Coupled stochastic soil moisture simulation‐optimization model of deficit irrigation

This study presents an explicit stochastic optimization‐simulation model of short‐term deficit irrigation management for large‐scale irrigation districts. The model which is a nonlinear nonconvex program with an economic objective function is built on an agrohydrological simulation component. The simulation component integrates (1) an explicit stochastic model of soil moisture dynamics of the crop‐root zone considering interaction of stochastic rainfall and irrigation with shallow water table effects, (2) a conceptual root zone salt balance model, and 3) the FAO crop yield model. Particle Swarm Optimization algorithm, linked to the simulation component, solves the resulting nonconvex program with a significantly better computational performance compared to a Monte Carlo‐based implicit stochastic optimization model. The model has been tested first by applying it in single‐crop irrigation problems through which the effects of the severity of water deficit on the objective function (net benefit), root‐zone water balance, and irrigation water needs have been assessed. Then, the model has been applied in Dasht‐e‐Abbas and Ein‐khosh Fakkeh Irrigation Districts (DAID and EFID) of the Karkheh Basin in southwest of Iran. While the maximum net benefit has been obtained for a stress‐avoidance (SA) irrigation policy, the highest water profitability has been resulted when only about 60% of the water used in the SA policy is applied. The DAID with respectively 33% of total cultivated area and 37% of total applied water has produced only 14% of the total net benefit due to low‐valued crops and adverse soil and shallow water table conditions.