3D Numerical Modeling of Hydrodynamics in the Dubai Coastal Zone

D

Journal of Shipping and Ocean Engineering 2 (2012) 283-292

DAVID PUBLISHING

3D Numerical Modeling of Hydrodynamics in the Dubai Coastal Zone Priyantha Gunaratna1 and Thamali Gunaratna2 1. Coastal Zone and Waterways Management Section, Dubai 90127, UAE 2. Asian Institute of Technology, Pathumthani 12120, Thailand Abstract: A three dimensional hydrodynamic was developed for the Dubai coastal zone including the Dubai Creek. The model is based on DHI (Danish Hydraulic Institute’s) MIKE 3 HD (FM) modeling software. The model was subjected to extensive calibration making use of recorded water levels, currents, water temperature and salinity. A high level of accuracy in calibration was achieved as indicated by the computed statistical error parameters at all recording stations. The model results combined with field recording of water levels were used to ascertain tidal wave propagation pattern in the Dubai coastal zone and in and out of the Dubai creek. This model will be a very useful tool in assessing impacts of planned connection of artificial waterways to the Dubai Creek. Key words: Hydrodynamics, numerical modeling, tidal constituents, tidal wave propagation.

1. Introduction The coastal zone of Dubai is contained in the 72 km stretch between the emirates of Abu Dhabi and Sharjah in the Arabian Gulf and defined as extending 10 nautical miles offshore (Fig. 1). The Dubai Creek is a natural waterway connected to sea, about 15 km long and deepened for inland navigation of vessels to an average water depth of 6 m. The Dubai coastal zone has undergone extensive change within the last decade due to the construction of mega offshore reclamations, ports and harbors and coastal and marine structures. The developments along the Dubai Creek, such as reclamations and outfall discharges have altered tidal flow regime locally. It is planned to construct and connect artificial canals to the Dubai Creek to promote inland navigation. In order to assess impacts of such developments with respect to water circulation and flushing it is imperative to develop a baseline Corresponding author: Priyantha Gunaratna, Dr., Senior Marine Projects Specialist, research fields: coastal engineering, numerical modeling of coastal processes, and oceanographic data analysis. E-mail: [email protected].

numerical model for the existing Dubai coastal zone—Dubai Creek system. A three dimensional hydrodynamic model based on DHI (Danish Hydraulic Institute’s) MIKE 3 HD (FM) numerical modeling system was developed to contain the Dubai coastal zone and including the Dubai Creek in the present context. In the horizontal domain, a triangular variable mesh representation coupled with a three layer equidistant discretization in the vertical direction was used. The main computational variables of the model are water levels, currents, water temperature and salinity. The CWMS (Coastal Zone & Waterways Management Section) of the DM (Dubai Municipality) has been conducting a monitoring program of the Dubai coastal zone for several years. Since May 2010, this program was enhanced with the establishment of number of recording instrument stations for water levels, waves, currents, water quality and meteorological parameters (Fig. 1). The time series data available from these instruments as well as other sources were used in very extensive and rigorous validation of the hydrodynamic model.

284

3D Numerical Modeling of Hydrodynamics in the Dubai Coastal Zone

World Islands Palm Jebel Ali Palm Deira Palm Jumeirah

Fig. 1 Dubai coastal zone indicating CWMS monitoring stations.

The model results coupled with the analysis of available data provided reliable information to assess the propagation of tide in the coastal sea of Dubai as well as in and out of the Dubai Creek. The model results were also useful in assessing the reliability and accuracy of the monitoring data.

2. Model Setup The hydrodynamic modeling was based on the application of DHI’s 3D unstructured mesh hydrodynamic model MIKE 3 HD (FM). This model is based on the numerical solution of the three dimensional incompressible Reynolds averaged Navier-Stokes equations involving the assumptions of Boussinesq and of hydrostatic pressure. The model consists of conservation equations for mass, momentum, temperature and salinity and is closed by a turbulent closure scheme [1]. The turbulence is characterized by the horizontal and vertical eddy viscosity coefficients. In the case of horizontal eddy viscosity, the option of adopting Smagorinsky formulation [2] is available, while in the case of vertical eddy viscosity log law or − formulation [3] options are available.

