National Assessment of Hurricane-Induced Coastal 

Erosion Hazards: Gulf of Mexico 

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

Asbury H. Sallenger

Open-File Report 2012–1084

U.S. Department of the Interior

U.S. Geological Survey
ii

U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia McNutt, Director

U.S. Geological Survey, Reston, Virginia 2012

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

http://pubs.usgs.gov/of/2012/1084/

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Suggested citation:

Stockdon, H.F., Doran, K.J., Thompson, D.M., Sopkin, K.L., Plant, N.G., and Sallenger, A.H., 2012, National 

assessment of hurricane-induced coastal erosion hazards: Gulf of Mexico: U.S. Geological Survey Open-File Report 

2012-1084, 51 p. 

Any use of trade, product, or firm names is for descriptive purposes only and does not imply 

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................................................................................................................................................................ 8

1.1 Impacts of hurricanes on coastal communities ........................................................................................................ 8

1.2 Prediction of hurricane-induced coastal erosion .................................................................................................12

1.3 Storm-scaling model ...........................................................................................................................................12

2. Methods....................................................................................................................................................................16

2.1 Lidar-derived beach morphology.........................................................................................................................17

2.1.1 Shoreline position.........................................................................................................................................18

2.1.2 Dune Crest ...................................................................................................................................................19

2.1.3 Dune Toe......................................................................................................................................................21

2.1.4 Beach slope..................................................................................................................................................23

2.2 Hurricane-induced water levels...........................................................................................................................23

2.2.1 Tide and Storm Surge ..................................................................................................................................24

2.2.2. Wave Height and Period..............................................................................................................................26

2.2.3 Wave Setup and Swash ...............................................................................................................................31

2.3 Probability of coastal change ..............................................................................................................................32

3. Results......................................................................................................................................................................34

3.1 Coastal morphology ............................................................................................................................................34

3.2 Hurricane-induced water levels...........................................................................................................................40

3.3 Probability of Coastal Change.............................................................................................................................41

4. Discussion ................................................................................................................................................................44

4.1 Validity of assumptions .......................................................................................................................................44

4.2 Relative importance of waves and storm surge ..................................................................................................45

4.3 Assessment updates...........................................................................................................................................46

5. Conclusion................................................................................................................................................................46

6. Acknowledgments.....................................................................................................................................................47

References Cited..........................................................................................................................................................48

Tables...........................................................................................................................................................................50
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Figures

Figure 1. A five-story condominium in Orange Beach, Alabama, collapsed when the dune underlying its foundation 

was eroded during the September 2004 landfall of Hurricane Ivan. .............................................................................. 9

Figure 2. Waves and surge left an overwash deposit covering the road in Pensacola Beach, Florida, after 

Hurricane Ivan in September 2004 .............................................................................................................................. 10

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. ................................................ 11

Figure 4. Lidar-based elevations of Hatteras Island, North Carolina, before and after the landfall of Hurricane 

Isabel in September 2003 show erosion of the seaward-most dune and subsequent breaching................................. 11

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

model of Sallenger (2000) ........................................................................................................................................... 13

Figure 6. Examples of collision (Nags Head, North Carolina; Isabel, 2003), overwash (Santa Rosa Island, Florida; 

Ivan, 2004), and inundation (Dauphin Island, Alabama; Katrina, 2005)....................................................................... 14

Figure 7. Cross-shore profile of lidar-based 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). ............................... 19

Figure 8. Automatically extracted dune crest (zc) and toe (zt) overlain on three-dimensional gridded lidar 

topography from Santa Rosa Island, Florida, collected September 8, 2008, after the landfall of Hurricane Gustav .... 21

Figure 9. Sea, Lake, and Overland Surges from Hurricanes (SLOSH) category 1 modeled surge Maximum of the 

Maximum (MOM) for the New Orleans basin............................................................................................................... 26

Figure 10. Simulating WAves Nearshore (SWAN) computational grid containing 169,188 active nodes.............. 28

Figure 11. Modeled significant wave height (Hs) and parameterized peak period (Tp) at the 20-meter isobath 

around the Gulf of Mexico extending from Texas to the Florida west coast ................................................................ 30

