- Email: [email protected]
Wave energy resource assessment along the Algerian coast based on 39-year wave hindcast
Journal Pre-proof Wave energy resource assessment along the Algerian coast based on 39-year wave hindcast Khalid Amarouche, Adem Akpınar, Nour El Islam Bachari, Houma Fouzia PII:
S0960-1481(20)30226-3
DOI:
https://doi.org/10.1016/j.renene.2020.02.040
Reference:
RENE 13061
To appear in:
Renewable Energy
Received Date: 15 July 2019 Revised Date:
1 February 2020
Accepted Date: 11 February 2020
Please cite this article as: Amarouche K, Akpınar A, El Islam Bachari N, Fouzia H, Wave energy resource assessment along the Algerian coast based on 39-year wave hindcast, Renewable Energy (2020), doi: https://doi.org/10.1016/j.renene.2020.02.040. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.
CRediT author statement
Khalid AMAROUCHE: Conceptualization, Software, Validation, Formal analysis, Writing - Original Draft Adem AKPINAR: Conceptualization; Methodology, Writing - Review & Editing Nour-el-islam BACHARI: Supervision ; Formal analysis. Fouzia HOUMA: Investigation, Resources, Data Curation.
1
Wave energy resource assessment along the Algerian Coast based on 39-year
2
wave hindcast
3
Khalid Amarouche1, *, Adem Akpınar2, Nour El Islam Bachari3, Houma Fouzia1
4 5 6 7 8 9
1
Ecole Nationale Supérieure des Sciences de la Mer et de l'Aménagement du Littoral (ENSSMAL), Département d’environnement et d’aménagement du littoral, Algiers, Algeria 2 Bursa Uludağ University, Department of Civil Engineering, Gorukle Campus, Bursa, Turkey 3 Université des Sciences et Technologie Houari Boumedien (USTHB), Laboratoire d’Océanographie Biologique et Environnement Marin, Algiers, Algeria
10
Abstract
11
This study investigates a long-term assessment of the wave energy resource propagated along the
12
Algerian basin, based on a 39-year wave hindcast. The wave energy hindcast dataset was
13
developed using the Simulating WAve Nearshore (SWAN) model, calibrated and validated [1]
14
against wave measurements performed on the Algerian coast. A detailed spatial and local
15
analysis was performed following the hindcast results. We have determined several parameters
16
including; hourly, monthly, seasonal and annual variations of wave energy resources, the
17
probability of occurrence distribution for different wave power ranges with different directions,
18
the probability of calm sea states, the wave energy development index (WEDI) and the total
19
annual wave energy and their distribution as a function of significant wave height and energy
20
period. All these results enabled a very important benchmark for decision making regarding the
21
future implementation and design of wave energy converters (WECs) and other offshore
22
structures in the Algerian basin. Our findings have shown that the Algerian coasts are
23
characterized by a considerable wave energy potential with a large hotspot area in the eastern
24
coasts. Thus, we have recorded a significant variability in the wave energy characteristics
25
available in each zone along the Algerian coast. The western zone was characterized by an
26
average energy of ~7.5 kW/m with a low monthly and seasonal variation (<1.2), the central zone
27
was characterized by a significant total annual wave energy of 63 MWh/m/year and a
28
considerable WEDI of 0.019, and the eastern Algerian coast was characterized by one of the
29
highest energy potential in the Mediterranean basin with a total annual energy exceeding 100
30
MWh/m for less than 15 km from the coast and a calm sea state probability lower than 18%.
31
Thus, it has been concluded that since 1995, wave energy resources have tended to increase
32
further.
33
Keywords: Wave Energy; Wave power; Variability; WEDI; Hotspots; Algerian basin. 1
1
1. Introduction
2
Algeria, like most countries in the word, is experiencing a significant growth in electricity
3
consumption [2]. This growth, in combination with the global warming problems and the need to
4
conserve fossil energy resources, requires the exploitation of renewable and sustainable energy
5
resources. To reach a global and sustainable solution to these problems, the Algerian Ministry of
6
Energy and Mines has developed a national program which aims to provide 40% of renewable
7
energies in electricity production by 2030 [3]. As part of this program, several potential areas
8
based on solar, wind and geothermal energy resources have been identified [4–7]. This research
9
showed that the important solar and wind energy resources are located in the southern Saharian
10
part of the country. However, according to the statistics of the national electricity production
11
company (Sonelgaz), more than 40% of the electricity are consumed by the coastal provinces
12
which cover 1.8% of the country [8]. As a result, and in view of the continuous development of
13
WECs in recent years [9–15] , wave energy presented as a condensed form of wind energy can
14
constitute an essential source of renewable energy [16] which can be exploited in these high-
15
consumption areas. Therefore, the assessment of wave energy propagation is a very important
16
task not only for its exploitation as a power resource but also for its destructive effects in the
17
coastal zones [17].
18
The Algerian coast is characterized by a narrow continental shelf that dissipates wave energy
19
near the shore or directly on the shore. During this last year several researches [18–21] have
20
concentrated on the evaluation of the wave energy potential in the West Mediterranean basin.
21
The results of these studies show that the Spanish and Italian coasts hold a very promising
22
potential, with an annual rate of more than 100 MWh/m/year on the western coasts of Sardinia in
23
Italy [19]. In the recent study developed by Besio et al, [18] the Algerian coast was determined
24
as one of the most energetic areas of the Mediterranean Sea. Nevertheless, most previous studies
25
[18, 20–24] have focused on European coasts and the recent wave hindcast databases [26–28]
26
widely used in previous studies was only validated based on wave measurements collected on
27
European coasts and no validation has been performed in the Algerian coast. However, the
28
morphology of the Algerian basin is completely different from that of the Mediterranean sub-
29
basin (Tyrrhenian Sea, Alboran Sea, Balearic Sea, Gulf of Lion, and etc.). The Algerian basin is
30
characterized by a very important fetch area with an open coast and a narrow continental shelf.
2
1
This specific morphological characteristic implies that a calibration and validation of the
2
prediction model based on local measurements can be necessary.
3
In this study, we present a first long-term detailed assessment of wave energy propagation in
4
the Algerian basin, based on 39-year wave hindcast, developed using a high-resolution (~3
5
km) wind wave model (SWAN) calibrated based on one-year wave observations of Azeffoune
6
buoy (Algerian coast) [1]. The calibrated SWAN model used for the development of this
7
hindcast database has been compared by the authors [1] against other models such as WAM [29],
8
TOMAWAC [30] and WaveWatch III [31]; implemented in previous studies [18, 25, 27, 32–36]
9
for the Western Mediterranean basin. The results of this comparison show that the SWAN model
10
calibrated by the Azeffoun buoy measurements is able to provide a higher accuracy. The
11
geographical location of the calibration buoy can contribute significantly to the improvement of
12
the model performance in the whole Mediterranean basin. The Azeffoun buoy is geographically
13
able to record the waves coming from the Tyrrhenian Sea, the Alboran Sea, the Balearic Sea and
14
even the Gulf of Lion. The wave power computed using this hindcast database was also validated
15
against the wave power computed using wave observations of three buoys installed in the
16
Algerian coast, provided by the Office National de Signalisation Maritime.
17
Based on the wave hindcast database, a spatio-temporal statistical analysis of wave energies was
18
developed and exploited to produce various thematic maps that reflect the propagation of wave
19
energies and its temporal variation. The results of this analysis allowed us to describe
20
quantitatively the wave energy distribution along the Algerian basin. In addition, 14 stations
21
located off each coastal province was subject of a detailed statistical analysis in which we have
22
evaluated the wave energy flux distribution variability and its potentiality at the annual, seasonal,
23
monthly and hourly scales. Thus, the total wave energy resources were quantified as a function
24
of significant wave height and energy period, with the probability of occurrence distribution for
25
different wave power ranges and different directions.
26
The wave energy hindcast database, the local analysis results, and the mapped spatial analysis
27
results including the WEDI map, constitute an essential benchmark for decision making
28
regarding the selection and design of WECs and other offshore structures in the Algerian basin.
29
The results obtained in this study can also contribute to the assessment of coastal vulnerability
30
and storms observed on the Algerian coast, knowing that the database developed during this
3
1
study is the first calibrated and validated wave hindcast database with high spatial resolution (~3
2
km) produced for Algerian coasts.
3 4
2. Study area
5
The study area is the only Algerian seafront which covers a coastline of about 1623 km in the
6
South West Mediterranean basin (Fig. 1). It covers the area which extends from -3o W to 9o E
7
and from 35o N to 40o N. This coast is oriented mainly towards the north and is characterized by
8
a very narrow continental shelf that varies from 50 km to 0.5 km. These characteristics make this
9
coast directly exposed to the northerly waves, which dissipates directly to the shore in the small
10
continental shelf area. According to the National Statistical Office, the Algerian coast is
11
distributed over 14 coastal provinces in which more than 40.7% of the total population lives at a
12
number that increases greatly during the summer periods. This demographic pressure is also at
13
the origin of the increase in electricity consumption recorded in the coastal provinces (Fig.1).
14
Thus, the maritime part which is the subject of this study; known as the Algerian basin, has a
15
strong economic exploitation with the presence of a very dense maritime traffic, knowing that
16
the Algerian basin is a point of intersection between ships coming from the Red Sea, the Black
17
Sea and the Atlantic Ocean.
18 19
3. Model description
20
Currently, several wind-wave models have been evaluated and implemented in the western
21
Mediterranean basin for the development of several wave hindcast databases. Among the most
22
recent, accurate and open-source models covering the Algerian basin, we highlight the WAM
23
model by Cavaleri and Sclavo [32], used for the development of the Atlas of Winds and Waves
24
of the Mediterranean Sea [26], the WAM-PRO by Ponce de León and Soares [37] for the
25
development of 29-year spectral wave hindcast, the Wavewatch III model by Mentaschi et al.
26
[23] for the development of 35-year wave hindcast from 1979 to 2013 [18], WAM model Cycle
27
4.5.3 by Liberti et al [19] for the development of 10-year wave hindcast from 2001 to 2010 [18,
28
21], TOMAWAC model by Tiberi-Wadier et al. [28] for the development of the wave hindcast
29
database (ANEMOC2), and the SWAN model by Lavidas et al [23, 38] for the development of
30
35-year wave hindcast from 1980 to 2014. The spatial resolution of these models in the Algerian 4
1
basin is respectively 25 km, 27.8 km, 10 km, ~7 km, 25 km and ~11 km. Thus, as mentioned in
2
the introduction section, all these models were calibrated and/or validated based only on wave
3
measurements recorded in European coasts and their accuracy on the Algerian coast remains
4
unknown.
5
Taking into account the morphological aspect of the Algerian basin which is completely different
6
from other European basins, with a very narrow continental shelf, an open coast and an extensive
7
fetch area, we considered that it is important to use an adapted model calibrated for this basin in
8
order to ensure accurate and reliable results. For the development of the 39-years wave hindcast
9
database, we preferred to use the SWAN model forced by the CFSR wind field, which has been
10
calibrated and validated especially for the Algerian basin by authors [1], with a high spatial
11
resolution of ~3 km. The good accuracy of the SWAN model in coastal areas [39] compared to
12
other models such as WaveWatch III [40], allows us to calibrate and validate it against wave
13
measurements carried out in coastal areas; knowing that most wave measurement buoys in the
14
Mediterranean Sea are installed in the coastal area. It is indeed that the SWAN model has already
15
been used by Lavidas et al [24] for the development of a 35-years wave hindcast database in the
16
Mediterranean Sea, however, their coarse grid model covering the whole Mediterranean basin
17
has been applied for the generation of boundary conditions in four other coastal domains which
18
exclude the Algerian coast. In addition, no calibration of the physical setup of the SWAN coarse
19
grid model has been provided in relation to the wave measurement in the West Mediterranean
20
basin. Actually, several studies [28, 41–46] have confirmed that the calibration of the physical
21
configuration in the SWAN model improves in many cases its performance and corrects the
22
underestimation of significant wave heights and mean wave periods. Moreover, the optimal
23
model setup varies depending on the study area as mentioned by Bingölbali et al, [47].
24
According to Lavidas et. al. [48] the use of numerical wave models adapted to a specific study
25
area offers significant advantages for the quantification of wave energy resources. The high
26
spatial resolution of the used model (~3 km ) and its performance in the Algerian coast will
27
allow us to better evaluate the wave energy potential even at a distance of 3 km from the coast,
28
knowing that an important part of the Algerian coastline is characterized by a very narrow
29
continental shelf (<5 km) and that the coast distance and the depth has an important role in the
30
selection of optimal wave energy resources areas [49–52].
31 5
1
3.1. Theoretical background
2
The wave hindcast data used in this study was developed based on the third generation wind-
3
wave hindcast model SWAN version 41.20 [39], a discrete spectral wave model describing the
4
evolution of the wave energy spectrum in two-dimensional mode under known wind fields and
5
bathymetries [39]. Like most third generation spectral models, this model is based on the
6
equilibrium equation of action by considering the interactions between waves and currents [53].
7
However, this model is characterized by a good efficiency in the coastal zone [39], and it is also
8
applicable on a large scale in deep waters [54]. The evolution equation of the wave spectrum in
9
SWAN model is described by the spectral action balance equation [55].
10 11
+
+
+
+
, , , ,
=
(1)
12 13
In this equation, the first term on the left-hand side is the local rate change of action density in
14
time, wherein
15
direction , horizontal coordinates x and y, and time t. The second and third terms represent the
16
geographic propagation of action density respectively in the x and y space in which cx and cy are
17
x, y components of the group velocity. The fourth term represents shifting of the relative
18
frequency due to variations in depths and currents. The fifth term represents depth and current
19
induced refraction. The source/sink term
20
generation, dissipation, and nonlinear wave-wave interactions. The processes contributing to the
21
sinks and source terms are given by the following equation.
( , , ,
, ) is the action density as a function of intrinsic frequency
,
,
on the right-hand side represents the effects of
22 23
=
+
+
+
!,"
+
!,#
+
!,#$
(2)
24 25
Here, the first term denotes the wave growth by the wind, the second and third terms represent
26
respectively the nonlinear transfer of wave energy through three-wave and four-wave
27
interactions and the fourth term denotes the wave decay due to whitecapping, and the last two
28
terms represent respectively the dissipations due to the bottom friction and depth induced wave
29
breaking. All description of the theoretical and numerical background of SWAN model, in
30
addition to the recent technical details, can be found in the SWAN technical manual [25].
31 6
1
3.2.
Forcing data
2
The wind speed, the fetch length, and the wind duration are mainly the parameters responsible on
3
the wave generation [57]. Currently, several wind reanalysis databases are available with
4
different spatial and temporal resolutions. The wind fields of the ERA-Interim reanalysis of the
5
European Centre for Medium-Range Weather Forecasts (ECMWF) [58] and those of Climate
6
Forecast System Reanalysis (CFSR) of the National Centers for Environmental Prediction
7
(NCEP) [58, 59] cover actually a period of 41 years and present the most used databases in the
8
recent studies. Several studies [61–69] showed that both databases have good performance and
9
that ECMWF wind speeds are slightly underestimated unlike the CFSR wind speeds. However,
10
the use of ECMWF winds as forcing for 3rd generation wind-wave models including SWAN
11
[67] induces a slight underestimation of significant wave heights Hm0 unlike the NCEP CFSR
12
winds which induces a slightly overestimate of Hm0. The global maps of the normalized Bias
13
related to the significant wave heights presented by Stopa & Cheung [68] illustrate clearly this
14
situation in the Mediterranean Sea. We consider that a slight overestimation of wind speeds and
15
significant wave heights may be favorable for hazard prediction studies, return period
16
measurements, measurements of the WEDI and for the design of offshore and coastal structures
17
in order to ensure maximum safety and sustainable development, with consideration of the
18
statistical error of the prediction model.
19
In this study, the SWAN model was forced using the CFSR wind fields developed by the NCEP.
20
This database (CFSR ds093.1) was provided by the research data archive website of the
21
University Corporation for Atmospheric Research and the National Centers for Environmental
22
Prediction, https://rda.ucar.edu/. The CFSR “ds093.1” wind fields are characterized by a high
23
spatial resolution of 0.3125° x 0.3125° between 1979 and 2011 (CFSR v1) [59] and 0.225° x
24
0.225° since 2011 (CFSR v2) [60]. It is also characterized by a very high temporal resolution of
25
1 hour. This high temporal resolution is considered important for a better estimation of storm
26
peaks [28]. According to Cox et al., [70], this wind field provides an excellent forcing source for
27
the third generation wave model. Besides, several other recent studies [27, 70–72] have also
28
approved the efficiency of this data source using different wind-wave models. According to Van
29
Vledder & Akpınar [74] the CFSR wind source gives a better result than the other reanalysis
30
used with the default SWAN model settings in the Black Sea.
7
1
Concerning bathymetric data, it is known that the bathymetry presents an important factor that
2
influence the wave propagation and wave energy dissipation by wave refraction and diffraction
3
phenomenon, and also by inducing wave breaking. According to Sartini et al. [40], the SWAN
4
model using during the present study, is able to perfectly reproduce the coastal diffraction and
5
refraction phenomena, related to coastal morphology. In addition, according to Wornom et al
6
[75] the SWAN model provides an accurate estimation of breaking waves in the surf area.
7
Nevertheless, in the western Mediterranean basin, an accurate bathymetry is necessary in order
8
to ensure an accurate estimate of the wave energy dissipation resulting from these three
9
phenomena; the western Mediterranean basin is characterized by complex bathymetry and a
10
complex coastline with the presence of several islands. In the Mediterranean Sea, the
11
bathymetric database ETOPO1 1 arc-minute global relief mode of the National Geophysical Data
12
Center of the National Oceanic and Atmospheric Administration (NOAA) is characterized by a
13
spatial resolution of 0.0166° in both directions compiled using a grid derived from multibeam
14
swath sonar bathymetric surveys of 500 m [76]. This source of bathymetric data was obtained
15
from the web-site (ngdc.noaa.gov/mgg/global/global.html) and used for the development of the
16
wind-wave hindcast data base. The efficiency of this bathymetric source was also approved using
17
different wind-wave models in different previous studies [71, 73, 76, 77].
18 19
3.3.
Model setup
20
The SWAN model used to perform the hindcast database was calibrated and validated for the
21
Algerian basin [1] based on wave dissipation parameterizations, especially the whitecapping
22
source term [79], and the coefficient of the whitecapping dissipation (Cds). Knowing that it is the
23
least known process in the numerical wind-wave models [41]. Thus, the selected exponential
24
wind growth, the whitecapping formulas and the coefficient of the whitecapping dissipation Cds
25
(empirical coefficient of proportionality) given in Table 1 was determined based on several
26
sensitivity tests, using one-year wave measurement data (from 01/07/1999 to 30/06/2000) of
27
Azeffoun buoy. The results of this calibration show that the use of the formulation of Janssen
28
[80, 81] with Cds1 = 1 for whitecapping and the formulation of Komen et al. [82] for wind
29
growth improves the model accuracy in the western Mediterranean. The detailed calibration
30
results and sensitivity tests’ results were given in Amarouche et. al. [1]. 8
1
The SWAN model was run in the third generation and non-stationary mode. The model domain
2
covers the entire West Mediterranean Sea discretized with a regular grid of 0.033° × 0.033° (~3
3
km) in spherical coordinates, from 17°E to 6°W and from 35°N to 45°N as shown in (Fig. 1).
4
The directional wave energy density spectrum was discretized using 35 frequency bins between
5
0.033 Hz and 1.0 Hz and 36 directional bins with constant increment. The numerical scheme was
6
the slightly dispersive scheme BSBT (first order upwind; Backward in Space, backward in
7
Time).
8
For the physical computation processes, all the used formulations [55, 80–86] and coefficients
9
are detailed in Table 1. Concerning the boundary condition, since we consider the Western
10
Mediterranean basin as a semi-enclosed basin, the shape of the spectra (both in frequency and
11
direction) at the open boundary of the computational grid was defined with JONSWAP [55]
12
spectrum with a peak enhancement parameter of gamma = 3.3.
13
Generally, compared to the default physical setting recommended by SWAN model, the
14
calibration of the physical setting in this model makes it more accuracy [28, 41–43]. Table 2
15
shows the statistical errors’ results obtained by the calibrated- and default-setting SWAN models
16
with respect to one-year wave measurement of Azefoun Buoy during the calibration period. The
17
model calibration has considerably improved the simulation results. The scatter index was
18
decreasing by 10% and 5% for Hm0 and Tm02 successively.
19
For the wave hindcast performance evaluation, we used five statistical error parameters
20
according to the following equations:
21
the correlation coefficient (r),
22 23
R=
+ &,-. ' (') * (*)
+ + / & ' (') 0 &,-. * (*) 0 ,-.
(3)
24 25
the mean absolute error (MAE),
26 27
4
MAE = ∑ ;4|78 − :8|
(4)
28 29
the root-mean-square error (RMSE),
30 9
1
4
RMSE = / & ;4 7 − :
=
(5)
2 3
the bias,
4 5
bias = B
;4
4
7 −:
(6)
6 7
and scatter-index (SI)
8 9 10 11
SI =
DE F
+ . &,-. *, +
(7)
where Oi and :) are respectively the observed values and the mean value of the observed data. Pi
12
and 7) are respectively the predicted values and the mean value of the predicted data. N is the
13
total data number.
14
In addition to these parameters usually measured for the evaluation of the wind and wave model
15
performance, the HH error indicator proposed by Hanna and Heinold [87] and evaluated by
16
Mentaschi et. al. [88] is also measured at the same buoys, following the recommendations of
17
Mentaschi et. al. [88] who demonstrated that the lower values of RMSE and SI do not provide a
18
reliable evaluation of the models’ performance. Mentaschi et. al. [88] recommends the use of this
19
index as it provides reliable results in the evaluation of wave simulation models in the
20
Mediterranean Sea.
21 22
GG = /
23
(8)
0 ∑+ ,-. ', (*,
∑+ ,-. ', *,
24 25
4. Wave power resource and variability computation
26
As almost the clean renewable energy, wave energy comes from the sun, which translates into
27
two fluid forms, water and atmospheric air [89]. Wave energy generated by wind per unit area is
10
1
expressed as the sum of potential energy related to the resting level and kinetic energy [82, 83]
2
by:
3 4
HIJ
K
= HL + H' =
4
4M
NOG = P
(9)
5 6
The power of the wave energy transmitted per unit of crest width in the direction of wave
7
propagation can be computed using the spectral output of the numerical wave model as energy
8
flux per wave crest width unit in kW/m [16, 92–94] as
9 10
=Y
W
7 = NO QX QX RS T , ℎ T,
VT V
(10)
11 12
where Cg(f, h) is the group velocity, S(f, Ɵ) is the directional wave variance density spectrum
13
and h, f, Ɵ, ρ, g are respectively the water depth, wave frequency, direction of propagation of a
14
spectral component, water density, and gravitational acceleration. In deep water (h > L/2), the
15
energy flux (Pw) transmitted by a regular wave per unit crest width can be approximated to
16 17
7Z =
18
(11)
[S²
M Y
= = × G^X × _` = 0.491 × G^X × _`
19 20
where Te is the energy period defined in terms of spectral moments, Hm0 is the spectral wave
21
height and ρ is the seawater density taken as 1027 kg/m3.
