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Journal of
Marine Science
and Engineering
Article
A Nourishment Performance Index for Beach
Erosion/Accretion at Saadiyat Island in Abu Dhabi
Waleed Hamza 1, Giuseppe Roberto Tomasicchio 2, Francesco Ligorio 2, Letizia Lusito 2, *
and Antonio Francone 3
1Biology Department, College of Science, United Arab Emirates University, 15551 Al Ain, UAE;
w.hamza@uaeu.ac.ae
2Department of Engineering for Innovation, University of Salento, Ecotekne, 73100 Lecce, Italy;
giuseppe.tomasicchio@unisalento.it (G.R.T.); francesco.ligorio@unisalento.it (F.L.)
3Department of Civil Engineering, University of Calabria, 87036 Arcavacata di Rende (CS), Italy;
antonio.francone@unical.it
*Correspondence: letizia.lusito@unisalento.it
Received: 20 April 2019; Accepted: 29 May 2019; Published: 1 June 2019
Abstract:
The present paper proposes a methodology to optimise the design of a beach protection
intervention at Saadiyat Island, of the Abu Dhabi city in the United Arab Emirates. In particular,
a nourishment performance index (NPI) has been introduced to select among different design
alternatives of a coastal engineering intervention related to the ongoing development of the island.
The NPI is based on general factors such as the initial volume of sand necessary for the nourishment,
the beach surface loss after the intervention and the closure depth. The proposed index, properly
integrated with a numerical simulation of the beach morphodynamics, is shown to be promising in the
evaluation of the feasibility for the planned coastal defence interventions. The adoption of different
design scenarios has showed that the NPI value depends mainly on the built nourishment shoreline.
Keywords:
beach morphology; beach nourishment performance; sustainable development; General
Shoreline beach model; United Arab Emirates; Saadiyat Island
1. Introduction
In the present paper, the definition of a nourishment performance index (NPI) for coastal
engineering interventions is proposed, based only on general factors such as the initial sand volume
necessary for the nourishment, the beach surface loss after the intervention and the closure depth
(defined in [1]), which indicates the seaward depth limit to the active profile.
In particular, the NPI has been determined for a coastal prediction system to be built at Saadiyat
beach, in Saadiyat Island, a large low-lying 27 km
2
island situated in the Arabian Gulf (also named
Persian Gulf) within the Emirate of Abu Dhabi of the United Arab Emirates (Figure 1). Saadiyat Island
comprises a SW–NE oriented, 9 km long natural sandy beach, Saadiyat beach, of moderate to flat slope.
The shape and orientation of the beach has been modified several times for the development of the
Cultural District of Saadiyat Island. The present study focuses on the western area of Saadiyat beach,
a 2 km long beach. Where the urban plan requires the realization of sustainable coastal protection
structures, different design scenarios have been proposed by the Tourism Development Investment
Company (TDIC), described in the relative TDIC master plan. According to the last development
plan as approved by TDIC, the intervention for Saadiyat beach involves a large sand nourishment
intervention and the construction of four groynes; the number and location of the four groynes has
been decided by TDIC and no contribution from the present Authors has been given.
J. Mar. Sci. Eng. 2019,7, 173; doi:10.3390/jmse7060173 www.mdpi.com/journal/jmse
J. Mar. Sci. Eng. 2019,7, 173 2 of 18
J. Mar. Sci. Eng. 2019, 7, x FOR PEER REVIEW 2 of 19
Figure 1. Top: view of the Arabic Gulf area showing the position of Abu Dhabi; bottom: aerial view
of Saadiyat island, in the Abu Dhabi Municipality; the red rectangle indicates the position of the
Saadiyat beach study area.
In order to quantify the efficiency feasibility for each of the design scenarios developed by TDIC,
a deep knowledge of the engineering details and the coastal conditions, such as the performance of
the intervention, the morphodynamics response of the shoreline to the various different coastal
defence scenarios of the TDIC development plan is required [2–5].
The objective of the present study is to identify a methodology to evaluate the nourishment
performance for different scenarios of coastal defence at Saadiyat beach. The proposed methodology
is based on the recent availability of observational data (i.e., data measured by in-situ instruments,
see Sections 2.1 and 2.2, and data recorded by remote observing systems like satellites, see Section
2.3.3), allowing to define with precision the wave climate (the distribution of wave characteristics
averaged over a period of time and for a particular location) and coast landforms, i.e., any of the relief
features present along the coast, which are the result of a combination of processes, sediments and
the geology of the coast itself. The observational data were collected by means of in-situ monitoring
instruments, survey campaigns and satellite imagery. The data collection has served as a basis for
numerical models-based simulations of the oceanographic conditions (wave climate) and of the
morphological changes in the natural shape of the coastline under the influence of the planned
interventions. The outcomes of the performed numerical simulations allow the identification of the
more environmentally sustainable scenario for Saadiyat beach.
2. Materials and Methods
The proposed methodology is based on the collection of observational data and the use of
numerical models in a joint way.
Figure 1.
Top: view of the Arabic Gulf area showing the position of Abu Dhabi; bottom: aerial view of
Saadiyat island, in the Abu Dhabi Municipality; the red rectangle indicates the position of the Saadiyat
beach study area.
In order to quantify the feasibility for each of the design scenarios developed by TDIC, a deep
knowledge of the engineering details and the coastal conditions, such as the performance of the
intervention, the morphodynamics response of the shoreline to the various different coastal defence
scenarios of the TDIC development plan is required [2–5].
The objective of the present study is to identify a methodology to evaluate the nourishment
performance for different scenarios of coastal defence at Saadiyat beach. The proposed methodology is
based on the recent availability of observational data (i.e., data measured by in-situ instruments, see
Sections 2.1 and 2.2, and data recorded by remote observing systems like satellites, see Section 2.3.3),
allowing to define with precision the wave climate (the distribution of wave characteristics averaged
over a period of time and for a particular location) and coast landforms, i.e., any of the relief features
present along the coast, which are the result of a combination of processes, sediments and the
geology of the coast itself. The observational data were collected by means of in-situ monitoring
instruments, survey campaigns and satellite imagery. The data collection has served as a basis for
numerical models-based simulations of the oceanographic conditions (wave climate) and of the
morphological changes in the natural shape of the coastline under the influence of the planned
interventions. The outcomes of the performed numerical simulations allow the identification of the
more environmentally sustainable scenario for Saadiyat beach.
2. Materials and Methods
The proposed methodology is based on the collection of observational data and the use of
numerical models in a joint way.
J. Mar. Sci. Eng. 2019,7, 173 3 of 18
2.1. Sediment Characteristics
Within a survey campaign in May 2017, different sediment samples have been collected at
points indicated in Figure 2and Table 1with water depths ranging from 1 m to 3 m. The samples
indicated as 8 and 9 have been collected at the same time with few meters distance from each other.
The concentration of each granulometric fraction was obtained through a sieve analysis with the
corresponding results reported also in Table 1.
