Inter-Regional Coordination to Improve Equality in the Agricultural Virtual Water Trade

The Drag Effect of Water Resources on China’s Regional Economic Growth: Analysis Based on the Temporal and Spatial Dimensions

Studying the influencing factors of the drag effect of water resources and its temporal–spatial variation can help governments to solve the problem of water resource constraints on the economic growth of different regions. Based on Romer’s hypothesis, this paper uses panel data to empirically analyze the drag effect of water resources in China’s 31 provinces from 1987 to 2017. The research shows that: (1) Water resources have certain constraints on the economic growth of each region. Regional economic growth has declined by 0.23% (eastern), 0.07% (western), 0.43% (central) and 0.09% (northeastern) annually. (2) In provinces with rapid labor growth, water resources have a greater impact on economic growth. In provinces with low labor growth rates, the drag effect of water resources on economic growth is affected by the capital stock of the region. (3) Through the analysis of the water drag effect in different time periods, this paper finds that in some periods, the government’s mobilization of water resources for the economic growth in some regions will not only promote the transfer of labor between regions, but also cause changes in the regional water resources. According to the results of this paper, the following conclusions can be drawn: (1) It is necessary to vigorously develop water-saving agriculture, improve technical efficiency, and reduce the strong dependence of economic growth on water resources in the provinces which has a strong water drag effect on economic growth; (2) Provinces with moderate water resource constraints should take the lead in deploying strategic emerging industries, and accelerate the development of the tertiary industry; (3) Provinces with weakly water resource restrictions can promote the development of capital-based industries. Not only can the development of the economy be rational, but it can also reduce the economy’s dependence on resources, thereby achieving the sustainable use of water resources and sustainable economic growth.

A Mixed Integer Linear Programming Method for Optimizing Layout of Irrigated Pumping Well in Oasis

Optimization of irrigation well layout plays a vital role in the rational utilization of groundwater and to balance the water–energy nexus, especially in arid irrigation districts. This study proposes the mixed integer linear programming model (MILP) for well layout optimization with minimum well irrigation costs. This model efficiently establishes a link between irrigation area and wells to express the constraints of ensuring that irrigation area can be covered with optimal wells by using grid points to represent the irrigation area. It also uses the special ordered sets (SOS) modeling tool to decompose the mixed integer nonlinear programming into a mixed integer linear programming by assigning SOS-constrained weights to discrete points of a nonlinear function. This method was used in Cele Oasis of the Tarim Basin of the Xinjiang Province, an arid region in northwestern China. Since the original well layout was already established, different economic criteria like implicit cost and explicit cost were considered and two optimization results were yielded. The results showed that (1) the implicit cost optimization (ICO) and explicit cost optimization (ECO) reduced total costs by 7.64% and 3.56% compared with the condition of without optimization; and (2) the ICO and ECO reduced the optimal number of wells by 52.89% and 10.74% compared with the existing number of wells. Based on the analysis of the results, it is suggested that the manager should close uneconomical wells after determining the economic criteria. This method for well layout optimization can assist managers to make more rational plans for irrigation systems to exploit groundwater more efficiently, economically, and in a more environmentally friendly manner.

Improving Estimation of Cropland Evapotranspiration by the Bayesian Model Averaging Method with Surface Energy Balance Models

Evapotranspiration (ET) is one of the key components of the global hydrological cycle. Many models have been established to obtain an accurate estimation of ET, but the uncertainty of each model has not been satisfactorily addressed, and the weight determination in multi-model simulation methods remains unclear. In this study, the Bayesian model averaging (BMA) method was adopted to tackle this issue. We explored the combination of four surface energy balance (SEB) models (SEBAL, SSEB, S-SEBI and SEBS) with the BMA method by using Landsat 8 images over two study areas in China, the Huailai flux station (semiarid region) and the Sidaoqiao flux station (arid/semiarid region), and the data from two stations were used as validation for this method. The performances of SEB models and different BMA methods is revealed by three statistical parameters (i.e., the coefficient of determination (R2), root mean squared error (RMSE), and the Nash-Sutcliffe efficiency coefficient (NSE)). We found the best performing SEB model was SEBAL, with an R2 of 0.609 (0.672), RMSE of 1.345 (0.876) mm/day, and NSE of 0.407 (0.563) at Huailai (Sidaoqiao) station. Compared with the four individual SEB models, each of the BMA methods (fixed, posterior inclusion probability, or random) can provide a more accurate and reliable simulation result. Similarly, in Huailai (Sidaoqiao) station, the best performing BMA random model provided an R2 of 0.750 (0.796), RMSE of 0.902 (0.602) mm/day, and NSE of 0.746 (0.793). We conclude that the BMA method outperformed the four SEB models alone and obtained a more accurate prediction of ET in two cropland areas, which provides important guidance for water resource allocation and management in arid and semiarid regions.