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Application of Bayesian Decision Networks for Groundwater Resources Management Under the Conditions of High Uncertainty and Data Scarcity

Author

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  • Hadis Mohajerani

    (University of Trier)

  • Majid Kholghi

    (University of Tehran)

  • Abolfazl Mosaedi

    (Ferdowsi University of Mashhad (FUM))

  • Raziyeh Farmani

    (University of Exeter)

  • Amir Sadoddin

    (Gorgan University of Agricultural Sci & Natural Resources)

  • Markus Casper

    (University of Trier)

Abstract

This paper presents management of groundwater resource using a Bayesian Decision Network (BDN). The Kordkooy region in North East of Iran has been selected as study area. The region has been sub-divided into three zones based on transmissivity (T) and electrical conductivity (EC) values. The BDN parameters: prior probabilities and Conditional Probability Tables - CPTs) have been identified for each of the three zones. Three groups of management scenarios have been developed based on the two decision variables including “Crop pattern” and “Domestic water demand” across the three zones of the study area: 1) status quo management for all three zones represent current conditions; 2) the effect of change in cropping pattern on management endpoints and 3) the effect of future increased domestic water demand on management endpoints. The outcomes arising from implementing each scenario have been predicted by use of the constructed BDN for each of the zones. Results reveal that probability of drawdown in groundwater levels of southern areas is relatively high compared with other zones. Groundwater withdrawal from northern and northwestern areas of the study area should be limited due to the groundwater quality problems associated with shallow groundwater of these two zones. The ability of the Bayesian Decision Network to take into account key uncertainties in natural resources and perform meaningful analysis in cases where there is not a vast amount of information and observed data available – and opportunities for enabling inputs for the analysis based partly on expert elicitation,emphasizes key advantages of this approach for groundwater management and addressing the groundwater related problems in a data-scarce area.

Suggested Citation

  • Hadis Mohajerani & Majid Kholghi & Abolfazl Mosaedi & Raziyeh Farmani & Amir Sadoddin & Markus Casper, 2017. "Application of Bayesian Decision Networks for Groundwater Resources Management Under the Conditions of High Uncertainty and Data Scarcity," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(6), pages 1859-1879, April.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:6:d:10.1007_s11269-017-1616-7
    DOI: 10.1007/s11269-017-1616-7
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    References listed on IDEAS

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    1. 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.
    2. Yan Han & Yue-Fei Huang & Guang-Qian Wang & Imran Maqsood, 2011. "A Multi-objective Linear Programming Model with Interval Parameters for Water Resources Allocation in Dalian City," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(2), pages 449-463, January.
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    2. Javad Shafiee Neyestanak & Abbas Roozbahani, 2021. "Comprehensive Risk Assessment of Urban Wastewater Reuse in Water Supply Alternatives Using Hybrid Bayesian Network Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(14), pages 5049-5072, November.

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