Long-term and Storm-related Shoreline Change 

Trends in the Florida Gulf Islands National Seashore 

U.S. DEPARTMENT OF THE INTERIOR 

U.S. GEOLOGICAL SURVEY

Open-File Report 2007- 1392 
Long-term and Storm-related Shoreline Change 

Trends in the Florida Gulf Islands National Seashore 

By Cheryl J. Hapke1

 and Mark Christiano2 

 

1

U.S. Geological Survey, Department of Geosciences, University of Rhode Island, 

Kingston, RI 02881 

2

 Environmental Data Center, Department of Natural Resource Sciences, University 

of Rhode Island, Kingston, RI 02881 

Open-File Report 2007-1392 

U.S. DEPARTMENT OF THE INTERIOR 

U.S. GEOLOGICAL SURVEY 

2
U.S. Department of the Interior 

Dirk Kempthorne, Secretary 

U.S. Geological Survey 

Mark Myers, Director 

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Suggested citation:

Hapke, C.J. and Christiano, M., 2007, Long-term and Storm-related Shoreline Change Trends in the Florida Gulf 

Islands National Seashore: U.S. Geological Survey Open-file Report 2007-1392, 18p. 

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. 

3
CONTENTS 

EXECUTIVE SUMMARY................................................................................5 

INTRODUCTION.............................................................................5 

METHODS.....................................................................................7 

Uncertainties and Errors...........................................................................9 

DISCUSSION................................................................................................11 

Fort Pickens Unit................................................................................12 

Santa Rosa Unit.................................................................................13 

SUMMARY/CONCLUSIONS.........................................................................14 

ACKNOWLEDGMENTS................................................................................17 

REFERENCES..............................................................................................17 

LIST OF FIGURES 

Figure 1. Location map of the Gulf Islands National Seashore in Florida. 

Figure 2. Shoreline change plots for the Fort Pickens unit. 

Figure 3. Shoreline change plots for the Santa Rosa unit. 

LIST OF TABLES 

Table 1. Ages and sources of compiled shorelines. 

Table 2. Maximum estimated positional and measurement errors for GUIS shorelines. 

Table 3. Average and maximum erosion rates for GUIS. 

4
EXECUTIVE SUMMARY 

Coastal erosion on Northern Gulf of Mexico barrier islands is an ongoing issue that was 

exacerbated by the storm seasons of 2004 and 2005 when several hurricanes made landfall 

in the Gulf of Mexico. Two units of the Gulf Islands National Seashore (GUIS), located on 

Santa Rosa Island, a barrier island off the Panhandle coast of Florida, were highly impacted 

during the hurricanes of 2004 (Ivan) and 2005 (Cindy, Dennis, Katrina and Rita). In addition 

to the loss of or damage to natural and cultural resources within the park, damage to park 

infrastructure, including park access roads and utilities, occurred in areas experiencing rapid 

shoreline retreat. The main park road was located as close as 50 m to the pre-storm (2001) 

shoreline and was still under repair from damage incurred during Hurricane Ivan when the 

2005 hurricanes struck. A new General Management Plan is under development for the Gulf 

Islands National Seashore. This plan, like the existing General Management Plan, strives to 

incorporate natural barrier island processes, and will guide future efforts to provide access 

to units of Gulf Islands National Seashore on Santa Rosa Island. 

To assess changes in island geomorphology and provide data for park management, the 

National Park Service and the U.S. Geological Survey are currently analyzing shoreline 

change to better understand long-term (100+ years) shoreline change trends as well as 

short-term shoreline impact and recovery to severe storm events. Results show that over an 

~140-year period from the late 1800s to May 2004, the average shoreline erosion rates in 

the Fort Pickens and Santa Rosa units of GUIS were -0.7m/yr and -0.1 m/yr, respectively. 

Areas of historic erosion, reaching a maximum rate of -1.3 m/yr, correspond to areas that 

experienced overwash and road damage during the 2004 hurricane season.. The shoreline 

eroded as much as ~60 m during Hurricane Ivan, and as much as ~88 m over the course of 

the 2005 storm season. The shoreline erosion rates in the areas where the park road was 

heavily damaged were as high as -70.2 m/yr over the 2004-2005 time period. Additional 

post-storm monitoring of these sections of the island, to assess whether erosion rates 

stabilize, will help to parks to determine the best long-term management strategy for the 

park infrastructure. 

