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OCR for page 120
6
Predicting Future
Shoreline Changes
INTRODUCTION
In order for the Federal Emergency Management Agency (FEMA)
to administer the Upton-3ones Amendment, reliable erosion rate data
for the U.S. coastlines are required. There are essentially two ap-
proaches for the acquisition of such data: analysis of historical shore-
line changes to forecast future evolution and a statistical method
(Monte CarIo simulations) based on synoptic oceanographic data
(Table 6-1~.
The first method is based on an analysis of the long-term data
base of shoreline location, which must also take into account the time
history of human interferences (e.g., beach nourishment, navigation
entrances, dredging projects, sea wails, and groin emplacement).
This analysis provides an average rate of evolution as well as a
distribution of the fluctuations around the trend caused by seasonal
variations and episodic storm events. The existing data base of
shoreline locations generally is long term (many decades to over a
century) and site specific.
The statistical approach is based on a knowledge of the deep and
shallow water oceanographic environment (e.g., waves, wind cur-
rent, storm surge) and sand sources and sinks that affect shoreline
position. This information can be compiled as time series or statis-
tical summaries. Sediment transport corresponding to a succession
120
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PREDICTING FUTURE SHORELINE CHANGES
TABLE 6-1 Methods for Determining the Ram of Shoreline Change
121
Methods
Shoreline Change
Analysis
Monte Carlo Simulations
Data base
Phenomenological Not needed
relationships
Calibration from
shoreline
change data
Adv antages
Disadvantages
Shoreline data are site
specific and discrete
Interpretation only
Relatively simple
Risk of bias on Me
effects of extreme
events
End product Average yearly rate of
erosion plus
fluctuations around
the average
Implementation Short term
Oceanographic data are
synoptic arid of coarse
resolution
Alongshore transport
plus cross-shore
transport combined
Calibration of model for
all categories of events
Physics-based approach
Lack of accuracy of
functional relationships
Determination of probability
distribution of shoreline
locations with confidence
bands
Long term
of oceanographic events then is calculated based on physics-based
equations relating the magnitude of forces causing the change to
observed shoreline evolution.
The data base of deep-water oceanographic events is broad (i.e.,
valid for very large areas) and can be summarized statistically. Deep-
water wave information for an ocean basin can theoretically be trans-
formed into site-specific, shallow-water wave data that, in theory,
can be used to determine long-term shoreline changes. The pre-
dicted shoreline fluctuations are then the result of the vagaries of the
forcing functions (e.g., storm occurrence, E! Nina conditions).
An additional consideration in evaluating these two approaches
is the need of any insurance-based program to be able to assess
the relative distribution of risks in order to establish appropriate
insurance premiums. This task of establishing a comrnonaTity of risk
is not easy, considering the following points:
1. A single common method of predicting shoreline changes,
valid for all situations, does not appear to be possible, owing to
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122
MANAGING COASTAL EROSION
the extreme variations in coastal morphology and oceanographic
climatology.
2. Risk is based on a combination of time-dependent effects from
both Tong-term trend averages, which can be established from past
observations of shoreline locations, and large fluctuations of stochas-
tic processes, caused by the random nature of storms. On an eroding
beach of established evolution, the average risk increases with time.
Seasonal fluctuations (winter-summer profiles in some regions) can
be added (based on previous field data) to long-term profile change.
However, damage often occurs during an unpredictable short-term
episodic event or perturbation that takes place in addition to (and
contributes to) general shore erosion.
This chapter reviews the validity and Irritations of the two
major approaches for predicting future shoreline changes. The rec-
ommended approach for FEMA in determining shoreline change is
the Emote Cario" method, but its utility presently is limited by the
lack of sufficient correlated oceanographic and shoreline change data.
Therefore, an interim methodology (historical shoreline analysis),
which is based on good-quality shoreline change data and an appro-
priate computer-based processing system, should be implemented.
It is proposed that this interim methodology should be improved by
incorporating existing data on oceanographic forces with correlated
observations of shoreline change.
HISTORICAL SHORELINE CHANGE METHOD
Available Data Base
A wide variety of data and information on beach erosion exist
for the Atlantic, Gulf of Mexico, Pacific, and Great Lakes coasts.
The information, however, ranges from highly accurate engineering
surveys to fairly general comparisons of historical photographs and
maps at various scales. The primary federal agencies engaged in
the systematic collection of coastal information are the U.S. Army
Corps of Engineers (COE), the National Oceanic and Atmospheric
Administration (NOAA), and the U.S. Geological Survey (USGS).
In 1971 the COE published an inventory of the nation's coastlines
in HA Report on the National Shoreline Study." Unfortunately, the
report was broad and had limited usefulness, but it is the only
comprehensive, nationwide assessment of America's coasts.
