Evaluation of Next Generation Beach and Dune Erosion Model to Predict High Frequency Changes Along the Panhandle Coast of Florida

Home , Hurricane Dennis (1981), Hurricane Eloise, Hurricane Opal

EVALUATION OF NEXT GENERATION BEACH AND DUNE EROSION MODEL TO PREDICT HIGH FREQUENCY CHANGES ALONG THE PANHANDLE COAST OF FLORIDA

NICOLE SHELBY SHARP

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2008

To my mother and father

ACKNOWLEDGMENTS

I would like to thank my supervisory committee chair, Dr. Robert G. Dean, for his

continuous support and guidance. His insight and knowledge into the subject is inspiring, and

his time that he has spent with me over the past two years has been very insightful. I also thank

Dr. Arnoldo Valle Levinson for serving on my supervisory committee.

I would also like to thank Jamie MacMahan for introducing to me the topic of coastal engineering. If it were not for his spirit and enthusiasm for the subject, I feel as though I would not be where I am today.

Lastly, I cannot forget to thank my parents and my sister for their patience in my schooling

process and for always answering the phone in my times of need.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES...... 7

LIST OF FIGURES ...... 8

ABSTRACT...... 11

CHAPTER

1 INTRODUCTION ...... 12

Coastal Processes...... 12 Shoreline Forecasting ...... 14 Objective and Scope ...... 15

2 LITERATURE REVIEW...... 18

Mean High Water (MHW) Erosion Calculation Methods...... 18 Hurricane Model...... 19 Bathystrophic Storm Tide Model ...... 21 Shoreline Change Models...... 23

3 METHODOLOGY ...... 29

Hurricane Model...... 29 Hurricane Storm Surge Model...... 30 NEXTGEN Erosion Model...... 35 Contour Changes ...... 36 Measured Contour Change...... 36 Predicted Contour Change...... 37 Statistical Analysis of Results ...... 38 Measured versus Predicted...... 39 Measured 10 Foot Contour versus Measured Zero Foot Contour...... 39

4 RESULTS AND ANALYSIS...... 44

Measured Shoreline Change Results ...... 44 Storm Surge Results ...... 46 Cross-Shore Transport Model...... 49 Statistical Analysis of Data...... 53 Measured 10 Foot versus Predicted 10 Foot Predicted Contour ...... 53 Measured 10 Foot versus Measured Zero Foot Contour...... 54 Model Sensitivity to Input Variables...... 55

5 CONCLUSIONS AND RECOMMENDATIONS...... 77

Summary and Conclusions ...... 77 Storm Surge Model...... 77 Measured Contour Change...... 78 Representation of High-Frequency Shoreline Changes ...... 78 Recommendations for Future Study ...... 79 Storm Surge Model...... 79 Measured Contour Change...... 79 Representation of High-Frequency Shoreline Changes ...... 80

APPENDIX

A STORM SURGE HYDROGRAPHS ...... 81

B NEXTGEN PROFILE EVOLUTION RESULTS...... 87

LIST OF REFERENCES...... 117

BIOGRAPHICAL SKETCH ...... 120

LIST OF TABLES

Table page

4-1 Comparison between predicted and measured storm surge...... 72

4-2 Individual setup values and maximum adjusted surge ...... 73

4-3 Individual R-squared and r values of predicted versus measured +10 contour change for six common profiles in each storm event...... 74

4-4 Individual R-squared values for measured +10 contour change versus measured zero contour change for each storm event ...... 75

4-5 Predicted average change of +10 contour from NEXTGEN model for the three cases of storm surge ...... 76

LIST OF FIGURES

Figure page

1-1 General location of study area and Department of Environmental Protection monuments...... 17

2-1 Sketch of idealized hurricane model...... 27

2-2 Basic concepts for Kriebel and Dean’s erosion model ...... 28

3-1 Definition sketch of hurricane “catchment” zone...... 41

3-2 Geometric sketch of θrot ,θl , and coordinate system...... 42

3-3 Example sketch of μ and θslnew_ ...... 43

4-1 Average +10 and zero foot contour change over nine common Monuments...... 57

4-2 Zero foot contour accretion due to large storms...... 58

4-3 Predicted storm surge hydrograph for Hurricane Eloise...... 59

4-4 Plot of maximum predicted un-scaled surge versus maximum measured surge at location of maximum surge...... 60

4-5 Measured storm surge hydrograph for Hurricane Eloise from historical tide gage data...... 61

4-6 Measured storm surge hydrograph for Hurricane Opal from NOAA CO-OPS database...... 62

4-7 Measured storm surge hydrograph for Hurricane Ivan from NOAA CO-OPS database...... 63

4-8 Measured storm surge hydrograph for Hurricane Dennis from NOAA CO-OPS database...... 64

4-9 Storm surge comparison between scaled and un-scaled values from one-dimensional model...... 65

4-10 Adjusted maximum predicted surge versus maximum measured surge at Walton County...... 66

4-11 Example profile response from the NEXTGEN model for Hurricane Eloise...... 67

4-12 Comparison of measured and predicted recessions of the +10 foot contour. Cumulative values are presented for Erin and Opal because no intermittent surveys are available to quantify individual measured recession...... 68

4-13 Plot of predicted erosion versus measured erosion for individual profiles (6) all storm events (5)...... 69

4-15 Comparison of measured and predicted recessions of the +10 foot contour with error bars to account for the sensitivity of the model output due to storm surge scaled such that the peaks varied by +/- 1 foot...... 71

A-1 Predicted storm surge hydrograph for Hurricane Eloise from one-dimensional storm surge model...... 81

A-2 Predicted storm surge hydrograph for Hurricane Erin from one-dimensional storm surge model...... 82

A-3 Predicted storm surge hydrograph for Hurricane Opal from one-dimensional storm surge model...... 83

A-4 Predicted storm surge hydrograph for Hurricane Georges from one-dimensional storm surge model...... 84

A-5 Predicted storm surge hydrograph for Hurricane Ivan from one-dimensional storm surge model...... 85

A-6 Predicted storm surge hydrograph for Hurricane Dennis from one-dimensional storm surge model...... 86

B-1 Calculated profile evolution for Monument 21 for Hurricane Eloise...... 87

B-2 Calculated profile evolution for Monument 57 for Hurricane Eloise...... 88

B-3 Calculated profile evolution for Monument 63 for Hurricane Eloise...... 89

B-4 Calculated profile evolution for Monument 66 for Hurricane Eloise...... 90

B-5 Calculated profile evolution for Monument 87 for Hurricane Eloise...... 91

B-6 Calculated profile evolution for Monument 102 for Hurricane Eloise...... 92

B-7 Calculated profile evolution for Monument 21 for Hurricane Erin...... 93

B-8 Calculated profile evolution for Monument 57 for Hurricane Erin...... 94

B-9 Calculated profile evolution for Monument 63 for Hurricane Erin...... 95

B-10 Calculated profile evolution for Monument 66 for Hurricane Erin...... 96

B-11 Calculated profile evolution for Monument 87 for Hurricane Erin...... 97

B-12 Calculated profile evolution for Monument 102 for Hurricane Erin...... 98

B-13 Calculated profile evolution for Monument 21 for Hurricane Opal...... 99

B-14 Calculated profile evolution for Monument 57 for Hurricane Opal...... 100

B-15 Calculated profile evolution for Monument 63 for Hurricane Opal...... 101

B-16 Calculated profile evolution for Monument 66 for Hurricane Opal...... 102

B-17 Calculated profile evolution for Monument 87 for Hurricane Opal...... 103

B-18 Calculated profile evolution for Monument 102 for Hurricane Opal...... 104

B-19 Calculated profile evolution for Monument 21 for Hurricane Ivan...... 105

B-20 Calculated profile evolution for Monument 57 for Hurricane Ivan...... 106

B-21 Calculated profile evolution for Monument 63 for Hurricane Ivan...... 107

B-22 Calculated profile evolution for Monument 66 for Hurricane Ivan...... 108

B-23 Calculated profile evolution for Monument 87 for Hurricane Ivan...... 109

B-24 Calculated profile evolution for Monument 102 for Hurricane Ivan...... 110

B-25 Calculated profile evolution for Monument 21 for Hurricane Dennis...... 111

B-26 Calculated profile evolution for Monument 57 for Hurricane Dennis...... 112

B-27 Calculated profile evolution for Monument 63 for Hurricane Dennis...... 113

B-28 Calculated profile evolution for Monument 66 for Hurricane Dennis...... 114

B-29 Calculated profile evolution for Monument 87 for Hurricane Dennis...... 115

B-30 Calculated profile evolution for Monument 102 for Hurricane Dennis...... 116

Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

EVALUATION OF NEXT GENERATION BEACH AND DUNE EROSION MODEL TO PREDICT HIGH FREQUENCY CHANGES ALONG THE PANHANDLE COAST OF FLORIDA

Nicole Shelby Sharp

August 2008

Chair: Robert G. Dean Major: Coastal and Oceanographic Engineering

High-frequency, or shorter-term, changes can pose a significant threat to coastal structures

and development along the Panhandle of Florida. In order to help mitigate this threat, a new erosion model, NEXTGEN, has been tested in order to evaluate the applicability of this model for prediction purposes. Historic storms have been analyzed and results have been compared with available survey data. Statistical analyses were performed on the predicted results to

evaluate the accuracy of the predictions and sensitivity of the model to input variables. Overall,

analysis of the results obtained from the model confirms its reasonableness in predicting

hurricane effects at the +10 foot contour in causing recession.

CHAPTER 1 INTRODUCTION

Coastal regions are the most developed areas within the United States. It has been

estimated that 139 million people are currently living along the coast, and by 2015, the coastal

population will reach 165 million (Cullington et al., 1990). The coastal zone of the United States

can be described as the land region that is approximately 31 miles from the coast. Overall, 53

percent of all Americans live within 50 miles of the coastline (Edwards, 1989). Living within

the dynamic coastal system comes with inherent dangers; property owners are constantly

threatened by shoreline retreat due to wind, waves, and hurricanes. With such a large population

in the U.S. living near our shorelines, it is important that we be able to accurately predict the location of our shorelines for land-use planning and for the safety of human life.

Coastal Processes

Coastal erosion is the result of many processes both on large and small scales. Some of these processes have a great deal known about them, while others are still yet to be fully

understood. Some coastal processes are actually beneficial and cause coastal land to be

reclaimed from the ocean; however, most processes lead to erosion which is detrimental to the

beach and structures in the way. The erosion that can be seen when one steps out on the beach

can be the result of two causes: humans or nature.

Erosion due to humans can be attributed to coastal structures such as inlets, groins, and

seawalls. In some countries, mining of sand from the active nearshore region is still allowed.

Additionally, structures placed in the coastal system for erosion mitigation can have unintended

adverse effects on the nearshore system. These structures interrupt the natural sediment flow of

the system and oftentimes erosion is the end result of the modification to the coastal system. As

coastal engineers, it is our job to attempt to mitigate this erosional tendency and to return a

natural equilibrium to the system. Systems such as sand bypassing plants have been used by

coastal engineers to help reinstate, to the degree possible, the natural flows within the dynamic system.