INTRODUCTION 

The Gulf Islands National Seashore (GUIS), in the northern Gulf of Mexico, is composed 

of a series of barrier islands that lie along the Florida Panhandle and Mississippi coasts. 

GUIS consists of seven barrier islands that extend along a total of 240 km of coast, with two 

in Florida (Perdido Key and Santa Rosa Island; Figure 1) and five in Mississippi. 

5
Santa Rosa Island is a low-profile, late Holocene barrier island (Otvos, 1982; Morton and 

others, 2004). Prior to the 2004-05 hurricanes, the island ranged in width from 160-1800 m, 

and fordune elevations were in the 5 m range. The Gulf Islands in Florida have historically 

been more stable than their Mississippi counterparts with respect to shoreline change 

(Stone and others, 2004). In 1995, Santa Rosa Island was almost entirely overwashed by 

Hurricane Opal which made landfall just east of Pensacola Beach. Opal caused extensive 

erosion and removed dunes as high as 5 meters (Stone and others, 2004), leaving the 

island and park resources more susceptible to future storms and erosion. In 2004 and 2005, 

a series of large and catastrophic hurricanes and tropical storms again directly impacted 

GUIS causing extensive damage to the coastal barrier islands and to coastal infrastructure. 

During such catastrophic events and/or storm seasons, dynamic but fragile barrier 

islands can become completely geomorphologically reconfigured, which directly affects 

habitat availability and functionality. Management of the highly impacted coastal resources 

in GUIS requires a working knowledge of how the system has changed. In order to 

understand the signal of severe storm shoreline change in the long-term record we assess 

whether the spatial patterns of the long-term erosion rates can indicate where future stormrelated hazards are greatest. 

The objective of this research is to build on existing long-term data that are critical to the 

understanding of short-term storm response in the Gulf Islands in Florida. The intention of 

this study is to determine, based on long-term trends (>100 years) and short-term (storm or 

seasonal) changes, where high rates of shoreline erosion may occur during future severe 

storms and thus provide useful information on the placement and maintenance of park 

infrastructure. 

6
 Figure 1. Location map of the Florida Units of the Gulf Islands National Seashore. 

METHODS 

The shoreline change rates used for this analysis are derived from both three 

historical time periods (1800s, 1930s and 1970s) and recent (2001, 2004, 2005) lidar data. 

The 2001 data are available for download from Miller and others (2004). The more recent 

lidar shorelines (2004 and 2005) were either obtained from the USGS or a mean high water 

(MHW) shoreline was extracted in-house for this analysis. Table 1 outlines the years and 

the sources of the data used for the shoreline change analysis. The map data consist of 

three historical shorelines that were digitized from georeferenced National Ocean Service 

(NOS) T-sheets, and represent the field interpretation of a high water line (HWL) on the 

beach (Shalowitz, 1964). The dates of the oldest map-derived shorelines are 1856, 1858 

and 1859 for the Santa Rosa unit, and 1871 for the Fort Pickens unit. For the 1930s and 

1970s-era T-sheets, the dates of the map-derived shorelines are 1934, and 1978 for both 

units. The modern shorelines are mean high water (MHW) shorelines derived from lidar 

7
data. For the long-term (~150 yr) shoreline change the most recent shoreline is from May 

2004 lidar data. Morton and others (2004) used the historical maps and 2001 lidar data to 

produce shoreline change rates for the entire Gulf of Mexico, including Santa Rosa Island. 

However, there are several distinct differences between our analysis and theirs. We 

recalculated the shoreline change rates using the Digital Shoreline Analysis System (DSAS; 

Thieler and others, 2005), producing rates on shore-perpendicular transects spaced at 20 m 

intervals, compared to the 50 m spacing used by Morton and others (2004). We also 

included a fifth shoreline, from May 2004, in the long-term, pre-storms analysis. 

Additionally, Morton and others (2004) did not account for the bias in the data that exists as 

a function of using different shoreline proxies, a HWL versus a MHW. A HWL shoreline is 

higher on the beach than the MHW shoreline which does not take total water level (wave 

run-up and tide height) into account (Ruggiero and others, 2003; Moore and others, 2006). 

