UFL/COEL98/009
STATISTICAL CHARACTERISTICS OF
FLORIDA SHORELINE CHANGES
by
Jie Cheng
Thesis
1998
STATISTICAL CHARACTERISTICS OF FLORIDA SHORELINE CHANGES
By
JIE CHENG
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
1998
ACKNOWLEDGEMENTS
I would like to express my gratitude to my advisor and supervisory committee
chairman, Dr. Robert G. Dean, for his guidance and support for this thesis. Without his
help and inspiration, this work could never have been done. I would also like to thank Dr.
Daniel M. Hanes, Dr. Hsiang Wang and Dr. Ashish J. Mehta (who later represented Dr.
Wang who was on leave) for serving on my committee.
My thanks also go to Becky, Sandra, Lucy, Helen, Cynthia, Laura, John and Subarna
for their support.
I must also thank all my friends in the Coastal and Oceanographic Engineering
Department for their help in difficult times.
I am also indebted to the Florida Sea Grant Program which provided support for this
work under Grant R/CS35. The Bureau of Beaches and Coastal Systems of the Florida
Department of Environmental Protection provided the shoreline position data used in this
study. These high quality data contributed greatly to the results developed in this thesis.
Finally, I want to thank my husband, Xueliang, for his help and encouragement.
TABLE OF CONTENTS
ACKNOWLEDGEMENTS .................................................................... ii
LIST OF TABLES ............................................................................... v
LIST OF FIGURES ............................................................................. vi
ABSTRACT .......................................................... ...................... xii
CHAPTERS
1 INTRODUCTION .............................................................. 1
1.1 Purpose of the Study .................................................................... 1
1.2 Florida Shoreline Position Data Set ...................................... ............ 2
1.3 Literature Review ....................................................................... 3
1.4 Report Organization .................................................................... 7
2 FLORIDA'S COAST SETTING ................................................... .......... 8
2.1 Longshore Sediment Transport Characteristics ........................................ 8
2.2 Sediment Characteristics ............................................................... 9
2.3 Sea Level Change ......................................................................... 10
2.4 Human Effects ................................................................................. 10
3 ANALYSIS PROCEDURES .................................................................. 11
3.1 Shoreline Change Trend Analysis ..................................................... 11
3.1.1 Shoreline Change Trend ........................................................... 12
3.1.2 Sand Conservation ................................................. ............... 12
3.1.3 Total Net Longshore Sediment Transport Difference ........................ 15
3.1.4 Local Crossshore Sediment Transport ......................................... 16
3.2 Analysis of Standard Deviation of Shoreline Deviations ............................. 17
3.2.1 Definition of Standard Deviations of Shoreline Deviations ................ 18
3.2.2 Model consideration ............................................................... 19
3.2.3 Analysis of Normalized Standard Deviations ................................... 19
3.2.4 Analysis of Unsealed Standard Deviations ................................... 24
3.2.5 Model Test .......................................................... ........ 26
3.2.6 The Influence of Inlets ............................................................. 27
4 RESULTS AND DISCUSSION ............................................................. ...28
4.1 Shoreline Change Rates and Sediment Transport .................................... 28
4.1.1 East Coast Counties and Islands ...................................... ......... 28
4.1.2 West Coast Counties and Islands ................................................. 34
4.2 Standard Deviation of Shoreline Deviation .............................................. 39
4.2.1 The effects of Inlets on Standard Deviations .................................... 39
4.2.2 Normalized Standard Deviations ...................................... .......... 39
4.2.3 Unscaled Standard Deviations ................................................ 41
5 SUMMARY AND CONCLUSIONS ..................................................... 107
REFERENCES .................................................................................... 110
BIOGRAPHICAL SKETCH .................................................................. 112
iv
LIST OF TABLES
41: Summary of shoreline change rates for Florida's east coast sandy beach
counties ................................................................... 43
42: Summary of shoreline change rates for Florida's east coast .........................43
43: Summary of shoreline change rates for Florida's east coast island by island ......44
44: Summary of shoreline change rates for Florida's west coast sandy beach
counties ......................................... ......... ............ ............. 46
45: Summary of shoreline change rates for Florida's west coast .........................46
46: Summary of shoreline change rates for Florida's northwest coast island by
island ............................................................................................................. . 47
47: Summary of shoreline change rates for Florida's southwest island by island ......48
48: Summary of the KolmogorovSmirnov test for goodness of model fitting to
normalized standard deviations for Florida's east coast sandy beach counties .... 50
49: Summary of the KolmogorovSmirnov test for goodness of model fitting to
normalized standard deviations for Florida's west coast sandy beach counties .... 51
410: Summary of the parameters for the models fitting to normalized standard
deviations for Florida's east coast sandy beach counties .............................52
411: Summary of the parameters for the models fitting to normalized standard
deviations for Florida's west coast sandy beach counties .............................53
412: Summary of the parameters for the models fitting to unsealed standard
deviations for Florida's east coast sandy beach counties ...............................54
413: Summary of the parameters for the models fitting to unsealed standard
deviations for Florida's west coast sandy beach counties .........................55
LIST OF FIGURES
11: Numbers of stations for which shoreline position data are available along
Florida's coast sandy beach counties ..................................... ............. 4
31: Coordinate system for sediment transport ..............................................13
32: Illustration of the iterative process .........................................................23
41: Longterm shoreline change rates along the east coast of Florida. These change
rates are determined by the best least squares fit procedures for the complete
record of data availability ................................................................................56
42: Examples of shoreline positions versus time at individual monument locations
for Florida's east coast sandy beach counties ............................................57
43: Histograms of longterm shoreline change rates in meters per year for the east
coast of Florida ................................................................................ 58
44: Average shoreline change over time for Florida's east coast sandy beach
counties .......................................................... ............... ......... 60
45: Total net longshore sediment transport difference along Florida's east coast.
Based on consideration of zero net longterm crossshore sediment transport ....61
46: Local crossshore sediment transport along Florida's east coast. Based on
considering a uniform gradient of net longshore sediment transport. Gradient
is based on previous estimates of net transport at the northern and southern
lim its of region considered ..............................................................62
47: Longterm shoreline change rates along Florida's northwest coast. These
change rates are determined by the best least squares fit procedures
procedures for the complete record of data availability .....................................63
48: Longterm shoreline change rates along Florida's southwest coast. These
change rates are determined by the best least squares fit procedures
procedures for the complete record of data availability ...................................64
49: Examples of shoreline positions versus time at individual monument locations
for Florida's west coast sandy beach counties .........................................65
410: Histogram of longterm shoreline change rates in meters per year for the
northwest coast of Florida .....................................................................66
411: Histogram of longterm shoreline change rates in meters per year for the
south estcoast of Florida ....................................................................................67
412: Total net longshore sediment transport difference along Florida's northwest
coast. Based on consideration of zero net longterm crossshore sediment
transport ............................................................................................................ 68
413: Total net longshore sediment transport difference along Florida's southwest
coast. Based on consideration of zero net longterm crossshore sediment
transport ................................................................................................................69
414: Local crossshore sediment transport along Florida's northwest coast. Based
on considering a gradient of +0.4 m3/m/yr net longshore sediment transport
over the limits of the region considered .....................................................70
415: Local crossshore sediment transport along Florida's southwest coast. Based
on considering a zero gradient of net longshore sediment transport over the
limits of the region considered ............................................................ 71
416: Shoreline change rates and unsealed standard deviations along the coastline
of Nassau County, 18571991. The bold segment of the standard deviation
curve in the lower panel represents the shoreline portions considered to be
acceptable for analysis of standard deviations ........................................72
417: Shoreline change rates and unsealed standard deviations along the coastline
of Duval County, 18531990. The bold segment of the standard deviation
curve in the lower panel represents the shoreline portions considered to be
acceptable for analysis of standard deviations .................................... 73
418: Shoreline change rates and unsealed standard deviations along the coastline
of St. Johns County, 18581992. The bold segment of the standard deviation
curve in the lower panel represents the shoreline portions considered to be
acceptable for analysis of standard deviations .....................................74
419: Shoreline change rates and unsealed standard deviations along the coastline
of Flagler County, 18721987. The bold segment of the standard deviation
curve in the lower panel represents the shoreline portions considered to be
acceptable for analysis of standard deviations ........................................75
420: Shoreline change rates and unsealed standard deviations along the coastline
of Volusia County, 18731989. The bold segment of the standard deviation
curve in the lower panel represents the shoreline portions considered to be
acceptable for analysis of standard deviations .........................................76
421: Shoreline change rates and unsealed standard deviations along the coastline
of Brevard County, 18741993. The bold segment of the standard deviation
curve in the lower panel represents the shoreline portions considered to be
acceptable for analysis of standard deviations .........................................77
422: Shoreline change rates and unsealed standard deviations along the coastline
of Indian River County, 18801993. The bold segment of the standard
deviation curve in the lower panel represents the shoreline portions
considered to be acceptable for analysis of standard deviations ..................78
423: Shoreline change rates and unsealed standard deviations along the coastline
of St. Lucie County, 18601989. The bold segment of the standard deviation
curve in the lower panel represents the shoreline portions considered to be
acceptable for analysis of standard deviations .......................................79
424: Shoreline change rates and unsealed standard deviations along the coastline
of Martin County, 18831986. The bold segment of the standard deviation
curve in the lower panel represents the shoreline portions considered to be
acceptable for analysis of standard deviations .......................................80
425: Shoreline change rates and unsealed standard deviations along the coastline
of Palm Beach County, 18831991. The bold segment of the standard
deviation curve in the lower panel represents the shoreline portions
considered to be acceptable for analysis of standard deviations ....................81
426: Shoreline change rates and unsealed standard deviations along the coastline
of Broward County, 18831986. The bold segment of the standard deviation
curve in the lower panel represents the shoreline portions considered to be
acceptable for analysis of standard deviations .........................................82
427: Shoreline change rates and unsealed standard deviations along the coastline
of Dade County, including period of extensive nourishment (18511986).
The bold segment of the standard deviation curve in the lower panel
represents the shoreline portions considered to be acceptable for analysis
of standard deviations .................................................................................. ... 83
428: Shoreline change rates and unsealed standard deviations along the coastline
of Dade County, prior to period of extensive nourishment (18511973).
The bold segment of the standard deviation curve in the lower panel
represents the shoreline portions considered to be acceptable for analysis
of standard deviations ........................................ ..... ................................84
429: Shoreline change rates and unsealed standard deviations along the coastline
of Escambia and Santa Rosa Counties, 18561978. The bold segment of the
standard deviation curve in the lower panel represents the shoreline portions
considered to be acceptable for analysis of standard deviations ....................85
430: Shoreline change rates and unsealed standard deviations along the coastline
of Okaloosa County, 18711990. The bold segment of the standard
deviation curve in the lower panel represents the shoreline portions
considered to be acceptable for analysis of standard deviations ....................86
431: Shoreline change rates and unsealed standard deviations along the coastline
of Walton County, 18721977. The bold segment of the standard deviation
curve in the lower panel represents the shoreline portions considered to be
acceptable for analysis of standard deviations ........................................ ....87
432: Shoreline change rates and unsealed standard deviations along the coastline
of Bay County, 18551977. The bold segment of the standard deviation
curve in the lower panel represents the shoreline portions considered to be
acceptable for analysis of standard deviations .........................................88
433: Shoreline change rates and unsealed standard deviations along the coastline
of Gulf County, 18571984. The bold segment of the standard deviation
curve in the lower panel represents the shoreline portions considered to be
acceptable for analysis of standard deviations ........................................89
434: Shoreline change rates and unsealed standard deviations along the coastline
of Franklin County, 18561979. The bold segment of the standard deviation
curve in the lower panel represents the shoreline portions considered to be
acceptable for analysis of standard deviations .........................................90
435: Shoreline change rates and unsealed standard deviations along the coastline
of Pinellas County, 18731987. The bold segment of the standard deviation
curve in the lower panel represents the shoreline portions considered to be
acceptable for analysis of standard deviations .......................................91
436: Shoreline change rates and unsealed standard deviations along the coastline
of Manatee County, 18741986. The bold segment of the standard deviation
curve in the lower panel represents the shoreline portions considered to be
acceptable for analysis of standard deviations ........................................92
437: Shoreline change rates and unsealed standard deviations along the coastline
of Sarasota County, 18831984. The bold segment of the standard deviation
curve in the lower panel represents the shoreline portions considered to be
acceptable for analysis of standard deviations ........................................93
438: Shoreline change rates and unsealed standard deviations along the coastline
of Charlotte County, 18601992. The bold segment of the standard deviation
curve in the lower panel represents the shoreline portions considered to be
acceptable for analysis of standard deviations .........................................94
439: Shoreline change rates and unsealed standard deviations along the coastline
of Lee County, 18581989. The bold segment of the standard deviation curve
in the lower panel represents the shoreline portions considered to be
acceptable for analysis of standard deviations .......................................95
440: Shoreline change rates and unsealed standard deviations along the coastline
of Collier County, 18851988. The bold segment of the standard deviation
curve in the lower panel represents the shoreline portions considered to be
acceptable for analysis of standard deviations ........................................96
441: Comparisons of empirical and theoretical cumulative distributions of
normalized standard deviations for Nassau, Duval and St. Johns
Counties ..................................................................... ................. 97
442: Comparisons of empirical and theoretical cumulative distributions of
normalized standard Deviations for Flagler, Volusia and Brevard
Counties ................................................................ ....... ... 98
443: Comparisons of empirical and theoretical cumulative distributions of
normalized standard deviations for Indian River, St. Lucie and Martin
Counties .................................................... ...... ...............................99
444: Comparisons of empirical and theoretical cumulative distributions of
normalized standard deviations for Palm Beach, Broward and Dade
Counties ...................................... ............ ............ ................ 100
445: Comparisons of empirical and theoretical cumulative distributions of
normalized standard deviations for Escambia, Santa Rosa, Okaloosa and
W alton Counties ........................................... ............... ..............................101
446: Comparisons of empirical and theoretical cumulative distributions of
normalized standard deviations for Bay, Gulf and Franklin Counties ............102
447: Comparisons of empirical and theoretical cumulative distributions of
normalized standard deviations for Pinellas, Manatee and Sarasota
C counties ....................................................................................................... 103
448: Comparisons of empirical and theoretical cumulative distributions of
normalized standard deviations for Charlotte, Lee and Collier Counties ..........104
449: Mean square error contour for xo = 0.0 for the Weibull cumulative
distribution fitting to normalized standard deviations of shoreline
deviations about the trend lines for Martin County ................................. 105
450: Mean square error at xo = 0.31 for the Rayleigh cumulative distribution
fitting to normalized standard deviations of shoreline deviations about
the trend lines for Martin County ......................................... ......106
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
STATISTICAL CHARACTERISTICS OF SHORELINE CHANGES
FOR FLORIDA'S COAST
By
Jie Cheng
May 1998
Chairman: Dr. Robert G. Dean
Major Department: Coastal and Oceanographic Engineering
A comprehensive shoreline position data base for the Florida shoreline is analyzed and
the results presented and interpreted in terms of shoreline change trends, net longshore
and crossshore sediment transport components, and fits of the standard deviations of
shoreline deviations about the trend line to theoretical models.
