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Shoreline response to variations in waves and water levels

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Title:
Shoreline response to variations in waves and water levels an engineering scale approach
Creator:
Miller, Jonathan K., 1976-
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English
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xvi, 173 leaves : ill. ; 29 cm.

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Subjects / Keywords:
Beaches ( jstor )
Calibration ( jstor )
Coasts ( jstor )
Datasets ( jstor )
Modeling ( jstor )
Parametric models ( jstor )
Sediments ( jstor )
Shorelines ( jstor )
Simulations ( jstor )
Waves ( jstor )
Civil and Coastal Engineering thesis, Ph. D ( lcsh )
Dissertations, Academic -- Civil and Coastal Engineering -- UF ( lcsh )
City of Daytona Beach ( local )
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bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

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Thesis:
Thesis (Ph. D.)--University of Florida, 2004.
Bibliography:
Includes bibliographical references.
General Note:
Printout.
General Note:
Vita.
Statement of Responsibility:
by Jonathan K. Miller.

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University of Florida
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University of Florida
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Copyright [name of dissertation author]. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
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SHORELINE RESPONSE TO VARIATIONS IN WAVES AND WATER LEVELS:
AN ENGINEERING SCALE APPROACH















By

JONATHAN K. MILLER


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2004


___ __

























This dissertation is dedicated to my beautiful wife Diana, who has always been there for
me when I needed it the most. During this long journey, she has patiently followed me
across the globe, sacrificing much of herself, now it is finally time to go home.














ACKNOWLEDGMENTS

First and foremost, I would like to thank my advisor, Robert Dean, for his

inspiration and guidance. Many a time I came to him frustrated and dejected, only to be

reinvigorated by his infectious enthusiasm during an enlightening Sunday morning

meeting. Sharing in his passion for coastal engineering over the past five years has been

an extraordinary experience.

I would also like to thank the other members of the Coastal Engineering

Department for their insightful seminars and thought provoking discussions. In particular

I would like to thank Becky Hudson and Robert Thieke for making sure everything ran

smoothly, and Dan Hanes, Ashish Mehta, Joann Mossa, and Robert Thieke once again,

for agreeing to serve on my supervisory committee. It has been a pleasure working with

all of them.

For welcoming Diana and I to a foreign land with wide open arms, I would like to

express my appreciation to Peter Nielsen and everyone at the University of Queensland.

From the moment we arrived in Brisbane we were treated like family, making our

transition "down under" much easier. Having the chance to work with Peter, Tom, Ling,

and all of the coastal students was an honor and sincere pleasure. Our time in Australia

was truly unforgettable thanks to the many wonderful people we encountered during our

journeys.

Several teachers that I was fortunate enough to encounter prior to beginning my

graduate studies have also played an integral role in my development as a student and as








a person. Br. Paul Joseph and K.Y. Billah always believed in me and encouraged me to

learn for the love of learning and not for the grade attached to it. Both epitomize the true

meaning of the word teacher. I need to thank Dimitris Dermatas and Michael Bruno for

encouraging me to follow my heart and study coastal engineering when others told me I

would be better off studying something more practical.

I would be remiss in not thanking the Florida Sea Grant, the American Society for

Engineering Education, the United States Department of Defense, and the Australian-

American Fulbright Commission, all of whom provided financial support for various

stages of this project. Their contributions have been greatly appreciated.

Along the way, I made many friends who have had a profound impact on my life

and whom I will never forget. To Kristen, Justin, Chris, Jamie, Sean, Al, Dave, Nick,

Ian, Finney, Carlos and everyone else who has helped me get through the last five years, I

truly value each of their friendships. Cliff deserves a special mention for taking a leap of

faith and moving to Gainesville with me at the start of this unforgettable journey.

Most importantly, I need to thank my family for their continued love and support,

without whom this would not have been possible. They have always been there to back

me and encourage me in whatever I have chosen to do, and I am much indebted to them.

Finally, I need to thank my wonderful wife Diana who inspires me each and every day.

She has shown me the true meaning of love, and without her encouragement and

emotional support over the past five years I would not have made it through the past five

years. She has always been there for me and I can never thank her enough for her infinite

love and patience.















TABLE OF CONTENTS

page

ACKNOW LEDGM ENTS ................................................................................................. iii

LIST OF TABLES ............................................................................................................ vii

LIST OF FIGURES ...................................................................................................... x

ABSTRACT..................................................................................................................... xiv

1 INTRODUCTION .................................................................................................. 1

2 BACKGROUND .................................................................................................... 8

2.1 Longshore (Planform) M odels............................................. ......................... 10
2.1.1 Analytical M odels .................................................... .......................... 11
2.1.2 Numerical One-Line/N-Line M odels .................................................... 12
2.2 Cross-shore (Profile) M odels...................................................... .................. 12
2.2.1 Analytical M odels .................................................... .......................... 13
2.2.2 Empirical M odels ............................................................ ...................13
2.2.3 Energy Dissipation M odels .....................................................................14
2.2.4 Process Based M odels ................................... ........................................17
2.2.5 Alternative M odels ................................................................................ 20
2.3 Need for Innovative Approaches .....................................................................20

3 M ODEL DEVELOPM ENT..................................................................................22

3.1. Theoretical Background................................. ................. .............................22
3.2. Defining the Equilibrium Shoreline, yq(t) ............................................. .....25
3.3. Defining the Rate Parameter, k ................................ ........................................ 29
3.4 Solution Technique .........................................................................................36
3.4.1 Numerical Scheme.................................... .............. ............................36
3.4.2 Forcing Data........................................... ................ ...........................37
3.4.3 Shoreline Data. ................................................................... .....................38
3.4.4 M odel Calibration................................................. ..............................39
3.4.5 M odel Evaluation .................................................................................. 40

4 FIELD DATA AND SITE SUITABILITY .................................................. .........46

4.1 East Coast Data............................................................................................... 48








4.2 W est Coast Data ............................................................................................. 53
4.3 Australian Data ............................................................................................... 55
4.4 Evaluation Tools................... .............. ........................................................57
4.4.1 Time Dom ain Based Statistics......................... .......................................58
4.4.2 Frequency Dom ain Based Statistics ............................................... .... 59
4.4.3 Method of Empirical Orthogonal Functions.......................................60
4.5 Site Suitability ..................................................................................................64

5 RESULTS .............................................................................................................75

5.1 New Jersey ...................................................................................................... 84
5.2 Florida............................................................................................................ 86
5.3 W ashington and Oregon ....................................................................................92
5.4 California........................................................................................................ 93
5.5 Australia.......................................................................................................... 94

6 DISCUSSION ..................................................................................................... 100

6.1 Timescale of Response ............................................................. ..................101
6.2 Selection of Appropriate Rate Parameters.......................................................... 103
6.3 M odified Error Estim ates / Cost Functions ...................................................... 109
6.4 Time Varying Sediment Scale Parameter, A(Q(t)) ..........................................111
6.5 Application to EOF Filtered Data....................................................................... 17

7 SUMMARY, CONCLUSIONS, AND FUTURE DIRECTIONS..........................19

7.1 Summ ary...................................................................................................... 119
7.2 Conclusions...................................................................................................121
7.2 Future Directions .......................................................................................... 123

APPENDIX

A M ODEL SOURCE CODE ....................................... ..............................................126

B COMPLETE SET OF MODEL RESULTS ....................................... .............. 135

LIST OF REFERENCES ............................................................................................ 164

BIOGRAPHICAL SKETCH ..................................................................................... 172


3














LIST OF TABLES


Table pag

3-1. Established erosion/accretion criteria.............................................................33

3-2. Categorical assessment procedure score matrix developed for this study ..............44

3-3. Subjective rating system based upon model performance statistics.......................44

4-1. Summary of data sources. ................................................................................... 47

4-2. Relevant site characteristics ................................................ ......................... 48

4-3. Data analysis techniques applied at each site.......................... ...................65

4-4. Summary of time domain analysis results ..........................................................66

4-5. Summary of EOF analysis results.......................................................................73

5-1. Summary of SLMOD results............................................................................76

5-2. NMSE associated with various rate parameter combinations at Long Beach, WA,
and typical of the NMSE tables presented in Appendix B.....................................80

5-3. CAP associated with various rate parameter combinations at Long Beach, WA, and
typical of the CAP tables presented in Appendix B...............................................81

5-4. Calibration coefficients for Long Beach, WA, and typical of the coefficient tables
presented in Appendix B.....................................................................................83

5-5. NMSE associated with various rate parameter combinations at Crescent Beach,
FL. ....................................................................................................................... 88

5-6. CAP associated with various rate parameter combinations at Crescent Beach,
FL. ....................................................................................................................... 89

6-1. Best performing rate parameters for each site...................................................1..04

6-2. Rate coefficient statistics according to geographic region...................................108

6-3. Percent change in NMSE values at Torrey Pines, CA for Case 1 (A(Q)) ...........116








6-4. Percent change in NMSE values at Torrey Pines, CA for Case 2 (minimum W.
im posed). .......................................................................................................... 116

6-5. Percent change in NMSE values at Torrey Pines, CA for Case 3 (A(Q) and
minimum W imposed). ...................................................................................117

6-6. Percent change in NMSE values when only the longshore uniform EOF modes are
considered at the Gold Coast, Australia..........................................................118

B-1. Calculated NMSE values for model hindcasts at Island Beach, NJ.....................136

B-2. Calculated CAP scores for model hindcasts at Island Beach, NJ ........................137

B-3. Calibration coefficients ka, ke, and Ayo for Island Beach, NJ ..............................137

B-4. Calculated NMSE values for model hindcasts at Wildwood, NJ...................... 138

B-5. Calculated CAP scores for model hindcasts at Wildwood, NJ ............................139

B-6. Calibration coefficients ka, ke, and Ayo for Wildwood, NJ ..................................139

B-7. Calculated NMSE values for model hindcasts at St. Augustine, FL....................140

B-8. Calculated CAP scores for model hindcasts at St. Augustine, FL.......................141

B-9. Calibration coefficients ka, ke, and Ayo for St. Augustine, FL.............................141

B-10. Calculated NMSE values for model hindcasts at Crescent Beach, FL ................142

B-11. Calculated CAP scores for model hindcasts at Crescent Beach, FL....................143

B-12. Calibration coefficients ka, ke, and Ayo for Crescent Beach, FL..........................143

B-13. Calculated NMSE values for model hindcasts at Daytona Beach, FL................ 144

B-14. Calculated CAP scores for model hindcasts at Daytona Beach, FL ....................145

B-15. Calibration coefficients ka, ke, and Ayo for Daytona Beach, FL ..........................145

B-16. Calculated NMSE values for model hindcasts at New Smyrna Beach, FL. ..........146

B-17. Calculated CAP scores for model hindcasts at New Smyrna Beach, FL.............147

B-18. Calibration coefficients ka, ke, and Ayo for New Smyrna Beach, FL.....................147

B-19. Calculated NMSE values for model hindcasts at North Beach, WA.....................148

B-20. Calculated CAP scores for model hindcasts at North Beach, WA.......................149


I_ _








B-21. Calibration coefficients ka, ke, and Ayo for North Beach, WA.............................149

B-22. Calculated NMSE values for model hindcasts at Long Beach, WA....................150

B-23. Calculated CAP scores for model hindcasts at Long Beach, WA .......................151

B-24. Calibration coefficients ka, ke, and Ayo for Long Beach, WA..............................151

B-25. Calculated NMSE values for model hindcasts at Clatsop Plains, OR .................152

B-26. Calculated CAP scores for model hindcasts at Clatsop Plains, OR.....................153

B-27. Calibration coefficients ka, ke, and Ayo for Clatsop Plains, OR...........................153

B-28. Calculated NMSE values for model hindcasts at Torrey Pines, CA....................154

B-29. Calculated CAP scores for model hindcasts at Torrey Pines, CA........................155

B-30. Calibration coefficients ka, ke, and Ayo for Torrey Pines, CA..............................155

B-31. Calculated NMSE values for model hindcasts at Brighton Beach, AS..................156

B-32. Calculated CAP scores for model hindcasts at Brighton Beach, AS ...................157

B-33. Calibration coefficients ka, ke, and Ayo for Brighton Beach, AS .........................157

B-34. Calculated NMSE values for model hindcasts at Leighton Beach, AS ...............158

B-35. Calculated CAP scores for model hindcasts at Leighton Beach, AS...................159

B-36. Calibration coefficients ka, ke, and Ayo for Leighton Beach, AS.........................159

B-37. Calculated NMSE values for model hindcasts at the Gold Coast, AS.................160

B-38. Calculated CAP scores for model hindcasts at the Gold Coast, AS ....................161

B-39. Calibration coefficients ka, ke, and Ayo for the Gold Coast, AS...........................161

B-40. Calculated NMSE values for model hindcasts at the Gold Coast, AS, using filtered
(fc = 0.033 days') data. ........................................................................................ 162

B-41. Calculated CAP scores for model hindcasts at the Gold Coast, AS, using filtered (fc
= 0.033 days-') data............................................................................................. 163

B-42. Calibration coefficients ka, ke, and Ayo for the Gold Coast, AS, using filtered (fc =
0.033 days-') data ................................................................................................. 163















LIST OF FIGURES


Figure page

2-1. Profile schematization in Swart model.............................. ...................................14

2-2. SBEACH profile schematization .........................................................................17

2-3. Typical process based model schematic..............................................................19

3-1. Beach recession due to a combination of an increased water level, S and wave
induced setup, b(y). ...........................................................................................28

3-2. Example illustrating the role of Ayo in correcting for differences in the baseline
conditions of y(t) and yob(t) ................................................................................. 29

3-3. Schematic of model calibration routine. ............................................................39

4-1. Location of data sets from the East Coast of the United States .............................49

4-2. Improvement in the consistency of the Duck shoreline data after adjusting for the
volume change between subsequent profiles .................................... ............. 52

4-3. Location of available shoreline data along the west coast of the United States.......53

4-4. Location of Australian shoreline data sets .........................................................55

4-5. Calculation of the mean correlation profile including r. and ravg........................67

4-6. Spectral analyses of Gold Coast shoreline data where the thick line represents the
mean spectra .......................................................................................................70

4-7. Mean coherence and phase for three selected shorelines at the Gold Coast...........70

4-8. Spatial eigenfunctions el(x)-e3(x) for Duck, NC............................................ ....72

4-9. Spatial eigenfunctions el(x)-e3(x) for Washington State.......................................72

4-10. Spatial eigenfunctions el(x)-e3(x) for the Gold Coast, QLD .................................73

5-1. Complete hindcast shoreline time series for Daytona Beach, FL..........................78








5-2. Example hindcast plot of observed and predicted shorelines at Long Beach, WA,
and typical of those presented in Appendix B. Error bars indicate the variation in
predicted shoreline position for different rate parameter combinations. .................79

5-3. Comparison of measured shorelines and "best" simulations according to the NMSE
and CAP criteria at Crescent Beach, FL. Error bars indicate the variation in
predicted shoreline position for different rate parameter combinations ................88

5-4. Comparison of measured shorelines and "best" simulations according to the NMSE
and CAP criteria at Daytona Beach, FL. Error bars indicate the variation in
predicted shoreline position for different rate parameter combinations. .................90

5-5. Comparison of measured shorelines and "best" simulations according to the NMSE
and CAP criteria at New Smyrna Beach, FL. Error bars indicate the variation in
predicted shoreline position for different rate parameter combinations ................91

5-6. Comparison of measured shorelines and "best" simulations according to the NMSE
and CAP criteria at Torrey Pines, CA. Error bars indicate the variation in predicted
shoreline position for different rate parameter combinations ................................94

5-7. Comparison of measured shorelines and "best" simulations according to the NMSE
and CAP criteria at Brighton Beach, AS. Error bars indicate the variation in
predicted shoreline position for different rate parameter combinations. .................96

5-8. Complete hindcast shoreline time series for the Gold Coast, QLD .......................96

5-9. Comparison of measured shorelines and "best" simulations according to the NMSE
and CAP criteria at the Gold Coast, AS, using filtered (fc = 0.033 days-) data.
Error bars indicate the variation in predicted shoreline position for different rate
parameter combinations. ..................................................................................... 98

5-10. Comparison of the extreme values of the measured shorelines and "best"
simulations according to the NMSE and CAP criteria at the Gold Coast, AS, using
filtered (fc = 0.033 days-') data. Error bars indicate the variation in predicted
shoreline position for different rate parameter combinations ................................99

6-1. Variability of the amplitude response function, IF(o),k1 with forcing frequency, o,
and rate coefficient, k ........................................................................................ 102

6-2. Variability of the phase response function, <(o,ka) with forcing frequency, o, and
rate coefficient, k ............................................................................................... 102

6-3. Histograms of accretion coefficients, ka, determined by the procedure detailed in
Chapter 3. ......................................................................................................... 106

6-4. Histograms of erosion coefficients, ke, determined by the procedure detailed in
Chapter 3. ......................................................................................................... 107








6-5. Comparison of "best" modified NMSE predictions with the standard NMSE
prediction and the measured data at Torrey Pines, CA........................................111

6-6. New relationship for A proposed by Wang (2004), where Afit/A, is the ratio of the
new A value to that given by Moore (1982), and Hb/wT is the breaking form of the
non-dimensional fall velocity parameter, Q ..........................................................113

6-7. Variation of active surfzone width, W., with A(Q). ............................................113

6-8. Effect of A(Q) on calculated Ay, values at Torrey Pines, CA.............................. 114

B-1. Comparison of measured shorelines and "best" simulations according to the NMSE
and CAP criteria at Island Beach, NJ. Error bars indicate the variation in predicted
shoreline position for different rate parameter combinations ..............................136

B-2. Comparison of measured shorelines and "best" simulations according to the NMSE
and CAP criteria at Wildwood, NJ. Error bars indicate the variation in predicted
shoreline position for different rate parameter combinations ..............................138

B-3. Comparison of measured shorelines and "best" simulations according to the NMSE
and CAP criteria at St. Augustine, FL. Error bars indicate the variation in predicted
shoreline position for different rate parameter combinations ..............................140

B-4. Comparison of measured shorelines and "best" simulations according to the NMSE
and CAP criteria at Crescent Beach, FL. Error bars indicate the variation in
predicted shoreline position for different rate parameter combinations ..............142

B-5. Comparison of measured shorelines and "best" simulations according to the NMSE
and CAP criteria at Daytona Beach, FL. Error bars indicate the variation in
predicted shoreline position for different rate parameter combinations. ...............144

B-6. Comparison of measured shorelines and "best" simulations according to the NMSE
and CAP criteria at New Smyrna Beach, FL. Error bars indicate the variation in
predicted shoreline position for different rate parameter combinations ..............146

B-7. Comparison of measured shorelines and "best" simulations according to the NMSE
and CAP criteria at North Beach, WA. Error bars indicate the variation in
predicted shoreline position for different rate parameter combinations ..............148

B-8. Comparison of measured shorelines and "best" simulations according to the NMSE
and CAP criteria at Long Beach, WA. Error bars indicate the variation in predicted
shoreline position for different rate parameter combinations ..............................150

B-9. Comparison of measured shorelines and "best" simulations according to the NMSE
and CAP criteria at Clatsop Plains, OR. Error bars indicate the variation in
predicted shoreline position for different rate parameter combinations ..............152








B-10. Comparison of measured shorelines and "best" simulations according to the NMSE
and CAP criteria at Torrey Pines, CA. Error bars indicate the variation in predicted
shoreline position for different rate parameter combinations ..............................154

B-11. Comparison of measured shorelines and "best" simulations according to the NMSE
and CAP criteria at Brighton Beach, AS. Error bars indicate the variation in
predicted shoreline position for different rate parameter combinations ..............156

B-12. Comparison of measured shorelines and "best" simulations according to the NMSE
and CAP criteria at Leighton Beach, AS. Error bars indicate the variation in
predicted shoreline position for different rate parameter combinations ..............158

B-13. Comparison of measured shorelines and "best" simulations according to the NMSE
and CAP criteria at the Gold Coast, AS. Error bars indicate the variation in
predicted shoreline position for different rate parameter combinations. ...............160

B-14. Comparison of measured shorelines and "best" simulations according to the NMSE
and CAP criteria at the Gold Coast, AS, using filtered (fc = 0.033 days"') data.
Error bars indicate the variation in predicted shoreline position for different rate
parameter combinations. .................................................................................. 162














Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

SHORELINE RESPONSE TO VARIATIONS IN WAVES AND WATER LEVELS:
AN ENGINEERING SCALE APPROACH

By

Jonathan K. Miller

December 2004

Chair: Robert G. Dean
Major Department: Civil and Coastal Engineering

A simple new shoreline change model has been developed, calibrated and evaluated

with several sets of high quality field data. The model is based upon previous research,

which indicates that the shoreline will approach an equilibrium position exponentially

with time, when subjected to constant forcing in the form of waves and water levels. The

engineering scale approach used here simulates the shoreline response to these cross-

shore processes in an extremely efficient and practical manner, while requiring only

readily obtainable wave and water level data as input. The equilibrium shoreline is

defined by using a conservation of volume argument and equilibrium beach profile theory

to derive an expression for the equilibrium shoreline change due to a combination of local

tide, storm surge, and wave induced setup. The rate at which the equilibrium condition is

approached is governed by a rate coefficient that can either be taken as a constant, or

parameterized in terms of the local wave and sediment properties. A total of eight

physically based rate parameters are evaluated, where the erosion and accretion are








parameterized separately. According to the results, the most effective parameterization

of the accretion rate is obtained using a surf zone Froude number, while the erosion rate

is best parameterized by either the surf similarity parameter or the breaking wave height

cubed. Three calibration coefficients representing a baseline for converting the

equilibrium shoreline changes into equilibrium shoreline positions, and separate erosion

and accretion constants, are evaluated by minimizing the error between model hindcasts

and historical shoreline data. The extensive set of shoreline data used to calibrate and

evaluate the model was compiled from a variety of sources, and consists of shoreline

measurements from a total of thirteen sites within the United States and Australia.

Overall the model successfully simulates the shoreline changes at 11 of the 13 study sites

with an average normalized mean square error of 0.643. Other tools designed to help

evaluate the model, such as a categorical assessment procedure and a model performance

index, also indicate a similar high degree of success.













CHAPTER 1
INTRODUCTION

In the past half-century, coastal populations worldwide have swelled as more and

more people have begun to recognize the recreational benefits and economic potential

associated with beaches. Eleven of the world's fifteen largest cities lie within the coastal

region, and over 400 million people live within twenty meters of sea level and twenty

kilometers of the coast (Small et al., 2000). In 1990, 133 million people or

approximately 54% of the U.S. population lived in one of 673 coastal counties (Culliton,

1990). In Australia, nearly 85% of the population lives within fifty kilometers of the

coast and nearly one-quarter of the population growth between 1991 and 1996 occurred

within three kilometers of the coastline (CSIRO Atmospheric Research, 2002; Australian

State of the Environment Committee, Coasts and Oceans, 2001).

The economic investment in these coastal regions is substantial. In the U.S., nearly

75% of the gross domestic product is generated in coastal states (Colgan, 2003).

According to Houston (2002), travel and tourism is America's leading industry and

employer, of which beaches are the primary component contributing an estimated $257

billion to the national economy in 1999. The economic impact of tourism is not limited

to the United States as similar statistics are reported in Australia where nearly 50% of

international tourists and 42% of domestic tourists visit the coast, contributing over $15

billion annually to the marine tourism industry (Australian State of the Environment

Committee, 2001). Australia and the U.S. are not alone in recognizing the economic

value of the coastal region as countries such as Japan, Germany, and Spain have been








known to spend as much $1.5 billion on shore protection and restoration in a single year

(Marine Facilities Panel, 1991).

In the U.S. and Australia, the most significant threat to this substantial investment

comes in the form of beach erosion due to a combination of natural and anthropogenic

factors. Galgano (1998) recently estimated that as much as 80-90% of the non-

engineered U.S. Atlantic coastline was experiencing net erosion, while a 1994 report by

the U.S. Army Corps of Engineers classified 33,000 km of the U.S. coastline as erosional,

4,300 km of which was classified as critical. While the exact numbers are often the

subject of intense debate, the importance of understanding the processes leading to beach

erosion (and accretion) is indisputable.

Unfortunately, beaches are extraordinarily complex, dynamic systems and

describing the governing physical process over the wide range of relevant spatial and

temporal scales is an extremely difficult task. Individual swash events alter the beach

topography with spatial and temporal scales on the order of millimeters and seconds,

while sporadic storms can cause tens of meters of erosion in only a few hours. Some

natural processes altering the shoreline such as sand waves exhibit both temporal and

spatial periodicity, while others such as storm related erosion occur randomly. The sheer

number and complexity of the physical processes responsible for inducing coastal change

make representing them all in a fully three-dimensional, time-dependent, process based

numerical model impossible given our current level of understanding. Fortunately for

engineering and planning purposes, the spatial and temporal scales are related, which

allows us to simplify the problem somewhat by considering only those scales important

to a specific problem. Arguably, the most relevant scale is the so-called engineering








scale, which refers to the range of temporal and spatial changes expected to impact a

structure during its lifetime. Typically, the expected lifespan of a structure is on the order

of 50-100 years, corresponding to relevant time and space scales of hours-decades, and

meters-hundreds of meters.

Even when the analysis is limited to the engineering time scale however, modeling

shoreline changes remains a difficult task, therefore a number of different approaches

have been developed. These range from simple extrapolations based on historical data to

highly detailed, fully three-dimensional, process based models. The fact that rudimentary

extrapolation techniques are still used despite the potential for considerable inaccuracies

is testament to the need for improved models. State of the art 3-D models have been

shown to be fairly accurate over shorter time scales after significant calibration; however

they tend to break down near the shoreline and remain cost-prohibitive for most

applications. One-line models provide simple yet accurate solutions for predicting

shoreline changes adjacent to structures related to longshore processes; however no

comparable technique exists for accurately modeling the shoreline response to cross-

shore processes. Although long-term predictions are often based upon the assumption

that the effects of cross-shore processes will cancel over the long run, the most significant

changes likely to impact a structure, particularly on a natural coastline, are in fact related

to these neglected processes. Accurately representing the potential shoreline change due

to cross-shore processes, such as those related to seasonal variations in wave energy or

extreme storms, must be included as an essential component of any complete shoreline

model. Unfortunately, it is much easier to diagnose the problem than to fix it, as

modeling the shoreline response to these cross-shore processes is not a trivial task.








Numerous cross-shore models have been developed with a variety of different

goals; however none have proven particularly successful at modeling shoreline changes

at the engineering scale. Although the specific capabilities of each model vary widely,

there are some common factors that make the majority of existing cross-shore models

inadequate for long-term predictions. Most conspicuous is the fact that nearly all models

predict erosion more accurately than accretion, and while this inability to accurately

model recovery processes has long been recognized, it remains a significant limitation.

The general applicability of most process based models is often restricted by the

extensive data required for calibration, as the paucity of available data, combined with

the need for site-specific information, means extensive costly field work is often required.

More disheartening is the fact that even the most detailed models tend to break down in

the vicinity of the shoreline, which for engineering purposes is nearly always the region

of greatest interest. In many studies, model performance is either not evaluated near the

shoreline or evaluated separately so as not to negatively impact the otherwise "good"

results. Furthermore, even if these event-based models were able to successfully handle

accretion, there is no guarantee that the results could be integrated up to yield reliable

predictions over longer timescales (Hanson et al. 2004). The above factors, when

combined with the extensive computational resources often required, make most state-of-

the-art models extremely inefficient, cost prohibitive, and ultimately impractical for many

engineering applications.

The objective of the present work is to present a new shoreline change model

which is capable of reproducing the shoreline response to cross-shore forcing over a

variety of temporal scales. In order to provide the widest possible range of applicability,








simplicity and efficiency along with a high degree of accuracy, were primary

considerations. Rather than approach the problem from a purely process based

standpoint, simple physical concepts were used in combination with empirical evidence

to create a new tool capable of fulfilling the proposed objectives. The result is a simple

model, which can be of immediate use to the engineering profession. Details of the

model are discussed in Chapter 3, however the basic concept borrows from classical

equilibrium theory, where the shoreline strives to reach an equilibrium state which

continuously changes in response to the dynamic conditions of the nearshore

environment. In accordance with physical observations, the rate at which this

equilibrium is approached is proportional to the degree of disequilibrium between the

instantaneous shoreline position and that suggested by the local forcing as a result of time

varying wave and water level conditions. Consistent with nature, such a model predicts

the strongest shoreline recovery immediately after the passage of a major storm, a result

which few (if any) process-based models have been able to reproduce.

High-quality data from both coasts of the United States as well as Australia were

collected to calibrate and evaluate the model. Unlike previous model studies that may

have been hampered by a lack of available data, recent emphasis on field data collection

and dissemination has resulted in an abundance of suitable data for this project. Instead

of haphazardly applying the model at each site for which sufficient data were available,

several criteria were used to eliminate those locations for which a cross-shore model was

considered inappropriate. These criteria helped identify and eliminate several sites where

the shoreline behavior exhibited significant longshore variations, potentially indicating

the predominance of longshore processes. The geographical diversity of the data sets








provided an interesting platform for examining the natural variability in the nearshore

system, and for evaluating the model over a wide range of wave, tide, and geologic

conditions. By incorporating shoreline measurements made using a number of different

techniques, the skill of the model could be evaluated over a variety of timescales ranging

from daily to multi-decadal.

Undoubtedly, process based models containing full detailed descriptions of the

governing hydrodynamics and resulting sediment transport will eventually yield the most

accurate predictions of shoreline change; however our present knowledge of the complex

relationships and feedback mechanisms is insufficient to justify their use in long-term

shoreline studies. The shoreline model developed and discussed herein is significant in

that it is able to accurately predict shoreline changes, while requiring only minimal,

readily available forcing and calibration data. The simplicity, efficiency, and adaptability

of the new model make it a useful tool for a variety of engineering applications. With

additional research, it should be possible to adapt this simple cross-shore model to work

in concert with the existing simple longshore models (e.g., Hanson and Larson, 1998) to

obtain a robust, quasi-two-dimensional shoreline model. Although the emphasis here has

been placed upon maintaining the simplicity of the model, it can readily be adapted and

used with more detailed wave transformation models to analyze the potential implications

of alongshore variations in the incident wave field. The current analysis has been limited

to the comparison of model hindcasts with measured data; however the exhibited skill

suggests it should be possible to apply the model in either a predictive sense using

statistical descriptions of the forcing parameters and Monte Carlo simulations, or in a

real-time sense using instantaneous measurements or storm forecasts of the forcing








parameters. In the first case the results would represent the probabilities associated with

various magnitudes of shoreline change based upon the statistical characteristics of the

forcing parameters, while in the second case the model could provide first-approximation

predictions of the erosive potential of approaching storms. The efficiency of the new

model will make it particularly useful for long-term studies ranging from the prediction

of seasonal shoreline changes, to the prediction of decadal shoreline migration patterns

for coastal management applications.

In order to help the reader navigate through the remainder of this document, it is

useful to provide a roadmap detailing its layout. In Chapter 2, some background

information is provided including a more detailed discussion of the problem, as well as

some of the more popular techniques for modeling the shoreline response to cross-shore

forcing. Details of the new model including a description of the numerical approach are

presented in Chapter 3, while the available field data and the tools used to help eliminate

the inappropriate sites for a cross-shore model are discussed in Chapter 4. The results are

presented in Chapter 5, followed by a detailed discussion of some of the key aspects of

the model in Chapter 6. Finally, Chapter 7 summarizes the results and presents some

suggestions for future work.













CHAPTER 2
BACKGROUND

The complexity of the extremely dynamic nearshore environment makes accurate

predictions of morphological evolution in this region over even limited temporal and

spatial scales extremely difficult. Unfortunately, practical design requirements demand

that a wide range of scales be taken into consideration, as the relevant engineering

timescale ranges from hours to decades and encompasses spatial scales ranging from

meters to hundreds of meters. The societal relevance of understanding and predicting

changes in the nearshore region is illustrated by the long and varied history of attempts to

model it using physical, analytical, and numerical techniques. Hanson et al. (2004)

reviewed in detail some of the conventional and less conventional modeling approaches

that have been used to predict coastal evolution over yearly to decadal timescales.

Despite considering over twenty different types of models, the authors were unable to
Th
identify any capable of reproducing adequate results over the full range of time scales

considered. The remainder of this chapter is devoted to a discussion of some of these

conventional modeling techniques, which through their inadequacy stress the need for

innovative approaches.

The four basic tools available to coastal engineers consist of experience/empirical

models, physical models, analytical models, and numerical models. In some respects,

local experience constitutes the best model, as a thorough understanding of the local

processes (waves, tides, currents, sediment transport) and geomorphology are essential

tools in understanding a coastal system. Similar projects on adjacent beaches often








provide invaluable information regarding unexpected results attributed to localized

phenomena. Relying on previous experience alone however, is insufficient for a number

of reasons, including the inability to consider innovative approaches or optimize design.

Although useful, experience or empirical models are nearly always best when applied in

combination with either physical, analytical, or numerical models.

Coastal physical models typically consist of scaled down versions of a natural

system and are often constructed in a laboratory. The primary advantage of a physical

model is the ability to control the ambient conditions so that specific design scenarios can

be isolated and evaluated more precisely. Although physical models play a critical role

in understanding coastal processes, they do have several significant disadvantages as

well. In order to ensure similar behavior between the model and prototype, both scaling

effects as well as laboratory effects must be considered and accounted for. Scaling

problems can occur when the correct balance of forces is not preserved in the model,

while laboratory effects can be equally detrimental, and include the generation of higher

harmonics and presence of boundary induced reflections. These and other considerations

combine to make physical models quite labor intensive and often extremely expensive, as

highly skilled labor and specialized facilities are frequently required.

Analytical models consist of closed form mathematical solutions to simplified

versions of the equations governing shoreline and profile change, and are often derived

for schematized geometries and basic input and boundary conditions. The objective of an

analytical model is to capture the essential physics of the problem in a simplified manner

that allows the fundamental features of the beach response to be derived, isolated, and

more readily comprehended. Unfortunately, these types of models are generally too








crude for design purposes; however they can provide a means to identify characteristic

trends and investigate the basic dependencies of the shoreline response to different

combinations of input and boundary conditions.

Increasingly numerical models are being used to study complex coastal systems as

advancements in our ability to represent the dominant physical processes, combined with

rapid advancements in computational capability, make them ever more efficient.

Numerical models provide greater flexibility in the selection of boundary conditions and

allow for the representation of arbitrary forcing. In addition, numerical models are

extremely dynamic in the sense that recent scientific advances are easily incorporated due

to their typically modular nature. The model presented here falls into this category;

therefore the majority of this chapter is devoted to a discussion of some of the

conventional numerical modeling approaches that have thus far failed to produce a

generally accepted cross-shore model, applicable at the engineering timescale.

2.1 Longshore (Planform) Models

Because of the complexity of the nearshore system, it is common to separate

longshore and cross-shore processes, and to treat planform and profile evolution

separately. In planform models, shoreline changes are assumed to result from gradients

in the longshore sediment transport, while cross-shore effects such as storm induced

erosion, or seasonal shoreline fluctuations, are either assumed to cancel over the length of

the simulation, or are accounted for separately. These assumptions make planform

models much more appropriate when applied over longer periods at segmented coastlines

with systematic long term trends, and less applicable over shorter periods on more natural

uninterrupted coastlines without dominant trends.








2.1.1 Analytical Models

Perhaps the most often utilized analytical model in coastal engineering is the one-

line model developed by Pelnard-Considere (1956) for predicting shoreline evolution due

to gradients in the longshore sediment transport. The key assumption of the model is that

the cross-shore profile remains in equilibrium, and does not change along the extent of

the shoreline being studied. The "one-line" moniker relates to the assumption that the

movement of the entire profile can be represented by the translation of a single contour,

usually the shoreline. If the one-line assumption holds, then the principle of mass

conservation in the longshore direction must apply at all times,

+D- =0 (2.1)
ax at

where x and y are the longshore and cross-shore coordinates, respectively, Qi is the

longshore sediment transport rate, t is time, and D is the vertical extent of the active

profile, defined as the sum of the depth of closure, h., and the berm height, B. The

longshore sediment transport rate is given by Qs1 = Qosinab, where Qo is the amplitude of

the longshore sediment transport rate, and ab is the angle between the breaking wave

crests and the shoreline. Under the assumptions of constant longshore forcing, and small

constant breaking wave angles, Equation 2.1 reduces to the classic heat conduction

equation for which numerous analytical solutions exist,

ay a'y
aye a2y (2.2)
at ax,

In Equation 2.2, the diffusion coefficient e is given by e = 2QdD. Larson et al. (1997)

provide a collection of analytical solutions to Equation 2.2 describing the basic shoreline

behavior under various combinations of simplified forcing and boundary conditions.








2.1.2 Numerical One-Line/N-Line Models

In order to obtain more realistic one-line solutions, Equation 2.1 can also be solved

using numerical techniques capable of handling more realistic forcing and boundary

conditions. Similar to the analytical solutions, cross-shore effects are assumed to cancel,

as these models are typically applied over periods of years to decades and at sites with

dominant long term trends. In terms of predictive skill, one-line models such as

GENESIS (Hanson and Kraus, 1989) have proven to be fairly successful, despite their

inability to simulate cross shore effects.

N-line models are an extension of typical one-line models where the profile is

divided into a series of N mutually interacting layers. In these quasi-3D models, cross-

shore effects are included in a highly schematized sense through interaction terms.

Models in this category include those of Bakker (1968) and Perlin and Dean (1983).

Hanson and Larson (2000) attribute the lack of success of conventional N-line models to

inappropriate representations of both the cross-shore sediment transport and the cross-

shore distribution of longshore transport, and suggest innovative approaches are required.

2.2 Cross-shore (Profile) Models

Cross-shore or profile models are generally used to describe the nearshore response

to events over limited temporal (hours-years) and spatial (meters-hundreds of meters)

scales. In contrast to planform models where gradients in the longshore sediment

transport drive the bathymetric evolution, profile models are most readily applied along

coastlines dominated by the influence of cross shore processes. In general, these models

have been most successful at simulating storm induced erosion, and have been less

successful at reproducing post-storm recovery, and therefore medium to long term profile

evolution. Schoonees and Theron (1995) reviewed ten different cross-shore models with








respect to theoretical merit and validation criteria, grouping the models into "best,"

"acceptable," and "less suitable" categories. According to their conclusions, none of the

models could be identified as clearly superior, as each potentially performs the best under

certain conditions.

2.2.1 Analytical Models

Although several analytical cross-shore models have been developed, none have

proven nearly as useful as the Pelnard-Considere equation. Larson and Ebersole (1999)

used a simple diffusion equation to describe the evolution in time of an offshore mound

placed in the x-z plane, as direct physical analogies exist between several of the

analytical solutions to the classical diffusion equation and the filling of dredged holes and

spreading of offshore mounds. Bender and Dean (2003) reviewed many of the analytical

solutions for wave transformation over bathymetric anomalies, including the potential

shoreline impacts. Another useful analytical model developed by Kriebel and Dean

(1993) for describing the time dependent evolution of the nearshore profile is discussed

in some detail in the next chapter.

2.2.2 Empirical Models

Most empirical models are based upon observations of morphological evolution

made in the laboratory under controlled circumstances, and as such are subject to certain

limitations. The empirical model of Swart (1974) was developed based upon

observations of profile recession under monotonic waves, in both small and large scale

tests. Swart schematized the profile into three separate zones as shown in Figure 2-1, and

developed empirical relations for the equilibrium profile as well as the onshore and

offshore limits of the active profile. In the Swart model, the time dependent cross-shore





14

sediment transport rate, Qs, is driven by the disequilibrium of profile characteristics, and

is given by

Q. = s, W (W ) (2.3)

where (Li-L2)t is a time dependent profile width, W is the profile width at equilibrium,

and Sy is an empirical constant for a given set of boundary conditions. Although the

model was subsequently applied to field data with some success (Swain and Houston,

1984; Swart, 1986), the intensive calibration and complicated empirical formulas

involved make the method too complex for widespread application.

1,ItM0 uNE

UPPCR UN SOUat 0 -OFIL *I 0W
11 SCHtATIZATIC A

| -~ -- -
L_ N A






r ----M-M1



Figure 2-1. Profile schematization in Swart model (from Swart, 1974).

2.2.3 Energy Dissipation Models

Based on an analysis of over 500 beach profiles from the Atlantic and Gulf Coasts

of the United States, Dean (1977) derived an equilibrium beach profile of the form h =

Ay2, using linear wave theory and the premise of uniform wave energy dissipation per








unit volume due to breaking. The key assumption in the derivation is that sediment of a

certain size will be stable for a certain level of wave energy dissipation per unit volume,

I d(EC,)
D.(d) = (2.4)
h dy

where h is the total depth, E is the wave energy density, and Cg is the group velocity. In

this case, the y coordinate is shore normal and increases offshore. Using equilibrium

beach profile theory, Kriebel and Dean (1985) proposed an expression for cross-shore

sediment transport based upon the difference between the actual and equilibrium levels of

wave energy dissipation in the surfzone,

Q, = K(D-D.) (2.5)

where D is the actual time dependent energy dissipation per unit volume, and K is an

empirical sediment transport parameter. The bracketed term represents the degree of

disequilibrium and suggests that for steep profiles sediment will be transported offshore

to restore equilibrium and vice-versa. Zheng and Dean (1997) subsequently modified

Equation 2.5 by raising the disequilibrium term to the third power, in order to satisfy the

appropriate scaling relationship given by Qr = Lr3n, where Qr is the sediment transport

ratio, and L, is the length ratio. In both models, profile adjustments occur in response to

gradients in the cross-shore sediment transport according to the continuity equation,

-y =_, (2.6)
at oh

Equations 2.5 and 2.6 form a closed system of equations which can be evaluated

numerically. On a storm time scale, both models are capable of adequately representing

the storm induced erosion but are less successful at reproducing post-storm recovery.








Inspired by the success of the EDUNE model (Kriebel and Dean, 1985), Larson

and Kraus (1989) attempted to extend the capabilities of energy dissipation based models

with SBEACH. In SBEACH, the nearshore region is separated into four distinct zones as

illustrated in Figure 2-2, each having its own sediment transport relationship. In the

breaking zone (Zone II), the magnitude of the cross-shore sediment transport, Q,, is

calculated based upon energy dissipation arguments, with an extra term added to account

for down slope transport,

K D-D.+ E D>D.- A
Q. Kdy K dy (2.7)
VK = (2.7)
e dh
0 D< D.-
K dy

where dh/dy is the local beach slope, and K and e are sediment transport coefficients for

the energy dissipation and slope dependent terms, respectively. In SBEACH, the

direction of transport is calculated separately, and is based upon an empirical criterion

relating the deep water wave steepness, HoLo, to the non-dimensional fall velocity

parameter, Ho/w,T, according to

O- < 0.00070 2- Q
(2.8)
H > 0.00070 + Q


where Ho, Lo, and T are deepwater wave parameters representing the wave height, wave

length, and wave period, and w, is the sediment fall velocity. Sediment transport

magnitudes in the remaining zones are calculated based upon empirical relationships for

which the energy dissipation based transport (as calculated from Equations 2.7 and 2.8)

serves as a boundary condition. In these regions, the magnitude of the sediment transport








typically follows an exponential decay, with coefficients that vary from region to region.

At both the onshore and offshore boundary, the magnitude of the sediment transport

reduces to zero, such that there is no bathymetric change. The SBEACH model has been

widely applied in numerical studies of storm related erosion and beach nourishment

equilibration with adequate results; however the inability of the model to accurately

simulate accretional events and onshore bar migration limits its usefulness for long term

studies.

pp

j I Wave Height




SI1




SWASH BREAKER TRANSITION
ZONE BROKEN WAVE ZONE ZONE PREBREAKING ZONE


Figure 2-2. SBEACH profile schematization (from Larson and Kraus, 1989).

2.2.4 Process Based Models

Process based models aim to reproduce profile evolution on the basis of first

physical principles. Roelvink and Broker (1993), and van Rijn et al. (2003) provide in

depth reviews of several state-of-the-art process based models including: UNIBEST

(Delft Hydraulics), LITCROSS (Danish Hydraulics Institute), SEDITEL (Lab Nationale

d'Hydraulique), WATAN3 and BEACH (University of Liverpool), COSMOS (H. R.

Wallingford), CROSMOR (University of Utrecht), and CIRC (University of Catalunya).

Although the exact details of the methods used to calculate the hydrodynamics, sediment








transport, and bed evolution vary from model to model, the schematic in Figure 2-3

illustrates the general solution procedure. Gradients in the time averaged cross-shore

sediment transport rate drive bed level changes according to the continuity equation,


(1-n) A = (2.9)
at ay

where n is the sediment porosity, Zb is the local bed elevation, and y is once again the

shore normal coordinate. In general terms, the time averaged cross-shore sediment

transport rate is given by

1 t2 Z,
Q(y () =- lu(y, z, t) c(y, z, t)dzdt (2.10)
t2 -ti Z4

where u is a horizontal velocity, and c is the sediment concentration. From a practical

standpoint, a complete time dependent solution of these equations is virtually impossible,

as specification of the velocity and concentration fields down to the scales associated

with turbulence is required. In order to arrive at workable solutions, most process-based

models distinguish between four different process scales, of which only those scales

relevant to a particular application may be considered. The turbulent scale is the smallest

scale and is usually not considered due to its relatively minor influence on the horizontal

flow field. The intra-wave time scale includes processes such as time lag effects within

the wave period and wave asymmetry, which can be particularly important for onshore

transport. Processes related to long waves and wave groups, such as variations in

sediment concentration, make up a third scale. The fourth and final scale consists of

mean variations of the wave field over time scales associated with the tidal period, and

includes tidal currents and time averaged return flows.
















>Hydrodynamic Module
1) Waves across the profile
2) Currents across the profile



Sediment Transport Module
1) Many different formulations



Morphologic Module
1) Conservation of sediment volume


Figure 2-3. Typical process based model schematic.

Although invaluable in terms of understanding the complex physical relationships

between hydrodynamic forcing and sediment response at the micro-scale, process based

models are inadequate for modeling long term profile development, particularly in the

vicinity of the shoreline. The combination of computational effort and the extensive data

required to calibrate these models makes them extremely inefficient and expensive to run,

especially for long-term studies. Stive and DeVriend (1995), Kobayashi and Johnson

(2001), and van Rijn et al. (2003) all reached the same conclusion, that given our current

"rudimentary" understanding of cross-shore sediment transport processes in the surf and

swash zones, the prediction of long term shoreline change using any of the existing

models based on first physical principles was virtually impossible. Even as our

understanding of micro-scale hydrodynamics and sediment transport processes improve,


Input
1) Initial bathymetry
2) Waves and water levels at the boundary
3) Sediment parameters








uncertainties such as those associated with prediction windows and the effects of storm

sequencing, bring into question whether the results of these small scale models can be

integrated up to the relevant engineering scales.

2.2.5 Alternative Models

Alternative models can be broadly described as those which do not follow any of

the traditional approaches, and which often combine elements of proven techniques in an

attempt to fill specific voids in our modeling capabilities. Some examples of alternate

models include Steetzel (1995) who added a semi-empirical cross-shore transport to

extend the capabilities of N-line models, and both Inman (1987) and Larson and Kraus

(1991) who modified Equation 2.2, adding an advective term to simulate sand wave

propagation. Hanson and Larson (1998) attempted to incorporate seasonal effects into a

traditional N-line model by schematizing the cross-shore sediment transport, but were

only moderately successful. Plant et al. (1999) and Madsen and Plant (2001) used

alternative methods which closely parallel the proposed shoreline model to successfully

simulate bar evolution and nearshore beach slope changes at Duck, NC. These

equilibrium based techniques are discussed in more detail in Chapter 3.

2.3 Need for Innovative Approaches

The models mentioned in the preceding two sections represent only a small subset

of those available, yet they illustrate an important point. In spite of intensive efforts to

develop robust numerical models of the nearshore region and the shoreline in particular,

conventional modeling approaches have failed to produce a generally accepted

engineering scale model. In general, planform models have proven somewhat successful

at describing long-term shoreline changes in the vicinity of structures; however attempts

to extend their capabilities by parameterizing the cross-shore sediment transport have








been much less successful. As a result, these models still cannot reproduce changes at

the storm and seasonal time scales, and are inappropriate on long, straight, natural

coastlines. Profile models, whether based on energy dissipation arguments or first

physical principles, have typically been unable to adequately simulate beach recovery;

therefore their applicability over longer time scales related to sequences of storms, or

seasons is limited. Despite intensive research, there still exists a range of relevant scales

of practical importance to engineering design, for which conventional cross-shore

modeling approaches have proven unsuitable. The model described herein represents an

attempt to fill this void using an innovative engineering scale approach.

The new model combines empirical evidence with basic theory to produce a

simple, yet effective, cross-shore shoreline change model, applicable at the engineering

scale. The primary objective is to create a robust, model capable of simulating shoreline

changes over a variety of different time scales, under a variety of conditions, in an

accurate and efficient manner, which is considered suitable for practical engineering

applications. The model, is described in detail in the following chapter, and takes the

form of a classic equilibrium equation as suggested by previous empirical studies of the

shoreline response to variations in waves and water levels, where tides, storm surges, and

wave induced setup have been included. Unlike many of the conventional approaches,

this innovative model requires only readily available wave and water level information,

and is extremely computationally efficient. Because of its simplicity, the new approach

has many conceivable applications, providing a potentially crucial link between profile

models which are unable to accurately reproduce the erosion-recovery sequence, and

coastal area models which ignore cross-shore processes completely.













CHAPTER 3
MODEL DEVELOPMENT

3.1. Theoretical Background

The shoreline model presented here differs from the conventional models discussed

in the previous chapter which thus far have been unable to produce a robust, generally

applicable shoreline change model based on cross-shore processes. Instead, an

innovative approach is proposed that uses empirical evidence based upon previous

laboratory and numerical investigations of shoreline change, to guide the development of

a simple new shoreline change model. Both small (Swart, 1974) and large-scale

laboratory experiments (as reported by Dette and Uliczka, 1987; Sunamura and

Maruyama, 1987; and Larson and Kraus, 1989) have suggested that an initially plane

beach subjected to steady erosional forcing in the form of a fixed elevated water level and

constant wave action, will evolve towards an equilibrium state with an approximately

exponential time scale. Numerical simulations performed by Kriebel and Dean (1985)

and Larson and Kraus (1989) support these observations, suggesting that shoreline

change can be modeled heuristically using an equation of the form,

dy (t)
-t= k (y (t)- y(t)) (3.1)
dt

where y(t) and yq(t) are the instantaneous actual and equilibrium shoreline positions at

time t, and ka is an empirical rate coefficient. Equation 3.1 is a classical equilibrium

equation, and implies that the rate of shoreline change is proportional to the degree of

shoreline disequilibrium. In previous studies, analogous relations of this form have been








used successfully to describe large-scale coastal phenomena where detailed knowledge of

the complex physical mechanisms producing the phenomena were lacking.

Wright et al. (1985) suggested that the rate at which a beach transitioned between

the various beach states in the morphodynamic classification scheme of Wright and Short

(1984), could be described by an equation similar in form to Equation 3.1,

st)= a + b ((t)) (S(t) S (t)) (3.2)

where the term (S(t)-Sq(t)) represents the disequilibrium between the actual and

predicted beach state at any given time, and b((t))P is a rate parameter dependent on the

non-dimensional fall velocity parameter, Q(t). The combination of these two terms was

described by the authors as representing the disequilibrium stress. Unfortunately, a lack

of available data outside the stable region meant the empirical coefficients a, b, and p

were left undetermined pending the collection of more field data.

