A statistical representation of oil spill fate in the Salish Sea (Part 1)

Details

• Journal Title:
  Marine Pollution Bulletin
• Personal Author:
  Mueller, Rachael D. ; Allen, Susan E. ; Chang, Stephanie ; Niu, Haibo ; Latornell, Douglas J. ; Li, Shihan ; Bagshaw, Ryah ; Bhudia, Ashutosh ; Do, Vicky ; Forysinski, Krista ; Moore-Maley, Ben ; Power, Cameron
• NOAA Program & Office:
  NMFS (National Marine Fisheries Service)
• Description:
  Transport of different oil types as fuel or cargo varies within the Salish Sea and over time. Although many studies have focused on the future health of Salish Sea ecosystems under a warming climate, no study has addressed how market-based dependencies on fossil fuels introduces geographic and temporal variations in ecosystem vulnerabilities. This paper aims to help address this knowledge gap with details of oil transport and oil spill risk in the Salish Sea. Part 1 describes the method that we developed to statistically generate individual oil spill scenarios based on ship traffic data, Washington state oil transfer data, and information on past oil spill events. We examine a set of these 10,000 random spill scenarios, referred to as “Study Spill Set,” and we compare this Study Spill Set to other sets of 10,000 spills. The Study Spill Set generated by this Monte Carlo method is the spill set that was used in the model simulations described and presented in a second paper in this special issue, Part 2 (Mueller et al., 2025). While Part 1 focuses on the methods and results from statistically generating 10,000 spill scenarios, Part 2 focuses on the likelihood of oil spill impacts in the Salish Sea based on the fate and transport of these 10,000, statistically-generated spill scenarios. In this paper, we explain the development of our Monte Carlo approach and show that Salish Sea oil spill risks are regionally variable by oil type and spill volume.
• Source:
  Marine Pollution Bulletin, 222, 118452
• DOI:
  https://doi.org/10.1016/j.marpolbul.2025.118452
• ISSN:
  0025-326X
• Format:
  PDF
• Publisher:
  Elsevier BV
• Document Type:
  Journal Article
• License:
  https://creativecommons.org/licenses/by/4.0/
• Rights Information:
  CC BY
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:4f1b020ef46ccea9c2123a4799f3cff86ba72758c900654787b05e419702f39b68ecc7a67f5652aeb34ca0279467925a165648199131e1343fa15a907822c2cf
• Download URL:
  https://repository.library.noaa.gov/view/noaa/73544/noaa_73544_DS1.pdf
• File Type:
  [PDF - 7.82 MB ]