Drifter-Based Predictions of the Spread of Surface Contamination Using Iterative Statistics: A Local Example with Global Applications

In the aftermath of the 2011 Fukushima nuclear power plant accident, it became critical to determine how radionuclides, both from atmospheric deposition and direct ocean discharge, were spreading in the ocean. One successful method used drifter observations from the Global Drifter Program (GDP) to predict the timing of the spread of surface contamination. U.S. coasts are home to a number of nuclear power plants as well as other industries capable of leaking contamination into the surface ocean. Here, the spread of surface contamination from a hypothetical accident at the existing Pilgrim nuclear power plant on the coast of Massachusetts is used as an example to show how the historical drifter dataset can be used as a prediction tool. Our investigation uses a combined dataset of drifter tracks from the GDP and the NOAA Northeast Fisheries Science Center. Two scenarios are examined to estimate the spread of surface contamination: a local direct leakage scenario and a broader atmospheric deposition scenario that could result from an explosion. The local leakage scenario is used to study the spread of contamination within and beyond Cape Cod Bay, and the atmospheric deposition scenario is used to study the large-scale spread of contamination throughout the North Atlantic Basin. A multiple-iteration method of estimating probability makes best use of the available drifter data. This technique, which allows for direct observationally-based predictions, can be applied anywhere that drifter data are available to calculate estimates of the likelihood and general timing of the spread of surface contamination in the ocean.