22 ^f.
23
_` =
24
G^X = 4hiX
(12)
^g
(13)
25 26
m-1 and m0 are spectral moments of nth-order spectral moment (mn) used to define the useful
27
wave statistic parameters [94].
28 29
W
i = QX
T T VT
(14)
30 11
1
For the evaluation of the total wave energy (ET in kWh/m) recorded during a given period with a
2
time step ∆t (3 hours in this study) we used the following common formula [15, 17]:
3 4
HJ
K
= ∑ 7 × ∆
(15)
5 6
The spatio-temporal variability of wave energy resources was carried out by computing the
7
coefficient of variation (COV) proposed by Cornett [92] which is the standard deviation (σp)
8
)))). This parameter allows us to identify the potential normalized to the mean power resource Pw
9
areas characterized by a significant wave energy resource with the lowest variation [23, 84].
10 11
no
R:m = )))))
(16)
pq
12 13
For the evaluation of monthly (MV) and seasonal (SV) variability [92] we also use two indices
14
for the wave energy resource variability defining as
15 16
rm =
17
m =
'stuv ( 'st,w 'xyuz
'{tuv ( '{t,w 'xyuz
(17) (18)
18 19
where Pm, max and Pm, min are respectively the mean wave power for the most energetic month and
20
for the least energetic month. Ps, max and Ps, min are respectively the mean wave power for the
21
most energetic season and for the least energetic season, and Pyear is the yearly average wave
22
power.
23 24
5. Wave power resource validation
25
For the validation of the SWAN model in the SW Mediterranean basin, we used the observation
26
data of six buoys (Fig. 1). All Algerian buoys are non-directional (DATAWELL Waverider),
27
they measure waves with periods from 1.6 to 30 seconds with a maximum error of 3%. More
28
characteristics concerning the periods used in the validation part, temporal resolutions, the
29
number of the wave observations, buoy sensor and the depths at buoy locations are detailed in
30
Table 3. Three physical wave characteristics (the significant wave height Hm0, the mean wave 12
1
period Tm02 and the energy period Te) were considered during the validation. Also, according to
2
the Eq (11) we compared wave power resource Pw computed using the observation data in the
3
Algerian coast
4
SWAN model results Pw,
5
Table 4 and the Q-Q plots (Fig. 2) show a very good performance of the calibrated SWAN model
6
in the six buoys, with a mean bias of 0.09 m and -0.17 s and a mean scatter index of 30% and
7
16% for both significant wave height Hm0 and the mean wave period Tm02, respectively. The
8
error indicator HH proposed by Hanna and Heinold [87] and evaluated by Mentaschi et. al. [88],
9
indicates a very good performance of the SWAN model calibrated for the Algerian basin
10
compared to the results obtained by Mentaschi et. al. [23] in seven other Mediterranean sub-
11
basins. The mean HH recorded in this study against validation buoys are 0.22, 0.13 and 0.15 for
12
Hm0, Tm02 and Te respectively. Concerning the energy period observed in the three Algerian
13
buoys and the computed wave power resource, we observe also a very good accuracy of these
14
two parameters with a mean scatter index of 14% and a bias of 0.16 s for the energy period. The
15
time series’ plots (Fig. 3) also present a very good accuracy of the predicted wave power
16
resource (Pw).
Pw,
observed
against the wave power resource computed using the calibrated
predicted
(Fig. 2). The error statistics’ results for all buoys detailed in
17 18
6.
Wave energy resource and variability assessment
19
6.1. Spatial distributions of mean and max wave energy fluxes
20
The spatial distribution map of the average wave energy flux at the West Mediterranean basin
21
(Fig. 4) shows the presence of a high energy resource in the northern part of the basin between
22
Sardinia and Mahon that exceeds 18 kW/m of average with a coefficient of variation of 0.19
23
(Fig. 7) which propagates to the East of the Algerian coast. In the western part of the basin,
24
another hot spot is also observed with an average wave power flux of 12.5 kW/m and a
25
coefficient of variation of 0.10. Referring to the bathymetric map, we observe that the wave
26
reaches the Algerian continental shelf (200 m deep) with an average wave power flux of about
27
7.5 kW/m in the western part, ~10 kW/m in the central part, and ~12 kW/m in the eastern part of
28
the basin. Regarding the spatial distribution of wave power along the Western Mediterranean Sea
29
(Fig. 4), we observed that the mean wave power distribution is strongly dependent on the
30
distribution of mean significant wave heights and it is similar to that obtained by previous studies
31
[17, 18, 20, 21, 38, 51]. However, from a quantitative viewpoint, the results of the inter-annual 13
1
mean obtained during the present study are closer to those obtained by Vannucchi et. al. [49],
2
with a mean wave powerreaching 20 kW/m in the central west Mediterranean basin. On the other
3
hand, an overestimation is observed by comparing our results with the other studies [18,19,38].
4
This difference may be related to several reasons in addition to the accuracy of the used models.
5
Among these reasons, the period considered to calculate the mean wave power; a significant
6
variation of the mean wave power flux during decadal years has been observed (Fig. 5) with a
7
considerable growth of the mean wave power flux between 2008 and 2017. The second reason
8
considered is the spatial and temporal resolution of the wind and wave data; a high temporal
9
resolution of the wind fields allow to better estimate the storm peaks [28] and according to Besio
10
et. al. [18] an increase of the spatial and temporal resolution of the model decreases its
11
underestimation of the significant wave heights. The third reason is the wind inputs used as
12
forcing in the model; e.g. the ECMWF winds underestimate wind speeds contrary to the CFSR
13
winds.
14
In the Algerian coast, Liberti et. al. [19] obtained an average wave power flux of 10.33 kW/m off
15
Cape Bougaroune (6.43°E x 37.2°N) during 10 years between 2001 - 2010, a result that
16
underestimates the average energy flux obtained during the present study using the SWAN
17
model calibrated and validated against observations made on the Algerian coast, which is 12.35
18
kW/m (underestimation of 16%) during the same period and in the same location. This
19
underestimation is considered justified, since the validation results of the model presented and
20
used by Liberti et. al. [19], show a negative bias for the significant wave heights at 72% of the
21
validation buoys, unlike the bias of the calibrated SWAN model used during this study, which is
22
positive for all the validation buoys (Table 4). Also, by comparing the results of the mean scatter
23
index of significant wave heights obtained against eleven wave buoys in the West
24
Mediterranean, Liberti et. al. [19] has reported a mean scatter index of 38% while a mean scatter
25
index of 30% was obtained against the same number of buoys for the calibrated SWAN model
26
[1]. It should be noted that the wave hindcast database used by Liberti et. al. [19] is the same one
27
used by Arena et. al. [22]. The same observation was noted by comparing the obtained results
28
with those of Besio et.al. [18] who found an average wave power flux of 9.10 kW/m off the
29
Annaba province 7.675°E x 37.250°N during 35 years against an average wave power flux of
30
12.16 kW/m obtained in the present study during the same period and at the same station
31
(underestimation of 25%). The wave hindcast database used by Besio et. al. [18], was developed
32
using Wavewatch III model implemented by Mentaschi et. al. [23], who indicate clearly 14
1
(graphically) in their paper that the normalized bias is negative for significant wave heights
2
greater than 1.7 m and decreases further considerably by increasing wave heights. This fact may
3
be at the origin of the remarkable difference in average wave power presented by Besio et. al.
4
[18] in the Mediterranean hotspots compared to the results of the present study. Thus, as already
5
specified in section 5, the results of the HH error indicators obtained for the calibrated SWAN
6
model show a very good performance of the calibrated SWAN model in the Algerian basin
7
compared to the results of the HH error indicators obtained by Mentaschi et. al. [23] in seven
8
other Mediterranean sub-basins. A straight comparison of the slightly overestimated results
9
obtained in this study with the slightly underestimated results of some previous studies reveals
10
logically a significant difference for the extreme values. The wave power bias computed depends
11
on the estimation errors of significant wave heights Hm0 and energetic periods Te and grows
12
exponentially by increasing Hm0 and Te (please see Eq. 11). It is therefore very important, when
13
processing the results obtained from wave energy assessment studies, to refer on the error
14
statistics of the model used for the development of the wave hindcast database.
15
Concerning the maximum wave power distribution, Fig. 4 presents the maximum significant
16
wave height and maximum wave power observed during the 39-year period at each point of the
17
computation grid. It can be observed that the spatial distribution of the maximum wave powers is
18
strongly depending on the significant wave heights. Along the Algerian coast, the maximum
19
wave powers are rarely lower than 300 kW/m and exceed 700 kW/m in the eastern and western
20
parts of the Algerian coast. Higher wave power values have already been observed by Arena et.
21
al. [22] as shown in their seventh figure concerning the wave power and average wave power
22
calculated for a certain directional sector. The knowledge of the spatial distribution of the
23
maximum wave power recorded in the 39-year period is an important parameter for the selection
24
of suitable areas for the installation of wave power farms and to ensure a durable and sustainable
25
development of these farms and other offshore and coastal installations.
26 27
6.2. Temporal variations of wave power flux
28
The variations of the wave power flux distribution and its potential at the decadal, annual,
29
seasonal, monthly and hourly scales along the Algerian coast present an essential parameter for
30
the evaluation and classification of areas with high energy potential characterized by a
31
considerable amount of energy that is omnipresent. The decadal spatial distribution maps (Fig. 5) 15
1
show a spatial distribution of wave power that is qualitatively similar to the distribution observed
2
over a 39-year period. On the other hand, from a quantitative point of view, we observe that the
3
wave power has increased over the last decades in the eastern and central part of the basin, with
4
very low decadal variation in the western part of the basin. This area can therefore have a climate
5
distinct from that of the rest of the western Mediterranean basin. The results of the decadal
6
variations may constitute an important information source as for the wave recovery projects with
7
a lifetime of 10 to 20 years [16].
8
According to the results of the seasonal and monthly mean power flux distributions (Fig. 6), a
9
similar spatial distribution can be observed by comparing the winter, autumn and annual average
10
distribution (Fig. 4) with higher power fluxes in the eastern part in comparison with that in the
11
western part of the Algerian basin. On the other hand, the spring and summer season has a
12
balance between the average energy recorded in the east and west of the Algerian basin.
13
Quantitatively, during the autumn and spring seasons, the average wave powers recorded are
14
almost equivalent along the Algerian coast, the average wave power varying between 4 kW/m
15
and 8 kW/m in a considerable part of the coast. For the winter season, the mean power exceeds
16
10 kW/m along the east coast at a distance of less than 15 km from the shore, and decreasing
17
progressively towards the west. Comparing these results against the mean wave power quantities
18
obtained seasonally by Besio et. al. [18] and Liberti et. al. [19], it is noticeable that the results
19
obtained for the summer, spring and autumn seasons are very similar compared to those of the
20
winter seasons, where higher values were recorded during the present study. This significant
21
difference can be justified by the points presented in section 6.1 as well as by the significant
22
increase of wave power during the last ten years (Fig. 5), knowing that the period between 2013
23
and 2017 have not been considered in the previous studies [18,19,22,38,49].
24
At the monthly scale, three different distributions are observed. The first is that during the
25
months of October, November, December, January, February, and March, where the power flux
26
is greater in the eastern part of the basin than in the western part. This case represents the
27
dominant distribution during the year. The second case of distribution is observed during the
28
month of June where the power flux is greater in the eastern part than in the western part of the
29
basin. Finally, the third case of distribution is observed during the months of April, May, July
30
and August, where a balance is observed in the distribution of energy between the east and west
31
of the basin. Quantitatively, the lowest wave power flux is recorded during the summer season, 16
1
precisely during the month of August, and the highest wave power flux are recorded during the
2
winter season precisely in December with a maximum average of 32 kW/m.
3
The consideration of areas according to their energy potential depends on the continuous
4
availability of this energy as well as their capacity. The spatial distribution maps of the
5
coefficients of variation (Fig. 7) allow us to evaluate the inter-annual, inter-monthly, and inter-
6
seasonal spatio-temporal variability of the mean wave power flux. The area characterized by a
7
considerable annual or monthly average wave power flux with a low COV, reflects the
8
continuous presence of a promising wave power flux during an exploitation period. At an inter-
9
annual scale, the COV represents the ratio between the standard deviation of the yearly mean
10
wave power fluxes during 39 years and the total average wave power flux. At this temporal
11
scale, the COV does not exceed 0.15 in the western part of the basin and reaches a maximum of
12
0.30 in the east. In the monthly scale, the COV represents the ratio between the standard
13
deviation of the monthly mean wave power fluxes during 39 years and the total monthly average
14
computed for each considered month. At this temporal scale, the highest coefficients of variation
15
are recorded during the months of January and February with a maximum of 0.80 in both the east
16
and west of the Algerian coast and the lowest coefficients of variation are recorded in the central
17
region of the basin where they vary between 0.20 and 0.40 over the 12 months. At the inter-
18
seasonal scale, the COV is the ratio between the standard deviation of the seasonal mean wave
19
power fluxes during 39 years and the total average computed for each considered season. At this
20
temporal scale, the highest COVs are recorded during the winter season between 0.30 and 0.40
21
over the entire Algerian basin. For the autumn season the strongest variations are observed in the
22
Eastern zone, and for the spring and summer seasons the coefficients of variation are between
23
0.15 and 0.30 over the entire Algerian basin.
24
According to Cornett [92], the monthly and seasonal variability (MV and SV) shows the
25
difference between the most energetic month and season and the least energetic month and
26
season normalized to the annual average, respectively. The spatial distributions of these two
27
parameters presented in Fig. 9 are very similar with a significant monthly and seasonal variation
28
in the eastern part, which decreases progressively from 2.4 to 0.3 moving towards the west.
29 30
6.3. The wave energy development index (WEDI)
17
1
WEDI as presented by Hagerman [95] is a ratio between the mean annual wave power and the
2
maximum observed power in a given area. This index is presented as an indicator of the risk to
3
which the WECs or the offshore structures are exposed during their activity. It allows to estimate
4
the economic report between the necessary consolidations of the mooring and the WECs hull and
5
the average wave energy potential available in the exploited area. The areas with low WEDI are
6
characterized by very higher wave power peaks compared to the wave power averages. In order
7
to resist to these extreme waves in such areas, WECs require a very heavy and expensive design
8
comparing to the exploitable wave power averages in these areas [24,96]. The spatial distribution
9
map of the WEDI index (Fig. 10) shows that the most important values, which range from 0.2 to
10
0.3, are recorded in the offshore of Tizi-Ouzou province and in the northwestern and
11
northeastern part of the Algerian basin. The WEDI index was also evaluated for the 14 stations
12
(Fig. 12) and used in the detailed evaluation of the wave energy resource along the Algerian
13
coast and the results are presented in following section.
14 15
6.4. Wave energy resource assessment at selected locations
16
For a quantitative evaluation of the wave energy resources along the Algerian coasts, a detailed
17
analysis was carried out at fourteen stations (Fig. 1) located off the fourteen Algerian coastal
18
provinces. These stations are located at a distance from the shoreline that varies between 16.8 km
19
and 3.2 km, corresponding to a depth ranging from 106 m to 327 m (Table 5). Thus, distances
20
from the ports are also considered. Distance from shore and depth constitute an important
21
economic factor [49, 50] and an essential element in the selection of the most appropriate WECs.
22
The results of the seasonal and annual mean wave power resources at the fourteen stations
23
presented in Fig. 11 show the availability of an average wave power flux that varies from 11.78
24
kW/m in the East (S2) to 4.4 kW/m in the center of the Algerian basin (S10). During the autumn,
25
winter and spring season, the stations from S1 to S4 located in the eastern part of the Algerian
26
basin represent the most energetic areas with an average energy which exceeds 15 kW/m in the
27
winter season and gets closer to the annual averages in the autumn and the spring seasons.
28
However, during the summer, there is a significant decrease in wave power flux at all stations.
29
Regarding the maximum wave power flux, an energy greater than 530 kW/m was recorded in
30
stations from S1 to S4, which had the highest average values, as well as in station S12 (Oran)
31
located in the western part. This extreme value recorded at station S12 compared to its annual 18
1
average of 5.73 kW/h explains the lowest WEDI value at this station (Fig. 12). The installation
2
of a WEC in this area may require a heavy and expensive design.
3
For the evaluation of the wave power flux availability at the monthly scale, we have presented in
4
Fig. 13 the distribution of the monthly wave power flux recorded over 39 years. The results show
5
that among the fourteen selected stations, seven stations (S1, S2, S3, S4, S6, S8, S11 and S12)
6
are characterized by a monthly wave power average that declines very rarely below 2 kW/m
7
during the years. Among these stations, the western ones have the lowest monthly (MV) and
8
seasonal (SV) variability rates, however, these stations are characterized by the lowest wave
9
power average (Table 6). Further, a significant increase in wave power over the last five years is
10
observed at all stations (Fig. 13), particularly during the months of January and February. This
11
observation explains the significant difference observed when comparing the results of this study
12
with the results of previous studies during the winter season; as mentioned above the period of
13
2013-2017 were not evaluated in the previous studies [17, 18].
14
Considering a calm sea state, the significant wave heights that does not exceed 0.5 m, the
15
stations S1, S2, S6 and S8 have a probability to have calm sea less than 20% (Table 6) which it is
16
lower than 17.2% between 15:00 and 00:00 (Fig. 14). This makes the average amount of the
17
wave power flux more important during the night than that of the morning periods in all stations
18
(Fig. 14). According to the daily consumption profile [97] defined by Sonelgaz Spa, the peaks in
19
domestic electricity consumption are recorded between 6 pm and 10 pm.
20
The probability of occurrence and the directional variability of wave energy flux presented in
21
Fig. 15 allows us to obtain more detailed information on the wave power resource characteristics
22
at the fourteen stations. These results contribute to the selection, design and orientation of the
23
WECs suitable for each of these areas to ensure an optimal exploitation. The probability of
24
occurrence presented in Fig. 15 is limited to the wave powers below 30 kW/m which represents
25
between 90% and 98% of the occurrences in all the fourteen stations. The most relevant wave
26
energy levels are in the range of 0 to 1 kW/m, with a proportion ranging from 31% to 33% in the
27
stations S1, S2, S6, S8, from 34% to 36% of the time in S4, S9, S10, S12 stations and from 39%
28
to 49% of the time in the other stations. Concerning the wave energy levels which vary between
29
1 kW/m and 30 kW/m, we notice that the probability of occurrence is more balanced between the
30
14 stations. The differentiation between stations can be based on the occurrence probabilities of
31
wave power ranges lower than 1 kW/m which represents largely proportion of the calm sea states 19
1
(Fig. 15) and also on the occurrence probabilities of wave power ranges higher than 30% which
2
varies between 2% at stations S9, S10, S14 and 10% at stations S1 and S2.
3
By examining the wave energy roses (Fig. 15) we notice that the directional distribution of wave
4
energy resources depends strongly on the geographical distribution of the 14 stations, this
5
distribution allows us to classify the stations in 3 different zones. The first zone covers the
6
stations located in the east of the Algerian basin between el-Taref (S1) and Jijel (S4), this zone is
7
characterized by a strong dominance of the NW waves with considerable energies. The second
8
zone is limited to the center of the Algerian basin between the Bejaia station (S5) and Chlef
9
station (S10), the dominant waves in this area are coming from the NNE and NE. The third zone,
10
is located in the western part of the Algerian basin and characterized by a strong dominance of
11
waves coming from the NNE, W and WNW direction and the most energetic are the W and
12
WNW waves. In these west stations it is more interesting to choose a WECs with low
13
dependence on direction or a WEC with a system that orientates it against the direction of the
14
most energetic waves as The Floating Wave Power Vessel [9]. In addition to the sea states
15
distributed over a range of direction, the wave energy contribution as a function of significant
16
wave height Hm0 and wave energy period Tm-10 is also an important parameter for the selection of
17
the promising WECs [19] or for the designing of the WEC wave farms [16]. The operation of
18
WECs is often optimized over specific ranges of wave heights and periods, the Fig. 16 represents
19
the distribution of wave energy as a function of significant wave height and energy period with
20
the contributions of different ranges of wave heights and periods to the annual wave energy
21
resources (Eq. 15). The selected stations show a remarkable difference in the distribution of the
22
total wave energy in terms of Te and Hm0. The highest total annual energies are recorded in El-
23
Taref S1 and Annaba S2 station located in the east of the Algerian basin, with a total annual
24
wave energy of 98.01 MWh/m and 103.27 MWh/m respectively (Table 6). In these two stations
25
this energy is distributed over a large period and significant wave height range concentrated
26
respectively between 6 s and 10 s and between 1 m and 6 m (Fig. 16). The stations of Jijel S3
27
and Skikda S4 have almost the same distribution, with a considerable amount of annual wave
28
power of 74.9 MWh/m and 73.5 MWh/m respectively. The main sum of this energy is
29
distributed over a significant range of Te and Hm0 concentrated between 1 m to 4 m for Hm0 and
30
between 6 s to10 s for Te. Compared to stations S1, S2, S3 and S4, the stations located in the
31
western part of the Algerian basin have a lower annual wave energy resources but concentrated 20
1
over a narrower range, ranging from 1 m to 3.5 m and from 5.5 s to 8.5 s in terms of significant
2
wave height and energy period successively.
3 4
6.5.
Hotspots coastal areas
5
The Algerian coasts are exposed to the eastern waves generated in the Tyrrhenian Sea, to the
6
western waves generated in the Alboran Sea, and to the northern waves. This characteristic
7
ensures that the Algerian coasts know the lowest probability to have calm seas (Fig. 17) during
8
the year, which is less than 18% off Annaba S2 (Table 6). On the other hand, compared to the
9
European coast, the Algerian coast has a very narrow continental shelf, which means that the
10
wave energy propagates near the shore with a low dissipation. Considering the total annual wave
11
energy, the annual probability to have a calm sea state and the distance from the coast as criteria
12
for the selection of coastal hot spots. We have selected the areas with a total annual wave energy
13
exceeding 100 MWh/m/year at 15 km from the coast, and a probability to have a calm sea state
14
less than 18%. The result of this multi-criteria spatial analysis allowed us to classify the coastal
15
areas with the highest wave energy resources in the western Mediterranean basin (Fig. 19). The
16
selected areas are the east coast of Mahon Island (Spain) which covers 442 km², the Carbonia
17
coast in the south-west of Sardinia (Italy) which covers 273 km² and Eastern coast of Algeria
18
(Annaba and Skikda province) which covers 546 km². These results show that the Algerian coast
19
has the highest energy potential in the western Mediterranean basin with an availability of waves
20
that exceed 0.5 m during 299 days of the year (Fig. 14).
