A cloud-based dual-objective nonlinear programming model for irrigation water allocation in Northwest China

Abstract Agricultural water management has become an essential problem in recent years due to the increasing water demands. Irrigation water resources allocation is a dynamic decision making process associated with various uncertainties, which often exist in a complex and composite format. In this study, a new uncertainty quantification technique, the cloud model, is introduced to a dual-objective nonlinear programming (DONP) framework, and a cloud-based dual-objective nonlinear programming (CDONP) model is developed to support irrigation water allocation and agricultural water planning under composite uncertainties. The cloud model is applied to address the complex composite uncertainties associated with reference evapotranspiration (ET0) and surface water availability (SWA). A case study of the Yingke irrigation district (YID) in Northwest China is conducted to demonstrate the applicability of the developed model. The results show that the net economic profit (ENP) and irrigation system efficiency (ISE) are influenced by ET0 more than SWA. The obtained results are also compared to those of a traditional dual-objective nonlinear programming model to illustrate the advantages of the proposed CDONP model. In addition, four water shortage scenarios are built and discussed for risk analysis.