MARINE FORECASTING AND OIL SPILL MODELING IN DUBAI AND THE GULF REGION

COPEDEC VII, 2008, Dubai, UAE

MARINE FORECASTING AND OIL SPILL MODELING IN DUBAI AND THE GULF REGION

by E. Howlett1, K. Jayko2, T. Isaji3, P. Anid4, Gary Mocke5 and Francois Smit6

ABSTRACT

The Coastal Management Section (CMS) of Dubai Municipality (DM) has been operating a coastal zone monitoring program since 2002 with initial emphasis on data collection and establishment of an in-house survey capability. The program has evolved to focus on data analysis and generation of products and interfacing with CMS-operated predictive numerical models. A further evolution is the establishment of a hydrodynamic and water quality forecasting system for the Dubai Coast. One of the uses of high resolution forecasting in Dubai coastal waters is for emergency response to pollution events. DM is working with a number of private consulting companies to integrate monitoring programs and high resolution hydrodynamic models with oil and chemical spill models. This paper discusses the use of oil spill models in the Dubai region with discussion of previous spills and new technology being implemented to improve oil spill predictions in the coastal region.

1. INTRODUCTION

The OILMAP oil spill model system was configured for Dubai coastal waters for Dubai Municipality. The OILMAP model is used extensively in the region and has been applied to a number of spills in hindcast and forecast modes. This paper briefly discusses the application of the model to a number of these spills, and also discusses some of the latest developments required to apply the model in this complex coastal region.

2. DATA ISSUES

Oil spill models are generally used in three modes of operation:

• Oil spill trajectory predictions for actual spills or exercises. These are referred to as deterministic models and provide single expected forecasts for spills.

• Backtrack models run deterministic models backwards in time and are typically used to try to determine the source of oil that has been observed at sea or on a beach. This mode is often used for “mystery spills” and identification of intentional discharges.

• Stochastic models are probability models that are used in response planning, risk assessment, and environmental impact assessments. In this mode, hundreds or thousands of simulations are performed to generate probability contours of where oil is likely to go. Stochastic models can also be run in backtrack mode (receptor) and are used to look at sensitive infrastructure such as intakes and their vulnerability paths to possible oil spills.

The primary difference between the deterministic and stochastic models is data requirements. In stochastic mode, the models use large amounts of historical or climatological data, such as 10 years of wind time series data, often collected at an airport, or output from re-analysis meteorological models. Statistical climatology of wind speed and direction summarized in frequency tables is also a source. Current data is typically derived from hydrodynamic models that are run for long periods, at least a year, or current analysis of seasonal patterns and tidal components.

In deterministic mode, typically for oil spill response needs, the time scales for response are much less and the pressures on the modeller to make accurate predictions for response planning in a timely

1 Mr., CEO/Principal, ASA, [email protected] 2 Ms., Senior Researcher, ASA, [email protected] 3 Mr., Principal, ASA, [email protected] 4 Dr., Vice President / Principal, HydroQual, [email protected] 5 Dr., Coastal & Oceans Manager, Worley Parsons, [email protected] 6 Mr., Lead Coastal Engineer, Worley Parsons, [email protected]

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fashion are significant. Access to high resolution wind and current forecasts are essential and it must be possible to efficiently integrate this data with the oil spill model. There is generally not time to search for data and import data in different formats. The evolution of internet-based web services that allow applications to communicate with servers that store vast amounts of data from models, regional forecasting systems, and observation networks has enabled integrated systems to dramatically improve both the time and quality of oil spill forecasts, as well as other marine predictions such as search & rescue and chemical spills.

2.1 Data Needs

One of the challenges in accurate oil spill predictions is the availability of high quality forecast data, specifically for winds and currents (Gilbert (1998)). A key issue in oil spill modelling is that data from a specific wind station or current meter is of limited value since it does not provide a forecast and it only provides data for a very localized region. Oil tends to move quite quickly and can cover large areas. Current data is even more difficult to obtain; current meters are site-specific and provide no forecast. Technology such as high frequency (HF) radar now allows for larger areas of water to be observed for surface currents. However, HF Radar does not have a forecast component, although analysis tools such as the Short Term Predictive System (STPS) add some forecast capabilities to HF Radar observations in tidal regimes (O’Donnell et al. (2005)).

A variety of web sites provide weather forecasts, primarily winds. Global winds, observations and forecasts, are often available from commercial sites that meet the needs of surfers such as the sites www.weatherflow.com, www.windguru.com, and weatherunderground.com. These are valuable sources of information and their data can be entered manually into the OILMAP system using specialized wind data entry tools (Fig. 1).