If a correction is not applied, the shoreline change rates will have an accretionary bias. The 

proxy bias was calculated using methods developed by Ruggiero and others (2003) and 

Moore and others (2006), and ranged from 5.0 – 10.2 m. The application of the proxy bias 

results in an overall increase in the erosion rate of -0.1 m/yr for the portion of Santa Rosa 

Island extending from Pensacola Pass to Navarre Beach. 

 In order to assess storm-driven shoreline change, we acquired pre-Hurricane Ivan 

(May 2004), post-Hurricane Ivan (Sept. 2004) and post-Hurricane Katrina (Sept. 2005) lidar 

data for Santa Rosa Island, from Pensacola Pass to Navarre Beach. Shorelines had been 

extracted from the pre- and post-Ivan lidar data by the USGS Center for Coastal and 

Wetland Studies. The shoreline from the most recent (post-Katrina) lidar data was extracted 

by researchers at the USGS Woods Hole Science Center (WHSC) specifically for this 

analysis. We used the same 2300 transects to calculate short-term (May 2004 to September 

2005) storm-driven shoreline change rates as were used in the long-term, pre-storms 

analysis. However, due to numerous gaps in the post-storm shoreline from island breaching 

and washover, only about 60% of the original transects crossed all three lidar shorelines. 

The data presented are in Universal Transverse Mercator (UTM) projection with the North 

American Datum of 1983 (NAD83). 

8
Table 1. Dates and original sources of shorelines for GUIS Santa Rosa Island. 

Date Original Data Source 

1856, 1858, 1859 or 1871 NOS T-sheets 

1934 NOS T-sheets 

1978 NOS T-sheets 

2001 USGS ATM* lidar 

May 2004 (2004a) USGS EAARL** lidar 

Sept. 2004 (2004b) USGS EAARL** lidar 

Sept. 2005 USGS EAARL** lidar 

 *ATM: Airborne Topographic Mapper; 

 **EAARL: Experimental Advanced Airborne Research Lidar 

Rates of shoreline change were generated in a GIS with the Digital Shoreline Analysis 

System (DSAS), an ArcGIS© tool developed by the USGS in cooperation with TPMC 

Environmental Services (Thieler and others, 2005). This tool contains three main 

components that define a baseline, generate transects perpendicular to the baseline that 

intersect the shorelines at a user-defined separation along the coast, and calculate rates of 

change. 

Baselines were constructed seaward of, and roughly parallel to, the general trend of the 

coastline. Using DSAS, transects were spaced at 20 m intervals. Rates of shoreline change 

were calculated at each transect using a linear regression rate where there were four or 

more shorelines and an end point rate for periods when there were three or less shorelines. 

Uncertainties and Errors 

Trends and calculated rates of shoreline change are only as reliable as the 

measurement errors that determine the accuracy of each shoreline position and statistical 

errors associated with compiling and comparing shoreline positions. A variety of authors 

have provided general estimates of the typical measurement errors associated with mapping 

methods and materials for historical shorelines, referencing shoreline position relative to 

geographic coordinates, and shoreline digitizing (Anders and Byrnes, 1991; Crowell and 

others, 1991; Thieler and Danforth, 1994; and Moore, 2000). 

The largest errors in this analysis were positioning errors of ±10 m, which were attributed 

to scales and inaccuracies in the original T-sheet surveys. However, the influence of large 

position errors on long-term rates of change can be reduced if the rate is calculated over a 

sufficiently long period of time. Additional data source errors implicit in this analysis result 

9
from GPS positioning errors (± 1 m), which Stockdon and others (2002) associated with the 

lidar data. Estimates of the maximum measurement errors for this study are provided in 

Table 2 to show how each error contributes to inaccuracy in the shoreline position. 

End-point rate uncertainty 

 End-point rates were calculated for those time periods with three or fewer shorelines, 

and include the 2004a-2004b (Ivan) time period and the 2004b-2005 (Katrina, Dennis, Rita) 

time period. The total shoreline position error for the end-point retreat rate (Ea) is the 

quadrature sum of the lidar shoreline position uncertainty (Esp) for each lidar shoreline 

divided by the time period over which the rate was determined: 

time

EE

E sp sp

a

2

2

2

1 + = (Equation 1) 

For this analysis, all end-point rates included only lidar shorelines, and each of these has 

an estimated total positional error of 1.5 m. The shoreline change rate uncertainties were 

determined to be 2.1m/yr for the end point rates (Table 2). 

