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Integrating climate model projections into environmental risk assessment: A probabilistic modeling approach
Moe, S.J.; Brix, K.V.; Landis, W.G.; Stauber, J.L.; Carriger, J.F.; Hader, J.D.; Kunimitsu, T.; Mentzel, S.; Nathan, R.; Noyes, P.D.; Oldenkamp, R.; Rohr, J.R.; van den Brink, P.J.; Verheyen, J.; Benestad, R.E. (2024). Integrating climate model projections into environmental risk assessment: A probabilistic modeling approach. Integr. Environ. Assess. Manag. 20(2): 367-383. https://dx.doi.org/10.1002/ieam.4879
In: Integrated Environmental Assessment and Management. Wiley: Pensacola. ISSN 1551-3777; e-ISSN 1551-3793
Peer reviewed article  
Beschikbaar in  Auteurs 
Author keywords
    Bayesian network; Climate information; Downscaling; Exposure model; Probabilistic risk assessment
Auteurs  Top 
  • Moe, S.J.
  • Brix, K.V.
  • Landis, W.G.
  • Stauber, J.L.
  • Carriger, J.F.
  • Hader, J.D.
  • Kunimitsu, T.
  • Mentzel, S.
  • Nathan, R.
  • Noyes, P.D.
  • Oldenkamp, R.
  • Rohr, J.R.
  • van den Brink, P.J.
  • Verheyen, J.
  • Benestad, R.E.
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. (c) 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). A SETAC workshop was organized in 2022 to address the integration of future projections from global climate models (GCMs) into environmental risk assessment models.The modeling approach presented is based on deriving on robust "climate information" with relevance for the assessment: future climate projections from ensembles of GCMs, regionally downscaled, and summarized by statistical properties.Three case studies in Norway, Australia, and the United States were used to show examples of quantification of climate change impacts on traditional risk assessment components such as chemical exposure and hazard, as well as on the vulnerability of assessment endpoints to other stressors.The case studies also demonstrated that probabilistic modeling methods such as Bayesian networks can be useful for integrating all quantified climate change impacts on risk components, together with estimated uncertainty, into a probabilistic risk characterization.
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