Citation
The Influence of seasonal variation in longshore sediment transport with applications to the erosion of the downdrift beach at Jupiter Inlet, Florida

Material Information

Title:
The Influence of seasonal variation in longshore sediment transport with applications to the erosion of the downdrift beach at Jupiter Inlet, Florida
Abbreviated Title:
UFLCOEL
Creator:
Harris, Philip S., 1966- ( Dissertant )
Thieke, Robert J. ( Thesis advisor )
Mehta, Ashish ( Reviewer )
Dean, Robert ( Reviewer )
University of Florida -- Coastal and Oceanographic Engineering Dept
Publisher:
Coastal & Oceanographic Engineering Dept., University of Florida
Publication Date:
Language:
English
Physical Description:
xiii, 138 leaves : ill. ; 28 cm.

Subjects

Subjects / Keywords:
Coastal and Oceanographic Engineering thesis M. Eng ( local )
Dissertations, Academic -- Coastal and Oceanographic Engineering -- UF ( local )
Genre:
bibliography ( marcgt )
non-fiction ( marcgt )
theses ( marcgt )

Notes

Abstract:
Erosion of downdrift beaches Is a problem commonly associated with most tidal inlets. Thus, it is undoubtedly a major consideration when pursuing an effective tidal inlet management plan. Various sand bypassing simulations at Jupiter Inlet, Florida, were examined through the use of synthetic longshore transport rates generated from wind hindcast was data in incorporation with local sediment budgets. In order to determine whether sand from the downdrift beach was infiltrating around the south jetty and into the inlet during flood tide, a sand tracer study was conducted three different times over a one year period. Similarly, sediment transport patterns pertaining to the downdrift beach o Jupiter Inlet were also investigated throughout the use of a drogue study performed in a physical model located at the Coastal and Oceanographic Laboratory at the University of Florida. Finally, in an attempt to determine the most optimal jetty configuration at Jupiter Inlet, different jetty modifications were implemented into the drogue study. It was found that annual renourishment of the downdrift beach at Jupiter Inlet before the specified nonpumping window would be most beneficial. By renourishing the downdrift beach in early March, a larger beach would be present throughout the summer, aiding nesting turtle s and appealing to summer vacationers. It was also determined that the jetty adjacent to the downdrift beach at Jupiter Inlet was indeed “leaking” (and was migrating from the downdrift beach, around the downdrift jetty, and into the inlet). Traces of fluorescent colored sand were found inside the inlet on two different occasions, some of the sand remnants of a tracer experiment conducted eight months prior. Finally, it was concluded that the optimal jetty configuration s it concerns the downdrift beach would involve either an extension of the north jetty with a downdrift curvature or an arm-like attachment to the south jetty which would extend southward with the goal being to create a larger “shadow-zone” downdrift of Jupiter Inlet.
Thesis:
Thesis (M. Eng.)--University of Florida, 1991.
Bibliography:
Includes bibliographical references (leaves 136-137).
General Note:
Typescript.
General Note:
Vita.
Funding:
This publication is being made available as part of the report series written by the faculty, staff, and students of the Coastal and Oceanographic Program of the Department of Civil and Coastal Engineering.
Statement of Responsibility:
by Philip S. Harris.

Record Information

Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
25540966 ( OCLC )

Full Text



UFL/COEL-91/015


THE INFLUENCE OF SEASONAL VARIATION IN LONGSHORE SEDIMENT TRANSPORT WITH APPLICATIONS TO THE EROSION OF THE DOWNDRIFT BEACH AT JUPITER INLET, FLORIDA






by



Philip S. Harris III






Thesis


1991























THE INFLUENCE OF SEASONAL VARIATION IN LONGSHORE SEDIMENT TRANSPORT WITH APPLICATIONS TO THE EROSION OF THE DOWNDRIFT
BEACH AT JUPITER INLET, FLORIDA


By

PHILIP S. HARRIS III


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


UNIVERSITY OF FLORIDA


1991


I














ACKNOWLEDGEMENTS


To begin, I would like to sincerely thank my advisor and supervisory committee chairman, Dr. Robert Thieke, for his insight, guidance, and support throughout this project. I consider it a true honor to have worked under his patient leadership. My thanks are also extended to Dr. Ashish Mehta and Dr. Robert Dean for their encouragement and advice.

The Jupiter Inlet District provided the sponsorship upon which this research is based and this support is greatly appreciated.

Thanks are extended to Richard Weggel, Scott Douglass, and all others involved in the pioneering of methodology as well as techniques used in this thesis. Appreciation is also given to Ron Dixon (Dixon and Associates Engineers Inc.) and Robin Hoban (Coastal Engineering Research Center) for providing important information necessary for successful completion of this project.

Enough cannot be said about the assistance of the Coastal Engineering staff, particularly Helen Twedell, Connie Burgess, and Lillean Pieter.

Heartfelt thanks are extended to fellow students and friends Jon "in the Caribbean" Grant, Mike DelTaco, little Phil, Mark Pirrello (the best athlete I have ever had the pleasure of meeting), Maximum, Domino's owner, Paul Work, Jei Choi, Eric Thosteson and last but not least, the crew of the Palaemon.

Finally, I wish to thank my father and mother for their support, both financial and personal, during the course of my education. No sacrifice was too big for them to make. By example, they have taught my brother, sister, and I that only through hard work and persistence can success be achieved.


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TABLE OF CONTENTS


ACKNOWLEDGEMENTS


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

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

ABSTRACT .............

CHAPTERS

1 INTRODUCTION ........

1.1 Rationale ............

1.2 Previous Research ......

1.3 Objectives and Scope .... 2 EROSION OF SOUTH BEACH

2.1 Introduction ..........

2.2 Sediment Transport ....

2 3 Ebbh Shoal


JUPITER


INLET


2. JEtty Confgrain.......................................
2.4 Jetty Configuration . . . . . . . . . . . . . . . .

2.5 Ecological Considerations ..................................

2.6 Conclusion .. .. ... ... .... ... .. .. ... .. .. ... ..

3 METHODOLOGY ..................................

3.1 WIS Wave Data ....... .................................

3.2 WIS Computed Longshore Sediment Transport Data . . . . . . .

3.3 Synthetic Longshore Transport .........................

3.4 Sand Bypassing Simulations . . . . . . . . . . . . . .

4 RESULTS AND ANALYSIS . . . . . . . . . . . . . . .

4.1 W IS W ave Data . . . . . . . . . . . . . . . . .


iii


.ii


v

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1

1

4

7

10 10 10

12 15

18

22

24 24 30 33 38

42 42


. . . . . . .

. . . . . . .


AT









4.2 WIS Computed Longshore Transport Data . . . . . . . . . 59

4.3 Results of Generated Synthetic Longshore Transport Values . . . . 68 4.4 Analysis of Sand Bypassing Simulations . . . . . . . . . . 79

5 SUMMARY AND CONCLUSIONS . . . . . . . . . . . . 99

5.1 Investigative Summary . . . . . . . . . . . . . . . 99

5.2 Conclusions . . . . . . . . . . . . . . . . . . 100

5.3 Future Recommendations . . . . . . . . . . . . . . 101

APPENDICES

A SAND TRACER STUDY . . . . . . . . . . . . . . . 103

B PHYSICAL MODEL EXPERIMENTS: DROGUE STUDY . . . . . . 117

C MONTHLY LOG-NORMAL PROBABILITY PLOTS .................. 129

REFERENCES .......... ...................................... 136

BIOGRAPHICAL SKETCH ............................... 138


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LIST OF TABLES


3.1 Estimated Longshore Transport Values (Q, cubic yards per year) for 20
Year Period 1956-1975 (calculated from WIS wave hindcast data). . 34

3.2 Statistical Parameters Describing Normally Distributed Monthly Populations of Daily Log-transport Values for 20 Years of WIS Data 19561975 (for use in generation of synthetic transport data). . . . . 37

4.1 Global Wave Parameters Collected from PUV Station. . . . . 43

4.2 1990 March Distribution of Wave Heights H and Wave Angles 0 (in
percent) at 7-meter Contour from PUV Data Collecting Package. . . 43

4.3 1990 April Distribution of Wave Heights H and Wave Angles 9 (in percent) at 7-meter Contour from PUV Data Collecting Package. . . . 44

4.4 1990 August Distribution of Wave Heights H and Wave Angles 9 (in
percent) at 7-meter Contour from PUV Data Collecting Package. . . 44

4.5 1990 September Distribution of Wave Heights H and Wave Angles 9 (in
percent) at 7-meter Contour from PUV Data Collecting Package. . . 44

4.6 1990 October Distribution of Wave Heights H and Wave Angles 9 (in
percent) at 7-meter Contour from PUV Data Collecting Package. . . 45

4.7 1990 November Distribution of Wave Heights H and Wave Angles 9 (in
percent) at 7-meter Contour from PUV Data Collecting Package. . . 45

4.8 1990 December Distribution of Wave Heights H and Wave Angles 9 (in
percent) at 7-meter Contour from PUV Data Collecting Package. . . 45

4.9 1991 January Distribution of Wave Heights H and Wave Angles 0 (in
percent) at 7-meter Contour from PUV Data Collecting Package. . . 46

4.10 1991 February Distribution of Wave Heights H and Wave Angles 9 (in
percent) at 7-meter Contour from PUV Data Collecting Package. . . 46

4.11 1991 March Distribution of Wave Heights H and Wave Angles 9 (in
percent) at 7-meter Contour from PUV Data Collecting Package. . . 46

4.12 1991 April Distribution of Wave Heights H and Wave Angles 9 (in percent) at 7-meter Contour from PUV Data Collecting Package. . . . 47


v








4.13 1991 May Distribution of Wave Heights H and Wave Angles 9 (in percent) at 7-meter Contour from PUV Data Collecting Package. . . . 47 4.14 Wave Parameters Collected from West Palm Beach CDN Station. . 49 4.15 Wave Parameters Collected from WIS Station # 156. . . . . . 49

4.16 January Distribution of Wave Heights H and Wave Angles 9 (in percent)
at 10-meter Contour from WIS Hindcast for Period 1956-1975. . . 50

4.17 February Distribution of Wave Heights H and Wave Angles 9 (in percent)
at 10-meter Contour from WIS Hindcast for Period 1956-1975. . . 50

4.18 March Distribution of Wave Heights H and Wave Angles 9 (in percent)
at 10-meter Contour from WIS Hindcast for Period 1956-1975. . . 50

4.19 April Distribution of Wave Heights H and Wave Angles 9 (in percent)
at 10-meter Contour from WIS Hindcast for Period 1956-1975. . . 51

4.20 May Distribution of Wave Heights H and Wave Angles 9 (in percent) at
10-meter Contour from WIS Hindcast for Period 1956-1975 . . . 51

4.21 June Distribution of Wave Heights H and Wave Angles 9 (in percent) at
10-meter Contour from WIS Hindcast for Period 1956-1975 . . . 51

4.22 July Distribution of Wave Heights H and Wave Angles 0 (in percent) at
10-meter Contour from WIS Hindcast for Period 1956-1975 . . . 52

4.23 August Distribution of Wave Heights H and Wave Angles 0 (in percent)
at 10-meter Contour from WIS Hindcast for Period 1956-1975. . . 52

4.24 September Distribution of Wave Heights H and Wave Angles 9 (in percent) at 10-meter Contour from WIS Hindcast for Period 1956-1975. . 52

4.25 October Distribution of Wave Heights H and Wave Angles 9 (in percent)
at 10-meter Contour from WIS Hindcast for Period 1956-1975. . . 53

4.26 November Distribution of Wave Heights H and Wave Angles 9 (in percent) at 10-meter Contour from WIS Hindcast for Period 1956-1975. . 53

4.27 December Distribution of Wave Heights H and Wave Angles 9 (in percent) at 10-meter Contour from WIS Hindcast for Period 1956-1975. . 53

4.28 Estimated Synthetic Longshore Transport Values (Q, cubic yards per
year) for 20 Year Period 1956-1975 (calculated from monthly statistics
of WIS wave hindcast data). . . . . . . . . . . . . 69

A.1 Location of Individual Sediment Samples Collected in the Vicinity of
Jupiter Inlet (Note: indicates D5o was < 0.06 mm) . . . . . 105


vi








B.1 Final Positions (given in terms of physical model template numbers)
of Spherical Floats Two Minutes After Placement Along South Beach.
Wave Height = 4 feet; Wave Period = 8 seconds; Wave Angle = 310 from North; Water Elevation: MSL; Beach Level: Normal; Note: An asterisk (*) before an entry signifies that the drogue passed into the inlet within the allotted two minutes, in which case the elapsed time of travel is
recorded. ....... ................................... 120

B.2 Final Positions (given in terms of physical model template numbers)
of Spherical Floats Two Minutes After Placement Along South Beach.
Wave Height = 4 feet; Wave Period = 8 seconds; Wave Angle = 31' from North; Water Elevation: MSL; Beach Level: Nourished; Note: An asterisk (*) before an entry signifies that the drogue passed into the inlet within the allotted two minutes, in which case the elapsed time of travel
is recorded. ....... .................................. 120

B.3 Final Positions (given in terms of physical model template numbers)
of Spherical Floats One Minute After Placement Along South Beach.
Wave Height = 8 feet; Wave Period = 12 seconds; Wave Angle = 310 from North; Water Elevation: +8.0 feet; Beach Level: Normal; Note: An asterisk (*) before an entry signifies that the drogue passed into the inlet within the allotted one minute, in which case the elapsed time of travel
is recorded. . . . . . . . . . . . . . . . . . 121

B.4 Final Positions (given in terms of physical model template numbers)
of Spherical Floats One Minute After Placement Along South Beach.
Wave Height = 8 feet; Wave Period = 12 seconds; Wave Angle = 31' from North; Water Elevation: +8.0 feet; Beach Level: Nourished; Note: An asterisk (*) before an entry signifies that the drogue passed into the inlet within the allotted one minute, in which case the elapsed time of
travel is recorded. ....... .............................. 121

B.5 Final Positions (given in terms of physical model template numbers)
of Spherical Floats One Minute After Placement Along South Beach.
Wave Height = 4 feet; Wave Period = 8 seconds; Wave Angle = 31' from North; Water Elevation: MSL; Beach Level: Normal; Note: An asterisk (*) before an entry signifies that the drogue passed into the inlet within the allotted one minute, in which case the elapsed time of
travel is recorded. ....... .............................. 123


vii














LIST OF FIGURES


1.1 April 18, 1990 photo of Jupiter Inlet before nourishment of the south
beach. ....... ..................................... 3

1.2 May 25, 1990 photo of Jupiter Inlet after nourishment of the south beach. 3

2.1 Erosion suffered by the south beach at Jupiter Inlet immediately after
northeaster in December, 1989 (Mehta et al., 1990a). . . . . . 13

2.2 Location of the ebb shoal directly offshore of Jupiter Inlet during March,
1979 (Mehta et al., 1990a). . . . . . . . . . . . . 16

2.3 Location of the ebb shoal directly offshore of Jupiter Inlet during August,
1979 (Mehta et al., 1990a). . . . . . . . . . . . . 16

2.4 Good sea turtle nesting beach, looking north toward Jupiter Inlet. . 19

2.5 Steep scarp considered to discourage nesting sea turtles just south of the
south jetty at Jupiter Inlet. . . . . . . . . . . . . 19

2.6 Favorable beach-slope profile south of Jupiter Inlet on three different
occasions in 1990-1991 (Mehta et al., 1991b). . . . . . . . 20

2.7 Unfavorable beach-slope profile adjacent to south jetty at Jupiter Inlet
on three different occasions in 1990-1991 (Mehta et al., 1991b). . . 20

2.8 Proximity of rocky outcroppings in the vicinity of Jupiter Inlet, Florida
(M ehta et al., 1991b). . . . . . . . . . . . . . . 21

3.1 Description of Phase I, Phase II, and Phase III (Jensen, 1983). . . 26

3.2 Location of offshore WIS hindcast data stations in the vicinity of Jupiter
Inlet (Jensen, 1983). . . . . . . . . . . . . . . 27