The spatial discretization of the primitive equations is performed using a cell-centered finite volume method. The spatial domain is discretized by subdivision of the continuum into non overlapping elements. In the horizontal plane an unstructured grid is used while in the vertical domain a structured discretization is used. In 2009, DHI conducted a numerical model study to assess the impacts on flushing and water circulation related to the implementation of all planned waterways and offshore development schemes in Dubai [4]. This model study was based on the application of MIKE 3 HD (FM) model together with the Transport Model MIKE 3 TR (FM) for the assessment of flushing characteristics. This model contained three open sea boundaries with two lateral boundaries demarcating the southern and northern extremities of Dubai and an offshore boundary more or less parallel to the Dubai coast. Tidal forcing was introduced at these boundaries through the specification of water level time series at equidistant locations along them. These water level time series spanning 15 months in 2007-2008, extracted from a Regional hydrodynamic model covering the entire Arabian Gulf [4] was available from this study.


The hydrodynamic model developed for this research study was set-up to have the same open sea boundaries as those used by DHI. This was considered to be the most appropriate strategy to make best use of the available water level time series at several locations along the model boundaries. The model domain covers approximately the area between DLTM (Dubai local transverse Mercator) 434,000-504,000 m easting and 2,750,000-2,821,000 m northing. It represents a domain of 80 km along the coast of Dubai by 25 km offshore. The model domain in the horizontal plane was discretized with triangular elements of varying sizes. In the vertical dimension an equidistant three layer discretization was used. This was considered to be adequate as past field measurements have shown that the variation of water temperature and salinity across the water depth was not that significant [5]. Smaller mesh elements were used, in particular in the Dubai Creek, nearshore coastal waters and in areas where spacing of the nodal points had to be reduced to accurately define the land-water boundaries of the model. The latter areas refer to, as example coastal and marine structures and port and harbor entrances in the Dubai coastal zone. The model bathymetry is shown in Fig. 2. All water depths are expressed with respect to DMD (Dubai Municipality Datum), which is the standard reference elevation used in Dubai. It is 0.2 m above the computed LAT (lowest astronomical tide) at Port Rashid, Dubai. The locations of CWMS monitoring stations are also indicated in Fig. 2. Table 1 gives the ranges of applied mesh sizes for different sub-regions of the model domain. Several sources of bathymetric data were used in creating the model mesh bathymetry. The major part of the Arabian Gulf bathymetry was created from high resolution LIDAR data obtained from a survey commissioned by the CWMS in 2007. Additionally, latest bathymetric survey data acquired by periodic surveys conducted by the CWMS in nearshore and within Dubai Creek were also used.

285

Fig. 2 MIKE 3 HD (FM) model bathymetry. Table 1 Mesh sizes used in different regions of the model. Region Offshore Nearshore Dubai Creek Inner Dubai Creek Deira Corniche

Mesh size (m2) 500,000-3,500,00 250-1,000,000 125-10,000 75-4,500 150-9,000

3. Model Forcing Data Model forcing at the open boundaries was introduced as water level, water temperature and salinity variations. The model was calibrated for the month of July 2011 and verified by continuing the simulation in the month of August 2011. As a further verification model runs were performed within the July-August period in the year 2010. Since the water levels at model boundaries were available only for a 15 month period in 2007-2008, in order to generate boundary conditions for the model simulation periods, harmonic analysis of water level time series was performed to establish tidal constituents. These tidal constituent amplitude and phase information was re-used to generate tidal water level time series covering the model simulation periods. Since the water levels computed in this way contains only the tidal effect, the wind correction facility available within MIKE 3 model was used at all three open boundaries.