J. Mar. Sci. Eng. 2019, 7, x FOR PEER REVIEW 3 of 19
2.1. Sediment Characteristics
Within a survey campaign in May 2017, different sediment samples have been collected at points
indicated in Figure 2 and Table 1 with water depths ranging from 1 m to 3 m. The samples indicated
as 8 and 9 have been collected at the same time with few meters distance from each other. The
concentration of each granulometric fraction was obtained through a sieve analysis with the
corresponding results reported also in Table 1.
Table 1 shows also the value of the mean diameter of the sediment sample, 𝐷 (given in mm)
and the classification of the beach type according to [6] at each collection point.
Although important for a complete sedimentological mapping of the area, it was not possible to
gain information on theml the textural characteristics of sand [7–9].
Figure 2. Location of sediment samples.
Table 1. Sediment characteristics of Saadiyat beach: coordinates and water depths of collection points,
sample weight percentages passing different meshes (of sizes 5 μm, 10 μm and so on), corresponding
𝐷 values and, finally, sand classification according to [6].
ID Location Weight (%) Passing Each Mesh (μm) Sand Classification
Coordinates Water Depth (m) 5 μm 10 μm 18 μm 35 μm 60 μm 120 μm 230 μm >230 μm 𝑫𝟓𝟎 (mm) Type
1 54°23′10″ E 24°32′18″ N 2 - 0.4% 0.4% 12.2% 61.8% 22.0% 2.3% 0.9% 0.44 Medium
2 54°25′20″ E 24°32′40″ N 2 - 0.2 1.0 4.2 14.7 63.1 16.1 0.7 0.26 Medium
3 54°24′07″ E 24°32′31″ N 2 - 0.2 0.5 5.2 30.3 61.3 1.6 0.9 0.33 Medium
4 54°24′55″ E 24°32′42″ N 2 - 0.0 0.1 1.8 17.8 71.1 8.6 0.6 0.27 Medium
5 54°25′43″ E 24°33′02″ N 3 - 0.5 7.0 33.5 49.0 9.3 0.4 0.3 0.65 Coarse
6 54°25′53″ E 24°32′52″ N 1 - 0.3 1.4 7.5 37.7 40.8 11.4 0.9 0.34 Medium
7 54°26′39″ E 24°33′24″ N 2 - 0.1 0.2 0.6 1.0 34.8 59.8 3.5 0.16 Fine
8 54°26′52″ E 24°33′39″ N 2 - 0.3 0.4 0.9 7.2 27.1 57.7 6.4 0.16 Fine
9 54°26′52″ E 24°33′39″ N 2 - 0.1 0.5 2.1 9.5 59.5 27.7 0.6 0.22 Fine
10 54°26′57″ E 24°33′35″ N 2 - 0.0 0.4 2.2 32.3 54.7 9.9 0.5 0.30 Medium
2.2. Local Wave Climate
Direct wave measurements are considered the most reliable source of information. In the Gulf
area, this type of information is rare or even missing. However, recently, in vicinity of Saadiyat beach,
the Abu Dhabi Municipality (ADM) installed two Argonaut-XR ADCP (Acoustic Doppler Current
Profiler) produced by the company “SonTek—A Xylem Brand” (San Diego, CA, USA) to observe the
atmospheric and oceanographic conditions (water level, significant wave height, peak wave period,
water temperature and wind speed and direction). Courtesy of the ADM, this observations dataset
Figure 2. Location of sediment samples.
Table 1.
Sediment characteristics of Saadiyat beach: coordinates and water depths of collection points,
sample weight percentages passing different meshes (of sizes 5
µ
m, 10
µ
m and so on), corresponding
D50 values and, finally, sand classification according to [6].
ID
Location Weight (%) Passing Each Mesh (µm) Sand
Classification
Coordinates Water
Depth (m) 5µm 10 µm 18 µm 35 µm 60 µm 120 µm 230 µm>230 µmD50 (mm) Type
1 54◦23010” E 24◦32018” N 2 - 0.4% 0.4% 12.2% 61.8% 22.0% 2.3% 0.9% 0.44
Medium
2 54◦25020” E 24◦32040” N 2 - 0.2 1.0 4.2 14.7 63.1 16.1 0.7 0.26
Medium
3 54◦24007” E 24◦32031” N 2 - 0.2 0.5 5.2 30.3 61.3 1.6 0.9 0.33
Medium
4 54◦24055” E 24◦32042” N 2 - 0.0 0.1 1.8 17.8 71.1 8.6 0.6 0.27
Medium
5 54◦25043” E 24◦33002” N 3 - 0.5 7.0 33.5 49.0 9.3 0.4 0.3 0.65 Coarse
6 54◦25053” E 24◦32052” N 1 - 0.3 1.4 7.5 37.7 40.8 11.4 0.9 0.34
Medium
7 54◦26039” E 24◦33024” N 2 - 0.1 0.2 0.6 1.0 34.8 59.8 3.5 0.16 Fine
8 54◦26052” E 24◦33039” N 2 - 0.3 0.4 0.9 7.2 27.1 57.7 6.4 0.16 Fine
9 54◦26052” E 24◦33039” N 2 - 0.1 0.5 2.1 9.5 59.5 27.7 0.6 0.22 Fine
10 54◦26057” E 24◦33035” N 2 - 0.0 0.4 2.2 32.3 54.7 9.9 0.5 0.30
Medium
Table 1shows also the value of the mean diameter of the sediment sample,
D50
(given in mm) and
the classification of the beach type according to [6] at each collection point.
Although important for a complete sedimentological mapping of the area, it was not possible to
gain information on the textural characteristics of sand [7–9].
2.2. Local Wave Climate
Direct wave measurements are considered the most reliable source of information. In the Gulf
area, this type of information is rare or even missing. However, recently, in vicinity of Saadiyat beach,
the Abu Dhabi Municipality (ADM) installed two Argonaut-XR ADCP (Acoustic Doppler Current
Profiler) produced by the company “SonTek—A Xylem Brand” (San Diego, CA, USA) to observe the
atmospheric and oceanographic conditions (water level, significant wave height, peak wave period,
J. Mar. Sci. Eng. 2019,7, 173 4 of 18
water temperature and wind speed and direction). Courtesy of the ADM, this observations dataset
was made available. The coordinates of the positions of the two instruments and the relative water
depth are reported in Table 2. The data from instrument “04” present a very high percentage of data
gaps and they have not been analysed. The recorded data from instrument “03”, indicated as ADMins
in the following, span the period from June 2015 to January 2018 (included), with a time resolution of
10 min and 30 min for the atmospheric and oceanographic variables, respectively [
10
]. The percentage
of data gaps is around 4.5%, keeping into account that the instrument did not work for a time equal to
around 42 days in total in the entire period June 2015–January 2018. Data successfully collected have
been considered of good quality, since the quality control is ensured by the robustness of the native
software/data acquisition system of the Argonaut-XR. Figure 3shows the location of the ADMins.
In addition, Figure 3shows also the grid node of the NOAA (National Oceanic and Atmospheric
Administration, a scientific agency within the United States Department of Commerce) offshore wave
data (coordinates 25
◦
N and 54
◦
E, 16 m water depth) used to calculate the closure depth (Section 2.3.3).
Table 2.
Coordinates of the positions of the two instruments installed by the Abu Dhabi Municipality
(ADM) and the relative water depth.
