International Journal of GEOMATE

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29/5/2018

International Journal of GEOMATE ISSN:2186-2982 (Print)

International Journal of GEOMATE

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Geotechnique, Construction Materials and Environment, Tsu, Mie, Japan

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The "International Journal of GEOMATE" is a Scientific Journal of the GEOMATE International Society that encompasses a broad area in Geotechnique, Construction Materials and Environment. Special Issue: The journal includes papers on Science, Engineering and Environment under the category of special issue. The key objective of this journal is to promote interdisciplinary research from various regions of the globe. Geomate meaning as GEO-MATE indicating earth friend or nature friend. The editorial board of the journal is comprised of extensively qualified researchers, academicians, scientists from Japan and other countries of the world. It is peer-reviewed journal that is published monthly (2011-2015 quarterly). All articles published in this journal are available on line. Contributors may download the manuscript preparation template for submitting paper or contact to the Editors-in-Chief [[email protected]].

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Aims & Scopes | International Journal of GEOMATE ISSN:2186-2982 (Print)

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The journal aims to become an efficient mean of publishing and distributing high quality information from the researchers, scientists and engineers. The main scopes are as follows:

Advances in Composite Materials Computational Mechanics Foundation and Retaining Walls Slope Stability Soil Dynamics Soil-Structure Interaction Pavement Technology Tunnels and Anchors Site Investigation and Rehabilitation

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Ecology and Land Development Water Resources Planning Environmental Management Public Health and Rehabilitation Earthquake and Tsunami Issues Safety and Reliability Geo-Hazard Mitigation Case History and Practical Experience Others

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Environmental Engineering Chemical Engineering Civil and Structural Engineering Computer Software Eng. Electrical and Electronic Eng. Energy and Thermal Eng. Aerospace Engineering Agricultural Engineering Biological Engineering and Sciences Biological Systems Engineering Biomedical and Genetic Engineering Bioprocess and Food Engineering Geotechnical Engineering Industrial and Process Engineering Manufacturing Engineering Mechanical and Vehicle Eng. Materials and Nano Eng. Nuclear Engineering Petroleum and Power Eng. Forest Industry Eng.

Environmental Technology Environmental Science Chemistry and Chemical Sci. Fisheries and Aquaculture Sciences Astronomy and Space Sci. Atmospheric Sciences Botany and Biological Sciences •Genetics and Bacteriolog Forestry Sciences Geological Sciences Materials Science and Mineralogy Statistics and Mathematics Microbiology and Medical Sciences Meteorology and Palaeo Ecology Pharmacology Physics and Physical Sci. Plant Sciences and Systems Biology Psychology and Systems Biology Zoology and Veterinary Sciences

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Recycle Solid Wastes Environmental dynamics Meteorology and Hydrology Atmospheric and Geophysics Physical oceanography Bio-engineering Environmental sustainability Resource management Modelling and decision support tools Institutional development Suspended and biological processes Anaerobic and Process modelling Modelling and numerical prediction Interaction between pollutants Water treatment residuals Quality of drinking water Distribution systems on potable water Reuse of reclaimed waters

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29/5/2018

Articles | International Journal of GEOMATE ISSN:2186-2982 (Print)

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Publication Ethics

1. SHEAR STRENGTH ENHANCEMENT OF COMPACTED SOILS USING

Review Policy

HIGH-CALCIUM FLY ASH-BASED GEOPOLYMER Soe Thiha, Chanodorm Lertsuriyakul and Decho Phueakphum Article Type: Research Article

Content List

View Abstract

No of Download = 344

Pages (1-9)

2. LIQUEFACTION SIMULATION AND RELATED BEHAVIOR OF

Discussion

UNDERGROUND STRUCTURE ON OSAKA GULF COAST

Erratum

Keita Sugito, Tetsuya Okano and Ryoichi Fukagawa Article Type: Research Article

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No of Download = 323

Copyright, Template (Form 1,2,3)

Pages (10-15)

Evaluation Form Galley Proof Submission

3. ASSESSING THE IMPACT OF POSITIVE PRESSURE VENTILATION ON

THE BUILDING FIRE – A CASE STUDY

Rajmund Kuti, Geza Zolyomi and Orsolya K. Kegyes-Brassai Article Type: Research Article

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No of Download = 312

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4. REINFORCEMENT EFFECT OF GEOGRID IN THE BALLAST AND SUB-

BALLAST OF THE RAILWAY TRACK

Saad Farhan Ibrahim, Ali Jabbar Kadhim and Harith Basim Khalaf Article Type: Research Article

View Abstract

No of Download = 299

Pages (22-27)