286


The water temperature and salinity data 1 m below the water surface at 30 min sampling interval were available as time series at two water quality buoys deployed nearshore off JOB (Jumeirah open beach) and offshore of Palm Deira. During the period July August 2011, Palm Deira Offshore Buoy was not in operation. Due to the insignificant stratification in relatively shallow coastal waters, the salinity and water temperature settings have only a very marginal effect on flow hydrodynamics. Therefore, water temperatures and salinities recorded at JOB were used as boundary conditions at all open boundaries (Fig. 3). However, additional model simulations of the calibrated model was conducted for the period July August 2010 using salinities and temperatures recorded at Palm Deira Offshore Buoy as boundary conditions. In this case, the computed salinities and temperatures at the JOB water quality buoy were compared with corresponding recorded parameters. Table 2 summarizes details of all model simulations conducted. In the case of meteorological forcing, primarily wind field over the model domain was introduced in order to reproduce wind generated currents, which are significantly stronger compared to tidal currents. The wind field over the model domain was assumed to be spatially uniform and was represented by the recorded wind speed and wind direction at Jebel Ali offshore buoy (Fig. 4). The wind sensors are located about 2 m above the water surface with recordings made at a sampling interval of 10 min. The evaporation rate is highest during the summer months of July and August covered by the model simulations. In this respect, the time series of evaporation adopted by DHI for the year 2007 was considered to be representative as well for the years 2010 and 2011 covered by the model simulations (Fig. 5). There was no precipitation occurring during the model simulation periods. In the case of atmospheric heat exchange, one of the main governing parameters is the air temperature. The air temperature was obtained from 10 minutes recordings at Jebel Ali offshore station (Fig. 6).

Fig. 3 Recorded water temperature and salinity at JOB water quality buoy—July 2011. Table 2 Summary of model simulations. Simulation Purpose No. 1.

2. 3.

Fig. 4 2011.

Boundary Conditions

Surface elevations: estimated Calibration and tidal constituents flushing capacity Temperature and salinity: (July 2011) JOB water quality buoy Wind: Jebel Ali offshore met station Verification Same as (1) (August 2011) Re-validation Same as (1) except (July-August Temperature and Salinity: 2010) Palm Deira Offshore Buoy

Recorded wind at Jebel Ali offshore buoy—July

Fig. 5 Assumed variation of annual evaporation.


Fig. 6 Recorded air temperature at Jebel Ali offshore buoy—July 2011.

287

Fig. 7 Computed and recorded water levels at JBH.

4. Model Calibration and Verification The model calibration simulations in the first phase were performed for the month of July 2011. Prior to this, initially model was run for a five day “warm-up” period to establish compatible initial conditions with model boundary conditions within the model domain. The model calibration involved obtaining best possible comparison between the model simulated and recorded parameters at different stations. In this respect, primarily water level comparison was used in establishing a well calibrated model. Following this, model calibration parameters were further adjusted to match the recorded currents as closely as possible. The recorded water temperatures and salinities were eventually compared with model simulated values to ensure that these two parameters are within the range of actual recordings. It was necessary to adjust eddy viscosities, bottom roughness and wind friction factor starting from their default values to obtain the best possible match with the recorded currents. The model parameters adjusted during the calibration with their final established values are given in Table 3. The comparison of water levels at JBH monitoring station tide gauge is shown in Fig. 7. The comparison of depth averaged east current component at JOB ADCP location is shown in Fig. 8. It is seen from the above visual comparisons, the model has reproduced recorded water levels and

Fig. 8 Computed and recorded depth averaged east current component at JOB ADCP station. Table 3 Model parameters. Model parameter

Eddy viscosity

Description Horizontal: Smagorinsky formulation (Smagorinsky constant = 0.2) Vertical: k-ε formulation with default empirical constants in MIKE 3 HD (FM). Bed roughness = 0.03 m 0.002425

