wave energy potential along the east coast of peninsular malaysia

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Energy xxx (2014) 1e13

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Energy

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Wave energy potential along the east coast of Peninsular Malaysia

Ali Mirzaei, Fredolin Tangang*, Liew JunengSchool of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor,Malaysia

a r t i c l e i n f o

Article history:Received 31 October 2013Received in revised form31 January 2014Accepted 1 February 2014Available online xxx

Keywords:Wave powerPeninsular MalaysiaWAVEWATCH III�Sheltering effect

* Corresponding author. Tel.: þ60 192718986.E-mail addresses: [email protected], ftangang@gm

http://dx.doi.org/10.1016/j.energy.2014.02.0050360-5442/� 2014 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Mirzaei Adx.doi.org/10.1016/j.energy.2014.02.005

a b s t r a c t

The wave power potential along the east coast of Peninsular Malaysia was investigated using the 31-year(1979e2009) output simulation of the NOAA WAVEWATCH III�. The result shows strong seasonalfluctuation in which the wave power during winter monsoon is much higher than other seasons.Additionally, the wave power also fluctuates inter-annually due to the El Niño-Southern Oscillation(ENSO). It was revealed that wave power along the northern section of the coast is more energetic thanthe southern region, with mean annual of 4 and 2.5 kW/m, respectively. The significant difference be-tween the two regions is due to the sheltering effects of the multiple islands. The 5% exceedance values,which represent the highest wave power, range from 8 to 15 kW/m and 1.5 to 4.2 kW/m for the northernand southern sections of the coast, respectively. It was also found that the bulk of the wave energy flux isgenerated by waves with significant wave height between 1 to 3 m and mean wave energy periodsbetween 6 to 9 s. Generally, with efficient wave energy converters, the renewable wave energy can beviable to be harvested, particularly in the northern region during winter monsoon period.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

In nature, there are different kinds of renewable resources thatpotentially can be used for the production of clean energy. As thesun heats the earth, winds are generated to transfer energy to theocean surface in the form of wind-waves. Waves transmit thisstored energy thousands of kilometers without significant loss andhence wave energy becomes one of the most important renewableenergy resources with low emission. In a maritime country withlong coastlines, wave energy can potentially be harvested to meetthe energy demands and reduce dependency on fossil fuel. How-ever, harvesting of wave energy requires a survey, research, anddevelopmental aspect to determine its viability [1].

As the wave travels from offshore toward the coast, its cumu-lative energy is reduced due to bottom topography friction [2].Nearshore, wave power is influenced by several factors includingcoastal refraction (and diffraction), wave breaking, and sea bottomroughness [3]. However, there may be locations both nearshore andoffshore that can be considered as a potential site for a wave farm;that is, the installation of wave energy convertors (WECs). Addi-tionally, in some regions, wave power fluctuates seasonally aswinds are stronger in a particular season compared to others. On

ail.com (F. Tangang).

, et al., Wave energy potenti

the long-term time scale, regional climatic condition is also influ-enced by rising carbon emissions [4] and hence may affect theharvested wave energy [5,6].

The global distribution of wave energy indicates that there aremany countries that have a coastal wave climate favorable for theexploitation of this energy [3]. However, assessment on theviability of wave energy requires long-term measurements ofwaves, which can be very expensive and time consuming. Wavemodeling is likely the first tool to investigate the potential of waveenergy. In Malaysia, the east coast of Peninsular Malaysia inparticular, due to its direct exposure to the South China Sea, canpotentially be the source of harvestable wave energy. The east coastof Peninsular Malaysia forms the western boundary of the southernSouth China Sea (SCS), where incident waves can travel from farnorth into this area. With a strong northeast monsoon generatinghigh waves, wave energy in this regionmay have the potential to beharvested. However, there has yet to be a study of the feasibility forthe potential of wave energy in this region.

The most energetic waves on the earth are generated betweenthe latitudes of 30� to 60�. However, attractive wave climate can bealso found within �30� of the equator where the trade winds blow[3]. Arinaga and Cheung [7] provided an atlas of global wave energyusing 10 years of reanalysis and hindcast data. According theirstudy, the monthly median wave power from wind waves above30�N ranges from 17 to 130 kW/m while the power below 30�S issteadier throughout the year with a range of 50e100 kW/m. For a

al along the east coast of Peninsular Malaysia, Energy (2014), http://

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A. Mirzaei et al. / Energy xxx (2014) 1e132

particular location, a thorough study of feasibility is needed todetermine the potential amount of energy that can be harvested.

