INFEWS/T1: Mesoscale Data Fusion to Map and Model the U.S. Food, Energy, and Water (FEW) System

INFEWS/T1：中尺度数据融合，用于绘制美国食品、能源和水 (FEW) 系统并对其进行建模

基本信息

批准号：
1639529
负责人：
Benjamin Ruddell
金额：
$ 300万
依托单位：
Northern Arizona University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1639529&HistoricalAwards=false
关键词：
INFEWS T1 Mesoscale Data Fusion

项目摘要

The Food, Energy, and Water (FEW) system is complex, vulnerable to societal and environmental changes, yet critical for national well-being. This project's major contribution is to create and exploit the first detailed mapping of the Food, Energy, and Water System of the United States. Using this capability will improve understanding of how local Food, Energy, and Water policy decisions and technologies cause ripple effects throughout the system (for example, how electricity usage in an American city affects rivers hundreds of miles away). Policies and technologies often pose trade-offs between Food, Energy, and Water systems, and this project is measuring those trade-offs so costs and benefits may be understood and balanced in future decisions. By studying how past events like droughts, storms, wars, or economic crises have affected the nation's Food, Energy, and Water System, this project is developing the capacity to anticipate the impacts of future events. The project provides an empirical basis for advances in theory and scientific modeling of the complete food-energy-water (FEW) system of the United States. The system is primarily composed of mesoscale phenomena in which regional trade, river basins and aquifers, irrigation districts, crop belts, states, tribes, counties and cities, power grids, climate gradients, and seasonal timescales interact in a dynamic, inter-connected coupled natural-human system. To advance understanding of these interactions, a reliable and complete empirical description of the FEW system is needed. This requires a dataset containing consumption, production, and bilateral trade data for the United States, with sub-county resolution. A retrospective version of this dataset (containing data from the mid-20th century to the present), will serve as a model network for the FEW system's emergent performance metrics, sustainability metrics, and supply-chain teleconnections, along with observed historical dynamics of system response, vulnerability, and resilience to stresses and shocks. A wide range of diverse and disparate (but mostly pre-existing) economic, climate, and environmental data will be assembled to create the first comprehensive empirical map of the U.S. Food, Energy, and Water system (the FEWSion v1.0-US database). This capability will then be used to achieve four high-value science and modeling objectives: (1) quantify the multiple-objective trade-offs between performance and sustainability metrics, (2) analyze historical sensitivity, vulnerability, resilience, and evolution of the FEW network with attribution to observed stresses and shocks, (3) establish the role of cities within the FEW system, and (4) provide a standards-based benchmarking assessment capability that can be used by other projects awarded under Track 1 (FEW System Modeling) and Track 3 (Research to Enable Innovative System Solutions) of this INFEWS solicitation. A public online educational tool uses this information to visualize how individual and local decisions create environmental footprints, and how those decisions create impacts throughout the food, energy, and water system.

食物，能源和水（少数）系统很复杂，容易受到社会和环境变化的影响，但对于国家福祉至关重要。该项目的主要贡献是创建和利用美国食品，能源和水系统的第一个详细映射。使用这种能力将提高人们对当地食品，能源和水政策决策和技术如何在整个系统中引起连锁反应的理解（例如，美国城市中的电力使用如何影响数百英里外的河流）。政策和技术经常在食品，能源和水系统之间进行权衡，该项目正在衡量这些权衡，因此在将来的决策中可以理解和平衡成本和收益。通过研究过去的事件，例如干旱，风暴，战争或经济危机如何影响美国的粮食，能源和水系统，该项目正在发展能够预测未来事件的影响。该项目为美国完整食品能 - 水（少数）系统的理论和科学建模的进步提供了经验基础。该系统主要由中尺度现象组成，其中区域贸易，河流盆地和含水层，灌溉区，农作物带，州，部落，县和城市，电网，气候梯度以及季节性时间表在动态，相互连接的偶联自然人类系统中相互作用。为了促进对这些相互作用的理解，需要对少数系统进行可靠而完整的经验描述。这需要具有子县分辨率的美国的数据集，其中包含消费，生产和双边贸易数据。该数据集的回顾性版本（包含从20世纪中叶到现在的数据），将作为模型网络，用于少数系统的新兴绩效指标，可持续性指标和供应链远程连接，以及观察到的历史动态，对系统响应，脆弱性，对压力和震惊的弹性。将组装各种多种多样和不同的经济，气候和环境数据，以创建美国食品，能源和水系统的首个综合经验图（少数v1.0-us数据库）。然后，该能力将用于实现四个高价值的科学和建模目标：（1）量化性能和可持续性指标之间的多重目标权衡取舍，（2）分析历史敏感性，脆弱性，脆弱性，弹性，少数网络的演变，少数网络与归因于观察到的压力和震惊的作用在少数城市中，（3）建立了少数人的作用，（3）（3）be（3）be（3）be（3）be（3）（4）（4）（4）（4）（4）（4）（4）4）通过轨道1（少数系统建模）和轨道3（可实现创新系统解决方案）授予的其他项目。公共在线教育工具使用这些信息来可视化个人和地方决策如何创建环境足迹，以及这些决策如何在整个食物，能源和水系统中产生影响。