Linear regression (long-term) shoreline change error

Linear regression is a commonly applied statistical technique for determining rates of 

shoreline change (Crowell and Leatherman, 1999) where there are a statistically valid 

number of samples. The long-term rates of shoreline change (1800s to 2004a, and 1800s 

to 2005) were determined at each transect by taking the slope of the regression line applied 

to all shoreline positions. The resulting rates are reported in units of m/yr (Table 3). 

Two uncertainty terms are estimated in the calculation of the long-term shoreline change 

rates. The first term is the 90% confidence interval of the linear regression shoreline change 

rate for each transect. The second term arises from the uncertainty in our best estimates of 

the proxy bias offsets. We calculate linear regression slopes using shoreline data that have 

been adjusted based on our best estimate of the shoreline proxy bias as well as data that 

have been adjusted according to our best estimate of the ± bias uncertainty. From this 

analysis we get a best estimate of the shoreline change rate and an uncertainty of the rate 

due to the bias uncertainty. At each transect we add the regression error and the proxy10
datum bias uncertainty error to get a total uncertainty of the shoreline change rate at a given 

location. 

However, in terms of calculating regionally-averaged shoreline change rate uncertainties 

the two terms discussed above need to be treated differently. Because the 90% confidence 

interval on the linear regression of each transect is assumed to be random and independent, 

when averaged over many transects the resulting average uncertainty associated with this 

term can be quite small; the greater the number of transects over which the uncertainty is 

averaged, the smaller the uncertainty on the average rate. However, for the second term 

we need to account for the fact that the proxy-datum offset is a bias and always acts in one 

direction. Therefore, the regionally averaged shoreline change rate uncertainty associated 

with the proxy-datum bias is simply the average value of the error resulting from the 

uncertainty of the proxy-datum bias. The regionally averaged total shoreline change 

uncertainty terms can be expressed by: 

n

B

n

C n

i

i

n

i

∑ i ∑ = = += 1 1

2

 U (Equation 2) 

where is the alongshore averaged shoreline change rate uncertainty, C is the linear 

regression 90% confidence interval, B is the shoreline change rate uncertainty associated 

with the proxy-datum bias, and n is the number of transects included in average. For this 

study the error associated with the linear regression rates was determined to be <0.1 m/yr. 

U

DISCUSSION 

The shoreline change analysis covers a total of approximately 25 km of Santa Rosa 

Island. The long-term average rate of shoreline change prior to the 2004-05 storm seasons 

was -0.3±0.1 m/yr. However, the rates of shoreline change vary dramatically within the two 

park units. In both the long-term and storm-driven change analyses, rates of change within 

the Fort Pickens unit were much more variable than those within the Santa Rosa unit. 

During the hurricanes of 2004 and 2005, the shoreline change rates increased by two orders 

of magnitude (Table 3). Historically, the maximum rate of change, -1.3±0.1 m/yr, was 

measured within the Fort Pickens unit. The maximum rate of change driven by the storms 

was -70.2±2.1 m/yr. 

Overall, the impacts of the 2004-05 storms to the long-term trends of shoreline change 

and the mean rates of change in both the FPU and the SRU were variable, and the Santa 

11
Rosa unit was more greatly impacted, as discussed below. A Student’s t-test run on the 

datasets indicate that there is a statistically significant difference between the 1800s – May 

2004 (pre-Ivan) and the 1800s – Sept. 2005 (post-storms) rates (p < 0.0001). 

Table 2. Maximum estimated positional errors for GUIS shorelines. 

Time Period Shoreline change rate uncertainties 

(m/yr) 1800s2004a 

2004a2004b2004a2005 1800s-2005 

Linear regression rate <0.1 -- -- <0.1 

End point rate -- 2.1 2.1 -- 

Table 3. Average and maximum long-term and storm-associated erosion rates for the Florida GUIS 

park units. FPU = Fort Pickens Unit; SRU = Santa Rosa Unit. 