In recent decades, the wave energy potential assessmentbenefited from the rapid development and optimization of thirdgeneration spectral wave models. With the availability of measuredin situ and altimeter data, these models can be calibrated andvalidated. Usingwavemodels, researchers can estimate the amountof energy being produced by the waves in different regions of theworld. However, most of the studies have been carried out alongEuropean and North American coasts, although similar in-vestigations have also been conducted in some parts of Asia andAustralia. For instance, a number of studies have investigated thepotential of thewave power in different regions of Spain [8e12]. Onthe other hand, temporal trends as well as the spatial distribution ofwave characteristics (significant wave height, wave period, andwave power) have been analyzed along the Atlantic coast of thesouthern United States [13] and Hawaiian Island chain [14]. In theAsia region, Ching-Piao et al. [15] used SWAN (Simulating WAvesNearshore) model to investigate wave climate in Taiwan, in whichresearchers found higher wave energy during the winter than thesummer monsoon. Moreover, an assessment on wave energyaround the Korea Peninsula showed a sensitivity of wave energy

Fig. 1. The map providing the locations of selected sites along the east coast of Peninsular Mcontext of a much wider region of the South China Sea. M1, M2 and M3 represent the thre

Please cite this article in press as: Mirzaei A, et al., Wave energy potentidx.doi.org/10.1016/j.energy.2014.02.005

resources to seasonality and regional features [16]. A numericalwave model has also been used to carry out a nationally consistentwave resource assessment for the Australian shelf [17].

In this study, we used NOAA WAVEATCH III� to simulate wavecharacteristics in southern region of the South China Sea toexamine the potential of the waves approaching the east coast ofPeninsular Malaysia for producing a source of power. The rest of thepaper is organized as follows: Section 2 provides a description ofthe model and data. Results and discussion are given in Section 3,which is followed by a conclusion in Section 4.

2. Methods and data

2.1. Wave model description

The NOAA WAVEATCH III� (NWW3) is a third generation nu-merical wave model, widely used for the simulation of wavecharacteristics in different sea states. The development of thismodel started in 1993 and it has been comprehensively used andvalidated in many parts of the globe and in many sea conditions.The NWW3 is based on the spectral action density equation thatallows the model to incorporate large-scalewave-current

alaysia with bathymetry in the region. The lower mapplaces the region of interest in thee nested domains.


A. Mirzaei et al. / Energy xxx (2014) 1e13 3

interactions [18]. The model solves the linear balance equation forspectral wave action density A in terms of wavenumber k andwave direction q, as a slowly varying function of space x andtime t,

DAðk; q;x; tÞDt

¼ Sðk; q; x; tÞ (1)

where S is the wave spectrum. The action density spectrum A isrelated to the energy spectrum F as,

A ¼ F=s (2)

and therefore,

S ¼ F=s (3)

where s is the intrinsic wave frequencywhich is related to thewavenumber as,

s2 ¼ gktanhkd; (4)

where d is the mean water depth [18]. On the other hand, therelative frequency is related to the absolute frequency u throughthe Doppler equation:

u ¼ sþ k$U; (5)

whereU is the mean current velocity vector. However, parameterUcan be ignored if there is no current (U ¼ 0) and in this case u ¼ s.Only two-phase parameters are independent among each other(s,k, q).

2.2. Model setup

The assessment of wave energy along the east coast of Penin-sular Malaysia is based on the wave simulation in Ref. [22]. A multi-grid NWW3 model, which is a mosaic of grids with a two-way

Fig. 2. The time series comparisons o


exchange of information, was implemented in the southern re-gion of the SCS to simulate wave climate for a period of 31-year(1979e2009). Three nested domains (with resolutions ofM1 ¼ 0.333�, M2 ¼ 0.25�, and M3 ¼ 0.15�) were applied to betterresolve the underlying bathymetry and swells entering the south-ern region of the SCS [22]. Additionally, these three computationalgrids were embedded with obstacle grids to represent islands asdescribed by Ref. [19]. The model was setup using ETOPO2 [20]bathymetry at 2-minute resolution and forced by the ClimateForecast System Reanalysis (CFSR) wind data with a resolution ofw38 km (T382) [21]. The effect of currents and sea ice wereexcluded in the computation. Details of the model setup can befound in Ref. [22]. Fig. 1 shows the location of selected sites alongthe east coast of Peninsular Malaysia where the wave energy wasassessed.