Along with humans altering the dynamic coastal system, nature has its own accretional and

erosional tendencies and cycles. During the winter months, the beach tends to have a bar profile

characterized by a narrow and steep beachface. This is due to the highly energetic winter waves

causing the beach to lose its fine material to offshore bars. As the season changes to the summer,

the waves tend to decrease in energy which allows the berm to grow in width by sand being

transported landward. This latter type of beach profile is normally designated as the berm or

summer profile (Dean and Dalrymple, 2002). Although the bar and berm profiles are fairly

predictable seasonal events, hurricanes and extreme storm events are highly unpredictable

erosional events and cause profiles similar to the winter waves with additional impact to the

higher contour.

Much of the natural impact seen within the coastal system is caused on the short-term time

scale by hurricanes or extreme winter storms which have a longer recovery period than that of

the seasonal processes. In a majority of these extreme events, the storm surge, high waves, and

wind can be the cause of most of the damage that occurs. Although the high waves and wind can

cause a substantial amount of damage to a beachfront property, it is the storm surge combined

with the high waves that is the most costly and deadly component of the storm.

The coastal impact due to high storm surges can be devastating. The storm surge can flood

land and greatly alter the shoreline within the affected area. Generally, on the Panhandle of

Florida, storm surges can range up to 15 or more feet. However, a maximum storm surge of 25’

has been reported in Mississippi due to Hurricane Katrina in 2005 (Fritz et al, 2007). The storm

surge can be broken down into four components. The strong winds associated with a hurricane are responsible for the first component of the storm surge. These winds create shear stresses on the water surface which push the water ahead of the storm until it is forced to pile upon the beach. The next component of surge, wave setup, primarily occurs in the breaking wave zone in shallow water and causes an increase in the mean water level due to transfer of momentum from the breaking waves to the water column. In addition to the onshore shear stress, the winds from a hurricane also create longshore currents, the third component of storm surge. The longshore current causes a force (the Coriolis force) which can either augment or reduce the overall surge.

The final component of the storm surge is barometric tide in response to the barometric pressure reduction in the low pressure storm. The extreme low pressure in the eye of the storm acts as a suction and draws up the water surface in the affected region. Of the four components, most of the surge is attributed to the wind stresses (Dean and Dalrymple, 2002).

Shoreline Forecasting

The primary application of shoreline position data is to understand the natural and/or altered characteristics of a coastal area of interest to develop an improved basis for management and design alternatives. One application is by land-use planners to establish regulatory coastal construction setbacks. In general, about one-third of all coastal states use shoreline-change data as a basis for setting regulatory coastal setbacks (National Research Council, 1990). Regulatory setbacks and restrictions generally vary from state to state and are calculated by using the average annual erosion rate (AAER) at a specific location. This AAER is then multiplied by a specific number of years, and the computed setback is established.

Recently, due to an increased interest in the response of the coastline due to hurricanes, it is now necessary to be able to accurately identify the impact of a storm on shoreline position.

The shoreline location must be predicted accurately by a shoreline erosion model. Ideally, this

model must be verified against historic storms to confirm accuracy of the results. Once the

model’s accuracy has been confirmed, it will then be able to be applied to various surge levels

and durations. Regulatory and management interests would then be able to apply this capability along with return periods of storms in order to establish coastal setbacks from the predicted erosion data.

Objective and Scope

The main objective of this thesis is to evaluate the profile response model, NEXTGEN,

and evaluate the model validity in predicting dune erosion. To achieve this goal, a storm surge

model will be modified in order to predict the required storm surge input to NEXTGEN.

Historic hurricanes will be selected and measured erosion from the hurricanes will be compared

with the forecast contour change. Statistical analyses will also be performed on the data to

confirm the accuracy of the predicted results against measured.

The scope of the project is the Panhandle counties of the Florida coast, with an emphasis

on Walton County. Figure 1-1 illustrates the study area. The time period associated with the project ranges from 1872 to present, with an emphasis on the 1975 to present data. The study area is located on the northern Gulf coast of Florida. Typically, the area is associated with low wave and tide energy. Wave periods tend to have magnitudes of 6.0 seconds of less. The tides found within the Panhandle are mostly diurnal with a range of less than 1.6 feet (Morang, 1992).

The measured seasonal variation experienced in Walton County is approximately 30 feet annually.

In the past 33 years, the study area has been impacted by nine major storms. Due to this

large number of storms, the study area is in a critically eroded state. The Florida Department of

Environmental Protection defines a critically eroded area as

“critically eroded area is a segment of the shoreline where natural processes or human activity have caused or contributed to erosion and recession of the beach or dune system to such a degree that upland development, recreational interests, wildlife habitat, or important cultural resources are threatened or lost. Critically eroded areas may also include peripheral segments or gaps between identified critically eroded areas which, although they may be stable or slightly erosional now, their inclusion is necessary for continuity of management of the coastal system or for the design integrity of adjacent beach management projects.”

The main threat attributed to critical erosion that applies to the Panhandle study area is recession

that could damage upland development and structures. In all, there are eight sections of Walton

County that make up 14.3 miles of critically eroded shoreline resulting in 57% of the County shoreline with this designation (Bureau of Beaches and Coastal Systems, 2007).

Figure 1-1. General location of study area and Department of Environmental Protection monuments [Reprinted with permission from Foster, E.R. 2000. Shoreline Change Rate Estimates: Walton County. Report No. BCS-2000-02 (Page 4, Figure 1). Office of Beaches and Coastal Systems, Florida Department of Environmental Protection, Tallahassee, Florida.]

CHAPTER 2 LITERATURE REVIEW

Mean High Water (MHW) Erosion Calculation Methods

Shoreline position is one of the most commonly available coastal descriptors available to

coastal engineers and provides the basis for quantifying shoreline change rates. The calculated

rates can be applied to classify areas of high hazard and erosion along the coastline (Dolan,

Fenster, and Holme, 1991). There are many methods utilized to calculate erosion rates by

coastal engineers, scientists, and land planners. Although all methods to be discussed can predict

a shoreline position at a future time, some methods produce more reliable results than others.

The simplest method used to calculate shoreline rate-of-change is the end point rate (EPR)

method. This method uses a simple calculation involving just two shoreline positions. Here, the total distance of shoreline change is divided by the elapsed time. The two points used to calculate a rate-of-change are usually the earliest and latest data points (Genz et al, 2007). The

advantage of this method is ease of calculation and its common use among many State agencies

(Dolan, Fenster, and Holme, 1991). The main disadvantage for utilizing this method is that

major shoreline changes that occur between the two time periods are disregarded by using the

earliest and latest points.

A second method that is used commonly by engineers, scientists, and planners is linear

regression (LR). With LR, a best fit line is calculated using the method of Least Squares. The

slope from the best fit line is then used as an estimate of shoreline change rate. Unlike the EPR

method, LR uses all available data points for shoreline position. The main disadvantage of this

method occurs when the data sets are clustered. This can cause the clustered data to have more

of an effect on the regression than the less gathered shoreline positions (Dolan, Fenster, and

Holme, 1991).

Foster and Savage (1989) developed the average of rates (AOR) method in order to calculate shoreline changes This method calculates individual EPRs for all shoreline position data, and averages them to determine a shoreline change rate. It is recommended, however, that this method not be used as a computational method alone. It is most commonly used with LR as a means to verify results.

There are several other more complex methods that are currently available, but not as widely used as the three listed above. The Jackknifing (JK) method is similar to that of LR, but it uses multiple linear regressions to find a shoreline change rate (Genz et al, 2007). Fenster,

Dolan, and Elder (1993) adapted the minimum description length (MDL) method as a way to

identify influential short-term changes. This method does not assume a linear trend and uses a

non-linear polynomial that best fits the data (Crowell, Douglas, and Letherman, 1993). The main

disadvantages of the latter two methods are in the complexity of calculations needed in order to

find the rate of change.

Hurricane Model

The hurricane model utilized throughout this thesis was developed by Wilson (1957) and

consists of a parameterized pressure field along with a wind field. The assumptions made within

the model are that the pressure field is perfectly symmetrical with circular isobars. The pressure

field is defined by the equation

−Rr/ p =+pppeoo()∞ − (2-1)

where po is the central pressure, p∞ is the ambient atmospheric pressure that is unaffected by the hurricane’s influence, R is the radius to maximum winds, and r is the distance from the

hurricane center to point of interest.

Wind velocities over the ocean are described in terms of three different velocities. The first of these is the cyclostrophic wind, defined as

ΔpR −Rr/ Uc = e (2-2) ρa r where Δp is the difference in pressure between the ambient pressure and the central pressure and

ρa is the density of air. The geostrophic wind velocity,U g , is defined as

ΔpR e−Rr/ ρ r 2 U = a (2-3) g 2sinω φ where ω is the angular velocity of rotation of the earth and φ is the latitude of the point of interest. In order to calculate the gradient wind speed, the wind speed at 30 feet above the water surface, the parameterγ must first be found by

1sin⎛⎞V β U γ =+c (2-4) ⎜⎟ 2 ⎝⎠UUcg where V is forward velocity of the hurricane and β is the angle of bearing of the point of interest.

β is further defined in Figure 2-1. The gradient wind speed is then defined as

2 UUGc=+0.83 (γ 1−γ ) (2-5) which accounts for the frictional effect of the water surface.

Holland (1980) developed a parametric hurricane model that utilized concentric pressure and wind profiles to define a “standard” hurricane. The model is similar to that of Wilson, but it utilizes a scaling parameter B that affects the radial reach of the maximum winds. Typical values of B can range from 1.0 to 2.5. The defining formulas for Holland’s model are

−(/)RrB pr()=+ poo() p∞ − p e (2-6)

1/2 ⎡⎤B 2 BR⎛⎞ −(/)RrB ⎛⎞rfcc rf Vr()=−+⎢⎥⎜⎟()p∞ po e ⎜⎟ − (2-7) ⎣⎦⎢⎥ρa ⎝⎠r ⎝⎠22 where p(r) is the pressures at a distance r from the hurricane, and V(r) is the gradient wind at a distance r from the hurricane. The other variables have been previously defined by Wilson’s hurricane model.

Bathystrophic Storm Tide Model

Freeman, Baer, and Jung (1957) developed a simple one-dimensional storm surge model termed the bathystrophic storm tide model. This model considers the governing equations along a single transect oriented perpendicular to the shore. The model is based on the following four assumptions which reduce the model to a time-dependent problem which can be solved at each point with the wind history considered to be known.

1. There is little or no cross-shore transport.

2. There is no significant change in the height to the water surface due to the divergence of the velocity field.

3. The variations in the alongshore direction are negligible.

4. The spatial derivatives of the longshore current are small enough to be neglected with respect to the Coriolis parameter.