Bed resistance Wind friction factor Dispersion Horizontal and vertical eddy viscosity Coefficients (salinity values were scaled by a factor of 0.1 & temperature)

currents with reasonable accuracy. The effectiveness of the model calibration was further assessed by computing statistical error parameters. The statistical error parameters considered were, RMSE (root mean square error), MAE (mean absolute error), CC (correlation coefficient) and IOA (index of agreement). The definitions of these parameters are given by Eqs. (1) to (4):


288

=

=

=

=

∑ ∑

∑ ∑

∑

(

∑

(|

(

|( |

(

−

−

̅ )(

̅) ∑ | |

|

(

)

(1)

)| )

|

(2) (3) )

(4)

in which, xi represents the recorded data and yi represents the computed data. The superscript bar stands for the average and N is the total number of recorded/computed data being compared. The RMSE is a good error indicator since it gives a relatively high weightage to large errors. The MAE measures the average magnitude of deviations between the datasets. The CC may assume a value between -1 to 1. The focus on modeling was to obtain CC values between 0.5 and 1, to ensure a strong positive correlation between the computed and the recorded data. IOA is used to assess dispersion between model predictions and field recordings. The closer IOA to 1.0, stronger the agreement between the two data sets. While it is difficult to find guidelines for what values of the IOA might represent a good agreement, values meaningfully larger than 0.5 is considered to represent good model performance [6]. The statistical error parameters corresponding to water levels at selected stations are listed in Table 4 for the model calibration period (July 2011) and in Table 5 for the verification period (August 2011). The same parameter values for currents recorded at JOB ADCP station are listed in Table 6. It is seen that all statistical error parameters obtained indicate a very strong agreement between the computed and recorded parameters at all stations. The model simulation during the month of August 2011 commenced by using the initial condition obtained from the previous month simulation. The improvement of statistical error parameters in August 2011 could be due to the use of more compatible and stabilized initial conditions in the model simulation.

Table 4 Statistical error parameters for computed water levels at selected stations—Model Calibration Period: July 2011. Station

RMSE (m)

JBH Mamzar DFC Jadaf

0.141 0.089 0.087 0.104

MAE (m) 0.109 0.070 0.067 0.085

CC

IOA

0.972 0.979 0.983 0.987

0.971 0.988 0.990 0.986

Table 5 Statistical error parameters for computed water levels at selected stations—Model Verification Period: August 2011. Station

RMSE (m)

JBH Mamzar DFC Jadaf

0.097 0.097 0.088 0.086

MAE (m) 0.077 0.080 0.071 0.068

CC

IOA

0.981 0.987 0.986 0.983

0.988 0.988 0.991 0.990

Table 6 Statistical error parameters for computed current components at JOB ADCP station. Model Calibration Period: July 2011 Current RMSE MAE (m/s) CC Component (m/s) East 0.02 0.016 0.781 North 0.024 0.018 0.863 Model Verification Period: August 2011 Current RMSE MAE (m/s) CC Component (m/s) East 0.017 0.013 0.81 North 0.021 0.017 0.884

IOA 0.861 0.916 IOA 0.881 0.925

In model simulations carried out in July August 2010, the salinity and water temperature boundary conditions were obtained from recordings at Palm Deira offshore buoy mounted sensors. The comparison of surface water temperature at JOB water quality buoy for the month of July 2010 is shown in Fig. 9. The comparison of surface salinities at the same location is shown in Fig. 10. The computed values of these parameters were extracted at the surface layer (layer 3) from the 3D model output. The statistical error parameters are given in Table 7. Since the salinity and temperature variations over the water depth are not that pronounced, they will have marginal influence on flow hydrodynamics. As such, the computed salinities and water temperatures lying within the same range as recorded values should be