Manually entering wind or current data during a spill response is error-prone and time-consuming. The next generation of model tools allows for direct access to data servers that automatically provide global, national, and regional wind and current forecasts. The server can also provide observation data from in-situ observations, radar, and satellites. The COASTMAP Environmental Data Server (EDS) is now used by the U.S Coast Guard for national and international U.S Coast Guard search & rescue operations with the SAROPS tool, and other maritime agencies such as Maritime New Zealand (MNZ), the U.S. Navy, and the oil industry use this technology.

Figure 1: Wind tools in OILMAP to manually enter wind data

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One of the considerable challenges in modeling oil spills and other pollutants in the Dubai coastal region is the need for high resolution current data that resolves the complex coastal structures. Traditional hydrodynamic grid systems cannot resolve this complexity so OILMAP is coupled with an unstructured flexible mesh hydrodynamic system provided by DHI. MIKE 3 is DHI's general three-dimensional hydrodynamic model system, using an unstructured mesh technology that provides the necessary flexibility for complex coastlines in Dubai. Vertical discretization is by sigma layers. It solves the shallow water equations using an efficient explicit finite-volume method with a Roe scheme upwinding type of description of advective terms. Other features are baroclinic flows due to temperature and salinity variations, turbulence parameters and linkage to water quality and transport models. The model for Dubai coastal waters consists of 56,000 elements with a spatial resolution from 100 m - 2000 m. The model has been validated using observed water levels and currents from two 2-week periods during 2006. An example of a predicted oil spill trajectory using this data is shown in Fig. 2.

Figure 2: Oil Spill Model using hydrodynamic data from DHI model

Additional challenges and solutions related to complicated shorelines and oil interaction are discussed in a later section. 2.2 Data Server

OILMAP connects to the COASTMAP Environmental Data Server (EDS) which provides homogenous access to meteorological and hydrodynamic data via web services (Fig. 3). It is designed to be scalable and modular and utilizes a Service Oriented Architecture (SOA). Users (clients) access the available data by using a web service (making a XML request to the server specifying what is needed), and the web service returns data based on the request. The web service may return data to the client from data products stored in distributed servers. A more common approach is that the catalog server retrieves data to a central server as a background process so all the data is stored and archived on a central server. For mission critical applications that need archive and forecast data with very fast response times, this solution is preferred.

The EDS consists of three major sub-systems: data collection (the Catalog Server), data storage (the Data store), and data distribution (the EDS web services). A flow chart of the EDS is given in Fig. 4.

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The EDS process of data collection and cataloging services (collectively known as the Catalog Server) allows administrators to customize the data to be made available to their client applications. The Catalog server is a series of data collection services governed by a timing service that automatically collects, processes and stores data from any number of disparate sources according to source availability schedules. The services use metadata stored in a SQL database that tell the catalog server when and where to collect data.

Figure 3: Dialog that accesses the Coastmap Data Server to obtain forecast data

OILMAP or other Client Application

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OR

XML Request that includes geographic region, start and end time, and data product(s)

Returns Image (WMS) or XML (WFS)

- OR - Remote Servers

Catalog Server

Data Services Aggregation (Space and Time)

Product Aggregation Conversion Services (converts data to NetCDF)

Objective Analysis Services

Returns CF 1.0 compliant NetCDF in gridded or unstructured grid format


Figure 4: Flow chart of EDS data transfer

The catalog server collects data via HTTP, FTP, or OPeNDAP and generally stores the data in its native format. The Catalog Server does not generally change the original data, and always maintains the native grid structure of the data.

Typical collection formats are NetCDF, GRIB (v1 & v2) and ASCII. The data collected includes: • point observation data (e.g., NDBC) • gridded observation data (satellite, sea surface radar) • numerical model products (regular and irregular grids)

OILMAP connects to the EDS through a series of web services. Three primary services that are available to client applications are:

• NetCDF Data Service • OGC WMS Service (as shown in Fig. 5) • OGC WFS Service

Figure 5: Example of weather data returned by the EDS

The EDS web services provide a catalog of the data available via XML as well as subsets of any of the data within the Data Store according to spatial and temporal parameters and client access privileges. A ranking (Quality) system may also be used so the user simply specifies a region, and an appropriate data set will be provided.

This NetCDF data service returns a consistent NetCDF CF 1.0 compliant NetCDF file to the client based upon the request. NetCDF, or Network Common Data Form – a self-describing, portable, array-oriented file format – is used as the base output format for the EDS web services. Output may be consumed by clients, such as OILMAP, MATLAB, and ARCGIS.