Instrument Longitude (E) Latitude (N) Water Depth (m)
03 (ADMins) 54◦24029.52” 24◦34017.04” 6
04 54◦16039.72” 24◦44031.56” 18
J. Mar. Sci. Eng. 2019, 7, x FOR PEER REVIEW 4 of 19
was made available. The coordinates of the positions of the two instruments and the relative water
depth are reported in Table 2. The data from instrument “04” present a very high percentage of data
gaps and they have not been analysed. The recorded data from instrument “03”, indicated as ADMins
in the following, span the period from June 2015 to January 2018 (included), with a time resolution
of 10 min and 30 min for the atmospheric and oceanographic variables, respectively [10]. The
percentage of data gaps is around 4.5%, keeping into account that the instrument did not work for a
time equal to around 42 days in total in the entire period June 2015–January 2018. Data successfully
collected have been considered of good quality, since the quality control is ensured by the robustness
of the native software/data acquisition system of the Argonaut-XR. Figure 3 shows the location of the
ADMins. In addition, Figure 3 shows also the grid node of the NOAA (National Oceanic and
Atmospheric Administration, a scientific agency within the United States Department of Commerce)
offshore wave data (coordinates 25° N and 54° E, 16 m water depth) used to calculate the closure
depth (Section 2.3.3).
Figure 3. Location of ADMins, and NOAA nearshore and offshore grid points for the wind/wave
model data.
Table 2. Coordinates of the positions of the two instruments installed by the Abu Dhabi Municipality
(ADM) and the relative water depth.
Instrument Longitude (E) Latitude (N) Water Depth (m)
03 (ADMins) 54°24′29.52″ 24°34′17.04″ 6
04 54°16′39.72″ 24°44′31.56″ 18
The NOAA National Centers for Environmental Prediction (NCEP) developed the Climate
Forecast System (CFS), a fully coupled model representing the interaction between the Earth’s
atmosphere, oceans, land and sea ice. A reanalysis of the sea and atmosphere state for the period of
1979–2009 has been conducted, resulting in the CFS Reanalysis (CFSR) dataset [11]. The vertical
discretization of the atmosphere consists of 64 layers. The temporal resolution for the atmospheric
variables is 3 h. Using the CFSR dataset, the NOAA Marine Modeling and Analysis Branch (MMAB)
has produced a wave hindcast for the same period. The wave hindcast dataset has been generated
using the WAVEWATCH III (WW3) model (v3.14), and it is suitable for use in climate studies. The
wave model suite consists of global and regional nested grids. The rectilinear grids have been
developed using ETOPO-1 bathymetry [12], together with v1.10 of the Global Self-consistent
Hierarchical High-resolution Shoreline (GSHHS) database. The spatial resolution of the considered
data is 1/6°, which corresponds to roughly 20 km. The North West Indian Ocean computational grid,
Figure 3.
Location of ADMins, and NOAA nearshore and offshore grid points for the wind/wave
model data.
The NOAA National Centers for Environmental Prediction (NCEP) developed the Climate Forecast
System (CFS), a fully coupled model representing the interaction between the Earth’s atmosphere,
oceans, land and sea ice. A reanalysis of the sea and atmosphere state for the period of 1979–2009
has been conducted, resulting in the CFS Reanalysis (CFSR) dataset [
11
]. The vertical discretization
of the atmosphere consists of 64 layers. The temporal resolution for the atmospheric variables is 3 h.
Using the CFSR dataset, the NOAA Marine Modeling and Analysis Branch (MMAB) has produced
a wave hindcast for the same period. The wave hindcast dataset has been generated using the
WAVEWATCH III (WW3) model (v3.14), and it is suitable for use in climate studies. The wave model
suite consists of global and regional nested grids. The rectilinear grids have been developed using
ETOPO-1 bathymetry [
12
], together with v1.10 of the Global Self-consistent Hierarchical High-resolution
Shoreline (GSHHS) database. The spatial resolution of the considered data is 1/6
◦
, which corresponds
J. Mar. Sci. Eng. 2019,7, 173 5 of 18
to roughly 20 km. The North West Indian Ocean computational grid, adopted in the considered
data, extends in longitude from 30
◦
E to 70
◦
E (with 241 grid nodes) and in latitude from 20
◦
S to
31
◦
N (307 grid nodes). The NOAA datasets (both wind and waves) are freely available. The NOAA
WAVEWATCH III/CFSR webpages [13,14] present additional details about the datasets.
2.3. Morphodynamic Modelling Techniques
2.3.1. Overview of Popular Commercial Software
A large and growing number of models have been developed to compute the morphodynamic
evolution of coastal environments. These span a large range of process combinations, scales and levels
of detail. The majority of existing large-scale coastline models address sandy coastline evolution.
The spatial scales addressed in these models range from meters to kilometres while temporal scales
range from hours to decades. The smaller space and time scale models typically employ explicitly
reductionist methodologies where conservation of momentum forms the explicit means for evolving
the system [
15
,
16
]. These models are typically used to simulate response from specific forcing events.
Belonging to this category, XBeach [
17
] uses conservation of momentum and advection diffusion
equations for sediment transport to simulate the response of the coast and dune to individual storm
events. Larger scale models use a range of approaches to evolve system characteristics. On the contrary,
GENESIS (GENEralized model for Simulating Shoreline change) [
18
], is designed to simulate long-term
shoreline change at coastal engineering projects as produced by spatial and temporal changes in
longshore sand transport. Typical longshore extents and time periods of modelled projects can be
in the ranges of 1 to 100 km and 1 to 100 months, respectively, and almost arbitrary numbers and
combinations of groins, detached breakwaters, seawalls, jetties and beach fills can be represented.
The model called Cascade [
19
] was developed to simulate regional sediment transport and coastal
evolution. Representation of inlets is of special interest in how they function in the regional sediment
transport system in terms of storing and transferring sediment, with consequences for the adjacent
beaches. In Cascade, the coupling between the regional and local scale occurs in a hierarchical manner,
that is, information is supplied from the regional scale to the local scale.
GenCade (from the combination of the words “Genesis” and “Cascade”) [
20
] is a newly developed
numerical model, which combines the engineering power of GENESIS and the regional processes
capability of the Cascade model. The main utility of the modelling system lies in simulating the response
of the shoreline to structures sited in the nearshore. Shoreline change produced by cross-shore sediment
transport as associated with storms and seasonal variations in wave climate cannot be simulated.
The model LITPACK is developed by the Danish Hydraulic Institute (DHI) and it requires a
commercial license [
21
]. LITPACK consists of an integrated system of modelling of coastal processes
and dynamics of the coastline, capable to manage interventions in the coastline such as optimisation of
beach creations and costal protection interventions, impact assessments of coastal constructions.
Within the Delft3D modelling package, a large variation of coastal and estuarine physical
and chemical processes can be simulated [
22
]. These include waves, tidal propagation, wind- or
wave-induced water level setup, flow induced by salinity or temperature gradients, sand and mud
transport, water quality and changing bathymetry (morphology). Delft3D is a very powerful Open
Source Software, but its range of applications go far beyond the beach morphology evolution modelling
that is required in the present work. Therefore, the shoreline evolution simulation has been performed
by means of the General Shoreline beach (GSb) model.
