Streaming Media
Presentation Abstract
Bequeathing future generations a Salish Sea that is absent of oil spill impacts requires good information on the spatial distribution of the most likely oil spill scenarios and their consequences in order to help develop effective plans for oil spill prevention and response. We developed a data-informed modeling framework for generating statistical maps of oil spill fate in the Salish Sea to help provide this information. Oil spill location, month and volume are randomly generated from a year’s worth of AIS ship track data that was organized into vessel time exposure maps for seven different vessel classifications. For oil cargo vessels, we use the time attribution in AIS ship tracks to create voyages that identify the ship’s origin and destination. Ships that are either identified as having U.S. origin or destination or that are in U.S. waters and without a Canadian origin or destination are attributed with an oil type that is determined by the Washington State Department of Ecology oil transfer data. We randomly select a spill day, hour and year between January 1, 2015 and December 31, 2018 to capture a wide range of spill conditions. Our 7-day spill scenarios use currents, winds and waves that are predicted by the SalishSeaCast, HRDPS and WW3 models, respectively. We generated 10,000 random oil spills with our Monte Carlo simulation and predicted oil dispersion, emulsification, dilution, biodegradation, beaching and advection for these spills using a modified version of the MOHID oil spill model. In this talk, we will detail the design of the Monte Carlo simulation and present maps of the likelihood of oil presence and volume based on region and oil type.
Session Title
Oil Spills and Road-based Contaminants
Conference Track
SSE10: Contaminants
Conference Name
Salish Sea Ecosystem Conference (2022 : Online)
Document Type
Event
SSEC Identifier
SSE-traditionals-157
Start Date
27-4-2022 11:30 AM
End Date
27-4-2022 1:00 PM
Rights
Copying of this document in whole or in part is allowable only for scholarly purposes. It is understood, however, that any copying or publication of this document for commercial purposes, or for financial gain, shall not be allowed without the author's written permission.
Type
Text
Language
English
A statistical representation of oil spill fate in the Salish Sea based on AIS ship traffic, oil transfer data, and a Monte Carlo model framework.
Bequeathing future generations a Salish Sea that is absent of oil spill impacts requires good information on the spatial distribution of the most likely oil spill scenarios and their consequences in order to help develop effective plans for oil spill prevention and response. We developed a data-informed modeling framework for generating statistical maps of oil spill fate in the Salish Sea to help provide this information. Oil spill location, month and volume are randomly generated from a year’s worth of AIS ship track data that was organized into vessel time exposure maps for seven different vessel classifications. For oil cargo vessels, we use the time attribution in AIS ship tracks to create voyages that identify the ship’s origin and destination. Ships that are either identified as having U.S. origin or destination or that are in U.S. waters and without a Canadian origin or destination are attributed with an oil type that is determined by the Washington State Department of Ecology oil transfer data. We randomly select a spill day, hour and year between January 1, 2015 and December 31, 2018 to capture a wide range of spill conditions. Our 7-day spill scenarios use currents, winds and waves that are predicted by the SalishSeaCast, HRDPS and WW3 models, respectively. We generated 10,000 random oil spills with our Monte Carlo simulation and predicted oil dispersion, emulsification, dilution, biodegradation, beaching and advection for these spills using a modified version of the MOHID oil spill model. In this talk, we will detail the design of the Monte Carlo simulation and present maps of the likelihood of oil presence and volume based on region and oil type.
