Full text of DOI:10.1046/j.1526-0984.2000.73002.x

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GIS Modeling of Impacts of an Accelerated Rate
of Sea-Level Rise on Coastal Inlets and Deeply
Embayed Shorelines
WERNER G. HENNECKE and PETER J. COWELL
Coastal Studies Unit, School of Geosciences, University of Sydney, NSW 2006, Australia
climate change, prove correct (Intergovernmental Panel on
ABSTRACT
•
Climate Change (IPCC), 1998; McInnes et al., 1998). With
half of the world’s population living in the coastal zone
(Leatherman and Nicholls, 1995), human assets are poten-
tially at risk from SLR. Practical decision-making in coastal
management requires cost-effective methods that allow fast
determination of high-risk areas in the coastal zone at a re-
gional scale. To model long-term changes, the nature and ex-
tent of physical changes in different coastal settings must be
identified (Thom and Roy, 1988; Intergovernmental Panel on
Climate Change (IPCC), 1996). Ideally, if we are able to
identify those processes responsible for a hazard, we should
be in a position to take appropriate action to avoid or miti-
gate the impacts associated with the hazard (Hennecke et al.,
1997). By defining mathematical equations to represent the
processes of these hazards, a computer can be used to simu-
late the impacts on areas of interest.
Scientific evidence suggests that an accelerated sea level rise will have
a significant impact on shorelines over the next 50–100 years.
Hence, coastal management and planning in the near future needs
to evaluate the physical impacts on vulnerable coastlines. In Aus-
tralia, the need to estimate potential coastal erosion and potential
coastal property damage is acute, because Ͼ80% of the Australian
population live in coastal areas. However, a limitation that has to be
considered in Australia and in many other parts of the world is the
lack of data for the coastal zone. Principles drawn from presently
available coastal process models are used in this study to identify
the potential physical impacts of sea level rise on barrier inlets and
deeply embayed shorelines, including estuaries.
The models (Hybrid-Bruun, Aggradation, and Translation Models)
are implemented into a geographic information system (GIS) envi-
ronment as a set of equations, which are then applied to the terrain
data. In principle, these are data that exist in the public domain,
such as the 1:25,000 topographic map series and bathymetric maps
at various scales. Changing parameter values in the models (such
as the rate of sea level rise, depth of closure, dune height, the size
of the flood-tide delta, sediment input from outside the embay-
ment, or the rate of flood-tide delta translation) allows the user to
run a number of impact scenarios for each model and locality. Out-
put maps of these scenarios typically present the spatial distribu-
tion of hazards caused by rising sea level in a grading triage of risk
classes: a definite risk, an uncertain risk, and a negligible risk ac-
cording to each model. These modeling results can then be used by
local government authorities for further detailed studies of individ-
ual sites potentially at risk.
In practice this deterministic approach may not be readily
applied, because:
1. the hazard may be created by a combination of coupled
processes;
2. the process(es) may be understood too poorly to allow
representation by a physical model (de Vriend, 1991;
Wadge et al., 1993; Cowell and Thom, 1994);
3. simulation may be too computationally intensive for
practical purposes; and
4. information on physical variables may not be available
(Wadge et al., 1993).
The modeling of geomorphological processes, which are re-
sponsible for the erosion and deposition of coastal sediments, is
difficult, as these processes are poorly understood especially at
time scales relevant to engineering and planning (Cowell and
Thom, 1994). Therefore, long-term morphological predictions
need to be met with knowledge that is available today (Stive
and de Vriend, 1993). This is often difficult due to the lack of
high-resolution data, especially terrain data, in many coastal ar-
eas (Nicholls, 1993). Furthermore, whereas most studies so far
have focused on responses of shorelines per se to SLR, little at-
tention has been paid to the behavior of shorelines within inlets
and bays, especially in respect to the coupling between shore-
Key Words: coastal erosion, GIS-based modeling, sea-level rise.
INTRODUCTION
•
Studies by the International Geographical Union’s Com-
mission on the Coastal Environment have shown that more
than two thirds of the world’s sandy coastlines have retreated
during the past few decades, and only Ͻ10% have prograded
(Bird, 1996). The erosion of sandy coasts is expected to dete-
riorate if predicted rates of sea level rise (SLR), induced by
© 2000, AAPG/DEG, 1075-9565/00/$15.00/0
Environmental Geosciences, Volume 7, Number 3, 2000 137–148

E N V I R O N M E N T A L G E O S C I E N C E S
138
line and inlet processes that govern the overall adjustments to
changes in sea level. This article adapts established ideas in-
volving sediment-mass balance and coastal equilibrium for
sea-level change on beaches to the equivalent problem for
shorelines in bays and inlets. The purpose of the article is to de-
scribe the general principles of the underlying models and their
incorporation into a geographic information system (GIS) and
to consider some operational implications of the models.
storminess, are beyond the scope of this study. Furthermore,
the models are applied to erodible shorelines only. Erosion-
resistant, rocky shorelines are not considered, and passive
inundation is regarded as the only agent of loss on rocky
shores.
