A model study of wind-induced sea level fluctuations in the Persian Gulf and the Gulf ofOmanMohammad Javad Ketabdari, Amin Ilia, and Mehdi Karimi Citation: AIP Conference Proceedings 1648, 770010 (2015); doi: 10.1063/1.4912980 View online: http://dx.doi.org/10.1063/1.4912980 View Table of Contents: http://scitation.aip.org/content/aip/proceeding/aipcp/1648?ver=pdfcov Published by the AIP Publishing Articles you may be interested in Wind-induced roughening of thin liquid films Appl. Phys. Lett. 84, 532 (2004); 10.1063/1.1644335 Nonlinear modeling of jet atomization in the wind‐induced regime Phys. Fluids 7, 964 (1995); 10.1063/1.868572 A study of recent data sets exhibiting wind‐induced ambient noise J. Acoust. Soc. Am. 69, S95 (1981); 10.1121/1.385989 Mechanisms of wind‐induced low‐frequency ambient sea noise in the deep ocean J. Acoust. Soc. Am. 67, S95 (1980); 10.1121/1.2018499 Wind‐induced low‐frequency ambient sea noise: Mechanism and modeling J. Acoust. Soc. Am. 62, S38 (1977); 10.1121/1.2016161

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A Model Study of Wind-Induced Sea Level Fluctuations in the Persian Gulf and the Gulf Of Oman

Mohammad Javad Ketabdaria, Amin Iliab and Mehdi Karimic

aAssociate Professor, Faculty of Marine Technology, Amirkabir University of Technology, Tehran, Iran

bSenior coastal engineer, Fara Darya Arshe Co., Tehran, Iran cJunior coastal engineer, Fara Darya Arshe Co., Tehran, Iran

* Corresponding Author: ketabdar@aut.ac.ir (M. J. Ketabdari)

Abstract. A 2D hydrodynamic model, with ability to use unstructured triangular grids, was used to evaluate wind-induced sea level variations in two important gulfs in the Middles East. The model was forced by the modified QuikSCAT wind data above the sea level for a 10-years period, (from 1999 to 2009). Measured water levels at Kish and Lavan Islands were used to calibrate and verify the model. A special effort was carried out to eliminate tidal levels data from the measurements to obtain raw wind-induced fluctuations. The wind drag coefficient, used as main calibration factor was estimated as a function of wind speed. The results proved that the wind drag coefficients for this area are much greater than those which are used in open-oceans. Comparison with the eld data proved that the wind-induced setups in the Persian Gulf are much greater than those which were predicted in the previous studies. The final results show that the model is completely applicable for the development of an operational system for predicting wind-induced extreme sea levels in the Persian Gulf and The Oman Sea. In this paper, the wind-induced extreme levels were determined for different coastal areas in the Persian Gulf and the Gulf of Oman. In addition, two useful maps were developed to present extreme wind setup and set-down throughout 10-year modeling period for entire the Persian Gulf and the Gulf of Oman. The results show that extreme wind-induced water levels, in the Northwest and Southeast of the Persian Gulf, are much higher than other places in this region.

Keywords: The Persian Gulf, Wind-induced extreme levels, 2D hydrodynamic model, Wind Setup. PACS: 02.60.Cb

INTRODUCTION

A wind stress over water bodies can generate large water level fluctuations if the wind stress is sufficiently strong and the water body is semi-shallow over a large enough area [1]. The Persian Gulf and the Gulf of Oman are two semi-enclosed basins. The Persian Gulf is connected to the Gulf of Oman in the east through the strait of Hormoz and the Gulf of Oman exchanges with open-oceans in the east by the Indian Ocean. The water level variations in the Persian Gulf are mainly induced by tides and winds. The range of tide levels vary between 1.5 to 4.5m in the Persian Gulf. Due to this predominant fluctuations induced by tides, wind-induced level variations haven’t been considered enough in the previous studies. Also, separating wind-induced levels from tidal levels in measured data may not be easily achievable. This study takes special attention to the sea level fluctuations which are induced by wind in the Persian Gulf and the Gulf of Oman. A 2D hydrodynamic model was applied with an unstructured triangular grid. Special effort was made to extract wind-induced level fluctuations from raw measured data at the two stations in the Northern part of the Persian Gulf. These extracted measurements were used to calibrate and validate the model. The extreme wind-induced levels were calculated for different coastal ports and cities around the Persian Gulf and the western part of the Gulf of Oman. Also, the extreme wind setups and set-downs for entire modeling area were presented.

