H. Runghen1, M. Bhuruth2 and S.D.D.V. Rughooputh3
1Department of Mathematics, [email protected]
2Department of Mathematics, [email protected]
3Department of Physics, [email protected]
Faculty of Science, University of Mauritius, Réduit, Mauritius
Abstract
One of the main challenges faced by countries, especially small island states, during an oil spill combat is the identification of vulnerable coastal locations. The lack of precise information of this nature has often led to the inappropriate use of combat materials and response strategies. In this study we present the application of GIS in the organization of information that will determine the degree of vulnerability in standard formats. Information on relevant factors such as shoreline sensitivity, biological resources, exposure to wave and tidal energy, and human-use resources are systematically presented using the Environmental Sensitivity Index technique. This paper presents an application taking Mauritius as a case study for oil spill preparedness. Significant factors affecting rescue efforts are investigated, as a result of which priorities are established and cleanup strategies identified. The paper shows the applicability of GIS tools and technology in governing actions taken during oil spill accidents, thus ensuring that the response is not only prompt but also appropriate. The methodology is demonstrated on a map of the North-West of Mauritius.
1. Introduction
Oil spill disasters have been a major concern due to increasing number of accidents that have occurred in recent years, for example, Sea Empress (1996), Pallas (1998), Erika (1999) and Prestige (2002). Since direct impact of oil spills in the marine environment are generally wide spread and of long-term, they can have devastating consequences on wildlife, fisheries, coastal and marine habitats, human health, economy as well as recreational resources of immediate coastal communities. Mauritius and its outer islands are located along a very dense maritime route for oil transportation. Tankers along this route carry around 750 million Metric tonnes of petroleum products annually. Every year 36 vessels of 6000 Metric tonnes and 23 vessels of 22000 Metric tonnes offload petroleum products in Port-Louis harbour. As a result, the Government of Mauritius through the Environmental Protection Act of 2002 mandated prescriptions of procedures for cleanup and removal operations. In this context, oil spill combat authorities have set up atlases that provide a means of determining marine and coastal areas of sensitivity that might be impacted should such a pollution incident occur. Many countries, such the United States of America, Australia, Greenland and Mauritius, have included oil spill sensitivity atlases as an integral part of their contingency plan.
The basic requirements for an understandable and usable oil spill sensitivity atlas have been discussed in the IMO/IPIECA report series [1]. Shoreline types, sub-tidal habitats, wildlife and protected areas, fishing activities and other socio-economic features as well as oil-spill response features are important factors to consider when setting up such an atlas. Another determining factor is the seasonal aspect which may alter the sensitivity of some resources. The inability of a hard-copy map to convey these complex logistics has significantly increased the use of Geographical Information Systems (GIS). Halls et al. [2], Fisher et al. [3] and Muskat [4] explain how GIS technology can be applied as a more efficient tool for oil spill preparedness, during an emergency response, and an aid for quantifying natural resource damage. GIS has proven to be an excellent data management, organizational and analysis tool. As the system becomes more widely used, the possibilities of linking different GIS systems and data required for oil spill response and contingency planning are increasing.
The aim of this paper is to present the different steps to build an oil spill sensitivity atlas using GIS technologies. The ESRI’s ArcGISTM 9.0 platform is chosen as it guarantees an efficient and effective means of managing geospatial data such as enabling easy alterations and updates. The Environmental Sensitivity Index (ESI) technique developed by the National Oceanic and Atmospheric Agency (NOAA) [5] is used to organize the information in standard formats for shoreline sensitivity, biological resources, exposure to wave and tidal energy and human-use resources. An oil spill sensitivity atlas for Mauritius was developed by Gunlach et al. [6]. In this paper the technique used to set up an updated and accurate oil spill sensitivity map is described. Based on all these information, appropriate methods to respond to oil spills in the different areas of Mauritius have also been assessed. Digital maps of Mauritius with scale 1:25,000 were used as base map for the thematic layers and listings of each processed data. Spatial and non-spatial data were analyzed through various functions of GIS techniques, such as geoprocessing, data analysis and overlaying, and modelling to yield the risk management system as thematic layers.