Littoral transport budgets are implemented in the models
as a second-order effect, but engineering structures, such as
dikes, seawalls, or groynes, are not taken into consideration,
except so far as these changes are incorporated into defini-
tion of the terrain model (rock/sand and elevation). Also, the
role of currents in the redistribution of sediments brought
into the embayment is outside the scope of the study. The
work is relevant to regional scales that extend over distances
typically covered by individual local government areas and
to an engineering and planning time scale of 50–100 years.
Due to the typically medium to low resolution input data
and uncertainties associated with climate change (Hender-
son-Sellers, 1993), results derived from all models must be
understood as preliminary, first-order estimates that ignore
details of internal redistribution of sediments and the effects
of engineering structures.
The Aggradation and the Translation Models are based on
theoretical concepts described in the literature (Van Straaten,
1954; Curray, 1964; Thom, 1984; Eysink, 1991; Louters and
Gerritsen, 1994; Roy, 1994) and are developed for GIS applica-
tion. Both models have not been tested and validated with field
data. Each of the models is based on an aggregated approach
and therefore suffers from the sort of limitations identified in re-
lation to the Bruun Rule by Pilkey et al. (1993), although uncer-
tainty in each of the models is amendable to sensitivity analysis.
Further limitations stem from nongeneric properties of GIS soft-
ware because different proprietary packages use different algo-
rithms for data acquisition, conversion, interpolation, and calcu-
lations (van der Knaap, 1992). Therefore, the selection of a
particular GIS software may have a significant influence on
modeling results. However, the GIS applied here (Arc/Info, En-
vironmental Systems Research Institute, Redlands, CA) was
chosen due to its widespread availability and use. A comparison
with modeling results from different GIS software was beyond
the scope of the article.
Approach
The main emphasis of this article is the description of a
method that allows a quick and cost-effective first estimate
of physical impacts of an accelerated rise in sea level on
tidal inlets over the next 50–100 years, before undertaking
more intensive analysis. The study is concerned with gen-
eral trends of shoreline change rather than small-scale
changes, such as single property loss. Readily available data
and knowledge obtained from previous studies are used and
rule-based models are adopted to predict the range of broad-
scale impacts on coastal inlets due to SLR.
Implementation of the models within a GIS allows site-spe-
cific data to be readily integrated into the models providing sim-
ple and pragmatic predictive tools. GIS models provide a useful
method of dealing with uncertainty through computer simula-
tion experiments (Thompson, 1992; Steyaert, 1993). They can
be utilized to deal with the high uncertainty accompanied when
predicting the impacts of climate change and the limited knowl-
edge of near-future morphological changes as a set of scenarios,
representing the potential outcome. The simplicity of the mod-
els is important to allow use of limited available data (usually
bathymetric charts and 1:25,000 topographic maps).
The approach represents a departure from previous stud-
ies concerned with the prediction of coastal erosion. We im-
plement process models within the GIS as a set of equations
that are applied in remapping terrain data to predict continu-
ous variation in shoreline recession alongshore due to a
given SLR, which is in turn generalized and remapped more
simply in terms of relative hazard levels. By using topo-
graphic maps and charts to define initial conditions, the au-
tomated GIS models estimate a range of coastal change for
user-defined regional or local scenarios. The GIS models
developed here are (1) Bruun-GIS Model; (2) Aggradation
Model; and (3) Flood-Tide Delta Translation Model.
MODEL IMPLEMENTATIONS
An aggregated model approach is adopted to side step the
dynamic and stochastic complexity (Cowell and Thom,
1994) that characterises coastal inlets in nature. The ratio-
nale for this type of approach has a long tradition in geo-
morphology (Thorne and Swift, 1991) and at present ap-
pears to be the only viable option for quantitative prediction
of long-term (decades to centuries) coastal change (Cowell
et al., 1995; Niedoroda et al., 1995; Stive and de Vriend,
1995; Stive et al. 1998). Although full morphodynamic
models are available for tidal inlets, their predictive capac-
ity is limited to the subdecadal time scale due to strong
Scope and Limitations
Several restrictions limit this study. First, the models are
applied only to coastal inlets and their flood-tide deltas.
Coastal inlets are defined broadly here as shoreline re-
entrants constricted by barriers or rocky shores and also
deeply embayed shorelines, including estuaries. Second,
SLR and sediment inputs from rivers or the offshore region
are considered as the only factors driving shoreline change.
Other potential threats to the coast, such as an increase in

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state-dependent nonlinearity (Roelvink and van Banning,
1994).
Retaining pre-set digitizing rules for the direction of digi-
tizing (e.g., generally North–South or South–North) is also
important as some GIS commands (e.g., segmentation and
buffer commands) are applied to the direction and therefore
to the left or right of a digital line or vector. Not retaining
digitizing rules will eventually cause an incorrect display of
the results, as shown in Figure 1. For this study, landward
shoreline changes are displayed to the left of a vector and
seaward changes to the right. Vector a has been digitized ac-
cording to the pre-defined digitizing rule and landward
shoreline change is defined as a left buffer function in the
GIS with respect to the direction of the vector. In contrast,
applying the left buffer convention for landward shoreline
changes to vector b, which is directed opposite to vector a,
results in the display of a seaward but not landward shore-
line change, i.e., an incorrect display of modeling results.