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PREDICTING FUTURE SHORELINE CHANGES
123
Additional information ~ available from the 30 coastal states
as well as local governmental departments, colleges, and universities
carrying out coastal research and from private engineering and en-
vironmental consulting firms. For example, New Jersey, Delaware,
North Carolina, Florida, Louisiana, California, Michigan, and Illi-
nois all have active coastal data collection programs for management
purposes.
Changes in shore position have been delineated using a wide
range of methodologies, including field measurements of beach pro-
files (e.g., Dewall and Richter, 1977), visual comparison of historic
changes from hand-held photography (Johnson, 1961; Kuhn and
Shepard, 1980), and quantitative analysis of historical maps and
vertical aerial photography through various photogramrnetric proce-
dures (Leatherman, 1983a). Although field measurements have the
potential to yield the most accurate data to determine beach changes,
the utility of such information is severely constrained because of its
temporal and spatial nonhomogeneity. Florida has the best statewide
program for the collection of such information, where 10 to 15 years
of data are available. For the other coastal states, such data are not
even available for most beaches, and the record often extends for a
decade or less for even the best-monitored recreational beaches.
The data needed for shoreline mapping can be obtained from
maps and charts and aerial photographs. Within NOAA the Na-
tional Ocean Service (NOS) performs coastal surveys of the United
States and its territories. For many coastal areas NOS topographic
surveys are available dating from the m~-1800s. When historical
data are compared to modern high-quality maps, long-term rates
of coastal erosion and accretion can be computed. These maps can
be augmented and updated with historical aerial photographs (late
1930s to present) available from many public agencies; the USGS's
National Cartographic Information Center (NCIC) serves as a cen-
tral repository of such data. The NOS IT" (topographic) sheets
are generally the most accurate maps available for the coastal zone.
Stable points located on these maps are accurate to within 0.3 mm
of their actual positions at the scale of the map (often I:10,000~. The
smallest field distance measurable is between 7 and 16 feet. This
high accuracy makes them quite useful in delineating the land-water
boundary and particularly for determining net changes over the long
term. Along the Great Lakes, seasonal and long-term changes in
water levels make the bluff top edge a better measure of erosional
trends than the wetted bound.
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124
MANAGING COASTAL EROSION
The USGS topographic maps can provide additional detail in
comparison to NOS T sheets, but these maps are updated at infre-
quent intervals. Also, their accuracy is a problem since the USGS
topographic maps are produced just within the guidelines of National
Map Accuracy Standards, which allows for no more than 10 percent
of the stable points tested to be in error by more than one-fiftieth of
an inch at the scale of the map. On the standard 7.5-minute quad-
rangles (1:24,000), one-fiftieth of an inch would mean an error of 40
feet in the actual location of a stable point. Other points, such as
shoreline positions, are located with an even larger potential error.
Aerial photographs can be used to provide the necessary detail
and short-time interval required to detect and evaluate the processes
shaping the coastline. The use of vertical air photographs to de-
termine the rates of shoreline change at selected points was well
documented by Stafford (1971~. Since then a number of coastal sci-
entists have used air photos to monitor shoreline recession (Dolan et
al., 1978; Leatherman, 1979) and to quantify changes in barrier envi-
ronments (Leatherman and Zaremba, 19863. It must be remembered
that air photos are not maps, and corrections for a range of distor-
tions must be made to rectify this imagery for usage in quantitative
analysis.
In most cases final maps are produced by transferring the air
photo data to an appropriate base map, and the readily available
USGS topographic maps often have been selected for this purpose.
As previously discussed, these maps have a 40-foot potential error to
which any errors associated with air photo mapping would be added.
The product is a map of relatively low accuracy, and only major
changes in the shoreline can be measured meaningfully.
Shoreline Indicator for Mapping Purposes
The shoreline is defined as the interface between the land and
water. However, the position of the shoreline on the beach face is
highly variable because of changes in water level caused by tides
waves, and wind and on the Great Lakes by hydrologic factors.
The mean high water line is depicted on NOS T sheets as the
shoreline indicator. It is preferred to any other tidal boundary for
the shoreline indicator because this wetted bound can be recognized
in the field and approximated from air photos. This allows for the
direct comparison of data obtained from the NOS T sheets and ver-
tical aerial photographs. While vegetation lines are easily recognized
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PREDICTING FUTURE SHORELINE CHANGES
125
on photographs, such information was not always recorded on the
historic NOS T sheets. Because the T sheets are referenced to mean
high water (MHW) and this is virtually the only long-term data
on historical shoreline changes available, this shoreline indicator has
been adopted by most coastal investigators for mapping purposes.
It should be kept in mind that the longer the period of record, the
greater confidence and reliability can be placed on the trend mea-
surement of shoreline change, provided there are a sufficient number
of data sets.
Error Analysis of Map Compilation
Beach erosion measurements are subject to a variety of error
sources, depending on the exact methodology used in the histori-
cal shoreline change analysis. A discussion and comparison of the
various mapping procedures appear elsewhere (Leatherman, 1983a).