The web service may return a time series (most often a grid of data points, although it can be a single point) of the data requested. The service may also employ spatial aggregation, allowing clients to request an aggregation of data products (e.g., deep water currents and tidal currents). Temporal aggregation will aggregate analysis (observation or hindcast data) products with forecast products.

Aggregated products are not fitted to a new grid, but always kept to the original grid of the data. For irregular data and aggregated products, the NetCDF product is an “unstructured” grid, essentially

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arrays of x,y coordinates with associated values. For regular gridded products, the NetCDF data can be returned as structured grid data.

The NetCDF data service can access data products stored on remote servers (such as OPeNDAP servers), but the response time is slower. For data requests to data stored on the Catalog Server, requests are fast (e.g., a 2 degree square with 48 hours of data takes typically less than 10 seconds). 3. THE OILMAP SYSTEM

The basic OILMAP model is a trajectory and fates model that predicts the surface movement of oil and its distribution in the environment. Variations on this model include stochastic and receptor models to predict the probability of the oil’s distribution in the environment based on historical wind/current conditions, and a subsurface model able to track oil that becomes entrained in the water column.

The trajectory and fates model predicts the transport and weathering of oil from instantaneous or continuous spills. Oil is represented by a large number of individual spillets, each of which is initialized with an equal fraction of the total oil mass spilled. The spillets move and weather independently. The model predicts the surface distribution of the spilled oil and the oil mass balance: the amount of oil on the free surface, in the water column, evaporated, on the shore, and removed by mechanical cleaning versus time. The fate processes in the model include spreading, evaporation, entrainment (natural dispersion), and emulsification (ASA (2004), (2007)). Oil-shoreline interaction is modeled in terms of a shore type dependent holding capacity and exponential removal rate. The model also estimates the temporal variation of the oil’s areal coverage, oil thickness, and oil viscosity.

The subsurface model includes all the features noted above for the trajectory and fates model, but also tracks the fate of subsurface particles. The subsurface oil concentration field is predicted using a particle based, random walk technique and includes oil droplet rise velocities by size class. Resurfacing of oil droplets due to buoyant effects is explicitly included and generates new surface slicks (Kolluru et al. (1994)).

The stochastic and receptor models perform a large number of simulations randomly varying the environmental data used to transport the oil. Either winds, currents, or both may be varied. The stochastic model predicts where oil is likely to go if released at a particular location; the receptor model predicts where oil would have originated in order to arrive at a particular location. The output of each model is contour maps, generated from the numerous simulations run, showing the probability of surface oiling and travel time contours. The stochastic model also determines the probability of shoreline oiling. Using resource information stored in the GIS database, a resource hit calculation can be performed to predict the probability of oiling important resources.

OILMAP represents oil as individual spillets, each with an associated location, mass and radius. Each spillet moves and weathers independently. In order to provide a more realistic representation of the surface oil thickness and areal extent covered, OILMAP provides the option to display surface oil thickness contours. Thickness contours are determined based on a Gaussian spreading algorithm which sums the contributions of individual spillets and accounts for limitations to spreading due to the presence of nearby land masses. Contours are displayed as gridded data, color coded to represent the range of oil thicknesses.

3.1 Shoreline Representation

Since its inception, OILMAP required a gridded representation of the area of interest to differentiate between land and water. A recent improvement to the models now allows the shoreline to be represented by polygons – such as the polygon representation used for the base map. Both BDM and ESRI SHP file formats are supported.

This improvement has two immediate benefits. First, it is no longer necessary to create a grid differentiating land and water before running a spill scenario. Second, a much more accurate resolution of oiled shoreline is possible, particularly in areas with complex shorelines.

Another issue not evident from the maps showing the distribution of shoreline oiling is the amount of oil deposited onshore. Due to the improved resolution of the shoreline provided by the polygon representation, a more realistic determination of the amount of oil retained on the shoreline can be made.

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Fig. 6 shows a comparison of simulations run with gridded and polygon shoreline representations in an area with many small inlets and islands. The model traditionally managed land recognition by using a grid representation of the land. This example shows that the grid does not fully resolve the small islands and closes off channels that should be open. The map on the right of figure 6 shows the new model that uses the actual GIS map for the land recognition algorithm and this allows for very accurate definition of land and can significantly improve the predictions.

