WAVE HEIGHT FORECAST ING USING … recorder, 1 – 28 February and 26 Maret 1997 – 22 Augtust 1999), Rembang-Central Java (3 – 13 July 2007) , pacitan – east java(16 – 20 October

http://www.iaeme.com/IJCIET/index.

International Journal of Civil Engineering and Technology (IJCIET)Volume 8, Issue 5, May 2017, pp.

Available online at http://www.iaeme.com/IJCIET/issues.

ISSN Print: 0976-6308 and ISSN

© IAEME Publication

WAVE HEIGHT FORECAST

MEASUREMEN

DISTRIBUTION EQUATION IN JA

Denny Nugroho Sugianto, Muhammad Zainuri

Department of Oceanography,

Department Of Physics, Faculty Science And Mathematic,

Diponegoro University, Indonesia

Departement Of Civil Engineering, Faculty Of Engineering,

Diponegoro University, Ind


Gadjah Mada University, Indonesia

ABSTRACT

The information of wave

meteorological communication

data of daily or maximum wind speed average

condition on the ground. T

based on the group of wind speed

able to provide more specific

results obtained from wave forecasting showed

empirical and significant relationship between U wind speed and wave forecast

term of wave height.

Keywords: Forecasting, Wind Speed Distribution Equation, Wave Height, Java Sea

Cite this Article: Denny Nugroho Sugianto, Muhammad Zainuri, Alfin Darari,

Suripin, Suseno Darsono and Nur Yuwono Wave Height Forecasting Using

Measurement Wind Speed Distribution Equation In Java Sea, Indonesia

Journal of Civil Engineering and Techn

http://www.iaeme.com/IJCIET/issues.

IJCIET/index.asp 604 [email protected]

International Journal of Civil Engineering and Technology (IJCIET) 2017, pp. 604–619, Article ID: IJCIET_08_05_068

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=8&IType=5

6308 and ISSN Online: 0976-6316

Scopus Indexed

WAVE HEIGHT FORECASTING USING

MEASUREMENT WIND SPEED

UTION EQUATION IN JAVA SEA,

INDONESIA

Denny Nugroho Sugianto, Muhammad Zainuri

Oceanography, Faculty of Fisheries and Marine Sciences,

Diponegoro University Indonesia.

Alfin Darari


Diponegoro University, Indonesia.

Suripin, Suseno Darsono


Diponegoro University, Indonesia.

Nur Yuwono


Gadjah Mada University, Indonesia.

waves is an important factor in the service of marine

communication. During that time, analyzing wind always use

daily or maximum wind speed average, that is less able to describe the

. This research aims to determinate the duration of the wind

wind speed using Beaufort scale and of wind dire

specific information to describe the condition of the season.

wave forecasting showed equation model that demonstrate

significant relationship between U wind speed and wave forecast

Forecasting, Wind Speed Distribution Equation, Wave Height, Java Sea

Denny Nugroho Sugianto, Muhammad Zainuri, Alfin Darari,


Measurement Wind Speed Distribution Equation In Java Sea, Indonesia

Journal of Civil Engineering and Technology, 8(5), 2017, pp. 604–619.

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=8&IType=5

[email protected]

asp?JType=IJCIET&VType=8&IType=5

ING USING

T WIND SPEED

VA SEA,

Sciences,




is an important factor in the service of marine

d always uses the

less able to describe the real

the duration of the wind

wind direction that is

to describe the condition of the season. The

that demonstrate

significant relationship between U wind speed and wave forecasting in

Forecasting, Wind Speed Distribution Equation, Wave Height, Java Sea

Denny Nugroho Sugianto, Muhammad Zainuri, Alfin Darari,


Measurement Wind Speed Distribution Equation In Java Sea, Indonesia. International

.

asp?JType=IJCIET&VType=8&IType=5

Wave Height Forecasting Using Measurement Wind Speed Distribution Equation In Java Sea, Indonesia

http://www.iaeme.com/IJCIET/index.asp 605 [email protected]

1. INTRODUCTION

Waves of the Java Sea have characteristics generated by the wind, many of which are limited

by the mainland such as the island of Sumatera, Kalimantan, and Sulawesi. These conditions

make the Java Sea area has strategic position in Indonesia as international shipping routes for

the benefit of the world economy [1] [2].

