Collaborative Research: Sustainability in the Food-Energy-Water nexus; integrated hydrologic modeling of tradeoffs between food and hydropower in large scale Chinese and US basins

水对于种植粮食和发电至关重要。 本文以中国黑河流域和美国加州中央谷两个全球重要的农业系统为研究对象，探讨了大尺度水-能系统的复杂性。 黑河和中央谷代表着数十亿美元的经济生产力，每年产生数十亿千瓦时的电力。 虽然这两个盆地在许多方面都很相似（水从高山上流下来滋养下面的作物），但它们在历史和管理方面存在关键差异，提供了许多重要信息。该项目汇集了来自美国和中国的研究人员，以更好地了解这些复杂农业系统中水和能源供应之间的权衡。利用美国和中国团队在当地流域的计算机模拟，数据集和研究，并合作促进我们对这些流域的共同理解。 这里开发和应用的最先进的计算机仿真平台旨在捕捉人类和自然系统之间的联系，这是以前的建模方法所不可能实现的。该项目还旨在通过为美国和中国开发K-12教育材料，并在两国的真实的课堂上进行试点，对下一代水资源使用者、规划者和科学家进行地下水可持续性教育。该项目将帮助我们更好地了解黑河和中央谷等粮食-水-能源系统管理的弱点，以加强它们的发展。 在灌溉农业系统中，水将粮食生产、能源需求和能源生产联系起来。 集中管理的流域通常有地表水灌溉、地下水灌溉和水电生产同时进行。虽然已经有许多大规模灌溉系统的业务研究，大多数工具应用于这些问题的重点是人类系统和简化自然水文。这项研究弥合了这一差距，开发了新的工具，可以模拟复杂的人类和自然系统中的少数相互作用。在这个项目中，国际先进的物理为基础的综合数值模拟的杠杆作用是通过汇集来自美国和中国的两个建模团队完成的。其目标是探讨两个全球重要农业系统-美国加州的中央谷和中国黑河流域-的农业供水、水力发电和环境退化之间的权衡。具体而言，探索（1）粮食和能源系统的脆弱性如何不同，（2）利益冲突可能导致系统效率低下和环境退化，以及（3）将综合水文模型应用于这些人类系统的优势。该项目还试图教育下一代水用户、规划人员和科学家了解地下水的可持续性。项目成果将用于为美国和中国开发K-12教育材料，并将在真实的课堂上试用。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Water is critical for growing food and generating power. This study deals with two globally important agricultural systems, the Heihe River Basin in China and the Central Valley of California, USA, that exemplify the complexities of large scale water-energy systems. The Heihe and the Central Valley represent billions of dollars in economic productivity and produce billions of kilowatt hours of electricity every year. While the two basins are in many ways similar (water flows from high in the mountains to nourish crops below), there are key differences in their history and management that provides many important information. This project brings together researchers from the US and China to better understand tradeoffs between water and energy supply in these complex agricultural systems. Advantage is taken of computer simulations, datasets and research from US and Chinese teams in their local basins and collaborate to advance our shared understanding of these basins. The state of the art computer simulation platforms developed and applied here are designed to capture connections between humans and natural systems not possible with previous modeling approaches. This project also seeks to educate the next generation of water users, planners and scientists on groundwater sustainability by developing K-12 education materials for both the US and China that will be piloted in real classrooms in both countries. This project will help us better understand weaknesses in managed food-water-energy systems like the Heihe and Central Valley to strengthen them moving forward. Water connects food production, energy demand and energy production in irrigated agricultural systems. Intensively managed basins routinely have surface water irrigation, groundwater irrigation and hydropower production operating in tandem. While there have been many operational studies of large scale irrigated systems, the majority of tools applied to these problems focus on the human systems and simplify the natural hydrology. This study bridges this gap developing novel tools that can simulate FEW interactions in complex human and natural systems. In this project leverage of international advances in physically based integrated numerical modeling is accomplished by bringing together two teams of modelers from the US and China. The goal is to explore the tradeoffs between agricultural water supply, hydropower production and environmental degradation in two globally important agricultural systems: the Central Valley of California (USA) and the Heihe River basin in China. Specifically, exploring (1) how the vulnerabilities of food and energy systems differ, (2) where conflicting interests can lead to system inefficiency and environmental degradation, and (3) the advantages of applying integrated hydrologic models to these human systems. The project also seeks to educate the next generation of water users, planners and scientists on groundwater sustainability. Project outputs will be used to develop K-12 education materials for both the US and China that will be piloted in real classrooms.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.