More recently, the equilibrium concept has been used by Plant et al. (1999), and

Madsen and Plant (2001), to describe bar and beach slope evolution at Duck, NC. In the

earlier study, Plant et al. showed that bar morphology, as represented by the time varying

location of the bar crest, could be described by an equilibrium equation of the form,

dX (t) = ( (t))'(X (t)- X, (t)) (3.3)

where X(t) and X,(t) are the time dependent actual and equilibrium bar positions,

respectively, and a(H(t))3 is a parameter influencing the rate at which equilibrium is

approached. By comparing the model to observations Plant et al. were able to show that

this relatively simple model had significant predictive capability over periods of nearly a

decade. Madsen and Plant used a very similar model to describe beach slope evolution,








where the form of the model is identical to that of Plant et al., with X(t) and Xq(t),

replaced by D(t) and qB(t), the time varying shore normal and equilibrium beach slopes,

respectively. While Plant et al. found that the rate parameter was proportional to the

wave height cubed, Madsen and Plant determined the corresponding dependence for the

beach slope change was closer to (H(t))42, where the range of exponents produced

similar, acceptable results. The Madsen and Plant model was found to explain between

30 and 40 percent of the observed beach slope changes at Duck.

Kriebel and Dean (1993) used an analytical approach to solve Equation 3.1 for an

idealized case. Sensitivity studies performed on the EDUNE model (Kriebel and Dean,

1985), indicated that the equilibrium shoreline response varied nearly linearly with

changes in the water level, and that the water level did not affect the rate of shoreline

response. This information allowed decomposition of the equilibrium response, yq(t),

into a term associated with the magnitude of the response, Yq, and a unit amplitude

function of time containing the temporal dependence, f(t). The simplified differential

equation,

d(t)= a(Yf (t) y(t)) (3.4)


was then solved in terms of the convolution of the time dependent forcing and a

characteristic solution for steady input conditions,

I
y(t) = aY, f (r)e-a(-T)d (3.5)
0

where T was a time lag. The analytical solution given in Equation 3.5 possesses several

attractive characteristics that suggest the method is worthy of further study. The

convolution solution indicates that antecedent conditions are important, and that the








actual shoreline response occurring in nature will be damped and lagged with respect to

the maximum or equilibrium state. The analytic model is also consistent with nature in

that it predicts the maximum rate of shoreline recovery will occur immediately after the

passage of a storm, and even provides an analytic justification for the different time

scales associated with beach erosion and accretion. As discussed in the previous chapter,

the analytic solution is limited however, due to its simplified nature and inability to

handle complex, realistic forcing conditions. Even the simplified Equation 3.4, can only

be solved analytically for a limited number of cases where the time dependence of the

equilibrium response is known and can be represented by a simple analytical function.

Although extensions of the analytical solution are possible, only a numerical approach

will be able to provide a realistic representation of the time dependent forcing function,

y q(t).

3.2. Defining the Equilibrium Shoreline, yq(t)

Equilibrium beach profile methodology and a modified version of the Bruun (1962)

rule, which considers increases in the local water surface elevation due to a combination

of tide, storm surge, and wave induced setup, are used to calculate the equilibrium

shoreline response, yq(t), for a given set of forcing conditions. Although the

applicability of equilibrium concepts in the nearshore environment remains a

controversial issue (see for example Thieler et al. (2000)), Bruun (1954) and Dean (1977)

have illustrated the ability of a single empirical equilibrium beach profile relationship, h

= Ay3, to adequately describe the nearshore bathymetry at numerous sites throughout

the United States and Denmark. When applied to field data where conditions are

constantly changing, the empirical relationship refers to a dynamic equilibrium state and

average profile conditions. Dean even showed the equilibrium beach profile could also








be derived analytically based on the assumption of uniform wave energy dissipation due

to breaking waves through the surfzone. Subsequently, Moore (1982) and Dean (1991)

were able to develop graphical, empirical relationships between the profile scale

parameter, A, and sediment characteristics such as median diameter and fall velocity.

The Bruun rule was originally developed to describe shoreline changes resulting

from an increase in the local water surface elevation, S. If the assumption is made that

the entire profile (not necessarily an equilibrium beach profile) shifts landward and

upward without changing form with respect to the new water line, and that sediment

volume is conserved, the resulting shoreline recession, Ay, is

Ay = -S ( (3.6)
(h4 + B)

where h. and W* are the vertical and horizontal extents of the active profile, and B is the

berm height. This expression has proven adequate in the absence of waves; however

previous studies have indicated that the most significant shoreline changes occur when

increased water levels are accompanied by large waves. Figure 3-1 illustrates this

modified situation, where the wave induced setup, q (y), alters the water surface

elevation across the profile. The assumptions required in order to derive an analytical

solution remain the same, namely the volume of sediment eroded from the foreshore

equals that deposited offshore, and the form of the equilibrium profile remains unchanged

with respect to the increased water level. In order to simplify the resulting expression,

the common volume can be added to both sides of the conservation of volume equation,

(B S (y))d+ A(y -Ay)dy
(3.7)
= Ay"dy + r (S + (y))y








which after integration simplifies to

Ay 3h. Ay)3 =h.(3/5-K) S -7b
W. 5B W.) B (1-K) B B(3.8)
K 3x/8
1+ 3 /8

where K is the depth limited breaking coefficient. Equation 3.8 relates the non-

dimensional shoreline recession, Ay/W., to the dimensionless berm height, B/h*, storm

surge, S/B, and wave setup, jdbB. In general, the non dimensional recession will be

small and with K = 0.78, Equation 3.8 can be simplified even further,
.0( .068H, (t)+ S(t)
Ayeq(t) = -W, (t).068H) (39)
A B+1.28Hb Wt

where Hb(t) is the breaking wave height, B is the berm height, and W.(t) is the width of

the active surf zone. Here W.(t) is defined as the distance to the break point, such that it

may be represented in terms of the breaking wave height, as W. = (HI/KA)3/2. The wave

height and breaking index used in the derivation of the above equation assumes constant

or average wave conditions. An alternate form of Equation 3.9 may also be derived for

significant wave conditions, where K is taken as 0.5 and the coefficients in the numerator

and denominator are replaced by 0.106 and 2.0, respectively. Although all of the

quantities in Equation 3.9 exhibit some degree of temporal dependence, the berm height

is taken as a constant, as information regarding its variability is sparse. In the field,

where conditions are constantly changing, Equation 3.9 represents a theoretical condition

which will hardly, if ever, be reached.

The quantity Ayeq(t) calculated from Equation 3.9 gives the shoreline change from a

stable or baseline condition; therefore in order to convert this time series of equilibrium








shoreline change into a time series of equilibrium shoreline positions, this baseline

condition must be identified. If the assumption is made a priori that the baseline

condition corresponds to the average measured shoreline, the Ayq(t) calculated by

Equation 3.9 are identically equal to the equilibrium shoreline positions (since the data

are detrended). However in general this assumption is incorrect, as the baseline

conditions for yq(t) and yob(t) are not necessarily the same. In fact, it has been argued

(Wright, 1995) that the average shoreline position actually represents an average

disequilibrium condition. In order to account for any potential offset in the baseline

conditions, a constant calibration parameter, Ayo, is introduced which provides an

additional degree of freedom. The role of Ayo is illustrated in Figure 3-2, where in the

example provided, a shift of Ayo = 25 m is required to align the reference frames for yeq(t)

and yb(t). The calibration routine discussed in Section 3.4.4 is used to determine the

values of Ayo and ka for each simulation. Once the baseline condition has been

identified, the equilibrium shoreline displacement, ycq(t), is given by

y(t)Ay + Ayeq(t) (3.10)















Figure 3-1. Beach recession due to a combination of an increased water level, S and wave
induced setup, ?(y) (from Dean 1991).






29




E 40



I-
20


0 MVyo = -25
-20 Yob
--,-- y,,

-40
0 100 200 300 400 500 600 700 800 900 1000
40Days
E
c 20-





-2 ba Fc s of
-40
0 100 200 300 400 500 600 700 800 900 1000
Days
Figure 3-2. Example illustrating the role of Ayo in correcting for differences in the
baseline conditions of yq(t) and yob(t).

3.3. Defining the Rate Parameter, ka

The coefficient ka, governing the rate of shoreline response in Equation 3.1, can

either be taken as an empirically determined constant or parameterized to incorporate

some measure of the local conditions. Both alternatives have been considered here. In

the simplest case, ka is assumed to be a locally determined constant, where the subscript

a is used to signify that ka may be double valued, with one value, ke, representing erosion

and a second, ka, associated with accretion. For most situations, it is assumed that ke will

be much larger than ka, as in nature the time scales of erosion are generally much shorter

than those of accretion.








Although convenient, this representation is perhaps overly simplified, as it is more

logical to assume the shoreline response rate depends in some manner on the local wave

and sediment properties. Two different approaches were considered to determine

effective parameterizations of the rate function. In the first approach, the rate parameter

was related to a measure of the local wave energy, while in the second, non-dimensional

parameterizations involving measures of both the wave and sediment properties, were

considered. In both cases, the final form of ka(t) is given by ka(t) = kaf(t), where ka is

the previously mentioned double valued coefficient, and f(t) is the time dependent

parameterization. By parameterizing the rate function, the spread of the empirical

coefficients is expected to reduce, as f(t) explicitly includes a measure of some of the

important differences between the sites. The exact values of ka for each simulation are

obtained by calibrating the model against historical data according to the procedure

described in Section 3.4.4.

Both Plant et al (1999) and Madsen and Plant (2001) based their rate coefficients

on parameterizations of the local wave energy, adopting relationships of the form

a(t)=a(H(t))P, where H(t) was a representative wave height and p was determined

through an empirical fit to the data. Rather than explore an infinite range of possible

values for p (p need not necessarily be an integer), a similar dependence is considered

here, where the potential parameterizations are limited to ka(t) = kali(t)2 and lk(t) =

kaHb(t)3. While Hb2(t) is obviously related to the wave energy, Hb3(t) can be thought of

as approximating the wave energy flux into the surfzone which actually has an HbI5

dependence. One of the major disadvantages of the assumed wave energy relationship

however, is that it contains no dependency on either sediment size, beach slope or wave








period. In addition, in order for Equation 3.1 to remain dimensionally consistent, wave

energy based parameterizations impose increasingly complex units of time-'length1p on

the empirical coefficients.

Kraus et al. (1991) reviewed many of the non-dimensional parameters often used to

separate erosional and accretional conditions and found several, that when plotted

together with the deep water wave steepness, Ho/Lo, were capable of differentiating

between the two. Table 3-1 lists some of the more common beach change discriminators.

Although some of the criteria listed in Table 3-1 were based upon consideration of profile

type, bar bermm) profiles are generally considered to be representative of erosive

accretivee) conditions, and here the assumption is made that these conditions will have an

in kind impact on the shoreline. The various parameter combinations listed in the table

typically incorporate measures of both the wave environment (Ho, T, Lo), as well as

morphologic and sediment properties (d50, ws, tan 3). Although some of the parameters

in Table 3-1 have thus far only been used to distinguish between the expected type of

change, the assumption made here is that the magnitude of several of these parameters

can potentially be related to the shoreline change rate through the parameter ka(t).

One of the advantages of using non-dimensional parameterizations is that the rate

coefficient, ka, retains the units of inverse time, which is more appealing from a physical

perspective, as the inverse of this coefficient can be interpreted as the time scale of the

shoreline response (see Section 6.2 for a more complete discussion). Using Table 3-1 as

guidance, numerous non-dimensional parameterizations were considered; however the

following five were deemed most appropriate:

* Fall velocity parameter, Q(t) = H(t) Gourlay (1968), Dean (1973)
w,T(t)









* Froude number, F,(t) = Kraus et al. (1991), Dalrymple (1992)
S FroudenumberF F() (t)


* Inverse Froude number, IF,(t) = F,(t)-'

gH, (t)
* Profile parameter, P(t) = gH(t) Dalrymple (1992)
wT(t)

HU (t)
* Surf similarity parameter, (t) = ,(t) Battjes (1974)
L (t)(tan s)2

where Hb is the breaking wave height, T the period, w, the sediment fall velocity, g

gravity, tan B the local beach slope, and Lo the deep water wave length (LI=gT2I2n).

Each parameter contains a description of both the wave (Hb, T, Lo) and sediment

(either w, or tan 0) properties and has a sound physical interpretation. The fall velocity

parameter has been used extensively in sediment transport and profile evolution studies

(Dean 1973, Wright and Short 1984, Kraus et al. 1991), and provides a measure of the

ratio of the amount of time a particle with a settling velocity ,ws, remains suspended

relative to the wave period under a wave of height Hb. Under a breaking wave crest, the

wave particle velocities are directed onshore, so if the particle manages to settle during

the first half wave period the net displacement is onshore resulting in accretion. If the

particle takes longer than half a wave period to settle, the net displacement is offshore

and erosion occurs. Kraus et al. (1991) showed that Q(t) is related to the wave energy

dissipation in the surfzone, and suggested that as the magnitude of the fall velocity

parameter increases, so should the transport magnitude. This alternate derivation has a

similar physical interpretation, where above some critical value of Q(t), sediment is

suspended and transported seaward by the near bottom return flow, while below this

value accretion will occur as wave asymmetry moves sediment onshore as bedload.









Table 3-1. Established erosion/accretion criteria.
Reference Parameters*


Waters (1939), Johnson (1949)

Rector (1954)

Iwagaki & Noda (1962)
Nayak (1970)

Dean (1973), Kriebel et al. (1987)

Sunamura & Horikawa (1975),
Sunamura (1980)

Hattori & Kawamata (1981)

Wright & Short (1984)



Larson & Kraus (1989)




Kraus et al. (1991)




Dalrymple (1992)


MacMahan & Thieke (2000)


Ho/Lo, Ho/dso
Ho/Lo, HodsoS

Ho/Lo, inw8/gT

Ho/Lo, dso/Lo, tanl

(Ho/Lo)tanp, w,/gT

Hb/w.T



Ho/Lo, Holw,T, rw,/gT




Ho/Lo, Ho/wT,
w/(gHo)5




gHo2/w83T, gHo2/w3T



Cb, TH/H, 2nUb/Tg,
Ub/uca, UblUbl 42/T2g


Erosion : Ho/Lo > 0.025
Accretion : Ho/Lo < 0.025
Erosion : do/Lo < 0.0146(Ho/Lo)1.25
Accretion : do/Lo < 0.0146(Ho/L)1.25
Graphical Method
Graphical Method
Erosion: Ho/Lo > CI[nw,/gT]
Accretion: Ho/Lo < C [nw./gT]
Erosion: H/Lo > C2[tanp (dso/Lo)067]
Accretion : Ho/L > C2[tan -(dW/L)0 67]
Erosion : tanp(Ho/Lo) > 0.5w,/gT
Accretion : tanp(HJ/L) > 0.5w./gT
Erosion : Hb/wT > 6
Accretion : Ht/w,T < 1
Erosion : Ho/L > Cs[(lew/gT)15]
Accretion : Ho/Lo < Ca[(nw/gT)15]
Erosion : Ho/Lo > C4[(Ho/wT)3]
Accretion : Ho/Lo < C4[(Ho/wT)]
Erosion: Ho/Lo > C5{[wJ(gHo)06}
Accretion : Ho/Lo < Cs{[w/(gHo)6}
Erosion : Ho/wT >C6[w2/gHo]
Accretion : H/w,T > Cs[w,2/gHo]
Erosion; gHo0/w,3T >-10,000
Accretion : gHo2/w 3T < -10,000
Erosion : gHb2/wsT >~22,400
Accretion : gHb2/wT < -22,400


Graphical Method


* H. = deepwater wave height, Lo = deep water wave length, do = median sediment size, S = sediment specific gravity, w =
sediment fall velocity, g = gravity, T = wave period, tano = beach slope, Hb = breaking wave height, U = wave orbital velocity
under a trough, u, = critical velocity required to initiate sediment motion according to Hallermeler (1980), ;b = surf similarity
parameter, Ti, = water surface displacement at the wave trough, Ub = uniform seaward directed return flow, ub = near bed
wave orbital velocity at breaking.

"Ci-e refer to empirical constants that vary depending upon scale effects in the data, i.e. small-large scale or lab-field.

Adapted from:
Larson, M. and Kraus, N.C., 1989. SBEACH: Numerical Model to Simulate Storm-Induced Beach Change. Technical
Report No. 89-9, Coastal Engineering Research Center, U. S. Army Corps of Engineers, Vicksburg, MS.


Criteria**








The surf zone Froude number was used in combination with the deepwater wave

steepness by both Kraus et al. (1991) and Dalrymple (1992) to distinguish between

erosional and accretional conditions. As formulated, the surf zone Froude number is a

ratio of competing forces, where Hb represents an upward suspending force, while w, and

g are related to particle settling. Although the Froude number contains measures of both

the wave and sediment properties, it is a potentially less accurate discriminator than the

fall velocity parameter since it does not include the wave period, and thus the wave

steepness, in its formulation. Kraus et al. presented two derivations where the Froude

number was shown to be related to both the wave energy dissipation in the surfzone, and

the power per unit volume expended by the waves via the bottom shear stress on

suspending the sediment. The relationship between wave energy dissipation and the

surfzone Froude number indicates that the magnitude of the shoreline response might be

expected to vary with the magnitude of Fr(t). A rate parameter based upon an inverse

Froude number, IFr(t), is also considered here in order to remain consistent with the

expectation that larger waves will result in an increased erosion rate.

The profile parameter derived by Dalrymple (1992) is essentially a rearrangement

of the empirical relationship between H/L, and (tcws/gT)3a and Ho/L and (Ho/wT)3

presented in Kraus and Larson (1988) and Larson and Kraus (1989). By taking the ratio

of each set of terms and canceling common factors, Dalrymple showed that a single

parameter, P(t), was extremely effective at separating the erosional and accretional events

described by Larson and Kraus (1989). Furthermore, he illustrated that the resulting

parameter was composed of a combination of two of the non-dimensional functions

discussed earlier, namely P(t) = IFr(t)2Q(t). By extension of the results presented in








Kraus et al. (1991), the magnitude of P(t) is also potentially related to the rate of

shoreline change.

Although first introduced by Irabarren and Nogales (1949), Battjes (1974) is

generally credited with illustrating the ability of a single parameter, C(t), to describe a

variety of surfzone characteristics. The surf similarity parameter, ,(t), has been related

to breaker type, breaking index, run-up, reflection coefficient, and beach type, all of

which have the potential to strongly influence the shoreline change rate. The inverted

form of the surf similarity parameter used here closely resembles the surf zone

interference index used by Wang and Yang (1980), who interpret the parameter as the

ratio of the natural swash period, to the period of the incoming waves. Using this

interpretation, larger values correspond to an increasing degree of interference from

successive waves, which manifests itself as a stronger return flow in the main water

column resulting in enhanced offshore transport. Compared to the other

parameterizations, the one glaring weakness of the surf similarity parameter is that it

requires knowledge of the beach slope a priori. This is problematic in that even if the

initial profile shape is known, there is no consensus as to which slope (mean? foreshore?

active profile?) to use. Here, an average nearshore beach slope was used, where both the

surf zone and subaerial beach up to and including the berm were considered.

A total of eight different rate parameters were evaluated, one where ka was

assumed to be a locally determined constant, two where ka(t) was assumed to scale with

the wave height alone, and five where both sediment and wave properties were used to

parameterize ka(t). The possibility that the same parameterization may not apply for both

erosion and accretion has been addressed by considering separate parameterizations for








ka(t) and ke(t) resulting in a total of 64 (8x8) possible rate parameter combinations. The

quantities w, and tan 0 involved in some of the parameterizations have been considered

constant, since information regarding the temporal variability of these properties is

sparse. The fall velocity, ws, used in each of the proposed parameterizations is that

calculated from the median sediment size using the Hallermeier (1981) relation, while the

beach slope, tan 0, is typically determined based upon a visual analysis of several

nearshore profiles.

3.4 Solution Technique

3.4.1 Numerical Scheme

Equation 3.1 can be discretized using a semi-implicit, finite difference scheme

according to

kcAf/ t+i
y7 l 2 (3.11)
1+ ka~t
2

where n is a time index. The unconditionally stable Crank-Nicholson scheme used

provides order two accuracy along with computational efficiency, while the oscillatory

nature of the forcing function yq(t) limits the buildup of numerical error, as errors tend to

cancel rather than perpetually increase. The maximum response or equilibrium shoreline

position, yq(t), is defined as that which would be attained if the forcing conditions were

held constant indefinitely, and may be calculated from either observed or simulated data.

In reality the equilibrium shoreline is a dynamic quantity, changing significantly with

time scales on the order of hours; however over a single time step the forcing is assumed

to remain constant. As with the analytical solution, the actual shoreline response will be

lagged and damped with respect to the equilibrium shoreline. The particular time step








used in model simulations varies with the temporal density of the input data, but is

generally on the order of several hours. In all cases, the resolution of the forcing data is

sufficient to capture the shortest (storm related) time scales intended to be reproduced by

the model.

3.4.2 Forcing Data

The model is forced by a combination of increased water levels due to tides, storm

surge, and wave induced setup. Water levels used as input to the model have been

obtained from tide gauges located near the sites of interest. In some cases, local tide

information was unavailable or inadequate during the period of analysis and the nearest

tide gauge with a complete record was used as a surrogate for the local water levels.

Where necessary, tide factors (both in time and space) based upon a comparison of the

local and surrogate data were applied in an effort to more closely match the local

conditions. A visual comparison of regions of overlap in the records indicates that the

application of tide factors improves the agreement between the local and surrogate tide

records.

Wave data were obtained from a combination of buoy measurements and statistical

Wave Information Study (WIS) hindcasts made by the U.S. Army Corps of Engineers.

Wherever possible an attempt was made to use buoy data; however in many cases a

combination of data sources was needed to help eliminate significant gaps in the wave

record. In these cases the secondary source was related to the primary source (both Hso

and Tp) through a linear regression analysis performed on overlapping sections of the

record. Directional information was added to non-directional wave data sets by assuming

all waves approached from the median deep water wave direction reported in the WIS

statistical summaries. The median angle was adopted after preliminary results using








wind direction as a proxy for wave direction proved unsatisfactory. These preliminary

results agreed with observations made by Masselink and Pattiaratchi (2001) in Western

Australia, where the wind and wave direction tended to become decoupled during the

falling leg of a storm. Once the offshore wave conditions were determined, linear wave

theory was used to convert the offshore conditions to breaking values for input to the

shoreline change model.

3.43 Shoreline Data

The individual shoreline data sets used to calibrate and evaluate the model are

discussed in detail in Chapter 4; however some general information is provided here. An

attempt was made to identify and utilize only data that exhibited characteristics likely to

be modeled well by a purely cross-shore model. Cross-shore processes tend to be most

important on long, straight, natural coastlines, and wherever possible adjacent shorelines

have been compared with one another to assess the degree of longshore uniformity.

Longshore averaged shorelines were used for model comparisons to help minimize the

influence of small-scale spatial irregularities that the model is not designed to reproduce

and which may have an impact on the perceived accuracy of simulations. Persistent

long-term shoreline trends were assumed to be related to gradients in the longshore

sediment transport rate and were removed prior to applying the model. While a portion

of these removed trends may in fact be the result of cross-shore processes (long term

increases in wave energy, sea level rise), it has been assumed that they play a subservient

role in comparison to the aforementioned longshore processes. The temporal resolution

of the shoreline data sets varies, which allows the skill of the model to be evaluated over

a range of time scales from daily to multi-decadal.
























Yes


An Initial range, R, Is selected for
each parameter


Iterations are performed using a user
specified step (e.g. R/20)


The value of the cost function, J is
evaluated at each Iteration and
compared to Jm


Does Jn lie on an extreme of the
range Ra?

No

Select k, k, and Ay used to
calculate J.in


Figure 3-3. Schematic of model calibration routine.

3.4.4 Model Calibration

The completely specified model contains three empirical coefficients, ka, ke, and

Ayo, which are evaluated based upon a comparison between model hindcasts and

historical data. This is achieved by minimizing an objective or cost function, J,


J (k.,ke,,y) = E(yo,(W- y,,p(ka, k,Ay., t))


(3.12)


where yob(t) and yp(ka,ke,Ayo,t) refer to the observed and predicted shorelines

respectively. Several numerical procedures were considered to help identify the most

appropriate values of ka, ke, and Ayo, where in the end, the simple numerical routine

illustrated in Figure 3-3 was used to locate the minimum of J. In general, the error

minimization procedure gives satisfactory results in terms of both computational time, as








well as accuracy, with the infrequent exception of a few cases where local minima in the

cost function are misidentified as the global minimum. This occurrence is rare, and while

other numerical routines, specifically simulated annealing (Bohachevsky et al., 1986),

were considered to attempt to correct this deficiency, none were satisfactory due to the

poorly defined constraints on ka and ke. At present, the method illustrated in Figure 3-3 is

deemed acceptable, as the misidentification of the global cost function minimum is

extremely uncommon.

3.4.5 Model Evaluation

Unfortunately, model evaluation remains as much an art as a scientific technique.

Despite recent calls for the development of a set of standardized, non-subjective model

evaluation criteria, this is simply not feasible. It is impossible to evaluate a model in a

completely objective sense and effectively consider all of the factors which have

contributed to its success or failure. While objective measures of model performance are

required to help quantify model skill, a number of factors including an appreciation of the

model's objectives, an analysis of the quality of data used with the model, and a

subjective interpretation of the quantitative measures are all required to accurately judge

a model. Although objective measures may be useful in identifying problems, ultimately

it is a subjective analysis of the objective criteria that identifies what the problem is and

how to fix it. Three criteria, two objective and one subjective, have been used to help

evaluate the new shoreline change model. Various quantitative measures including the

normalized mean square error (NMSE), and several related criteria were used to

objectively measure the prediction skill of the model. Since none of these are capable of

evaluating the ability of the model to discriminate between erosional, accretional, and

stable conditions, a separate objective categorical assessment procedure (CAP) is used for








this purpose. Finally, a subjective measure of model performance, the Model

Performance Index (MPI), was used to summarize all of the pertinent information,

including both objective skill measures, in order to provide a single composite measure of

model performance.

Three of the more popular parameters used to evaluate model accuracy are the

relative mean absolute error (RMAE), the normalized mean square error (NMSE), and

the Brier Skills Score (BSS). The RMAE is defined as


RMAE= 0, O < RMAE< w (3.13)
t

where means is the measurement error associated with yob. The RMAE has been used

extensively by the European community (Sutherland et al., 2004, van Rijn et al., 2003)

and is favored over the NMSE in part because it is less sensitive to outliers, as the

difference term in the numerator is not squared as it is in the NMSE. One of the primary

disadvantages of the RMAE however, is that the modulus appearing in the numerator

makes the statistic non-analytic and thus more difficult to work with than the NMSE.

Plant et al. (2004) advocate the use of a skill measure based loosely upon the

NMSE because it allows the computation of confidence limits. They define skill as

[(Y yp2] 2
C w6
mW eas
Skill = 1 -0o > Skill > 1 (3.14)


t








where w is a weighting function, and yobs,t=o is the initial observation made at time t = 0.

Although potentially appropriate for profile data, the skill as defined in Equation 3.14 is

inadequate for shoreline studies, as the skill becomes a strong function of yobs,t= due to

the significant shoreline changes that are routinely observed between successive surveys.

One of the primary advantages of using this skill statistic is the capability of quantifying

its significance; however this advantage is negated in shoreline studies, as even the most

extensive shoreline data sets are too short (not enough degrees of freedom) for this to

become meaningful.

Sutherland and Soulsby (2003) and Sutherland et al. (2004) advocate the use of a

similar skill measure for the evaluation of morphodynamic models. The Brier Skills

Score (BSS) is defined as

:(Yp, y.)2
BSS =1- ', -oo< BSS <1 (3.15)
Z (Yb Yob)2
t

where yb refers to a baseline condition. The BSS is slightly preferable to the previous

measure of skill in that the baseline condition is not specified a priori. In fact, if

detrended shoreline data are used, and yb is specified as the mean of yob, the BSS reduces

to the complement of the traditional NMSE,

E(Y,r- Yb)2
NMSE = Y -- =1-BSS, 0 < NMSE < (3.16)


Careful consideration of the aforementioned measures of model skill reveals that each has

its distinct strengths and weaknesses. Because of its relative simplicity and compatibility

with the often utilized BSS, the NMSE was selected as the most appropriate tool for

evaluating the new model. The NMSE has a direct physical interpretation as the ratio of








the error variance to the measured shoreline variance, and unlike the RMAE it can be

evaluated analytically. A perfect model in which the predictions exactly match the

observations is characterized by a NMSE of zero. Although unbounded at its upper limit,

errors on the order of one indicate model predictions with mean square deviations from

the measured data approximately equal to the variance of the data. A subjective rating of

the objective NMSE criterion is given in Table 3-3 In general, the NMSE is an extremely

effective measure of model performance; however, since the difference term in the

numerator is squared, it has the unfortunate property of being oversensitive to large

deviations. The consequences of this are discussed in more detail in relation to model

predictions in Chapters 5 and 6, along with several techniques that were used to attempt

to overcome this shortcoming.

None of the aforementioned measures of model skill are capable of evaluating the

ability of the model to predict the correct type of shoreline change, i.e. erosion or

accretion. This is illustrated clearly by considering two separate cases. In the first case,

the correct type of change is predicted, but the magnitude is severely over predicted. In

the second case, the wrong type is predicted, but the difference between yob and yp, is

small. All of the measures discussed previously will assign less skill to the model that

over predicts the magnitude of shoreline change even though the direction of change was

predicted correctly. In order to assess the ability of the model to accurately distinguish

between erosion and accretion, a separate categorical assessment technique was applied.

Predictions and measurements were divided into three general categories: accretion,

erosion, and no change (or stable), where a shoreline is defined as stable if the change in

shoreline position between two successive surveys is less than five percent of the









maximum range over the entire data set. This relative definition provides a useful sliding

scale whereby energetic coastlines can undergo more significant changes and still be

considered stable. A score from 0-1 is assigned to each possible combination of

conditions as indicated in Table 3-2, where 1 represents a match between the prediction

and observation, and 0 represents a complete mismatch. Since stable events are hardest

to predict, they are assigned values reflecting the seriousness of the mismatch. For

example miscasting an erosional period as stable has more serious potential consequences

than miscasting an accretional period as stable hence the lower score (0.3 vs. 0.6). The

categorical assessment procedure score, or CAP, is simply the average of all the

individual scores, where higher values indicate more accurate predictions as reflected in

Table 3-3.

Table 3-2. Categorical assessment procedure score matrix developed for this study.
Pr"- ted Erosion Stable Accretion
Measured ___________
Erosion 1.0 0.3 0.0

Stable 0.4 1.0 0.5

Accretion 0.0 0.6 1.0


Table 3-3. Subjective rating system based upon model performance statistics.
Rating Range of Values
NMSE CAP MPI

Excellent <0.3 >0.8 5
Good 0.3-0.6 0.6-0.8 4
Reasonable 0.6-0.8 0.4-0.6 3
Poor 0.8-1.0 0.2-0.4 2
Bad >1.0 <0.2 1

The Model Performance Index (MPI) provides a holistic evaluation of model skill

by incorporating the aforementioned objective measures of model performance with a

subjective analysis of some of the more subtle aspects. The MPI takes into account many








factors including: the quality and completeness of the input data, the characteristics of the

modeled shoreline, the character of the NMSE (is it unduly influenced by a single data

point), and the CAP. Higher expectations are placed on model performance when it is

applied at sites with better data. For example, the availability of local tide data and

measured directional wave information is expected to significantly improve the accuracy

of predictions. The subjective analysis allows for the consideration of the character of

the NMSE. Is it providing an accurate measure of model performance, or is a

disproportionate amount of error resulting from a single outlier? These are important

questions relating to the skill of the model that cannot be answered using purely objective

methods. The MPI classification system ranges from 1-5 and is presented in Table 3-3.













CHAPTER 4
FIELD DATA AND SITE SUITABILITY

In order to accurately evaluate the model, data were collected from numerous

sources encompassing a variety of geographical locations and typical beach conditions.

Broadly, the field sites may be separated into three groups, one each representing the East

and West coast of the United States, and a third representing Australia. Since the model

only simulates shoreline changes due to cross-shore processes an attempt was made to

select data from long uninterrupted natural coastlines. The format of the available

shoreline information ranges from beach width measurements obtained from aerial

photography and video analysis, to several sets of complete profiles, some surveyed to

depths of nearly twenty meters. The cross-shore model is not intended to reproduce

small-scale features with alongshore wavelengths less than several hundred meters;

therefore generally alongshore averaged shorelines have been used. The varying

temporal resolution of the available shoreline measurements is such that it allows the

performance of the model to be evaluated over a number of time scales ranging from

days to decades. Wherever possible actual wave and tide data were used; however

statistical hindcasts were substituted for physical measurements where required.

Summaries of the available data and relevant site characteristics are provided in Tables

4-1 and 4-2. With so many good, high-quality data sets to choose from, it is essential to

develop tools capable of identifying potentially inappropriate sites. The remainder of this

chapter is devoted to providing a brief description of the collected data as well as






47


describing the techniques which were used to help select the most suitable locations for

the evaluation of the proposed cross-shore model.

Table 4-1. Summary of data sources.


Shoreline
Latitude Longitude Orientation
Orientation


Wave Data Nearest Tide Shoreline
Source Gauge ID Data


East Coast Sites
East Hampton, NY 40.93N

Harvey Cedars, NJ 39.70N

Island Beach, NJ 39.83N

Wildwood, NJ 38.98N

Duck, NC 36.18N

St. Augustine, FL 29.95N

Crescent Beach, FL 29.75N

Daytona Beach, FL 29.17N

New Smyma Beach, FL 28.88N

West Coast Sites
North Beach, WA 47.20N

Grayland Plains, WA 46.80N

Long Beach, WA 46.50N

Clatsop Plains, OR 46.100N

Torrey Pines, CA 32.87N

Australian Sites

Brighton Beach, WA 31.92S

Leighton Beach, WA 32.08S

Gold Coast, QLD 27.97S


72.20MW

74.1 3W

74.100W

74.80W

75.75OW

81.33W

81.25W

81.05W

80.93W



124.05W

124.05W

124.05W

124.05W

117.26W



115.750E

115.75E

153.42E


1510

1270

98

1420

680

750

75"

600

600



2700

2700

2700

270"

2700



2700

2700

900


Buoy-44025 8531680
WISII-79 (Sandy Hook)
Buoy-44025 8531680
WISII-69 (Sandy Hook)
Buoy-44025 8531680
WISII-69 (Sandy Hook)
Buoy-44009 8534720
WISII-66 (Atlantic City)


FRF Gauge

WISII-23

WISII-23

WISII-22

WISII-22


Buoy-46029
CDIP-036
Buoy-46029
CDIP-036
Buoy-46029
CDIP-036
Buoy-46029
CDIP-036

WISSC-002



Buoy-38

Buoy-38

Buoy-23
Buoy-13


8651370
(Duck)
8720220
(Mayport)
8720220
(Mayport)
8720220
(Mayport)
8720220
(Mayport)

9440910
(Willapa Bay)
9440910
(Willapa Bay)
9440910
(Willapa Bay)
9440910
(Willapa Bay)
9410660
(Los Angeles)


Freemantle

Freemantle

100035
(Gold Coast)


Profiles

Profiles

Profiles

Profiles

Profiles

Profiles &
Aerials
Profiles &
Aerials
Profiles &
Aerials
Profiles &
Aerials


Profiles

Profiles

Profiles

Profiles

Profiles



Profiles

Profiles

Video


" Direction is the approximate azimuth of the outward shoreline normal.
b Data from the nearby Cape May tide gauge, ID# 8536110, was also used to fill in missing data.









Table 4-2. Relevant site characteristics
Approximate Median Sediment
site Tal Range a Significant Grain Size Fal Vc\ Nearhore Bermnn
Site Tidal Range Hb (m) Beach Slope Height (m)
(m) (mm) (cm/s)

East Coast Sites
East Hampton, NY 1.14 1.24 0.375 4.66 2.45
Harvey Cedars, NJ 1.38 1.24 0.305 3.72 2.60
Island Beach, NJ 1.38 1.21 0.370 4.57 1:30 2.30
Wildwood, NJ 1.50 1.30 0.200 2.33 1:65 2.30
Duck, NC 1.12 1.13 0.200b 2.33 2.50
St. Augustine, FL 1.57 1.27 0.149 1.64 1:25 1.85
Crescent Beach, FL 1.40 1.27 0.139 1.56 1:40 1.85
Daytona Beach, FL 1.35 1.29 0.153 1.74 1:55 1.85
New Smyrna Beach, FL 1.25 1.29 0.138 1.55 1:55 1.85

West Coast Sites
North Beach, WA 2.72 2.46 0.135 1.37 1:70 3.00
Grayland Plains, WA 2.72 2.46 0.178 2.05 1:45 3.00
Long Beach, WA 2.92 2.46 0.193 224 1:55 3.00
Clatsop Plains, OR 2.92 2.46 0.160 1.82 1:45 3.00
Torrey Pines, CA 1.62 1.76 0.194 2.27 1:50 2.40

Australian Sites
Brighton Beach, WA 0.6c 1.10 0.574 7.92 1:15 2.00
Leighton Beach, WA 0.60 1.10 0.375 5.00 1:15 2.00
Gold Coast, QLD 1.50 1.25 0.290 3.51 1:20 2.20

* Defined as MHHW-MLLW.
b Calculated from the median grain size using the Hallermeier (1981) relation.
Mean spring tide range.

4.1 East Coast Data

In general terms, much of the Atlantic and Gulf coast of the United States is

characterized by low-lying barrier island topography. Most of the coastline experiences

low-moderate wave energy, with larger waves occurring most frequently during the

winter months from November to March, and in association with isolated tropical

weather systems. In all, shoreline data from nine east coast sites spread amongst four

different states was considered. Figure 4-1 shows the location of each site as well as the

approximate location of nearby wave and tide gauges that were used in the analysis.











DA185WD BUhC.
















XMW SNYA SIHI v w WS R SATIC


Figure 4-1. Location of data sets from the East Coast of the United States.

The northernmost site on the Atlantic Coast is East Hampton, NY. Located along

the southern shore of Long Island, the coastline has been monitored extensively since at

least 1979 by a variety of state agencies, with the current monitoring being performed by

the Marine Sciences Research Center of Stony Brook University. Profiles collected on

inconsistent intervals over the past 25 years have indicated that the East Hampton

shoreline is relatively stable. The dominant mode of variability along this stretch of

coastline is a strong annual fluctuation, which corresponds to a distinct seasonal pattern

in the wave climate. The mean annual significant wave height recorded by NOAA buoy

44025 in 40 m of water is 1.2 m, although waves as large as 9.2 m have been recorded

during the winter months. The tidal range at the site is approximately 1.1 m and contains

a dominant semi-diurnal component.








The Richard J. Stockton College of New Jersey and the New Jersey Department of

Environmental Protection maintain an extensive set of beach profile data for the State of

New Jersey. Wading depth profiles were performed at over 100 sites along the Atlantic

and Delaware Bay coastlines annually between 1986 and 1994, with the frequency

increasing to bi-annually beginning in 1995. Shoreline data from three sites, Island

Beach State Park, Harvey Cedars, and Wildwood were used in the current study.

Conditions along the coast vary from location to location; however typical offshore wave

heights as measured by NOAA buoy 44025 in 40 m of water range from 0.9-1.6 m, with

a reported mean annual significant wave height of 1.2 m. Tides along the coast are

mainly semi-diurnal, with a range on the order of 1.4 m. Although occasionally impacted

by hurricanes and tropical storms, the most significant threats to the New Jersey shoreline

are large waves and storm surges produced by strong winter storms and northeasters.

The Duck, NC shoreline data were extracted from profiles collected by U. S. Army

Corps of Engineers Field Research Facility (FRF) staff, and form only a small subset of

the available data along one of the most intensively studied coastlines in the world.

Detailed profiles have been collected monthly (bi-weekly at four selected sites), along the

one-kilometer stretch of coastline since 1981. A variety of instruments simultaneously

collect additional data ranging from wave heights and tide information to air temperature

and wind direction. Despite a recognized seasonality in the wave climate in both height

and direction, the dominant shoreline fluctuations at Duck occur with periods greater than

one year (Plant and Holman, 1996; Miller and Dean, 2003). The mean annual significant

wave height recorded at NOAA buoy 44014 in 47.5 m of water was 1.4 m, while the

mean tidal range recorded by a gauge mounted on the FRF pier is 1.12 m.








Despite the quantity and quality of the available data at Duck, the presence of the

research pier significantly alters the nearshore environment, making it a potentially

inappropriate site for the evaluation of a cross-shore shoreline model. Bathymetric

changes related to the disruption of the natural longshore sediment transport by the pier

have been found to be particularly pronounced in the vicinity of the shoreline (Plant et

al., 1999; Miller and Dean, 2003). Miller and Dean (2003, 2004) discussed a method for

attempting to isolate the shoreline changes due to cross-shore processes, by using a

simple conservation of volume argument. According to the procedure, the change in

sediment volume within a profile between two successive surveys, AV(t), is presumed to

be the result of longshore processes. Under the assumptions that the profile translates

without changing form and that the volume change is distributed evenly over the vertical

dimension of the active profile, (h.+B), the shoreline change due to longshore processes,

or the shoreline adjustment, Ay(t), can be obtained,


Ay( t) = (4.1)
( h + B)

Since the model only considers cross-shore forcing, shorelines that have been adjusted by

Ay(t) may potentially be more appropriate for evaluation purposes. Figure 4-2 illustrates

the qualitative improvement in the consistency of shoreline changes between two profiles

located on opposite sides of the Duck pier, suggesting that longshore effects are at least

partially responsible for some of the initial non-uniform shoreline behavior. The same

procedure was applied to the Torrey Pines profile data discussed in the next section;

however the absence of any significant disturbances on the relatively straight, natural

coastline resulted in only small shoreline adjustments.









Interpolated Raw Shorelines, Duck, NC
60
Profile 2
4E 0I Profile 188
40


S 20 I .

-20
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002
Year
Interpolated Adjusted Shorelines, Duck, NC
60
Profile 62
E -- Profile188


t : : iI i "

-20
-20C- I I I I I I I
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002
Year
Figure 4-2. Improvement in the consistency of the Duck shoreline data after adjusting for
the volume change between subsequent profiles.

The Florida shoreline data consist of a combination of beach profile data collected

by the Florida Department of Environmental Protection Bureau of Beaches and Coastal

Systems, and beach width measurements obtained through the analysis of aerial

photographs from a variety of sources (Miller, 2001). Based on the availability of WIS

hindcasts in the region and a lack of reliable buoy data, the duration of the analysis in this

region was restricted to the period between 1956 and 1995 even though additional

shoreline data exist. Shoreline measurements during this forty-year interval were highly

irregular due to the combination of data sources used but generally increased in

frequency with time. All four Florida sites experience similar wave conditions

characterized by a mean annual significant wave height on the order of 1.2 m as

measured by NOAA Buoy 41009 in 42 m of water. The tidal range increases slightly

from south to north along the coastline, with a mean value of 1.35 m reported at Daytona








Beach, and a slightly larger value of 1.57 m reported for St. Augustine Beach. Typical

threats to this coastline include the catastrophic effects of both hurricanes and severe

winter storms.

4.2 West Coast Data

In contrast with much of the Atlantic coast, compartmentalized beaches interrupted

by numerous rocky headlands characterize a majority of the Pacific coastline of the

United States. The wave climate ranges from moderate to severe, with the largest waves

typically occurring in the Pacific Northwest. Global weather systems have a considerable

impact along the Pacific coast, where El Nifio/La Nifia weather patterns significantly alter

the typical conditions. The West Coast data set is composed of a total of five sites, one

located in Southern California, with the rest concentrated around the Washington-Oregon

border. The approximate location of each site, along with nearby wave and water level

gauges is indicated in Figure 4-3


F WeS oasTZOn
n BlWa 3nT
T T=D Geat
















Figure 4-3. Location of available shoreline data along the west coast of the United States.








An excellent set of shoreline data for the Pacific Northwest has been collected as a

part of the Washington State Coastal Erosion Study, which is being conducted jointly by

the United States Geological Survey and the Washington Department of Ecology. Profile

data have been collected quarterly since 1997 (bi-annually prior to 1999) at more than

fifty sites along a 160 km stretch of coast between Tillamook Head, Oregon and Point

Grenville, Washington, the approximate boundaries of the Columbia River Littoral Cell

(CRLC). The CRLC contains four sub-regions, separated by three inlets, known as the

North Beach, Grayland Plains, Long Beach, and Clatsop Plains subcells. Each subcell

consists of long (>30 km) straight sections of coastline bound by natural headlands and

navigational entrances. Shorelines in the CRLC are subjected to a strongly seasonal,

high-energy wave climate where offshore significant wave heights measured in 228 m of

water average 2.3 m and increase to over 3.0 m during the winter months. Local tides are

semi-diurnal with an average range of approximately 2.8 m. In addition to storm and

seasonal related changes, the shoreline also responds to El Nifio/La Nifia weather

patterns, and long-term events including geologically frequent subduction zone

earthquakes.

The two years of profile data collected at Torrey Pines, California, by Nordstrom

and Inman (1975), represents one of the first complete sets of nearshore bathymetric

measurements ever obtained. Surveys were conducted to depths of nearly 18 m on a

monthly basis along three shore-perpendicular transects between June 1972 and April

1974. Nearshore wave conditions along this section of the California coast vary

significantly due to the extremely irregular offshore bathymetry, including numerous

submarine canyons. The mean annual significant wave height reported by Scripps Buoy








101 in 549 m of water over the period 2001-2003 was approximately 1.1 m. The wave

climate exhibits a distinct seasonal periodicity similar to the Pacific Northwest; however

the range of variability (0.8-1.3 m) is reduced considerably. The local tide is semi-

diurnal with an average range of 1.6 m, and like most Pacific coast locations, has a large

diurnal inequality. Shoreline changes in Southern California occur over a variety of time

scales; however the short duration of the data set limits the current analysis to changes

with periods of two years or less.






















Figure 4-4. Location of Australian shoreline data sets.

4.3 Australian Data

A total of three data sets were obtained from Australia, with two sites located near

Perth in Western Australia, and the third located near Brisbane along the east coast. Each

of the three Australian sites provides a unique test for the model. The two west coast

sites are composed of the coarsest sand and experience the smallest tides of any site








studied. The unique aspect of the east coast site is related to the daily shoreline sampling

interval, which allows the model to be evaluated over a wider range of timescales. The

three Australian sites are depicted in Figure 4-4, along with the location of nearby wave

and tide gauges.

Masselink and Pattiaratchi (2001) collected shoreline data from a number of

beaches in the Perth region of Western Australia. Although the focus of their study was

on beach changes dominated by the seasonal reversal in longshore sediment transport,

data were collected at several long, uninterrupted, natural beaches where the shoreline

responded primarily to changes in the incident wave energy. The available shoreline data

consisted of a combination of beach width measurements and nearshore profiles collected

over weekly to bi-weekly intervals between November 1995 and November 1998. Perth

beaches are sheltered by a series of submerged shore parallel sand ridges that

significantly reduce the incident wave energy reaching the coast. The average mean

significant wave height and peak period measured by a buoy inside the outermost sand

banks in 17 m of water were 0.9 m and 10.1 s, respectively. The sheltering effect of the

sand banks can be seen when the inshore wave height is compared to that observed at a

buoy just offshore in 48 m of water, where H, averages 2.2 m. Perth experiences a

primarily diurnal tide with a maximum spring tide range of only 0.6 m. Unique factors

influencing beach changes in the region include passing weather systems that can

overwhelm the local tide and extremely energetic alongshore seabreezes that can average

up to 8 m/s during the summer (Masselink and Pattiaratchi, 2000).

Shoreline position data from Narrowneck located at the northern end of the famed

Gold Coast have been collected since mid-1999 as part of the post-construction








monitoring program of the Northern Gold Coast Beach Protection Strategy. A beach

nourishment was carried out between February 1999 and June 2000 in conjunction with

the construction of an artificial surfing reef completed in December of 2000, just to the

north of the site. An ARGUS video monitoring system (Holman et al., 1993) was set up

to monitor the resulting shoreline changes with both a high temporal and spatial

resolution. Daily shoreline measurements along the southernmost 1500 m of the site,

where the effects of the nourishment and reef construction are least pronounced, have

been extracted from video images using a technique described in Turner and Leyden

(2000) and Turner (2003). The wave climate at Narrowneck as determined from a

waverider buoy located offshore of the site in 16 m of water is characterized by a mean

significant wave height of 1.1 m with an associated peak period of 9.2 s. Tides at the site

are mainly semidiurnal with a maximum spring tide range of approximately 1.5 m.

Beach changes at the site have typically been described as "event driven;" however

recent work by Turner (2004) has identified a significant seasonal component.

4.4 Evaluation Tools

Only recently has the selection of appropriate data for the evaluation of new long

term models become an issue, as multiple high-quality data sets simply did not exist.

With the increased availability of good quality data, comes a new responsibility and

challenge to choose the data sets most appropriate for the intended application. In order

to do this, a variety of statistical techniques and data analysis tools can be applied to help

reveal some of the relevant characteristics of the data sets. The nature of the proposed

model is such that it is expected to reproduce only those changes related to cross-shore

processes; therefore longshore uniformity is an extremely important characteristic. By

utilizing simple statistical techniques to analyze the shoreline data at several adjacent








locations, the impact of the unpredictable (for this model), non-uniform shoreline

movements can be quantified and compared to the more uniform, predictable, large-scale

changes. An appropriate data set will be one in which the ratio of the uniform behavior

to the non-uniform behavior is high.

4.4.1 Time Domain Based Statistics

Traditional time domain based statistics provide valuable insight into the relevant

characteristics of a data set with minimal computational effort. The linear association

between two data sets x and y is given by the correlation coefficient, rxy,



r = n -1 r,5 1 (4.2)
SxS

where overbars denote mean quantities, and s, and Sy are the standard deviations. A

correlation coefficient of 1 indicates perfect correlation (a negative indicates the

variables change inversely to one another), while 0 indicates the lack of any linear

relationship between x and y. If the spacing between values of x and y is constant, a

lagged correlation coefficient can be calculated which may provide useful information as

to the phase relationship of x and y (i.e. it is possible for x to either lag or lead y). If x

and y are beach width measurements taken at adjacent locations along a uniform beach, a

large positive correlation is expected.

Although the correlation coefficient is useful for comparing two variables with

each other, it is less useful for comparing large numbers of variables. For this purpose,

the longshore uniformity index provides a meaningful, non-dimensional measure of the

degree of shoreline homogeneity. The two-dimensional method applied here is a

simplification of a three-dimensional method used by Plant et al. (1999). The non-








dimensional longshore uniformity index, ILs, is simply the ratio of the longshore uniform

2 2
portion of the shoreline variance, shu2, to the total variance, sto, in the system. In the two

dimensional case, the longshore uniform portion of the variance is essentially the

temporal variance of the longshore averaged deviations from the time mean shoreline

location,

y'(x,t)= y(x,t)-y,(x) (4.3)


s = Yt'(t ,) (4.4)
nt j=i

where x and y represent the longshore and cross-shore coordinates respectively, and the

subscripts t and x refer to temporally and spatially averaged quantities. In Equation 4.3,

the shoreline data are separated into the time mean component, yt(x), and a time and

space dependent deviation from this mean, y'(x,t). The temporal variance of the

alongshore average of these deviations defines the uniform component of the total

shoreline variance. The longshore uniformity index, Ilsu, is then

S2
=I, = (4.5)


4.4.2 Frequency Domain Based Statistics

In some cases it is possible to look at the relationship between adjacent shoreline

fluctuations in the frequency domain. If shoreline'data are available over constant

intervals (or can be interpolated to constant intervals with out too much loss of accuracy)

spectral analysis can be used to examine relationships between the various frequency

components of the overall signal. In particular, the coherence (or squared coherence or

coherency), Coh, indicates the degree of linear correlation between the various frequency








components of two signals, while the phase, <(f), indicates the lag or lead. The

coherence and phase are given by

IG, (f f) 2
Coh(f) = (4.6)
G (f)G,(f)


(f tan-- (f (4.7)
C, ( f )

where Gx and Gyy are the auto-spectra of x and y respectively, and Gxy is the cross-

spectrum of x and y, which is made up of both a real, Cxy (coincident), and an imaginary,

Qxy (quadrature), part. In general, if x and y are two adjacent shoreline data sets a high

coherence would be expected, particularly in the low frequency domain. Unfortunately,

when dealing with relatively small data sets such as even the most comprehensive

bathymetric data, spectral confidence limits become difficult to apply and are not

presented here.

