Due to population growth, environmental pollution and climate change, the lack of water resources has become a critical factor which threatens sustainable agricultural development. Reasonable irrigation scheduling strategies can reduce the waste of water and enhance agricultural water-use efficiency. In the present study, the decomposition-coordination theory was adopted to analyze the hierarchical canal system. A novel nonlinear multi-level multi-objective optimization model for complex canal systems was established, taking account of the multiple demands from decision makers and realistic factors of canal operation. An interactive method of the technique for order preference using similarity algorithm and genetic algorithm was proposed to solve the developed model. The developed model was successfully applied for the operational strategy making of a canal system located in the arid area of northwest China. The results indicated that the optimization model could help shorten the operational duration by two days, achieve about 26% reduction of irrigation water consumption, and improve the efficiency of water delivery from 0.566 to 0.687. That will be very favorable for the promotion of the agricultural water productivity, the relief of water shortage crisis and the sustainable development of agriculture. The outcomes can provide a wide range of support for decision making and make irrigation decision-making more scientific and systematic.

To optimize the water distribution of three industries based on the water demand prediction under multiple uncertainties, a fuzzy credibility-constrained interval two-stage stochastic programming (FCITSP) model base on the Cobb-Douglas production (CD) function was developed. The CD-FCITSP model integrated fuzzy credibility-constrained programming (FCP), an interval two-stage stochastic programming (ITSP) method and CD function. The developed model could deal with uncertainties with interval, random, and fuzzy features, reflect tradeoffs between different water use sectors, and provide water managers in arid regions with sustainable and reasonable water-allocation schemes under different credibility scenarios of local policies. Moreover, the relationships between economic benefits and water consumption were taken into consideration via the Cobb-Douglas production function. The developed model was applied to support the optimal allocation of limited water resources in Minqin County, northwest China. The obtained solution demonstrated that the developed method could help local water managers to effectively allocate limited water under multiple uncertainties and different credibility scenarios. In addition, water use efficiency could be promoted and the emissions of major pollutants could be reduced. The developed method could be extended to water management practices in other arid regions.

This paper developed an interval multiobjective two-stage stochastic programming (IMO-TSP) model. The model integrated interval two-stage stochastic programming (ITSP), interval linear multiobjective programming (IMOP), and the analytic hierarchy process (AHP) method. The IMO-TSP model could not only address the conflicts of multiple objectives under uncertainty but could also obtain the optimal allocation results based on the water managers’ experience, knowledge, and local policies. In addition, a solution method was proposed to solve the IMO-TSP model by integrating the Zimmermann’s fuzzy method and the typical ITSP solution method. The developed model was applied to a real-world case for supporting the allocation of limited water resources to various water-use sectors. From the results analysis, the single objective model with a certain indicator can obtain the maximum value of that objective but can hardly take other significant factors into consideration. Conversely, the IMO-TSP model can effectively balance the three objectives of maximizing the system net benefit, minimizing the total COD discharge, and minimizing the water deficit by obtaining the best coordinated satisfaction. The obtained coordination satisfaction by the IMO-TSP model was [0.74, 0.83] in this study, which indicates that the results obtained by the developed model can allocate limited water resources to various water users with a high degree of coordination among the three objectives under different flow levels. The developed model can help the water managers of arid regions to develop the desired and reasonable water resources management policies under uncertainty.

With population growth and water scarcity, efficient crop production has drawn attention worldwide. In the Hexi Corridor, the largest production base of maize seed in China, it is desired to develop efficient irrigation strategies for seed maize. Considering the double criteria of yield and seed quality, an integrated water-saving and quality-guarantee uncertain programming approach (IWQUP) was developed in this study to help with agricultural sustainable development. The IWQUP combined deficit irrigation theory, soil-water balance, and multiple uncertainties. The water-flowering model (WFM) and kernel weight prediction model with water production functions were used to reflect the relationship among water consumption, crop yield, and seed quality. Meanwhile, to deal with the widespread existence of uncertainties in nature and the decision-making process, interval programming and fuzzy programming were integrated within the framework of IWQUP, along with the use of the genetic algorithm and Monte Carlo simulation. The results showed that when the climatic condition is moist, decision-makers may use a low tolerance level in order to reduce the water waste, enhance the water use efficiency, and guarantee a relatively high seed quality. When the climate is harsh, a high tolerance level to water use constraints is recommended in order to guarantee yield. In addition, optimistic decision-makers could choose a relatively high tolerance level, but in moist years they should be careful in order to avoid water waste. The established model was compared with three other models to represent its practicability for offering decision-makers various references under different scenarios.

Different from the traditional irrigation optimization model based only on the water production function, in this study, we explored the water–yield–quality–benefit relationship and established a general irrigation scheduling optimization framework. To establish the framework, (1) an artificial neural network coupled with ensemble empirical mode decomposition (EEMD-ANN) is used to decompose the original price time series into several subseries and then forecast each of them; (2) factor analysis and a technique for order of preference by similarity to ideal solution (FA-TOPSIS), as an integrated evaluation method, is used to comprehensively evaluate the fruit quality parameters; and (3) regression analysis is used to simulate water-yield and water-fruit quality relationships. The model is applied to a case study of greenhouse tomato irrigation schedule optimization. The results indicate that EEMD-ANN can improve the accuracy of price forecasting. Jensen and additive models are selected to simulate the relationships of tomato yield and quality with water deficit at various stages. Besides, the model can balance the contradiction between higher yields and better quality, and optimal irrigation scheduling is obtained under different market conditions. Comparison between the developed model and a traditional modeling approach indicates that the former can improve net benefits, fruit quality, and water use efficiency. This model considers the economic mechanism of market price changing with fruit quality. Forecasting and optimization results can provide reliable and useful advices for local farmers on planting and irrigation.