21 22
7. Conclusions
23
This study presents an assessment of the potential wave energy resources along the Algerian
24
coast. 39-year wave climate and wave energy hindcast dataset was developed for the Algerian
25
basin using SWAN model. This model was calibrated and validated based on three wave buoy
26
measurements recorded in the Algerian coast; with a high spatial resolution of 0.033˚ [1]. This
27
resolution allowed us to evaluate the wave energy potential at a distance of ~3 km from the
28
coast, considering the Algerian narrow continental shelf. A spatio-temporal analysis of the wave
29
power variations allowed us to observe that the strongest wave energy resources in the western
30
Mediterranean are located near the Eastern coasts of Algeria; taking into account the distance 21
1
from the coasts and its omnipresence during the year. The East Algerian coasts are exposed to
2
the waves coming mainly from the Algerian basin, the North West Mediterranean basin and the
3
Tyrrhenian basin. Therefore, wave energy potential is also considerable on the west and central
4
coasts of Algeria, with a lower monthly and seasonal variation. The detailed local analysis
5
carried out for fourteen stations distributed along the Algerian basin, show a significant
6
variability in the wave energy characteristics at each zone. The stations located in the eastern
7
part of the Algerian basin (S1 and S2) are characterized by the highest total annual wave energy,
8
exceeding 100 MWh/m/year with an annual probability of 18% of calm sea state, within 15 km
9
of the coastline. Station S3 (Skikda) is characterized by a high dominance of NNE waves and a
10
total annual wave energy of 74.9 MW/m/year. The S6 station has a total annual wave energy of
11
63 MWh/m/year, with an average wave power flux of 7.28 kW/m and a maximum of 382.82
12
kW/m, resulting in the highest WEDI of 0.019. From the eastern to the western stations we
13
notice a progressive decrease in the total annual energy and a decrease in the WEDI. Station 14
14
in the west has recorded the lowest resources.
15
By comparing the average wave energy resource obtained during this study on the Algerian coast
16
against that obtained during the previous studies [18,19,22,49]; we observe that our results are
17
close to those reported by Vannucchi et. al.[49], and underestimated compared to those recorded
18
by Liberti et. al. [19] and Besio et. al. [18]. The main reason for this underestimation is not
19
necessarily due to the model accuracy, but mainly caused by positive or negative signs of the
20
model bias. A straight comparison of the slightly overestimated results obtained in this study
21
with the slightly underestimated results of some previous studies reveals a significant difference.
22
When processing or comparing the results obtained from the different wave energy assessment
23
studies, it is strongly recommended to refer on the error statistics derived from the models used
24
for the development of the wave energy datasets. In addition, the results of this study confirm the
25
conclusion reached by Besio et. al. [18] concerning the increasing trend in wave power
26
availability between 2005 and 2013, and we add that this growth has been observed since 1995
27
and has persisted over the last five years between 2013 and 2017, more precisely in the West and
28
Central parts of the Algerian coast, during the months of January and February (Fig.13).
29
The wave energy dataset presented during this study will allow to design and estimate the
30
capacity and profitability of wave energy farms at any selected location. Therefore, the long-term
31
wave energy resource assessment results present a necessary tool to identify suitable locations 22
1
for the implementation of wave energy farms; to be used by the private sector or public
2
institutions. We also suspect that a more detailed local assessment, using a nested grid model
3
with a higher spatial resolution, can provide a paramount result in the selected hotspots areas.
4 5
Acknowledgement
6
The authors would like to express their special thanks to the late Dr. Gerbrant van Vledder who
7
assisted us during the implementation of the SWAN model in W-Mediterranean. Although he is
8
no longer with us, he continues to inspire us by his example and dedication to science. We
9
gratefully acknowledge Dr. Salim Lamine, for his suggestions, which have helped in improving
10
the English language of the paper. The authors acknowledge the NGDC/NOAA for providing the
11
bathymetry data ETOPO1 1 arc-minute, the NCEP/UCAR Research data archive service for
12
providing the wind forcing data of NOAA NCEP Climate Forecast System Reanalysis (CFSR),
13
the ONSM (Offıce National de Signalisation Maritime) and the Público Puertos del Estado for
14
providing the wave measurements data. This work is based on the PhD thesis of the first author.
15 16
References
17 18
[1]
K. Amarouche, A. Akpınar, N.E.I. Bachari, R.E. Çakmak, F. Houma, Evaluation of a
19
high-resolution wave hindcast model SWAN for the West Mediterranean basin, Appl.
20
Ocean Res. 84 (2019) 225–241. https://doi.org/10.1016/J.APOR.2019.01.014.
21
[2]
Radiation Using {MSG}-{HRV} images, Int. J. Appl. Environ. Sci. 11 (2016) 351–368.
22 23
K. Bouchouicha, A. Razagui, N.E.I. Bachari, N. Aoun, Estimation of Hourly Global Solar
[3]
A.B. Stambouli, Z. Khiat, S. Flazi, Y. Kitamura, A review on the renewable energy
24
development in Algeria: Current perspective, energy scenario and sustainability issues,
25
Renew. Sustain. Energy Rev. 16 (2012) 4445–4460.
26
https://doi.org/10.1016/J.RSER.2012.04.031.
27
[4]
Y. Himri, A.S. Malik, A. Boudghene Stambouli, S. Himri, B. Draoui, Review and use of
28
the Algerian renewable energy for sustainable development, Renew. Sustain. Energy Rev.
29
13 (2009) 1584–1591. https://doi.org/10.1016/J.RSER.2008.09.007.
30
[5]
K. Bouchouicha, A. Razagui, N. El, I. Bachari, N. Aoun, Mapping and geospatial analysis 23
1
of solar resource in Algeria, Int. J. Energy, Environ. Econ. 23 (2015) 735–751.
2
https://search.proquest.com/openview/dd3b32539d92855486578967dc899d20/1?pq-
3
origsite=gscholar&cbl=2034867 (accessed October 28, 2018).
4
[6]
https://doi.org/10.1016/S0960-1481(00)00090-2.
5 6
N.K. Merzouk, Wind energy potential of Algeria, Renew. Energy. 21 (2000) 553–562.
[7]
H. Mahmoudi, N. Spahis, M.F. Goosen, N. Ghaffour, N. Drouiche, A. Ouagued,
7
Application of geothermal energy for heating and fresh water production in a brackish
8
water greenhouse desalination unit: A case study from Algeria, Renew. Sustain. Energy
9
Rev. 14 (2010) 512–517. https://doi.org/10.1016/J.RSER.2009.07.038.
10
[8]
K. Kateb, Changements démographiques et organisation familiale en Algérie, Maghreb
11
Machrek. (2003) 95–110. https://www.africabib.org/rec.php?RID=253697506 (accessed
12
October 28, 2018).
13
[9]
A. Clément, P. McCullen, A. Falcão, A. Fiorentino, F. Gardner, K. Hammarlund, G.
14
Lemonis, T. Lewis, K. Nielsen, S. Petroncini, M.-T. Pontes, P. Schild, B.-O. Sjöström,
15
H.C. Sørensen, T. Thorpe, Wave energy in Europe: current status and perspectives,
16
Renew. Sustain. Energy Rev. 6 (2002) 405–431. https://doi.org/10.1016/S1364-
17
0321(02)00009-6.
18
[10]
B. Drew, A.R. Plummer, M.N. Sahinkaya, A review of wave energy converter technology,
19
Proc. Inst. Mech. Eng. Part A J. Power Energy. 223 (2009) 887–902.
20
https://doi.org/10.1243/09576509JPE782.
21
[11]
Energy Rev. 14 (2010) 899–918. https://doi.org/10.1016/J.RSER.2009.11.003.
22 23
A.F. de O. Falcão, Wave energy utilization: A review of the technologies, Renew. Sustain.
[12]
F.J.M. Farley, The underwater resonant airbag: a new wave energy converter, Proc. R.
24
Soc. A Math. Phys. Eng. Sci. 474 (2018) 20170192.
25
https://doi.org/10.1098/rspa.2017.0192.
26
[13]
H. Fernandez, G. Iglesias, R. Carballo, A. Castro, J.A. Fraguela, F. Taveira-Pinto, M.
27
Sanchez, The new wave energy converter WaveCat: Concept and laboratory tests, Mar.
28
Struct. 29 (2012) 58–70. https://doi.org/10.1016/J.MARSTRUC.2012.10.002.
29
[14]
J.P. Kofoed, P. Frigaard, E. Friis-Madsen, H.C. Sørensen, Prototype testing of the wave
30
energy converter wave dragon, Renew. Energy. 31 (2006) 181–189.
31
https://doi.org/10.1016/J.RENENE.2005.09.005.
32
[15]
L. Martinelli, B. Zanuttigh, J.P. Kofoed, Selection of design power of wave energy 24
1
converters based on wave basin experiments, Renew. Energy. 36 (2011) 3124–3132.
2
https://doi.org/10.1016/J.RENENE.2011.03.021.
3
[16]
A. Akpınar, B. Bingölbali, G.P. Van Vledder, Long-term analysis of wave power potential
4
in the Black Sea, based on 31-year SWAN simulations, Ocean Eng. 130 (2017) 482–497.
5
https://doi.org/10.1016/J.OCEANENG.2016.12.023.
6
[17]
K. Amarouche, N.-E.-I. Bachari, F. Houma, Study of the coastal wave energy propagation
7
using {GIS} and hydrodynamic model, in: R.C.E. Suha Ozden, Cengiz Akbulak, F.S. and
8
M.A. Oznur Karaca (Eds.), PROCEEDING B. Int. Symp. GIS Appl. Geogr. Geosci.,
9
Çanakkale Onsekiz Mart University, Canakkale, Turkey, 2017: pp. 55–64. https://doi.org/ISBN : 978 - 605 - 4222 - 54 - 4.
10 11
[18]
G. Besio, L. Mentaschi, A. Mazzino, Wave energy resource assessment in the
12
Mediterranean Sea on the basis of a 35-year hindcast, Energy. 94 (2016) 50–63.
13
https://doi.org/10.1016/J.ENERGY.2015.10.044.
14
[19]
L. Liberti, A. Carillo, G. Sannino, Wave energy resource assessment in the Mediterranean,
15
the Italian perspective, Renew. Energy. 50 (2013) 938–949.
16
https://doi.org/10.1016/J.RENENE.2012.08.023.
17
[20]
D. Vicinanza, P. Contestabile, V. Ferrante, Wave energy potential in the north-west of
18
Sardinia (Italy), Renew. Energy. 50 (2013) 506–521.
19
https://doi.org/10.1016/J.RENENE.2012.07.015.
20
[21]
S. Ponce de León, A. Orfila, G. Simarro, Wave energy in the Balearic Sea. Evolution from
21
a 29 year spectral wave hindcast, Renew. Energy. 85 (2016) 1192–1200.
22
https://doi.org/10.1016/J.RENENE.2015.07.076.
23
[22]
F. Arena, V. Laface, G. Malara, A. Romolo, A. Viviano, V. Fiamma, G. Sannino, A.
24
Carillo, Wave climate analysis for the design of wave energy harvesters in the
25
Mediterranean Sea, Renew. Energy. 77 (2015) 125–141.
26
https://doi.org/10.1016/J.RENENE.2014.12.002.
27
[23]
L. Mentaschi, G. Besio, F. Cassola, A. Mazzino, Performance evaluation of Wavewatch
28
III in the Mediterranean Sea, Ocean Model. 90 (2015) 82–94.
29
https://doi.org/10.1016/J.OCEMOD.2015.04.003.
30
[24]
G. Lavidas, V. Venugopal, A 35 year high-resolution wave atlas for nearshore energy
31
production and economics at the Aegean Sea, Renew. Energy. 103 (2017) 401–417.
32
https://doi.org/10.1016/J.RENENE.2016.11.055. 25
1
[25]
V. Franzitta, D. Curto, D. Milone, D. Rao, V. Franzitta, D. Curto, D. Milone, D. Rao,
2
Assessment of Renewable Sources for the Energy Consumption in Malta in the
3
Mediterranean Sea, Energies. 9 (2016) 1034. https://doi.org/10.3390/en9121034.
4
[26]
Armaments Organisation Research Cell, 2004.
5 6
Medatlas Group, Wind and wave atlas of the Mediterranean Sea, Western European
[27]
L. Donatini, G. Lupieri, G. Contento, A. Pedroncini, L.A. Cusati, A. Crosta, MWM: A 35
7
years wind&wave high resolution hindcast dataset and an operational forecast service
8
for the mediterranean sea, in: 18th Int. Conf. Ships Shipp. Res. NAV 2015, ITA, Lecco,
9
Italy, 2015: pp. 116–125. https://arts.units.it/handle/11368/2919934#.XBd8tGko_IU (accessed December 17, 2018).
10 11
[28]
A.-L. Tiberi-Wadier, A. Laugel, M. Benoit, Construction of the Numerical Wave
12
Databases Anemoc-2 on the Mediterranean Sea and the Atlantic Ocean Through Hindcast
13
Simulations Over the Period 1979–2010, in: Adv. Hydroinformatics, Springer, Singapore,
14
2016: pp. 127–143. https://doi.org/10.1007/978-981-287-615-7_9.
15
[29]
Wamdi Group, The {WAM} Model—A Third Generation Ocean Wave Prediction Model,
16
J. Phys. Oceanogr. 18 (1988) 1775–1810. https://doi.org/10.1175/1520-
17
0485(1988)018<1775:TWMTGO>2.0.CO;2.
18
[30]
H.L. Tolman, A Third-Generation Model for Wind Waves on Slowly Varying, Unsteady,
19
and Inhomogeneous Depths and Currents, J. Phys. Oceanogr. 21 (1991) 782–797.
20
https://doi.org/10.1175/1520-0485(1991)021<0782:ATGMFW>2.0.CO;2.
21
[31]
M. Benoit, F. Marcos, F. Becq, Development of a Third Generation Shallow-Water Wave
22
Model with Unstructured Spatial Meshing, in: Coast. Eng. Proc., 1996: pp. 465–478.
23
https://doi.org/10.1061/9780784402429.037.
24
[32]
L. Cavaleri, M. Sclavo, The calibration of wind and wave model data in the Mediterranean
25
Sea, Coast. Eng. 53 (2006) 613–627.
26
https://doi.org/10.1016/J.COASTALENG.2005.12.006.
27
[33]
55 (2008) 872–880. https://doi.org/10.1016/j.coastaleng.2008.02.024.
28 29
S. Musić, S. Nicković, 44-year wave hindcast for the Eastern Mediterranean, Coast. Eng.
[34]
F. Ardhuin, L. Bertotti, J.-R. Bidlot, L. Cavaleri, V. Filipetto, J.-M. Lefevre, P. Wittmann,
30
Comparison of wind and wave measurements and models in the Western Mediterranean
31
Sea, Ocean Eng. 34 (2007) 526–541. https://doi.org/10.1016/j.oceaneng.2006.02.008.
32
[35]
R. Bolaños-Sanchez, A. Sanchez-Arcilla, J. Cateura, Evaluation of two atmospheric 26
1
models for wind–wave modelling in the {NW} Mediterranean, J. Mar. Syst. 65 (2007)
2
336–353. https://doi.org/10.1016/j.jmarsys.2005.09.014.
3
[36]
A. Martínez-Asensio, M. Marcos, G. Jordà, D. Gomis, Calibration of a new wind-wave
4
hindcast in the Western Mediterranean, Ournal Mar. Syst. 121–122 (2013) 1–10.
5
https://doi.org/10.1016/j.jmarsys.2013.04.006.
6
[37]
S. de León, A. Orfila, G. Simarro, Wave energy in the Balearic Sea. Evolution from a 29
7
year spectral wave hindcast, 85 (n.d.) 1192–1200.
8
https://doi.org/10.1016/j.renene.2015.07.076.
9
[38]
G. Lavidas, V. Venugopal, A. Agarwal, Long-term evaluation of the wave climate and
10
energy potential in the Mediterranean Sea, in: Sustain. Hydraul. Era Glob. Chang., CRC
11
Press, 2016: pp. 247–253. https://doi.org/10.1201/b21902-46.
12
[39]
R.C. Ris, L.H. Holthuijsen, N. Booij, A third-generation wave model for coastal regions:
13
2. Verification, J. Geophys. Res. 104 (1999) 7667–7681.
14
https://doi.org/10.1029/1998JC900123.
15
[40]
L. Sartini, L. Mentaschi, G. Besio, Evaluating third generation wave spectral models
16
performances in coastal areas. An application to Eastern Liguria, in: Ocean. 2015 -
17
Genova, IEEE, 2015: pp. 1–10. https://doi.org/10.1109/OCEANS-Genova.2015.7271395.
18
[41]
W.E. Rogers, P.A. Hwang, D.W. Wang, Investigation of Wave Growth and Decay in the
19
{SWAN} Model: Three Regional-Scale Applications, J. Phys. Oceanogr. 33 (2003) 366–
20
389. https://doi.org/10.1175/1520-0485(2003)033<0366:IOWGAD>2.0.CO;2.
21
[42]
M.H. Moeini, A. Etemad-Shahidi, Application of two numerical models for wave
22
hindcasting in Lake Erie, Appl. Ocean Res. 29 (2007) 137–145.
23
https://doi.org/10.1016/j.apor.2007.10.001.
24
[43]
A. Akpinar, S. Ponce de León, An assessment of the wind re-analyses in the modelling of
25
an extreme sea state in the Black Sea, Dyn. Atmos. Ocean. 73 (2016) 61–75.
26
https://doi.org/10.1016/J.DYNATMOCE.2015.12.002.
27
[44]
V. Kutupoğlu, R.E. Çakmak, A. Akpınar, G.P. van Vledder, Setup and evaluation of a
28
SWAN wind wave model for the Sea of Marmara, Ocean Eng. 165 (2018) 450–464.
29
https://doi.org/10.1016/J.OCEANENG.2018.07.053.
30
[45]
R. Atan, S. Nash, J. Goggins, Development of a nested local scale wave model for a 1/4
31
scale wave energy test site using SWAN, J. Oper. Oceanogr. 10 (2017) 59–78.
32
https://doi.org/10.1080/1755876X.2016.1275495. 27
1
[46]
B. Kamranzad, A. Etemad-Shahidi, V. Chegini, Assessment of wave energy variation in
2
the Persian Gulf, Ocean Eng. 70 (2013) 72–80.
3
https://doi.org/10.1016/j.oceaneng.2013.05.027.
4
[47]
B. Bingölbali, A. Akpınar, H. Jafali, G.P. Van Vledder, Downscaling of wave climate in
5
the western Black Sea, Ocean Eng. 172 (2019) 31–45.
6
https://doi.org/10.1016/J.OCEANENG.2018.11.042.
7
[48]
G. Lavidas, V. Venugopal, Application of numerical wave models at European coastlines:
8
A review, Renew. Sustain. Energy Rev. 92 (2018) 489–500.
9
https://doi.org/10.1016/j.rser.2018.04.112.
10
[49]
V. Vannucchi, L. Cappietti, V. Vannucchi, L. Cappietti, Wave Energy Assessment and
11
Performance Estimation of State of the Art Wave Energy Converters in Italian Hotspots,
12
Sustainability. 8 (2016) 1300. https://doi.org/10.3390/su8121300.
13
[50]
E. Rusu, F. Onea, Study on the influence of the distance to shore for a wave energy farm
14
operating in the central part of the Portuguese nearshore, Energy Convers. Manag. 114
15
(2016) 209–223. https://doi.org/10.1016/j.enconman.2016.02.020.
16
[51]
K. Thorburn, H. Bernhoff, M. Leijon, Wave energy transmission system concepts for
17
linear generator arrays, Ocean Eng. 31 (2004) 1339–1349.
18
https://doi.org/10.1016/J.OCEANENG.2004.03.003.
19
[52]
U. Henfridsson, V. Neimane, K. Strand, R. Kapper, H. Bernhoff, O. Danielsson, M.
20
Leijon, J. Sundberg, K. Thorburn, E. Ericsson, K. Bergman, Wave energy potential in the
21
Baltic Sea and the Danish part of the North Sea, with reflections on the Skagerrak, Renew.
22
Energy. 32 (2007) 2069–2084. https://doi.org/10.1016/J.RENENE.2006.10.006.
23
[53]
N. Booij, R.C. Ris, L.H. Holthuijsen, A third-generation wave model for coastal regions:
24
1. Model description and validation, J. Geophys. Res. 104 (1999) 7649–7666.
25
https://doi.org/10.1029/98JC02622.
26
[54]
P.A. Umesh, P.K. Bhaskaran, K.G. Sandhya, T.M. Balakrishnan Nair, An assessment on
27
the impact of wind forcing on simulation and validation of wave spectra at coastal
28
Puducherry, east coast of India, Ocean Eng. 139 (2017) 14–32.
29
https://doi.org/10.1016/J.OCEANENG.2017.04.043.
30
[55]
K. Hasselmann, T.P. Barnett, E. Bouws, H. Carlson, D.E. Cartwright, K. Enke, J.A.
31
Ewing, H. Gienapp, D.E. Hasselmann, P. Kruseman, A. Meerburg, P. Müller, D.J. Olbers,
32
K. Richter, W. Sell, H. Walden, Measurements of wind-wave growth and swell decay 28
1
during the Joint North Sea Wave Project ({JONSWAP}), Dtsch. Hydrogr. Z. A8 (1973)
2
1–95. http://resolver.tudelft.nl/uuid:f204e188-13b9-49d8-a6dc-4fb7c20562fc (accessed
3
July 2, 2018).
4
[56]
S. team, {SWAN} technical documentation version 41.20A, Delft University of
5
Technology, Netherlands., 2018.
6
http://swanmodel.sourceforge.net/online_doc/swantech/swantech.html.
7
[57]
A. Akpınar, G.P. van Vledder, M.İ. Kömürcü, M. Özger, Evaluation of the numerical
8
wave model (SWAN) for wave simulation in the Black Sea, Cont. Shelf Res. 50–51
9
(2012) 80–99. https://doi.org/10.1016/J.CSR.2012.09.012.
10
[58]
D.P. Dee, S.M. Uppala, A.J. Simmons, P. Berrisford, P. Poli, S. Kobayashi, U. Andrae,
11
M.A. Balmaseda, G. Balsamo, P. Bauer, P. Bechtold, A.C.M. Beljaars, L. van de Berg, J.