4.43 Method of Empirical Orthogonal Functions

Although technically a time domain based statistic, the method of empirical

orthogonal functions (EOFs) is discussed separately due to some unique characteristics.

Pearson (1901) and Hotelling (1933) originally developed the method in the early 1900's

as a means of extracting the dominant behavioral patterns from a set of data. Winant et

al. (1975), Vincent et al. (1976), and Dolan et al. (1977) were among the first to apply the

technique to geophysical data sets in the coastal environment. Although much of the

subsequent work with EOFs has centered around applications of the method to profile

data, it can also be used to analyze the longshore variations in a data set. Here they are

used to examine the longshore variability of data sets consisting of beach width

measurements. When applied in this manner, EOF's are able to extract the dominant








modes of variability, which may correspond to either longshore uniform behavior, which

can potentially be modeled with a cross-shore model, or non-homogenous behavior,

which cannot.

In simple terms, the EOF method exploits the properties of matrices to identify

patterns of standing oscillations within the data. These patterns allow individual modes

of variability to be analyzed separately, which can then used for a variety of purposes,

among them simplifying the representation of the original data. The first step in an EOF

analysis is to separate the spatial and temporal variability of the data by representing the

original data set, y(x,t), as a series of linear combinations of functions of time and space,


y(x,t)= c, (t)ek (x) (4.8)
k-I

where ek(x) are referred to as the spatial eigenfunctions, and Ck(t) are referred to as either

weighting functions or the temporal eigenfunctions. The summation is carried out from k

= 1 to n, where n is the lesser of nx or nt, the number of spatial and temporal samples

respectively. In some derivations, the temporal coefficient ck(t) is given as the product of

a unit amplitude function of time ck(t) and a normalizing factor ak given by jn-n,,

where Xk is the eigenvalue associated with the kth eigenfunction. The requirement,

Se, (x)e (x) = 8 (4.9)
x

where S. is the Kronecker delta, ensures that the eigenfunctions, ek(x), form a set of

statistically independent, or uncorrelated vectors, which are normalized to unity. So far,

the derivation has remained fairly general, and an infinite number of functions ek(x) may

be specified that satisfy the conditions of Equations 4.8 and 4.9. What separates the EOF

method from other series decomposition techniques such as Fourier analysis, is the fact






62

that the data are used to select the eigenfunctions rather than specifying them a priori.

The selection is made such that the eigenfunctions best-fit the data in a least squares

sense, with the first eigenfunction representing the bulk of the variability in the data set,

and each subsequent eigenfunction accounting for the majority of the remaining

variability. Mathematically, the Lagrange multiplier approach is used to formalize this

requirement, resulting in a solvable eigenvalue problem,

Aek(x)= ek(x) or AE=AE (4.10)

where E is a matrix containing the spatial eigenfunctions, ek(x), and A is a diagonal

matrix containing the eigenvalues, Xk. The matrix A represents some measure of the

spatial covariability of the original data set y(x,t). Winant et al. (1975) defined A as a

correlation or sum of squares and cross-products matrix, while Aubrey and Ross (1985)

utilized a demeaned version of y(x,t) and defined A as the covariance matrix. Either

method is correct as long as the results are interpreted in the context of the frame of

reference from which they were derived. Here A is taken as the correlation or sum of

squares and cross-products matrix,

A=- (YYT) [n,n.] (4.11)
nxn,

where the bracketed term, [n, nx], indicates the dimensions of A.

Two methods exist for determining the temporal eigenfunctions, Ck(t). The first

method is directly analogous to the technique set forth for calculating the spatial

eigenfunctions and involves solving the set of equations,

Bc (t)= (t) or BC=AC (4.12)


B= -- (YTY) [n,,nt (4.13)
nfxn


"r'








Where A provided a measure of the spatial covariability within the data set, B measures

the temporal covariabilty. Analogous to Equations 4.10 and 4.11, [nt, nd defines the

dimensions of B, and C contains the temporal eigenfunctions ck(t). In comparing

Equations 4.11 and 4.13, it should be obvious that in general, matrices A and B have

different dimensions, and thus the A's calculated from Equations 4.10 and 4.12 also must

have different dimensions. Although disconcerting at first glance, it can be proven that

only the first k values of X are non-trivial (non zero), and in fact that the first k values

from either equation will be identical. Alternatively, the temporal eigenfunctions may be

calculated directly as


ck t) y(x,, t)ek x) (4.14)
ak i=1

where ak (=J nn, ) is the normalizing factor mentioned previously.

Square matrices have many interesting properties, some of which can be exploited

to help explain the physical significance of the calculated quantities. Given the

definitions of A and B, the trace, or sum of the diagonal elements of both A and B is

simply the mean square value of the data. From Equations 4.10 and 4.12 it can be shown

that the sum of eigenvalues must equal the mean square value of the data, thus each

individual eigenvalue, Xk, can be thought of as representing the relative contribution of

mode k to the overall variability of the data set. The percent contribution of level k is

given by


S- x100 (4.15)
k








As mentioned previously, the first few modes will contain the bulk of the variability, and

the significance of each mode will decrease. In the present context, the usefulness of the

EOF technique is rooted in the longshore variability of the primary mode, ei(x). Extrema

in el(x) define regions of maximum variability, while nodes indicate regions of zero

variability. With rare exception, nodes separate eroding and accreting regions; and

therefore shoreline changes are said to be out of phase across nodes. The presence of

nodal points usually indicates the influence of longshore processes, as they provide the

mechanism for transferring sediment across the node from eroding to accreting regions.

Here, multiple nodal points are used to identify those data sets which are not likely to be

well represented by a cross-shore model. In cases where some modes are longshore

uniform and others are not, it is possible to use the EOF method as a means to filter out

the non-uniform behavior by reconstructing the data set according to

K
y,(x,t) = ack(t)ek(x) (4.16)
k=1

where k refers to the uniform modes, and yrK(X,t) refers to a reconstructed data set which

retains only the longshore uniform information.

4.5 Site Suitability

Although intuitively obvious, the fact that not every data set is appropriate for

every application is rarely discussed. In the past there has been an overabundance of

models calibrated and evaluated with whatever data was available, regardless of the

synergy between the data and the model. A good example is the fact that data from

Duck, has indiscriminately been used to validate many models regardless of whether the

influence of the pier has been accounted for. Because of the abundance of good, high-

quality data sets available here, inappropriate data can be eliminated using some of the






65

methods discussed in section 4.4. Table 4-3 summarizes the methods used at each site as

only certain techniques are applicable at each location.

Table 4-3. Data analysis techniques applied at each site.

Site Length Sampling Duration Interval Time Frequency EOF
(km) Locations Domain Domain


East Coast Sites
East Hampton, NY 1.50 3 1979-1997 Variable Yes No No
Harvey Cedars, NJ 9.00 3 1986-2002 Biannually Yes No No
Island Beach, NJ 4.00 3 1986-2002 Biannually Yes No No
Wildwood, NJ 3.50 2 1986-2002 Biannually Yes No No
Duck, NC 1.00 20 1980-2002 Monthly Yes Yes Yes
St. Augustine, FL 2.50 3 1955-1995 Variable Yes No No
Crescent Beach, FL 3.25 2 1955-1996 Variable Yes No No
Daytona Beach, FL 3.50 3 1955-1997 Variable Yes No No
New Smyrna Beach, FL 2.75 3 1955-1998 Variable Yes No No

West Coast Sites
North Beach, WA 41.00 12 1998-2002 Quarterly Yes No Yes
Grayland Plains, WA 17.00 8 1998-2003 Quarterly Yes No Yes
Long Beach, WA 38.00 16 1998-2004 Quarterly Yes No Yes
Clatsop Plains, OR 25.00 6 1998-2005 Quarterly Yes No Yes
Torrey Pines, CA 1.00 3 1972-1974 Monthly Yes Yes No

Australian Sites
Brighton Beach, WA NA 1 1995-1998 Weekly Yes No No
Leighton Beach, WA NA 1 1997-1998 Weekly Yes No No
Gold Coast, QLD 1.50 300 2000-2003 Daily Yes Yes Yes

In the time domain, the appropriateness of each data set was evaluated by the

combination of the correlation coefficient, rxy, and the longshore uniformity index, I,.

Large positive correlations are characteristic of the type of homogenous shoreline change

desired for this particular application. At sites with fewer than three sampling points the

correlation between each individual sampling location and each of the others was

calculated according to Equation 4.2. In Table 4-4, both the mean and maximum

correlation is reported, along with the 95% significance level as determined from a

standard t-test (Davis, 1986). In most cases, the calculated correlation coefficient is









larger than the 95% significance level, meaning the null hypothesis (that the correlations

may have occurred by chance) can be rejected. Bold, italic values denote cases where

insufficient evidence exists to reject the null hypothesis. Of the sites with less than three

sampling locations, the East Hampton, Harvey Cedars, and Wildwood data sets appear to

be non-uniform and hence inappropriate for the model.

Table 4-4. Summary of time domain analysis results.

Number of Maximum Average Significant
ite Correlations Correlation Correlation Correlation li
(95%)

East Coast Sites
East Hampton, NY 3 0.356 0.231 0.325 0.588
Harvey Cedars, NJ 3 0.211 0.072 0.404 0.325
Island Beach, NJ 3 0.677 0.564 0.433 0.608
Wildwood, NJ 2 0.238 0.238 0.413 0.629
Duck, NC 20 0.401 0.315 0.122 0.433&
Adjusted Data 20 0.305 0.222 0.122 0.355b
St. Augustine, FL 3 0.767 0.693 0.482 0.766
Crescent Beach, FL 2 0.709 0.709 0.532 0.928
Daytona Beach, FL 3 0.871 0.778 0.576 0.868
New Smyma Beach, FL 3 0.887 0.817 0.468 0.888

West Coast Sites
North Beach, WA 12 0.643 0.491 0.468 0.4160
Grayland Plains, WA 8 0.530 0.385 0.482 0.1540
Long Beach, WA 16 0.606 0.489 0.482 0.597c
Clatsop Plains, OR 6 0.623 0.497 0.482 0.4990
Torrey Pines, CA 3 0.869 0.775 0.288 0.827
Adjusted Data 3 0.777 0.715 0.288 0.834

Australian Sites
Brighton Beach, WAd 1 0.519 0.519 0.279 0.796
Leighton Beach, WAd 1 0.519 0.519 0.279 0.796
Gold Coast, OLD 300 0.888 0.867 0.081 0.877

a Considering subsections on either side of the pier: lw =0.821 north of the pier, 6u =0.704 south of the pier.
b Considering subsections on either side of the pier: lIu =0.586 north of the pier, lbu =0.597 south of the pier.
When only the data from the center of the site which was used to calculate the longshore averaged shorelines is
considered, I.u increases to at least 0.835.
d For the purposes of the time domain analysis shoreline data from the two Western Australian sites were compared.










Correlation Analysis Duck, NC (Adjusted)


. ., .. .


Mean r: =0.2634
Xy


FRF ler





Mean r = 0.412
... Correlation with yx.
Correlation with Yxgl.


1

0.5

. 0

-0.5

-1



1

0.8

Aj.6

0.4

0.2


1000


0.3049
0.2224
0.1221


0 200 400 600 800 1000
Longshore Distance (m)
Figure 4-5. Calculation of the mean correlation profile including rm. and ravg.

At sites with more than three sampling locations, the average and maximum

correlation coefficients were calculated in a slightly different manner. As with the

smaller data sets, Equation 4.2 was used to calculate the correlation between each

individual data set and all of the others. The result is a symmetrical nx x nx matrix of

correlation coefficients describing the covariability of the data. Each column of this

matrix was then averaged to obtain a mean correlation coefficient for each sampling

location. In Figure 4-5, an example is provided using the adjusted data set from Duck,

NC. The upper panel shows correlation profiles for two specific profiles (yx=183 and

y,=777), where the correlation of data set with itself is identically one. Similar profiles

exist at each shoreline measurement location. The average correlation coefficient can

then be calculated at each of the sites as was done for the two profiles in the upper panel


600 800


FRF 01W







'_ _5% Signlficanoealenl~


II


. =


' 'I


I


0 200 400








. . .








(rxy=0.263 and ry=0.241). In the lower panel, these average r.y values are plotted along

with lines representing the mean and maximum values, and the 5% statistical significance

level. The data reported in Table 4-4 are these average and maximum correlation

coefficients. From the results of the correlation analysis of the larger data sets, the

shorelines at both Grayland Plains, WA and Duck, NC behave non-uniformly and are

therefore considered inappropriate for evaluating the proposed model.

The longshore uniformity index, ILa, provides an additional useful measure of

shoreline homogeneity. Larger values of Isu indicate stretches of coastline where cross-

shore processes are most likely dominant and the entire shoreline tends to translate in

unison. Calculated values of the longshore uniformity index are presented in the last

column of Table 4-4, where a value of 0.6 is used to identify potentially inappropriate

data sets. Although the results suggest that all of the Washington and Oregon data are

unsuitable, information gained from other analyses, particularly the EOF method, clearly

show that the majority of the non-uniform behavior at these sites is due to isolated end

effects. In fact when Iau is recalculated using the subset of data used to calculate the

longshore averaged shoreline (taken at the center of each site), all the values increase to

at least 0.835. Although in general the longshore uniformity index supports the results of

the correlation analysis, there is one glaring discrepancy. While the correlation analysis

suggests the Wildwood data set be eliminated, a longshore uniformity index above the

cutoff criterion was calculated (0.629). Closer examination reveals that while the two

shorelines comprising this data set generally move in unison with one another, a rather

large discrepancy is observed in the data point collected immediately after the infamous

"Perfect Storm" occurring in late October 1991. The profile located closest to the nearest








down drift inlet exhibits significant accretion while the majority of the coastline

experienced severe erosion. The hypothesis is that the unusual severity of the storm

extended the typical region of influence associated with the inlet to encompass the

southernmost profile location. Rather than eliminate a potentially good data set on the

basis of a single inconsistent data point, the decision was made to retain the Wildwood

site after eliminating the data set closest to the inlet.

Spectral analysis, coherence, and phase were used to analyze the frequency domain

behavior of those data sets that were either sampled at a constant interval, or were

sampled frequently enough to allow the original data to be interpolated with an

acceptable degree of accuracy. Spectra similar to those presented for the Gold Coast in

Figure 4-6 were calculated for the Duck and Torrey Pines data as well. Figure 4-6 clearly

shows that the periodic trends at each longshore location are consistent, as would be

expected. Similar behavior is observed at Torrey Pines; however the Duck spectra are

much more scattered indicating non-uniform behavior. The coherence and phase plotted

in Figure 4-7 are the average values using three selected shorelines as the basis for

comparison. As expected for uniform shorelines, the coherence is high and the phase

oscillates slightly about zero in the high-energy region (f = 0.001-0.04 cycles/day).

Similar behavior is exhibited at Torrey Pines; however low coherence values and wildly

fluctuating phase estimates once again illustrate the inconsistent behavior of adjacent

shorelines at Duck. In general, the conclusions of the frequency domain based analysis

are consistent with the previous results, and support the assertion that Duck is an

inappropriate location for the evaluation of the proposed model.










Shoreline Change Spectra Gold Coast, QLD


f (cycles/day)
Figure 4-6. Spectral analyses of Gold Coast shoreline data where the thick line represents
the mean spectra.


Mean Coherence & Phase Gold Coast, QLD


0.8

i 0.6

~0.4
:E


Coh & with Yx-17so
-e- Coh & O with y-125
S Coh & with y-7


0-3 1/yr 2/yr 10-


f (cycles/day)
Figure 4-7. Mean coherence and phase for three selected shorelines at the Gold Coast.


_


It~P_~1P~4P~L;rd~a~pl~








The EOF method was used to analyze the dominant spatial and temporal modes of

variability at sites with multiple (>3) longshore sampling locations. The dominant modes

of spatial variability are of primary interest here, and are plotted in Figures 4-8 to 4-10.

A summary of the nature and the amount of variability explained by the first two modes

is given in Table 4-5. In every case, the first two modes account for nearly 90% of the

total variability of the data, where the majority of modes can be classified as uniform.

Non-uniform behavior is indicated by the presence of nodal points in Figures 4-8 to 4-10,

where they tend to represent transition points that separate eroding and accreting regions.

Typically, shoreline changes are referred to as out of phase across the nodes. The

absence of nodal points in a given mode is a reflection of longshore uniform behavior, as

the entire coastline tends to advance and retreat in unison. In terms of the principal

modes of transport, cross-shore processes tend to dominate on uniform coastlines, while

longshore processes provide the primary mechanism for transporting sand across nodal

points from eroding to accreting regions. As indicated in Table 4-5, most of the primary

eigenfunctions, el(x), exhibit some form of uniform behavior. Inlets and rocky headlands

tend to have a significant impact on the Washington data as pronounced "end effects" are

present. Uniform regions are most difficult to identify at Duck and Grayland Plains. At

most sites, the second and third spatial eigenfunctions, e2(x) and es(x), begin to describe

the deviations from the dominant longshore uniform behavior described by ei(x). Based

upon the form of the primary and secondary eigenfunctions and their relative importance,

the EOF analysis suggests that potentially neither the Duck nor the Grayland Plains data

are appropriate for the evaluating a cross-shore model.










First Three Spatial Eigenfunctions Duck, NC


0 200 400
Longshore C
First Three Spatial Eigenfun


0.5 ...


-00 0 200 400
Longshore D
Figure 4-8. Spatial eigenfunctions ei(x)-e3(x) f


_1


First Three Spatial Eigenfunctions Washington State


e,(x) -76.41%
-e- e2(x)- 16.61%
0.5 e3(x) 3.71% .... ... .




-0.5 .... .. ......... ...

Clatsop Plains
-1 L- nahoe-Dstace-kin
5 10 15 20 25 30 35
Longshore Distance (km)


Long Beach
0 .5................ .......




e,(x)-77.09%
-0.5 -e- ex) 14.34 ....
e3(x) 5.63%
-1 -
40 50 60 70 80
Longshore Distance (km)


-0.5 ....... .......... ... .... ...- : e,(x)- .n % .t
:/ .y. -e e~(x) 7.03% ......
rayla i Plain e3(x) 2.93%
-1 -1 -
95 100 105 110 115 120 130 140 150 160
Longshore Distance (km) Longshore Distance (km)
Figure 4-9. Spatial eigenfunctions el(x)-e3(x) for Washington State.


FRF Per
N
.. ... ... .. ...... ... .... .. .. ...... .




S....... ...... e (x)- 81.98%
S-e- e(x) 7.45%
Se(x) 3.63%
600 800 1000 1200
stance (m)
actions Duck, NC (Adjusted)

FRF Pier VOi






......... .... e,(x)-55.25%
-e- e2(x)- 16.37%
1 --.. e -(x) 9.68%
600 800 1000 1200
distance (m)
or Duck, NC.


-200


0


I I I I


I


I


........................................


-0.5






73


First Three Spatial Eigenfunctions Gold Coast, QLD


0.1




0-
0 1 .. ...... .. ... ... ......... ............... ... ..... ............ ......




-0.1 -

0.15 e1(x) 88.6%
-e- e(x) 2.58%
9-e3(x) 2.22%
no-I-I


-2000 -1500 -1000
Longshore Distance (m)
Figure 4-10. Spatial eigenfunctions ez(x)-e3(x) for the Gold Coast, QLD.

Table 4-5. Summary of EOF analysis results.
e (x) e2(x)
Site % Variance m % Variance Fo
Explained Form Explain Form
Explained Explained


e2.n(x)
% Variance
Remaining


East Coast Sites
Duck, NC 81.98% Uniform 7.45% Variable 10.57%
Duck, NC (adjusted) 55.25% Variable 16.37% Variable 28.38%

West Coast Sites
North Beach, WA 82.11% Uniform 7.03% Uniform 10.86%
Grayland Plains, WA 90.09% Variable 8.89% Uniform 1.02%
Long Beach, WA 77.09% Uniform 14.34% Uniform 8.57%
Clatsop Plains, OR 76.41% Uniform 16.61% Uniform 6.98%

Australian Site
Gold Coast, QLD 88.59% Uniform 2.58% Variable 8.83%

As a result of the site suitability analysis performed using the tools described in

Section 4.4, several data sets have been determined to be inappropriate for evaluating the

new model. The cross-shore nature of the model makes longshore uniformity an


N








important characteristic of suitable sites. A combination of inconsistent survey data

along with poor performance in the correlation analysis resulted in the elimination of the

East Hampton data. The Harvey Cedars site was included primarily as a check of the site

evaluation technique since its location within a groin field makes it extremely unsuitable

for analysis with a cross-shore model. Surprisingly, the majority of the Washington and

Oregon State data exhibits enough longshore uniform behavior to be considered suitable,

with the lone exception of the Grayland Plains data set. A low average correlation

coefficient, and an extremely small longshore uniformity index, combined with the EOF

analysis that failed to identify any significant longshore uniform behavior, all support the

decision to eliminate the Grayland Plains site. Last but not least is the revered Duck data

set. The significant impact of the pier on the adjacent shorelines has long been

recognized, but often ignored when selecting appropriate data for the evaluation of new

models. The Duck data performed poorly in all of the suitability tests illustrating its

inappropriateness for the proposed application. Fortunately, the number of high quality,

readily accessible data sets is constantly increasing, providing plenty of more suitable

alternatives.













CHAPTER 5
RESULTS

At most of the selected sites, the model is able to reproduce the historical shoreline

changes with a degree of accuracy that is on par with or better than most traditional

approaches, but at a fraction of the computational cost and effort. A total of 64

simulations representing all possible rate parameter combinations were performed at each

of the 13 sites, for a total of 832 separate hindcasts. As expected, the results varied from

site to site, and even at a given location depending upon the parameterizations selected

for ka(t) and ke(t). A succinct summary of the results is provided in Table 5-1, where the

columns from left to right represent: the average NMSE and classification (from Table

3.2), the minimum NMSE and classification, the mean CAP and classification, the

maximum CAP and classification, and the MPI and associated classification. The

column averages given in the last row indicate that overall the model is successful

according to all three criteria, particularly when only the best simulations (columns 4 &

8) corresponding to the most suitable rate parameters are considered. There are however,

some cases where the model does not perform nearly as well (Island Beach, NJ for

example), although at least in some cases this poor performance can be partially

explained by the unexpected, and somewhat anomalous behavior of the observed

shoreline. A complete tabulation of the results at each of the thirteen sites is presented in

Appendix B. The remainder of this chapter is devoted to a description of the typical

model performance using the Daytona Beach, FL data, examples of the types of results








presented in Appendix B using the Long Beach, WA data, and a general description of

the results in New Jersey, Florida, Washington, California, and Australia.

Table 5-1. Summary of SLMOD results.
NMSE CAP MPI
Site Mean Minimum Mean Maximum Score Rating

Island Beach, NJ 0.932 P* 0.885 P 0.632 G 0.705 G 1 B
Wildwood, NJ 0.686 R 0.596 G 0.659 G 0.779 G 3 R
St. Augustine, FL 0.782 R 0.668 R 0.640 E 0.805 E 4 G
Crescent Beach, FL 0.849 P 0.259 E 0.663 G 0.835 E 2(5) P-E
Daytona Beach, FL 0.703 R 0.619 R 0.748 G 0.841 E 4 G
New Smyrna Beach, FL 0.765 R 0.595 G 0.684 G 0.800 E 4 G
North Beach, WA 0.628 R 0.537 R 0.828 E 0.917 E 4 G
Long Beach, WA 0.363 G 0.281 E 0.873 E 0.926 E 5 E
Clatsop Plains, OR 0.423 G 0.312 G 0.902 E 0.974 E 5 E
Torrey Pines, CA 0.745 R 0.48 G 0.596 G 0.779 G 4 G
Brighton Beach, AS 0.615 R 0.524 G 0.647 G 0.656 G 3 R
Leighton Beach, AS 0.624 R 0.522 G 0.655 G 0.680 G 3 R
Gold Coast, AS 0.521 G 0.470 G 0.715 G 0.718 G 4 G
Gold Coast (filt), AS 0.367 G 0.298 G 0.985 G 0.987 G 4 G
Average 0.643 R 0.503 G 0.731 G 0.814 E 3.79 G-E

Classification according to Table 3-3, where B=Bad, P=Poor, R=Reasonable, G=Good, E=Excellent

The unconditionally stable nature of the numerical technique employed in the

model allows for simulations to be performed with an arbitrary time step. Since one of

the objectives of the model is to encompass as much of the broad engineering scale as

possible, the shortest time step, corresponding to the temporal density of the input data, is

used. Depending upon the source of the wave data, this time step varies, but is generally

on the order of 1-3 hours. This is short enough to capture the smallest scale intended to

be reproduced by the model corresponding to storms, and also allows for realistic

simulations to be completed in a reasonable amount of time. For clarity, only predictions

for those days where a corresponding measured data point was recorded are plotted in the

figures appearing in Appendix B; however for each simulation a complete time series of

equilibrium and hindcast shoreline positions are calculated at each time step. An








example of these time series is plotted in Figure 5-1, where dissecting the figure helps to

illustrate the typical model behavior. The specific simulation presented corresponds to

the hindcast with the lowest NMSE at Daytona Beach, FL, where the accretion rate

parameter is a function of the breaking wave height squared, namely ka(t) = 1.08x10-

4H2(t) hr1, and the erosion rate parameter is a function of the Froude number,

specifically ke(t) = 0.45Fr(t) hr'1. All three plotted time series contain dominant seasonal

signals, corresponding to strong annual periodicities in both the wave and water level

forcing. The noise or variability in yq(t) reflects the fact that the equilibrium shoreline

represents a complete and instantaneous response to the forcing, which includes high

frequency phenomena such as the semi-diurnal tidal signal. The predicted shoreline on

the other hand, responds with a much longer timescale that is highly dependent on the

value of ka. The seasonal trend predicted for Daytona Beach is generally consistent with

previous observations that have been made along the Florida coastline (DeWall, 1977).

The inset chart in Figure 5-1, provides a close up view of the typical annual cycle that

occurs in nature and is well predicted by the model. The inset plot shows an initially

eroded winter shoreline, which recovers gradually over the spring and summer months,

only to be eroded by a succession of storms, in this case Hurricane Diana (9/8/1984-

9/16/1984) and the Thanksgiving Day Storm (11/22/1984-11/25/1984). The significant

difference between the predicted erosion and accretion time scales, exhibited in nearly all

of the simulations is characteristic of the natural response, and is particularly pronounced

in Figure 5-1. Although it is somewhat difficult to tell based upon Figure 5-1 alone, a

more complete analysis including an evaluation of the NMSE, CAP, and MPI indicates

that the model performs reasonably well at Daytona Beach.









Shoreline Hindcast Time Series
Daytona Beach, FL
80
y (t)
eq
Y y (t)
60- pr Mt
x ox Y(t
40 -







.G -20-



-20 N (N.S T 1 ,
C -40-40
Hu DIan
-60 O ; 4 .

-20 1964)
-80 ...... ...........
":-40
-100 0 1984 1984.5 1985
1960 1965 1970 1975 1980 1985 1990 1995
Date
Figure 5-1. Complete hindcast shoreline time series for Daytona Beach, FL.

The figures and tables presented in Appendix B are designed to provide a concise

yet informative description of the model results at each site. A typical hindcast generated

for the Long Beach, WA site is shown in Figure 5-2, where only those model predictions

corresponding to the dates for which measured data were available have been plotted.

The two hindcasts in each figure represent those generated using the parameter

combinations resulting in the best predictions as selected objectively using the NMSE

and CAP criteria. In some cases, both criteria suggest the same parameter combination

and the two hindcasts overlie one another. The error bars appearing in Figure 5-2 are

used to indicate the range of shoreline predictions produced by alternate parameter








combinations. In other words, all shoreline predictions for a given time fall within the

bounds defined by the error bars. Although the model is sensitive to the particular form

of the rate parameter, the error bands typically show that regardless of the

parameterization, the predictions fall within an acceptable range of one another.

Long Beach, Washington
20

15

10. ... ..








-25 ................ C.......... ....... CAP)
-0
5 ............. : .......
0-5 .. .... .........
_Il I" !II









-30' '
1997 1998 1999 2000 2001 2002 2003



WA, and typical of those presented in Appendix B. Error bars indicate the
variation in predicted shoreline position for different rate parameter
combinations.


Accompanying each figure is a series of three tables similar to Tables 5-2 to 5-4.

In Table 5-2, the NMSE for each possible parameter combination at a given site is

presented, where each column corresponds to a different form of ke(t), while each row

represents a different form of ka(t). In addition, column and row averages have been








included, which give the mean NMSE for the various forms of ka(t) and ke(t),

respectively. Two separate criteria were used to select the "best" rate parameters at a

given site. The first criterion is based upon the average performance of the model for

each form of ka(t) and ke(t) taken over all eight forms of the opposing parameter. Gray

shading has been used to identify the "best" parameters according to the first NMSE

criterion. The second criterion uses the best individual simulation (lowest overall

NMSE), identified by the bold outlined value, to select the most appropriate parameter

combination. There is a subtle difference in the two criteria, in that the first criterion

identifies the best rate parameters, ka(t) and ke(t) independently of one another, while the

second criterion identifies a parameter combination. In the example given in Table 5-2,

the most appropriate individual parameterizations are given by ka(t) = kaFr(t), and ke(t) =

ke4b(t), while the best parameter combination also happens to correspond to ka(t) =

kaFr(t), ke(t) = keb(t). The fact that both methods suggest the same parameter set is not

surprising since the criteria are related; however a quick glance at the results in Appendix

B will confirm that this is frequently not the case.

Table 5-2. NMSE associated with various rate parameter combinations at Long Beach,
WA, and typical of the NMSE tables presented in Appendix B.
Erosion Parameter ke(t) =
Con f(Q) f(Hb2) f(Hb3) f(Fr) f(IF,) f(P) Avg
Con 0.307 0.311 0.335 0.338 0.308 0.314 0.321 0.316
S f(Q) 0.332 0.330 0.383 0.369 0.337 0.341 0.383 0.349
f(Hb2) 0.390 0.400 0.441 0.471 0.375 0.400 0.426 0.411
SfHb) 0.436 0.463 0.493 0.486 0.426 0.455 0.493 0.463

c f(IFr) 0.322 0.333 0.355 0.365 0.320 0.337 0.343 0.336
f f(b) 0.327 0.322 0.350 0.348 0.319 0.333 0.333 0.330
f(P) 0.396 0.390 0.426 0.445 0.368 0.392 0.413 0.401
Avg 0.351 0.355 0.388 0.393 0.343 0.360 0.377 0.363








Table 5-3 is an example of the second type of table generated for each site, where

the format is very similar to that of Table 5-2, but rather than containing NMSE values, it

contains CAP values. Once again row and column averages have been calculated and

have been used to help select the "best" parameters according to the first criterion, which

in the CAP tables are shaded with a diagonal striped pattern to distinguish them from the

previous table. Using the CAP criterion, the "best" rate parameters at Long Beach are

given by ka(t) = kalb2(t) and kI(t) = kcIFr(t). Similar to the NMSE table, the maximum

CAP for an individual simulation is outlined (only italicized rather than boldfaced) in

order to indicate the "best" parameter combination based on the second CAP criterion.

Because the CAP value is essentially an average of a finite number of specified weights,

it is not unusual for multiple hindcasts to receive the same CAP score. This is the case at

Long Beach, as multiple parameter combinations result in the same maximum CAP score

of 0.926, indicating several simulations that perform equally well according to the second

criterion. It should be noted that several of these combinations do not correspond to

either of the optimal rate parameters based upon the row and column average CAP.

Table 5-3. CAP associated with various rate parameter combinations at Long Beach,
WA, and typical of the CAP tables presented in Appendix B.
Erosion Parameter k, =
Con f(Q) f((Hb) f(Hb3) f(Fr) f() f(P) Avg
Con 0.858 0.858 0.853 0.853 0.858 0.858 0.853 0.856
f(Q) 0.895 0.890 0.853 0.853 0.895 0.890 0.853 0.882

F(Hb) 0.926 0.874 0.868 0.816 0.926 0.874 0.853 0.883
S f(Fr) 0.858 0.858 0.821 0.853 0.858 0.858 0.821 0.848
f(IF,) 0.890 0.853 0.853 0.853 0.890 0.890 0.853 0.871
Sf(&) 0.895 0.858 0.858 0.890 0.895 0.821 0.890 0.875
f(P) 0.911 0.911 0.853 0.816 0.890 0.911 0.874 0.882


Avg 0.893 0.874 0.854 0.846


0.890


0.874


0.859


0.873








The last type of table created for each site is illustrated by Table 5-4, and gives the

specific coefficient values for each simulation as determined by the procedure discussed

in Section 3.4.4. Once again, the columns represent different forms of ke(t), while the

rows indicate different forms of ka(t). The set of coefficients, ka, k1, Ayo, for each

parameter combination are listed vertically. Row and column averages are also

calculated, where ka and ke can only be averaged horizontally and vertically, respectively,

as different forms of the rate coefficients have different units. The shading patterns used

in the NMSE and CAP tables to indicate the best parameterizations of ka(t) and ke(t) are

repeated in Table 5-4, in order to make cross referencing the tables easier. In the

example presented, the row and column corresponding to ka(t) = kaFr(t) and ke(t) = ke b(t)

are shaded, indicating the best parameters according to the NMSE, while the optimum

parameterization based on the CAP score, ka(t) = kaHb2(t), ke(t) = keFr(t), is represented

by the striped row and column. The individual cells resulting in the best simulations

according to the second criterion are also identified in Table 5-4, where the bold outlined

text denotes the best individual simulation based upon the NMSE, and the italicized

outlined text indicates the simulations receiving the maximum CAP score. At Long

Beach, the best individual simulation according to the NMSE occurs for the specific

parameter combination, ka(t) = 0.05Fr(t) hri, ke(t) = 0.000003(b(t) hr"', Ayo = 24.38 m,

while several parameter combinations result in the same maximum CAP value.

While the availability of numerous sets of field data has advantages in terms of

allowing the model to be evaluated over a wide range of conditions, it makes the

presentation of the results, even in a compact form, quite cumbersome. In order to

streamline the process, the remainder of this chapter is devoted to qualitative descriptions






83


of the overall model performance in each of five geographic regions: New Jersey,

Florida, Washington, California, and Australia. Where necessary, figures or tables such

as those previously discussed are presented within the text to illustrate specific points;

however the complete set of figures and tables for all thirteen sites can be found in

Appendix B.

Table 5-4. Calibration coefficients for Long Beach, WA, and typical of the coefficient
tables presented in Appendix B.


Con ka
[hr1] k.
Av.


f(Q)
[hr"]


Con
[hr1il


3.0E-04
5.0E-04
24.384


3.0E-05
5.5E-04
27.432


f(Q)
[hr'1


4.5E-04
2.5E-05
12.192


4.0E-05
3.0E-05


f(Hb2)
hr'1m'21


5.0E-04
1.6E-05
6.096


1.0E-03
1.6E-05


f(Hb3)
Ihr-1m4


4.5E-04
2.5E-06
3.048


4.5E-05
2.8E-06


f(Fr)
[hr'1


3.5E-04
1.5E-01
24.384


3.0E-05
1.5E-01


f(P)

4.5E-04
2.5E-10
0.144
1.OE-03
2.5E-10


Avg

3.9E-04

15.240
2.8E-04


3.4E-05 4.6E-04
5.5E-10
IiR An 9A nCn


It fH 3)k, 1.8E-05 2.1E-05 5.3E-05 3.5E-0311.8E-05
[h(1&] k. 1.5E-03 7.OE-05 3.8E-05 5.3E-06 5.OE-01
A 42.672 33.528 6.096 -36.576 45.720


00

ka 1.5E-06 1.5E-06 2.5E-06 2.OE-06 1.5E-06
k 6 E


[hr'] ]
Av.


.u -4 t
27.432


J3.Uc-0u
21.336


I .6O96UO
6.096


2., E-u0
6096


2.UE-U I
30.480


k, 2.5E-06 2.0E-06 3.OE-06 3.OE-06 2.5E-06
[h( ke 5.5E-04 3.OE-05 1.6E-05 2.7E-06 1.5E-01
AyI 27.432 27.432 12.192 9.144 27.432
ka 1.0E-08 1.0E.OE 1.0E-08 1.0E-08 6.OE-10
[hr ] ke 9.0E-04 4.5E-05 2.2E-05 3.2E-06 3.0E-01
AYo 27.432 21.336 12.192 6.096 39.624


ka
Avg k.
Ayo


1.9E-03
29.337


3.8E-05
22.479


2.SE-06
2.5E-10
6.096
4.5E-06
2.5E-10
6.096
1.5E-09
3.SE-10
6.096

3.1E-10
7.239


2.1E-05 7.1E-06 2.4E-01
8.001 0.381 33.147


1.8E-06
-
18.669
2.8E-06

19.431
6.6E-09

20.193



19.574


I I I I


1 I I


I


, ,


I


I


I


. .








5.1 New Jersey

The New Jersey region consists of two different sites, Island Beach along the

central New Jersey coastline, and Wildwood located in the southern portion of the state.

Hydrodynamic conditions at both sites are similar however, both the shoreline orientation

(Ono = 98 Island Beach, non = 142- Wildwood) and sediment size (dso = 0.370 mm

Island Beach, d50 = 0.200 mm Wildwood) vary considerably. Model performance

also varies significantly between the two sites, performing poorly at Island Beach, but

reasonably well at Wildwood. While the model successfully simulates a majority of the

large shoreline changes at Wildwood, including an extreme erosional event in 1991

related to the "Perfect Storm", it fails to reproduce most of the changes at Island Beach.

Closer examination of the data at Island Beach however, reveals that the measured

shoreline exhibits some characteristics that would make simulating it with any model

extremely difficult. Given the similarities in the forcing conditions for both New Jersey

simulations, it is reasonable to assume that if the shoreline observations at each site were

fairly consistent, the results should be as well. Unfortunately, this is not the case, mainly

due to two glaring differences involving the magnitude of the erosion related to the

"Perfect Storm", and the sudden increase in the severity of shoreline changes experienced

at Island Beach after 1995. The infamous "Perfect Storm" occurring in late October of

1991 impacted a majority of the east coast of the United States, causing widespread

damage and significant beach erosion. According to the data points spanning this event,

the storm resulted in relatively minor erosion at Island Beach (~ 5 m), which was not well

predicted by the model, and significant erosion at Wildwood (~ 60 m ), which was

successfully predicted. Since the forcing data for both hindcasts are similar, it is not








surprising that the model was only able to reproduce one of the two scenarios, and that

the result that was reproduced corresponded to the severe erosion that was more typical

of the situation along the majority of the east coast. The sudden sharp increase in the

magnitude of shoreline changes after 1995 at Island Beach is also considered somewhat

abnormal. Neither the Wildwood shoreline data, nor the forcing data exhibit a similar

trend, indicating that perhaps some outside factor not considered by the model is

influencing that particular stretch of shoreline.

Both objective evaluation criteria support these qualitative observations, as

according to the classification system set forth in Table 3-3, the model performance with

respect to the NMSE is poor at Island Beach and reasonable to good at Wildwood. Based

upon the average NMSE for each form of the rate parameters, the best parameterizations

are given by ka(t) = ka (constant), ke(t) = keHb3(t) at Island Beach, and ka(t) = kaP(t), ke(t)

= keP(t) at Wildwood. The CAP scores are somewhat misleading as the performance of

the model at both sites can be classified as good according to this criterion. The abrupt

jump in the magnitude of the shoreline fluctuations at Island Beach after 1995, minimizes

the influence of the pre-1995 data on the overall CAP score as most of these data points

correspond to a stable shoreline as defined by the sliding scale. The optimum parameters

based upon the average CAP scores for Island Beach and Wildwood are, ka(t) = ka, ke(t) =

kHb2(t) and ka(t) = kaP(t), ke(t) = keCb(t), respectively.

The inability of one of the two objective measures of model skill to correctly

characterize the performance of the model at Island Beach, presents a clear indication of

the necessity of incorporating some subjectivity into the analysis of numerical models.

The previously described Model Performance Index (MPI) provides a useful measure of




Full Text

PAGE 1

SHORELINE RESPONSE TO VARIATIONS IN WAVES AND WATER LEVELS: AN ENGINEERING SCALE APPROACH By JONATHAN K. MILLER A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2004

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This dissertation is dedicated to my beautiful wife Diana, who has always been there for me when I needed it the most. During this long journey, she has patiently followed me across the globe, sacrificing much of herself, now it is finally time to go home.

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ACKNOWLEDGMENTS First and foremost, I would like to thank my advisor, Robert Dean, for his inspiration and guidance. Many a time I came to him frustrated and dejected, only to be reinvigorated by his infectious enthusiasm during an enlightening Sunday morning meeting. Sharing in his passion for coastal engineering over the past five years has been an extraordinary experience. I would also like to thank the other members of t~e Coastal Engineering , Department for their insightful seminars and thought provoking discussions. In particular I would like to thank Becky Hudson and Robert Thieke for making sure everything ran smoothly, and Dan Hanes, Ashish Mehta, Joann Mossa, and Robert Thieke once again, for agreeing to serve on my supervisory committee. It has been a pleasure working with all of them. For welcoming Diana and I to a foreign land with wide open arms, I would like to express my appreciation to Peter Nielsen and everyone at the University of Queensland. From the moment we arrived in Brisbane we were treated like family, making our transition "down under" much easier. Having the chance to work with Peter, Tom, Ling, and all of the coastal students was an honor and sincere pleasure. Our time in Australia was truly unforgettable thanks to the many wonderful people we encountered during our journeys. Several teachers that I was fortunate enough to encounter prior to beginning my graduate studies have also played an integral role in my development as a student and as iii

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a person. Br. Paul Joseph and K.Y. Billah always believed in me and encouraged me to learn for the love of learning and not for the grade attached to it. Both epitomize the true meaning of the word teacher. I need to thank Dimitris Dermatas and Michael Bruno for encouraging me to follow my heart and study coastal engineering when others told me I would be better off studying something more practical. I would be remiss in not thanking the Florida Sea Grant, the American Society for Engineering Education, the United States Department of Defense, and the Australian American Fulbright Commission, all of whom provided financial support for various stages of this project. Their contributions have been greatly appreciated. Along the way, I made many friends who have had a profound impact on my life and whom I will never forget. To Kristen, Justin, Chris, Jamie, Sean, Al, Dave, Nick, Ian, Finney, Carlos and everyone else who has helped me get through the last five years, I truly value each of their friendships. Cliff deserves a special mention for taking a leap of faith and moving to Gainesville with me at the start of this unforgettable journey. Most importantly, I need to thank my family for their continued love and support, without whom this would not have been possible. They have always been there to back me and encourage me in whatever I have chosen to do, and I am much indebted to them. Finally, I need to thank my wonderful wife Diana who inspires me each and every day. She has shown me the true meaning of love, and without her encouragement and emotional support over the past five years I would not have made it through the past five years. She has always been there for me and I can never thank her enough for her infinite love and patience. iv

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TABLE OF CONTENTS ACKNOWLEDGMENTS ................................................................................................. iii LIST OF TABLES ............................................................................................................ vii LIST OF FIGURES X ABSTRACT ..................................................................................................................... xiv 1 INTRODUCTION ........................................................................................................ 1 2 BACKGROUND .......................................................................................................... 8 2.1 I..ongshore (Planfonn) Models .............................................................................. 10 2.1.1 Analytical Models ...................................................................................... 11 2.1.2 Numerical One-Line/N-Line Models ......................................................... 12 2.2 Cross-shore (Profile) Models ................................................................................ 12 2.2.1 Analytical Models ...................................................................................... 13 2.2.2 Empirical Models ....................................................................................... 13 2.2.3 Energy Dissipation Models ........................................................................ 14 2.2.4 Process Based Models ................................................................................ 17 2.2.5 Alternative Models ..................................................................................... 20 2.3 Need for Innovative Approaches .......................................................................... 20 3 MODEL DEVELOPMENT ........................................................................................ 22 3.1. Theoretical Background ....................................................................................... 22 3.2. Defining the Equilibrium Shoreline, yeq(t) .......................................................... 25 3.3. Defining the Rate Parameter, ka .......................................................................... 29 3.4 Solution Technique ............................................................................................... 36 3.4.1 Numerical Scheme ...................................................................................... 36 3.4.2 Forcing Data .............................................................................................. 37 3.4.3 Shoreline Data ........................................................................................... 38 3.4.4 Model Calibration ....................................................................................... 39 3.4.5 Model Evaluation ....................................................................................... 40 4 FIELD DATA AND SITE SUITABILITY ................................................................ 46 4.1 East Coast Data ..................................................................................................... 48 V

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4.2 West Coast Data ................................................................................................... 53 4.3 Australian Data ..................................................................................................... 55 4.4 Evaluation Tools ................................................................................................... 57 4.4.1 Time Domain Based Statistics .................................................................... 58 4.4.2 Frequency Domain Based Statistics ........................................................... 59 4.4.3 Method of Empirical Orthogonal Functions ............................................... 60 4.5 Site Suitability ...................................................................................................... 64 5 RESULTS ................................................................................................................... 75 5.1 New Jersey ...................... .. .................................................................................... 84 5.2 Florida ................................................................................................................. 86 5.3 Washington and Oregon ...................................................................... ................. 92 5.4 California .............................................................................................................. 93 5.5 Australia ............................................................................................................... 94 6 DISCUSSION ........................................................................................................... 100 6.1 Timescale of Response ....................................................................................... 101 6.2 Selection of Appropriate Rate Parameters .......................................................... 103 6.3 Modified Error Estimates I Cost Functions ........................................................ 109 6.4 Time Varying Sediment Scale Parameter, A(.Q(t)) ............................................ 111 6.5 Application to EOF Filtered Data ....................................................................... 117 7 SUMMARY, CONCLUSIONS, AND FUTURE DIRECTIONS ............................ 119 7.1 Summary ................................... ........................................................................ 119 7.2 Conclusions ......................................................................................................... 121 7 .2 Future Directions ................................................................................................ 123 APPENDIX A MODEL SOURCE CODE .................................................... .. ............................... 126 B COMPLETE SET OF MODEL RESULTS ............................................................. 135 LIST OF REFERENCES ................................................................................................. 164 BIOGRAPHICAL SKETCH ........................................................................................... 172 vi

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LIST OF TABLES 3-1. Established erosion/accretion criteria ....................................................................... 33 3-2. Categorical assessment procedure score matrix developed for this study ............... 44 3-3. Subjective rating system based upon model performance statistics ......................... 44 4-1. Summary of data sources ......................................................................................... 47 4-2. Relevant site characteristics ..................................................................................... 48 4-3. Data analysis techniques applied at each site ........................................................... 65 4-4. Summary of time domain analysis results ................................................................ 66 4-5. Summary of EOF analysis results ............................................................................ 73 5-1. Summary of SIMOD results .................................................................................... 76 5-2. NMSE associated with various rate parameter combinations at Long Beach, WA, and typical of the NMSE tables presented in Appendix B ....................................... 80 5-3. CAP associated with various rate parameter combinations at Long Beach, WA, and typical of the CAP tables presented in Appendix B ................................................. 81 5-4. Calibration coefficients for Long Beach, WA, and typical of the coefficient tables presented in Appendix B .......................................................................................... 83 5-5. NMSE associated with various rate parameter combinations at Crescent Beach, FL ............................................................................................................................. 88 5-6. CAP associated with various rate parameter combinations at Crescent Beach, FL ............................................................................................................................. 89 6-1. Best performing rate parameters for each site ........................................................ 104 6-2. Rate coefficient statistics according to geographic region ..................................... 108 6 3. Percent change in NMSE values at Torrey Pines, CA for Case 1 (A(Q)) ............. 116 vii

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6-4. Percent change in NMSE values at Torrey Pines, CA for Case 2 (minimum W imposed) .............. .............. .......... ............................................ ....... ........................ 116 6-5. Percent change in NMSE values at Torrey Pines, CA for Case 3 (A(Q) and minimum W. imposed) .......................................................................................... 117 6-6. Percent change in NMSE values when only the longshore uniform EOF modes are considered at the Gold Coast, Australia ................................................................. 118 B-1. Calculated NMSE values for model hindcasts at Island Beach, NJ ....................... 136 B-2. Calculated CAP scores for model hindcasts at Island Beach, NJ .......................... 137 B-3. Calibration coefficients ka, kc, and /:iy0 for Island Beach, NJ ................................ 137 B-4. Calculated NMSE values for model hindcasts at Wildwood, NJ.. ......................... 138 B-5. Calculated CAP scores for model hindcasts at Wildwood, NJ .............................. 139 B-6. Calibration coefficients ka, kc, and /:iy0 for Wildwood, NJ .................................... 139 B-7. Calculated NMSE values for model hindcasts at St. Augustine, FL ...................... 140 B-8. Calculated CAP scores for model hindcasts at St. Augustine, FL ......................... 141 B-9. Calibration coefficients ka, kc, and /:iy0 for St. Augustine, FL ............................... 141 B-10. Calculated NMSE values for model hindcasts at Crescent Beach, FL .................. 142 B-11. Calculated CAP scores for model hindcasts at Crescent Beach, FL ...................... 143 B-12. Calibration coefficients ka, kc, and /:iy0 for Crescent Beach, FL ............................ 143 B-13. Calculated NMSE values for model hindcasts at Daytona Beach, FL ................... 144 B-14. Calculated CAP scores for model hindcasts at Daytona Beach, FL ...................... 145 B-15. Calibration coefficients ka, kc, and /:iy0 for Daytona Beach, FL ............................ 145 B-16. Calculated NMSE values for model hindcasts at New Smyrna Beach, FL ........... 146 B-17. Calculated CAP scores for model hindcasts at New Smyrna Beach, FL ............... 147 B-18. Calibration coefficients ka, kc, and /:iy0 for New Smyrna Beach, FL ..................... 147 B-19. Calculated NMSE values for model hindcasts at North Beach, WA ..................... 148 B-20. Calculated CAP scores for model hindcasts at North Beach, W A. ........................ 149 viii

PAGE 9

B-21. Calibration coefficients ka, ke, and Ay0 for North Beach, WA ............................... 149 B-22. Calculated NMSE values for model hindcasts at Long Beach, WA ...................... 150 B-23. Calculated CAP scores for model hindcasts at Long Beach, WA ......................... 151 B-24. Calibration coefficients ka, ke, and Ay0 for Long Beach, W A ....................... ........ 151 B-25. Calculated NMSE values for model hindcasts at Clatsop Plains, OR. .................. 152 B-26. Calculated CAP scores for model hindcasts at Clatsop Plains, OR. ...................... 153 B-27. Calibration coefficients ka, ke, and Ay0 for Clatsop Plains, OR. ............................ 153 B-28. Calculated NMSE values for model hindcasts at Torrey Pines, CA ...................... 154 B-29. Calculated CAP scores for model hindcasts at Torrey Pines, CA. ......................... 155 B-30. Calibration coefficients ka, ke, and Ay0 for Torrey Pines, CA ................................ 155 B-31. Calculated NMSE values for model hindcasts at Brighton Beach, AS .................. 156 B-32. Calculated CAP scores for model hindcasts at Brighton Beach, AS ..................... 157 B-33. Calibration coefficients ka, ke, and Ay0 for Brighton Beach, AS ........................... 157 B-34. Calculated NMSE values for model hindcasts at ~ighton Beach, AS ................. 158 B-35. Calculated CAP scores for model hindcasts at Leighton Beach, AS ..................... 159 B-36. Calibration coefficients ka, ke, and Ay0 for Leighton Beach, AS ........................... 159 B-37. Calculated NMSE values for model hindcasts at the Gold Coast, AS ................... 160 B-38. Calculated CAP scores for model hindcasts at the Gold Coast, AS ...................... 161 B-39. Calibration coefficients ka, ke, and Ay0 for the Gold Coast, AS ............................. 161 B-40. Calculated NMSE values for model hindcasts at the Gold Coast, AS, using filtered (fc = 0.033 days-1 ) data ........................................................................................... 162 B-41. Calculated CAP scores for model hindcasts at the Gold Coast, AS, using filtered (fc = 0.033 days-1 ) data ..................................................................... ... ........................ 163 B-42. Calibration coefficients ka, ke, and Ay0 for the Gold Coast, AS, using filtered (fc = 0.033 days -1 ) data ................................................................................................... 163 ix

