Proceedings of International Forum for Green Asia 2017
Special issue of Evergreen - Joint Journal of Novel Carbon Resource Sciences & Green Asia Strategy3
Wave Energy Resource Assessment around Kyushu based on Numerical Hindcast

Tomoaki Hirakawa
IGSES, Kyushu UniversityAbstra ct
To promote wave energy technologies, including wave energy converters (WECs), this study
aims to present comprehensive analyses of wave climates around Kyushu. This paper calculates wave
climate around Kyushu for 10 years (2007 -2016) using the Simulating Waves Nearshore (SWAN)
model with an approximately 800 m high spatial and 1 -hour temporal resolution. Results revealthat the south side of Kyushu has more energetic waves than the north, and the southeast has
the strongest energy among neighboring seas of Kyushu. In the southeast, the major portion of
the wave energy transport is generated by extreme weather events. Wave climates in the southwest
are less affected by extreme weather events, and the wave energy transport shows the opposite
tendency to that of the southeast, suggesting that wave energy production in the southeast andsouthwest can supply high stable energy.

1. Introduction

were not discussed.

While acknowledging the difficulty for renewables to take up nuclear energy’s share in near
future, the promotion of renewable energy technology is one of the key policies for Japan ’s
stable future energy structure. In Japan, thefirst wave energy resource assessments were performed by Maeda and Kinoshita (1979)[1], stimulated by two oil crises. Maeda and Kinoshita(1979) calculated the distribution of mean wave
energy transport P¯ (kW/m) around Japan from1954 to 1963. For Kyushu, the results showedthat P¯ = 11.57 in the east, ranging a latitude of30 ° -35 ° and a longitude of 130 ° -135 ° , and P¯= 9.91 in the west, ranging a latitude of 30°- 35 ° anda longitude of 125 ° -130 ° . Maeda et al. (1981) [2]also obtained P¯ = 12.7 for the east region and P¯ =11.0 for the west region from 1954 to 1963.
Tabata et al. (1980) [3] obtained P = 7.28 forsoutheast of Kyushu and P = 0.623 southwest of
Kyushu for 1975 to 1978. Takahashi et al.
(1989) [4] obtained P¯ = 6.7 southeast of Kyushu andP¯ = 1.6 southwest of Kyushu for 1970 to 1984.
Directions and positions of P ¯ propagatingfrom the southeast and southwest toward
Kyushu described by Tabata et al. (1980) andTakahashi et al. (1989) are depicted in figure 1.
Takahashi et al. (1989) also studied the joint
distribution of H 1 / 3 and T1 /3 around Japan;
however the wave direction and directional spectrum were not discussed. Recently, a wave
energy assessment was performed for Japan between 1994 and 2014 using WaveWatchIII. The
mean wave energy transport 30 km off Japanesecoasts was estimated as P¯ = 7.35. However,comprehensive analyses of waves around Kyushu

This study aims to present comprehensive
analyses of statistics of wave characteristics
around Kyushu using a high-resolution numerical model.
2. Data and Model

FIGURE 1.
Five computational domains andbathymetry in Kyushu. Red dots describe NCEP
data used for boundary conditions of SWAN’s computation. Two arrows describe directions and positions of the mean wave energy transport P¯ calculated by Tabata et al. (1980)[3] and Takahashi
et al. (1989) [4] according to their papers.

10

Proceedings of International Forum for Green Asia 2017
Special issue of Evergreen - Joint Journal of Novel Carbon Resource Sciences & Green Asia Strategy3
The present study makes use of a third generatio n wave mod el, Simulating W aves
Nearshore (SWAN)[5, 6]. The basis of the for mulation is the wave action balance equation:
""

"

"

"

resolution in our computation (see table 1 fordetails).

Σ!"# + $% "% + $& "& + $σ "σ + $' "'( ) = σ (1)w h e r e N , d e f i n e d a s N ( x , y , t , σ , θ ) = S(x,y, t, σ, θ) /σ, is the action density, S is thedirectional wave energy density spectrum (herein after referred to as directional spectrum), θis the direction, x and y are the horizontal coordinates, and t is time. σ is the relative wave
frequency, defined as σ = ω-k u where ω is theabsolute wave frequency, k is the wave numbervector, and u is the ambient current velocityvector. The Σ in (1) descries source terms. For
inputs to SWAN, wind forcing was providedby JMA. Bathymetry data are provided fromGEBCO 30 arc-second gridded data.
We used datasets containing significant wave
heights, primary wave mean periods and primary wave directions for every three hours f rom

FIGURE 2. Spatial

distribution of the mean wave
energy transport P¯ in domain 5 for 10 years.

