Development and application of a stochastic optimization model for groundwater management: crop pattern and conjunctive use consideration

The need for irrigation water in arid and semi-arid regions is mostly supplied by groundwater. Furthermore, the agricultural development in these areas is not generally based on a comprehensive plan, which can cause aquifers depletion. On the other hand, to properly manage an aquifer and to have an optimal crop plan, the stochastic nature of the different parameters of a groundwater system such as groundwater recharge and water demands should be taken into consideration. In this paper, we develop an explicit stochastic optimization model for Firouzabad aquifer in Iran. This formulation is based on the first and second moment analysis for groundwater head which has been initially proposed for surface water resources management by Fletcher and Ponnambalam. We extend the model to create a new random withdrawal policy for conjunctive use setting in which the randomness in available precipitation is taken into account. The interesting point is that the model provides the respective probabilities of shortage and surplus without imposing the extra decision variables into the optimization model. A genetic-based algorithm is used to solve the stochastic nonlinear and non-convex formulation. The outcome results indicate that the current crop pattern should be changed, that is, the allocated areas of some crops have to be meaningfully reduced. Finally, to validate our model efficiency, we demonstrate that how much close the statistical characteristics obtained from the optimization model are to those estimated from the Monte Carlo simulation. Furthermore, the optimal benefits obtained using the proposed optimization model are as suitable as the benefits achieved using the corresponding Monte Carlo-based optimization model.