Long-term (pre-storms) 

(1800s –05/04) 

Erosion rate (m/yr) 

Hurricane Ivan 2004-05 Hurricanes Long-term (post-storms) 

05/04 – 05/05 

Erosion rate (m/yr) 

09/04 – 09/05 

Erosion rate (m/yr) 

(1800s – 2005) 

Erosion rate (m/yr) 

Unit Av. Max. Av. Max. Av. Max. Av. Max. 

FPU -0.7+0.1 -1.3+0.1 -22.6+2.1 -43.6+2.1 -38.5+2.1 -70.2+2.1 -0.9+0.1 -1.5+0.1 

SRU -0.1+0.1 -0.3+0.1 -24.6+2.1 -59.3+2.1 -35.2+2.1 -60.3+2.1 -0.4+0.1 -0.6+0.1 

Fort Pickens Unit (FPU) 

The average rate of long-term, pre-storm (1858 - 2004a) shoreline erosion (the 

average of transects that had negative change) in the FPU was -0.7±0.1 m/yr (Table 3). 

The maximum erosion rate during this period, -1.3±0.1 m/yr, occurred near the eastern 

portion of the unit where the island is narrow (Figure 2a). The long-term, pre-storm shoreline 

change rates reverse from an erosional trend to an accretional trend at the western end of 

the FPU where the island width increases to greater than 370 m. Indicative of the 

documented east-to-west sediment transport direction (Otvos, 1982), the western end of 

Santa Rosa Island has been prograding over the ~140 yrs of the study. However, this 

accretional trend was interrupted during the 2004-05 hurricanes. As a result of storm surge 

related to Hurricane Ivan, much of this previously stable (or accretional) portion of the FPU 

became erosional (Figure 2b). 

12
Large stretches of the FPU were overwashed or completely breached during the 2004-

05 hurricanes and as a result, there are sizable gaps in the shoreline datasets (Figures 2 bd). The overwash/breach areas correspond spatially to the narrowest portions of the island 

within the FPU. 

Average rates of shoreline erosion that resulted from the storms were two orders of 

magnitude greater than the long-term trends. During the Hurricane Ivan time period, the 

average rate of erosion increased from the long-term average of -1.3±0.1 m/yr to -22.6±2.1 

m/yr and the shoreline erosion associated with both Ivan and the 2005 storms was -38.5±2.1 

m/yr. Although the proximity of the hurricane eye and northeast quadrant to Santa Rosa 

Island were more favorable for causing high rates of beach erosion during Ivan than those 

produced during the 2005 storms (Katrina, Rita and Dennis), these storm likely resulted in 

more damage because the island was decimated by Ivan, and still in a state of recovery 

when the storms of 2005 occurred. 

The combined impact of the 2004-2005 storms on the long-term shoreline change 

trend was measurable, although not as dramatic as expected, especially in the FPU. The 

average long-term erosion rate increased by -0.2 m/yr to -0.9±0.1 m/yr, and the maximum 

rate increased by the same amount to -1.5±0.1 m/yr. The overall patterns of long-term 

shoreline change within the FPU remained similar (Figure 2d). However, the point of 

reversal from erosion to accretion near the western end of the park shifted 1 km to the west 

in the long-term dataset that include the storms, resulting in a shorter extent of shoreline that 

is accretional. 

Santa Rosa Unit (SRU) 

 The shoreline change trends in the SRU are much less variable along-shore than the 

trends in the FPU. Rates of long-term, pre-storm erosion averaged -0.1±0.1 m/yr, a 

nonsignificant signal given the uncertainties in the data. The maximum long-term (prestorm) erosion rate was -0.3±0.1 m/yr, and was measured near the western end of the SRU, 

at one of the narrowest portion of the island within the SRU (Figure 3a). As shown in Figure 

3a, rates are systematically low throughout this unit, with a slight trend toward erosion at 

both the east and west ends of the unit, and a slightly more accretional (or less erosional) 

trend in the center portion of the unit. Spatially, the pattern of shoreline change tends to 

mimic the shape of the island, with low erosion rates or accretion occurring where the island 

is wider - in general, at the locations of the bayside cuspate shoreforms. 

