CDI-Type II: Understanding Water-Human Dynamics with Intelligent Digital Watersheds

CDI-Type II：通过智能数字流域了解水与人类的动态

• 批准号: 0835607
• 负责人: Jerald Schnoor
• 金额: $ 89.94万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-11-01 至 2012-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0835607&HistoricalAwards=false
• 关键词: CDI; Type; II; Understanding; Water

0835607 Schnoor The dynamics of water and the role of humans in the water cycle are not well understood largely because: (a) environmental and socio-economic analyses have traditionally been performed separately; and (b) the methods, tools, and data needed for true multidisciplinary work do not exist. In the absence of a full understanding of coupled human-natural system dynamics, there is a risk of unintended consequences (e.g., increased nutrient pollution, Gulf Hypoxia, greenhouse gas emissions, and food prices as a result of a government policy to improve energy independence through agricultural production of corn for biofuels). This project's overarching goal is to develop a prototype cyberinfrastructure-based intelligent digital watershed (IDW) that enables discovery and innovation in ways never before possible. IDW will facilitate novel insights into human/environment interactions through multi-disciplinary research focused on watershed-related processes at multiple spatio-temporal scales. Project objectives are to: (a) Determine the time delay between socio-economic decisions in land management practices and the resulting water quality at multiple scales, (b) Discover patterns, emergent behavior, dynamic interactions and underlying principles from the integration of heterogeneous data collected or generated by multi-disciplinary simulation models, (c) Develop computational intelligence to fill-in data gaps, and improve model parameterization, and (d) Determine needs (data transformation, knowledge extraction/representation, and visualization) for a real-time, multi-domain cyberinfrastructure system using distributed resources to engage a variety of users in watershed science & management.Intellectual Merit. Use of the novel IDW will enable the prediction of stream-flow and water quality fluxes based on rainfall runoff forecasts and human-management decisions, including dependencies on hydrologic, biogeochemical, and anthropogenic parameters (e.g., vegetation, subsurface structure, land use and cover). The associated fluxes, stores, and properties will be combined to determine estimates of water quality and quantity at the watershed-scale level. Direct benefits of the research will be: (a) creating prototype tools and technology needed to collect near real-time data related to land use; (b) understanding the impact of local land-use decisions on stream water quality at multiple scales; and (c) enhancing the eco-hydrological data management systems currently used to monitor water quality.Broader Impacts. The broader benefit of the proposed research will be an increased understanding of how humans: (a) process information about natural systems, (b) make decisions under uncertainty, and (c) evaluate social and environmental impacts of technological change and substitution among competing societal objectives. First, the modeling and cyberinfrastructure approaches proposed in this research are applicable not only to the emerging observatory effort led by the WATERS Network community, but to many other disciplines where distributed databases are used for decision making. The domain studied in this research makes a useful test-bed for envisioned applications of research in energy, medicine, technology, and other domains. Such generalizations are suggested by general systems theory and the similarities among nonlinear, complex, coupled human behavioral and natural science systems. Second, the Intelligent Digital Watershed operates on a web-based platform, so anyone with internet access will be able to benefit from the research including environmental specialists, engineers, hydrologists, regulators, non-governmental organizations, environmental consulting firms, and academic institutions. Broader impacts will include the capability to utilize this Intelligent Digital Watershed in problem-based learning (PBL) like the Invent Iowa Program, a network of 30,000 active student participants (K-12).

0835607 Schnoor水的动态和人类在水循环中的作用还没有得到很好的理解，主要是因为：（a）环境和社会经济分析传统上是分开进行的;（B）真正的多学科工作所需的方法，工具和数据不存在。在对人类-自然系统耦合动力学缺乏充分理解的情况下，有可能产生意想不到的后果（例如，营养污染增加、海湾缺氧、温室气体排放和食品价格上涨，这是政府通过农业生产玉米作为生物燃料来提高能源独立性的政策造成的）。该项目的总体目标是开发一个基于网络基础设施的智能数字流域（IDW）原型，以前所未有的方式实现发现和创新。IDW将通过在多个时空尺度上侧重于流域相关过程的多学科研究，促进对人类/环境相互作用的新见解。项目目标是：（a）确定土地管理做法中的社会经济决定与由此产生的多尺度水质之间的时间延迟，（B）通过整合多学科模拟模型收集或生成的异质数据，发现模式、紧急行为、动态相互作用和基本原则，（c）开发计算智能，以填补数据空白，并改进模型参数化，（d）确定实时、多领域网络基础设施系统的需求（数据转换、知识提取/表示和可视化），该系统使用分布式资源使各种用户参与流域科学&管理。使用新的IDW将能够根据降雨径流预测和人类管理决策预测径流和水质通量，包括对水文、地球化学和人为参数的依赖性（例如，植被、地下结构、土地利用和覆盖）。相关的通量，商店和属性将结合起来，以确定在流域尺度水平的水质和水量的估计。这项研究的直接好处将是：（a）创造收集与土地使用有关的近实时数据所需的原型工具和技术;（B）了解地方土地使用决定在多个尺度上对溪流水质的影响;（c）加强目前用于监测水质的生态水文数据管理系统。拟议的研究的更广泛的好处将是增加对人类如何：（a）处理有关自然系统的信息，（B）在不确定的情况下作出决定，（c）评估技术变革和相互竞争的社会目标之间的替代的社会和环境影响的理解。首先，本研究中提出的建模和网络基础设施方法不仅适用于由沃茨网络社区领导的新兴天文台工作，而且适用于许多其他学科，其中分布式数据库用于决策。本研究中研究的领域为能源，医学，技术和其他领域的研究提供了一个有用的试验平台。一般系统理论和非线性、复杂、耦合的人类行为和自然科学系统之间的相似性提出了这样的概括。第二，智能数字流域在基于网络的平台上运行，因此任何能够访问互联网的人都能够从研究中受益，包括环境专家，工程师，水文学家，监管机构，非政府组织，环境咨询公司和学术机构。更广泛的影响将包括在基于问题的学习（PBL）中利用这一智能数字流域的能力，如发明爱荷华州计划，这是一个由30,000名活跃的学生参与者（K-12）组成的网络。