Journal
RIVER RESEARCH AND APPLICATIONS
Volume 36, Issue 4, Pages 668-680Publisher
WILEY
DOI: 10.1002/rra.3620
Keywords
adaptive management; decision-making; environmental water; monitoring and evaluation; Murray-Darling Basin; social learning; uncertainty
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Adaptive management is central to improving outcomes of environmental water delivery. The Australian Government's Murray-Darling Basin (MDB) Plan 2012 explicitly states that adaptive management should be applied in the planning, prioritisation and use of environmental water. A Long Term Intervention Monitoring (LTIM) program was established in 2014 to evaluate responses to environmental water delivery for seven Areas within the MDB, with evaluation also undertaken at the Basin scale. Adaptive management at the Area scale was assessed using two approaches: (a) through a reflective exercise undertaken by researchers, water managers and community members and (b) through an independent review and evaluation of the program, where relevant reports were reviewed and managers and researchers involved in the LTIM program were interviewed. Both assessment approaches revealed that the scale of management actions influenced the extent to which learnings were incorporated into subsequent actions. Although there were many examples where learnings within an Area had been used to adaptively manage subsequent environmental water deliveries within that Area, there was inconsistent documentation of the processes for incorporating learnings into decision making. Although this likely limited the sharing of learnings, there were also examples where learnings from one Area had influenced environmental water management in another, suggesting that sharing between concurrent projects can increase learning. The two assessments identified ways to improve and systematically document the adaptive management learnings. With improved processes to increase reflection, documentation and sharing of learnings across projects, there is an opportunity to improve management of environmental water and ecosystem outcomes.
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