The least squares method is applied to the data to quantify shoreline change trends. It
is found that, on average, the shoreline change rates for the entire time period available
for the east coast, northwest coast and southwest coast are +0.22 m/yr, 0.16 m/yr and
+0.12 m/yr, respectively. For the entire period time, the shoreline change trends are
interpreted in terms of longshore and crossshore sediment transport components by
employing the equation for conservation of sand. For the east coast, the result of
calculation of the longshore transport difference commencing from the FloridaGeorgia
border strongly indicates the presence of a net landward crossshore sediment transport
and/or significant biogenetic production. For the northwest and southwest coasts, the
magnitudes of the longshore sediment transport difference are on the order of + 105 m3/yr,
which are consistent with earlier estimates. The average net crossshore transport
components for the east coast, northwest coast and southwest coast are 0.8 m3/m/yr
(landward), +0.45 m3/m/yr (seaward), 0.68 m3/m/yr (landward), respectively.
To examine the characteristics of shoreline deviations about the trend lines, the
standard deviations of shoreline deviations along Florida's coastline are calculated. The
results show that the greater standard deviations tend to be associated with the greater
shoreline change rates and often occur near inlets. The statistical distributions of standard
deviations are modeled and compared with two candidate models: the modified Weibull
and the modified Rayleigh cumulative distributions. These distributions are fit to the
empirical cumulative distributions of the standard deviations outside of the influence of
inlets for each county. The KolmogorovSmirov test is used to evaluate the goodness of
fit of these models to the standard deviations. This statistical analysis of the cumulative
distributions of the standard deviations for each county results in good agreement
between the empirical and theoretical distributions proposed in this study.
CHAPTER 1
INTRODUCTION
1.1 Purpose of the Study
With increasing human activities and investment in the coastal zone, including
recreation, tourism, residential and industrial installations, there is a greater need for
understanding shoreline changes and coastal processes. Due to this investment and the
possible impact on adjacent shorelines, rational coastal management that scientifically
and economically determines whether a coastal engineering project is feasible becomes
more important. Shoreline change can be decomposed into coherent and fluctuating
components. The former is the longterm shoreline change trend, and the latter is
shoreline deviation about this trend. Shoreline stability, the quantification of shoreline
change trend including both shoreline advancement and shoreline recession and their
causes are significant elements in the decision making process. The statistics of shoreline
deviations about the shoreline change trends can be used as a basis for the prediction of
shoreline fluctuations which is an important basis for coastal zone management. An
understanding of the causes and characteristics of shoreline changes is essential for
interpreting the effects of coastal engineering works and shoreline protection. The
characteristics of shoreline changes along the coast of Florida are a result of the natural
and human "forces" exerted on this shoreline. Natural effects include sea level rise,
seasonal wave and water level variations, and episodic storms. The dominant human
interventions in the area of interest are the construction of new inlets and the modification
of existing inlets. Within the approximately last two decades, beach nourishment has
played an extensive role. These processes and constructed elements can cause both
shoreline erosion and shoreline accretion. This study provides general views on these
causes and factors along the Florida coastline, and based on the Florida shoreline position
data set, analyzes the statistical characteristics of shoreline changes for the coast of
Florida.
1.2 Florida Shoreline Position Data Set
The original shoreline position data were obtained from the following organizations:
* The U. S Coastal and Geodetic Survey (U.S.C. & G.S.)
* The Division of Beaches and Shores (B & S) of the Florida Department of Natural
Resources (FDNR, now Bureau of Beaches and Coastal Systems of the Florida
Department of Environment & Protection, FDEP)
Historical shoreline positions have been digitized and compiled by FDNR, which were
obtained from aerial photographs, maps and charts, and hydrographic and beach surveys.
Shoreline position is defined by the intersection of land and water. The datum for
measuring shoreline position is mean high water (MHW), which is the average height of
the high waters over a 19year period. The shoreline position data are referenced to the
FDNR monuments. The Florida data set includes historical shoreline positions for the 25
predominantly sandy shoreline counties. There are approximately 3785 individual
monuments that are located at nominal spacings of 300 m along Florida's coastline of
about 1320 km. Historical shoreline position data are available on a countybycounty
basis for most counties. Because of the FDEP precedent of combining Escambia and
Santa Rosa Counties in the shoreline position data base, these counties are treated as a
single county in this study. Figure 11 identifies these twentyfive counties and shows the
numbers of monuments corresponding to each county. As shown in Figure 11, these
counties are distributed over three segments of Florida's coastline. The sandy east coast
of Florida extends approximately 570 km from the FloridaGeorgia border south to Key
Biscayne, along which there are 12 predominantly sandy shoreline counties and 19 inlets.
The upper part of Florida's west coast has seven predominantly sandy shoreline counties
and 12 inlets, extending approximately 400 km from Escambia County at the Florida
Alabama border through Franklin County. The lower part of Florida's west coast has six
predominantly sandy shoreline counties and 28 inlets, extending approximately 300 km
from Pinellas County at the north through Collier County at the south.
1.3 Literature Review
Several investigators have developed and applied methods for interpreting shoreline
change trends. Crowell et al. (1991) have evaluated errors in available data sources which
depict historical shorelines, including "T" sheets, aerial photographs of various scales and
ground surveys. Error estimates are recommended for the various data sources. Dolan et
al. (1991) compared various methods of analysis of shoreline position data, including:
endpoint, average of rates, linear regression, and jackknife. The potential errors
associated with the various methods were examined for data of different characteristics
and by application to the Outer Banks of North Carolina. Crowell et al. (1993) have
Nassau (82)
/.N' '
e ~
I..c
rzB
(386)
aarasoLas\ i Martin (127)
Charlotte (68)am Be
alm Beach
Lee (239) (127)
Collier (148) Broward (128)
Dade (112)
Figure 11 Numbers of stations for which shoreline position data are available
along Florida's coastal sandy beach Counties.
compared the merits of using long term (>60 years) versus short term (<10 years) data to
establish shoreline change trends. That paper was motivated in part by conclusions by
Dolan et al. (1980) that errors associated with the "T" sheets preclude their use in the
computation of shoreline change trends. Crowell et al. (1993) found significant
advantages in incorporating the long term data, including increasing both the accuracy
and smoothing. Fenster et al. (1993a) have considered the appropriateness of several
methods of extracting trends from shoreline data and recommended the use of a socalled
"Minimum Descriptor Length" method (MDL) in which a linear trend is fit to the more
recent data based on consideration of the times when changes occurred in more complete
polynomial fits to the data. In the simplest situation, all available data provide a
reasonable fit to a linear line; and in such cases, all data are used in establishing the line
used in projections. In more complicated and realistic cases, various tests tempered by
judgement are applied to the data to establish that recent portion of the data from which a
linear trend line is to be established. Fenster et al. (1993b) applied this method to identify
changes in shoreline changes along the Outer Banks of North Carolina. Crowell et al.
(1997) have compared the merits of the MDL method proposed by Fenster et al. (1993a)
with the least squares method using tide gage data as a surrogate to shoreline positions
and have concluded that for projections of shoreline change, the least squares method is
superior to the MDL method. Foster and Savage (1989) have suggested a method by
which the end point slopes of various qualifying pairs of data are averaged to establish the
appropriate trend. In order for a pair of data points to be included in the average, the time
separating the two points must be large enough that the associated trend change is large
compared to the uncertainties in the shoreline positions. If N data points are available and
if all pairs of data points qualify, there would be N(N1)/2 slopes to be averaged. Fenster
and Dolan (1996) have examined the longshore extent of the influence of inlets on the
adjacent barrier islands using three criteria, all of which employed shoreline change rates.
Two sites were examined and it was found that along the outer banks of North Carolina,
the influence of Oregon Inlet extended up to 13.0 km from the inlet whereas for the tide
dominated inlets of the Virginia shoreline, the influence extended up to 6.1 km from the
three inlets examined.
Overall, the results of examinations of various methods of shoreline position analysis
have shown linear regression based on the best least squares fit to be a very reasonable
method unless known changes have occurred that will induce a different trend over recent
times. These changes could include modifications of an inlet, beach nourishment,
placement of a structure that will impede the natural flow of sand along the shoreline, or a
landslide or flood that delivers a large source of new sand to the shoreline. Although
natural shorelines exhibit cyclic behavior which is not captured by a linear trend, a priori
knowledge of the future timing of the cycles is required to appropriately incorporate this
cyclicity into the analysis.
Knowledge of the trends of shoreline change and the fluctuations about these trends
are both important in considerations of rational coastal management policies. Although
the magnitudes of the fluctuations about the trend can amount to several decades of the
trend component, there have been very few studies focused on shoreline variability. No
studies have been found which examined the character of the shoreline deviations about
the trend line and the relationship between the magnitudes of these deviations to inlet
proximity.
1.4 Report Organization
This study consists of 5 chapters. Chapter 2 provides general views on causes of
shoreline changes and factors related to shoreline changes along the coast of Florida.
Chapter 3 discusses shoreline change analysis procedures. In Chapter 3.1, longterm
shoreline change trends along the coast of Florida are analyzed, and based on these results
and various considerations, interpreted in terms of net longshore and crossshore
sediment transport components. The best least squares fit method is used to analyze the
shoreline change trends and the conservation of sand equation is applied in the analysis of
sediment transport. In Chapter 3.2, the standard deviation of shoreline deviations is
calculated for each location and the empirical cumulative distribution of the standard
deviations for each county is modeled by two bestfit theoretical distributions: the
modified Rayleigh distribution with two parameters and the modified Weibull
distribution with three parameters. The KolmogorovSmirnov test is employed to
evaluate the goodness of fit of these theoretical distributions to the standard deviations.
The effects of inlets on the standard deviations are identified. Chapter 4 presents and
discusses results. All of the results are presented county by county, and some are also
shown island by island, regionally. Chapter 5 provides the summary and conclusion for
this study. Because of the large numbers of tables and figures in Chapter 4, the tables and
figures are organized at the end of this chapter rather than after firstmention.
CHAPTER 2
FLORIDA'S COAST SETTING
There are twentyfive predominantly sandy shoreline counties along Florida's
coastline of 1270 km. In this study, the Florida's coastline is considered in three
segments: the east coast with 12 sandy counties, the north west coast with 7 sandy
counties and south west coast with 6 sandy counties, see Figure 11, which also shows the
number of DNR numbers in each county. These monuments denote the locations at which
longterm shoreline positions are available and to which the shoreline position data are
referenced. The characteristics of Florida coast setting are addressed in the following
sections.
2.1 Longshore Sediment Transport Characteristics
Coastal sediment transport includes components of longshore and crossshore
transport. The former is dominantly the result of oblique waves generating waveinduced
longshore currents, while the latter is due to waveinduced crossshore water particle
motions and the undertow. Crossshore transport is most pronounced during periods of
storminduced elevated water levels and severe waves. Longshore sediment transport can
occur along a coastline in two directions, depending on the wave direction. For Florida,
southerly directed transport is defined as positive for the east coastline, while westerly
and northerly directed transports are defined as positive for the northwest and southwest
coastlines respectively. Net transport is the difference between the longshore transport in
the two directions. Gross transport is the sum of the absolute transports in the two
directions, thus only net transport has an associated sign. Both net and gross transports
are important quantities in coastal engineering design. The U. S. Army Corps of
Engineers (1954) estimated that the total westward longshore transport amounts to an
average of about 140,000 m3/yr at the state's western boundary. Dean and 0' Brien
(1987) presented comprehensive estimates of net annual longshore sediment transport
along Florida's coast. Considering shoreline change trends and previous estimates of
longshore transport, this paper will provide a detailed examination of sediment transport
along Florida's coast.
2.2 Sediment Characteristics
The beach generally consists of a variety of materials, although the dominant
components are quartz, and shell. Sediment characteristics, which include grain size,
density, shape, and surface texture, or fall velocity that contains comprehensive
information of sediment behavior, play an important role in the shoreline response to
storms and human activities. Along Florida's east coast, the average sediment size ranges
from 0.1 mm to 1 mm. Most average sediment sizes of the northern sandy counties range
from 0.1 mm to 0.2 mm (Charles,1994). For the southern sandy counties, the variation of
average sediment size is relatively greater, and the increase in the average sediment size
from north to south is due dominantly to the increasing shell content. This longshore
gradient in sediment size affects the equilibrium beach profile, response to storms and
possibly sediment transport rates. Along Florida's west coast, however, no
comprehensive studies of sediment characteristics have been conducted.