2.3. Data and validation

The simulated outputs were validated against available 3-monthADCP data, which was recorded in nearshore Terengganu (102.92�Eand 5.5�N) from January toMarch 2009. The time series of observedand simulated values of significant wave height (Hs) and meanwave period (Tm) were compared and shown in Fig. 2. Moreover,Fig. 3 illustrates a wave rose of simulated and observed mean wavedirection. Validation shows an acceptable agreement between theNWW3 output and observation. However, the model slightlyunderestimated the Hs and Tm. The model performed relativelybetter in simulating Hs than Tm as indicated by the correlation co-efficients and RMSE values (Fig. 2). For the observed waves, thedirection was rather broad, ranging from southeast to northeast(Fig. 3b) with waves of higher Hs dominantly impacting from theeast direction. During this period, the wind direction is mostlynortheasterly and is associated with the winter monsoon. The un-derestimation of Hs and Tm as well as broader range in the observedwave direction could be a result of various factors. These includethe influences of local winds, wave-current interaction, bathymetry

f ADCP and simulated Hs and Tm.


Fig. 3. The wave roses of mean wave direction (a) simulated and (b) ADCP.


complexity in the very shallow area, and the wave scattering effectof the islands. These effects are either not considered or not well-resolved in the model and therefore, the biases exist in Hs and Tmwhile the range of direction of simulated waves is rather narrowandmainly east-northeasterly, consistent with the large-scale winddirection (Fig. 3b). More details about the model validation insouthern SCS can be found in Ref. [22].

Fig. 4. The annual mean spatial distribution of wave energy during the 31-yearsimulation period.

2.4. Wave energy

To determine wave energy potential along the east coast ofPeninsular Malaysia, simulation outputs of a sub-domain(100.25�Ee107�E and 1�Ne10�N) within the third domain M3(98�Ee122�E and 2�Se18�N) were considered (Fig. 1). Wave energyis a function of significant wave height (Hs) and wave energy period(Te), in which each value is independent of the direction of wavepropagation. In the model computation, the significant wave heightis defined when using a spectral approach [18] as,

Hs ¼ 4ðm0Þ12 (6)

where mn represents the spectral moment of order n,

mn ¼Z2p

0

ZN

0

f nSðf ; qÞdf dq (7)

where f is the wave frequency, q is wave direction and S(f,q) is thespectral density.

The energy period is also defined as,

Te ¼ m�1

m0(8)

where m�1 and m0 are minus the first moment and the zerothmoment (the variance) of the wave spectrum, respectively. The Tewas defined using the peak wave period Tp since the NWW3modeldoes not compute this parameter directly. The relationship be-tween Tp and Te depends on the shape of the wave spectrum andcan be expressed as,

Te ¼ aTp (9)

where the coefficient a depends on the shape of the wavespectrum and can be calculated by the numerical integration of the


wave spectrum [23]. The value of a was set to 0.9 following Goda’s[24] approximation for the JONSWAP spectrum. Moreover, theenergy propagation in traveling waves depends on the group ve-locity CG since the energy transport velocity equals the group ve-locity. Hence, the wave energy flux (Ef), through a vertical plane ofunit width perpendicular to the wave propagation direction [25] isequal to:

Ef ¼ ECG (10)

where E is the wave energy and CG is the group velocity, which isdescribed as a function of wave frequency f and water depth d:


Fig. 5. The seasonal mean spatial distribution of wave energy flux during the 31-year simulation period.


CGðf ; dÞ ¼ g4pf

�1þ 2kd

sinhð2kdÞtanhðkdÞ�

(11)

and k ¼ 2p/L is the wave number and L is the wave length. In deepwater conditions, (d > 0.5L) the group velocity is defined as,

CG ¼ g=4pf (12)

For a sinusoidal wave of height Hs, the average energy stored on ahorizontal square meter of the water surface is:

E ¼ rgZN

0

Sðf Þdf ¼ 116

rgH2s (13)


Half of this is potential energy due to the weight of the waterlifted from thewave troughs to thewave crest. The remaining half iskinetic energy residing in the motion of the water. Therefore Eq.(10) can be rewritten as,

Ef ¼ rg2

64pH2s Tezð0:49ÞH2

s Te kW=m (14)

where r and g are seawater mass density (1025 kg/m2) andgravity acceleration (9.8 m/s2), respectively. The above formulastates the wave power is proportional to the wave period and tothe square of the wave height. Moreover, when a significant waveheight is given in meters and the wave period in seconds, theresult is power in kilowatts (kW) per meter of the wave frontlength. In deep water, the group velocity equals half of the phasevelocity and hence it is independent of water depth (Eq. (12)).


