Evaluating the results of Hormuz strait wave simulations using WAVEWATCH-III and MIKE21-SW

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1 Int. J. Mar. Sci. Eng., 2 (2), , Spring 2012 ISSN IAU Evaluating the results of Hormuz strait wave simulations using WAVEWATCH-III and MIKE21-SW *F. S. Sharifi; M. Ezam; A. Karami Khaniki Graduate school of marine science and technology, Science and research branch, Islamic Azad University, Tehran, Iran Received 20 March 2012; revised 9 April 2012; accepted 17 May 2012 ABSTRACT: In this study, the capabilities of WAWEWATCH-III and MIKE21-SW for predicting the characteristics of wind-generated waves in Hormuz Strait are evaluated. The numerical models have been set up using Input data including GFS wind with 5 spatial resolution and 6 hours time intervals, along with the ETOPO1 bathymetry data with 2 arc-minute spatial resolutions. The results of the two model simulations were compared with the available satellite altimetry measurements of significant wave heights at the modeling area. The comparisons show that in deep water WAVEWATCH-III results in more reliable prediction of wave characteristics in comparison to the MIKE-21 SW. While in shallow area the MIKE-21 gives more consistencies with altimetry measurements. These may be due to the benefits of the unstructured grid which are used in MIKE- 21, lead to better representations of the coastal area. The investigation on the direction of wind generated waves in the modeling area show that in some regions despite of the increase in wind speed, significant wave height remains nearly unchanged. This is mainly because of rapid changes in wind direction over the Strait of Hormuz. Key words: Numerical Modeling; Wave Simulation; Hormuz Strait; WAVEWATCH III; Mike21-SW INTRODUCTION Wind induced waves are among the most important subjects in coastal and ocean engineering. A reliable prediction of wind induced wave characteristics is an indispensable part of offshore and coastal structure design. The random nature of sea surface waves makes it one of the most complicated phenomena in marine environments. Ocean wave characteristics are mainly determined through field measurements, numerical simulation, physical models and analytical solutions. Each method has its own advantages and disadvantages nowadays, numerical models emerge as one of the most powerful tools for the study of surface water waves. On the basis of underlying wave theories, present water wave models are mainly categorized as spectral, mild slope equation (MSE), boussinesq and shallow water equation wave models. Nowadays, the third generation spectral wave models, which are based * Corresponding Author f.sharifi72@yahoo.com on wave action balance equation, are widely being used in wave climates predictions. The WAVEWATCH-III and MIKE 21 SW, which are used in the present study, are the two representative spectral wave models, developed and validated for use in offshore and coastal areas, Liu et al., (2002) evaluated the reliability of four spectral wave models. They applied different models to simulate wave induced waves in Lake Michigan and found that results of all models used in the study, nearly lie in the same order of accuracy. Eventually, they concluded that in spite of different numerical schemes used in the models, all models yield nearly same results. Since wind data are introduced in wave action balance equation via source terms, wind input data may considerably affect the results of spectral wave models. Many studies have already been conducted

2 F.S. Sharifi et al. on the reliability of wind data which are produced by operational forecast model for wave simulation purposes. Signell et al. (2005) evaluated wind data produced by four different forecast models including the ECMWF, for wave simulation purposes in the Adriatic Sea. They used the SWAN (Ris, et al., 1999) for wave simulation and found that the ECMWF T511 (with spatial resolution of 40 km) underestimates the wind speed by 36%. They also showed that data produced by the ECMWF model are very smooth and do not reproduce the spatial structure of strong wind events. According to this study, the predicted wave heights averagely show at least 50% underestimation with respect to field measurements. Cavaleri and Scalvo (2006) conducted a similar research in the Mediterranean Sea. They showed that wind data, produced by the ECWMF, are considerably underestimated with respect to field measurements and consequently cause an under-prediction in wave simulation results. Brenner et al. (1999) carried out a numerical simulation of wind induced waves in the Mediterranean Sea using the WAM (Hasselman et al., 1988) model.the wind data used in this study were produced by various models including ECMWF ERA. Results of the study show that introducing ECWMF wind data in the WAM model leads to considerable under-prediction in wave height simulation. This is mainly because of low resolution and underestimated wind data produced by ECWMF. Caires and Sterl (2006), Cavaleri and Bertotti (2005) and Ardhuin et al. (2003), also reported many cases where the use of the ECMWF results in underestimation in wind field data. Caires and Sterl (2006) estimated global return values of significant wave height based on the ERA- 40 data sets. They found that the use of ERA-40 data results in underestimating the higher significant wave heights. Accordingly, on the basis of buoy data, they proposed a linear relationship to correct estimated return values. Despite the fact that Caires and Sterl estimated global extreme wave heights distribution, results of their study cannot provide reliable information for the Persian Gulf. This is mainly due to the lack of sufficient field measurement data and high spatial resolution of the model. MATERIALS AND METHODS The study area The Strait of Hormuz is a waterway connecting the Persian Gulf to the Oman Sea. It is situated between Iran (on the northern coast) and Musandam peninsula (on the Southern coast). The Strait of Hormuz is one of the most important sea passages in the world. It has about 180 km long and 50 km wide. The maximum depth of the Persian Gulf is about 100m and is related to the south and southeast of the Strait of Hormuz. Fig. 1 shows the geographical location and bathymetry of the Strait of Hormuz. Fig. 3 shows a graphical representation of the Persian Gulf bathymetry. As can be observed in the figure, water depth decreases toward the Iranian coasts. Fig. 1: The Geographical Location and bathymetry of Hormuz Strait Numerical Simulation As mentioned before, the WAVEWATCH-III and Mike 21 SW are the third generation wind wave models which are used in the present study. 164