Figure 12. Significant wave height (Hs) and peak (Tp) wave periods for deep (water depth, h, > 30 m; black) and 

shallow (h < 30 m; red) water observed at National Data Buoy Center (NDBC) buoys in the Gulf of Mexico.............. 31

Figure 13. Maximum shoreline water level (98) for a category 1 hurricane and raw and smoothed dune crest 

elevation (zc) for a 1-kilometer alongshore section. ..................................................................................................... 34

Figure 14. Distributions of dune crest elevation (zc, a), dune toe elevation (zt, b), and mean beach slope (m, c) 

for the U.S. Gulf of Mexico sandy coastlines. .............................................................................................................. 35

Figure 15. Mean dune crest elevation (zc) for 1-kilometer sections of coastline compared to the standard 

deviation (zc) in those section.. ................................................................................................................................. 36
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Figure 16. Distribution of dune crest (zc) and dune toe (zt) elevations for Texas, Louisiana, Mississippi, Alabama, 

Florida Panhandle, and Florida west coast.................................................................................................................. 37

Figure 17. Dune crest (zc) and toe (zt) elevations (top) and hurricane-induced mean (50) and maximum (98) 

shoreline water levels around the Gulf of Mexico extending from Texas to the Florida west coast ......................... 38

Figure 18. Distributions of mean beach slope (m) elevations for Texas, Louisiana, Mississippi, Alabama, Florida 

Panhandle, and Florida west coast.............................................................................................................................. 39

Figure 19. Probabilities of collision (c), overwash (o), and inundation (i) during a category 1 hurricane for Texas 

and Louisiana............................................................................................................................................................. 42

Figure 20. Probabilities of collision (c), overwash (o), and inundation (i) during a category 1 hurricane for 

Mississippi, Alabama, and west Florida. ...................................................................................................................... 43

Tables

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

along the Gulf of Mexico coast .................................................................................................................................... 50

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 ................................................................................ 50

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

inundation during category 1-5 hurricanes................................................................................................................... 51
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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

meters per second (m/s) 2..237 mile per hour (mi/h)

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

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

Additional Abbreviations and Acronyms

ACRONYM OR ABBREVIATION DEFINITION 

CHARTS Compact Hydrographic Airborne Rapid Total Survey

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
vii

NAVD88 North American Vertical Datum 1988

NGVD29 National Geodetic Vertical Datum 1929

NHC National Hurricane Center

NOAA National Oceanic and Atmospheric Administration

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
8

National Assessment of Hurricane-Induced Coastal 

Erosion Hazards: Gulf of Mexico 

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

Sallenger 

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, such as powerful hurricanes, 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. Structures built on or a near a dune can be undermined during wave attack and 

subsequent erosion. During Hurricane Ivan in 2004, a five-story condominium in Orange Beach, Ala., 

collapsed after the sand dune supporting the foundation eroded (fig. 1). 
9

Figure 1. A five-story condominium in Orange Beach, Alabama, collapsed when the dune 

underlying its foundation was eroded during the September 2004 landfall of Hurricane 

Ivan.

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).
10

Figure 2. Waves and surge left an overwash deposit covering the road in Pensacola Beach, 

Florida, after Hurricane Ivan in September 2004 (photo credit: Jeff Lillycrop, U.S. Army 

Corps of Engineers).

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 and on the 

mainland. During Hurricane Isabel the protective sand dunes near the breach were completely eroded, 

increasing vulnerability to future storms (fig. 4). 
11

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 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 seawardmost 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.
12

1.2 Prediction of hurricane-induced coastal erosion

In the last decade, gulf coast communities have been devastated by many powerful hurricanes 

including Charley (2004), Ivan (2004), Katrina (2005), Rita (2005), and Ike (2008). Waves and surge 

accompanying these storms resulted in widespread beach and dune erosion and extensive overwash 

(Sallenger and others, 2006; Doran and others, 2009a; Doran and others, 2009b). There exists a clear 

need 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 associated with building houses or 

infrastructure on land that shifts and moves with each storm landfall. A decade of research on stormdriven 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 USGSdeveloped 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, we focus on the sandy beaches on the U.S. Gulf of Mexico 

coastline. These beaches are among the most vulnerable in the Nation due to low coastal elevations and 

frequent hurricane landfalls. 