PAGE 10

LIST OF FIGURES Figure 2-1. Profile schematization in Swart model. .................................................................... 14 2-2. SBEACH profile schematization ............................................................................. 17 2-3. Typical process based model schematic ................................................................... 19 3-1. Beach recession due to a combination of an increased water level, Sand wave induced setup, Tlb(y) ................................................................................................. 28 3-2. Example illustrating the role of fly0 in correcting for differences in the baseline conditions of yeq(t) and Yob(t) ................................................................................... 29 3-3. Schematic of model calibration routine ................................................................... 39 4-1. Location of data sets from the East Coast of the United States ............................... 49 4-2. Improvement in the consistency of the Duck shoreline data after adjusting for the volume change between subsequent profiles ........................................................... 52 4-3. Location of available shoreline data along the west coast of the United States ....... 53 4-4. Location of Australian shoreline data sets ............................................................... 55 4-5. Calculation of the mean correlation profile including rmax and ravg 4-6. Spectral analyses of Gold Coast shoreline data where the thick line represents the mean spectra ............................................................................................................. 70 47. Mean coherence and phase for three selected shorelines at the Gold Coast. ........... 70 4-8. Spatial eigenfunctions e1(x)-e 3 (x) for Duck, NC ...................................................... 72 4-9 Spatial eigenfunctions e 1 (x)-e 3 (x) for Washington State ......................................... 72 4-10. Spatial eigenfunctions e1(x)-e3(x) for the Gold Coast, QLD ................................... 73 5-1. Complete hindcast shoreline time series for Daytona Beach, FL ............................ 78 X

PAGE 11

5-2. Example hindcast plot of observed and predicted shorelines at Long Beach, WA, and typical of those presented in Appendix B. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations .................. 79 5-3. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Crescent Beach, FL. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations .................. 88 5-4. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Daytona Beach, FL. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations .................. 90 5-5. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at New Smyrna Beach, FL. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations .................. 91 5-6. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Torrey Pines, CA. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations .................................. 94 57. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Brighton Beach, AS. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations .................. 96 5-8. Complete hindcast shoreline time series for the Gold Coast, QID ......................... 96 5-9. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at the Gold Coast, AS, using filtered (fc = 0.033 days 1 ) data. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations ................. ............... .......................................................... 98 5-10. Comparison of the extreme values of the measured shorelines and "best" simulations according to the NMSE and CAP criteria at the Gold Coast, AS, using filtered (fc = 0.033 days 1 ) data. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations .................................. 99 6-1. Variability of the amplitude response function, IF(co,ka)I with forcing frequency, co, and rate coefficient, ka ........................................................................................... 102 6-2. Variability of the phase response function, cp{co,ka) with forcing frequency, co, and rate coefficient, ka .................................................................................................. 102 6-3. Histograms of accretion coefficients, ka, determined by the procedure detailed in Chapter 3 ............ ................................................................................................... 106 6-4. Histograms of erosion coefficients, kc, determined by the procedure detailed in Chapter 3 ........................................................ ....................................................... 107 xi

PAGE 12

6-5. Comparison of "best" modified NMSE predictions with the standard NMSE prediction and the measured data at Torrey Pines, CA .......................................... 11 l 6-6. New relationship for A proposed by Wang (2004), where Artt!Arec is the ratio of the new A value to that given by Moore (1982), and Ht/wT is the breaking form of the non-dimensional fall velocity parameter, .Q ... ........................................... ... ......... 113 6-7. Variation of active surfzone width, w., with A(.Q) ............................................... 113 6-8. Effect of A(.Q) on calculated ~Yeq values at Torrey Pines, CA .............................. 114 B-1. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Island Beach, NJ. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations ................................ 136 B-2. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Wildwood, NJ. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations ....... ......................... 138 B-3. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at St. Augustine, FL. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations ................................ 140 B-4. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Crescent Beach, FL. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations ................ 142 B-5. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Daytona Beach, FL. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations ................ 144 B-6. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at New Smyrna Beach, FL. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations ................ 146 B-7. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at North Beach, WA. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations ................ 148 B-8. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Long Beach, WA. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations ...... .. ........................ 150 B-9. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Clatsop Plains, OR. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations ................ 152 xii

PAGE 13

B-10. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Torrey Pines, CA. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations ................................ 154 B-11. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Brighton Beach, AS. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations ................ 156 B-12. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Leighton Beach, AS. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations ................ 158 B-13. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at the Gold Coast, AS. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations ................ 160 B-14. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at the Gold Coast, AS, using filtered (fc = 0.033 days-1 ) data. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations ......................................................................................... 162 xiii

PAGE 14

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy SHORELINE RESPONSE TO VARIATIONS IN WAVES AND WATER LEVELS: AN ENGINEERING SCALE APPROACH By Jonathan K. Miller December 2004 Chair: Robert G. Dean Major Department: Civil and Coastal Engineering A simple new shoreline change model has been developed, calibrated and evaluated with several sets of high quality field data. The model is based upon previous research, which indicates that the shoreline will approach an equilibrium position exponentially with time, when subjected to constant forcing in the form of waves and water levels. The engineering scale approach used here simulates the shoreline response to these cross shore processes in an extremely efficient and practical manner, while requiring only readily obtainable wave and water level data as input. The equilibrium shoreline is defined by using a conservation of volume argument and equilibrium beach profile theory to derive an expression for the equilibrium shoreline change due to a combination of local tide, storm surge, and wave induced setup. The rate at which the equilibrium condition is approached is governed by a rate coefficient that can either be taken as a constant, or parameterized in terms of the local wave and sediment properties. A total of eight physically based rate parameters are evaluated, where the erosion and accretion are XIV

PAGE 15

parameterized separately. According to the results, the most effective parameterization of the accretion rate is obtained using a surf zone Froude number, while the erosion rate is best parameterized by either the surf similarity parameter or the breaking wave height cubed. Three calibration coefficients representing a baseline for converting the equilibrium shoreline changes into equilibrium shoreline positions, and separate erosion and accretion constants, are evaluated by minimizing the error between model hindcasts and historical shoreline data. The extensive set of shoreline data used to calibrate and evaluate the model was compiled from a variety of sources, and consists of shoreline measurements from a total of thirteen sites within the United States and Australia. Overall the model successfully simulates the shoreline changes at 11 of the 13 study sites with an average normalized mean square error of 0.643. Other tools designed to help evaluate the model, such as a categorical assessment procedure and a model performance index also indicate a similar high degree of success. x v

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CHAPTER 1 INTRODUCTION In the past half-century, coastal populations worldwide have swelled as more and more people have begun to recognize the recreational benefits and economic potential associated with beaches. Eleven of the world's fifteen largest cities lie within the coastal region, and over 400 million people live within twenty meters of sea level and twenty kilometers of the coast (Small et al., 2000). In 1990, 133 million people or approximately 54% of the U.S. population lived in one of 673 coastal counties (Culliton, 1990). In Australia, nearly 85% of the population lives within fifty kilometers of the coast and nearly one-quarter of the population growth between 1991 and 1996 occurred within three kilometers of the coastline (CSIRO Atmospheric Research, 2002; Australian State of the Environment Committee, Coasts and Oceans, 2001). The economic investment in these coastal regions is substantial. In the U.S., nearly 75% of the gross domestic product is generated in coastal states (Colgan, 2003). According to Houston (2002), travel and tourism is America's leading industry and employer, of which beaches are the primary component contributing an estimated $257 billion to the national economy in 1999. The economic impact of tourism is not limited to the United States as similar statistics are reported in Australia where nearly 50% of international tourists and 42% of domestic tourists visit the coast, contributing over $15 billion annually to the marine tourism industry (Australian State of the Environment Committee, 2 001). Australia and the U.S. are not alone in recogni z ing the economic value of the coastal region as countries such as Japan, Germany, and Spain have been 1

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2 known to spend as much $1.5 billion on shore protection and restoration in a single year (Marine Facilities Panel, 1991). In the U.S. and Australia, the most significant threat to this substantial investment comes in the form of beach erosion due to a combination of natural and anthropogenic factors. Galgano (1998) recently estimated that as much as 80-90% of the non engineered U.S. Atlantic coastline was experiencing net erosion, while a 1994 report by the U.S. Army Corps of Engineers classified 33,000 km of the U.S. coastline as erosional, 4,300 km of which was classified as critical. While the exact numbers are often the subject of intense debate, the importance of understanding the processes leading to beach erosion (and accretion) is indisputable. Unfortunately, beaches are extraordinarily complex, dynamic systems and describing the governing physical process over the wide range of relevant spatial and temporal scales is an extremely difficult task. Individual swash events alter the beach topography with spatial and temporal scales on the order of millimeters and seconds, while sporadic storms can cause tens of meters of erosion in only a few hours. Some natural processes altering the shoreline such as sand waves exhibit both temporal and spatial periodicity, while others such as storm related erosion occur randomly. The sheer number and complexity of the physical processes responsible for inducing coastal change make representing them all in a fully three-dimensional, time-dependent, process based numerical model impossible given our current level of understanding. Fortunately for engineering and planning purposes, the spatial and temporal scales are related, which allows us to simplify the problem somewhat by considering only those scales important to a specific problem. Arguably, the most relevant scale is the so-called engineering

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3 scale, which refers to the range of temporal and spatial changes expected to impact a structure during its lifetime. Typically, the expected lifespan of a structure is on the order of 50-100 years, corresponding to relevant time and space scales of hours-decades, and meters-hundreds of meters. Even when the analysis is limited to the engineering time scale however, modeling shoreline changes remains a difficult task, therefore a number of different approaches have been developed. These range from simple extrapolations based on historical data to highly detailed, fully three-dimensional, process based models. The fact that rudimentary extrapolation techniques are still used despite the potential for considerable inaccuracies is testament to the need for improved models. State of the art 3-D models have been shown to be fairly accurate over shorter time scales after significant calibration; however they tend to break down near the shoreline and remain cost-prohibitive for most applications. One-line models provide simple yet accurate solutions for predicting shoreline changes adjacent to structures related to longshore processes; however no comparable technique exists for accurately modeling the shoreline response to cross shore processes. Although long-term predictions are often based upon the assumption that the effects of cross shore processes will cancel over the long run, the most significant changes likely to impact a structure, particularly on a natural coastline, are in fact related to these neglected processes Accurately representing the potential shoreline change due to cross shore processes, such as those related to seasonal variations in wave energy or extreme storms, must be included as an essential component of any complete shoreline model. Unfortunately, it is much easier to diagnose the problem than to fix it, as modeling the shoreline response to these cross-shore processes is not a trivial task.

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4 Numerous cross-shore models have been developed with a variety of different goals; however none have proven particularly successful at modeling shoreline changes at the engineering scale. Although the specific capabilities of each model vary widely, there are some common factors that make the majority of existing cross-shore models inadequate for long-term predictions. Most conspicuous is the fact that nearly all models predict erosion more accurately than accretion, and while this inability to accurately model recovery processes has long been recognized, it remains a significant limitation. The general applicability of most process based models is often restricted by the extensive data required for calibration, as the paucity of available data, combined with the need for site-specific information, means extensive costly field work is often required. More disheartening is the fact that even the most detailed models tend to break down in the vicinity of the shoreline, which for engineering purposes is nearly always the region of greatest interest. In many studies, model performance is either not evaluated near the shoreline or evaluated separately so as not to negatively impact the otherwise "good" results. Furthermore, even if these event-based models were able to successfully handle accretion, there is no guarantee that the results could be integrated up to yield reliable predictions over longer timescales (Hanson et al. 2004). The above factors, when combined with the extensive computational resources often required, make most state-of the-art models extremely inefficient, cost prohibitive, and ultimately impractical for many engineering applications The objective of the present work is to present a new shoreline change model which is capable of reproducing the shoreline response to cross-shore forcing over a variety of temporal scales. In order to provide the widest possible range of applicability,

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5 simplicity and efficiency along with a high degree of accuracy, were primary considerations. Rather than approach the problem from a purely process based standpoint, simple physical concepts were used in combination with empirical evidence to create a new tool capable of fulfilling the proposed objectives. The result is a simple model, which can be of immediate use to the engineering profession. Details of the model are discussed in Chapter 3, however the basic concept borrows from classical equilibrium theory, where the shoreline strives to reach an equilibrium state which continuously changes in response to the dynamic conditions of the nearshore environment. In accordance with physical observations, the rate at which this equilibrium is approached is proportional to the degree of disequilibrium between the instantaneous shoreline position and that suggested by the local forcing as a result of time varying wave and water level conditions. Consistent with nature, such a model predicts the strongest shoreline recovery immediately after the passage of a major storm, a result which few (if any) process-based models have been able to reproduce. High-quality data from both coasts of the United States as well as Australia were collected to calibrate and evaluate the model. Unlike previous model studies that may have been hampered by a lack of available data recent emphasis on field data collection and dissemination has resulted in an abundance of suitable data for this project. Instead of haphazardly applying the model at each site for which sufficient data were available, several criteria were used to eliminate those locations for which a cross shore model was considered inappropriate. These criteria helped identify and eliminate several sites where the shoreline behavior exhibited significant longshore variations potentially indicating the predominance of longshore processes. The geographical diversity of the data sets

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6 provided an interesting platform for examining the natural variability in the nearshore system, and for evaluating the model over a wide range of wave, tide, and geologic conditions. By incorporating shoreline measurements made using a number of different techniques, the skill of the model could be evaluated over a variety of timescales ranging from daily to multi-decadal. Undoubtedly, process based models containing full detailed descriptions of the governing hydrodynamics and resulting sediment transport will eventually yield the most accurate predictions of shoreline change; however our present knowledge of the complex relationships and feedback mechanisms is insufficient to justify their use in long-term shoreline studies. The shoreline model developed and discussed herein is significant in that it is able to accurately predict shoreline changes, while requiring only minimal, readily available forcing and calibration data. The simplicity, efficiency, and adaptability of the new model make it a useful tool for a variety of engineering applications. With additional research, it should be possible to adapt this simple cross-shore model to work in concert with the existing simple longshore models (e.g., Hanson and Larson, 1998) to obtain a robust, quasi-two-dimensional shoreline model. Although the emphasis here has been placed upon maintaining the simplicity of the model, it can readily be adapted and used with more detailed wave transformation models to analyze the potential implications of alongshore variations in the incident wave field. The current analysis has been limited to the comparison of model hindcasts with measured data; however the exhibited skill suggests it should be possible to apply the model in either a predictive sense using statistical descriptions of the forcing parameters and Monte Carlo simulations, or in a real-time sense using instantaneous measurements or storm forecasts of the forcing

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7 parameters. In the first case the results would represent the probabilities associated with various magnitudes of shoreline change based upon the statistical characteristics of the forcing parameters, while in the second case the model could provide first-approximation predictions of the erosive potential of approaching storms. The efficiency of the new model will make it particularly useful for long-term studies ranging from the prediction of seasonal shoreline changes, to the prediction of decadal shoreline migration patterns for coastal management applications In order to help the reader navigate through the remainder of this document, it is useful to provide a roadmap detailing its layout. In Chapter 2, some background information is provided including a more detailed discussion of the problem, as well as some of the more popular techniques for modeling the shoreline response to cross-shore forcing. Details of the new model including a description of the numerical approach are presented in Chapter 3, while the available field data and the tools used to help eliminate the inappropriate sites for a cross-shore model are discussed in Chapter 4. The results are presented in Chapter 5, followed by a detailed discussion of some of the key aspects of the model in Chapter 6. Finally, Chapter 7 summarizes the results and presents some suggestions for future work.

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CHAPTER2 BACKGROUND The complexity of the extremely dynamic nearshore environment makes accurate predictions of morphological evolution in this region over even limited temporal and spatial scales extremely difficult. Unfortunately, practical design requirements demand that a wide range of scales be taken into consideration, as the relevant engineering timescale ranges from hours to decades and encompasses spatial scales ranging from meters to hundreds of meters. The societal relevance of understanding and predicting changes in the nearshore region is illustrated by the long and varied history of attempts to model it using physical, analytical, and numerical techniques. Hanson et al. (2004) reviewed in detail some of the conventional and less conventional modeling approaches that have been used to predict coastal evolution over yearly to decadal timescales. Despite considering over twenty different types of models, the authors were unable to identify any capable of reproducing adequate results over the full range of time scales considered The remainder of this chapter is devoted to a discussion of some of these conventional modeling techniques, which through their inadequacy stress the need for innovative approaches. The four basic tools available to coastal engineers consist of experience/empirical models, physical models, analytical models, and numerical models. In some respects, local experience constitutes the best model, as a thorough understanding of the local processes (waves, tides, currents, sediment transport) and geomorphology are essential tools in understanding a coastal system. Similar projects on adjacent beaches often 8

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9 provide invaluable information regarding unexpected results attributed to localized phenomena. Relying on previous experience alone however, is insufficient for a number of reasons, including the inability to consider innovative approaches or optimize design. Although useful, experience or empirical models are nearly always best when applied in combination with either physical, analytical, or numerical models. Coastal physical models typically consist of scaled down versions of a natural system and are often constructed in a laboratory. The primary advantage of a physical model is the ability to control the ambient conditions so that specific design scenarios can be isolated and evaluated more precisely. Although physical models play a critical role in understanding coastal processes, they do have several significant disadvantages as well. In order to ensure similar behavior between the model and prototype, both scaling effects as well as laboratory effects must be considered and accounted for. Scaling problems can occur when the correct balance of forces is not preserved in the model, while laboratory effects can be equally detrimental, and include the generation of higher harmonics and presence of boundary induced reflections. These and other considerations combine to make physical models quite labor intensive and often extremely expensive, as highly skilled labor and specialized facilities are frequently required. Analytical models consist of closed form mathematical solutions to simplified versions of the equations governing shoreline and profile change, and are often derived for schematized geometries and basic input and boundary conditions. The objective of an analytical model is to capture the essential physics of the problem in a simplified manner that allows the fundamental features of the beach response to be derived, isolated, and more readily comprehended Unfortunately, these types of models are generally too

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10 crude for design purposes; however they can provide a means to identify characteristic trends and investigate the basic dependencies of the shoreline response to different combinations of input and boundary conditions. Increasingly numerical models are being used to study complex coastal systems as advancements in our ability to represent the dominant physical processes, combined with rapid advancements in computational capability, make them ever more efficient. Numerical models provide greater flexibility in the selection of boundary conditions and allow for the representation of arbitrary forcing. In addition, numerical models are extremely dynamic in the sense that recent scientific advances are easily incorporated due to their typically modular nature. The model presented here falls into this category; therefore the majority of this chapter is devoted to a discussion of some of the conventional numerical modeling approaches that have thus far failed to produce a generally accepted cross-shore model, applicable at the engineering timescale. 2.1 Longshore (Planform) Models Because of the complexity of the nearshore system, it is common to separate longshore and cross-shore processes, and to treat planform and profile evolution separately. In planform models, shoreline changes are assumed to result from gradients in the longshore sediment transport, while cross-shore effects such as storm induced erosion, or seasonal shoreline fluctuations, are either assumed to cancel over the length of the simulation, or are accounted for separately. These assumptions make planform models much more appropriate when applied over longer periods at segmented coastlines with systematic long term trends, and less applicable over shorter periods on more natural uninterrupt e d coastlines without dominant trends.

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11 2.1.1 Analytical Models Perhaps the most often utilized analytical model in coastal engineering is the oneline model developed by Pelnard-Considere (1956) for predicting shoreline evolution due to gradients in the longshore sediment transport. The key assumption of the model is that the cross-shore profile remains in equilibrium, and does not change along the extent of the shoreline being studied. The "one-line" moniker relates to the assumption that the movement of the entire profile can be represented by the translation of a single contour, usually the shoreline. If the one-line assumption holds, then the principle of mass conservation in the longshore direction must apply at all times, aQ1s +Day =0 ax at (2.1) where x and y are the longshore and cross-shore coordinates, respectively, Q1s is the longshore sediment transport rate, tis time, and D is the vertical extent of the active profile, defined as the sum of the depth of closure, h and the berm height, B. The longshore sediment transport rate is given by Q1s = QoSin
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12 2.1.2 Numerical One-Line/N-Line Models In order to obtain more realistic one-line solutions, Equation 2.1 can also be solved using numerical techniques capable of handling more realistic forcing and boundary conditions. Similar to the analytical solutions, cross-shore effects are assumed to cancel, as these models are typically applied over periods of years to decades and at sites with dominant long term trends. In terms of predictive skill, one-line models such as GENESIS (Hanson and Kraus, 1989) have proven to be fairly successful, despite their inability to simulate cross shore effects. N-line models are an extension of typical one-line models where the profile is divided into a series of N mutually interacting layers. In these quasi-3D models, cross shore effects are included in a highly schematized sense through interaction terms. Models in this category include those of Bakker (1968) and Perlin and Dean (1983). Hanson and Larson (2000) attribute the lack of success of conventional N-line models to inappropriate representations of both the cross-shore sediment transport and the cross shore distribution of longshore transport, and suggest innovative approaches are required. 2.2 Cross-shore (Profile) Models Cross-shore or profile models are generally used to describe the nearshore response to events over limited temporal (hours-years) and spatial (meters-hundreds of meters) scales. In contrast to planform models where gradients in the longshore sediment transport drive the bathymetric evolution, profile models are most readily applied along coastlines dominated by the influence of cross shore processes. In general, these models have been most successful at simulating storm induced erosion, and have been less successful at reproducing post storm recovery, and therefore medium to long term profile evolution. Schoonees and Theron (1995) reviewed ten different cross-shore models with

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13 respect to theoretical merit and validation criteria, grouping the models into "best," "acceptable," and "less suitable" categories. According to their conclusions, none of the models could be identified as clearly superior, as each potentially performs the best under certain conditions. 2.2.1 Analytical Models Although several analytical cross-shore models have been developed, none have proven nearly as useful as the Pelnard-Considere equation. Larson and Ebersole (1999) used a simple diffusion equation to describe the evolution in time of an offshore mound placed in the x-z plane, as direct physical analogies exist between several of the analytical solutions to the classical diffusion equation and the filling of dredged holes and spreading of offshore mounds. Bender and Dean (2003) reviewed many of the analytical solutions for wave transformation over bathymetric anomalies, including the potential shoreline impacts. Another useful analytical model developed by Kriebel and Dean (1993) for describing the time dependent evolution of the nearshore profile is discussed in some detail in the next chapter. 2.2.2 Empirical Models Most empirical models are based upon observations of morphological evolution made in the laboratory under controlled circumstances, and as such are subject to certain limitations. The empirical model of Swart (1974) was developed based upon observations of profile recession under monotonic waves, in both small and large scale tests. Swart schematized the profile into three separate zones as shown in Figure 2-1, an9 developed empirical relations for the equilibrium profile as well as the onshore and offshore limits of the active profile. In the Swart model, the time dependent cross-shore

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14 sediment transport rate, Qcs, is driven by the disequilibrium of profile characteristics, and is given by (2.3) where (L1-Li)t is a time dependent profile width, Wis the profile width at equilibrium, and Sy is an empirical constant for a given set of boundary conditions. Although the model was subsequently applied to field data with some success (Swain and Houston, 1984; Swart, 1986), the intensive calibration and complicated empirical formulas involved make the method too complex for widespread application. Z[AO LIN[ .,._.----.--------1 "' I -,.;,,ll.E1i .. rzin~-1-~ J. l -----------,, L --. .l!U,U( .J.1.V(i.._ ---. . r-------------~--Figure 2-1. Profile schematization in Swart model (from Swart, 1974). 2.2.3 Energy Dissipation Models t Based on an analysis of over 500 beach profiles from the Atlantic and Gulf Coasts of the United States, Dean (1977) derived an equilibrium beach profile of the form h = Ayw, using linear wave theory and the premise of uniform wave energy dissipation per

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15 unit volume due to breaking. The key assumption in the derivation is that sediment of a certain size will be stable for a certain level of wave energy dissipation per unit volume, (2.4) where his the total depth, Eis the wave energy density, and Cg is the group velocity. In this case, the y coordinate is shore normal and increases offshore. Using equilibrium beach profile theory, Kriebel and Dean (1985) proposed an expression for cross-shore sediment transport based upon the difference between the actual and equilibrium levels of wave energy dissipation in the surfzone, (2.5) where D is the actual time dependent energy dissipation per unit volume, and K is an empirical sediment transport parameter. The bracketed term represents the degree of disequilibrium and suggests that for steep profiles sediment will be transported offshore to restore equilibrium and vice-versa. Zheng and Dean (1997) subsequently modified Equation 2.5 by raising the disequilibrium term to the third power, in order to satisfy the appropriate scaling relationship given by Qr= Li,312, where Qr is the sediment transport ratio, and Lr is the length ratio. In both models, profile adjustments occur in response to gradients in the cross-shore sediment transport according to the continuity equation, (2.6) Equations 2.5 and 2.6 form a closed system of equations which can be evaluated numerically. On a storm time scale, both models are capable of adequately representing the storm induced erosion but are less successful at reproducing post storm recovery.

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16 Inspired by the success of the EDUNE model (Kriebel and Dean, 1985), Larson and Kraus (1989) attempted to extend the capabilities of energy dissipation based models with SBEACH. In SBEACH, the nearshore region is separated into four distinct zones as illustrated in Figure 2-2, each having its own sediment transport relationship. In the breaking zone (Zone ill), the magnitude of the cross-shore sediment transport, Qcs, is calculated based upon energy dissipation arguments, with an extra term added to account for down slope transport, K D-D +--( dhJ K dy dh D>D-- K dy 0 D D. !_ dh K dy (2.7) where dh/dy is the local beach slope, and K and E are sediment transport coefficients for the energy dissipation and slope dependent terms, respectively. In SBEACH, the direction of transport is calculated separately, and is based upon an empirical criterion relating the deep water wave steepness, Ho/Lo, to the non-dimensional fall velocity parameter, HJw5T, according to (2.8) where Ho, Lo, and T are deepwater wave parameters representing the wave height, wave length, and wave period, and w5 is the sediment fall velocity. Sediment transport magnitudes in the remaining zones are calculated based upon empirical relationships for which the energy dissipation based transport (as calculated from Equations 2.7 and 2.8) serves as a boundary condition. In these regions, the magnitude of the sediment transport

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17 typically follows an exponential decay, with coefficients that vary from region to region. At both the onshore and offshore boundary, the magnitude of the sediment transport reduces to zero, such that there is no bathymetric change. The SBEACH model has been widely applied in numerical studies of storm related erosion and beach nourishment equilibration with adequate results; however the inability of the model to accurately simulate accretional events and onshore bar migration limits its usefulness for long term studies. BP t Wave Height "--~1..------:'."'-----------,-t--r---,------"!!l!;..---x SWASH ZONE BROKEN WAVE ZONE BREAKER TRANSITION ZONE I PREBREAKING ZONE Figure 2-2. SBEACH profile schematization (from Larson and Kraus, 1989). 2.2.4 Process Based Models Process based models aim to reproduce profile evolution on the basis of first physical principles. Roelvink and Broker (1993), and van Rijn et al. (2003) provide in depth reviews of several state-of-the-art process based models including: UNIBEST (Delft Hydraulics), LITCROSS (Danish Hydraulics Institute), SEDITEL (Lab Nationale d'Hydraulique), WATAN3 and BEACH (University of Liverpool), COSMOS (H. R. Wallingford), CROSMOR (University of Utrecht), and CIRC (University of Catalunya). Although the exact details of the methods used to calculate the hydrodynamics, sediment

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18 transport, and bed evolution vary from model to model, the schematic in Figure 2-3 illustrates the general solution procedure. Gradients in the time averaged cross-shore sediment transport rate drive bed level changes according to the continuity equation, (2.9) where n is the sediment porosity, zb is the local bed elevation, and y is once again the shore normal coordinate. In general terms, the time averaged cross-shore sediment transport rate is given by 1 l2 z, Q0(y)=--J Ju(y,z,t)c(y,z,t)dzdt t2 -t, ,, Zi. (2.10) where u is a horizontal velocity, and c is the sediment concentration. From a practical standpoint, a complete time dependent solution of these equations is virtually impossible, as specification of the velocity and concentration fields down to the scales associated with turbulence is required. In order to arrive at workable solutions, most process-based models distinguish between four different process scales, of which only those scales relevant to a particular application may be considered. The turbulent scale is the smallest scale and is usually not considered due to its relatively minor influence on the horizontal flow field. The intra-wave 'time scale includes processes such as time lag effects within the wave period and wave asymmetry, which can be particularly important for onshore transport. Processes related to long waves and wave groups, such as variations in sediment concentration, make up a third scale. The fourth and final scale consists of mean variations of the wave field over time scales associated with the tidal period, and includes tidal currents and time averaged return flows.

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19 Input 1) Initial bathymetry 2) Waves and water levels at the boundary 3) Sediment parameters Hydrodynamic Module 1) Waves across the profile 2) Currents across the profile Sediment Transport Module 1) Many different formulations Morphologic Module 1) Conservation of sediment volume Figure 2-3. Typical process based model schematic. Although invaluable in terms of understanding the complex physical relationships between hydrodynamic forcing and sediment response at the micro-scale, process based models are inadequate for modeling long term profile development, particularly in the vicinity of the shoreline. The combination of computational effort and the extensive data required to calibrate these models makes them extremely inefficient and expensive to run, especially for long-term studies. Stive and DeVriend (1995), Kobayashi and Johnson (2001), and van Rijn et al. (2003) all reached the same conclusion, that given our current "rudimentary" understanding of cross-shore sediment transport processes in the surf and swash zones, the prediction of long term shoreline change using any of the existing models based on first physical principles was virtually impossible. Even as our understanding of micro-scale hydrodynamics and sediment transport processes improve,

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20 uncertainties such as those associated with prediction windows and the effects of stonn sequencing, bring into question whether the results of these small scale models can be integrated up to the relevant engineering scales. 2.2.S Alternative Models Alternative models can be broadly described as those which do not follow any of the traditional approaches, and which often combine elements of proven techniques in an attempt to fill specific voids in our modeling capabilities. Some examples of alternate models include Steetzel (1995) who added a semi-empirical cross-shore transport to extend the capabilities of N-line models, and both Inman (1987) and Larson and Kraus (1991) who modified Equation 2.2, adding an advective tenn to simulate sand wave propagation. Hanson and Larson (1998) attempted to incorporate seasonal effects into a traditional N-line model by schematizing the cross-shore sediment transport, but were only moderately successful. Plant et al. (1999) and Madsen and Plant (2001) used alternative methods which closely parallel the proposed shoreline model to successfully simulate bar evolution and nearshore beach slope changes at Duck, NC. These equilibrium based techniques are discussed in more detail in Chapter 3. 2.3 Need for Innovative Approaches The models mentioned in the preceding two sections represent only a small subset of those available, yet they illustrate an important point. In spite of intensive efforts to develop robust numerical models of the nearshore region and the shoreline in particular, conventional modeling approaches have failed to produce a generally accepted engineering scale model. In general, planfonn models have proven somewhat successful at describing long -tenn shoreline changes in the vicinity of structures; however attempts to extend their capabilities by parameterizing the cross-shore sediment transport have

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21 been much less successful. As a result, these models still cannot reproduce changes at the storm and seasonal time scales, and are inappropriate on long, straight, natural coastlines. Profile models, whether based on energy dissipation arguments or first physical principles, have typically been unable to adequately simulate beach recovery; therefore their applicability over longer time scales related to sequences of storms, or seasons is limited. Despite intensive research, there still exists a range of relevant scales of practical importance to engineering design, for which conventional cross-shore modeling approaches have proven unsuitable. The model described herein represents an attempt to fill this void using an innovative engineering scale approach. The new model combines empirical evidence with basic theory to produce a simple, yet effective, cross-shore shoreline change model, applicable at the engineering scale. The primary objective is to create a robust, model capable of simulating shoreline changes over a variety of different time scales, under a variety of conditions, in an accurate and efficient manner, which is considered suitable for practical engineering applications. The model, is described in detail in the following chapter, and takes the form of a classic equilibrium equation as suggested by previous empirical studies of the shoreline response to variations in waves and water levels, where tides, storm surges, and wave induced setup have been included. Unlike many of the conventional approaches, this innovative model requires only readily available wave and water level information, and is extremely computationally efficient. Because of its simplicity, the new approach has many conceivable applications, providing a potentially crucial link between profile models which are unable to accurately reproduce the erosion-recovery sequence, and coastal area models which ignore cross-shore processes completely.

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CHAPTER3 MODEL DEVELOPMENT 3.1. Theoretical Background The shoreline model presented here differs from the conventional models discussed in the previous chapter which thus far have been unable to produce a robust, generally applicable shoreline change model based on cross-shore processes. Instead, an innovative approach is proposed that uses empirical evidence based upon previous laboratory and numerical investigations of shoreline change, to guide the development of a simple new shoreline change model. Both small (Swart, 1974) and large-scale laboratory experiments (as reported by Dette and Uliczka, 1987; Sunamura and Maruyama, 1987; and Larson and Kraus, 1989) have suggested that an initially plane beach subjected to steady erosional forcing in the form of a fixed elevated water level and constant wave action, will evolve towards an equilibrium state with an approximately exponential time scale. Numerical simulations performed by Kriebel and Dean (1985) and Larson and Kraus (1989) support these observations, suggesting that shoreline change can be modeled heuristically using an equation of the form, dy(t) = ka (Yeq(t)-y(t)) dt (3.1) where y(t) and yeq(t) are the instantaneous actual and equilibrium shoreline positions at time t, and ka is an empirical rate coefficient. Equation 3.1 is a classical equilibrium equation, and implies that the rate of shoreline change is proportional to the degree of shoreline disequilibrium. In previous studies, analogous relations of this form have been 22

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23 used successfully to describe large-scale coastal phenomena where detailed knowledge of the complex physical mechanisms producing the phenomena were lacking. Wright et al. (1985) suggested that the rate at which a beach transitioned between the various beach states in the morphodynamic classification scheme of Wright and Short (1984), could be described by an equation similar in form to Equation 3.1, dS(t) = a+b(Q(t)Y (S(t)-Seq(t)) dt where the term (S(t)-Seq(t)) represents the disequilibrium between the actual and (3.2) predicted beach state at any given time, and b(Q(t))P is a rate parameter dependent on the non-dimensional fall velocity parameter, Q(t). The combination of these two terms was described by the authors as representing the disequilibrium stress. Unfortunately, a lack of available data outside the stable region meant the empirical coefficients a, b, and p were left undetermined pending the collection of more field data. More recently, the equilibrium concept has been used by Plant et al. (1999), and Madsen and Plant (2001), to describe bar and beach slope evolution at Duck, NC. In the earlier study, Plant et al. showed that bar morphology, as represented by the time varying location of the bar crest, could be described by an equilibrium equation of the form, dX(t) =a(H(t))3(X(t)-Xeq(t)) dt where X(t) and Xeq(t) are the time dependent actual and equilibrium bar positions, (3.3) respectively, and a(H(t))3 is a parameter influencing the rate at which equilibrium is approached. By comparing the model to observations Plant et al. were able to show that this relatively simple model had significant predictive capability over periods of nearly a decade. Madsen and Plant used a very similar model to describe beach slope evolution,

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24 where the form of the model is identical to that of Plant et al., with X(t) and Xcq(t), replaced by ~(t) and ~eq(t), the time varying shore normal and equilibrium beach slopes, respectively. While Plant et al. found that the rate parameter was proportional to the wave height cubed, Madsen and Plant determined the corresponding dependence for the beach slope change was closer to (H(t))4, where the range of exponents produced similar, acceptable results. The Madsen and Plant model was found to explain between 30 and 40 percent of the observed beach slope changes at Duck. Kriebel and Dean (1993) used an analytical approach to solve Equation 3.1 for an idealized case. Sensitivity studies performed on the EDUNE model (Kriebel and Dean, 1985), indicated that the equilibrium shoreline response varied nearly linearly with changes in the water level, and that the water level did not affect the rate of shoreline response. This information allowed decomposition of the equilibrium response, yeq(t), into a term associated with the magnitude of the response, Y eq, and a unit amplitude function of time containing the temporal dependence, f(t). The simplified differential equation, dy(t) = a(~qf(t)-y(t)) dt was then solved in terms of the convolution of the time dependent forcing and a characteristic solution for steady input conditions, I y(t) = aYeq f f(r)e-adr 0 (3.4) (3.5) where 'twas a time lag. The analytical solution given in Equation 3.5 possesses several attractive characteristics that suggest the method is worthy of further study. The convolution solution indicates that antecedent conditions are important, and that the

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25 actual shoreline response occurring in nature will be damped and lagged with respect to the maximum or equilibrium state. The analytic model is also consistent with nature in that it predicts the maximum rate of shoreline recovery will occur immediately after the passage of a storm, and even provides an analytic justification for the different time scales associated with beach erosion and accretion. As discussed in the previous chapter, the analytic solution is limited however, due to its simplified nature and inability to handle complex, realistic forcing conditions. Even the simplified Equation 3.4, can only be solved analytically for a limited number of cases where the time dependence of the equilibrium response is known and can be represented by a simple analytical function. Although extensions of the analytical solution are possible, only a numerical approach will be able to provide a realistic representation of the time dependent forcing function, yeq(t). 3.2. Defining the Equilibrium Shoreline, yeq(t) Equilibrium beach profile methodology and a modified version of the Bruun (1962) rule, which considers increases in the local water surface elevation due to a combination of tide, storm surge, and wave induced setup, are used to calculate the equilibrium shoreline response, yeq(t), for a given set of forcing conditions. Although the applicability of equilibrium concepts in the nearshore environment remains a controversial issue (see for example Thieler et al. (2000)), Bruun (1954) and Dean (1977) have illustrated the ability of a single empirical equilibrium beach profile relationship, h = Ay2'3 to adequately describe the nearshore bathymetry at numerous sites throughout the United States and Denmark. When applied to field data where conditions are constantly changing, the empirical relationship refers to a dynamic equilibrium state and average profile conditions. Dean even showed the equilibrium beach profile could also

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26 be derived analytically based on the assumption of uniform wave energy dissipation due to breaking waves through the surfzone. Subsequently, Moore (1982) and Dean (1991) were able to develop graphical, empirical relationships between the profile scale parameter, A, and sediment characteristics such as median diameter and fall velocity. The Bruun rule was originally developed to describe shoreline changes resulting from an increase in the local water surface elevation, S. If the assumption is made that the entire profile (not necessarily an equilibrium beach profile) shifts landward and upward without changing form with respect to the new water line, and that sediment volume is conserved, the resulting shoreline recession, 6.y, is fl. =-S W. y (h. +B) (3. 6) where h and w are the vertical and h orizontal extents of the active profile, and Bis the berm height. This expression has proven adequate in the absence of waves; however previous studies have indicated that the most significant shoreline changes occur when increased water levels are accompanied by large waves. Figure 3-1 illustrates this modified situation where the wave induced setup, 1J (y), alters the water surface elevation across the profile. The assumptions required in order to derive an analytical solution remain the same, namely the volume of sediment eroded from the foreshore equals that deposited offshore, and the form of the equilibrium profile remains unchanged with respect to the increased water level. In order to simplify the resulting expression, the common volume can be added to both sides of the conservation of volume equation, f,(BS -q(y))dy+ ,.+Ay A(y6.y)213dy = r+Ay Ay2/3dy + r + A y ( s +q(y) )cty (3.7)

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27 which after integration simplifies to !ly + 3h. (1 + !ly)513 = h. (315-K) ___ T/b W. 5B W. B (1-K) B B K = 3t' /8 1+3t' /8 (3.8) where K is the depth limited breaking coefficient. Equation 3.8 relates the non dimensional shoreline recession, !ly/W , to the dimensionless berm height, B/h., storm surge, SIB, and wave setup, 'f}t/B. In general, the non dimensional recession will be small and with K = 0.78, Equation 3.8 can be simplified even further, !1 ( )=-W ( >(0.068Hb(t)+S(t)) Yeq t t B+l.28Hb(t) (3.9) where Hb(t) is the breaking wave height, B is the berm height, and W (t) is the width of the active surf zone. Here W (t) is defined as the distance to the break point, such that it may be represented in terms of the breaking wave height, as W =
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28 shoreline change into a time series of equilibrium shoreline positions, this baseline condition must be identified. If the assumption is made a priori that the baseline condition corresponds to the average measured shoreline, the ayeq(t) calculated by Equation 3.9 are identically equal to the equilibrium shoreline positions (since the data are detrended). However in general this assumption is incorrect, as the baseline conditions for yeq(t) and Yob(t) are not necessarily the same. In fact, it has been argued (Wright, 1995) that the average shoreline position actually represents an average disequilibrium condition. In order to account for any potential offset in the baseline conditions, a constant calibration parameter, ay0 is introduced which provides an additional degree of freedom. The role of ay0 is illustrated in Figure 3-2, where in the example provided, a shift of ayo = 25 mis required to align the reference frames for yeq(t) and Yoo(t). The calibration routine discussed in Section 3.4.4 is used to determine the values of ay0 and lea for each simulation. Once the baseline condition has been identified, the equilibrium shoreline displacement, yeq(t), is given by Y e q (t) = ay0 + ayeq (t) (3.10) ------w.,-------- ft(y It ---'-=" -----~ --:~:;r~-~ Figure 3-1. Beach recession due to a combination of an increased water level, S and wave induced setup, 1/(Y) (from Dean 1991).

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29 E 40 -40.__ __.. __ _._ __ __.__ __ ....._ ___. __ _._ __ _._ __ _._ ___..__ _, 0 100 200 300 400 500 600 700 800 900 1000 Das 40.-------,-----.---....---.---------T~---.---""T"""--..-----,---, 3.3. Defining the Rate Parameter, ka The coefficient ka, governing the rate of shoreline response in Equation 3.1, can either be taken as an empirically determined constant or parameterized to incorporate some measure of the local conditions. Both alternatives have been considered here. In the simplest case, lea is assumed to be a locally determined constant, where the subscript a is used to signify that lea may be double valued, with one value, ke, representing erosion and a second, ka, associated with accretion. For most situations, it is assumed that kc will be much larger than ka, as in nature the time scales of erosion are generally much shorter than those of accretion.

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30 Although convenient, this representation is perhaps overly simplified, as it is more logical to assume the shoreline response rate depends in some manner on the local wave and sediment properties. Two different approaches were considered to determine effective parameterizations of the rate function. In the first approach, the rate parameter was related to a measure of the local wave energy, while in the second, non-dimensional parameterizations involving measures of both the wave and sediment properties, were considered. In both cases, the final form of ka(t) is given by ka(t) = kaf(t), where ka is the previously mentioned double valued coefficient, and f(t) is the time dependent parameterization. By parameterizing the rate function, the spread of the empirical coefficients is expected to reduce, as f(t) explicitly includes a measure of some of the important differences between the sites. The exact values of ka for each simulation are obtained by calibrating the model against historical data according to the procedure described in Section 3.4.4. Both Plant et al (1999) and Madsen and Plant (2001) based their rate coefficients on parameterizations of the local wave energy, adopting relationships of the form a(t)=a(H(t))P, where H(t) was a representative wave height and p was determined through an empirical fit to the data. Rather than explore an infinite range of possible values for p (p need not necessarily be an integer), a similar dependence is considered here, where the potential parameterizations are limited to ka(t) = kalit,(t)2 and ka(t) = kalit,(t}3. While Ht,2(t) is obviously related to the wave energy, Hb3(t) c~ be thought of as approximating the wave energy flux into the surfzone which actually has an Ht,512 dependence. One of the major disadvantages of the assumed wave energy relationship however, is that it contains no dependency on either sediment size, beach slope or wave

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31 period. In addition, in order for Equation 3.1 to remain dimensionally consistent, wave energy based parameterizations impose increasingly complex units of time11engthP on the empirical coefficients. Kraus et al. (1991) reviewed many of the non-dimensional parameters often used to separate erosional and accretional conditions and found several, that when plotted together with the deep water wave steepness, Ho/Lo, were capable of differentiating between the two. Table 3-1 lists some of the more common beach change discriminators. Although some of the criteria listed in Table 3-1 were based upon consideration of profile type, bar (berm) profiles are generally considered to be representative of erosive (accretive) conditions, and here the assumption is made that these conditions will have an in kind impact on the shoreline. The various parameter combinations listed in the table typically incorporate measures of both the wave environment CHo, T, Lo), as well as morphologic and sediment properties (dso, Ws, tan~). Although some of the parameters in Table 3-1 have thus far only been used to distinguish between the expected type of change, the assumption made here is that the magnitude of several of these parameters can potentially be related to the shoreline change rate through the parameter ka(t). One of the advantages of using non-dimensional parameterizations is that the rate coefficient, ka, retains the units of inverse time, which is more appealing from a physical perspective, as the inverse of this coefficient can be interpreted as the time scale of the shoreline response (see Section 6.2 for a more complete discussion). Using Table 3-1 as guidance, numerous non-dimensional parameterizations were considered; however the following five were deemed most appropriate: Fall velocity parameter, .Q(t) = Hb(t) Gourlay (1968), Dean (1973) wsT(t)

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32 w Froude number, F,(t) = s Kraus et al. (1991), Dalrymple (1992) ~gHb(t) Inverse Froude number, IF,(t) = F,(tf1 gH (t) Profile parameter, P(t) = 3 b Dalrymple (1992) wsT(t) Surf similarity parameter, (b(t) = Hb(t) /1)2 Battjes (1974) L0(t)(tan where Hb is the breaking wave height, T the period, Ws the sediment fall velocity, g gravity, tan J3 the local beach slope, and Lo the deep water wave length (Lo=gT2/27t). Each parameter contains a description of both the wave
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33 Table 3-1. Established erosion/accretion criteria. Reference Waters (1939), Johnson (1949) Rector (1954) lwagaki & Noda (1962) Nayak (1970) Dean (1973), Kriebel et al. (1987) Sunamura & Horikawa (1975), Sunamura (1980) Hattori & Kawamata (1981) Wright & Short (1984) Larson & Kraus (1989) Kraus et al. (1991) Dalrymple (1992) MacMahan & Thieke (2000) Parameters* He/Lo Hollo, dso/lo He/Lo, Hc/dso He/Lo, Hc/dsoS Hollo, 7rNJgT Hollo, dso/lo, tanf3 (Hc/Lo)tanj3, wJgT Ht/w1T He/Lo, Hc/w, T, 7rNJgT Hollo, HJw._T, wJ(gHo)-5 gH/!w/T, gH/lw/T ~. T1t,/H, 21tUt/Tg, Ut/ucrtt, ublubl 47t/T2g Criteria** Erosion : Hollo > 0.025 Accretion : Hollo < 0.025 Erosion : dso/lo < 0.0146(Hc/Lo) 1.25 Accretion: dso/lo < 0.0146(Hc/Lo)1.25 Graphical Method Graphical Method Erosion : He/Lo> C1[1rNJgT) Accretion: Hollo< C1[1rNJgT) Erosion : He/Lo> C2[tan13-027(dsollo)0 67 ] Accretion : Hollo> Ci[tan13-0.27(dso/lo)'67] Erosion : tanj3(Hc/Lo1 > 0.5wJgT Accretion : tanj3(Hc/Lo1 > 0.5wJgT Erosion : Ht/W8 T > 6 Accretion: Ht/w,T < 1 Erosion: He/Lo> C3[(7rNJgT)1 Accretion: Hollo< C3[(7rNJgT)1 1 Erosion : Hollo > C4[(HJw1 T)~ Accretion: Hollo< C 4 [(HJw,T)~ Erosion : Hollo > Cs{[wJ(gHo)-16} Accretion : Hollo < C5{[wJ(gH0)'16 } Erosion: Hofw,T >C6[w.2/gH0 ] Accretion : HJw,T > C6[w.2/gH0 ] Erosion ; gH/lw.3T >-10,000 Accretion : gH/lw.3T < -10,000 Erosion: gHb2/w,3r >-22,400 Accretion : gHb2/w,3r < -22,400 Graphical Method Ho = deepwater wave height, Lo = deep water wave length, d50 = median sediment size, S = sediment specific gravity, w = sediment fall velocity, g = gravity T = wave period tanl3 = beach slope, Hb = breaking wave height, U1r = wave orbital velocity under a trough, Ucrtt = critical velocity required to initiate sediment motion according to Hallermeier (1980), i;., = surf sinilarity parameter TJ1r = water surface displacement at the wave trough, Ub = uniform seaward directed retum flow, Ub = near bed wave orbital velocity at breaking. c,-e refer to empirical constants that vary depending upon scale effects in the data i e small-large scale or lab-field Adapted from: Larson M. and Kraus, N.C., 1989 SBEACH: Numerical Model to Simulate Storm-Induced Beach Change. Technical Report No. 89-9 Coastal Engineering Research Center, U S. Army Corps of Engineers Vicksburg, MS.