Fig. 6. The wave power roses for each of the selecte

Table 1The coordinates of the selected sites including their positions with respect to thenearby island.

Site names Location Depth (m) Position

P1 103.10� Ee5.80� N 51 In front of IslandP2 103.30� Ee5.23� N 17 e

P3 103.72� Ee4.85� N 31 e

P4 103.55� Ee4.50� N 27 e

P5 103.50� Ee3.80� N 17 e

P6 103.60� Ee2.80� N 8 ShelteredP7 104.25� Ee2.80� N 24 In front of IslandP8 104.00� Ee2.50� N 9 ShelteredP9 104.20� Ee2.00� N 16 Sheltered



However, as the waves propagate toward the coastal shallowwaters, group velocity changes according to the water depth andwave length to the point where it becomes equal to phase speedin a given region where d < 0.05 L. In this instance, Eq. (14) re-quires adjustment to take into account the energy fluctuationsdue to bathymetric changes. Following [26] the adjusted wavepower is given as,E*f ¼ bEf (15)

where b is the proposed correction factor based on the cross-correlation between the energy period (Te) and significant waveheight (Hs):

d site based on the 31-year model simulation.


Fig. 7. The annual mean wave energy flux for each selected site during the 31-year simulation period.


b ¼ 1þCOV Te;Hs

2 (16)

�2�

Te$Hs

Due tomany uncertainties in computing the correction factors, amuch smaller value of the second term on the right hand side mayindicate how applying the correction factor is at times unnecessary.On the other hand, large values imply an underestimation of waveenergy flux.

Table 2The correction factor values used for wave energy flux calculation for each of the site.

Sitenames

Annual Winter(DJF)

Spring(MAM)

Summer (JJA) Autumn(SON)

P1 1.50 1.11 1.44 1.13 1.45P2 1.54 1.11 1.45 1.08 1.42P3 1.45 1.10 1.38 1.08 1.33P4 1.44 1.11 1.37 1.09 1.31P5 1.46 1.09 1.33 1.08 1.28P6 1.58 1.08 1.26 0.82 1.29P7 1.47 1.09 1.38 1.07 1.29P8 1.00 0.97 0.99 0.98 0.98P9 1.21 1.02 1.20 1.10 1.11

3. Results and discussion

3.1. Spatial distribution of wave energy

The east coast of Peninsular Malaysia is directly exposed to theSCS and it can be greatly affected by waves generated from regionsfar away (e.g. northern and central SCS). However, the existence ofmultiple islands and the bathymetric steepness in Sunda shelf mayobstruct, refract, and reduce the wave heights and consequentlyaffect wave energy. Fig. 4 depicts the annual average of wave energyin the entire sub-domain calculated using Eq. (14). Consistent withthe distribution of Hs described in Ref. [22], wave energy distribu-tion shows decreasing magnitude towards the east coast ofPeninsular Malaysia due to increasing the bottom friction over theSunda shelf. The amount of energy in the coastal areas is about aquarter of those in the eastern open boundary of the domain. Inaddition to the bathymetric effect, wave energy is also influencedby the blockage of multiple islands. The propagation of wave en-ergy in the northeast corner of the domain is significantly blockedby the Can Dáoisland in the southern coast of Vietnam.Moreover, inthe southeastern corner of the domain, the existence of theAnambas islands provides an effective obstruction of wave energyfrom reaching the southern region of Peninsular Malaysia. Inaddition, Tioman Island, located off the east coast of PeninsularMalaysia, also plays a significant role in reduction of wave energy.

