Adaptive modelling for adaptive water quality management in the Great Barrier Reef region, Australia

  • Authors:

  • Tim Lynam;John Drewry;Will Higham;Carl Mitchell

  • Affiliations:

  • CSIRO Sustainable Ecosystems, University Road, Davies Laboratory, Douglas, Townsville, Queensland 4814, Australia;Reef Catchments, P.O. Box 815, Mackay, Queensland 4740, Australia (formerly called Mackay Whitsunday Natural Resource Management Group);Reef Catchments, P.O. Box 815, Mackay, Queensland 4740, Australia (formerly called Mackay Whitsunday Natural Resource Management Group);Reef Catchments, P.O. Box 815, Mackay, Queensland 4740, Australia (formerly called Mackay Whitsunday Natural Resource Management Group)

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

The development and use of a Bayesian Belief Network (BBN) model, within an adaptive management process for the management of water quality in the Mackay Whitsunday region of Queensland, Australia is described. The management goal is firstly to set achievable targets for water quality entering the Great Barrier Reef lagoon from the Mackay Whitsunday natural resource management region and then secondly to define and implement a strategy to achieve these targets. The BBN serves as an adaptive framework that managers and scientists may use to articulate what they know about the managed system. It then provides a tool to guide where, when and what interventions (including research) are most likely to achieve management outcomes. Importantly the BBN provides a platform for collective learning. BBN estimates of total suspended sediment (TSS) loads and event mean concentrations (EMCs) were compared to observed data and results from current best practice models. The BBN estimates were reasonable relative to empirical observations. Example results from the BBN are thereafter used to illustrate the use of the model in estimating the likelihood of exceeding water quality targets with and without proposed actions to improve water quality. Example results are also used to illustrate what spatial or land use elements might contribute most to exceeding water quality targets. Finally key limitations of the tool are discussed and important learnings from the process are highlighted.