The waves is an important factor in marine meteorological services. Frequent occurrence

of high waves certainly can cause disruption to marine transportation activities between

islands which can affect people`s live in land. Such things as scarcity of foodstuffs in several

small islands and any kinds of development activities are relies heavily on the lack of

construction materials supply. The waves are more likely to cause the forces acting on the

existing infrastructure and building on the coast. Moreover, it is able to generate currents and

on sediment mobilization in the coastal areas [3]. Hence, the need for knowledge about the

condition of the Java Sea wave which is used for planning, determining the geometry of

beach building coast, port planning, handling coastal damage, as well as the power of

building infrastructure in coastal areas is urgently needed [4]

Currently the wave data which is exist in the Java Sea is still limited and generally

difficult to be obtained [5]. Therefore to overcome this limitations, wave forecasting usually

used wind data obtained from wind measurement stations of the Meteorology, Climatology

and Geophysics (BMKG)[6]. During this time, data analysis is always conducted by using the

daily average wind speed data or maximum. Consequently, the data are less describe the real

condition [7]. Wave forecasting method generally use wave forecasting curve of height and

the wave period, similar with the one developed by Darbyshire and Draper [8] for coastal

waters and by Green and Dorrestein [9] for the deep sea [10], the SMB Method Sverdrup-

Munk-Bretschneide [11] and a method of using a nomogram to determine height and wave

period forecasts with analysis [12] [13]. However, this method still has weaknesses such as

the distribution of wind speed of the wave generator can not describe the duration and

distribution of wind speed per hour. In addition, it can not classify based on wind direction

that can describe the general condition, and there are no waves data that can be calibrated

with the model. Thus it could not determine the duration of the wind (td) that influence

significantly to the wave forecasting in Java Sea by using maximum wind data [14] [15].

Thereby, it is necessary to conduct research regarding how to predict wave height in the

Java Sea based on the modelling of wind speed distribution, and daily maximum wind speed

in a real-time (every hour). In this research, conducted determination of the wind duration

based on a Group of wind speed (Beaufort scale) and the wind direction which has the

tendency of certain direction to describe condition of the season. Wave forecasting method of

wind data uses Darbyshire curves, which is further developed to determine duration of the

wind is equivalent to the maximum speeds. Wind data that will be used is the wind speed data

every hour, with periods of wind data be processed for more than 10 years. However, the

previous research about the modelling wind speed distribution in the western region of

Indonesia [16]; Indonesia central region [17] and the eastern region of Indonesia [18], are

using wind data every three hours and the period of wind data are processed only three years.

This research aims to harness the modelling wind speed distribution to obtain the empirical

relationship between wind speed and wave height in the Java Sea for the wave forecasting

needs.

Denny Nugroho Sugianto, Muhammad Zainuri, Alfin Darari, Suripin, Suseno Darsono and Nur Yuwono


2. METHODS

The research was conducted in the Java Sea which is located 5 area bojonegoro – banten (18

– 21 July 2008), semarang - central java (28 – 30 December), Jepara-central java ( take from

wave recorder, 1 – 28 February and 26 Maret 1997 – 22 Augtust 1999), Rembang-Central

Java (3 – 13 July 2007) , pacitan – east java(16 – 20 October 2009), fig 1

Figure 1 The Location of collecting data Area

The method of collecting wind data directly conducted by using survey techniques in situ,

where the tools used in the form of a simple anemometer, on the screen that read

subsequently recorded anemometer wind speed and direction, wind direction on the wind data

recording is taken from wind came from or arrive. As for the wind data for the years 2007-

2013 was obtained from wind measurement stations of the Meteorology, Climatology and

Geophysics (BMKG) every location in 2007-2013, in the form of wind data every hour. Data

gathered around a period of more than 10 years for each location recording wind data by

these agencies. Data retrieval is done to present the model of the distribution of wind speed in

Semarang, Jakarta, Surabaya. Maximum wind speed values obtained with the classification of

the moderate breeze, fresh breeze, and strong breeze. These data are the result of average

modelling wind speed distribution in all seasons table 1.



