Qunchang Liu’s research while affiliated with China Institute of Water Resources and Hydropower Research and other places

In order to optimize the planting structure and use water more efficiently, an inaccurate two-stage planning model is proposed in this paper. This model can not only reflect uncertainty of the probability distribution in the form of the possible distribution interval, but also build a recourse relationship between expected benefits and penalties for failing to achieve target goals. The two-stage planning model, combined with the gray GM (1.1) model, is applied to Daxing district of Beijing to optimize and adjust planting areas of the grain crops, fruits and vegetables, and garden plots in 2020. In the meantime, three scenarios were established for comparative analysis. Results show that after optimization, the economic benefits of above-mentioned three planting areas in Daxing district in 2020 is 3.71 billion CNY, an increase of 348 million from 2016 CNY; the total water consumption is 64.17 million cubic meters, a decrease of 62.79 million cubic meters from 2016. Results indicate that this model method is feasible for optimizing planting structure, and to some extent, can provide decision-making support and a theoretical basis for planting structure optimization and prediction in similar areas to Daxing district.

Full implicit solutions are constructed for all terms of the Saint–Venant equations and advection–dispersion equation. Thereafter, a global coefficient matrix for forming algebraic equations is established to realize the simultaneous solution of both surface water flow and solute transport equations and a full-coupled model between the surface water flow and solute transport is constructed for basin fertigation. This model completely eliminates splitting errors, which exist in common solutions for both of the aforementioned equations. Moreover, the proposed model can take any time steps and is no longer constrained by stability conditions, such as the CFL (Courant, Friedrichs and Lewy) number and Peclet number. These advantages of the full-coupled model obviously improve the computational efficiency and accuracy, and its applicability in practical simulations. Three basin fertigation experiments are conducted to validate the performance of the proposed full-coupled model. The results show that the developed model achieves good fit between the simulated and observed results, and presents more efficiency than the existing model.

Saint-Venant equations are accurately solved by a fully implicit solution, and a numerical model for furrow water flow is proposed with unconditional stability. Furthermore, this numerical model is extended to furrow networks. There are continuous hydraulic connections among multiple furrows by network junctions, thus the surface water flows of all furrows in a furrow network are simultaneously simulated. Then, a fully hydrodynamic coupled model with unconditional stability is constructed for furrow networks. The dam-break problem with an analytical solution is first used to test the proposed model, and a good performance with different time steps is obtained. Meanwhile, two field-scale experiments for a furrow network are selected to validate the proposed model. The validated results show that the proposed model can well simulate water flows of a furrow network and presents good mass conservation ability. Meanwhile, the computational efficiency of the proposed model can satisfy the requirement of irrigation application.

This paper used a back-propagation (BP) neural network to conduct statistical downscaling based on the data (two factors: precipitation and temperature) downscaled by the National Climate Centre of China from over 20 global circulation models (GCMs) and input these downscaled data into the Basin-wide Holistic Integrated Water Assessment (BHIWA) model to analyse irrigation water demand as well as water stress situations in both water quantity and quality in the Yellow River Basin, China. The results of the changes in climate in scenarios A1B, A2 and B1/B2 showed that irrigation water demand will increase and the water situation will become more stressed in 2030 and 2050, though there is still some increase in precipitation in most of the sub-basins in these scenarios. Meanwhile, with the same variation range of precipitation and ET0, ET0 will have a bigger impact on irrigation water demand and water stress situations than precipitation, This implies that ET0 is a more dominant factor influencing water demand and water stress situations than other meteorological parameters such as precipitation. In addition to this, groundwater quality is more vulnerable to climate change than surface water quality and better water management will result in a decrease in irrigation water demand. Copyright (c) 2013 John Wiley & Sons, Ltd.

Quantitative analysis techniques have gained a great deal of popularity with decision‐makers and analysts in recent years, and this is also the case in the hydrology sector. In this paper, the Basin‐wide Holistic Integrated Water Assessment (BHIWA) model developed by the International Commission on Irrigation and Drainage (ICID) was updated by the author to simulate the water balance of the Yellow River Basin in China, as well as to analyse the impacts of land and water use on return flows of this basin under past (1980), present (2000), and future (2030 and 2050) conditions. Stochastic analysis was then carried out using the Monte Carlo simulation method, by randomly selecting sets of values for the probability distributions in the cell values and formulas to quantify the risks in terms of water quantity and quality resulting from return flow. The model amply demonstrates the serious water shortage situation in the future. Developing the strategy of water‐saving measures would greatly enhance the efficiency of irrigation water use, and decrease water withdrawal for irrigation. In addition to this, a possible opportunity to transfer water from the Yangtze River to the Yellow River also came to light. Copyright © 2013 John Wiley & Sons, Ltd.