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 
13

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).

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). 
14

 swash (98 < zt)

 collision (98 > zt)

 overwash (98 > zc) 

 inundation (50 > zc)

(Note: Following Plant and Stockdon (in press), 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 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 
15

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 more 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, or 

even permanent, 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. Within this regime some of the most 

extreme coastal changes occur on barrier islands: 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 due to 

transport of sand 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, 

carving breaches. 

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

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

Ivan (2004) (Stockdon and others, 2007b) and comparing this 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 between regimes and was highest for the overwash 

conditions. Underprediction of the actual storm response was more likely than over prediction. 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.
16

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

hurricanes (Plant and others, 2010). 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 the storm makes landfall.

Using a similar methodology, this report quantifies the likely impact of a hypothetical hurricane 

landfall on the Gulf of Mexico coastline. The probabilities of hurricane-induced 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. 

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. Lidar topographic 

surveying has made it possible to accurately measure dune elevations along long (hundreds of 

kilometers) stretches of coastline. The combination of high-resolution measurements and advanced 
17

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 2000 to 2008 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 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) 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 crossshore 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 and toe, as well as shoreline position and beach slope, at a 

regular alongshore interval, here, 10 m (fig. 7). These features are ultimately used to estimate wave 

runup and the corresponding storm-response regimes, as well as to measure actual morphologic changes 

before and after storms.
18

2.1.1 Shoreline position

Shoreline position (xsl) is defined as the horizontal location of the intersection of mean-high 

water (MHW) contour with the beach profile. The elevation of MHW around the Gulf of Mexico was 

defined using long-term observations from open-coast tide gages (Weber and others, 2005). The crossshore location of the MHW shoreline was automatically extracted from gridded lidar data using a 

probabilistic approach that makes use of the RMS error surface, providing for a statistically robust 

estimate. The probability that each gridded elevation was equal to the local MHW value was computed 

from a normal distribution of the beach elevation, using the interpolated elevation as the mean () and 

the lidar scatter (noise) as the standard deviation (). Using a prior shoreline as a first approximation, 

the grid cell with the highest probability within a defined distance (3 times the standard deviation of all 

shoreline points within the grid segment) of the prior shoreline was selected as the most likely shoreline. 

A linear regression using the selected point and adjacent grid cells was used to identify a more precise 

cross-shore location of the MHW line. The confidence interval on the estimate was defined from the 

regression error and the error on lidar data. 
19

Figure 7. Cross-shore profile of lidar-based 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.

2.1.2 Dune Crest

For this analysis, the seaward-most sand dune at any location along the coast defines the 

landward limit of the beach system. The position and elevation (xc,zc) of the dune crest or, in the 

absence of a dune, the beach berm were extracted every 10 m along the coast from cross-shore profiles 

of gridded lidar topography (fig. 7). An automatic algorithm was used to select the peak of the most 

seaward dune along each cross-shore profile (Stockdon and others, 2009). The approach is based on 

finding inflection points in the cross-shore slope, computed across a sliding 5-m window, and 

classifying them as morphologic crests and troughs. The highest crest elevation was identified as the 

crest of the primary dune. Several quality control checks ensure that an appropriate dune/berm location 

was selected. Dune/berm selection is restricted to locations where:
20

 xc is within 200 m of the shoreline,

 xc is seaward of built structures, identified as locations where local slope exceeds a threshold 

limit of 34°,

 RMS error on zc is less than 0.5 m, a pre-defined noise tolerance, and

 zc is higher than the MHW elevation.

