11 Applications of an Oil Drift and Fate Model for Fairway Design
383
11.4.1 Calculation of Drift Patterns
An analysis of these questions in terms of purely current-driven motions of passive
particles has been presented in the previous chapters. It provides certain spatial distributions of the probability for different offshore areas to serve as starting points
of pollution that hits a coast within a certain time limit and the time it takes for
the pollution to reach the coast. The analogues of such spatial fields are called drift
patterns below. To construct these patterns, the area of interest is covered by a grid
of release positions of realistic oil spills. Similarly to the above, the resulting ensembles of drift of oil spills are analysed statistically. The ‘where’ is then referring
to a marker (a certain grid point) on a spatial map, and the ‘when’ determines the
residence time of oil at sea. In a similar way, the probability of oil landing is derived
from an ensemble of oil drift simulations.
The exposure of coastal stretches to the pollution released at a specific site depends on the drift pattern, i.e., on the landing probability and residence time. The
anisotropy of oil propagation in many sea areas makes these factors a complex function of topography, metocean forcing and flow regime. In other words, they are not
just geometrical functions depending on the ‘downwind’ or ‘downwave’ distance to
the shoreline. Moreover, they apparently vary within the year and are definitely different for different seasons (Soomere et al. 2011d). Therefore, there is little hope to
identify certain dynamical, well-defined links between the oil release sites and potentially affected coastal sections. A feasible solution is to use a statistical approach.
Similarly to efforts described in Chaps. 4, 9 and 10, a large number of simulations of
oil propagation should be performed and analysed to establish statistical links. The
results are statistically significant spatial distributions of certain measures characterizing environmental risk factors, which reflect certain frequently occurring drift
patterns. The obvious next step towards practically useful results is the merging of
risk factors into one (risk) parameter φ (see Eq. (11.2) below) for the assessment of
environmentally safe fairways in Sect. 11.5.
Model based investigations of oil drift patterns involve two kinds of simulations:
high-resolution weather and ocean circulation model runs with a goal to evaluate the
forcing fields impacting oil drift and fate, and oil drift model runs for drift assessments. All processes 2 along the chain from the atmosphere to the ocean, and further
on to the drift and decay of oil at sea have to be well represented by the models that
are applied. The quality and resolution of the forcing data is crucial for an adequate
replication of the drift and fate of oil, especially in near coastal waters (Broström
et al. 2011).
To ensure the necessary accuracy of the drift predictions, high-quality operational
ocean model and atmosphere reanalysis results were selected for the simulations.
Special focus was put on the accurate modelling of the atmospheric and hydrodynamic conditions in the Gulf of Finland. As discussed in Sect. 11.3, the operational
ocean circulation model HBM, normally used for the Baltic Sea in a resolution of
2 Currents and winds drive oil advection and affect oil chemistry and physics in sea water.
383
11.4.1 Calculation of Drift Patterns
An analysis of these questions in terms of purely current-driven motions of passive
particles has been presented in the previous chapters. It provides certain spatial distributions of the probability for different offshore areas to serve as starting points
of pollution that hits a coast within a certain time limit and the time it takes for
the pollution to reach the coast. The analogues of such spatial fields are called drift
patterns below. To construct these patterns, the area of interest is covered by a grid
of release positions of realistic oil spills. Similarly to the above, the resulting ensembles of drift of oil spills are analysed statistically. The ‘where’ is then referring
to a marker (a certain grid point) on a spatial map, and the ‘when’ determines the
residence time of oil at sea. In a similar way, the probability of oil landing is derived
from an ensemble of oil drift simulations.
The exposure of coastal stretches to the pollution released at a specific site depends on the drift pattern, i.e., on the landing probability and residence time. The
anisotropy of oil propagation in many sea areas makes these factors a complex function of topography, metocean forcing and flow regime. In other words, they are not
just geometrical functions depending on the ‘downwind’ or ‘downwave’ distance to
the shoreline. Moreover, they apparently vary within the year and are definitely different for different seasons (Soomere et al. 2011d). Therefore, there is little hope to
identify certain dynamical, well-defined links between the oil release sites and potentially affected coastal sections. A feasible solution is to use a statistical approach.
Similarly to efforts described in Chaps. 4, 9 and 10, a large number of simulations of
oil propagation should be performed and analysed to establish statistical links. The
results are statistically significant spatial distributions of certain measures characterizing environmental risk factors, which reflect certain frequently occurring drift
patterns. The obvious next step towards practically useful results is the merging of
risk factors into one (risk) parameter φ (see Eq. (11.2) below) for the assessment of
environmentally safe fairways in Sect. 11.5.
Model based investigations of oil drift patterns involve two kinds of simulations:
high-resolution weather and ocean circulation model runs with a goal to evaluate the
forcing fields impacting oil drift and fate, and oil drift model runs for drift assessments. All processes 2 along the chain from the atmosphere to the ocean, and further
on to the drift and decay of oil at sea have to be well represented by the models that
are applied. The quality and resolution of the forcing data is crucial for an adequate
replication of the drift and fate of oil, especially in near coastal waters (Broström
et al. 2011).
To ensure the necessary accuracy of the drift predictions, high-quality operational
ocean model and atmosphere reanalysis results were selected for the simulations.
Special focus was put on the accurate modelling of the atmospheric and hydrodynamic conditions in the Gulf of Finland. As discussed in Sect. 11.3, the operational
ocean circulation model HBM, normally used for the Baltic Sea in a resolution of
2 Currents and winds drive oil advection and affect oil chemistry and physics in sea water.