3 Int. J. Mar. Sci. Eng., 2 (2), , Spring 2012 The Mike 21 SW, which is based on unstructured grids, is capable of simulating wave generation and propagation in both deep water and coastal areas. The WAVEWATCH-III, based on rectangular structured grids, is developed by NOAA, in the spirit of the WAM model. WAVEWATCH-III is the further development of WAVEWATCH and WAVEWATCH II, which were developed by Delft University of Technology and NASA Godard Space Flight Center respectively. WAVEWATCH-III differs from its predecessors in many aspects namely, governing equations, numerical schemes, model structure and physical parameterization. More details about the model are represented by Tolman (2002). Model Set Up In the present study, wind data are derived from the Global Forecast System (GFS), a weather prediction system run by NOAA. The wind data are available with spacing (in both longitudinal and latitudinal directions) and 6 hours temporal resolution. Fig. 2 represents the wind field over the Persian Gulf, extracted from the GFS data. In the numerical simulation domain, the wind speed data were derived from the available data sets for the January The wind data have been interpolated within spatial and temporal domain, so as to use in the wave models. In addition, the bathymetry data used in the numerical simulation are extracted from the ETOPO1, a 1-arc-minute global model of the Earth s surface (see Fig. 3). A representation of the bathymetry data in 0.05 uniform structured grid is also shown in Fig. 4. Fig. 3: Graphical representation of bathymetry data extracted from ETOPO1. Creating an appropriate computational mesh of the area under study is a fundamental step in numerical modeling of wave generation and propagation in the area. In the present study, structured and non uniform unstructured grids have been utilized to create the computational mesh. Fig. 4: Bathymetry of the area with spatial resolution of 0.05 Fig. 2 : A representation of input wind field, based on GFS data for Persian Gulf and Gulf of Oman Fig. 5: The computational mesh used in Mike 21-SW 165

4 Evaluating the results of Hormuz strait wave simulations using WAVEWATCH-III and MIKE21-SW RESULTS AND DISCUSSION In order to predict wave characteristics in the Hormuz Strait area, the WAVEWATCH III has been utilized. Then the results of numerical simulation by the WAVEWATCH III have been compared with those of MIKE 21 SW. The unstructured computational grid in MIKE 21 consists of maximum element areas ranging from 2000 m 2 in shallow near-shore to m 2 in offshore regions, and the rectangular structured grid in WAVEWATCH III assumed as about 0.05 degree (~ 5km) after performing some sensitivity analysis. Considering the open boundaries as far away from the Strait of Hormuz, permit us to treat the open lateral boundaries as closed (land) boundaries with nearly good approximation. In order to evaluate the capabilities of the two models in simulations of the wind wave characteristics we focus on the time series of the significant wave heights and wave directions which are driven in some selective locations in the modeling area. The geographical positions of these locations are represented in Fig. 6. Fig. 7a: A comparison between significant wave height at M1 station, yielded by MIKE 21 SW and WAVEWATCH III during January 2009 Fig. 7b: A scatter plot showing the degree of correlation between significant wave heights at M1 predicted by MIKE 21 SW and WAVEWATCH III Fig. 6: The geographical location of the selective points The MIKE 21 SW has been set up and run over the study area using input wind field and initial conditions as described previously. The time series of significant wave heights and scatter diagrams yielded from both model simulations at stations M1 and M2 are represented by Fig. 7 and Fig. 8 respectively. As can be observed, there is no considerable difference between significant wave height predicted by MIKE 21 SW and WAVEWATCH and the comparison suggests a close agreement between the waves characteristics predicted by the two models, especially in deep water. Fig. 8a: A comparison between significant wave height at M2 station, yielded by MIKE 21 SW and WAVEWATCH III during January 2009 Fig. 8b: A scatter plot showing the degree of correlation between significant wave heights at M2 predicted by MIKE 21 SW and WAVEWATCH III. 166