Using the above assumptions, the governing equations of the model are found by integrating over depth to obtain

∂∂η 11⎡⎤τ sx p =++⎢⎥fqcy (2-6) ∂+x gh()ηρ⎣⎦ww ρg∂ x

∂qy 1 =−(τ sybyτ ) (2-7) ∂t ρw where η is the water surface elevation, x and y are normal and parallel to the shoreline, g is

gravity, h is local undisturbed water depth, τ s is the shear stress at the water surface due to wind,

ρw is the density of water, fc is the Coriolis parameter, q is the volumetric flow per unit width,

p is the atmospheric pressure, and τ b is the shear stress due to bottom friction. Equation 2.6 allows for a hydrostatic balance of the forces associated with the surge in the cross-shore direction with the time dependent Coriolis induced flow in the alongshore direction and the shear stresses due to the wind in the x (onshore) direction. Equation 2.7 is a balance between the surface and bottom shear stresses in the alongshore direction and the inertial force in the alongshore direction.

Dean and Chui (1982) developed a two-dimensional model based on more complete representations of the equations of motion and a one-dimensional model from the governing equations developed by Freeman, Baer, and Jung. The two-dimensional model was applied to calibrate the one-dimensional model. Both models were calibrated against tide gage measurements.

Although this thesis utilizes a one-dimensional model only, it is important to note that there are many two-dimensional models available. These models are more complex and require more resources. Such examples of the more complex models are the Sea, Lake, and Overland

Surges from Hurricanes (SLOSH) model developed by Jelesnianski, Chen, and Shaffer (1992) which is utilized by the National Weather Service. This model runs on a cylindrical grid system and incorporates some nonlinearities into the calculations. Luettich and Westerink (1992) developed the ADvanced CIRCulation (ADCIRC) Finite Element Hydrodynamic Model for

Coastal Oceans, Inlets, Rivers and Floodplains. This model simulates the rise in water levels in any area of interest. ADCIRC is a two-dimensional model that solves the depth-integrated equations of momentum and continuity within a time domain. The U.S. Army Corps of

Engineers and a number of other entities apply this model to predict water levels in the United

States and internationally.

The more complex models mentioned above are primarily used within the United States, however, it is important to note other reputable models that are utilized by European countries to predict storm surge. The Danish developed MIKE 21 which is a model that simulates currents, waves, and ecology within inland waters, coastal areas, and seas. The hydrodynamic portion of the model simulates the water level variations due to various forcing processes. The water level response is found through the depth integrated equations of continuity and momentum on a grid system covering the coastal area (Danish Hydraulic Institute, 2008). The Dutch have developed

Delft 3D which has the ability to simulate two and three-dimensional flow, waves, morphodynamics, and water quality. Along with calculating the individual effects, the model has the capability of handling interactions of the processes mentioned above (Delft Hydraulics,

2001).

Shoreline Change Models

The GENESIS shoreline change model (Hanson, 1989) is a one-line numerical planform model designed to predict long-term shoreline changes associated with coastal engineering structures or beach nourishment projects which perturb the nearshore system. The model has been generalized in comparison to previous models so that it can be applied to most sandy coastlines. There are two governing equations: a conservation equation and a transport equation.

The conservation of mass equation is

∂∂yQ1 ⎛⎞ ++⎜⎟q =0 (2-9) ∂+∂tDBc D⎝⎠ x

where y is the shoreline position, t is the time, x is the longshore coordinate, DB is the berm

height above mean water level, Dc is the depth of closure, Q is the longshore transport rate, and q represents sources or sinks along the coast. The longshore transport, Q, in Equation 2.9 is based upon the CERC longshore transport formula. The equation can be expressed as follows

∂H QHCa=−()(sin2cos2 αα a ) (2-10) g bb12s bs∂x b where H is significant wave height, Cg is wave group velocity, the b subscript denotes breaking

wave condition, and αbs is the angle of wave crests to the shoreline. The non-dimensional

coefficients a1 and a2 are defined as

K1 a1 = 5/2 (2-11) 16(ρρs /−− 1)(1p )1.416

K2 a2 = 5/2 (2-12) 8(ρρs /−− 1)(1p ) tan β 1.416

where K1 and K2 are calibration parameters, ρs and ρ are the densities of sand and water, p is the sediment porosity, and tan β is the average bottom slope from the shoreline to the depth of minimal longshore transport. The factor of 1.416 is a conversion between significant and RMS wave heights.

Larson and Kraus (1989) developed the SBEACH numerical model to predict storm- induced beach and dune erosion. The important feature included within this model is the capability to reproduce the main morphologic features of the beach profile, such as bars and berms. The assumptions made by the authors of the model are

1. Profile change is the result of cross-shore processes due to breaking waves.

2. There is no net loss or gain of material.

3. The rate of longshore transport is based upon empirical calculations from wave tank experiments.

4. Longshore processes are uniform and not considered.

Like GENESIS, the model is based on the conservation of mass equation where cross-shore transport rates are required. However, these rates can be either theoretical or from an empirical rate formula.

Kriebel and Dean (1985) developed a two-dimensional model to predict beach and dune erosion during major storm events. The model uses a simplified set of governing equations for beach profile evolution, the complete time history of the storm surge, and a more realistic representation of the beach profile in order to predict the shoreline evolution. Figure 2-2 illustrates the basic concepts of the model. The model represents the general equilibrium profile by a curve that has been defined as

hAx= 2/3 (2-13) where h is water depth related to some distance, x, offshore. The parameter A depends on sediment size and governs the profile steepness and is related to the equilibrium wave energy

dissipation per unit volume, D* within the surf zone.

The model represents profile evolution through the concept that a profile will attain a dynamic equilibrium for a given surge and wave conditions after a certain amount of time.

Using this assumption, the net transport rate is assumed to be proportional to the disequilibrium of wave energy dissipation that is occurring at all points across the surf zone. Through these concepts, and through shallow water wave theory, the cross-shore sediment transport rate, Q, is determined by the model through the difference between the actual and equilibrium levels of wave energy dissipation per unit volume throughout the surf zone, or by

QKDD=−(* ) (2-14)

where D and D* are the actual and equilibrium time-dependent energy dissipation per unit volume, and K is a transport rate coefficient which is determined empirically. Using the calculated transport rate, the time-dependent profile evolution is determined by the equation of conservation of sand over a profile by an implicit finite-difference solution which is defined as

∂∂x Q = (2-15) ∂∂th where x is the distance offshore to the depth, h.

Figure 2-1. Sketch of idealized hurricane model.

Figure 2-2. Basic concepts for Kriebel and Dean’s erosion model [Reprinted with permission from Dean, R.G. 1986. Verification study of a dune erosion model. Shore and Beach 54(3), (Page 14, Figure 1).]

CHAPTER 3 METHODOLOGY

The primary objective of this thesis is to evaluate the accuracy of new erosion methodology in predicting high-frequency changes along the Panhandle of the Florida coast.

The main long-term goal is to be able to accurately predict how beach profiles respond to major storm events, especially hurricanes. Several historic storms will be studied along with various contour changes associated with these storms, and from this information, a deterministic/statistical approach will be taken in order to evaluate the predicted results within the study area.

The input data required to calculate contour positions for the profile response model and for the bathystrophic storm tide model was obtained from the Bureau of Beaches and Coastal

Systems (BBCS) of the Florida Department of Environmental Protection (FDEP)

(http://www.dep.state.fl.us/beaches/). The profile change model utilizes as input the initial profile and storm surge time history. To predict storm surge, hurricane track information was obtained from the National Hurricane Center’s website (http://www.nhc.noaa.gov/pastall.shtml) in the Hurricane Best Track Files (HURDAT) directory. The information within the HURDAT file contains six hourly hurricane center locations in longitude and latitude, intensities in maximum one minute surface wind speeds in knots, and for some storms, minimum central pressure in millibars for all Tropical Storms and Hurricanes in the Atlantic from 1851-2006.

Hurricane Model

In order to calculate the storm surges for individual hurricanes within our study area, a hurricane program was modified to compute the following parameters for the one-dimensional storm surge model: Hurricane position, translational speed, translational direction, radius to

maximum winds and pressure deficit. All values within the model are referenced with respect to a defined offshore origin.

The program has been modified from its previous form in two ways. The first alteration of the program is to include a filter that selects hurricanes within a selected radius to the study area and disregards the hurricanes that occur outside this catchment area. The designated area is limited to 100 miles to the west of Walton County and 50 miles to the east of Walton County.

Figure 3-1 provides a detailed sketch of the catchment area.

The second modification to the program is a change in the coordinate system. Previously, the model had employed a left-hand coordinate system; however, for our study area, the use of a right-handed system was desired. The coordinate system has also been aligned relative to the nominal coastline, and the storm coordinates determined from the program are also relative to this coordinate system. Figure 3-2 provides a sketch of the offshore coordinate system.

Hurricane Storm Surge Model

The hurricane storm surge model utilized throughout this thesis was first obtained from the

Florida Department of Environmental Protection’s (FDEP) Bureau of Beaches and Coastal

Systems (BBCS). The model has been altered from the original version to allow individual real- world hurricane parameters instead of random model hurricane parameters. The model determines the storm tide by combining calculations of wave setup, wind stress tide, barometric tide, and the Coriolis tide at grid and selected time increments1. For calculation purposes, the model requires input of the following parameters: hurricane position, pressure deficit, forward velocity, and translational direction. The model uses the hurricane characteristics from the

1 The astronomical tide has not been accounted for at this point, but it is included in the final values of adjusted storm surge.

hurricane track model and advances the hurricane along a track in accordance with HURDAT specifications.

Theory. The governing expressions of the one-dimensional model in finite difference form are

Δx ⎡⎤τ pnn+11− p + ηηnn++11=+xi +fqn+1 +ii+1 (3-1) ii+1 ⎢⎥cyi gh()+ηρi ⎣⎦wwρg

1 ⎡⎤Δt qqnn+1 =+τ (3-2) yyii⎢yi⎥ BB ⎣⎦ρw

Δtqn f yi BB =+1.0 2 (3-3) ()h +η i where i is grid location, n is the corresponding time step, and f is a Weisbach-Darcy bottom friction factor taken as 0.0025. The remaining terms have been previously defined in Chapter 2 in the description of Freeman, Baer, and Jung’s (1957) one-dimensional bathystrophic surge model. Continuing with the hurricane track model above, x is defined as the cross-shore location and is positive landward.

The only boundary condition needed in the model is the seaward boundary condition of water surface displacement due to the barometric tide. This boundary condition is defined as

p∞ − p1 η1 = (3-4) ρw g where the subscript one represents the most seaward point for each transect. Initial conditions

for the model include a condition of rest where qy is equal to zero and a zero water surface displacement.

As mentioned above, the main governing equations for the one-dimensional program contain the four components of surge that were described in Chapter 1. The Coriolis tide is represented within the parenthesis in the first part of Equation 3-1 by

Δx (fcyq ) (3-5) gh()+η

where fc is the Coriolis parameter. The Coriolis parameter is equal to 2ω sinφ where ω is the angular rotation of the earth ( 7.272× 10−5 rad/s), and φ is the latitude of Walton County (30° ).

This portion of the storm surge can be critical if the alongshore current and the Coriolis force act in the same direction, but the Coriolis contribution can reduce the storm induced surge if the current is flowing in the opposite direction.