Taking the present state of the coast, the automated GIS
models are used to provide continuous estimates for the
alongshore variation in magnitude of coastal recession or
progradation for a region. If input variables such as SLR,
the depth of closure, sediment supply from outside the em-
bayment, or the rate of translation are uncertain, GIS is used
to simulate a range of potential impacts. By changing the
values of the variables after each model run, the user has
control of the model and the number of scenarios performed
for every site. To improve the user-friendliness of the mod-
els and also to enhance their speed, menu-driven user inter-
faces have been developed.
GIS Modeling of Natural Hazards
The United Nations adopted the 1990s as the Interna-
tional Decade for Natural Disaster Reduction, focusing on
the application of advances in science and information tech-
nology, including remote sensing and GIS (Coppock, 1995).
GIS applications in environmental modeling are relatively
new but not uncommon (Brinkley, 1997), and GIS models
are believed to be well suited to integrating real-world data
with deterministic and stochastic process models (Thomp-
son, 1992).
GIS has been applied in climate-change modeling; how-
ever, such applications are rare in combination with coastal-
process models, the only example found being that by Lee
et al. (1992). Despite the general applicability of the models
described in this article, each model can be expanded to
more detailed applications, simply by developing add-in
modules to the base model. Such modules, for example, could
implement Hallermeier’s (1981) or Bruun and Schwartz’s
(1985) approaches for the determination of the depth of clo-
sure. Also, more detailed information can be added to the exist-
ing database, e.g., detailed hydro-survey data, wave data, infor-
mation on currents, detailed topography or cadastral data, and
retrieved when necessary to calculate land loss.
Data Assimilation and Mapping
The main advantage of using GIS as a platform for the
modeling is that it readily allows the assimilation of mapped
data to constrain the models. Spatial data are not only used
to define the initial conditions for the modeling. The rule-
based models also draw information from the mapped data
with respect to morphological conditions and dimensions
that govern the modeled responses (e.g., initial area of the
flood-tide delta, offshore distance of closure depths, propor-
tion between erodible sandy shorelines and erosion-resistant
rocky shorelines) (Hennecke et al., 1997). In this study, the
models are constrained by limited data and the limited
knowledge about physical processes involved. These con-
straints coupled with uncertainties in SLR estimates pro-
duce modeling outcomes. GIS procedures include the trans-
formation of charts to a digital map and also the embedding
of the process models as a set of equations and procedures
within the map database.
Practical considerations in the preparation of the digital
maps with the GIS are essential for computationally fast and
efficient running of the models. All maps need to have the
same pre-defined protocol when remapped and identical
identifiers for different morphological features to allow the
model to be applied correctly. This is guaranteed by a de-
tailed coding system, as shown in Table 1.
MODEL CONCEPTS
The fundamental reason for modeling is a lack of full ac-
cess, either in time or space, to the phenomena of interest
(Oreskes et al., 1994), a situation pertaining to the predic-
tion of coastal impacts of climate change. Although numeri-
cal models are used widely in earth sciences to represent
Table 1: Table of codes identifying morphological features in a
digital bathymetric chart.
Feature
Identifier
Erodible shoreline
Sandy shore
Muddy shore
Erosion-resistant shoreline
Rocky shore
Seawall/training wall
Bathymetric contour
Ϫ1-m contour line
Ϫ2-m contour line
Ϫ5-m contour line
Ϫ10-m contour line
Ϫ15-m contour line
Ϫ20-m contour line
Seaward boundary of the flood-tide delta
Landward boundary of the flood-tide delta
100
101
102
103
Ϫ1
Ϫ2
Ϫ5
Ϫ10
Ϫ15
Ϫ20
200
201

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140
The model is sensitive to the selection of the seaward limit
of morphologic change (i.e., the definition of the depth of
closure). In the absence of sufficient data, the theoretical
and empirical relationships by Hallermeier (1981) or Bruun
and Schwartz (1985) can be used as default values for
shoreface parameters with respect to the limiting depth. In
general, it is considered that the Bruun model is useful in
providing an estimate of the minimum rate of recession as-
sociated with SLR, provided that it is applied over a suffi-
ciently long-term period (Schwartz, 1967; Komar, 1991). In
this study, the concept of the Bruun Model itself has not
been modified as it has been in the past, for example, by
Dubois (1977) or Dean and Maurmeyer (1983). The only
“modification” described here is the implementation of the
model in a GIS to allow for continuous alongshore variabil-
ity of shoreline responses. The Bruun-GIS model is flexible
enough to be applied to different sites. Each variable of the
model is stored in the GIS as a separate column in the data-
base of the digital map to account for differences in local
Bruun parameters (Table 2).