2.3.2. GSb Model Description
Numerical simulations have been conducted by means of a newly proposed morphodynamic
model, named General Shoreline beach 1.0 (GSb), belonging to the one-line model typology [
23
]. This
typology assumes that the beach cross-shore profile remains unchanged [
24
,
25
], thereby allowing
beach change to be described uniquely in terms of the shoreline position. The peculiarity of the GSb
J. Mar. Sci. Eng. 2019,7, 173 6 of 18
model consists of simulating shoreline evolution based on a longshore transport formula/procedure
suitable at any coastal mound: sand, gravel, cobbles, shingle and rock beaches [
26
–
32
]. It is mainly
based on the General Longshore Transport (GLT) model as in [
28
], where the longshore transport rate,
QLT, in terms of m3/s is given by the following equation:
QLT =SND3
50
(1−n)Tm
(1)
with
Tm
=mean wave period,
n
=sediment porosity,
SN
=the number of units passing a given control
section in one wave [23–27]. In case of beaches, units are sand grains.
The GSb morphodynamics model considers the following equation for the longshore transport
rate Q:
Q=QLT −KGSb
8ρs
ρ−1(1−n)tan β1.4165/2H2
bcgb cos(θbs)∂Hb
∂x(2)
with
ρs
=density of sediment,
ρ
=density of water,
tan β
=average bottom slope,
Hb
=breaking wave
height,
cgb
=group celerity at breaking,
θbs
=wave obliquity at breaking. The breaking wave height is
considered as a wave forcing. For more references, see [33,34].
The second term in Equation (2), i.e., the term:
KGSb
8ρs
ρ−1(1−n)tan β1.4165/2H2
bcgb cos(θbs)∂Hb
∂x(3)
accounts for the longshore current and associated sediment transport induced by the alongshore
gradient in wave height [
35
,
36
]. The average bottom slope is determined assuming cross transects of
the bathymetric charts.
The GSb model presents one calibration coefficient solely,
KGSb
, which does not depend on
the grain size diameter and depends on the alongshore gradient in breaking wave height [
23
].
The general formula/procedure considers an energy flux approach combined with an empirical/statistical
relationship between the wave-induced forcing and the number of moving units. GSb model allows to
determine short-term (daily base) or long-term (yearly base) shoreline change for arbitrary combinations
and configurations of structures (groynes, jetties, detached breakwaters and seawalls) and beach fills
that can be represented on a modelled reach of coast.
To model the longshore sediment transport with the GSb numerical model, the 2 km long analysed
shoreline has been divided in three cells and 6 sectors. The cells are indicated as the West cell, the VVIP
cell, in the centre, and St. Regis cell at the eastern boundary of the beach. The sectors divide the West
cell, the St. Regis cell and the area between groynes 3 and 4 each one in half and they have been used
as a reference for the computation of the maximum accretion/erosion areas along the beach. Figure 4
shows the adopted initial design scenario, the cells and the reference sectors.
The actual adopted solution by the contracting company, involves the construction of four groynes:
groyne 1 is 287 m long and reaches water depth 2.5 m; and groynes 2, 3 and 4 are, respectively, 230 m,
263 m and 287 m long and reach water depth 2.6 m, 3.3 m and 3.1 m. The intervention comprises a large
initial nourishment to create an area suitable for human beach recreational facilities and to increase the
longevity of the beach. The sand will be taken from stockpiles in the south of Saadiyat island.
J. Mar. Sci. Eng. 2019,7, 173 7 of 18
J. Mar. Sci. Eng. 2019, 7, x FOR PEER REVIEW 6 of 19
model consists of simulating shoreline evolution based on a longshore transport formula/procedure
suitable at any coastal mound: sand, gravel, cobbles, shingle and rock beaches [26–32]. It is mainly
based on the General Longshore Transport (GLT) model as in [28], where the longshore transport
rate, 𝑄, in terms of m
3
/s is given by the following equation:
𝑄 = 𝑆𝐷
1 − 𝑛𝑇 (1)
with 𝑇 = mean wave period, 𝑛 = sediment porosity, 𝑆 = the number of units passing a given
control section in one wave [23–27]. In case of beaches, units are sand grains.
The GSb morphodynamics model considers the following equation for the longshore transport
rate 𝑄:
𝑄=𝑄
−𝐾
8𝜌
𝜌−11−𝑛tan 𝛽 1.416
⁄𝐻
𝑐 cos𝜃𝜕𝐻
𝜕𝑥 (2)
with 𝜌 = density of sediment, 𝜌 = density of water, tan𝛽 = average bottom slope, 𝐻 = breaking
wave height, 𝑐 = group celerity at breaking, 𝜃 = wave obliquity at breaking. The breaking wave
height is considered as a wave forcing. For more references, see [33,34].
The second term in Equation (2), i.e., the term:
𝐾
8𝜌
𝜌−11−𝑛tan𝛽 1.416
⁄𝐻
𝑐 cos𝜃𝜕𝐻
𝜕𝑥 (3)
accounts for the longshore current and associated sediment transport induced by the alongshore
gradient in wave height [35,36]. The average bottom slope is determined assuming cross transects of
the bathymetric charts.
The GSb model presents one calibration coefficient solely, 𝐾, which does not depend on the
grain size diameter and depends on the alongshore gradient in breaking wave height [23]. The
general formula/procedure considers an energy flux approach combined with an empirical/statistical
relationship between the wave-induced forcing and the number of moving units. GSb model allows
to determine short-term (daily base) or long-term (yearly base) shoreline change for arbitrary
combinations and configurations of structures (groynes, jetties, detached breakwaters and seawalls)
and beach fills that can be represented on a modelled reach of coast.
To model the longshore sediment transport with the GSb numerical model, the 2 km long
analysed shoreline has been divided in three cells and 6 sectors. The cells are indicated as the West
cell, the VVIP cell, in the centre, and St. Regis cell at the eastern boundary of the beach. The sectors
divide the West cell, the St. Regis cell and the area between groynes 3 and 4 each one in half and they
have been used as a reference for the computation of the maximum accretion/erosion areas along the
beach. Figure 4 shows the adopted initial design scenario, the cells and the reference sectors.
Figure 4.
Schematic initial design scenario. Indicated are also the reference sectors for the computation
of the maximum accretion/erosion along the beach.
2.3.3. Calibration of the GSB Model
The alongshore model computational domain has been assumed equal to 2000 m. The model grid
cell resolution, DX, has been set equal to 40 m with a total number of cells, NX, equal to 50, whereas
the model experiment has been carried out adopting a calculation time step, DT, equal to 1 h. GSb
boundaries have been selected as pinned beach, meaning that the shoreline does not change over time
in the extremes of the domain [23].
The closure depth, hc[1], has been calculated by the following equation:
hc=2.28 Hs−68.5 H2
s,0−12/gT2
s,0−12(4)
where
Hs,0−12
is the significant wave height exceeded for 12 h in one year and
Ts,0−12
is the associated
wave period;
g
is the gravitational acceleration. The closure depth has been calculated by means of the
NOAA offshore wave data (at the grid node with coordinates 25
◦
N and 54
◦
E, indicated in Figure 3) at
16 m water depth. The calculated closure depth results equal to 3.6 m.