The results of the automated analysis were checked to verify that an alongshore coherent dune 

feature was identified and that it was located seaward of coastal structures and roads. ArcGIS shapefiles 

of dune peaks generated from the two-dimensional cross-shore profiles during the automated process 

were viewed in combination with the three-dimensional gridded lidar dataset (fig. 8) and GoogleEarth 

images. In regions of complex topography, this step ensured that regions of steep topography such as 

escarpments were not erroneously labeled as buildings. When inconsistencies were found, the algorithm 

was re-initialized, and the results were viewed in a graphical user interface that displayed interpolated 

and point cloud lidar data. Errors were corrected by removing or editing dune crest positions. 
21

Figure 8. Automatically extracted dune crest (zc) and toe (zt) overlain on three-dimensional

gridded lidar topography from Santa Rosa Island, Florida, collected September 8, 2008,

after the landfall of Hurricane Gustav. Gaps in the data indicate locations where the 

automated algorithm was unable to identify a dune crest. Abbreviation: m, meter.

2.1.3 Dune Toe

Specification of the elevation and cross-shore location of the dune toe (xt,zt) is essential both for 

differentiating between swash and collision storm regimes and for computing beach slope. A

hydrodynamic-based elevation criteria, the high water line (HWL), was used to distinguish between 

swash-generated berms and aeolian-generated dunes in order to avoid selection of a dune toe on a berm 

(fig. 7). The HWL, which has contributions from both waves and tides, was defined as the sum of 

MHW elevation and a 20-year (yr) 2-percent exceedance runup level (Ruggiero and List, 2009). The 
22

runup elevation was parameterized using Stockdon and others (2006) and a 20-yr mean wave height 

(Hs) and peak period (Tp) from the U.S. Army Corps of Engineers Wave Information System (WIS) 

hindcasts (Hs = 0.85 m, Hs = 0.22 m; p = 5.90 seconds (s), p = 0.34 s). The resulting HWL

elevation around the Gulf of Mexico basin was approximately 60 cm NAVD88. Local HWL values 

were calculated as the spatial mean within a 2-km sliding window. An error of 40 cm was included to 

account for uncertainty in wave runup computations (Stockdon and others, 2006), lidar data uncertainty,

and spatial variability with the 2-km window. If the dune crest elevation (zc) did not exceed the 

elevation of HWL, then the feature was classified as a berm peak and xt and zt were not determined.

Where zc was greater than HWL, the feature was assumed to be a dune, and xt and zt were

mathematically derived from beach profiles using a process similar to the automated method of dune 

crest identification. Dune toe was defined as the location of maximum slope change between xc and xsl. 

The cross-shore location of the dune toe was constrained in the landward limit by the primary dune crest 

location and at the seaward boundary by the shoreline location. Additionally, the dune toe was 

restricted to locations where:

 zt > HWL to ensure that a water-laid feature was not selected,

 zc - zt > 0.5 m to prevent selection of small depressions on the dune face itself, and

 curvature of the profile around the (xt,zt) was negative.

Similar to the dune crest analysis, (xt,zt) identified by the automated process was checked by 

viewing ArcGIS shapefiles of the dune toe in overlay with the dune crest and gridded lidar datasets (fig.

8). The shape of a natural beach can vary greatly from the idealized dune profile, and visual verification 

ensured spatially consistent dune features are chosen. 
23

2.1.4 Beach slope 

Wave runup is dependent on the local beach slope over which the swash progresses as it 

interacts with the beach or dune. Under average wave conditions, swash flows up- and down-slope

within a short horizontal distance (meters) of the MHW shoreline, an area defined as the foreshore. 

However, during a hurricane, storm surge can raise the still water level to meters above the MHW 

shoreline. Therefore, the landward limit of storm-induced swash excursion is constrained by the base of 

the dune (xt,zt). If a dune is not present, then the berm crest (xc,zc) becomes the landward swash limit. 

Using these features as landward limits and the shoreline (xsl) as the seaward, the mean beach slope (m)

was calculated using an endpoint method (fig. 7). 

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-driven swash and setup, the super-elevation of the water surface at the shoreline 

due to wave breaking. Predictions of coastal change during hurricanes require estimates of both the 

mean water level and the wave runup that can be expected for each category. The mean hurricaneinduced 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 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 
24

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 employed by NOAA in 

inundation risk studies and operational storm surge forecasting. The numerical model is based on 

linearized, depth-integrated 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 timevarying 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 16 model 

domains subdividing the gulf coast 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. 9). (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 
25

hurricane wind field (Houston and others, 1999) as well as discrepancies between the coarse model grid 

and the real topography and bathymetry over which the storm will travel. 