A conservative (worst-case) estimate of the maximum possible error
in high-quality techniques of analyzing historical shoreline data am
preaches 40 feet, which is within National Map Accuracy Standards.
In practice, errors often have been shown to be less than 20 feet,
the average being 11 feet for the Delaware coastal erosion mapping
project (GaIgano and Leatherman, 19893.
Projection of Shoreline Positions
Extrapolation of trends bayed on historical shoreline change anal-
ysis takes into consideration the inherent variability in shoreline
response based on differing coastal processes, sedimentary environ-
ments, and coastal exposures. The following discussion concerns the
validity of determining long-term shoreline position changes from
limited observations (e.g., snapshot views of the beach through time
via time-series air photos).
For the sake of simplicity, consider a hypothetical case where
the shoreline location Is defined as a function of time. The trends in
shoreline position shown on the two curves (Figure ~1) correspond
to differences in the length of record available. The average rate of
shoreline position change with respect to time is different for the
record extending from 1920 to 1930 as compared to the 1920 to 1940
record. This difference is caused by the occurrence of extreme events
in 1927-1930, for example, a hurricane in 1930 and the fact that
accretion took place between 1930 and 1937.
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126
MANAGING COASTAL EROSION
The important point to consider in this hypothetical illustration
(Figure ~~) is that the estimation of shoreline trend depends on
both the long-term trend ( ~signal" ) and short-term "noises caused
by seasonal and storm (hurricane-induced) effects. To quantify this
point, suppose the shoreline position trend rate is 1 foot per year and
the noise range ~ 100 feet (an extreme example). If it is desired to
limit the error in defining the trend to +30 percent, the required time
period of measurement to meet these conditions is 333 years. As a
comparison, suppose the shoreline position trend rate is 20 feet per
year, with the noise range and error limit the same. In this case the
required time period of measurement is only 16.7 years. The signifi-
cance of these two examples is that if it is necessary to establish, with
confidence, the shoreline change trend in locations where the trend is
small to moderate, the required time periods of measurement can be
longer than the data record avaflable. Realizing that a portion of the
noise at a point is time dependent for example, caused by seasonal
and storm effects—and a portion is caused by spatial (alongshore)
effects, it is possible to decrease the necessary measurement time
interval by averaging the shoreline changes alongshore. In addition,
poststorm and wintertime photos should not be compared to imagery
acquired in the summer.
Rates of beach recession can be calculated from the change in
shoreline or bluff position over time. If a number of historic shore-
line positions are available, then it is possible to determine rates of
change in beach recession both temporally and spatially. Of course,
this straightforward projection of new shoreline position based on his-
torical change assumes that all the oceanographic forces (e.g., waves
and sea level change) remain essentially constant. If the greenhouse-
induced climate warming increases, then the rate of sea level rise
likely will accelerate in the future, so adjustments must be made
to project further sea level rise/shoreline movement relationships
(weatherman, 1983b).
In summary, erosional trend rates can only be established accu-
rately in those areas where long-term shoreline positions are available
or where the trend rates are large. Where beach erosion rates are
calculated to be in the low range (1 foot or less per year), it must
be realizecl that the reliability of this measurement is probably low
owing to natural fluctuations in beach width. Therefore, prudence
demands that a certain minimum setback distance be added to the
average annual erosion rate to compensate for the larger possible
error in trend measurements.
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PREDICTING FUTURE; SHORELINE CHANGES
Shoreline Location in Feet
~ 90
O 0 -
~ G 70
O
Z
_
~' ~ 50
O ~
I Oh
Oh
30
Benchmark
127
10
Year Base Erosion Rate
I'm Event
~ ~ 1 ~
. ~ 1
20 Year Base Erosion Rate
4
1920 1 930
YEARS
90-30
1940
Rate of Erosion 1920 to 1930: = 6 ft/yr
10
90-50
Rate of Erosion 1920 to 1950: = 2 ft/yr
20
FIGURE 6-1 Example of the variation of the rate of erosion as a function of
the duration of the period of Aberration, 1920 to 1940.
E~tmg Nationwide Data on Beach Erosion
Maps depicting shoreline changes along portions of the U.S.
coasts have been made for a host of reasons and by literally hun-
dreds of researchers using a range of methodologies. For instance,
the New Jersey coast has been mapped to determine beach erosion
rates by at least three separate groups during the past few decades
(Dolan et al., 1978; Farrell and Leatherman, 1989; Nordstrom, 1977~.
In addition to these statewide mapping efforts, other agencies (e.g.,
U.S. Army COE) have mapped specific portions of the New Jersey
coast. Mapping methodologies have ranged from photocopy reduc-
tion/eniargement of historical maps and photos for direct overlay to
sophisticated computer-based mapping methodologies that permit
correction of the inherent errors and distortions of the raw data.