Figure 6: Simulations with grid (left) and GIS polygon representation of land (right)

3.2 Response Options

OILMAP contains numerous options for including response activities in a spill scenario. One option allows the user to adjust model predications based on actual observations of the oil’s location. Another allows boom segments to be added to stop or deflect the movement of surface oil. Several cleanup options are available including dispersant application and mechanical removal in user-specified regions.

The Response Calculator, a tool for estimating the equipment necessary to respond to an oil spill, is now included. It was developed with the assistance of Alan Allen (Spiltec) and is similar to NOAA’s Spill Tools™. It incorporates a database containing the available response equipment and equipment-specific operational parameters. Equipment is organized by function: Skimmers lists resources for mechanically removing oil from the water surface, Dispersant lists both vessel and aircraft resources for dispersing oil.

Response actions can be specified for the first three days following the start of the spill. For each day the Response Calculator form displays a typical oil thickness (assumed) and the maximum oil on the surface based on the active scenario. These values affect the amount of oil that can be cleaned and may be changed if desired. Generally within three days oil will have spread or weathered to the extent that it can no longer be effectively recovered, but this may not be the case for a particular scenario.

As each piece of response equipment is deployed, the figure at the bottom of the Response Calculator shows the amount of oil that could potentially be cleaned (Fig. 7).

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Figure 7: The OILMAP Response Calculator input form

The Response Calculator determines an average cleanup rate for each piece of deployed equipment and these rates can be incorporated into a simulation to determine the effect of the proposed response on the oil’s trajectory and fate. The oil removal determined by the Response Calculator assumes ideal response conditions. The simulation must be re-run with the Response Calculator rates to assess the cleanup’s effectiveness under the physical and environmental constraints of the scenario, and the actual amount cleaned will typically be less than that estimated by the Response Calculator. 4. OIL SPILL CASES

The possibility of oil spills in the Arabian Gulf region will always be high, simply due to the large volume of oil that is shipped in the region. A number of major spills have occurred in recent years, including the oil spill in 1991 during the Gulf War, the Akari spill off Jebel Ali, and the Ajman spill in 1998 (Anderson et al (1999)). In 2007, a “mystery” spill was also observed for many days of the coast of Dubai and OILMAP was used to predict the trajectory of that spill (Fig. 8). The Gulf War spill was modeled using OILMAP but has been extensively documented in other publications (Spaulding et al. (1991b)) and is not described here.

Figure 8: OILMAP prediction of the track of a mystery slick observed to the west of Palm Jebel

Ali, February 2007

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Below are brief descriptions of the application of OILMAP to two spills in the region as well as to a recent spill in Korea. 4.1 Akari Spill off Jebel Ali

At about 22:00 on August 25, 1987 the tanker Akari lost control and grounded about one mile northeast of the eastern breakwater at Jebel Ali port. An estimated 900 tons of “heavy fuel oil” (bunker type 180 cs) was released during the four-day salvage operation. Of particular concern was the impact of the spilled oil on sea water intakes (Spaulding et al. (1991a)).

OILMAP predicted the trajectory of the spilled oil using a combination of wind-driven currents and tidal constituents to drive the oil spill model. Analysis of the model output showed many correspondences between the predicted and observed locations of floating and beached oil. The predictions for August 28 at 18:00 are shown in Fig. 9. The successful alignment of the predicted sea surface oil positions to the observed surface signature of spilled oil indicated the combination of the tidal and wind forced current fields with the Dubai airport wind record were able to yield a good representation of sea surface oil movement.

Predicted concentrations of naturally dispersed oil in the water column at the sea water intakes for the Akari spill hindcast ranged from 2 ppb to 3 ppm.

Figure 9: OILMAP predictions of the Akari spill, August 28, 1987 at 18:00.

4.2 Ajman Spill

On January 7, 1998 at about midday, the barge Pontoon 300 sank about 5 miles off the coast of Ajman releasing 4000 tonnes of product, probably bunker fuel (“CST”). The initially reported spill volume did not change in later reports, despite the fact that oil continued to leak at an unknown rate for many days after the sinking. After salvage and recovery of the remaining oil, total loss was estimated at 5000 tonnes. All reports on impacts and oil coverage from this spill were taken from newspaper reports. There was very little reliable information on which to base the spill simulation. As with the Akari spill, not knowing the time course of the release was a serious disadvantage. There were, however, enough points reported about the spill to make a reasonable estimation of what occurred.

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For the hindcast simulation, 4000 tonnes of Bunker C were simulated spilled over a two week period. Four hundred surface particles were used to better display this long-term release. The best results were achieved using both tidal and wind-driven currents in combination with a 2% wind factor.