12
Bidlot, N. Bormann, C. Delsol, R. Dragani, M. Fuentes, A.J. Geer, L. Haimberger, S.B.
13
Healy, H. Hersbach, E. V. Hólm, L. Isaksen, P. Kållberg, M. Köhler, M. Matricardi, A.P.
14
McNally, B.M. Monge-Sanz, J.-J. Morcrette, B.-K. Park, C. Peubey, P. de Rosnay, C.
15
Tavolato, J.-N. Thépaut, F. Vitart, The ERA-Interim reanalysis: configuration and
16
performance of the data assimilation system, Q. J. R. Meteorol. Soc. 137 (2011) 553–597.
17
https://doi.org/10.1002/qj.828.
18
[59]
S. Saha, S. Moorthi, H.-L. Pan, X. Wu, J. Wang, S. Nadiga, P. Tripp, R. Kistler, J.
19
Woollen, D. Behringer, H. Liu, D. Stokes, R. Grumbine, G. Gayno, J. Wang, Y.-T. Hou,
20
H. Chuang, H.-M.H. Juang, J. Sela, M. Iredell, R. Treadon, D. Kleist, P. Van Delst, D.
21
Keyser, J. Derber, M. Ek, J. Meng, H. Wei, R. Yang, S. Lord, H. van den Dool, A. Kumar,
22
W. Wang, C. Long, M. Chelliah, Y. Xue, B. Huang, J.-K. Schemm, W. Ebisuzaki, R. Lin,
23
P. Xie, M. Chen, S. Zhou, W. Higgins, C.-Z. Zou, Q. Liu, Y. Chen, Y. Han, L. Cucurull,
24
R.W. Reynolds, G. Rutledge, M. Goldberg, The {NCEP} Climate Forecast System
25
Reanalysis, Bull. Am. Meteorol. Soc. 91 (2010) 1015–1058.
26
https://doi.org/10.1175/2010BAMS3001.1.
27
[60]
S. Saha, S. Moorthi, X. Wu, J. Wang, S. Nadiga, P. Tripp, D. Behringer, Y.-T. Hou, H.
28
Chuang, M. Iredell, M. Ek, J. Meng, R. Yang, M.P. Mendez, H. van den Dool, Q. Zhang,
29
W. Wang, M. Chen, E. Becker, The {NCEP} Climate Forecast System Version 2, J. Clim.
30
27 (2014) 2185–2208. https://doi.org/10.1175/JCLI-D-12-00823.1.
31 32
[61]
M.H. Moeini, A. Etemad-Shahidi, V. Chegini, Wave modeling and extreme value analysis off the northern coast of the Persian Gulf, Appl. Ocean Res. 32 (2010) 209–218. 29
https://doi.org/10.1016/j.apor.2009.10.005.
1 2
[62]
S. Ponce de León, C. Guedes Soares, Sensitivity of wave model predictions to wind fields
3
in the Western Mediterranean sea, Coast. Eng. 55 (2008) 920–929.
4
https://doi.org/10.1016/j.coastaleng.2008.02.023.
5
[63]
R.M. Campos, C. Guedes Soares, Comparison and assessment of three wave hindcasts in
6
the North Atlantic Ocean, J. Oper. Oceanogr. 9 (2016) 26–44.
7
https://doi.org/10.1080/1755876X.2016.1200249.
8
[64]
Atlantic Ocean, J. Oper. Oceanogr. 10 (2017) 30–44.
9
https://doi.org/10.1080/1755876X.2016.1253328.
10 11
R.M. Campos, C. Guedes Soares, Assessment of three wind reanalyses in the North
[65]
F. Ardhuin, J. Hanafin, Y. Quilfen, B. Chapron, P. Queffeulou, M. Obrebski, J.
12
Sienkiewicz, D. Vandemark, Calibration of the IOWAGA global wave hindcast (1991–
13
2011) using ECMWF and CFSR winds, in: Proc. 12th International Workshop of Wave
14
Hindcasting and Forecating . 2011, Hawaii, 2011.
15
[66]
D.B. Chelton, M.H. Freilich, Scatterometer-Based Assessment of 10-m Wind Analyses
16
from the Operational ECMWF and NCEP Numerical Weather Prediction Models, Mon.
17
Weather Rev. 133 (2005) 409–429. https://doi.org/10.1175/MWR-2861.1.
18
[67]
L. Rusu, M. Gonçalves, C. Guedes Soares, Prediction of storm conditions using wind data
19
from the ECMWF and NCEP reanalysis, in: C. Soares Guedes, Â.. Teixeira (Eds.), Dev.
20
Marit. Transp. Harvest. Sea Resour. (2 Vol. Set) Proc. 17th Int. Congr. Int. Marit. Assoc.
21
Mediterr. (IMAM 2017), Taylor & Francis Group, 2017, Lisbon, Portugal, 2017: p. 1278.
22
https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Prediction+of+storm+condi
23
tions+using+wind+data+from+the+ECMWF+and++NCEP+reanalysis&btnG= (accessed
24
January 19, 2020).
25
[68]
J.E. Stopa, K.F. Cheung, Intercomparison of wind and wave data from the ECMWF
26
Reanalysis Interim and the NCEP Climate Forecast System Reanalysis, Ocean Model. 75
27
(2014) 65–83. https://doi.org/10.1016/j.ocemod.2013.12.006.
28
[69]
R.P. Signell, S. Carniel, L. Cavaleri, J. Chiggiato, J.D. Doyle, J. Pullen, M. Sclavo,
29
Assessment of wind quality for oceanographic modelling in semi-enclosed basins, J. Mar.
30
Syst. 53 (2005) 217–233. https://doi.org/10.1016/j.jmarsys.2004.03.006.
31 32
[70]
A.T. Cox, V.J. Cardone, V.R. Swail, On the Use of the Climate Forecast System Reanalysis Wind Forcing In Ocean Response Modeling, in: Proceedings, 12th Int. Work. 30
Wave, Hindcasting, Forecast. Hawaii, USA, 2011: p. 20.
1 2
[71]
A. Akpınar, B. Bingölbali, Long-term variations of wind and wave conditions in the
3
coastal regions of the Black Sea, Nat. Hazards. 84 (2016) 69–92.
4
https://doi.org/10.1007/s11069-016-2407-9.
5
[72]
A. Chawla, D. Spindler, H. Tolman, 30 Year Wave Hindcasts using {WAVEWATCH}
6
{III} R with {CFSR} winds† Phase, NOAA/NWS/NCEP/MMAB. Maryl. USA. (2012)
7
23.
8
[73]
extratropical cyclones, Ocean Model. 81 (2014) 78–88.
9
https://doi.org/10.1016/J.OCEMOD.2014.07.005.
10 11
S. Ponce de León, C. Guedes Soares, Extreme wave parameters under North Atlantic
[74]
G.P. Van Vledder, A. Akpınar, Wave model predictions in the Black Sea: Sensitivity to
12
wind fields, Appl. Ocean Res. 53 (2015) 161–178.
13
https://doi.org/10.1016/j.apor.2015.08.006.
14
[75]
S.F. Wornom, D.J.S. Welsh, K.W. Bedford, On coupling the SWAN and WAM wave
15
models for accurate nearshore wave predictions, Coast. Eng. J. 43 (2001) 161–201.
16
https://doi.org/10.1142/S0578563401000335.
17
[76]
C. Amante, B.W. Eakins, {ETOPO}1 Global Relief Model converted to {PanMap} layer
18
format, NOAA-National Geophys. Data Cent. (2009).
19
https://doi.org/https://doi.org/10.1594/PANGAEA.769615.
20
[77]
J. Perez, M. Menendez, P. Camus, F.J. Mendez, I.J. Losada, Statistical multi-model
21
climate projections of surface ocean waves in Europe, Ocean Model. 96 (2015) 161–170.
22
https://doi.org/10.1016/J.OCEMOD.2015.06.001.
23
[78]
L. Sartini, F. Cassola, G. Besio, Extreme waves seasonality analysis: An application in the
24
Mediterranean Sea, J. Geophys. Res. Ocean. 120 (2015) 6266–6288.
25
https://doi.org/10.1002/2015JC011061.
26
[79]
statistics, Ocean Model. 70 (2013) 62–74. https://doi.org/10.1016/j.ocemod.2013.03.007.
27 28
F. Leckler, F. Ardhuin, J.-F. Filipot, A. Mironov, Dissipation source terms and whitecap
[80]
P.A.E.M. Janssen, P.A.E.M. Janssen, Wave-Induced Stress and the Drag of Air Flow over
29
Sea Waves, J. Phys. Oceanogr. 19 (1989) 745–754. https://doi.org/10.1175/1520-
30
0485(1989)019<0745:WISATD>2.0.CO;2.
31 32
[81]
P.A.E.M. Janssen, Quasi-linear Theory of Wind-Wave Generation Applied to Wave Forecasting, J. Phys. Oceanogr. 21 (1991) 1631–1642. https://doi.org/10.1175/152031
0485(1991)021<1631:QLTOWW>2.0.CO;2.
1 2
[82]
G.J. Komen, K. Hasselmann, K. Hasselmann, On the Existence of a Fully Developed
3
Wind-Sea Spectrum, J. Phys. Oceanogr. 14 (1984) 1271–1285.
4
https://doi.org/10.1175/1520-0485(1984)014<1271:OTEOAF>2.0.CO;2.
5
[83]
L. Cavaleri, P.M. Rizzoli, Wind wave prediction in shallow water: Theory and
6
applications, J. Geophys. Res. 86 (1981) 10961–10973.
7
https://doi.org/10.1029/JC086iC11p10961.
8
[84]
S. Hasselmann, K. Hasselmann, Computations and Parameterizations of the Nonlinear Energy Transfer in a Gravity-Wave Spectrum. Part I: A New Method for Efficient
9 10
Computations of the Exact Nonlinear Transfer Integral, J. Phys. Oceanogr. 15 (1985)
11
1369–1377. https://doi.org/10.1175/1520-0485(1985)015<1369:CAPOTN>2.0.CO;2.
12
[85]
Waves, in: Coast. Eng., 1978: pp. 569–587. https://doi.org/10.1061/9780872621909.034.
13 14
[86]
M. Zijlema, G.P. van Vledder, L.H. Holthuijsen, Bottom friction and wind drag for wave models, Coast. Eng. 65 (2012) 19–26. https://doi.org/10.1016/j.coastaleng.2012.03.002.
15 16
Battjes J. A., Janssen J. P. F. M., Energy Loss and Set-Up Due to Breaking of Random
[87]
S.R. Hanna, D.W. Heinold, A.P.I. Health, E.A. Dept, I. Environmental Research &
17
Technology, Development and application of a simple method for evaluating air quality
18
models, American Petroleum Institute, 1985.
19
https://books.google.dz/books?id=lKrpAAAAMAAJ.
20
[88]
L. Mentaschi, G. Besio, F. Cassola, A. Mazzino, Problems in RMSE-based wave model
21
validations, Ocean Model. 72 (2013) 53–58.
22
https://doi.org/10.1016/j.ocemod.2013.08.003.
23
[89]
1981. https://archimer.ifremer.fr/doc/00001/11266/7812.pdf.
24 25
[90]
R. Bonnefille, Cours d’hydraulique maritime, Masson, Paris, 1992. https://infoscience.epfl.ch/record/25376/ (accessed October 27, 2018).
26 27
G. Damy, M. Gauthier, Production d’énergie à partir de la houle, {CNEXO}-, Ifremer,
[91]
L.H. Holthuijsen, Waves in oceanic and coastal waters, Cambridge University Press,
28
2007.
29
https://books.google.dz/books/about/Waves_in_Oceanic_and_Coastal_Waters.html?id=4
30
mUsvgAACAAJ&source=kp_book_description&redir_esc=y (accessed October 28,
31
2018).
32
[92]
A.M. Cornett, A Global Wave Energy Resource Assessment, in: Eighteenth Int. Offshore 32
1
Polar Eng. Conf., International Society of Offshore and Polar Engineers, Vancouver,
2
Canada, 2008: p. 9. https://www.onepetro.org/conference-paper/ISOPE-I-08-370
3
(accessed October 31, 2018).
4
[93]
E. Kasiulis, J. Kofoed, A. Povilaitis, A. Radzevičius, E. Kasiulis, J.P. Kofoed, A.
5
Povilaitis, A. Radzevičius, Spatial Distribution of the Baltic Sea Near-Shore Wave Power
6
Potential along the Coast of Klaipėda, Lithuania, Energies. 10 (2017) 2170.
7
https://doi.org/10.3390/en10122170.
8
[94]
Waves in Finite Water Depths, Energies. 10 (2017) 460.
9
https://doi.org/10.3390/en10040460.
10 11
[95]
G. Hagerman, Southern New England wave energy resource potential., in: Proceedings of the Building Energy, Boston, USA, 2001.
12 13
W. Sheng, H. Li, W. Sheng, H. Li, A Method for Energy and Resource Assessment of
[96]
J.R. Joubert, An investigation of the wave energy resource on the South African Coast,
14
focusing on the spatial distribution of the South West coast, (2008).
15
http://scholar.sun.ac.za/handle/10019.1/2666 (accessed October 29, 2018).
16
[97]
M Amirat, S.M.K. El Hassar, Economies d’Energie dans le Secteur de l’Habitat
17
Consommation Electrique des Ménages “‘Cas d’un foyer algérien typique en période
18
d’hiver,’” Rev. Des Énergies Renouvelables. 8 (2005) 27–38.
19
https://www.cder.dz/download/Art8-1_3.pdf.
20
33
Table 1. The calibrated physical processes’ settings of SWAN model used for the development of the wave hindcast data base. Physical process
Formula
Parameters
Linear wind growth Exponential wind growth
Cavaleri and Malanotte-Rizzoli [83] Komen et al. [82]
Whitecapping
Janssen [80,81]
Cds1=1.0 &
Quadruplets wave–wave interactions
the discrete Interaction approximation (DIA) of Hasselmann et al. [84]
ƛ=0.25
Depth-induced breaking
Battjes and Janssen [85]
αBJ=1.0 γBJ=0.73
Bottom friction
JONSWAP [55]
CFJON=0.038 according to Zijlema et al. [86]
delta=1
Cn/4= 3.0 x 107
Table 2. Error statistic of Hm0 and Tm02 using the calibrated- and the default-setting of SWAN model Buoy
B2
Measurement Period 01-07-1999 30-06-2000
to
RMSE (m and s)
SI
Hm0 Tm02
Hm0 Tm02
Hm0 Tm02
Default setting
0.36 1.04
0.38 0.22
-0.17 -0.42 0.24 0.86
0.94 0.88
Calibrated settings
0.27 0.77
0.28 0.17
0.03 0.09 0.19 0.62
0.95 0.90
Physical Process
Bias (m and s)
MAE (m and s) Hm0 Tm02
R Hm0 Tm02
Table 3. Validation wave buoys characteristics. Buoy Name
Buoy Type/Sensor
Validated Parameters
Used Period
Tamentfoust (B1)
Non-Directional Waverider/Datawell
Hm0 _Tm02_Te
Azeffoun (B2)
Non-Directional Waverider /Datawell
Kala (B3) Palos (B4) Dragonera (B5)
Mahon (B6)
Temporal resolutions
Nbr of Observation
01-10-1998 to 31-03-1999
3h
1304
50
Hm0 _Tm02_Te
01-07-1999 to 30-06-2000
3h
2352
30
Non-Directional Waverider/Datawell
Hm0 _Tm02_Te
01-01-2002 to 31-12-2002
3h
2480
50
Directional SeaWatch/Datawell
Hm0 _ Tm02
Jan 2007 to Dec-2009
1h
25470
230
Hm0 _ Tm02
Jan 2007 to Dec-2009
1h
25222
135
Hm0 _ Tm02
Jan 2007 to Dec-2009
1h
23257
300
Directional WaveScan/HIPPY 120/Wavesense Directional WaveScan/HIPPY120/Wavesense
Depths
Table 4. Error statistics of the calibrated SWAN model results (Hm0, Tm02 and Te).
R
Buoys
Hm0 Tm02
SI Te
Tamentfoust (B1) 0.92 0.88 0.89 Azeffoun (B2) 0.95
0.9
Hm0 Tm02
Te
Bias
RMSE
MAE
(m, s and s)
(m, s and s)
(m, s and s)
HH
Hm0 Tm02 Te Hm0 Tm02 Te Hm0 Tm02 Te Hm0 Tm02 Te
0.3
0.15 0.13 0.15 0.17 0.08 0.37 0.74 0.81 0.27 0.59 0.62 0.23 0.15 0.13
0.92 0.28
0.17 0.15 0.03 0.09 0.31 0.27 0.77 0.85 0.19 0.62 0.64 0.17 0.16 0.14
Kala (B3)
0.93 0.89 0.93
0.3
0.18 0.13 0.01 -0.43 0.09 0.28 0.81 0.73 0.19 0.66 0.55 0.23 0.17 0.12
Palos (B4)
0.92 0.82
/
0.30
0.14
/
0.15 -0.07
/
0.32 0.56
/
0.22 0.44
/
0.24 0.14
/
Dragonera (B5) 0.92 0.84
/
0.30
0.18
/
0.05 -0.45
/
0.32 0.74
/
0.22 0.60
/
0.24 0.18
/
0.94 0.88
/
0.29
0.15
/
0.16 -0.30
/
0.37 0.67
/
0.26 0.54
/
0.22 0.14
/
Mahon (B6) Average
0.93 0.87 0.91 0.30
0.16 0.14 0.09 -0.17 0.16 0.32 0.72 0.80 0.23 0.58 0.60 0.22 0.15 0.13
Table 5. Characteristic of the fourteen stations selected for the detailed analysis.
Station N°
Province
Coordinate
1 2 3 4 5 6 7 8 9 10 11 12 13 14
El-Taref Annaba Skikda Jijel Bejaia TIZI-Ouzou Boumerdes Algiers Tipaza Chlef Mostaganem Oran Ain-Temouchent Tlemcen
8.20°E 7.37°E 6.90°E 5.77°E 5.23°E 4.40°E 3.53°E 3.07°E 2.37°E 1.13°E 0.17°E -0.40°E -1.40°E -1.90°E
37.10°N 37.16°N 37.00°N 36.90°N 36.73°N 36.93°N 36.83°N 36.83°N 36.70°N 36.53°N 36.20°N 35.93°N 35.43°N 35.23°N
Depth
Distance from the shoreline
Distance from the port
106 203 108 224 213 255 223 327 155 185 111 131 107 110
16.8 km 9.3 km 6.58 km 8.0 Km 9.2 km 3.3 km 3.9 km 3.5 km 6.3 km 4.2 km 10.4 km 3.2 km 13.3 km 14.1 km
30 km 11.9 km 11.7 km 08.50 km 12.00 km 3.7 km 3.95 km 6 km 13.8 km 5.3 km 21 km 16.5 km 14.5 km 14.8 km
Table 6. Some statistical results of the wave power resources in the fourteen selected stations Station S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14
MEAN (kW/m) 11.18 11.78 8.54 8.38 4.99 7.28 5.45 5.63 4.72 4.40 5.53 5.73 4.59 4.37
MAX (kW/m) 593.12 641.84 532.23 557.03 328.55 382.82 367.70 444.70 359.21 425.24 392.62 575.48 274.73 302.08
Hm0<0.5 (%) 19.37 17.53 25.91 23.33 38.05 18.20 26.00 19.26 20.97 20.53 24.80 20.11 29.32 24.18
SV
MV
COV
1.57 1.58 1.50 1.51 1.34 1.41 1.42 1.29 1.17 0.90 0.96 0.80 0.94 0.75
1.72 1.73 1.65 1.67 1.51 1.59 1.62 1.48 1.36 1.05 1.05 0.89 0.96 0.75
0.21 0.20 0.20 0.20 0.21 0.19 0.18 0.16 0.15 0.14 0.20 0.17 0.19 0.13
Pw-Total (MWh/m/year) 98.01 103.27 74.90 73.50 43.75 63.78 47.76 49.38 41.35 38.58 48.45 50.24 40.28 38.36
Fig 1. Illustration maps of the model setup domain, the interest area, the Algerian population density, the annual consumption of electricity in each coastal province, the position of the validation buoys, and the position of the stations selected for the detailed analysis.
Fig. 2. Q – Q plots of simulated wave power resource against the buoys observation from 01-10-1998 to 31-031999 in Tamentfoust buoy, from 01-07-1999 to 30-06-2000 in Azeffoun Buoy and from 01-01-2002 to 31-122002 in Kala Buoy.
Fig. 3. Time series plot of the significant wave height Hm0, the energy period Te and the computed wave power Pw results obtained by the calibrated SWAN model and buoy observation from 01-07-1999 to 30-062000.
Fig. 4. Spatial distributions of mean and maximum significant wave heights (top) and mean and maximum wave power (bottom) over 39 years.
Fig. 5. Spatial distributions of mean wave power flux during decadal years 1979 – 1988, 1989 – 1998, 1999 – 2008 and 2009 - 2017
Fig. 6. Monthly and seasonal distribution of the wave power flux average during 39 years
Fig. 7. Spatial distribution of the seasonal and inter-annual wave power flux COV during 39 years
Fig. 8. Spatial distribution of the monthly wave power flux COV during 39 years
Fig. 9. Monthly variability index and seasonal variability index of the wave power flux from 1979 to 2017
Fig. 10. Spatial distribution of WEDI index for 39-year hindcast
Fig. 11. Annual and seasonal averaged wave power resources and the maximum wave power observed during 39 years at fourteen locations along the Algerian coast
Fig. 12. Wave energy development index at fourteen locations along the Algerian coast
Fig. 13. Monthly mean wave power flux availability during 39 years at the fourteen selected stations
Fig. 14. Proportion of significant heights below 0.5 m recorded on a three-hour scale over 39 years and the hourly variation profiles of the average wave energy
Fig. 15. Probability of occurrence and the wave energy flux roses during 39 years at fourteen locations along the Algerian coast
Fig. 16. Total wave energy distribution as a function of significant wave height and energy period at fourteen locations along the Algerian coast
Fig. 17. Map of total annual wave energies in the 15 km of the shore band
Fig. 18. Probability map of calm sea states in the 15 km of the shore band
Fig. 19. Results of multi-criteria analysis, the areas in red color are characterized by a total annual energy that exceed 100 MWh/m/year at 15 km from the coast, and a probability to have a calm sea stat less than 18%
Highlights: •
A first detailed long-term assessment of wave energies in the Algerian coast.
•
Largest hotspot area in the W-Med basin is located in the Eastern coast of Algeria.
•
High resolution SWAN model (~3km) calibrated for the Algerian basin was used.
