Water is an essential recurring input for agriculture productivity and key to food security. Its availability in space and time has been a matter of great concern in many parts of the world. In India there is growing awareness about the scarcity of water under the increased water demand from agriculture and other sectors. Further, the projected reduction in water availability to the agriculture sector from the share of 89

Sustainable use and management of nutrients is an important issue for food, energy and water systems. The close connections between the three systems, reflected by the “nexus” concept, warrant an integrated approach to nutrients management across the nexus. In this paper, dynamic modelling of nutrient flows in a local food-energy-water system is presented and applied to a simplified case study. The model was used to simulate several scenarios affecting nitrogen flows and stocks to assess the impact of a) the level of local wheat production, b) the selection of energy generation technology, and c) the management of available nutrient resources (digestate and straws). The simulation results showed that varying the proportion of locally produced wheat significantly affects the surface runoff and the nitrogen content in a local water body, with the latter increasing by nearly 70% in 50 years if about half of the wheat consumed is produced locally as opposed to being 100% imported. The introduction of anaerobic digestion as an energy generation option helps to supply more electricity, reduce the imported fertiliser, and also significantly reduce the landfilled nitrogen nutrient by up to 60 times, due to the reuse of the anaerobic digestate. On the other hand, a balanced consideration should be given between using the straw as fertiliser and as feedstock for energy generation. This work offers a first analysis of the food-energy-water nexus with a focus on nutrient flows and stocks. The modelling approach has the potential to inform holistic decision making with respect to nutrient usage, efficiency and the related environmental impact in the design of a local system for meeting the demand for food, energy and water.

Rice production not only consumes large amounts of irrigation water and fertilizer, but also poses a high risk of water pollution by delivering nitrogen (N) through surface runoff. To ensure sustainable rice production, many water-saving irrigation managements have been proposed and implemented, but drainage water managements receive far less attention and need to be further explored. This study aimed to determine the paddy drainage optimization management and assess its potential to water and food security in China via different scale methods (from pot and field experiments to national assessment). The national investigation of water and N fertilizer use in paddy fields implied that diffuse N pollution was expected to continue increasing, especially in the Yangtze river basin. Two-years field experiments at typical sites identified that the tillering and jointing–booting stages were critical risk stages for N runoff loss, and pot experiments on the critical stages were conducted to determine the optimal drainage water level without yield reduction. Then, the applicability of paddy drainage optimization was verified and evaluated by drainage optimization field experiment and precipitation characteristics analysis. Finally, the potential of drainage optimization on mitigating N runoff loss was estimated by scenario analysis at the national scale. After implementing paddy drainage optimization in field experiments, surface runoff and nitrogen runoff loss decreased by 27.97–78.94% and 35.17–67.95%, respectively, without affecting rice yield. By full implementation of the optimal drainage and fertilization management, N runoff loss could be reduced by 0.19 Tg yr⁻¹ at the national scale. These results suggest that paddy drainage optimization is an agro-ecosystems friendly water management for sustainable rice production, and has notable potential to ensure water and food security in China.

With continuous population increase and economic growth, challenges on securing sufficient energy, water, and food supplies are amplifying. Water plays the most important role in the energy-water-food (E-W-F) nexus issue such as energy supply (clean hydropower energy generation), water supply (drinking water), and food supply (agricultural irrigation water). Therefore, water quantity simulation & forecasting become an important issue in E-W-F nexus problem. Water quantity simulation & forecasting model, such as rainfall-runoff (RR) hydrological model has become a useful tool which can significantly improve efficiency of the hydropower energy generation, water supply management, and agricultural irrigation water utilization. The accuracy and reliability of the water quantity simulation & forecasting model are significantly affected by the model parameters. Therefore, demand of effective and fast model parameter optimization tool for solving the E-W-F nexus problem increases significantly. The shuffled complex evolution developed at University of Arizona (SCE-UA) has been recognized as an effective global model parameter optimization method for more than 20years and is highly suited to solve the E-W-F nexus problem. However, the computational efficiency of the SCE-UA dramatically deteriorates when applied to complex E-W-F nexus problem. For the purpose of solving this conundrum, a fast parallel SCE-UA was proposed in this paper. The parallel SCE-UA was implemented on the novel heterogeneous computing hardware and software systems which were constituted by the Intel multi-core CPU, NVIDIA many-core GPU, and PGI Accelerator Visual Fortran (with OpenMP and CUDA). Performance comparisons between the parallel and serial SCE-UA were carried out based on two case studies, the Griewank benchmark function optimization and a real world IHACRES RR hydrological model parameter optimization. Comparison results indicated that the parallel SCE-UA outperformed the serial one and has good application prospects for solving the water quantity simulation & forecasting model parameter calibration in the E-W-F nexus problem.