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Water scarcity, the intensification of agricultural nonpoint pollution, and continuous deterioration of the ecosystem are serious problems in arid areas. Optimal allocation of water resources is the key way to achieve sustainable and efficient development of irrigated agriculture. An integrated multiobjective optimization model considering water-balance processes was developed under shallow groundwater environments to enhance comprehensive objectives and obtain optimal irrigation solutions. A simulation module of the physical processes of water movement among crop root zones, soil water, and groundwater aquifers, and a multiobjective optimization module of irrigation water allocation were incorporated into a general framework. It has the following advantages in terms of (1) illustrating variations of agricultural hydrological parameters (e.g., soil water content, groundwater depth, groundwater-based evaporation, and so on) in water movement processes; (2) obtaining a set of noninferior decision-making solutions through a modified nondominated sorting genetic algorithm (NSGA-II), which can maximize the interests for multiple parties; and (3) generating optimal solutions of irrigation water allocation and alleviating water scarcity, improving irrigation water productivity, and reducing negative environmental effects. To demonstrate its applicability, it was applied to optimize irrigation water allocation in the Jiefangzha Irrigation Subarea (JIS), China. It was firstly split into 44 irrigation subsystems, and each subsystem was assumed to be a homogeneous unit in meteorology, soil texture, and groundwater. Under the water competition between subsystems and rigid constraints of water availability, optimized results enhanced net economic benefits by 14.7%, saved 7.74% of irrigation water amount, and improved irrigation water productivity by 13% compared with status quo. Meanwhile, it can reduce graywater footprint of grain production by 58% compared with the average level of Hetao Irrigation District, China. [ABSTRACT FROM AUTHOR]

Due to the complex feedback cycle relationship among water resources, energy, and food, the agricultural water management mode and concept with a single object or two objects as the core can no longer meet the inherent requirements of the coordinated, sustained, and healthy development of economy and society. A two-stage multiobjective decision-making method based on a θ -dominance evolutionary algorithm (θ -DEA) is proposed for the optimization of agricultural systems with a water-energy-food nexus. First, the Pareto noninferior solution set of the model is obtained by the evolutionary algorithm based on θ -DEA. Then, the economic benefit and carbon emission of each solution are evaluated, and the optimal solution is selected by using the multiobjective decision-making method based on interval-valued hesitant fuzzy sets and the technique for order preference by similarity to ideal solution (TOPSIS). Through a case study of the Hetao Irrigation District (HID), the best optimization results are obtained when the carbon footprint is 4.16×109 kg CO2 eq, and the economic benefit is 8.09×109 yuan. The example shows that the model can well select the optimal allocation solution of water resources, which not only has high economic benefits and low pollution discharge but also meets the subjective wishes of decision-makers. The model has good practicability and stability. [ABSTRACT FROM AUTHOR]