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34 The surf zone Froude number was used in combination with the deepwate~ wave steepness by both Kraus et al. (1991) and Dalrymple (1992) to distinguish between erosional and accretional conditions. As formulated, the surf zone Froude number is a ratio of competing forces, where Hb represents an upward suspending force, while Ws and g are related to particle settling. Although the Froude number contains measures of both the wave and sediment properties, it is a potentially less accurate discriminator than the fall velocity parameter since it does not include the wave period, and thus the wave steepness, in its formulation Kraus et al. presented two derivations where the Froude number was shown to be related to both the wave energy dissipation in the surfzone, and the power per unit volume expended by the waves via the bottom shear stress on suspending the sediment. The relationship between wave energy dissipation and the surfzone Froude number indicates that the magnitude of the shoreline response might be expected to vary with the magnitude of Fr(t). A rate parameter based upon an inverse Froude number, IFr(t), is also considered here in order to remain consistent with the expectation that larger waves will result in an increased erosion rate. The profile parameter derived by Dalrymple (1992) is essentially a rearrangement of the empirical relationship between HJLo and {1twJgT)312 and HJLo and {HJw5T)3 presented in Kraus and Larson (1988) and Larson and Kraus (1989) By taking the ratio of each set of terms and canceling common factors, Dalrymple showed that a single parameter, P(t), was extremely effective at separating the erosional and accretional events described by Larson and Kraus (1989). Furthermore, he illustrated that the resulting parameter was composed of a combination of two of the non-dimensional functions discussed earlier, namely P(t) = 1Fr(t)2.Q(t). By extension of the re sults pres e nted in

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35 Kraus et al. (1991), the magnitude of P(t) is also potentially related to the rate of shoreline change. Although first introduced by Irabarren and Nogales (1949), Battjes (1974) is generally credited with illustrating the ability of a single parameter, ~b(t), to describe a variety of surfzone characteristics. The surf similarity parameter, ~b(t), has been related to breaker type, breaking index, run-up, reflection coefficient, and beach type, all of which have the potential to strongly influence the shoreline change rate. The inverted form of the surf similarity parameter used here closely resembles the surf zone interference index used by Wang and Yang (1980), who interpret the parameter as the ratio of the natural swash period, to the period of the incoming waves. Using this interpretation, larger values correspond to an increasing degree of interference from successive waves, which manifests itself as a stronger return flow in the main water columri resulting in enhanced offshore transport. Compared to the other parameterizations, the one glaring weakness of the surf similarity parameter is that it requires knowledge of the beach slope a priori. This is problematic in that even if the initial profile shape is known, there is no consensus as to which slope (mean? foreshore? active profile?) to use. Here, an average nearshore beach slope was used, where both the surf zone and subaerial beach up to and including the berm were considered. A total of eight different rate parameters were evaluated, one where ka was assumed to be a locally determined constant, two where ka(t) was assumed to scale with the wave height alone, and five where both sediment and wave properties were used to parameterize ka(t). The possibility that the same parameterization may not apply for both erosion and accretion has been addressed by considering separate parameterizations for

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36 ka(t) and ke(t) resulting in a total of 64 (8x8) possible rate parameter combinations. The quantities w5 and tan f} involved in some of the parameterizations have been considered constant, since information regarding the temporal variability of these properties is sparse. The fall velocity, w5 used in each of the proposed parameterizations is that calculated from the median sediment size using the Hallermeier (1981) relation, while the beach slope, tan f}, is typically determined based upon a visual analysis of several nearshore profiles. 3.4 Solution Technique 3.4.1 Numerical Scheme Equation 3.1 can be discretized using a semi-implicit, finite difference scheme according to (3.11) where n is a time index. The unconditionally stable Crank-Nicholson scheme used provides order two accuracy along with computational efficiency, while the oscillatory nature of the forcing function yeq(t) limits the buildup of numerical error, as errors tend to cancel rather than perpetually increase. The maximum response or equilibrium shoreline position, yeq(t), is defined as that which would be attained if the forcing conditions were held constant indefinitely, and may be calculated from either observed or simulated data. In reality the equilibrium shoreline is a dynamic quantity, changing significantly with time scales on the order of hours; however over a single time step the forcing is assumed to remain constant. As with the analytical solution, the actual shoreline response will be lagged and damped with respect to the equilibrium shoreline. The particular time step

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37 used in model simulations varies with the temporal density of the input data, but is generally on the order of several hours. In all cases, the resolution of the forcing data is sufficient to capture the shortest (storm related) time scales intended to be reproduced by the model. 3.4.2 Forcing Data The model is forced by a combination of increased water levels due to tides, storm surge, and wave induced setup. Water levels used as input to the model have been obtained from tide gauges located near the sites of interest. In some cases, local tide information was unavailable or inadequate during the period of analysis and the nearest tide gauge with a complete record was used as a surrogate for the local water levels. Where necessary, tide factors (both in time and space) based upon a comparison of the local and surrogate data were applied in an effort to more closely match the local conditions. A visual comparison of regions of overlap in the records indicates that the application of tide factors improves the agreement between the local and surrogate tide records. Wave data were obtained from a combination of buoy measurements and statistical Wave Information Study (WIS) hindcasts made by the U.S. Army Corps of Engineers. Wherever possible an attempt was made to use buoy data; however in many cases a combination of data sources was needed to help eliminate significant gaps in the wave record. In these cases the secondary source was related to the primary source (both H5 0 and Tp) through a linear regression analysis performed on overlapping sections of the record. Directional information was added to non directional wave data sets by assuming all waves approached from the median deep water wave direction reported in th e WIS statistical summaries. The median angle was adopted after preliminary results using

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38 wind direction as a proxy for wave direction proved unsatisfactory. These preliminary results agreed with observations made by Masselink and Pattiaratchi (2001) in Western Australia, where the wind and wave direction tended to become decoupled during the falling leg of a storm. Once the offshore wave conditions were determined, linear wave theory was used to convert the offshore conditions to breaking values for input to the shoreline change model. 3.4.3 Shoreline Data The individual shoreline data sets used to calibrate and evaluate the model are discussed in detail in Chapter 4; however some general information is provided here. An attempt was made to identify and utilize only data that exhibited characteristics likely to be modeled well by a purely cross-shore model. Cross-shore processes tend to be most important on long, straight, natural coastlines, and wherever possible adjacent shorelines have been compared with one another to assess the degree of longshore uniformity. Longshore averaged shorelines were used for model comparisons to help minimize the influence of small-scale spatial irregularities that the model is not designed to reproduce and which may have an impact on the perceived accuracy of simulations. Persistent long-term shoreline trends were assumed to be related to gradients in the longshore sediment transport rate and were removed prior to applying the model. While a portion of these removed trends may in fact be the result of cross-shore processes (long term increases in wave energy, sea level rise), it has been assumed that they play a subservient role in comparison to the aforementioned longshore processes. The temporal resolution of the shoreline data sets varies, which allows the skill of the model to be evaluated over a range of time scales from daily to multi-decadal.

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Adjust Ra Yes 39 An lnltlal range, Ra Is selected for each parameter The value of the cost function, J Is evaluated at each Iteration and compared to Jm1n Does Jmin lie on an extrema of the range Ra? No Select k., k. and 4y0 used to calculate Jm,n Figure 3-3. Schematic of model calibration routine. 3.4.4 Model Calibration The completely specified model contains three empirical coefficients, lea, ke, and !ly0 which are evaluated based upon a comparison between model hindcasts and historical data. This is achieved by minimizing an objective or cost function, J, (3.12) where Yob(t) and Ypr(lea,ke,lly0,t) refer to the observed and predicted shorelines respectively. Several numerical procedures were considered to help identify the most appropriate values of lea, ke, and !ly0 where in the end, the simple numerical routine illustrated in Figure 3-3 was used to locate the minimum of J. In general, the error minimization procedure gives satisfactory results in terms of both computational time, as

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40 well as accuracy, with the infrequent exception of a few cases where local minima in the cost function are misidentified as the global minimum. This occurrence is rare, and while other numerical routines, specifically simulated annealing (Bohachevsky et al., 1986), were considered to attempt to correct this deficiency, none were satisfactory due to the poorly defined constraints on ka and kc. At present, the method illustrated in Figure 3-3 is deemed acceptable, as the misidentification of the global cost function minimum is extremely uncommon. 3.4.S Model Evaluation Unfortunately, model evaluation remains as much an art as a scientific technique. Despite recent calls for the development of a set of standardized, non-subjective model evaluation criteria, this is simply not feasible. It is impossible to evaluate a model in a completely objective sense and effectively consider all of the factors which have contributed to its success or failure. While objective measures of model performance are required to help quantify model skill, a number of factors including an appreciation of the model's objectives, an analysis of the quality of data used with the model, and a subjective interpretation of the quantitative measures are all required to accurately judge a model. Although objective measures may be useful in identifying problems, ultimately it is a subjective analysis of the objective criteria that identifies what the problem is and how to fix it. Three criteria, two objective and one subjective, have been used to help evaluate the new shoreline change model. Various quantitative measures including the normalized mean square error (NMSE), and several related criteria were used to objectively measure the prediction skill of the model. Since none of these are capable of evaluating the ability of the model to discriminate between erosional, accretional, and stable conditions, a separate objective categorical assessment procedure (CAP) is used for

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41 this purpose. Finally, a subjective measure of model performance, the Model Performance Index (MPI), was used to summarize all of the pertinent information, including both objective skill measures, in order to provide a single composite measure of model performance. Three of the more popular parameters used to evaluate model accuracy are the relative mean absolute error (RMAE), the normalized mean square error (NMSE), and the Brier Skills Score (BSS). The RMAE is defined as OSkill> 1 (3.14) I

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42 where w is a weighting function, and Yobs,t=O is the initial observation made at time t = 0. Although potentially appropriate for profile data, the skill as defined in Equation 3.14 is inadequate for shoreline studies, as the skill becomes a strong function of Yobs,t=O due to the significant shoreline changes that are routinely observed between successive surveys. One of the primary advantages of using this skill statistic is the capability of quantifying its significance; however this advantage is negated in shoreline studies, as even the most extensive shoreline data sets are too short (not enough degrees of freedom) for this to become meaningful. Sutherland and Soulsby (2003) and Sutherland et al. (2004) advocate the use of a similar skill measure for the evaluation of morphodynamic models. The Brier Skills Score (BSS) is defined as -oo
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43 the error variance to the measured shoreline variance, and unlike the RMAE it can be evaluated analytically. A perfect model in which the predictions exactly match the observations is characterized by a NMSE of zero. Although unbounded at its upper limit, errors on the order of one indicate model predictions with mean square deviations from the measured data approximately equal to the variance of the data. A subjective rating of the objective NMSE criterion is given in Table 3-3 In general, the NMSE is an extremely effective measure of model performance; however, since the difference term in the numerator is squared, it has the unfortunate property of being oversensitive to large deviations. The consequences of this are discussed in more detail in relation to model predictions in Chapters 5 and 6, along with several techniques that were used to attempt to overcome this shortcoming. None of the aforementioned measures of model skill are capable of evaluating the ability of the model to predict the correct type of shoreline change, i.e. erosion or accretion. This is illustrated clearly by considering two separate cases. In the first case, the correct type of change is predicted, but the magnitude is severely over predicted. In the second case, the wrong type is predicted, but the difference between Yob and Ypr is small. All of the measures discussed previously will assign less skill to the model that over predicts the magnitude of shoreline change even though the direction of change was predicted correctly. In order to assess the ability of the model to accurately distinguish between erosion and accretion, a separate categorical assessment technique was applied. Predictions and measurements were divided into three general categories: accretion, erosion, and no change (or stable), where a shoreline is defined as stable if the change in shoreline position between two successive surveys is less than five percent of the

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44 maximum range over the entire data set. This relative definition provides a useful sliding scale whereby energetic coastlines can undergo more significant changes and still be considered stable. A score from 0-1 is assigned to each possible combination of conditions as indicated in Table 3-2, where 1 represents a match between the prediction and observation, and O represents a complete mismatch. Since stable events are hardest to predict, they are assigned values reflecting the seriousness of the mismatch. For example miscasting an erosional period as stable has more serious potential consequences than miscasting an accretional period as stable hence the lower score (0.3 vs. 0.6). The categorical assessment procedure score, or CAP, is simply the average of all the individual scores, where higher values indicate more accurate predictions as reflected in Table 3-3. T bl 3 2 C a e -. al ateiionc d assessment proce ure s core matrix developed for this study I~ Erosion Stable Accretion Measu ed Erosion 1. 0 0.3 0.0 Stable 0.4 1.0 0.5 Accretion 0.0 0.6 1.0 Table 3 3. Subjective rating system based upon model performance statistics. Range of Values CAP Rating NMSE MPI Excellent <0.3 >0.8 5 Good 0 3-0.6 0 6-0.8 4 Reasonable 0.6-0.8 0 4-0.6 3 Poor 0.8-1.0 0.2-0.4 2 Bad >1.0 <0.2 1 The Model Performance Index (MPI) provides a holistic evaluation of model skill by incorporating the aforementioned objective measures of model performance with a subjective analysis of some of the more subtle aspects. The MPI takes into account many

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45 factors including: the quality and completeness of the input data, the characteristics of the modeled shoreline, the character of the NMSE (is it unduly influenced by a single data point), and the CAP. Higher expectations are placed on model performance when it is applied at sites with better data. For example, the availability of local tide data and measured directional wave information is expected to significantly improve the accuracy of predictions. The subjective analysis allows for the consideration of the character of the NMSE. Is it providing an accurate measure of model performance, or is a disproportionate amount of error resulting from a single outlier? These are important questions relating to the skill of the model that cannot be answered using purely objective methods. The MPI classification system ranges from 1-5 and is presented in Table 3-3.

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CHAPTER4 FIELD DATA AND SITE SUIT ABILITY In order to accurately evaluate the model, data were collected from numerous sources encompassing a variety of geographical locations and typical beach conditions. Broadly, the field sites may be separated into three groups, one each representing the East and West coast of the United States, and a third representing Australia. Since the model only simulates shoreline changes due to cross-shore processes an attempt was made to select data from long uninterrupted natural coastlines. The format of the available shoreline information ranges from beach width measurements obtained from aerial photography and video analysis, to several sets of complete profiles, some surveyed to depths of nearly twenty meters. The cross-shore model is not intended to reproduce small-scale features with alongshore wavelengths less than several hundred meters; therefore generally alongshore averaged shorelines have been used. The varying temporal resolution of the available shoreline measurements is such that it allows the performance of the model to be evaluated over a number of time scales ranging from days to decades. Wherever possible actual wave and tide data were used; however statistical hindcasts were substituted for physical measurements where required. Summaries of the available data and relevant site characteristics are provided in Tables 4-1 and 4-2. With so many good, high-quality data sets to choose from, it is essential to develop tools capable of identifying potentially inappropriate sites. The remainder of this chapter is d e voted to providing a brief description of the collected data as well as 46

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47 describing the techniques which were used to help select the most suitable locations for the evaluation of the proposed cross-shore model. Table 4-1. Summary of data sources. Site Latitude Longitude Shoreline Wave Data Nearest Tide Shoreline Orientation Source Gauge ID Data East Coast Sites East Hampton, NY 40.93N 72.20W 151 Buoy-44025 8531680 Profiles WISll-79 (Sandy Hook) Harvey Cedars NJ 39 70N 74.13W 127 Buoy-44025 8531680 Profiles WISll-69 (Sandy Hook) Island Beach NJ 39 83N 74.10W 98 Buoy-44025 8531680 Profiles WISll -69 (Sandy Hook) Wildwood, NJ 38.98N 74. 80W 142 Buoy-44009 8534720 Profiles WISll-66 (Atlantic City') Duck, NC 36.18N 75.75W 680 FRF Gauge 8651370 Profiles (Duck) St. Augustine FL 29.95N 81. 33W 75 WISll-23 8720220 Profiles & (Mayport) Aerials Crescent Beach, FL 29.75 N 81.25W 75 WISll-23 8720220 Profiles & (Mayport) Aerials Daytona Beach, FL 29.17N 81.05W 60 WISll-22 8720220 Profiles & (Mayport) Aerials New Smyrna Beach, FL 28. 88N 80. 93W 60 WISll-22 8720220 Profiles & (Mayport) Aerials West Coast Sites North Beach WA 47. 20N 124 05W 270 Buoy-46029 9440910 Profiles CDIP-036 (Willapa Bay) Grayland Plains, WA 46.80N 124 05W 270 Buoy-46029 9440910 Profiles CDIP -036 (Willapa Bay) Long Beach, WA 46.50 N 124.05W 270 Buoy-46029 9440910 Profiles CDIP -036 (Willapa Bay) Clatsop Pla i ns, OR 46. 10 N 124.05W 270 Buoy 46029 9440910 Profiles CDIP-036 (Willapa Bay) Torrey Pines CA 32 87N 117.26W 270 WISSC-002 9410660 (Los Angeles) Profiles Australian Sites Brighton Beach, WA 31. 92 S 115.75 E 270 Buoy-38 Free m a ntle Pro fi l es Leighton Beach, WA 32.08S 115 75 E 270 Buoy-38 Freemantle Profi l es Gold Coast, OLD 27.97 S 153 42E 90 Buoy-23 100035 Video Buoy 13 (Gold Coast) Direction is th e app roxim a te az imuth of th e outw ard shor e lin e norm al. b D ata from the n ea rby Cape May tide gauge, ID# 8536110 was also u sed to fill in missing da ta

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48 Table 4-2. Relevant site characteristics Site Approximat~ Significant ~dia~ Sedime~t Nearshore Berm Tidal Range H (m) Gram Size Fall Velocity' Beach Slope Height (m) (m) b (mm) (cm/s) East Coast Sites East Hampton, NY 1.14 Harvey Cedars, NJ 1.38 Island Beach, NJ 1.38 Wildwood, NJ 1.50 Duck, NC 1.12 St. Augustine, FL 1.57 Crescent Beach, FL 1.40 Daytona Beach, FL 1.35 New Smyrna Beach, FL 1.25 West Coast Sites North Beach, WA 2.72 Grayland Plains, WA 2.72 Long Beach, WA 2.92 Clatsop Plains, OR 2.92 Torrey Pines, CA 1.62 Australian Sites Brighton Beach, WA 0.6c Leighton Beach, WA 0.6c Gold Coast, OLD 1.5c Defined as MHHW-MLLW. 1.24 1.24 1.21 1.30 1.13 1.27 1.27 1.29 1.29 2.46 2.46 2.46 2.46 1.76 1.10 1.10 1.25 0.375 0.305 0.370 0.200 0.200b 0.149 0.139 0.153 0.138 0.135 0.178 0.193 0.160 0.194 0.574 0.375 0.290 b Calculated from the median grain size using the Hallermeier (1981) relation. b Mean spring tide range. 4.1 East Coast Data 4.66 3.72 4.57 2.33 2.33 1.64 1.56 1.74 1.55 1.37 2.05 2.24 1.82 2.27 7.92 5.00 3.51 1:30 1:65 1:25 1:40 1:55 1:55 1:70 1:45 1:55 1:45 1:50 1:15 1:15 1:20 2.45 2.60 2.30 2.30 2.50 1.85 1.85 1.85 1.85 3.00 3.00 3.00 3.00 2.40 2.00 2.00 2.20 In general terms, much of the Atlantic and Gulf coast of the United States is characterized by low-lying barrier island topography. Most of the coastline experiences low moderate wave energy, with larger waves occurring most frequently during the winter months from November to March, and in association with isolated tropical weather systems. In all, shoreline data from nine east coast sites spread amongst four different states was considered Figure 4-1 shows the location of each site as well as the approximate location of nearby wave and tide gauges that were used in the analysis.

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49 V = WIS STATIOJr B MVB B'OOY T 'l"IDB a&tJl'IB Figure 4-1. Location of data sets from the East Coast of the United States. The northernmost site on the Atlantic Coast is East Hampton, NY. Located along the southern shore of Long Island, the coastline has been monitored extensively since at least 1979 by a variety of state agencies, with the current monitoring being performed by the Marine Sciences Research Center of Stony Brook University. Profiles collected on inconsistent intervals over the past 25 years have indicated that the East Hampton shoreline is relatively stable. The dominant mode of variability along this stretch of coastline is a strong annual fluctuation, which corresponds to a distinct seasonal pattern in the wave climate. The mean annual significant wave height recorded by NOAA buoy 44025 in 40 m of water is 1.2 m, although waves as large as 9.2 m have been recorded during the winter months. The tidal range at the site is approximately 1.1 m and contains a dominant semi-diurnal component.

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50 The Richard J. Stockton College of New Jersey and the New Jersey Department of Environmental Protection maintain an extensive set of beach profile data for the State of New Jersey. Wading depth profiles were performed at over 100 sites along the Atlantic and Delaware Bay coastlines annually between 1986 and 1994, with the frequency increasing to bi-annually beginning in 1995. Shoreline data from three sites, Island Beach State Park, Harvey Cedars, and Wildwood were used in the current study. Conditions along the coast vary from location to location; however typical offshore wave heights as measured by NOAA buoy 44025 in 40 m of water range from 0.9-1.6 m, with a reported mean annual significant wave height of 1.2 m. Tides along the coast are mainly semi-diurnal, with a range on the order of 1.4 m. Although occasionally impacted by hurricanes and tropical storms, the most significant threats to the New Jersey shoreline are large waves and storm surges produced by strong winter storms and northeasters. The Duck, NC shoreline data were extracted from profiles collected by U.S. Army Corps of Engineers Field Research Facility (FRF) staff, and form only a small subset of the available data along one of the most intensively studied coastlines in the world. Detailed profiles have been collected monthly (bi-weekly at four selected sites), along the one-kilometer stretch of coastline since 1981. A variety of instruments simultaneously collect additional data ranging from wave heights and tide information to air temperature and wind direction. Despite a recognized seasonality in the wave climate in both height and direction, the dominant shoreline fluctuations at Duck occur with periods greater than one year (Plant and Holman, 1996; Miller and Dean, 2003). The mean annual significant wave height recorded at NOAA buoy 44014 in 47.5 m of water was 1.4 m, while the mean tidal range recorded by a gauge mounted on the FRF pier is 1.12 m.

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51 Despite the quantity and quality of the available data at Duck, the presence of the research pier significantly alters the nearshore environment, making it a potentially inappropriate site for the evaluation of a cross-shore shoreline model. Bathymetric changes related to the disruption of the natural longshore sediment transport by the pier have been found to be particularly pronounced in the vicinity of the shoreline (Plant et al., 1999; Miller and Dean, 2003). Miller and Dean (2003, 2004) discussed a method for attempting to isolate the shoreline changes due to cross-shore processes, by using a simple conservation of volume argument. According to the procedure, the change in sediment volume within a profile between two successive surveys, fl V(t), is presumed to be the result of longshore processes. Under the assumptions that the profile translates without changing form and that the volume change is distributed evenly over the vertical dimension of the active profile, (h+B), the shoreline change due to longshore processes, or the shoreline adjustment, /ly(t), can be obtained, LI (t)= LIV(t) (h.+B) (4.1) Since the model only considers cross-shore forcing, shorelines that have been adjusted by /ly(t) may potentially be more appropriate for evaluation purposes. Figure 4-2 illustrates the qualitative improvement in the consistency of shoreline changes between two profiles located on opposite sides of the Duck pier, suggesting that longshore effects are at least partially responsible for some of the initial non-uniform shoreline behavior. The same procedure was applied to the Torrey Pines profile data discussed in the next section; however the absence of any significant disturbances on the relatively straight, natural coastline resulted in only small shoreline adjustments.

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52 Interpolated Raw Shorelines, Duck, NC 60r.=======-====.----r--.-----.-----r---,--,----,----,----, Profile62 g 40 Profile 188 ~ 20 e _g 0 rJ'J -20...._ _._ __.__ ___,_ __ ....._ __.__ ......... __. __ .....__ _._ __._ __ 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 Year Interpolated Adjusted Shorelines, Duck, NC 60=====-====.---.--.-----.-----r---,r--,----,----,------, -20...._ _._ __.__ ___,_ __ ....._ __._ ___._ __.....__.....__ _._ __._ __ 1~ 1~ 1~ 1~ 1~ 1~1~ 19941~ Year Figure 4-2. Improvement in the consistency of the Duck shoreline data after adjusting for the volume change between subsequent profiles. The Florida shoreline data consist of a combination of beach profile data collected by the Florida Department of Environmental Protection Bureau of Beaches and Coastal Systems, and beach width measurements obtained through the analysis of aerial photographs from a variety of sources (Miller, 2001). Based on the availability of WIS hindcasts in the region and a lack of reliable buoy data, the duration of the analysis in this region was restricted to the period between 1956 and 1995 even though additional shoreline data exist. Shoreline measurements during this forty-year interval were highly irregular due to the combination of data sources used but generally increased in frequency with time. All four Florida sites experience similar wave conditions characterized by a mean annual significant wave height on the order of 1.2 m as measured by NOAA Buoy 41009 in 42 m of water. The tidal range increases slightly from south to north along the coastline, with a mean value of 1.35 m reported at Daytona

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53 Beach, and a slightly larger value of 1.57 m reported for St. Augustine Beach. Typical threats to this coastline include the catastrophic effects of both hurricanes and severe winter storms. 4.2 West Coast Data In contrast with much of the Atlantic coast, compartmentalized beaches interrupted by numerous rocky headlands characterize a majority of the Pacific coastline of the United States. The wave climate ranges from moderate to severe, with the largest waves typically occurring in the Pacific Northwest. Global weather systems have a considerable impact along the Pacific coast, where El Nino/La Nina weather patterns significantly alter the typical conditions. The West Coast data set is composed of a total of five sites, one located in Southern California, with the rest concentrated around the Washington-Oregon border. The approximate location of each site, along with nearby wave and water level gauges is indicated in Figure 4-3 W 1IZS S"l'A'l'IOB B IIAVB BUOY T Tim caoa Figure 4-3. Location of available shoreline data along the west coast of the United States.

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54 An excellent set of shoreline data for the Pacific Northwest has been collected as a part of the Washington State Coastal Erosion Study, which is being conducted jointly by the United States Geological Survey and the Washington Department of Ecology. Profile data have been collected quarterly since 1997 (bi-annually prior to 1999) at more than fifty sites along a 160 km stretch of coast between Tillamook Head, Oregon and Point Grenville, Washington, the approximate boundaries of the Columbia River Littoral Cell (CRLC). The CRLC contains four sub-regions, separated by three inlets, known as the North Beach, Grayland Plains, Long Beach, and Clatsop Plains subcells. Each subcell consists of long (>30 km) straight sections of coastline bound by natural headlands and navigational entrances. Shorelines in the CRLC are subjected to a strongly seasonal, high-energy wave climate where offshore significant wave heights measured in 228 m of water average 2.3 m and increase to over 3.0 m during the winter months. Local tides are semi-diurnal with an average range of approximately 2.8 m. In addition to storm and seasonal related changes, the shoreline also responds to El Nifio/La Nifia weather patterns, and long-term events including geologically frequent subduction zone earthquakes. The two years of profile data collected at Torrey Pines, California, by Nordstrom and Inman (1975), represents one of the first complete sets of nearshore bathymetric measurements ever obtained. Surveys were conducted to depths of nearly 18 m on a monthly basis along three shore perpendicular transects between June 1972 and April 1974. Nearshore wave conditions along this section of the California coast vary significantly due to the extremely irregular offshore bathymetry, including numerous submarine canyons. The mean annual significant wave height reported by Scripps Buoy

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55 101 in 549 m of water over the period 2001-2003 was approximately 1.1 m. The wave climate exhibits a distinct seasonal periodicity similar to the Pacific Northwest; however the range of variability (0.8-1.3 m) is reduced considerably. The local tide is semi diurnal with an average range of 1.6 m, and like most Pacific coast locations, has a large diurnal inequality Shoreline changes in Southern California occur over a variety of time scales; however the short duration of the data set limits the current analysis to changes with periods of two years or less. Figure 4-4. Location of Australian shoreline data sets. 4.3 Australian Data A total of three data sets were obtained from Australia, with two sites located near Perth in Western Australia, and the third located near Brisbane along the east coast. Each of the three Australian sites provides a unique test for the model. The two west coast sites are composed of the coarsest sand and experience the smallest tides of any site

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56 studied. The unique aspect of the east coast site is related to the daily shoreline sampling interval, which allows the model to be evaluated over a wider range of timescales. The three Australian sites are depicted in Figure 4-4, along with the location of nearby wave and tide gauges. Masselink and Pattiaratchi (2001) collected shoreline data from a number of beaches in the Perth region of Western Australia. Although the focus of their study was on beach changes dominated by the seasonal reversal in longshore sediment transport, data were collected at several long, uninterrupted, natural beaches where the shoreline responded primarily to changes in the incident wave energy. The available shoreline data consisted of a combination of beach width measurements and nearshore profiles collected over weekly to bi-weekly intervals between November 1995 and November 1998. Perth beaches are sheltered by a series of submerged shore parallel sand ridges that significantly reduce the incident wave energy reaching the coast. The average mean significant wave height and peak period measured by a buoy inside the outermost sand banks in 17 m of water were 0.9 m and 10.1 s, respectively. The sheltering effect of the sand banks can be seen when the inshore wave height is compared to that observed at a buoy just offshore in 48 m of water, where H s averages 2.2 m. Perth experiences a primarily diurnal tide with a maximum spring tide range of only 0.6 m. Unique factors influencing beach changes in the region include passing weather systems that can overwhelm the local tide and extremely energetic alongshore seabreezes that can average up to 8 mis during the summer (Masselink and Pattiaratchi, 2000). Shoreline position data from Narrowneck located at the northern end of the famed Gold Coast have been collected since mid 1999 as part of the post-construction

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57 monitoring program of the Northern Gold Coast Beach Protection Strategy. A beach nourishment was earned out between February 1999 and June 2000 in conjunction with the construction of an artificial surfing reef completed in December of 2000, just to the north of the site. An ARGUS video monitoring system (Holman et al., 1993) was set up to monitor the resulting shoreline changes with both a high temporal and spatial resolution. Daily shoreline measurements along the southernmost 1500 m of the site, where the effects of the nourishment and reef construction are least pronounced, have been extracted from video images using a technique described in Turner and Leyden (2000) and Turner (2003). The wave climate at Narrowneck as determined from a waverider buoy located offshore of the site in 16 m of water is characterized by a mean significant wave height of 1.1 m with an associated peak period of 9.2 s. Tides at the site are mainly semidiurnal with a maximum spring tide range of approximately 1.5 m. Beach changes at the site have typically been described as "event driven;" however recent work by Turner (2004) has identified a significant seasonal component. 4 4 Evaluation Tools Only recently has the selection of appropriate data for the evaluation of new long term models become an issue as multiple high quality data sets simply did not exist. With the increased availability of good quality data, comes a new responsibility and challenge to choose the data sets most appropriate for the intended application. In order to do this, a variety of statistical techniques and data analysis tools can be applied to help reveal some of the relevant characteristics of the data sets. The nature of the proposed model is such that it is expected to reproduce only those changes related to cross-shore processes ; th e refore longshore uniformity is an extremely important characteristic By utilizing simple statistical techniques to analyze the shoreline data at several adjacent

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58 locations, the impact of the unpredictable (for this model), non-uniform shoreline movements can be quantified and compared to the more uniform, predictable, large-scale changes. An appropriate data set will be one in which the ratio of the uniform behavior to the non-uniform behavior is high. 4.4.1 Time Domain Based Statistics Traditional time domain based statistics provide valuable insight into the relevant characteristics of a data set with minimal computational effort. The linear association between two data sets x and y is given by the correlation coefficient, rxy, (4.2) where overbars denote mean quantities, and Sx and Sy are the standard deviations. A correlation coefficient of indicates perfect correlation (a negative indicates the variables change inversely to one another), while O indicates the lack of any linear relationship between x and y. If the spacing between values of x and y is constant, a lagged correlation coefficient can be calculated which may provide useful information as to the phase relationship of x and y (i.e. it is possible for x to either lag or lead y). If x and y are beach width measurements taken at adjacent locations along a uniform beach, a large positive correlation is expected. Although the correlation coefficient is useful for comparing two variables with each other, it is less useful for comparing large numbers of variables. For this purpose, the longshore uniformity index provides a meaningful, non-dimensional measure of the degree of shoreline homogeneity The two-dimensional method applied here is a simplification of a three-dimensional method used by Plant et al. (1999). The non-

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59 dimensional longshore uniformity index, l1su, is simply the ratio of the longshore uniform portion of the shoreline variance, sis/, to the total variance, sti, in the system. In the two dimensional case, the longshore uniform portion of the variance is essentially the temporal variance of the longshore averaged deviations from the time mean shoreline location, y'( x,t )= y( x,t )-y,( x) (4.3) (4.4) where x and y represent the longshore and cross-shore coordinates respectively, and the subscripts t and x refer to temporally and spatially averaged quantities. In Equation 4.3, the shoreline data are separated into the time mean component, Yt(X), and a time and space dependent deviation from this mean, y'(x,t). The temporal variance of the alongshore average of these deviations defines the uniform component of the total shoreline variance. The longshore uniformity index, l1su, is then 2 ] = S/su lsu 2 s,o1 4.4.2 Frequency Domain Based Statistics (4.5) In some cases it is possible to look at the relationship between adjacent shoreline fluctuations in the frequency domain. If shoreline data are available over constant intervals (or can be interpolated to constant intervals with out too much loss of accuracy) spectral analysis can be used to examine relationships between the various frequency components of the overall signal. In particular, the coherence (or squared coherence or coherency), Coh, indicates the degree of linear correlation between the various frequency

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60 components of two signals, while the phase, cp(O, indicates the lag or lead. The coherence and phase are given by (4.6) (4.7) where Gxx and Gyy are the auto-spectra of x and y respectively, and Gxy is the cross spectrum of x and y, which is made up of both a real, Cxy (coincident), and an imaginary, Qxy (quadrature), part. In general, if x and y are two adjacent shoreline data sets a high coherence would be expected, particularly in the low frequency domain. Unfortunately, when dealing with relatively small data sets such as even the most comprehensive bathymetric data, spectral confidence limits become difficult to apply and are not presented here. 4.4.3 Method of Empirical Orthogonal Functions Although technically a time domain based statistic, the method of empirical orthogonal functions (EOFs) is discussed separately due to some unique characteristics. Pearson (1901) and Hotelling (1933) originally developed the method in the early 1900's as a means of extracting the dominant behavioral patterns from a set of data. Winant et al. (1975), Vincent et al. (1976), and Dolan et al. (1977) were among the first to apply the technique to geophysical data sets in the coastal environment. Although much of the subsequent work with EOFs has centered around applications of the method to profile data, it can also be used to analyze the longshore variations in a data set. Here they are used to examine the longshore variability of data sets consisting of beach width measurements. When applied in this manner, EOF's are able to extract the dominant

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61 modes of variability, which may correspond to either longshore uniform behavior, which can potentially be modeled with a cross-shore model, or non-homogenous behavior, which cannot. In simple terms, the EOF method exploits the properties of matrices to identify patterns of standing oscillations within the data These patterns allow individual modes of variability to be analyzed separately, which can then used for a variety of purposes, among them simplifying the representation of the original data. The first step in an EOF analysis is to separate the spatial and temporal variability of the data by representing the original data set, y(x,t), as a series of linear combinations of functions of time and space, n y(x,t) = Ic" (t)e1(x) (4.8) A:=l where ek(x) are referred to as the spatial eigenfunctions, and ck(t) are referred to as either weighting functions or the temporal eigenfunctions. The summation is carried out from k = 1 to n, where n is the lesser of nx or nt, the number of spatial and temporal samples respectively. In some derivations, the temporal coefficient ck(t) is given as the product of a unit amplitude function of time ck(t) and a normalizing factor ak given by ~AicnJA where Ak is the eigenvalue associated with the kth eigenfunction. The requirement, (4.9) .x where 6nm is the Kronecker delta, ensures that the eigenfunctions ek(x), form a set of statistically independent, or uncorrelated vectors, which are normalized to unity. So far, the derivation has remained fairly general, and an infinite number of functions ek(x) may be specified that satisfy the conditions of Equations 4.8 and 4 9 What separates the EOF method from other series decomposition techniques such as Fourier analysis, is the fact

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62 that the data are used to select the eigenfunctions rather than specifying them a priori. The selection is made such that the eigenfunctions best-fit the data in a least squares sense, with the first eigenfunction representing the bulk of the variability in the data set, and each subsequent eigenfunction accounting for the majority of the remaining variability. Mathematically, the Lagrange multiplier approach is used to formalize this requirement, resulting in a solvable eigenvalue problem, (4.10) where E is a matrix containing the spatial eigenfunctions, eit(x), and A is a diagonal matrix containing the eigenvalues, "-k The matrix A represents some measure of the spatial covariability of the original data set y(x,t). Winant et al. (1975) defined A as a correlation or sum of squares and cross-products matrix, while Aubrey and Ross (1985) utilized a demeaned version of y(x,t) and defined A as the covariance matrix. Either method is correct as long as the results are interpreted in the context of the frame of reference from which they were derived. Here A is taken as the correlation or sum of squares and cross-products matrix, (4.11) where the bracketed term, [nx nx], indicates the dimensions of A. Two methods exist for determining the temporal eigenfunctions, ck(t). The first method is directly analogous to the technique set forth for calculating the spatial eigenfunctions and involves solving the set of equations, Be t (t) = -Ct (t) or BC = AC (4.12) [n ,,n,] (4.13)

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63 Where A provided a measure of the spatial covariability within the data set, B measures the temporal covariabilty. Analogous to Equations 4.10 and 4.11, [nt, nJ defines the dimensions of B, and C contains the temporal eigenfunctions C1t(t). In comparing Equations 4.11 and 4 13, it should be obvious that in general, matrices A and B have different dimensions, and thus the A's calculated from Equations 4.10 and 4.12 also must have different dimensions. Although disconcerting at first glance, it can be proven that only the first k values of A are non-trivial (non zero), and in fact that the first k values from either equation will be identical. Alternatively, the temporal eigenfunctions may be calculated directly as (4.14) where a" (=~~n,.n, ) is the normalizing factor mentioned previously. Square matrices have many interesting properties, some of which can be exploited to help explain the physical significance of the calculated quantities. Given the definitions of A and B, the trace, or sum of the diagonal elements of both A and B is simply the mean square value of the data. From Equations 4.10 and 4.12 it can be shown that the sum of eigenvalues must equal the mean square value of the data, thus each individual eigenvalue, A1t, can be thought of as representing the relative contribution of mode k to the overall variability of the data set. The percent contribution of level k is given by (4.15)

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64 As mentioned previously, the first few modes will contain the bulk of the variability, and the significance of each mode will decrease. In the present context, the usefulness of the EOF technique is rooted in the longshore variability of the primary mode, e1(x). Extrema in e1(x) define regions of maximum variability, while nodes indicate regions of zero variability. With rare exception, nodes separate eroding and accreting regions; and therefore shoreline changes are said to be out of phase across nodes. The presence of nodal points usually indicates the influence of longshore processes, as they provide the mechanism for transferring sediment across the node from eroding to accreting regions. Here, multiple nodal points are used to identify those data sets which are not likely to be well represented by a cross-shore model. In cases where some modes are longshore uniform and others are not, it is possible to use the EOF method as a means to filter out the non-uniform behavior by reconstructing the data set according to K Y,K(x,t) = Latck(t)ek(x) (4.16) k = I where k refers to the uniform modes, and YrK(x,t) refers to a reconstructed data set which retains only the longshore uniform information. 4.5 Site Suitability Although intuitively obvious, the fact that not every data set is appropriate for every application is rarely discussed. In the past there has been an overabundance of models calibrated and evaluated with whatever data was available, regardless of the synergy between the data and the model. A good example is the fact that data from Duck, has indiscriminately been used to validate many models regardless of whether the influence of the pier has been accounted for. Because of the abundance of good, high quality data sets available here, inappropriate data can be eliminated using some of the

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65 methods discussed in section 4.4. Table 4-3 summarizes the methods used at each site as only certain techniques are applicable at each location. Table 4-3. Data analysis techniques applied at each site. Site Length Sampling Duration Interval Time Frequency EOF (km) Locations Domain Domain East Coast Sites East Hampton, NY 1.50 3 1979-1997 Variable Yes No No Harvey Cedars, NJ 9.00 3 1986-2002 Biannually Yes No No Island Beach, NJ 4.00 3 1986-2002 Biannually Yes No No Wildwood, NJ 3.50 2 1986-2002 Biannually Yes No No Duck, NC 1.00 20 1980-2002 Monthly Yes Yes Yes St. Augustine, FL 2.50 3 1955-1995 Variable Yes No No CrescentBeach,FL 3.25 2 1955-1996 Variable Yes No No Daytona Beach, FL 3.50 3 1955-1997 Variable Yes No No New Smyrna Beach, FL 2.75 3 1955-1998 Variable Yes No No West Coast Sites North Beach, WA 41.00 12 1998-2002 Quarterly Yes No Yes Grayland Plains, WA 17.00 8 1998-2003 Quarterly Yes No Yes Long Beach, WA 38.00 16 1998-2004 Quarterly Yes No Yes Clatsop Plains, OR 25.00 6 1998-2005 Quarterly Yes No Yes Torrey Pines, CA 1.00 3 1972-1974 Monthly Yes Yes No Australian Sites Brighton Beach, WA NA 1 1995-1998 Weekly Yes No No Leighton Beach, WA NA 1 1997-1998 Weekly Yes No No Gold Coast, OLD 1 50 300 2000-2003 Daily Yes Yes Yes In the time domain, the appropriateness of each data set was evaluated by the combination of the correlation coefficient, rxy, and the longshore uniformity index, l1su Large positive correlations are characteristic of the type of homogenous shoreline change desired for this particular application At sites with fewer than three sampling points the correlation between each individual sampling location and each of the others was calculated according to Equation 4.2. In Table 4-4, both the mean and maximum correlation is reported, along with the 95% significance level as determined from a standard t-test (Davis, 1986). In most cases, the calculated correlation coefficient is

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66 larger than the 95% significance level, meaning the null hypothesis (that the correlations may have occurred by chance) can be rejected. Bold, italic values denote cases where insufficient evidence exists to reject the null hypothesis. Of the sites with less than three sampling locations, the East Hampton, Harvey Cedars, and Wildwood data sets appear to be non-uniform and hence inappropriate for the model. Table 4-4. Summary of time domain analysis results. Site Number of Maximum Average Correlations Correlation Correlation East Coast Sites East Hampton, NY 3 0.356 0.231 Harvey Cedars, NJ 3 0.211 0.072 Island Beach, NJ 3 0.677 0.564 Wildwood, NJ 2 0.238 0.238 Duck, NC 20 0.401 0.315 Adjusted Data 20 0.305 0.222 St. Augustine, FL 3 0.767 0.693 Crescent Beach, FL 2 0.709 0.709 Daytona Beach, FL 3 0.871 0.778 New Smyma Beach, FL 3 0.887 0.817 West Coast Sites North Beach, WA 12 0.643 0.491 Grayland Plains, WA 8 0.530 0.385 Long Beach, WA 16 0.606 0.489 Clatsop Plains, OR 6 0.623 0.497 Torrey Pines, CA 3 0.869 0.775 Adjusted Data 3 0.777 0.715 Australian Sites Brighton Beach, WAd 1 0.519 0 519 Leighton Beach, WAd 1 0.519 0.519 Gold Coast, OLD 300 0.888 0.867 Significant Correlation (95%) 0.325 0.404 0.433 0.413 0.122 0.122 0.482 0.532 0.576 0.468 0.468 0.482 0 482 0.482 0.288 0.288 0.279 0.279 0 .081 Considering subsections on either side of the pier: l11u =0.821 north of the pier, l11u =0. 704 south of the pier b Considering subsections on either side of the pier: l11u =0. 586 north of the pier, l11u =0. 597 south of the pier. 0.588 0.325 0.608 0.629 0.4338 0.355b 0.766 0.928 0.868 0.888 0.416c 0.154c 0.59~ 0.499c 0.827 0.834 0.796 0 796 0.877 When only the data from the center of the site which was used to calculate the longshore averaged shorelines is considered, I., increases to at least 0 .835. d For the purposes of the time domain analysis shoreline data from the two Western Australian sites were compared.

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... 0.5 iii ::, j 0 '6 C: -0.5 0 67 Correlation Analysis Duck, NC (Adjusted) 200 400 800 1000 0.8 ....... ................ .. ....... . ...... ................ ......... ... it>.6 ........................................... ................ ................ ....... ........ . . C: . . . . I 0.4 . . . . . .................. ............... ................ ............. .... . . . . 0 .__ __.__ ___ ___. ____ _._ ___..______..__ ___ ___._ ___ _.... 0 200 400 600 800 1000 Longshore Distance (m) Figure 4-5. Calculation of the mean correlation profile including rmax and ravg At sites with more than three sampling locations, the average and maximum correlation coefficients were calculated in a slightly different manner. As with the smaller data sets, Equation 4.2 was used to calculate the correlation between each individual data set and all of the others. The result is a symmetrical nx x nx matrix of correlation coefficients describing the covariability of the data. Each column of this matrix was then averaged to obtain a mean correlation coefficient for each sampling location. In Figure 4-5, an example is provided using the adjusted data set from Duck, NC. The upper panel shows correlation profiles for two specific profiles (yx=183 and Yx=777), where the correlation of data set with itself is identically one. Similar profiles exist at each shoreline measurement location. The average correlation coefficient can then be calculated at each of the sites as was done for the two profiles in the upper panel

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68 (rxy=0.263 and rxy=0.241). In the lower panel, these average rxy values are plotted along with lines representing the mean and maximum values, and the 5% statistical significance level. The data reported in Table 4-4 are these average and maximum correlation coefficients. From the results of the correlation analysis of the larger data sets, the shorelines at both Grayland Plains, WA and Duck, NC behave non-uniformly and are therefore considered inappropriate for evaluating the proposed model. The longshore uniformity index, l1su, provides an additional useful measure of shoreline homogeneity. Larger values of l1su indicate stretches of coastline where cross shore processes are most likely dominant and the entire shoreline tends to translate in unison. Calculated values of the longshore uniformity index are presented in the last column of Table 4-4, where a value of 0.6 is used to identify potentially inappropriate data sets. Although the results suggest that all of the Washington and Oregon data are unsuitable, information gained from other analyses, particularly the EOF method, clearly show that the majority of the non-uniform behavior at these sites is due to isolated end effects. In fact when l1su is recalculated using the subset of data used to calculate the longshore averaged shoreline (taken at the center of each site), all the values increase to at least 0.835. Although in general the longshore uniformity index supports the results of the correlation analysis, there is one glaring discrepancy. While the correlation analysis suggests the Wildwood data set be eliminated, a longshore uniformity index above the cutoff criterion was calculated (0.629). Closer examination reveals that while the two shorelines comprising this data set generally move in unison with one another, a rather large discrepancy is observed in the data point collected immediately after the infamous "Perfect Storm" occurring in late October 1991. The profile located closest to the nearest

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69 down drift inlet exhibits significant accretion while the majority of the coastline experienced severe erosion. The hypothesis is that the unusual severity of the storm extended the typical region of influence associated with the inlet to encompass the southernmost profile location. Rather than eliminate a potentially good data set on the basis of a single inconsistent data point, the decision was made to retain the Wildwood site after eliminating the data set closest to the inlet. Spectral analysis, coherence, and phase were used to analyze the frequency domain behavior of those data sets that were either sampled at a constant interval, or were sampled frequently enough to allow the original data to be interpolated with an acceptable degree of accuracy. Spectra similar to those presented for the Gold Coast in Figure 4-6 were calculated for the Duck and Torrey Pines data as well. Figure 4-6 clearly shows that the periodic trends at each longshore location are consistent, as would be expected. Similar behavior is observed at Torrey Pines; however the Duck spectra are much more scattered indicating non uniform behavior. The coherence and phase plotted in Figure 4-7 are the average values using three selected shorelines as the basis for comparison. As expected for uniform shorelines, the coherence is high and the phase oscillates slightly about zero in the high-energy region (f = O.O(H0.04 cycles/day). Similar behavior is exhibited at Torrey Pines; however low coherence values and wildly fluctuating phase estimates once again illustrate the inconsistent behavior of adjacent shorelines at Duck. In general, the conclusions of the frequency domain based analysis are consistent with the previous results, and support the assertion that Duck is an inappropriate location for the evaluation of the proposed model.

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70 X 104 Shoreline Change Spectra Gold Coast, OLD 10 9 8 7 $: CG 6 "C I 1. 5 :ii4 3 2 1 0 10 -3 Mean Coherence & Phase Gold Coast OLD 0.8 ~0. 6 u C :g 0 4 ::E -Coh & with y x ,._1750 0.2 -eCoh & with Yx= -1250 _,._ Coh & with y x=-750 OL....J.. ________ ....__....____.___.__._......_ .......... ....___~.___.__._......_ .................... 10-3 1/yr 2/yr 102 1/mo 10 ,1/wk 100 50 C 0 CG Q) ::E 50 1/yr 2/yr 1/mo f (cycles/day) Figure 47. Mean coherence and phase for three selected shorelines at the Gold Coast.

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71 The EOF method was used to analyze the dominant spatial and temporal modes of variability at sites with multiple (>3) longshore sampling locations. The dominant modes of spatial variability are of primary interest here, and are plotted in Figures 4-8 to 4-10. A summary of the nature and the amount of variability explained by the first two modes is given in Table 4-5. In every case, the first two modes account for nearly 90% of the total variability of the data, where the majority of modes can be classified as uniform. Non-uniform behavior is indicated by the presence of nodal points in Figures 4-8 to 4-10, where they tend to represent transition points that separate eroding and accreting regions. Typically, shoreline changes are referred to as out of phase across the nodes. The absence of nodal points in a given mode is a reflection of longshore uniform behavior, as the entire coastline tends to advance and retreat in unison. In terms of the principal modes of transport, cross-shore processes tend to dominate on uniform coastlines, while longshore processes provide the primary mechanism for transporting sand across nodal points from eroding to accreting regions. As indicated in Table 4-5, most of the primary eigenfunctions, e1(x), exhibit some form of uniform behavior. Inlets and rocky headlands tend to have a significant impact on the Washington data as pronounced "end effects" are present. Uniform regions are most difficult to identify at Duck and Grayland Plains. At most sites, the second and third spatial eigenfunctions, e2(x) and e3(x), begin to describe the deviations from the dominant longshore uniform behavior described by e1(x). Based upon the form of the primary and secondary eigenfunctions and their relative importance, the EOF analysis suggests that potentially neither the Duck nor the Grayland Plains data are appropriate for the evaluating a cross-shore model.

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72 First Three Spatial Eigenfunctions Duck, NC 1 ~----,-----.-------r---w---,----~----r----:--. FRF : Pler 0 5 . .... . . ........... .. ............................ ......................... . . . 0 Q) -0.5 e1(x)-81.98% -e-e2(x) 7.45% . . . . . . . ..... ............. ......... . . . ................... ...... . . . . __ e3(x) 3.63% -1 .__ __ ___._ ___ __,_ ___ _,__ _._ __._ ___ _.___--L ___;::..._ ___ _J -200 0 200 400 600 800 1000 1200 . . Longshore Distance (m) First Three Spatial Eigenfunctions Duck, NC (Adjusted) 1~------~---~-~-~---...-----..------. r FRFPler 0.5 '. : :0 Q) e1(x)-55.25% -e-e2(x) 16.37% -0.5 __ e3(x) 9.68% -1~--~---~---~-~-~---~--_ __;::..._ ___ _J -200 0 200 400 600 800 1000 1200 Longshore Distance (m) Figure 4-8. Spatial eigenfunctions e1(x)-e3(x) for Duck, NC. First Three Spatial Eigenfunctions Washington State e1(x) -76.41% LongB~h : : ----... : N : -e-eix) 16 61% 0 5 __ ea
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73 First Three Spatial Eigenfunctions Gold Coast, OLD 0.2~-------.---------.--------....,....., N 0 15 ............................... ; ..... .. ..... ..................... ; ....................... 0.1 : : ........ -0. 1 : -0.15 e1(x) 88.6% .... : .............................. .. '. .... ................. . . . . -ee2(x) 2.58% __ e3(x) -2.22% -0.2L...!::::=======--L--------L------'--____J -2000 -1500 -1000 -500 Longshore Distance (m) Figure 4-10. Spatial eigenfunctions e1(x)-e3(x) for the Gold Coast, QLD. Table 4-5. Summary of EOF analysis results. e1(x) Site East Coast Sites % Variance Explained Duck, NC 81.98% Duck, NC (adjusted) 55.25% West Coast Sites North Beach, WA Grayland Plains, WA Long Beach, WA Clatsop Plains, OR Australian Site Gold Coast, OLD 82.11% 90.09% 77.09% 76. 41% 88.59% Form Uniform Variable Uniform Variable Uniform Uniform Uniform e2(X) % Variance Explained Form 7.45% 16.37% 7.03% 8.89% 14.34% 16.61% 2.58% Variable Variable Uniform Uniform Uniform Uniform Variable e2-nx(X) % Variance Remaining 10.57% 28.38% 10.86% 1.02% 8.57% 6.98% 8.83% As a result of the site suitability analysis performed using the tools described in Section 4.4, several data sets have been determined to be inappropriate for evaluating the new model. The cross-shore nature of the model makes longshore uniformity an

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74 important characteristic of suitable sites. A combination of inconsistent survey data along with poor performance in the correlation analysis resulted in the elimination of the East Hampton data. The Harvey Cedars site was included primarily as a check of the site evaluation technique since its location within a groin field makes it extremely unsuitable for analysis with a cross-shore model. Surprisingly, the majority of the Washington and Oregon State data exhibits enough longshore uniform behavior to be considered suitable, with the lone exception of the Grayland Plains data set. A low average correlation coefficient, and an extremely small longshore uniformity index, combined with the EOF analysis that failed to identify any significant longshore uniform behavior, all support the decision to eliminate the Grayland Plains site. Last but not least is the revered Duck data set. The significant impact of the pier on the adjacent shorelines has long been recognized, but often ignored when selecting appropriate data for the evaluation of new models. The Duck data performed poorly in all of the suitability tests illustrating its inappropriateness for the proposed application. Fortunately, the number of high quality, readily accessible data sets is constantly increasing, providing plenty of more suitable alternatives.

