland 33 59 100 100

 Delaware 0 0 73 93

 New Jersey 11 46 81 98

 New York 9 27 61 85

Inundation

 U.S. mid-Atlantic coast 6 25 43 66

 Virginia 22 63 73 82

 Maryland 0 22 55 84

 Delaware 0 0 0 38

 New Jersey 1 11 47 75

 New York 1 14 23 49