Several additional constraints were applied to address some practical implementation problems 

associated with our use of the MOMs. First, SLOSH model domains were constructed to focus highest 

grid resolution in the most vulnerable coastal regions (fig. 9). As a result, the different model domains

overlap with each other. In these cases, MOM results were weighted to give preference to (1) results 

produced nearer the focal region of one of the overlapping grids, (2) results from higher resolution grid 

cells, and (3) more recently published results. Preference was given to different grid cells by scaling 

their results by a factor of 1 for the smallest cells to zero for the largest cells. Resulting surge levels

were interpolated, incorporating the assigned scaling factor, to the evenly spaced shoreline and 

smoothed with a Hanning window set to a length of 2 km alongshore. Additionally, since topographic 

and hydrodynamic results are compared to each other, we converted all elevations to the same NAVD88 

vertical datum (using the same GEOID model).
26

Figure 9. Sea, Lake, and Overland Surges from Hurricanes (SLOSH) category 1 modeled surge 

Maximum of the Maximum (MOM) for the New Orleans 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 buoys tend to fail during conditions stronger than tropical storm. Long-term hindcasts 

of historical wave conditions (for example, USACE WIS) also under-represent storm conditions. 

Therefore, wave conditions were estimated using Simulating WAves Nearshore (SWAN) model, a 
27

spectral wave model that resolves random, short-crested 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 Gulf of Mexico 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 Gulf of Mexico. 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 of many 

storms. 

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 longitudinal resolution of the SWAN computational grid was 3.34 km. The latitudinal 

resolution varies from 2.87 km in the northern gulf to 3.17 km at the southern limits (fig. 10). SWAN 

was run 24 times for each wind speed with direction varying from 15° to 360° by 15° (fig. 10). For each 
28

wind speed the model results from the 24 wind direction runs were combined by retaining the wave 

parameters from the wind direction that generated the largest significant wave height (Hs) in each grid 

cell.

Figure 10. Simulating WAves Nearshore (SWAN) computational grid containing 169,188 active 

nodes. Longitudinal resolution is 3.34 kilometers and latitudinal resolution varies from 2.87 

kilometers in the northern Gulf of Mexico to 3.17 kilometers in the south. The 20-meter isobath 

along the U.S. coastline is shown in red. Arrows indicate 24 wind directions used in simulation.

Abbreviation: m, meter.

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

Gulf of Mexico basin (fig. 11). The resulting maximum wave heights at the 20-m isobaths typically 

ranged from about 7 m to about 9 m, except where the continental shelf was very narrow. To put these 

model results in context, we compared the modeled wave heights to observed wave heights at several 
29

buoys located in different water depths throughout the Gulf of Mexico. Maximum observed wave 

heights were between 6 m and 9 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. 

Mean period estimates ranged between 7 and 18 s and tended to be higher where the significant 

wave height was higher (again, due to the presence or absence of dissipation). 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 15 shallow-water buoys operating in the gulf. The analysis 

yielded a parameterized relationship between significant wave height, obtained from the SWAN model, 

and peak wave periods (fig. 12). 

Tp = b0 + b1*Hs + b2*Hs

2 + b3h, (4)

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 = 3.8460 (0.0617)

b1 = 1.7812 (0.0289) 

b2 = -0.0120 (0.0052) 

b3 = -0.0049 (0.0957)

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

consistent with Gulf of Mexico observations, using the simulated significant wave heights and the 20-m 

depth as input. Recomputed wave periods typically increase from about 16 s to 22 s (fig. 11). 
30

Figure 11. Modeled significant wave height (Hs, top) and parameterized peak period (Tp, 

bottom) at the 20-meter isobath around the Gulf of Mexico extending from Texas (left) to 

the Florida west coast (right). State boundaries are indicated by vertical lines.

Abbreviations: m, meter; km, kilometer; s, second. 
31

Figure 12. Significant wave height (Hs) and peak (Tp) wave periods for deep (water depth, h, > 

30 m; black) and shallow (h < 30 m; red) water observed at National Data Buoy Center 

(NDBC) buoys in the Gulf of Mexico. The weighted regression based on shallow water 

buoys is shown in red. Abbreviations: m, meter; s, second.