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CHAPTERS RESULTS At most of the selected sites, the model is able to reproduce the historical shoreline changes with a degree of accuracy that is on par with or better than most traditional approaches, but at a fraction of the computational cost and effort. A total of 64 simulations representing all possible rate parameter combinations were performed at each of the 13 sites, for a total of 832 separate hindcasts. As expected, the results varied from site to site, and even at a given location depending upon the parameterizations selected for ka(t) and ke(t). A succinct summary of the results is provided in Table 5-1, where the columns from left to right represent: the average NMSE and classification (from Table 3.2), the minimum NMSE and classification, the mean CAP and classification, the maximum CAP and classification, and the MPI and associated classification. The column averages given in the last row indicate that overall the model is successful according to all three criteria, particularly when only the best simulations (columns 4 & 8) corresponding to the most suitable rate parameters are considered. There are however, some cases where the model does not perform nearly as well (Island Beach, NJ for example), although at least in some cases this poor performance can be partially explained by the unexpected, and somewhat anomalous behavior of the observed shoreline. A complete tabulation of the results at each of the thirteen sites is presented in Appendix B. The remainder of this chapter is devoted to a description of the typical model performance using the Daytona Beach, FL data, examples of the types of results 75

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76 presented in Appendix B using the Long Beach, WA data, and a general description of the results in New Jersey, Florida, Washington, California, and Australia. Table 5-1. Summary of SLMOD results. NMSE CAP MPI Site Mean Minimum Mean Maximum Score Rating Island Beach, NJ 0.932 p 0.885 p 0.632 G 0.705 G 1 B Wildwood, NJ 0.686 R 0.596 G 0.659 G 0.779 G 3 R St. Augustine, FL 0.782 R 0.668 R 0.640 E 0.805 E 4 G Crescent Beach, FL 0.849 p 0.259 E 0.663 G 0.835 E 2 (5) P-E Daytona Beach, FL 0.703 R 0.619 R 0.748 G 0.841 E 4 G New Smyrna Beach, FL 0.765 R 0.595 G 0.684 G 0.800 E 4 G North Beach, WA 0.628 R 0.537 R 0.828 E 0.917 E 4 G Long Beach, WA 0.363 G 0.281 E 0.873 E 0.926 E 5 E Clatsop Plains, OR 0.423 G 0.312 G 0.902 E 0.974 E 5 E Torrey Pines, CA 0.745 R 0.48 G 0.596 G 0.779 G 4 G Brighton Beach, AS 0.615 R 0.524 G 0.647 G 0.656 G 3 R Leighton Beach, AS 0.624 R 0.522 G 0.655 G 0.680 G 3 R Gold Coast, AS 0.521 G 0.470 G 0.715 G 0.718 G 4 G Gold Coast (tilt), AS 0.367 G 0.298 G 0.985 G 0.987 G 4 G Average 0.643 R 0.503 G 0.731 G 0.814 E 3.79 G-E Classification according to Table 3-3, where B=Bad, P=Poor, R=Reasonable, G=Good, E=Excellent The unconditionally stable nature of the numerical technique employed in the model allows for simulations to be performed with an arbitrary time step. Since one of the objectives of the model is to encompass as much of the broad engineering scale as possible, the shortest time step, corresponding to the temporal density of the input data, is used. Depending upon the source of the wave data, this time step varies, but is generally on the order of 1-3 hours. This is short enough to capture the smallest scale intended to be reproduced by the model corresponding to storms, and also allows for realistic simulations to be completed in a reasonable amount of time. For clarity, only predictions for those days where a corresponding measured data point was recorded are plotted in the figures appearing in Appendix B; however for each simulation a complete time series of equilibrium and hindcast shoreline positions are calculated at each time step. An

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77 example of these time series is plotted in Figure 5-1, where dissecting the figure helps to illustrate the typical model behavior. The specific simulation presented corresponds to the hindcast with the lowest NMSE at Daytona Beach, FL, where the accretion rate parameter is a function of the breaking wave height squared, namely ka(t) = l.08x10-4Hb2(t) hr -1 and the erosion rate parameter is a function of the Froude number, specifically ke(t) = 0.45Fr(t) hr-1 All three plotted time series contain dominant seasonal signals, corresponding to strong annual periodicities in both the wave and water level forcing. The noise or variability in yeq(t) reflects the fact that the equilibrium shoreline represents a complete and instantaneous response to the forcing, which includes high frequency phenomena such as the semi-diurnal tidal signal. The predicted shoreline on the other hand, responds with a much longer timescale that is highly dependent on the value of ka. The seasonal trend predicted for Daytona Beach is generally consistent with previous observations that have been made along the Florida coastline (De Wall, 1977). The inset chart in Figure 5-1, provides a close up view of the typical annual cycle that occurs in nature and is well predicted by the model. The inset plot shows an initially eroded winter shoreline, which recovers gradually over the spring and summer months only to be eroded by a succession of storms, in this case Hurricane Diana (9/8/19849/16/1984) and the Thanksgiving Day Storm (ll/22/1984-11/25/1984). The significant difference between the predicted erosion and accretion time scales, exhibited in nearly all of the simulations is characteristic of the natural response, and is particularly pronounced in Figure 5 1 Although it is somewhat difficult to tell based upon Figure 5-1 alone, a more complete analysis including an evaluation of the NMSE, CAP, and MPI indicates that the model performs reasonably well at Daytona Beach.

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-E C 0 E en 0 a.. Q) .s 0 .l: CfJ 80 60 l 40 20 -20 L -60 -80 78 Shoreline Hindcast Time Series Daytona Beach, FL ?C .. : 1984.5 : 1985 -100'-----'----'-....__ __ __.__ ...__...._ __ ___.__,____..... _,_ __ ___._ ___ .........., 1960 1965 1970 1975 1980 1985 1990 1995 Date Figure 5-1 Complete hindcast shoreline time series for Daytona Beach, FL. The figures and tables presented in Appendix B are designed to provide a concise yet informative description of the model results at each site. A typical hindcast generated for the Long Beach, WA site is shown in Figure 5-2, where only those model predictions corresponding to the dates for which measured data were available have been plotted. The two hindcasts in each figure represent those generated using the parameter combinations resulting in the best predictions as selected objectively using the NMSE and CAP criteria. In some cases, both criteria suggest the same parameter combination and the two hindcasts overlie one another. The error bars appearing in Figure 5 2 are used to indicate the range of shoreline predictions produced by alternate parameter

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79 combinations. In other words, all shoreline predictions for a given time fall within the bounds defined by the error bars. Although the model is sensitive to the particular form of the rate parameter, the error bands typically show that regardless of the parameterization, the predictions fall within an acceptable range of one another. Long Beach, Washington 20 15 10 5 -E -C: 0 0 E u, 0 -5 a.. Q) .E Q) 0 -10 :II : .............. : { . .. ; .s::. en -15 -20 -25 : I I / \ HF \ : ..... .. .. .. . . . . . . ..... .1././. i \ ....................... -_:_-~-:~-(M-in-im;_u m N M _S_E_) ..,_1 iii -A y (Maximum CAP) pr + Prediction Ran e -30L_ __ ____J ___ __,! ___ _..1. ___ ___:I======:=::r:=======::...1 1997 1998 1999 2000 2001 2002 2003 Year Figure 5-2. Example hindcast plot of observed and predicted shorelines at Long Beach, WA, and typical of those presented in Appendix B. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations. Accompanying each figure is a series of three tables similar to Tables 5-2 to 5-4. In Table 5-2, the NMSE for each possible parameter combination at a given site is presented, where each column corresponds to a different form of ke(t), while each row represents a different form of ka(t). In addition, column and row averages have been

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80 included, which give the mean NMSE for the various forms of ka(t) and ke(t), respectively. Two separate criteria were used to select the "best" rate parameters at a given site. The first criterion is based upon the average performance of the model for each form of ka(t) and ke(t) taken over all eight forms of the opposing parameter. Gray shading has been used to identify the "best" parameters according to the first NMSE criterion. The second criterion uses the best individual simulation (lowest overall NMSE), identified by the bold outlined value, to select the most appropriate parameter combination. There is a subtle difference in the two criteria, in that the first criterion identifies the best rate parameters, ka(t) and ke(t) independently of one another, while the second criterion identifies a parameter combination. In the example given in Table 5-2, the most appropriate individual parameterizations are given by ka(t) = kaFr(t), and ke(t) = ke~b(t), while the best parameter combination also happens to correspond to ka(t) = k3Fr{t), ke(t) = ke~b(t). The fact that both methods suggest the same parameter set is not surprising since the criteria are related; however a quick glance at the results in Appendix B will confirm that this is frequently not the case. Table 5-2 NMSE associated with various rate parameter combinations at Long Beach WA, and typical of the NMSE tables presented in Appendix B. Erosion Parameter ke(t) = Con f(Q) f(Hb2 ) f(H/) f(F r ) f(IFr) f(P) Avg II Con 0.307 0.311 0.335 0.338 0.308 0.321 0 316 -0.332 0.330 0 383 0.369 0.337 0.383 0.349 -.:2 0.390 0.471 0.375 ... 0.411 Q) Q) E as ... as a. C 0 ;. 0.327 0.322 0.350 0.348 0.319 0.333 0.330 (J ii. f(P) 0.396 0.390 0.426 0.445 0 368 0.413 0.401 Avg 0.351 0.355 0.388 0.393 0.343 0 377 0.363

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81 Table 5-3 is an example of the second type of table generated for each site, where the format is very similar to that of Table 5-2, but rather than containing NMSE values, it contains CAP values. Once again row and column averages have been calculated and have been used to help select the "best" parameters according to the first criterion, which in the CAP tables are shaded with a diagonal striped pattern to distinguish them from the previous table. Using the CAP criterion, the "best" rate parameters at Long Beach are given by ka(t) = kaHi, 2(t) and ke(t) = keIFr(t). Similar to the NMSE table, the maximum CAP for an individual simulation is outlined (only italicized rather than boldfaced) in order to indicate the "best" parameter combination based on the second CAP criterion. Because the CAP value is essentially an average of a finite number of specified weights, it is not unusual for multiple hindcasts to receive the same CAP score. This is the case at Long Beach, as multiple parameter combinations result in the same maximum CAP score of 0.926, indicating several simulations that perform equally well according to the second criterion. It should be noted that several of these combinations do not correspond to either of the optimal rate parameters based upon the row and column average CAP. Table 5-3. CAP associated with various rate parameter combinations at Long Beach, WA, and typical of the CAP tables presented in Appendix B. Con f(Q) f(Hb2 ) f(P) Avg Con 0.858 0.858 0.853 0.853 0.853 0.856 ---0.895 0.890 0.853 0.853 Q) Q) 0.868 0.816 E 0.874 0.853 0.883 as f(Fr) 0.858 0.821 0.853 ... as 0.858 0.821 0.848 a. f(IFr) C 0.890 0.853 0.853 0.853 0 0.890 0.853 0.871 :.= Q) ... f(l;i,) 0.895 0.858 0.858 0.890 0.821 0.890 0.875 (J (J
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82 The last type of table created for each site is illustrated by Table 5-4, and gives the specific coefficient values for each simulation as determined by the procedure discussed in Section 3.4.4. Once again, the columns represent different forms of ke(t), while the rows indicate different forms of ka(t). The set of coefficients, lea, kc, fl.yo, for each parameter combination are listed vertically. Row and column averages are also calculated, where lea and kc can only be averaged horizontally and vertically, respectively, as different forms of the rate coefficients have different units. The shading patterns used in the NMSE and CAP tables to indicate the best parameterizations of lea(t) and kc(t) are repeated in Table 5-4, in order to make cross referencing the tables easier. In the example presented, the row and column corresponding to ka(t) = leaFr(t) and kc(t) = kc~b(t) are shaded, indicating the best parameters according to the NMSE, while the optimum parameterization based on the CAP score, ka(t) = kaH1,2(t), kc(t) = kcIFr(t), is represented by the striped row and column. The individual cells resulting in the best simulations according to the second criterion are also identified in Table 5-4, where the bold outlined text denotes the best individual simulation based upon the NMSE, and the italicized outlined text indicates the simulations receiving the maximum CAP score. At Long Beach, the best individual simulation according to the NMSE occurs for the specific parameter combination, lea(t) = 0.05Fr(t) hr-1 kc(t) = 0.000003~b(t) hr-1 /l.y0 = 24.38 m, while several parameter combinations result in the same maximum CAP value. While the availability of numerous sets of field data has advantages in terms of allowing the model to be evaluated over a wide range of conditions, it makes the presentation of the results, even in a compact form, quite cumbersome. In order to streamline the process, the remainder of this chapter is devoted to qualitative descriptions

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83 of the overall model performance in each of five geographic regions: New Jersey, Florida, Washington, California, and Australia. Where necessary, figures or tables such as those previously discussed are presented within the text to illustrate specific points; however the complete set of figures and tables for all thirteen sites can be found in Appendix B. Table 5-4. Calibration coefficients for Long Beach, WA, and typical of the coefficient tables resented in A endix B. Con ka [hr1J f(Q) [hr J II -j .: .S! Cl) E a, ... l'O a. Cl) iii f(IFf) a: [hr J f(/;t,). [hr J f(P) [hr J Avg Con hr1 3.0E-04 5.0E-04 f(Q) hr1 4.5E-04 5.0E-04 4.5E-04 2.5E-05 1.6E-05 2.5E-06 12.192 6.096 3.048 4.0E-05 1.0E-03 4.SE-05 3.0E-05 1.6E-05 2.8E-06 1 SE-06 2.SE-06 2.0E-06 3.0E-05 1.6E-05 2.7E-06 21.336 6.096 6.096 2.0E-06 3.0E-06 3.0E-06 3.0E-05 1.6E-05 2.7E-06 27.432 12.192 9.144 1.0E-08 1. 0E-08 1 0E-08 4.5E-05 2.2E-05 3.2E 06 21. 336 12.192 6.096 1. 9E-03 3 8E-05 2 1 E-05 7.1 E-06 2.4E-01 22.479 8.001 0.381 f(P) Avg hr1 4.5E-04 3.9E-04 15 240 1.0E-03 2.8E-04 18.669 4.5E-06 2.8E-06 19.431 1.SE-09 6.6E-09 3. SE-10 20.193 19.574

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84 5.1 New Jersey The New Jersey region consists of two different sites, Island Beach along the central New Jersey coastline, and Wildwood located in the southern portion of the state. Hydrodynamic conditions at both sites are similar however, both the shoreline orientation (0nonn = 98 Island Beach, Snonn = 142Wildwood) and sediment size (dso = 0.370 mm Island Beach, d50 = 0.200 mm Wildwood) vary considerably. Model performance also varies significantly between the two sites, performing poorly at Island Beach, but reasonably well at Wildwood. While the model successfully simulates a majority of the large shoreline changes at Wildwood, including an extreme erosional event in 1991 related to the "Perfect Storm", it fails to reproduce most of the changes at Island Beach. Closer examination of the data at Island Beach however, reveals that the measured shoreline exhibits some characteristics that would make simulating it with any model extremely difficult. Given the similarities in the forcing conditions for both New Jersey simulations, it is reasonable to assume that if the shoreline observations at each site were fairly consistent, the results should be as well. Unfortunately, this is not the case, mainly due to two glaring differences involving the magnitude of the erosion related to the "Perfect Storm", and the sudden increase in the severity of shoreline changes experienced at Island Beach after 1995. The infamous "Perfect Storm" occurring in late October of 1991 impacted a majority of the east coast of the United States, causing widespread damage and significant beach erosion. According to the data points spanning this event, the storm resulted in relatively minor erosion at Island Beach (-5 m), which was not well predicted by the model, and significant erosion at Wildwood (-60 m ), which was successfully predicted. Since the forcing data for both hindcasts are similar, it is not

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85 surprising that the model was only able to reproduce one of the two scenarios, and that the result that was reproduced corresponded to the severe erosion that was more typical of the situation along the majority of the east coast. The sudden sharp increase in the magnitude of shoreline changes after 1995 at Island Beach is also considered somewhat abnormal. Neither the Wildwood shoreline data, nor the forcing data exhibit a similar trend, indicating that perhaps some outside factor not considered by the model is influencing that particular stretch of shoreline. Both objective evaluation criteria support these qualitative observations, as according to the classification system set forth in Table 3-3, the model pedormance with respect to the NMSE is poor at Island Beach and reasonable to good at Wildwood. Based upon the average NMSE for each form of the rate parameters, the best parameterizations are given by ka(t) = ka (constant), ke(t) = kJib3(t) at Island Beach, and ka(t) = kaP(t), ke(t) = keP(t) at Wildwood. The CAP scores are somewhat misleading as the pedormance of the model at both sites can be classified as good according to this criterion. The abrupt jump in the magnitude of the shoreline fluctuations at Island Beach after 1995, minimizes the influence of the pre-1995 data on the overall CAP score as most of these data points correspond to a stable shoreline as defined by the sliding scale. The optimum parameters based upon the average CAP scores for Island Beach and Wildwood are, ka(t) = ka, ke(t) = kJib 2(t) and ka(t) = kaP(t), ke(t) = ke~b(t), respectively. The inability of one of the two objective measures of model skill to correctly characterize the pedormance of the model at Island Beach, presents a clear indication of the necessity of incorporating some subjectivity into the analysis of numerical models. The previously described Model Pedormance Index (MPI) provides a useful measure of

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86 model skill by considering both the NMSE and CAP, as well as other factors which cannot be quantified objectively. In this case, these factors primarily include the proximity of the available forcing data and the quality and frequency of the shoreline measurements. Based upon these considerations, the potential exists for reasonable shoreline predictions at both sites; therefore the poor overall performance of the model at Island Beach, merits an MPI score of 1, while the more reasonable hindcasts generated at Wildwood warrant an MPI of 3. 5.2 Florida In general, the model performs well at all four Florida sites with rare exception. At St. Augustine, model hindcasts match the measured shoreline changes extremely well, despite NMSE values that are slightly higher than expected. The explanation for this behavior lies in the over-sensitivity of the NMSE to outliers, particularly when the variance of the observed data (actually LY!) is small, as is the case here. The relative stability of the St. Augustine shoreline magnifies the importance of differences between the hindcasts and measured data, resulting in larger NMSE values due predominantly to only a few relatively poor predictions. The CAP scores support the assertion that the model performs well at this site despite the NMSE values, as the mean CAP score is greater than 0.8, which is classified as excellent according to Table 3-3. On average, the rate parameters resulting in the lowest NMSE are given by k-a(t) = kaH1,2(t), ke(t) = keil(t), while the best individual simulation according to both objective criteria occurs for ka = 0.2 hr-1 ke(t) = 0.0045Q(t) hr-1 /:iy0 = 0.0 m. Considering the quality of the input data which consists of hindcast wave information and remotely collected tide data, the overall behavior of the model is considered good, warranting an MPI rating of 4.

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87 The performance of the model at Crescent Beach can be classified as anywhere between poor (MPI = 2) and excellent (MPI = 5), hence the variability in the MPI rating reported in Table 5-1. The hindcast plotted in Figure 5-3 (ka = 0.9il(t) hr"1 kc= 0.0006il(t) hr -1 ~Yo= -27.43 m) represents an excellent fit to the data; however it can not be considered representative of the overall model performance at the site, given the information in Tables 5-5 and 5-6. According to these tables, the plotted hindcast represents an anomaly for the site, where poor predictions are actually much more common. Data from other sites indicates that the model is not nearly sensitive enough to the specific form of the rate parameter to be able to explain the variability of the results. Although there are many potential reasons for this seemingly inconsistent behavior, the most likely explanation is that the calibration algorithm fails to accurately identify the global minimum of the cost function, incorrectly selecting a local minima associated with relatively poor predictions, instead. It is likely, that better predictions, more similar to those appearing in Figure 5-3, exist for other rate parameter combinations; however the selected calibration routine simply fails to locate them. Assigning an MPI for the site is difficult, because it is unclear whether to judge the model performance on the basis of its potential and the hindcasts plotted in Figure 5-3, or on the actual results including the deficiencies in the calibration algorithm reflected in Tables 5-5 and 5-6. In the end, the most equitable characterization of the model is that it fails to reproduce the measured data, meriting an MPI of 2; however this result should be qualified by the observation that if the calibration routine is indeed the problem, the potential exists for excellent predictions (MPI = 5) at the site.

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88 Crescent Beach, Florida 25r=========;7-------r----.-------r-------..--r---, _.,_ Yob . . . 20 -oy pr (Minimum NMSE) -A y (Maximum CAP) pr i . . . .. . .. .. :-.......... [ .. ...... "l + Prediction Range 15-------=----: : 10 e C 5 .Q ;t:= Cl) 0 -~ -5 0 .c en -10 -15 -20 . . . ; ,.:. ...... : . ... ...... ......... : : ...... : . . . . . : : : \ . ... ....... = .......... : .......... i ......... \ r .......... : ........... : .......... : ... : : : \'. : : -25L----...L..-----'----.L---......_ __ __._ ___ ..__ __ ___._ __ __, 1955 1960 1965 1970 1975 1980 1985 1990 1995 Year Figure 5-3. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Crescent Beach, FL. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations. Table 5-5. NMSE associated with various rate parameter combinations at Crescent Beach,FL. Erosion Parameter k.(t) = Con f(Hb2 ) f(Hb3 ) f(Fr) f(IFr) f(l;t,) f(P) Avg II Con 0.931 0 940 0.939 0.895 0.926 -0.934 0 930 0.437 0.890 0.760 -.::2 ... Cl) Cl) E as ... as a. f(IFr) 0.935 C 0.922 o.n1 0.885 0.856 0 -:;::::; Cl) ... f( l;t,) 0.934 0.933 0.937 0.932 0.910 0.896 0.924 (.) (.) <( f(P) 0.914 0.925 0.927 0.867 0.717 0.854 0.851 Avg 0.901 0.916 0.897 0.822 0.803 0.861 0.849

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89 Table 5-6. CAP associated with various rate parameter combinations at Crescent Beach, FL. Erosion Parameter k.(t) = Con f(Q) f(Hb2 ) f(Hb3 ) f(Fr) f(IFr) Con 0.541 0.565 0.659 0.600 0.541 II :Z."',.,,,l"~/~f! "".$,.,,,,. ,,,...:~: ,;::.,..,..~., ""1Z'f~"~# ~f/~1/. ~~-,,.;; tr:~~,;;,: ;,: ;,: ~,.;W-1%f1"-f%%~ ~:,: Z"' ,,,,, v1,,,,,~/, ~/2 ~'/. v~~v. -~;,:;,: r. wffi . ~$-1 ~,;,,, .. W%~,. .... f(H/) 0. 724 0.688 0.629 0.665 0 582 Q) f(Hb3 ) Q) 0.724 0.653 0.665 0.629 0.724 0.706 0.692 E CG f(Fr) 0.624 0.588 0.629 0.635 0.577 0.577 0.671 0.627 .... CG a. f(IFr) C 0.588 0.771 0.647 0.665 0.565 0.606 0.662 0 f(l;i,) 0.647 0.718 0.641 0.600 0.577 0.618 0.650 () f(P) 0 706 0.724 0.647 0.682 0.600 0.688 0.692 Avg 0.654 0.693 0.652 0.645 0.593 0.662 0.699 0.663 The model performs well at Daytona Beach, with the exception of a single extreme accretion-erosion sequence, the magnitude of which the model is unable to reproduce. As shown in Figure 5-4, shoreline measurements over the four year span between 1976 and 1980 indicate that the Daytona Beach shoreline experienced an unprecedented period of rapid growth (+30 m) between 1976 and 1978, followed by an equally rapid recession in the ensuing two years of nearly equal magnitude (-25 m). While both hindcasts presented in Figure 5-4 reproduce the correct sequence of events, neither comes close to reproducing the extraordinary magnitude of the observed shoreline changes. Examination of the hydrodynamic data forcing the model failed to reveal any abnormal behavior in the data over the four year period capable of explaining the measurements. One possible explanation for this behavior is that one or more of the measured shoreline data points may be inaccurate. This possibility is given strong consideration as the most accreted shoreline was obtained from an older blue-line aerial photograph. Although there was not enough evidence to exclude the data point, the possibility exists that the shoreline measurement obtained from this type of aerial photograph might be inaccurate.

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90 With such a small data set, a single bad measurement can profoundly affect the overall simulation, as the model attempts to fit the bad data point at the expense of better agreement elsewhere. Despite this potentially inaccurate data point, the model still performs well overall at Daytona Beach, with an average NMSE of 0. 703 and an average CAP score of 0.748. The best parameterizations as suggested by the NMSE and CAP are ka(t) = kaIFr(t), ke(t) = ke~b(t) and ka(t) = kaP(t), ke(t) = keHt,3(t), respectively. As with all of the Florida sites, the forcing data are only considered adequate as they consist of hindcast wave information and remotely measured tide data. Overall, the mode l performance at Daytona Beach is considered good enough to deserve an MPI of 4. -E -C: 0 .:: u; 0 (l, (I) .5 "iD ... 0 .c (I) Daytona Beach, Florida 30~-~--~----,-------y---r------.----~---, ---yob . 25 : : : -: . . -15~--~--~--~--~------_._ __ ___._ __ __. 1955 1960 1965 1970 1975 1980 1985 1990 1995 Year Figure 5-4. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Daytona Beach, FL. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations.

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91 New Smyrna Beach, Florida 30.-------r-______:~--r---.-------.-------.----.------.-----, 20 . . .. . .. ..... .. . ..... s 10 > L I':". . f C: .2 a. Q) C: . :,..,. ~ .... : '~' -~-10 ... .. : ....... .. ; .. ' : / . .... i 0 .r:. en -20 -30 ... -. . . . : / .... .. \ : ...... ... : . . . ...... ; .... ...... -: . .. 1 ---Yob .. ... -oy (Minimum NMSE) pr -A y (Maximum CAP) pr + Prediction Ran e -40L-__ __._ ___ _,__ __ _._ ___ _._ __ ___. ___ __._ ___ ..__ __ __. 1955 1960 1965 1970 1975 1980 1985 1990 1995 Year Figure 5-5. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at New Smyrna Beach, FL. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations. The model performs well at New Smyrna Beach as shown in Figure 5-5; however the plotted hindcasts also help to illustrate one of the disadvantages of relying on purely objective measures to select the "best" simulation. While the hindcast corresponding to the maximum CAP aptly simulates most of the major shoreline changes, the hindcast with the minimum NMSE is ineffective at reproducing some of the changes, particularly in the first half of the simulation. One of the disadvantages of using both a cost function and evaluation criterion where the difference term is squared, is that conservative hindcasts, where the number of large outliers are minimized tend to be favored over more

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92 aggressive, and sometimes more effective (qualitatively) predictions (this is discussed in more detail in relation to the Torrey Pines data set in Section 5.4). The lower than average CAP value (0.604 vs. 0.684) associated with the minimum NMSE simulation and the higher than average NMSE (0.650 vs. 0.595) associated with the maximum CAP simulation reflect this behavior. On average, the best simulations according to the NMSE and CAP criteria occur for rate parameters of the form, ka(t) = kaH1,2(t), ke(t) = ke~b(t) and ka(t) = ka, ke(t) = kc, respectively. Despite the inability of the NMSE to accurately identify the "best" prediction, the model still performs reasonably well overall at the site given the less than ideal forcing and calibration data, warranting an MPI of 4. 5.3 Washington and Oregon Undoubtedly, the model is most successful at simulating the shoreline data sets from the Pacific Northwest. All three sites located in Washington and Oregon are modeled extremely well both qualitatively as well as quantitatively. At both Long Beach and Clatsop Plains, the NMSE routinely falls below 0.500, while the CAP often approaches 0.900. Even at North Beach where the model is slightly less successful, the NMSE and CAP average 0.628 and 0.828, respectively. The parameterizations most frequently identified by the NMSE and CAP criteria as corresponding to the best simulations are ka(t) = kaFr(t) and ke(t) = ke~b(t). This is indeed the case at North Beach, where the best individual simulation occurs for the parameter set, ka(t) = 0.025Fr(t) hr-1 ke(t) = lxl0-6 ~b(t) hr-1 lly0 = 54.86 m. The lowest NMSE for any simulation in the study was calculated for the Long Beach hindcast plotted in Figure 5-2, where a NMSE of 0.281 was obtained using ka(t) = 0 05Fr(t) hr-1 ke(t) = 3x10-6 ~(t) hr -1 and lly0 = 24.38 m. The maximum CAP score achieved by any one simulation occurs at Clatsop Plains,

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93 where Hb3 and ~b were found to best parameterize the rate coefficients according to, ka(t) = 2.3x10 -5Hb3(t) hr-1 ke(t) = l.5x10-5 ~b(t) hr-1 Ayo= 45.72 m. The excellent results at all three sites are not surprising, as both the shoreline and forcing data, which consist of local tide and measured directional wave information, are of the highest quality. Excellent data lead to high expectations which were for the most part surpassed, warranting an MPI of 4 at North Beach, and 5 at both Long Beach and Clatsop Plains. 5.4 California The Torrey Pines simulations shown in Figure 5-6, illustrate a tendency of the model to give conservative predictions. While the model is able to reproduce the seasonal trend exhibited by the Torrey Pines shoreline well, it is less skilled at simulating some of the smaller scale fluctuations. Theoretically, the model should be fully capable of simulating changes with time scales as small as several hours; therefore the most likely explanation for this behavior relates to the form of the cost function, J. One of the consequences of selecting a cost function where the difference term is squared, is that conservative hindcasts are favored because they are less likely to generate large outliers. At Torrey Pines, this manifests itself in a set of "safe" hindcasts where the dominant seasonal trend is preserved at the expense of some of the smaller, high-frequency oscillations. Lower than normal CAP values at the site support this observation. Despite the conservative nature of the predictions, the model performs reasonably well overall at the site. The average NMSE and CAP values suggest that the hindcasts are particularly sensitive to the forms of the rate parameters ka(t) and ke(t) at Torrey Pines. The NMSE criterion suggests a parameterization of the form ka(t) = kaFr(t), ke(t) = ke~b(t), while the CAP criterion suggests ka(t) = kaFr(t) and ke(t) = keP(t). Since the forcing data composed

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94 of remote water surface elevations and old WIS wave hindcasts is less than ideal, the ability of the model to reproduce the dominant annual signal in the data is considered enough to merit an MPI of 4 for the site. E -C: 0 E U) 0 a. Q) 0 .s::. Torrey Pines, California 1s.---.---=------;---.-----,--.---r-----.--.------, 10 .. . . ........ ... ........... . . : f : \ .. : \ 5 r \ -l----:-l 0 / I I : : \ : \ . . . . . . . . . . . ---1/ . \ I \1 :1' : \ ---yob -o-y (Minimum NMSE) pr -A y (Maximum CAP) pr + Prediction Range . ...... \-. . en -5 -10 . ........ . .. ..... ................ -................. -15'-------'----'---......L...--......._ __ .....__ __ ..._ __.'-_ _._ __ ___._ __ 1972.4 1972.6 1972.8 1973 1973 2 1973.4 1973.6 1973.8 197 4 197 4.2 197 4.4 Year Figure 5 6. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Torrey Pines, CA. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations. 5.5 Australia In general, the performance of the model at all three Australian sites is considered good. As was the case at Torrey Pines, the model hindcasts tend to be rather conservative; however the dominant overall trends in the data are simulated successfully. The Australian data represent interesting test cases for the model because the high density of the available shoreline data provides a means to asses the skill of the model over

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95 shorter timescales. Typically, model hindcasts at all three sites behave similarly to those depicted in Figure 5.7 for Brighton Beach, where the dominant trend is one of mild accretion, regardless of the small short-term fluctuations in the measured data. The model does account for most of the significant erosional events in the data set, accurately representing them as a series of sudden changes related to severe storms. The explanation for this behavior lies in the typical value of the calibration coefficient, Ay0 which for these short data sets generally lies above most of the measured shorelines. A complete simulation (ka(t) = 0.015Fr(t) hf 1, kc= 0.1 hr-1 Ay0 = 27.43 m) of the Gold Coast data set is plotted as Figure 5.8 to help demonstrate the influence of Ay0 On the surface, Figure 5.8 is very similar to Figure 5-1; however the length of the Gold Coast data set (2.5 years) represents only a small fraction of the forty-year simulation performed at Daytona Beach. The large Ay0 selected by the calibration routine effectively shifts the equilibrium shoreline in Figure 5.8 landward with respect to the measured shoreline, suggesting this short period corresponds to a condition of disequilibrium. According to Equation 3.1, the modeled shoreline approaches the equilibrium shoreline exponentially, a trend readily observed in Figure 5.8 during accretional periods. During large storms however, the equilibrium shoreline briefly dips below the predicted shoreline, resulting in a drastic, rapid erosion of the predicted shoreline related to the instantaneous value of kc. Due to the conservative nature of the simulations, the predicted erosion often does not match the measured erosion in terms of magnitude; however the timing of the predicted events coincides well with the observations. Hindcasts for Leighton Beach and Brighton Beach exhibit similar behavior.

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96 Brighton Beach, Australia 25r.=========,---,-----,,----,--..------,----, --yJ -oy (Minimum NMSE) 20 ~ /' (Maximum CAP) pr ..... > ..... .. ..... -:................... 15 -10 s :g 5 n. GI .s o e 2 en -5 . . . + Prediction Ran -10 .......... ..... . -15 : ............ ......... :: -20~--~--~~--~--~~--~--~--~ 1995.5 1996 1996 5 1997 1997 5 1998 1998.5 1999 Year Figure 57. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Brighton Beach, AS. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations. 40 30 g 20 C .Q :c l 10 GI s e 0 -10 -20 Shoreline Hindcast Time Series Gold Coast, OLD -30~---~ ___ ..._ ___ _._ ___ .....__ ___ _._ ___ 2 000 5 2001 2001 .5 2002 2002.5 2003 2003 5 Date Figure 5-8. Complete hindcast shoreline time series for the Gold Coast, QLD.

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97 Based upon the NMSE measure, the model is moderately successful at all three sites, as the average NMSE ranges from 0.520-0.624. These values for the most part reflect the ability of the model to simulate the dominant trends in the data sets, including the major erosional events, while the lower than normal CAP values reflect the inability of the model to reproduce many of the short period fluctuations. Typical CAP values range from 0.631 to 0.715, and are among the lowest recorded for any data set; therefore neither criteria based on the CAP is used to evaluate the rate parameters at these sites. While the lowest average NMSE at all three locations occurs for ka(t) = kaFr(t), three different parameterizations are suggested for ke(t), namely kc(t) = kc (Gold Coast), kc(t) = kcHb3(t) (Leighton Beach), and kc(t) = ke,Q(t) (Brighton Beach). Unfortunately, although the daily shoreline measurements obtained at the Gold Coast are an excellent source of information, the data are fairly noisy and contain some unnatural fluctuations on the order of m over consecutive days. In order to remove some of the noise, the data set was linearly interpolated and lowpass filtered using a variety of cutoff frequencies. The results obtained using a cutoff frequency of fc = 0.033 days 1 are plotted in Figure 5.9. As expected, the agreement between the measured and hindcast shorelines increases substantially, although in most cases the calibration constants, ka, kc, flyo, change only slightly, if at all. Both the NMSE and CAP reflect this improvement as their respective average values over all of the simulations improve to 0.367 and 0.985. The only period for which the model performs poorly is during the early part of 2002, where the magnitude of a severe erosional event is underpredicted by the model. Previously, there has been some question as to whether this event was real, or a result of a shift in the position of one of the cameras. The fact that the model

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98 reproduces the timing of the erosion exactly confirms that this event was in fact real, and that it corresponds to period of increased water level and wave activity early in 2002. Overall, the model does a good job of simulating the filtered Gold Coast data set; therefore it receives an MPI of 4. Gold Coast, Australia (Low-Pass Filtered Data) 30~---~-----.--------.-------.-------,------, 20 .. .. . . .. .... ....... .. . .... . .. ......................................... e 10 ...... C 0 .:; "ii) 0 ........ Q) 0 .c (/J-10 -20 ---yob -oy (Minimum NMSE) pr -A y (Maximum CAP) pr + Prediction Ran e -30L_ ___ .1.._ ___ ...,.L_ ___ _l_ ___ ~======:::::::r:==::::::::==~ 2000.5 2001 2001.5 2002 2002.5 2003 2003.5 Year Figure 5-9. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at the Gold Coast, AS, using filtered (fc = 0.033 days-1) data. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations. While evaluating the model on its ability to simulate daily changes is considered instructive, its ability to successfully predict the extreme shoreline states is ultimately of more importance. A final set of hindcasts was generated using an abridged shoreline data set consisting only of the local extrema of the monthly filtered data. The results are shown in Figure 5-10, where once again the model performs extremely well. Although

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99 the typical NMSEs increase slightly from those based on the complete filtered data set, the model does a slightly better job of fitting the extrema of the abridged data set. In terms of assigning an MPI to the simulations, this is seen as a even trade-off; therefore the MPI remains unchanged from the previous case and is equal to 4. Monthly Extremes Gold Coast, Australia (Low-Pass Filtered Data) 30.---------.-----..---------.------.-------,-----, 20 ........ . . .... .... .. . .......... .................. ......... .............. ............ . . . ..... . e 10 C 0 .: 'in 0 Q) .E 0 .c C/)-10 -20 --yob oy (Minimum NMSE) -A. /r (Maximum CAP) pr + Prediction Ran e -3o~---~----~---~-------------....... 2000.5 2001 2001.5 2002 2002.5 2003 2003.5 Year Figure 5-10. Comparison of the extreme values of the measured shorelines and "best" simulations according to the NMSE and CAP criteria at the Gold Coast, AS, using filtered (fc = 0.033 days-1 ) data. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations.

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CHAP'TER6 DISCUSSION Whereas previous chapters concentrated on providing a general evaluation of the model performance at each of the thirteen sites, this chapter describes some of the key aspects of the model in more detail. The timescale of the model response is examined by interpreting Equation 3.1 as a simple linear filter with time constant ka, which allows the shoreline response to different periodic forcing to be analyzed in terms of an amplitude and phase response function. The results of the two methods described in Chapter 5 for selecting suitable parameterizations for ka(t) at each site are discussed and summarized. The variability in the coefficients associated with each of the different forms of the rate parameter, ka(t), are examined with the assistance of histograms and bulk statistics, where it is shown that by separating the coefficients according to geographic location, the variability is reduced significantly. In addition, several variations of the NMSE which were developed and applied in an effort to encourage more aggressive model predictions are discussed. The impact of new research by Wang (2004), who identified a relationship between the sediment scale parameter, A, and the non-dimensional fall velocity parameter, .Q, is considered. This work has implications here through the potential impact of A(.Q) on the calculation of W , and hence the equilibrium shoreline position, yeq(t). Finally, an interesting application of EOFs is discussed, where they have been used to remove the non-uniform component of the shoreline data sets prior to simulating them with the model. 100

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101 6.1 Timescale of Response The shoreline response timescale can be determined by recognizing that Equation 3.1 with ka = Constant, is the equation of a simple linear filter, where the shoreline response can be represented in terms of amplitude and phase response functions. Equation 3.1 can be rewritten in a more useful form 1 dy(t) ---+ y(t) = y (t) k dt eq a (6.1) where ka has been taken as a constant, and yeq(t) and y(t) are the forcing and response functions, respectively. Considering a periodic forcing function of the form yeq(t) = Aeirot, where A is the amplitude and co (=27tlf) is the frequency, it can be assumed the response will have a similar form, y(t) = Beirot_ Inserting these expressions into Equation 6.1, 1 . -B(im)e'OJt + Be'OJt = Ae'OJt ka (6.2) which after canceling the common terms simplifies to (6.3) The function F( co,ka) is an output response function which provides the relationship between the forcing, yeq(t), and the output, or in this case the predictions y(t). The output response function contains both amplitude and phase information which are given by jF(co,ka)I and cl>(co,ka), respectively (6.4)

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102 Amplitude Response Function, IF((l),kJI .. ,5.. 810-1 f l10..z f 10"" 10-3 104 Rate Coefficient, k (hr-1 ) a t. ; ."!', ... 0.1 0.2 0.3 0.4 0.5 0.6 o. 7 0.8 0.9 Figure 6-1. Variability of the amplitude response function, IF(co,ka)I with forcing frequency, co, and rate coefficient, ka. Phase Response Function, +((l),kJ .. ,5.. 810-1 f l10..z if 10"" 10-6 10 ... 10-3 104 Rate Coefficient, k (hr-1 ) a .... 1 ~--0.2 0 4 0.6 0.8 1 1.2 1.4 Figure 6-2. Variability of the phase response function, co,ka) with forcing frequency, co, and rate coefficient, ka.

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103 (6.5) The response, y(t), will be lagged and damped according to IF(co,ka)I and cp(co,lea) with respect to the forcing yeq(t). Figures 6-1 and 6-2 illustrate the variability of the amplitude and phase response functions over the relevant ranges of lea and co, where for reference, the mean values of ka and kc for the parameterization lea= Constant (ka = 2.9xlff3 hr"1 and kc= 3.74x102 hr"1 ) have been plotted. If a response time scale, tr, is arbitrarily defined as IF(co,lea)I = 0.5, there exists a factor thirteen difference between the approximate erosion (4 days) and accretion (52 days) time scales, as indicated in Figures 6-1 and 6-2. This provides a partial explanation of why the model is more successful at predicting events with longer timescales, such as seasonal shoreline changes, and confirms that in order to accurately simulate events with varying timescales, it is advantageous to include some measure of the event itself, in the rate function parameterization. 6.2 Selection of Appropriate Rate Parameters Selecting an appropriate parameterization for the rate coefficient in Equation 3.1 is critical to the performance of the model. As discussed in Chapter 3, a virtually infinite number of parameterizations could be considered; however the eight examined here I represent some of the more theoretically and physically sound possibilities. The criteria discussed in the previous chapter are used to select two sets of suitable parameters at each site, one based upon the mean value of the NMSE for specific forms of ka and kc, and one based upon the single lowest NMSE at a given site. The parameters suggested by the first method are not linked as they are based upon the average error of a specific

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104 parameterization (for example ka(t) = kaO(t)), taken against all possible forms of the other parameter, while the second method gives a parameter combination. The most suitable rate parameters suggested by each method are listed in Table 6-1, where if more than one parameterization performs equally well at a site (often the case for CAP scores), multiple parameters are listed. In the last row, the most frequently occurring parameterizations are given where according to the mean NMSE criterion, the most suitable rate parameters are ka(t) = kaFr(t) and ke(t) = ke~b(t). If the best individual simulation criterion is used instead, a slightly different parameter combination, (ka(t) = kaFr(t), ke(t) = kJfb3(t)), is obtained .. Although the CAP criteria may also be used to select two sets of suitable parameters, the NMSE is considered to be a more rigorous measure of model skill; hence it alone was used as the primary criterion for selecting the best rate parameters. Table 6-1. Best performing rate parameters for each site. NMSE CAP Mean Individual Mean Individual Site ka ke (ka.ke) ka ke (ka.ke) Island Beach, NJ Con Hb3 (Fr,H/) Con H/ Multiple Wildwood, NJ p p (l;i,,Hb3 ) p Yi Multiple St. Augustine, FL Hb2 Q (Con,Q) Q Q (Q,IFr) Crescent Beach, FL Hb3 Q (Q,Q) Q p (Q,Q) Daytona Beach, FL IFr Yi (Hb2,Fr) p Hb3 (Fr,Hb3 ) New Smyrna Beach, FL H/ Yi (Hb3 ,P) Con Con (Hb3,1Fr) North Beach, WA Fr Yi (Fr,Y>) Fr Yi Multiple Long Beach, WA Fr Yi (Fr,l;i,) Hb2 IFr Multiple Clatsop Plains, OR Fr Hb3 (Fr,Hb3 ) Hb2 Yi (Hb3,l;i,) Torrey Pines, CA Fr Yi (Fr,Hb3 ) Fr p Multiple Brighton, AS Fr Q (Fr,Hb3 ) Hb3 Q Multiple Leighton, AS Fr Hb3 (Fr,Hb3 ) p Q Multiple Gold Coast, AS Fr Con (Fr,Con) Q y, Hb2,Hb3 Multiple Gold Coast (filtered) Fr Con,IFr (Fr,Fr) Con, Fr, IFr, l;i, Hb3 Multiple Most Frequently Occurring Fr Yi (Fr,Hb3 )

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105 An important factor if the model is to be extended into the predictive realm is the variability of the coefficients associated with each form of the rate parameter. Ideally, most of the differences between the sites will be reflected in the parameterizations, thereby minimizing the variability of the calibration coefficients. In Figures 6-3 and 6-4 histograms illustrating the variability of the coefficients associated with each of the eight different parameterizations for ka(t) and ke(t) are presented. The histograms are plotted on a logarithmic scale, such that each bin corresponds to an order of magnitude variation. Accompanying each histogram are a set of statistics indicating the mean, median, and coefficient of variation for each data set. The coefficient of variation or COV provides an indication of the degree of variability in the data and is defined as the standard deviation of the sample divided by its mean. The histograms themselves contain all of the data; however four outliers representing the upper (2) and lower (2) extrema were eliminated prior to the calculation of the bulk statistics. While overall the variability is significant, it reduces considerably if the data are grouped according to geographic region. In Table 62, the statistics (mean, median, COV) have been recalculated for five separate geographic regions, New Jersey, Florida, Washington, California, and Australia The percent change between the regional COV and the initial COV is also calculated, where on average the COV is reduced by over 45% for ka(t), and over 65% for ke(t) The statistics are not recalculated for California since there is only one site within the region. Bold values in Table 6 2 indicate cases where the regional variability actually exceeds the overall variability, while the shaded columns correspond to the "best" parameterizations of the rate function as indicated in Table 6-1. Some of the largest reductions in the COV are associated with these most appropriate parameterizations.

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k8(t) = Constant 50~----~-------Mean = 2.908-3 40 Median = 6 .00eCOV = 1.86 100 k (hr-1 ) a k (t) = k H2(t) 40 ~M_e_an_=-3-.2,11'-7-e--4-....,._~ Median= 7.27e-5 30 cov = 2 04 20 :, 10 105 100 10 5 106 k8(t) = k8n(t) 40~--~---~----, Mean = 1 .548-3 Median = 1.00e f/)30 g COV = 4.93 20 :, 10 0 -5 0 105 10-10 10 k (hr-1) 10 a k (t) = k H3(t) 40 Mean = 4.12e-4 Median = 6.1 Be-5 fl) 30 g COV=2. 07 20 :, 10 0 10-15 10 -10 105 10 50 40 fl) g30 G) t:: ~20 10 0 10-10 5 k (hr 1m-3 ) a k.(t) = k_F;1(t) Mean = 3.448-5 Median = 2 .508COV = 3.55 0L---------~ 0'----10 -10 10 5 10 10 1 5 10-10 10 5 k (hr 1 ) k (hr 1 ) a a Figure 6 3. Histograms of accretion coefficients, k1 determined by the procedure detailed in Chapter 3.

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k8(t) = Constant II) 30 g 20 ::, 810 o.__ __ ___ 10 -10 10-5 10 k (hr-1 ) k (t) = k H2b(t) 60 Mean= 1 -3 50 Median = 2.698-5 COV=3.O7 ~40 ~30 ::, 820 10 oL----.-~ 10-15 10-10 105 k (hr-1m-2 ) k8(t) = k8Fr(t) 40-------~--Mean=2.37 Median= 1.508-1 Ill 30 cov = 2.55 g 20 10 107 40 II) 830 ~20 10 40 Mean = 8.86e-4 Median = 7.068-6 ~30 COV=2.78 20 ::, 810 o.__ __...._ ___ 10-15 10-10 10-5 k (hr-1m-3 ) k (t) = k F1(t) e r 10-5 10 k (hr-1 ) k (t) = k P(t) so.----~--:-:-e-a-n~=-=5,..,,.8,....,1-e--=1=--"' 40 Median = 3 508-1 COV=3.89 O'----o~--10 -10 10-5 10 10 -15 10 -10 10-5 10 k (hr-1 ) k (hr -1 ) Figure 6-4. Histograms of erosion coefficients, kc, determined by the procedure detailed in Chapter 3.

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108 Con f(Q) f(Hb2 ) f(1;i,) f(P) hr"1 hr"1 hr"1m-2 hr"1 hr"1 hr"1 Mean 4.47E-03 6 41E-04 1 19E-03 4 99E-04 r;g;:-;'.l'/.1. 1 68E-05 2.70E-08 W/,~ New Jersey Med 4.25E-03 5.25E-04 2 69E-04 3 00E-04 1.00E-05 1.75E-08 co 0.96 0.94 2.36 0.95 0.74 0.86 % -48 5% -81. 0% 15.7% -54.0% -81.2% -62.1% Mean 3.SOE-03 3 72E-03 9 76E-05 6 42E-05 1 58E-09 Florida Med 1.SOE-03 2.SOE-04 6.19E-05 3 53E-05 1.SOE-05 1.00E-09 co 1.32 3.59 0.94 0.97 3.34 2 .71 1.19 % -29.1% -27 2% -53.8% -5.9% -31.0% -47.5% 1 82E-05 3 58E-05 1 40E-06 1 .SOE-09 -. Washington Med 1 25E-04 1 SOE-05 1 61E-05 4 00E-07 1 SOE-06 1.25E-10 j co 0.64 0.65 0.81 0.72 1.09 1.99 % -65.7% -86 7% -60 1% -72.3% -12.5% California Mean 4 49E-03 5 33E-04 1 16E-03 1 87E-04 1.31E-07 Australia Med 1.SOE-03 6 SOE-04 8 59E-04 1.35E-04 1.23E-07 1.94 0.83 0 97 0.99 1.09 7.49 0.78 4.3% -83.2% -52.3% -52.3% 'W~ -69.3% 90.5% -65 6% 1 54E-03 3 27E-04 4 12E-04 -ii" 6 24E-04 2 48E-08 II Data 1.00E-04 7 27E-05 6.18E-05 2.SOE-06 1 00E-05 1 75E-09 4 93 2.04 2.27 <:! 4 46E-09 4 25E-03 2.25E-04 5 .11 E-05 "" 7.75E-03 5.00E-09 New Jersey 1 10 2.65 2.43 0.32 Florida -73.3% ashington 1 .SOE-05 1 0SE-05 1 .SOE-01 1 00E-10 0.55 0.27 0.39 0 77 -77 8% -79 6 % -91. 2% California 8 37E-02 1 01E-02 ustralia 1 75E-01 3.25E-02 9.69E-03 9.75E+OO 1 75E-03 1.75E-06 0.68 2.14 0.83 0 .70 0.99 1.12 % -75.3% -20.7% 72.9% -72 6% -70 .8% -71.2% Mean 3 74E-02 5.92E-03 1 30E-03 ;~ 2.37E+OO 4 27E-04 5 .81E -0 7 Med 1.SOE -0 3 9 00E-05 ,r~ 4 00E-06 3.SOE-10 2.69E-05 ~ ,,, ,.,, 1.SOE-01 co 2.76 2.70 3.07 f@" ~~;' 2.55 3.38 3.89

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109 6.3 Modified Error Estimates I Cost Functions As discussed in the previous chapter, one of the weaknesses of the model is that it tends to be conservative due to the nature of the cost function utilized in the calibration. In many cases, the calibration routine actually selects the safest prediction containing the fewest outliers, rather than the best prediction. Several attempts were made to address this issue by considering alternate cost functions and error estimates, which were intended to encourage more aggressive simulations. Rather than completely abandon the NMSE which has been used extensively in numerous numerical models, several modifications to the traditional definition were considered. The first approach was to simply ignore a set number of outliers, thereby preventing a few bad predictions from dominating the NMSE. The selection of the appropriate number of outliers to exclude is important, because if too many are excluded the NMSE becomes irrelevant, while if too few are eliminated there will be no improvement in the results. After some consideration, 15% of the total number of observations was selected as an acceptable number of outliers. The second modification to the NMSE consisted of applying a weighting factor to the individual difference terms in the numerator of Equation 3.16, creating what is referred to as the weighted normalized mean square error or WNMSE { w =0.25 W =0.15 W=l.0 fly pr >fly00 fl Y pr < fl Y ol, flyprfly00 <0 (6.6) where flypr and fly00 are the calculated and observed shoreline changes at each time step t. The weighting factor, W, is used to encourage more aggressive predictions by reducing the penalty or cost associated with correctly predicting the direction of shoreline change

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110 (Ayp~yoo>()) but over predicting the magnitude (Aypr > Ayoo). The third approach is similar to the second, as a weighting factor is applied to the standard NMSE, only this time the weighting is applied a posteriori. Unlike the second method, where an arbitrary constant weighting is applied at each time step, an empirical function relating the predicted variance, Sp/, to the measured variance, s00 2 is used to weight the final calculated NMSE. This modified version of the NMSE is referred to as the variance ratio normalized mean square error or VRNMSE 2 0 1 s VRNMSE = NMSE 1--f sob (6.7) The exponent 0.1 was determined somewhat arbitrarily using a trial and error technique. In cases where the model is overly conservative, the ratio Sp/ls002 is small and the VRNMSE approaches the standard NMSE. On the other hand, when the total variance of the predictions approaches that of the observations, the NMSE is reduced significantly. The best simulations at Torrey Pines, CA according to the three modified error estimates are plotted in Figure 6-5 along with the measured data and the best prediction according to the standard NMSE. As was the intent, all three modified simulations are more aggressive than the original; however they are still unable to reproduce the high frequency component of the shoreline change. Of the three methods, the VRNMSE appears to encourage the most aggressive prediction, while simply eliminating a few outliers results in the smallest improvement over the original. Due to the fact that only small improvements were observed, and since the capacity to predict the high frequency shoreline behavior was not increased, the standard NMSE was retained as the primary error measure.