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128
MANAGING COASTAL EROSION
The USGS has compiled these widely disparate historical data
on erosion rates as part of the National Atlas series (Dolan et al.,
1985~. The National Atlas is plotted at a scale of 1:6,000,000, and
recession rates are displayed at intervals of ~ meter per year of shore
change. Although this map provides an overview of the national
coastal recession situation, it has limited utility because of its coarse
resolution.
The data used for the USGS National Atlas on shore change
exist at a better resolution for most locations and are on an IBM-
compatible PC. This system, called the Coastal Erosion Information
System (CElS), also contains a bibliography of reference sources
and is capable of computing standard statistical parameters (erosion
rates, standard deviation, and running means) at selected distances
along the shoreline.
The CElS approach has limited utility for FEMA because there
is essentially no quality assessment of the input data. Further limi-
tations exist because most of the data records are temporally short
(decades) and a variety of mapping methodologies have been used to
produce the data, which therefore vary widely in resolution and reli-
ability. The CElS data assembly does provide a general qualitative
index of erosion-prone areas along U.S. coasts, but the data are not
sufficient to help implement an interim methodology for FEMA.
An example of the unplementation of the historic shoreline
change method is illustrated by the New Jersey coastal erosion
project. The raw data used included all available NOS T sheets,
New Jersey Department of Environmental Protection orthophotos,
and a specially acquired set of large-scale vertical aerial photographs
for the Atlantic coast beaches. Five sets of T sheets were available
from the NOS archives, ranging in age from the mid-1800s to the
1960s. The state orthophotos (based on air photos corrected for dis-
torsion by stereoplotters) were of 1970s vintage. A professional aeria]
survey company was commissioned to fly the coast to obtain the most
recent data on shoreline position. Engineering surveys of particular
areas were available for many towns but were not used because of
their limited coverage on a statewide basis. Also, the USGS T maps
(7.5-minute quadrangles) were not used because the historical shore-
line change maps and hence calculation of beach erosion rates would
have been considerably less accurate.
The Metric Mapping computerized technique of data entry, dis-
tortion correction, and map plotting was used to generate the his-
torical shoreline change information (Leatherman and Clow, 1983~.
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PREDICTING FUTURE SHORELINE CHANGES
129
Approximately eight shorelines covering a period of the past 130
years now are available to determine the long-term trend in beach
erosion. Only good, high-quality raw data were used; the orthophotos
and air photos acquired during summertime for seasonal consistency
were selected to complement and update the NOS T sheets. Other
aerial photographic data were available, but the number of sets used
was limited by the funds available. The total cost for erosion map-
ping was approximately $2,000 per mile of shoreline. New Jersey's
data requirements for the continued implementation of its building
setback regulations are for an erosion update every 5 to 10 years.
PREDICTIVE MODELS
Batkgronnd
Within the past several decades there has been substantial in-
terest in the development of calculation procedures (herein termed
"models") for quantitative prediction of future shoreline changes as
a result of natural or human-induced effects. These models, which
include both analytical and numerical types, are well beyond the
infancy stage and provide a sound foundation for the recommended
longer-term methodology, yet they are not presently at a level where
they can be applied and interpreted without substantial effort and
skill. Therefore, these models cannot be applied easily in their
present form to the type of predictions needed to implement a FEMA
erosion program.
The paragraphs below provide a brief overview of the status of
modeling of beach systems. Appendix C presents a more detailed
review, including a discussion of the features of individual models.
Shoreline retreat can occur as a result of longshore sediment
transport, offshore sediment transport, or a combination. Offshore
sediment transport primarily is responsible for shoreline retreat dur-
ing storms, whereas long-term retreat can be caused by either or
by a combination of these transport components. Individual models
have tended to concentrate on shore response to either longshore or
cross-shore transport. Models are generally site specific for erosion
and require validation against the history of a particular site.
A process-based mode! requires two types of equations: (1) a
transport equation relating the volumetric movement of sediment to
the causative forces (e.g., waves, tides, etc.), and (2) an equation that
carries out the bookkeeping of changes as a result of the sediment
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130
MANAGING COASTAL EROSION
movement. Some of the earliest modeling efforts simplified the above
equations for the case of longshore sediment transport, thus allow-
ing analytical solutions to be developed that provide considerable
insight into the effects of individual parameters, such as wave height
and direction. Larson et al. (1987) have summarized a number of
such solutions, including the effect of constructing a groin along the
shoreline, evolution of a beach nourishment project, and shoreline
changes from delivery of sedunent to the coast by a river. In addition
to analytical solutions, numerical solutions have been developed that
allow specification of time-varying waves and tides.