5. DETERMINATION OF THE APPROPRIATE IRRIGATION METHODS

BASED ON SOIL ANALYSIS FOR UPLAND FIELDS IN MIE PREFECTURE OF JAPAN Abdul Saboor Rahmany, Hajime Narioka, Takamitsu Kajisa and Homayoon Ganji Article Type: Research Article

View Abstract

No of Download = 294

Pages (28-33)

6. REVIEW OF THE INFLUENCING FACTORS OF INTEGRATED WASTE

MANAGEMENT

Mohamad Satori, Erri N. Megantara, Ina Primiana F.M.S, Budhi Gunawan Article Type: Review Article

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No of Download = 298

Pages (34-40)

7. DEVELOPMENT OF A PROFESSIONAL QUALIFICATION FOR

CONSTRUCTION SURVEYORS IN THAILAND

Supacha Siriwongyingcharoen, Sunchai Inthapichai and Narin Sridokmai Article Type: Research Article

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No of Download = 279

Pages (41-47)

8. LESSONS AND ACHIEVEMENTS FROM THE MERSEY FOREST BY

NETWORKING PARTNERSHIP FOR TWENTY YEARS

Tomoko Miyagawa, Clare Olver, Noriko Otsuka, Takefumi Kurose and Hirokazu Abe Article Type: Research Article

View Abstract

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No of Download = 266

Pages (48-54)

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9. THE INFLUENCE OF THE JAKARTA BAY RECLAMATION ON THE

SURROUNDING TIDAL ELEVATION AND TIDAL CURRENT Harman Ajiwibowo and Munawir Bintang Pratama Article Type: Research Article

View Abstract

No of Download = 258

Pages (55-65)

10. A REVIEW OF SELECTED UNEXPECTED LARGE SLOPE FAILURES Marthinus Sonnekus and John Victor Smith Article Type: Review Article

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No of Download = 241

Pages (66-73)

11. RESPONSE OF DIODIA VIRGINIANA (RUBIACEAE) APPLIED TO DAM

RESERVOIR SLOPES AS A COVER PLANT, JAPAN

Taizo Uchida, Yuya Imamura, Yoshifumi Kochi, Mamoru Yamada, Kunihiko Fukaura, Aki Matsumoto, William T. Haller and Lyn A. Gettys Article Type: Research Article

View Abstract

No of Download = 226

Pages (74-78)

12. MULTIPLE OBJECTIVE MANAGEMENT STRATEGIES FOR COASTAL

AQUIFERS UTILIZING NEW SURROGATE MODELS Alvin Lal and Bithin Datta Article Type: Research Article

View Abstract

No of Download = 215

Pages (79-85)

13. CORRELATION AMONG THE SOIL PARAMETERS OF THE

KARNAPHULI RIVER TUNNEL PROJECT

Khondoker Istiak Ahmad, Masnun Abrar, Sultan Al Shafian and Hossain Md. Shahin Article Type: Research Article

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No of Download = 200

Pages (86-90)

14. BEHAVIOR OF SEGMENTAL TUNNEL LININGS UNDER THE IMPACT

OF EARTHQUAKES: A CASE STUDY FROM THE TUNNEL OF HANOI METRO SYSTEM Gospodarikov Alexandr and Thanh Nguyen Chi Article Type: Research Article

View Abstract

No of Download = 231

Pages (91-98)

15. INFLUENCE OF HEAT-AND-POWER ENTERPRISES ON THE

HYDROSPHERE

Tatyana Germanovna Korotkova, Svyatoslav Andreevich Bushumov, Svetlana Dmitrievna Burlaka, Natalya Yurevna Istoshina and Hazret Ruslanovich Siukhov Article Type: Research Article

View Abstract

No of Download = 252

Pages (99-106)

16. PORTLAND CEMENT CONTAINING FLY ASH, EXPANDED PERLITE,

AND PLASTICIZER FOR MASONRY AND PLASTERING MORTARS

Satakhun Detphan, Tanakorn Phoo-ngernkham, Vanchai Sata, Chudapak Detphan and Prinya Chindaprasirt Article Type: Research Article

View Abstract

No of Download = 256

Pages (107-113)

17. APPLICATION OF SWAN MODEL FOR HINDCASTINGWAVE HEIGHT

IN JEPARA COASTAL WATERS, NORTH JAVA, INDONESIA

Yati Muliati, Ricky Lukman Tawekal, Andojo Wurjanto, Jaya Kelvin, and Widodo Setiyo Pranowo, Article Type: Research Article

View Abstract

No of Download = 268

Pages (114-120)

18. CO-BENEFIT ASSESSMENT OF LOGISTICS OPTIMIZATION

PROGRAMS: THE CASE OF THE PHILIPPINE GREATER CAPITAL REGION

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Articles | International Journal of GEOMATE