3.3 Log-normal probability plot of annual longshore transport magnitudes
(southward and northward) from WIS hindcasts for the period 1956-1975. 35

3.4 Southward sediment budget in the vicinity of Jupiter Inlet (Mehta et al.,
1991b). . . . . . . . . . . . . . . . . . . 41

3.5 Northward sediment budget in the vicinity of Jupiter Inlet. . . . 41 4.1 Comparison of PUV, CDN, and WIS monthly average wave height. . 55


viii








4.2 Comparison of PUV, CDN, and WIS monthly deviation of wave height. 56 4.3 Comparison of PUV, CDN, and WIS monthly maximum wave height.. 57 4.4 Comparison of PUV, CDN, and WIS monthly average wave period. . 58 4.5 Comparison of PUV, CDN, and WIS monthly deviation of wave period. 60

4.6 Daily and cumulative longshore transport rates generated from WIS
hindcast wave data for the year of 1963 (positive assumed southward). 62

4.7 Daily and cumulative longshore transport rates generated from WIS
hindcast wave data for the year of 1964 (positive assumed southward). 63

4.8 Daily and cumulative longshore transport rates generated from WIS
hindcast wave data for the year of 1958 (positive assumed southward). 64

4.9 Comparison of southward, northward, and net monthly variation of longshore transport . . . . . . . . . . . . . . . . 66

4.10 Comparison of seasonal variation of southward, northward, and zero
longshore transport. . . . . . . . . . . . . . . 67

4.11 Example #1 of a single realization of daily and cumulative synthetic
longshore transport rates (positive assumed southward). . . . . 72

4.12 Example #2 of a single realization of daily and cumulative synthetic
longshore transport rates (positive assumed southward). . . . . 73

4.13 Example #3 of a single realization of daily and cumulative synthetic
longshore transport rates (positive assumed southward). . . . . 74

4.14 A single realization of daily and cumulative twenty year synthetic longshore transport rates (positive assumed southward). . . . . . 77

4.15 Comparison of southward, northward, and net synthetic longshore transport rates. . . . . . . . . . . . . . . . . . 78

4.16 Comparison of seasonal variation of southward, northward, and zero
synthetic longshore transport rates. . . . . . . . . . . 80

4.17 Log-normal probability plot of annual synthetic longshore transport magnitudes (southward and northward) for a twenty year period. . . . 81

4.18 Comparison of twenty years of WIS net transport rates with the amount
of sand dredged onto the south beach at Jupiter Inlet. . . . . . 86

4.19 Comparison of the cumulative twenty years of WIS net transport rates
with the cumulative amount of sand dredged onto the south beach at
Jupiter Inlet. . . . . . . . . . . . . . . . . 86


ix








4.20 Five year post-window simulation of volume of sand in trap and volume
of sand on south beach (Note: 90,000 cubic yards of sand pumped over
a twenty-five day period once every two years). . . . . . . . 87

4.21 Five year pre-window simulation of volume of sand in trap and volume
of sand on south beach (Note: 90,000 cubic yards of sand pumped over
a twenty-five day period once every two years). . . . . . . . 88

4.22 Post-window bypassing simulation for a total dredged amount of 34,000
cubic yards per year. . . . . . . . . . . . . . . 89

4.23 Post-window bypassing simulation for a total dredged amount of 70,000
cubic yards per year. . . . . . . . . . . . . . . 90

4.24 Post-window bypassing simulation for a total dredged amount of 45,000
cubic yards per year. . . . . . . . . . . . . . . 91

4.25 Pre-window bypassing simulation for a total dredged amount of 45,000
cubic yards per year. . . . . . . . . . . . . . . 94

4.26 Pre-window twenty year bypassing simulation for a total dredged amount
of 45,000 cubic yards per year. . . . . . . . . . . . 96

4.27 Pre-window twenty year bypassing simulation for a total dredged amount
of 45,000 cubic yards per year. . . . . . . . . . . . 97

4.28 Pre-window twenty year bypassing simulation for a total dredged amount
of 45,000 cubic yards per year. . . . . . . . . . . . 98

A.1 Historical sand sample locations in the vicinity of Jupiter Inlet. . . 106

A.2 Typical sand trap used in the nearshore zone at the Coastal Engineering
Research Center in Duck, North Carolina (Rosati and Kraus, 1989). . 108

A.3 Sand trap incorporated into the sand tracer study of the south beach at
Jupiter Inlet. . . . . . . . . . . . . . . . . 110

A.4 Location of recovered red tracer sand as a result of a tracer experiment
conducted on June 1, 1990. . . . . . . . . . . . . 112

A.5 Location of recovered green tracer sand as a result of a tracer experiment
conducted on June 18,1990. . . . . . . . . . . . . 113

A.6 Location of recovered red, green, and orange tracer sand as a result of a
tracer experiment conducted on December 6, 1990. . . . . . . 115

B.1 Model grid system setup for drogues study (physical model 1:100 scale). 118

B.2 Jetty modifications (A-D) investigated in the drogue study conducted
on Jupiter Inlet (Mehta et al., 1991a). . . . . . . . . . 125

B.3 Jetty modifications (E-G) investigated in the drogue study conducted
on Jupiter Inlet (Mehta et al., 1991a). . . . . . . . . . 126


x








C.1 Log-normal probability plot for January longshore transport values (southward and northward) computed from WIS hindcast wave data. . . 130

C.2 Log-normal probability plot for February longshore transport values
(southward and northward) computed from WIS hindcast wave data.. 130 C.3 Log-normal probability plot for March longshore transport values (southward and northward) computed from WIS hindcast wave data. . . 131

C.4 Log-normal probability plot for April longshore transport values (southward and northward) computed from WIS hindcast wave data. . . 131

C.5 Log-normal probability plot for May longshore transport values (southward and northward) computed from WIS hindcast wave data. . . 132

C.6 Log-normal probability plot for June longshore transport values (southward and northward) computed from WIS hindcast wave data. . . 132

C.7 Log-normal probability plot for July longshore transport values (southward and northward) computed from WIS hindcast wave data. . . 133

C.8 Log-normal probability plot for August longshore transport values (southward and northward) computed from WIS hindcast wave data. . . 133

C.9 Log-normal probability plot for September longshore transport values
(southward and northward) computed from WIS hindcast wave data.. 134 C.10 Log-normal probability plot for October longshore transport values (southward and northward) computed from WIS hindcast wave data. . . 134

C.11 Log-normal probability plot for November longshore transport values
(southward and northward) computed from WIS hindcast wave data. 135 C.12 Log-normal probability plot for December longshore transport values
(southward and northward) computed from WIS hindcast wave data. 135


xi














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

THE INFLUENCE OF SEASONAL VARIATION IN LONGSHORE SEDIMENT TRANSPORT WITH APPLICATIONS TO THE EROSION OF THE DOWNDRIFT BEACH AT JUPITER INLET, FLORIDA

By

PHILIP S. HARRIS III

December 1991

Chairman: Dr. Robert J. Thieke
Major Department: Coastal and Oceanographic Engineering

Erosion of downdrift beaches is a problem commonly associated with most tidal inlets. Thus, it is undoubtedly a major consideration when pursuing an effective tidal inlet management plan. Various sand bypassing simulations at Jupiter Inlet, Florida, were examined through the use of synthetic longshore transport rates generated from wind hindcast wave data in incorporation with local sediment budgets. In order to determine whether sand from the downdrift beach was infiltrating around the south jetty and into the inlet during flood tide, a sand tracer study was conducted three different times over a one year period. Similarly, sediment transport patterns pertaining to the downdrift beach of Jupiter Inlet were also investigated through the use of a drogue study performed in a physical model located at the Coastal and Oceanographic Laboratory at the University of Florida. Finally, in an attempt to determine the most optimal jetty configuration at Jupiter Inlet, different jetty modifications were implemented into the drogue study.

It was found that annual renourishment of the downdrift beach at Jupiter Inlet before the specified nonpumping window would be most beneficial. By renourishing the downdrift beach in early March, a larger beach would be present throughout the summer, aiding nesting turtles and appealing to summer vacationers. It was also determined that the


xii








jetty adjacent to the downdrift beach at Jupiter Inlet was indeed "leaking" (sand was migrating from the downdrift beach, around the downdrift jetty, and into the inlet). Traces of fluorescent colored sand were found inside the inlet on two different occasions, some of the sand remnants of a tracer experiment conducted eight months prior. Finally, it was concluded that the optimal jetty configuration as it concerns the downdrift beach would involve either an extension of the north jetty with a downdrift curvature or an arm-like attachment to the south jetty which would extend southward with the goal being to create a larger "shadow zone" downdrift of Jupiter Inlet.


xiii














CHAPTER 1
INTRODUCTION



1.1 Rationale

Tidal inlets can have a tremendous impact on adjacent coastlines. Erosion of downdrift beaches can involve issues of property loss and important environmental considerations. Stabilization of these depleting beaches is a major issue when confronting the question of tidal inlet management.

Inlets originate when a small strip of land acting as a barrier between the ocean and an estuarine body of water or bay is breached. These breakthroughs are formed when large waves that accompany major storms first erode the exposed beach and eventually cut a path from the ocean to the inland body of water. Once the storm passes, one of two things happens. If the tidal prism is large, creating strong tidal currents, then the inlet will remain open (Bruun and Gerritsen, 1959). However, if the tidal currents are weak and the longshore transport is not only large, but dominant in one direction, the inlet will typically close.

Inlets which remain open without the interference of man are classified as natural inlets. Natural inlet systems contain both ebb and flood shoals, and sand is naturally bypassed along the ebb shoal or deposited within the inlet. Because natural inlets tend to migrate and because they are often characterized as unnavigable due to shallow depths, man-made structures such as jetties are sometimes put into place. Not only do they act as protection from large nearshore waves, but jetties also increase tidal currents which in turn, create a deeper, more navigable channel through the inlet (Marino and Mehta, 1989). However, while jetties make for a more navigable channel and deter inlets from further migration, the adjacent beaches are also impacted.


1






2


Beaches adjacent to inlets can erode when the longshore transport of sand is interrupted due to a littoral barrier. Where longshore sediment transport is dominant in one direction, accumulation of sand updrift of an inlet and erosion of sand from the downdrift beach typically occurs (Dean, 1990), accompanied by sedimentation within the inlet and an enlarged ebb shoal. The sediment pathways in the vicinity of tidal inlets can be complicated, with sand not only trapped by jetties, but also rerouted into the inlet or completely bypassed and deposited further down the beach. During flood tide, sediment may even be transported directly from the downdrift beach into the inlet. Over a period of time, the eroded downdrift beach may reach a critical point after which any further depletion of sand will have costly effects not only on local towns and communities, but plant and animal life as well. Because of the potential economic loss due to an eroded beach and for beach protection during storm conditions, it is common practice to replenish these eroded beaches. Figure 1.1 and 1.2 are photos of Jupiter Inlet, Florida, which were taken in 1990 before and after nourishment of the downdrift (south) beach.

Sand bypassing, which involves the artificial transfer of sand from either the updrift beach, flood shoal or ebb shoal, is a common method used to supply new sand to unprotected beaches downdrift of tidal inlets. However, before a beach is renourished, there are many issues concerning the sand bypassing schedule, sand availability, and the environmental impact on the proposed nourishment site that must first be addressed. For example, when devising a renourishment schedule, is it better to renourish the beach continuously, annually, every two years, or only when the beach appears to be in an endangered state? More specifically, are there certain months during a year that should be avoided because of seasonal variability in wave climate or increased risk to aquatic life inhabiting the beach in its present condition? Another question is the availability of enough sand to successfully nourish the downdrift beach. When considering viable sand source options such as ebb and flood shoals or the updrift beach, what repercussions can be expected with the extraction of a large amount of sand from one localized area? What will be the global response of the tidal inlet system and how will individual areas be effected? Once the eroded beach







3


&i














Figure 1.1: Apri 18. 1990 photo of Jupiter Inlet before nourishment of the south beach.


AG .*.x


Eig2ure .2: '.a 1 If O plou o f .lJpitcr Inlet after itourisliment of the scuti. heucIl.






4


has been renourished, how will the nourished beach respond to seasonal changes in wave climate and over what period of time will this response take place? These are some of the questions which must be addressed when beach nourishment is considered.

Jupiter Inlet, located in Palm Beach County, Florida, is like most tidal inlets in Florida in the sense that its downdrift beach is subjected to large amounts of erosion and has a history of beach renourishment projects. This erosion not only effects the public, but also sea animals which inhabit these downdrift beaches. Because it exhibits problems typical of Florida inlets and is the focus of a University of Florida study, Jupiter Inlet will serve as the basis of this investigation. Most of the field data, laboratory experiments, and numerical simulations will be directly referenced to Jupiter Inlet.

1.2 Previous Research

The common initial objective when beginning research or work that involves the coastal zone is to ascertain characteristics of the local wave climate. Often, this is accomplished by collecting actual wave data from instrumentation placed offshore. However, in many cases, actual wave data is unavailable and some other source is required.

Walton (1973) presented a technical report in which he utilized a large data source of ship wave observations for the computation of longshore sediment transport along segments of Florida's coast. The wave data that was used was the Summary of Synoptic Meteorological Observations, a long term wind hindcast wave data set referred to as SSMO. This data set was a compilation of meteorological and sea state observations taken from ships located at various latitudes and longitudes within the boundaries of the study.

When using the SSMO wave hindcast data set, some assumptions had to be made: i) swell waves were considered to be in the same direction as the sea waves, which in turn corresponded to the wind direction, ii) both sea and swell waves characterized as having the same wave period and wave height were treated alike, losing no energy to the atmosphere between the point of observation and the shoreline considered, and iii) all wave observations were made in "deep water" (h > 2.56T2 in ft.) for the wave periods recorded.






5


The hindcast waves were transformed from the observation site to a section of shore, undergoing shoaling, refraction, friction, and percolation effects. Assumptions were also made that limited the analysis to an ideal beach with no anomalies in offshore bathymetry. The results of the study were presented in a series of littoral drift roses, making it possible to find annual longshore sediment transport rates for various sections of the Florida coast. Computed values of littoral drift were compared to existing estimates for specific locations. It was concluded that although most of the net longshore transport directions were in agreement, the corresponding magnitudes differed considerably.

Weggel and Perlin (1988), through the use of twenty years of Wave Information Study (WIS) hindcast wave data compiled by the U.S. Army Corps of Engineers, also demonstrated that hindcast wave data developed from offshore wind fields can be a sufficient alternative for actual wave data. The WIS hindcast wave data set which separately took into account the wave characteristics of both "sea" and "swell" conditions, appeared to be the next generation of long term hindcast wave data (SSMO wave data was considered the first generation of long term hindcast wave data). A successful description of the longshore transport environment was made through the use of six parameters. Log-normal probability distributions were used to describe the upcoast and downcoast populations. The mean and variance of each of these distributions and the percentage of time the transport was in the upcoast and downcoast directions, constituted the six parameters of interest. When comparing the longshore transport values computed from WIS hindcast data with longshore transport values calculated from LEO observations over the same 2.3 year period, the net transport rates of the two methods were within 3% of each other. Although the daily transport rates differed significantly, the cumulative transport curves indicated that the two estimates of net transport were reasonably close. Also, by showing that both the positive and negative transport populations followed a log-normal probability distribution, Weggel and Perlin demonstrated that transport values could be synthetically generated through the use of the statistical parameters describing those populations.