The barometric pressure term within the model is defined by

p − p ii+1 (3-6) ρw g where the change in pressure is measured in pounds per square foot (psf). Commonly, the surge contributed by the barometric pressure is considerably smaller that the wind stress contribution.

The wind stresses, as mentioned in Chapter 1, are normally the largest contributor to the storm surge. The contribution of wind stresses is represented in the model in the first part of

Equation 3-1 by

Δx ⎛⎞τ ⎜x ⎟ (3-7) gh()+η ⎝⎠ρw

where τ x is the wind shear stress in the cross-shore direction. The wind shear stress is defined as

2 τ x = ρwkW sinθd (3-8)

where k is the Van Dorn friction factor. Numerous studies have been conducted for the relationship for k and the value employed herein is

⎧1.2× 10−6 WW≤ c ⎪ 2 k = ⎨ ⎛W ⎞ (3-9) 1.2×+× 10−−66 2.25 10 1 −c ⎪ ⎜⎟ WW> ⎩⎪ ⎝⎠W c

where Wc is the critical velocity (23.6 feet per second). The friction factor usually has smaller values for mild winds and a greater value for larger winds due to the increased roughness of the

water surface. The quantity θd in Equation 3-8 is defined as

θds=−μθl_ new (3-10)

where θslnew_ is defined by

θslnew_ =−θ rotθ l (3-11)

where θrot is the orientation of the shoreline to which the axes are rotated such that the x axis is

shore normal, and θl is the orientation of the shoreline at the transect of interest that is read in from the input data file. The angle μ is defined in Figure 3-3 and is the angle between the shore normal x-axis and the wind vectors. Figures 3-2 and 3-3 illustrate the geometry of the angles referred to in Equation 3-10 and 3-11.

The last component of storm surge calculated in the program is the total wave setup,ηwsu .

This term is defined as

⎛⎞H η =−0.19⎜ 1.0 2.82 b ⎟H (3-12) wsu ⎜gT 2 ⎟b ⎝⎠o

where Hb is breaking wave height and To is the deepwater wave period. In order to apply the

equation above, the Bretschneider relationship is used to find H max . From this relationship, the following equation is obtained

⎛⎞0.208V He=+16.5Rp(Δ /100) ⎜ 1 F ⎟ (3-13) max ⎜⎟ ⎝⎠U max

where R is the radius of maximum winds in nautical miles, Δp is the central pressure deficit, VF

is the forward velocity of the hurricane in knots, and U max is the maximum wind speed in knots.

The local deepwater wave height is determined from local winds, U, and maximum hurricane

winds,U max . The deepwater significant wave height is represented by

⎛⎞U 2 HHo = max ⎜2 ⎟ (3-14) ⎝⎠Umax

The breaking wave height, Hb , is based on the deepwater significant wave height and approximated by

Hb= 0.936Ho (3-15)

By substituting the value found from Equation 3-15 into Equation 3-12, the total wave setup is found and added to the storm surge at each time step in the shoreward grid.

The volumetric longshore transport rate represented by Equation 3-2 is caused by wind stresses acting parallel to the coastline of interest. Like the calculation for the wind stress tide, the volumetric transport rate is determined by shear stresses in the longshore direction which are represented by the following

2 τ yw=−ρkW cosθd (3-16)

The negative sign within Equation 3-16 is due to our coordinate system and reverses the direction of the wind stresses to be compatible with the positive onshore coordinate system.

NEXTGEN Erosion Model

After developing a storm surge history for our area of interest, it was then necessary to apply a cross-shore sediment transport model that could predict profile evolution due to a storm tide and elevated waves. The cross-shore transport model used within the study is the two- dimensional Next Generation Beach and Dune Erosion Model, also known as NEXTGEN, developed by Dean (2004). The model contains both a sediment transport equation and a continuity equation to represent processes in nature. The model’s input requires a beach profile for our study area of interest, a storm surge history, and the following parameters that define beach and storm characteristics within the study area:

• Sediment transport coefficient • Exponent in transport equation • Onshore limiting slope • Offshore limiting slope • Beach face slope (associated with erosion) • Time increment • Wave height

The NEXTGEN model was run with the default values recommended by Dean in the program’s users manual. This allowed the study to prove the applicability of the model to various study areas and historic storms. The model utilized a recommended wave height of 10 feet, which affects the offshore depth to which sediment is moved. By using the 10 foot wave height, the predicted amount of erosion or accretion experienced during the storm could be affected. This could possibly result in some error being factored into cross-shore transport calculations.

Theory. As mentioned above, the cross-shore sediment transport equation within the model is defined as

M −1 QKDD=−*( DD −*) (3-17)

Where Q is the cross-shore transport rate, K is the sediment transport coefficient, D is the wave energy dissipation per unit volume, M is an exponent that partially governs the rate of profile

evolution, and D* is the equilibrium value of wave energy dissipation per unit volume. The values of M and K are 2.0 and 0.005, which are defined in the input file.

The continuity equation within the model allows for a conservation of sand and is defined as

∂∂hQ = (3-18) ∂∂tx where h is the depth at the center of a grid. Equation 3-18 is coupled with Equation 3-17, and both are solved simultaneously. The profile is updated after each computation, and the equations are repeated until completion of the simulation.

Contour Changes

Measured Contour Change

To quantify the historic beach profile changes over time, methodology was developed to interpolate the contour changes at different elevations. This was accomplished by locating the shoreline position at each zero foot contour and +10 foot contour for each desired monument within the study area. The shoreline position was found by

⎛⎞zh− yy=+i−1 yy − (3-19) contourim, i−−11⎜⎟(i i ) ⎝⎠hhii− −1 where i is the location, m indicates measured, y is the cross-shore coordinate, h is the elevation, and z is the desired elevation of the contour line. Equation 3-19 delivers a single contour

location for one individual monument. In some cases, two +10 foot contours were present within the dataset. This was accounted for in the contour change methodology by allowing the model to read in all data and retain the most seaward value of the +10 foot contour. In order to obtain an average contour for all monuments at one period in time, the following equation was used

1 n y= y (3-20) contour_ avgm∑ contouri,m n i=1 where n is the number of monuments within the study area.

Once an average contour location was found for a given elevation, it was then desired to determine the contour change based on the initial pre-storm profile and the corresponding post- storm profile for the selected time segment. The change in the contour position is

Δ=yycontour contour − ycontour (3-21) m post− stormmpre− stormm

Equation 3-21 can be used for all individual contour positions at one time or for average position at one time.

Predicted Contour Change

Once results were obtained from the NEXTGEN model, it was then desired to compare shoreline erosion at different height contours. This was accomplished by using Equations 3-19 and 3-20, but inserting predicted profiles instead of measured. The shoreline recession was found similar to Equation 3-21, with the equation changed to the following

Δ=yycontour contour − ycontour (3-22) ppost−stormp pre−stormm where m indicates measured data, and p indicates predicted data. As mentioned above, Equation

3-22 can be used for all individual contour positions at one time or for average position at one time.

Statistical Analysis of Results

Results obtained for both measured and predicted contour changes were analyzed to determine the effectiveness of the model. The Pearson correlation coefficient, r, was used in order to determine how certain one can be in making predictions from the NEXTGEN model.

The square of the correlation coefficient was the last step taken in order to determine the goodness of fit. The square of the correlation coefficient will be referred to as R-squared in following chapters. For ease of use and lack of confusion, the following notation is defined for the analysis. A contour position is defined by

ympB or , or A,10 or 0 (,ij ) (3-23) where m is measured, p is predicted, B is before or pre-storm, A is after or post-storm, 10 indicates the 10 foot contour, 0 indicates the zero foot contour, i is the storm event (5 total), and j is the monument number (6 total). Using Equation 3-23, the contour change was defined for statistical analysis by

Δ=ymm,10 (,ij ) y,Am ,10 (, ij ) − y,B ,10 (, ij ) (3-24)

Δ=yijypp,10 (, ),Am ,10 (, ijy ) −,B ,10 (, ij ) (3-25)

By using these definitions, the correlation equations can be written much more compactly. The zero foot contour change notation is represented in the same manner as Equations 3-24 and 3-25 with the subscript 10 replaced by 0.

The r value was found for two different cases using two different methods. The two cases considered within the analysis were a comparison between the measured and predicted 10 foot contour data and a comparison between the measured 10 foot and zero contour data. The two methods performed for each case considered all data within a single storm event and data over all storm events.

Measured versus Predicted

The first method performed upon the measured and predicted data for the 10 foot contour

was for each storm event, j. The correlation coefficient, r1,i , is defined as

6 ⎡⎤ ∑ ⎣⎦Δ−Δymp,10 (,ij ) y,10 (, ij ) r = j=1 (3-26) 1,i 66 22 ∑∑ΔΔymp,10 (,ij ) y,10 (, ij ) jj==11 where the variables have been previously defined in the section above. The next method

determined the correlation coefficient r2 for all storm events and is defined as

56 ⎡⎤ ∑∑⎣⎦Δ−Δyijyijmp,10 (, ),10 (, ) r = ij==11 (3-27) 2 56 56 22 ∑∑ΔΔymp,10 (,ij ) ∑∑ y,10 (, ij ) ij==11 ij==11

Measured 10 Foot Contour versus Measured Zero Foot Contour

The comparison between the measured +10 foot contour and measured zero contour was

performed in the same manner as mentioned above. The correlation coefficient, r1,i , for the first method is as follows

6 ∑ ⎣⎦⎡⎤Δ−Δymm,10 (,ij ) y,0 (, ij ) r = j=1 (3-28) 1,i 66 22 ∑∑ΔΔymm,10 (,ij ) y,0 (, ij ) jj==11

The second method used to calculate the correlation coefficient, r2 , for all storm events is defined by

56 ∑∑⎣⎦⎡⎤Δ−Δyijyijmm,10 (, ),0 (, ) r = ij==11 (3-29) 2 56 56 22 ∑∑ΔΔymm,10 (,ij ) ∑∑ y,0 (, ij ) ij==11 ij==11

Results of these analyses are presented and interpreted in the following chapters.

Figure 3-1. Definition sketch of hurricane “catchment” zone.

Figure 3-2. Geometric sketch of θrot ,θl , and coordinate system.

Figure 3-3. Example sketch of μ and θslnew_ .

CHAPTER 4 RESULTS AND ANALYSIS

As discussed in the previous chapters, evaluating erosion predicted by the NEXTGEN model was the main focus of this thesis. In order to complete this task, various models were utilized and numerous results were developed. From these results, a better quantitative assessment of the shoreline erosion model could be attained through improving the storm surge data.

Measured Shoreline Change Results

To determine the magnitude of changes that occur along the shoreline in Walton County, it was necessary to analyze the changes for various contour lines. A contour change program was run for nine common monuments spaced periodically throughout Walton County. Common monuments within our study area are defined as monuments that are located in the same position since 1973 and are common to all data sets. By using common monuments that had not been relocated, no error from this possible source was included in the calculations. The common monuments used to analyze the measured beach profile data were monuments 21, 57, 60, 63, 66,

84, 87, 102, and 117.