Cross-shore transport models have received little attention until
the last few decades. These models generally are based on the concept
that if the prevailing waves and tides are of sufficient duration the
profile will evolve to an equilibrium shape. The complexity of these
models ranges from simple ones based on correlation with field and
laboratory data to those that simulate profile evolution based on
time-varying wave heights and storm surges as input. The state of
Florida uses a simplified version of a cross-shore mode! in defining
the zone of impact owing to a Goodyear storm event.
Within the last decade models have been extended to represent
both longshore and cross-shore transport. This capability is espe-
cially important in situations involving structures where the inter-
ruption of longshore transport (e.g., by a groin) causes increases and
decreases in the profile slope on the uplift and downdrift sides of
the structure and corresponding cross-shore transport components.
At present, longshore transport models probably are accurate
to within +30 percent and cros~shore transport models to within
+60 percent, providing there ~ no storm-induced interruption to
the assumed prevailing condition. They are, however, the type of
operational too] that would be highly useful eventually to a FEMA
erosion program.
Perlin and Dean (1985) have developed an e-line mode] in which
both longshore sediment transport and cross-shore sediment trans-
port are represented. As an example of its application, consider
transport in the vicinity of one or more groins. UpUrift and downdrift
of a groin, the shoreline would be displaced seaward and landward,
causing an increase and decrease in the profile slopes, respectively,
and thereby inducing offshore and onshore components, respectively,
of cross-shore sediment transport. At each time step, the governing
equation is solved by matrix inversion.
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132
MANAGING COASTAL EROSION
1980; Perlin and Dean, 1986) and (2) shoreline change is the result of
cross-shore movement from waves and storm surge combined (Kriebel
and Dean, 1985~. This is described in Appendix C of this report.
However, it is reemphasized that these two methods are based on
functional relationships that must be improved by further research.
A COMPREHENSIVE METHOD Ok PREDICTING
SlIORE[INE CHANGES
Backgrolmd
Shoreline displacement is influenced both by the long-term pre-
vailing wave climate of frequent occurrence (i.e., of high probability)
and by rare, extreme episodic events of low probability. It is influ-
enced also by the quantity and quality of sand sources and sinks and
by human-induced factors.
Wave climate summaries provide a large population of oceano-
graphic events over a relatively short period of time (years to decades).
The sequential time history of these numerous events is seldom avail-
able. Most likely they are grouped statistically in the form of prom
ability of exceedance curves and wave roses. Because these data are
based on a large population, they generally are reliable. However,
because of cumulative errors over a large number of events (and
their sequential and nonlinear effects), shoreline changes cannot be
determined by the phenomenological deterministic approach at this
time. Their average effects can be obtained theoretically from the
site-specific data base of shoreline change over a relatively short pe-
riod of time, excluding the effect of extreme events. Unfortunately,
adequate data for this purpose are seldom available.
Episodic storms present a relatively smaller population of events,
requiring a much longer period of observation than may be available
from the site-specific and duration-lim~ted shoreline data base. How-
ever, a data base of the causative oceanographic events valid for a
broad area over generally longer periods of time may be available,
from which site-specific forcing events can be determined. Then,
formulation of the functional relationships relating sediment trans-
port to these forcing events can allow for determination of shoreline
changes. These functional relationships can be calibrated from the
site-specific shoreline data under extreme conditions and the post-
storm recovery time. Then, by adding their effects linearly, the cu-
mulative shoreline changes from a series of extreme events of known
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PREDICTING FUTURE SHORELINE CHANGES
133
probability can be calculated determ~nistically. For example, this
approach can cover a distribution of extreme events occurring over
a century. The data base can be extended synthetically and the
method can be refined by application of a method currently in use
by FEMA to establish storm surge statistics.
Method Based on Monte CarIo Simulations
The method described in the preceding paragraphs requires that
it be valid to add linearly the effects of all oceanographic events.
Actually, beach changes are caused by every uncorrelated event,
depending upon the initial conditions resulting from the previous
change. In particular, beach accretion following a storm (i.e., during
recovery time) depends upon the plan and profile, the topography
and bathymetry left by the storm, and the movement of nearby es-
tuaries, etc. Owing to the lack of a complete time history of sea
state and the sequence dependence and nonlinear nature of the re-
lated beach processes, a stochastic approach to shoreline evolution
is necessary for scientific exactness. The problem is indeed inher-
ently chaotic (i.e., deterministic but unpredictable). A Monte CarIo
simulation of incident waves provides a method consistent with the
natural processes. This method translates the random nature of the
sea state into deterministic events, the sum of which gives the same
wave energy rose as provided by summary atlases. Shoreline evolu-
tion then is deterrn~ned statistically by a succession of Monte CarIo
simulations of wave climatology, including both prevailing wave con-
ditions and storms (Le Mehaute et al., 1983~. The forcing functions
are randomly defined by season and by a multiplicity of time series of
varying events (e.g., direction, intensity, etc.) that can all be grouped
in the same statistical summaries.