2. Method of study
Gunlach et al. [7], Mosbech et al. [8] and Anderson et al. [9] describe various techniques for building sensitivity maps for oil spill response. Using the Environmental Sensitivity Index (ESI), shoreline ranking, biological resources and human-use resources were delineated on ArcMAP workspace by colour coding, symbols and other markings. The ESI method compiles the data on shoreline sensitivity, biological resources, exposure to wave and tidal energy and human-use resources into standard and comprehensible formats. The shoreline habitats of Mauritius are delineated and presented in order of increasing sensitivity to spilled oil as listed in Table 1. Factors such as their vulnerability to shoreline type, exposures to wave and tidal energy, biological productivity and weakness and ease of cleanup of an intertidal habitat have determined their relative sensitivity. A ranking of “1” represents shorelines least susceptible to be damaged by oiling, and “10” represents the locations most likely to be damaged. Examples of shorelines ranked as “1” include steep, exposed rocky cliffs, where oil cannot penetrate into the rock and will quickly be washed off by the action of waves and tides. Shorelines ranked as “10” include protected, vegetated wetlands, such as mangrove swamps and saltwater marshes. Oil in these areas will remain for a long period of time, penetrate deeply into the substrate, and inflict damage to plants and animals.
Table 1: Sensitivity Index Ranking for Shorelines of Mauritius 
The intertidal habitats of Mauritius, which cover a shoreline of about 173 km, were identified and mapped during ground surveys conducted from June 2003 to January 2004. The readings were taken starting from 09 hr 00 min to 14 hr 30 min daily. These intertidal habitats were delineated directly onto 1:25000 scale Mauritian geological topographic maps (CAD format). Data were collected using a handheld Global Position System (GPS) receiver which has an accuracy of 3 meters. The geodetic reference (datum) used for GPS is the World Geodetic System 1984 (WGS84). Since the base map is on National Grid Coordinates with origin Le Pouce (20011’42.25 S, 57031’18.58 E), false coordinates (1000000.000 mE, 1000000.000 mN), conversions from WGS84 to Lambert Conical (-one parallel) projection were processed. Additional information through the use of historical sources [6 and 10], maps and aerial photo interpretation were applied. Accuracy of all descriptive and spatial attributes were tested by visual comparison of hard copy check plots to the source materials and verifying the location of the data on screen relative to other data layers in the same geographic area.
The ArcToolbox application in ArcGISTM 9.0 provides a powerful set of geoprocessing functions, one of which is used to import the CAD layers into ArcMap application. Each of the different shoreline types is imported as different layers. In the geographic data view, geographic layers representing these shoreline types are compiled into GIS data sets. A table of contents interface organizes and controls the drawing properties of the GIS data layers in the data frame. The shoreline types are color coded in a ranked format on a scale from 1 to 10 as described before. ArcMap also enables the use different inbuilt symbols to represent some important environmental and human resources that could be affected by an oil spill. These map elements include birds (sea birds, shore birds and herons), public beaches, hotels and special areas designated by legislation such as fishing resources and nature reserves. These areas are indicated specifically to aid and direct the response effort. An example of the legend showing the different color codes is shown in Fig 2. In the page layout view, we can modify the layout to improve the design and visual balance of the composition by adding new map elements and changing the properties of the existing map elements. ArcMap also allows the inclusion of several key components when producing a map. These include the title, scale bar, legend and north arrow. ArcMap also integrates attributes tables, text files, digital photographs and video imagery in the digital maps.