6

In a subsequent publication, Weggel, Douglass, and Tunnell (1988) applied this procedure of generating synthetic transport values to Indian River Inlet in Delaware. Once again, the longshore transport rate data was divided into two populations, northward and southward transport. Monthly values for the mean and variance of each of the two lognormal distributions were then established. Through the use of random number generators, log-normal distributions for a particular month, along with the frequency of transport either northward or southward, were sampled and daily synthetic transport values were generated. The synthetic data was then used to simulate the operation of a sand bypassing system. By varying the pumping rate and operating constraints, different five year simulations were produced. The net longshore transport, the amount of sand naturally passing the inlet, and the amount of sand pumped across the inlet were computed for each of the five years. Weggel, Douglass, and Tunnell concluded that increasing the storage of sand beyond a certain capacity did not further increase the amount of sand that could be bypassed because of limitations due to pumping capacity. It was also found that by increasing the pumping capacity, more sand was effectively being bypassed. However, in extreme cases, the amount of sand being pumped was limited by the amount of sand being carried into the system by longshore transport.

The combination of the previous two reports provided a method of projecting, on a daily basis, the amount of sand entering a tidal inlet system, whether it be from the upcoast or downcoast direction. These techniques did not, however, provide a direct means of evaluating the condition of the downdrift beaches.

Walther, Sasso, and Lin (1987) conducted a detailed sediment budget analysis of Sebastian Inlet, Florida, in which the volume of sand lost from the downdrift beach due to the effects of the inlet, was assessed. The methodology behind the sediment budget was presented in such a way that it could be applied to other inlets. Results from the evaluation of the sediment budget were given in terms of shoaling rates as well as deficits in the net






7

longshore drift deemed attributable to the inlet. Historical dredge records and surveys were used to develop a historical sediment budget, while an existing sediment budget was simultaneously created through the use of local shoaling rates.

When renourishing a beach, it is not only useful to know the volume of sand located on the beach, but it is also advantageous to have some idea of how the nourished beach will respond over time. Dean and Grant (1989) presented a "one-line" model for calculating thirty-year shoreline positions in the vicinity of beach nourishment projects. The report was comprised of a computer program that had the capabilities of computing shoreline position over a thirty-year period. The beach could either be straight or nourished and littoral barriers were optional. Background erosion rates and constant wave characteristics were the two driving forces for the dispersion of the beach which could be examined directly by looking at superimposed plan view plots. The development of the one-line model not only provided a means of calculating the volume of sand lost from the beach at any particular time, but it also indicated the position of the eroded shoreline given a constant wave climate.

By combining a long term hindcast wave data set such as the Wave Information Study, with the development of local sediment budgets in the vicinity of Jupiter Inlet, the seasonal variation in longshore sediment transport and its effects on the condition of the downdrift beach can be evaluated. Incorporation of such analysis along with detailed field and laboratory experiments will provide a basis for the optimization of solutions for the erosion of downdrift beaches such as the south beach at Jupiter Inlet.

1.3 Objectives and Scope

The primary objective of this investigation is to provide a framework for determining the most appropriate methods and scheduling for renourishing depleted beaches downdrift of tidal inlets. The renourishment schedule will depend directly on the availability of sand and the daily bypassing rate. Considerations involving environmental effects to the south beach and the direct impact on home owners both updrift and downdrift of the inlet will be investigated as well.






8

Another objective of this study, which is consistent with the primary objective, is to determine seasonal variation of the volume of sand present on the downdrift beach. Through the use of twenty years of WIS hindcast wave data, the seasonal variation in wave climate over a twenty year period will be examined. Twenty year computer simulations will be conducted not only to predict the loss of sand volume from the downdrift beach, but also to examine the volume of sand collecting in and being pumped from a "trap" within the inlet system. By incorporating these different facets, an improved scheme for management of downdrift beaches at tidal inlets will result.

This thesis will address the following topics. Chapter 2 will discuss tidal inlet systems in general and investigate how downdrift beaches are affected by changes in longshore sediment transport, ebb shoal mining, and modifications to jetties. The importance of a statistical description of the local wave climate in order to make long term projections will also be stressed. Chapter 3 will involve a detailed explanation of the methods and equations used throughout this study. A brief explanation of the WIS wave hindcast data system will be followed by the methodology used to generate synthetic transport values. Techniques involved when simulating numerous sand bypassing scenarios will be discussed, along with the methods used to model the erosion of the downdrift beach. Computed results and subsequent analysis will be presented in Chapter 4. A comparison of WIS hindcast wave data to actual wave data, coupled with a detailed analysis of the WIS transport data, will be presented. Results from different bypassing scenarios will also be analyzed. Chapter 5 will contain an investigative summary and will include conclusions based not only on computer research, but field and laboratory studies as well.

Appendix A will discuss in detail the procedure, equipment used, and results taken from a tracer study that was conducted at Jupiter Inlet in Florida. The tracer study incorporated a self-designed sand trap for placement in the mouth of the inlet during flood tide. The study was carried out on three different occasions using three different lots of fluorescent colored sand. The primary objective of this tracer study was to establish if, and under what






9
conditions, sand from the downdrift beach was being transported around the south jetty and into the inlet. Appendix B will deal with a laboratory study that took place at the Coastal and Oceanographic Laboratory on the University of Florida campus. It entails the use of a physical model of Jupiter Inlet to examine the transport of miniature drogues around the south jetty under different wave climates and jetty configurations. Both Appendix A and Appendix B are supported by a field drogue study that was conducted at the actual inlet. Appendix C is an extension of the detailed examination involving the WIS transport data in Chapter 4. Monthly log-normal probability plots of both the southward and northward longshore transport rates are presented.














CHAPTER 2
EROSION OF SOUTH BEACH AT JUPITER INLET



2.1 Introduction

Since the beginning of waterborne commerce in the coastal areas, tidal inlets have played an important role in linking estuaries and bays to the world's oceans. Such inlets are found on both the east and west coasts of the United States and while they share many general features, each has specific characteristics which set it apart. Whether it be because of a difference in longshore transport, ebb shoal location, jetty configuration, or ecological concerns, the potential variations between tidal inlets are nearly endless.

When the management of a particular inlet is undertaken, each of these issues must be examined closely. Disruption of the transport of sand across an inlet, dredging of delicate ebb shoals, or reorientation of existing jetties all can cause potentially important changes to adjacent shorelines. Not only could there be a hazardous environmental impact, but erosion of the downdrift beach could possibly be intensified.

2.2 Sediment Transport

Longshore sediment transport occurs within the surf zone and is defined as the movement of sand in a direction parallel to the beach. The longshore transport depends primarily on the incident wave height and wave angle, however it is not easily measured. Two of the most popular methods of evaluating this transport are by either comparing measured beach volumes or by using simple empirical equations that relate the transport to basic wave properties such as wave energy flux. Because sediment transport is directly dependent on the local wave climate, there tend to be seasonal variations in this transport, whether it be a change in magnitude, a shift in direction or a combination of both. Not only can longshore


10






11

transport vary seasonally, it can also be made up of a few episodic events that account for a great deal of the transport for that particular year. However, when considering the transport of sand in the vicinity of tidal inlets, the influence of tidal currents as well as the presence of jetties make this process become even more complicated.

In the vicinity of tidal inlets, longshore sediment transport is combined with the transport of sediment due to tidal currents. Sand which makes up this longshore transport many times moves into the inlet or deposits on the ebb shoal directly outside the inlet. Once the sand has deposited on the inner and outer shoals, it is either lost to the system or it may move back and forth within the tidal inlet for days, weeks, or even months. Eventually, this sand too will migrate downdrift or be entirely lost to the system. Although a great deal of sand temporarily deposits on the ebb and flood shoals, there is a percentage of sand that naturally bypasses the inlet and continues on its path downdrift of the inlet system. With the addition of jetties, the sediment pattern mentioned above can become further complicated.

The presence of jetties can not only increase tidal currents, but more importantly can interrupt the longshore transport of sand to the beach directly downcoast of the inlet. An increase in tidal currents would presumably increase the amount of sand deposited both within the inlet and on the ebb shoal. These increased currents could also push the flood shoal further back into the inlet and the ebb shoal into deeper water (Marino and Mehta, 1989). Thus, the jetties act as a littoral barrier at an inlet, since the beach directly downdrift of the inlet is deprived of the longshore transport, and the sediment that is not directly trapped by the jetties, is rerouted either into the inlet or onto the ebb shoal where it then migrates downdrift. Hence, the addition of jetties, in many cases, is accompanied by an increase in downdrift beach erosion. Because most coastal areas have dominant directions of longshore transport, a typical jettied inlet will exhibit a build up of sand on one side of the inlet, accompanied by a depletion of sand on the downdrift side.






12

In the same manner, because of seasonal variability in wave climate and therefore longshore transport, which is a typical feature at many coastal areas, the shoreline position of the downdrift beach, as well as the updrift beach, may undergo a great deal of change throughout the span of a year. During the winter months, the coastline may be subjected to large wave heights which many times come in the form of northeasters (on the east coast). These episodic events move a great deal of sand and in the process, may erode large portions of shoreline. The summer months on the other hand, are characterized as the period of the year in which smaller waves cause onshore transport, bringing offshore bars back toward the beach, and aiding in the natural recovery.

Jupiter Inlet, located on the east coast of Florida, is a typical inlet which has many of these characteristics. It is an artificially stabilized inlet, with a pair of jetties, and both a flood and ebb shoal. Jupiter Inlet is also similar to many east coast inlets in the sense that the updrift beach is built out, whereas the south, or downdrift beach, is generally receding. The characteristic longshore sediment transport in the area is similar to that described above. There are indications that during the winter months, the net transport is much larger and it is in the southward direction. On the contrary, during the summer months, the longshore transport, although much weaker, seems to be in a northward direction. The inlet is also subject to episodic events. During a storm in 1989, the downdrift beach reached a relatively critical point of erosion in which valuable trees and property were lost to the infringing ocean (Figure 2.1).

Although there are seasonal variations in the transport and occasional events when erosion is intensified, the annual net transport at Jupiter Inlet usually approximates 230,000 cubic yards (Dean and O'Brien, 1987; Marino and Mehta, 1986). Because it is difficult to obtain field verification of the longshore transport and because of the natural variability in the transport from year to year, the accuracy of this estimate is somewhat questionable.

2.3 Ebb Shoal

Considered one of the most dominant features of a coastal inlet, ebb shoals are crescentshaped sand bar formations located offshore of tidal inlets. The ebb shoal is a direct result







13















4v
7






44






































Figure 2.1: Erosion suffered byv th" south beach at .1uplier. lini i mmled t(A ;I i l(rt 11
I's 'r 1! D ec me r I99 I I i) C r. if /.. I 99 f ) .






14

of ebb currents transporting sand from within the inlet or taking sediment out of the littoral drift, and moving this sand offshore. In tidal inlet systems, the ebb shoal serves two primary purposes. Not only does it serve as a sand bridge for bypassing sand naturally around inlets, but ebb shoals are also a type of natural protection for adjacent beaches (Marino and Mehta, 1987).

As sediment approaches a tidal inlet system, depending on the tide, it may be moved into the inlet (flood tide) or onto the ebb shoal (ebb tide). Once the sand reaches the ebb shoal, it moves along this bar until the bar typically comes back into contact with the beach further downdrift of the inlet. In this case, sand has not only naturally bypassed the inlet, but it has also bypassed the beach adjacent to the downdrift jetty.

Ebb shoals can also serve as protection to many downdrift beaches. Because ebb shoals are usually shifted downdrift of the inlet due to longshore currents, they are typically located offshore of downdrift beaches rather than directly in front of the inlet. As waves propagate shoreward, the wave heights grow and the wavelengths decrease, up to a point where breaking occurs. In the presence of ebb shoals, waves tend to break offshore, dramatically decreasing the wave energy. By the time these waves get to shore, they are no longer as effective in eroding sand from the beach.

In the past, suggestions have been made to use the potentially large quantities of sand available on ebb shoals to renourish eroded beaches downdrift of inlets. In view of the protective nature of many ebb shoals, this type of approach may involve some risk and the long-term effects are not that well understood. There is also the question of whether renourishing the downdrift beach with sand taken from the ebb shoal is actually solving the problem or creating a more severe one. If sand is taken from the ebb shoal, then the large waves that were dissipating offshore, will now concentrate their wave energy directly on the downdrift beach. Although many ebb shoals are stationary, there are other ebb shoals that move around quite frequently, and such migration of the ebb shoal makes mining extremely difficult to evaluate and carry out.






15
Movement of the ebb shoal also decreases the stability of the channel which may be used for navigational purposes. Many times, boaters who do not have knowledge of the local area, will not only become grounded on the outer bar, but in some cases, there may be fatalities.

Jupiter Inlet contains an ebb shoal which shifts a great deal, causing some of the same problems previously mentioned. Figures 2.2 and 2.3 give a comparative look at the location of the Jupiter Inlet ebb shoal over a period of six months. Because of the movement of the ebb shoal, navigation through the inlet is somewhat hazardous and results in a handful of deaths annually (U.S. Army Corps of Engineers, 1966; Mehta et al., 1990a). It is for this reason that Jupiter Inlet has been classified as an unnavigable inlet. It is suggested that only local individuals who are familiar with the bathymetry of the area navigate the inlet.

2.4 Jetty Configuration

Jetties are coastal structures placed at the mouth of an inlet that usually extend seaward, redirecting and confining the stream or tidal flow so as to provide protection and stabilization for a ship channel. Many coastal communities choose the option of placing jetties at their inlet in order to deter migration of the inlet and to increase tidal currents which will deepen and create a more defined channel for boaters. The speed at which the water flows through the inlet depends on the width between the jetties as well as the size of the tidal prism. For the same inlet, jetties which are placed closer together tend to have larger currents than jetties which are spaced further apart. As mentioned earlier, stronger tidal currents will result in the ebb shoal being positioned further offshore and the flood shoal moving back into the inlet (Marino and Mehta, 1989). In the same way that the tidal currents can be controlled by the spacing of the jetties, so too can the erosion to the downdrift beach be potentially minimized through the use of an ideal jetty configuration.

Although erosion to beaches downdrift of inlets is in general unavoidable, minimization of erosion can be achieved by an ideal alignment of the jetties. Jetties that fail to extend a satisfactory distance seaward, are susceptible to sediment migrating from adjacent beaches,







16


MARCH 1979 SURVEY






















Figure 2.2: Location of the ebb shoal directly offshore of Jupiter Inlet during March, 1979 (Mehta et al., 1990a).
AUGUST 1979 SURVEY
-7























Figure 2.3: Location of the ebb shoal directly offshore of Jupiter Inlet during August, 1979 .(Mehta et al., 1990a).






17
around the jetties, and into the inlet. It is common practice to attach an arm to the end of the downdrift jetty which will extend downcoast, parallel to the shoreline. This orientation is useful in stopping sand from flowing into the inlet from the downdrift beach (Brunn, 1953). On the other hand, longer updrift jetties tend to trap larger amounts of the littoral drift, which in turn cuts off the supply of sand to the downdrift beach. Many times these jetties are also curved so that the approaching waves are perpendicular to the jetty wall. This cuts down the wave energy in the channel and provides safe passage for ships attempting to navigate the inlet. This type of curved jetty is also effective in redirecting the littoral drift around the inlet rather than completely trapping the sand. Another viable jetty option is the placement of a weir jetty on the updrift side of the inlet. A weir jetty allows for sediment to deposit in a specified "trap" within the inlet. This sand can then be actively bypassed when the accumulation exceeds a certain level.

Until 1922, Jupiter Inlet was a natural inlet which maintained an open, although shallow, inlet channel. In 1922, two jetties, each 120 m in length and 107 m apart, were constructed and subsequently extended and strengthened. In 1940, the Inlet District built an angular groin at the seaward end of the south jetty. The intended purpose was to increase current velocities and induce scouring between the jetties where closure of the inlet had recurred. However, in 1942, Jupiter Inlet once again closed and remained closed until 1947. In 1947, a dredging plan was put into action and the inlet was once again reopened. In 1956, a 90 m long concrete capped sheet pile jetty was built approximately 30 m north of the existing north jetty. Finally, in 1967 an angular groin at the seaward end of the south jetty was removed and the jetty was extended by 30 m (Mehta et al, 1990a).