2.3 Sea Level Change
Sea level change is a longterm process, which affects shoreline stability. Generally,
increases and decreases in relative sea level will cause the shoreline to recede and
advance, respectively. There are 6 longterm tide stations in Florida, from which the
average annual sea level change rates can be obtained. In Florida, contrary to the above
relationship, the average shoreline is advancing while the longterm average relative sea
level is rising at approximately 2 mm/year (Dean, 1994).
2.4 Human Effects
Human effects on shorelines can cause beach erosion or accretion. The dominant
erosion cause in the area of concern by far is the result of construction of new entrance
channels and the modification of existing entrance channels for navigational purposes.
Usually these entrances include two jetties to prevent sand from entering the channel and
dredging of the channel to a depth greater than natural. Additionally, past practices of
placing channel maintenance material in deep water have left a legacy of erosion near
many entrances along Florida's coast. Groins, and in some cases seawalls, can
redistribute sand available in the nearshore system. Nourishment, the placement of large
quantities of good quality sediment in the nearshore system to advance the shoreline
seaward can cause accretion over fairly large segments of shoreline.
CHAPTER 3
ANALYSIS PROCEDURES
3.1 Shoreline Change Trend Analysis
The longterm shoreline change rates at individual locations are first examined directly
from the data set using the linear regression based on the best least squares fit procedure,
which is described in the following section. To obtain an overall assessment of shoreline
stability, the average longterm shoreline change rates are calculated by averaging the
shoreline change rates at individual monument locations within the regions of interest.
Two different geographic entities are considered in developing shoreline change
characteristics: (1) counties and (2) the various islands along Florida's coast. For these
counties and islands, the analyses are carried out for two periods: (1) the complete period
for which the shoreline position data are available and (2) pre1970s. The latter data are
of interest as they preceded the extensive nourishment activities that have occurred over
the last 25 or so years. For comparison and verification, the average shoreline change
rates for the complete period are also computed by the second approach (Method 2),
which is based on the best least squares fit to the average shoreline positions within the
regions of interest. The average shoreline position for a series of monuments is
determined by averaging the shoreline positions of the individual locations for each
survey. Method 2 is somewhat less suitable for analysis since it requires more complete
data sets for the particular county or island under consideration. Based on the shoreline
change trends, the conservation of sand equation, and various reasonable considerations,
net longshore and crossshore sediment transport components are calculated to interpret
shoreline changes.
3.1.1 Shoreline Change Trend
The longterm shoreline change rate at each location is determined by the linear model
to fit the available shoreline position with time:
(Y)ij= a jti+bj (3.1)
where (y)ij is the predicted shoreline position corresponding to a given value of time ti at
a longshore position, xj, and aj is the longterm shoreline change rate. A zero value of aj
indicates that the shoreline position is stable over time whereas positive or negative
values of aj denote an advancing or receding shoreline, respectively. The quantities aj and
bj in this model can be calculated based on the leastsquares method, in which the sums of
squares of the differences in shoreline position between the measured data and the bestfit
straight line are minimized.
3.1.2 Sand Conservation
The changes in shoreline position are interpreted separately as if they were caused
entirely by either gradients in longshore sediment transport or crossshore sediment
transport; both interpretations are based on considerations of sand conservation. It is
realized that the actual changes are due to a combination of longshore and crossshore
sediment transport components.
Sediment transport is computed in a local coordinate system in which the xaxis is
oriented along the shoreline and the yaxis is directed offshore, as illustrated in Figure 3
1.
Az
h(x,y,t)
h.
Figure 31. Coordinate system for sediment transport
For Florida's east coast, the origin of the coordinate system is located at the north border
of Nassau County, the northernmost county on Florida's east coast. For Florida's
northwest coast and southwest coast, the origins of the coordinate systems are located at
the western end of Franklin County and at the southern end of Collier County,
respectively (Figure 11).
The following governing equation is based on the conservation of sediment volume
for a control area and is the basis for computing transport components.
Sh aqx 4qy
= +a s (3.2)
at ax ay
where qx and qy denote the local sediment transport rates per unit width in the longshore
and offshore directions respectively, h and s denote the local depth of water and an
additional source term representing any material added per unit area per unit time.
Integrating Eq. (3.2) across the profile from y = yi to y = y2 leads to
d a 33Y2
at ax +qy(x,yz,t) qy(x, y,t) s dy (3.3)
dt dx Y,
and since
S+ _s = 0 (3.4)
at ax
aQ x av, Y2
ax Vs qy(x,y2,t)+q(x, yl,t)+ Jsdy (3.5)
Sx a t yl
where Vw is the volume of water per unit length of shoreline; V, is the volume of sand
per unit length of shoreline; Qx denotes total volumetric longshore sediment transport
rate; qy (x, Y2, t) is the sand transport rate out of the control volume at the offshore limit,
Y2, and qy (x, yl, t) is the transport rate into the control volume at the inshore end ( y ) of
the profile, the latter of which is considered to be zero. If no material is added to the
profile artificially, such as beach nourishment, or removed by dredging, the value of the
fourth term on the right hand side of Eq. (3.5) is also zero.
For a shoreline position, y(x,t), relative to a fixed reference, it is assumed that as y(x,t)
changes, the entire profile moves without change of form over the active vertical
dimension, h.+B, where h* is the socalled "depth of closure" and B is the berm height
(see Figure 31). Thus AVs can be expressed as
AVs = Ay(h, + B) (3.6)
The following equation can be established from Eqs. (3.5) and (3.6).
(h. ) Y x + q+y(, y2, t) (3.7)
a t 6x
3.1.3 Total Net Longshore Sediment Transport Difference
With the above framework, we will first examine the longshore sediment transport
distribution along Florida's east coast, northwest coast and southwest coast, respectively,
considering the influence of crossshore transport to be negligible (q( Y2 )=0) in Eq. (3.7),
which results in
x =(h + B) (3.8)
x t
Now, integrating along the beach from one end of the study area (at x = x, ) to another
Xi, we have
Q(xi,t)Q(x1, t)= (h* +B)dx (3.9)
xi at
where h* and B for Florida's coast are obtained from Dean and Grant (1989). The partial
derivative from Eq. (3.1) is
at
a y(x,t )
= a(x) (3.10)
at
where a(x) is obtained from Eq. (3.1).
The integral of Eq. (3.10) is approximated by the trapezoidal rule
Xi 1 i
Sf(x,t )dx = i[f(Xkl,1tj)+f(xk,tj)](xk Xk1) (3.11)
xI 2 k=2
where k = 2,3,4,..., i.
Based on Eqs. (3.9), (3.10) and (3.11) the difference in net longshore sediment
transport at point xi from that at xi can be determined by
I i
Q (xi, t) Q (x,tj) = (h, + B) I[a(xk1) + a(xk)](xk Xk1) (3.12)
2 k=2
where i = 2,3,4,...,n.
3.1.4 Local CrossShore Sediment Transport
Local crossshore sediment transport is computed using Eq. (3.13) obtained from
rewriting Eq. (3.7) with the consideration of a uniform average gradient of longshore
sediment transport.
qy(x,y2,t) = a(h + B) (3.13)
a
where a is the longterm value obtained from Eq. (3.1) and is calculated based on
ax
previous estimates of net longshore sediment transport along Florida's coast. For the east
coast, these estimates were obtained predominantly of sand impoundment against the
updrift sides of newly constructed jetties. The net longshore sediment transport at the
northern end of the state on the east coast is believed to be on the order of 460,000 m3/yr
(south), whereas near the southerly terminus of the sandy beaches, the value is estimated
to be approximately 7,000 m3/yr (south) (Dean and O'Brien, 1987a). Thus the
approximate gradient in longshore sediment transport can be obtained based on the length
of 570 km along the east coast of Florida, resulting in
iQ
= 0.8 (2 / year) (3.14)
ax
For the northwest coast, the approximate gradient in longshore sediment transport is +0.4
m3/m/yr based on a transport of 140,000 m3/yr (U. S. Army Corps of Engineers, 1954) at
the state's western boundary and zero transport at the eastern limit of the northwest
counties. An average zero gradient in longshore sediment transport is assumed for the
southwest counties.
3.2 Analysis of Standard Deviation of Shoreline Deviations
The previous section discusses the longterm shoreline change trend, which can be
represented by a bestfit straight line. However, the shoreline positions exhibit large
deviations about the trend lines. The shoreline deviations could be due to many factors,
for example, seasonal variations, storm events, cumulative storm effects, migrating sand
waves, anthropogenic alterations, such as construction of jetties, etc., and of course,
errors in the recorded shoreline positions. The processes that govern shoreline
fluctuations are far from being completely understood. This section addresses
measurements of shoreline deviations and the characterization of the distributions of the
standard deviations of shoreline deviations about the trend lines. Each standard deviation
is associated with one profile, which quantifies the distribution of shoreline deviations
about the trend line at that particular location. The statistics of the standard deviations can
be used as a basis for the prediction of shoreline deviations which in turn is applicable to
coastal zone management. For this purpose, based on a combination of the features of the
standard deviations and some knowledge of the processes, this section focuses on two
components: (1) the statistical cumulative distribution of the standard deviations of
shoreline deviations and a test whether or not the Rayleigh and Weibull distributions fit
the standard deviations reasonably, and (2) the effects of inlets on shoreline deviations. In
this study, both normalized data and unsealed data are considered. Since the influence of
inlets on shoreline changes is great and may belong to a different statistical distribution,
in (1), only the shoreline position data outside of the influence of inlets are analyzed
based on the criterion that the standard deviations of shoreline deviations must be
approximately uniform along the shoreline. It will be shown that the standard deviations
of shoreline changes are substantially greater in the vicinity of inlets. The examinations
described above are carried out for all of the twentyfour Florida predominantly sandy
shoreline counties, see Figure 11.
3.2.1 Definition of Standard Deviations of Shoreline Deviations
The shoreline deviation about the trend line is defined by
(Ay)i= (Ym)ij (Yt)ij (3.15)
where ym and Yt indicate the measured shoreline position data and the shoreline position
based on Eq. (3.1), respectively. It is significant to note that this approach forces the mean
of shoreline deviations over time for all locations to be zero, as well as the average value
of shoreline deviations over time to be 0 for a fixed location.
Based on Eq. (3.15), the standard deviation of shoreline deviations for each location is
computed by
S= l ((Ay)(Ay))2 /(k1) (3.16)
i=1
where Ay, is the shoreline change deviation about the bestfit line with time at each j
location and Ay is the average of Ay,; i = 1,2,3,...,k., and k denotes the number of the
years for which shoreline position data are available at the examined location.
3.2.2 Model Consideration
Since the standard deviations of shoreline deviations are positive, the Weibull and
Rayleigh cumulative distributions, which use random variables, the values of which are
greater than 0, are considered as candidate models to fit the standard deviation
distributions. To improve the goodness of fit of these models, the Weibull and Rayleigh
modes are proposed which allow for zero offsets for these distributions. The solution for
the normalized standard deviations is easier to obtain and also provides a basis for the
more difficult solution of the unsealed results. Thus, the normalized data are analyzed
first, followed by an examination of the unsealed data. The normalized data are calculated
by scaling the individual values of the unsealed data by the same constant, which is the
rootmeansquare value of unsealed standard deviation within the group being considered.
3.2.3 Analysis of Normalized Standard Deviations
This section focuses on comparisons of the cumulative distribution functions (CDF)
(also called probability distribution functions) of sample normalized standard deviations
for all locations within each county with the Weibull and Rayleigh distributions.
Since the size of the data set of interest is relatively small and every individual in the
data has the same chance of being selected, the CDF is calculated by a step function,
F(xi) = i 1/n, where xi and n are the normalized standard deviation and the data size,
respectively; X, in ascending order: x1 < X2 <...< xi <... Xn .
The Weibull probability density function is defined as
f(x) = Anx"eA", 0< x
Integrating from 0 to x yields the cumulative distribution function
F(x) = 1 e, O0x< o (3.18)
where n is the parameter that determines the shape of the distribution and A is the scale
parameter that determines the spread. Both A and n are positive and nondimensional.
These parameters can be estimated by fitting data to the model based on the best least
squares method and NewtonRaphson procedure. However, with the estimated
parameters, it was found that the distribution does not provide a good fit to the individual
points corresponding to the smaller data values. One way of addressing this deficiency is
to include an "offset". Thus a new parameter, xo, is introduced to yield a modified
Weibull distribution, called a threeparameter Weibull distribution, as shown below.
F(x)= 1 e(xxor), 0 < xo< xmin, Ox < o (3.19)
where n and A have the same definitions as those in the twoparameter Weibull
distribution; xo is the nondimensional offset parameter; Xmin represents the minimum of
the normalized data values. The reasons for the range of x0 are (1) a restriction of the
CDF is that the probability represented must increase from 0 to 1 monotonically, and (2)
all the parameters should be real numbers. If xo is equal to or greater than xnin, based on
the iterative process for solving the parameters, the probability could be multivalued or
the numerical solution could become unstable or imaginary. Based on the best least
squares method and NewtonRaphson procedure, the iterative process determining the
three parameters is described as follows.
(1) Fix xo = 0. For the trial values of Ak and nk, the theoretical cumulative frequency is
equal to
F(xi) = 1 eAk(xixO)nk (3.20)
where for this starting case, xo = 0 and xi is an individual data value; i = 1,2,..., m, m is
data size; k = 1,2,..., K, K is number of iterations. Then, the error between data and model
is defined as
Si (Ak, nk) = Fe (xi) + eAk(xix0)nk (3.21)
Si+1(Ak +AAjn+Anj) =i(Ak, k) + An+j AAj (3.22)
where Fe (x) denotes the empirical cumulative frequency; AA and An are unknown
changes of A and n, respectively; j denotes locale;  and are the partial derivatives
dA an
of E i with respect to A and n, respectively, given by
ae = (xixo)nk e Ak(xixk (3.23)
aA
r i = Ak (Xi)nk eAk(xix)nk ln(xi x) (3.24)
an
k
To find the minimum of Ye2(Ak+AAj,nk+Anj), differentiate with respect to the
i=l
increase of each parameter and equate to zero. Differentiating with respect to AA and
equating to zero gives
asc m a aE i aE ii
(esi)+Anyj ( )+AAi4 () =0i (3.25)
i=1 iA i=1 On oA i=1 aA aA
Differentiating with respect to An and equating to zero gives
m M m "ai a8 i aE i =_
(Ei)+Anj I( e)+ AAj l( )=0 (3.26)
i=1 an i=1 an an i=1 aA an
Anj and AAj can be solved from the two basic equations obtained above.