The estimation of the longshore sediment transport calibration coefficient,
KGSb
, has been obtained
based on the available historical data. It is worth to point out that the
KGSb
does not depend on the
grain size, while it depends on the characteristics of wave propagation. Two available Google Earth
satellite images, from the years 2008 and 2009, have been considered to set the initial/final shoreline
position and to determine the optimal value for the calibration coefficient. Different values of
KGSb
,
ranging between 0.005 and 0.5 have been adopted; for each of them, a measure of the error between the
resulting calculated 2009 shoreline and the actual one has been determined, with a similar procedure
as in [
37
]. The minimum value of the error is related to the optimal value for the calibration coefficient
KGSb
, which has been assumed equal to 0.3. Figure 5shows the 2008 and 2009 satellite shorelines and
the shoreline resulting from the GSb calibration procedure. The resulting Root Mean Square Difference
(RMSD) value is also shown in Figure 5, together with the distribution of the difference between the
2009 shoreline and the GSb model output (with
KGSb
=0.3) and the difference of the two 2008 and
2009 shorelines.
A period of one year has been simulated, from 1 January 2008 to 31 December 2008, considering
the wave time series resulting from [
10
] with NOAA climate forecast system reanalyses dataset input
winds (years 1979–2009) as input data (indicated as NOAA nearshore in Figure 3).
J. Mar. Sci. Eng. 2019,7, 173 8 of 18
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Figure 5. Top: historical shorelines adopted for the calibration of the General Shoreline beach (GSb)
model; bottom: distribution of the difference between the 2009 shoreline and the GSb model output
(with 𝐾 = 0.3) and the difference of the two 2008 and 2009 Google Earth shorelines.
A period of one year has been simulated, from 1 January 2008 to 31 December 2008, considering
the wave time series resulting from [10] with NOAA climate forecast system reanalyses dataset input
winds (years 1979–2009) as input data (indicated as NOAA nearshore in Figure 3).
2.4. Other Numerical Models: Ghost
In the present work, use is also made of a well-established wave propagation numerical model,
Ghost [38–40], which is a half plane and steady state marginal directional wave spectral
transformation model solving the wave action conservation equation with an implicit finite-
difference method on a rectilinear grid. The marginal directional spectrum for the wave
transformation is a directional wave spectrum integrated in the frequency range [39,40]. The model
is capable of simulating wave-structure and wave-current interactions: in particular, the combined
effects of wave reflection, wave breaking, diffraction, shoaling, refraction and wave transmission
through and over submerged structures. A more extended description of the Ghost model and also
a comparison of the performances of Ghost with respect to other wave propagation numerical models
such as STWave (Steady-State Spectral Wave Mode), for example for the propagation of waves in
coastal inlets, can be found in [41], where the authors found that, overall, wave direction estimates
from Ghost in inlets and near structures compared slightly better with measurements with respect to
the STWave performances.
2.5. The Nourishment Performance Index
In the present work, a nourishment performance index (NPI) is defined, considering the
maximum recession that will occur after 1/5 year from the nourishment intervention related to the
initial volume of sand necessary for the nourishment, according to:
NPI =W
S ⋅ℎ (5)
where W is the initial design volume necessary for the nourishment, S is the area in recession in
the emerged beach after 1 year, with respect to the initial shoreline and ℎ the closure depth.
Figure 5.
Top: historical shorelines adopted for the calibration of the General Shoreline beach (GSb)
model; bottom: distribution of the difference between the 2009 shoreline and the GSb model output
(with KGSb =0.3) and the difference of the two 2008 and 2009 Google Earth shorelines.
2.4. Other Numerical Models: Ghost
In the present work, use is also made of a well-established wave propagation numerical model,
Ghost [
38
–
40
], which is a half plane and steady state marginal directional wave spectral transformation
model solving the wave action conservation equation with an implicit finite-difference method on a
rectilinear grid. The marginal directional spectrum for the wave transformation is a directional wave
spectrum integrated in the frequency range [
39
,
40
]. The model is capable of simulating wave-structure
and wave-current interactions: in particular, the combined effects of wave reflection, wave breaking,
diffraction, shoaling, refraction and wave transmission through and over submerged structures. A more
extended description of the Ghost model and also a comparison of the performances of Ghost with
respect to other wave propagation numerical models such as STWave (Steady-State Spectral Wave
Mode), for example for the propagation of waves in coastal inlets, can be found in [
41
], where the
authors found that, overall, wave direction estimates from Ghost in inlets and near structures compared
slightly better with measurements with respect to the STWave performances.
2.5. The Nourishment Performance Index
In the present work, a nourishment performance index (NPI) is defined, considering the maximum
recession that will occur after 1/5 year from the nourishment intervention related to the initial volume
of sand necessary for the nourishment, according to:
NPI =W
S1yr ·hc(5)
where
W
is the initial design volume necessary for the nourishment,
S1yr
is the area in recession in the
emerged beach after 1 year, with respect to the initial shoreline and hcthe closure depth.
3. Results
3.1. Nearshore Wave Climate
The nearshore wave climate has been calculated in terms of significant wave height, Hs, wave
peak period, T
p
and mean wave direction,
θi
, at eight virtual buoys near the coast at different depths.
Figure 6shows the location of the virtual buoys, chosen in order to analyse the wave effects along the
entire beach, with a focus in the proximity of the study area at the western side of the beach.
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3. Results
3.1. Nearshore Wave Climate
The nearshore wave climate has been calculated in terms of significant wave height, Hs, wave
peak period, T
p
and mean wave direction, θ
i
, at eight virtual buoys near the coast at different depths.
Figure 6 shows the location of the virtual buoys, chosen in order to analyse the wave effects along the
entire beach, with a focus in the proximity of the study area at the western side of the beach.
Figure 6. Positions of the virtual buoys along Saadiyat beach.
Results are reported in terms of wave rose plots indicating the wave appearance frequencies.
Figure 7 shows the corresponding wave roses at the virtual buoys indicated in Figure 6 with labels
from A to H: the influence of the bottom (seabed) determines a dissipation of the waves close to the
beach. Results show that the most frequent wave’s events have directions in the sector 270° N–360°
N, with a dominant North-West component, and maximum wave height lower than 1.8 m.
Figure 6. Positions of the virtual buoys along Saadiyat beach.
Results are reported in terms of wave rose plots indicating the wave appearance frequencies.
Figure 7shows the corresponding wave roses at the virtual buoys indicated in Figure 6with labels
from A to H: the influence of the bottom (seabed) determines a dissipation of the waves close to the
beach. Results show that the most frequent wave’s events have directions in the sector 270
◦
N–360
◦
N,
with a dominant North-West component, and maximum wave height lower than 1.8 m.
J. Mar. Sci. Eng. 2019, 7, x FOR PEER REVIEW 10 of 19
Figure 7. Wave climate at the eight virtual buoys, indicated as A–H in Figure 6, along Saadiyat
beach.
