A Bayesian decision network approach for assessing the ecological impacts of salinity management

• DOI: 10.1016/j.matcom.2005.02.020
• Publication Date: 2005-03-13T02:56:03Z
• Authors: A. Sadoddin; R.A. Letcher; A.J. Jakeman; L.T.H. Newham
• Keywords: Mathematical models; Keywords: Catchments; Ecology; Economic and social effects; 0208 environmental biotechnology; New South Wales (NSW); 710; 02 engineering and technology; Salinity measurement; Salinity management; 15. Life on land; 6. Clean water; Dryland; Rivers; 13. Climate action; Terrestrial and riparian ecology; Decision support systems Bayesian decision networks; Bayesian decision networks; Probability

This paper outlines one component of a study being undertaken to provide a new tool for integrated management of dryland salinity, a major environmental problem in Australia. The Little River Catchment in the upper Macquarie River basin of New South Wales (NSW) is used as a case study. A Bayesian decision network (BDN) approach integrates the various system components - biophysical, social, ecological, and economic. The method of integration of the system components is demonstrated through an example application showing the impacts of various management scenarios on terrestrial and riparian ecology. The ecological impacts of management scenarios are assessed using a probabilistic approach to evaluate ecological criteria which are compared with those for the present situation. In considering different ecological indices, the direction and magnitude of change under different management scenarios varies because of the diverse influence of habitat fragmentation.