5 Int. J. Mar. Sci. Eng., 2 (2), , Spring 2012 Comparisons between significant wave height time series, which are plotted in Fig 7a, suggests that at M1 station there is a close agreement between simulation results of the MIKE 21 SW and WAVEWATCH III. The calculated value of coefficient of determination (R 2 ) also suggests that at M1, there is a close correlation between significant wave height predicted by MIKE 21 SW and WAVEWATCHIII. However, at M2, M3 and M4, which are located near to the coast, there are more differences between numerical simulation results of the MIKE 21 SW and that of WAVEWATCH III. As mentioned before, the MIKE 21 SW adopts unstructured mesh grid, while WAVEWATCH III is based on structured grids. That is main reason why results of significant wave height prediction by MIKE 21 in shallow near-shore waters, differ from that of WAVEWATCH III. It should be noted that the use of unstructured grids enhances numerical simulation accuracy in complex geometry of the computational domain which is caused by the shape of the coastline. Fig.8 shows the results of the M2, which is a representative station located in shallow water area. As can be observed in the figure, in comparison with other stations, the smaller R-squared value at the M2 suggests the lower degrees of correlation between results of significant wave height prediction by the MIKE 21 SW and WAVEWATCH III. In the present study, in order to minimize the inaccuracy caused by shoaling effect and complex geometry of the computational domain,, a local refinement with higher spatial resolution has been made at the M2. However, in comparison with other measurement stations, at the M2, there are more differences between significant wave heights predicted by the two models. Fig. 9 represents a comparison between wave direction time series at M1 station, predicted by the MIKE21-SW and WAVEWATCH-III. Generally, it can be concluded that except for the near-shore area, where MIKE 21 SW over-predicts the significant wave height, there is a reasonable agreement between numerical simulation results of the MIKE 21- SW and that of WAVEWATCH-III. Fig.9: Comparison between temporal variation of wind direction predicted by the MIKE21- SW, and the WAVEWATCH III, at the M1. Fig. 10a represents time series of wind speed at 10 m above sea surface (U 10 ) and significant wave height at M1 stations, for the January As can be observed in the figure, black arrows make three time intervals. In the first and last intervals, a direct correlation between wind speed and significant wave height variations can be observed. However, in the second time interval, because of rapid changes in wind direction over the area and the effect of short fetch, a low degree of correlation between U 10 and significant wave height is observable. At the M1, located in the center of the straight, the wave growth is not limited by the fetch and wind duration. However, it can be observed that despite of the increase in wind speed during an approximate period between 370 to 470 hours from the start of the simulation, significant wave height remains nearly constant in this period. U(10) January (m/s) Fig. 10a: A comparison between time series of wind speed and predicted significant wave height at the M1 during January 2009 Fig. 10b: A comparison between time series of wind direction and predicted wave direction at the M1 during January 2009 Significant wave hight January (m) - U(10) M1 January - Significant wave hight 167