Krister Ian Daniel Roquel, Alexis Fillone and Krista Danielle Yu Article Type: Research Article

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No of Download = 274

Pages (121-128)

19. CHARACTERIZATION OF RECYCLED AGGREGATE FOR USE AS BASE

COURSE MATERIAL Kongrat Nokkaew

Article Type: Research Article

View Abstract

No of Download = 285

Pages (129-136)

20. PHYSICAL MODELING OF PROPOSED POROUS DRAINAGE SYSTEM

TO SOLVE INUNDATION PROBLEM Ahmad Rifa and Noriyuki Yasufuku Article Type: Research Article

View Abstract

No of Download = 297

Pages (137-142)

21. REMOVAL OF COPPER IONS FROM AQUEOUS SOLUTION USING

PALM SHELL CHARCOAL ACTIVATED BY NAOH Muhammad Faisal and Asri Gani, Abubakar Article Type: Research Article

View Abstract

No of Download = 309

Pages (143-147)

22. EARTHQUAKE MICROZONATION STUDY ON BATUBESI DAM OF

NUHA, EAST LUWU, SOUTH SULAWESI, INDONESIA Sunaryo, Harti Umbu Mala, and Anom Prasetio Article Type: Research Article

View Abstract

No of Download = 322

Pages (148-154)

23. THE CARBON FOOTPRINT OF NATURAL GAS AND ITS ROLE IN THE

CARBON FOOTPRINT OF ENERGY PRODUCTION

Oleg E. Aksyutin, Alexander G. Ishkov, Konstantin V. Romanov, Vladimir A. Grachev Article Type: Research Article

View Abstract

No of Download = 332

Pages (155-160)

24. PRELIMINARY STUDY OF LANDSLIDE IN SRI MULYO, MALANG,

INDONESIA USING RESISTIVITY METHOD AND DRILLING CORE DATA Adi Susilo, Eko Andi Suryo, Fina Fitriah, Muwardi Sutasoma and Bahtiar Article Type: Research Article

View Abstract

No of Download = 371

Pages (161-168)

25. COMPRESSIVE STRENGTH MODELLING OF CONCRETE MIXED

WITH FLY ASH AND WASTE CERAMICS USING k-NEAREST NEIGHBOR ALGORITHM Kenneth Jae T. Elevado, Joenel G. Galupino and Ronaldo S. Gallardo Article Type: Research Article

View Abstract

No of Download = 355

Pages (169-174)

26. STABILIZATION OF SANDY SOIL USING RECYCLE WASTE TIRE

CHIPS

Mohammed Abdullateef Al-Neami Article Type: Research Article

View Abstract

No of Download = 339

Pages (175-180)

27. THE CHANGES OF ENVIRONMENT AND AQUATIC ORGANISM

BIODIVERSITY IN EAST COAST OF SIDOARJO DUE TO LAPINDO HOT MUD Tarzan Purnomo and Fida Rachmadiarti Article Type: Research Article

View Abstract

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No of Download = 285

Pages (181-186)

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28. ASSESSING URBAN WATER SUPPLY SYSTEM IN NORTHEASTERN

THAILAND: WATER QUALITY AND AUTHORITY ORGANIZATION Jareeya Yimrattanabavorn, Oranee Rungrueang, Sudjit Karuchit and Pensupa Wirikitkhul Article Type: Research Article

View Abstract

No of Download = 227

Pages (187-194)

29. STABILITY CHART FOR UNSUPPORTED SQUARE TUNNELS IN

HOMOGENEOUS UNDRAINED CLAY

Jim Shiau, Mohammad Mirza Hassan and Zakaria Hossain Article Type: Research Article

View Abstract

© 2011-2018, The Geomate International Society.

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No of Download = 208

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International Journal of GEOMATE, Aug., 2018 Vol.15, Issue 48, pp.114-120 Geotec., Const. Mat. & Env., DOI: https://doi.org/10.21660/2018.48.56067 ISSN: 2186-2982 (Print), 2186-2990 (Online), Japan

APPLICATION OF SWAN MODEL FOR HINDCASTING WAVE HEIGHT IN JEPARA COASTAL WATERS, NORTH JAVA, INDONESIA *Yati Muliati1,2, Ricky Lukman Tawekal1, Andojo Wurjanto1, Jaya Kelvin3, and Widodo Setiyo Pranowo3,4 1

2

Faculty of Civil and Environmental Engineering, Institut Teknologi Bandung, Indonesia, Faculty of Civil Engineering and Planning, Institut Teknologi Nasional (Itenas) Bandung, Indonesia, 3 Marine Research Center, Indonesian Ministry of Marine Affairs & Fisheries, 4 Department of Tech. Hydrography, Naval Postgraduate School (STTAL). *Corresponding Author, Received: 26 Feb. 2018, Revised: 19 Mar. 2018, Accepted: 19 Apr. 2018