In their present condition, the jetties at Jupiter Inlet seem to be causing some of the same problems mentioned earlier. The updrift jetty and the tidal currents are depriving the south beach of much needed sand. Their are also indications of sediment being transferred around the south jetty and into the inlet during flood tide, indicating that the south jetty is not of sufficient length (Dean and Walton, 1973).






19


Figure 2.4: Good sea turtle nesting beach, looking north toward Jupiter Inlet.


Figure 2.5: Steep scarp considered to discourage nesting sea turtles just south of the south jetty at Jupiter Inlet.






18

2.5 Ecological Considerations

When discussing the management of downdrift beaches, attention must be given to the effects extreme amounts of erosion will have on the environment. One of the major concerns is how eroded or recently renourished beaches will effect sea animals that annually nest on the beach. There is also the question of how habitats such as exposed rocky outcroppings and coastal vegetation downdrift of inlets will be effected. An assessment of the local ecological system is an important facet of inlet management decisions.

Annual nesting of sea animals such as sea turtles is greatly dependent on the condition of the beach. If the beach has been recently renourished or is in a highly eroded state, the slope of the beach may be unfavorable for sea turtles who must crawl up the beach before burying their eggs. Figures 2.4 and 2.5 give an example of a favorable as well as an unfavorable beach for turtle nesting.

In Florida, a slope ratio of one to ten is considered an optimal beach slope for nesting turtles (Mehta et aL., 1991b) but of course, the optimal slope could change depending on the geographical location and the type of sea animal. Figures 2.6 and 2.7 show examples of an optimum beach slope for nesting turtles (located approximately one mile south of Jupiter Inlet) versus the beach slope found on the south beach (adjacent to the south jetty) at Jupiter Inlet soon after the completion of a beach renourishment project. If renourishment of the beach begins before the buried eggs have hatched, the eggs either have to be moved or suffer the consequence of being buried too deep for the baby turtles to dig their way to the surface.

Shown in Figure 2.8 are rocky outcroppings which lie in the nearshore area at Jupiter Inlet. Exposed rocky outcrops like these provide habitat for small fish as well as under water vegetation and microorganisms. However, these outcrops can be covered up with the construction of jetties or the renourishment of an eroded beach. Coastal vegetation such as sea grass, sea oats, and palm trees, are also endangered when the beach downdrift of an inlet experiences extreme amounts of erosion.







20


~~4III~rq





N
~h. ~rr.


--- 4/13/90
- 5/29/90
*--- 2/4/91


'~ ~'\


I I


2uu
DISTANCE (feet)


300


Figure 2.6: Favorable beach-slope profile south of Jupiter Inlet on three different occasions in 1990-1991 (Mehta et al., 1991b).

.d t


200
DISTANCE (feet)


300


Figure 2.7: Unfavorable beach-slope profile adjacent to south jetty at Jupiter Inlet on three different occasions in 1990-1991 (Mehta et al., 1991b).


1


'-


51-


0
a>
z
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-J
w


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- 5/29/90
---.- 2/4/91


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-101


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I I


100


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21


Figure 2.8: Proximity of rocky outcroppings in the vicinity of Jupiter Inlet, Florida (Mehta et al., 1991b).






22

The beaches adjacent to Jupiter Inlet are a thriving ecological community. Not only are there exposed rocky outcroppings and vegetation on the south beach, but Jupiter Inlet also serves as one of the largest summer homes for nesting sea turtles on the east coast. Turtle nestings occur on such a regular basis that these annual visits have begun to attract tourists. The time period in which the turtles nest is an important factor when considering beach renourishment plans.

2.6 Conclusion

It should be the goal of all coastal engineers to make improvements in the coastal zone without disrupting the natural processes that take place. However, because tidal inlets are such dynamic systems, any modifications to the natural state of the inlet usually result in global changes. Reorientation of the jetties may lessen the erosion to the downdrift beach, but at the same time might also shift the navigational channel or cause the ebb shoal to readjust. The realignment of the ebb shoal could cause a change in the erosional pattern downdrift of the inlet. Another scenario involves the mining of the ebb shoal for beach renourishment purposes. Although the placement of sand on the downdrift beach may solve the immediate problem, reduction of the ebb shoal may lessen its protective capacity and result in intensified erosion. Additionally, renourishment of an eroded beach during the turtle nesting season could have important effects on their long-term existence. Not only is the sea turtle an endangered species, but it also provides a certain economic value to the community through the influx of tourists who come to view the nesting process.

These are just a few examples of how small modifications to jetties, mining of ebb shoals, or renourishment scheduling, can have a greater impact on the entire tidal inlet system. Thus careful management at tidal inlets is essential.

This is why the need for a long term wave data set is so important. A long term wave data set can allow planners to successfully estimate annual longshore sediment transport rates. Furthermore, the seasonal variation (changes in magnitude as well as direction) in these transport rates can be better understood, enabling prior evaluation of renourishment






23

rates for the downdrift beach. By incorporating local sediment budgets with synthetically generated longshore transport rates, a number of statistical sand bypassing simulations can be conducted showing the seasonal variation of sand volume on the downdrift beach. As a result, a more efficient bypassing schedule can be created. Through the use of a physical model, drogue experiments can be run for typical and storm wave conditions. Additional drogue experiments incorporating different jetty modifications can lead to an optimal jetty configuration, reducing the erosion of the downdrift beach. By developing a more efficient bypassing schedule as well as optimizing the alignment of the jetties, stabilization of the downdrift beach can be achieved.














CHAPTER 3
METHODOLOGY



3.1 WIS Wave Data

In order to assess potential littoral impacts of alternative inlet management solutions, the sediment budget and historical transport patterns must be described. Further, to understand the physical processes that affect (drive/control) sediment dynamics in the coastal zone, it was important to first ascertain characteristics of the local wave climate. Sometimes this is accomplished by collecting actual wave data from instrumentation placed in the nearshore area. But in many cases, actual data is not available. Thus, some type of wind hindcast data must be used. Because of the proximity of Jupiter Inlet, the following data sources/methods were considered: i) PUV instrumentation package (Note: P= pressure, U= current velocity in the x-direction, and V= current velocity in the y-direction) designed to collect short term wave statistics and maintained by the Coastal and Oceanographic Engineering Department at the University of Florida. It should be mentioned that wave heights can be generated from the pressure data, wave directions can be calculated from the directional current data, and wave periods can be produced from the pressure frequency, ii) Florida Coastal Data Network (CDN): a long term study devoted to the generation of wave statistics along the Florida coast. The CDN data collecting packages were designed and maintained by the Coastal and Oceanographic Engineering Department at the University of Florida in accordance with the U.S. Army Corps of Engineers, and iii) U.S. Army Corps of Engineers Wave Information Study (WIS) Hindcast Model. It should be noted that because the WIS hindcast wave data was considered an updated version of the Summary of Synoptic Meteorological Observations (SSMO), the SSMO hindcast wave data was not considered in this investigation.


24







25

Each of these wave data sources have advantages and disadvantages. Because PUV data contains real time measurements of wave angle, wave height, and wave period, it would be the most useful data source. However, because only fifteen months of this data was collected, obtaining an understanding of the annual wave climate through the use of such a small data source was unlikely. Because the CDN wave data incorporated five years of data, the wave statistics generated from this study were more dependable than the PUV wave statistics. However, although the long-term data set was useful, without the correlation of the wave height and wave period with a wave direction, the CDN data alone proved to be ineffective. The WIS hindcast wave data, which incorporated twenty years of wave height, wave period, and wave angle, gave a larger overview of the annual wave climate and seasonal variability. The main disadvantage of the WIS wave data was the fact that it was hindcast wave data from offshore winds rather than actual wave data. However, by comparing the real wave data compiled from CDN and PUV stations with the long-term WIS hindcast wave data, a realistic twenty year wave data set (1956-1975) justified by real wave data, could be developed.

The Wave Information Studies (WIS) produced wave climate information for the Atlantic, Pacific, Gulf of Mexico, and the Great Lakes for the years 1956-1975. The wave information was generated by numerical hindcasting models which created wind fields from historical meteorological records (Resio et al., 1982) and calculated wind wave growth and propagation (Corson et al., 1981). The numerical hindcasting programs assume spectral transformation of sea and swell waves, no additional wind effects, and straight, parallel bottom contours. The wave hindcast information is stored at selected points on a numerical grid in the vicinity of the U.S. coastline (Jensen, 1983).

Because wave information is ordinarily needed for specific application at nearshore points, the WIS wave data is transformed from deep water to shallow water. This transformation of wave climate information is divided into three different phases. Phase I is defined as the deep ocean where the hindcast waves originate. The waves generated in this phase







26

are spawned from large offshore storm systems which may stretch for hundreds of miles. In Phase II, the waves that were created in Phase I are transformed across the shelf zone. Finally, the waves from Phase II are carried across the nearshore zone to the 10 m contour, making up the Phase III wave data set. A diagram briefly describing these three phases is presented in Figure 3.1.

PHASE 111 PHASE II PHASE I










NEARSHORE ZONE SHELF ZONE DEEP OCEAN SYNOrTIC, MESOSCALE MESOSCALE AND SYNOPTIC SYNOPTIC AND LARGE SCALE T 2z CONVECTIVE
Lx LESS THAN 10 MILES Lx 10'S OF MILES Lx 100'S OF MILES At LESS THAN 3 HOURS At 3 TO 6 HOURS At GREATER THAN 6 HOURS AIR -SEA INTERACTION AIR-SEA INTERACTION AIR -SEA INTERACTION REFRACTION
DIFFRACTION
0
Ix SHOALING
BOTTOM FRICTION
LONG WAVES (TIDES
AND SURGE)
SECONDARY
WAVE TRANSFORMATION ENERGY SOURCE PRIMARY ENERGY SOURCE


Figure 3.1: Description of Phase I, Phase II, and Phase III (Jensen, 1983).


Because the research conducted during this report specifically dealt with the east coast of Florida, the Atlantic Coast Wave Information Study (ACWIS) hindcast wave data set was applied. Because of the location of Jupiter Inlet, Florida, the specific stations used in this study were station 10 in deep water, station 67 in the shelf zone, and station 156 in the nearshore zone. The locations of these three stations are presented in Figure 3.2.

Phase I data is initially compiled for points in the spherical orthogonal grid which encompasses the North Atlantic Ocean. Thirteen locations adjacent to the U.S. Atlantic coast are selected for data analysis. Each of these thirteen stations contains twenty years (1956-1975) of hindcast, deepwater, significant wave data stored at three hour intervals. It



















'1

154

155


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PHASE


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PHASE



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ISLAND


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ANDROS ISLAND


S I __ _ _ _ _ _ __ _ __ _ __ _ I


Figure 3.2: Location of offshore WIS hindcast data stations in the vicinity of Jupiter Inlet (Jensen, 1983).


82


FLORIDA


-57 158 159
BOCA RATON 60 FT. LAUDERDALE


MIAMI 1 3

164

165


250


'


I -


-;- -






28

should be noted that tropical storms and hurricanes are not included in the twenty year data set (Abel et al., 1989).

In Phase II of the wave hindcast model, three hour averaged wave conditions for an entire year are transferred across the shelf zone. During this transformation, the original wave characteristics are changed due to the following wave processes: refraction, diffraction, shoaling and bottom friction.

For this thesis, WIS data for the Atlantic Phase II was received from the Corps of Engineers in the following format:


1. Date/time

2. "Sea" maximum wave height (meters)

3. "Sea" wave period (seconds)

4. "Sea" wave direction (degrees WIS)

5. "Swell" maximum wave height (meters)

6. "Swell" wave period (seconds)

7. "Swell" wave direction (degrees WIS)

8. Wind speed (knots)

9. Wind direction (degrees WIS)


As seen above, the wave data is divided into "sea" and "swell" conditions. "Sea" conditions indicate locally generated waves while "swell" conditions indicate waves generated further afield.

In preparation for Phase III transformation, the wave angles were changed from WIS values to azimuth values. This was accomplished by subtracting the WIS values from 2700. If the difference was less than zero, 3600 was added (Jensen, 1983).


I






29

After the angles had been transferred from WIS to their corresponding azimuth values, the Phase II wave data set was correctly formatted and Phase III wave data could be generated. The Phase III program can be divided into three major segments as follows:

1. Computation of the sea operator

2. Computation of the swell operator

3. Application of the sea and swell operators to a WIS time history from the offshore

location

The Phase III program is set up to transform wave height and direction for each sea and swell observation from the offshore point into the shallower depth by initializing values in the sea and swell operators. Within the Phase III program, there is also an option for sheltering effects that may be caused by offshore islands.

Once again there are a few things that should be pointed out that pertain directly to the Phase III program. The first thing being that the selection of the orientation of the bottom contours is very important due to the fact that it directly effects the transformation process. Furthermore, Phase III output gives an approaching wave angle of 900 if the waves migrate normal to shore, 1800 if the waves migrate parallel to the shoreline from the north, etc (Jensen, 1983).

As a result of the generation of the Phase III output, a twenty year wave data set for waves occurring at the 10 m contour off the coast of Jupiter Inlet was created. Once this nearshore wave data was collected, the next step was to use basic linear wave theory to translate the waves to breaking conditions and compute the corresponding longshore transports. Note that because the Wave Information Study encompasses both coasts as well as the Gulf of Mexico and the Great Lakes, it is reasonable to assume that this same approach can be duplicated in other parts of the United States.






30

3.2 WIS Computed Longshore Sediment Transport Data

When doing coastal research that is directly related to the nearshore zone or shoreline, it is advantageous to have an idea of the magnitude as well as direction of the longshore transport in the local vicinity. In order to study the erosion or accretion of a beach over a long period of time, annual longshore transport values are essential. It may also be important to quantify the seasonal or short-term variability that can be expected at a particular site. By incorporating WIS nearshore wave data with basic linear wave theory, wave heights and directions in the nearshore zone can be transformed to breaking conditions. Afterwards, the wave characteristics at breaking can be used with the transport equation to produce longshore transport rates on time scales as short as three hours.

When transforming the Phase III wave data set from station 156 into breaking conditions, the sea conditions and swell conditions were computed separately. Through a detailed examination of the WIS hindcast wave data set, it was found that for the most part, swell conditions were represented by smaller wave angles and larger wave heights and wave periods. Sea conditions on the other hand, contained larger wave angles and smaller wave heights and wave periods. After combining the sea and swell wave conditions to produce a single wave height, wave angle, and wave period for a particular three hour period, what resulted was uncharacteristically large longshore sediment transport rates. Thus, it was concluded that by calculating longshore sediment transport rates for sea and swell conditions separately, and then combining the two to make one three hour longshore transport rate, the most realistic results would be generated. Note that this separation of sea and swell waves during transformation is consistent with linear wave theory.

Initially, both sea and swell significant wave heights were changed from centimeters to meters and their respective root mean square (r.m.s.) wave heights were calculated in accordance with a Raleigh distribution by using



H,ea, H I1e = 1414 (3.1)






31

where H, indicates the significant wave height for a three hour period, whether it be sea or swell conditions, and He and HeI stand for the corresponding r.m.s. wave heights. Once these r.m.s. wave heights had been calculated, the longshore sediment transport resulting from sea conditions was first calculated by setting the swell wave height to zero while the sea wave height remained unchanged. Subsequently, in the process of arriving at a single r.m.s. wave height, wave angle, and wave period, the swell wave angle and wave period had no effect on these calculations. After obtaining three hour wave characteristics based solely on sea conditions, basic linear wave theory was used in producing wave characteristics at breaking conditions. The final result was a three hour longshore transport rate based on sea conditions.

After a three hour longshore sediment transport rate was computed using sea conditions, a similar three hour longshore transport rate was calculated using the corresponding swell conditions. Incorporating the same technique as mentioned above, the sea wave height was set to zero and the swell wave height remained unchanged (equal to the initial value calculated from Equation 3.1). As a result, three hour wave characteristics based only on swell conditions were generated and with the incorporation of basic linear wave theory, these wave characteristics were then transferred to breaking conditions. Subsequently, a second longshore sediment transport rate based on swell conditions was produced for the same three hour period. By combining these two transport rates, a net longshore sediment transport rate for one three hour period was produced. The following is a brief description of the equations used in this process.