WIND DATA

The NASA QuikSCAT Scatterometer is a windsea observation satellite, which has measured the surface wind speed and direction over the ice-free global oceans throughout July 1999 to November 2009. QuikSCAT provided measurements of the wind speed and direction referenced to 10 meters above the sea surface at a spatial resolution of 25 km with 12 hour time intervals [2]. A special study was made to process, expand and eliminate errors and gaps

Proceedings of the International Conference on Numerical Analysis and Applied Mathematics 2014 (ICNAAM-2014)AIP Conf. Proc. 1648, 770010-1–770010-5; doi: 10.1063/1.4912980

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from QuikSCAT wind data in the Persian Gulf and the Gulf of Oman. The data was used to force the 2D hydrodynamic model to determine wind-induced sea level fluctuations.

MODEL SET UP

In this case study, a 2D hydrodynamic model, MIKE21-FM-HD Flow model, was used to simulate water level. This model uses an unstructured triangular grids system and is able to consider the wind drag coefficients as a function of wind speed. This model is a module of MIKE Zero software, developed by Danish Hydraulic Institute (DHI) and is based on the numerical solution of the depth averaged 2D shallow water equations [3].

The domain of study covers the Persian Gulf, the Strait of Hormoz and the eastern part of the Gulf of Oman until the longitude E58°. In order to determine the location of boundary of the model in the eastern part of the Oman Sea, a sensitivity analysis was performed. The results of this analysis proved that the impact of eastern boundary data is negligible in water level modeling results in the western area of the longitude E58°, while the boundary is located at longitude E62° or more. So, in this study, the model eastern boundary in the Indian Ocean was located at longitude E62°. Fig. 1 shows the final model grids for studying wind-induced water level fluctuations in the Persian Gulf and the Gulf of Oman. The mesh sizes vary from 0.01º (1km) in the near coast area to 0.3º (30 km) in the middle of the Gulfs. More decrease in the mesh length didn’t have effect on the water levels derived from the model. Also, the bed resistance is specified based on Manning drag coefficient. In this modeling, the Manning number was considered to 32m1/3

/s. Specifying Manning numbers in range 20 to 40 m1/3/s didn’t have sensible effect on the results of the model.

A sensitive analyze was done in order to determine space discretization method. Fig. 2 represents the comparison of the model results for low order and high order space discretization in Bandar Lengeh. As can be seen, the water levels for the high order scheme are larger than the low order scheme in some peaks. So, the high order scheme was used for modeling in this study.

FIGURE 1. Sudy domain for wind-induced water level variations in the Persian Gulf and the Gulf of Oman.

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FIGURE 2. Comparison of the model results for low order and high order space discretization in Bandar Lengeh.

The most important calibration parameter for the wind-induced fluctuation models is the wind friction coefficient

(drag coefficient). Some researches such as Guan et al., proposed a linear relation between the wind drag coefficient and wind speed [4]. As the Persian Gulf is a semi-enclosed basin, the wind drag coefficient used in this model was much greater than those which were used for usual open seas [5].

As MIKE21FM HD model could consider this linear relation, the wind drag coefficient was considered as 0.003625 for the wind speeds over 20 m/s and 0.00125 for the wins speeds under the 5 m/s. Also the linear values were considered for the wind speeds between these extents.

The model was validated for the three of the most important storms in the measurements period. In order to determine the water level variation due to wind in measured data, it is obliged to calculate the difference between raw measurements and predicted tidal data. Fig. 3 shows the validation result at Kish measurements for a storm on 04/04/2007.