•
A new accurate 39-year wave energy hindcast dataset has been developed.
•
Wave energy resources tend to increase further since 1995 in the Algerian coast.
Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
S0960-1481(20)30226-3
DOI:
https://doi.org/10.1016/j.renene.2020.02.040
Reference:
RENE 13061
To appear in:
Renewable Energy
Received Date: 15 July 2019 Revised Date:
1 February 2020
Accepted Date: 11 February 2020
Please cite this article as: Amarouche K, Akpınar A, El Islam Bachari N, Fouzia H, Wave energy resource assessment along the Algerian coast based on 39-year wave hindcast, Renewable Energy (2020), doi: https://doi.org/10.1016/j.renene.2020.02.040. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.
CRediT author statement
Khalid AMAROUCHE: Conceptualization, Software, Validation, Formal analysis, Writing - Original Draft Adem AKPINAR: Conceptualization; Methodology, Writing - Review & Editing Nour-el-islam BACHARI: Supervision ; Formal analysis. Fouzia HOUMA: Investigation, Resources, Data Curation.
1
Wave energy resource assessment along the Algerian Coast based on 39-year
2
wave hindcast
3
Khalid Amarouche1, *, Adem Akpınar2, Nour El Islam Bachari3, Houma Fouzia1
4 5 6 7 8 9
1
Ecole Nationale Supérieure des Sciences de la Mer et de l'Aménagement du Littoral (ENSSMAL), Département d’environnement et d’aménagement du littoral, Algiers, Algeria 2 Bursa Uludağ University, Department of Civil Engineering, Gorukle Campus, Bursa, Turkey 3 Université des Sciences et Technologie Houari Boumedien (USTHB), Laboratoire d’Océanographie Biologique et Environnement Marin, Algiers, Algeria
10
Abstract
11
This study investigates a long-term assessment of the wave energy resource propagated along the
12
Algerian basin, based on a 39-year wave hindcast. The wave energy hindcast dataset was
13
developed using the Simulating WAve Nearshore (SWAN) model, calibrated and validated [1]
14
against wave measurements performed on the Algerian coast. A detailed spatial and local
15
analysis was performed following the hindcast results. We have determined several parameters
16
including; hourly, monthly, seasonal and annual variations of wave energy resources, the
17
probability of occurrence distribution for different wave power ranges with different directions,
18
the probability of calm sea states, the wave energy development index (WEDI) and the total
19
annual wave energy and their distribution as a function of significant wave height and energy
20
period. All these results enabled a very important benchmark for decision making regarding the
21
future implementation and design of wave energy converters (WECs) and other offshore
22
structures in the Algerian basin. Our findings have shown that the Algerian coasts are
23
characterized by a considerable wave energy potential with a large hotspot area in the eastern
24
coasts. Thus, we have recorded a significant variability in the wave energy characteristics
25
available in each zone along the Algerian coast. The western zone was characterized by an
26
average energy of ~7.5 kW/m with a low monthly and seasonal variation (<1.2), the central zone
27
was characterized by a significant total annual wave energy of 63 MWh/m/year and a
28
considerable WEDI of 0.019, and the eastern Algerian coast was characterized by one of the
29
highest energy potential in the Mediterranean basin with a total annual energy exceeding 100
30
MWh/m for less than 15 km from the coast and a calm sea state probability lower than 18%.
31
Thus, it has been concluded that since 1995, wave energy resources have tended to increase
32
further.
33
Keywords: Wave Energy; Wave power; Variability; WEDI; Hotspots; Algerian basin. 1
1
1. Introduction
2
Algeria, like most countries in the word, is experiencing a significant growth in electricity
3
consumption [2]. This growth, in combination with the global warming problems and the need to
4
conserve fossil energy resources, requires the exploitation of renewable and sustainable energy
5
resources. To reach a global and sustainable solution to these problems, the Algerian Ministry of
6
Energy and Mines has developed a national program which aims to provide 40% of renewable
7
energies in electricity production by 2030 [3]. As part of this program, several potential areas
8
based on solar, wind and geothermal energy resources have been identified [4–7]. This research
9
showed that the important solar and wind energy resources are located in the southern Saharian
10
part of the country. However, according to the statistics of the national electricity production
11
company (Sonelgaz), more than 40% of the electricity are consumed by the coastal provinces
12
which cover 1.8% of the country [8]. As a result, and in view of the continuous development of
13
WECs in recent years [9–15] , wave energy presented as a condensed form of wind energy can
14
constitute an essential source of renewable energy [16] which can be exploited in these high-
15
consumption areas. Therefore, the assessment of wave energy propagation is a very important
16
task not only for its exploitation as a power resource but also for its destructive effects in the
17
coastal zones [17].
18
The Algerian coast is characterized by a narrow continental shelf that dissipates wave energy
19
near the shore or directly on the shore. During this last year several researches [18–21] have
20
concentrated on the evaluation of the wave energy potential in the West Mediterranean basin.
21
The results of these studies show that the Spanish and Italian coasts hold a very promising
22
potential, with an annual rate of more than 100 MWh/m/year on the western coasts of Sardinia in
23
Italy [19]. In the recent study developed by Besio et al, [18] the Algerian coast was determined
24
as one of the most energetic areas of the Mediterranean Sea. Nevertheless, most previous studies
25
[18, 20–24] have focused on European coasts and the recent wave hindcast databases [26–28]
26
widely used in previous studies was only validated based on wave measurements collected on
27
European coasts and no validation has been performed in the Algerian coast. However, the
28
morphology of the Algerian basin is completely different from that of the Mediterranean sub-
29
basin (Tyrrhenian Sea, Alboran Sea, Balearic Sea, Gulf of Lion, and etc.). The Algerian basin is
30
characterized by a very important fetch area with an open coast and a narrow continental shelf.
2
1
This specific morphological characteristic implies that a calibration and validation of the
2
prediction model based on local measurements can be necessary.
3
In this study, we present a first long-term detailed assessment of wave energy propagation in
4
the Algerian basin, based on 39-year wave hindcast, developed using a high-resolution (~3
5
km) wind wave model (SWAN) calibrated based on one-year wave observations of Azeffoune
6
buoy (Algerian coast) [1]. The calibrated SWAN model used for the development of this
7
hindcast database has been compared by the authors [1] against other models such as WAM [29],
8
TOMAWAC [30] and WaveWatch III [31]; implemented in previous studies [18, 25, 27, 32–36]
9
for the Western Mediterranean basin. The results of this comparison show that the SWAN model
10
calibrated by the Azeffoun buoy measurements is able to provide a higher accuracy. The
11
geographical location of the calibration buoy can contribute significantly to the improvement of
12
the model performance in the whole Mediterranean basin. The Azeffoun buoy is geographically
13
able to record the waves coming from the Tyrrhenian Sea, the Alboran Sea, the Balearic Sea and
14
even the Gulf of Lion. The wave power computed using this hindcast database was also validated
15
against the wave power computed using wave observations of three buoys installed in the
16
Algerian coast, provided by the Office National de Signalisation Maritime.
17
Based on the wave hindcast database, a spatio-temporal statistical analysis of wave energies was
18
developed and exploited to produce various thematic maps that reflect the propagation of wave
19
energies and its temporal variation. The results of this analysis allowed us to describe
20
quantitatively the wave energy distribution along the Algerian basin. In addition, 14 stations
21
located off each coastal province was subject of a detailed statistical analysis in which we have
22
evaluated the wave energy flux distribution variability and its potentiality at the annual, seasonal,
23
monthly and hourly scales. Thus, the total wave energy resources were quantified as a function
24
of significant wave height and energy period, with the probability of occurrence distribution for
25
different wave power ranges and different directions.
26
The wave energy hindcast database, the local analysis results, and the mapped spatial analysis
27
results including the WEDI map, constitute an essential benchmark for decision making
28
regarding the selection and design of WECs and other offshore structures in the Algerian basin.
29
The results obtained in this study can also contribute to the assessment of coastal vulnerability
30
and storms observed on the Algerian coast, knowing that the database developed during this
3
1
study is the first calibrated and validated wave hindcast database with high spatial resolution (~3
2
km) produced for Algerian coasts.
3 4
2. Study area
5
The study area is the only Algerian seafront which covers a coastline of about 1623 km in the
6
South West Mediterranean basin (Fig. 1). It covers the area which extends from -3o W to 9o E
7
and from 35o N to 40o N. This coast is oriented mainly towards the north and is characterized by
8
a very narrow continental shelf that varies from 50 km to 0.5 km. These characteristics make this
9
coast directly exposed to the northerly waves, which dissipates directly to the shore in the small
10
continental shelf area. According to the National Statistical Office, the Algerian coast is
11
distributed over 14 coastal provinces in which more than 40.7% of the total population lives at a
12
number that increases greatly during the summer periods. This demographic pressure is also at
13
the origin of the increase in electricity consumption recorded in the coastal provinces (Fig.1).
14
Thus, the maritime part which is the subject of this study; known as the Algerian basin, has a
15
strong economic exploitation with the presence of a very dense maritime traffic, knowing that
16
the Algerian basin is a point of intersection between ships coming from the Red Sea, the Black
17
Sea and the Atlantic Ocean.
18 19
3. Model description
20
Currently, several wind-wave models have been evaluated and implemented in the western
21
Mediterranean basin for the development of several wave hindcast databases. Among the most
22
recent, accurate and open-source models covering the Algerian basin, we highlight the WAM
23
model by Cavaleri and Sclavo [32], used for the development of the Atlas of Winds and Waves
24
of the Mediterranean Sea [26], the WAM-PRO by Ponce de León and Soares [37] for the
25
development of 29-year spectral wave hindcast, the Wavewatch III model by Mentaschi et al.
26
[23] for the development of 35-year wave hindcast from 1979 to 2013 [18], WAM model Cycle
27
4.5.3 by Liberti et al [19] for the development of 10-year wave hindcast from 2001 to 2010 [18,
28
21], TOMAWAC model by Tiberi-Wadier et al. [28] for the development of the wave hindcast
29
database (ANEMOC2), and the SWAN model by Lavidas et al [23, 38] for the development of
30
35-year wave hindcast from 1980 to 2014. The spatial resolution of these models in the Algerian 4
1
basin is respectively 25 km, 27.8 km, 10 km, ~7 km, 25 km and ~11 km. Thus, as mentioned in
2
the introduction section, all these models were calibrated and/or validated based only on wave
3
measurements recorded in European coasts and their accuracy on the Algerian coast remains
4
unknown.
5
Taking into account the morphological aspect of the Algerian basin which is completely different
6
from other European basins, with a very narrow continental shelf, an open coast and an extensive
7
fetch area, we considered that it is important to use an adapted model calibrated for this basin in
8
order to ensure accurate and reliable results. For the development of the 39-years wave hindcast
9
database, we preferred to use the SWAN model forced by the CFSR wind field, which has been
10
calibrated and validated especially for the Algerian basin by authors [1], with a high spatial
11
resolution of ~3 km. The good accuracy of the SWAN model in coastal areas [39] compared to
12
other models such as WaveWatch III [40], allows us to calibrate and validate it against wave
13
measurements carried out in coastal areas; knowing that most wave measurement buoys in the
14
Mediterranean Sea are installed in the coastal area. It is indeed that the SWAN model has already
15
been used by Lavidas et al [24] for the development of a 35-years wave hindcast database in the
16
Mediterranean Sea, however, their coarse grid model covering the whole Mediterranean basin
17
has been applied for the generation of boundary conditions in four other coastal domains which
18
exclude the Algerian coast. In addition, no calibration of the physical setup of the SWAN coarse
19
grid model has been provided in relation to the wave measurement in the West Mediterranean
20
basin. Actually, several studies [28, 41–46] have confirmed that the calibration of the physical
21
configuration in the SWAN model improves in many cases its performance and corrects the
22
underestimation of significant wave heights and mean wave periods. Moreover, the optimal
23
model setup varies depending on the study area as mentioned by Bingölbali et al, [47].
24
According to Lavidas et. al. [48] the use of numerical wave models adapted to a specific study
25
area offers significant advantages for the quantification of wave energy resources. The high
26
spatial resolution of the used model (~3 km ) and its performance in the Algerian coast will
27
allow us to better evaluate the wave energy potential even at a distance of 3 km from the coast,
28
knowing that an important part of the Algerian coastline is characterized by a very narrow
29
continental shelf (<5 km) and that the coast distance and the depth has an important role in the
30
selection of optimal wave energy resources areas [49–52].
31 5
1
3.1. Theoretical background
2
The wave hindcast data used in this study was developed based on the third generation wind-
3
wave hindcast model SWAN version 41.20 [39], a discrete spectral wave model describing the
4
evolution of the wave energy spectrum in two-dimensional mode under known wind fields and
5
bathymetries [39]. Like most third generation spectral models, this model is based on the
6
equilibrium equation of action by considering the interactions between waves and currents [53].
7
However, this model is characterized by a good efficiency in the coastal zone [39], and it is also
8
applicable on a large scale in deep waters [54]. The evolution equation of the wave spectrum in
9
SWAN model is described by the spectral action balance equation [55].
10 11
+
+
+
+
, , , ,
=
(1)
12 13
In this equation, the first term on the left-hand side is the local rate change of action density in
14
time, wherein
15
direction , horizontal coordinates x and y, and time t. The second and third terms represent the
16
geographic propagation of action density respectively in the x and y space in which cx and cy are
17
x, y components of the group velocity. The fourth term represents shifting of the relative
18
frequency due to variations in depths and currents. The fifth term represents depth and current
19
induced refraction. The source/sink term
20
generation, dissipation, and nonlinear wave-wave interactions. The processes contributing to the
21
sinks and source terms are given by the following equation.
( , , ,
, ) is the action density as a function of intrinsic frequency
,
,
on the right-hand side represents the effects of
22 23
=
+
+
+
!,"
+
!,#
+
!,#$
(2)
24 25
Here, the first term denotes the wave growth by the wind, the second and third terms represent
26
respectively the nonlinear transfer of wave energy through three-wave and four-wave
27
interactions and the fourth term denotes the wave decay due to whitecapping, and the last two
28
terms represent respectively the dissipations due to the bottom friction and depth induced wave
29
breaking. All description of the theoretical and numerical background of SWAN model, in
30
addition to the recent technical details, can be found in the SWAN technical manual [25].
31 6
1
3.2.
Forcing data
2
The wind speed, the fetch length, and the wind duration are mainly the parameters responsible on
3
the wave generation [57]. Currently, several wind reanalysis databases are available with
4
different spatial and temporal resolutions. The wind fields of the ERA-Interim reanalysis of the
5
European Centre for Medium-Range Weather Forecasts (ECMWF) [58] and those of Climate
6
Forecast System Reanalysis (CFSR) of the National Centers for Environmental Prediction
7
(NCEP) [58, 59] cover actually a period of 41 years and present the most used databases in the
8
recent studies. Several studies [61–69] showed that both databases have good performance and
9
that ECMWF wind speeds are slightly underestimated unlike the CFSR wind speeds. However,
10
the use of ECMWF winds as forcing for 3rd generation wind-wave models including SWAN
11
[67] induces a slight underestimation of significant wave heights Hm0 unlike the NCEP CFSR
12
winds which induces a slightly overestimate of Hm0. The global maps of the normalized Bias
13
related to the significant wave heights presented by Stopa & Cheung [68] illustrate clearly this
14
situation in the Mediterranean Sea. We consider that a slight overestimation of wind speeds and
15
significant wave heights may be favorable for hazard prediction studies, return period
16
measurements, measurements of the WEDI and for the design of offshore and coastal structures
17
in order to ensure maximum safety and sustainable development, with consideration of the
18
statistical error of the prediction model.
19
In this study, the SWAN model was forced using the CFSR wind fields developed by the NCEP.
20
This database (CFSR ds093.1) was provided by the research data archive website of the
21
University Corporation for Atmospheric Research and the National Centers for Environmental
22
Prediction, https://rda.ucar.edu/. The CFSR “ds093.1” wind fields are characterized by a high
23
spatial resolution of 0.3125° x 0.3125° between 1979 and 2011 (CFSR v1) [59] and 0.225° x
24
0.225° since 2011 (CFSR v2) [60]. It is also characterized by a very high temporal resolution of
25
1 hour. This high temporal resolution is considered important for a better estimation of storm
26
peaks [28]. According to Cox et al., [70], this wind field provides an excellent forcing source for
27
the third generation wave model. Besides, several other recent studies [27, 70–72] have also
28
approved the efficiency of this data source using different wind-wave models. According to Van
29
Vledder & Akpınar [74] the CFSR wind source gives a better result than the other reanalysis
30
used with the default SWAN model settings in the Black Sea.
7
1
Concerning bathymetric data, it is known that the bathymetry presents an important factor that
2
influence the wave propagation and wave energy dissipation by wave refraction and diffraction
3
phenomenon, and also by inducing wave breaking. According to Sartini et al. [40], the SWAN
4
model using during the present study, is able to perfectly reproduce the coastal diffraction and
5
refraction phenomena, related to coastal morphology. In addition, according to Wornom et al
6
[75] the SWAN model provides an accurate estimation of breaking waves in the surf area.
7
Nevertheless, in the western Mediterranean basin, an accurate bathymetry is necessary in order
8
to ensure an accurate estimate of the wave energy dissipation resulting from these three
9
phenomena; the western Mediterranean basin is characterized by complex bathymetry and a
10
complex coastline with the presence of several islands. In the Mediterranean Sea, the
11
bathymetric database ETOPO1 1 arc-minute global relief mode of the National Geophysical Data
12
Center of the National Oceanic and Atmospheric Administration (NOAA) is characterized by a
13
spatial resolution of 0.0166° in both directions compiled using a grid derived from multibeam
14
swath sonar bathymetric surveys of 500 m [76]. This source of bathymetric data was obtained
15
from the web-site (ngdc.noaa.gov/mgg/global/global.html) and used for the development of the
16
wind-wave hindcast data base. The efficiency of this bathymetric source was also approved using
17
different wind-wave models in different previous studies [71, 73, 76, 77].
18 19
3.3.
Model setup
20
The SWAN model used to perform the hindcast database was calibrated and validated for the
21
Algerian basin [1] based on wave dissipation parameterizations, especially the whitecapping
22
source term [79], and the coefficient of the whitecapping dissipation (Cds). Knowing that it is the
23
least known process in the numerical wind-wave models [41]. Thus, the selected exponential
24
wind growth, the whitecapping formulas and the coefficient of the whitecapping dissipation Cds
25
(empirical coefficient of proportionality) given in Table 1 was determined based on several
26
sensitivity tests, using one-year wave measurement data (from 01/07/1999 to 30/06/2000) of
27
Azeffoun buoy. The results of this calibration show that the use of the formulation of Janssen
28
[80, 81] with Cds1 = 1 for whitecapping and the formulation of Komen et al. [82] for wind
29
growth improves the model accuracy in the western Mediterranean. The detailed calibration
30
results and sensitivity tests’ results were given in Amarouche et. al. [1]. 8
1
The SWAN model was run in the third generation and non-stationary mode. The model domain
2
covers the entire West Mediterranean Sea discretized with a regular grid of 0.033° × 0.033° (~3
3
km) in spherical coordinates, from 17°E to 6°W and from 35°N to 45°N as shown in (Fig. 1).
4
The directional wave energy density spectrum was discretized using 35 frequency bins between
5
0.033 Hz and 1.0 Hz and 36 directional bins with constant increment. The numerical scheme was
6
the slightly dispersive scheme BSBT (first order upwind; Backward in Space, backward in
7
Time).
8
For the physical computation processes, all the used formulations [55, 80–86] and coefficients
9
are detailed in Table 1. Concerning the boundary condition, since we consider the Western
10
Mediterranean basin as a semi-enclosed basin, the shape of the spectra (both in frequency and
11
direction) at the open boundary of the computational grid was defined with JONSWAP [55]
12
spectrum with a peak enhancement parameter of gamma = 3.3.
13
Generally, compared to the default physical setting recommended by SWAN model, the
14
calibration of the physical setting in this model makes it more accuracy [28, 41–43]. Table 2
15
shows the statistical errors’ results obtained by the calibrated- and default-setting SWAN models
16
with respect to one-year wave measurement of Azefoun Buoy during the calibration period. The
17
model calibration has considerably improved the simulation results. The scatter index was
18
decreasing by 10% and 5% for Hm0 and Tm02 successively.
19
For the wave hindcast performance evaluation, we used five statistical error parameters
20
according to the following equations:
21
the correlation coefficient (r),
22 23
R=
+ &,-. ' (') * (*)
+ + / & ' (') 0 &,-. * (*) 0 ,-.
(3)
24 25
the mean absolute error (MAE),
26 27
4
MAE = ∑ ;4|78 − :8|
(4)
28 29
the root-mean-square error (RMSE),
30 9
1
4
RMSE = / & ;4 7 − :
=
(5)
2 3
the bias,
4 5
bias = B
;4
4
7 −:
(6)
6 7
and scatter-index (SI)
8 9 10 11
SI =
DE F
+ . &,-. *, +
(7)
where Oi and :) are respectively the observed values and the mean value of the observed data. Pi
12
and 7) are respectively the predicted values and the mean value of the predicted data. N is the
13
total data number.
14
In addition to these parameters usually measured for the evaluation of the wind and wave model
15
performance, the HH error indicator proposed by Hanna and Heinold [87] and evaluated by
16
Mentaschi et. al. [88] is also measured at the same buoys, following the recommendations of
17
Mentaschi et. al. [88] who demonstrated that the lower values of RMSE and SI do not provide a
18
reliable evaluation of the models’ performance. Mentaschi et. al. [88] recommends the use of this
19
index as it provides reliable results in the evaluation of wave simulation models in the
20
Mediterranean Sea.
21 22
GG = /
23
(8)
0 ∑+ ,-. ', (*,
∑+ ,-. ', *,
24 25
4. Wave power resource and variability computation
26
As almost the clean renewable energy, wave energy comes from the sun, which translates into
27
two fluid forms, water and atmospheric air [89]. Wave energy generated by wind per unit area is
10
1
expressed as the sum of potential energy related to the resting level and kinetic energy [82, 83]
2
by:
3 4
HIJ
K
= HL + H' =
4
4M
NOG = P
(9)
5 6
The power of the wave energy transmitted per unit of crest width in the direction of wave
7
propagation can be computed using the spectral output of the numerical wave model as energy
8
flux per wave crest width unit in kW/m [16, 92–94] as
9 10
=Y
W
7 = NO QX QX RS T , ℎ T,
VT V
(10)
11 12
where Cg(f, h) is the group velocity, S(f, Ɵ) is the directional wave variance density spectrum
13
and h, f, Ɵ, ρ, g are respectively the water depth, wave frequency, direction of propagation of a
14
spectral component, water density, and gravitational acceleration. In deep water (h > L/2), the
15
energy flux (Pw) transmitted by a regular wave per unit crest width can be approximated to
16 17
7Z =
18
(11)
[S²
M Y
= = × G^X × _` = 0.491 × G^X × _`
19 20
where Te is the energy period defined in terms of spectral moments, Hm0 is the spectral wave
21
height and ρ is the seawater density taken as 1027 kg/m3.