To determine contour changes since 1973, the +10 and zero contour line positions for

Walton County’s beaches were selected as the references for subsequent changes. The shoreline changes were referenced to the 1973 position. All fluctuations after 1973 on the negative y-axis indicate a recessed contour and on the positive y-axis, a contour advancement. As can be seen from Figure 4-1, there have been substantial changes in the zero and +10 foot contour positions over the past 30 years.

The impact from Hurricane Eloise can be seen in late 1975 with a large amount of recession occurring in the +10 foot contour line. From the time span of 1975 to 1995, this

contour recovered slightly past its pre-Eloise position due to mild weather conditions. In 1995, it can be seen that the contour responds significantly to Hurricanes Erin and Opal. These storms bring the +10 foot contour back to a recessed location of -36 feet. From the time period of 1998 to 2004, there is only a slight recovery at the upper contour level. In 2004, there is another large contour recession due to Hurricanes Ivan and Dennis. These hurricanes moved the +10 foot contour back approximately 34 feet from its previously eroded condition. The last data point available in Walton County was in July, 2007, and from this point, one can note the minimal recovery that has occurred.

Although the +10 foot contour indicated large amounts of recession due to major storm events, the zero contour line may indicate accretion after large storms. As for the 10 foot contour, the 1973 position for the zero contour is used as a datum and is set equal to zero. In

1975, the waterline is moved 36 feet shoreward due to Hurricane Eloise. After some time, it can be seen that the profile evolves back to within approximately seven feet from the datum. From

1981 to 1995, there is little data available to indicate the position of the zero contour line. In

1995, Hurricanes Erin and Opal moved the zero foot contour line approximately 35 feet shoreward. The next major hurricane to impact the zero foot contour was Hurricane Georges in late 1998, which caused a retreat of approximately 30 feet. Over the next six years, the profile recovers to approximately its 1973 pre-storm conditions. Then in 2004, the zero contour experiences retreat by Hurricane Ivan. After Hurricane Ivan, the profile is impacted by

Hurricane Dennis in 2005. Hurricane Dennis, however, did not cause the zero foot contour line to be displaced seaward. The profile response from Dennis was a retreat of over 40 feet.

However, from Figure 4-1 it can be seen that the zero foot contour line is recovering quite rapidly back toward the 1973 datum.

One explanation for the accretion at the zero foot contour following some storms is the large amount of sediment eroded from the upper berm during a storm. The sand is taken from the upper beach and transported seaward where it advances the lower contours. In response to the steep berm caused by erosion, the upper portion of the profile erodes in an attempt to form a new equilibrium profile for the elevated water levels. As the profile approaches equilibrium for the new water level, the zero contour line is displaced farther seaward. Once the waters recede, the zero foot contour is located at the adjusted seaward position from the profile response and from the large amount of sand moved to the lower contours from the storm. Figure 4-2 also illustrates this concept. A second possible explanation is that the lower contours respond to the post-storm constructive forces much more rapidly than the upper contours. Thus, during the time between the storm and the post-storm surveys, the lower contours may have experienced substantial recovery.

The results for the contour changes due to storms are also used later in this chapter to establish conclusions between measured retreat versus predicted retreat.

Storm Surge Results

The storm surge model was first run with Hurricane Eloise storm parameters from the model hurricane. Hurricane Eloise was chosen as a means of calibration due to the amount of data available for that storm. For Hurricane Eloise, the model creates a 23 hour hydrograph of storm surge with it reaching its peak surge of 5.6 feet at 18.0 hours, which is presented as Figure

4-3.

The calculated results from the one-dimensional model do not compare favorably with those measured during Hurricane Eloise. A tide gage at East Pass recorded a maximum storm tide of 6.4 feet, which did not include wave setup. Other measurements include a MHW mark of

13.8 feet which would indicate an under-prediction of approximately a factor of two, but this measurement may include wave runup or setup (Clark, 2006).

During hurricanes, wave runup can drastically increase the storm surge’s reach upon a beach. When a comparison was made between predicted and measured water levels, it was important that the most accurate source was used for comparison. It was determined that the best source for comparison would be from tide gages that were operational during the hurricane, and if they were not available, the MHW marks should be used with caution.

In addition to Hurricane Eloise, the one-dimensional model was run for nine other storms that entered the study area. For each of the storms, the one-dimensional model produced results for the hurricanes making landfall at the center of Walton County. Appendix A contains the predicted surge for the remaining major hurricanes within the study area. Table 4-1 shows a comparison between calculated and measured storm tides at several locations for Hurricane

Eloise and nine other hurricanes within our study area.

Error analysis done upon the measured surge values indicate that none of the model results compare favorably with recorded surge data. Figure 4-4 illustrates the maximum predicted un- scaled surge versus the maximum measured surge at the location of greatest surge. The predicted data was plotted against the measured data in order to analyze how well or how poorly the predicted data compared with the measured. As shown in Figure 4-4, a best-fit line was placed through the origin in order to see the magnitude of variation from the line. As can be seen in Figure 4-4, all of the results fall under the line of equivalence. The lack of fit to the line indicates an under-prediction which lead to an in-depth analysis of variables within the model.

After much scrutiny, it was decided that the one-dimensional model was under-predicting the values for the wind stress tide, thereby decreasing the overall surge. This under-prediction

could result in a misleading evaluation of the applicability of NEXTGEN. If too small surge values are input into the NEXTGEN model, the erosion will be underestimated.

Several possible explanations are given for the model’s shortcomings in accurately predicting the surge. The study area of Walton County has a relatively steep beach (slope of approximately 1/50) and a narrow shelf width. With these physical characteristics, the conditions are unfavorable to predict large wind setup values with the current equations described in Chapter 3. The calculations for wind setup are depth dependent, and with the steep slope, the water depth decreases rapidly. It is recalled that the Bathystrophic Storm Surge Model considers the system to be in static equilibrium in the cross-shore direction. It may be that dynamic effects play a significant role, especially on steep profiles.

Because the main purpose of this effort was to evaluate the profile response model,

NEXTGEN, it is essential to use the best storm surge results as possible. In order to represent more reliable storm surge values, two methods were developed. The first method utilized historic storm surge hydrographs obtained from the National Oceanic and Atmospheric

Administration (NOAA) Center for Operational Oceanographic Products and Services (CO-

OPS) (http://tidesandcurrents.noaa.gov/). These hydrographs were measured by a tide gage located at the end of a pier in Panama City, Florida and were available for eight of the nine storms of interest. This location was chosen for its close proximity to our study area, and the lack of wave setup in the measurements due to the tide gage position at the end of the pier.

Figures 4-5 to 4-8 illustrate the measured storm surge for several storms at the Panama City Pier tide station. Since the gage measures the astronomical tide, wind setup, barometric, and Coriolis components of the surge due to the hurricane, the measured values of the surge were able to be combined with the predicted wave setup values from the model. This combination of predicted

wave setup plus measured surge which includes all components other than wave setup provides more reasonable surge values. Each of the setup values along with the total surge can be seen in

Table 4-2.

As noted, the method described above was applied to eight of the nine storms; however, due to the lack of such data for Hurricane Erin, a second method was developed to scale the storm surge hydrograph created by the one-dimensional model. This scale factor was applied to the Walton County centered storm surge hydrograph, which increased the surge levels by approximately a factor of 2 for Hurricane Erin. An example of a scaled hydrograph can be seen in Figure 4-9.

The combined values of measured wind stress tide, barometric tide, Coriolis force, and predicted wave setup increased the storm surge to more reasonable levels. Figure 4-10 presents a comparison of the combined or scaled surge values and established. The values are closer to the line of equivalence in this plot, with some values falling very close to the equivalence line. The values for the predicted surge are centered at Walton County, but the values of measured surge were for areas close to the study area. This difference of location could explain why some values fall either above or below the line of equivalence.

Cross-Shore Transport Model

The NEXTGEN model was run for major storms affecting Walton County from 1975 to present. The major storms affecting the study area were Hurricane Eloise (1975), Hurricane Erin

(1995), Hurricane Opal (1995), Hurricane Ivan (2004), and Hurricane Dennis (2005). Since the model uses one beach profile per run, it was necessary to run the model at several locations throughout the county. The average locations selected were Monuments 21, 57, 63, 66, 87, and

102. Through selecting various monuments spaced throughout the study area, we were able to obtain a broader representation of storm related profile response. Like the calculations made for

measured contour change above, the monuments selected for use within the NEXTGEN model are all common monuments to the data set.

The model predicted erosion for each of the storms adequately when the adjusted storm surge was used as an input. Figure 4-11 illustrates the predicted profile evolution seen for

Hurricane Eloise at Monument 66. All profile evolution plots from the NEXTGEN model are presented in Appendix B. Each figure indicates the initial, pre-storm profile by the cyan line, with the other lines representing the profile evolution at several times during the storm event.

The final post-storm equilibrium profile is represented in each plot by the tan dashed line.

Predicted contour change. Hurricane Eloise caused a significant amount of erosion within the upper contours. The average recession of the +10 foot contour for the six profiles included in this study was found to be -39.5 feet. Post-storm studies done by Chui in 1977 measured an average retreat of 38 feet, which is in agreement with the calculated erosion from the model.

Hurricane Erin was a weaker tropical storm when it impacted Walton County, and this can be seen in the amount of erosion caused by the storm. On average, Walton County’s beaches retreated 9.4 feet at the +10 foot contour line for this storm. In the case of Hurricane Opal, the pre-storm beach condition was impacted by Hurricane Erin two months prior. Since there were no post-storm beach profiles taken after Erin, it is assumed that the pre-storm profile was the same as used for Erin. Under this assumption, results indicated a larger amount of erosion than that experienced by Hurricane Erin. On average, the model predicted a retreat of the +10 foot contour line of 18.6 feet for Hurricane Opal. Overall, for the 1995 hurricane season, a combined predicted retreat was found to be -28 feet, which is in good agreement with the observed retreat of -36 feet.

Although the predicted versus measured results for Hurricane Opal from this study compare well with one another. Studies done by the Bureau of Beaches and Coastal Systems observed a retreat at the +10 foot contour line at an average of 45 feet. However, it must be noted that the datum considered for pre-storm conditions could be different than the one used in the model, and the monuments used could be different than those of our study.

The next major hurricane to impact Walton County occurred nine years later in 2004. The pre-Ivan shoreline had undergone gradual recovery from Hurricane Opal, and the +10 foot contour line was located at 57.8 feet. As can be seen from a comparison from the two measurements of the +10 contour from 1975 and 1995, the beach was in an eroded state when

Hurricane Ivan impacted the coast. The calculated retreat of the +10 contour line post-Ivan was found to be 16.4 feet which compares reasonably to a measured retreat of 23 feet.

The final hurricane considered in this study is Hurricane Dennis. Hurricane Dennis was the first hurricane of the season to affect Walton County, and since its close proximity to

Hurricane Ivan, the pre-storm beach condition was in an eroded state. The pre-storm +10 foot contour was located approximately 50 feet shoreward of the 1973 position. The calculated average retreat for the +10 foot contour line for Hurricane Dennis was found to be 28 feet for the six Walton County profiles which is greater than the measured retreat of 16 feet.