ABSTRACT: SWAN (Simulating Wave Near-shore) is a numerical wave model for hindcasting/forecasting wave parameters in coastal areas. This numerical model is chosen because is suitable for shallow water. This study was conducted to verify the results of wave height hindcasting in Jepara coastal waters. This is expected to support wave characteristic research based on wave forecasting for 10 years in the waters between Java, Sumatera and Kalimantan. The model is run with the third-generation mode (GEN3), which allow wind input, quadruplet and triad interactions, whitecapping, and breaking. Wind data is obtained from ECMWF (European Centre for Medium-Range Weather Forecasts) and the bathymetry from GEBCO (General Bathymetric Chart of The Oceans). The validation of the model and buoy data during July - December 1993 shows a good result (Root Mean Square Error = 0.166 and correlation/ linear regression = 0.807). Based on the literature, qualitatively the model has been verified with other simulation from another model in the same location. Keywords: SWAN Model, Hindcasting, Jepara Coastal Waters, Significant Wave Height, Validation

bottom friction). It is expensive in terms of computer time. Running long time series on a PC is prohibitive [3]. Besides that, the difference in density gives very significance effect to the relative wave amplitude [4]. This research is concerned the development of a methodology for nesting from ocean to local scale using SWAN, where waves are first simulated for a larger area using a coarse grid and then downscaled to a finer grid covering a smaller area. The boundary conditions for the finer grid are derived from the coarse grid computation. There are several nesting techniques that can be implemented to produce a high-resolution local scale model. One common difference in techniques is the source of the boundary data for the coarse model. The most holistic approach is to nest from a global domain to a regional/sub-oceanic domain and, lastly, to a local coastal domain [5]. Gorman et.al [6] show the simulations were validated using data from an inshore site in 30 m water depth at Mangawhai on the north-east coast of the North Island. Use of the nested model improved the agreement between model and measured significant wave height, decreasing the scatter index from 0.50 to 0.26. The suite of tools provided by the hindcast and localized, shallow water models can provide accurate new wave information for most of New Zealand's coastline.

1. INTRODUCTION Considering the difficulty to obtain waveform measurement data in Indonesia, wind wave hindcasting was often used in onshore and offshore building planning. There is a significant difference between measurement results and forecasting [1], so it needs to be verified with the measurement results. The purpose of this study is to find whether the SWAN set up give results in accordance with the measurement results in Jepara coastal waters. This study was conducted to support the research of wave characteristics based on wave forecasting for 10 years in the waters between Java, Sumatera and Kalimantan using SWAN model from TU Delft (Delft University of Technology). SWAN (Simulating Wave Near-shore) is a numerical wave model for hindcasting wave parameters in coastal areas. This numerical model was chosen because the reference is suitable for shallow water. Shallow water has many nonlinear factors that affect the wave greatly. In addition, this model can be accessed directly without the need to pay licenses and has been used widely by researchers in various countries. SWAN is now a viable option for operational high-resolution nonstationary wave predictions at sub-regional scale [2]. It is relatively quick to set up and userfriendly in operation, but some terms should be improved and not all interactions are included (e.g. 114

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In shallow water, six processes contribute to Stot:

2. PHYSICAL PROCESSES All information about the sea surface is contained in the wave variance spectrum or energy density E(), distributing wave energy over (radian) frequencies (as observed in a frame of reference moving with current velocity) and propagation directions (the direction normal to the wave crest of each spectral component). Usually, wave models determine the evolution of the action density N (x,t;) in space x and time t. The action density is defined as N=E/and is conserved during propagation in the presence of ambient current, whereas energy density E is not. It is assumed that the ambient current is uniform with respect to the vertical co-ordinate and is denoted as U [7]. The evolution of the action density N is governed by the action balance equation, which reads [8]: 𝜕N 𝜕t

+ ∇𝑥 [(𝑐𝑔 + U)N] +

𝜕𝑐𝜎 N 𝜕𝑐 + 𝜃 𝜕𝜎 𝜕𝜃

=

Stot σ

Stot = Sin + Snl3 + Snl4 + Sds,w + Sds,b + Sds,br

These terms denote, respectively, wave growth by the wind, nonlinear transfer of wave energy through three-wave and four-wave interactions and wave decay due to whitecapping, bottom friction and depth-induced wave breaking [7]. There are some options in SWAN regarding the model set-up which pertains to the type and/or parameterization of the formulations used for the source terms in Eq.(5). The user can choose between three different formulations for Sin, which accounts for the linear and exponential growth of waves due to wind [5]. Wind energy to waves is commonly described as the sum of linear and exponential growth. There are two wind growth models in SWAN that are available for us. Both expressions of wind growth model of them share the following form (Eq.(6)) and the same linear growth (Eq.(7)), while the exponential growth term is different.

