Assessment of the optimized scenarios for economic-environmental conjunctive water use utilizing gravitational search algorithm

Holding proper ratio between groundwater extraction and surface water allocation in conjunctive use of water resources may be a desirable way for simultaneously controlling the aquifer sustainability and meeting the increasing agricultural water demands. This paper focuses on maximizing the agricultural net benefits (NB) while avoiding the vital groundwater resources to excessively withdraw when utilized to supply the water demands and provide desirable net benefits. For this purpose, the gravitational search algorithm (GSA) is employed as the optimizer and linked to the artificial neural network (ANN) as the simulator of the groundwater level (GWL) variations. The study area is the west of the irrigation network of Qazvin plain in Iran. This area is divided into two zones: low-GWL-drawdown zone (zone 1); and high-GWL-drawdown zone (zone 2). For each zone, nine scenarios are defined considering three climatic years each of which is assigned three different crop patterns, totally resulting in 18 scenarios. The results show that the simulation-optimization models can decrease the groundwater drawdown in all scenarios by 0.77-1.84 m in zone 1, and by 1.28-1.97 m in zone 2. Furthermore, by replacing the existing crop pattern with two other ones, NB is increased by 35.5-53.7% in the zone 1 and by 24.9-59.7% in the zone 2. Additionally, the net benefit per unit water consumption volume (NBPD) is increased by 18.9-32.2% in zone 1 and by 9-52.6% in zone 2.

Automated summary

This study effectively reduced groundwater drawdown in the western part of the Qazvin plain irrigation network in Iran by utilizing gravitational search algorithm and artificial neural network, while increasing agricultural net benefits and net benefits per unit water consumption.