This paper reports on the current state of surface water and ocean contamination models-based on the needs of US Government agencies, their Information Technology (IT) systems, and business processes. In addition, down-selection and evaluation criteria were applied in a two-step process. In Step 1, sixty five surface water and ocean models were identified and researched. In Step 2, the following criteria were explored for each model: 1) model environment (river, lake estuary, coastal ocean and watershed); 2) degree of analysis (screening model intermediate model, advanced model); 3) availability (public domain, proprietary); 4) temporal variability (steady state or time variable/dynamic); 5) spatial resolution (one, two or three dimensional); 6) processes (flow, transport, both flow and transport in an integrated system); 7) water quality (chemical, biological, radionuclides, sediment); and 8) support (user support/training available, user manuals/documents available).
This paper describes the development of an oil spill modeling system that is operational on a global scale and can be used for both real-time response, forecast simulations and probabilistic risk analysis based on climatological wind and ocean current data. For ocean and estuarine spills, the system makes use of the General NOAA Operational Modeling Environment (GNOME) oil spill model, Trajectory Analysis Planner and the Automated Data Inquiry for Oil Spills weathering model. Hydrodynamic and meteorological data is obtained from the US Navy and National Oceanic and Atmospheric Administration. Data access is provided through the Naval Oceanographic Office, the Fleet Numerical Meteorological and Oceanographic Center and the GNOME Online Oceanographic Data Server. For riverine spills, the GeoSpatial Stream Flow Model and the Incident Command Tool for Drinking Water Protection are used to respectively, build river networks with associated flows and velocities and, transport and disperse oil spill contamination downstream. Case study examples are presented for both forecast simulations and probabilistic risk analysis.
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