Therefore,
Ak+1 = Ak + AAj (3.27)
nk+1 = nk + Anj (3.28)
We may now use Ak+1 and nk+l and repeat the process to obtain improved A and n values
until a check on the sizes of AA and An indicates that additional iterations are not
necessary. The final values of AA and An should be very small. Calculate the mean square
error after obtaining the solutions. This procedure for estimating parameters under
constraint is illustrated in Figure 32.
(2) The value of xo is increased in steps of 0.01. For each xo, repeat 1) until the
smallest error for the range of xo is obtained when A, n and xo corresponding to this error
are the solutions.
Also, the parameters may be examined graphically by a twodimensional error contour
plot, which portrays the magnitude of the error and provides an estimate of the unknowns
and demonstrates the sensitivity of the solution to the parameters. The objective is to
display the error as a function of the two variables A and n at a given Xo that describes the
error resulting from the modeling,
Figure 32 Illustration of the iterative process
1 m 2
error(xo, A,n) = E;2 (xo,A,n) (3.29)
m i=1
The function can then be evaluated directly and graphed. Compared with the iteration
process, more work and less accuracy are associated with obtaining estimates A and n by
using error contour plotting. However, the error contour plotting has the advantage over
the iteration process of allowing visualization of the magnitude of the error and the
solutions of the parameters. Therefore, the error contour plotting may be employed to
verify the solutions and character of the error surface resulting from the iteration process.
For comparison and verification, a twoparameter Rayleigh distribution, which is a
particular case of a threeparameter Weibull distribution, is used as a model.
F(x) = 1 eA(xxx, 0 < xo< Xmin, 0x < (3.30)
The Rayleigh distribution is equivalent to n=2 of the Weibull distribution described
above. The approach to determining parameters for this model is similar to that for the
Weibull distribution, and similarly, the error plotting method can also be used to examine
the variation of the error as a function of the parameters for this model. Compared to the
Weibull model, the error associated from the Rayleigh model is a single curve, instead of
contours, due to only one variable, A, for a given xo.
3.2.4 Analysis of Unscaled Standard Deviations
For the unsealed standard deviations, the parameters in the above models also can be
obtained via the iteration process. However, if the parameters for the models fitting to the
normalized standard deviations are available, an easier method may be employed to
determine the parameters for the models for the unsealed standard deviations. This
method is given as follows.
As described in the previous section, the Weibull cumulative distribution model fitting
to the normalized data is
F(x) = 1 eA(xx, 0 < Xmin, 0
The Weibull cumulative distribution model fitting to the unsealed data is defined as
F(x') = 1 eA'(x'xo'' (3.31)
where x'= Xrms x or x=x'/Xrm, and x,,, is the rootmeansquare value of the unsealed data;
x' denotes the unsealed data values; A', n' and xo' are the parameters for this model. It is
noted that in Eq. (3.31), A' has units corresponding to the units of x', for example, the
units of A' are 1/mn if the unit of x' is m, while n' is nondimensional. Since x is
obtained by dividing x' by the rootmeansquare value of the unsealed data that is
constant, random variables X' and X must be identically distributed.
F' (x' ) F(x) (3.32)
Then,
A'(x'Ixo'' = (Xxox. ) (3.33)
Xrms
Thus,
n' n (3.34)
A'= A/xns (3.35)
xo' XO Xrms (3.36)
By the same approach above, the analysis of the Rayleigh cumulative distribution model
fitted to the unsealed data leads to
A' A/x2 (3.37)
xo =XOXxrms (3.38)
3.2.5 Model Test
To evaluate the significance of the fits to the cumulative distribution function models
described above, the KolmogorovSmirnov (KS) test is employed. This procedure
evaluates the goodness of fit of a model to the data of interest. It is a comparison between
two quantities: (1) the KS statistic, Dn, which is the maximum absolute difference
between observed and expected cumulative frequencies obtained based on a proposed
model, and (2) a critical value K corresponding to different significance levels and
different sample sizes. The proposed model is the null hypothesis (Ho) against the
alternative (Hi) that it is not correct. A certain significance level, a, is the probability that
we erroneously reject Ho if it is true, which can be expressed as
a = P{Rejecting Ho /Ho is correct (3.39)
The larger the value of a, the higher the risk of erroneous rejection. Therefore, the
significance levels used in the test are very small. If the sample size is greater than 35, the
relationship between a critical value K and a certain significance level corresponding to K
can be expressed approximately as
K = ln(a/2) / 2n (3.40)
Three commonly used standards are
a= 0.10,K = 1.22/Vn
a = 0.05, K = 1.36 / n
a = 0.01, K = 1.63/Fn
Another standard which will be used in this study is
a = 0.001, K = 1.95 /n
If Dn > K(a, n), which means that the probability that we get a Dn value, if the null
hypothesis is true, is smaller than the significance level a, the null hypothesis will be
rejected. If Dn < K(a, n), on the contrary, the null hypothesis will be accepted. The
significance level a may be called a Type I error. On the other hand, if we fail to reject it
when it is not correct, a Type II error occurs. However, in this study, only a Type I error is
considered.
3.2.6 The Influence of Inlets
Both natural and constructed inlets can result in significant interruptions of longshore
sediment transport patterns and alterations of crossshore sediment transport. In general,
these effects of jettied inlets on shoreline changes will typically lead to updrift accretion
and downdrift erosion, whereas for unmodified inlets, the effects are an increase in the
variance in the adjacent shoreline positions. It will be demonstrated that the shoreline
change trends in proximity to inlets are significantly greater than those outside the
influence of inlets. The magnitudes of shoreline changes near inlets are usually greater
than those far away from inlets and the shoreline deviations including the influence of
inlets tend to have large variances.
CHAPTER 4
RESULTS AND DISCUSSION
4.1 Shoreline Change Rates and Sediment Transport
Shoreline change rates at individual monument locations along Florida's coast are
calculated by the linear regression method. The average of the best least squares fit to
individual shoreline positions at each location (Method 1) results in the average shoreline
change rates for the regions of interest. For comparison and verification, the average
shoreline change rates for the 12 counties on the east coast are also computed by the
second method (Method 2), which is based on the best least squares fit to average
shoreline positions. Additionally, the averages are obtained over two different regions:
countybycounty and islandbyisland, and for two different time periods: all data and
pre1970 data. Based on the shoreline change rates, the longshore and crossshore
sediment transport components are examined by using the conservation of sand equation.
The following two sections present the shoreline change rates and sediment transport for
Florida's east coast and west coast, respectively. As noted previously, due to their large
numbers the figures and tables in this chapter are grouped at the end of the chapter.
4.1.1 East Coast Counties and Islands
Figure 41 presents the variation of longterm shoreline change rates along Florida's
east coast. Duval County has the most severe localized shoreline erosion and it also has a
much larger shoreline accretion. The largest positive shoreline change occurs in St. Johns
County. Both Volusia County and Martin County have significant shoreline changes,
advancing for the former and retreating for the latter. In general, the larger shoreline
change rates occur near inlets, usually accretion updrift and erosion downdrift. Without
exception, all of the "spikes" observed in Figure 41 are at inlet locations. Figure 42
presents 12 examples of shoreline positions at individual monuments versus time, one for
each county on the east coast. The dashed line presents the trend line based on the best
least squares fit, while the solid line connects the measured shoreline positions. These
examples were selected to be reasonably representative of the longterm shoreline change
rates for the individual counties. These results also demonstrate the variability in
shoreline positions with time at individual monuments. To better understand the
distribution of the recession or advancement rates along each county on the east coast,
histograms of longterm shoreline change rates (all data) are provided in Figure 43. In
each histogram, the horizontal axis represents shoreline change rates, with the vertical
axis indicating the number of monuments that have the corresponding erosion or
accretion range in that county. The horizontal scales in Figure 43 are the same for all
counties to facilitate comparison.
Table 41 summarizes the longterm shoreline change rates for each county on the east
coast using the two procedures and the periods of time for which data are available. With
the concern of effects of beach nourishment projects which started in the 1970's on the
east coast of Florida, the longterm shoreline change rates limited to pre1970s obtained
by Method 1 are also presented in this table. As shown in this table, the values of the
shoreline change rates for the two periods based on Method 1 are approximately equal for
all sandy beach counties except Dade County. For Dade County, the shoreline change rate
including the 1986 data is +0.30 m/yr, whereas, the shoreline before 1970 exhibited a
longterm retreat trend at 0.01 m/yr. The absolute difference between these two values
are very large relative to those for other counties. In a more detailed examination of the
longterm shoreline change rates at 74 individual monument locations available north of
Government Cut within Dade County, 24 of the 74 beach profiles have a recessionary
trend before the 19761981 Beach Nourishment Project, whereas, including the 1986
data, 72 of the 74 beach profiles were characterized by longterm advancement.
Obviously, this is due to the effect of the major Miami Beach Nourishment Project
constructed north of Government Cut from 1976 to 1981. The Miami Beach Nourishment
Project is considered a major success with over 20 years experience (Wiegel, 1992). To
illustrate the shoreline change trends more completely, the following discussions are
based on the entire period of time for which data are available unless noted otherwise. Of
the 12 counties, 10 are characterized by average shoreline advancements based on the
results from Method 1. The maximum rate of shoreline advancement rate is +1.01 m/yr
for Duval County while the minimum value is +0.01 m/yr for Broward County. Only two
counties, St. Lucie County and Martin County, which are contiguous on the central east
coast of Florida, have a longterm shoreline retreat trend. In comparison with the results
from Method 1, Method 2 results in 9 counties with advancing shorelines and 3 counties
with receding shorelines. The results obtained by the two methods are approximately the
same for most counties. Although the shoreline change rate at Broward County is
negative (0.02 m/yr) by Method 2 and positive (0.01 m/yr) by Method 1, the magnitudes
of these two values are very small relative to those for other counties. Therefore, Broward
County can be considered to be characterized by an approximately neutral shoreline.
Figure 44, based on Method 2, provides the detailed average shoreline change over time
for each county on the east coast. As shown in this figure, all of the counties except
Martin County exhibit either a trend of advancement or a nearly neutral condition. Also
for some of the counties, especially Dade County, the recent effects of beach nourishment
are evident. These results presented in Figure 44 are consistent with those in Table 41.
The following discussions of results in Tables 42 and 43 are based on Method 1
since there are no significant differences between results obtained from the two methods
and Method 2 requires more complete data sets.
As shown in Table 42, the shoreline change rates for the complete time period
available for the upper east coast (6 counties, 1113 profiles) and lower east coast (6
counties, 828 profiles) are 0.48 m/yr and 0.12 m/yr, respectively. On average, Florida's
east coast shoreline is advancing at a rate of 22 centimeters per year. The average erosion
on the lower east coast counties is due to Martin County where St. Lucie Inlet (cut in
1892) has resulted in severe beach erosion to the southerly Jupiter Island. Limiting
considerations to pre1970 data, the average rate of advancement of the east coast of
Florida is 16 cm/yr. Thus, by inference, the effects of the extensive beach nourishment
projects have resulted in an average shoreline advancement rate of 6 cm/year prorated
over the approximately 120 year period or 0.52 m/yr prorated over the approximately 20
years of nourishment activity. It is noted that the increasing trend toward shoreline
advancement over the past 20 years is much greater in the southern six counties than in
the northern six counties which is consistent with the concentration of extensive
nourishment activities in the southern portion of the state.
Grouping the data by islands results in the shoreline change rate of each island
presented in Table 43. Comparing the shoreline change rates limited to pre1970s with
that limited to the more complete period leads to a result similar to that in Table 41.
Based on the more complete period of time, of the 20 islands, 12 exhibit longterm
advancement and 8 have a recessional trend. The maximum value (2.02 m/yr) for
advancing shoreline occurs at Island No.2 (Little Talbot Island) and the maximum
recessional value (1.69 m/yr) occurs at Island No.10 (Jupiter Island) which is south
(downdrift) of St. Lucie Inlet.
The longshore variation of longterm net longshore sediment transport difference
along the eastern coast of Florida is presented in Figure 45. Recall that this result, based
on Eq. (3.12), considers the crossshore sediment transport and source terms to be zero.
Only two counties, Martin County and St. Lucie County, clearly show positive values of
longshore gradient of longshore sediment transport, implying a longterm shoreline
recessional trend. Most other counties have negative values of longshore gradients of
longshore sediment transport, due to longterm shoreline advancements. Since these
results are based on the conservation of sediment, they are consistent with the shoreline
change characteristics in Table 41 and Figure 41. Based on the considerations here, the
longshore transport decreases along the northeast coast and increases along the coastlines
of St. Lucie and Martin Counties. The gradient of the longshore transport along the
southeast coast is relatively small. The maximum value of the longshore transport
differences occurs at the border between Nassau County and Duval County, while the
minimum occurs at St. Lucie County. Figure 45 is based only on longterm shoreline
position changes, with the consideration of zero crossshore transport. Considering the
net southerly transport at the north end of the State to be 460,000 m3 /yr, Figure 45
indicates the net transport to change direction (to northerly) somewhat south of the mid
part of St. Johns County and to remain northerly to the southern end of the region
considered, where the net transport would be northerly at a rate of approximately 480,000
m3 /yr. This unrealistic result supports the presence and significance of a net crossshore
(landward) sediment transport as a major contribution of shoreline advancement along the
east coast of Florida.