2.2.3 Wave Setup and Swash

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

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

Setup is parameterized as 

 

 

(5)
32

where L0 = gTp

2

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

parameterized as

 [ 

 ]

 

(6)

Combining equations (5) and (6) with modeled estimates of tide + 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 considered arise from topographic elevation errors (for example, lidar error and 

interpolation error) and errors 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 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. 13, top). The variables were represented with a normal distribution of values at each 

location, now with a 1-km alongshore spacing, using the interpolated value as the mean and the RMS 

error as the standard deviation (fig. 13, bottom). This produced mean and standard deviations for both 

the hydrodynamic and the morphologic variables. 
33

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

 

 

 √ 

∫ 

 

 

 

 

(7)

where μ is the mean difference between the 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 13 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.
34

Figure 13. 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 around the Gulf of Mexico vary extensively over both short and long spatial 

scales (ta1ble 1). Mean dune crest elevation, zc, for sandy beaches along the gulf coast is 2.42 m, and 

the standard deviation, , is 1.02 m, indicating spatial variability high enough that low elevations ( –
35

2) are just above the HWL (fig. 14a). Elevations range from 0.5 to over 8 m. Dune toe elevations are 

consistently low with less spatial variability (fig. 14b; zt = 1.60 m; zc = 0.59 m). In general, local 

spatial variability scales with dune elevation: locations with higher mean elevations also exhibit greater 

spatial variability (fig. 15).

Figure 14. Distributions of dune crest elevation (zc, a), dune toe elevation (zt, b), and mean 

beach slope (m, c) for the U.S. Gulf of Mexico sandy coastlines. Abbreviation: m, meter.
36

Figure 15. Mean dune crest elevation (zc) for 1-kilometer sections of coastline compared to the 

standard deviation (zc) in those section. r2 = 0.48, N = 1620. Abbreviation: m, meter.

The highest dunes (zc in excess of 8 m) are located in south Texas and in the Florida Panhandle 

(figs. 16-17); however, variability is high in both States, as noted by zc of 1.50 m (zc = 2.72 m) and 

1.69 m (zc = 3.18 m), respectively. Some of the lowest elevations are also found in Florida, located 

along the west-central coast (zc = 1.84 m, zc = 0.74 m). However, the lowest dunes are found in 

Louisiana (zc = 1.57 m, zc = 0.64 m), particularly along the bermed beaches of the Chenier plain,

where beach ridges and berms mix with muddy coasts and marshes (fig. 17; distance, y, = 700-775 km). 

Louisiana beaches also have the lowest dune toes (figs. 16-17). 
37

Figure 16. Distribution of dune crest (zc) and dune toe (zt) elevations for Texas, Louisiana, 

Mississippi, Alabama, Florida Panhandle, and Florida west coast. Abbreviation: m, meter.
38

Figure 17. Dune crest (zc) and toe (zt) elevations (top) and hurricane-induced mean (50; middle) and maximum (98; 

bottom) shoreline water levels around the Gulf of Mexico extending from Texas (left) to the Florida west coast (right). 

State boundaries are indicated by vertical lines. Gaps in the data are locations of marshy shorelines. Abbreviation: m, 

meter; km, kilometer.
39

While also spatially variable, mean beach slope is generally low (fig. 18), leading to dissipative 

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

coast ( = 0.016,  = 0.009). Steeper slopes are present along the central Florida west coast ( = 

0.047); however, the standard deviation was also greatest here ( = 0.033). 

Figure 18. Distributions of mean beach slope (m) elevations for Texas, Louisiana, Mississippi, 

Alabama, Florida Panhandle, and Florida west coast. 

 
40

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 (Hs = 10-13m) were located offshore of the 

Mississippi River Delta where a narrow shelf allows larger waves to make it to the nearshore (fig. 11), 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 minimal; standard deviation of Hs ranges from 14 cm for a category 1 storm to 6 

cm for a category 5 storm (table 2).