User-defined variables (⌬s, h, D) are entered after com-
mencement of a model run. The profile length (L) is calcu-
lated automatically by the GIS as the offset distance be-
tween the shoreline and the depth of closure, and the rate of
recession (R) is then determined from Equation 1. A loop
function is implemented with the GIS model so that by
changing the values of one or more variables, a number of
scenarios can be run based on parameter variations. This
also facilitates sensitivity analysis.
FIGURE 1: GIS buffer function with respect to the direction of a vector. (A) digitiz-
ing direction according to the pre-defined convention (south–north); (B) digitizing di-
rection opposite the convention, resulting in the wrong display of modeling results.
complex, poorly understood systems (Oreskes et al., 1994),
they also are being used in the public arena, often to justify
or support controversial decisions. It is important to ac-
knowledge therefore that the primary value of these models
is heuristic. Coastal-impact models in this context are repre-
sentations of natural processes, useful for guiding further
study but not susceptible to proof, as explained clearly by
Oreskes et al. (1994). This point is illustrated by the Bruun
Model of shoreline responses to SLR (Bruun, 1988), the
many derivative Bruun-type models, and the many attempts
to validate these models. We therefore emphasize that our
approach so far is heuristic in applying the Bruun-GIS
Model, the Aggradation, and the Flood-Tide Delta Transla-
tion Model to describe and simulate responses of rising sea
level in different coastal-inlet conditions. The models are
designed for application in coastal environments dominated
by waves or tides and areas with SLR lag effects, i.e., where
a delayed response of the flood-tide delta to the rise of the
water level occurs, such as the Dutch Wadden Sea (Peer-
bolte et al., 1991). Practical application must incorporate
sensitivity analyses to assess uncertainty, and results must
be evaluated in the light of the real-world complications
outlined by Pilkey et al. (1993).
Aggradation Model
The Aggradation Model describes the vertical growth of a
flood-tide delta in tide-dominated environments. The model
is based on the work of Van Straaten (1954) which de-
scribed the idea of tidal flat sedimentation for the Dutch
Wadden Sea (Eysink, 1991). Van Straaten explained that
one of the main processes of sedimentation in the Wadden
Sea is the vertical deposition and upward growth of tidal
flats at the same order as the relative SLR. Eysink (1991)
supported Van Straaten’s theory, stating that sedimentation
in the Dutch Wadden Sea estuary and the local rate of SLR
appear to be in equilibrium. Peerbolte et al. (1991) and
Bruun-GIS Model
The Bruun-GIS Model applies Bruun’s concept (Bruun,
1962, 1988) for continuous alongshore variability in shore-
line response to SLR in general and to inlet and embayment
shorelines in particular. This GIS-based approach to appli-
cations of the Bruun Model has been applied previously at
least once in developing coastal-management plans in New
Zealand (J. Gibb, 1998, personal communication). Accord-
ing to Bruun,
Table 2: Database of a chart with Bruun-GIS variables for sample
transections along a beach in southeastern Australia.
Name
⌬sa
Lb
hc
Dd
Re
Narrabeen (Sydney)
Narrabeen
Narrabeen
0.2
0.2
0.2
1350
1450
1280
24
24
24
5
7
8
9.3
9.4
8.0
L
h + D
-------------
R = ∆s
(1)
a⌬s, magnitude of sea level rise (m).
bL, length of profile (m).
ch, depth of closure (m).
where R is the rate of shoreline recession (m); ⌬s the local/
regional rate of SLR (m); L the length of the active profile
(m); h the depth of closure (m); and D the dune height (m).
dD, dune elevation (m).
eR, rate of recession (m).

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141
Louters and Gerritsen (1994) described processes responsi-
ble for the rise of the Wadden Sea floor.
1. a sediment volume ⌬Ve, supplied from outside the em-
bayment and being transported into the inlet (an as-
sumed response to flood-tide currents and waves);
2. fluvial sediments, ⌬Vfl., being deposited in the inlet;
and
Niemeyer et al. (1995) stated that the large-scale morpho-
dynamic balance in the Wadden Sea has not changed signif-
icantly during the last few hundred years, because sufficient
sediment supply to the floor of the Wadden Sea allows it to
follow the rising sea level. The tidal volume of the East Fri-
sian Wadden Sea has not increased since 1650 despite a rise
in sea level according to Niemeyer et al. (1995). However,
the authors did not comment on the potential significance of
land reclamation in that area since the Middle Ages, which
could partly be responsible for a decrease of the tidal area.
Jelgersma (1992) found that sedimentation in the Wadden
Sea has kept up with a SLR of 1.5 mm/yr during the last 100
years.