Figure 46 presents local crossshore sediment transport along the east coast of Florida
under the assumption of a uniform gradient in longshore sediment transport based on
previously estimated values, see Eq. (3.13). Large values of local crossshore transport
along this coast are not evident, except at locations of inlets. The maximum local cross
shore transport is approximately 220 m3/m/yr and occurs in Duval county, while the
minimum (approximately 155 m3/mlyr ) is in St. Johns County. Since these local cross
shore rates are based on the conservation of sediment, they are consistent with the total
average net longshore sediment transport considered and shoreline changes. The average
crossshore transport is 0.8 m3/m/yr (directed onshore), some of which could be
accounted for by biogenetic production (shells and coral) representing a continuing source
term.
4.1.2 West Coast Counties and Islands
Figures 47 and 48 present the variation of longterm shoreline change rates along
Florida's northwest and southwest coasts, respectively. For the northwest coast, Franklin
County has the largest positive shoreline change rate and it also has a large shoreline
recession; Gulf County has the most severe localized shoreline erosion. For the southwest
coast, the largest positive and negative shoreline changes occur near Hurricane Pass at
Pinellas County. Compared with Pinellas County, other counties have much smaller
shoreline changes, but they all have substantial shoreline fluctuations around zero. With
one exception all of the "spikes" observed in Figure 47 and Figure 48 are at inlet
locations. This exception is near a cape (Cape San Bias in Gulf County). Figure 49
presents 12 examples of shoreline positions at individual monuments versus time, one for
each county on the west coast. Histograms of longterm shoreline change rates (all data)
are provided in Figures 410 and 411 for each county on the west coast. The information
shown in Figures 49, 410 and 411 is similar to that shown in Figures 42 and 43 for
the counties on the east coast.
Table 44 summarizes the longterm shoreline change rates for each county on the
west coast using the two periods of time described earlier by Method 1. It is seen that the
values of the shoreline change rates for the pre1970s and total period are approximately
equal for most counties. Any differences attributable to beach nourishment would
indicate a greater advancement/less recession for the total period compared to the pre
1970s. There has been much less nourishment on Florida's west coast compared to its
east coast with only Pinellas, Sarasota and Lee Counties on the west coast receiving
substantial nourishment during the periods encompassed in the data base. Of these three
counties, only Pinellas and Lee Counties show significant differences consistent with
anticipated nourishment effects. The relative increase for Pinellas and Lee Counties are
0.55 m/yr and 0.26 m/yr, respectively, prorated over the full period of record. Over the
post1970 period, the change in rates for Pinellas and Lee Counties are +3.4 m/yr and
+1.5 m/yr, respectively. For the entire period, of the 12 counties, 6 are characterized by
average shoreline advancements and 6 are characterized by average shoreline retreats
based on the "All Data" results in Table 44. The maximum rate of shoreline
advancement rate is +0.50 m/yr for Okaloosa County and the maximum rate of shoreline
retreat rate is 0.51 m/yr for Gulf County.
Table 45 summarizes the longterm shoreline change rates for the northwest coast, the
southwest coast and the entire west coast, respectively. The shoreline change rates for the
complete time period available for the northwest coast (6 counties, 936 profiles) and
southwest coast (6 counties, 892 profiles) are 0.16 m/yr and +0.12 m/yr, respectively,
whereas, the shoreline change rates before 1970 for the northwest coast and southwest
coast are 0.27 m/yr and 0.03 m/yr, respectively. On an overall average, Florida's west
coast shoreline is eroding at a rate of 2 cm/year for the complete time period. Limiting
considerations to pre1970s data, the average rate of erosion of the west coast of Florida
is 15 cm/yr. Thus, considering this difference to be due to beach nourishment, the effect
prorated over the full 110 year time period is a relative advancement of 13 cm/year.
Prorated over the approximate 20 years encompassing beach nourishment, the prorated
effect is a relative advancement of 56 cm/year.
Grouping the data by islands results in the shoreline change rate for each island on the
west coast presented in Tables 46 and 47 for the northwest and southwest coasts,
respectively. Comparing the shoreline change rates limited to pre1970s with those for the
more complete period leads to a result similar to that in Table 44. For the northwest
coast, based on the more complete period of time, of the 11 islands for which shoreline
position data are available, 5 exhibit longterm advancement and 6 have a recessional
trend. The maximum value (1.36 m/yr) for shoreline advancement occurs at Island No. 5
which is west of Mexico Beach Inlet and the maximum recessional value (2.34 m/yr)
occurs at Island No. 4 which is west of East Pass into St. Andrews Bay. For the southwest
coast, based on the more complete period of time, of the 28 islands, 15 exhibit longterm
advancement and 13 have a recessional trend. The maximum value (2.32 m/yr) for
shoreline advancement occurs at Island No. 2 (Caladesi Island) and the maximum
recessional value (2.33 m/yr) occurs at Island No. 22 which is north of Big Hickory Pass.
The longshore variations of longterm net longshore sediment transport rate difference
along the northwest coast and southwest coast, of Florida, are presented in Figures 412
and 413, respectively. For those sandy beach segments without data (e.g., western end of
Franklin County, Figure 412) or segments across inlets (e.g., Tampa Bay, Figure 413), it
is considered for purposes here, that the shoreline changes and/or volumetric storage are
zero. However, the potential contributions of entrances to longshore sediment transport
can be substantial. For the northwest coast, Bay County clearly shows a positive value of
longshore gradient of longshore sediment transport, implying a longterm shoreline
recessional trend; Okaloosa County clearly shows a negative value of longshore gradient
of longshore sediment transport, implying a longterm shoreline advancement trend; the
maximum value of the longshore transport differences occurs at Gulf County, while the
minimum occurs at Franklin County; the longshore transport difference values are
between 1.4x105 +1.4x105 m3/year. This range of values is considered reasonable and
considering the transport to be small at the eastern end of Franklin County, the positive
transport values (westward) in western Bay County and further west are consistent with
previous estimates of 140,000 m3/yr (U. S. Army Corps of Engineers, 1955). For the
southwest coast, Collier County and Pinellas County clearly show negative values of
longshore gradients of longshore sediment transport, due to longterm shoreline
advancements; the maximum value of the longshore transport differences occurs at the
south end of Collier County while the minimum occurs at Lee County; the longshore
transport decreases along the coastlines of Collier County and the south part of Lee
County, whereas, the gradient of the longshore transport along the remaining part of the
southwest coast is relatively small; the magnitudes of longshore sediment transport
difference are on the order of 105 m3/yr. Again this range of sediment transport values is
considered reasonable compared to existing estimates (Dean and O'Brien, 1987). These
results are consistent with the shoreline change characteristics in Tables 44, and 45 and
Figures 47 and 48.
Figures 414 and 415 present local crossshore sediment transport along the northwest
and southwest coastlines, of Florida, respectively, under the assumption of an average
zero gradient in longshore sediment transport for the southwest counties and a gradient
+0.4 m3/m/yr based on a transport of 140,000 m3/yr (U. S. Army Corps of Engineers,
1954) at the state's western boundary and zero transport at the eastern limit of the
northwest counties for the northwest counties, see Equations (3.13) and (3.14). Large
values of inferred local crossshore transport along this coast are not evident, except at
locations of inlets. For the northwest coast, the maximum local crossshore transport is
approximately 115 m3/m/yr and occurs in Gulf County, while the minimum
(approximately 80 m3/m/yr ) is in Franklin County. For the southwest coast, Pinellas
County has the maximum and minimum local crossshore transport, which are
approximately 45 m3/m/yr and 180 m3/m/yr, respectively. These results are consistent
with the total average net longshore sediment transport gradient considered and shoreline
change rates. On an average basis, the net crossshore sediment transport for the
northwest and southwest counties are +0.45 m3/m/yr (offshore) and 0.68 m3/m/yr
(onshore) respectively. The latter is similar to the value found for the east coast of
Florida.
It is noted that county maps are provided for reference in Figures 41, 45, 46, 47, 4
8, 412, 413, 414, and 415, respectively. As shown in these figures, the county lines
approximate those shown in the plots on the left hand side of each figure. However, the
locations of the county lines shown in the data plots are to be used for accurate positional
reference.
4.2 Standard Deviation of Shoreline Deviations
4.2.1 The Effects of Inlets on Standard Deviations
Figures 416 to 440 present shoreline change rates (upper panels) and standard
deviations (lower panels) versus monument number for each county. These lower panels
in these figures also provide the basis for considering the shoreline position data without
the influence of inlets. The vertical scales of these figures vary for each county to provide
greater detail. Two features which these figures portray are noteworthy. One is that the
greater the shoreline change rates are, in general, the greater the standard deviations. The
second is that the larger standard deviations often occur near inlets. For these shoreline
segments considered to be outside the major influence of inlets / entrances those portions
of the standard deviation plots are indicated by a bold line. It is noted that one figure has
been presented for Dade County for the period 18511986 (Figure 427) and Figure 428
represents the period 18511973, a period prior to the extensive 19761981 nourishment
project.
4.2.2 Normalized Standard Deviations
The shoreline position data at monument locations which have approximately
uniform standard deviations are considered as those without the influence of inlets, see
Figures 416 to 440 for the bold line segments. Based on these selected shoreline
position data, the standard deviations of the shoreline deviations at each predominantly
sandy county along Florida's coastline have been analyzed by the techniques described
earlier.
Figures 441 to 448 present the empirical cumulative distribution of normalized
standard deviations compared with two models: the modified Weibull and Rayleigh
cumulative distributions. All these figures show good agreement between the empirical
cumulative distribution and the theoretical results. It is evident that the Weibull
cumulative distribution fits the normalized standard deviations better than the Rayleigh
cumulative distribution, since the Weibull cumulative distribution has one more
parameter to be adjusted to fit the data. Tables 48 and 49 include results from applying
the KS test to test the goodness of fit for the models. It is interesting to see that at any
significance level equal to or less than 0.01, the models are accepted for all counties.
The parameters of the models for the normalized deviations are summarized in Tables
410 and 411 for the east coast and west coast counties respectively. For the Rayleigh
distributions, the least squares error values range from 1.87 x 104 to 6.1 x 103. For the
Weibull cumulative distributions, the least squares error values range from 1.31 x 104 to
4.4 x 103. As shown in these tables, for each county, the errors associated with the
Weibull model are dump less than those for the Rayleigh model due to one more fitting
parameter in the Weibull model, and it appears that the errors associated with the two
models approach certain limits. Additionally, the model parameters appear to be within
certain ranges except for Nassau County. For the Rayleigh distributions, the range of A is
from 0.96 to 7.85 with an average of 2.20; the range of xo is from 0.00 to 0.67 with an
average of 0.29. For the Weibull distributions, the range of n is from 1.44 to 5.82 with an
average of 2.79; the range of A is from 0.79 to 7.28 with an average of 1.67; the range of
xo is from 0.00 to 0.62 with an average of 0.17. No relationship is apparent between the
error and the maximum deviation between the empirical CDF and the theoretical CDF. In
other words, if the model has a smaller error, that does not mean its maximum deviation
is smaller, or vice versa.
Figures 449 and 450 provide two examples for examining graphically the error
dependency on the parameters. Figure 449 shows the mean square error contour at xo = 0
for the Weibull cumulative distribution fitting to normalized standard deviations for
Martin County. Figure 450 presents the mean square error line at xo = 0.31 for the
Rayleigh cumulative distribution fitting to normalized standard deviation for Martin
County. These graphical relationships between the parameters and the mean square errors
are consistent with the best fit values of the parameters in Table 410 for Martin County.
4.2.3 Unscaled Standard Deviations
The comparisons between the empirical cumulative distributions of unsealed standard
deviations and the Weibull and Rayleigh cumulative distributions demonstrate the same
information as represented in Figures 441 to 448, which is consistent with the method
by which the normalized data are obtained and the method by which the empirical
cumulative distributions are calculated; therefore, the figures presenting the comparisons
of these distributions are not shown in this paper. Tables 412 and 413 summarize the
parameters and errors for the Rayleigh and the Weibull cumulative distributions. It is
significant to note four expected features: (1) for the unsealed data, the n' values for the
Weibull cumulative distributions are the same as n for the normalized data, (2) For the
unsealed data, the value of the parameter A' for the two models can be obtained by
dividing A for the normalized data by the nt power of the rootmeansquare value of the
42
unsealed data, (3) For the unsealed data, the value of the parameter xo' for the two models
can be obtained by multiplying xo for the normalized data by the rootmeansquare value
of the unsealed data, and (4) For the unsealed data, the errors from the models are the
same as those for the normalized data. These features are consistent with the relationships
between the model for the normalized data and the unsealed data, which are presented in
Equations (3.34) to (3.38).
Table 41 Summary of shoreline change rates for Florida's east coast sandy
beach counties
All Data
Pre1970 Data
Method 1 Method 2 Method 1
Nassau
18571991
+0.50
+0.52
+0.64
Duval 18531990 78 +1.01 +1.61 +1.17
St.John 18581992 209 +0.88 +0.86 +0.92
Flagler 18721987 100 +0.04 +0.03 +0.03
Volusia 18731989 272 +0.33 +0.21 +0.26
Brevard 18741993 386 +0.36 +0.40 +0.27
Indian River 18801993 119 +0.18 +0.18 +0.15
St.Lucie 18601989 115 0.14 0.13 0.24
Martin 18831986 127 1.23 1.37 1.36
Palm Beach 18831991 227 +0.06 +0.05 0.08
Broward 18831986 128 +0.01 0.02 +0.00
Dade 18511986 112 +0.30 +0.25 0.01
Note:
Method 1 represents the average of the shoreline change rates at individual monument locations. These change rates are
determined by the best least squares fit procedure.
