PELETS-2D: Drift simulations / drift climatologies

Current fields from coastDat also provide a basis for particle drift simulations and establishing long-term drift climatologies. The Langrangian transport module PELETS-2D allows for a) tracking individual particles or particle clouds and b) analysing large ensembles of such simulations with regard to transport paths, transport rates and characteristic travel times.

Three interactive examples shall illustrate the procedure and the kind of possible applications.

The first example is a game that invites you to experience the complexity of the North Sea hydrodynamic regime. You may release a virtual bottle message at any location in the German Bight and watch its movements during a period of 20 days. Depending on both the location and the release time you choose (any hour within the years 1948-2003) the bottle will behave very differently. Possibly you may prefer to release a rubber duck instead of a bottle. The duck sitting on the water will be much more affected by prevailing wind conditions. As simulations are based on realistic environmental conditions, it is possible to refer to specific events in the past like major storm surges, for instance.

For a more quantitative evaluation of the fate of particle clouds released at some source region of interest one should refer to particle densities in problem dependent receptor regions. The following example allows for the display of some pre-calculated simulations that illustrate the general approach. Unfortunately, at present it is not possible to start any simulation you defined on your own.

This example presents some aspects of a scientific study that was published. Current and wind data from coastDat were used for tracking hypothetical (illegal) oil releases at different locations along main shipping routes. After subdividing the German North Sea coast into a couple of receptor regions, detailed simulations revealed correspondences between source and receptor regions and the role of both prevailing weather conditions and the half-life of an assumed pollutant. These results were then summarized in terms of probabilistic relationships, which allows for an interactive exploration of spatial dependences and sensitivities between parameters addressed in the study.