Table 1 The equation of Wind Speed Distribution Model: Classified in the moderate breeze, fresh

breeze, and strong breeze in Semarang, Jakarta, Surabaya, and Java Sea

Location Breeze

Classification t = 0 – 6 hours t = 6 – 12 hours

Semarang

Moderate U/Umax (%) = 1,208 t2

+ 0,435 t +

54,37 (R² = 0,996) Pers. (4.1)

U/Umax (%) = 1,242 t2 – 29,98 t +

235,3 (R² = 0,997) Pers. (4.2)

Fresh U/max (%) = 1,592 t2

– 0,226 t +

44,50 (R² = 0,975) Pers. (4.3)

U/Umax (%) = 1,571 t2 – 37,89 t+

271,3 (R² = 0,988) Pers. (4.4)

Strong U/Umax (%) = 1,926 t2

– 0,423 t +

33,59 (R² = 0,993) Pers. (4.5)

U/Umax (%) = 1,846 t2 – 44,46 t +

300,8 (R² = 0,994) Pers. (4.6)

Jakarta


+ 1,041 t +

57,30 (R² = 0,991) Pers. (4.7)

U/Umax (%) = 1,091 t2 – 27,08 t +

225,1 (R² = 0.990) Pers. (4.8)

Fresh U/Umax (%) = 1,583 t2

– 0,144 t +

44,42 (R² = 0,998) Pers. (4.9)

U/Umax (%) = 1,667 t2 – 39,61 t +

278,2 (R² = 0,998) Pers. (4.10)


– 2,460 t +

36,13 (R² = 0,978) Pers. (4.11)

U/Umax (%) = 2,292 t2 – 52,38 t +

332,4 (R² = 0,990) Pers. (4.12)

Surabaya


+ 1,711 t +

58,23 (R² = 0,981) Pers. (4.13)

U/Umax (%) = 0,996 t2 – 25,11 t +

216,2 (R² = 0,974) Pers. (4.14)


+ 0,510 t +

46,71 (R² = 0,997) Pers. (4.15)

U/Umax (%) = 1,341 t� - 33,39 t +

252,6 (R² = 0,997) Pers. (4.16)


– 1,895 t +

34,59 (R² = 0,982) Pers. (4.17)

U/Umax (%) = 2,234 t2 – 50,88 t+

324,8 (R² = 0,981) Pers. (4.18)

LautJava


+ 1,148 t +

56,55 (R² = 0,988) Pers. (4.19)

U/Umax (%) = 1,11 t2 – 27,49 t +

226,6 (R² = 0,987) Pers. (4.20)


+ 0,159 t +

45,10 (R² = 0,994) Pers. (4.21)

U/Umax (%) = 1,526 t2 – 37,08 t +

268,6 (R² = 0,994) Pers. (4.22)


– 1,837 t +

34,44 (R² = 0,986) Pers. (4.23)

U/Umax (%) = 2,175 t2 – 50,20 t +

323,3 (R² = 0,991) Pers. (4.24)

The next step is calculation and determination of wind duration equivalent (tdEQ) by using

Darbyshire curves[8]. Moreover, processing data to obtain the relationship chartbetween

wind duration equivalent and the wind speed U in the Java island. The equation of its

relationship is as follows :

tdEQ = 0,006 U 2 – 0,437 U + 10,70 (1)

the equation is be able to use for wave forecasting in Java Sea based on the model of wind

speed distribution or daily wind speed maximum.

3. RESULT OF DISCUSSION

The relationship chart of wind duration equivalenttdEQ (hours) with the wind speed U (knot)

in the Java Sea is showed by the equation (1). If the value of the wind speed U are 10, 15, 20,

25, 30, 35, and 40 knot, therefore the value of wind duration equivalent (tdEQ) are able to be

obtained. By using Darbyshire [8] curves, the significant of wave height and period are also

be able to be determined, given by fig. 2.



Figure 1 Determination Curves of Significant Wave Height and Period Obtained from The Equivalent

Wind Speed Duration(tdEQ) in the Java Sea by using Darbyshire Methods

linede scribes particular wind speed with its wind duration equivalent (tdEQ), thus the

significant of wave height and period are be able to be obtained.

Table 2 The Relationship between Wind speed U and wind duration equivalent(tdEQ) to Determine

Significant of Wave Height (Hs) and Period (Ts) by Using Darbyshire Methods.

No Wind Speed

U (Knot)

Wind Speed

U (m/sec)

Wind Duration

EquivalenttdEQ

(hours)

Significant

of Wave

Height

Hs (m)

Significant of

Wave Period

Ts (sec)

1 10 5,1 7 0,6 4,1

2 15 7,7 5 0,9 4,8

3 20 10,3 4 1,2 5,3

4 25 12,9 3,5 1,7 6,0

5 30 15,4 3 2,1 6,4

6 35 17,9 3 2,8 7,1

7 40 20,6 3 3,7 7,8

The results of processing data regarding the significant wave height and period are formed

to become a modelling through regression approach, which describe the wind speed U with

significant wave height (Hs) and period (Ts) as the simplification from Darbyshire methods

[9]. The results descrine the relationship between the equation and significant of wave height

forecasting curve in Java Sea.