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-E -C Q :t: Cl) 0 c.. Q) -~ e 0 .c en 111 Torrey Pines, California 15r-------r---=::a-----'---r--.----,-----,---,,-----,--,---, 5 -5 -10 . . . . -15 . .... .... ......... ., ..... .... .... . . . ............... .... ..... ..... ..... ........ .. ... . . . . ...... ...... ,., \ rt :, 'y' I : I 'ii ... : .......... : .......... : ... ....... : .. ..... : \ . / .. : : : I / ---l'alb : v; -oy (Original NMSE} pr ..t::,. y (Outliers Removed} pr O y (WNMSE} pr --vy pr (VRNMSE} -20'-----'----'-----'---'-----'-'---......:...---------' ........... __ _. 1972.4 1972.6 1972.8 1973 1973.2 1973.4 1973.6 1973.8 197 4 197 4.2 197 4.4 Year Figure 6-5. Comparison of "best" modified NMSE predictions with the standard NMSE prediction and the measured data at Torrey Pines, CA. 6.4 Time Varying Sediment Scale Parameter, A(Q(t)) An effort was made to examine the potential impacts of some very recent and potentially significant findings related to the variability of the sediment scale parameter, A, in the well-known equilibrium beach profile relationship, h = Ay213 Wang (2004) analyzed several sets of laboratory data and found that value of A which had previously been assumed to be a function of the sediment properties alone, according to the work of Moore (1982) and Dean (1991), varied substantially with the non-dimensional fall velocity parameter, .Q (=Hi/w5T). The new relationship is more flexible, incorporating measures of the wave environment through the quantities Ht, and T, and results in milder

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112 sloped profiles for larger wav.es with shorter periods (typically stonn conditions), and steeper sloped profiles for small waves with longer periods (typically accretional conditions). This is of course more consistent with the dynamic behavior of natural beaches, and the typical seasonal pattern predicted by the summer/winter model of beach profile evolution. Figure 6-6, is a plot taken from Wang (2004) which presents the data and the proposed relationship, Anew= Ao1d x (0.868+ 1.406/Q), where Ao1d is the value of A according to Moore (1982). The potential implications of this new research on the present work are significant, as the active surfzone width, W , used to calculate the equilibrium shoreline change, Liyeq, and subsequently the equilibrium shoreline itself, is highly dependent on the value of A. According to the definition of W presented in Chapter 3, an analytic expression for the variation of W with Q exists and is given by, W new = Woid x (0.868+1.406/Qr312 as shown in Figure 6-7. In reassessing the value of W and its impact on the model, the cases of extremely low wave heights were also reconsidered. As a consequence of defining the active surfzone width as the distance to the breakpoint, w. shrinks to nearly zero during extremely mild wave conditions. Given the derivation of the quantity Liyeq presented previously, this seems unrealistic and inappropriate. In order to more accurately represent the expected shoreline changes associated with small waves, a limiting condition was considered for W , such that the minimum active surfzone width is given by, w. = (0.4HJKA)3 2 where Hb is the average breaking wave height at the site. The coefficient 0.4 was chosen somewhat arbitrarily after several trial runs, because it seemed to give realistic limiting values for W (on the order of fifty meters) at most sites.

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113 ,. ~-uz,tim =t-o , CE~-0.
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114 Case 1 W.(O) Case 2 Limited W. 75..----.----.--.----.----.--.----, 75..----.----.--...---.----.--...---, 50 ..... ~.. ~: ~50 ... . . . 25 25 .. ...... < .. : ..... .... 0 .... 0 .. ...... .. : ..... : .. .. -25 .... . . ... . . ...... ... . . -25 .......... : .. .. . . . . -50 .... :: : : . -50 .. .. .. .... ; .... ; .... :" .. : .. . . . . . -75 .. . . ...... .... ...... .. -... . ... . . . . . . . .. ...... . . .. . -75 .. .. . -100-_.__....__~_.__....__~~ -100 -75 -50 -25 0 25 50 75 -100 . -100 -75 -50 -25 0 25 50 75 Original Ay (m) Original Ay (m) eq eq Case 3 Limited W. O 75~-~--~---.---~----''-'--r'~---.----, : X 50 ........ : ....... ..... : ......... : ......... : ...... 1: ...... . ......... . S 25 .. ~ -~-. . i . 0 :: < :: . . .............. .. ... . . . "'O . G> -25 .. .. .. ; ........ : .. :E . . . . . -. ............ . . . . -50 ........ ; ..... . . . -75 ..... ..... .......... .. .. ........... . . . . . . . . . . -100"'-----'---..._ ___._ __ _._ ____. __ ...._ ____. -100 -75 -50 -25 0 25 50 Original liy (m) eq 75 Figure 6-8. Effect of A(.Q) on calculated Ayeq values at Torrey Pines, CA. Three new scenarios were run at several sites. In Case 1, the value of A was modified according to the relationship in Figure 6 7 and yeq(t) was recalculated. In Case 2, the limiting condition was imposed upon W . Case 3 consists of the combination of Cases 1 and 2, where W is limited and the modified A value is used. Figure 6-8 illustrates the differences between the three cases, and their overall impact on the calculation on Ayeq. The three panels in Figure 6-8 each illustrate a different case, where the original Ayeq is plotted against the modified version In all three panels, a straight line with a slope of one indicates no difference between the original and modified Ayeq. In the upper left panel representing Case 1, most of the values are located near the 45 line

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115 indicating that the amount of variability introduced by the modified A value is small. For Case 2, which is depicted in the upper right hand panel, only small values of ~Yeq corresponding to cases where Hb<0.4 Hb are affected. The magnitude of these small changes increase due to the lower limit imposed on W . Case 3 is shown in the lower panel, and demonstrates the combined influence of limiting W , and incorporating A(.Q). The impact of these changes to W on the hindcasts were examined by rerunning the simulations, including the calibration routine, at several sites. Despite potentially profound impacts elsewhere, the variability of A with .Q has little if any impact on the overall model performance. Because the differences between the original hindcasts and the modified versions are typically so small, they tend to overlie one another when plotted. The net effect of the three modifications to W at Torrey Pines are presented in Tables 6-3 to 6-5, where the format of the tables is identical to the NMSE tables discussed in the previous chapter, only the values indicate the percent change in the NMSE compared to the original simulations. The solid shading denotes the row and column with the lowest average NMSE, while the bold outlined value indicates the lowest NMSE for an individual simulation. The diagonal striping signifies the row and column with the lowest NMSE in the original simulation. The fact that all three modified simulations suggest a different parameterization for ke(t) (keHb3
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116 somewhat, as for a few parameter combinations the error is actually reduced by over 20%; however smaller changes to the NMSE on the order of 1 % are actually much more common. Although the modifications to A reduced the NMSE in most cases, it is unlikely that the unusually large error reductions can be attributed entirely to these changes. One possible explanation is that the calibration routine incorrectly identified the cost function minimum in the original simulation, a problem which is not encountered when the modified data is used. Table 6-3. Percent change in NMSE values at Torrey Pines, CA for Case 1 (A(.Q)). Erosion Parameter ke(t) = Con f(Q) f(Hb2 ) f(Fr) f(IFr) f(P) Avg II Con -2.09% -2.25% 0.07% -8.79% f(Q) -2.32% -3.45% 1.30% 1.30% -1.21% -.::2 f(Hb2 ) -0.91% -7.31% -3.99% -1.81% ... Q) Q) E as ... as a. C 0 :;:::. Q) ... -1.09% -1.89% 0.14% -9.13% 0 -1.18% -1.81% 0.14% -2.85% 0 ct Avg -0.91% -4.81% -2.89% -4.36% Table 6-4. Percent change in NMSE values at Torrey Pines, CA for Case 2 (minimum W. imposed). II Con f(Q) f(H/) Con f(Q) -0.53% 0.26% 0.12% 0.34% 0.02% -5.22% -1.08% -0.71% 0.04% 0.04% 1.22% -0. 72% f(H/) Avg -7.36% -5.37% -0.65% -0.27% -8.55% -1.61% -3.53%

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117 Table 6-5. Percent change in NMSE values at Torrey Pines, CA for Case 3 (A(Q) and minimum W imposed). II --... i E as a. C 0 Con f(Q) -1.91% -2.04% -2.20% -3.33% -0.89% -1.29% -1.98% -1 .15% -1. 78% -0.85% -4.73% Erosion Parameter k.(t) = 2 ~ff,""~~/. w, f(Hb ) f(Fr) f(IFr) f(P) Avg -43.44% 0.07% 0.64% -1.03% -8.52% 1.30% 1.30% -1.43% -1.76% -1.11% 0.14% 0.14% -2.90% -1.32% -1.80% -9.13% -2.82% -4.27% 6.5 Application to EOF Filtered Data As discussed in Chapter 4, EOFs can be used to filter data or equivalently to extract the relevant information from a data set. Normally, this means reducing noise, by eliminating modes that fall below a certain variance threshold; however this is not always the case. Here, EOFs were used to identify and isolate those modes corresponding to longshore uniform behavior, as these modes are more likely reflective of the shoreline response to cross-shore processes. Unfortunately, many of the data sets which were identified as containing one or more longshore uniform modes also contained most of their variance (-90%) within these modes, meaning the difference between the EOF filtered data and the original data is minimal. Table 6-6 is formatted identically to Tables 6-3 to 6 5, and indicates the percent ch~nge in the NMSE when an EOF filtered data set is used at the Gold Coast site. As expected there is a slight improvement over the original predictions; however this improvement averages less than one percent, and is considered trivial. Tests at other sites confirm that the dominant nature of the longshore uniform modes at each of the study sites ensures that only minor differences exist

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118 between the original simulations and those performed against the EOF filtered data. Although the EOF filtering technique is not useful here, this is due primarily to the characteristics of the specific data sets used in the study, and is not considered representative of the overall usefulness of the method. Table 6-6. Percent change in NMSE values when only the longshore uniform EOF modes are considered at the Gold Coast, Australia. Erosion Parameter f(Q) f(Hb2 ) f(Hb3 ) f(Fr) f(y,) f(P) Avg II Con -0.66% -0.60% -0.63% -0.83% -0.84% -0.73% f(Q) -0.62% -0.44% -0.64% -0.83% -0.60% -0.72% -:2 f(Hb2 ) -0 53% -0.56% -0.52% ... -0.57% -0.64% -0.58% Q) Q) E as ... as a. C: 0 .. Q) ... -0.77% -0.74% -0.67% -0.82% -0.74% -0.72% (,) -0.43% -0.52% -0.55% -0.64% -0 67% -0.55% (,) c:( -0.66% -0.58% -0.60% 0.08% -0.74% -0 .66% -0.58%

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CHAPTER 7 SUMMARY, CONCLUSIONS, AND FUTURE DIRECTIONS 7.1 Summary The worldwide increase in coastal populations during the past half-century has enhanced both the profile and the importance of coastal research. As the level of economic investment in these regions continues to increase, so must our understanding of the complex physical processes that shape these extremely dynamic regions. In many places beach erosion is a persistent threat, and in the wake of the recent hurricanes that have impacted the State of Florida, the development of innovative methods to predict the shoreline response to waves and water levels is arguably more relevant than ever. Unfortunately, several recent studies comparing some of the state of the art numerical models have resulted in some alarming conclusions. Current knowledge of hydro-, sediment and morpho-dynamics in the shoreface environment is insufficient to undertake shoreface-profile evolution modeling on the basis of first physical principles(cf. Niederoda et al. 1995). (Stive and de Vriend, 1995 p. 246) Quantitative understanding of the cross-shore sand transport processes in surf and swash zones on beaches is still rudimentary. None of the existing models can explain the post-storm recovery of beaches with sediment grains deposited above the mean shoreline. Consequently, it is not possible to predict the long-term shoreline change resulting from the cycle of beach erosion and recovery caused by sequences of storms. (Kobayashi and Johnson, 2001 p. 9363) The profile models are not yet suitable for detailed modeling of the beach (foreshore) and dune zones at the end of the profile on the seasonal time scale. (van Rijn et al., 2003 p. 324) At the present stage of research, it may be well concluded that the prediction of the precise bed evolution in the inner surf and beach zone is not feasible, no matter what type of model is used. The only solution here is to focus on the prediction of 119

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120 bulk volumes integrated over larger space and time scales rather than on the prediction of precise bed levels. (van Rijn et al., 2003 p. 325) These findings all point to a need for a new generation of models that can be applied near the shoreline over longer time frames. In particular, there is not a single generally accepted cross-shore model capable of reproducing shoreline changes over the engineering timescale. The proposed model fills this void by using an innovative engineering scale approach to develop a practical new tool for simulating the shoreline response to wave and water level forcing, over these relevant timescales. In the current study, the model has only been applied to historical data sets in a hindcast mode; however the positive results indicate that the model could be used to generate shoreline predictions with only minor modifications. The alternative shoreline change model presented here approaches the problem from a unique perspective, which differs from most of the traditional methods that have thus far failed to produce a reliable cross-shore model at the time scale of interest. Empirical evidence in the form of laboratory data and previous numerical studies was used to derive a simple equation relating the rate of shoreline change, dy/dt, to the degree of shoreline disequilibrium, (yeq-y). The equilibrium shoreline position was defined by combining equilibrium beach profile theory and a Bruun-type conservation of volume argument to derive an expression for the equilibrium shoreline change, Ayeq, due to wave and water level variations. Once a stable or baseline condition was determined through site-specific model calibration, the time series of equilibrium shoreline changes was converted into a time series of equilibrium shoreline positions. The rate at which the equilibrium shoreline is approached is controlled by a rate coefficient, ka, which can either be taken as a constant or parameterized in terms of local variables. A total of eight

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121 physically based rate parameters were analyzed, where the possibility that the erosion and accretion rate can be parameterized differently has been considered. Once completely defined, the governing shoreline evolution equation was solved numerically using an unconditionally stable, semi-implicit, Crank-Nicholson scheme. The solution procedure includes the definition and minimization of a cost function based upon a normalized mean square error that provides values for the three calibration coefficients, t:,.y0 ka, and kc, and results in an optimal solution. 7 .2 Conclusions The model was calibrated and evaluated using extensive sets of high quality shoreline data from a total of thirteen sites along both coasts of the United States and Australia. Although more data (three additional locations) were actually available, a combination of traditional and non-traditional statistical analysis techniques were used to eliminate several sites where the longshore shoreline behavior was decidedly non uniform. Model skill was evaluated using several performance indicators, where both objective criteria such as the NMSE and CAP, and subjective measures such as the MPI were used. Overall, the model performed well at most sites, as reflected in the average NMSE and CAP scores of 0.643 and 0.731, respectively. When only the best simulations at each site, corresponding to the most suitable parameter combinations, were averaged instead of all 800+ simulations, the average NMSE reduced significantly to 0.503, while the average CAP score increased to 0.814. According to the subjective MPI, over 50% of the simulations could be classified as either good or excellent, with reasonable results obtained in nearly every other case. Of the two sites where the model did not perform well according to the MPI, the cause at one (Island Beach, NJ) could be attributed to a

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122 case where the measured shoreline exhibits some unnatural behavior, and the other (Crescent Beach, FL) was most likely linked to a potentially correctable problem with the calibration routine. Based upon the results from all thirteen sites, two potentially appropriate parameter sets were obtained. Using a criterion based upon the average NMSE for a particular form of ka(t), the independent parameters, ka(t) = kaFr(t) and ke(t) = ke~b(t) were determined to be most suitable, where Fr(t) = wJ(gHb)l/2 is a surfzone Froude number, and ~b(t) = Ht,/{l,utan2f3) is a form of the surf similarity parameter. A separate criterion based upon the best individual simulations at each site according to the NMSE gave a slightly different parameter combination, ka(t) = kaFr(t), ke(t) = keHb\t), where these two parameters are linked. The variability of the coefficients associated with each parameterization has important implications in terms of the potential predictive capacity of the model. A series of histograms and bulk statistics were used to illustrate the significant spread associated with most of the coefficients when the data were considered as a whole. However when the coefficients were grouped regionally, the variability reduced significantly, and some of the most dramatic improvements occur for those parameters selected by the aforementioned criteria. In addition to these critical issues, several supplementary topics were explored as well. The potential impact of new research suggesting that the sediment scale parameter, A, varies with the non-dimensional fall velocity parameter, Q, was investigated by adjusting the calculation of the active surf zone width, W and subsequently, ycq(t). In conjunction with the recalculation of yeq(t), a lower limit was adopted for the active surf zone width, such that in mild wave conditions realistic minimum values of W were

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123 obtained. Three separate cases were evaluated (A(W), limited W , A(W) and limited W. ), where the overall impact of these changes was determined to be small, as the results only improved by an average of four percent. Several modified versions of the NMSE were tested in hopes that they would improve upon the conservative nature of the model and encourage more aggressive simulations. Three modified cost functions were considered; however the resultant relatively minor improvements in the results did not justify using any of them in place of the typical NMSE. Finally, the model was applied to a data set which was filtered using EOFs, such that only the longshore uniform components of the shoreline change were retained. Although potentially useful, the nature of the data used in this study was such that the primary mode of shoreline variability was sufficiently longshore uniform; therefore little (on the order of one percent) difference could be observed between simulations carried out using the filtered data as opposed to the original data. 7 .2 Future Directions Overall, the model is considered to be a powerful new tool for simulating long term shoreline changes due to forcing by variable wave heights and water levels at the engineering timescale. There are improvements which can be made however, and these can be broken down into two broad categories related to the theoretical basis of the model itself, and the numerical technique. The good results obtained at a majority of the thirteen sites, suggests that the overall approach is sound. As discussed in Chapter 3, successful simulations are predicated on accurate representations of three quantities, the equilibrium shoreline, yeq(t), and the rate parameters, ka and kc. The equilibrium shoreline position is a difficult quantity to define, and while the simple conservation of volume arguments used here appear to be adequate, a more rigorous method for

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124 determining this parameter could be developed and adopted. In the short term, more accurate representations of the local wave and water level conditions are considered essential for good results. The existing wave transformation model based on linear theory, which is used to shoal and refract the remotely measured deep-water waves to their breakpoint, could be improved to include a more advanced wave transformation model. Of course, any simulation is dependent on the quality of the available wave and water level data, which at many sites could be improved. Although the present work suggests that ka(t) = kaFr(t) and ke(t) = keHb3(t) or ke~b(t) are appropriate parameterizations for the rate coefficient, there exists a strong potential for improving the model through studying these coefficients in more detail. The significant variability across geographic regions, and the relative stability within a given region, of the calibration coefficients ka and kc, indicates that additional factors may need to be included in future studies. Perhaps the most significant chance for improving the model is related to the computational technique, as the current numerical scheme and calibration routine are both fairly simple. Although these simple routines seem to generate accurate predictions with remarkable efficiency, more advanced schemes could undoubtedly improve model performance. The tendency of the model to give conservative predictions is an undesirable feature, which might be improved through the adaptation of a different cost function and error definition. Several attempts were made to address this issue in the present work by modifying the NMSE; however more additional research is required. Alternate error estimates and cost functions which could encourage more aggressive

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125 predictions at the shorter time scales, while not significantly reducing the model skill over the longer time scales, should be considered. Once the suggested improvements have been made, the simplicity and efficiency of the model will make it an ideal tool for a variety of applications. Properly calibrated, the proposed model will be a useful cross-shore complement to traditional one-line models, allowing both longshore and cross-shore processes to be considered in a single simple, yet practical, engineering scale model. As an engineering tool, the model is ideal for quickly evaluating several different design alternatives in order to find the optimal solution. If the effectiveness of the model at the shorter timescales can be improved, its modest data requirements will make it useful for providing real-time nowcasts using either measured data or short term hydrodynamic forecasts. As a predictive tool, the efficiency of the model makes it ideally suited for use with the Monte Carlo method, where statistical distributions of the forcing parameters can be used to quantify the probable shoreline changes. These represent only a small portion of the many potential uses of an accurate new engineering scale shoreline model such as the one developed, calibrated, and evaluated here.

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APPENDIX A MODEL SOURCE CODE The source code (Fortran 90) for the model discussed in this work is presented in the following pages. 126

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!SLMOD.FOR !JON MILLER !OCTOBER 2004 !LAST UPDATED: 10/12/04 implicit none 127 external NDAYS,JERROR,JOUTERROR,WERROR,VERROR real JERROR,YERROR,KSELECT,K,Ka,Ke,AA,FV ,JOUTERROR,WERROR,VERROR realDY2ERO,MINYERROR,DYEQ real Y(150000),YEQ(150000),YEQws(150000),Hb(l50000),PER(l50000) real DYOBS(l 000),DYPRED(l 000),YPRED( 1000) real YPNUM(lOOO),YOBS(lOOO),YOBSDATE(lOOO),YPSUM(lOOO) real PI,G,BCHSLP,KAMIN,KEMIN,DYMIN,DKE,DKA,DY0START,DDY ,NMSEMATRIX(S,8) integer YR(150000),MONTH(150000),DAY(150000),HR(150000) integer DTT(150000),JPRED(150000) integer NUMPHOTOSj,i,NDA YS,KAA,KEE,DDYEQ,IMAX integer TSTEP,ika,ike integer OBSDAY(lOOO),JOBS(lOOO) character*3 KA TYPE(8),LOC2, YEQTYPE,KETYPE(8) character*2 LOCI character*! RERUN LOC1='TP';LOC2='A VG';YEQTYPE='SIG'; open (unit=23,file=LOCl/f\'//LOC2//'\'//LOCl//LOC2//'(EMatrix)'//YEQTYPE/f.out',status='NEW') Pl=3.14159;0=32.l KATYPE(l)='CON';KATYPE(2)='FVP';KATYPE(3)='HB2';KATYPE(4)='HB3 ; KATYPE(5)='FRD';KATYPE(6)='1FR';KATYPE(7)='SSP';KATYPE(8)='PRP'; KETYPE(l)='CON';KETYPE(2)='FVP';KETYPE(3)='HB2';KETYPE(4)='HB3'; KETYPE(5)='FRD';KETYPE(6)='1FR';KETYPE(7)='SSP';KETYPE(8) = 'PRP'; do ika=l,8 do ike=l,8 MINYERROR=lOOOO.0 DYEQ=l.0 call RUNSETUP (L0Cl,LOC2,YEQTYPE,KATYPE(ika),KETYPE(ike),BCHSLP ,FV,TSTEP) call SL_IN (NUMPHOTOS,YOBS,DYOBS JOBS,YOBSDATE,OBSDA Y) i=l do while (.TRUE.) read ( 1, ,end = 1000) YR(i),MONTH(i),DA Y(i),HR(i) YEQws(i),Hb(i),PER(i) JPRED(i) = NDAYS(DAY(i),MONTH(i),YR(i)) DTT(i)=(JPRED(i)-JPRED(i-1))*24+(HR(i)-HR(i-1)) IMAX=i i=i+l end do 1000 continue write( *,* ) write(*,*) KATYPE(ika), KETYPE(ike)

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128 DKA=0.0005 ;DKE--0.0005 ;DY0ST ART=-100.0;DDY = 10.0 RERUN='Y' do while (RERUN.eq.'Y') RERUN='N' !aaaaaaaaaaaaaaaaaa Ka LOOP aaaaaaaaaaaaaaaaaaa doKAA=l,20 !eeeeeeeeeeeeeee Ke LOOP eeeeeeeeeeeeeee doKEE=l,20 !qqqqqqqqqqqqqqqqqqq DYEQ LOOP qqqqqqqqqqqqqqqqq do DDYEQ=l,20 Y(l)=YOBS(l) KA=KAA *DKA+0.00 KE=KEE*DKE+0.00 DYZERO=DY0START+DDYEQ*DDY j=l YEQ(l )= YEQws(l)+DYZERO !pppppppppppppppppppp PREDICTION LOOP ppppppppppppp doi=2,IMAX DYEQ=DYZERO YEQ(i)= YEQws(i)+DYEQ !Select appropriate rate parameter if(Y(i-1).lt.YEQ(i)) then K=KSELECT(KATYPE(ika),KA,HB(i),FV ,PER(i),BCHSLP) else K=KSELECT(KETYPE(ike),KE,HB(i),FV ,PER(i),BCHSLP) end if !Calculate predicted shoreline Y(i) AA=K*DTT(i)/2.0 Y(i)=(Y(i-l)+AA *(YEQ(i)+ YEQ(i-l)-Y(i-1)))/(1.0+AA) if (JPRED(i).eq.JOBS(j)) then YPSUM(j)= YPSUM(j)+ Y(i);YPNUM(j)= YPNUM(j)+ 1 end do else if ((JPRED(i).gt.JOBS(j)).and.(j.lt.NUMPHOTOS)) then j =j+l end if do j=l ,NUMPHOTOS if(YPNUM(j).eq 0)YPNUM(j)=l YPRED(j) = YPSUM(j)/YPNUM(j) if(YPSUM(j).eq.0)YPRED(j)=(YPSUM(j1 )+ YPSUM(j+ 1 ))/(YPNUM(j-1 )+ YPNUM(j+ 1 )) DYPRED(j)= YPRED(j)YPRED(j-1) if(j.eq.l)DYPRED(l)=YPRED(l)-Y(l) end do YERROR = jerror(YOBS, YPRED,NUMPHOTOS) YERROR=jouterror(YOBS,YPRED,NUMPHOTOS) YERROR=werror(YOBS,YPRED,NUMPHOTOS)

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129 YERROR=verror(YOBS,YPRED,NUMPHOTOS) if ((YERROR.le.MINYERROR).and.(YERROR.gt.0)) then rewind(7) MINYERROR=YERROR;KAMIN=KA;KEMIN=KE;DYMIN=DY2ERO write (7,'(f12.5,2ES12.2E2,2f12.2)') YERROR,KA,KE,DYZERO,DYEQ write (7,'(5f12.3)') (YOBSDA TE(i), YOBS(i), YPRED(i),DYOBS(l),DYPRED(i),i= l ,NUMPHOTOS) end if end do write (5,'(2Fl5.9,4Fl2.4)') Ka,Ke,DY2ERO,YERROR do i=l,IMAX;Y(i)=O;YEQ(i)=O;end do do j=l,NUMPHOTOS;YPSUM(j)=O;YPNUM(j)=O;end do do i=l,1000 DYPRED(i)=O YPRED(i)=O end do end do !qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq write(*,*) Kaa end do !aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa if ((abs(KAMIN-DKA)).lt.DKN2.0) then DKA=DKNIO.0;RERUN='Y';WRITE(*, *) 'KA-' else if (abs(KAMIN-DKA*20.0).lt.DKN2.0) then DKA=DKA 10.0;RERUN='Y';WRITE(*, *) 'KA+' end if if (abs(KEMIN-DKE).lt.DKF/2.0) then DKE=DKF/10.0;RERUN='Y';WRITE(*, *) 'KE-' else if (abs(KEMIN-DKE*20.0).lt.DKF/2.0) then DKE=DKE* 10 0;RERUN='Y';WRITE(*, *) 'KE+' end if if (DYMIN.eq.DY0ST ART +DDY) then DY0ST ART=DY0ST ART-DDY*IO.0;RERUN='Y';WRITE(*, *) 'DYO-'; else if (DYMIN.eq.DY0ST ART +DDY*20.0) then DY0START=DY0START+DDY*IO.0;RERUN='Y';WRITE(*,*) 'DYO+' end if rewind(S) write(*,*) KAMIN,KEMIN,DYMIN,MINYERROR end do close(l);close(3);close(5);close(7); NMSEMA TRIX(ika,ike)=MINYERROR; end do;end do do i =l,8 write(23,'(8Fl0.5)') (NMSEMATRIX(ij)j=l,8) end do end program

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130 !SubRoutines !SubRoutines !SubRoutines !SubRoutines !SubRoutines!SubRoutines!SubRoutines !SubRoutines !SubRoutines !SubRoutines !SubRoutines !SubRoutines!SubRoutines!SubRoutines !SubRoutines !SubRoutines!SubRoutines !SubRoutines !SubRoutines!SubRoutines!SubRoutines !RUNSETUP !LOCI,LOC2,YEQTYPE,COTYPE variables from main program indicating the type of run !BCHSLP ;FV beach slope and fall velocity (ft/s) !creates files, opens them, and format statements subroutine RUNSETUP (LOCI,LOC2,YEQTYPE,KATYPE,KETYPE,BCHSLP ;FV,TSTEP) external NOA YS character*2 LOCI character*3 KATYPE,KETYPE,LOC2,YEQTYPE character*50 EQFILE,SLFILE,ELFILE, YFILE integer TSTEP real BCHSLP,FV if (LOCI.eq.TP') then BCHSLP=0.02;TSTEP=3; if(LOC2.eq.'NOR') then; FV=0.0781 end if else if (LOC2.eq.'SOU') then; FV=0.0745 else if (LOC2.eq.'IND') then; FV=0.0696 else if (LOC2.eq.'AVG') then; FV=0.0745 else if (LOCI.eq.'DU') then BCHSLP=O 0;FV=0.0764;TSTEP=l else if (LOCI.eq.'FL') then TSTEP=3 if (LOC2.eq.'DAB') then BCHSLP=O.0IS;FV=0.0571 else if (LOC2.eq.'SAB') then BCHSLP=0.040;FV=0.0538 else if (LOC2 eq.'CRB') then BCHSLP=0.025;FV=0.0512 else if (LOC2.eq.'NSB') then BCHSLP=O.0IS;FV=0.0509 end if else if (LOCI.eq.'NJ') then if ((LOC2.eq.WWD').or.(LOC2.eq.'109').or.(LOC2.eq.'l 10')) then BCHSLP=0.015;FV=0.0764;TSTEP=l else if (LOC2 eq.'ISB') then BCHSLP=0.032;FV=0.150;TSTEP=l end if else if (LOCI.eq.'LI') then BCHSLP=O.OOO;FV=0 153;TSTEP=l else if (LOCI.eq.'W A') then TSTEP=l if (LOC2.eq.'LGB') then BCHSLP=0.020;FV=0.074 else if (LOC2.eq 'NRB') then BCHSLP=0.014;FV=0.045; else if (LOC2.eq.'CLP') then

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BCHSLP=0.022;FV=0.060; else if (LOC2.eq.'GRP') then BCHSLP=0.023;FV=0.067; end if else if (LOCl.eq.'OZ') then TSTEP=l if (LOC2.eq.'LEI') then BCHSLP=0.075;FV =0.164 else if (LOC2.eq.'BRI') then BCHSLP=0.075;FV=0.260 else if (LOC2.eq.'GLD') then BCHSLP=0.05;FV=0.115 end if else if (LOCl.eq.'AA') then BCHSLP=0.02;FV=7 .64e-2;TSTEP=l end if 131 EQFILE='infiles\'//LOC1//'\'//LOC1//LOC2//'yeq'/NEQTYPF1f.inp' SLFILE='infiles\'//LOC 1//'\'//LOC 1//LOC2//'SL.inp' ELFILE=LOC1/f\'//LOC2/f\'//LOC1//LOC2/f(Elist)'//K.ATYPF1f _'IIKETYPEJf _'INEQTYPFlf.out' YFILE=LOC1//'\'//LOC2/f\'//LOC1//LOC2//'(Y)'//K.ATYPF1f _'I/KETYPEJf _'INEQTYPEJf.out' open ( unit=l ,file=EQFILE,status='OLD') open (unit=3,file=SLFILE,status='OLD') open (unit=5,file=ELFILE) open (unit=7,file=YFILE) return end subroutine !SL_IN !NUMPHOTOS Number measured shorelines !YOBS, DYOBS Observed shoreline and shoreline change !JOBS, YOBSDATE serial date and decimal date !reads in shoreline input file and calculates date measures subroutine SL_IN(NUMPHOTOS,YOBS,DYOBS,JOBS,YOBSDATE,OBSDAY) external NDAYS integer k,OBSYR( 1000),OBSMO( 1000),0BSDA Y( 1000),JOBS( 1000) integer NUMPHOTOS,NDA YS real YOBS( 1000),DYOBS( 1000), YO BSD A TE( 1000) k = l do while (.true ) end do read(3, *,end=l 111) OBSYR(k),OBSMO(k) OBSDAY(k),YOBS(k),DYOBS(k) JOBS(k)=NDAYS(OBSDAY(k),OBSMO(k),OBSYR(k)) YOBSDATE(k)=OBSYR(k)+NDAYS(OBSDAY(k) OBSMO(k),1900)/365.0 k =k+l 1111 continue NUMPHOTOS = k 1 return end subroutine

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132 !Functions !Functions !Functions !Functions !Functions !Functions !Functions !Functions !Functions !Functions !Functions !Functions !Functions !Functions !Functions !Functions !Functions !Functions !Functions !Functions !Functions !Functions !Functions !Functions !Functions !Functions !Functions function KSELECT (KTYPE,KALPHA,WVHT ,FVEL,WVPE,M) real KSELECT ,G,PI,WVHT ,FVEL,WVPE,M,KALPHA character*3 KTYPE G=32.l;Pl=3.14159 if(KTYPE.eq.'CON') then KSELECT=KALPHA elseif(KTYPE.eq. 'HB2') then KSELECT=KALPHA *(WVHT**2)*10e-5 elseif(KTYPE.eq. 'HB3') then KSELECT=KALPHA*(WVHT**3)*10e-5 elseif(KTYPE.eq. 'FVP') then KSELECT=KALPHA *(WVHT/(FVEL *WVPE)) elseif(KTYPE.eq.'SSP') then KSELECT=KALPHA*((2.0*PI/WVPE)**2.0*WVHT/(2.0*G*M**2.0))*10e-3 elseif(KTYPE.eq. 'FRD') then if(WVHT. eq.0.0) WVHT=O.l KSELECT=KALPHA *(FVEU(G*WVHT)**0 5) elseif(KTYPE.eq.'IFR') then if(WVHT.eq.0.0) WVHT=0.1 KSELECT=KALPHA *((FVEU(G*WVHT)**0.5)**(-1 ))* 1 Oe-3 elseif(KTYPE.eq. 'PRP') then KSELECT=KALPHA *(G*WVHT**2.0/FVEL **3.0/WVPE)*lOe-7 endif end function function jerror (MEAS,PRED,n) integer n,i real MEAS(lOOO),PRED(lOOO),NUM(lOOO),DENOM(lOOO),JERROR do i=2,n num(i)=(meas(i)-pred(i))**2 denom(i)=(meas(i)-(0.00))**2 end do jerror=(sum(num))/(sum(denom)) do i=l,n;num(i)=O;denom(i)=0;end do end function function JOUTERROR (MEAS,PRED,n) integer n,i,OUTLIERS,OUTLOC(l) real MEAS( 1000),PRED( 1000),NUM( 1000),DENOM( 1000),JOUTERROR,PMAX real OUTNUM(lOOO),OUTDENOM(lOOO) OUTLIERS = floor(0.15*n) PMAX =lOOOOO.0 do i=2,n NUM(i)=(MEAS(i)-PRED(i))**2

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133 DENOM(i)=(MEAS(i)-(0.00))**2 end do do i=l,OlITLIERS OUTNUM(i)=maxval(NUM,MASK=NUM.lt.PMAX) OlITLOC=maxloc(NUM,MASK=NUM lt.PMAX) OUTDENOM(i)=DENOM(OlITLOC(l)) PMAX=OUTNUM(i) end do JOUTERROR=(sum(NUM)-sum(OUTNUM))/(sum(DENOM)-sum(OUTDENOM)) do i=l,n;NUM(i)=O;DENOM(i)=0;OUTNUM(i)=O;OUTDENOM(i)=O;end do end function function WERROR (MEAS,PRED,n) integer n,i real MEAS(lOOO),PRED(lOOO),DMEAS(lOOO),DPRED(lOOO),NUM(lOOO),DENOM(lOOO),WERROR real WEIGIIT DMEAS( 1 )=O.0;DPRED( 1 )=0.0; do i=2,n end do DMEAS(i)=MEAS(i)-MEAS(i-l);DPRED=PRED(i)-PRED(i-1); if(DMEAS(i)*DPRED(i).gt.0.0) then WEIGIIT=0.250 if(abs(DPRED(i)).lt.abs(DMEAS(i))) WEIGIIT=0.75 else WEIGIIT=l.0 end if NUM(i)=WEIGIIT*(MEAS(i)-PRED(i))**2 DENOM(i)=(MEAS(i) )**2 WERROR=(sum(NUM))/(sum(DENOM)) do i = l,n;NUM(i)=O;DENOM(i)=O;end do end function function VERROR (MEAS,PRED,n) integer n,i real MEAS(lOOO),PRED(lOOO),NUM(lOOO),DENOM(lOOO),VERROR real MEANPRED,DP(IOOO),VR MEANPRED=sum(PRED)/n do i=2,n NUM(i)=(MEAS(i)-PRED(i))**2 DENOM(i)=(MEAS(i)-(0.001) )**2 DP(i)=(PRED(i)-MEANPRED)**2 !Deviation from mean end do

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134 VR=sum(DP)/sum(DENOM) !Variance ratio (1/n terms cancel) VERROR=(sum(NUM)/sum(DENOM))*(abs( 1-VR))**0.1 do i=l,n;NUM(i)=O;DENOM(i)=O;end do end function !NMSE*ll-vrl

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APPENDIXB COMPLETE SET OF MODEL RESULTS Results of the model simulations for each of the 64 possible parameter combinations at each site are presented within this appendix. Detailed information on the interpretation of the tables and figures appears in Chapter 5 along with a qualitative description of the results. Where necessary, some of the information presented here has been reproduced within the text in order to illustrate important points. 135

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136 Island Beach, New Jersey 15-----'-'--'-~--~---~----..-----.----..------, e -C: 0 E II) 0 a. 10 ... ... .. ..... . 5 ................................. ,. 0 .... .~ e -5 . . . . : .. ,. ....... .. . . . < .. = .. . . 0 .c en . . . . . . . . . . . . . . -10 . -15 .._ Yob -oy (Minimum NMSE) pr -A y (Maximum CAP) pr . . ., .. .. .............. ..... . 1-Prediction Ran e -2ou..----------''--L-----'---.......__ __ ____. ___ ....,___ __ _. 1986 1988 1990 1992 1994 1996 1998 2000 2002 Year Figure B-1. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Island Beach, NJ. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations. Table B-1. Calculated NMSE values for model hindcasts at Island Beach, NJ. Erosion Parameter ke(t) = f IFr II ... 0.995 0.938 0.995 0.996 0.931 1.001 0.964 G> G> 0.921 0.936 0.938 0.924 0.949 0.929 0.926 0.932 E e 0.947 0 912 0.892 0.952 0.930 0.905 0.892 0.914 IU Q. f(IFr) 0.975 0.925 0.934 C: 0 0.940 0.948 0.928 0.907 0.934 f(l;t,) 0.959 0.915 0.921 0.942 0.949 0 919 0 914 0.928 f(P) 0.959 0.937 0.928 0.965 0.952 0.935 0.921 0.939 Avg 0.953 0.926 0.921 0.950 0.948 0.923 0.923 0.932

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137 Table B-2. Calculated CAP scores for model hindcasts at Island Beach, NJ. II f(C) 0.564 0.618 0.605 0.605 0.627 0.615 j ... f(Hb2 ) 0.596 0.636 0.655 0.596 0.596 0.659 0.596 0.621 Q) G) f(Hb3 ) 0.618 0.632 0.605 0.609 0.650 0.605 0.609 0.622 E t!! f(Fr) 0.577 0.682 0.677 0.573 0.600 0.696 0.686 0.649 "' Cl. f(IFr) 0.527 0.618 0.605 0.614 0.668 0.650 0.632 0.615 C 0 f(l;ti) 0.582 0.636 0.682 0.596 0.618 0.614 0.682 0.632 f(P) 0.596 0.632 0.655 0.564 0.618 0.664 0.664 0.628 Avg 0.596 0.640 0.646 0.607 0.632 0.646 0.639 0.632 Table B-3. Calibration coefficients ka, kc, and~ 0 for Island Beach, NJ. Erosion Parameter ke t = Avg f(C) [hr" 1 2.0E-04 1.5E-06 2.5E-06 2.0E-09 -3.048 -3.048 -3.048 -3.048 -3.048 -2.286 f(Hb:.i) 9.7E-05 2.2E-04 1.6E-11 1.8E-04 [hr"1m-2] 6.0E-08 8.5E-06 4.0E-10 -9.144 -9.144 -3.048 6.096 76.200 8.763 1.8E-03 1.8E-03 3.4E-04 1.8E-04 1.2E-04 6.1E-04 5.0E-06 1.0E-05 6.0E-09 ..: -3.048 3.048 6.096 6.096 2.667 Q) G) 1.0E-02 9.0E-03 8.5E-03 4.5E-01 1.9E-01 E f(F~ "' 1.0E-03 7.0E-06 1 0E-05 2.5E-09 ... [hr" 1 "' Cl. 9.144 9.144 9.144 -3 .048 4.572 C 0 7.5E-05 3.5E-06 f(IFf) 2.5E-06 1.0E-04 5.6E-05 [hr" 1 1.0E-03 7.5E-03 4.5E-06 1.0E-05 3.0E-09 0.000 -3.048 6.096 9.144 -3.048 0.000 f(l;ii) 5.5E-06 6.5E-05 1.0E-05 3.5E-05 9.5E-03 4.0E-06 2.0E-06 4.0E-09 [hr" 1 6.096 -3.048 6.096 -3.048 3.048 0.762 7.0E-08 7.5E-08 2.0E-08 2.5E-08 4.9E-08 f(P} 1.5E-04 2.0E-06 1.0E-05 4.5E-09 [hr" 1 0.000 0.000 6.096 3.048 1.524 Avg 3.3E-06 6.9E-06 3.1E 09 -0.381 -1.524 1.905 3.429 9.525 1.619

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138 Wildwood, New Jersey 40=========::;-----,----,-----,-----,-----,----, ---Yob -oy (Minimum NMSE) 30 .. /r (Maximum CAP) pr + Prediction Range 20 .. e 10 c:: 0 E fl) 0 a. Cl) 0 .!: -10 0 .c en -20 -30 -40 ... .... I ......... ........ .... I I I /' I 7 . ... I . . . . \ I i . ...... .. ............ . ... . . .. \1: j .... ... : ..... ..... : ... ...... : I I .. \ I I . .. : ..... : .... . /' ..... . : .. ... ... ....... '.' ' . I . . .. : .......... : ....... ... : .......... :. . . . . . . . t t . . . . : .......... : ........ ' -50..._ __ ...._ __ ...._ __ ......... __ ___._ __ __. _____ .._ __ ...._ __ _.__ __ _, 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 Year Figure B-2. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Wildwood, NJ. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations. Table B-4. Calculated NMSE values for model hindcasts at Wildwood, NJ. Erosion Parameter ke(t) = Con f(Q) f(Hb2 ) f(Hb3 ) f(F r ) f(IFr) f(l;i,) Avg II Con 0.662 0.663 0.627 0.627 0.976 0 .651 0.688 0.692 f(Q) 0.648 0.644 0.611 0 879 0.980 0.636 0 675 0 712 ... f(Hb2 ) 0.646 0.647 0.980 0.634 0 980 0.648 0.649 0.729 I f(Hb3 ) 0.632 0.637 0.984 0.650 0.982 0.635 0.642 0.728 E !!! f(Fr) 0.668 0.671 0.634 0.632 0.975 0.656 0.695 0.698 IU a. 0.660 0.658 0.624 0.674 0.648 C

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139 Table B-5. Calculated CAP scores for model hindcas ts at Wildwood, NJ. Erosion Parameter ke(t) = Con f(Q) f(Hb2 ) f(Hb3 ) f(Fr) f(IFr) f(P) Avg II Con 0.663 0.675 0.604 0 592 0.525 0.688 0.629 0.624 f(Q) 0.646 0.617 0.617 0.625 0.525 0.617 0.646 0.630 ... f(Hb2 ) 0 754 0 754 0 525 0.658 0 525 0 .754 0.700 0.676 a, ai f(Hb3 ) 0 .721 0.738 0.496 0.613 0 496 0 738 0.658 0.644 E 1!! f(Fr) 0.675 0 629 0.575 0.604 0 542 0 617 0 629 0.611 cu a. f(IFr) 0 717 0 642 0 .621 0.588 0.629 0.648 C 0 :;:: it': ~ JJ,,,,,,,,,, Q : ~!'?%' a, ... ;,; :,,~,/;,,,,...,.,,,,,,:w/,w/,,:v.;,,,: v;, 1//, ,,..,.,..,,,;,;~--~ 1///. ;,; '.i'//, :,-: :-'/, ;,;y; ,../$. 1/, ;,;m-////, 00:,:: ;.,J1//.W/M Avg 0 705 0.695 0 589 0 .621 0 579 0.668 0 659 Table B-6. Calibration coefficients ka, ke, and fl. o for Wildwood NJ. Con f(Q) f(Hb2 ) f(Fr ) Avg hr'1 hr'1 hr 1 m 2 hr'1 hr'l ke 2.0E-04 3.0E-04 2.5E-04 3.0E-04 1 5E-03 Con [hr'1] ke 8 5E-03 4.0E 04 3.8E-04 7 1 E-05 1.0E-07 !J. 0 36. 576 27. 432 27.432 21. 336 -18 288 ke 2.5E-05 2 5E-05 3.5E-05 7.5E-04 1.5E-04 1.7E-04 f(Q} ke 1 0E-01 5.0E-04 3.8E-04 3.4E-06 2.0E-05 [hr' l 36.576 27.432 -15 240 -18 288 19.050 f(Hbl!) 1.6E-03 4 3E-04 2 2E-04 4 3E-04 [hr'1m-2] 1.0E -02 3 2E-08 8.8E-05 1 5E-05 !J. 0 21.336 18 288 -18.288 21.336 -18.288 ke 7 1E-04 8.8E-04 1.8E-04 1.1E-04 2 1E-04 II f(Hb 3 ) ke 2 0E-02 1 0E-02 9 2E-10 8 8E-05 9.0E-04 [hr' 1 m ~ j !J. 0 24. 384 18.288 -18 288 24.384 -15.240 .: ke 3 0E 02 4 0E -02 3.5E 02 5 0E-02 4.0E-01 i f(F~ E [hr' l 3 5E 04 3.8E 04 7.1 E -05 2 0E-06 1!! cu 27.432 27. 432 18.288 18.288 a. 1.5E-06 2 0E-06 2.5E-06 1 0E-06 f(IFf) a: [hr' l 4 5E-04 4.3E 04 7 1E-05 2.5E+OO 33. 528 27. 432 18 288 42. 672 f(/;b). 1.0E-05 1 0E 06 1.SE-06 7 0E -07 6 .SE-04 3.8E 04 8.SE-05 [hr' l 21.336 Avg 2. 9 E 03 3.0 E04 7 1 E-05 1 1 E+00 24.384 16.002 16.383 2 286

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15 10 5 -E 0 C: 0 .:; u; 0 -5 a. .~ _g -10 Cl) -15 -20 140 St. Augustine, Florida : : : i ~ ;{: / \ ... I w I I : I : I,,. ..... .... ......... I . : \ \ I / : I / I : : I. : / / :. I : \ ........ ... , ... : I 1 . I I I 1 I I . = : ......... ;. . . ---Yob oy (Minimum NMSE) pr -8. y (Maximum CAP) pr + Prediction Ran e I + i': ........ . ....... 1'; ........ II h \ .. ~ .. -25.__ __ _._ __ ...._ __ _._ __ _,_ __ _._ __ .......... __ __._ __ _._ __ __, 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 Year Figure B-3. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at St. Augustine, FL. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations. Table B7. Calculated NMSE values for model hindcasts at St. Augustine, FL. Erosion Parameter ke(t) = f(Hb2 ) f(Hb3 ) f(Fr) f(IFr) f(y,) f(P) Avg II 0.816 0.839 0.800 0.731 0.791 ... G> iii E I!! 0.790 0.790 0.790 0.821 0.837 as n. f(IFr) 0.763 0 814 0.814 C: 0 0.813 0.794 0.672 0.768 0.765 .:; !!! f(y,) 0.893 0.736 0.737 0.736 0.937 0.766 0.814 0.795 f(P) 0.764 0.835 0.827 0.837 0.780 0.697 0.761 o.n8 Avg 0 802 0.801 0.818 0.779 0.815 0.737 0.779 0.782

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141 Table B-8. Calculated CAP scores for model hindcasts at St. Augustine, FL. II j ..: I E e ca a. a: f(Fr) f(IFr) f(y,) f(P) 0.547 0.584 0.579 0.584 Avg 0 613 Con hr"1 Con [hr"1] f(Fq. [hr"] f(IFf) [hr"] f(l;i,). [hr"] f(Pl [hr"] Avg 0.632 0.674 0.653 0.721 0.674 0.665 5.0E+OO 1.1 E-10 -3.048 1.6E-10 -3 048 -3.048 -3.048 1.5E-05 1.143 0.595 0.674 0.653 0.690 0.626 0.653 7.1E-06 15.240 5.0E+OO 7.1 E-12 -3.048 6.0E-05 3.5E-11 -3.048 5.0E-04 8 8E-07 -3.048 5.0E-09 7.1 E-06 0.000 4 6E-06 0.381 0.611 0 674 0.658 0.721 0.742 0.666 f(Fr) hr"1 1.5E-02 7.0E-03 4.0E-01 27.432 5.0E+OO 1.0E-06 -3.048 6.5E-05 9.5E-03 -3.048 5.0E-04 3 5E-03 -3.048 1.0E-08 8.0E-03 3.048 1.4E-01 6.096 0.616 0.647 0.547 0.611 0.584 0.618 f(IFr) hr", 1.0E-04 4.0E-06 3 0E-06 27. 432 8.5E-02 1.0E-06 6.096 4.0E-07 4.5E-06 30.480 6.5E-06 2.0E-06 18.288 2.0E-10 4.0E-06 30.480 1.2E-04 22.860 f(y,) 0.568 0.547 0.584 0.547 0.621 0.596 f(y,) hr"1 1.0E-04 3.5E-05 2.5E-05 30.480 1.5E-02 4 0E-05 30.480 3.5E-07 4.5E-05 33. 528 4.0E-06 4 0E-05 30.480 1.5E-10 5.0E-05 36.576 3 3E-05 27.051 f(P) 0.616 0.647 0.626 0.642 0.579 0.634 f(P) hr"1 7.0E-04 2.0E-10 3.5E-10 18.288 2.0E-01 1.5E-10 0.000 1.0E-06 2.5E-10 12.192 6.5E-05 1.5E-10 0.000 5.0E-10 3.0E-10 15 240 2.2E 10 8.001 Avg 0.632 0.621 0.654 0.629 0.640 Avg 3.1E-02 22.860 1.9E+OO 8.763 1.4E-04 13.335 2.7E-04 8.382 2.9E-09 16.764 12.621

