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The Implementation of a Bayesian Network for Watershed Management Decisions

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  • Ahmed Said

Abstract

Recently, the U.S. EPA issued the 303(d) list of impaired waters in Idaho State that contained the causes of impairment. This 303(d) list provides useful information that can be used to determine the Total Maximum Daily Loads (TMDLs). Implementation of TMDLs should result in pollutant reductions, which, in turn can lead to the restoration of these water bodies. Flow alteration is one of the potential sources of impairments in the Big Lost River in south-central Idaho, which have some negative impacts on the water quality and beneficial uses. Flow in the Big Lost River is altered, both in quantity and quality, and this reduces recreation activities, affects the fish assemblage, and changes the composition and relative abundance of aquatic species. The effect of riparian vegetation is another factor that needs to be predicted. In addition, three conservation schemes (construction of upstream reservoirs, downstream reservoirs, and canal linings) were proposed to restore flow in the downstream reaches of the river and compensate for water loss during the low flood seasons. However, there is no single predictive model that can be used to appropriately represent each of these issues as management decisions. In this paper, an expert system in the form of a Bayesian network, a graphical diagram of nodes and arcs, was implemented to examine all significant water management variables and relationships among these variables. Lining the irrigation canals was found to be the best scheme, followed by constructing an upstream reservoir. The TMDLs would benefit the water quality in the watershed but would not significantly increase the water quantity and solve the flow alteration problem. Consequently, this can be used to determine the sequence of decisions that can be taken in the future. Copyright Springer Science + Business Media, Inc. 2006

Suggested Citation

  • Ahmed Said, 2006. "The Implementation of a Bayesian Network for Watershed Management Decisions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(4), pages 591-605, August.
    • Handle: RePEc:spr:waterr:v:20:y:2006:i:4:p:591-605
    DOI: 10.1007/s11269-006-3088-z
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    References listed on IDEAS

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    1. Z. Kundzewicz & L. Somlyódy, 1997. "Climatic Change Impact on Water Resources in a Systems Perspective," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 11(6), pages 407-435, December.
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    Cited by:

    1. José-Luis Molina & Santiago Zazo, 2017. "Causal Reasoning for the Analysis of Rivers Runoff Temporal Behavior," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(14), pages 4669-4681, November.
    2. Xinghui Xia & Zhifeng Yang & Yuxiang Wu, 2009. "Incorporating Eco-environmental Water Requirements in Integrated Evaluation of Water Quality and Quantity—A Study for the Yellow River," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(6), pages 1067-1079, April.
    3. 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.
    4. 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.
    5. Rahman Khatibi & Farzin Salmasi & Mohammad Ghorbani & Hakimeh Asadi, 2014. "Modelling Energy Dissipation Over Stepped-gabion Weirs by Artificial Intelligence," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 1807-1821, May.
    6. Qiuxiang Jiang & Tian Wang & Zilong Wang & Qiang Fu & Zhimei Zhou & Youzhu Zhao & Yujie Dong, 2018. "HHM- and RFRM-Based Water Resource System Risk Identification," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(12), pages 4045-4061, September.
    7. José-Luis Molina & Santiago Zazo & Ana-María Martín-Casado & María-Carmen Patino-Alonso, 2020. "Rivers’ Temporal Sustainability through the Evaluation of Predictive Runoff Methods," Sustainability, MDPI, vol. 12(5), pages 1-21, February.

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