Figure 3 The Java Sea Wave Height Forecasting Curve: Wind Speed Relationship Chart with

Significant of Wave Height Forecasting in Java Sea

Figure 4 The Java Sea Wave Period Forecasting Curve: Wind Speed Relationship Chart with

Significant of Wave Periods Forecasting in Java Sea

Fig3 shows the relationship of wind speed with significant of wave height forecasting in Java

Sea, whereas fig. 4 shows the relationship of wind speed with significant of wave periods

forecasting in Java Sea. Fig 3 uses linier regression system, owing to the facts that the periods

data are more likely to have linier trend. In contrast, the significant of wave height is likely to

have arched form, accordingly the approach of quadratic regression is used [19]. Empirical

relationship of wind speed U (knot) with wave height (H) in meter and period (T) in seconds

is showed by the equation as follows:

a) Significant of Wave Height



Wind speed starts from U = 0 knot

Hs = 0,00162 U2+ 0,0275 U (R

2 = 0,997) (2)

b) Wave Period

Wave Period starts from U = 10 knot

Ts = 0,12 U + 2,928 (R2 = 0,996) (3)

In which the significant of wave period is only valid and able to be calculated for wind

speed more that 10 knot, because of the wind speed ranging from 0 – 10 knot is not be able to

be formed as a modelling.

The results of equation (2) and (3) are examined regarding its validation and model

callibration, to see if the model represents the actual conditions. The data used in the

validation is in Semarang (28 – 30 December 2010), Rembang (3 – 13 July 2007), Jepara (1 –

28 February 1998 and 1 – 28 February 1999) Bojonegoro, (18 – 21 July 2008), and Pacitan

(18 – 21 July 2008). The data of wave height measurement field is depicted on a model or

significant wave height forecasting curve shown in Fig. 5.

Figure 5 Data of Wind Speed Relationship Chart with Significant Wave Height Measurement Field

Using Java Sea Wave Height Forecasting Curve.

Fig. 5 shows the significant of maximum wave height in waters area of Rembang and

Jepara. Some data are bigger than the results obtained from Java Sea wave height forecasting

curve. But overall, the data have trends which is adjoined with the curve.

The aim of wave forecasting callibration is to represent significant of maximum wave

height in the waters, by matching the model with the actual condition. Therefore, it is able to

be used for the needs of coastal practitioner engineering[20]. Callibration of wave height

forecasting is conducted by regulating wind duration equivalent (tdEQ). Moreover, there are 2

parameters which influence the determination of significant wave height and periods

forecasting. The other parameter such as fetch is not being considerated, because of the wind

speed has already been determined. The wind speed is in the form of U= 10, 15, 20, 25, 30,



35, and 40 knots. Callibration is able to be conducted by regulating the valueof wind duration

equivalent(tdEQ).

The calibration results of wind duration equivalent (tdEQ) is approximately similar with

wind duration equivalent (tdEQ) obtained before callibration as given by table 2. In addition,

there are the value of wind duration equivalent (tdEQ) obtained after callibration as given by

table 3.

Table 3 The Value Of Wind Duration Equivalent(tdEQ) for The Callibration Model of Wave

Forecasting in Java Sea

No Wind Speed

U (Knot)

Wins Speed

U (m/sec)

Wind Duration

EquivalenttdEQ

(hours)

Before

Callibration

Wind Duration

EquivalenttdEQ

(hours)

After

Callibration

1 10 5,1 7 8

2 15 7,7 5 6

3 20 10,3 4 5

4 25 12,9 3,5 4,5

5 30 15,4 3 4

6 35 17,9 3 4

7 40 20,6 3 4

On wind duration equivalent (tdEQ), the results of callibration show that each of the

tdEQvalue is added by 1 hour. As a consideration from the fig. 5 that data distribution of

significant wave height measurement field in Rembang and Jepara is higher than the Java Sea

wave height forecasting curve.The calculation of wave forecasting (height and periods) using

Darbyshire curve is obtained based on the value of wind speed U and equaivalent wind

duration (tdEQ) as given by table 3. Whereas, the results of callibration are presented in the

table 4, and depicted into Java Sea wave forecasting curve as given by fig. 6 and fig. 7.

