National Assessment of Hurricane-Induced Coastal 

Erosion Hazards: Southeast Atlantic Coast

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

Open-File Report 2013–1130

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

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Suggested citation:

Stockdon, H.F., Doran, K.J., Thompson, D.M., Sopkin, K.L., and Plant, N.G., 2013, National assessment of hurricaneinduced coastal erosion hazards: Southeast Atlantic Coast: U.S. Geological Survey Open-File Report 2013-1130, 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.
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Contents

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

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

1.2 Prediction of Hurricane-Induced Coastal Erosion................................................................................................. 4

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

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

2.1 Lidar-Derived Beach Morphology......................................................................................................................... 7

2.2 Hurricane-Induced Water Levels.......................................................................................................................... 8

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

2.2.2. Wave Height and Period............................................................................................................................... 9

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 ..........................................................................................................................................24

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

6. Acknowledgments.....................................................................................................................................................25

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

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

Figures

1. Parking areas, septic systems, and foundations in South Nags Head, North Carolina, collapsed when the 

underlying dune was eroded during the September 1999 landfall of Hurricane Dennis.................................... 1

2. Waves and surge left an overwash deposit covering roads during Hurricane Isabel in September 2003 (top). 

Parking areas near the beach and elevated homes (bottom) in Rodanthe, North Carolina, were filled during 

Hurricane Dennis in September 1999............................................................................................................... 2

3. Storm surge, waves, and currents from Hurricane Isabel (August 2003) cut a breach across Highway 12 on 

Hatteras Island, North Carolina, severing the single road in and out of the village. .......................................... 3

4. Lidar-based elevations of Hatteras Island, North Carolina, before (left) and after (middle) the landfall of 

Hurricane Isabel in September 2003 show erosion of the seaward-most dune and subsequent breaching ..... 3

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

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

6. 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

7. 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

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

maximum (MOM) for the Wilmington/Myrtle Beach basin................................................................................. 9

9. Simulating WAves Nearshore (SWAN) model computational grid. ................................................................. 11
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10. Modeled significant wave height (Hs, top) and parameterized peak period (Tp, bottom) at the 20-meter isobath 

extending from Florida (left) to North Carolina (right)...................................................................................... 12

11. Significant wave height (Hs) and peak (Tp) wave periods for shallow (h < 30 m) water observed at National 

Data Buoy Center buoys................................................................................................................................. 13

12. 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

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

bottom) for the southeast Atlantic sandy coastlines........................................................................................ 16

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

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

15. Distribution of dune crest (zc) and dune toe (zt) elevations for Florida, Georgia, South Carolina, and North 

Carolina. ......................................................................................................................................................... 18

16. Dune crest (zc) and toe (zt) elevations (top) and hurricane-induced mean (50; middle) and maximum (98; 

bottom) shoreline water levels extending from Florida (left) to North Carolina (right).. ................................... 19

17. Distributions of mean beach slope (m) elevations for Florida, Georgia, South Carolina, and North Carolina.20

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

hurricane for Florida, Georgia, South Carolina, and North Carolina. .............................................................. 22

Tables

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

the southeast Atlantic coast. [Standard deviation is given in parentheses. m, meter]..................................... 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 1-5 hurricanes. [Standard deviation is given in parentheses. m, 

meter; m/s, meters per second; s, second]..................................................................................................... 27

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

inundation during category 1–5 hurricanes..................................................................................................... 28
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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
vi

RMS Root-Mean-Square

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: Southeast Atlantic Coast

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

1. Introduction 

1.1 Impacts of Hurricanes on Coastal Communities

Beaches serve as a natural barrier 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 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. The September 1999 

landfall of Hurricane Dennis caused erosion and undermining that destroyed roads, foundations, and 

septic systems (fig. 1). 

Figure 1. Parking areas, septic systems, and foundations in South Nags Head, North Carolina, collapsed when the 

underlying dune was eroded during the September 1999 landfall of Hurricane Dennis.
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 new inlet, completely severing evacuation routes. Waves and 

surge during the 2003 landfall of Hurricane Isabel left a 200-meter (m) wide breach that cut the only 

road to and from the village of Hatteras, N.C. (fig. 3).

 

Figure 2. Waves and surge left an overwash deposit covering roads during Hurricane Isabel in September 2003 

(top). Parking areas near the beach and elevated homes (bottom) in Rodanthe, North Carolina, were filled during

Hurricane Dennis in September 1999.