The random nature of oceanographic events is accounted for by
a large number of Monte Cario simulations for the same location.
The Monte Cario sunulations of wave approach direction at fine
resolution, in addition to the multiplicity of wave energy levels, also
alleviates the discrepancy resulting from the coarse discretization of
the wave angle from the atlases. Multiple Monte Cario simulations
for the same location allow the determination of a multiplicity of
shoreline distances from a reference point, from which a probability
distribution of shoreline locations can be obtained as a function of
time. Standard deviation and confidence bands that increase with
time and number of simulations also can be obtained.
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134
MANAGING COASTAL EROSION
The Monte CarIo sunulation also could be used for determin-
ing storm surge statistics ~ well as the Joint Probability Method
presently used by FEMA. The Monte CarIo simulation has not been
used due to the relative complexity of storm surge calculations and
the large number of simulations that this method requires. (Typi-
cally, 300 storm surge calculations for each study area are needed for
the Joint Probability Method.) However, the Monte Cario simulation
for storm surge Is not needed, since storm surges are independent
events and their effect on flooding generally is not cumulative.
Even though it represents a physically more complex phenomenon
than storm surges, the mathematical modeling of shoreline evolution
is relatively straightforward. This allows the processing of a great
number of simulations as required by the Monte CarIo method. In
principle, the Monte CarIo simulations provide as many shoreline
positions as needed] for determining confidence bands and standard
deviations. The most significant limitation of Monte Cario simulation
is our present poor understanding of beach recovery time scales that
would be required to provide an initial condition for each succeeding
event to be modeled.
Even in the case of well-documented, long-term statistics of
prevailing climatology and episodic events, the state of the art is such
that this approach, even though desirable, is difficult at this time.
This is due primarily to the lack of reliable functional relationships
relating the physics of sediment transport to the forcing events.
In particular, more research and understanding are needed of the
processes relating to the long-term erosion trend and the recovery
of beaches following storms. Nevertheless, because it is practically
the only theoretically rational approach to the problem, every effort
should be made for its development and application. A summary
and comparison of the Historical Shoreline Analysis and Monte Cario
simulation methods are presented in Table ~1.
FEMA's Present Methodology
DEFINITION OF IMMINENT COLLAPSE
A house is defined as in danger of imminent collapse (1) if its
distance from shore is less than a critical distance defined below
and (2) if its structural integrity and, in particular, its foundation
do not satisfy construction code criteria such as described in the
FEMA report (January 22, 1988) on "structure subject to imminent
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PREDICTING FUTURE SHORELINE CHANGES
135
collapse." The reference line from which the requisite distance is to
be measured ~ defined as follows:
1. bluff edge;
2. top edge of escarpment on an eroding dune (normal high tide
should be near the toe of the dune and there should be indications
that the dune is actively eroding);
3. normal high tide line, which is indicated by the following:
a. vegetation line (must be in an area of low tidal range where
permanent vegetation exists just above high tide),
b. beach scarp (a 4- to Chinch cut at the upper limit of high
tide),
debris line (deposited by the normal high tide), and
d. upper limit of wet sand; and
4. vegetation line (when none of the above can be located, use
the seaward-most edge of permanent vegetation this is intended to
be seldom used).
This list is presented in priority order; if the first recommended
feature is not present or suitable for use, the next feature should be
used, and so forth. However, only the first two reference features
are appropriate for use with the interim methodology recommended
herein.
In most state programs the setback line refers to a fixed baseline
determined at a given time and rarely changed. In the present
context the reference line is continuously moving shoreward and
must be reviewed annually at the time of the construction permit
process. Accordingly, the reference line may vary for two adjacent
structures built at two different periods of time.
The distance from that line defining the zone of imminent col-
lapse is obtained by the sum of five times the annual rate of erosion
and an additional distance defined by a 50 percent probability that
the distance will be exceeded within the next 3 years. For lack of
accurate determination of the confidence bands allowing an accurate
definition of the 50 percent probability deviation from the mean, a
[(}foot distance will be added to the five times annual rate of erosion.
RECOMMENDED METHODOLOGIES
Introduction
The following two methodologies are recommended by the com-
mittee for use in determining shoreline change rates. The committee
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MANAGING COASTAL EROSION
recognizes that the use of available shoreline recession data such
as aerial photos and profiles would be the least costly to implement
shoreline change mapping. However, it would be preferable, although
more costly, to utilize oceanographic data in the determination of
shoreline change rates.
Historical Shorelme Change Method
Historical shoreline mapping can immediately provide the req-
uisite data on erosional trends for implementation of the FEMA
program. While there is already a plethora of such data available for
the U.S. coasts, standards must be set and met for inclusion of such
information into the national computerized data bank.