3. Test case: Description of Map 18
Although a greater number of maps with greater accuracy can be produced, we have restricted ourselves to 19 maps as shown in Fig 1. As a result, rapid search will be possible and implementation on the model will be more structured. With all the necessary information gathered, the sensitivity maps can now be produced. Oil spill countermeasure considerations are described for each of the 19 operational maps. In this section, we give an overview of their basis and content. As an example, we present below the case of Map 18 (North-West part of Mauritius) in Fig 2.
Fig 1: Map index for oil spill sensitivity atlas 
Fig 2: Oil spill sensitivity Map 18
Map 18 covers the coastal area from Albion to Baie du Tombeau. The shoreline consists mainly of structures and coastal developments at Port-Louis. The coastline of the harbour comprises mostly of metal or concrete walls and sheltered rocky shores. Natural shorelines to the north and west are composed of sand or sand mixed with coarser material. Mangroves and marshes are not very common; they are found only along the sheltered areas of upper Grand-Rivi?re Bay. The reef platform is several hundred meters wide to the north and west of Port-Louis, and nonexistent within the port area.
Resources at risk
The waters off Port-Louis are designated as a fishing reserve. The shoreline environment needing protection is the marsh and mangrove area in upper Grand-Riv?re Bay. A small public beach is present at mare Samson and there are two coastal hotels in Baie du Tombeau area (Corotel and Hotel Les Cocotiers). Precautions need to be taken during the application of dispersant or other chemical because of the fishing reserve present in the area. An electricity-generating facility, which uses seawater for cooling, is located at Bain des Dames. A number of small boats are moored in Grande Rivi?re Bay.
Response strategies
The port facilities are a possible source of an oil spill. In such an event, the response strategy is to contain the oil within the industrialized port area and not allow it to impact adjacent, more sensitive environments. The calm waters of the port area enable a full response using booms, skimmers and sorbents to be undertaken. The electical station at Bain des Dames must be notified of a spill to determine if intakes should be protected, monitored or closed. Booms and sorbents should also be used to protect the mangrove and marsh area in Grand-Rivi?re Bay. Oil that impacts adjacent shorelines should be cleared up with the minimal removal of sand to avoid potential erosion problems.
4. Discussion
The application of GIS technology has resulted in the development of an oil spill sensitivity atlas that is no longer a static product of limited usage. One of the major differences between the atlas produced by Gunlach et al. [6] and the new one is that it is now an automated information system that is capable of recording and maintaining data, readily producing relevant maps. The use of digital maps facilitates updating of available data, thus allowing spatial queries to be performed at any time. Along with a digital map, pictures and short movies have been inserted into the map to provide the responder with an idea of the shore under study. The primary motivation for making a digital oil sensitivity atlas was to identify the shoreline at risk during oil spill scenarios in conjunction with building an oil spill model for Mauritius. However, the use of the atlas is not only restricted to oil spill response and planning, but may also be applied to coastal management in a broader context. In order to facilitate the understanding of and access to the different factors at play in the oil spill combat, the atlas compiled during this study will be distributed on free to all stakeholders and easy-to-use software such as print published map files (PMF) and portable document files (PDF) which can be accessed using ESRI ArcReader and Adobe Acrobat, respectively. In addition, the facilities offered by the software mentioned above will help safeguard the accuracy of the original information thereby preventing tampering.
6. Conclusion
The new sensitivity atlas has been incorporated into ArcMap GIS format to help the oil spill responder to immediately identify the shore types and possible combat techniques. The various shore types have been visually presented using colour schemes to differentiate between them on the digital map. In this way the decision taking process during incidents requiring immediate action such as during oil spill is facilitated. In addition, some techniques for developing an oil spill sensitivity atlas utilizing GIS technologies have been discussed. The shore types were described and used to update existing sensitivity maps for Mauritius. ESI was used to outline the different types of shoreline identified so far. From thereon a sensitivity ranking has been established and indicated on the map. Methods were discussed and their high degree of accuracy confirmed.
6. Acknowledgement
This work is part of the first author’s MPhil research and the support of the Tertiary Education Commission is gratefully acknowledged.
7. References
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