Integrating diverse model results into decision support for good environmental status and blue growth

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http://urn.fi/URN:NBN:fi-fe2022050432735 http://hdl.handle.net/10138/343315

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Laura Uusitalo, Thorsten Blenckner, Riikka Puntila-Dodd, Annaliina Skyttä, Susanna Jernberg, Rudi Voss, Bärbel Müller-Karulis, Maciej T. Tomczak, Christian Möllmann, Heikki Peltonen. Integrating diverse model results into decision support for good environmental status and blue growth. Science of The Total Environment 806, part 2 (2022), 150450, ISSN 0048-9697. https://doi.org/10.1016/j.scitotenv.2021.150450

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Title: Integrating diverse model results into decision support for good environmental status and blue growth
Author: Uusitalo, Laura; Blenckner, Thorsten; Puntila-Dodd, Riikka; Skyttä, Annaliina; Jernberg, Susanna; Voss, Rudi; Müller-Karulis, Bärbel; Tomczak, Maciej T.; Möllmann, Christian; Peltonen, Heikki
Contributor organization: Suomen ympäristökeskus
The Finnish Environment Institute
Publisher: Elsevier
Date: 2022
Language: en
Belongs to series: Science of the Total Environment
ISSN: 0048-9697
1879-1026
DOI: https://doi.org/10.1016/j.scitotenv.2021.150450
URI: http://urn.fi/URN:NBN:fi-fe2022050432735
http://hdl.handle.net/10138/343315
Abstract: Sustainable environmental management needs to consider multiple ecological and societal objectives simultaneously while accounting for the many uncertainties arising from natural variability, insufficient knowledge about the system's behaviour leading to diverging model projections, and changing ecosystem. In this paper we demonstrate how a Bayesian network- based decision support model can be used to summarize a large body of research and model projections about potential management alternatives and climate scenarios for the Baltic Sea. We demonstrate how this type of a model can act as an emulator and ensemble, integrating disciplines such as climatology, biogeochemistry, marine and fisheries ecology as well as economics. Further, Bayesian network models include and present the uncertainty related to the predictions, allowing evaluation of the uncertainties, precautionary management, and the explicit consideration of acceptable risk levels. The Baltic Sea example also shows that the two biogeochemical models frequently used in future projections give considerably different predictions. Further, inclusion of parameter uncertainty of the food web model increased uncertainty in the outcomes and reduced the predicted manageability of the system. The model allows simultaneous evaluation of environmental and economic goals, while illustrating the uncertainty of predictions, providing a more holistic view of the management problem.
Description: Highlights • Environmental management needs to integrate multiple tools and objectives. • Bayesian network based decision support system is used as an integrating meta model. • Uncertainty of model projections is incorporated, enabling the evaluation of risks. • The model enables evaluation of synergies and trade-offs in management. • There are large uncertainties related to the future projections of the Baltic Sea.
Subject: ympäristönhoito
ympäristön tila
sosiaaliekologia
päätöksenteko
bayesilainen menetelmä
mallit
epävarmuus
sininen kasvu
meret
strategia
meristrategia
Itämeri
Subject (yso): decision support system
bayesian network
environmental management
ecosystem novelty
model emulator
socio-ecological system
blue growth
MSFD
good environmental status
Baltic Sea
Rights: CC BY 4.0


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