The weather and climate over the region is modulated by theAsian-Australian monsoon system, which features pronouncednortheast and southwest winds during winter (DJF) and summer(JJA) seasons, respectively [27]. However, during the transitionalseasons of spring (MAM) and autumn (SON), inconsistent windsprevail. These seasonal changes in the wind affect the seasonalwave energy and thus also affect wave power distribution. Fig. 5shows the spatial distribution of wave power during DJF, MAM,JJA, and SON. Generally, the wave power distributions during SONand DJF resemble to that of the mean wave energy shown in Fig. 4.The highest magnitude of wave power occurs during DJF implyingthe dominance of waves during the winter monsoon. During thisperiod, the magnitude of wave power exceeds 12 kW/m in the openeastern boundary of the domain but toward the east coast ofPeninsular Malaysia the amplitude decreases to approximately


5 kW/m. The wave power during SON is lower due to the weakerwinds with magnitudes ranging between 1e3 kW/m along thecoast. DuringMAM, the magnitudes of wave power are much lowerdue to much weaker winds condition. Nevertheless, the distribu-tion still resembles those of DJF and SON. Along the coastal region,the wave power becomes less than 0.5 kW/m. Moreover, the role ofislands is obvious in the reduction of wave power especially in thesouthern region.

The distribution of wave power during JJA is remarkablydifferent, with higher values concentrating in eastern part of theGulf of Thailand. Moreover, the area with relatively higher wavepower of around 2 kW/m extends to southern region of the domain.These patterns are mainly due to the southwest monsoon windconditions that generate strong local waves [22]. Along the eastcoast of Peninsular Malaysia, these local waves could interact withincoming swells from the northern and central regions of the SCS,resulting in shorter wave periods and heights and therefore areduction of wave power. However, in the Gulf of Thailand, theabsence of incoming swells from the SCS, due to the shadowingeffects of the Indo-China Peninsula, results in less wave-waveinteraction. Such a lack of wave-wave interaction promotes wavegrowth towards the eastern coast of Cambodia. In the region southof the Anambas islands, the diffracted swells could interact with thelocally generated waves resulting in relatively lower wave power(<2 kW/m) (Fig. 5).

3.2. Temporal distribution of wave power

The wave conditions along the east coast of Peninsular Malaysiaare influenced by seasonal and inter-annual changes of the climate[22]. Moreover, due to the existence of nearshore islands, waveconditions are also dependent on site location along the coast. Ninesites with different depths were selected to investigate the tem-poral distribution of wave power along the coastal area of Penin-sular Malaysia (Fig. 1; Table 1). The annual wave roses wereconstructed based on the 31-year simulated data for these sites


02468

1012141618

Wav

e en

ergy

flux

(kW

/m)

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010Year

(a) − DJF

0

1

2

3

4

5

6

7

Wav

e en

ergy

flux

(kW

/m)

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010Year

(b) − MAM

0.0

0.5

1.0

Wav

e en

ergy

flux

(kW

/m)

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010Year

(c) − JJA

1980

1

2

3

4

5

6

7

8

Wav

e en

ergy

flux

(kW

/m)

1978 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010Year

(d) − SON

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9

Fig. 8. As in Fig. 7, except for seasonal mean wave energy flux.


(Fig. 6). As indicated, the incident waves are dominantly east-northeasterly with northern (southern) sites exhibit higher(lower) intensity of wave power. Most sites in the southern regionare sheltered by multiple islands except P7.

Fig. 7 shows the time series of annual means of wave power inthe 31-year period from 1979 to 2009 for these sites, calculatedaccording to Eq. (15). The wave power values were correctedusing Eq. (16) with correction coefficients listed in Table 2.Generally, the annual mean of wave power decreases when goingsouthward, with P1 and P8 having the highest and the lowestvalues, respectively. For the northern sites, the wave power


ranges between 4e6 kW/m. On the other hand, for the southernsites the values do not exceed 2 kW/m, with the exception of P7.The relatively large amount of wave energy in northern andcentral parts of the coast is due to their open exposure to the SCS.As a comparison, in an enclosed basin like the Black Sea, themagnitude of wave power is much lower because of limited fetch[28]. In southern part of Peninsular Malaysia, the shadowing ef-fect of Tioman and Anambas islands significantly reduces waveenergy. Nevertheless, the values also are dependent on whetherthe location of the selected site is behind or in front of an island.For example, P1 and P7 are located in front of Redang and Tioman


Annual0

2

4

6

8

10

Wav

e en

ergy

flux

(kW

/m)

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9

Winter Spring Summer Autumn

Fig. 9. The averaged annual and seasonal means of wave energy flux in selected sites.

Fig. 10. The annual mean of wave energy flux 5% exceedance values for the selected sites based on the 31-year simulation period.

0.00

0.05

0.10

0.15

0.20

Wav

e en

ergy

flux

(kW

/m)

1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010Year

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9

Fig. 11. As in Fig. 10, except for the wave energy flux 90% exceedance values.

