Keywords
salinity management, bayesian decision network, terrestrial and riparian ecology
Start Date
1-7-2004 12:00 AM
Abstract
This paper outlines a component of a study currently being undertaken to provide a new tool for the holistic management of dryland salinity. The Little River catchment in the upper Macquarie River basin of New South Wales (NSW), Australia, is used as a case study. The model uses a Bayesian Decision Network (BDN) approach to integrating 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 scenarios on terrestrial and riparian ecology. The paper outlines these scenarios and demonstrates the way in which they are spatially incorporated in the model. The ecological impacts of management scenarios have been assessed using a probabilistic approach to evaluating ecological criteria for a range of management actions compared with the present situation.
Assessing the ecological impacts of salinity management using a Bayesian Decision Network
This paper outlines a component of a study currently being undertaken to provide a new tool for the holistic management of dryland salinity. The Little River catchment in the upper Macquarie River basin of New South Wales (NSW), Australia, is used as a case study. The model uses a Bayesian Decision Network (BDN) approach to integrating 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 scenarios on terrestrial and riparian ecology. The paper outlines these scenarios and demonstrates the way in which they are spatially incorporated in the model. The ecological impacts of management scenarios have been assessed using a probabilistic approach to evaluating ecological criteria for a range of management actions compared with the present situation.
