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Environmental Geology

, Volume 2, Issue 2, pp 107–117 | Cite as

Application of an oil spill vulnerability index to the shoreline of lower Cook Inlet, Alaska

  • Jacqueline Michel
  • Miles O. Hayes
  • P. Jeffrey Brown
Article

Abstract

Offshore tracts in Alaska's lower Cook Inlet are scheduled to be opened for exploratory petroleum drilling in the near future. Because of the potential for oil spills resulting from this activity, a field study of the coastal zone was conducted in June 1976. A total of 1216 km of shoreline was classified into 3 types: erosional (45 percent), neutral (38 percent), and depositional (17 percent). These were further subdivided into 16 subclasses on the basis of small scale morphological features. This classification was used in conjunction with a vulnerability index of potential oil spill damage, developed through study of three major oil spills, to predict the longevity of oil in the different coastal environments of the Inlet. On a scale from 1 to 10, 45 percent of the shoreline has low values of 1 to 4, which means that oil would be dispersed by natural processes within less than six months after a spill on these coasts. Values from 4 to 6 were assigned to 13.4 percent of the shoreline, where oil residence time may be up to one year. A 6 to 10 rating was assigned to 41.5 percent of the shoreline, where oil contamination may remain for periods of two to ten years, or possibly longer should no major clean-up procedures be initiated. We propose that the use of this type of vulnerability indexing, in conjunction with a biological susceptibility index and oil spill trajectory models, would provide a rational basis for decision making concerning the location of on- and off-shore oil facilities and the design of oil spill contingency plans.

Keywords

Vulnerability Index Exploratory Petroleum Susceptibility Index Biological Susceptibility Petroleum Drilling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York Inc. 1978

Authors and Affiliations

  • Jacqueline Michel
    • 1
  • Miles O. Hayes
    • 1
  • P. Jeffrey Brown
    • 1
  1. 1.Coastal Research Division, Department of GeologyUniversity of South CarolinaColumbia

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