289

water levels at CWMS monitoring stations and additionally with predicted tides at ATT (Admiralty Tide Table) stations [7] and DM Survey Department stations [8] listed in Table 8. At all these stations excellent agreement with high quality statistical error parameters were obtained. This indicates that these stations are likely to demonstrate a consistent pattern of tidal wave propagation. The tidal wave propagation pattern for different tidal constituents can be obtained by developing contour Fig. 9 Computed and recorded surface water temperature at JOB water quality buoy.

plots of phases of these constituents. This process was carried out for the principal tidal constituents M2, S2, K1 and O1. The tidal constituents at number of locations along the model open boundaries were obtained through harmonic analysis of 15 months of water level time series in 2007-2008 as already mentioned. In the case of ATT stations, published principal tidal constituent information is available. At all other tide recording stations and ADCP instrument stations, the tidal constituents were obtained from the

Fig. 10 Computed and recorded surface salinity at JOB water quality buoy. Table 7 Statistical error parameters for computed water temperature and salinity at JOB water quality buoy. Model Simulation Period: July 2010 RMSE MAE Parameter (°C/psu) (°C/psu) Water 0.41 0.323 temperature Salinity 1.104 0.955

CC

IOA

0.46

0.629

0.038

0.358

sufficient for acceptable model performance. This requirement is fulfilled as seen from Figs. 9 and 10, although the statistical indicators IOA and CC are outside the acceptable limits. The maximum difference between computed and recorded water temperatures was about 1.1oC and in the case of salinity it was about 1.8 psu.

5. Tidal Wave Propagation In the calibration and verification process, the model computed water levels were compared with recorded

harmonic analysis of water level time series. DM Survey Department declined to release tidal constituents established from long term tide recordings. Therefore, tidal constituents were determined from the published hourly tide predictions for the year 2011. Initially cotidal lines were developed considering only principal tidal constituent phases at model boundaries. This will result in an approximate pattern of tidal wave propagation in the coastal area of the model. Thereafter, the tidal constituent phases of all interior stations were compared with this contour pattern as a consistency check. Only two stations in the sea, JOB ADCP station and the Sharjah ATT station indicated significant deviation. Therefore, disregarding these two stations and considering tidal constituents at model boundaries and remaining interior stations cotidal lines were re-generated. In the case of Dubai Creek, the tidal constituent phases were examined to see whether they indicate a gradual variation starting from the Shindagha tide station at creek mouth and moving towards interior.

290


Table 8 Locations of DM Survey Department and ATT stations. Station

DLTM Coordinates (m) East North DM Survey Department Stations Al Mamzar Park 501874 2801718 Umm Suqeim II 486220 2782862 Jebel Ali Marina 468927 2764982 Dhow Wharfage* 498321 2794486 Al Jadaf* 500000 2806672 ATT Stations Sharjah 503355 2806672 Maktoum Bridge* 498600 2794100 Dubai 493200 2793700 Mina Jebel Ali 471397 2766084

Fig. 11 Cotidal lines for the Dubai coastal zone—M2 tidal constituent.

* stations located in the Dubai Creek

The two DM Survey Department Stations contradicted gradual variation of tidal phases and were disregarded. Therefore, cotidal lines generated within Dubai Creek can be considered only as approximate as they depended only on data from three stations. Fig. 11 shows the tidal wave propagation pattern for the Principal Lunar (M2) tidal constituent in terms of cotidal lines. The cotidal lines within the Dubai Creek for the same tidal constituent are shown in Fig. 12. The cotidal lines for the principal tidal constituent K1 (Luni Solar) are illustrated in Figs. 13 and 14. The approximate travel time for the tidal wave constituents can be estimated based on the above cotidal line patterns. The M2 and K1 principal tidal constituents have periods of 12.42 hr and 23.93 hr,

Fig. 12 Cotidal lines for the Dubai Creek—M2 tidal constituent.

respectively. Therefore, phase difference between any two locations can be converted to an equivalent travel time. The computations carried out in this way showed that the travel time for the M2 tidal constituent wave across the model domain parallel to the Dubai coast is about 46 min. In the case of K1 tidal constituent, the corresponding travel time was found to be 63 min. The M2 tidal constituent wave takes about 27 min to travel along the Dubai Creek from one end to the other. In the case of K1 tidal constituent the same travel time was found to be 36 min.

