Integrating climate model projections into environmental risk assessment: A probabilistic modeling approach

S.J. Moe*, Kevin V. Brix, Wayne G. Landis, Jenny L. Stauber, John F. Carriger, John D. Hader, Taro Kunimitsu, Sophie Mentzel, Rory Nathan, Pamela D. Noyes, Rik Oldenkamp, Jason R. Rohr, Paul J. van den Brink, Julie Verheyen, Rasmus E. Benestad

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)

Abstract

The Society of Environmental Toxicology and Chemistry (SETAC) convened a Pellston workshop in 2022 to examine how information on climate change could be better incorporated into the ecological risk assessment (ERA) process for chemicals as well as other environmental stressors. A major impetus for this workshop is that climate change can affect components of ecological risks in multiple direct and indirect ways, including the use patterns and environmental exposure pathways of chemical stressors such as pesticides, the toxicity of chemicals in receiving environments, and the vulnerability of species of concern related to habitat quality and use. This article explores a modeling approach for integrating climate model projections into the assessment of near- and long-term ecological risks, developed in collaboration with climate scientists. State-of-the-art global climate modeling and downscaling techniques may enable climate projections at scales appropriate for the study area. It is, however, also important to realize the limitations of individual global climate models and make use of climate model ensembles represented by statistical properties. Here, we present a probabilistic modeling approach aiming to combine projected climatic variables as well as the associated uncertainties from climate model ensembles in conjunction with ERA pathways. We draw upon three examples of ERA that utilized Bayesian networks for this purpose and that also represent methodological advancements for better prediction of future risks to ecosystems. We envision that the modeling approach developed from this international collaboration will contribute to better assessment and management of risks from chemical stressors in a changing climate. Integr Environ Assess Manag 2024;00:1–17.

Original languageEnglish
Pages (from-to)367-383
JournalIntegrated Environmental Assessment and Management
Volume20
Issue number2
Early online date11 Dec 2023
DOIs
Publication statusPublished - Mar 2024

Keywords

  • Bayesian network
  • Climate information
  • Downscaling
  • Exposure model
  • Probabilistic risk assessment

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