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142 Crescent Beach, Florida 25r.=========~-------r---.---~-~i-r--, .,._ Yob 20 -oy pr (Minimum NMSE) -A y (Maximum CAP) pr . . .. ........... : .... . + Prediction Ran e 15r--------~~-~ : E C 0 .. 10 5 0 a. , : 0 .......... :+ -, ..... : .. -~ -5 _g en -10 -15 -20 ........ .... _. : ;, .... . . . . . . . . . . . . . . ............ ........... ... ... . . . . . . . . .. : I ........ r .. ..... . .. .......... .. . -25,..__--......L..---L-----'------""'L---......,_--___.---...._--__. 1955 1960 1965 1970 1975 1980 1985 1990 1995 Year Figure B-4. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Crescent Beach, FL. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations. Table B-10. Calculated NMSE values for model hindcasts at Crescent Beach, FL. Erosion Parameter k.(t) = Con f(Hb2 ) f(Hb3 ) f(Fr) f(IFr) f(Y>) f(P) Avg II 0.941 0.919 0.931 0.940 0.939 0.917 0.895 0.926 0.925 0.930 0.437 0.801 0.890 0.760 E 1G a.. f(IFr) 0.913 r:: 0 0.922 0.935 0 .1n 0.856 f(Y>) 0 933 0.934 0.933 0.937 0.932 0.910 0.896 0.924 f(P) 0.790 0.914 0.925 0.927 0.867 0.717 0.854 0.851 Avg 0.857 0.901 0.916 0.897 0.822 0.803 0.861 0.849

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143 Table B-11. Calculated CAP scores for model hindcasts at Crescent Beach, FL. Erosion Parameter ke(t) = Con f(Q) f(Hb2 ) f(Hb3 ) f(Fr) f(IFr) II ... a, -a; 0.724 0.653 0 629 0.724 0.747 0.692 E e f(Fr) 0.624 0.588 0.629 0.635 o.5n o.5n 0.671 0.627 as a. f(IFr) C 0.588 o .n1 0 .647 0.665 0.565 0 .606 0.724 0.662 0 :.:: !! f(!;i,) 0.647 0.718 0.641 0.600 0.577 0.618 0.659 0.650 () f(P) 0.706 0.724 0 .647 0.682 0.600 0.688 0.806 0.692 Avg 0.654 0.693 0.652 0.645 0.593 0 .662 0.699 0 663 Con f(Fr) f(IFr) Avg hr"1 hr"1 hr", Con ke 5.5E-04 [hr"1] ke 1.0E-02 3.5E-07 II s ..: I f(F~ E [hr" 1 9.0E-03 5.0E-08 e as -3.048 -3.048 -3.048 -6.096 a. 3.0E-06 3.0E-06 2.5E-06 2.5E-06 f(IFr) a: [hr"1] 2.2E-05 7.1E-06 2 5E-02 3.5E-07 0 .000 0.000 0.000 1.5E-05 1.5E-05 1.0E-05 1.5E-05 f(l;i,) 9.7E-06 3.5E-06 9.5E-03 4.5E-07 [hr" 1 -3 048 -3.048 -3.048 0.000 1.5E-09 1.0E-09 1.5E-10 f(P) 5.3E-06 6.5E-03 8.5E-06 [hr" 1 0.000 -3.048 42.672 Avg 7.0E -06 2.0E -01 1.4E -05 0.762 4.191 12.954 10.335

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144 Daytona Beach, Florida 30-------~------.----~-----.--,-;::::::::::::::=r:::======~=-=-=--=--=--=l 25 . ......... ... :-. ..... . 20 .. . .. .. ......... ... .. ........... . Yob -oy (Minimum NMSE) pr -A y (Maximum CAP) pr + Prediction Ran e e 15 . . ..... . . ............................... .. ............ ' ............ ....... ..... ....... -C: 0 :e 10 U) 0 a. Q) .5 0 .c rn 5 0 -5 .. ....... : ........... : ... .. ....... -:--.... . .. . . . . . . . . . . . ' .... ... t / t / I -10 . ....... .. .. :, -15._ __ __._ ___ ...____ __ ___., ___ ...._ ___ ,.._ __ __._ ___ _._ __ ____ 1955 1960 1965 1970 1975 1980 1985 1990 1995 Year Figure B-5. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Daytona Beach, FL. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations. Table B-13. Calculated NMSE values for model hindcasts at Daytona Beach, FL. Erosion Parameter ke(t) = Con f(Q) f(Hb2 ) f(Hb3 ) f(Fr) f(IFr) f(P) Avg II Con 0.671 0.658 0.699 0.711 0.707 0.673 0.678 0.679 f(Q) 0.691 0.670 0.714 0.721 0.667 0.688 0.692 0.687 j ... 0.672 0.681 0.833 0.870 0.619 0.699 0 723 0.717 Q) ai 0.689 0.811 0.834 0.628 0.736 0.793 0.736 E e as Q. C: 0 0 685 0.689 0.739 0.754 0.791 0.711 0.706 0.716 Avg 0.683 0.676 0.761 0.755 0.694 0.695 0.706 0.703

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145 Table B-14. Calculated CAP scores for model hindcasts at Daytona Beach, FL. II --j II --j .: I E CG a. Q) iii a: Con f(Q) f(Hb2 ) f(Hb3 ) f(O) [hr"] f(F~ [hr"] Avg Con 0.715 0.682 0.715 0.759 Con hr"1 f(Q) 0.741 0.763 0.763 f(Q) hr"1 f(Hb2 ) f(l;i,) 0.756 0.741 0.759 0.715 0.793 0.693 0.756 0.693 0.644 0.715 0.715 0.759 0.693 0.796 0.767 0.811 15.621 f(P) 0.756 0.793 0.785 0.763 f(P) hr"1 Avg 0.753 0.749 0.718 0.762 Avg 2.0E-03 1.7E-03 2.286 2.5E-04 1.8E-04 3.810 1.9E-04 19.431 2.8E-05 6.2E-05 21.336 19.812 3.5E-01 2.4E-01 9.335

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-E -C: 0 E U) 0 a. Q) C: 146 New Smyrna Beach, Florida 30..----~----.------,----~-------,,-------,----..----, 20 . ................... . 10 0 l: ,,, I + / @ I ':, ... : .. : ', .Q) -10 ... I 0 .c UJ -20 -30 . : / .. I : / ..... ,-: \ : / :.' ,; . . ....... : ...... . ..... yob ............... .................. ...... -oy (Minimum NMSE) pr -A y (Maximum CAP) pr + Prediction Ran e -40L._ __ L_ ___JL...,_ ___/. __ ___l __ ---1,--===::::I=====:r:=====::'...l 1955 1960 1965 1970 1975 1980 1985 1990 1995 Year Figure B-6. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at New Smyrna Beach, FL. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations. Table B-16. Calculated NMSE values for model hindcasts at New Smyrna Beach, FL. Erosion Parameter ke(t) = f('2) f(Hb 2 ) f(Hb3 ) f(Fr) f(IFr) f(P) Avg II 0.803 0.895 0 880 0.901 0.809 0.836 0 839 s .::2 ... I E !!! 0.770 0 782 0.694 0.723 0.761 0.615 0 722 as a. f(IFr) C 0 745 0 .611 0 860 0 0.886 0.887 0 733 0.829 0.788 f(l;t,) 0 801 0.810 0.901 0.884 0.773 0.819 0.837 0.822 f(P) 0.743 0.770 0.663 0.699 0.982 0.884 0.649 0.765 Avg 0.760 0.724 0.767 0.789 0.868 0.742 0.752 0.765

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C: 0 II .: s G> E !!! a, a.. a: f(Fr) f(IFr) f(t;i,) f(P) Avg f(Hb3 ) [hr1m~ f(F~ [hr"] f(IFf) [hr"] f(l;i,) [hr" l f(PJ [hr"] Avg 0.785 0.742 0.619 0.723 0.712 0.704 0.000 1.5E-08 1.0E-04 1.0E-09 1.0E-03 147 0.589 0.785 0.612 0.619 0.715 0.712 0.712 0.646 0.612 0.635 0.651 0.674 94.488 94.488 4.0E-06 8.5E-06 2.5E-05 1.5E-05 4.5E-12 3.0E-12 9.7E-05 1.1E-05 94.488 94.488 1.8E-04 4.9E-05 44.196 33.528 0.785 0.604 0.697 0.731 0.646 0.608 0.785 0.670 0.565 0.623 0.715 0.662 0.661 0.765 0.723 0.646 0.723 0.714 0.523 0.704 0.723 0.689 0.669 0.636 0.702 0.696 0.695 0.684 a Beach, FL. Avg 1.5E-09 -39.624 32.766 2.6E-01 2.5E-10 6.096 0.000 55.626 1.0E-03 1.7E-04 6.096 24.765 5.0E-06 1.0E-04 1.3E-03 3.048 5 334 1.5E-10 6.1E-09 2.0E-09 9.5E-07 -39.624 3.048 33.147 2.1E-01 1.2E-08 -3.048 29.432

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E C 148 North Beach, Washington 40r.=========i:======::::c;-----r-----,-----,--------, .... Yob -oy (Minimum NMSE) pr 30 -A y pr (Maximum CAP) . . . . . . . ... . . . . . . . . . . . . . . . ............ + Prediction Ran e 20 10 . ..... . . . . . 'riL a. -~ 0 .c en 0 .. .. -10 -20 : I / . ... ,...... ........ . .. ..... ..... ..... : I -30.__ ___ ___._ ____ ....,__ ___ ___. ____ ___.__ ____ .._ ___ ___. 1997 1998 1999 2000 2001 2002 2003 Year Figure B7. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at North Beach, WA. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations. Table B-19. Calculated NMSE values for model hindcasts at North Beach, WA .. Erosion Parameter ke(t) = Con f(Q) f(Hb2 ) f(Hb3 ) f(Fr) f(IFr) f(P) Avg II "Con 0.624 0.569 0.590 0.563 0.614 0.591 0.617 0.590 f(Q) 0.665 0.608 0.632 0.611 0.630 0.742 0.614 0.636 }' ... 0.675 0.655 0.668 0.635 0.648 0.656 Q) ai E cu a. C 0 i ... 0.629 0.606 0.616 0.590 0.627 0.618 0.746 0.691 0.665 0.620 0.677 0.730 0 674 0.679 Avg 0.654 0.618 0.630 0.604 0.639 0.660 0.628 0.628

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149 Table B-20. Calculated CAP scores for model hindcasts at North Beach, WA. Erosion Parameter ke(t) = Con f(Q) f(Hb2 ) f(Hb3 ) f(Fr) f(IFr) f(P) Avg II Con 0.917 0.878 0.839 0.800 0.878 0.917 0.800 0.863 f(Q) 0.878 0.839 0.839 0.800 0.878 0.700 0.839 0.831 j ... f(Hb2 ) 0.772 0.811 0.789 0.789 0.772 0.796 Q) Q) 0.711 0.789 0.817 0.788 E e a:J a. f(IF,) C: 0.800 0.839 0.800 0.800 0.826 0 f(t;ii) 0.878 0.878 0.800 0.800 0.878 0.878 0.800 0.849 f(P) 0.739 0.778 0.811 0.811 0.794 0.739 0.811 0.790 Avg 0.826 0.841 0.817 0.800 0.843 0.819 0.814 0.828 Table B-21. Calibration coefficients ka, ke, and I). 0 for North Beach, WA. Con f(Q) f(Hb2 ) f(Fr) f(P) Avg hr"1 hr"1 hr"1 hr1 hr"1 Con ke 1.5-04 1.0E-04 1.5E-04 1.0E-04 1.0E-04 1.4E-04 [hr"1] ke 2.0E-04 9.5E-06 8.6E-06 1.6E-06 9.5E-02 42.672 24.384 33 528 39.624 -3.048 32.004 f(O). 7.0E-06 1.0E-05 1.0E-05 5.0E-06 1.0E-05 9.8E-06 1.0E-05 9.2E-06 1.6E-06 1.5E-01 [hr" l 42.672 24.384 21.336 57.912 30.861 1.6E-05 1.1 E-05 1.1 E-05 1.6E-05 1.6E-05 1.5E-05 1.5E-05 1.1E-05 2.3E-06 2.0E-01 57.912 57.912 64.008 60.960 5.3E-06 3.5E-06 1.2E-06 5.3E-06 II 1.5E-05 1.oe~o5 3.5E-06 2.5E-01 ..: i E e a:J a. Q) iii f(IF,) a: [hr"1] 1.0E-05 1.2E-06 1.5E-01 45.720 27.432 -6.096 51.816 f(t;ii). 4.0E-07 4.0E-07 2.5E-07 3.0E-07 1.5E-06 4.9E-07 9.5E-06 9.2E-06 1.9E -06 1.5E-01 2.0E-11 [hr" l 45.720 39.624 57.912 60.960 -6.096 44.196 f(P} 1.0E-10 1.0E-10 4.5E-11 2.0E-11 3.5E-11 1.0E-10 6.9E-11 [hr" l ke 2.5E-04 9.5E-06 1.1E-05 2.8E-06 2.0E-01 fl O 27.432 24.384 45.720 88.392 70.104 43.053 lea Avg ke 3.0E-04 1.1E-05 9.6E-06 2.1E-06 1.7E-01 fl O 47.625 45.339 39.243 53.721 57.531 45. 196

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20 15 10 5 -E -C: 0 0 E II) 0 -5 a. Q) .5 Q) 0 -10 .s::. (J) -15 -20 -25 150 Long Beach, Washington : II ............. :,\. : \ ,, I : \ ,, .......... :1, ,-. . I : \\ / . \ .. I . . . . . . . :-.. i j .......... :. \ I ............ : .. t .......... i . . . . .. + l I j ---Yob -oy (Minimum NMSE) pr -A y (Maximum CAP) pr + Prediction Range -30L_ ___ L_ ___ .1,_ ___ ..,L_ __ ___:::::c::======:::::c::======:...J 1997 1998 1999 2000 2001 2002 2003 Year Figure B-8. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Long Beach, WA. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations. Table B-22. Calculated NMSE values for model hindcasts at Long Beach, WA. Erosion Parameter ke(t) = Con f(Q) f(Hb2 ) f(Hb3 ) f(Fr) f(IFr) f(P) Avg II Con 0.307 0.311 0.335 0.338 0.308 0.314 0.321 0.316 ........ f(Q) 0.332 0.330 0.383 0.369 0.337 0.341 0.383 0 349 j ... 0.390 0.400 0.441 0.471 0.375 0.400 0.426 i E I!! a, a.. C 0 .:= !!? 0.327 0.322 0.333 0.333 0.330 8 < 0 396 0.390 0.426 0.445 0.368 0 392 0 .4 13 0.401 Avg 0.351 0.355 0.388 0.393 0.343 0.360 o.3n 0.363

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151 Table B-23. Calculated CAP scores for model hindcasts at Long Beach, WA. Con f(Q) f(Hb2 ) f(l;i,) f(P) Avg II Con 0.858 0.858 0.853 0.858 0.853 0.856 ... a, Q) f(Hb3 ) 0.868 0.816 0.853 0.883 E e f(Fr) 0.858 0.821 0.853 0.858 0.821 0.848 cu 0. f(IFr) 0.890 C 0.890 0.853 0.853 0.853 0.890 0.853 0.871 0 f(l;i,) 0.895 0.858 0.858 0.890 0.895 0.821 0.890 0.875 8 c( f(P) 0.911 0.911 0.853 0.816 0.890 0.911 0.874 0.882 Avg 0.893 0.874 0.854 0.846 0.890 0.874 0.859 0.873 Con f(Q) Avg hr"1 hr"1 ka 3.0E-04 Con [hr"1) ka 5.0E-04 2.5E-05 1.6E-05 2.5E-06 12.192 6.096 3.048 15.240 f(O) 4.0E-05 1.0E-03 4.5E-05 1.0E-03 2.8E-04 [hr" l 3.0E-05 1.6E-05 2 8E-06 II -..: s a, E e cu 0. I f(IF{) a: [hr" 1 1.5E-06 2.5E-06 2.0E-06 3.0E-05 1.6E-05 2.7E-06 2.0E-01 21.336 6.096 6.096 18.669 f(l;i,). [hr" 1 2.0E-06 3.0E-06 3.0E-06 4.5E-06 2.8E-06 3.0E-05 1.6E-05 2.7E-06 1.5E-01 27.432 12.192 9.144 19.431 f(PJ [hr" l 1.0E-08 1.0E-08 1.0E-08 1.0E-08 1.5E-09 6.6E-09 4.5E-05 2.2E-05 3.2E-06 21.336 12.192 6.096 20.193 Avg 1.9E-03 3.8E-05 2.1E 05 7.1E-06 2.4E-01 22.479 8.001 0.381 19.574

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152 Clatsop Plains, Oregon 30~---~----~----~----..---------,-------. 20 ........ E 10 I \ 1 I -C: 0 E u, 0 a. -~ 0 .c: ,_:i I: I 0 ............... ,. \ l /; : \ I / . I+ / en -10 : ~/; : I / ......... :. i . . .... : I : ; : I i 'I -20 \1 : : . ,.. Yob -oy (Minimum NMSE) pr -A y (Maximum CAP) pr + Prediction Ran e -30,...._ ___ ___,_ ____ ......... ____ ....._ ____ .._ ___ ___. ____ __ 1997 1998 1999 2000 2001 2002 2003 Year Figure B-9. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Clatsop Plains, OR. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations. Table B-25. Calculated NMSE values for model hindcasts at Clatsop Plains, OR. Erosion Parameter ke(t) = Con f(Q) f(Hb2 ) f(Fr) f(IFr) f(~) f(P) Avg II 0 403 0.360 0.356 0 420 0.387 0.341 0.360 0 369 -0.471 0.424 0 384 0.495 0.423 0.393 0.407 0.419 j' ... 0.516 0.472 0.463 0.510 0.446 0.444 G) Gi E co a. C: 0 i ... 0.459 0.379 0.451 0.433 0 379 0.374 0.400 0.580 0 .491 0.468 0.526 0.543 0.437 0.441 0.488 Avg 0.465 0.425 0.412 0.462 0.455 0.390 0.402 0.423

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153 Table B-26. Calculated CAP scores for model hindcasts at Clatsop Plains, OR. -j ... i E ca a.. C 0 f(Hb3 ) f(Fr) f(IFr) f(l;t,) f(P) Avg Con [hr"1] f(Q) [hr" l f(l;t,). [hr" l f(Pj [hr" l Avg ka ka f(Q) 0.895 0 916 0.911 0.911 0 .911 0.911 0.879 0.911 0.900 0.874 0 909 0.906 Con f(Q) hr"1 hr"1 39.624 1.5E-09 2.0E-10 1 5E-05 12.192 27.432 7.1 E-04 2.6E-05 f(P) 0.805 0.805 0 895 0.942 0.916 0 893 0.911 0.911 0 .911 0 .911 0.911 0.911 0.874 0.874 0 .911 0.947 0.874 0.901 0.874 0.874 0.879 0.879 0.911 0.890 0.905 0.905 0.874 0.911 0.905 0.897 0.882 0.882 0.902 0.910 0.905 0.902 f(P) Avg hr1 hr", hr"1 1.5E-01 41.529 1.1E-05 40.386 1.5E-06 1.2E-06 36.576 1.0E-10 3.1E-10 35.052 41.958

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E C .2 "iii Q) .5 0 .s:::. 154 Torrey Pines, California 15r------r---=------ir-------r---r----,------r---r------r--, 10 ..... 5 : \ ? . t. : : t :; : \ I : \ . . . .............. ---yob -oy (Minimum NMSE) pr -A -y (Maximum CAP) pr + Prediction Range ........... . . .. . 0 I : . : .... .... . . .-1:. .................. l' .j, :,' : \ I . . . . I . en -5 ... .. r ~ ; : i : . ... i ....... ":"" .. . . . . . . -10 -15'-------L-----'----'----......_--.....__--...._-__. __ .......... __ __._ __ ....., 1972.4 1972.6 1972.8 1973 1973.2 1973.4 1973.6 1973.8 1974 1974.2 1974.4 Year Figure B-10. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Torrey Pines, CA. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations. Table B-28. Calculated NMSE values for model hindcasts at Torrey Pines, CA. Erosion Parameter k.(t) = Con f(Q) f(Hb2 ) f(Hb3 ) f(Fr) f(IFr) f(P) Avg II Con 0.623 0.614 0.998 0.554 0.998 0.608 0.583 0.701 :::-f(Q) 0.691 0.685 0.986 0.619 0.986 0.687 0.655 0.749 ,_ 0.853 0.855 0.835 0.874 0.857 0.848 0.829 CD G) E !!! ca 0. C: 0 0.591 1.000 0.543 1.000 0.586 0.560 0.687 0.791 0.791 0 985 0.751 0.985 0.792 0.780 0.820 Avg 0.715 0.711 0.907 0.660 0.920 0 708 0.683 0.745

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155 Table B-29. Calculated CAP scores for model hindcasts at Torrey Pines, CA. Erosion Parameter ke(t) = Con f(Q) f(Hb2 ) f(Hb3 ) f(Fr) f(IFr) f(y,) Avg II Con 0.629 0.621 0.561 0.632 0.561 0.654 0.586 0.610 f(Q) 0.557 0.571 0.539 0.639 0.539 0.614 0.561 0.582 -... f(Hb2 ) 0.579 0.625 0.607 0.575 0.611 0.589 0.650 0.606 i E !!! as a.. C 0 0.561 0.561 0.611 8 <( 0.579 0.614 0.539 0.632 0.539 0.589 0.650 0.592 Avg 0.612 0.621 0.568 0.628 0 570 0.623 0.613 0.608 Table B-30. Calibration coefficients ka, ke, and /l. o for Torre Pines, CA. Rate Parameter ke t = Con f(Q) f(Hb2 ) f(Hb3 ) f(Fr) f(IFr) Avg hr"1 hr"1 hr"1 hr"l Con ka 3.0E-04 3.0E-04 9 5E-04 2.5E-04 9.5E-04 [hr"1] ka 2.0E-03 2.0E-04 3.8E-11 8.8E-05 6.0E-09 fl. 0 18.288 18.288 -36.576 18.288 -36.576 ka 1.5E-05 1.5E-05 7.0E-04 3.5E-05 7.0E-04 f(O) 8.8E-05 1.0E-07 [hr"] fl. 0 24.384 24.384 -51. 816 18.288 -51.816 ka 3 2E-05 2.2E-05 3.2E-05 4.3E-05 9.7E-06 f(Hb") [hr"1m-2] 8.8E-05 5.5E-01 fl. 0 24.384 27.432 24 384 21.336 27.432 f(Hb3 ) ka 7.1E-06 7.1E-06 3.5E-03 1.2E-05 3.5E-03 II [hr" 1 m-~ ka 3.5E-03 3.0E-04 2.7E-11 8.8E-05 -j fl. 0 27.432 27.432 -54.864 ..: i E !!! as a.. Q) iii a: f(IFr) [hr"1] 8.8E-05 4.0E-09 fl. 0 18. 288 18.288 -54.864 18 288 -54.864 f(!;i,). ka 4.0E-06 4.0E-06 9 5E-06 3.5E-06 9.5E-06 [hr"] ka 1.5E-03 1.5E-04 5 4E-09 7 1E-05 2 5E-08 fl. 0 15.240 15.240 -36 576 15.240 -36.576 ka 4.5E-10 5.0E-10 2.0E-08 9.0E-10 2.0E-08 f(PJ [hr"] 1 6E-11 8.8E-05 1.0E-07 fl. 0 24. 384 24.384 -54 864 18.288 -54.864 ka Avg 8.4E-05 1.3E-01 18.669 -30.480

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156 Brighton Beach, Australia 25========:=::;:----.--------.----.---,-----, ---Yob -oy (Minimum NMSE) 2 0 -A /r (Maximum CAP) pr 15 e 10 C 0 :t:: 5 1/) 0 a. -~ 0 0 .c en -5 -10 -15 + Prediction Ran e . ........... ...... ....... . . . : ::--.. . . . . . . . . ..... ................... ........ . . . . . . . . . ..... : . . . . . . . .. .......... ...... : ......... = . . . . . . . . . -20.__ ___ ..__ __ ____,.__ __ __. ___ __. ___ __.. ___ __.. ___ ___. 1995.5 1996 1996.5 1997 1997.5 1998 1998.5 1999 Year Figure B-11. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Brighton Beach, AS. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations. Table B-31. Calculated NMSE values for model hindcasts at Brighton Beach, AS. Erosion Parameter ke(t) = Con f(Hb2 ) f(Hli3 ) f(Fr) f(IFr) f(y,) f(P) Avg II Con 0.545 0 859 0.840 0.575 0.542 0.548 0.864 0.665 f(Q) 0.576 0.571 0.567 0.577 0.575 0.579 0.868 0.608 ... 0.658 0.653 0.628 0 669 0.625 0 624 0.639 I E e cu a.. C 0 0.577 0.596 0.562 0.578 0.615 0.575 0.566 0.580 0.624 0 647 0 612 0.624 0.622 0.622 0 616 0.623 Avg 0.587 0.677 0.622 0.600 0.596 0.592 0.662 0.615

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157 Table B-32. Calculated CAP scores for model hindcasts at Brighton Beach, AS. Erosion Parameter ke(t) = Con f(Hb2 ) f(Hb3 ) f(Fr) f(IF,) f(y,) f(P) Avg II Con 0.648 0.631 0.631 0.644 0.648 0.648 0.631 0.640 f(Q) 0 .641 0.652 0.652 0.641 0.641 0.652 0.631 0.646 -... i E e 0.648 0.642 0.655 0.648 0.648 0.642 0.647 IU a.. C: f(IF,) 0.631 0.652 0.648 0.648 0.648 0.648 0.646 0 f(y,) 0.648 0.656 0.648 0.637 0.647 0.648 0.648 0.647 f(P) 0.656 0.656 0 633 0.656 0.648 0.656 0.650 Avg 0.647 0.643 0.647 0.642 0.648 0.650 0.646 0.647 Table B-33. Calibration coefficients ka, kc, and /l. o for Bri ton Beach, AS. Rate Parameter ke t = Con f(Hb3 ) f(Fr) f(IFr) f(y,) f(P) Avg hr"1 hr1m hr1 hr"l hr"1 hr"1 Con ka 2.5E-02 1.0E-04 1.0E-04 1.0E-04 2.5E-02 9.4E-03 [hr"1] 5.3E-05 7.5E+OO 8.5E-03 3.5E-02 2.5E-08 -3.048 -3.048 12.192 15.240 15.240 -3.048 8.001 f(Oj 7.5E-05 5.0E-05 5.0E-05 6.0E-05 1.5E-02 1.9E-03 1.2E-02 3.0E+01 8.5E-03 4.5E-02 2.5E-08 [hr" I 12.192 15.240 15.240 15.240 -3.048 12.192 f(Hbi) 1.1E-04 7.0E-05 1.1E-04 8.6E-05 1.1E-04 9.6E-05 [hr"1m-2] 3.0E-03 4.5E+01 7.5E-04 1.0E-02 1.SE-05 ..: i IU a.. a, iii f(IFf) a: [hr"] 1.1E-02 9.5E+OO 8.5E-03 9.0E-03 7.0E-05 -3 .048 12.192 12.192 15.240 12.192 15.240 11.430 f(l;i,). 1.5E-05 1 0E-05 2.0E-05 1.5E-05 1.5E-05 1.6E-05 1.0E-02 8.8E-03 2.0E+01 7.5E-04 9.SE-03 9.0E-06 [hr" I 9.144 12.192 15.240 9.144 12.192 12.192 11.811 f(Pl 4.SE-08 3.SE-08 2.SE-08 3.SE-08 3.0E-08 3.SE-08 3.3E-08 [hr"] 1.0E-02 1.2E-02 3 .0E+01 3.0E-03 9.SE-03 1.0E-05 9.144 12.192 15.240 12.192 12.192 12.192 12.573 Avg 1.7E-02 8.8E-03 2.0E+01 5.6E-03 1.8E-02 1.6E-05 7.239 9.906 13.716 12.954 12.954 8 763 11.621

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158 Leighton Beach, Australia 20.----------'-----.------------r--------,--,-----, 15 10 e 5 C .,._ Yob -oy (Minimum NMSE) pr -A -y (Maximum CAP) pr + Prediction Ran e .2 0 .................. ..... . .. ~ a. .! -5 0 .c cn-10 -15 : t . ... "t41 11ta!'ft / I . .... . ................... : ..... . .............. ... : . . . . . . . . -20 : ........ ; ...... . . . . . -25,..._ ________ _._ __________________ _. 1997.5 1998 1998.5 1999 Year Figure B-12. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at Leighton Beach, AS. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations. Table B-34. Calculated NMSE values for model hindcasts at Leighton Beach, AS. Erosion Parameter k.(t) = Con f(Q) f(Hb2 ) f(Fr) f(IFr) f(y,) f(P) Avg II 0.598 0.571 0.557 0 607 0.588 0.569 0.550 0.573 -0 .641 0.620 0.609 0.647 0.635 0.614 0.601 0.621 j ... 0.671 0.651 0.640 0.676 0.665 0.646 0.635 0.651 I E a, c.. C 0 0 656 0.647 0.624 0.616 8 < o.6n 0.658 0.659 0.639 0.672 0 .651 0.643 0.654 Avg 0.643 0.622 0.630 0.644 0.636 0.617 0.605 0.624

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159 Table B-35. Calculated CAP scores for model hindcasts at Leighton Beach, AS. Erosion Parameter ke(t) = Con f(Hb2 ) f(Hb3 ) f(Fr) f(IFr) I ;~~o I f(P) Avg II Con 0.650 0.660 0.668 0.638 0.650 0.674 0.661 f('2) 0.642 0.640 0.646 0.642 0.666 0.642 0.666 0.650 -..... f(Hb2 ) 0.680 0.658 0.652 0.646 0.659 ... 0.680 0.640 0.656 Cl) cii f(Hb3 ) 0.622 0.654 0.654 0.622 0.654 0.654 0.652 0.646 E a, f(Fr) 0.642 0.656 0.668 0.636 0.636 0.680 0.668 0.656 ... a, a. C: 0 i; ... 8 < Table B-36. Calibration coefficients ka, ke, and !:J. 0 for Lei hton Beach, AS. Con f(Fr) f(IFr) f(y,) f(P) Avg hr"1 hr"1 hr"l hr"1 hr"1 ke 1.5E-03 1.SE-03 1.5E-03 1.6E-03 Con [hr"1] 5.5E-04 3.0E-03 9.0E-07 6.096 6.096 6.096 6.096 6.096 1.0E-03 1.0E-03 1.0E-03 7.0E-04 8.9E-04 f('2). 1.0E+01 1.5E-03 7.5E-03 1.5E-06 [hr" 1 6.096 6 096 6.096 6.096 5.715 f(Hbi) 2.lE-03 2.2E-03 2.2E-03 2.2E-03 2.3E-03 [hr"1m-2] 9.SE+00 1.0E-03 6.5E-03 2.0E-06 6.096 6.096 6.096 6.096 6.096 2.3E-03 2.3E-03 2.8E-03 2.7E-03 II 5.0E-07 -j .: I E a, a. Cl) iii f(IFf) a: [hr" 1 9.0E+OO 5.5E-04 4.5E-03 1.0E-06 6.096 6 096 6.096 6.096 5.334 4.0E-04 3.5E-04 2.5E-04 4.0E-04 f(l;t,). 1.5E-06 [hr" 1 Avg 3.9E+02 1.2E-03 6.4E-03 1.3E-06 6.4n 6.096 6.096 5.715 5.620

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160 Gold Coast, Australia so~---~----..-------.-----......... -----.----..... ---Yob -oy (Minimum NMSE) 40 -8 /r (Maximum CAP) .. pr + Prediction Ran e 30 I 20 C: 0 .:; u; 10 ...... Q) ~ 0 ..c: en 0 .. .. .... -10 ... ..... ........ -20 -3o~---~----~---~--------~-----' 2000.5 2001 2001.5 2002 2002.5 2003 2003.5 Year Figure B-13. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at the Gold Coast, AS. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations. Table B-37. Calculated NMSE values for model hindcasts at the Gold Coast, AS. Erosion Parameter k.(t) = f(Q) f(Hb2 ) f(Hb 3 ) f(Fr) f(IFr) f(y,) f(P) Avg II Con 0.503 0.483 0.492 0.477 0.480 0.519 0.503 0 492 f(Q) 0.519 0.502 0.503 0.508 0.499 0.543 0.517 0.511 j ... f(Hb2 ) 0.564 0.538 0.543 0 558 0.541 0.554 E e as Q. C 0 0.507 0 .484 0.490 0 495 0 487 0.535 0.552 0.574 0.530 0.529 0.525 0.577 0.550 0.546 Avg 0.531 0.515 0.515 0.512 0.509 0.553 0.529 0.521

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161 Table B-38. Calculated CAP scores for model hindcasts at the Gold Coast, AS. Con f(y,) f(P) Avg f(Hb2 ) ... 0.713 0.715 0.714 0.715 I f(Hb3 ) 0.710 0.710 0.716 0.714 E e 0.715 0.716 0.715 ca 0.. 0.716 0.714 0.715 C: 0 8 c( f(P) 0.717 0.715 0.714 0.715 Avg 0.715 0.714 0.715 0.715 Table B-39. Calibration coefficients lea, kc, and 11 0 for the Gold Coast, AS. f(Fr) f(IF,) f(y,) f(P) Avg hr"1 hr"l hr"1 hr"1 1.5E-04 1.5E-04 1.0E-04 1.5E-04 1.SE-04 Con [hr"1] 6.0E-04 3.0E-03 1.5E-07 1.3E-04 6.5E-04 9.5E-04 2.0E-07 24.384 21.336 21.336 22.479 1.2E-04 1.1 E-04 3.5E-05 1.1 E-04 1.1 E-04 11 7.0E-04 8.0E-04 6.5E-07 f(Pj [hr"] Avg 7.5E-09 7.0E-09 3.0E-09 5.0E-09 7.1E-09 5.0E+01 7.5E-04 9.0E-04 2.0E-07 24.384 21.336 21.336 21.336 5.SE-04 1.6E-03 2.3E-07 25.146 23.622 20.574 22.431

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162 Gold Coast, Australia (Low-Pass Filtered Data) 30.-----~----..------r------,--------,-,------, 20 ..... e 10 -C 0 .:: u; 0 ........ Q) .s 0 ,r; C/)-10 -20 ..................... ---yob oy (Minimum NMSE) pr -e.-y (Maximum CAP) pr + Prediction Ran e -30.__ ___ __._ ____ _._ ___ __._ ____ ........_ ___ ___, ____ _, 2000.5 2001 2001.5 2002 2002.5 2003 2003.5 Year Figure B-14. Comparison of measured shorelines and "best" simulations according to the NMSE and CAP criteria at the Gold Coast, AS, using filtered (fc = 0.033 days 1 ) data. Error bars indicate the variation in predicted shoreline position for different rate parameter combinations. Table B-40 Calculated NMSE values for model hindcasts at the Gold Coast, AS, using filtered (fc = 0.033 days-1 ) data. Erosion Parameter k. t = f(Q) f(Hb2 ) f(Hb3 ) f(Fr) f(l;i,) f(P) Avg II Con 0.339 0.314 0.329 0.309 0.362 0.340 0.327 f(Q) 0.361 0 342 0.345 0 348 0.391 0.358 0.353 j ... 0.389 0.397 0.444 0.409 CD -a; E I!! al a. C 0 I 0.317 0.328 0.381 0.337 0.407 0.435 0.381 0.383 0.436 0.403 0.400 Avg 0.377 0.357 0.360 0.360 0.404 0.375 0.367

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163 Table B-41. Calculated CAP scores for model hindcasts at the Gold Coast, AS, using filtered (fc = 0.033 days-1 ) data. Avg 0.981 0.983 0.985 0.986 0.985 Table B-42. Calibration coefficients ka, kc, and /ly0 for the Gold Coast, AS, using filtered (fc = 0.033 da s -1 ) data. II f(O) [hr"] f(Pl [hr"] Avg 1.3E-03 1.5E-07 18.288 1.1E-04 2.0E-07 21.336 7.1E-05 2.0E-07 21.336 1.7E-07 20.193 Avg 21.717 1.3E-04 22.479 8.6E-05 20.955 22.146

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LIST OF REFERENCES Aubrey, D.G. and Ross, R.M., 1985. The Quantitative Description of Beach Cycles. Marine Geology, Vol. 69: 155-170. Australian State of the Environment Committee, Coasts and Oceans, 2001. Australia State of the Environment Report 2001. CSIRO Publishing, Collingwood, Australia. Bakker, W.T., 1968. A Mathematical Theory About Sand Waves and Its Application on the Dutch Wadden Isle of Vlieland. Shore and Beach, Vol. 36(2): 4-14. Battjes, I.A., 1974. Surf Similarity. Proceedings of the 14th International Conference on Coastal Engineering, ASCE, New York, NY: 466-479. Bender, C.J. and Dean, R.G., 2003. Wave Field Modification by Bathymetric Anomalies and Resulting Shoreline Changes: A Review with Recent Results. Coastal Engineering, Vol. 49: 125-153. Bohachevsky, 1.0., Johnson, M.E. and Stein, M.L., 1986. Generalized Simulated Annealing for Function Optimization. Technometrics, Vol. 28(3): 209-217. Bruun, P., 1954. Coast Erosion and Development of Beach Profiles. Technical Memorandum No. 44, Beach Erosion Board, U.S. Army Corps of Engineers, Washington, D.C. Bruun, P., 1962. Sea level Rise as a Cause of Shore Erosion. Journal of Waterways, Harbors Division ASCE, Vol. 88: 117-130. Colgan, C.S., 2003. The Changing Ocean and Coastal Economy of the United States: A Briefing Paper for Conference Participants. NGA Center for Best Practices Conference, Waves of Change: Examining the Role of States in Emerging Ocean Policy. Commonwealth Scientific and Industrial Research Organisation (CSIRO) Atmospheric Research, 2002. Climate Change and Australia's Coastal Communities. CSIRO Atmospheric Research, Apsendale, Australia. Culliton, T.J., 1990. 50 Years of Population Change along the Nation's Coasts, 19602010. Strategic Assessment Branch, Ocean Assessments Division, Office of Oceanography and Marine Assessment, National Ocean Service, National Oceanic and Atmospheric Administration, Rockville, MD. 164

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165 Dalrymple, R.A., 1992. Prediction of Storm/Normal Beach Profiles. Journal of Waterway, Port, Coastal, and Ocean Engineering, Vol. 118(2): 193-200. Davies, A.G., van Rijn, A.G., Damgaard, J.S., van de Graaff, J. and Ribberink, J.S., 2002. Intercomparison of Research and Practical Sand Transport Models. Coastal Engineering, Vol. 46: 1-23. Davis, J.C., 1986. Statistics and Data Analysis in Geology. John Wiley & Sons, Inc., New York, NY: 646pp. Dean, R.G., 1973. Heuristic Models of Sand Transport in the Surfzone. Proceedings of the Conference on Engineering Dynamics in the Surfzone, Institute of Engineers, Australia, Sydney, Australia: 208-214. Dean, R.G., 1977. Equilibrium Beach Profiles: U.S. Atlantic and Gulf Coasts. University of Delaware, Department of Civil Engineering, Ocean Engineering Report No. 12. Dean, R.G., 1991. Equilibrium Beach Profiles: Principles and Applications. Journal of Coastal Research, Vol. 7(1): 53-84. Dette, H. and Uliczka, K., 1987. Prototype Investigation on Time-Dependent Dune Recession and Beach Erosion. Proceedings of Coastal Sediments '87, ASCE, New York, NY: 1430-1444. Dewall, A.E., 1977. Littoral Environment Observations and Beach Changes Along the Southeast Florida Coast. Technical Paper No. 77-10, Coastal Engineering Research Center, U.S. Army Corps of Engineers, Fort Belvoir, VA. Dolan, R., Hayden, B.P. and Felder, W., 1979. Systematic Variations in Inshore Bathymetry. Journal of Geology, Vol. 85: 129-141. Galgano, F.A., 1998. Geomorphic Analysis of Modes of Shoreline Behavior and the Influence of Tidal Inlets on Coastal Configuration, U S. East Coast. Ph.D. Thesis, University of Maryland, College Park, MD. Gourlay, M.R., 1968. Beach and Dune Erosion Tests. Report No. M935/M936, Delft Hydraulics Laboratory, Delft, The Netherlands. Hallermeier, R.J., 1980. Sand Motion Initiation by Water Waves: Two Asymptotes. Journal of Waterway, Port, Coastal, and Ocean Engineering, Vol. 106(3): 299-318. Hallermeier, R.J., 1981. Terminal Settling Velocity of Commonly Occurring Sands. Sedimentology, Vol. 28(6): 859-865.

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166 Hanson, H. and Kraus, N.C., 1989. GENESIS-Generalized Model for Simulating Shoreline Change. Volume 1: Reference manual and Users Guide. Technical Report No. 89-19, Coastal Engineering Research Center, U.S. Army Corps of Engineers, Vicksburg, MS. Hanson, H. and Larson, M., 1998. Seasonal Shoreline Variations by Cross-shore Transport in a One-line Model Under Random Waves. Proceedings of the 26th International Conference on Coastal Engineering, ASCE, New York, NY: 26822695. Hanson, H. and Larson, M., 2000. Simulating Coastal Evolution Using a New Type of N-Line Model. Proceedings of the 27th International Conference on Coastal Engineering, ASCE, New York, NY: 2808-2821. Hanson, H., Aarinkhof, S., Capobianco, M., Jimenez, J.A., Larson, M., Nicholls, R.J., Plant, N.G., Southgate, H.N., Steetzel, H.J., Stive, M.J.F. and de Vriend, H.J., 2003. Modeling of Coastal Evolution on Yearly to Decadal Time Scales. Journal of Coastal Research, Vol. 19( 4 ): 790-811. Hattori, M. and Kawamata, R., 1981. Onshore-Offshore Transport and Beach Profile Change. Proceedings of the 17th International Conference on Coastal Engineering, ASCE, New York, NY: 1175-1193. Holman, R.A., Sallenger Jr., A.H., Lippmann, T.C.D. and Haines, J.W., 1993. The Application of Video Image Processing to the Study of Nearshore Processes. Oceanography, Vol. 6(3), 78-85. Hotelling, H. 1933. Analysis of a Complex of Statistical Variables into Principle Components. Journal of Educational Psychology, Vol. 24: 417-441, 498-520. Houston, J.R., 2002. The Economic Value of Beaches. Shore and Beach, Vol. 70(1): 912. Inman, D L., 1987. Accretion and Erosion Waves on Beaches. Shore and Beach, Vol. 55(3):61-66. Iribarren, C.R. and Nogales, C., 1949. Protection des Portes, Section II, Comm. 4, XVIIth International Navigational Congress, Lisbon, Portugal: 31-80. lwagaki, Y. and Noda, H., 1963. Laboratory Study of Scale Effects in Two Dimensional Beach Processes. Proceedings of the 8th International Conference on Coastal Engineering, ASCE, New York, NY: 194-210. Johnson, J.W., 1949. Scale Effects in Hydraulic Models Involving Wave Motion. Transactions of the American Geophysical Union, Vol. 30(4): 517-525.

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167 Kobayashi, N. and Johnson, B.D., 2001. Sand Suspension, Storage, Advection, and Settling in Surf and Swash Zones. Journal of Geophysical Research, Vol. 106(C5): 9363-9376. Kraus, N.C. and Larson, M., 1988. Beach Profile Change Measured in the Tank for Large Waves, 1956-1957 and 1962. Technical Report No. 88-6, Coastal Engineering Research Center, U. S. Army Corps of Engineers, Vicksburg, MS Kraus, N.C., Larson, M. and Kriebel, D.L., 1991. Evaluation of Beach Erosion and Accretion Predictors. Proceedings of Coastal Sediments '91, ASCE, New York, NY: 572-587. Kriebel, D.L. and Dean, R.G., 1985. Numerical Simulation of Time Dependent Beach and Dune Erosion. Coastal Engineering, Vol. 9: 221-245. Kriebel, D.L. and Dean, R.G., 1993. Convolution Method for Time-Dependent Beach Profile Response. Journal of Waterway, Port, Coastal, and Ocean Engineering, Vol. 119(2): 204-226. Kriebel, D.L., Dally, W.R. and Dean, R.G., 1987. Undistorted Froude Model for Surfzone Sediment Transport. Proceedings of the 20th International Conference on Coastal Engineering, ASCE, New York, NY: 1296-1310. Larson, M. and Kraus, N.C., 1989. SBEACH: Numerical Model to Simulate Storm Induced Beach Change. Technical Report No. 89-9, Coastal Engineering Research Center, U. S. Army Corps of Engineers, Vicksburg, MS. Larson, M. and Kraus, N.C., 1991. Mathematical Modeling of the Fate of Beach Fill. Coastal Engineering, SI Vol. 16: 83-114. Larson, M., Hanson, H. and Kraus, N.C., 1997. Analytical Solutions of the One-Line Model of Shoreline Change Near Coastal Structures. Journal of Waterway, Port, Coastal, and Ocean Engineering, Vol. 123(4): 180-191. Larson, M. and Ebersole, B.A., 1999. An Analytical Model to Predict the Response of Mounds Placed in the Offshore. Technical Note No. 11-42, Coastal Engineering Research Center, U.S. Army Corps of Engineers, Vicksburg, MS. MacMahan, J. and Thieke, R.J., 2000. Cross-shore Sediment Transport Indices. Proceedings of the 27th International Conference on Coastal Engineering, ASCE, New York, NY: 3193-3204. Madsen, A.J. and Plant, N.G., 2001. Intertidal Beach Slope Predictions Compared to Field Data. Marine Geology, Vol. 173: 121-139. Marine Facilities Panel, 1991. Report of the U.S. Japan Wind and Seismic Effects Panel. Report No. J-4, 17th Meeting of the Marine Facilities Panel of the United State Japan Cooperative Program in Natural Resources, Tokyo, Japan.

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169 Plant, N.G. and Holman, R.A., 1996. lnterannual Shoreline Variations at Duck, NC, USA. Proceedings of the 25th International Conference on Coastal Engineering, ASCE, New York, NY: 3521-3533. Plant, N.G., Holman, R.A., Freilich, M.H. and Birkemeier, W.A., 1999. A Simple Model for Interannual Sandbar Behavior. Journal of Geophysical Research, Vol. 104(C7): 15755-15776. Plant, N.G., Holland, K.T., Puleo, J.A. and Gallagher, E.L., 2004. Prediction Skill of Nearshore Profile Evolution Models. Journal of Geophysical Research, Vol. 109(C01006): 1-19. Rector, R.L., 1954. Laboratory Study of Equilibrium Profiles of Beaches. Technical Memorandum No. 41, Beach Erosion Board, U.S. Army Corps of Engineers, Washington, D.C. Roelvink, J.A. and Broker, I., 1993. Cross-shore Profile Models. Coastal Engineering, Vol. 21: 163-191. Schoones, J.S. and Theron, A.K., 1995 Evaluation of 10 Cross-shore Sediment Transport/Morphological Models. Coastal Engineering, Vol. 25: 1-41. Small, C., Gornitz, V. and Cohen, J.E., 2000. Coastal Hazards and the Global Distribution of Human Population. Environmental Geoscience, Vol. 7: 3-12. Steetzel, H.J., 1995. Prediction of Development Coastline and Outer Deltas of Wadden Coast for the Period 1990-2040. Report No. H1887, Delft Hydraulics Laboratory, Delft, The Netherlands. Stive, M.J.F. and deVriend, H.J., 1995. Modeling Shoreface Profile Evolution. Marine Geology, Vol. 126: 235-248. Sunamura, T., 1980. Parameters for Delimiting Erosion and Accretion of Natural Beaches Institute of Geoscience Annual Report No. 6, University of Tsukuba, Japan. Sunamura, T . and Horikawa, K., 1975. Two Dimensional Beach Transformation Due to Waves. Proceedings of the 14th International Conference on Coastal Engineering, ASCE, New York, NY: 920-938. Sunamura, T. and Maruyama, K., 1987. Wave-Induced Geomorphic Response of Eroding Beaches-With Special Reference to Seaward Migrating Bars. Proceedings of Coastal Sediments '87, ASCE, New York, NY: 788-801. Sutherland, J. and Soulsby, R.L., 2003. Use of Model Performance Statistics in Modelling Coastal Morphodynamics. Proceedings of Coastal Sediments '03. CD ROM, World Scientific Publishing Corp. and East Meets West Productions, Corpus Christi, TX: 1-14.

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171 Wang, X., 2004. Improved Representation of Cross-shore Sediment Transport Characteristics. Ph.D. Thesis, University of Florida, Gainesville, FL. Waters, C.H., 1939. Equilibrium Slopes of Sea Beaches. M.S. Thesis, University of California, Berkeley, CA. Winant, C.D., Inman, D.L. and Nordstrom, C.E., 1975. Description of Seasonal Beach Changes Using Empirical Eigenfunctions. Journal of Geophysical Research, Vol. 80(15): 1979-1986. Wright, L.D. and Short, A.D., 1984. Morphodynamic Variability of Surfzones and Beaches: A Synthesis. Marine Geology, Vol. 56: 93-118. Wright, L.D., Short, A.O. and Green, M.O., 1985. Short-Term Changes in the Morphodynamic States of Beaches and Surf Zones: An Empirical Predictive Model. Marine Geology, Vol. 62: 339-364. Wright, L.D., 1995. Morphodynamics of Inner Continental Shelves. CRC Press, Boca Raton, FL, 241pp. Zheng, J. and Dean, R.G., 1997. Numerical Models and lntercomparisons of Beach Profile Evolution. Coastal Engineering, Vol. 30: 169-201.

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BIOGRAPHICAL SKETCH Jonathan Kevin Miller was born December 16th, 1976, at the Jersey Shore Medical Center in Long Branch, New Jersey. Maybe it was the first breath of cool, crisp, salty sea air that instilled in him his love of the beach, or maybe it was the many summers spent with the family "down the shore," but whatever it was, he has always felt drawn to the coast, all the while never imagining he would one day have the privilege of studying it for a living. Jon spent much of his early childhood roaming the playgrounds of beautiful Rahway, New Jersey, honing his basketball skills in hopes of becoming the next Magic Johnson. While he learned much about life in general in Rahway, it was not until he enrolled in St. Joseph's High School that he discovered his passion for learning. After graduating in 1994, he attended Stevens Institute of Technology, originally intending to become an underpaid, overworked civil engineer. While there he became intrigued with a specialization called coastal engineering. After graduating from Stevens in 1999, he enrolled at the University of Florida intent on pursuing a PhD in the field of coastal engineering. In May of 2001, Jon married his wonderful wife Diana, and in August of that year he cleared the first hurdle by completing his Master of Science degree. On a whim, Jon decided to apply for a Fulbright Fellowship to complete a portion of his research in Australia, and in August 2003 he and Diana moved to Brisbane, where Jon would spend the next year studying at the University of Queensland. Upon completion of his doctorate, Jon intends to pursue a career in academia, so that he can continue to 172

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173 indulge his appetite for knowledge, while hopefully inspiring a new generation of coastal engineers. After many years of hard work and dedication, Jon hopes to one day earn the title of overworked, underpaid, college professor.

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I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. ~~&(.~ Robert G. Dean Graduate Research Professor Emeritus of Civil and Coastal Engineering I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. AB~ Professor of Civil and Coastal Engineering I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. -::,J.~ Assistant Professor of Civil and Coastal Engineering I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. ;!0;/~ ~ I Daniel M Hanes Professor of Civil and Coastal Engineering I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philo c .1 r n Mossa ssociate Professor of Geography

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This dissertation was submitted to the Graduate Faculty of the College of Engineering and to the Graduate School and was accepted as partial fulfillment of the requirements for the degree of Doctor of Philo{)hy. December2004 I ~ K ~ Pramod P. Khargonekar Dean, College of Engineering Kenneth Gerhardt Interim Dean, Graduate School

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