Table 4 The Relationship Table between The Callibration Results of Equivalent Wind Speed Duration

to Determine Significant of Wave Height by Using Darbyshire Methods.

No Wind Speed

U (Knot)

Wind Speed

U (m/sec)

Wind Duration

Equivalent Tdeq

(Hours)

After

Callibration

Significant

of Wave

Height

Hs (m)

Significant of

Wave Period

Ts (sec)

1 10 5,1 8 0,65 4,2

2 15 7,7 6 1 5,1

3 20 10,3 5 1,48 6

4 25 12,9 4,5 2,1 7

5 30 15,4 4 2,5 7,4

6 35 17,9 4 3,2 8

7 40 20,6 4 4,2 8,8



Figure 6 Wind Speed and Java Sea Wave Height Forecasting Relationship Chart with Wind Duration

Equivalent (tdEQ) Base Callibration

Figure 7 Wind Speed and Java Sea Significant Wave Periods Relationship Chart with Wind Duration

Equivalent (tdEQ) Base Callibration

Fig. 6 and Fig. 7 is obtained from the equation and Jawa Sea wave height and period

forecasting curve based on the data of wind speed and wind duration equivalent (tdEQ) after



callibration. Empirical relationship of wind speed U (Knot) with wave height (meter) and

wave period (seconds) as given by fig 6 and 7 is showed by equation (4) and (5).

• Wave height forecasting for wind duration equivalent (tdEQ) base callibration


Hs = 0,0016 U2+ 0,0406 U (R

2 = 0,996) (4)

• Wave period forecasting for wind duration equivalent (tdEQ) base callibration


Ts = 0,15 U + 2,892 (R2 = 0,987) (5)

Equation (5) for wave period forecasting are valid and able to be calculated by the wind

speed with > 10 knot, because of the wind speed between 0-10 knot is unlikely to be formed

as model. In addition, wind speed < 10 knot is less speed for the wave formatting as given by

fig. 8.

The following step is the validation conducted to validate the empirical relationship of

wind speed U (Knot) with wave height (meter) of wind duration equvalent (tdEQ) base

callibration, by using the data of wind speed relationshipwith significant wave height

measurement field onJava Seawave height forecasting curve. The similar step is used to the

data distribution of significant wave period measurement field in Java Sea. The graph to

validate the wind duration equivalent (tdEQ) base callibration is depicted on fig 8.

Figure 8 Distribution Wind Speed and Significant Measurement Directly Relationship Chart Jawa

Sea Wave Height Forecasting Using Wind Duration Equivalent (tdEQ) callibration

Fig.8 illustrates the maximum wave height mainly in Rembang and Jepara. It is more

likely similar with Java Sea wave height forecasting curve. Additionally, the callibration

results of Java Sea wave height forecasting curve for wind duration equivalent (tdEQ) shows a

better illustration, primarily regarding the maximum wave height. It means, the model is



selected conservatively. Thus, in coastal planning the results of Java Sea wave height

forecasting curve can be used or implemented. Thereby, the wave forecasting is able to

present significant wave height happened in the waters. Despite it is difficult to determine its

fit in deks, because of the comparison reference used is the maximum wave height. There is

whole description regarding Java Sea wave height forecasting curve for wind duration

equivalent (tdEQ) before callibration, as given by fig.9.

Figure 9 Distribution Data of Wind and Significant Wave Height Direct Measurement Relationship

Chart with Wind Duration Equivalent (tdEQ) before Callibration

Based on fig.9 it is concluded that the wave height forecasting by using wind duration

equivalent (tdEQ) after callibration has better results in interpreting the empirical relationship

between wind speed U (knot) and wave height significant Hs (meter). Specifically for waves

that have maximum wave height. The validation and callibration model in this study are

based on maximum of the wave height significant (Hs). It is with consideration for "security"

wave forecasting results especially for waves that have the maximum value less the results of

the generated wave forecasting model in this study.

The validation is conducted by determining the selection of the model or wave forecasting

curves based on data only at the height of a wave of significant field and wave forecasting

curves model for the Java Sea wave height forecasting curve. Because of the significant wave

period of variation is large enough for each measurement location, thus that consideration on

period aspects have yet to be used in determining which model is better to be applied [21].

Duration data regarding the significant wave period and its measurement are depicted

by Java Sea wave height froecasting as given by fig. 10 and fig.11. However, waters in

Pacitan is unlikely to be analyze due to the wave which is relatively small.