22 ^f.
23
_` =
24
G^X = 4hiX
(12)
^g
(13)
25 26
m-1 and m0 are spectral moments of nth-order spectral moment (mn) used to define the useful
27
wave statistic parameters [94].
28 29
W
i = QX
T T VT
(14)
30 11
1
For the evaluation of the total wave energy (ET in kWh/m) recorded during a given period with a
2
time step ∆t (3 hours in this study) we used the following common formula [15, 17]:
3 4
HJ
K
= ∑ 7 × ∆
(15)
5 6
The spatio-temporal variability of wave energy resources was carried out by computing the
7
coefficient of variation (COV) proposed by Cornett [92] which is the standard deviation (σp)
8
)))). This parameter allows us to identify the potential normalized to the mean power resource Pw
9
areas characterized by a significant wave energy resource with the lowest variation [23, 84].
10 11
no
R:m = )))))
(16)
pq
12 13
For the evaluation of monthly (MV) and seasonal (SV) variability [92] we also use two indices
14
for the wave energy resource variability defining as
15 16
rm =
17
m =
'stuv ( 'st,w 'xyuz
'{tuv ( '{t,w 'xyuz
(17) (18)
18 19
where Pm, max and Pm, min are respectively the mean wave power for the most energetic month and
20
for the least energetic month. Ps, max and Ps, min are respectively the mean wave power for the
21
most energetic season and for the least energetic season, and Pyear is the yearly average wave
22
power.
23 24
5. Wave power resource validation
25
For the validation of the SWAN model in the SW Mediterranean basin, we used the observation
26
data of six buoys (Fig. 1). All Algerian buoys are non-directional (DATAWELL Waverider),
27
they measure waves with periods from 1.6 to 30 seconds with a maximum error of 3%. More
28
characteristics concerning the periods used in the validation part, temporal resolutions, the
29
number of the wave observations, buoy sensor and the depths at buoy locations are detailed in
30
Table 3. Three physical wave characteristics (the significant wave height Hm0, the mean wave 12
1
period Tm02 and the energy period Te) were considered during the validation. Also, according to
2
the Eq (11) we compared wave power resource Pw computed using the observation data in the
3
Algerian coast
4
SWAN model results Pw,
5
Table 4 and the Q-Q plots (Fig. 2) show a very good performance of the calibrated SWAN model
6
in the six buoys, with a mean bias of 0.09 m and -0.17 s and a mean scatter index of 30% and
7
16% for both significant wave height Hm0 and the mean wave period Tm02, respectively. The
8
error indicator HH proposed by Hanna and Heinold [87] and evaluated by Mentaschi et. al. [88],
9
indicates a very good performance of the SWAN model calibrated for the Algerian basin
10
compared to the results obtained by Mentaschi et. al. [23] in seven other Mediterranean sub-
11
basins. The mean HH recorded in this study against validation buoys are 0.22, 0.13 and 0.15 for
12
Hm0, Tm02 and Te respectively. Concerning the energy period observed in the three Algerian
13
buoys and the computed wave power resource, we observe also a very good accuracy of these
14
two parameters with a mean scatter index of 14% and a bias of 0.16 s for the energy period. The
15
time series’ plots (Fig. 3) also present a very good accuracy of the predicted wave power
16
resource (Pw).
Pw,
observed
against the wave power resource computed using the calibrated
predicted
(Fig. 2). The error statistics’ results for all buoys detailed in
17 18
6.
Wave energy resource and variability assessment
19
6.1. Spatial distributions of mean and max wave energy fluxes
20
The spatial distribution map of the average wave energy flux at the West Mediterranean basin
21
(Fig. 4) shows the presence of a high energy resource in the northern part of the basin between
22
Sardinia and Mahon that exceeds 18 kW/m of average with a coefficient of variation of 0.19
23
(Fig. 7) which propagates to the East of the Algerian coast. In the western part of the basin,
24
another hot spot is also observed with an average wave power flux of 12.5 kW/m and a
25
coefficient of variation of 0.10. Referring to the bathymetric map, we observe that the wave
26
reaches the Algerian continental shelf (200 m deep) with an average wave power flux of about
27
7.5 kW/m in the western part, ~10 kW/m in the central part, and ~12 kW/m in the eastern part of
28
the basin. Regarding the spatial distribution of wave power along the Western Mediterranean Sea
29
(Fig. 4), we observed that the mean wave power distribution is strongly dependent on the
30
distribution of mean significant wave heights and it is similar to that obtained by previous studies
31
[17, 18, 20, 21, 38, 51]. However, from a quantitative viewpoint, the results of the inter-annual 13
1
mean obtained during the present study are closer to those obtained by Vannucchi et. al. [49],
2
with a mean wave powerreaching 20 kW/m in the central west Mediterranean basin. On the other
3
hand, an overestimation is observed by comparing our results with the other studies [18,19,38].
4
This difference may be related to several reasons in addition to the accuracy of the used models.
5
Among these reasons, the period considered to calculate the mean wave power; a significant
6
variation of the mean wave power flux during decadal years has been observed (Fig. 5) with a
7
considerable growth of the mean wave power flux between 2008 and 2017. The second reason
8
considered is the spatial and temporal resolution of the wind and wave data; a high temporal
9
resolution of the wind fields allow to better estimate the storm peaks [28] and according to Besio
10
et. al. [18] an increase of the spatial and temporal resolution of the model decreases its
11
underestimation of the significant wave heights. The third reason is the wind inputs used as
12
forcing in the model; e.g. the ECMWF winds underestimate wind speeds contrary to the CFSR
13
winds.
14
In the Algerian coast, Liberti et. al. [19] obtained an average wave power flux of 10.33 kW/m off
15
Cape Bougaroune (6.43°E x 37.2°N) during 10 years between 2001 - 2010, a result that
16
underestimates the average energy flux obtained during the present study using the SWAN
17
model calibrated and validated against observations made on the Algerian coast, which is 12.35
18
kW/m (underestimation of 16%) during the same period and in the same location. This
19
underestimation is considered justified, since the validation results of the model presented and
20
used by Liberti et. al. [19], show a negative bias for the significant wave heights at 72% of the
21
validation buoys, unlike the bias of the calibrated SWAN model used during this study, which is
22
positive for all the validation buoys (Table 4). Also, by comparing the results of the mean scatter
23
index of significant wave heights obtained against eleven wave buoys in the West
24
Mediterranean, Liberti et. al. [19] has reported a mean scatter index of 38% while a mean scatter
25
index of 30% was obtained against the same number of buoys for the calibrated SWAN model
26
[1]. It should be noted that the wave hindcast database used by Liberti et. al. [19] is the same one
27
used by Arena et. al. [22]. The same observation was noted by comparing the obtained results
28
with those of Besio et.al. [18] who found an average wave power flux of 9.10 kW/m off the
29
Annaba province 7.675°E x 37.250°N during 35 years against an average wave power flux of
30
12.16 kW/m obtained in the present study during the same period and at the same station
31
(underestimation of 25%). The wave hindcast database used by Besio et. al. [18], was developed
32
using Wavewatch III model implemented by Mentaschi et. al. [23], who indicate clearly 14
1
(graphically) in their paper that the normalized bias is negative for significant wave heights
2
greater than 1.7 m and decreases further considerably by increasing wave heights. This fact may
3
be at the origin of the remarkable difference in average wave power presented by Besio et. al.
4
[18] in the Mediterranean hotspots compared to the results of the present study. Thus, as already
5
specified in section 5, the results of the HH error indicators obtained for the calibrated SWAN
6
model show a very good performance of the calibrated SWAN model in the Algerian basin
7
compared to the results of the HH error indicators obtained by Mentaschi et. al. [23] in seven
8
other Mediterranean sub-basins. A straight comparison of the slightly overestimated results
9
obtained in this study with the slightly underestimated results of some previous studies reveals
10
logically a significant difference for the extreme values. The wave power bias computed depends
11
on the estimation errors of significant wave heights Hm0 and energetic periods Te and grows
12
exponentially by increasing Hm0 and Te (please see Eq. 11). It is therefore very important, when
13
processing the results obtained from wave energy assessment studies, to refer on the error
14
statistics of the model used for the development of the wave hindcast database.
15
Concerning the maximum wave power distribution, Fig. 4 presents the maximum significant
16
wave height and maximum wave power observed during the 39-year period at each point of the
17
computation grid. It can be observed that the spatial distribution of the maximum wave powers is
18
strongly depending on the significant wave heights. Along the Algerian coast, the maximum
19
wave powers are rarely lower than 300 kW/m and exceed 700 kW/m in the eastern and western
20
parts of the Algerian coast. Higher wave power values have already been observed by Arena et.
21
al. [22] as shown in their seventh figure concerning the wave power and average wave power
22
calculated for a certain directional sector. The knowledge of the spatial distribution of the
23
maximum wave power recorded in the 39-year period is an important parameter for the selection
24
of suitable areas for the installation of wave power farms and to ensure a durable and sustainable
25
development of these farms and other offshore and coastal installations.
26 27
6.2. Temporal variations of wave power flux
28
The variations of the wave power flux distribution and its potential at the decadal, annual,
29
seasonal, monthly and hourly scales along the Algerian coast present an essential parameter for
30
the evaluation and classification of areas with high energy potential characterized by a
31
considerable amount of energy that is omnipresent. The decadal spatial distribution maps (Fig. 5) 15
1
show a spatial distribution of wave power that is qualitatively similar to the distribution observed
2
over a 39-year period. On the other hand, from a quantitative point of view, we observe that the
3
wave power has increased over the last decades in the eastern and central part of the basin, with
4
very low decadal variation in the western part of the basin. This area can therefore have a climate
5
distinct from that of the rest of the western Mediterranean basin. The results of the decadal
6
variations may constitute an important information source as for the wave recovery projects with
7
a lifetime of 10 to 20 years [16].
8
According to the results of the seasonal and monthly mean power flux distributions (Fig. 6), a
9
similar spatial distribution can be observed by comparing the winter, autumn and annual average
10
distribution (Fig. 4) with higher power fluxes in the eastern part in comparison with that in the
11
western part of the Algerian basin. On the other hand, the spring and summer season has a
12
balance between the average energy recorded in the east and west of the Algerian basin.
13
Quantitatively, during the autumn and spring seasons, the average wave powers recorded are
14
almost equivalent along the Algerian coast, the average wave power varying between 4 kW/m
15
and 8 kW/m in a considerable part of the coast. For the winter season, the mean power exceeds
16
10 kW/m along the east coast at a distance of less than 15 km from the shore, and decreasing
17
progressively towards the west. Comparing these results against the mean wave power quantities
18
obtained seasonally by Besio et. al. [18] and Liberti et. al. [19], it is noticeable that the results
19
obtained for the summer, spring and autumn seasons are very similar compared to those of the
20
winter seasons, where higher values were recorded during the present study. This significant
21
difference can be justified by the points presented in section 6.1 as well as by the significant
22
increase of wave power during the last ten years (Fig. 5), knowing that the period between 2013
23
and 2017 have not been considered in the previous studies [18,19,22,38,49].
24
At the monthly scale, three different distributions are observed. The first is that during the
25
months of October, November, December, January, February, and March, where the power flux
26
is greater in the eastern part of the basin than in the western part. This case represents the
27
dominant distribution during the year. The second case of distribution is observed during the
28
month of June where the power flux is greater in the eastern part than in the western part of the
29
basin. Finally, the third case of distribution is observed during the months of April, May, July
30
and August, where a balance is observed in the distribution of energy between the east and west
31
of the basin. Quantitatively, the lowest wave power flux is recorded during the summer season, 16
1
precisely during the month of August, and the highest wave power flux are recorded during the
2
winter season precisely in December with a maximum average of 32 kW/m.
3
The consideration of areas according to their energy potential depends on the continuous
4
availability of this energy as well as their capacity. The spatial distribution maps of the
5
coefficients of variation (Fig. 7) allow us to evaluate the inter-annual, inter-monthly, and inter-
6
seasonal spatio-temporal variability of the mean wave power flux. The area characterized by a
7
considerable annual or monthly average wave power flux with a low COV, reflects the
8
continuous presence of a promising wave power flux during an exploitation period. At an inter-
9
annual scale, the COV represents the ratio between the standard deviation of the yearly mean
10
wave power fluxes during 39 years and the total average wave power flux. At this temporal
11
scale, the COV does not exceed 0.15 in the western part of the basin and reaches a maximum of
12
0.30 in the east. In the monthly scale, the COV represents the ratio between the standard
13
deviation of the monthly mean wave power fluxes during 39 years and the total monthly average
14
computed for each considered month. At this temporal scale, the highest coefficients of variation
15
are recorded during the months of January and February with a maximum of 0.80 in both the east
16
and west of the Algerian coast and the lowest coefficients of variation are recorded in the central
17
region of the basin where they vary between 0.20 and 0.40 over the 12 months. At the inter-
18
seasonal scale, the COV is the ratio between the standard deviation of the seasonal mean wave
19
power fluxes during 39 years and the total average computed for each considered season. At this
20
temporal scale, the highest COVs are recorded during the winter season between 0.30 and 0.40
21
over the entire Algerian basin. For the autumn season the strongest variations are observed in the
22
Eastern zone, and for the spring and summer seasons the coefficients of variation are between
23
0.15 and 0.30 over the entire Algerian basin.
24
According to Cornett [92], the monthly and seasonal variability (MV and SV) shows the
25
difference between the most energetic month and season and the least energetic month and
26
season normalized to the annual average, respectively. The spatial distributions of these two
27
parameters presented in Fig. 9 are very similar with a significant monthly and seasonal variation
28
in the eastern part, which decreases progressively from 2.4 to 0.3 moving towards the west.
29 30
6.3. The wave energy development index (WEDI)
17
1
WEDI as presented by Hagerman [95] is a ratio between the mean annual wave power and the
2
maximum observed power in a given area. This index is presented as an indicator of the risk to
3
which the WECs or the offshore structures are exposed during their activity. It allows to estimate
4
the economic report between the necessary consolidations of the mooring and the WECs hull and
5
the average wave energy potential available in the exploited area. The areas with low WEDI are
6
characterized by very higher wave power peaks compared to the wave power averages. In order
7
to resist to these extreme waves in such areas, WECs require a very heavy and expensive design
8
comparing to the exploitable wave power averages in these areas [24,96]. The spatial distribution
9
map of the WEDI index (Fig. 10) shows that the most important values, which range from 0.2 to
10
0.3, are recorded in the offshore of Tizi-Ouzou province and in the northwestern and
11
northeastern part of the Algerian basin. The WEDI index was also evaluated for the 14 stations
12
(Fig. 12) and used in the detailed evaluation of the wave energy resource along the Algerian
13
coast and the results are presented in following section.
14 15
6.4. Wave energy resource assessment at selected locations
16
For a quantitative evaluation of the wave energy resources along the Algerian coasts, a detailed
17
analysis was carried out at fourteen stations (Fig. 1) located off the fourteen Algerian coastal
18
provinces. These stations are located at a distance from the shoreline that varies between 16.8 km
19
and 3.2 km, corresponding to a depth ranging from 106 m to 327 m (Table 5). Thus, distances
20
from the ports are also considered. Distance from shore and depth constitute an important
21
economic factor [49, 50] and an essential element in the selection of the most appropriate WECs.
22
The results of the seasonal and annual mean wave power resources at the fourteen stations
23
presented in Fig. 11 show the availability of an average wave power flux that varies from 11.78
24
kW/m in the East (S2) to 4.4 kW/m in the center of the Algerian basin (S10). During the autumn,
25
winter and spring season, the stations from S1 to S4 located in the eastern part of the Algerian
26
basin represent the most energetic areas with an average energy which exceeds 15 kW/m in the
27
winter season and gets closer to the annual averages in the autumn and the spring seasons.
28
However, during the summer, there is a significant decrease in wave power flux at all stations.
29
Regarding the maximum wave power flux, an energy greater than 530 kW/m was recorded in
30
stations from S1 to S4, which had the highest average values, as well as in station S12 (Oran)
31
located in the western part. This extreme value recorded at station S12 compared to its annual 18
1
average of 5.73 kW/h explains the lowest WEDI value at this station (Fig. 12). The installation
2
of a WEC in this area may require a heavy and expensive design.
3
For the evaluation of the wave power flux availability at the monthly scale, we have presented in
4
Fig. 13 the distribution of the monthly wave power flux recorded over 39 years. The results show
5
that among the fourteen selected stations, seven stations (S1, S2, S3, S4, S6, S8, S11 and S12)
6
are characterized by a monthly wave power average that declines very rarely below 2 kW/m
7
during the years. Among these stations, the western ones have the lowest monthly (MV) and
8
seasonal (SV) variability rates, however, these stations are characterized by the lowest wave
9
power average (Table 6). Further, a significant increase in wave power over the last five years is
10
observed at all stations (Fig. 13), particularly during the months of January and February. This
11
observation explains the significant difference observed when comparing the results of this study
12
with the results of previous studies during the winter season; as mentioned above the period of
13
2013-2017 were not evaluated in the previous studies [17, 18].
14
Considering a calm sea state, the significant wave heights that does not exceed 0.5 m, the
15
stations S1, S2, S6 and S8 have a probability to have calm sea less than 20% (Table 6) which it is
16
lower than 17.2% between 15:00 and 00:00 (Fig. 14). This makes the average amount of the
17
wave power flux more important during the night than that of the morning periods in all stations
18
(Fig. 14). According to the daily consumption profile [97] defined by Sonelgaz Spa, the peaks in
19
domestic electricity consumption are recorded between 6 pm and 10 pm.
20
The probability of occurrence and the directional variability of wave energy flux presented in
21
Fig. 15 allows us to obtain more detailed information on the wave power resource characteristics
22
at the fourteen stations. These results contribute to the selection, design and orientation of the
23
WECs suitable for each of these areas to ensure an optimal exploitation. The probability of
24
occurrence presented in Fig. 15 is limited to the wave powers below 30 kW/m which represents
25
between 90% and 98% of the occurrences in all the fourteen stations. The most relevant wave
26
energy levels are in the range of 0 to 1 kW/m, with a proportion ranging from 31% to 33% in the
27
stations S1, S2, S6, S8, from 34% to 36% of the time in S4, S9, S10, S12 stations and from 39%
28
to 49% of the time in the other stations. Concerning the wave energy levels which vary between
29
1 kW/m and 30 kW/m, we notice that the probability of occurrence is more balanced between the
30
14 stations. The differentiation between stations can be based on the occurrence probabilities of
31
wave power ranges lower than 1 kW/m which represents largely proportion of the calm sea states 19
1
(Fig. 15) and also on the occurrence probabilities of wave power ranges higher than 30% which
2
varies between 2% at stations S9, S10, S14 and 10% at stations S1 and S2.
3
By examining the wave energy roses (Fig. 15) we notice that the directional distribution of wave
4
energy resources depends strongly on the geographical distribution of the 14 stations, this
5
distribution allows us to classify the stations in 3 different zones. The first zone covers the
6
stations located in the east of the Algerian basin between el-Taref (S1) and Jijel (S4), this zone is
7
characterized by a strong dominance of the NW waves with considerable energies. The second
8
zone is limited to the center of the Algerian basin between the Bejaia station (S5) and Chlef
9
station (S10), the dominant waves in this area are coming from the NNE and NE. The third zone,
10
is located in the western part of the Algerian basin and characterized by a strong dominance of
11
waves coming from the NNE, W and WNW direction and the most energetic are the W and
12
WNW waves. In these west stations it is more interesting to choose a WECs with low
13
dependence on direction or a WEC with a system that orientates it against the direction of the
14
most energetic waves as The Floating Wave Power Vessel [9]. In addition to the sea states
15
distributed over a range of direction, the wave energy contribution as a function of significant
16
wave height Hm0 and wave energy period Tm-10 is also an important parameter for the selection of
17
the promising WECs [19] or for the designing of the WEC wave farms [16]. The operation of
18
WECs is often optimized over specific ranges of wave heights and periods, the Fig. 16 represents
19
the distribution of wave energy as a function of significant wave height and energy period with
20
the contributions of different ranges of wave heights and periods to the annual wave energy
21
resources (Eq. 15). The selected stations show a remarkable difference in the distribution of the
22
total wave energy in terms of Te and Hm0. The highest total annual energies are recorded in El-
23
Taref S1 and Annaba S2 station located in the east of the Algerian basin, with a total annual
24
wave energy of 98.01 MWh/m and 103.27 MWh/m respectively (Table 6). In these two stations
25
this energy is distributed over a large period and significant wave height range concentrated
26
respectively between 6 s and 10 s and between 1 m and 6 m (Fig. 16). The stations of Jijel S3
27
and Skikda S4 have almost the same distribution, with a considerable amount of annual wave
28
power of 74.9 MWh/m and 73.5 MWh/m respectively. The main sum of this energy is
29
distributed over a significant range of Te and Hm0 concentrated between 1 m to 4 m for Hm0 and
30
between 6 s to10 s for Te. Compared to stations S1, S2, S3 and S4, the stations located in the
31
western part of the Algerian basin have a lower annual wave energy resources but concentrated 20
1
over a narrower range, ranging from 1 m to 3.5 m and from 5.5 s to 8.5 s in terms of significant
2
wave height and energy period successively.
3 4
6.5.
Hotspots coastal areas
5
The Algerian coasts are exposed to the eastern waves generated in the Tyrrhenian Sea, to the
6
western waves generated in the Alboran Sea, and to the northern waves. This characteristic
7
ensures that the Algerian coasts know the lowest probability to have calm seas (Fig. 17) during
8
the year, which is less than 18% off Annaba S2 (Table 6). On the other hand, compared to the
9
European coast, the Algerian coast has a very narrow continental shelf, which means that the
10
wave energy propagates near the shore with a low dissipation. Considering the total annual wave
11
energy, the annual probability to have a calm sea state and the distance from the coast as criteria
12
for the selection of coastal hot spots. We have selected the areas with a total annual wave energy
13
exceeding 100 MWh/m/year at 15 km from the coast, and a probability to have a calm sea state
14
less than 18%. The result of this multi-criteria spatial analysis allowed us to classify the coastal
15
areas with the highest wave energy resources in the western Mediterranean basin (Fig. 19). The
16
selected areas are the east coast of Mahon Island (Spain) which covers 442 km², the Carbonia
17
coast in the south-west of Sardinia (Italy) which covers 273 km² and Eastern coast of Algeria
18
(Annaba and Skikda province) which covers 546 km². These results show that the Algerian coast
19
has the highest energy potential in the western Mediterranean basin with an availability of waves
20
that exceed 0.5 m during 299 days of the year (Fig. 14).
