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

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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.

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Acknowledgements

This work was performed with the support of Gorgan University of Agricultural Sci.& Natural Resources research council.

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Authors and Affiliations

  1. Department of Physical Geography, University of Trier, Campus II, 54296, Trier, Germany

    Hadis Mohajerani & Markus Casper

  2. Department of Irrigation & Reclamation Engineering, Faculty of Agronomy Engineering & Technology, College of Agriculture & Natural Resources, University of Tehran, Karaj, Iran

    Majid Kholghi

  3. Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad (FUM), Mashhad, Iran

    Abolfazl Mosaedi

  4. Centre for Water Systems, University of Exeter, North Park Road, Exeter, EX4 4QF, UK

    Raziyeh Farmani

  5. Faculty of Range land and Watershed Management, Gorgan University of Agricultural Sci & Natural Resources, Golestan Province, Gorgan, Iran

    Amir Sadoddin

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  1. Hadis Mohajerani
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  2. Majid Kholghi
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  4. Raziyeh Farmani
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  5. Amir Sadoddin
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  6. Markus Casper
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Correspondence to Hadis Mohajerani.

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Mohajerani, H., Kholghi, M., Mosaedi, A. et al. Application of Bayesian Decision Networks for Groundwater Resources Management Under the Conditions of High Uncertainty and Data Scarcity. Water Resour Manage 31, 1859–1879 (2017). https://doi.org/10.1007/s11269-017-1616-7

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  • DOI: https://doi.org/10.1007/s11269-017-1616-7

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