Basic linear wave theory equations were used to transform the wave characteristics at a 10 m depth, to a point in the surf zone where the waves would break. This was accomplished by first obtaining the deep water wave length.

L, = 1.56 T2 (3.2) In this equation, T stands for the particular wave period and L, is the deep water wave length in meters. Once the deep water wave length was calculated, the wave length at the


I






32

10 m depth was computed. This was accomplished through interpolation of the following equation.



L = L, tanh [L(h)] (3.3) In Equation 3.3, h indicates the water depth and L is the wave length at that particular depth. Having done this, the group velocity could now be calculated.
L [ 4r
C9 = g 1+ 4,] (3.4) 2T sinh L

As a result of the computation of the group velocity, the depth of breaking and the breaking wave height could be computed.

hb= 1 [H, (3.5)




Hb = rhb (3.6) In Equation 3.5 and 3.6, n is equivalent to 0.78 (Weggel, 1972) and hb is the term used to signify the depth of breaking as Hb is similarly used to represent the breaking wave height.

Snell's Law was incorporated with Equation 3.3 in order to calculate the wave length and wave angle during breaking conditions.

T1 sin a1 T2 sin a2 (37) L1 L2

Equation 3.7 is a modified version of Snell's Law. If the celerity at one depth is known and the wave angles at two depths are known, then Snell's Law can be used to calculate the celerity at the second depth.

Once breaking wave characteristics for a three hour period had been produced, incorporation with the transport equation (U.S. Army Corps of Engineers, 1984) enabled a longshore transport rate, whether it be for sea or swell conditions, to be calculated.

Q = kpHbX/Fsin2ab (3.8) 16(p, p)a'






33

ab = The angle the wave makes with the shore normal at breaking

p = Fluid density


P. = Density of the sediment

a' = Solid fraction of the sediment deposit (1- porosity)

k = An empirical coefficient determined by comparing calculated values
of Q with measured transport rates
Q = Longshore transport rate


Having a set procedure to generate the longshore transport rates for any three hour period, longshore sediment transport rates were subsequently generated for every three hours over the entire twenty year period (1956-1975). Next the net transports were compiled for every one of the twenty years. These net transport values were then summed and an average was taken. This average was adjusted to the best observed value of 230,000 cubic yards in the southward direction in the vicinity of Jupiter Inlet. At that point, the empirical coefficient (k) was set at 0.28 and the annual transport values were recalculated. Table 3.1 presents the twenty years of longshore data as calculated by the WIS hindcast wave data.

The annual net transport for all twenty years were then ranked in ascending order and plotted on a log-normal scale (Figure 3.3). Because the annual net transports represented good fit to a log-normal distribution, it was concluded that longshore transport rates could indeed be adequately described statistically (Weggel and Perlin, 1988). Therefore, having created a twenty year data base of transport values, these transport rates could now be manipulated in order to produce synthetic longshore transport data.

3.3 Synthetic Longshore Transport

Having collected twenty years of longshore transport values at three hour intervals, it was possible to statistically describe the transport. The twenty years of longshore transport rates were divided up into their respective months, enabling the seasonal variability to






34


Table 3.1: Estimated Longshore Transport Values (Q, cubic yards per year) for 20 Year Period 1956-1975 (calculated from WIS wave hindcast data).

YEAR Qnet Qouth Qnorth Qouth Percentage of Gross Drift + Qnorth % South % North
1956 433,633 473,253 -39,621 512,874 92.3 7.7 1957 207,214 278,450 -71,236 349,686 79.6 20.4 1958 381,848 441,072 -59,223 500,295 88.2 11.8 1959 310,889 395,503 -84,614 480,117 82.4 17.6 1960 236,324 302,862 -66,538 369,400 82.0 18.0 1961 147,716 228,766 -81,050 309,816 73.8 26.2 1962 261,973 321,219 -59,245 380,464 84.4 15.6 1963 232,033 286,333 -54,299 340,632 84.0 16.0 1964 146,029 210,080 -72,050 282,130 74.5 25.5 1965 225,910 291,087 -65,177 356,264 81.7 18.3 1966 255,891 346,656 -90,766 437,422 79.2 20.8 1967 270,685 315,307 -44,622 359,929 87.6 12.4 1968 63,430 98,922 -35,492 134,414 73.6 26.4 1969 194,132 253,204 -59,072 312,276 81.1 18.9 1970 203,480 273,624 -70,143 343,767 79.6 20.4 1971 190,001 246,245 -56,244 302,489 81.4 18.6 1972 316,316 375,529 -59,214 434,743 86.4 13.6 1973 316,063 382,845 -66,781 449,626 85.1 14.9 1974 200,821 227,965 -27,144 255,109 89.4 10.6 1975 21,181 78,744 -57,563 136,307 57.8 42.2
AVERAGE 230,779 291,383 -61,383 81.2 18.8






35


10 6


wU

w
0.





CI




0
C,,
z


I II I I I I I I I I


1 2 5 10 30 50 70 90
EXCEEDANCE PROBABILITY


I I


9899
(%)


Figure 3.3: Log-normal probability plot of annual longshore transport magnitudes (southward and northward) from WIS hindcasts for the period 1956-1975.


SOUTHWARD TRANSPORT
















NORTHWARD TRANSPORT


*


105


104


I I







36

be examined in detail. After the twenty year statistical populations for each month were produced, sampling of these populations led to the generation of daily synthetic longshore transport values. The creation of the synthetic transport values would eventually lead to the execution of a number of sand bypassing simulations.

The first step in obtaining the synthetic longshore transport rates was to extract monthly statistics describing the actual transport. The twenty years of transport rates derived from the WIS wave hindcast study were separated into their respective months. Thus, twelve new data sets were created. Once this had been accomplished, the percentage of time the transport was either south, north, or zero was found. After this was done to all twelve data sets, each data set was split into two parts. One group of data contained southward transport rates and the other data set contained only northward transport rates. Subsequently, the mean and standard deviation for both the data sets were calculated. When the southward and northward transport rates were grouped in ascending order on a log-normal scale, they approximated a log-normal distribution considerably well. Having computed the mean and standard deviation for both north and south data sets for all twelve months, the next step was to convert these values from log-normal to the corresponding normal mean and standard deviation.

The mean and standard deviation for the associated normal distribution was derived through the use of the following two equations.

1 012
n 109l1g 2 +1 (3.9)


1 [ ,a2 1
a' = log10 / logic0 + I (3.10) In Equations 3.9 and 3.10, it and o stand for the log-normal mean and standard deviation, respectively. Likewise, a' and 0' represent the normal mean and standard deviation, in that order.

Once the mean and standard deviation for the normal distribution had been computed, the synthetic longshore transports were generated. When producing a data set containing






37


these transport rates, a pair of random number generators ranging from 0.7718, transport for the would be assumed northward, and if 0.0226
MONTH % NORTH % SOUTH % ZERO north /3o,' Osouth O.south January 22.82 74.92 2.26 2.29 0.48 2.90 0.55 Febuary 27.85 68.94 3.21 2.56 0.50 2.87 0.51 March 26.67 70.46 2.86 2.51 0.47 2.77 0.51 April 33.46 63.67 2.88 2.50 0.50 2.56 0.54 May 44.72 47.24 8.04 2.28 0.48 2.30 0.56 June 54.10 34.13 11.77 2.07 0.61 1.91 0.66 July 64.50 23.73 11.77 2.01 0.51 1.46 0.54
August 54.07 34.64 11.29 1.93 0.48 1.76 0.65
September 36.90 59.29 3.81 2.08 0.61 2.58 0.60
October 22.42 76.17 1.41 2.38 0.52 2.99 0.53
November 21.56 75.60 2.83 2.39 0.48 3.04 0.51 December 20.09 78.71 1.21 2.50 0.50 2.99 0.50


Once the direction of the transport was determined, the second random number was used to determine the magnitude of the transport rate. This was accomplished through the use of Equation 3.11 for the standard normal curve (Abramowitz and Stegun, 1972):






38


T Co + C1T + C2T 2
1+ D1T + D2T2 + D3T3

Co = 2.5155 C1 = 0.8029 C2 = 0.0103 D = 1.4330 D2 = 0.1893 D3 = 0.0013

ST = n N


RN = Random number produced by second random number generator

After determining the values for z, the monthly statistics for a particular direction were used to solve for x.

x = a' + #'z (3.12) Finally, the antilogarithm of the result was taken to determine the synthetic transport for that day. With the methodology to produce synthetic longshore transport rates established, it was now possible to produce a wide variety of sand bypassing simulations.

3.4 Sand Bypassing Simulations Through the development of a synthetic longshore transport data base, sand bypassing scenarios were simulated. By incorporating sediment budgets for the cases of both southward and northward transport with the synthetic transport rates, the volume of sand accumulating in different areas of the inlet system could be calculated. The different areas of the inlet included the ebb shoal, the "sand trap" within the inlet, and the downdrift beach. By examining the rate at which the downdrift beach accretes or erodes, the most opportune solution for nourishment of the downdrift beach could be devised.






39

Because the annual net transport at Jupiter Inlet, Florida, was in the southward direction, a sediment budget for the southward transport had been created previously. Most of the numbers used in the production of this sediment budget were arrived at by either the U.S. Army Corps of Engineers or through the use of old beach surveys (Buckingham, 1984). When the transport was from the north, there was 94% bypassing of the inlet system and 18.6% of the transport for that particular day was eroded from the south beach. Likewise, 3% of the daily transport deposited on the ebb shoal and was eventually lost from the system. The sand trap within the inlet collected 13% of the sand transported for that day. Figure 3.4 presents a sediment budget (percentages) at Jupiter Inlet when the longshore sediment transport is in the southward direction.

Through the use of simplistic arguments, a northward sediment budget was developed. Because the downdrift beach was typically in an eroded condition, the south jetty created a more effective littoral barrier, causing sediment transported northward to be halted easier. Likewise, because the updrift beach was typically built out, more sand was available to be taken into the longshore transport. Incorporating this logic leads to a crude estimate in which 72.2% of the original northward transport bypassed the inlet system. Because the transport was coming from the south, sand was being deposited on the south beach and eroded from the north beach. On the south beach, 20% of the transport was being deposited. On the contrary, 25% of the transport was being eroded from the north beach. Likewise, 3.8% of the transport was deposited on the ebb shoal and 17.45% was predicted to settle in the trap inside the inlet. Figure 3.5 presents a sediment budget (percentages) at Jupiter Inlet when the longshore sediment transport is in the northward direction.

With both southward and northward sediment budgets established, a number of sand bypassing scenarios could be simulated. This was accomplished by adding sand to the south beach on an appropriate basis while simultaneously subtracting the same amount of sand from either the ebb shoal, flood shoal, or trap inside the inlet, depending on where the






40

beach nourishment material was being excavated. In this study, most of the sand was taken from the trap inside the inlet while the accumulated sand further back in the inlet was used as a safety factor.

By varying the pumping rate and number of consecutive days the sand was pumped, different volumes of sand were placed on the south beach. When the amount of sand placed on the south beach was increased, the amount of sand available in the trap decreased. If the quantity of sand placed on the south beach exceeded the supply available within the trap, other mining options had to be devised.







41


7








940.0
-- 5.6
2

Fir s n b t 2 .2 3.0*




- -94.0





Figure 3.4: Southward sediment budget in the vicinity of Jupiter Inlet (Mehta et al., 1991b).


-.72.2



11.5 25.0 44.2
No 29.o .



..0


100.0





Figure 3.5: Northward sediment budget in the vicinity of Jupiter Inlet.














CHAPTER 4
RESULTS AND ANALYSIS



4.1 WIS Wave Data

In order to acquire a better understanding of the wave characteristics in the nearshore zone at Jupiter Inlet, the advantages and disadvantages of the PUV, CDN, and WIS hindcast wave data sources should be examined in detail. An indepth analysis of the wave statistics compiled by all three data sources is provided the following discussion.

South of Jupiter Inlet (LAT: 26 57' 45" N, LONG: 80 04' 48" W) at a depth of 7.0 m, a PUV data collecting package was deployed. Short term wave data, including wave height, wave period, and wave angle, was collected from March, 1990 through May, 1991. However, during three of these months, May, June, and July of 1990, instrumentation problems inhibited the collection of directional wave characteristics. Table 4.1 shows that the average monthly wave height was smaller during the summer months (June through August) and that it increased in October. It should be noted that PUV wave data for the months of March, April, and May was collected in 1990 and 1991. As a result, Table 4.1 presents a compilation of one year wave characteristics with the exception of the three months mentioned above in which case, wave data for that particular month for both years was averaged to produce a single value.

This increase in wave height was probably due to sub-tropical storms. The maximum wave heights were also larger during the winter months as well as during the first couple of months of the year. There were also indications that the nearshore wave climate was less variable during July and August. In comparison, Tables 4.2 through 4.13 show that while the waves were predominantly from the northeast, there were indicators that in the months of August, 1990 and March through May of 1991, a larger percentage of waves approached


42






43


Table 4.1: Global Wave Parameters Collected from PUV Station.

MONTH H,(mean) H,(std dev) H,(max) T(mean) T(std dev)
(meter) (meter) (meter) (sec) (sec) January 0.59 0.35 1.75 8.23 2.86 February 0.73 0.44 2.15 7.43 2.51 March 0.69 0.38 1.71 7.70 3.40 April 0.72 0.48 2.40 7.27 3.11 May 0.62 0.36 1.92 5.49 2.48 June 0.38 0.27 1.50 8.27 0.75 July 0.17 0.09 0.34 8.13 1.07 August 0.25 0.09 0.55 8.34 0.86 September 0.58 0.28 1.50 8.45 0.77 October 0.92 0.41 2.45 8.41 0.77 November 0.84 0.38 1.70 7.70 2.50 December 0.75 0.36 1.78 8.45 2.76


Table 4.2: 1990 March Distribution of Wave Heights H and Wave Angles 0 (in percent) at 7-meter Contour from PUV Data Collecting Package.


H<1m lm3m
0 > 300 21.3 7.5 2.1 0.0 200 < 0 < 300 11.7 8.5 0.0 0.0 100 < 0 < 200 9.6 3.2 0.0 0.0 00 < 0 < 100 3.2 0.0 0.0 0.0
-100 < 0 < 0 6.4 1.1 0.0 0.0
-200 < 0 < -10" 5.3 1.1 0.0 0.0
-300 < 0 < -200 11.7 4.3 0.0 0.0
0 5 -30" 3.2 0.0 0.0 0.0






44


Table 4.3: 1990 April Distribution of Wave Heights H and Wave Angles 9 (in percent) at 7-meter Contour from PUV Data Collecting Package.


H<1m lm3m
9 > 300 19.4 3.2 0.0 0.0 200 < 9 < 300 24.2 6.5 1.6 0.0 100 < 9 < 200 16.1 3.2 0.0 0.0 00 < 9 < 100 14.5 0.0 0.0 0.0
-100 < 9 < 00 0.0 1.6 0.0 0.0
-200 < 9 < -100 0.0 1.6 0.0 0.0
-300 < 9 < -200 0.0 0.0 0.0 0.0
9 < -300 8.1 0.0 0.0 0.0


Table 4.4: 1990 August Distribution of Wave Heights H and Wave Angles 9 (in percent) at 7-meter Contour from PUV Data Collecting Package.


H<1m lm3m
9 > 300 3.2 0.0 0.0 0.0 200 < 9 < 300 19.4 0.0 0.0 0.0 100 < 9 < 200 37.6 0.0 0.0 0.0 00 < 0 < 100 12.9 0.0 0.0 0.0
-100 <9O < 0* 7.5 0.0 0.0 0.0
-200 <9 < -100 15.1 0.0 0.0 0.0
-300 < 9 < -200 4.3 0.0 0.0 0.0
9 < -300 0.0 0.0 0.0 0.0


Table 4.5: 1990 September Distribution of Wave Heights H and Wave Angles 9 (in percent) at 7-meter Contour from PUV Data Collecting Package.