Data for southeastern Australia have shown similar trends
to the Wadden Sea. Based on research undertaken by
Nielsen and Roy (1981) and Hennecke (1999), it can be
suggested that flood-tide delta aggradation in estuaries in
southeastern Australia started ف11,000 B.P. and was able to
keep pace with rising sea level until ف6000–5000 B.P., i.e.,
between 500 and 1000 years into the stillstand period.
de Ronde (1993) assumed that the sedimentation in the
Wadden Sea will also increase under future SLR conditions
and that the Wadden Sea is able to keep pace with a 1-m
SLR in 100 years. Louters and Gerritsen (1994) expect that
most of the Wadden Sea is able to keep up if relative rise in
sea level is only 20–60 cm per century in the coming cen-
tury but not if SLR is as much as 1 m, as expected by de
Ronde. Only in extreme situations, at a rise in sea level of
85 cm or more and a sharp increase in wind speeds, Louters
and Gerritsen anticipate changes in the morphology of the
Wadden Sea to such an extent that the tidal flats may disap-
pear forever. The authors expect rather an effection of the
morphological structure of the Wadden Sea at local level
due to SLR, but the characteristic dynamic system of chan-
nels and tidal flats will continue to exist in the next hundred
years in their opinion.
The sediment sources for the rise of the Wadden Sea tidal
basin are the North Holland coast and barrier islands, accord-
ing to Dieckmann (1985), Stive et al. (1990), and Louters
and Gerritsen (1994). The concept described for the Wadden
Sea is borrowed for the Aggradation Model; however, the
model is not restricted to applications in the Wadden Sea.
The sediment volume needed for the rise of the flood-tide
delta is here defined as a function of the size of the area of
the flood-tide delta (⌬A) and the rate of SLR (⌬s). Such a
simplified approach is believed to be an appropriate initial
estimation for the calculation of the sediment demand of a
tidal basin under SLR conditions (Niemeyer et al., 1995).
Model adaptations proposed here extend the concept by
identifying three main sources of sediment (Figure 2) for
raising the floor of the estuary (i.e., the flood-tide delta, ⌬A):
3. a sediment volume, ⌬Vi, derived through the erosion
of erodible shorelines inside the embayment.
For the present study, ⌬Ve is defined as a composition of
three sources of net sediment input:
1. littoral transport (⌬Vlit.);
2. offshore sediment supply (⌬Vof.); and
3. overwash processes (⌬Vov.).
Therefore, the total demand ⌬Vtot. (⌬A * ⌬s) ϭ ⌬Vfl.
ϩ
⌬Ve ϩ ⌬Vi. Generally speaking, the larger (⌬Ve ϩ ⌬Vfl.),
the smaller is ⌬Vi and therefore the risk of shoreline erosion
in the embayment.
GIS procedures for the Aggradation Model are similar to
the Bruun-GIS Model from a GIS perspective. Like the
Bruun-GIS Model, the Aggradation Model (Equations 2–5)
can be applied to different locations. All variables of the
Aggradation Model (i.e., ⌬A, ⌬s, ⌬Vfl., ⌬Vlit., ⌬Vof., ⌬Vov.,
⌬Vi., D, and Les, where Les is the length of erodible shore-
line in the embayment) are also defined as separate columns
in the database of the digital map (Table 3).
The rate of recession R per meter of erodible shoreline
can therefore be defined as follows:
Ϫ1
Ϫ1
R = (∆A ∆s)(L ) (D)
(2)
(3)
(4)
es
Ϫ1
Ϫ1
= (∆V )(L ) (D)
tot.
es
Ϫ1
Ϫ1
= (∆V + ∆V + ∆V )(L ) (D)
e
lit.
i.
es
= (∆V + ∆V + ∆V
+
fl.
lit.
ov.
Ϫ1
Ϫ1
∆V + ∆V )(L ) (D)
(5)
of.
i.
es
The user supplies the values of the variables (⌬s, ⌬Vfl., ⌬Vlit.,
⌬Vof., and ⌬Vov.) or an equation defining them when prompted
by the GIS interface procedure. The net external sediment
volume, ⌬Ve ϩ ⌬Vfl., supplied from outside the bay, is then
subtracted from ⌬Vtot. to determine the remaining sediment
FIGURE 2: Sediment sources for the aggradation of a flood-tide delta in the Aggra-
dation Model.

E N V I R O N M E N T A L G E O S C I E N C E S
142
Table 3: Database of a chart with Aggradation Model variables for a coastal inlet in southeastern Australia.
c
d
e
f
g
h
i
Name
⌬Aa
⌬sb
⌬Vtot.
⌬Vfl.
⌬Vlit.
⌬Vof.
⌬Vov.
⌬Vi.
Les
Dj
3
Rk
Batemans Bay
19,630,000
0.2
3,926,000
0
0
0
0
3,926,000
12,000
109
a⌬A, area of the flood-tide delta (m2).
b⌬s, magnitude of sea level rise.
c⌬Vtot., total sediment demand (m3).
d⌬Vfl., sediment supply from fluvial sources (m3).
e⌬Vlit., sediment supply through littoral sediment transport into the inlet (m3).
f⌬Vof., sediment supply from offshore (m3).
g⌬Vov., sediment supply through overwash processes (m3).
h⌬Vi., sediment supply from erodible shorelines along the flood-tide delta (m3).
iLes, length of erodible shorelines (m).
jD, dune elevation (m).
kR, rate of recession (m).
demand to be supplied from inside the embayment (⌬Vi).