Wave roses corresponding to virtual buoys C, D and E in Figure 6 are, indeed, comparable
similar when considering the main wave direction and Hs values. In particular, it can be noticed how
changes of the coastline shape and the presence of groynes, acting as defence structures, obstruct the
wave propagation, resulting in a wave energy reduction in C, D, and E with respect to the waves
corresponding to A and B.
Results relative to the remaining virtual buoys show a good exposure of the beach to different
wave conditions, i.e., the position of the virtual buoys has been chosen well enough so that the
simulated beach conditions are sensitive to the action of the waves coming from all the possible
directions; therefore, the climate at the virtual buoys is well representative of the real conditions of
the beach. The bottom friction, the bathymetry of the area and the alignment of the beach determine
a slight rotation of the fronts and only the directions from east to south are lost with respect to the
original wave climate at the bathymetry 6 m.
3.2. Shoreline Design Scenarios
To increase the longevity of the beach and propose a sustainable solution over the years, some
possible alternatives have been simulated. In particular, a different orientation of the as-built
shoreline and different volume of sand for the nourishment have been tested. Figure 8 shows the
scenarios with different orientations of the initial design shoreline. Based on the results of the
sediment analysis (Section 2.1), it is assumed that the area of interest is characterised by medium sand
with the median grain size 𝐷 = 0.26 mm and the sorting parameter 𝐷/𝐷 = 2.44 (where: D
15
(transition layer) is the 15th
percentile particle size in the transition layer material, meaning that 15% of
Figure 7.
Wave climate at the eight virtual buoys, indicated as A–H in Figure 6, along Saadiyat beach.
J. Mar. Sci. Eng. 2019,7, 173 10 of 18
Wave roses corresponding to virtual buoys C, D and E in Figure 6are, indeed, similar when
considering the main wave direction and Hs values. In particular, it can be noticed how changes of the
coastline shape and the presence of groynes, acting as defence structures, obstruct the wave propagation,
resulting in a wave energy reduction in C, D, and E with respect to the waves corresponding to A and B.
Results relative to the remaining virtual buoys show a good exposure of the beach to different wave
conditions, i.e., the position of the virtual buoys has been chosen well enough so that the simulated
beach conditions are sensitive to the action of the waves coming from all the possible directions;
therefore, the climate at the virtual buoys is well representative of the real conditions of the beach.
The bottom friction, the bathymetry of the area and the alignment of the beach determine a slight
rotation of the fronts and only the directions from east to south are lost with respect to the original
wave climate at the bathymetry 6 m.
3.2. Shoreline Design Scenarios
To increase the longevity of the beach and propose a sustainable solution over the years, some
possible alternatives have been simulated. In particular, a different orientation of the as-built shoreline
and different volume of sand for the nourishment have been tested. Figure 8shows the scenarios
with different orientations of the initial design shoreline. Based on the results of the sediment analysis
(Section 2.1), it is assumed that the area of interest is characterised by medium sand with the median
grain size
D50
=0.26 mm and the sorting parameter
D15
/
D85
=2.44 (where: D
15
(transition layer) is the
15th percentile particle size in the transition layer material, meaning that 15% of the sand is smaller
than D15 mm, and D85 (filter media) is the 85th percentile particle size in the filter media.
In scenario 1, the initial alignment of the design shoreline is obtained from the nourishment of the
entire stretch of coast. In particular, 600,000 m
3
are necessary for the West cell, 240,000 m
3
for the VVIP
cell and 180,000 m3for the St. Regis cell.
In the scenario 2, a rotation of the shoreline alignment for the west cell and the VVIP cell,
respectively 5
◦
and 10
◦
counter clockwise, is proposed, with a consequent increase in the sand volume,
which is necessary for the initial nourishment (respectively 830,000 m
3
for the West cell and 270,000 m
3
for the VVIP cell).
The difference of scenario 3 with respect to scenario 2 consists in the fact that the shoreline
alignment in the West cell is around the centreline.
In the scenario 4, the initial shoreline alignment is the same as in scenario 1 but a counter clockwise
rotation of 5
◦
is expected for the West cell, from the centre of the cell to the groyne 2, with a relative
increase in the nourishment sand requirement, from 600,000 m3to 650,000 m3.
In the scenario 5, the shoreline alignment is similar to scenario 1, but a clockwise rotation of 5
◦
has been imposed for the West cell, from the centre of the cell to the groyne 1. The volume of sand
required is 50,000 m3more than the volume foreseen in the scenario 1.
A summary of the volumes of sand needed for the initial nourishment in the different scenarios is
reported in Table 3.
Table 3. Summary of the five design scenarios.
Scenarios Sand Volume (×103m3)
West Cell VVIP Cell St. Regis Cell
1 600 240 180
2 830 270 180
3 600 270 180
4 650 240 180
5 650 270 180
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the sand is smaller than D
15
mm, and
D
85
(filter media) is the 85th percentile particle size in the filter
media.
(A)
(B)
(C)
(D)
(E)
Figure 8.
Schematic scenarios. (
A
) scenario 1; (
B
) scenario 2; (
C
) scenario 3; (
D
) scenario 4; (
E
) scenario 5.
J. Mar. Sci. Eng. 2019,7, 173 12 of 18
3.3. Shoreline Evolution for the Design Scenarios
The shoreline evolution for each of the five considered scenarios has been modelled with the
GSb model. The numerical simulations have been performed considering the evolution of the initial
shoreline after 1 year, 2 years and 5 years from the end of the intervention.
Figure 9; Figure 10 show a comparison between the results for, respectively, the evolution in 1 year
and in 5 years, in terms of maximum accretion/recession.
J. Mar. Sci. Eng. 2019, 7, x FOR PEER REVIEW 12 of 19
Figure 8. Schematic scenarios. (A) scenario 1; (B) scenario 2; (C) scenario 3; (D) scenario 4; (E) scenario
5.
In scenario 1, the initial alignment of the design shoreline is obtained from the nourishment of
the entire stretch of coast. In particular, 600,000 m
3
are necessary for the West cell, 240,000 m
3
for the
VVIP cell and 180,000 m
3
for the St. Regis cell.
In the scenario 2, a rotation of the shoreline alignment for the west cell and the VVIP cell,
respectively 5° and 10° counter clockwise, is proposed, with a consequent increase in the sand
volume, which is necessary for the initial nourishment (respectively 830,000 m
3
for the West cell and
270,000 m
3
for the VVIP cell).
The difference of scenario 3 with respect to scenario 2 consists in the fact that the shoreline
alignment in the West cell is around the centreline.
In the scenario 4, the initial shoreline alignment is the same as in scenario 1 but a counter
clockwise rotation of 5° is expected for the West cell, from the centre of the cell to the groyne 2, with
a relative increase in the nourishment sand requirement, from 600,000 m
3
to 650,000 m
3
.
In the scenario 5, the shoreline alignment is similar to scenario 1, but a clockwise rotation of 5°
has been imposed for the West cell, from the centre of the cell to the groyne 1. The volume of sand
required is 50,000 m
3
more than the volume foreseen in the scenario 1.
A summary of the volumes of sand needed for the initial nourishment in the different scenarios
is reported in Table 3.