H<1m lm3m
9 > 300 2.5 0.0 0.0 0.0 200 < 9 < 300 29.4 2.5 0.0 0.0 100 < 9 < 200 42.0 3.4 0.0 0.0 00 < 9 < 100 9.2 1.7 0.0 0.0
-0 < 9 < 0" 7.6 0.8 0.0 0.0
-200 < 9 < -10* 0.8 0.0 0.0 0.0
-300 < 0 < -200 0.0 0.0 0.0 0.0
9 < -300 0.0 0.0 0.0 0.0






45


Table 4.6: 1990 October Distribution of Wave Heights H and Wave Angles 0 at 7-meter Contour from PUV Data Collecting Package.


H3m
0 > 300 0.0 0.0 0.0 0.0 200 < 0 < 300 13.7 8.6 0.0 0.0 100 < 0 < 200 35.9 19.7 0.0 0.0 00 < 0 < 100 7.7 7.7 0.9 0.0
-100 < 0 < 00 2.6 1.7 0.0 0.0
-200 <0 < -100 0.0 1.7 0.0 0.0
-300 < 0 < -200 0.0 0.0 0.0 0.0
0 < -300 0.0 0.0 0.0 0.0


Table 4.7: 1990 November Distribution of Wave Heights H and Wave Angles 0 at 7-meter Contour from PUV Data Collecting Package.

H3m
0 > 300 20.9 1.1 0.0 0.0
200 < 0 < 300 20.9 14.3 0.0 0.0 100 < 0 < 20* 8.8 5.5 0.0 0.0 00 < 0 < 100 0.0 4.4 0.0 0.0 -100 < 0 < 00 4.4 2.2 0.0 0.0
-200 <0 < -10" 5.5 6.6 0.0 0.0 -300 < 0 < -200 4.4 1.1 0.0 0.0
0 < -300 0.0 0.0 0.0 0.0


(in percent)


(in percent)


Table 4.8: 1990 December Distribution of Wave Heights H and Wave Angles 0 (in percent) at 7-meter Contour from PUV Data Collecting Package.


H3m
0 > 300 15.0 0.8 0.0 0.0 200 < 0 < 300 29.2 7.5 0.0 0.0 100 < 0 < 200 12.5 0.8 0.0 0.0 00 < 0 < 100 3.3 2.5 0.0 0.0
-100 < 0 < 00 4.2 5.8 0.0 0.0
-200 < 0 < -100 2.5 6.7 0.0 0.0
-300 < 0 < -200 5.0 0.0 0.0 0.0
0 < -300 4.2 0.0 0.0 0.0






46


Table 4.9: 1991 January Distribution of Wave Heights H and Wave Angles 0 at 7-meter Contour from PUV Data Collecting Package.

H<1m lm3m
9 > 300 16.1 0.8 0.0 0.0
200 < 9 < 300 22.6 1.6 0.0 0.0 100 < 9 < 200 19.4 4.8 0.0 0.0 00 < 9 < 100 5.7 0.0 0.0 0.0 -100 < 9 < 0" 2.4 0.0 0.0 0.0
-200 < 9 < -100 5.7 0.0 0.0 0.0 -300 < 9 < -200 12.9 0.0 0.0 0.0
0 < -30* 8.1 0.0 0.0 0.0


Table 4.10: 1991 February Distribution of Wave Heights H and Wave Angles 0 at 7-meter Contour from PUV Data Collecting Package.

H<1m lm3m
0 > 300 9.8 1.2 0.0 0.0
200 < 9 < 300 4.9 4.9 0.0 0.0 100 < 9 < 200 28.1 12.2 1.2 0.0 00 < 9 < 100 6.1 0.0 0.0 0.0 -100 < 9 < 00 6.1 4.9 0.0 0.0
-200 < 9 < -100 7.3 2.4 0.0 0.0 -300 < 9 < -200 3.7 0.0 0.0 0.0
9 < -300 7.3 0.0 0.0 0.0


Table 4.11: 1991 March Distribution of Wave Heights H and Wave Angles 0 7-meter Contour from PUV Data Collecting Package.


H<1m lm3m
9 > 300 0.8 0.0 0.0 0.0 200 < 9 < 300 3.3 0.8 0.0 0.0 100 < 9 < 200 17.9 12.2 0.0 0.0 00 < 9 < 100 23.6 3.3 0.0 0.0
-100 < 9 < 00 5.7 0.0 0.0 0.0
-200 < 0 < -100 13.0 1.6 0.0 0.0
-300 < 9 < -20" 13.8 1.6 0.0 0.0
9 < -30* 2.4 0.0 0.0 0.0


(in percent)


(in percent)


(in percent) at







47


Table 4.12: 1991 April Distribution of Wave Heights H and Wave Angles 9 (in percent) at 7-meter Contour from PUV Data Collecting Package.


H3m
9 > 300 2.6 0.0 0.0 0.0 200 < 0 < 300 0.9 0.0 0.9 0.0 100 < 9 < 200 22.4 10.4 0.9 0.0 00 < 9 < 100 16.4 0.9 0.9 0.0
-10* < 9 < 0* 6.9 4.3 0.9 0.0
-200 < 9 < -100 15.5 1.7 0.0 0.0
-300 < 9 < -20* 9.5 0.0 0.0 0.0
9 < -30* 5.2 0.0 0.0 0.0


Table 4.13: 1991 May Distribution of Wave Heights H 7-meter Contour from PUV Data Collecting Package.


and Wave Angles 9 (in percent) at


H3m
9 > 300 0.0 0.0 0.0 0.0 200 < 9< 300 0.0 0.9 0.0 0.0 100 < 9 < 200 10.5 2.6 0.0 0.0 00 < 9 < 100 14.0 0.0 0.0 0.0
-100 < 9< 00 17.5 6.1 0.0 0.0
-20" < 0 < -10" 33.3 3.5 0.0 0.0
-30" < 0 < -20" 7.9 0.0 0.0 0.0
9 < -30" 3.5 0.0 0.0 0.0






48


from the southeast. Even more pronounced was the increased wave heights occurring during the winter months, as well as a few isolated events during March and April of both years.

The Florida Coastal Data Network (CDN) (Wang et al., 1990) involved a compilation of long term wave statistics for stations located on both coasts of Florida. One of the stations nearest to the Jupiter Inlet study area was located at West Palm Beach. The West Palm Beach station was deployed in a depth of 9.0 m at LAT: 26 42' 07" N and LONG: 80 01' 42" W, from the period 1984 to 1989. Table 4.14 shows that the average monthly wave height decreased during the summer months (June and August) and the standard deviation and maximum monthly wave height appeared to follow a similar pattern.

As expected, the average monthly wave period became lower from May through August while the standard deviation of the period showed no seasonal variation.

An alternative long term data source option when investigating the wave climate in the vicinity of a coastal project involved the use of the twenty years of WIS hindcast wave data compiled from historical meteorological records by the U.S. Army Corps of Engineers.

Table 4.15 presents the monthly variation in WIS wave statistics compiled over a twenty year period in the vicinity of Jupiter Inlet. It is shown that the average monthly wave heights are smaller from June through August. The standard deviation and maximum wave heights adhere to a similar pattern. During the fall months, the increased wave energy was probably the result of calm summers interrupted by tropical storms followed by northeasters in November through March. These northeasters can lie off the east coast for long periods of time, creating large wave conditions. Tables 4.16 through 4.27 indicate that during the months of July through October, a larger percentage of the waves were propagating towards shore from the southeast.

Furthermore, it was obvious that the waves were smaller from May through September. The highest percentage of waves were less than 1.0 m in height. However, during the winter months, waves were hindcast exceeding 3.0 m in height.







49


Table 4.14: Wave Parameters Collected from West Palm Beach CDN Station.

MONTH H,(mean) H,(std dev) H.,(max) T(mean) T(std dev)
(meter) (meter) (meter) (sec) (sec)
January 0.58 0.35 1.70 7.66 2.17 February 0.46 0.28 1.44 7.32 2.35
March 0.50 0.30 1.44 7.11 2.55 April 0.43 0.27 1.29 8.15 2.54 May 0.39 0.26 1.13 6.55 2.25 June 0.31 0.21 0.97 6.75 2.18 July 0.24 0.16 0.89 6.69 1.84
August 0.24 0.16 0.80 5.93 2.16 September 0.46 0.26 1.21 8.16 2.84 October 0.65 0.31 1.90 8.42 2.68 November 0.59 0.36 1.96 6.47 2.14 December 0.60 0.38 1.88 7.49 2.65






Table 4.15: Wave Parameters Collected from WIS Station # 156.

MONTH H,(mean) H,(std dev) H,(max) T(mean) T(std dev)
(meter) (meter) (meter) (sec) (sec)
January 0.65 0.40 2.66 6.64 2.70 February 0.57 0.33 2.04 6.35 2.86
March 0.58 0.36 2.52 6.67 3.17 April 0.57 0.34 1.77 6.04 3.15 May 0.43 0.28 1.61 4.56 2.17 June 0.37 0.25 1.96 4.12 2.02 July 0.30 0.20 1.59 3.66 2.43
August 0.29 0.20 1.72 3.51 2.31 September 0.51 0.37 2.80 4.93 2.31 October 0.66 0.40 2.10 5.82 2.12 November 0.72 0.42 2.45 5.99 2.26 December 0.73 0.37 1.98 6.97 2.67






50


Table 4.16: January Distribution of Wave Heights H and Wave Angles 0 (in percent) at 10-meter Contour from WIS Hindcast for Period 1956-1975.


H3m
9 > 300 4.4 0.0 0.0 0.0 200 < 9 < 300 2.7 0.0 0.0 0.0 100 < 9 < 200 19.0 2.5 0.0 0.0 00 < 9 < 100 36.5 27.0 3.3 0.6
-10 < 9 < 00 3.4 0.0 0.0 0.0
-200 <9 < -100 0.0 0.0 0.0 0.0
-300 < 9 < -200 0.0 0.0 0.0 0.0
9 < -30* 0.6 0.0 0.0 0.0


Table 4.17: February Distribution of Wave Heights H and Wave Angles 9 (in 10-meter Contour from WIS Hindcast for Period 1956-1975.

H<1m 1m3m
9 > 300 3.9 0.0 0.0 0.0
200 < 9 < 30* 4.9 0.0 0.0 0.0 100 < 0 < 200 19.7 1.7 0.0 0.0 00 < 9 < 10* 37.1 25.5 3.1 0.6 -10* < 0 < 00 2.9 0.0 0.0 0.0
-200 < 9 < -100 0.0 0.0 0.0 0.0 -300 < 9 < -20* 0.0 0.0 0.0 0.0
9 < -300 1.2 0.0 0.0 0.0


percent) at


Table 4.18: March Distribution of Wave Heights H and Wave 10-meter Contour from WIS Hindcast for Period 1956-1975.


Angles 9 (in percent) at


H<1m lm3m
9 > 300 4.8 0.1 0.0 0.0 200 < 9 < 30* 2.2 0.2 0.0 0.0 100 < 9 < 200 24.0 1.8 0.1 0.1 00 < 9 < 100 37.3 25.5 2.2 0.2
-100 < 9 < 00 0.5 0.0 0.0 0.0
-200 < 9 < -100 1.0 0.0 0.0 0.0
-300 < 0 < -200 0.0 0.0 0.0 0.0
9 < -300 0.6 0.0 0.0 0.0






51


Table 4.19: April Distribution of Wave Heights H and Wave Angles 8 (in 10-meter Contour from WIS Hindcast for Period 1956-1975.

H3m
8 > 300 2.3 0.0 0.0 0.0
200 < 0< 300 2.4 0.0 0.0 0.0 100 < 0 < 200 29.4 0.0 0.0 0.0 00 < 0 < 100 36.6 26.4 2.1 0.0 -10 < 0 < 00 0.4 0.0 0.0 0.0
-1200 < 8 < -10* 0.0 0.0 0.0 0.0 -300 < 8 < -200 0.0 0.0 0.0 0.0
0 < -300 0.5 0.0 0.0 0.0


percent) at


Table 4.20: May Distribution of Wave Heights H and Wave Angles 0 (in percent) at 10-meter Contour from WIS Hindcast for Period 1956-1975.


H3m
8 > 300 2.7 0.0 0.0 0.0 200 < 8 < 300 7.9 0.0 0.0 0.0 100 < 0 < 20* 14.7 0.0 0.0 0.0 00 < 8 < 100 55.5 14.8 0.6 0.0
-10 < 8 < 00 3.4 0.0 0.0 0.0
-200 < 0 -10* 0.0 0.0 0.0 0.0
-300 < 8 < -20* 0.0 0.0 0.0 0.0
8 < -300 0.4 0.0 0.0 0.0


Table 4.21: June Distribution of Wave Heights H and Wave Angles 8 (in percent) at 10-meter Contour from WIS Hindcast for Period 1956-1975.


H<1m lm3m
8 > 300 3.7 0.0 0.0 0.0 200 < 0< 300 2.4 0.0 0.0 0.0 100 < 8< 200 11.5 0.2 0.0 0.0 00 < 8 < 100 67.3 10.4 1.0 0.2
-100 < 8 < 00 2.9 0.0 0.0 0.0
-200 < 0 < -100 0.0 0.0 0.0 0.0
-300 < 0 < -20" 0.0 0.0 0.0 0.0
0 < -300 0.4 0.0 0.0 0.0






52


Table 4.22: July Distribution of Wave Heights H and Wave Angles 9 (in percent) at 10-meter Contour from WIS Hindcast for Period 1956-1975.


H3m
9 > 300 1.0 0.0 0.0 0.0 200 < 9 < 30* 0.7 0.0 0.0 0.0 100 < 9 < 200 18.6 0.0 0.0 0.0 00 < 9 < 10* 69.6 3.5 0.0 0.0
-100 < 9 < 00 7.4 0.0 0.0 0.0
-200 < 9 < -100 0.0 0.0 0.0 0.0
-30* < 9 < -200 0.0 0.0 0.0 0.0
9 < -300 0.3 0.0 0.0 0.0


Table 4.23: August Distribution of Wave Heights H and Wave Angles 9 (in percent) at 10-meter Contour from WIS Hindcast for Period 1956-1975.


_H3m
9 > 300 2.7 0.0 0.0 0.0 200 < 9 < 300 1.2 0.0 0.0 0.0 100 < 9 < 200 20.1 0.0 0.0 0.0 00 < 9 < 100 67.0 3.4 0.0 0.0
-100 < 9 < 00 5.5 0.0 0.0 0.0
-200 < 9 < -100 0.0 0.0 0.0 0.0
-300 < 9 < -200 0.0 0.0 0.0 0.0
9 < -300 0.1 0.0 0.0 0.0


Table 4.24: September Distribution of Wave Heights H and Wave Angles 9 (in percent) at 10-meter Contour from WIS Hindcast for Period 1956-1975.


H3m
9 > 300 5.0 0.0 0.0 0.0 200 < 9 < 300 2.7 0.2 0.0 0.0 100 < 9 < 20* 15.2 0.2 0.0 0.0 00 < 9 < 100 53.2 16.7 1.8 0.2
-10* < 9< 00 4.3 0.3 0.0 0.0
-200 < 9 < -100 0.0 0.0 0.0 0.0
-300 < 0 < -200 0.0 0.0 0.0 0.0
9 < -300 0.3 0.0 0.0 0.0






53


Table 4.25: October Distribution of Wave Heights H and Wave Angles 9 (in 10-meter Contour from WIS Hindcast for Period 1956-1975.

H<1m lm3m
9 > 300 3.3 0.1 0.0 0.0
200 < 9 < 300 4.2 0.2 0.0 0.0 100 < 9 < 200 15.4 0.8 0.0 0.0 00 < 9 < 100 37.3 24.5 7.9 0.1 -100 < 9 < 00 5.5 0.6 0.0 0.0
-200 < 9 < -100 0.0 0.0 0.0 0.0 -300 < 9 < -200 0.0 0.0 0.0 0.0
9 < -300 0.2 0.0 0.0 0.0


percent) at


Table 4.26: November Distribution of Wave Heights H and Wave Angles 9 (in percent) at 10-meter Contour from WIS Hindcast for Period 1956-1975.


