Water Resources Management

, Volume 29, Issue 10, pp 3841–3861

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

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

DOI: 10.1007/s11269-015-1032-9

Cite this article as:
Fowe, T., Nouiri, I., Ibrahim, B. et al. Water Resour Manage (2015) 29: 3841. doi:10.1007/s11269-015-1032-9

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.

Keywords

Irrigation Water resources Allocation Optimization Genetic algorithms 

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • T. Fowe
    • 1
  • I. Nouiri
    • 2
  • B. Ibrahim
    • 3
  • H. Karambiri
    • 1
  • J. E. Paturel
    • 1
    • 4
  1. 1.Hydrology and Water Resources LaboratoryInternational Institute for Water and Environmental Engineering (2iE)Ouagadougou 01Burkina Faso
  2. 2.National Institute of Agronomy of TunisiaTunisTunisia
  3. 3.West African Science Service Center on Climate Change and Adapted Land Use (WASCAL)OuagadougouBurkina Faso
  4. 4.IRD-HydroSciences MontpellierUniversité Montpellier 2Montpellier Cedex 5France

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