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

Author

Listed:
  • Sadoddin, A.
  • Letcher, R.A.
  • Jakeman, A.J.
  • Newham, L.T.H.

Abstract

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.

Suggested Citation

  • Sadoddin, A. & Letcher, R.A. & Jakeman, A.J. & Newham, L.T.H., 2005. "A Bayesian decision network approach for assessing the ecological impacts of salinity management," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 69(1), pages 162-176.
    • Handle: RePEc:eee:matcom:v:69:y:2005:i:1:p:162-176
    DOI: 10.1016/j.matcom.2005.02.020
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    Cited by:

    1. Zhang, Lu & Cui, Li & Chen, Lujie & Dai, Jing & Jin, Ziyi & Wu, Hao, 2023. "A hybrid approach to explore the critical criteria of online supply chain finance to improve supply chain performance," International Journal of Production Economics, Elsevier, vol. 255(C).
    2. Kragt, Marit Ellen & Bennett, Jeffrey W., 2009. "Integrating economic values and catchment modelling," 2009 Conference (53rd), February 11-13, 2009, Cairns, Australia 48057, Australian Agricultural and Resource Economics Society.
    3. F De Carlo & O Borgia & M Tucci, 2011. "Risk-based inspections enhanced with Bayesian networks," Journal of Risk and Reliability, , vol. 225(3), pages 375-386, September.
    4. Barton, David N. & Benjamin, Tamara & Cerdán, Carlos R. & DeClerck, Fabrice & Madsen, Anders L. & Rusch, Graciela M. & Salazar, à lvaro G. & Sanchez, Dalia & Villanueva, Cristóbal, 2016. "Assessing ecosystem services from multifunctional trees in pastures using Bayesian belief networks," Ecosystem Services, Elsevier, vol. 18(C), pages 165-174.
    5. Jumeniyaz Seydehmet & Guang Hui Lv & Ilyas Nurmemet & Tayierjiang Aishan & Abdulla Abliz & Mamat Sawut & Abdugheni Abliz & Mamattursun Eziz, 2018. "Model Prediction of Secondary Soil Salinization in the Keriya Oasis, Northwest China," Sustainability, MDPI, vol. 10(3), pages 1-22, February.
    6. Keshtkar, A.R. & Salajegheh, A. & Sadoddin, A. & Allan, M.G., 2013. "Application of Bayesian networks for sustainability assessment in catchment modeling and management (Case study: The Hablehrood river catchment)," Ecological Modelling, Elsevier, vol. 268(C), pages 48-54.
    7. Jose-Luis Molina & Jose García-Aróstegui & John Bromley & Jose Benavente, 2011. "Integrated Assessment of the European WFD Implementation in Extremely Overexploited Aquifers Through Participatory Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(13), pages 3343-3370, October.
    8. Morando, S. & Jemei, S. & Hissel, D. & Gouriveau, R. & Zerhouni, N., 2017. "ANOVA method applied to proton exchange membrane fuel cell ageing forecasting using an echo state network," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 283-294.

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