There are three basic requirements for the acquisition of reli-
able, accurate, and readily usable information on erosion rates as
determined from historical shoreline analysis:
1. Use of only good-quality raw data (from high-quality maps,
large-scale air photographs, and survey profiles).
2. Utilization of a mapping procedure that allows for the com-
pilation of both map and air photo data and that permits the rec-
tification of these raw data such that they meet or exceed National
Map Accuracy Standards.
3. Output of PC-based digital data in the State Plane Coordi-
nate System that readily permits the calculation of erosion rates at a
predetermined basis along the shoreline or at specified locations and
includes a map-plotting capability on a PC-based system.
Determination of the erosion trend should be based on the
longest period of record available, but provisions must be made for
any human-inducec] effects (e.g., groins, jetties) on shoreline position
during recent times. The earliest reliable maps commonly available
for the U.S. coasts are the NOS T sheets. There are approximately
6,000 T sheets available from NOS archives in Rockville, Maryland.
The earliest information dates from the m~-1800s and extends to
the 1970s; for many coastal areas, approximately four sets of histor-
ical maps are available at 30~year intervals. All rectifiable NOS T
sheets should be utilized in a long-term analysis of historical shoreline
changes.
Vertical aerial photography should be used to complement and
update the NOS map data. Literally ganglions of historical air photos
exist of the U.S. coasts. The earliest imagery dates from the late
1930s and early 1940s to the present. From the 1960s to date, most
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PREDICTING FUTURE SHORELINE CHANGES
137
coasts have been photographed at 5-year intervals by various agencies
(e.g., USDA, SCS, COE, DOl). Urbanized coasts, such as Ocean City,
Maryland, are now photographed several times a year by professional
aerial survey companies. Therefore, lack of data generally is not a
problem, and its selection for mapping typically is governed by the
coverage and scale.
Good-quality raw maps and air photo data still must be corrected
for distortion or otherwise rectified to make them usable for the
determination of reliable, accurate rates of beach or bluff erosion.
A host of methodologies have been utilized in the past, ranging
from photocopy reduction/enlargement of air photos and maps for
direct overlay comparisons to sophisticated computer-based mapping
systems (Leatherman, 1983a). It must be clearly understood that
the best raw data cannot be expected to yield high-quality erosion
rate information without proper corrections/rectifications to remove
inherent errors and distortions. The compiled data on historical
shoreline changes should in any case meet or exceed National Map
Accuracy Standards.
Shoreline change maps and the derived rates of beach erosion
must be interpreted by professionals; otherwise, misleading or even
wrong conclusions can be drawn from a causal inspection of the data.
For example, a variety of patterns of shoreline behavior (e.g., lin-
ear recession, cyclic changes near inlets, and engineering structural-
induced trends) exhibited along the New Jersey coast are determined
from the historical data (Farrell and Leatherman, 1989~. Therefore,
professional judgment ~ required for proper interpretation and ap-
plication of erosion data for the FEMA program.
There are two possible outputs for the erosion data: tabular
format or map products. Both products are desirable as each has
special advantages to FEMA in terms of information display and an-
alytic calculations. The historical shoreline change data should be in
digital format on a State Plane Coordinate System for computational
ease. A PC-based, user-friendly, menu-driven system should be uti-
lized to facilitate calculation of erosion rates on a predeterminated
basis (e.g., 50 meters) along the shoreline or at specified locations
of particular interest. In addition, FEMA personnel should have the
option of viewing the data in map format, wherein all or a specified
portion of the historical shorelines appear in their spatial context.
Oftentimes, this spatial representation of the shoreline data can be
invaluable in understanding apparent anomalies in the beach erosion
data as it appears in tabular form.
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MANAGING COASTAL EROSION
[ong-Term Methodology
In a previous section the recommended ~10, ~30, and ~60
zones are defined by the distance between the reference line and 10
times, 30 times, or 60 times the annual rate of erosion, respectively.
This is based on the assumption that the erosion rate is constant over
time and is determined by the historical shoreline change method.
In actuality, erosion rates can vary through time, depending
upon the geological features of the substrata. For example, a rock
formation may be uncovered by erosion, which would not respond
to waves as readily as loose sand. Conversely, dune overtopping and
massive overwashing could increase the amount of storm-induced
beach erosion.
The long-term methodology provides a better definition of the ~
zones by taking into account the probability distribution of shoreline
location from short-term climate variation (such as storms) about
a slowly changing average location. Once this long-term methodol-
ogy is implemented, a redefinition of the Zones is recommended,
commensurate with the new knowledge obtained.
Beach erosion trends can be determined by the historical shore-
line mapping methodology until this preferred long-term approach
of using oceanographic data and statistical treatments can be un-
dertaken. In actuality, the preferred methodology involves utilizing
available records of shoreline recession for analysis of the time history
of oceanographic forces (e.g., wind waves, storm surges, etc.~. For
lack of data, established functional relationships (relating longshore
transport to the incident alongshore wave energy on the one hand and
cross-shore transport to the onshore wave energy and storm surge on
the other hand) will be used, as described in a previous section.
