Now showing 1 - 2 of 2
ItemThe politicisation of science in the Murray-Darling Basin, Australia: discussion of 'Scientific integrity, public policy and water governance'Stewardson, MJ ; Bond, N ; Brookes, J ; Capon, S ; Dyer, F ; Grace, M ; Frazier, P ; Hart, B ; Horne, A ; King, A ; Langton, M ; Nathan, R ; Rutherfurd, I ; Sheldon, F ; Thompson, R ; Vertessy, R ; Walker, G ; Wang, QJ ; Wassens, S ; Watts, R ; Webb, A ; Western, AW (Taylor & Francis, 2021-10-30)Many water scientists aim for their work to inform water policy and management, and in pursuit of this objective, they often work alongside government water agencies to ensure their research is relevant, timely and communicated effectively. A paper in this issue, examining 'Science integrity, public policy and water governance in the Murray-Darling Basin, Australia’, suggests that a large group of scientists, who work on water management in the Murray-Darling Basin (MDB) including the Basin Plan, have been subject to possible ‘administrative capture'. Specifically, it is suggested that they have advocated for policies favoured by government agencies with the objective of gaining personal benefit, such as increased research funding. We examine evidence for this claim and conclude that it is not justified. The efforts of scientists working alongside government water agencies appear to have been misinterpreted as possible administrative capture. Although unsubstantiated, this claim does indicate that the science used in basin water planning is increasingly caught up in the politics of water management. We suggest actions to improve science-policy engagement in basin planning, to promote constructive debate over contested views and avoid the over-politicisation of basin science.
ItemInforming Environmental Water Management Decisions: Using Conditional Probability Networks to Address the Information Needs of Planning and Implementation CyclesHorne, AC ; Szemis, JM ; Webb, JA ; Kaur, S ; Stewardson, MJ ; Bond, N ; Nathan, R (SPRINGER, 2018-03-01)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.