(1)

The left-hand side is the kinematic part of this equation. The second term denotes the propagation of wave energy in two-dimensional geographical xspace, with the group velocity cg = ∂σ/∂k following from the dispersion relation σ2 = g|k| tanh(|k|d) where k is the wave number vector and d the water depth. The third term represents the effect of shifting of the radian frequency due to variations in depth and mean currents. The fourth term represents depth-induced and current-induced refraction. The quantities cσ and cθ are the propagation velocities in spectral space (σ,θ). The right-hand side contains Stot, which is the source/sink term that represents all physical processes which generate, dissipate, or redistribute wave energy. They are defined for energy density E(σ, θ). The second term in Eq. (1) can be recast in Cartesian, spherical or curvilinear co-ordinates. For small-scale applications, the spectral action balance equation may be expressed in Cartesian co-ordinates as given by [7] 𝜕N 𝜕𝑡

+

𝜕𝑐𝑥 N 𝜕𝑥

+

𝜕𝑐𝑦 N 𝜕𝑦

+

𝜕𝑐𝜎 N 𝜕𝜎

+

𝜕𝑐𝜃 N 𝜕𝜃

=

Stot 𝜎

Sin (σ,θ) = A + BxE(σ,θ)

𝛼

A = 𝑔22𝜋 [U∗ max(0, cos(𝜃 − 𝜃𝑤 ))]4 H with H = exp(−(σ/σ*PM )-4)

and σ*PM =

0.13𝑔 28𝑈∗

(7)

2𝜋 (8)

Exponential growth: a. Expression from [10]: 𝐵 = max[0,0.25

𝜌𝑎 𝑈 (28 ∗ 𝜌𝑤 𝐶𝑝ℎ

cos(𝜃 − 𝜃𝑤 ) − 1)]𝜎

(9)

in which U* is friction velocity, wis wind direction, Cph is the phase speed and aand ware the density of air and water, respectively.

(2)

b. Expression from [11]: c𝑥 = 𝑐𝑔,𝑥 + U𝑥 ,

𝑐c𝑦 = c𝑔,𝑦 + U𝑦

(3)

𝜌

With respect to applications at shelf sea or oceanic scales the action balance equation may be recast in spherical co-ordinates as follows [7]:

+

𝜕𝑐𝑥 𝑁 𝜕𝑥

+

𝜕𝑐𝑦 𝑁 𝜕𝑦

+

𝜕𝑐𝜎 𝑁 𝜕𝜎

+

𝜕𝑐𝜃 𝑁 𝜕𝜃

=

Stot σ

𝑈

𝐵 = 𝛽 𝜌 𝑎 (𝐶 ∗ )2 (max(0, cos(𝜃 − 𝜃𝑤 )))2 𝜎 𝑤

𝜕𝑡

(6)

In which A describes linear growth and BxE exponential growth [9]. Linear growth by wind:

with

𝜕𝑁

(5)

𝑝ℎ

(10)

where  is the Miles“constant”. The dissipation term of wave energy is represented by the summation of three different contributions: white-capping Sds,w, bottom friction Sds,b and depth-induced breaking Sds,br [7].

(4)

with longitude and latitude  115

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(110.450oE -110.918oE and 5.996oS - 6.450oS). The JS and JCW domains have 1/8 degree and 1/96 grid resolutions with the total of 176x120 and 44x48 grid-cells, respectively. The bathymetry in this region is relatively shallow (<100 m), with the presence of narrow straits (e.g. Malacca Strait) and small islands that add the complexity of the model domain (Fig. 1). The deep waters are concentrated in the edge of model domain, i.e. North of Sumatera (top-left), North of Kalimantan (top-right), and North of Bali (bottom-right). The depth range is 500-3300 m.

3. MATERIALS AND METHODS 3.1 Available Data The scarcity of time series oceanographic datasets, especially the observational wave data, is one of the challenges to develop the ocean model in Indonesia. However, data is obtained from longterm wave observation located in Jepara, Central Java (110.7722oE, 6.3983oS), which has granted the access from PT. Geomarindex. The data is from July to December 1993 with three-hour temporal resolution. The available parameter is only the wave height values. The bathymetry data is obtained from General Bathymetric Chart of the Oceans (GEBCO) with a spatial resolution of 30 arc-sec (~1 km). There is no available local bathymetry dataset to cover the coastal waters. Therefore, it is applied to all model domains. The only forcing included in this wave model is from the wind. It is obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) with a spatial resolution of 0.125 degrees (~13.75 km) and 6-hour temporal resolution. The FNMOC global WW3 model is vastly used for open boundary condition of wave forcing in several studies, e.g. [5], however, wave data in 1993 is unavailable. The significant wave height output from the ECMWF reanalysis (ERA)-Interim reanalysis is used as a comparison to our model. The nearest point to the buoy station is located at 110.75oE and 6.375oS. The distance between these two points is 4.16 km or 4 grid cells in the smallest domain.

