Bayesian Network Modelling for assessing the biophysical and socio-economic impacts of dryland salinity management

Improving dryland salinity management at catchment scales requires an integrated modelling approach, in which the dominant bio-physical and socio-economic drivers, processes and impacts are considered. This paper presents and evaluates the use of a Bayesian Decision Network (BDN) model as an integrated approach for considering the trade-offs associated with the management of dryland salinity, a major environmental problem in Australia. The ability and effectiveness of the BDN approach in building integrated catchment assessment and management tools are demonstrated through a case study in the Little River Catchment (LRC) in the upper Macquarie River basin, NSW. This integrated model was developed to co-ordinate the various disciplines involved in salinity problems, integrate available data and information, and to allow the investigation of the potential outcomes arising from implementing salinity management options at the catchment scale. A conceptual model framework underlying the BDN for salinity management in the LRC was developed. This framework incorporates ecological, physical, economic and social aspects of dryland salinity problems in the catchment. To complete the BDN model, a range of techniques, data and information was used. Various outcomes of implementing 32 possible salinity management scenarios at the catchment scale are investigated and discussed. The investigation was conducted based on the following indices from different disciplines: surface runoff, baseflow, stream salt concentration, terrestrial habitat condition, community attitude, establishment costs, and total gross margin. The BDN approach implemented in this research serves as a valuable tool to represent the catchment system as a whole, to incorporate output from models and expert judgment, to examine the trade-offs among outcomes necessary for decision-making, and to communicate uncertainty of the parameters in the BDN model. The analysis of the trade-offs presented in this paper also shows that due to the influences of the various possible outcomes on decision-making analysis, as well as the diversity in the factors influencing the characteristics of stream flow, blanket solutions for managing the quality and quantity of stream flow cannot be suggested. To reach informed and feasible decisions for salinity management the social and economic preferences and priorities, along with the ecological and hydrological consequences of salinity management options, need to be considered in quantifying the trade-offs among salinity management outcomes.