Table 3. Summary of the five design scenarios.
Scenarios Sand Volume (×10
3
m
3
)
West Cell VVIP Cell St. Regis Cell
1 600 240 180
2 830 270 180
3 600 270 180
4 650 240 180
5 650 270 180
3.3. Shoreline Evolution for the Design Scenarios
The shoreline evolution for each of the five considered scenarios has been modelled with the
GSb model. The numerical simulations have been performed considering the evolution of the initial
shoreline after 1 year, 2 years and 5 years from the end of the intervention.
Figure 9; Figure 10 show a comparison between the results for, respectively, the evolution in
1 year and in 5 years, in terms of maximum accretion/recession.
Figure 9.
Evolution of the initial shoreline after 1 year, for the different considered the scenarios simulated.
J. Mar. Sci. Eng. 2019, 7, x FOR PEER REVIEW 13 of 19
Figure 9. Evolution of the initial shoreline after 1 year, for the different considered the scenarios
simulated.
Figure 10. Evolution of the initial shoreline after 5 years, for the different considered scenarios.
The maximum accretion/recession volumes, i.e., the new beachline dry area as resulting from
the shoreline shifts multiplied by the closure depth, in relation to the sand volume of the initial
nourishment in the six sectors along the analysed shoreline, have been calculated. The shoreline shifts
are calculated considering the new beach shoreline position as resulting after a fixed amount of time
from the end of the intervention and the “as built shoreline after the nourishment intervention”. The
results are shown in Table 4 and Table 5.
Table 4. Maximum accretion/erosion occurred after 1 year, for the proposed scenarios.
Scenarios Sand Volume (×10
3
m
3
) Max Recession after 1 year (m) Max Accretion after 1 year (m)
West Cell VVIP Cell St. Regis Cell S1 S2 S3 S4 S5 S6
1 600 240 180 26 20 21 20 25 4
2 830 270 180 20 13 31 44 16 4
3 600 270 180 20 13 31 30 16 4
4 650 240 180 26 20 21 30 25 4
5 650 270 180 10 20 21 20 25 4
Table 5. Maximum accretion/erosion occurred after 5 years, for the proposed scenarios.
Scenarios
Sand Volume (×10
3
m
3
) Max Recession after 5
years (m)
Max Accretion after 5
years (m)
West
Cell
VVIP
Cell
St. Regis
Cell S1 S2 S3 S4 S5 S6
1 600 240 180 80 27 62 75 39 14
2 830 270 180 65 22 77 71 29 14
3 600 270 180 63 22 77 79 24 14
4 650 240 180 79 27 62 79 39 14
5 650 270 180 85 27 61 76 39 14
3.4. The Nourishment Performance Index
The optimal intervention scenario among the five proposed has to be individuated on the basis
of the NPI, calculated using Equation (5), considering the maximum recession that will occur for each
of the considered scenarios after 1 year from the intervention at sector 1 (S1) (Figure 4), related to the
initial volume of sand necessary for the nourishment, with W the initial design volume necessary for
the nourishment of the West cell, ℎ= 3.6 m, and S the area in recession in the emerged beach
after 1/5 year, with respect to the initial shoreline. The areas in recession in the emerged beach result
from the comparison of the original shoreline and the one simulated with GSb after 1 year or 5 years.
Figure 10. Evolution of the initial shoreline after 5 years, for the different considered scenarios.
The maximum accretion/recession volumes, i.e., the new beachline dry area as resulting from the
shoreline shifts multiplied by the closure depth, in relation to the sand volume of the initial nourishment
in the six sectors along the analysed shoreline, have been calculated. The shoreline shifts are calculated
considering the new beach shoreline position as resulting after a fixed amount of time from the end of
the intervention and the “as built shoreline after the nourishment intervention”. The results are shown
in Tables 4and 5.
J. Mar. Sci. Eng. 2019,7, 173 13 of 18
Table 4. Maximum accretion/erosion occurred after 1 year, for the proposed scenarios.
Scenarios Sand Volume (×103m3)Max Recession
after 1 year (m)
Max Accretion
after 1 year (m)
West Cell VVIP Cell St. Regis Cell S1 S2 S3 S4 S5 S6
1 600 240 180 26 20 21 20 25 4
2 830 270 180 20 13 31 44 16 4
3 600 270 180 20 13 31 30 16 4
4 650 240 180 26 20 21 30 25 4
5 650 270 180 10 20 21 20 25 4
Table 5. Maximum accretion/erosion occurred after 5 years, for the proposed scenarios.
Scenarios Sand Volume (×103m3)Max Recession
after 5 years (m)
Max Accretion
after 5 years (m)
West Cell VVIP Cell St. Regis Cell S1 S2 S3 S4 S5 S6
1 600 240 180 80 27 62 75 39 14
2 830 270 180 65 22 77 71 29 14
3 600 270 180 63 22 77 79 24 14
4 650 240 180 79 27 62 79 39 14
5 650 270 180 85 27 61 76 39 14
3.4. The Nourishment Performance Index
The optimal intervention scenario among the five proposed has to be individuated on the basis of
the NPI, calculated using Equation (5), considering the maximum recession that will occur for each of
the considered scenarios after 1 year from the intervention at sector 1 (S1) (Figure 4), related to the
initial volume of sand necessary for the nourishment, with
W
the initial design volume necessary for
the nourishment of the West cell,
hc=
3.6 m, and
S1yr
the area in recession in the emerged beach after
1/5 year, with respect to the initial shoreline. The areas in recession in the emerged beach result from
the comparison of the original shoreline and the one simulated with GSb after 1 year or 5 years.
4. Discussion
From the results obtained in the present study, it has been found that the NPI allows selecting
the optimal scenario based on the efficiency of the intervention. Table 6shows the values of NPI for
the different considered scenarios. In the first column, the initial sand volume
W
, necessary for the
nourishment intervention is indicated. The second column shows the area of the beach that will be
eroded 1 year or 5 years after the intervention, simulated with the GSb numerical model. This area is
calculated as the area enclosed by the simulated shoreline after 1/5 years from the intervention and the
corresponding initial shoreline for each scenario separately. The third column shows the calculated NPI
values. The fourth column shows the increase in percentage of NPI in the beach longevity, calculated
with respect to scenario 1, assumed as a reference.
Regarding the West cell and the results relative to 1 year, with respect to scenario 1, the result for
scenario 2 shows an increase of NPI (+29%), which is due in a large part to a significant larger volume
of sand (from 600,000 m
3
to 830,000 m
3
). Scenario 3 can represent a good option because of the good
longevity percentage increase (+15%) obtained, while keeping the same volume of sand necessary for
the initial nourishment (600,000 m
3
). Scenario 4 shows almost the same NPI of scenario 1, but at the
price of a slightly larger increase in the necessary sand volume for the nourishment (from 600,000 m
3
to 650,000 m3).
J. Mar. Sci. Eng. 2019,7, 173 14 of 18
Table 6. nourishment performance index (NPI) values at Saadiyat beach.