The GIS is finally used to calculate an average rate of shore-
line change for those erodible shorelines Les inside the bay
with an average dune height D and considered in the mod-
eling.
Shoreline changes associated with the flood-tide delta
translation in the FTDTM are defined as volumetric changes
of the flood-tide delta before (A-B) and after (AЈ-BЈ) transla-
tion (Figure 4). The delta front and the ramp of the flood-
tide delta are dealt with separately to account for shoreline
changes in the inner and outer part of the embayment. The
sediment supply from offshore is initially assumed to be un-
restricted for this model. Like the Bruun and the Aggrada-
tion Model, the FTDTM is implemented with GIS and de-
signed for applications for three different embayment
configurations. These are embayments with rectangular
shapes, funnel-shaped embayments, and inlet-constricted
embayments (Figure 5).
Flood-Tide Delta Translation Model
The Flood-Tide Delta Translation Model (FTDTM) de-
scribes, in addition to behavior captured by the Aggradation
Model, the horizontal translation of a sediment body in an
embayment under SLR conditions. Shoreface translation
has been described by Curray (1964) and further docu-
mented by Boyd and Penland (1984), Thom (1984), Wind
and Peerbolte (1993), and Roy (1984, 1994). The model is
essentially a combination of the Aggradation Model and a
simplified Bruun Model (Dubois, 1977) and computation-
ally resembles elements of the Shoreface Translation Model
(Cowell et al., 1995). Waves and flood-tide currents are the
agents of landward flood-tide delta translation. However,
the redistribution of the sediment within the embayment or
inlet is not considered as this is subject to local factors not
captured in the model.
Rectangular Embayments
In bays with rectangular shapes (Figure 6), the landward
(Lb) and seaward (Sb) boundaries are similar in length (Lb Ϸ
Sb). The overall water volume above the flood-tide delta
and thus the proportion between the water and sediment
volume of the flood-tide delta remain constant during trans-
lation. As a result, the accommodation space for the trans-
lated flood-tide delta remains constant, and the flood-tide
delta is assumed to rise at the same rate as sea level. The
water depth at the seaward end of the flood-tide delta is ex-
pected to increase over time due to rising sea level and the
landward translating flood-tide delta, eventually causing
shoreline recession on the adjacent erodible shorelines. It is
evident from Figures 5B and 5C that embayments with up-
stream shoreline convergence suffer less shoreline retreat
The translation distance is defined as a function of local
SLR and the average slope of the embayment (Figure 3), i.e.,
T = ∆s ⁄ tanα
(6)
where T is horizontal distance (translation) (m); ⌬s is mag-
nitude of SLR (m); and ␣ ϭ average slope of the embay-
ment floor (degree).
FIGURE 3: Translation distance of a flood-
tide delta in an embayment under SLR condi-
tions.

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143
the seaward boundary (Sb) is shorter than is the landward
boundary (Lb) of the flood-tide delta and the area of the
flood-tide delta before translation (FTD0) is smaller than at
the end of the translation (FTD1).
During flood-tide delta translation, the embayment wid-
ens toward the inner part of the bay, i.e., the horizontal ac-
commodation space increases. Again, sediment is required
to provide the vertical aggradation due to SLR, but an addi-
tional volume also is required due to the increase in hori-
zontal accommodation space. To allow the flood-tide delta
to rise at the same rate as sea level, as assumed in the
Aggradation Model, the sediment deficit in this scenario has
to be provided from the adjacent erodible shorelines.
FIGURE 4: Profile of a flood-tide delta during translation from A to AЈ and B to BЈ
under SLR conditions.
than do embayments with parallel sides, whereas divergent
shorelines cause enhanced recession.
MODEL APPLICATIONS
Funnel-Shaped Embayments
The generic behavior of the models can be illustrated by
application to idealized, synthetic site data.
In funnel-shaped embayments (Figure 7), the seaward
boundary of the flood-tide delta is longer than is the land-
ward boundary (Sb Ͼ Lb) and the area of the flood-tide delta
(FTD) before translation (FTD0) is larger than at the end of
the translation (FTD1) (Figure 7B).
Bruun-GIS Model
The modeling results achieved with the Bruun-GIS
Model in principle do not differ from “traditional” applica-
tions of the Bruun Rule. Recession along the shoreline var-
ies inversely with dune and shoreface steepness, i.e., with
L/(hϩD) (Equation 1). Thus, alongshore variations in dune
height and distance of estimated closure depth from the
shore, both given by the terrain model, produce continuous
variation in recession along the coast; SLR impact is simply
a function of terrain. Different scenarios for the expected
magnitude of SLR (Figure 9), the resolution of the terrain
model, and uncertainty regarding closure depth estimates
can be examined through sensitivity analysis or as a set of
scenarios. Figure 9 also illustrates how the GIS allows the
terrain data to condition the Bruun Model to account for
real-world variability. This conditioning is probably too ex-
act because other processes, such as littoral transport, may
smear the local effects of dune height and bathymetry be-
tween adjacent areas alongshore. Nevertheless, the results
can be expected to provide a general indication of shoreline
response to Bruun effects as well as assessment of the un-
certainties pertaining to these effects. Figure 9 shows con-
ceptual modeling results with the Bruun-GIS model for dif-
ferent sections of a beach. The figure is based on a single
given SLR scenario and closure depth but with variation in
L and D for different sections. In this example, dune height
increases toward the upper part of the beach and the rate of
shoreline recession decreases. Therefore, alongshore varia-
tion in shoreline recession is accounted for with the GIS
model by providing the information for individual profiles
to the GIS.