H<1m lm3m
9 > 300 3.8 0.1 0.0 0.0 200 < 9 < 300 4.4 0.0 0.0 0.0 100 < 0 < 200 13.1 2.9 0.0 0.0 00 < 0 < 100 32.7 31.9 6.8 0.4
-100 < 0 < 00 3.3 0.2 0.0 0.0
-200 < 9 < -10* 0.0 0.0 0.0 0.0
-300 < 9 < -20* 0.0 0.0 0.0 0.0
9 < -300 0.5 0.0 0.0 0.0


Table 4.27: December Distribution of Wave Heights H and Wave Angles 9 (in 10-meter Contour from WIS Hindcast for Period 1956-1975.

H<1m lm3m
9 > 300 2.3 0.0 0.0 0.0
200 < 9 < 300 1.9 0.1 0.0 0.0 100 < 9 < 200 22.3 3.7 0.0 0.0 00 < 9 < 100 29.6 32.7 5.5 0.2 -10* < 9 < 00 1.3 0.2 0.0 0.0
-200 < 9 < -100 0.0 0.0 0.0 0.0 -300 < 9 < --200 0.0 0.0 0.0 0.0
9 < -300 0.2 0.0 0.0 0.0


percent) at






54
Comparisons were made between the fifteen months of PUV data, five years of CDN data, and twenty years of WIS hindcast wave data. By superimposing plots of each of the different data sets, correlation between these different data sets could be examined in detail. Figure 4.1 shows that the monthly average wave height of the WIS data corresponds fairly well to that of the CDN data. Although the PUV wave data has larger waves on the average, there still exists a trend of smaller waves during the summer months. It is highly possible that the 1990-91 wave conditions at Jupiter Inlet were extraordinarily high and therefore, do not give a good representation of the normal annual wave climate. This is always the difficulty in comparing long term data with a single year. Figure 4.2 indicates that for all three data sets, there is less of a deviation in wave height during the summer months.

Once again, the WIS data corresponds well with the CDN data. The PUV data appears to have abrupt changes which should be expected due to the nature of the short-term data set. Similar to the first two tables, Figure 4.3 shows that the monthly maximum wave heights were larger during the winter months and smaller during the summer months. The WIS wave heights, which were compiled over a twenty year period were larger than the corresponding CDN data. Once again, there were indications that a potentially irregularly high wave climate was present in the vicinity of Jupiter Inlet in 1990-91. The maximum wave heights collected by the PUV station south of the inlet were in some cases, larger than those collected from the WIS hindcast wave data set which spans a twenty year period (1956 through 1975). Although the wave height characteristics of the CDN data set compared fairly well with the WIS hindcast wave data set, a minor anomaly was noted when comparing the wave period. According to Figure 4.4, there is a decrease in wave period during the summer months. This decrease was obvious when examining the WIS wave data. However, the CDN wave data showed a small increase from May through July. After a smaller average wave period in August, there was an abrupt increase in the wave period over the two months that followed.

















I .S


CD

- 1.2



ci



0.9
a




1-''
Ci:





0.




...... ..... ..



0.0)








JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV D3C MONTHS ............Ct on s 11
.. .. ...i~ s osn


Figure 4.1: Comparison of PUV, CDN, and WIS monthly average wave height.















0.5


I- 0.4



0.3






0.0



- ~0.2




0.0








JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEBC MON THS Pu li
...................C......ll.I)..
-. ... H-in


Figure 4.2: Comparison of PUV, CDN, and WIS monthly deviation of wave height.















3. 1)


2.\ & 2.0



(f)















0.5







JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
MONT 11 A












M D T S...................... con ontoe
_._ .._.. -- w s on in


Figure 4.3: Comparison of PUV, CDN, and WIS monthly maximum wave height.



































... ... .


N.
N.


JAN FEB MAR


I I I 1 I 1 1 1 APR MAY JUN JUL AUG SEP OCT NOV DEC


MONTHS


PUV DRIA
...................... C DI D A I
........HIS UnIn


Figure 4.4: Comparison of PUV, CDN, and WIS monthly average wave period.


10.0


LI





'I" U1-)


9.0




0.0)




7.0




6.0




5.0









3.0



2.0




l.a -


7
7






* /


7


'. N.


.7
7


7
/
/
/
/


0.0


CA






59
The PUV wave data appeared to dip before the summer months but then increased slightly and remained constant. Figure 4.5 included superimposed plots comparing the standard deviation of the wave period. The standard deviation of the wave period collected by the PUV station appeared to contain deflated values during the summer months and into October. Although the CDN data and WIS data were of the same magnitude, there seemed to be no distinct pattern present.

In conclusion, it appears that although the three data sources spanned over three different time periods, identical seasonal trends in the wave climate were shown for each data source. With a few exceptions, a calm wave environment was presented during the summer months, accompanied by a more active wave environment during the fall, winter, and spring of the year. Although there were some disagreements between the PUV wave data and the two long term wave data sets (CDN and WIS), as previously mentioned, these discrepancies should be expected.

4.2 WIS Computed Longshore Transport Data Even though it is useful to be aware of annual longshore transport rates in the vicinity of a coastal project, it would be far more advantageous to have knowledge of daily transport values. By incorporating twenty years of WIS hindcast wave data recorded every three hours with basic linear wave theory, twenty years of longshore transport values in the vicinity of Jupiter Inlet were produced. Generation of daily transport rates will aid in the evaluation of seasonal trends. The following is a detailed investigation of these seasonal trends occurring from 1956-1975 in the vicinity of Jupiter Inlet.

Table 3.1 presented the twenty years of longshore transport data which was calculated from the WIS hindcast wave data. The average net transport over the twenty year period was computed to be 230,000 cubic yards. The average annual southward and northward transports were 291,000 cubic yards and 61,383 cubic yards, respectively. Examining the gross drift over the twenty year period, the average annual percentage of longshore transport southward and northward was 81.2% and 18.8%, respectively. It appeared that the















3.5



(no 5
3.0 C) N


............
2.5



.... ... ... .....















-....H -. I
FU2., aty t w r






LLj
c:)


0) .5 I-I








JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC MONTHS ............Ct r
IS 0010 Figure 4.5: Comparison of PUV, CDN, and WIS monthly deviation of wave period.






61
WIS longshore transport data for 1975 may have been incomplete. However, there was no substantial reason to omit this year from the twenty year data set. Henceforth, the year 1975 was included with the other nineteen years of WIS transport data.

Once a good understanding of the annual longshore transport was established, a more detailed look was taken at these transport rates. In order to study seasonal trends that can arise as a result of a change in magnitude or direction of the longshore transport, individual years were examined. Figures 4.6-4.8 show both the daily and cumulative net longshore transport rates at Jupiter Inlet for the years 1963, 1964, and 1958, respectively. The year 1963 was deemed a "normal" year because the resulting net transport (232,033 cubic yards) closely resembled the average annual net longshore transport rate. On the contrary, the year 1964, was a less active year with the annual net transport a great deal smaller (146,029 cubic yards) than the average annual net. The year 1958 was just the opposite of the year 1964 in the since that the annual net transport was much greater (381,848 cubic yards) than the average annual rate.

Although these three years span the general transport spectrum, the seasonal trend of the longshore transport was obvious. During the first three months of the year, the transport rates were in the southward direction and their magnitudes at times, were quite large. However, in the following five months (April through August), the transport rates were much smaller and it appeared that the dominant direction of transport shifted from southward to northward. With the arrival of September, the longshore transport rates gradually increased with the dominant direction being from north to south. It should be noted how much effect a few isolated transport events have on the net transport for the year. For example, in 1958 almost all of the transport for the year occurred during the first part of January along with a few large southward transport events at the beginning of October and December. This supports the observation that the transport can be highly episodic, with a large percentage of the annual net transport resulting from a few major transport events such as northeasters or tropical storms.











62


60000 .


50000 40000 30000


-) 20000









-30000
40000


















a: -jooo
-20000

-E

D -30000


-0000-50000 -600000 31 62 93 124 15 186 217 246 2179 310 341 TIME (DATS)


400000




350000




300000
4
a:
CD
CL 250000 a:

200000




C: 450000




4 00000




50000





0 341 62 93 424 455 486 217 248 21 9 3410 344 T IME (DRYS) Figure 4.6: Daily and cumulative longshore transport rates generated from WIS hindcast wave data for the year of 1963 (positive assumed southward).










63


60000 50000 40000 30000


20000


30000


0


-30000


-20000


-30000


-40000


-50000


-60000


350000 300000 250000 200000 150000 100000 -


0 3' 62 93 124 S 1'86 2,27 240 2'79 30 341 T IME (DAYS)


Figure 4.7: Daily and cumulative longshore transport rates generated from WIS hindcast wave data for the year of 1964 (positive assumed southward).


L:)
CL

(n
z
a:



a:
CZ-


0 33 6 2 913 1124 1 5 1186 2317 240 279 3310 3421

T IME (OATS)
-1


400000


In




F


(-I


CL

a:J


50000


0













60000 50000 '000 30000 20000


*0000





-10000


-20000


-30000


-40000


-50000


-60000





400000


I I I I -


0 31 62 93 124 15 186 T IME (DAYS)


217 248 279 30 341


Figure 4.8: Daily and cumulative longshore transport rates generated wave data for the year of 1958 (positive assumed southward).


from WIS hindcast


-A



























0 3A 62 93 11 AS 1186 2'17 As 8 A- 310 231 T IME (DRYS)


(n


0



CD,

CL to
z
cr






cc C3


C3




C-) 0
(
z a:



Lii F
CC


350000 300000 250000 200000 150000 100000 50000 -


0






65

By dividing the twenty years of WIS transport data into months, a detailed analysis of the monthly transport characteristics was developed. Figure 4.9 gives a monthly comparison of the variability of the southward, northward, and net longshore transports. Because it was the dominant transport direction and all the large transport events were associated with this direction, the southward longshore transport duplicated the pattern of the net transport but at a slightly smaller magnitude. The northward transport on the other hand, had a much smaller impact on the net transport for any particular year. From January through December, the average monthly northward transport was steady, although it decreased during the summer months as did the southward transport. In fact, because the southward transport decreased so drastically during the months of June, July, and August, the average monthly transport for these three months was in the northward direction.

Another view of the monthly variation of longshore transport is given in Figure 4.10. This figure displays the variation in occurrence of southward, northward, and zero transport events. It was obvious that the number of occurrences of southward transport decreased and reached a low during the month of July. On the contrary, the occurrence of northward transport increased during the summer months, reaching a peak also during July. Additionally, the occurrence of days when the longshore transport was nonexistent increased during the summer months as well. It should be noted that although the occurrences of northward transport outnumbered the occurrences of southward transport during the months of June, July, and August, Figure 4.9 indicated that the average longshore transport was northward only during the months of July and August. Although there were more days when the transport was northward during the month of June, these northward transport rates were much smaller than those transport events in the southward direction. It should also be pointed out that the decrease in the monthly average transports during the summer months (Figure 4.9) was directly related to the increase in "zero transport" days over the same period of time (Figure 4.10).















70001)


50000 -- \






-3
V)/




(T- .00 \ s




20000 /
CIL

'(10) -



















JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEDC


SNET N T H. ...................... Na n
.... ... SOUTHWAno

Figure 4.9: Comparison of southward, northward, and net monthly variation of lonlgshore
transport.


























0(












ta
50 or)

Li








30








10 --- N

I-












JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV D EC
110111fl1












MONTHS .....SPMUOCHRNO M................-SE0


Figure 4.10: Comparison of seasonal variation of southward, northward, and zero longshore

transport.






68
Once a detailed analysis of the twenty years of WIS longshore transport data had been achieved, the possibility of creating synthetic longshore transport rates was investigated. The southward and northward annual net transport rates for all twenty years of WIS transport data were ranked in ascending order. A log-normal probability plot of these annual transport events was then produced. Figure 3.3 showed that both the southward and northward plots followed a straight line considerably well. It should be pointed out that the smallest transport rate in the southward plot is not in agreement with the other nineteen points. This stray value happened to occur in 1975 which is the same year that was earlier suspected of containing a lack of adequate data. Nevertheless, because both the southward and northward plots followed a straight line fairly well, this indicated that synthetic longshore transport rates could indeed be faithfully created. A further examination of the log-normal probability plots for monthly longshore transport magnitudes (southward and northward) are located in Appendix C.

4.3 Results of Generated Synthetic Longshore Transport Values

After twenty years of longshore transport values had been generated through the use of WIS wave data, it was established that a statistical description of the longshore transport could be achieved. Once the twenty years of daily longshore transport rates were divided into their respective months, the seasonal variability was investigated in detail. Furthermore, the twenty year statistics for each month were compiled and through sampling of these statistics, daily synthetic longshore transport values were generated. Because these synthetic transport values were arrived at through the use of twenty year statistics, not only would the seasonal variability resemble that of the twenty years of WIS longshore transport data but more importantly, longshore transport rates could be produced for any time span. The following is a presentation of the synthetic longshore transport values and a comparison of these values with the twenty years of WIS longshore transport rates. Table 4.28 presents twenty years of synthetic longshore transport values compiled by selecting at random, twenty different combinations of numbers.






69


Table 4.28: Estimated Synthetic Longshore Transport Values (Q, cubic yards per year) for 20 Year Period 1956-1975 (calculated from monthly statistics of WIS wave hindcast data).

REALIZATION Qnet Q.,outh Qnorth Qasoth Percentage of Gross Drift + Qnorth % South % North
1 222,787 280,456 -57,668 338,124 83.0 17.0 2 193,063 245,753 -52,690 298,443 82.4 17.6 3 304,748 368,600 -63,851 432,451 85.2 14.8 4 179,500 237,404 -57,904 295,308 80.4 19.6 5 198,153 247,589 -49,436 297,025 83.4 16.6 6 221,682 264,627 -42,945 307,572 86.0 14.0 7 201,222 253,781 -52,559 306,340 82.8 17.2 8 165,996 210,004 -44,007 254,011 82.7 17.3 9 274,772 313,157 -38,385 351,542 89.1 10.9 10 245,964 292,625 -46,661 339,286 86.3 13.7 11 169,401 236,854 -67,453 304,307 77.8 22.2 12 269,671 314,089 -44,418 358,507 87.6 12.4 13 224,036 284,865 -60,829 345,694 82.4 17.6 14 232,395 274,139 -41,744 315,883 86.8 13.2 15 198,325 238,182 -39,857 278,039 85.7 14.3 16 185,583 237,327 -51,744 289,071 82.1 17.9 17 270,116 314,271 -44,155 358,426 87.7 12.3 18 226,118 279,802 -53,683 333,485 83.9 16.1 19 194,732 244,214 -49,482 293,696 83.2 16.8 20 224,710 268,956 -44,246 313,202 85.9 14.1
AVERAGE 220,149 270,335 -50,186 84.2 15.8






70

The annual net transport over the twenty year period was 220,149 cubic yards, an average annual net transport rate 4.5% lower than that calculated from the twenty years of WIS transport data (230,779 cubic yards). The average annual southward and northward transports were 270,335 cubic yards and 50,186 cubic yards, respectively. These annual average transports (southward and northward) when compared to the annual average WIS transport rates, were slightly lower. The annual average synthetic transport in the southward direction was 7.2% lower than the average annual WIS transport rate. Similarly, the annual average synthetic transport in the northward direction was 18.2% lower than the annual average WIS transport rate. It should be pointed out that the twenty years of synthetic transport data were randomly selected and that the averaged annual values were very sensitive to the selection of these random numbers. Two additional twenty year synthetic longshore transport data sets were generated with the average annual net transport rates equating 235,758 cubic yards and 214,877 cubic yards. Therefore, further random number combinations may have resulted in twenty years of synthetic transport data with an average greater than or less than that produced by the twenty years of WIS transport data.