Overall, the predicted retreat at the +10 foot contour compares favorably with the observed data. Hurricane Eloise had the best comparison of predicted versus measured contour change with an over-prediction of 2.5 feet. The retreat predicted for Hurricane Dennis contained the largest difference between measured and predicted contour change with an over-prediction of 11 feet. In general, the model was successful in predicting reasonable retreat due to major storms at

the upper contours. Figure 4-12 illustrates the comparison between predicted and measured +10 foot contour change.

Along with comparing the +10 foot contour change from the NEXTGEN model, it was also decided to analyze the zero foot contour change post-storm. Hurricane Eloise was predicted to have caused a shoreline advancement of 34.3 feet at the zero contour. As mentioned above in the measured shoreline change section, accretion about the zero contour mark is common for major storms due to the volume of sand eroded from the upper berm and possibly post-storm advancement prior to survey. This prediction compares well with the measured results of approximately 36 feet of accretion determined from the measured pre and post-storm data.

After Hurricane Eloise, the zero contour was impacted by Hurricanes Erin and Opal.

Hurricane Erin only resulted in a shoreline advancement of 4.6 feet at the contour line.

However, Hurricane Opal contributed to a major portion of the advancement during the 1995 hurricane season. The overall predicted shoreline advancement due to Hurricane Opal was approximately 40 feet. However, the total measured zero foot contour change for the 1995 hurricane season was a retreat of -35 feet, which does not compare well with the predicted results. Again, this error between predicted and measured can be attributed to the elapsed time between surveys.

The next predicted shoreline change about the waterline was for Hurricane Ivan. The model results indicate an advancement of approximately 28 feet for the six Walton County profiles. Hurricane Dennis was the last hurricane to impact the zero foot contour with a predicted shoreline advancement of about 50 feet. However, the post-storm measured zero contour for the 2004-2005 hurricane segment indicates that erosion, not accretion, occurred at the contour. Results for this segment in time do not compare favorably to one another.

Reasons for the difference in magnitudes for the predicted erosion and the measured can be attributed to several factors. The predicted erosion by the NEXTGEN model is for immediately after a storm. However, in the real world, it is not possible to survey the beach profile directly after the storm. This could lead to some recovery affecting the calculations made when comparing shoreline change. Other reasons for the discrepancies within calculations have previously been described in the section of Measured Shoreline Change

Statistical Analysis of Data

The statistical analysis performed on both data sets was a central element included within the study. This portion of the thesis was necessary in order to evaluate NEXTGEN, as well as quantify the program’s sensitivity to input variables. The major variable that will be examined will be the storm surge input. By altering the peak storm surge by +/-1 foot, the sensitivity of

NEXTGEN to storm surge will be established.

The method to find the correlation coefficient, r, was discussed in Chapter 3, but oftentimes in research the R-squared value is commonly used. The R-squared value is simply found by taking the square of the correlation coefficient. The coefficient of determination, or R- squared, is useful because it gives the proportion of variance shared by the two sets of data. One advantage of using the coefficient of determination over the correlation coefficient is the ease of interpretation. By simply moving the decimal point two places to the right, R-squared can be interpreted as a percentage, unlike the correlation coefficient.

Measured 10 Foot versus Predicted 10 Foot Predicted Contour

Once values for predicted shoreline change were obtained from the NEXTGEN model, it was necessary to compare them with measured values to meet the objective of the study. In order to determine how well the measured +10 foot contour values compared with the predicted values, a correlation test was performed. As mentioned in Chapter 3, the correlation coefficient,

r, was found using two different methods. The first method used all data points throughout each storm event. The five storm events analyzed were the time period of 1973 to 1975 which includes Hurricane Eloise, the next two time periods were from 1995 to 1997 and included

Hurricanes Erin and Opal, the next time segment was 2004-2005 for Hurricane Ivan, and the last event was from early 2005 to late 2005 which accounted for Hurricane Dennis. A list of the R- squared and r values can be found in Table 4-3. The best value from the R-squared values was for Hurricane Opal with R2 = 0.67, which indicates there is a 67% overlap between datasets.

Overall, all the calculated r values were between the ranges of 0.5 to 0.8, which indicates a moderate correlation amongst the datasets. This indicates that the NEXTGEN model was able to reasonably replicate profile response due to hurricanes.

This moderate correlation can possibly be attributed to the uncertainty in the storm surge predictions. Figure 4-13 represents all of the predicted and measured +10 contour change data for all storm events. As can be seen from the plot, two R-squared values are shown. The value of R2 =0.44 is for the case of a linear trend that is forced through the origin. The second value for R-squared is equal to 0.45 representing the best-fit line that is not forced through zero.

Measured 10 Foot versus Measured Zero Foot Contour

Along with analyzing trends in Figure 4-1 visually, a correlation test was performed to evaluate the correlation between the changes at the 0 and + 10 foot contours. Figure 4-14 represents the correlation between all measured +10 and zero contour change for all storm events. Again, there are two values for R-squared shown. The first value that contains the best- fit line passing through the origin indicates R2 = 0.0152. The small value indicates poor agreement between the two data sets. The value of R-squared where the best-fit line is not

forced through the origin is 0.0285. Both values indicate weak agreements amongst all of the data. Possible reasons for this weak correlation have previously been discussed.

When the storm events are analyzed individually, values for R-squared indicate better agreement among some data sets then others. Table 4-4 lists the R-squared values for each of the storm events. The best correlation amongst data sets is for Hurricane Georges with R2 =0.71, and the least correlation is for Hurricane Dennis with R2 =0.01. The remaining values of R- squared fall within the above range.

Model Sensitivity to Input Variables

As mentioned before, storm surge was the limiting factor in our study to accurately quantify contour changes throughout Walton County. In order to illustrate the extent that storm surge affected the study, storm surge was altered by +/- 1 foot to test the sensitivity of the model to the input variable. Table 4-5 indicates the predicted average +10 foot contour change for the adjusted storm surge and for the surge scaled such that the peak varied by +/- 1 foot. Results are presented in Figure 4-15.

For Hurricane Eloise, the predicted retreat was found to be -39.5 feet. By altering the storm surge +1 foot, predicted retreat was found to be -50.3 feet. When the storm surge was reduced by -1 foot, the retreat was reduced to approximately -26 feet. For this case, altering the storm surge resulted in at least a +10 foot difference of retreat. The retreat caused by Hurricane

Erin was found to be approximately -9 feet. The increased surge of +1 foot caused -18 feet of retreat, while the decreased surge caused a shoreline advancement of +8 feet. Hurricane Opal was least affected by altering the storm surge by +/-1 foot. Predicted retreat without altering the storm surge was found to be -18.6 feet. When the storm surge was increased by +1 foot, the retreat was found to be -20.4 feet. By lowering the storm surge by one foot, the expected retreat

was found to be -20.9 feet. Hurricane Ivan was found to have caused -16.4 feet of retreat. With

+1 foot surge, retreat was found to be approximately -30 feet. However, when surge was reduced by one foot, predicted retreat was only found to be -3.5 feet. The last amount of retreat that was analyzed was from Hurricane Dennis. Hurricane Dennis was predicted to have caused -

28 feet of retreat. With an increased surge of one foot, the retreat was found to be -33 feet. By decreasing the storm surge by one foot, the amount of predicted retreat was found to be 22.5 feet.

Overall, it can be seen from the results that by changing the storm surge by one foot can greatly alter the predicted retreat. For some cases, the difference between predicted retreat and predicted retreat with altered surge was approximately 15 feet. This indicates that the model is sensitive to the input of storm surge, and the best representation of the surge that occurred with each storm is necessary to obtain appropriate results.

Eloise 50

Georges

Erin and Opal Ivan

Dennis

-50

10' contour 0' contour

-100 Average Change of 0 and 10 foot Contour from 1973 Postition (ft) Postition 1973 from Contour foot 10 and of 0 Change Average 1970 1980 1990 2000 2010 Year

Figure 4-1. Average +10 and zero foot contour change over nine common Monuments.

Figure 4-2. Zero foot contour accretion due to large storms [Reprinted with permission from Clark, R.R. 2006. Hurricane Dennis & Hurricane Katrina: Final Report on 2005 Season Impacts to Northwest Florida. (Page 14, Figure 12). Office of Beaches and Coastal Systems, Florida Department of Environmental Protection, Tallahassee, Florida.]

3 Storm Surge (ft)

0 0 5 10 15 20 Time (hours)

Figure 4-3. Predicted storm surge hydrograph for Hurricane Eloise.

Eloise Dennis Kate Ivan Earl

5 Maximum Predicted Maximum Surge

Opal Georges Elena Frederic Erin 0 0 5 10 15 Maximum Measured Surge Figure 4-4. Plot of maximum predicted un-scaled surge versus maximum measured surge at location of maximum surge.

4 Surge (ft) Surge

0 5 10 15 20 Time (hours)

Figure 4-5. Measured storm surge hydrograph for Hurricane Eloise from historical tide gage data.

Figure 4-6. Measured storm surge hydrograph for Hurricane Opal from NOAA CO-OPS database.

Figure 4-7. Measured storm surge hydrograph for Hurricane Ivan from NOAA CO-OPS database.

Figure 4-8. Measured storm surge hydrograph for Hurricane Dennis from NOAA CO-OPS database.

6 Unscaled Surge Scaled Surge

4 Storm Surge (ft) Surge Storm

0 10203040 Time (hours)

Figure 4-9. Storm surge comparison between scaled and un-scaled values from one-dimensional model.

Opal

Dennis

Eloise

Ivan 5 Maxixmum Predicted Surge Erin

0 051015 Maximum Established Surge

Figure 4-10. Adjusted maximum predicted surge versus maximum measured surge at Walton County.

30 Initial 1 hour 10 hour 20 16 hour 19 hour 23 hour 10 Elevation (ft) 0

-10

0 400 800 1200 Cross-shore distance (ft)

Figure 4-11. Example profile response from the NEXTGEN model for Hurricane Eloise.

Eloise

Dennis Erin and Opal 20

Ivan

Predicted Recession of +10 Contour 10

0 0 10203040 Measured Recession of +10 Contour

Figure 4-12. Comparison of measured and predicted recessions of the +10 foot contour. Cumulative values are presented for Erin and Opal because no intermittent surveys are available to quantify individual measured recession.

100 Elois e Er in Opal 80 Iv an Dennis Linear (All data for all Storm Events) R2 = 0.4508 60 R2 = 0.4411 (ft)

20 Predicted Recession 0 0 102030405060708090

-20 Tw o points indicate contour advancement -40 Measured Recession (ft)

Figure 4-13. Plot of predicted erosion versus measured erosion for individual profiles (6) all storm events (5).

150 Meas. 0 and 10' contour change

Linear (Meas. 0 and 10' contour change) 100

R2 = 0.0152 50

R2 = 0.0285

0 -20-100 102030405060708090 Measured 0' Contour Recession Measured 0'

-50

-100 Measured 10' Contour Recession

Figure 4-14. Plot of measured 0 foot and measured +10 foot contour change for all storm events.