Following the availability of long-term oceanographic data, sta-
tistical methods can be introduced to improve the accuracy of shore
prediction. Standard variation and confidence bands about the av-
erage or most probable shoreline location also can be defined. As
previously stated, implementation has to remain flexible, consider-
ing the geographic variability of coasts, because a set relationship
valid at one place may not be valid elsewhere.
Time and availability of resources are also a factor in implemen-
tation of a statistical approach. Initially, a ranking based on storm
intensity may be used, but results from Monte Cario simulations
are much desired. The corresponding methodologies should be de-
veloped in parallel in order to assess the errors and discrepancies
between various approaches by sensitivity analysis.
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PREDICTING FUTURE SHORELINE CHANGES
139
It is anticipated that this program will be implemented county
by county. At a later date, the matching of the prediction at the
boundaries of each county anti be required so as to make the result
consistent for sake of fairness in the insurance rate.
ESTABLISHMENT OF A COMPUTERIZED NATIONAL
DATA BASE
A tremendous amount of shoreline change data must be as-
sembled, analyzed, and interpreted properly for implementation of
erosion-based setbacks by FEMA. High-quality data obtained from
historical shoreline change analysm should be acquired at close in-
tervals along the coast. Predicted shoreline change from a statistical
treatment of the long-term oceanographic information will be less
site specific, but interval measurements can be extracted from the
data set. In essence, this voluminous information available as output
from both methodologies must be entered, manipulated, and out-
put on a personal computer system for ease of operation by FEMA
personnel and other users.
Advent of the personal computer with high-capacity hard-disc
storage and computerized mapping capabilities makes this technol-
ogy most attractive to agencies involved in the analysis and re-
trieval of geographical-based data. These new technologies, which
include off-the-shelf Geographic Information Systems (GISs) and
user-friendly computerized plotting routines, can be readily utilized
by FEMA for their national data base on erosion rates.
A GIS data base management system is based on true geograph-
ical locations (latitude and longitude) but also utilizes town/city
boundaries, postage zones, and locality names in terms of search and
retrieval. This aspect greatly facilitates the utility of such a system,
making it readily understandable to the casual user.
Another major advantage of the GIS approach as compared to
just ~inventory"-type system is that it can be used to display erosion
information in a map format. Computerized mapping virtually has
replaced manual cartography at all major companies and government
agencies involved in mapping efforts of geographical-based data.
Erosion data in numerical strings (e.g., Metric Mapping; I.eather-
man, 1983a) or in discrete point format (e.g., state of Florida monu-
ment system) can be easily entered and utilized in a GIS computer-
ized format. Therefore, existing high-quality data sets can be entered
directly into an aIready-available data base management system. As
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140
MANAGING COASTAL EROSION
computer-based, long-term erosion data for the other coastal states
are generated by historical shoreline change analysis, all this infor-
mation can be incorporated to form a true national data base.
Analysm of the historical shoreline data wall be useful to FEMA
for comparative purposes once the data from the modeling efforts
become available. Fortunately, the GIS data management system
can accornrnodate both data sets and, in fact, serve as the host for
ancillary information, such as the location of buildings. This system
offers the capability for the overlay of erosion data (historical and/or
mode} derived) and insured beach-front buildings on a map at any
scale desired. This ability To view the situation" or gain a quick
spatial overview provides FEMA administrators with perhaps as
much utility as the actual sophisticated computer-based processing
and data base management utilities.
RESEARCH AND DATA NEEDS
The problem of shore erosion is not new, and unplementation
of the Upton-Jones program can capitalize on a large amount of
information from past investigations. Nevertheless, coastal processes
are complex, such that the state of the art in predicting coastal
erosion remains relatively poor in regard to applying it to the FEMA
program.
In order to improve the methodology for assessing beach erosion
and the risk of collapse of structures, much more research needs to
be undertaken by FEMA and other appropriate agencies, such as
NOAA and the COE on the following:
1. determination of the long-term wave climatology through
field data collection programs;
2. monitoring of beach response to wave climate variations and
episodic events; and
3. more research on predictive mathematical and probabilistic
models of probability distribution of shoreline locations by a Monte
CarIo simulation of the wave climate, taking into account both the
longshore and cros~shore transport.
It is clear that better and longer-term data on shoreline changes
should be collected. Specific efforts should be directed toward quan-
tifying storm-generated erosion through prestorm and poststorm sur-
veys as well as the period of beach recovery. The use of remote sensing
should be considered for monitoring of beach and dune erosion.
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PREDICTING FUTURE SHORELINE CHANGES
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141
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A~4NAGING COASTAL EROSION
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Representative terms from entire chapter:
shoreline changes
