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Environmental Management

, Volume 61, Issue 3, pp 347–357 | Cite as

Informing Environmental Water Management Decisions: Using Conditional Probability Networks to Address the Information Needs of Planning and Implementation Cycles

  • Avril C. Horne
  • Joanna M. Szemis
  • J. Angus Webb
  • Simranjit Kaur
  • Michael J. Stewardson
  • Nick Bond
  • Rory Nathan
Article

Abstract

One important aspect of adaptive management is the clear and transparent documentation of hypotheses, together with the use of predictive models (complete with any assumptions) to test those hypotheses. Documentation of such models can improve the ability to learn from management decisions and supports dialog between stakeholders. A key challenge is how best to represent the existing scientific knowledge to support decision-making. Such challenges are currently emerging in the field of environmental water management in Australia, where managers are required to prioritize the delivery of environmental water on an annual basis, using a transparent and evidence-based decision framework. We argue that the development of models of ecological responses to environmental water use needs to support both the planning and implementation cycles of adaptive management. Here we demonstrate an approach based on the use of Conditional Probability Networks to translate existing ecological knowledge into quantitative models that include temporal dynamics to support adaptive environmental flow management. It equally extends to other applications where knowledge is incomplete, but decisions must still be made.

Keywords

Environmental flow Instream flow Adaptive management Conditional probability network Ecological response Active management 

Notes

Acknowledgements

This study was funded by through ARC Linkage project LP130100174.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no competing interest.

Supplementary material

267_2017_874_MOESM1_ESM.docx (67 kb)
Supplementary Information

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Infrastructure EngineeringThe University of MelbourneParkvilleAustralia
  2. 2.The Murray-Darling Freshwater Research CenterLa Trobe University WodongaAustralia

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