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 sound-
3

side of the barrier, and on the mainland. During Hurricane Isabel the protective sand dunes near the 

breach were completely eroded, increasing vulnerability to future storms (fig. 4). 

Figure 3. Storm surge, waves, and currents from Hurricane Isabel (August 2003) cut a breach across Highway 12 

on Hatteras Island, North Carolina, severing the single road in and out of the village. The Atlantic Ocean is 

located to the left of the image. Asphalt from the highway is on the right side of the image. See figure 4 for aerial 

view of the breach. 

Figure 4. Lidar-based elevations of Hatteras Island, North Carolina, before (left) and after (middle) the landfall of 

Hurricane Isabel in September 2003 show erosion of the seaward-most dune and subsequent breaching (right). 

See figure 3 for a ground view of the breach. Note: Lidar surveys did not cover the same, or full, width of 

island during each survey.
4

1.2 Prediction of Hurricane-Induced Coastal Erosion

In the last decade, communities along the southeastern coast of the United States have been 

devastated by many powerful hurricanes including Isabel (2003), Frances (2004), Jeanne (2004), Irene 

(2011), and Sandy (2012). Waves and surge accompanying these storms resulted in widespread beach 

and dune erosion and extensive overwash (Sallenger and others, 2004, 2005, 2006; 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 the 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 southeast Atlantic shoreline. 

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

of Sallenger (2000) (modified from Stockdon and others, 2009).
5

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

seaward sand dunes that define 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. 5) (Sallenger, 

2000; Stockdon and others, 2007a). 

 swash (98 < zt)

 collision (98 > zt)

 overwash (98 > zc) 

 inundation (50 > zc)

(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 6. 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 transported offshore and will 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. 6, left). Again, 

sand is transported seaward and then re-deposited on the beach or sandbar or transported alongshore. 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.

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. 6, 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. 6, 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 
6

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 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. southeast 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 1–5 hurricanes. This report, along with a similar analysis for 

the Gulf of Mexico shorelines (Stockdon and others, 2012), forms the basis of a USGS 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 1–5 hurricanes and can 

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

1–5 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 

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.
7

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 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. 7). These features are ultimately used to estimate wave runup and the corresponding stormresponse regimes, as well as to measure 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 7. 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.
8

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)

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 1–5 

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 10 model 

domains subdividing the southeast 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 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. 8). (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 8. Sea, Lake, and Overland Surges from Hurricanes (SLOSH) category 1 modeled surge maximum of the 

maximum (MOM) for the Wilmington/Myrtle Beach basin. Abbreviation: m, meter.

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 1–5 hurricanes. Observations during hurricanes 

are incomplete as instrumented buoys tend to fail during conditions stronger than tropical storm. Longterm 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, short-crested, windgenerated waves varying in time and space (Holthuijsen and others, 1993). 
10

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 southeast 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 2 kilometers (km) x 2 km (fig. 9). 

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

(CRM). The Cape Hatteras Inundation digital elevation model (DEM) was used to fill a gap in the CRM 

off Cape Hatteras at the shelf break. A nested 100 m x 500 m (cross-shore x alongshore) rectangular 

grid for south Florida was nested in the larger domain. This was needed to resolve the 20 m isobath, 

where significant wave height (Hs) is extracted for analysis, along the south Florida coast where the 

continental shelf is very narrow. SWAN was run 24 times for each wind speed with direction varying 

from 15° to 360° 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 Hs in each grid cell.

Simulated Hs at the 20-m isobaths for category 1–5 hurricanes was relatively uniform along the 

southeast Atlantic coast with the exception of south Florida (fig. 10). The resulting maximum wave 

heights at the 20-m isobaths typically ranged from about 7 m to about 9 m, except in south Florida, 

where the continental shelf was very narrow resulting in higher maximum wave height values. To put 

these model results in context, we compared the modeled wave heights to observed wave heights at 

several buoys located in different water depths throughout the southeast Atlantic. Maximum observed 

wave heights were between 5 m and10 m at relatively shallow locations (that is, 15 to 30 m water 

depths, which included just three buoys). 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 28 shallow-water buoys (< 30 m) operating in the 

southeast Atlantic. The analysis yielded a parameterized relation between significant wave height, 

obtained from the SWAN model, and peak wave periods (fig. 11). 

Tp = b0 + b1*Hs + b2*Hs

2 + b3h, (3)

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 
11

b0 = 7.3111 (0.0374)

b1 = 0.4367 (0.0225) 

b2 = 0.0677 (0.0037) 

b3 = -0.0136 (0.0015)

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

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

m depth as input. 