Fig. 13 Cotidal lines for the Dubai coastal zone—K1 tidal constituent.


Fig. 14 Cotidal lines for the Dubai Creek—K1 tidal constituent.

6. Flushing Capacity of the Dubai Creek The Dubai Creek is known for its poor flushing capacity, particularly in the inner region. Various outfalls discharging into the creek, in particular the treated sewage discharge from the Al Aweer Sewage Treatment Plant has declined its water quality over the years. As an application of the calibrated hydrodynamic model, the flushing capacity of the Dubai Creek was assessed. For this purpose, advection-dispersion simulations of a hypothetical conservative tracer were carried out using the TR (Transport) module of the MIKE 3 model. The simulations were carried out in decoupled mode, in which hydrodynamic computations were carried out first followed by an advection-dispersion simulation based on the computed hydrodynamics. A conservative tracer was assumed to be distributed uniformly at a constant concentration of 1 kg/m3 within the entire Dubai Creek at the beginning of the simulation. The remaining

291

Fig. 15 Simulated surface layer tracer concentration distribution after one month in Dubai Creek.

area of the model was assumed to be free of this tracer. The simulated tracer concentration distribution contours after one month at the surface layer is shown in Fig. 15. It is seen that after 1 month, the inner part of the creek is flushed by only about 10 percent (tracer concentration = 0.9 kg/m3). Moving away from the inner creek, the tracer concentration is seen to reduce with about 0.6 kg/m3 concentration (40 percent flushing) shown in the mid region. In order to improve this inadequate flushing capacity it is planned to use uni-directional pumping of sea water through the system when a newly constructed artificial waterway system will be connected to the Dubai Creek. The present calibrated hydrodynamic will be used to assess effectiveness of this sea water pumping discharge in increasing the flushing capacity.

7. Conclusions A numerical model for simulating three dimensional hydrodynamics in the Dubai coastal zone and the Dubai


292

Creek was developed. The model was successfully calibrated and verified against water level, current, water temperature and salinity data available from number of monitoring stations. This calibrated model can be used to investigate any hydraulic and water quality impacts of planned connection of artificial waterways to the Dubai Creek.

Acknowledgments The authors wish to express their profound gratitude to the management of Coastal Zone and Waterways Management Section of the Dubai Municipality for providing the opportunity to engage in this research study.

[2]

[3] [4]

[5]

[6]

[7]

References [1]

Danish Hydraulic Institute, MIKE 21 & MIKE 3 Flow Model FM, Hydrodynamic and Transport Module,

[8]

Scientific Documentation, 2011. J. Smagorinsky, General circulation experiment with primitive equations, Monthly Weather Review 91 (3) (1964) 99-164. W. Rodi, Turbulence Models and Their Applications in Hydraulics, IAHR, Delft, The Netherlands, 1984. Dubai Municipality, Waterway Studies for Dubai Emirate (CM 10053), Baseline Modeling of Vision Case (Stage 1), A report prepared for Dubai Waterways Committee, 2009. HydroQual, Dubai Creek Water Quality and Sediment Characteristics Monitoring Study, Data Report, Project No. DUBI0010, Dubai, 2005. C. Willmott, S. Ackleson, R. Davis, J. Feedema, K. Klink, D. Legate et al., Statistics for the evaluation and comparison of models, Journal of Geophysical Research 90 (1985) 8995-9005. British Admiralty, NP203: Admiralty Tide Tables (ATT), Vol. 3, Indian Ocean and South China Sea (including Tidal Stream Tables), Great Britain, 2012. Dubai Municipality, Dubai Tide Tables, Survey Department, Dubai, 2011.