Accurate forecasting of annual runoff time series is of great significance for water resources planning and management. However, considering that the number of forecasting factors is numerous, a single forecasting model has certain limitations and a runoff time series consists of complex nonlinear and nonstationary characteristics, which make the runoff forecasting difficult. Aimed at improving the prediction accuracy of annual runoff time series, the principal components analysis (PCA) method is adopted to reduce the complexity of forecasting factors, and a modified coupling forecasting model based on multiple linear regression (MLR), back propagation neural network (BPNN), Elman neural network (ENN), and particle swarm optimization-support vector machine for regression (PSO-SVR) is proposed and applied in the Dongbei Hydrological Station in the Ganjiang River Basin. Firstly, from two conventional factors (i.e., rainfall, runoff) and 130 atmospheric circulation indexes (i.e., 88 atmospheric circulation indexes, 26 sea temperature indexes, 16 other indexes), principal components generated by linear mapping are screened as forecasting factors. Then, based on above forecasting factors, four forecasting models including MLR, BPNN, ENN, and PSO-SVR are developed to predict annual runoff time series. Subsequently, a coupling model composed of BPNN, ENN, and PSO-SVR is constructed by means of a multi-model information fusion taking three hydrological years (i.e., wet year, normal year, dry year) into consideration. Finally, according to residual error correction, a modified coupling forecasting model is introduced so as to further improve the accuracy of the predicted annual runoff time series in the verification period.

The application of automatic control to irrigation canals is an important means of improving the efficiency of water delivery. The Middle Route Project (MRP) for South-to-North Water Transfer, the largest water transfer project in China, is currently under manual control. Given the complexity of the MRP, there is an urgent need to adopt some form of automatic control. This paper describes the application of model predictive control (MPC), a popular real time control algorithm particularly suited to the automatic control of multi-pool irrigation water delivery systems, to the MRP using a linear control model. This control system is tested in part of the MRP by means of numerical simulations. The results show that the control system can deal with both known and unknown disturbances, albeit with a degree of resonance in some short pools. However, it takes a long time for the MRP to reach a stable state under the MPC system and the calculation time for the whole MRP network would be too long to satisfy the requirements of real-time control. Suggestions are presented for the construction of an automatic control system for the MRP.

Improving agricultural water use efficiency (WUE) and reducing the proportion of agricultural water consumption are of great significance for coping with the water crisis in the world, particularly in northwest China. However, considering the lack of WUE indicators for the selection and an evaluation of system construction in Xinjiang, the implementation of the strictest water resources management system in this province has been seriously restricted. In order to evaluate the agricultural water utilization situation of 14 administrative regions in Xinjiang, a synthetical evaluation model is proposed combining the analytic hierarchy process method and the fuzzy evaluation method. Firstly, an evaluation system composed of the target layer (i.e., water use efficiency), the criterion layer (i.e., water use situation, engineering measure, planting structure) and the index layer (e.g., utilization coefficient of irrigation water, weighting irrigation quota, grain production per cubic meter of irrigation water, etc.) is constructed. Secondly, a classification standard of evaluation indicators is put forward and a fuzzy evaluation model is introduced into calculate agricultural WUE. Finally, key evaluation indicators that lead to these administrative regions with very low WUE are identified, and discussions on improving agricultural WUE in Kashgar are made. The evaluation results of this study are beneficial for providing support for reforming agricultural water use and promoting sustainable agricultural development in Xinjiang.

In this paper, automatic control of the water level in an irrigation canal by automatic regulation of intermediate gates was studied. Previous scholars have proposed a water level difference control strategy that works to keep relative deviations in all pools the same for a particular situation where the operator does not have full control over the canal inflow, with the centralized linear quadratic regulator (LQR) control method used. While in practice, the deviation tolerance of pools may differ in some canals which limits the applicability of the control strategy. In this work, a weight coefficient was added to the deviation and the algorithm was improved to keep the relative deviations to certain proportions. The model predictive control (MPC) method was then used with this improved control strategy and was compared to the LQR control method using the same control strategy. The results showed that the improved strategy can keep the water level deviations in all pools to certain proportions, as is our objective. Also, under this difference control strategy, the MPC method greatly improved the control performance compared to the LQR control method.

Projecting surface runoff is a meaningful task for rational allocation and optimal scheduling of water resources. Aimed at exploring the impact of climate change on surface water resources, the basin-scale water-balance model coupled with the Budyko-type equation was developed for the upper Ganjiang River Basin (UGRB). Studies demonstrated that the Budyko-CY function is the optimal water-balance model, with a Nash–Sutcliffe efficiency (NSE) of 0.843 in the calibration period and 0.653 in the validation period, respectively. The increase in temperature by 1 °C may bring about a 2.5–4.7% runoff decline, while the 10% increase in precipitation may lead to a 12.1–14.3% runoff ascent. The annual mean temperature is expected to grow by 0.69, 0.68 and 0.97 °C in the next 30 years relative to that during the reference period, respectively. Similarly, the surface runoff is estimated to increase by 8.4, 6.5 and 5.0% on a multi-year average scale, respectively. This study is beneficial to provide possibilities for climate scenarios that may occur in the future, and the results presented herein are capable of giving a reference for the planning and management of water resources in the UGRB.