Fig. 12. The averaged annual and seasonal means of the wave energy flux 5% exceedance values for the selected sites based on the 31-year simulation period.


Please cite this article in press as: Mirzaei A, et al., Wave energy potential along the east coast of Peninsular Malaysia, Energy (2014), http://dx.doi.org/10.1016/j.energy.2014.02.005

0.0

0.5

1.0

1.5

Wav

e en

ergy

flux

(kW

/m)

Year

Mean annual Winter Spring Summer Autumn

3P1P 9P8P7P6P5P4P2P

Fig. 13. As in Fig. 12, except for the wave energy flux 90% exceedance values.


islands, respectively; hence they have relatively large wavepower.

The wave power along the coast also shows inter-annualvariability. Generally, the inter-annual signal is stronger in thenorthern and central parts of the coast due to their open expo-sure to the SCS. Sites in the southern region, especially P8, showlesser or insignificant variability. Mirzaei et al. [22] showed theinfluence of both conventional ENSO and ENSO Modoki on the Hs,particularly in the open sea region of the SCS. Hence, the largeinter-annual variabilities in the northern and central parts of thecoast are basically remote influences originating from the openregion of the SCS.

Fig. 8 depicts the time series of seasonal mean wave power forthe selected sites. Generally, wave power is higher during the DJFseason due to strong weather condition during this period [22]. Thewave power appears to be minimal during the JJA. Hence, theannual mean is mainly contributed by the DJF (Fig. 9). The sea-sonality of the wave power is more evident for the northern andcentral parts of the coast.

Additionally, thewave power also shows inter-annual variabilitythat has pronounced seasonality. The annual wave power indicateshigher peak value during 1984 (Fig. 7) coinciding with the 1984/85La Niña [29]. However, as shown in Fig. 8, it mainly benefited fromthe higher peak of wave power during December 1984 andJanuaryeFebruary 1985. Such a peak is clearly absent during the1984 JJA and SON seasons. The seasonal behavior of wave powerduring a La Niña event can be explained by how the event affectswave conditions in the SCS. During a La Niña event, the Niño3.4index is negatively (positively) correlated with Hs in DJF (JJA) [22].Hence, one expects that the Hs (and wave power) is higher (lower)in December 1984 and JanuaryeFebruary 1985 (JJA1984). In 1998,lower mean annual wave power is depicted in Fig. 7, which coin-cided with the 1997/98 El Niño.

3.3. Wave power exceedance

Due to the extreme weather conditions that may occur in theregion, especially during winter monsoon [30], it is equallyimportant to examine the statistics of the wave power tail dis-tribution in addition to the mean values described in previoussection. Figs. 10 and 11 represent the time series of the 5% and90% exceedance values of wave power, respectively, for theselected sites. The 5% exceedance represents the extreme valuesthat occupy the right-end tail of the wave power probabilitydistribution. Likewise, the 90% exceedance provides the lowestvalues representing the left-end tail of the wave power distri-bution. The distribution of extreme wave power along the coast


is consistent with the annual mean (Fig. 10). The northern andcentral parts of the coast exhibit higher 5% exceedance wavepower that ranges between 14e22 kW/m. In comparison, thesouthern parts experience lower extreme values (e.g.P8 < 2 kW/m). Generally, similar to the mean, the extremevalues are also modulated by inter-annual variability. On theother hand, the 90% exceedances are much lower with valuesnot exceeding 0.2 kW/m (Fig. 11). Nevertheless, the inter-annualvariability is still strongly featured in the 90% exceedancevalues.

Figs. 12 and 13 depict the seasonal variation of 5% and 90% ex-ceedance values of the selected sites, respectively. Consistent withseasonal means (Fig. 7), the extreme values of wave power arehigher during DJF compared with other seasons due to the extremeweather conditions. In northern section of the coast, the extremevalues are higher ranging between 22e24 kW/m due to openexposure to the SCS (Fig. 12). However, values gradually decreasetowards the south as the effect of island blockage becomesimportant. The lowest extreme values of around 1e4 kW/m occurin P8 due to the sheltering effect of Tioman Island. On the otherhand, as shown in Fig. 13, during the winter monsoon the 90%exceedance values are relatively higher (i.e. 0.5e1.5 kW/m)compared with other seasons.