The model predictions, shown in Fig. 10, roughly match the coverage of shoreline from the reports. All the runs represented the ‘huge new amounts’ of oil that reached all the coastlines of the region on January 13. Very strong onshore winds (>20 knots from the WNW) provided the forcing for the observed heavy shoreline oiling. It was apparent that a large quantity of oil was floating or in the water column and resurfacing in the waters immediately offshore for the onshore wind to cause the strong and general shoreline oiling observed. The hindcast did not simulate the strong reversal of flow and southwestward transport of oil on January 21. This is thought to result from nearshore currents not resolved by the hydrodynamic model and indicates the need for improved nearshore modeling and observation networks.

Figure 10: Ajman spill hindcast for January 13, 1998 at 14:00.

4.3 Hebei Spirit Spill, South Korea

On December 7, 2007, the oil tanker Hebei Spirit, anchored 100 kilometers (60 miles) south of Seoul, was pierced by a crane-carrying barge, releasing 10,500 metric tons of crude oil into the sea. The oil came ashore along a 300 kilometer stretch of shoreline on South Korea’s west coast, a region that is very active with fisheries. The spill resulted in blackened beaches, coated birds in oily tar and cast a foul smell over a nature reserve. Fig. 11 shows the Envisat satellite image released by the European Space Agency showing the extent of the oil spill in South Korean waters. Using local current and wind conditions OILMAP was used to reproduce the movement of the oil and the shoreline impacts. Also shown in Fig. 11 is the OILMAP predicted movement of the spill over the 4-day period. The satellite image confirmed the accuracy of the OILMAP prediction and provided spill responders with added confidence in the model’s ability during the response.

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Figure 11: Oil spill prediction compared to satellite image of spill

5. CONCLUSIONS

Dubai Municipality have integrated an oil spill model system, OILMAP, with a hydrodynamic model that uses an unstructured grid to resolve detailed coastal areas. The oil spill model system can be used for spill response and preparedness and has been proven as a useful tool for spills in the region. Specific advances have been made to the system to meet the challenges of the complex coastline in the United Arab Emirates. Significant improvements in the accuracy of oil spill predictions can be realized by connecting the system to on-line observation and forecasting data that provides accurate coastal currents and wind forecasts. 6. REFERENCES

Applied Science Associates, Inc. (ASA) (2004). Technical Manual, OILMAP for Windows, Applied Science Associates, Inc., Narragansett, RI.

Applied Science Associates, Inc.(ASA) (2007). OILMAP Version 6.1 Users Manual. Applied Science Associates, Inc., Narragansett, RI.

O’Donnell, J., D. Ullman, M. Spaulding, E. Howlett, T. Fake, P. Hall, I. Tatsu, C. Edwards, E. Anderson, T. McClay, J. Kohut, A. Allen, S. Lester, and M. Lewandowski (2005). Integration of Coastal Ocean Dynamics Application Radar (CODAR) and Short-Term Predictive System (STPS) Surface Current Estimates into the Search and Rescue Optimal Planning System (SAROPS). U.S. Coast Guard Technical Report DTCG39-00-D-R00008/HSCG32-04-J-100052

Gilbert, T. (1998) Maritime Response Operations – Requirements for Metocean Data and Services. Conference and workshop on meteorological and oceanographic services for marine pollution emergency response operations. July 13 – 17, 1998 Townsville, Australia.

Kolluru, V., M. L. Spaulding, and E. Anderson, 1994. A three dimensional subsurface oil dispersion model using a particle based technique, 17th Arctic and Marine Oil Spill Program, Technical Seminar, June 8-10, 1994, Vancouver, British Columbia, Canada, pp. 767-784.

Anderson, E., H. Rines, and P. Rybacki (1999). Oil Spill Model Applications Dubai Coastline and Offshore Areas. Gulf Area Oil Companies Mutual Aid Organization (GAOCMAO) Conference & Exhibition, Qatar, April.

Spaulding, M.L., E.L. Anderson, K. Jayko, T. Isaji and C. Turner (1991a). Oil Pollution Risk Assessment for Seawater Intakes along the Saudi Arabian Coast. 157pp. Prepared for Stone & Webster Engineering Corp. Houston, TX. ASA 91-39.

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Spaulding, M.L., E.L. Anderson, T. Isaji and E. Howlett (1991b). Simulation of the Oil Trajectory and Fate in the Arabian Gulf from the Mina Al Ahmadi Spill. Marine Environmental Research, 36:79-115.

MARINE FORECASTING AND OIL SPILL MODELING IN DUBAI AND THE GULF REGION

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