During flood-tide delta translation, the tidal delta is
forced towards the inner, narrower part of the embayment.
With the inner boundary of the flood-tide delta being
shorter than the outer boundary, the accommodation space
in the inner part of the embayment is reduced (Figure 7B).
The water volume above the flood-tide delta is larger before
translation than at the end, due to the funnel-shaped config-
uration of the embayment. Despite the usual assumption of
a rise in the elevation of the flood-tide delta corresponding
to SLR, the reduction of the horizontal accommodation
space implies a reduced demand for sediment required for
aggradation; a sediment surplus may even arise under these
conditions. Beaches in the embayment will experience less
erosion than under the Aggradation Model. If the reduced
accommodation volume results in a sediment surplus, then
the implication is that the beaches will prograde into the bay.
Constricted-Inlet Embayments
The third scenario (Figure 8) describes the translation of
the flood-tide delta in inlet constricted embayments. Here,
Aggradation Model
Modeling results of the Aggradation Model depend in
principle on the relation between the net sediment supply
FIGURE 5: Embayment shapes considered in the Flood-Tide Delta Translation
Model. (A) Rectangular embayments; (B) funnel-shaped embayments; and (C) inlet-
constricted embayments.

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144
FIGURE 6: Translation of the flood-tide delta in a rectangular embayment; the wet volume remains constant and the flood-tide delta rises at the same rate as sea level. (A) Profile
of the flood-tide delta during translation; (B) top view of the flood-tide delta during translation.
from outside (⌬Ve ϩ ⌬Vfl.) and inside (⌬Vi) the embay-
ment. In cases where the net external sediment volume (⌬Ve ϩ
⌬Vfl.) is smaller than the total sediment demand ⌬Vtot. re-
quired to raise the flood-tide delta, shorelines will erode ac-
cording to this model to supply the volumetric difference.
The model works at first approximation so that the sediment
demand extracted from shorelines within the coastal inlet is
assumed to be apportioned equally for all erodable parts of
the shoreline. Shoreline retreat, however, depends on the ca-
pacity of the local shoreline to supply sediments, and this is
a function of dune height. Therefore, the predicted shoreline re-
treat varies alongshore in accordance with the onshore Digital
Elevation Model (DEM).
In embayments where there is sufficient sediment supply,
i.e., where ⌬Ve ϩ ⌬fl. ϭ ⌬Vtot, no shoreline change occurs
according to the model, despite SLR (e.g., the case of the
Wadden Sea). In cases where ⌬Ve ϩ ⌬Vfl. Ͼ ⌬Vtot, the
shorelines in the embayment are predicted to build out into
the embayment. With respect to ⌬Ve, the magnitude of each
component contributing to the marine sediment budget (⌬Vlit.,
⌬Vof., and ⌬Vov.) varies between 0 and Ͼ100% of the flood-
tide delta demand volume. Hence, an infinite number of sce-
narios is feasible in every case study, unless expert knowl-
edge can provide more detailed information with regard to
local sediment sources. This is not a trivial problem for pro-
fession specialists in coastal processes, so applications of these
models by general staff, in for example local government,
requires considerable caution. The application of a sensitiv-
ity analysis is thus mandatory. Figure 10 displays the concept
of a range of modeling results achieved with the Aggrada-
tion Model. The figure includes one accretion and three re-
cession scenarios, which are based on different values of the
input variables. It is assumed for this example that parame-
ter values in Recession Scenarios 1 and 2 are identical, ex-
cept for the supply of marine sediments, i.e., the contribu-
tion of ⌬Ve to ⌬Vtot. is higher in Scenario 1 than in Scenario
2. To allow the flood-tide delta to aggradate at the rate of
SLR, ⌬Vi ϭ ⌬Vtot. Ϫ (⌬Ve ϩ ⌬Vfl.), implying a higher rate
in shoreline recession for Scenario 2. Additional to the as-
sumptions made for Scenarios 1 and 2, the dune height in
Scenario 3 is assumed to be lower than in Scenario 2. With
⌬Vi being a function of Les, D, and the width of the dune, a
FIGURE 7: Translation of the flood-tide delta in a funnel-shaped embayment; the wet volume decreases and sediment demand for the rise of the flood-tide delta is reduced. (A)
Profile of the flood-tide delta during translation; (B) top view of the flood-tide delta during translation.