Smaller scale spatial variability of 98 (fig. 18) is dominated by alongshore variations in m, as the 

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

conditions and local slopes (equations 5 and 6). 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 the Mississippi and west-central Florida coastlines. However, R2 is a meter 

higher on the Florida west coast than in Mississippi because of the steeper slopes: m = 0.047 in Florida 

as opposed tom = 0.016 in Mississippi.

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, 51 percent from waves and 51 percent from combined tides 

and surge. In a category 5 hurricane, surge dominates the signal, contributing 64 percent to the total 
41

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 extreme vulnerability of beaches throughout the Gulf of Mexico to dune 

erosion and overwash (figs. 19-20). For direct landfall of the lowest category hurricane, 99 percent of 

sandy beaches are very likely to experience dune erosion due to collision and over 70 percent of the 

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

along 27 percent of the coastline, typically near inlets (for example, the Mississippi barrier islands; fig.

21) and in areas with low elevation beach berms (the Chenier plain of Louisiana, fig. 20, and west-central 

Florida where zc < 2 m; table 1). For a category 3 hurricane landfall, 92 percent of the beaches in the 

Gulf 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 climbs to 73 percent. The basin-wide average 

storm surge associated with category 3 wind speeds exceeds 4 m, over 1.5 m higher than the average dune 

and berm elevation, zc = 2.43 m.

The higher dune elevations in the Florida Panhandle are less vulnerable to coastal change. During

a category 1 landfall, 98 percent of these beaches are likely to experience dune erosion; however, only 47

percent are expected to overwash (fig. 20). On the west-central Florida coast, vulnerability to dune 
42

erosion during a category 1 hurricane is the same, 99 percent, but crest elevations are more than 1 m 

lower than those on the panhandle, leading to increased vulnerability to overwash–98 percent of sandy 

beaches (fig. 20; table 3). For conditions related to a category 3 hurricane, 99 percent of Florida west 

coast beaches are very likely to inundate compared to only 51 percent of panhandle beaches. 

Figure 19. Probabilities of collision (c), overwash (o), and inundation (i) during a category 1 

hurricane for Texas and Louisiana.

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

complex dune fields or more three-dimensional beach morphology. For example, in south Texas, the mix 
43

of low and high dunes leads to a more complicated picture of hurricane-induced erosion hazards (fig. 19). 

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/gom/). 

Figure 20. Probabilities of collision (c), overwash (o), and inundation (i) during a category 1 

hurricane for Mississippi, Alabama, and west Florida.
44

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. This 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; Irish and others, 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 much 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.
45

4.2 Relative importance of waves and storm surge

Hurricane-induced coastal changes are due to 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 gulf-wide average of wave height and period for a category 1 hurricane (table 2), the predicted wavedriven component of shoreline water levels, R2, was 2.8 m, high enough to overwash the mean dune 

elevation (zc = 2.4), even without surge. For the category 2 conditions modeled in this analysis, the wavedriven and storm surge components were of equal magnitudes. During a category 5 storm, the magnitude 

of surge was almost double that of wave component; however, the magnitude of runup was over 3 m, 

exceeding the mean dune crest height and significant enough to determine the vulnerability of the highest 

dunes. 

The importance of waves in the lower strength wind categories, and the vulnerability of the entire 

basin to a category 1 storm, suggests that the impacts of less extreme weather events are likely to be 

significant and, under these conditions, waves may play a more important role than storm surge. Tropical 

storms and cold fronts in the gulf can provide the energy to significantly erode beaches and dunes. 

Waves during tropical storms can reach 5 m in height with wave periods of 12 s. Given these input 

conditions and a mean beach slope of 0.03, water levels (R2, = 1.63) would be high enough to force dune 

erosion over large portions of the basin (zt = 1.6 m). In fact, along much of the Louisiana coastline and 

in parts of west-central Florida, very low beaches would be vulnerable to overwash during these less

extreme conditions (fig. 17; table 1).
46

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, this assessment will 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/gom). 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 surveying needs. Examples of these updates 

associated with major hurricane landfall can be found at http://coastal.er.usgs.gov/hurricanes/.