Method 2 is based on the best least squares fit to the average shoreline positions for each county. The average shoreline
position is obtained by averaging the shoreline positions of the individual locations for each survey.
All Data
18531993
+0.48
18511993 828 0.12 0.24
18511993 1941 +0.22 +0.16
Pre1970 Data
+0.45
Table 43 Summary of shoreline change rates for Florida's east coast island by island
/ hnarld mnknthrl 1
All Data
St. Marys River
to Nassau Sound
(Amelia)
18571991
+0.49
Pre1970 Data
+0.64
Nassau River #2 18531990 23 +2.02 +2.75
to St. George Inlet (Little Talbot)
St. George Inlet #3 18581992 173 +0.15 +0.09
to St. Augustine Inlet
St. Augustine Inlet #4 18591986 73 +2.21 +2.22
to Matanzas Inlet
Matanzas Inlet #5 18691989 262 +0.15 +0.13
to Ponce De Leon Inlet
Ponce De Leon Inlet #6 18741989 290 +0.46 +0.35
to Port Canaveral Entrance
Port Canaveral Entrance #7 18761993 219 +0.23 +0.13
to Sebastian Inlet
Sebastian Inlet #8 18601993 152 +0.29 +0.24
to Ft. Pierce Inlet
Ft. Pierce Inlet #9 18601989 124 0.26 0.41
to St. Lucie Inlet (Hutchinson)
St. Lucie Inlet #10 18831991 97 1.69 1.72
to Jupiter Inlet (Jupiter)
Jupiter Inlet #11 18831991 63 +0.21 +0.10
to Port of Palm Beach Entrance
Port of Palm Beach Entrance #12 18831991 76 0.14 0.40
to South Lake Worth Entrance (Palm Beach)
South Lake Worth Entrance #13 18831991 71 +0.17 +0.13
to Boca Raton Inlet
Boca Raton Inlet #14 18841991 29 0.14 0.19
to Hillsboro Inlet
Hillsboro Inlet #15 18831972 61 +0.26 +0.25
to Port Everglades Entrance
Port Everglades Entrance #16 18831986 69 0.09 0.19
to Bakers Haulover Inlet
Bakers Haulover Inlet #17 18671986 48 +0.90 +0.43
to Government Cut (Miami Beach) __
All Data
Government Gut
to Norris Cut
#18
(Fisher Island)
18831972
0.12
Pre1970 Data
Norris Cut #19 18831986 10 1.32 1.62
to Bear Cut (Virginia Key)
Bear Cut #20 18671986 24 0.07 0.22
to South End of Key Biscayne (Key Biscayne)
Note:
The method represents the average of the shoreline change rates at individual monument locations. These change
rates are determined by the best least squares fit procedure.
46
Table 44 Summary of shoreline change rates for Florida's west coast sandy
All Data
Escambia+Santa Rosa
18561978
Pre1970 Data
0.19
Okaloosa 18711990 50 +0.50 +0.26
Walton 18721977 127 0.14 0.18
Bay 18551977 144 0.24 0.39
Gulf 18571984 162 0.51 0.59
Franklin 18561979 239 0.06 0.23
Pinellas 18731987 187 +0.29 0.26
Manatee 18741986 67 0.14 +0.17
Sarasota 18831994 183 +0.04 0.07
Charlotte 18601992 68 +0.18 +0.41
Lee 18581989 239 + 0.01 0.25
Collier 18851988 148 +0.27 +0.39
Note:
Shoreline change rates shown above are the average of the shoreline change rates at individual monument locations
within each county. These change rates are determined by the best least squares fit procedure.
ratoc fnr
All Data
18551990
18581994 892 +0.12 0.03
18551994 1828 0.02 0.15
Pre1970 Data
0.27
47
Table 46 Summary of shoreline change rates for Florida's northwest coast island by island
State Line
to Pensacola Pass
#1
(Perdido Key)
18581979
All Data
0.04
Pre1970 Data
0.12
Pensacola Pass #2 18591989 163 0.11 0.17
to East Pass. Destin (Santa Rosa Island)
East Pass. Destin #3 18551990 258 +0.03 0.06
to Panama City Channel (Mainland)
Panama City Channel #4 18551978 24 2.34 3.02
to St. Andrew Bay Entrance. East Pass (Shell Island)
to St. Andrew Bay Entrance. East Pass #5 18551978 6 +1.36 +1.02
to Mexico Beach Inlet (Crooked Island)
Mexico Beach Inlet #6 18681984 48 +0.60 +0.41
to Entrance to St. Joseph Channel (Mainland)
Entrance to St. Joseph Channel #7 18571984 131 0.80 0.85
to Indian Pass St. Joe Spit
Indian Pass #8 No Data
to West Pass (St. Vincent Island)
West Pass #9 18571980 51 +0.18 0.12
to Saint George Island Channel (St. George Island)
Saint George Island Channel #10 18561980 98 0.22 0.39
to East Pass (St. George Island)
East Pass #11 18581980 44 0.24 0.30
to Unnamed Channel (Dog Island)
Unnamed Channel to #12 18581980 46 0.26 0.01
Ochlochnee River (Alligator Point)
Note:
Shoreline change rates shown above are the averages of the shoreline change rates at individual monument
locations for each island. These change rates are determined by the best least squares fit procedure.
Table 47 Summary of shoreline change rates for Florida's southwest coast island by island
All Data
Pre1970
Data
Gulf of Mexico
to Hurricane Pass
#1
(Honeymoon Island)
18731987
+0.86
0.08
Hurricane Pass #2 18731987 16 +2.32 +0.44
to Dunedin Pass (Big Pass) (Caladesi Island)
Dunedin Pass (Big Pass) #3 18731987 19 +0.54 +0.07
to Clearwater Pass (Little Pass) (Clearwater Beach
Island)
Clearwater Pass (Little Pass) #4 18731987 73 0.27 0.56
to John's Pass (Sand Key)
John's Pass #5 18731987 17 +0.87 +0.14
to Blind Pass (Treasure Island)
Blind Pass #6 18731987 22 +0.34 +0.23
to Bunces Pass (Long Key)
Bunces Pass #7 18731987 25 0.40 0.84
to Tampa Bay Entrance (Mullet Key)
Tampa Bay Entrance #8 18741986 41 0.36 0.02
to Longboat Pass (Anna Maria Key)
Longboat Pass #9 18831987 55 0.27 0.07
to New Pass (Longboat Key)
New Pass #10 18831987 15 +0.71 +0.09
to Big Sarasota Pass (Lido Key)
Big Sarasota Pass #11 18831993 33 +0.83 +0.79
to Midnight Pass (Siesta Key)
Midnight Pass #12 18831994 37 +0.00 0.26
to Venice Inlet (Casey Key)
Venice Inlet #13 18831993 90 0.01 0.08
to Stump Pass (Manasota Peninsula,
Manasota Key)
Stump Pass #14 18601992 36 0.71 0.87
to Gasparilla Pass (Knight,Bocilla,Don
Pedro,Little Gasparilla
Islands)
Gasparilla Pass #15 18601992 37 +0.23 +0.67
to Boca Grande Pass (Gasparilla Island)
Boca Grande Pass #16 18591989 39 +0.22 +0.24
to Captiva Pass (Lacosta Island)_
Table 47continued
All Data
Captiva Pass
to Redfish Pass
#17
(North Captiva Isalnd)
18591989
Pre1970
Data
1.05 0.72
Redfish Pass #18 18591989 27 0.86 0.81
to Blind Pass (Captiva Island)
Blind Pass #19 18581989 65 +0.90 +0.88
to Entrance to San Carlos Bay (Sanibel Island)
Entrance to San Carlos Bay #20 18581989 36 +0.22 0.38
to Big Carlos Pass (Estero Island)
Big Carlos Pass #21 18851989 12 0.68 3.87
to New Pass (Lovers Key)
New Pass #22 18851989 3 2.33 2.64
to Big Hickory Pass (Big Hickory Island)
Big Hickory Pass #23 18851989 30 +0.04 0.16
to Wiggins Pass (Bonita Beach)
Wiggins Pass #24 18851988 25 0.07 +0.12
to Clam Pass (Vanderbilt Beach)
Clam Pass #25 18851988 16 0.05 +0.14
to Moorings (Doctors) Pass (Naples Island)
Moorings (Doctors) Pass #26 18851988 32 0.05 +0.09
to Gordon Pass (Naples Island)
Gordon Pass #27 18851988 38 +0.37 +0.47
to Big Marco Pass (Keewaydin Island)
Big Marco Pass #28 18851988 21 +1.53 +1.73
to Caxambas Pass (Marco Island)
Note:
Shoreline change rates shown above are the averages of the shoreline change rates at individual monument
locations for each island. These change rates are determined by the best least squares fit procedure.
Table 48 Summary of the KolmogorovSmirnov test for goodness of model fitting to
normalized standard deviations for Florida's east coast sandy beach counties
The KolmogorovSmirnov Test of Goodness of Fit
Reject (X) or Accept (V) the Proposed Models
Number Significance Level
County Years of Rayleigh CDF Weibull CDF
County Years of
Profiles Significance Level Significance Level
0.10 0.05 0.01 IFRFSL FFs,
0.10 0.05 0.01 0.10 0.05 0.01
Nassau 18711991 44 0.1839 0.2050 0.2457 0.1217 V V V 0.0680 V V V
Duval 18581990 48 0.1761 0.1963 0.2353 0.0774 / V V 0.0536 V V V
North St. Johns 18581992 170 0.0936 0.1043 0.1250 0.0631 V V V 0.0507 V V V
East_
Florida Flagler 18721987 100 0.1220 0.1360 0.1630 0.0859 V V V 0.0682 V V V
Coast
Volusia 18731989 241 0.0786 0.0876 0.1050 0.0518 V V V 0.0257 V V V
Brevard 18761993 198 0.0867 0.0967 0.1158 0.1047 X X V 0.0351 V V V
Indian River 18801993 119 0.1118 0.1247 0.1494 0.0691 V V V 0.0477 V V V
St. Lucie 18601987 84 0.1331 0.1484 0.1778 0.0544 V V V 0.0299 V V V
South Martin 18831986 82 0.1347 0.1502 0.1800 0.0951 V V V 0.0673 V V V
East
Florida PalmBeach 18831991 197 0.0869 0.0969 0.1161 0.0695 / V V 0.0350 V V V
Coast
Broward 18831986 94 0.1258 0.1403 0.1681 0.0365 V V V 0.0403 V V V
Dade 18511973 81 0.1360 0.1509 0.1808 0.0984 V V V 0.0662 V V
Table 49 Summary of the KolmogorovSmirnov test of goodness of model fitting to
normalized standard deviations for Florida's west coast sandy beach counties
The KolmogorovSmimov Test of Goodness of Fit
Number Reject (X) or Accept (V) the Proposed Models
County Years of Significance Level
Profiles Rayleigh CDF Weibull CDF
Significance Level Significance Level
0.10 0.05 0.01 FRF F,Fs 
0.10 0.05 0.10 0.10 0.05 0.10
Escambia 18561978 181 0.0907 0.1011 0.1212 0.0908 X V V 0.0455 / V V
Santa Rosa
Okaloosa 18711990 39 0.1954 0.2178 0.2610 0.0588 V V V 0.0536 / V V
North
West Walton 18721977 127 0.1083 0.1207 0.1446 0.0369 V V V 0.0416 V/ V
Florida
Coast Bay 18551977 39 0.1954 0.2178 0.2610 0.0719 V V V 0.0699 V V V
Gulf 18681984 87 0.1308 0.1458 0.1748 0.0636 V V V 0.0522 V V V
Franklin 18561979 149 0.0999 0.1114 0.1335 0.0296 V V V 0.0364 V V V
Pinellas 18731987 31 0.2191 0.2443 0.2928 0.0588 V V V 0.0443 V V V
Manatee 18741986 21 0.2662 0.2968 0.3557 0.1412 V V V 0.1278 V V V
South Sarasota 18831994 78 0.1381 0.1540 0.1846 0.0466 V V V 0.0379 V V V
West
Florida Charlotte 18831988 12 0.3522 0.3926 0.4705 0.1332 V V V 0.1028 V V V
Coast
Lee 18581989 53 0.1676 0.1868 0.2239 0.0814 V V V 0.0530 V V V
Collier 18851988 80 0.1364 0.1521 0.1822 0.0637 V V V 0.0605 V/ V
Table 410 Summary of the parameters for the fitting of models to normalized standard deviations
for Florida's east coast sandy beach counties
BESTFIT WEIBULL CUMULATIVE DISTRIBUTION: F(x) = 1 eA(xxo)
BESTFIT RAYLEIGH CUMULATIVEDISTRIBUTION: F (x) = 1 e"A(x)2
Number Rayleigh CDF Weibull CDF
County Years of x,
Profiles A xo Error n A xe Error
Nassau 18711991 44 0.68 7.85 0.67 3.00x103 5.82 7.28 0.31 9.76x104
Duval 18581990 48 0.37 2.23 0.37 1.60x103 3.81 0.86 0.00 6.04x104
North
East St. Johns 18581992 170 0.24 1.58 0.24 4.75x104 2.41 1.34 0.15 3.72x104
Florida Flagler 18721987 100 0.30 1.75 0.30 1.40x10l 3.19 0.82 0.00 9.28x104
Coast
Volusia 18731989 241 0.28 1.79 0.28 5.98x104 2.27 1.99 0.28 1.31x10"
Brevard 18761993 198 0.44 2.61 0.44 3.20x103 3.37 3.07 0.33 1.58x104
Indian River 18801993 119 0.34 2.27 0.34 6.85x104 2.23 2.57 0.34 3.66x104
St. Lucie 18601987 84 0.40 2.40 0.40 5.15x104 3.04 1.60 0.19 1.91x104