3.3 Model Setup The non-stationary 2D wave model within SWAN is simulated with 1-hour interval from July to December 1993. The frequency range is set at 0.3-1.1 Hz and divided linearly into 38 frequencies. The number of directional bins is set for 72 due to the physical characteristics of the study areas, such as the geographical conditions, bathymetry gradients, and global and local wind effects [12]. In addition, the first order, backward space, backward time (BSBT) numerical scheme are employed for both model domains with three maximum number of iterations and 98% percentage of accuracy for the wet/dry condition. The same physics setup is applied to both domains. GEN3 wave model with Komen linear growth formulation and the white capping default configurations were used [10]. Further, the triad and quad wave-wave interaction, as well as breaking and diffraction processes are activated by using the default configurations [7]. For bed friction, the dissipation coefficients (Cb) was 0.019 as suggested for the region with smooth sediment characteristic, while the default value was 0.038 [7]. The vegetation, turbulence, and fluid mud are omitted in the physical processes due to the absence of datasets. Finally, the model is simulated in parallel computing with OpenMP (Open Multi-Processing) to reduce computation times.

3.2 Model Domain The SWAN model provides nesting application to the parent grid. Hence, there is two model domains, the Java Sea (JS) domain as the parent grid and Jepara Coastal Waters (JCW) domain as the child/nested grid (Fig. 1). The JS domain extends from Aceh to Bali that includes two marginal seas, i.e. the South China Sea and the Java Sea, while the JCW domain covers the Jepara coastal waters

Fig.1 Grid-view of wave model domains; (left) JS domain with isobath at 50 m and (right) JCW domain with 10 m of isobath interval. Red point denotes a buoy location. 116

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Manny to Jepara occurred over 10 days (see Fig.2d). The result of forecasting with SWAN shows a wave distribution pattern corresponding to the buoy data, except for the duration of Oct-Nov 1993 for which the wave height of the measurement needs to be reconfirmed. Factors that may affect the inaccuracy of the model: 1. Coarse resolution of bathymetry dataset used in this model 2. Global wind data are usually unable to achieve the magnitude of extreme events 3. The absence of wave-current interaction in the model and static water level (zero value) 4. The grid on the model is also still rough and in rectangular form 5. The accuracy of the buoy data for validation also needs to be confirmed again, especially the Oct-Nov 1993 timeframe, because the wave height was only about 10-15 cm.

4. RESULTS AND DISCUSSION 4.1 Model Validation Wave statistics for the buoy sites were computed from the hindcast. Occurrence statistics for significant wave height Hs, mean direction Qmem, and second moment mean period Tm2 were computed. Significant wave height results were compared with data over the relevant deployment periods. Significant wave height at a wave buoy site as simulated by the wave model and as measured by the buoy, shown as time series in Fig.2a and regression in Fig.2c, with the line of best fit and equivalence lines shown by dashed and solid lines respectively. In December 1993, there appeared to be extreme wave height (see Fig.2b) and after further study of the cause, this was due to Manny typhoon where propagation of waves from the center of the cyclone

Fig.2 (a) and (b) are showing time-series of significant wave height from SWAN model (blue line), ECMWF model (red line) and buoy observation (black dots) for whole observation period and during Typhoon Manny, respectively. (c) Hs density plot of SWAN & Jepara Buoy, and (d) Typhoon Manny propagation track that obtained from Joint Typhoon Warning Center (JTWC) and plotted in Google Earth. 117

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Table 1. Significant wave height (Hs) statistic in the Jepara Buoy station and the model accuracy. R is correlation coefficient and SI is scattered index. Basic Stats Model Accuracy Min Max Mean Std RMSE R Bias SI x x x x Jepara Buoy 0.017 1.878 0.230 0.202 SWAN 0.041 1.406 0.339 0.203 0.166 0.807 -0.109 0.720 ECMWF* 0.071 1.955 0.535 0.288 0.354 0.778 -0.304 1.535 * Significant wave height output from the ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis (ERA)-Interim reanalysis Data