6 F.S. Sharifi et al. Time series plots of direction of wind and windgenerated wave at M1 station have been represented by Fig 10b. As can be observed, at the period when the direction of wind changes rapidly, despite of other favorable conditions for wave growth, significant wave height remains nearly constant. Comparison between Results and Satellite Altimetry Observations. In the present study, altimetry data provided by both JASON 2 and ENVISAT1, covering a period from 1 January 2009 to 31 July 2009, have been used. Fig 11 shows the altimetry observations along tracks of JASON 2 and ENVISAT1 in the Persian Gulf and Gulf of Oman. suggests a reasonable agreement between results of the numerical simulation and satellite altimetry observations. In the time series represented below, a relatively reasonable agreement between crest height, obtained by numerical simulation and altimetry observations, is also noticeable. It should be noted that, since both of the two models yields nearly the same results in deep water, it will suffice to represent a comparison between results of the WAVEWATCH-III and altimetry observations. Fig. 12a: A comparison between significant wave height time series predicted by numerical simulation and altimetry observations, Hormuz station, January 2009 Fig. 11a : Significant wave height observation along JASON 2 tracks in the Persian Gulf and Gulf of Oman Fig. 12b: Scatter plot representing the relation between numerical simulation results and altimetry data, Hormuz station, January 2009 Fig. 11b: Significant wave height observation along ENVISAT1 tracks in the Persian Gulf and Gulf of Oman A comparison between significant wave heights predicted by WAVEWATCH-III and altimetry data As can be seen in Fig. 12 b, the calculated R-squared value suggests that results of the numerical simulation are in relatively reasonable agreement with altimetry observations. Table 1 summarizes some statistical measures that have been used here to compare two sets of significant wave height data obtained from results of MIKE 21 SW numerical simulation, and satellite altimetry observations. Represented measures include, absolute difference between means of the two data sets, root mean square error, relative mean difference, dispersion 168

7 Int. J. Mar. Sci. Eng., 2 (2), , Spring 2012 coefficient and correlation coefficient, as denoted by BIAS, RMS, BI, SI and R respectively. The value of aforementioned statistical parameters at each station, suggest relatively good agreement between results of the MIKE 21 SW numerical simulation and altimetry measurements. It should be noted that the value of represented statistical measures is zero if both data sets are the same, and increases as both data sets become more diverse. Therefore, it can be said that there is a close agreement between the MIKE 21 SW predictions and altimetry observations. Table 2 represents the same statistical measures calculated for results of the WAVEWATCHIII. Again, it can be observed that results of numerical simulation are in good agreement with altimetry observations. Table 1: Statistical measures comparing results of the MIKE 21 SW and altimetry observations Name Bias RMS BI SI R Hormuz M M M Table 2: Statistical measures comparing results of the WAVEWATCH- III and altimetry observations Name Bias RMS BI SI R Hormuz M M M M M CONCLUSION In this study, the capabilities of the WAWEWATCH -III and MIKE21-SW for predicting wind wave characteristics in Hormuz Strait have been evaluated. The numerical models have been set up using a set of input data, including GFS wind data, and ETOPO1 bathymetry data. Results of the two model were compared with the significant wave heights derived from satellite altimetry data in the area under study. The comparisons suggest that, in the deepwater and in comparison with the MIKE-21 SW, the use of WAVEWATCH-III leads to more reliable prediction of wind wave characteristics, whereas in the shallow water areas the results of the MIKE-21 are in better agreement with altimetry data.. As mentioned before, rapid changes in wind direction, limit the growth of wind-generated waves in the area under study. The effect of surface layer instabilities on wave growth would be discussed in future studies. In the present study, bathymetry (topography) data with 2km spatial resolution have been used. It is recommended to use bathymetry data with higher spatial resolution in future studies. As mentioned above, in the present study, GFS data have been used as input wind field. It is recommended to validate GFS wind data against available data obtained from Iranian synoptic stations, and modify these data sets to use in future studies. The non-physical oscillations in wind direction imply some limitations of the GFS model. Therefore, The GFS model has to be modified and enhanced so that the use of GFS data in numerical simulation of wind-waves, leads to more realistic predictions. REFERENCES Battjes, J. A.; Janssen J.P.F.M., (1978). Energy loss and set-up due to breaking o random waves. in Proceeding of coastal engineering, 16th conference. Booij, N.; Ris, R. C.; Holthuijsen, L. H., (1999). A third generation wave model for coastal regions. 1. Model description and validation. Journal of Geophysical Research. 104, Cavaleri, L.; Sclavo, M., (2006). The calibration of wind and wave model data in the Mediterranean Sea. Coastal Engineering Caires, S; Sterl, A., (2005). A new nonparametric method to correct model data: Application to significant wave height from the ERA-40 reanalysis. Journal of Atmospheric and Oceanic Technology. 22 (4), Caires, S.; Sterl, A., (2005). 100-Year Return value estimates for ocean wind speed and significant wave 169

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