FIGURE 3. Model validation for wind-induced water fluctuations at Kish station for storm on 04/04/2007

CACULATING EXTREMES FLUCTUATIONS

In order to determine extreme values for wind setup and Wind set-down, water level fluctuations induced by wind were extracted from the model for different important ports and cities in the region. For instant, one of the important ports in northern coasts of the Persian Gulf is Bandar Lengeh. Water levels were extracted in front of Bandar Lengeh. An extreme value analysis was performed on this set of data by the EVA toolbox in MIKE Zero. Table 1 represents final results of wind setup and set-down. Similar processes were carried out to determine extreme wind setups and set-downs for other important ports and citied in the Persian Gulf and the Gulf of Oman, such as: Jask, Bandar Abbas, Bushehr, Kuwait, Doha, Abu Dhabi, Dubai, Fujairah and Muscat.

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TABLE 1. Wind setup and wind set-down for different return periods in Bandar Lengeh Return Period (yrs.) Set Up (m) Set-Down (m)

2 0.35 0.36 5 0.44 0.45

10 0.51 0.51 20 0.57 0.57 50 0.66 0.65 100 0.73 0.72

RESULTS

The 2D hydrodynamic modeling was carried out for a 10-years period (1999~2009) to determine wind-induced water level fluctuations. Extreme fluctuations were calculated by extreme value analysis for important coastal cities and ports in the Persian Gulf and the Gulf of Oman. Fig. 4 represents the location of the cities and ports which were considered for extreme value analysis.

In addition, maximum setups for different part of the Persian Gulf and the Gulf of Oman were presented. Fig. 5 shows maximum wind-setups in the Persian Gulf and the Gulf of Oman. It is evident in this figure that the wind-setup at the northeast of the Persian Gulf (from Kuwait to Hendijan in Iran) is more than 1.5m. Kuwait, Faw, Um Qasr, Bubian Island, Arvand-rud and Imam Khomeini port are the areas where wind-setup must be considered as serious factor in design of coastal structures. Also, the southern areas of Bahrain have a high maximum setup level induced by winds. The maximum setup levels in these areas are exceeded than 1.5m. Many construction projects have been doing in the coastline from Doha to Abu Dhabi and Dubai, where the wind setup levels are higher than 1.0m. Therefore it’s so important that designers consider the wind induced fluctuations in this region.

FIGURE 4. Important cities and ports in the Persian Gulf and the eastern part of the Gulf of Oman

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FIGURE 5. Maximum wind-Setups in the Persian Gulf and the Gulf of Oman

CONCLUSIONS

Tide is the main and continual factor in water level fluctuations in the Persian Gulf and causes a water level fluctuation between 1.5 to 4.5m in different parts of the Persian Gulf [6]. Some studies have been performed in tidal level fluctuations in the Persian Gulf. Wind setup and wind set-down are the second dominant factors in water level fluctuations in the Persian Gulf, which have not been studied enough in this region yet. This study focused on the wind-induced water level fluctuations in the Persian Gulf and the Gulf of Oman by modeling this phenomenon for a 10-years period. The model was calibrated and validated by separated wind-induced water levels from measured data. The wind drag coefficient, obtained in calibration process, is much larger than usual wind drag coefficient which is used for oceans and non-closed seas. The results show that extreme wind-induced water levels, in the Northwest and Southeast of the Persian Gulf, are much higher than other places in this region.

REFERENCES

1. R.M. Sorensen, Basic Coastal Engineering, 3rd Ed, XIV, 324 p, (2006). 2. ftp://podaac-ftp.jpl.nasa.gov/allData/quikscat/ 3. Mike21 Wave Modelling User Guide, DHI Water and Environment, (2009). 4. Ch. Guan, L. Xie, On the Linear Parameterization of Drag Coefficient over Sea Surface, American Meteorological Society,

(2004). 5. R.T. Wang, “Title of Chapter,” in Classic Physiques, edited by R. B. Hamil, Publisher City: Publisher Name, 212-213, (1999). 2. M.A. Badri, P. Wilders, and A.R. Azimian, Flow Estimation for the Persian Gulf Using a Kelvin Wave Expansion, Delft

University of Technology, Reports of the Department of Applied Mathematics, Report 10-06, (2010).

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