Using optimization to develop a “designer” environmental flow regime

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• 作者：Avril Horne^a ; ^{avril.horne@unimelb.edu.au} ; Simranjit Kaur^a ; Joanna Szemis^a ; Alysson Costa^a ; J. Angus Webb^a ; Rory Nathan^a ; Michael Stewardson^a ; Lisa Lowe^a ; Natashia Boland^b
• 关键词：Environmental flow ; Conditional probability network ; Bayesian network ; Optimization ; Designer flow ; Mixed integer programming
• 刊名：Environmental Modelling & Software
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：88
• 期：Complete
• 页码：188-199
• 全文大小：1670 K
• 卷排序：88

文摘

There are increasing numbers of rivers with large storages, resulting in changes to environmental condition downstream. In these systems, environmental flow regimes that are specifically designed to meet environmental management objectives, whilst continuing to support economic needs, may be the best approach. A challenge remains as to how best to design these novel flow regimes. Decision support tools such as optimization provide a potential tool to achieve this. In existing tools environmental outcomes are not represented with sufficient realism and this is a major barrier to successful adoption by decision-makers. Here, we employ conditional probability networks as a promising approach that provides both ease of modelling and a direct link to ecological outcomes and processes. We present a generic model that can be used to represent any ecological endpoint within a river system. We then demonstrate the approach using two fish species in the Yarra River, Victoria.