Figure 9. Simulating WAves Nearshore (SWAN) model computational grid. Longitudinal and latitudinal resolution is 

2 kilometers. The green box represents a finer (100 m x 500 m) nested grid designed to resolve the narrow 

continental shelf in southeast Florida. The 20-meter isobath along the U.S. coastline is shown in red. Abbreviation: 

m, meter.
12

Figure 10. Modeled significant wave height (Hs, top) and parameterized peak period (Tp, bottom) at the 20-meter isobath extending from Florida (left) 

to North Carolina (right). State boundaries are indicated by vertical lines. Abbreviations: m, meter; km, kilometer; s, second. 
13

Figure 11. Significant wave height (Hs) and peak (Tp) wave periods for shallow (h < 30 m) water observed at National 

Data Buoy Center buoys. The black curve represents the weighted regression based on shallow water buoys. 

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)
14

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

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

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. 12, 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. 12, 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, inundation) regime. 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 12 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 12. 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 southeast 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 3.84 m, and 

the standard deviation, , is 1.40 m, indicating spatial variability high enough that low elevations ( –

2) are just above the HWL (fig. 13, top). Elevations range from 1 to 10 m. Dune toe elevations are 

consistently low with less spatial variability (fig. 14, middle; zt = 2.26 m; zc = 0.69 m). Local spatial 

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

greater spatial variability (fig. 14).
16

Figure 13. Distributions of dune crest elevation (zc, top), dune toe elevation (zt, middle), and mean beach slope (m, 

bottom) for the southeast Atlantic sandy coastlines. Abbreviation: m, meter.
17

Figure 14. Mean dune crest elevation (zc) for 1-kilometer sections of coastline compared to the standard deviation 

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

2 = 0.25, N = 1455. 

Note data are plotted on a log-log scale. Abbreviation: m, meter.

The highest dunes (zc in excess of 9 m) are located in north Florida and northern North Carolina

(figs. 15–16); however, variability is high in both states, as noted by zc of 1.52 m (zc = 4.71 m) and 

1.73 m (zc = 4.44 m), respectively. The lowest elevations are found along the coast of South Carolina

(zc = 2.93 m; zc = 0.85 m) and northern Georgia (zc 3.28 m; zc = 1.50 m). 
18

Figure 15. Distribution of dune crest (zc) and dune toe (zt) elevations for Florida, Georgia, South Carolina, and North 

Carolina. Abbreviation: m, meter.
19

Figure 16. Dune crest (zc) and toe (zt) elevations (top) and hurricane-induced mean (50; middle) and maximum (98; bottom) shoreline water levels 

extending from Florida (left) to North Carolina (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. 17), leading to dissipative 

conditions during storms. The lowest slopes, and least spatial variability, are located along the Georgia

coast ( = 0.041;  = 0.019). Steeper slopes are present along the Florida coast ( = 0.082); however, 

the standard deviation was also greatest along the Florida coast ( = 0.041). 

Figure 17. Distributions of mean beach slope (m) elevations for Florida, Georgia, South Carolina, and North Carolina. 

 

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 south Florida where a 

narrow shelf allows larger waves to make it to the nearshore (fig. 10), as wave breaking and, to a lesser 

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

the sandy beaches used in this analysis, variability of Hs along the shoreline for a single category is 
21

minimal; standard deviation of Hs ranges from 28 cm for a category 1 storm to 11 cm for a category 5 

storm (table 2).

Smaller scale spatial variability of 98 is dominated by alongshore variations in m (fig. 17), 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, modeled category 3 wave 

heights were 8.2 m for both Florida and Georgia coastlines. However, R2 is a meter higher on the 

Florida coast than in Georgia because of the steeper slopes: m = 0.082 in Florida as opposed tom = 

0.041 in Georgia.

