Coastal environment is extremely sensitive area. The presence of oil and petroleum residues in the marine environment results from abusive spillages by ships and boats to the detriment of marine ecosystems navigation, and commercial actives such as fisheries, coastal industry development, and tourism; as well as to coastal resources when the oil reaches land. Up to now, there still isn¡¦t a complete evaluation and solution for the oil-spill risks in Taiwan. It is very important to organize and prepare an operational response for coastal oil pollution accidents. The use of computer models to predict the movement of oil helps to make best use of the various measures and equipment that can be employed in case of an oil spill accident. In this study, the numerical model MEDSLIK is used to directly model the transport of oil for the coast sea around Taiwan. MEDSLIK is a 3D oil spill model designed to predict the transport, fate and weathering of an oil spill. The MEDSLIK oil spill model in pre-operational mode was first developed in 1997 (Lardner et al. 1998) to assist the objectives of the EU LIFE project ¡§Subregional contingency Plan for Preparedness and response to Major Pollution Incidents in the Eastern Mediterranean-Levantine¡¨. The software consists of three parts, a graphical input interface through which the user enters data concerning the spill and environmental conditions, a run module that performs the computations that simulate the spill behaviors and a graphical output interface by means of which the user can examine the predictions of the model. The aim of this study is to adopt MEDSLIK model to predict the expected state of the oil when it arrives at a given location around Taiwan. The input data includes the type of oil and its characteristics, forecasts of wind direction and strength, sea temperature, currents and conditions at sea. By using MEDSLIK oil spill model to simulate the Tzini oil-spill accident, the oil disperses between the south part of Suao Harbor and Naao. The modeling results compare well with the actual situation. The main result is the MEDSLIK model provides the oil-spill movement pattern around Taiwan Sea and answers questions such as how much will evaporate, how much will be dispersed as fine droplets in the water, where the oil spill is most likely move to, and how soon it will get there. However, the wind input data is quantitative in this study. Future tasks must fully account the impact of regional wind field to oil movement and emphasize on anticipating likely impacts on the coast and provide an early warming and mitigation tool to plan an effective response to keep oil away from key coastal resources.

Sunken oil is difficult to locate because remote sensing techniques cannot as yet provide views of sunken oil over large areas. Moreover, the oil may re-suspend and sink with changes in salinity, sediment load, and temperature, making deterministic fate models difficult to deploy and calibrate when even the presence of sunken oil is difficult to assess. For these reasons, together with the expense of field data collection, there is a need for a statistical technique integrating limited data collection with stochastic transport modeling. Predictive Bayesian modeling techniques have been developed and demonstrated for exploiting limited information for decision support in many other applications. These techniques brought to a multi-modal Lagrangian modeling framework, representing a near-real time approach to locating and tracking sunken oil driven by intrinsic physical properties of field data collected following a spill after oil has begun collecting on a relatively flat bay bottom. Methods include (1) development of the conceptual predictive Bayesian model and multi-modal Gaussian computational approach based on theory and literature review; (2) development of an object-oriented programming and combinatorial structure capable of managing data, integration and computation over an uncertain and highly dimensional parameter space; (3) creating a new bi-dimensional approach of the method of images to account for curved shoreline boundaries; (4) confirmation of model capability for locating sunken oil patches using available (partial) real field data and capability for temporal projections near curved boundaries using simulated field data; and (5) development of a stand-alone open-source computer application with graphical user interface capable of calibrating instantaneous oil spill scenarios, obtaining sets maps of relative probability profiles at different prediction times and user-selected geographic areas and resolution, and capable of performing post-processing tasks proper of a basic GIS-like software. The result is a predictive Bayesian multi-modal Gaussian model, SOSim (Sunken Oil Simulator) Version 1.0rc1, operational for use with limited, randomly-sampled, available subjective and numeric data on sunken oil concentrations and locations in relatively flat-bottomed bays. The SOSim model represents a new approach, coupling a Lagrangian modeling technique with predictive Bayesian capability for computing unconditional probabilities of mass as a function of space and time. The approach addresses the current need to rapidly deploy modeling capability without readily accessible information on ocean bottom currents. Contributions include (1) the development of the apparently first pollutant transport model for computing unconditional relative probabilities of pollutant location as a function of time based on limited available field data alone; (2) development of a numerical method of computing concentration profiles subject to curved, continuous or discontinuous boundary conditions; (3) development combinatorial algorithms to compute unconditional multimodal Gaussian probabilities not amenable to analytical or Markov-Chain Monte Carlo integration due to high dimensionality; and (4) the development of software modules, including a core module containing the developed Bayesian functions, a wrapping graphical user interface, a processing and operating interface, and the necessary programming components that lead to an open-source, stand-alone, executable computer application (SOSim - Sunken Oil Simulator). Extensions and refinements are recommended, including the addition of capability for accepting available information on bathymetry and maybe bottom currents as Bayesian prior information, the creation of capability of modeling continuous oil releases, and the extension to tracking of suspended oil (3-D).