both monsoon wind patterns and can be seen in Fig. 3a and 3b (right). In normal condition as depicted in Fig. 3a and 3b, Hs is strongly associated with the wind pattern. Although, occasionally there are cyclones coming from Indian Ocean (south of Java) or typhoon in the South China Sea region and causing a disturbance within model domains for up to 15 days. One of the examples is the Typhoon Manny, which was originated in the Western Pacific. Fig. 3c shows the generation of typhoon within model domains and it has significantly amplified the wave height. In normal conditions, the wind speed 5-7 m/s produces Hs 0.5-1.2 meter (Fig, 3a and 3b), while at maximum Hs condition between July-December 1993, wind speed 8-15 m/s yield Hs 1-3 meters (Fig.3d).The relative maximum Hs in the model domain reaches 3.16 m. It is located in the south of Kalimantan (see Fig. 3d). Meanwhile, in Jepara coastal waters, the increasing maximum Hs is up to 1.41 m. The results of statistical calculations for the four areas in the study area obtained the Hs min-max and average Hs (meter) in the east monsoon conditions for the Java Sea 0.43-0.99 (average 0.64) Karimata Strait 0.18- 0.98 (0.51), Malacca Strait 0.02-0.58 (0.24), South China Sea 0.09-1.21 (0.47) and for the west monsoon; Java Sea 0.08-2.44 (average 0,57) Karimata Strait 0.09-2.09 (0,56), Malacca Strait 0.07-0.77 (0.28), South China Sea 0,21-2.93 (0.97). This results are suitable when compared to Hs for 9 years forecasting by Wicaksana et.al (2015) [14] where at the west monsoon in Karimata Strait of Hs 1.5-3 m (Hs SWAN 2.09 m) and Java Sea 0.52.5 m (Hs SWAN 2.44 m), while at the east monsoon in Karimata Strait Hs 1,5-2,5 m (Hs SWAN 0,98 m) and Java Sea 1-2 m (Hs SWAN 0,99 m). Suitable in question is data analysis results for 6 months entered in the range of 9-year forecasting results.

4.2 Comparison with Existing Models The European Centre for Medium-Range Weather Forecasts (ECMWF) is an independent intergovernmental organization supported by 34 states. ECMWF is both a research institute and a 24/7 operational service, producing and disseminating numerical weather predictions to its Member States. This data is fully available to the national meteorological services in the Member States [13]. The result of SWAN modeling is compared with the wave forecasting result from ECMWF as shown in Fig 2a and 2b. Both models exhibit similar wave distribution patterns, although Hs ECMWF model results tend to be always larger than the Hs model of SWAN. Statistical analysis for Hs model and Hs buoy included minimum, maximum, mean, standard deviation and model accuracy values against the measurement results are presented in Table 1 where the Hs model SWAN (0.807) showed a better correlation value than Hs ECMWF (0.778). Root Mean Square Error (RMSE) for Hs SWAN is smaller than Hs ECMWF. This shows the SWAN modeling more closely to the measurement results, in other words, the SWAN model setting is good. 4.3 Monsoonal Significant Wave Characteristics Examples of the significant wave (Hs) and wind pattern models in east monsoon and west monsoon are presented in Fig.3, both also show normal conditions and extreme wave (typhoon) condition. Wind patterns during east and west monsoon are distinctly recognized based on its direction. The east monsoon winds travel from southeast to northwest, while the west monsoon winds are the opposites. The east monsoon wind or Australian monsoon wind blows from Australia to the equator and is known as the dry season that peaks in JuneJuly-August. The west monsoon wind or Asian monsoon wind blows from the Asian continent with water vapors that cause rain, so it is called the rainy season and reaches its peak in December-JanuaryFebruary. Wind data treated in accordance with

4.4 Future Works Application This study is expected to support wave characteristic research based on wave forecasting for 10 years in the waters between Java, Sumatera and Kalimantan. The wave forecasting research needed 10-year wind data (2007 - 2016) from 118

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Fig. 3 Significant wave height with directional spreading (left) and wind speed (right) within the large model domain in different conditions: (a) east monsoon, (b) west monsoon, (c) typhoon Manny generation, and (d) relative maximum Hs for the period of July to December 1993. ECMWF and bathymetry data from GEBCO where both data use the same resolution used in this study. The results of the research are expected to help practitioners to plan the structure of the beach building, coastal protection, the structure of the building at sea, or marine structures. For example, as mentioned by Rathod et.al [15]; Piles used in marine structures are subjected to lateral loads from the impact of berthing ships and from waves. Piles used to support retaining walls, bridge piers and

abutments, and machinery foundations carry combinations of vertical and horizontal loads. The 10-year wave data can be used as a basis to determine the probability of 25, 50, or even 100 years in the future. The use of significant wave heights with specific return periods is associated with the risk of planned building structures. The higher the risk value the longer return period is chosen.