Furthermore, as modeling develops, such as improvements to include storm size (Irish and others, 2008), 

surge inputs to coastal change analyses will 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 Gulf of Mexico 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 
47

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

likelihood of encountering mild, moderate, or severe erosion 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 3 

hurricanes, wave runup is of greater or equal magnitude as storms 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 and low coastal elevations makes the entire gulf 

region vulnerable to significant coastal erosion during storms. For a category 1 hurricane direct landfall, 

99 percent of dune-backed beaches in the Gulf of Mexico are very likely (p>90 percent) to experience 

dune erosion during the collision regime. Overwash during a category 1 landfall is very likely along 71

percent of the coast. For wave and surge conditions representative of a category 3 hurricane, 92 percent

of the Gulf of Mexico coastal areas are very likely to experience overwash and associated erosion. 

During category 5 conditions, 89 percent of the Gulf of Mexico beaches and dunes are very likely to be 

vulnerable to erosion due to inundation. The morphologic impacts from frequent and intense tropical 

storm events have increased the vulnerability of the Gulf of Mexico region to impacts from less extreme 

events in the future.

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 
48

and data collections that made this assessment possible: Katy Serafin, Laura Fauver, Peter Howd, Joe 

Long, Kristy Guy, Karen Morgan, Brendan Dwyer, Jolene Gittens, Bryan McCloskey, and Rob Wertz.

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

the comprehensive lidar data set that formed the basis of this analysis. Erika Lentz and Jamie Cormier 

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

Marine Geology Program. 

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Tables

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

along the Gulf of Mexico coast. [Standard deviation is given in parenthesis. m, meter]

State/region zc(m) zt (m) m Survey date 

(month/year)

U.S. Gulf of Mexico 2.43 (1.02) 1.06 (0.59) 0.032 (0.021)

Texas 2.72 (1.50) 1.84 (0.88) 0.033 (0.026) 8/2000, 9/2005,9/2008

Louisiana 1.57 (0.64) 1.33 (0.37) 0.020 (0.014) 9/2005, 3/2008, 9/2008

Mississippi 2.24 (0.70) 1.34 (0.46) 0.016 (0.009) 6/2007, 9/2008

Alabama 3.05 (0.87) 1.93 (0.68) 0.038 (0.019) 9/2008

Florida Panhandle 3.18 (1.69) 1.85 (0.80) 0.039 (0.022) 6/2008, 9/2008

Florida west coast 1.84 (0.74) 1.32 (0.35) 0.047 (0.033) 11/2004, 5/2006

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 parenthesis. 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 7.58 (0.14) 16.85 (0.64) 0.69 (0.37) 2.86 (0.64) 1.68 (0.28)

2 49.2 7.83 (0.11) 17.22 (0.60) 0.71 (0.38) 2.98 (0.65) 2.86 (0.45)

3 58.1 8.18 (0.08) 17.75 (0.57) 0.75 (0.40) 3.14 (0.67) 3.96 (0.59)

4 69.3 8.62 (0.06) 18.42 (0.60) 0.80 (0.42) 3.34 (0.71) 5.01 (0.70)

5 73.8 8.79 (0.06) 18.68 (0.62) 0.82 (0.43) 3.42 (0.73) 6.02 (0.79)
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Table 3. Percent 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. Gulf of Mexico 99 100 100 100 100

 Texas 99 100 100 100 100

 Louisiana 100 99 100 100 100

 Mississippi 100 100 100 100 100

 Alabama 100 100 100 100 100

 Florida Panhandle 98 99 100 100 100

 Florida west coast 99 100 100 100 100

Overwash

 U.S. Gulf of Mexico 71 85 92 97 98

 Texas 61 75 88 98 99

 Louisiana 95 99 100 100 100

 Mississippi 96 100 100 100 100

 Alabama 69 99 100 100 100

 Florida Panhandle 47 74 83 89 93

 Florida west coast 98 99 100 100 100

Inundation

 U.S. Gulf of Mexico 27 58 73 83 89

 Texas 20 54 62 73 85

 Louisiana 62 86 95 100 100

 Mississippi 35 93 100 100 100

 Alabama 4 29 74 99 100

 Florida Panhandle 5 25 51 69 77

 Florida west coast 42 91 99 99 99