Figure 10 Distribution Data of Wind Speed and Significant Wave Periods Direct Measurement

Relationship Chart with Wind Duration Equivalent (tdEQ) before Callibration

Figure 11 Distribution Data of Wind Speed and Significant Wave Periods Direct Measurement

Relationship Chart with Wind Duration Equivalent (tdEQ) After Callibration

The result of the wave forecasting model of the study, additionally compared to the previous

studies. Regarding to the relationship between wind speed and significant wave height in the

Java Sea by Basuki Rajendra Budi [22], in Semarang and Surabaya by Thambas [17]



Figure 12 The Relationship Chart between Wind Speed and Significant Wave Height Hs (m)

Compared to The Previous Study.

Fig. 12. Shows that there is a visible difference of wave height, in which the results of the

previous study indicates significant wave height is larger compared to the results of this

study. Therefore, the calculation of wave forecasting stages use data every hour, thus the

duration and distribution of wind are more likely to be better to determine the duration of

wind speed. In the previous study used data on wind speed every 3 hours, so the alleged

approach for the calculation of the duration and distribution of wind are more likely to be

larger so that obtained results of calculation of wave forecasting from wind data the result is

also larger.

It is clearly seen that the previous study generated the relationship between wind speed U

(knots) and a significant wave height Hs (m). In addition, it produced also without a tangent

point on the conditions of wind speed 0 (zero). Besides the result of the equation looks also

applies to the values of wind speed less than 15 knots. Whereas the results of the model

equations and wave forecasting curves form the Java Sea produced in this study has been

made between the results of validation step model with measurement data field in which this

validation step is not be able to conduct by previous researchers, therefore the results

obtained in this study has better results. This is as presented in Fig. 13.



Figure 13 Distribution Data of Wind Speed and Significant Wave Height Compared to Some Studies

regarding Wave Forecasting in The Java Sea

Fig. 13 shows the distribution data of wind speed and wave height significant direct

measurement field on some studies of wave height forecasting in the Jawa Sea. The results of

Thambas (Semarang, 2003), Thambas (Surabaya, 2003) are more likely to have greater data

on wave forecasting then field measurement when the wind speed is above 20 knots.

However, for the lower wind speed has lower tren and its wave height forecasting is less then

the field measurement data. Therefore, it has low quality trend compared to the results of

field measurement. Whereas the model by Basuki[22] has greater value or above the wave

measurement. It can be concluded the recents studies have better quality than the previous

studies.

4. CONCLUSION

Wave forecasting theoretically can be done by using the model equation of empirical

relationship between wind speed and significant of wave height and period forecasting. The

results obtained from wave forecasting showed equation model that demonstrate empirical

and significant relationship between U wind speed and wave forecasting in term of wave

height. Therefore, it practically and easily provides the information both for researcher and

planner in the wave forecasting (storm) in Java Sea.

5. ACKNOWLEDGEMENT

We would thank to Oceanography Department, Diponegoro University for funding our

research and to Sulistiyono Susilo and Mirra Fatharani for valuable editing assistance.



REFERENCES

[1] Atmoko, R. A., Aisjah, A. S., & Nugroho, G. (2016). Analysis of Spatial Characteristic of

Maritime Weather in Java Sea. IPTEK Journal of Proceedings Series, 2(1).

[2] Nuryanto, D. E., Pawitan, H., Hidayat, R., & Aldrian, E. (2016). Heavy Rainfall

Distributions Over Java Sea in Wet Season. Procedia Environmental Sciences, 33, 178-

186.

[3] Horikawa, K. (Ed.). (1988). Nearshore dynamics and coastal processes: Theory,

measurement, and predictive models. University of Tokyo press.

[4] Coastal Hydraulic Laboratory, “Coastal Enginering Manual.,” in Part I-IV, Washington

DC: Department of the Army. U.S. Army Corp of Engineering, 2002.

[5] Yuwono, N. (1992). Dasar Dasar Perencanaan Bangunan Pantai. Keluarga Mahasiswa

Teknik Sipil Unuversitas Gadjah Mada, Yogyakarta.

[6] Kurniawan, R., Habibie, M. N., & Suratno, S. (2011). Variasi Bulanan Gelombang Laut

Di Indonesia. Jurnal Meteorologi dan Geofisika, 12(3).