60 Eloise Erin and Opal 50 Ivan Dennis 40

10 Predicted Recession of +10 foot Contour +10 Recession foot of Predicted

0 0 5 10 15 20 25 30 35 40 45 Measured Recession of +10 foot Contour

Figure 4-15. Comparison of measured and predicted recessions of the +10 foot contour with error bars to account for the sensitivity of the model output due to storm surge scaled such that the peaks varied by +/- 1 foot.

Table 4-1. Comparison between predicted and measured storm surge Storm Landfall location Max. measured Max. predicted Max. predicted surge surge surge and setup Eloise Dune Allen 13.8’* 5.67’ 13.69’ Beach, FL Frederic Dauphin Island, +12’* 3.68’ 5.05’ AL Elena Biloxi, MS 10.5’* 4.46’ 6.07’ Kate Crooked Island, 8.4’ 4.56’ 8.23’ FL Erin Navarre Beach, 6-7’* 3.06’ 4.25’ FL Opal Pensacola Beach, 14.13’* 4.80’ 6.27’ FL Earl Shell Island, FL 6-7’* 4.16’ 6.08’ Georges Biloxi, MS 8’* 4.16’ 5.66’ Ivan Pensacola, FL 12.2’* 5.25’ 8.14’ Dennis Santa Rosa 15’* 5.04’ 6.90’ Island, FL * indicates that measurement is a high water mark (HWM)

Table 4-2. Individual setup values and maximum adjusted surge Hurricane Wind setup Wave setup Combined Surge Eloise 6.0’ 4.21’ 10.21’ Opal 7.31’ 3.78’ 11.56’ Ivan 5.8’ 1.68’ 7.48’ Dennis 6.03’ 3.43’ 9.46’

Table 4-3. Individual R-squared and r values of predicted versus measured +10 contour change for six common profiles in each storm event Hurricane R-sqaured value r value Eloise 0.407 0.638 Erin 0.552 0.743 Opal 0.669 0.818 Ivan 0.254 0.504 Dennis 0.390 0.624

Table 4-4. Individual R-squared values for measured +10 contour change versus measured zero contour change for each storm event Hurricane R-sqaured value Eloise 0.075 Erin and Opal 0.233 Georges 0.713 Ivan 0.316 Dennis 0.010

Table 4-5. Predicted average change of +10 contour from NEXTGEN model for the three cases of storm surge Hurricane Predicted ∆x Predicted ∆x with Predicted ∆x with Measured ∆x +1’ surge -1’ surge Eloise -39.5’ -50.3’ -25.5’ -37 Erin -9.4’ -17.9’ 8.0’ -36 Opal -18.6’ -20.4’ -20.9’ Ivan -16.4’ -29.9’ -3.5’ -23 Dennis -28.0’ -33.0’ -22.5’ -16

CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS

The main objective of this thesis was to evaluate methodology for the prediction of high- frequency shoreline and upper dune contour changes along the Florida Panhandle. The objective was met with both success and failure. The project was successful in that we were able to predict shoreline and + 10 foot contour changes that compared favorably with measured data. A major shortcoming was the inability to accurately predict storm surge results. Attempting to identify and improve the calculated storm surge results occupied a significant portion of the project effort. This, in return, affected the time remaining to calibrate the new methodology to predict high-frequency changes along the Panhandle.

Summary and Conclusions

Storm Surge Model

The storm surge model was probably the largest weakness exhibited in this study. For all hurricanes within the study area, the results from the one-dimensional storm surge model underestimated the predicted water levels. The component that most likely contributed to the under-prediction of storm surge was the wind surge. Recall that the wind stress tide is depth dependent, and the study area was located on a steep and narrow shelf. Since the model considers the system to be in a state of static equilibrium, it may be that dynamic effects play a significant role in areas with steep profiles.

Once the problem with the wind surge was identified, attempts at rectifying the small values of storm surge were made. The method used to increase the storm surge values was to combine the calculated wave setup from the one-dimensional model with the measured wind stress, Coriolis, and barometric tides. The second method that was used for the one case that no hydrographic data were available was to scale the storm surge results from the maximum

established water level. Overall, both methods worked quite well in adjusting the storm surge to more reasonable values that compared favorably with measured data.

Measured Contour Change

The project was successful in identifying the contour changes at both the zero and +10 foot contour from 1970 to present. Both contours have had dramatic fluctuations in recent years due to the active hurricane seasons from 1973 to present. The present location of the measured +10 and zero contours are in an eroded state. However, the contours appear to be recovering based on the most recent profile survey. Absent of major storms in the near term, the dunes are expected to continue recovering, albeit slowly.

Representation of High-Frequency Shoreline Changes

Once reasonable time histories of water levels had been established for the five major erosional events, NEXTGEN was, for the most part, successful in predicting change of the zero foot and +10 foot contours from 1973 to present. The model adequately represented erosion at the +10 foot contour from increased water and wave activity that transported sand seaward to lower contours. Along with accurately modeling the response of the upper berm, the NEXTGEN model effectively represented the accretion at the waterline that is associated with severe dune erosion.

Statistical analysis done on the predicted results from NEXTGEN indicates that there is reasonable agreement between the predicted and measured +10 contour change. Overall, the predicted and measured +10 contour change data had a promising correlation. The scatter that occurs within the data is most likely due to the inability to accurately predict the storm surge.

The NEXTGEN model is dependent upon an accurate input of storm surge, and without it, the model is limited in its ability to predict shoreline change.

Recommendations for Future Study

Storm Surge Model

The lack of being able to predict accurate storm surges remains one of the principal limitations for hindcasting shoreline change for historical storms within the study. Any future attempt to model storm surge within our study area could possibly include the use of another storm surge model to predict the storm surges. Adapting a new model to our study area and conditions was beyond the timeframe for this thesis, but it would be interesting to see if the results compare with the one-dimensional model used within this thesis. Also, the inclusion of dynamic effects could increase the values for the wind stress tide within the study area. This would help to account for the steep slope which is believed to be affecting the storm surge values.

Measured Contour Change

To better represent contour change since 1973, further recommendations are to include more monuments in the calculations. The measured contour change within the study was limited by the number of common monuments available throughout the period 1973 to 2005 which had not been relocated. If more monuments were able to be incorporated in the average, the dependence on individual monuments would be less of a possible source or error. A possible solution to this problem is to use adjusted profiles that account for change in monument position from their 1973 positions. This solution, however, must be done carefully so that the adjusted profile does not become an additional source of error.

Although it was not the focus of the study, it is interesting to note the exponential recovery of the shoreline after major storm events. Further research could include an attempt to predict the recovery of both contours after major storm events. This would be quite a comprehensive task due to the dependence of the upper contours on aeolian processes. The only un-interrupted

recovery that can be seen from the data is from Hurricane Eloise in 1975 to the full recovery of the beach in 1995. After this, the coast was affected by several hurricanes in a small time span, and the beach was not able to experience substantial recovery. The suggested model would have to be able to account for the recovery from a previously eroded state.

Representation of High-Frequency Shoreline Changes

In order to better evaluate the NEXTGEN model’s performance in predicting contour change, it is recommended to compare the model output with a greater number of data sets. This would require a larger study area than that of the current study. Perhaps a comparison between all major hurricanes impacting the state of Florida from 1975 to present would allow for a larger data set for comparison. Also, by increasing the study area, more measured data would be available for comparison. The larger study area would also show the model’s ability to be used at any location within the state of Florida, which would be advantageous to government agencies. Additionally, storm-induced changes at additional contour elevations would be useful.

It is possible that contours lower than +10 feet are less sensitive to storm surges.

The final recommendation concerning the NEXTGEN model is related to the storm surge input from the one-dimensional model. More favorable comparisons will require incorporation of the most accurate storm surge data into the model’s input. With storm surges that compare favorably to real-time conditions during the hurricane, evaluation of the predictability of

NEXTGEN could be accomplished with greater confidence.

APPENDIX A STORM SURGE HYDROGRAPHS

3 Storm Surge (ft)

0 0 5 10 15 20 Time (hours)

Figure A-1. Predicted storm surge hydrograph for Hurricane Eloise from one-dimensional storm surge model.

2 Storm Surge (ft)

0 0 10203040 Time (hours)

Figure A-2. Predicted storm surge hydrograph for Hurricane Erin from one-dimensional storm surge model.

3 Storm Surge (ft) 2

0 0 5 10 15 Time (hours)

Figure A-3. Predicted storm surge hydrograph for Hurricane Opal from one-dimensional storm surge model.

2 Storm Surge (ft)

0 0 20406080100 Time (hours)

Figure A-4. Predicted storm surge hydrograph for Hurricane Georges from one-dimensional storm surge model.

2.0

1.5

1.0 Storm Surge (ft)

0.5

0.0 01020304050 Time (hours)

Figure A-5. Predicted storm surge hydrograph for Hurricane Ivan from one-dimensional storm surge model.

2 Storm Surge (ft)

0 0 5 10 15 20 25 Time (hours)

Figure A-6. Predicted storm surge hydrograph for Hurricane Dennis from one-dimensional storm surge model.

APPENDIX B NEXTGEN PROFILE EVOLUTION RESULTS

20 Initial 1 hour 10 hour 16 hour 10 19 hour 23 hour

0 Elevation (ft)

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-1. Calculated profile evolution for Monument 21 for Hurricane Eloise.

40 Initial 1 hour 30 10 hour 16 hour 19 hour 20 23 hour

10 Elevation (ft)

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-2. Calculated profile evolution for Monument 57 for Hurricane Eloise.

30 Initial 1 hour 10 hour 16 hour 20 19 hour 23 hour

10 Elevation (ft)

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-3. Calculated profile evolution for Monument 63 for Hurricane Eloise.

30 Initial 1 hour 10 hour 20 16 hour 19 hour 23 hour 10 Elevation (ft) 0

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-4. Calculated profile evolution for Monument 66 for Hurricane Eloise.

30 Initial 1 hour 10 hour 20 16 hour 19 hour 23 hour 10 Elevation (ft) 0

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-5. Calculated profile evolution for Monument 87 for Hurricane Eloise.

20 Initial 1 hour 10 hour 16 hour 10 19 hour 23 hour

0 Elevation (ft)

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-6. Calculated profile evolution for Monument 102 for Hurricane Eloise.

Initial 1 hour 15 hour 0 26 hour 30 hour 47 hour

-20 Elevation (ft)

-40

-60 0 500 1000 1500 2000 Cross-shore distance (ft)

Figure B-7. Calculated profile evolution for Monument 21 for Hurricane Erin.

Initial 1 hour 15 hour 0 26 hour 30 hour 47 hour

-20 Elevation (ft)

-40

-60 0 500 1000 1500 2000 Cross-shore distance (ft)

Figure B-8. Calculated profile evolution for Monument 57 for Hurricane Erin.

20 Initial 1 hour 15 hour 0 26 hour 30 hour 47 hour

-20 Elevation (ft)

-40

-60

0 500 1000 1500 2000 Cross-shore distance (ft)

Figure B-9. Calculated profile evolution for Monument 63 for Hurricane Erin.