3.4. Characterization of wave energy potential

The annual wave energy resources in selected study siteswere characterized according to the distribution of significantwave heights and energy periods over 31 years in the form ofa scatter diagram (Figs. 14 and 15). These scatter diagramsprovide a simultaneous visualization of the occurrence ofdifferent sea states and corresponding wave energy. Thenumber inside the figure indicates the mean annual occur-rences of sea states (number of hours per year), which weretabulated into a bin of intervals of DHs ¼ 0.5 m and DTe ¼ 1 s.Moreover, the color-shaded bins represent the annual energyflux in kWh/(m � year). For each bin, this value is calculatedby multiplying the mean annual of occurrences with the cor-responding wave power density (in kW/m) and 3, since thesimulated data is in 3 hourly. Meanwhile, the isolines in eachplot depict the corresponding wave power density. For sitesalong the northern section of the coast, the bulk of the energyflux is contributed by waves with a Hs between 1e3 m and Teof 6e9 s (Fig. 14). Consistent with Fig. 7, the distribution ofenergy flux in the selected sites decreases southward. Thelevel of energy flux for sites P6, P8 and P9 are much lowerdue to the sheltering of multiple islands and shallow depths.


Fig. 14. Scatter diagram of wave energy resource (based on the 31-year average) in terms of Hs and Te for sites along northern section of the coast. The color scale depicts annualwave energy per meter per year, the numbers within the graph represent the occurrence of sea estate in term of number of hour per year, and isoclines refer to wave power.


The bulk of the energy flux for these sites is mainly charac-terized by waves of Hs between 1e2 m and Te of 7e9 s(Fig. 15). However, for site P7, the energy flux is comparativelyhigher since it is located in front of Tioman island and it isexposed to a long fetch.


4. Conclusion

This study assesses the wave energy potential along the eastcoast of Peninsular Malaysia based on the 31 years (1979e2009)of simulation outputs for the 3rd generation NOAA WWIII�


Fig. 15. As in Fig. 14 except for sites in the southern section of the coast.


model. The model performance was reasonable in simulatingwave characteristics during the validation period. It was also ableto reproduce the effect of the islands’ obstruction of waves. Thespatial distribution of simulated Hs indicated higher values alongthe northern section of the coast, where waves generated fromthe central and northern South China Sea can penetrate directlydue to a long fetch. Along the southern section of the coast, thesheltering effect of multiple islands both scatters and obstructsthe incoming waves to result in a lower magnitude of Hs in thissection.

For a wave energy potential assessment along the coast, a totalof 9 sites were selected with variable depths and positions withrespect to nearby islands (Table 1). Due to the reduction of ba-thymetry in the Sunda Shelf region, it is expected that the calcu-lation of wave energy potential based on deep water wave powerformulation will result in an underestimation of wave energy. As aremedial measure, a correction factor based on the covariance of Hs

and Te was applied in calculating the wave energy potential for the9 selected sites.

Generally, the wave energy flux is higher for sites located alongthe northern section of the coast while in the southern part themagnitudes are relatively lower. However, higher wave energy fluxfor site P7 shows that the site’s location with respect of the nearbyisland is critical in determining the energy power. Moreover, the


wave energy flux exhibits strong seasonal fluctuation with higher(smaller) values during the winter (summer) monsoon. Addition-ally, significant inter-annual variabilities are also featured in thewave power time series. Overall, the annual average of wave powerfor sites in the northern (southern) section of the coast ranges from2.6 to 4.6 kW/m (0.5e1.5 kW/m). Interestingly, the bulk of theenergy flux is contributed bywaves withHs(Te) between 1e3m (6e9 s) and 1e2 m (7e9 s) for sites in northern and southern sectionsof the coast, respectively.

In general, wave power along the east coast of PeninsularMalaysia is lower than those regions located in open ocean withdeeper depths. However, with increasing wave energy converter(WEC) efficiency and advancing technologies in the foreseeablefuture, it may still be viable to extract the wave renewable energyfrom this region, especially during the winter monsoon. Never-theless, the results of this study suggest that the site selection of awave energy farm is critical.

Acknowledgments

This research is funded by the grants of MOHE LRGS/TD/2011/UKM/PG/01, MOSTI Science fund 04-01-02-SF0747 and the Uni-versiti Kebangsaan Malaysia DIP-2012-020 and DPP-2013-080. Theauthors are grateful to the Institute of Oceanography and



Environment, Universiti Malaysia Terengganu for providing theADCP data.

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