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Significant wave forecasting is also required for shipping safety. Until now the Karimata Strait (between south Sumatera and Kalimantan Island) is still a trading channel and the Java Sea becomes one of the important national service channels, especially in the present role in the Indonesian toll lane [14].

Proudman Oceanographic Laboratory Report, NO 57, 51PP, 2000, pp.ii-48. [4] Harun F.N. and Khairuddin M.A.A., Analytical Solution For a Long Waves Propagation in Two-Layer Fluid Over a Submerged Hump, International Journal of GEOMATE, Vol. 12, Issue 33, 2017, pp. 30-37. [5] Atan R., Nash S., and Goggins J., Development of a Nested Local Scale Wave Model for a 1/4 Scale Wave Energy Test Site Using SWAN, Journal of Operational Oceanography, 2017, pp.1-20. [6] Gorman R.M., Bryan K.R., and Laing A.K., Wave hindcast for the New Zealand region: Nearshore validation and coastal wave climate, New Zealand Journal of Marine and Freshwater Research, Vol.37, Issue 3, 2003, pp. 567-588. [7] The SWAN team, SWAN Technical Documentation, Delft University of Technology, 2006, pp. 11–27. [8] Komen G.J., Cavaleri L., Donelan M., Hasselmann K., Hasselmann S. and P.A.E.M. Janssen, Dynamics and Modelling of Ocean Waves, Cambridge Univ. Press, 1994, pp. 532. [9] Baoshu Y., Dezhou Y., Application Study of Shallow Water Wave Model (SWAN) in Bohai Sea, Proceedings of The Twelfth OMISAR Workshop on Ocean Models, 2004, pp. 3-1-3-8. [10] Komen G.J., Hasselmann S., and Hasselmann K, On the Existence of a Fully Developed Windsea Spectrum, Journal of Physical Oceanography, 14, 1984, pp.1271-1285. [11] Janssen P.A.E.M., Quasi-linear Theory of Wind-wave generation applied to Wave Forecasting, Journal of Physical Oceanography, 21, 1991, pp.1631-1641. [12] WMO, Guide to wave analysis and forecasting, Second ed, Geneva, Switzerland: Secretariat of the World Meteorological Organization (WMO), 1998. [13] ECMWF, Who We Are, www.ecmwf.int/en/ about, 2018. [14] Wicaksana S., Sofian I., Pranowo W.S., Kuswardani A.R.T.D., Saroso, Sukoco, N.B., Karakteristik Gelombang Signifikan di Selat Karimata dan Laut Jawa Berdasarkan Rerata Angin 9 Tahunan (2005-2013), Omni Akuatika, Vol.11, Issue 2, 2015, pp.33-40. [15] Rathod D., Muthukkumaran K., Sitharam T.G., Dynamic Response of Single Pile Located In Soft Clay Underlay by Sand, International Journal of GEOMATE, Vol. 11, Issue 26, 2016, pp. 2563-2567.

5. CONCLUSION The result of forecasting with SWAN shows a wave distribution pattern corresponding to the buoy data, except for the duration of Oct-Nov 1993 for which the wave height of the measurement needs to be reconfirmed. Refers to the Root Mean Square Error (RMSE) value (0,166) and correlation/ linear regression value (0,807), and the waveform pattern corresponding to the monsoon pattern, it can be stated that this SWAN model is valid. The setting up of wave hindcast for Jepara waters will be helpful for improving the level of shallow sea wave hindcast in the waters between Java, Sumatera, and Kalimantan. 6. ACKNOWLEDGEMENTS The authors would like to thank PT. Geomarindex/PT. Wiratman & Associates for providing observation wave data and also thank the ECMWF and GEBCO for providing access to wind and bathymetry data. Sincere gratitude to scientists at Delft University of Technology (TU Delft) who developed the SWAN model and special thanks to the Marine and Coastal Data Laboratory, Indonesian Ministry of Marine Affairs & Fisheries for providing places of simulations. 7. REFERENCES [1] Muliati Y., Wurjanto A., and Pranowo W.S., Validation of Altimeter Significant Wave Height Using Wave Gauge Measurement in Pacitan Coastal Waters, East Java, Indonesia, International Journal of Advances in Engineering Research, Vol. 2, Issue No. IV, 2016, pp. 25-33. [2] Rogers W., Kaihatu J.M., Hsu L., Jensen R.E., Dykes J.D., and Holland K.T., Forecasting and Hindcasting Waves with the SWAN Model in the Southern California Bight, Coastal Engineering, Vol, 54, Issue 1, 2007, pp. 1-15 [3] Wolf J., Hargreaves J.C., Flather R.A., Application of The SWAN Shallow Water Wave Model to Some U.K. Coastal Sites.

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