21 22
7. Conclusions
23
This study presents an assessment of the potential wave energy resources along the Algerian
24
coast. 39-year wave climate and wave energy hindcast dataset was developed for the Algerian
25
basin using SWAN model. This model was calibrated and validated based on three wave buoy
26
measurements recorded in the Algerian coast; with a high spatial resolution of 0.033˚ [1]. This
27
resolution allowed us to evaluate the wave energy potential at a distance of ~3 km from the
28
coast, considering the Algerian narrow continental shelf. A spatio-temporal analysis of the wave
29
power variations allowed us to observe that the strongest wave energy resources in the western
30
Mediterranean are located near the Eastern coasts of Algeria; taking into account the distance 21
1
from the coasts and its omnipresence during the year. The East Algerian coasts are exposed to
2
the waves coming mainly from the Algerian basin, the North West Mediterranean basin and the
3
Tyrrhenian basin. Therefore, wave energy potential is also considerable on the west and central
4
coasts of Algeria, with a lower monthly and seasonal variation. The detailed local analysis
5
carried out for fourteen stations distributed along the Algerian basin, show a significant
6
variability in the wave energy characteristics at each zone. The stations located in the eastern
7
part of the Algerian basin (S1 and S2) are characterized by the highest total annual wave energy,
8
exceeding 100 MWh/m/year with an annual probability of 18% of calm sea state, within 15 km
9
of the coastline. Station S3 (Skikda) is characterized by a high dominance of NNE waves and a
10
total annual wave energy of 74.9 MW/m/year. The S6 station has a total annual wave energy of
11
63 MWh/m/year, with an average wave power flux of 7.28 kW/m and a maximum of 382.82
12
kW/m, resulting in the highest WEDI of 0.019. From the eastern to the western stations we
13
notice a progressive decrease in the total annual energy and a decrease in the WEDI. Station 14
14
in the west has recorded the lowest resources.
15
By comparing the average wave energy resource obtained during this study on the Algerian coast
16
against that obtained during the previous studies [18,19,22,49]; we observe that our results are
17
close to those reported by Vannucchi et. al.[49], and underestimated compared to those recorded
18
by Liberti et. al. [19] and Besio et. al. [18]. The main reason for this underestimation is not
19
necessarily due to the model accuracy, but mainly caused by positive or negative signs of the
20
model bias. A straight comparison of the slightly overestimated results obtained in this study
21
with the slightly underestimated results of some previous studies reveals a significant difference.
22
When processing or comparing the results obtained from the different wave energy assessment
23
studies, it is strongly recommended to refer on the error statistics derived from the models used
24
for the development of the wave energy datasets. In addition, the results of this study confirm the
25
conclusion reached by Besio et. al. [18] concerning the increasing trend in wave power
26
availability between 2005 and 2013, and we add that this growth has been observed since 1995
27
and has persisted over the last five years between 2013 and 2017, more precisely in the West and
28
Central parts of the Algerian coast, during the months of January and February (Fig.13).
29
The wave energy dataset presented during this study will allow to design and estimate the
30
capacity and profitability of wave energy farms at any selected location. Therefore, the long-term
31
wave energy resource assessment results present a necessary tool to identify suitable locations 22
1
for the implementation of wave energy farms; to be used by the private sector or public
2
institutions. We also suspect that a more detailed local assessment, using a nested grid model
3
with a higher spatial resolution, can provide a paramount result in the selected hotspots areas.
4 5
Acknowledgement
6
The authors would like to express their special thanks to the late Dr. Gerbrant van Vledder who
7
assisted us during the implementation of the SWAN model in W-Mediterranean. Although he is
8
no longer with us, he continues to inspire us by his example and dedication to science. We
9
gratefully acknowledge Dr. Salim Lamine, for his suggestions, which have helped in improving
10
the English language of the paper. The authors acknowledge the NGDC/NOAA for providing the
11
bathymetry data ETOPO1 1 arc-minute, the NCEP/UCAR Research data archive service for
12
providing the wind forcing data of NOAA NCEP Climate Forecast System Reanalysis (CFSR),
13
the ONSM (Offıce National de Signalisation Maritime) and the Público Puertos del Estado for
14
providing the wave measurements data. This work is based on the PhD thesis of the first author.
15 16
References
17 18
[1]
K. Amarouche, A. Akpınar, N.E.I. Bachari, R.E. Çakmak, F. Houma, Evaluation of a
19
high-resolution wave hindcast model SWAN for the West Mediterranean basin, Appl.
20
Ocean Res. 84 (2019) 225–241. https://doi.org/10.1016/J.APOR.2019.01.014.
21
[2]
Radiation Using {MSG}-{HRV} images, Int. J. Appl. Environ. Sci. 11 (2016) 351–368.
22 23
K. Bouchouicha, A. Razagui, N.E.I. Bachari, N. Aoun, Estimation of Hourly Global Solar
[3]
A.B. Stambouli, Z. Khiat, S. Flazi, Y. Kitamura, A review on the renewable energy
24
development in Algeria: Current perspective, energy scenario and sustainability issues,
25
Renew. Sustain. Energy Rev. 16 (2012) 4445–4460.
26
https://doi.org/10.1016/J.RSER.2012.04.031.
27
[4]
Y. Himri, A.S. Malik, A. Boudghene Stambouli, S. Himri, B. Draoui, Review and use of
28
the Algerian renewable energy for sustainable development, Renew. Sustain. Energy Rev.
29
13 (2009) 1584–1591. https://doi.org/10.1016/J.RSER.2008.09.007.
30
[5]
K. Bouchouicha, A. Razagui, N. El, I. Bachari, N. Aoun, Mapping and geospatial analysis 23
1
of solar resource in Algeria, Int. J. Energy, Environ. Econ. 23 (2015) 735–751.
2
https://search.proquest.com/openview/dd3b32539d92855486578967dc899d20/1?pq-
3
origsite=gscholar&cbl=2034867 (accessed October 28, 2018).
4
[6]
https://doi.org/10.1016/S0960-1481(00)00090-2.
5 6
N.K. Merzouk, Wind energy potential of Algeria, Renew. Energy. 21 (2000) 553–562.
[7]
H. Mahmoudi, N. Spahis, M.F. Goosen, N. Ghaffour, N. Drouiche, A. Ouagued,
7
Application of geothermal energy for heating and fresh water production in a brackish
8
water greenhouse desalination unit: A case study from Algeria, Renew. Sustain. Energy
9
Rev. 14 (2010) 512–517. https://doi.org/10.1016/J.RSER.2009.07.038.
10
[8]
K. Kateb, Changements démographiques et organisation familiale en Algérie, Maghreb
11
Machrek. (2003) 95–110. https://www.africabib.org/rec.php?RID=253697506 (accessed
12
October 28, 2018).
13
[9]
A. Clément, P. McCullen, A. Falcão, A. Fiorentino, F. Gardner, K. Hammarlund, G.
14
Lemonis, T. Lewis, K. Nielsen, S. Petroncini, M.-T. Pontes, P. Schild, B.-O. Sjöström,
15
H.C. Sørensen, T. Thorpe, Wave energy in Europe: current status and perspectives,
16
Renew. Sustain. Energy Rev. 6 (2002) 405–431. https://doi.org/10.1016/S1364-
17
0321(02)00009-6.
18
[10]
B. Drew, A.R. Plummer, M.N. Sahinkaya, A review of wave energy converter technology,
19
Proc. Inst. Mech. Eng. Part A J. Power Energy. 223 (2009) 887–902.
20
https://doi.org/10.1243/09576509JPE782.
21
[11]
Energy Rev. 14 (2010) 899–918. https://doi.org/10.1016/J.RSER.2009.11.003.
22 23
A.F. de O. Falcão, Wave energy utilization: A review of the technologies, Renew. Sustain.
[12]
F.J.M. Farley, The underwater resonant airbag: a new wave energy converter, Proc. R.
24
Soc. A Math. Phys. Eng. Sci. 474 (2018) 20170192.
25
https://doi.org/10.1098/rspa.2017.0192.
26
[13]
H. Fernandez, G. Iglesias, R. Carballo, A. Castro, J.A. Fraguela, F. Taveira-Pinto, M.
27
Sanchez, The new wave energy converter WaveCat: Concept and laboratory tests, Mar.
28
Struct. 29 (2012) 58–70. https://doi.org/10.1016/J.MARSTRUC.2012.10.002.
29
[14]
J.P. Kofoed, P. Frigaard, E. Friis-Madsen, H.C. Sørensen, Prototype testing of the wave
30
energy converter wave dragon, Renew. Energy. 31 (2006) 181–189.
31
https://doi.org/10.1016/J.RENENE.2005.09.005.
32
[15]
L. Martinelli, B. Zanuttigh, J.P. Kofoed, Selection of design power of wave energy 24
1
converters based on wave basin experiments, Renew. Energy. 36 (2011) 3124–3132.
2
https://doi.org/10.1016/J.RENENE.2011.03.021.
3
[16]
A. Akpınar, B. Bingölbali, G.P. Van Vledder, Long-term analysis of wave power potential
4
in the Black Sea, based on 31-year SWAN simulations, Ocean Eng. 130 (2017) 482–497.
5
https://doi.org/10.1016/J.OCEANENG.2016.12.023.
6
[17]
K. Amarouche, N.-E.-I. Bachari, F. Houma, Study of the coastal wave energy propagation
7
using {GIS} and hydrodynamic model, in: R.C.E. Suha Ozden, Cengiz Akbulak, F.S. and
8
M.A. Oznur Karaca (Eds.), PROCEEDING B. Int. Symp. GIS Appl. Geogr. Geosci.,
9
Çanakkale Onsekiz Mart University, Canakkale, Turkey, 2017: pp. 55–64. https://doi.org/ISBN : 978 - 605 - 4222 - 54 - 4.
10 11
[18]
G. Besio, L. Mentaschi, A. Mazzino, Wave energy resource assessment in the
12
Mediterranean Sea on the basis of a 35-year hindcast, Energy. 94 (2016) 50–63.
13
https://doi.org/10.1016/J.ENERGY.2015.10.044.
14
[19]
L. Liberti, A. Carillo, G. Sannino, Wave energy resource assessment in the Mediterranean,
15
the Italian perspective, Renew. Energy. 50 (2013) 938–949.
16
https://doi.org/10.1016/J.RENENE.2012.08.023.
17
[20]
D. Vicinanza, P. Contestabile, V. Ferrante, Wave energy potential in the north-west of
18
Sardinia (Italy), Renew. Energy. 50 (2013) 506–521.
19
https://doi.org/10.1016/J.RENENE.2012.07.015.
20
[21]
S. Ponce de León, A. Orfila, G. Simarro, Wave energy in the Balearic Sea. Evolution from
21
a 29 year spectral wave hindcast, Renew. Energy. 85 (2016) 1192–1200.
22
https://doi.org/10.1016/J.RENENE.2015.07.076.
23
[22]
F. Arena, V. Laface, G. Malara, A. Romolo, A. Viviano, V. Fiamma, G. Sannino, A.
24
Carillo, Wave climate analysis for the design of wave energy harvesters in the
25
Mediterranean Sea, Renew. Energy. 77 (2015) 125–141.
26
https://doi.org/10.1016/J.RENENE.2014.12.002.
27
[23]
L. Mentaschi, G. Besio, F. Cassola, A. Mazzino, Performance evaluation of Wavewatch
28
III in the Mediterranean Sea, Ocean Model. 90 (2015) 82–94.
29
https://doi.org/10.1016/J.OCEMOD.2015.04.003.
30
[24]
G. Lavidas, V. Venugopal, A 35 year high-resolution wave atlas for nearshore energy
31
production and economics at the Aegean Sea, Renew. Energy. 103 (2017) 401–417.
32
https://doi.org/10.1016/J.RENENE.2016.11.055. 25
1
[25]
V. Franzitta, D. Curto, D. Milone, D. Rao, V. Franzitta, D. Curto, D. Milone, D. Rao,
2
Assessment of Renewable Sources for the Energy Consumption in Malta in the
3
Mediterranean Sea, Energies. 9 (2016) 1034. https://doi.org/10.3390/en9121034.
4
[26]
Armaments Organisation Research Cell, 2004.
5 6
Medatlas Group, Wind and wave atlas of the Mediterranean Sea, Western European
[27]
L. Donatini, G. Lupieri, G. Contento, A. Pedroncini, L.A. Cusati, A. Crosta, MWM: A 35
7
years wind&wave high resolution hindcast dataset and an operational forecast service
8
for the mediterranean sea, in: 18th Int. Conf. Ships Shipp. Res. NAV 2015, ITA, Lecco,
9
Italy, 2015: pp. 116–125. https://arts.units.it/handle/11368/2919934#.XBd8tGko_IU (accessed December 17, 2018).
10 11
[28]
A.-L. Tiberi-Wadier, A. Laugel, M. Benoit, Construction of the Numerical Wave
12
Databases Anemoc-2 on the Mediterranean Sea and the Atlantic Ocean Through Hindcast
13
Simulations Over the Period 1979–2010, in: Adv. Hydroinformatics, Springer, Singapore,
14
2016: pp. 127–143. https://doi.org/10.1007/978-981-287-615-7_9.
15
[29]
Wamdi Group, The {WAM} Model—A Third Generation Ocean Wave Prediction Model,
16
J. Phys. Oceanogr. 18 (1988) 1775–1810. https://doi.org/10.1175/1520-
17
0485(1988)018<1775:TWMTGO>2.0.CO;2.
18
[30]
H.L. Tolman, A Third-Generation Model for Wind Waves on Slowly Varying, Unsteady,
19
and Inhomogeneous Depths and Currents, J. Phys. Oceanogr. 21 (1991) 782–797.
20
https://doi.org/10.1175/1520-0485(1991)021<0782:ATGMFW>2.0.CO;2.
21
[31]
M. Benoit, F. Marcos, F. Becq, Development of a Third Generation Shallow-Water Wave
22
Model with Unstructured Spatial Meshing, in: Coast. Eng. Proc., 1996: pp. 465–478.
23
https://doi.org/10.1061/9780784402429.037.
24
[32]
L. Cavaleri, M. Sclavo, The calibration of wind and wave model data in the Mediterranean
25
Sea, Coast. Eng. 53 (2006) 613–627.
26
https://doi.org/10.1016/J.COASTALENG.2005.12.006.
27
[33]
55 (2008) 872–880. https://doi.org/10.1016/j.coastaleng.2008.02.024.
28 29
S. Musić, S. Nicković, 44-year wave hindcast for the Eastern Mediterranean, Coast. Eng.
[34]
F. Ardhuin, L. Bertotti, J.-R. Bidlot, L. Cavaleri, V. Filipetto, J.-M. Lefevre, P. Wittmann,
30
Comparison of wind and wave measurements and models in the Western Mediterranean
31
Sea, Ocean Eng. 34 (2007) 526–541. https://doi.org/10.1016/j.oceaneng.2006.02.008.
32
[35]
R. Bolaños-Sanchez, A. Sanchez-Arcilla, J. Cateura, Evaluation of two atmospheric 26
1
models for wind–wave modelling in the {NW} Mediterranean, J. Mar. Syst. 65 (2007)
2
336–353. https://doi.org/10.1016/j.jmarsys.2005.09.014.
3
[36]
A. Martínez-Asensio, M. Marcos, G. Jordà, D. Gomis, Calibration of a new wind-wave
4
hindcast in the Western Mediterranean, Ournal Mar. Syst. 121–122 (2013) 1–10.
5
https://doi.org/10.1016/j.jmarsys.2013.04.006.
6
[37]
S. de León, A. Orfila, G. Simarro, Wave energy in the Balearic Sea. Evolution from a 29
7
year spectral wave hindcast, 85 (n.d.) 1192–1200.
8
https://doi.org/10.1016/j.renene.2015.07.076.
9
[38]
G. Lavidas, V. Venugopal, A. Agarwal, Long-term evaluation of the wave climate and
10
energy potential in the Mediterranean Sea, in: Sustain. Hydraul. Era Glob. Chang., CRC
11
Press, 2016: pp. 247–253. https://doi.org/10.1201/b21902-46.
12
[39]
R.C. Ris, L.H. Holthuijsen, N. Booij, A third-generation wave model for coastal regions:
13
2. Verification, J. Geophys. Res. 104 (1999) 7667–7681.
14
https://doi.org/10.1029/1998JC900123.
15
[40]
L. Sartini, L. Mentaschi, G. Besio, Evaluating third generation wave spectral models
16
performances in coastal areas. An application to Eastern Liguria, in: Ocean. 2015 -
17
Genova, IEEE, 2015: pp. 1–10. https://doi.org/10.1109/OCEANS-Genova.2015.7271395.
18
[41]
W.E. Rogers, P.A. Hwang, D.W. Wang, Investigation of Wave Growth and Decay in the
19
{SWAN} Model: Three Regional-Scale Applications, J. Phys. Oceanogr. 33 (2003) 366–
20
389. https://doi.org/10.1175/1520-0485(2003)033<0366:IOWGAD>2.0.CO;2.
21
[42]
M.H. Moeini, A. Etemad-Shahidi, Application of two numerical models for wave
22
hindcasting in Lake Erie, Appl. Ocean Res. 29 (2007) 137–145.
23
https://doi.org/10.1016/j.apor.2007.10.001.
24
[43]
A. Akpinar, S. Ponce de León, An assessment of the wind re-analyses in the modelling of
25
an extreme sea state in the Black Sea, Dyn. Atmos. Ocean. 73 (2016) 61–75.
26
https://doi.org/10.1016/J.DYNATMOCE.2015.12.002.
27
[44]
V. Kutupoğlu, R.E. Çakmak, A. Akpınar, G.P. van Vledder, Setup and evaluation of a
28
SWAN wind wave model for the Sea of Marmara, Ocean Eng. 165 (2018) 450–464.
29
https://doi.org/10.1016/J.OCEANENG.2018.07.053.
30
[45]
R. Atan, S. Nash, J. Goggins, Development of a nested local scale wave model for a 1/4
31
scale wave energy test site using SWAN, J. Oper. Oceanogr. 10 (2017) 59–78.
32
https://doi.org/10.1080/1755876X.2016.1275495. 27
1
[46]
B. Kamranzad, A. Etemad-Shahidi, V. Chegini, Assessment of wave energy variation in
2
the Persian Gulf, Ocean Eng. 70 (2013) 72–80.
3
https://doi.org/10.1016/j.oceaneng.2013.05.027.
4
[47]
B. Bingölbali, A. Akpınar, H. Jafali, G.P. Van Vledder, Downscaling of wave climate in
5
the western Black Sea, Ocean Eng. 172 (2019) 31–45.
6
https://doi.org/10.1016/J.OCEANENG.2018.11.042.
7
[48]
G. Lavidas, V. Venugopal, Application of numerical wave models at European coastlines:
8
A review, Renew. Sustain. Energy Rev. 92 (2018) 489–500.
9
https://doi.org/10.1016/j.rser.2018.04.112.
10
[49]
V. Vannucchi, L. Cappietti, V. Vannucchi, L. Cappietti, Wave Energy Assessment and
11
Performance Estimation of State of the Art Wave Energy Converters in Italian Hotspots,
12
Sustainability. 8 (2016) 1300. https://doi.org/10.3390/su8121300.
13
[50]
E. Rusu, F. Onea, Study on the influence of the distance to shore for a wave energy farm
14
operating in the central part of the Portuguese nearshore, Energy Convers. Manag. 114
15
(2016) 209–223. https://doi.org/10.1016/j.enconman.2016.02.020.
16
[51]
K. Thorburn, H. Bernhoff, M. Leijon, Wave energy transmission system concepts for
17
linear generator arrays, Ocean Eng. 31 (2004) 1339–1349.
18
https://doi.org/10.1016/J.OCEANENG.2004.03.003.
19
[52]
U. Henfridsson, V. Neimane, K. Strand, R. Kapper, H. Bernhoff, O. Danielsson, M.
20
Leijon, J. Sundberg, K. Thorburn, E. Ericsson, K. Bergman, Wave energy potential in the
21
Baltic Sea and the Danish part of the North Sea, with reflections on the Skagerrak, Renew.
22
Energy. 32 (2007) 2069–2084. https://doi.org/10.1016/J.RENENE.2006.10.006.
23
[53]
N. Booij, R.C. Ris, L.H. Holthuijsen, A third-generation wave model for coastal regions:
24
1. Model description and validation, J. Geophys. Res. 104 (1999) 7649–7666.
25
https://doi.org/10.1029/98JC02622.
26
[54]
P.A. Umesh, P.K. Bhaskaran, K.G. Sandhya, T.M. Balakrishnan Nair, An assessment on
27
the impact of wind forcing on simulation and validation of wave spectra at coastal
28
Puducherry, east coast of India, Ocean Eng. 139 (2017) 14–32.
29
https://doi.org/10.1016/J.OCEANENG.2017.04.043.
30
[55]
K. Hasselmann, T.P. Barnett, E. Bouws, H. Carlson, D.E. Cartwright, K. Enke, J.A.
31
Ewing, H. Gienapp, D.E. Hasselmann, P. Kruseman, A. Meerburg, P. Müller, D.J. Olbers,
32
K. Richter, W. Sell, H. Walden, Measurements of wind-wave growth and swell decay 28
1
during the Joint North Sea Wave Project ({JONSWAP}), Dtsch. Hydrogr. Z. A8 (1973)
2
1–95. http://resolver.tudelft.nl/uuid:f204e188-13b9-49d8-a6dc-4fb7c20562fc (accessed
3
July 2, 2018).
4
[56]
S. team, {SWAN} technical documentation version 41.20A, Delft University of
5
Technology, Netherlands., 2018.
6
http://swanmodel.sourceforge.net/online_doc/swantech/swantech.html.
7
[57]
A. Akpınar, G.P. van Vledder, M.İ. Kömürcü, M. Özger, Evaluation of the numerical
8
wave model (SWAN) for wave simulation in the Black Sea, Cont. Shelf Res. 50–51
9
(2012) 80–99. https://doi.org/10.1016/J.CSR.2012.09.012.
10
[58]
D.P. Dee, S.M. Uppala, A.J. Simmons, P. Berrisford, P. Poli, S. Kobayashi, U. Andrae,
11
M.A. Balmaseda, G. Balsamo, P. Bauer, P. Bechtold, A.C.M. Beljaars, L. van de Berg, J.