Referring to Table 4.28, the distribution of gross drift over the twenty year period for the southward and northward directions was 84.2% and 15.8%, respectively. It was found that the gross drift in the southward direction was 3.7% higher than the southward gross drift produced from the WIS transport data. However, the gross drift in the northward direction was 16.0% lower than the gross drift obtained from the WIS transport data. Further examination of the synthetic transport results, concluded that although a larger percentage of the gross drift was in the southward direction, the magnitude of these transport values remained considerably lower. The fact that the average annual transports were lower than those generated from the WIS transport data was very significant. Even though the synthetic southward annual average was 7.2% lower than that obtained from the WIS trans-






71

port values, the northward annual average was 18.2% lower. Thus, the result in the annual average net transport for the twenty years of synthetic generated data was within 4.5% of the average annual net produced from the WIS transport data.

It should be pointed out that the annual net synthetic transport rates did not have as great of range as did the WIS transport rates. The WIS transport rates contained one annual net transport over 400,000 cubic yards and four others over 300,000 cubic yards while the synthetic annual net transport rates included only one over 300,000 cubic yards. Similarly, the WIS transport rates contained two annual net transports under 100,000 cubic yards while the synthetic annual net transport rates included none lower than 150,000 cubic yards.

With an overview of the twenty years of synthetic generated transport values completed, an investigation of the seasonal trends corresponding to particular years followed. Figures 4.11-4.13 shows the synthetically generated daily and cumulative net longshore transport rates for three different years. These three particular years were selected because they presented not only a "normal" year, but also years of extreme transport activity or inactivity. The annual net transport rates of these three years also compared well with their counterparts in Figures 4.6- 4.8, making the comparison between WIS transport years and synthetically generated transport years much easier. Figure 4.11 gives the daily and cumulative longshore transport rate distribution for a year which would be considered a "normal" year. Figure 4.12 presents the same two plots for a year which best represents a "calm" year. Figure 4.13 once again shows the same two plots but for a very active year.

The same seasonal trend which was present in the annual plots of the WIS longshore transport values was once again present in the synthetically generated yearly transport distribution. As expected, there was an obvious decrease in the transport during the summer months. This was best seen in Figure 4.12 where during the months of May through August, the net transport switched from a southward to a northward direction. Increased southward transport rates during the winter months were also very noticeable. Figure 4.13 gave the










72


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0 31 62 93 121 AS 1286 217 214 279 320 --1 T IME (DAYS) Figure 4.11: Example #1 of a single realization of daily and cumulative synthetic longshore transport rates (positive assumed southward).










-73


60000 50000


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-30000-110000-50000 -600000 31 62 93 124 1S 186 217 248 279 310 3q1 TIME (DAYS) 4100000




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a:

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0 31 62 93 12 As5 186 217 261 279 310 3111 TIME (DAYS)


Figure 4.12: Example #2 of a single realization of daily and cumulative synthetic longshore transport rates (positive assumed southward).










74


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Figure 4.13: Example #3 of a single realization of daily and cumulative synthetic longshore transport rates (positive assumed southward).


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75

best example of this phenomena. The presence of two large southward transport events occurring within a couple of days of one another, resulted in an annual net transport rate which was extremely large (304,748 cubic yards). In fact, these two large transport rates in September, coupled with a few large events during the months of November and March, made up a large percentage of the transport for that year. As previously mentioned, this coincides with the observations that a large percentage of the annual net transport was a result of a handful of highly episodic events such as northeasters or tropical storms.

In comparing the seasonal variation of the WIS longshore transport values to the synthetic longshore transport value, the most noted difference between the two was the variation of the transport over a period of days. Because the WIS transport data was generated from actual meteorological conditions, there appeared to be a more gradual increase of decrease in the longshore transport events. However, because the synthetic transport data was a result of the statistics of the WIS transport data, more abrupt changes occurred on a daily basis, as expected. On any particular day, the longshore transport rate was independent of the longshore transport rate occurring the previous day. A prime example of this drastic change in the longshore transport rate was observed during the third month in Figure 4.13. A southward transport rate of approximately 24,000 cubic yards was followed the next day by a northward transport rate of approximately 4,000 cubic yards. In comparing the "normal" synthetic transport year to the "normal" WIS transport year, although the large transport events occurred during different days, in both cases, they appeared to take place during the same months. In both Figures 4.6 and 4.11, the summer months were associated with small transport events. The same can be said when comparing the activity of low and high synthetic transport years with their corresponding WIS transport years (1964 and 1958, respectively). Although it is plausible that a correlation between successive synthetically generated transport days could be introduced into the simulation model, this detail does not seem justified in the scope of this thesis.






76

Once the seasonal trends of the synthetic longshore transport rates had been analyzed and compared to the actual WIS transport rates, the examination of these seasonal trends was taken a step further. Figure 4.14 presents the daily and cumulative longshore transport rates for a twenty year period. This particular random number combination was the same combination used to generate the "normal" one year longshore transport simulation in Figure 4.11. Note how the cumulative net transport distribution resembles a staircase. Furthermore, it should be pointed out how the southward longshore transport rates are practically a mirror image of the net transport, while the northward transport has little variation but continually increases. The seasonal trend that exists over the same twenty year period can be best seen by looking at the daily transport distribution. Note the calm regions separating the areas containing large transport events. Although some years may be more "spiky" than others, there still remains a common lull during the summer months.

Further examination of the seasonal variation in longshore characteristics was performed by dividing the twenty years of synthetic transport rates into months. Figure 4.15 presents a monthly comparison of the variability of the southward, northward, and net synthetic longshore transports. The variation in the net transport was most obvious during July and August when the average net longshore transport rate switched from a southward to a northward direction. Also noticeable was the abrupt decrease in the average southward transport during the summer months followed by a drastic increase during September and November. On the contrary, the average northward synthetic transport rates increased from January through April, had a small decrease during the summer months, and once again increased during September and November. Figure 4.15 corresponds considerably well with Figure 4.9 which was a similar comparison using the WIS longshore transport data. Both figures showed that the average southward transport rates followed the same pattern as did the net transport rates with the exception that the southward transport rates were greater in magnitude. It should be pointed out that both figures have an average net transport rate in the northward direction during July and August.












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Figure 4.14: A single realization of daily and cumulative twenty year synthetic longshore transport rates (positive assumed southward).


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77






















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I I I I I III JRN FEB MAR RPR MRT JUN JUL RUG




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NET
NORTHWARD SOUTHWARD


Figure 4.15: Comparison of southward, northward, and net synthetic longshore transport rates.


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79
A different perspective of the monthly variation of the longshore transport rates was presented in Figure 4.16. This figure shows the variation in occurrence of southward, northward, and zero transport events. Figure 4.16 corresponds very well with Figure 4.10 which is a similar comparison but with the use of actual WIS transport data. In both figures, the month of July was very significant. The number of occurrences of southward transport starts out high in the beginning of the year and gradually decreases, reaching a minimum number of occurrences during the month of July. Afterwards, the number of occurrences of southward transport once again increases. On the other hand, the number of occurrences of northward transport starts out low in January and gradually increases until July when it reaches a peak. It should be noted that during July when the number of occurrences of northward transport were a great deal larger than those in the southward direction, the corresponding average net transport rates were in the northward direction (Figure 4.15). It should also be pointed out that during the summer months, there was a noticeable increase in the number of days zero transport occurred. As a result, the average northward and southward longshore transport rates decreased during the summer, the average southward transport rate decreasing more drastically.

The final comparison of the synthetically generated longshore transport rates with those rates generated from the actual WIS data, was presented in Figure 4.17. This figure contains a log-normal probability plot of the annual northward and southward longshore transport events. These plots were the result of separating the twenty years of synthetic transport data into northward and southward directions, and subsequently ranking these numbers in ascending order.

4.4 Analysis of Sand Bypassing Simulations

With the development of synthetic longshore transport rates completed, a number of different sand bypassing simulations were produced. Combining the northward and southward sediment budgets (Figures 3.4 and 3.5) with the synthetic longshore transport data allowed the determination of the amount of sand accumulating at different locations within














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JAN FEB MR9 RPR MAY JUN JUL RUG SEP OCT NOV DEC MONTHS NORTHWARD
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longshore transport rates.






81


SOUTHWARD TRANSPORT





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NORTHWARD TRANSPORT


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82
the inlet system. Further investigation into the rate at which the downdrift beach eroded or accreted would provide the necessary criteria for developing an optimum sand bypassing schedule.

When renourishing depleted beaches downdrift of inlets, material within the inlet (flood shoal) or directly offshore of the inlet (ebb shoal), is commonly used. The primary goal is to avoid disturbing the natural processes of the tidal inlet system while at the same time, successfully replenishing the downdrift beach. One of the main concerns when undertaking a task of such magnitude is answering the question of whether there is enough sand available to continuously renourish the downdrift beach over a period of years. Although enough sand may be available to initially replenish the downdrift beach, this in no way guarantees that the same amount of material will be available for renourishment purposes in the future. There is also the concern of the present state of the downdrift beach. If the downdrift beach is eroding at an extensively high rate, then it is possible that the beach will reach a critically eroded state before it can once again be renourished. Thus, it is of the utmost importance to find a "common ground" where the downdrift beach can successfully be renourished, while minimizing any associated effects that may accompany the removal and placement of large quantities of sand.

The design criteria adopted when simulating different sand bypassing scenarios concerning the downdrift beach at Jupiter Inlet were the pumping rate, the "trap" size, and the pumping period. Although the size of the sand trap was of great importance, realistically speaking, the sand trap maintained by the Jupiter Inlet District is not of fixed dimensions and additional sand (within reason) can be obtained outside the trap "limits" if necessary. The period of time in which sand could be pumped onto the south beach at Jupiter Inlet was a very important issue as well. Due to the presence of nesting sea turtles during the summer months, (legally) beach nourishment is not permitted during the period of June 1 through October 31. This period of time is subsequently referred to as the non-pumping window and defined as the period of time in which no sand would be pumped on to the






83
south beach because of increased turtle nesting activity. The pumping rate also played an important role in ultimately finding the best sand bypassing schedule. By adjusting the pumping rate, an opportune bypassing rate could be formed in which enough would be placed on the south beach while avoiding removal of large quantities of sediment from within the tidal inlet system. By taking into account all three of these design criteria, an optimum bypassing schedule was possible.

The following is a detailed analysis involving a number of different bypassing scenarios. Various simulations were looked at not only from the perspective of examining the volume of sand on the south beach at Jupiter Inlet, but the quantity of sand dredged from the Jupiter Inlet District (JID) sand trap was also monitored. Each bypassing simulation was begun either before or after the "non- pumping" window. When renourishing the south beach annually, sand was pumped constantly for a period of twenty days. However, when renourishing the beach once every two years, constant pumping continued for a period of twenty-five days. Three different pumping rates were used in this investigation; 3500 cubic yards per day (70,000 cubic yards annual volume) which was considered an overabundance of sand being placed on the south beach (based on estimated sand budgets), 1700 cubic yards per day (34,000 cubic yards annual volume) which was a less than adequate amount of material, and 2250 cubic yards per day (45,000 cubic yards annual volume) which was deemed close to the optimum bypassing rate over a twenty day period at Jupiter Inlet. When renourishing the beach once every two years, these pumping rates would almost double and the bypassing would continue for an extra five days.

Five year bypassing simulations were first examined. When renourishing the beach before the non-pumping window, bypassing simulations were begun on day 60 of the first year. At this point, it is assumed that the JID trap had just been emptied and the renourishment of the south beach had just been completed. The critical beach condition was assumed to be the point in which local authorities decide that further erosion would cause considerable damage and that the beach should be renourished immediately. The critical beach condi-






84

tion was therefore assumed as the zero line in the figures presenting the volume of sand on the south beach over a period of time. Similarly, the zero line in the corresponding figure which presents the volume of sand in the JID trap over a period of time, was deemed the point at which time the trap was completely empty. Once a detailed analysis of various five year bypassing simulations was accomplished and the optimum bypassing schedule selected, the bypassing scheme was expanded to a twenty year period and a brief statistical analysis was conducted. By looking at ten totally different twenty year bypassing simulations, the success rate of the optimum bypassing schedule previously selected could be estimated.

Before going any further, it should be stressed that these bypassing simulations are somewhat limited by the sediment budgets that are incorporated into this bypassing technique. Because many of the values arrived at in the southward sediment budget are only estimates and because the northward sediment budget was a result of crude modifications of these estimates, it was possible that the technique used in this thesis may not be exactly representative of what would actually occur at Jupiter Inlet. However, this technique should provide a practical working basis, and if the actual values for both the southward and northward sediment budgets were known more exactly, the framework established in this bypassing simulation technique would still be quite adequate and highly useful.

In beginning the actual analysis of the results, Figure 4.18 presents the twenty years of net transport in the vicinity of Jupiter Inlet and corresponding amount of sand dredged onto the south beach. It would be expected that large transport years would be accompanied by large amounts of sand being dredged onto the south beach. However, this is definitely not the case. It appears the south beach at Jupiter Inlet was renourished only when it had reached a critical point. Often obstacles are encountered when attempting to gain approval to dredge within the inlet or mine the outer shoal. Consequently, this can become a long process. With unsuccessful management of the downdrift beach in the past, Jupiter Inlet was geared to a more continuous pattern of sand bypassing options. A long term average view of the dredging process is given in Figure 4.19, which presents plots of both the cumu-






85
lative amount of sand being dredged onto the south beach along with the cumulative annual net volume of sand being transported in the littoral drift. After extending a best fit line through both plots, comparison of the slopes of these lines indicated that on the long term average, 20.5% of the annual net longshore transport was being placed on the south beach at Jupiter Inlet. Assuming that the amount placed on the south beach is compatible with the erosion rate, then this agrees well with the southward sediment budget which predicted that 18.6% of a southward sediment transport event would be eroded from the south beach.

Figures 4.20 and 4.21 present post and pre- window five year simulations of the amount of sand contained in the JID trap as well as the volume of sand present on the south beach. In both figures, 90,000 cubic yards of sand is being placed on the south beach over a twentyfive day period once every two years. Whether pumping before the non-pumping window or afterwards, in both cases such a large amount of sand was pumped over such a small period of time that the JID trap was completely emptied and material had to be taken from elsewhere. It appeared that as far as the south beach was concerned, it would be more beneficial to pump before the non-pumping window. Although a larger quantity of material would have to be taken from the a different sector of the inlet, the south beach would remain built out for a longer period of time. Because there were problems with adopting either one of these bypassing scenarios, a more continuous bypassing schedule was investigated.

Annual sand bypassing simulations were first examined for the case of pumping after the non-pumping window. Figures 4.22-4.24 present post-window bypassing scenarios for pumping rates of 1700, 3400, and 2250 cubic yards per day. In Figure 4.22, 34,000 cubic yards of sand was placed on the south beach over a period of twenty days. It is clear that the south beach was showing a deficit of sand. Simultaneously, there was an excess amount of sediment accumulating in the JID trap within the inlet. Thus, a larger pumping rate was sought. Figure 4.23 showed the volume of sand that would be located on the south beach when a pumping rate of 70,000 cubic yards per year was adopted. The results given in Figure 4.23 were just the opposite of those seen in the previous figure. An overabundance







86



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ANNUAL WIS TRANSPORT o DREDGE VOLUME


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1950 1955 1960 1965 1970 1975

TIME(YEARS)


Figure 4.18: Comparison of twenty years of WIS net transport rates with the amount of sand dredged onto the south beach at Jupiter Inlet.

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5;


CUMULATIVE WIS TRANSPORT *
4 CUMULATIVE DREDGE VOLUME 0





2.


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------------------------------------- ------


1950 1955 1960 1965 1970 1975

TIME(YEARS)


Figure 4.19: Comparison of the cumulative twenty years of WIS net transport rates with the cumulative amount of sand dredged onto the south beach at Jupiter Inlet.


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