In figure 2, contour line for P = 15 is close to thesoutheast coasts of the main island. These coasts
have the most energetic waves around Kyushu.
However, the energy of strong waves is mainly

WaveWatchIII data provided by the U.S. Na tional Oceanic and Atmospheric Administration(NOAA). The red dots in figure 1 show locations
from NOAA datasets that were taken for theboundary shown in the black square.

concentrated in the end of 05-07 and 08-10.
We see this from figure 3. In 08 -10, Kyushu
is frequently struck by typhoons from thesouth tropical region; consequently, meanseasonal wave energy transport in 08-10 significantly increases up to approximately P¯ = 30 in thesoutheast.

To calculate, computational domains withmore detailed grid cells were nested in domains
with coarser grid cells and received boundary
conditions from results of coarser domains. Domain 1 is the largest of five domains. Domain
2, which is in domain 1, is the second largest
and holds three smaller domains, designated domains 3, 4, and 5. Detailed descriptions of do mains are shown in table 1.
Table 1: Setups of computational domains. The
first number in each cell is regarding the longitude,
and the other one is regarding latitude.
Domain
name
Domain 1
Domain 2
Domain 3
Domain 4
Domain 5Distances i des ( )12 1255111125 2◦,,,,.

,of Number
Grid cells
200 200
200 200150 150
150 150375 300,,,,,of Distancecells (m)6000 6700
2400 2800
620 740630 740640 740of,,,,,

3. Result and discussions
3 . 1 Y e a r l y a n d s e a s o n a l me a n w a v e e n ergy transport P ¯
The mean wave energy transport P ¯ is examined in this section. It is revealed that the south
side of Kyushu possesses larger wave energy than
the north side does, which was overlooked in previous studies due to the absence of measured
data. Figure 2 shows P ¯ calculated for 10 years indomain 5, which was performed with the highest

Considering less extreme weather events fromfigure 3, we selected a site 10 km off the tip of thepeninsula in the southwest, which is indicated by
x2 in figure 2. Figure 2 shows that waves at x2
are expected to have a yearly mean wave energy
transport of P ¯ 10. P at x2 is relatively constantand shows a completely different tendency tothat at x1. In 05-07, wind from the north west
starts shifting to the southwest and south;
consequently, P at x2 decreases and P accordingly increases for the southeast coast because
the long fetch and duration in the Pacific.
The
completelydifferent
tendencies
between P in the southeast and southwest
suggest that wave energy production in theseregions can compensate for each other tosupply high stable energy.
Statistical wave characteristics a re importantfor estimating and improving wave energy production. In the next section, we analyze statis tical wave characteristics at 1 and 2 using
several useful plots.
x11x

Proceedings of International Forum for Green Asia 2017
Special issue of Evergreen - Joint Journal of Novel Carbon Resource Sciences & Green Asia Strategy3

FIGURE 3. Seasonal spatial distributions of mean wave energy transport P calculated in domain 5 for (a)11-01; (b) 02-04; (c) 05-07; and (d) 08-10. The wind fields are obtained from JMA and extrapolated bySWAN.

3.2 Wave characteristics

The water depths at x1 and x2 are 120
and 187 m, respectively; therefore only offshore
WECs are suited for these sites. Considering
the distance from the coasts regarding electrical
power transmission, x1 and x2 are selected tobe approximately 10 km off the coasts. At bothx1 and x2, P can be approximated by the wave
energy transport for deep water Pdeep, assuming
a linear narrow-band spectrum and deep water:
P Pdeep = ρg 2H 2 m0 T e /(64π), where T e describes the period of wave energy, and we used
T e = Tmm10 in this study.
We studied the seasonal joint distribution andhistograms of Hm0 versus Tmm10. At x1 in 02- 04
and 11-01, joint distributions resemble eachother, having small mean significant wave height¯Hm0 and period ¯Tmm10 (not shown here). In05-07 to 08-10, as expected from figure 3. extreme weather events are apparent from large
Hm0 and long Tmm10 (see figure for season 0810). Peaks of the contour plot and histogramsin 08-10 indicate that (Tmm10, Hm0) (8, 1)
is the mode and more likely to occur, whereas
the mean significant wave height and period are( Tmm10, Hm0) = (8.71, 1.86). The large difference
between the mode and mean suggest that themean values ( Tmm10, Hm0) are strongly affectedby extreme weather events.
Figure 5 shows means of seasonal directional
spectra ¯S(f, 0) at x1 and x2 in the season 08-10∑M
and for 10 years: S ¯ = 1 j=1 Sj. In 02-04 andM11-01, S¯ also resemble each other, both showingthat the spectrum S(0.46, 85) carries the largest
energy (not shown here). In 05-07 and 08-10,
large two peaks are broadly distributed in 0.
Peaks in the direction 0 90 correspond to ordinary waves observed throughout the years, andthe other peak in the direction 0 150
corresponds to extreme waves. There is little
difference between frequencies f of ordinary

waves and extreme waves. Figure 6 shows
seasonal wave roses for the season 08-10 andx1 and x2 which are two-dimensional
histograms consisting of Hm0 and the mean wave
direction 0j. The wave rose for 08-10 showsthat extreme waves propagate in 0 140.
The relatively small seasonal variation in wave
direction suggests that the use of WECs, de pending upon wave direction, is possible in thesoutheast.