South
East Martin 18831986 82 0.31 1.76 0.31 2.50x103 3.77 0.79 0.00 7.36x104
Florida Palm Beach 18831991 197 0.20 1.32 0.19 8.82x104 2.90 0.85 0.00 2.20x104
Coast
Broward 18831986 94 0.28 1.87 0.27 2.99x104 2.04 1.90 0.27 2.91x104
Dade 18511973 81 0.15 1.25 0.15 2.40x103 3.09 0.94 0.01 6.69x104
Table 411 Summary of the parameters for the fitting of models to normalized standard deviations
for Florida's west coast sandy beach counties
BESTFIT WEIBULL CUMULATIVE DISTRIBUTION: F (x) = 1 eA(xx)
BESTFIT RAYLEIGH CUMULATIVE DISTRIBUTION: F (x) = 1 eA(xx)2
Number Rayleigh CDF Weibull CDF
County Years of x,
Profiles A x, Error n A xo Error
Escambia 18561978 181 0.15 1.21 0.14 2.80x103 3.15 1.05 0.04 2.91x104
Santa Rosa
Okaloosa 18711990 39 0.38 2.13 0.35 1.10x103 2.43 1.67 0.23 9.96x104
North Walton 18721977 127 0.00 0.96 0.00 2.70x104 1.96 0.96 0.00 2.55x104
West
Florida Bay 18551977 39 0.31 1.83 0.23 8.08x104 1.98 1.82 0.23 8.06x104
Coast
Gulf 18681984 87 0.23 1.14 0.05 5.88x104 1.59 1.46 0.19 4.14x104
Franklin 18561979 149 0.12 1.00 0.00 1.87x104 1.93 0.99 0.00 1.44x104
Pinellas 18731987 31 0.28 1.82 0.28 9.19x104 2.22 2.00 0.28 5.90x104
Manatee 18741986 21 0.34 1.66 0.29 6.10x103 3.27 0.79 0.00 4.40x103
South
West Sarasota 18831994 78 0.50 3.02 0.46 4.25x104 2.97 2.02 0.25 2.02x104
Florida Charlotte 18831988 12 0.53 2.65 0.42 4.40x103 3.99 0.85 0.00 2.70x103
Coast
Lee 18581989 53 0.68 4.56 0.53 9.19x104 1.44 4.08 0.62 6.17x104
Collier 18851988 80 0.31 2.14 0.31 6.88x104 2.05 2.19 0.31 6.70x104
Table 412 Summary of the parameters for the fitting of models to unsealed standard deviations
for Florida's east coast sandy beach counties
BESTFIT WEIBULL CUMULATIVE DISTRIBUTION: F(x) = 1 eA(xx)n
BESTFIT RAYLEIGH CUMULATIVE DISTRIBUTION: F(x) = 1 eA(xx)2
Number Rayleigh C.DF Weibull C.D.F
County Years of x, x,
Profiles A xo Error n A x, Error
Nassau 18711991 44 18.86 12.75 0.0221 12.64 3.00x103 5.82 2.71x107 5.85 9.76x104
Duval 18581990 48 20.24 7.57 0.0054 7.49 1.60x103 3.81 9.14x106 0.00 6.04x104
North
East St. Johns 18581992 170 13.35 3.27 0.0089 3.20 4.75x104 2.41 2.60x103 2.00 3.72x104
Floridast I 2.60xl2x 3 2.00 3.72
Florida Flagler 18721987 100 10.03 3.04 0.0174 3.01 1.40x103 3.19 5.22xl0 0.00 9.28x10
Coast
Volusia 18731989 241 9.30 2.65 0.0207 2.60 5.98x104 2.27 1.26x102 2.60 1.31x104
Brevard 18761993 198 11.68 5.19 0.0191 5.14 3.20x103 3.37 7.56x104 3.86 1.58x10
Indian River 18801993 119 9.31 3.18 0.0262 3.17 6.85x104 2.23 1.76x102 3.17 3.66x104
St. Lucie 18601987 84 9.94 4.00 0.0243 3.98 5.15x104 3.04 1.50x103 1.89 1.91x10
South
East Martin 18831986 82 9.34 2.93 0.0201 2.90 2.50x103 3.77 1.74x104 0.00 7.36x104
Florida Palm Beach 18831991 197 12.05 2.38 0.0091 2.29 8.82x104 2.90 6.18x104 0.00 2.20x104
Coast
Broward 18831986 94 10.34 2.89 0.0175 2.79 2.99x104 2.04 1.63x102 2.79 2.91x10"4
Dade 18511973 81 14.06 2.11 0.0063 2.11 2.40x103 3.09 2.67x104 0.14 6.69x10"
Table 413 Summary of the parameters for the fitting of models to unsealed standard deviations
for Florida's west coast sandy beach counties
BESTFIT WEIBULL CUMULATIVE DISTRIBUTION: F (x) = 1 eA(xx
BESTFIT RAYLEIGH CUMULATIVE DISTRIBUTION: F (x) = 1 eA(xo)2
Number Rayleigh CJD.F Weibull C.D.F
County Years of x, x
Profiles A xo Error n A xo Error
Escambia 18561978 181 17.82 2.64 0.0038 2.50 2.80x103 3.15 1.21x104 0.71 2.91x104
Santa Rosa
Okaloosa 18711990 39 9.84 3.70 0.0220 3.44 1.10x10l3 2.43 6.40x103 2.26 9.96x104
North Walton 18721977 127 13.66 0.04 0.0051 0.00 2.70x104 1.96 5.80x103 0.00 2.55x104
West
Florida Bay 18551977 39 7.11 2.24 0.0362 1.63 8.08x104 1.98 3.72x102 1.63 8.06x104
Coast
Gulf 18681984 87 9.42 2.17 0.0129 0.47 5.88x104 1.59 4.13x102 1.79 4.14x104
Franklin 18561979 149 12.23 1.51 0.0067 0.00 1.87x104 1.93 8.00x103 0.00 1.44x104
Pinellas 18731987 31 5.26 1.50 0.0659 1.47 9.19x104 2.22 4.98x102 1.47 5.90x104
Manatee 18741986 21 11.47 3.95 0.0127 3.33 6.10x103 3.27 2.71x104 0.00 4.40x103
South
West Sarasota 18831994 78 10.26 5.09 0.0288 4.72 4.25x104 2.98 2.00x103 2.56 2.02x104
Florida Charlotte 18831988 12 12.03 6.39 0.0183 5.05 4.40x103 3.99 4.14x10 0.00 2.70x103
Coast
Lee 18581989 53 14.72 9.95 0.0210 7.80 9.19x104 1.44 8.50x102 9.13 6.17x104
Collier 18851988 80 11.66 3.67 0.0157 3.61 6.88x104 2.05 1.42x102 3.61 6.70x104
.I I
...... . ........ assau .... . ........ .... ..
.... .. .. u.va .. .... ..... ......... .. .... .
St.Johns
.................. ..... ....... ..............
'fla ler
Volusia
Brevard:
S indian River
St.Lucie: :
......... M a in ................
Palm Beach i
Broward
Dade
I I 1 1 1
I
61
30 25 20 15 10 5 0 5 10 15 20
Shoreline Change Rate (meters/year)
Figure 41 Longterm shoreline change rates along the east coast of Florida. These
change rates are determined by the best least squares fit procedure
for the complete record of data availability.
.. .. . .. .. .. . .
L...................................
L ....... .........: ......... .........
r..................:.........:....
h. . ... ..... .... .... .. : .... ...
. . . . . . . . .
I
"'
Naemru.ERB5
3
  i
            1k      

:;i A 
2 i
A
Yoar
St.Johno.Rl 72
...... 
      
  . . .
  : ^
  ; ; 
Your
So  UY  
4o  1TT 
20  ,  Qf _ ^ ^
  s . ' 4'  \
o 
    
40   t
0 0            r         .
lOO
1880 I... 1;00 1020 1040 1000 1080 200
k ilo  *:"*:  ~t^^,
  4:      
;     
co s ^, 'Y .   i , 
  r
1 1 . .T . . . . . . . . . . . .
IO ... ... .. T D0 ...... T01 .... t0 .. . . .. .. .T.. .
. . i . . . . i . . .
VotuIua.R35
   
  a
 ~ tti
    
   t 
+ 4   
reso reso 1roo
1020 1o40 1 0. 1000 2000
Yeor
So  ._ ^ 'X 
So     
s  
..o  
o   ~ o Ye. o  .
00 ".  ,^   
Of T_ ,  
IsO 105.0 1 .00 120 g1040 1. 0 10a0 20
Year
l r     L oo*
1,o  so
Indianj Rlveri R43
oo .   
Ie .j' I.o
0       
70 4 1  7 102014010
" O r0oo 1ao l"40 o .o0
Year
Io ...  so
ISo    I e
,40   ,i : ... .. .. . . .
o    
S40 r AS
S1020 140
"  .  . as0
.0     
04
...  
o ......... ..
 . .
0 Year
  k .  40
.0   
20
r 7w r f       t       2
2.. 1
~ JL   
'OF i
0~o ,0oo 1,UO 1 r0 1 e 0
Year
_P I n nj __B o_ il_ R_4 ......__. _.. ... ... .. _
..     
0o 1000 100o 1040 1050 150 30000
Year
DdeRi2 
   j
_; T   
. . 
.  .
  
    
 L  
 ____~~~   
: L :  
        
1a0o 1o0o 020ao 140 105o 1.0o
Year
Figure 42 Examples of shoreline positions versus time at individual monument
locations for Florida's east coast sandy beach counties.
300
a
i10
.340
ZO
a
8t.~u~lsR7
z 7iri
~~~j~~ mi,~
? z
;~;~,~
I
7   
sa
Year
enl e a
o mr

0 ,
I
I .... .. .. i
10
rI.o
I a0
z000
BO
Ison I ..
IwO .. OO rIS
0s 1 Bo I.00
I .o 2000
2000
30 20 10 0 10 20 3C
Shoreline Change Rate
120
SL Johns County (209)
100
80 .....
6 0 ..... ....... . . . . ... . . ...... . . . . ... ... .
2 0 ............. . . ...... ..... . .. ... .. . ... ....... .. .. . ... ....... ...
20 1 n
30 20 10 0 10 20 31
Shoreline Change Rate
0
Shoreline Change Rate
in.
25 
Duval County (78)
30 20 10 0 10 20 3
Shoreline Change Rate
An.
0'
30 20 10 0 10 20 3
Shoreline Change Rate
160 .....
140.
120 .. ..........
0 "" I .. .
30 20 10 0 10 20
Shoreline Change Rate
Figure 43 Histograms of longterm shoreline change rates in meters per year
for the east coast of Florida. (Values in parenthesis indicate total
number of locations).
Nassau County (68)
Fagler County (100)
Brevard County (386)
. ...... ... ... ........... .. .......
.... .. ...... . . ...... .. ...... . . . .
. .. . .
14
I
^
ian'
30 20 10 0 10 20 3
Shoreline Change Rate
50 
Martin County (127)
40
35
20 
15
10 ...... ...
2 5 .... .... . . ............ . ..... ... ... .... ..... ...
30 20 10 0 10 20 3(
Shoreline Change Rate
0 I I l.llll I I
30 20 10 0 10 20 30
Shoreline Change Rate
Figure 43 Continued
rre.
30 20 10 0 10 20 30
Shoreline Change Rate
140
Palm Beach County (227)
120
100
80 ... . ... .
60 ......
40 .........
20
0 1 L~n
0 20 10 0 10 20 3C
Shoreline Change Rate
0
Shoreline Change Rate
Indian River County (119)
St. Lucie County (115)
.li . .. .. .
Broward County (128)
..... . .. ... ... ..
. ........... I ...........
. .........
. ........... ... . .
.. .. ......... ... .... . .........
.............................................
..... ... ...... . ........
.. . .. . . .. . .
Year
150 1900
1850 1900
Year
1950
Average shoreline change over time for Florida's east coast
sandy beach counties based on method 2.
Figure 44
2000
L I/ 14 12 10 8 6 4 2 0 2
Longshore Sediment Transport Difference (cubic meters x 10 5 / year)
Figure 45 Total net longshore sediment transport difference along Florida's
east coast. Based on consideration of zero net longterm cross
shore sediment transport.
.... ..............
% . .
..................
. . .. S
.
I I
I I '
0
200 150 100 50 0 50 100 150 200 250 300
Local Cross Shore Sediment Transport (m^3/mlyr)
Figure 46 Local crossshore sediment transport along Florida's east coast.
based on considering a uniform gradient of net longshore
sediment transport. Gradient is based on previous estimates
of net transport at the northern and southern limits of region
considered.
A
c
I
"
S............ Nassau.:.............. .......
'. .Duval ........
: St.Johns
^. .......................................................
...... ....... Fla ler .
SVolusia:
............ .................. ........... .
SBrevard
........ ....... ....Indian R river ...... ......... .........
................I d i..... ....v... .....
.......... S t.Lu ci .................. .......
M a . .
:Palm Beach
. . . ... .. .. .. : . .. ... .. . .. ... .. .. .... ... .... .
 Dade
I I
I
25 20 15 10 5 0 5 10 15 20
Shoreline Change Rate (meters/year)
Figure 47 Longterm shoreline change rates along Florida's northwest coast. These
change rates are determined by the best least squares fit procedure for
the complete record of data availability.
0E r
I I
I I
Pinellas:
....... : ..........: ......... : .................... ............................
Manatee
: Sarasota
...... .... .......... .......... ..... .. ......... ,.........
Charlotte
S Lee
Collier
I I
U
10 5 0 5 10 15 20 25 30 35 40
Shoreline Change Rate (meters/year)
Figure 48 Longterm shoreline change rates along Florida's southwest coast. These
change rates are determined by the best least squares fit procedure for
the complete record of data availability.
2.5
x
iij
o
1 .
1.5
r
~
c
......... : .........