West Cell VVIP Cell St. Regis Cell
Recession
(m2)
Sand Volume
×103(m3)
Recession
(m2)NPI NPI Increase
(%)
Sand volume
×103(m3)
Recession
(m2)NPI NPI Increase
(%)
Sand Volume
×103(m3)NPI NPI Increase
(%)
1 600 2227 75 reference 240 2685 25 reference 180 1110 45 reference
2 830 2394 96 29 270 1707 44 77 180 638 78 74
3 600 1944 86 15 270 1338 56 126 180 693 72 60
4 650 2368 76 2 240 2712 25 -1 180 962 52 15
5 650 874 207 176 270 2708 28 12 180 1140 44 -3
Scenarios
(5 years)
Sand Volume
×103(m3)
Recession
(m2)NPI NPI Increase
(%)
Sand Volume
×103(m3)
Recession
(m2)NPI NPI Increase
(%)
Sand Volume
×103 (m3)
Recession
(m2)NPI NPI Increase
(%)
1 600 15,450 11 reference 240 3643 18 reference 180 6175 8 reference
2 830 13,920 17 54 270 1642 46 150 180 5625 9 10
3 600 13,017 13 19 270 1958 38 109 180 5737 9 8
4 650 14,106 13 19 240 3615 18 1 180 5683 9 9
5 650 13,798 13 21 270 3769 20 9 180 5856 9 5
J. Mar. Sci. Eng. 2019,7, 173 15 of 18
For the West cell and the 1 year simulation, scenario 5 presents the larger NPI increase, equal to
+176%. This is not verified for the remaining cells and considering the results relative to 5 years; the
scenario that offers the overall best nourishment performance is scenario 2. Estimate of the nourishment
performance is not extended to the entire project lifetime but it is limited to 5 years. This limit is
chosen because at this time, the nourished shoreline retreats at some transects reaching an unacceptable
threshold for the touristic, aesthetic and recreational uses of the beach.
Consequently, comparison of different design scenarios has showed that the NPI value depends
mainly on the as built nourishment shoreline. The recommended scenario (i.e., scenario 2) durability
is sounding.
This interesting and potentially very useful methodology can be adopted for analysing and
predicting shoreline development under different coastal engineering interventions. The need for
such a methodology arises from the large number of coastal interventions that are planned in high
number worldwide and from the need to protect those from the climatically induced sea level rise
and its hazardous consequences. The advantage of this definition is that all factors are measurable
in-loco, are quantifiable by the development firms, are based on the physical characteristics of the
beach (e.g., morphology), but also on aspects concerning the planned intervention (nourishments,
longevity).
A potential benefit of this methodology is the fact that it also gives indication about the
environmental sustainability (ES) of the planned interventions. In fact, ES, specifically the careful
use of natural resources to preserve the ecosystems, is required in planning complex engineering
interventions. ES is also an essential factor in mitigating the effects of environmental catastrophic
phenomena, related to climate change. Since water covers up Earth’s largest portion, it is a complex
ecosystem; coastal engineers should include ES in the design of planned interventions.
The ES concept has been developed to ensure that in meeting its needs for water, food, shelter as
well as engaging in leisure activities and entertainment where humanity does not cause damage to
the environment or deplete resources that cannot be renewed [
42
]. However, despite many studies,
the practices in coastal management have not yet reached a point where natural resources are being
used sustainably.
In general, given the complexity of the environment and the ecosystem, environmental indexes
could provide a useful tool to highlight environmental conditions and trends for policy purposes by
isolating key aspects from an otherwise overwhelming amount of information [
43
]. Oftentimes,
however, ES is difficult to translate in operational terms and many of the indexes proposed
and mentioned above, include parameters, which are beyond the sphere of influence of local
authorities/development firms [
44
]. This supports the scope of this study, i.e., to investigate an
index to support also environmentally sustainable engineering applications.
Moreover, the NPI can be adopted not only as a valuable database for making management
decisions, but also it encourages the local community and stakeholders to engage in the safeguard of
the environment, given the simplicity of its definition and its immediate and easy application in real
cases. This is a key aspect in ES-oriented engineering interventions: in fact, since ecological boundaries
rarely meet up with political jurisdictions, it is necessary to be aware of major environmental issues
and the best option to preserve the environment for future generations [45].
Specifically, the proposed NPI definition satisfies all the requirements for an environmental
indicator to work well as a basis for policymaking [
46
]: (1) data availability; (2) ecosystem specificity of
indicators; (3) spatial and conceptual aggregation of indicators and (4) baseline or reference values for
indicators. The proposed NPI definition, the availability of data since the initial nourishment volume,
the recession area and the closure depth are all easily measurable and well defined. The ecosystem
specificity of the NPI is ensured through its dependence from the closure depth and the recession area,
the aggregation is ensured by the NPI dependence from both environmental aspects (
S1yr
and
hc
) and
specific aspects of the intervention (W) and the baseline is clearly drawn from scenario 1.
J. Mar. Sci. Eng. 2019,7, 173 16 of 18
5. Conclusions
Environmental sustainability (ES) is an essential factor in solving environmental degradation; this
is especially true when designing complex coastal engineering interventions.
The present paper describes the methodology followed to design a sustainable beach at Saadiyat
Island, of the Abu Dhabi Municipality in the United Arab Emirates. Specifically, a proposed
nourishment performance index appears suitable to quantify the level of sustainability for different
coastal engineering interventions. The nourishment performance index is based on factors such as the
initial volume necessary for the nourishment of the intervention area, the area in recession 1 year after
the intervention and the closure depth.
The proposed methodology can be used for analysing and predicting shoreline development
under different coastal engineering interventions. The need for such a methodology arises from
the large number of coastal interventions that are planned in high number worldwide and from the
need to protect those from the climatically induced sea level rise and its hazardous consequences.
The advantage of this definition is that all factors are measurable in-loco, are quantifiable by the
development firms and are based on the physical characteristics of the beach (e.g., morphology), but
also on aspects concerning the planned intervention (nourishments, longevity).
Results show that the NPI value depends mainly on the as built nourishment shoreline.
The recommended scenario (i.e., scenario 2) durability is sounding from the environmental
sustainability prospective.
In conclusion, the application of the presented methodology in the evaluation of the impacts of the
planned interventions at Saadiyat beach has been shown to be promising and can assist the engineers
and/or environmentalists for designing/evaluating coastal interventions that are foreseen in the area.
Author Contributions:
Conceptualization, W.H. and G.R.T.; methodology, G.R.T.; software, G.R.T., F.L., L.L.
and A.F.; validation, F.L.; formal analysis, F.L. and A.F.; investigation, G.R.T.; resources, W.H.; data curation,
L.L.; writing—original draft preparation, F.L.; writing—review and editing, G.R.T. and L.L.; visualization, F.L.;
supervision, G.R.T.; project administration, W.H.; funding acquisition, W.H.
Funding:
The present study is funded by the United Arab Emirates University research grant, through the National
Water Centre, Grant #31R115; “Impact of coastal (long-shore) currents on erosion/deposition and consequent
water/sediments quality variations along the coastal area of Abu Dhabi City”.
Acknowledgments:
The authors thank the Abu Dhabi Municipality for providing in-situ data relative to wave
conditions at Saadiyat beachfront.
Conflicts of Interest: The authors declare no conflict of interest.
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