20 Initial 1 hour 15 hour 26 hour 0 30 hour 47 hour

-20 Elevation (ft)

-40

-60

0 500 1000 1500 2000 Cross-shore distance (ft)

Figure B-10. Calculated profile evolution for Monument 66 for Hurricane Erin.

Initial 1 hour 15 hour 10 26 hour 30 hour 47 hour

-10 Elevation (ft)

-30

-50

0 500 1000 1500 2000 Cross-shore distance (ft)

Figure B-11. Calculated profile evolution for Monument 87 for Hurricane Erin.

Initial 1 hour 15 hour 0 26 hour 30 hour 47 hour

-20 Elevation (ft)

-40

-60

0 500 1000 1500 2000 Cross-shore distance (ft)

Figure B-12. Calculated profile evolution for Monument 102 for Hurricane Erin.

Initial 1 hour 3.5 hour 10 9.5 hour 14 hour 17 hour

0 Elevation (ft)

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-13. Calculated profile evolution for Monument 21 for Hurricane Opal.

30 Initial 1 hour 3.5 hour 20 9.5 hour 14 hour 17 hour

10 Elevation (ft)

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-14. Calculated profile evolution for Monument 57 for Hurricane Opal.

100

Initial 20 1 hour 3.5 hour 9.5 hour 14 hour 10 17 hour

Elevation (ft) 0

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-15. Calculated profile evolution for Monument 63 for Hurricane Opal.

101

Initial 1 hour 20 3.5 hour 9.5 hour 14 hour 17 hour 10 Elevation (ft) 0

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-16. Calculated profile evolution for Monument 66 for Hurricane Opal.

102

20 Initial 1 hour 3.5 hour 9.5 hour 14 hour 10 17 hour

Elevation (ft) 0

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-17. Calculated profile evolution for Monument 87 for Hurricane Opal.

103

20 Initial 1 hour 3.5 hour 9.5 hour 10 14 hour 17 hour

Elevation (ft) 0

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-18. Calculated profile evolution for Monument 102 for Hurricane Opal.

104

Initial 1 hour 19 hour 10 24 hour 35 hour 53 hour

0 Elevation (ft)

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-19. Calculated profile evolution for Monument 21 for Hurricane Ivan.

105

Initial 30 1 hour 19 hour 24 hour 35 hour 20 53 hour

10 Elevation (ft)

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-20. Calculated profile evolution for Monument 57 for Hurricane Ivan.

106

Initial 1 hour 20 19 hour 24 hour 35 hour 53 hour 10 Elevation (ft) 0

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-21. Calculated profile evolution for Monument 63 for Hurricane Ivan.

107

Initial 1 hour 20 19 hour 24 hour 35 hour 53 hour 10 Elevation (ft) 0

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-22. Calculated profile evolution for Monument 66 for Hurricane Ivan.

108

Initial 1 hour 20 19 hour 24 hour 35 hour 53 hour 10 Elevation (ft)

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-23. Calculated profile evolution for Monument 87 for Hurricane Ivan.

109

Initial 20 1 hour 19 hour 24 hour 35 hour 10 53 hour

Elevation (ft) 0

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-24. Calculated profile evolution for Monument 102 for Hurricane Ivan.

110

Initial 1 hour 9 hour 10 16 hour 21 hour 29 hour

0 Elevation (ft)

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-25. Calculated profile evolution for Monument 21 for Hurricane Dennis.

111

Initial 1 hour 30 9 hour 16 hour 21 hour 20 29 hour

10 Elevation (ft)

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-26. Calculated profile evolution for Monument 57 for Hurricane Dennis.

112

Initial 1 hour 20 9 hour 16 hour 21 hour 29 hour 10 Elevation (ft) 0

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-27. Calculated profile evolution for Monument 63 for Hurricane Dennis.

113

Initial 1 hour 20 9 hour 16 hour 21 hour 29 hour 10 Elevation (ft) 0

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-28. Calculated profile evolution for Monument 66 for Hurricane Dennis.

114

30 Initial 1 hour 9 hour 20 16 hour 21 hour 29 hour

10 Elevation (ft)

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-29. Calculated profile evolution for Monument 87 for Hurricane Dennis.

115

Initial 20 1 hour 9 hour 16 hour 21 hour 10 29 hour

Elevation (ft) 0

-10

0 400 800 1200 Cross-shore distance (ft)

Figure B-30. Calculated profile evolution for Monument 102 for Hurricane Dennis.

116

LIST OF REFERENCES

Burdin, W.W., 1977. Surge effects from Hurricane Eloise. Shore and Beach, 45(2), 3-8.

Bureau of Beaches and Coastal Systems, 2007. Critically Eroded Beaches in Florida. Tallahassee, Florida: Florida Department of Environmental Protection, 76p.

Chiu, T.Y., 1977. Beach and Dune Response to Hurricane Eloise of September 1975. Coastal Sediments ’77. New York: ASCE, pp. 116-134.

Clark, R.R., 1981. Beach and Dune Erosion. In: Hurricane Dennis: Beach and Dune Erosion and Structural Damage Assessment and Post-storm Recovery Recommendations for the Panhandle Coast of Florida. Florida Department of Environmental Protection, 54p.

Clark, R.R. and LaGrone, J., 2006. A Comparative Analysis of Hurricane Dennis and Other Recent Hurricanes on the Coastal Communities of Northwest Florida. Tallahassee, Florida: Bureau of Beaches and Coastal Systems, Florida Department of Environmental Protection, 19p.

Crowell, M.; Douglas, B.C., and Leatherman, S.P., 1997. On forecasting future U.S. shoreline positions: a test of algorithms. Journal of Coastal Research, 13(4), 1245-1255.

Cullington, T.J.; Warren, M.A.; Goodspeed, T.R.; Remer, D.G.; Blackwell, C.M., and McDonough, III, J.J., 1990. 50 Years of Population Growth Along the Nation’s Coasts 1960-2010. Silver Spring, Maryland: National Ocean Service, National Oceanic and Atmospheric Administration, 41p.

Danish Hydraulics Institute, 2008. MIKE 21. http://www.dhigroup.com/Software/ Marine/MIKE21.aspx (accessed April 7, 2008).

Dean, R.G., 2004. Next Generation Beach and Dune Erosion Model for Applications of the Bureau of Beaches and Coastal Systems. Beaches and Shores Resource Center, Florida State University, 15p.

Dean, R.G. and Chiu, T.Y., 1982. Walton County Storm Surge Model Study. Beaches and Shores Resource Center, Florida State University, 64p.

Dean, R.G. and Dalrymple, R.A., 2002. Coastal Processes with Engineering Applications. Cambridge, MA, Cambridge University Press.

Delft Hydraulics, 2001. A guide to integrated coastal zone management. Simulation models and modeling systems related to integrated coastal zone management. http://www.netcoast.nl/tools/rikz/delft3d.htm (accessed April 7, 2008).

Dolan, R.; Fenster, M.S., and Holme, S.J., 1991. Temporal analysis of shoreline recession and accretion. Journal of Coastal Research, 16(1), 145-152.

117

Edwards, S.F., 1989. Estimates of Future Demographic Changes in the Coastal Zone. Coastal Management 17, 229-240.

Fenster, M.S., Dolan, R., and Elder, J.F., 1993. A new method for predicting shoreline position from historical data. Journal of Coastal Research, 9(1), 147-171.

Foster, E.R. and Savage, R.J., 1989. Methods of historical shoreline analysis. In: Coastal Zone ’89. New York: American Society of Civil Engineers, 4434-448.

Foster, E.R., Spurgeon, D.L., and Cheng, J., 2000. Shoreline Change Rate Estimates: Walton County. Tallahassee, Florida: Office of Beaches and Coastal Systems, Florida Department of Environmental Protection, Report No. BCS-2000-02, 59p.

Freeman, J.C.; Baer, L., and Jung, G.H., 1957. The Bathystrophic Storm Tide. Journal of Marine Research, 16(1), 12-22.

Fritz, H.M., Blount, C., Sokoloski, R., Singleton, J., Fuggle, A., McAdoo, B.G., Moore, A., Grass, C., and Tate, B., 2007. Hurricane Katrina storm surge distribution and field observations on the Mississippi Barrier Islands. Estuarine, Coastal, and Shelf Science, 74(1-2), 12-20.

Genz, A.S., Fletcher, C.H., and Dunn, R.A., Frazer, L.N., and Rooney, J.J., 2007. The predictive accuracy of shoreline change rate methods and alongshore beach variation on Maui, Hawaii. Journal of Coastal Research, 23(1), 87-105.

Hanson, H. 1989. Genesis—A generalized shoreline change numerical model. Journal of Coastal Research, 5(1), 1-27.

Holland, G.J., 1980. An analytical model of wind and pressure profiles in hurricanes. Monthly Weather Review, 108, 1212-1218.

Jelesnianski, C.P., Chen, J., and Shaffer, W.A., 1992. SLOSH: Sea, Lake, and Overland Surges from Hurricanes. National Oceanic and Atmospheric Administration, Technical Report NWS 48, 71p.

Kriebel, D.L. and Dean, R.G., 1986. Verification study of a dune erosion model. Shore and Beach, 54(3), 13-21.

Kriebel, D.L. and Dean, R.G., 1985. Numerical simulation of time-dependent beach and dune erosion. Coastal Engineering, 9, 221-245.

Larson, M. and Kraus, N.C., 1989. SBEACH: Numerical Model for Simulating Storm- Induced Beach Change. Vicksburg, Mississippi: U.S. Army Corps of Engineers, WES, Technical Report DRP-92-5, 115p.

Morang A. 1992. Inlet migration and hydraulic processes at East Pass, Florida. Journal of Coastal Research, 8, 457–481.

118

National Research Council, 1990. Managing Coastal Erosion. Washington, D.C.: National Academy Press, 182p.

Westerink, J.J., Luettich, R.A., Baptista, A.M., Scheffner, N.W., and Farrar, P., 1992. Tide and storm-surge predictions using finite-element model. Journal of Hydraulic Engineering, 118(10), 1373-1390.

Wilson, B.L., 1957. Hurricane Wave Statistics for the Gulf of Mexico. U.S. Army Corps of Engineers, Beach Erosion Board, Technical Memorandum 98.

119

BIOGRAPHICAL SKETCH

Nicole Sharp was born in York, Pennsylvania. Since she was a small child, her parents brought her to the beaches of Ocean City, MD, every summer. The smell of the ocean, the unforgettable feeling of exfoliation from sand adhering to freshly applied sunscreen, and the refreshing taste of a big gulp of saltwater from a large wave are all fond memories from her childhood that she would never forget.

After graduating high school, Nicole enrolled at the University of Delaware where she pursued an undergraduate degree in civil engineering. While attending the University of

Delaware, she was fortunate to be offered classes in the field of coastal engineering. From those classes, a desire to pursue higher education in coastal engineering grew. After deciding that a general civil engineering degree wasn’t for her, Nicole enrolled in graduate school at the

University of Florida for coastal engineering. While attending UF, she obtained a well-rounded education along with a degree in coastal engineering.

Wherever life takes Nicole after UF, there is one thing for certain. She will be living by the coast and will have a career she enjoys.

120