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

For a category 1 storm, wave-driven components represent 63 percent of the total hurricane-induced 

water levels; the remaining 27 percent is attributed to tides and surge (table 2). For a category 2 storm, 

the contributions are approximately equal, 53 percent from waves and 47 percent from combined tides 

and surge. In a category 5 hurricane, surge dominates the signal, contributing 61 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 (33 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 southeast Atlantic coast to dune erosion

and overwash (fig. 18). For direct landfall of the lowest category hurricane, 89 percent of sandy beaches

are very likely to experience dune erosion due to collision and nearly half (47 percent) of the coastal 

areas are vulnerable to overwash (table 3). Inundation of the beach and dune system is expected along 

12 percent of the coastline, typically near areas with low elevation beach berms (the Georgia and South 

Carolina coasts, fig. 18). For a category 5 hurricane landfall, 92 percent of southeast 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 75 percent. 
22

Figure 18. Probabilities of collision (inner strip), overwash (middle strip), and inundation (outer strip) during a category 

1 hurricane for Florida, Georgia, South Carolina, and North Carolina.
23

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 north Florida, 

the mix of low and high dunes leads to a more complicated picture of hurricane-induced erosion hazards

(fig. 18). 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 1–5 landfalls, as well as supporting morphology and water level data, are available online

(http://coastal.er.usgs.gov/hurricanes/erosionhazards/). 

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. 7) 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 170 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 3.26 m, high enough to reach the mean dune toe 

elevation (zt = 2.36 m), even without surge. For the category 2 conditions modeled in this analysis, the 

wave-driven and storm surge components were of similar magnitudes (3.42 m and 3.05 m, respectively). 

During a category 5 storm, the magnitude of surge was almost one and a half times that of the wave 

component; however, the magnitude of runup was 4 m, exceeding the mean dune crest height (3.84 m; 

table 1). This indicates that the waves alone are large enough make dunes in this area vulnerable to 

extreme erosion. 
24

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/). 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, U.S. Army Corps of Engineers, State agencies, and other entities utilize increasingly effective 

lidar capabilities in acquiring coastal coverage for a variety of surveying needs. Examples of these 

updates associated with 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 1–5 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 southeast 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 to 2 

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 in a region with low coastal elevations makes the 

entire southeast 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 southeast 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 47 percent of the coast. During category 5 conditions, 92 percent of the 

southeast Atlantic beaches are very likely to experience overwash and associated erosion, and 75 percent 

of the beaches and dunes are very likely to be vulnerable to erosion due to inundation.
25

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 the National Assessment Coastal 

Change Hazards project. Extensive data processing and mapping support were provided by Kathryn 

Smith, Laura Fauver, 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. Emily Himmelstoss and Peter Howd provided thorough and thoughtful reviews. This 

research has been supported by the USGS Coastal and Marine Geology Program. 

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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 southeast Atlantic coast. [Standard deviation is given in parentheses. m, meter]

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

[month/year]

U.S. southeast Atlantic 

coast

3.84 (1.40) 2.26 (0.69) 0.062 (0.029)

Florida 4.71 (1.52) 2.72 (0.81) 0.082 (0.041) 8/2009, 5/2010

Georgia 3.28 (1.50) 2.16 (0.73) 0.041 (0.019) 5/2010

South Carolina 2.93 (0.85) 1.85 (0.46) 0.055 (0.029) 5/2010

North Carolina 4.44 (1.73) 2.29 (0.75) 0.054 (0.026) 8/2009, 5/2010

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 1-5 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 7.44 (0.28) 14.03 (0.41) 1.03 (0.50) 3.26 (0.94) 1.88 (0.63)

2 49.2 7.72 (0.20) 14.44 (0.30) 1.08 (0.52) 3.42 (0.97) 3.05 (0.95)

3 58.1 8.09 (0.14) 15.00 (0.22) 1.15 (0.55) 3.63 (1.02) 4.18 (1.19)

4 69.3 8.52 (0.11) 15.68 (0.18) 1.23 (0.59) 3.90 (1.09) 5.23 (1.40)

5 73.8 8.69 (0.11) 15.94 (0.18) 1.27 (0.60) 4.00 (1.12) 6.27 (1.57)
28

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

inundation during category 1–5 hurricanes. 

Hurricane intensity category

1 2 3 4 5

Collision 

 U.S. southeast Atlantic coast 89 95 96 96 96

 Florida 92 99 99 99 100

 Georgia 98 100 100 100 100

South Carolina 100 100 100 100 100

 North Carolina 91 99 100 100 100

Overwash

 U.S. southeast Atlantic coast 47 66 78 87 92

 Florida 30 56 78 91 97

 Georgia 71 89 95 97 97

 South Carolina 96 99 100 100 100

 North Carolina 31 52 65 78 89

Inundation

 U.S. southeast Atlantic coast 12 36 51 64 75

 Florida 1 7 29 52 75

 Georgia 40 68 84 93 95

 South Carolina 34 95 99 100 100

 North Carolina 4 23 39 50 59