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FIGURE 8: Translation of the flood-tide delta in a constricted-inlet embayment. The wet volume increases during translation and so does the sediment demand for the aggradation
of the flood-tide delta. (A) Profile of the flood-tide delta during translation; (B) top view of the flood-tide delta during translation.
lower dune height ultimately results in higher shoreline re-
cession. The Accretion Scenario is based on the assumption
that ⌬Ve ϩ ⌬Vfl. Ͼ ⌬Vtot, i.e., a surplus in sediment occurs
in the inlet over time causing the shoreline to prograde into
the inlet.
for funnel-shaped embayments (Figure 11B) as a result of a
decrease in accommodation space and the reduced demand
for sediment. Also, beaches landward of the delta front in
rectangular-shaped embayment (Figure 11A) are expected
to prograde into the inlet. For constricted-inlet embayments
(Figure 11C), where the accommodation space and there-
fore the sediment demand increases, a widespread shoreline
recession is the expected overall impact. Still, modeling re-
sults of the FTDTM are highly variable, but in principle de-
pend on the rate of flood-tide delta translation, the embay-
ment shape, and the sediment supply from offshore.
Flood-Tide Delta Translation Model
Generally speaking, modeling results for the FTDTM fol-
low those for the Aggradation Model but with the addition
that different embayment shapes and rates of flood-tide
delta translation are included. Therefore, more variables
need to be considered where flood-tide delta translation oc-
curs. Figure 11 shows anticipated impacts of flood-tide
delta translation based on embayment shapes shown in Fig-
ure 5. Shoreline accretion (reverse Bruun effect) is expected
CONCLUSIONS
•
The potential threat of an enhanced rise in sea level de-
mands methods that are useful for making practical predic-
tions of a range of coastal impacts on a large spatial scale.
Despite a number of uncertainties associated with climate
change and inaccuracies associated with the mapping of
low-resolution data and their further processing, a quick and
low-cost method is believed to be a good start for hazard
mapping at a large spatial scale.
The principles of three GIS-based models used to simu-
late and display a range of possible impacts of rising sea
FIGURE 9: Modeling of continuous variation of beach recession due to changes in
values for L and D between profiles, using the Bruun-GIS Model.
FIGURE 10: Conceptual recession and accretion scenarios derived with the Aggra-
dation Model.

E N V I R O N M E N T A L G E O S C I E N C E S
146
FIGURE 11: Conceptual modeling results for different embayment shapes with the Flood-Tide Delta Translation Model.
level on flood-tide deltas were introduced and discussed in
this article. The advantage of GIS-based models compared
to conventional methods clearly lies in their flexibility with
respect to parameter value adjustments allowing the users of
such systems to account quickly for morphological variabil-
ity under SLR conditions in space and time.
With respect to the three GIS models described in this ar-
ticle, it can be summarized that:
• the Bruun-GIS Model allows for continuous alongshore
variability of shoreline response, defining shoreline re-
cession as a function of terrain (L/(hϩD)) and the rate
of SLR on both open ocean beaches and in inlets;
government as tools in future shoreline change estimation,
but the decision about the selection of the most appropriate
model still remains with the user of those tools.
ACKNOWLEDGMENTS
•
This work was funded by the National Greenhouse Advi-
sory Committee (Australia) and the Deutscher Akademis-
cher Austauschdienst (Germany). The authors acknowledge
the useful comments provided on this manuscript by Dr.
Walter Barnhardt and Dr. David Bush.
• the Aggradation Model also considers terrain as a factor
of continuous shoreline change. Nevertheless, the pro-
portion of the external sediment volume (⌬Ve ϩ ⌬Vfl.)
required for the aggradation of the flood-tide delta com-
pared to ⌬Vtot. (ϭ ⌬A * ⌬s) is the dominant factor in
determining shoreline recession or advance;
• the Flood-Tide Delta Translation Model implements, in
addition to the variables in the Aggradation Model, the
translation of the flood-tide delta under SLR conditions
for different inlet shapes, therefore accounting for a
wider range of shoreline response than the other two
models.
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ABOUT THE AUTHORS
•
Werner G. Hennecke
Werner Hennecke is a postgraduate stu-
Peter J. Cowell
Peter Cowell is Senior Lecturer at the
Coastal Studies Unit in the Marine Studies
Centre and School of Geosciences at the
University of Sydney. His reasearch on
coastal morphodynamics ranges from in-
stantaneous processes to coastal evolution
on time scales of centuries and millennia.
This work focuses on the integration of
computer-modeling methods with process and geological data for
prediction of morphological change on coasts.
dent at the Coastal Studies Unit in the Ma-
rine Studies Centre and School of Geo-
science at the University of Sydney. He
submitted his Ph.D. thesis about GIS-based
modeling of sea-level rise impacts on
coastal inlets in August 1999. Previously,
Werner studied the geomorphology of the
North Sea and Baltic Sea using Geographical Information Systems
(GIS) at the University of Kiel in Germany, where he obtained a mas-
ter’s degree in 1994. His interests focus on the application of GIS in
coastal research.