12
Bidlot, N. Bormann, C. Delsol, R. Dragani, M. Fuentes, A.J. Geer, L. Haimberger, S.B.
13
Healy, H. Hersbach, E. V. Hólm, L. Isaksen, P. Kållberg, M. Köhler, M. Matricardi, A.P.
14
McNally, B.M. Monge-Sanz, J.-J. Morcrette, B.-K. Park, C. Peubey, P. de Rosnay, C.
15
Tavolato, J.-N. Thépaut, F. Vitart, The ERA-Interim reanalysis: configuration and
16
performance of the data assimilation system, Q. J. R. Meteorol. Soc. 137 (2011) 553–597.
17
https://doi.org/10.1002/qj.828.
18
[59]
S. Saha, S. Moorthi, H.-L. Pan, X. Wu, J. Wang, S. Nadiga, P. Tripp, R. Kistler, J.
19
Woollen, D. Behringer, H. Liu, D. Stokes, R. Grumbine, G. Gayno, J. Wang, Y.-T. Hou,
20
H. Chuang, H.-M.H. Juang, J. Sela, M. Iredell, R. Treadon, D. Kleist, P. Van Delst, D.
21
Keyser, J. Derber, M. Ek, J. Meng, H. Wei, R. Yang, S. Lord, H. van den Dool, A. Kumar,
22
W. Wang, C. Long, M. Chelliah, Y. Xue, B. Huang, J.-K. Schemm, W. Ebisuzaki, R. Lin,
23
P. Xie, M. Chen, S. Zhou, W. Higgins, C.-Z. Zou, Q. Liu, Y. Chen, Y. Han, L. Cucurull,
24
R.W. Reynolds, G. Rutledge, M. Goldberg, The {NCEP} Climate Forecast System
25
Reanalysis, Bull. Am. Meteorol. Soc. 91 (2010) 1015–1058.
26
https://doi.org/10.1175/2010BAMS3001.1.
27
[60]
S. Saha, S. Moorthi, X. Wu, J. Wang, S. Nadiga, P. Tripp, D. Behringer, Y.-T. Hou, H.
28
Chuang, M. Iredell, M. Ek, J. Meng, R. Yang, M.P. Mendez, H. van den Dool, Q. Zhang,
29
W. Wang, M. Chen, E. Becker, The {NCEP} Climate Forecast System Version 2, J. Clim.
30
27 (2014) 2185–2208. https://doi.org/10.1175/JCLI-D-12-00823.1.
31 32
[61]
M.H. Moeini, A. Etemad-Shahidi, V. Chegini, Wave modeling and extreme value analysis off the northern coast of the Persian Gulf, Appl. Ocean Res. 32 (2010) 209–218. 29
https://doi.org/10.1016/j.apor.2009.10.005.
1 2
[62]
S. Ponce de León, C. Guedes Soares, Sensitivity of wave model predictions to wind fields
3
in the Western Mediterranean sea, Coast. Eng. 55 (2008) 920–929.
4
https://doi.org/10.1016/j.coastaleng.2008.02.023.
5
[63]
R.M. Campos, C. Guedes Soares, Comparison and assessment of three wave hindcasts in
6
the North Atlantic Ocean, J. Oper. Oceanogr. 9 (2016) 26–44.
7
https://doi.org/10.1080/1755876X.2016.1200249.
8
[64]
Atlantic Ocean, J. Oper. Oceanogr. 10 (2017) 30–44.
9
https://doi.org/10.1080/1755876X.2016.1253328.
10 11
R.M. Campos, C. Guedes Soares, Assessment of three wind reanalyses in the North
[65]
F. Ardhuin, J. Hanafin, Y. Quilfen, B. Chapron, P. Queffeulou, M. Obrebski, J.
12
Sienkiewicz, D. Vandemark, Calibration of the IOWAGA global wave hindcast (1991–
13
2011) using ECMWF and CFSR winds, in: Proc. 12th International Workshop of Wave
14
Hindcasting and Forecating . 2011, Hawaii, 2011.
15
[66]
D.B. Chelton, M.H. Freilich, Scatterometer-Based Assessment of 10-m Wind Analyses
16
from the Operational ECMWF and NCEP Numerical Weather Prediction Models, Mon.
17
Weather Rev. 133 (2005) 409–429. https://doi.org/10.1175/MWR-2861.1.
18
[67]
L. Rusu, M. Gonçalves, C. Guedes Soares, Prediction of storm conditions using wind data
19
from the ECMWF and NCEP reanalysis, in: C. Soares Guedes, Â.. Teixeira (Eds.), Dev.
20
Marit. Transp. Harvest. Sea Resour. (2 Vol. Set) Proc. 17th Int. Congr. Int. Marit. Assoc.
21
Mediterr. (IMAM 2017), Taylor & Francis Group, 2017, Lisbon, Portugal, 2017: p. 1278.
22
https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Prediction+of+storm+condi
23
tions+using+wind+data+from+the+ECMWF+and++NCEP+reanalysis&btnG= (accessed
24
January 19, 2020).
25
[68]
J.E. Stopa, K.F. Cheung, Intercomparison of wind and wave data from the ECMWF
26
Reanalysis Interim and the NCEP Climate Forecast System Reanalysis, Ocean Model. 75
27
(2014) 65–83. https://doi.org/10.1016/j.ocemod.2013.12.006.
28
[69]
R.P. Signell, S. Carniel, L. Cavaleri, J. Chiggiato, J.D. Doyle, J. Pullen, M. Sclavo,
29
Assessment of wind quality for oceanographic modelling in semi-enclosed basins, J. Mar.
30
Syst. 53 (2005) 217–233. https://doi.org/10.1016/j.jmarsys.2004.03.006.
31 32
[70]
A.T. Cox, V.J. Cardone, V.R. Swail, On the Use of the Climate Forecast System Reanalysis Wind Forcing In Ocean Response Modeling, in: Proceedings, 12th Int. Work. 30
Wave, Hindcasting, Forecast. Hawaii, USA, 2011: p. 20.
1 2
[71]
A. Akpınar, B. Bingölbali, Long-term variations of wind and wave conditions in the
3
coastal regions of the Black Sea, Nat. Hazards. 84 (2016) 69–92.
4
https://doi.org/10.1007/s11069-016-2407-9.
5
[72]
A. Chawla, D. Spindler, H. Tolman, 30 Year Wave Hindcasts using {WAVEWATCH}
6
{III} R with {CFSR} winds† Phase, NOAA/NWS/NCEP/MMAB. Maryl. USA. (2012)
7
23.
8
[73]
extratropical cyclones, Ocean Model. 81 (2014) 78–88.
9
https://doi.org/10.1016/J.OCEMOD.2014.07.005.
10 11
S. Ponce de León, C. Guedes Soares, Extreme wave parameters under North Atlantic
[74]
G.P. Van Vledder, A. Akpınar, Wave model predictions in the Black Sea: Sensitivity to
12
wind fields, Appl. Ocean Res. 53 (2015) 161–178.
13
https://doi.org/10.1016/j.apor.2015.08.006.
14
[75]
S.F. Wornom, D.J.S. Welsh, K.W. Bedford, On coupling the SWAN and WAM wave
15
models for accurate nearshore wave predictions, Coast. Eng. J. 43 (2001) 161–201.
16
https://doi.org/10.1142/S0578563401000335.
17
[76]
C. Amante, B.W. Eakins, {ETOPO}1 Global Relief Model converted to {PanMap} layer
18
format, NOAA-National Geophys. Data Cent. (2009).
19
https://doi.org/https://doi.org/10.1594/PANGAEA.769615.
20
[77]
J. Perez, M. Menendez, P. Camus, F.J. Mendez, I.J. Losada, Statistical multi-model
21
climate projections of surface ocean waves in Europe, Ocean Model. 96 (2015) 161–170.
22
https://doi.org/10.1016/J.OCEMOD.2015.06.001.
23
[78]
L. Sartini, F. Cassola, G. Besio, Extreme waves seasonality analysis: An application in the
24
Mediterranean Sea, J. Geophys. Res. Ocean. 120 (2015) 6266–6288.
25
https://doi.org/10.1002/2015JC011061.
26
[79]
statistics, Ocean Model. 70 (2013) 62–74. https://doi.org/10.1016/j.ocemod.2013.03.007.
27 28
F. Leckler, F. Ardhuin, J.-F. Filipot, A. Mironov, Dissipation source terms and whitecap
[80]
P.A.E.M. Janssen, P.A.E.M. Janssen, Wave-Induced Stress and the Drag of Air Flow over
29
Sea Waves, J. Phys. Oceanogr. 19 (1989) 745–754. https://doi.org/10.1175/1520-
30
0485(1989)019<0745:WISATD>2.0.CO;2.
31 32
[81]
P.A.E.M. Janssen, Quasi-linear Theory of Wind-Wave Generation Applied to Wave Forecasting, J. Phys. Oceanogr. 21 (1991) 1631–1642. https://doi.org/10.1175/152031
0485(1991)021<1631:QLTOWW>2.0.CO;2.
1 2
[82]
G.J. Komen, K. Hasselmann, K. Hasselmann, On the Existence of a Fully Developed
3
Wind-Sea Spectrum, J. Phys. Oceanogr. 14 (1984) 1271–1285.
4
https://doi.org/10.1175/1520-0485(1984)014<1271:OTEOAF>2.0.CO;2.
5
[83]
L. Cavaleri, P.M. Rizzoli, Wind wave prediction in shallow water: Theory and
6
applications, J. Geophys. Res. 86 (1981) 10961–10973.
7
https://doi.org/10.1029/JC086iC11p10961.
8
[84]
S. Hasselmann, K. Hasselmann, Computations and Parameterizations of the Nonlinear Energy Transfer in a Gravity-Wave Spectrum. Part I: A New Method for Efficient
9 10
Computations of the Exact Nonlinear Transfer Integral, J. Phys. Oceanogr. 15 (1985)
11
1369–1377. https://doi.org/10.1175/1520-0485(1985)015<1369:CAPOTN>2.0.CO;2.
12
[85]
Waves, in: Coast. Eng., 1978: pp. 569–587. https://doi.org/10.1061/9780872621909.034.
13 14
[86]
M. Zijlema, G.P. van Vledder, L.H. Holthuijsen, Bottom friction and wind drag for wave models, Coast. Eng. 65 (2012) 19–26. https://doi.org/10.1016/j.coastaleng.2012.03.002.
15 16
Battjes J. A., Janssen J. P. F. M., Energy Loss and Set-Up Due to Breaking of Random
[87]
S.R. Hanna, D.W. Heinold, A.P.I. Health, E.A. Dept, I. Environmental Research &
17
Technology, Development and application of a simple method for evaluating air quality
18
models, American Petroleum Institute, 1985.
19
https://books.google.dz/books?id=lKrpAAAAMAAJ.
20
[88]
L. Mentaschi, G. Besio, F. Cassola, A. Mazzino, Problems in RMSE-based wave model
21
validations, Ocean Model. 72 (2013) 53–58.
22
https://doi.org/10.1016/j.ocemod.2013.08.003.
23
[89]
1981. https://archimer.ifremer.fr/doc/00001/11266/7812.pdf.
24 25
[90]
R. Bonnefille, Cours d’hydraulique maritime, Masson, Paris, 1992. https://infoscience.epfl.ch/record/25376/ (accessed October 27, 2018).
26 27
G. Damy, M. Gauthier, Production d’énergie à partir de la houle, {CNEXO}-, Ifremer,
[91]
L.H. Holthuijsen, Waves in oceanic and coastal waters, Cambridge University Press,
28
2007.
29
https://books.google.dz/books/about/Waves_in_Oceanic_and_Coastal_Waters.html?id=4
30
mUsvgAACAAJ&source=kp_book_description&redir_esc=y (accessed October 28,
31
2018).
32
[92]
A.M. Cornett, A Global Wave Energy Resource Assessment, in: Eighteenth Int. Offshore 32
1
Polar Eng. Conf., International Society of Offshore and Polar Engineers, Vancouver,
2
Canada, 2008: p. 9. https://www.onepetro.org/conference-paper/ISOPE-I-08-370
3
(accessed October 31, 2018).
4
[93]
E. Kasiulis, J. Kofoed, A. Povilaitis, A. Radzevičius, E. Kasiulis, J.P. Kofoed, A.
5
Povilaitis, A. Radzevičius, Spatial Distribution of the Baltic Sea Near-Shore Wave Power
6
Potential along the Coast of Klaipėda, Lithuania, Energies. 10 (2017) 2170.
7
https://doi.org/10.3390/en10122170.
8
[94]
Waves in Finite Water Depths, Energies. 10 (2017) 460.
9
https://doi.org/10.3390/en10040460.
10 11
[95]
G. Hagerman, Southern New England wave energy resource potential., in: Proceedings of the Building Energy, Boston, USA, 2001.
12 13
W. Sheng, H. Li, W. Sheng, H. Li, A Method for Energy and Resource Assessment of
[96]
J.R. Joubert, An investigation of the wave energy resource on the South African Coast,
14
focusing on the spatial distribution of the South West coast, (2008).
15
http://scholar.sun.ac.za/handle/10019.1/2666 (accessed October 29, 2018).
16
[97]
M Amirat, S.M.K. El Hassar, Economies d’Energie dans le Secteur de l’Habitat
17
Consommation Electrique des Ménages “‘Cas d’un foyer algérien typique en période
18
d’hiver,’” Rev. Des Énergies Renouvelables. 8 (2005) 27–38.
19
https://www.cder.dz/download/Art8-1_3.pdf.
20
33
Table 1. The calibrated physical processes’ settings of SWAN model used for the development of the wave hindcast data base. Physical process
Formula
Parameters
Linear wind growth Exponential wind growth
Cavaleri and Malanotte-Rizzoli [83] Komen et al. [82]
Whitecapping
Janssen [80,81]
Cds1=1.0 &
Quadruplets wave–wave interactions
the discrete Interaction approximation (DIA) of Hasselmann et al. [84]
ƛ=0.25
Depth-induced breaking
Battjes and Janssen [85]
αBJ=1.0 γBJ=0.73
Bottom friction
JONSWAP [55]
CFJON=0.038 according to Zijlema et al. [86]
delta=1
Cn/4= 3.0 x 107
Table 2. Error statistic of Hm0 and Tm02 using the calibrated- and the default-setting of SWAN model Buoy
B2
Measurement Period 01-07-1999 30-06-2000
to
RMSE (m and s)
SI
Hm0 Tm02
Hm0 Tm02
Hm0 Tm02
Default setting
0.36 1.04
0.38 0.22
-0.17 -0.42 0.24 0.86
0.94 0.88
Calibrated settings
0.27 0.77
0.28 0.17
0.03 0.09 0.19 0.62
0.95 0.90
Physical Process
Bias (m and s)
MAE (m and s) Hm0 Tm02
R Hm0 Tm02
Table 3. Validation wave buoys characteristics. Buoy Name
Buoy Type/Sensor
Validated Parameters
Used Period
Tamentfoust (B1)
Non-Directional Waverider/Datawell
Hm0 _Tm02_Te
Azeffoun (B2)
Non-Directional Waverider /Datawell
Kala (B3) Palos (B4) Dragonera (B5)
Mahon (B6)
Temporal resolutions
Nbr of Observation
01-10-1998 to 31-03-1999
3h
1304
50
Hm0 _Tm02_Te
01-07-1999 to 30-06-2000
3h
2352
30
Non-Directional Waverider/Datawell
Hm0 _Tm02_Te
01-01-2002 to 31-12-2002
3h
2480
50
Directional SeaWatch/Datawell
Hm0 _ Tm02
Jan 2007 to Dec-2009
1h
25470
230
Hm0 _ Tm02
Jan 2007 to Dec-2009
1h
25222
135
Hm0 _ Tm02
Jan 2007 to Dec-2009
1h
23257
300
Directional WaveScan/HIPPY 120/Wavesense Directional WaveScan/HIPPY120/Wavesense
Depths
Table 4. Error statistics of the calibrated SWAN model results (Hm0, Tm02 and Te).
R
Buoys
Hm0 Tm02
SI Te
Tamentfoust (B1) 0.92 0.88 0.89 Azeffoun (B2) 0.95
0.9
Hm0 Tm02
Te
Bias
RMSE
MAE
(m, s and s)
(m, s and s)
(m, s and s)
HH
Hm0 Tm02 Te Hm0 Tm02 Te Hm0 Tm02 Te Hm0 Tm02 Te
0.3
0.15 0.13 0.15 0.17 0.08 0.37 0.74 0.81 0.27 0.59 0.62 0.23 0.15 0.13
0.92 0.28
0.17 0.15 0.03 0.09 0.31 0.27 0.77 0.85 0.19 0.62 0.64 0.17 0.16 0.14
Kala (B3)
0.93 0.89 0.93
0.3
0.18 0.13 0.01 -0.43 0.09 0.28 0.81 0.73 0.19 0.66 0.55 0.23 0.17 0.12
Palos (B4)
0.92 0.82
/
0.30
0.14
/
0.15 -0.07
/
0.32 0.56
/
0.22 0.44
/
0.24 0.14
/
Dragonera (B5) 0.92 0.84
/
0.30
0.18
/
0.05 -0.45
/
0.32 0.74
/
0.22 0.60
/
0.24 0.18
/
0.94 0.88
/
0.29
0.15
/
0.16 -0.30
/
0.37 0.67
/
0.26 0.54
/
0.22 0.14
/
Mahon (B6) Average
0.93 0.87 0.91 0.30
0.16 0.14 0.09 -0.17 0.16 0.32 0.72 0.80 0.23 0.58 0.60 0.22 0.15 0.13
Table 5. Characteristic of the fourteen stations selected for the detailed analysis.
Station N°
Province
Coordinate
1 2 3 4 5 6 7 8 9 10 11 12 13 14
El-Taref Annaba Skikda Jijel Bejaia TIZI-Ouzou Boumerdes Algiers Tipaza Chlef Mostaganem Oran Ain-Temouchent Tlemcen
8.20°E 7.37°E 6.90°E 5.77°E 5.23°E 4.40°E 3.53°E 3.07°E 2.37°E 1.13°E 0.17°E -0.40°E -1.40°E -1.90°E
37.10°N 37.16°N 37.00°N 36.90°N 36.73°N 36.93°N 36.83°N 36.83°N 36.70°N 36.53°N 36.20°N 35.93°N 35.43°N 35.23°N
Depth
Distance from the shoreline
Distance from the port
106 203 108 224 213 255 223 327 155 185 111 131 107 110
16.8 km 9.3 km 6.58 km 8.0 Km 9.2 km 3.3 km 3.9 km 3.5 km 6.3 km 4.2 km 10.4 km 3.2 km 13.3 km 14.1 km
30 km 11.9 km 11.7 km 08.50 km 12.00 km 3.7 km 3.95 km 6 km 13.8 km 5.3 km 21 km 16.5 km 14.5 km 14.8 km
Table 6. Some statistical results of the wave power resources in the fourteen selected stations Station S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14
MEAN (kW/m) 11.18 11.78 8.54 8.38 4.99 7.28 5.45 5.63 4.72 4.40 5.53 5.73 4.59 4.37
MAX (kW/m) 593.12 641.84 532.23 557.03 328.55 382.82 367.70 444.70 359.21 425.24 392.62 575.48 274.73 302.08
Hm0<0.5 (%) 19.37 17.53 25.91 23.33 38.05 18.20 26.00 19.26 20.97 20.53 24.80 20.11 29.32 24.18
SV
MV
COV
1.57 1.58 1.50 1.51 1.34 1.41 1.42 1.29 1.17 0.90 0.96 0.80 0.94 0.75
1.72 1.73 1.65 1.67 1.51 1.59 1.62 1.48 1.36 1.05 1.05 0.89 0.96 0.75
0.21 0.20 0.20 0.20 0.21 0.19 0.18 0.16 0.15 0.14 0.20 0.17 0.19 0.13
Pw-Total (MWh/m/year) 98.01 103.27 74.90 73.50 43.75 63.78 47.76 49.38 41.35 38.58 48.45 50.24 40.28 38.36
Fig 1. Illustration maps of the model setup domain, the interest area, the Algerian population density, the annual consumption of electricity in each coastal province, the position of the validation buoys, and the position of the stations selected for the detailed analysis.
Fig. 2. Q – Q plots of simulated wave power resource against the buoys observation from 01-10-1998 to 31-031999 in Tamentfoust buoy, from 01-07-1999 to 30-06-2000 in Azeffoun Buoy and from 01-01-2002 to 31-122002 in Kala Buoy.
Fig. 3. Time series plot of the significant wave height Hm0, the energy period Te and the computed wave power Pw results obtained by the calibrated SWAN model and buoy observation from 01-07-1999 to 30-062000.
Fig. 4. Spatial distributions of mean and maximum significant wave heights (top) and mean and maximum wave power (bottom) over 39 years.
Fig. 5. Spatial distributions of mean wave power flux during decadal years 1979 – 1988, 1989 – 1998, 1999 – 2008 and 2009 - 2017
Fig. 6. Monthly and seasonal distribution of the wave power flux average during 39 years
Fig. 7. Spatial distribution of the seasonal and inter-annual wave power flux COV during 39 years
Fig. 8. Spatial distribution of the monthly wave power flux COV during 39 years
Fig. 9. Monthly variability index and seasonal variability index of the wave power flux from 1979 to 2017
Fig. 10. Spatial distribution of WEDI index for 39-year hindcast
Fig. 11. Annual and seasonal averaged wave power resources and the maximum wave power observed during 39 years at fourteen locations along the Algerian coast
Fig. 12. Wave energy development index at fourteen locations along the Algerian coast
Fig. 13. Monthly mean wave power flux availability during 39 years at the fourteen selected stations
Fig. 14. Proportion of significant heights below 0.5 m recorded on a three-hour scale over 39 years and the hourly variation profiles of the average wave energy
Fig. 15. Probability of occurrence and the wave energy flux roses during 39 years at fourteen locations along the Algerian coast
Fig. 16. Total wave energy distribution as a function of significant wave height and energy period at fourteen locations along the Algerian coast
Fig. 17. Map of total annual wave energies in the 15 km of the shore band
Fig. 18. Probability map of calm sea states in the 15 km of the shore band
Fig. 19. Results of multi-criteria analysis, the areas in red color are characterized by a total annual energy that exceed 100 MWh/m/year at 15 km from the coast, and a probability to have a calm sea stat less than 18%
Highlights: •
A first detailed long-term assessment of wave energies in the Algerian coast.
•
Largest hotspot area in the W-Med basin is located in the Eastern coast of Algeria.
•
High resolution SWAN model (~3km) calibrated for the Algerian basin was used.
•
A new accurate 39-year wave energy hindcast dataset has been developed.
•
Wave energy resources tend to increase further since 1995 in the Algerian coast.
Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: