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Safavi, H.R., Darzi, F., and Marino, M. A. (2010). Simulation-optimization modeling of conjunctive use of surface water and groundwater, J. of Water Resources Management,

Simulation-Optimization Modeling of Conjunctive Use of Surface Water and Groundwater


Hamid R. Safavi1, F. Darzi2, and M.A. Mariño3



1- Assistant Professor, Dept. of Civil Engineering, Isfahan University of Technology, Isfahan, Iran. E-mail:

2- Graduate Student, Dept. of Civil Engineering, Isfahan University of Technology, Isfahan, Iran. E-mail:

3- Distinguished Professor, Dept. of Civil and Environmental Engineering, University of California, Davis, CA 95616. E-mail:





Water resources management in semiarid regions with low precipitation and high potential of evapotranspiration is a great challenge for managers and decision makers. In those regions, both sources of water should be managed conjunctively so as to minimize shortages of water in dry seasons. In conjunctive use, the difficulty increases as one must represent the response of both systems interactions, and develop management strategies that simultaneously address surface water and aquifer regulation. This paper focuses on the simulation-optimization for conjunctive use of surface water and groundwater on a basin-wide scale, the Najafabad plain in west-central Iran. A trained artificial neural network model is developed as a simulator of surface water and groundwater interaction while a genetic algorithm is developed as the optimization model. The main goal of the simulation-optimization model is to minimize shortages in meeting irrigation demands for three irrigation systems subject to constraints on the control of cumulative drawdown of the underlying water table and maximum capacity of surface irrigation systems. To achieve the main goal, three scenarios are presented. Results of the proposed model demonstrate the importance of the conjunctive use approach for planning the management of water resources in semiarid regions.

Keywords- Surface water; Groundwater; Conjunctive use; Semiarid regions; Optimization; Simulation; Genetic algorithm; Artificial neural network.


DOI 10.1007/s11269-009-9533-z

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