""""'j""~
~'''''''"
Eacal nbiai Sainta Rioa. R138
  so
.  *i T I 7
     ,  A
a,~' j   s
40o
    . so
so
Oso
                 2
126
0a
Iss
10o0 1010 1040 10s0 1o80
Year
  , 7o   1
.. 
O o   
::::  i::?t ::::::: ::
 +:  
. ... .  i  .  .
i _  
.    t. ..  i       
o0 180 1000 1020o 1040 1so 1s 0
YIIr
 ..   . . 
      
70 .  ^^ 
70    .. .. 4 ....  ..   
e t       
.o... .;   
4Sc
as'tt
4O
10 lsso 1000 1.20 1940 0e0o 1...
Year
a,
A1
66
210
Z O
7.
140
130
1080 1000 1*00 201
Pln. ime. ? R 88
Year
'"   4 
r~   t  
1:   
0     
_B 5^    
1i80o 18o 1000 1.20 1040 o o 1000 3oI
Year
tsr ati  1 1 
,.0
e rr y *~    

180O 1200 1.20 1040 1a80 180 2000
Year
lO80 Year
. . . . .                
on   
0 t  
..ii~4 ~
oa _ 
o
; r~ ~~~ 
. . . . . .. . .    .. .i . . . . .
1.o. I.. ls sO 120
Year
7 0     T       7    
S Manateei R50
< ao      
go     4   
R It 
Oo   
o  4 
I 10 18 1000I... 1020 104I lo00 1000 20o
Year
_^;   
Chi rlott,. R51
Tre.d Ln:0: .i mri Mf
7 70 10 10 120 4  
 t  : 1
"8 180 I* 1920y 1.4o l ... 1 0 20
sar
S.Colli.r. R 107
.To     
To   
,o .  ^ O ^ >~ 'r
so   
o a
7   7     
180 1OO Ir o 1. 40 ls O
Y.lr
Figure 49 Examples of shoreline positions versus time at individual monument
locations for Florida's west coast sandy beach counties.
40 1r8o 180o
60 10.O 1o00 1020 140 1s00 1
Year
  _   . . . . . . . .. . . . . . . . 
     r .......
 
_    
..  ? ....  .  .
 L__. 
        I               
2000
            r   t    ~
.. r~i

      
e
1Ba
1040 1.0o Io80
Do
I
Lee. R164
e   
7r r d In: 0.01 n:y
    :
          
2000

1B0o 2000
I.oo
 Year
%'.or
1 O..
I.
Escambla + Santa Rosa County (214)
80 30 20 10 0 10 20 30
70 ......
60
50 . ..... .... .. .......
40
S 30 ........ ... ......
20
30 20 10 0 10 20 30
Shoreline Change Rate
8 Wa0onCoun0
Walton County (127)
n .... ..... ..... ... .. . ........... .. . . . . . . . . . .
30 20 10 0 10 20 30
Shoreline Change Rate
40
Gulf County (162)
35 .......
30 .............. ..
25
S20 ..... ... .. . ..
.. . .... .. ..... .. .. ....... .. . .. .. .......... . ...... .
1 0 .... ..... .................... .............. ...... .... .... .... ...... ................. ..
5n ..., m.n ,
30 20 10 0 10
Shoreline Change Rate
30 20
10 0 10
Shoreline Change Rate
10 0 10
Shoreline Change Rate
Figure 410 Histograms of longterm shoreline change rates in meters per year
for the northwest coast of Florida. (Values in parenthesis indicate
total number of locations).
Bay County (144)
. .
20 30
.........................................
...... I ... ............. I ... ... ........
....... ..................... ......
............................................
.. . ......... ...... . .......
............ I .. ..... .. ....................
............................................
.. .. ........... ........... ........
. JU
20 30
Pinellas County (187)
30 20 10 0 10 20 30
Shoreline Change Rate
80
Sarasota County (183)
70 ...... .. ... .. . ..
60 . . .
50 . ......
40 ... .
30 . .
2 0 ........... ... . ................ .. .......... .. .... ........ .... ........
10 ..._.....
10 L.... .. .. .. .... ............
30 20 10 0 10 20 30
Shoreline Change Rate
50 1  
SLee County (239)
A R ) ................I ................................
30 20 10 0 10
Shoreline Change Rate
20 30
30 20
10 0 10
Shoreline Change Rate
10 0 10
Shoreline Change Rate
30 20 10 0 10
Shoreline Change Rate
Figure 411 Histograms of longterm shoreline change rates in meters per year
for the southwest coast of Florida. (Values in parenthesis indicate
total number of locations).
Manatee County (67)
20 .. . ....... .. ................ . .. .. .... .... .. ...... ..... .... ....... .. . ..
0
5
5 ..................0 ..
.. . .. . .. ....... .......... .. ..... L . .i. . .. . . . . .
Collier County (148)
70
60
50
40
30
2 0 .......... .. ...... .. .. ... .. .. .. ...... ....... .. .. . .. .. ... ...
n 1 ____
I .I h
. ...... I .... .. ...... I ................
...............................................
. ........................ I .. ........... ....
. . ....... I ... . ... ........ .. .
n
Kn
I~~~~~ ~ ~ .. .. . .. . .. . .. . . .
nanisu ni
n
 .. .... ... ........... I 
.......... I .......... ................ I ........
..................................... ........
U
n
I
n r
20 30
20 30
2.5 2 1.5 1 0.5 0 0.5 1 1.5 2
Longshore Sediment Transport Difference (cubic meters x 1 0^5 / year)
Figure 412 Total net longshore sediment transport difference along Florida's
northwest coast. Based on consideration of zero net longterm
crossshore sediment transport.
2.5 2 1.5 1 0.5 0 0.5 1 1.5 2
Longshore Sediment Transport Difference (cubic meters x 1O^5 / year)
Figure 413 Total net longshore sediment transport difference along Florida's
southwest coast. Based on consideration of zero net longterm
crossshore sediment transport.
4
Escambia
Santa Rosa
Okaloosa
.
:....... ... ....
Walton
Bay
Gulf
t"
3
x
22.5
4
E
0
c
S1.5
0
r
0
J
f0ol
150 100 50 0 50
Local Crossshore Sediment Transport (m^2/yr)
Figure 414
Local Crossshore sediment transport along Florida's northwest
coast. Based on considering a gradient of 0.4m3/m/yr net longshore
sediment transport over the limits of the region considered.
: .......... o ............. .............
Franklin _
I I.I,.
I I
i.. ''* ''*.......1......
S....... .. .. . .. .. .. .... . .. .. .. .. .. ..
.. . . .
.. . . . . .. . . . .
...........
III
100 150
........... .. ............ ^
. .. . . . .... . . . .
Pinellas
Manatee
Sarasota
Charlotte
Lee
Collier
........... ..... .... .............
............. ..... . . . . . . . . . . . .
.: > I
150 100 50 0 50
Local Crossshore Sediment Transport (m^2/yr)
100 150
Figure 415 Local crossshore sediment transport along Florida's southwest
coast. Based on considering a zero gradient of net longshore
sediment transport over the limits of the region considered.
ILE
S2.5
x
a)
e2
E
C
0
C
0
t
0.5 .
Il I
....:.............
. .. .. .... ...
.. .... .... ...
. . . .
72
Nassau County
EI
E 10
a0
5 5
00
a,
C
* Ii
o 5
jc
W,
09 19
Figure 416
29 39 49 59 69
Monument Number
Shoreline change rates and unsealed standard deviations
along the coastline of Nassau County, 18571991. The bold
segment of the standard deviation curve in the lower panel
represents the shoreline portions considered to be acceptable
for analysis of standard deviations.
19 29 39 49 59 69
Monument Number
I I I I I
150
100
50
0
Duval County
4)  
 South
c 10
0
N suS nFort George Inlet
20 Nassau Sound St. Johns River Entrance
0 30 i 
02 12 22 32 42 52 62 72
Monument Number
" "400 r
400 I
E
r
o 300
S200
CO
r' 0 
02 12 22 32 42 52
Monument Number
Figure 417
62 72
Shoreline change rates and unsealed standard deviations
along the coastline of Duval County, 18531990. The bold
segment of the standard deviation curve in the lower panel
represents the shoreline portions considered to be acceptable
for analysis of standard deviations.
St. Johns County
E

5 15
010
5)
0
o 5
U 0
"600 
4 400
200
*O0
50 100 150 200
Monument Number
Figure 418
Shoreline change rates and unsealed standard deviations
along the coastline of St. Johns County, 18581992. The bold
segment of the standard deviation curve in the lower panel
represents the shoreline portions considered to be acceptable
for analysis of standard deviations.
50 100 150 200
Monument Number
Flagler County
i4
E 0.4
aD
S0.2
r0
S0.2
o 0.4
S20
C
0 15
S1o0
t0
%o5
CO n
20 40 60 80
Monument Number
100
20 40 60 80
Monument Number
Figure 419 Shoreline change rates and unsealed standard deviations
along the coastline of Flagler County, 18721987. The bold
segment of the standard deviation curve in the lower panel
represents the shoreline portions considered to be acceptable
for analysis of standard deviations.
100
Volusia County
50 100 150 200 250
Monument Number
50 100 150 200 250
Monument Number
Figure 420 Shoreline change rates and unsealed standard deviations
along the coastline of Volusia County, 18731989. The bold
segment of the standard deviation curve in the lower panel
represents the shoreline portions considered to be acceptable
for analysis of standard deviations.
0 South
Ponce De Leon Inlet
 200
C
. 150
o 100
I
S50
CO n
I I
Brevard County
E6
4
a,
22
0I
"C
o 2
00
a,0
50 100 150 200 250 300 350
Monument Number
S 50 100 150 200 250 300 350
Monument Number
Figure 421 Shoreline change rates and unsealed standard deviations
along the coastline of Brevard County, 18741993. The bold
segment of the standard deviation curve in the lower panel
represents the shoreline portions considered to be acceptable
for analysis of standard deviations.
'g100
c
50
a
 n
Indian River County
I
E2
a)
a)
o0
a)
S1
0
C,
E
3* 15
a)
lo
0 5
) n
20 40 60 80 100
Monument Number
Figure 422 Shoreline change rates and unsealed standard deviations
along the coastline of Indian River County, 18801993. The bold
segment of the standard deviation curve in the lower panel
represents the shoreline portions considered to be acceptable
for analysis of standard deviations.
20 40 60 80 100
Monument Number
St. Lucie County
E 3

0
cu
5 0
E
0 40 
3 30
a,0
o 20
C 0
*j2
6. o1
20 40 60 80 100
Monument Number
Figure 423
Shoreline change rates and unsealed standard deviations
along the coastline of St. Lucie County, 18601989. The bold
segment of the standard deviation curve in the lower panel
represents the shoreline portions considered to be acceptable
for analysis of standard deviations.
20 40 60 80 100
Monument Number
80
Martin County
I
E5
0
00
C=
0 5
E0
o 10
C,
0
150
o
a
c
V) n
20 40 60 80 100 120
Monument Number
Figure 424 Shoreline change rates and unsealed standard deviations
along the coastline of Martin County, 18831986. The bold
segment of the standard deviation curve in the lower panel
represents the shoreline portions considered to be acceptable
for analysis of standard deviations.
20 40 60 80 100 120
Monument Number
81
Palm Beach County
S3
, 2
o 1
0S o
O9)
S40
3 30
a 20
00
CO 0i
0 50 100 150 200
Monument Number
Figure 425 Shoreline change rates and unsealed standard deviations
along the coastline of Palm Beach County, 18831991. The bold
segment of the standard deviation curve in the lower panel
represents the shoreline portions considered to be acceptable
for analysis of standard deviations.
50 100 150 200
Monument Number
Broward County
a
E
a 1 
a)
0
0
C 2
o 3L
CO
0
100
50
a n
0) n
20 40 60 80 100 120
Monument Number
Figure 426
Shoreline change rates and unsealed standard deviations
along the coastline of Broward County, 18831986. The bold
segment of the standard deviation curve in the lower panel
represents the shoreline portions considered to be acceptable
for analysis of standard deviations.
20 40 60 80 100 120
Monument Number
83
Dade County
E
S2
( 0
2
ou
a,
c
02
S4
CO
80 100
C
o
. 40
20
O 0
0 20 40 60 80 100
Monument Number
Figure 427 Shoreline change rates and unsealed standard deviations
along the coastline of Dade County, including period
of extensive nourishment (18511986).
40 60
Monument Number
SSouth
(1) Bakers Haulover Cut
(2) Government Cut
(3) Norris Cut
(4) Bear Cut (1) (2)
I I
0)
I 1 I I I
84
Dade County
E
2
0,
e
o 4

" 0
'60
C
0
0 
c,
S40 
0
20 40 60 80 100
Monument Number
Figure 428
Shoreline change rates and unsealed standard deviations
along the coastline of Dade County, including period
of extensive nourishment (18511973). The bold
segment of the standard deviation curve in the lower panel
represents the shoreline portions considered to be acceptable
for analysis of standard deviations.
20 40 60 80 100
Monument Number
Escambia+Santa Rosa County
E
2a
tM
G) 1
C
0)
0
6 22
= 0
W,
50 100 150 200
Monument Number
g 100
E
0
) 50
0
0 50 100 150 200
Monument Number
Figure 429
Shoreline change rates and unsealed standard deviations
along the coastline of Escambia and Santa Rosa Counties,
18561978. The bold segment of the standard deviation
curve in the lower panel represents the shoreline portions
considered to be acceptable for analysis of standard
deviations.
Okaloosa County
E3
0
2
4)
a,0
0
) 0
o 1
10 20 30 40
Monument Number
Figure 430 Shoreline change rates and unsealed standard deviations
along the coastline of Okaloosa County, 18711990. The bold
segment of the standard deviation curve in the lower panel
represents the shoreline portions considered to be acceptable
for analysis of standard deviations.
10 20 30 40
Monument Number
g 150
E
0
.' 100
a5
. 50
*o
c i) n