[7] Hadikusumah. (2009 ). Karakteristik Gelombang dan Arus di Eretan Indramayu,” J.

Makara Seri Sains, vol. 13, no. 2, pp. 163–172.

[8] Darbyshire, M., & Draper, L. (1963). Forecasting wind-generated sea waves.

Engineering, 195, 482-484.

[9] Dorrestein, R. (1960). Simplified method of determining refraction coefficients for sea

waves. Journal of Geophysical Research, 65(2), 637-642.

[10] Yuwono, N., & Kodoatie, R. J. (2004). Pedoman Pengembangan Reklamasi Pantai dan

Perencanaan Bangunan Pengamanannya.

[11] Hands, E. B. (1984). The Great lakes as a test model for profile response to sea level

changes (No. CERC-84-14). Coastal engineering research center vicksburg ms.

[12] B. Triatmodjo. (1996). Pelabuhan. Yogyakarta: Beta Offset.

[13] B. Triatmodjo. (1999). Teknik Pantai. Yogyakarta: Beta Ofset.

[14] Wu, T., Timo, H., Qin, B., Zhu, G., Janne, R., & Yan, W. (2016). In-situ erosion of

cohesive sediment in a large shallow lake experiencing long-term decline in wind speed.

Journal of Hydrology, 539, 254-264.

[15] Draper, L. (1963). Derivation of adesign wavefrom instrumental records of sea waves.

Proceedings of the Institution of Civil Engineers, 26(2), 291-304.

[16] HERMAWAN, S., & Yuwono, I. H. N. (2002). Model distribusi kecepatan angin dan

pemanfaatannya dalam peramalan gelombang di wilayah barat Indonesia (Pulau

Sumatera) (Doctoral dissertation, [Yogyakarta]: Universitas Gadjah Mada).

[17] THAMBAS, A. H., & Yuwono, I. H. N. (2003). Model distribusi kecepatan angin dan

pemanfaatannya dalam peramalan gelombang di wilayah tengah Indonesia:: Pulau Java,

Sulawesi dan Kalimantan (Doctoral dissertation, [Yogyakarta]: Universitas Gadjah

Mada).

[18] Edy, H., Yuwono, I. H. N., & HE, D. (2004). Model Distribusi Kecepatan Angin Dan

Pemanfaatannya Dalam Peramalan Gelombang Di Wilayah Timur Indonesia (Pulau

Sulawesi, Nusa Tenggara, Maluku Dan Papua) (Doctoral dissertation, [Yogyakarta]:

Universitas Gadjah Mada).

[19] Mughal, M. O., Lynch, M., Yu, F., McGann, B., Jeanneret, F., & Sutton, J. (2017). Wind

modelling, validation and sensitivity study using Weather Research and Forecasting

model in complex terrain. Environmental Modelling & Software, 90, 107-125.

[20] Massel, S. R., & Mei, C. C. (1977). Transmission of random wind waves through

perforated or porous breakwaters. Coastal Engineering, 1, 63-78.

[21] Miller, S. T. K., Keim, B. D., Talbot, R. W., & Mao, H. (2003). Sea breeze: Structure,

forecasting, and impacts. Reviews of geophysics, 41(3).



[22] B. S. Budi (2000). “Model Distribusi Kecepatan Angin dan Pemanfaatannya dalam

Peramalan gelombang di Wilayah Tengah Indonesia,” Gadjah Mada University.

[23] Messi Alfred François, Tewa Jean Jule, Lezin Seba Minsili, Piam Njomo Fritz, Koumbe

Mbock, Kenne Rodrigue, Critical Analysis of the Methodology Adopted By Certain

Cameroonian Studies for the Evaluation of the Wind Speed: Case of the Economical City

of Douala. International Journal of Civil Engineering and Technology, 8(2), 2017, pp. 34–

50.

[24] S. Bhakiyaraja, Dr. S. Vaithyanathan, Dr. S. Palanivelraja and V. Krishna Prasad,

Dispersion of Air Pollutant in the Neighbourhood of a Typical Thermal Power Station

under Critical Wind Speed using CFD-Fluidyn PANACHE. International Journal of

Mechanical Engineering and Technology, 7(6), 2016, pp. 245–252.

Documents

WAVE HEIGHT FORECAST ING USING … recorder, 1 – 28 February and 26 Maret 1997 – 22 Augtust 1999), Rembang-Central Java (3 – 13 July 2007) , pacitan – east java(16 – 20 October