A Bayesian Decision Network Approach for Salinity Management in the Little River Catchment, NSW

Salinisation is a major environmental problem affecting land and water systems in Australia. This paper outlines a study currently being undertaken to provide a new tool to manage salinity from environmental and socioeconomic perspectives. In this study, biophysical and socio-economic aspects of the Little River Catchment and their relevance to salinity management will be investigated using a Bayesian Decision Network (BDN) approach. The Little River Catchment is located in the upper Macquarie River Basin in central western NSW. Salinity has been nominated by the catchment community as the main environmental problem. The focus of this study is on exploring the economic and biophysical impacts of scenarios for salinity management, consistent with salinity strategies both in NSW and the Murray Darling Basin. This will involve developing a modelling system to assist decision makers in formulating alternatives, analysing the impacts of these alternatives on salinity, water supply and the farming community, and interpreting and suggesting appropriate options for implementation in the catchment. The BDN approach has been selected because of its ability to represent the dryland salinity problem graphically and to model complex interactions between system variables. Additional reasons for selecting this approach are the ability of BDNs to integrate qualitative information and knowledge with quantitative information, and its capacity to deal with uncertainty.