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OPTIWAM: An Intelligent Tool for Optimizing Irrigation Water Management in Coupled Reservoir–Groundwater Systems

Author

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  • T. Fowe
  • I. Nouiri
  • B. Ibrahim
  • H. Karambiri
  • J. Paturel

Abstract

An approach based on a real coded Genetic Algorithm (GA) model was used to optimize water allocation from a coupled reservoir-groundwater system. The GA model considered five objectives: satisfying irrigation water demand, safeguarding water storage for the environment and fisheries, maximizing crop water productivity, protecting the downstream ecosystem against elevated soil salinity and hydromorphic issues, and reducing the unit cost of water. The model constraints are based on hydraulic and storage continuity requirements. The objectives and constraints were combined into a fitness function using a weighting factor and the penalty approaches. The decision variable was water allocation for irrigation demand from reservoir and groundwater. The irrigation water demands around the reservoir were estimated using the Food and Agriculture Organization (FAO) Penman-Monteith method in the water evaluation and planning (WEAP) software. The deterministic GA model was coded using Visual Basic 6 and a new tool for irrigation water management optimization (OPTIWAM) was developed. To validate the applicability of the deterministic model for the operation of coupled reservoir-groundwater systems, the Boura reservoir (in the center-west region of Burkina Faso) and the downstream irrigation area were used as a case study. Results show that the proposed methodology and the developed tool are effective and useful for determining optimal allocation of irrigation water. Furthermore, the methodology and tool can improve water resources management of coupled reservoir-groundwater systems. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • T. Fowe & I. Nouiri & B. Ibrahim & H. Karambiri & J. Paturel, 2015. "OPTIWAM: An Intelligent Tool for Optimizing Irrigation Water Management in Coupled Reservoir–Groundwater Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3841-3861, August.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:10:p:3841-3861
    DOI: 10.1007/s11269-015-1032-9
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    2. B. Rousta & S. Araghinejad, 2015. "Development of a Multi Criteria Decision Making Tool for a Water Resources Decision Support System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5713-5727, December.
    3. Ajay Singh, 2016. "Optimal Allocation of Resources for Increasing Farm Revenue under Hydrological Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(7), pages 2569-2580, May.
    4. C. Dai & Y. P. Cai & W. T. Lu & H. Liu & H. C. Guo, 2016. "Conjunctive Water Use Optimization for Watershed-Lake Water Distribution System under Uncertainty: a Case Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4429-4449, September.
    5. Wen-Ping Tsai & Yen-Ming Chiang & Jun-Lin Huang & Fi-John Chang, 2016. "Exploring the Mechanism of Surface and Ground Water through Data-Driven Techniques with Sensitivity Analysis for Water Resources Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4789-4806, October.

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