13
 Similar to the FPU, rates of shoreline change increased by two orders of magnitude, 

both from Hurricane Ivan and from the combined impacts of all the 2004-05 storms (Table 

3). The shoreline erosion rate associated with Hurricane Ivan averaged -24.6±2.1 m/yr, with 

a maximum erosion rate of -59.3±2.1 m/yr located on the eastern end of the SRU, 

immediately adjacent to the largest breach site, where there is a gap in the shoreline data 

(Figure 3b). The storms of 2005 resulted in additional areas of overwash and breaching 

(additional data gaps in Figure 3c). The maximum erosion rate during this period, -60.3±2.1 

m/y was again located on the eastern end of the SRU, adjacent to a locale that breached 

during Hurricane Ivan. 

 Unlike the FPU, the storms of 2004 and 2005 resulted in an overall regime shift in the 

long-term shoreline change pattern in the SRU (Figure 3d). Whereas the historical pre-storm 

shoreline change data indicated that this portion of the island was relatively stable, the longterm, post-storm data show an completely erosional regime with erosion rates averaging 

0.4±0.1 m/yr, and increase of -0.3 m/yr. 

SUMMARY/CONCLUSION

 Long-term (~140-year) shoreline change rates were generated for the Fort Pickens 

and Santa Rosa units of GUIS in Florida to assess the historical trends prior to the 2004 and 

2005 hurricanes. Results indicate that the rates of shoreline change were higher and more 

variable in the FPU and that the shoreline was relatively stable within the SRU. In the FPU, 

the western 1.5 km of the island had a long-term progradational trend, but the average rate 

of shoreline erosion for the rest of the unit was -0.7±0.1 m/yr. 

 Shoreline change rates along the same stretches of coast were calculated to assess 

the impacts of Hurricane Ivan in 2004 and for the total impacts of the 2005-05 hurricane 

season, which included Hurricanes Ivan, Cindy, Dennis, Katrina and Rita. During these time 

periods, the rates of shoreline change increased by two orders of magnitude from the longterm trends. Numerous areas of the island were breached and overwashed, leading to 

significant gaps in the data, especially within the FPU. In general, these areas tended to 

correspond to the narrowest portion of the island. 

 Overall, the average and maximum rates of shoreline erosion were greater in the SRU 

during the Hurricane Ivan period, which was historically the more stable part of GUIS, and 

higher in the FPU during the combined periods that include both Ivan and the storms of 

2005. This, in part, is likely related to the large data gaps in the FPU, where storm shoreline 

change rates could not be measured due to breaching or severe island elevation deflation. 

14
Figure 2. Orthophotomosaic from 1993 and shoreline change plots for the Fort Pickens unit 

of GUIS. 

15
Figure 3. Orthophotomosaic from 1993 and shoreline change plots for the Santa Rosa unit 

of GUIS.

16
 Long-term rates of shoreline change were measurably influenced by the 2004 and 

2005 hurricanes, although the impacts are greater in the SRU, which has direct park 

management implications. In the FPU, areas that had high rates of historical erosion prior to 

the storms were also those areas that experienced high rates of erosion during the storms, 

and thus the spatial patterns and distributions of shoreline change did not change 

significantly. One exception is an ~1 km section of coast in the western half of the FPU 

where the long-term shoreline change signal reversed from historically prograding to eroding 

at rates as high as -0.7 m/yr. In the SRU, the shoreline shifted from a historically stable 

system to one that has consistently significant rates of shoreline erosion along its entire 

length. The shoreline will likely experience rapid recovery in lieu of additional extreme storm 

events, and an important component of the long-term analysis will be the incorporation of 

such data as they become available. 

ACKNOWLEDGMENTS 

The shorelines from the pre- and post-Ivan lidar were graciously provided by Asbury 

Sallenger, Hilary Stockdon and Laura Fauver of the USGS Coastal and Marine Geology 

Program. Additionally, post-Katrina lidar coverage of Santa Rosa Island was made available 

by John Brock and Amar Nayegandhi of the USGS Center for Coastal and Watershed 

Studies. Jeff List, Kathy Konicki and Amy Farris (USGS Woods Hole) derived the postKatrina shoreline from lidar data, and calculated the proxy-datum bias. Their contributions 

were crucial to this study. Funding was generously provided by the National Park Service 

Natural Resource Preservation Program. 

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18