Joint distributions of (Tmm10, Hm0) andhistograms of Hm0 and Tmm10 at 1 (top) and 2 (bottom)
for 08-10. Contour lines in joint distributions show
wave energy transports for deep water P¯ (kW/m)of indicated on the values vertical axis at the right.
FIGURE 4.

x12xProceedings of International Forum for Green Asia 2017
Special issue of Evergreen - Joint Journal of Novel Carbon Resource Sciences & Green Asia Strategy

3
by Tabata et al. (1980) and Takahashi et al.
(1989).
According to the spatial distribution of sea sonal P ¯ , extreme weather events frequently
strik e th is reg ion in 0 8 -1 0 . In 0 8 -1 0, at x 1
which is approximately 10 km off the coast, themonthly P ¯ and the ratio of monthly P ¯ relative
to total yearly P¯ are expected to reach P¯ = 29 and0.45, respectively. Although the yearly P¯ isexpected to be powerful on the southeast coast,a stable high-energy supply cannot be possible,
and such extreme weather events may cause se vere damage to WECs. P at x2 is relatively
constant and shows the opposite tendency of
P at x 1 . T h e o p p o site ten d e n cie s b e twe e n
P in the southeast and southwest suggest thatif WECs can efficiently absorb wave energyincluding those of extreme waves, wave energy
product southeast and southwest of Kyushu cancompensate each other to supply high stableenergy.

FIGURE 5. Normalized 10-years mean directional
spectra ¯ S ( f,9 ) at 1 (left) and 2 (right) for 08- 10.
¯S (f, 9 ) are normalized with the maximum values
described in each figure.
xx

Statistical wave characteristics in the southeast and southwest represented by x1 and x2
also showed different tendencies in terms of seasonal wave direction variation.
The relatively small seasonal variation in wave
direction at x1 suggests that the use of WECs,depending upon wave direction, is possible inthe southeast, whereas marked seasonal variations in wave direction at x2 suggest that theuse of WECs without depending upon the wave
direction is advantageous southwest of Kyushu.
Reference

FIGURE 6. Wave roses of wave energy transport P
at 1 (left) and 2 (right) for 10 years in 08-10.
xxAt location 2 , in 02-04 and 11-01, joint distributions of Hmü and Tmm1ü are similar to each other,having larger ¯Hmü than seasons 05-07 and 08-10.
Means of seasonal directional spectra at 2 infigure 5 show more peaks and marked seasonalvariations in wave direction than those a t 1 .
O n e o f t h e p e a k s a p p r o x i m a t e l y a t ¯ S (0.5,30), is consistently observed in all seasons. In 0507 and 08-10, peaks at ¯ S (0. 4 , 90) are thought tobe caused by typhoons from the south, which issimilar to the case in 1 . In 11- 01 and 02-04, peaksat ¯S(0.5, 350) are thought to be caused by monsoon
from the continent (not shown here).
xxx[1] H. Maeda and T. Kinoshita, “Wave Power Absorption,” Inst. Ind. Sci. Univ. Tokyo , vol. 31, no.
11, pp. 1–10, 1979.
[2] H. Maeda, T. Kinoshita, and S. Kato, “Fundamental Research on Absorbing Energy from
Ocean Waves (2nd report),” J. Soc. Nav. Archit.
Japan , no. 149, pp. 65–72, 1981.
[3] T. Tabata, T. Yagyu, and I. Fukuda, “W ave
Energy on Japanese Coast,” Rep. Port Harb.
Res. Inst. , no. 364, pp. 1–15, 1980.
[4] S. Takahashi and T. Adachi, “W ave Poweraround Japan from a Viewpoint of Its Uti lization,” Rep. Port Harb. Res. Inst. , no. 654,pp. 1–19, 1989.
[5] N. Booij, R. C. Ris, and L. H. Holthuijsen, “AThird-Generation Wave Model for Coastal Regions: 1. Model Description and Validation, ” J.
Geophys. Res., vol. 104, pp. 7649–7666, apr
1999.
[6] R. C. Ris, L. H. Holthuijsen , and N. Booij,“A Third-generation Wave Model for CoastalRegions: 2. Verification,” J. Geophys. Res. ,vol. 104, pp. 7667–7681, apr 1999.

x

Seasonal wave roses at 2 also show marked
seasonal variations in wave direction; accord ingly, it is suggested that the use of WECs, withoutdepending upon wave direction, is advantageousin the southwest.
x4. Conclusion

The spatial distribution of the mean wave
energy transport P ¯ for 10 years revealed that
Kyushu has more wave energy in the south thanin the north, which was overlooked in previous
studies due to the absence of measured data.
Mean wave energy transport P ¯ is highest in thesouthwest. The spatial distribution of P ¯ for 10years showed wave energy up to P ¯ 16 in thesoutheast and P 10 in the southeast can beobtained if offshore waves are taken into con sideration, which is greater than P ¯ estimated13