CDI-Type II: Collaborative Research: New knowledge from the Global Lake Ecological Observatory Network (GLEON)

CDI-Type II：协作研究：来自全球湖泊生态观测站网络（GLEON）的新知识

• 批准号: 0941573
• 负责人: Kenneth Chiu
• 金额: $ 22.8万
• 依托单位: SUNY at Binghamton
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-10-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0941573&HistoricalAwards=false
• 关键词: CDI; Type; II; Collaborative; Research

Ecosystems have changed more rapidly in the past 50 years than at any other time in human history. Lakes are exemplars of change, as a variety of social, landscape and climate factors drive globally pervasive degradation of lake water quality and quantity. National and international ecological networks play a valuable role in monitoring the world?s ecosystems through continuous automated sensing of key environmental variables. The Global Lake Ecological Observatory Network (GLEON) has been amassing data from lake-sensor networks around the world. In this project, GLEON scientists transform ecological sensor networks from data collectors to knowledge generators through integration of the people, data, and cyberinfrastructure of lake-sensor networks. Scientists use three-dimensional lake simulation and advanced signal processing algorithms to exploit information embedded in sensor network data, as well as data from non-traditional sources, such as Web sites that log observations of city and state employees who monitor the lakes. Automated coupling of diverse data sources with simulation models will provide near-real-time prediction of lake conditions. With new data and model integration, scientists will gain new understanding into socially relevant environmental issues, such as the development of harmful algal blooms and the roles lakes play in the global carbon cycle. The collaborative efforts of lake ecologists, computer scientists, and information technologists will yield transformations for the scientific disciplines and benefits for the broader community. The use of non-traditional data from the Internet will reveal new pathways for multi-discipline collaborations that study how ecosystems and societies interact. The rapidly expanding field of environmental sensor networks will benefit by use of the data-model integration techniques developed here. The insights into data-model coupling gained by computer scientists will be of benefit to other disciplines, such as the social sciences and biological epidemiology, in which diverse data sources and complex models are applied to complex problems. Finally, the novel and advanced techniques developed will enable the next generation of scientists to study lakes in ways not previously possible. Teams of students from multiple disciplines will participate in the creation of these new technologies, fostering collaborations that will lead to exchange of ideas and the emergence of a new way of studying our natural systems.

在过去的50年里，生态系统的变化比人类历史上的任何时候都要快。湖泊是变化的典范，因为各种社会、景观和气候因素在全球范围内导致湖泊水质和数量的普遍退化。国家和国际生态网络通过对关键环境变量的连续自动传感，在监测世界-S生态系统方面发挥着宝贵的作用。全球湖泊生态观测网络(GLEON)一直在从世界各地的湖泊传感器网络收集数据。在这个项目中，GLEON科学家通过整合湖泊传感器网络的人员、数据和网络基础设施，将生态传感器网络从数据收集器转变为知识生成器。科学家使用三维湖泊模拟和先进的信号处理算法来利用传感器网络数据中嵌入的信息，以及来自非传统来源的数据，例如记录监测湖泊的市政和州雇员观察的网站。各种数据源与模拟模型的自动耦合将提供对湖泊状况的近乎实时的预测。通过新的数据和模型集成，科学家将对与社会相关的环境问题有新的理解，例如有害藻华的发展以及湖泊在全球碳循环中的作用。湖泊生态学家、计算机科学家和信息技术专家的合作努力将为科学学科带来变革，并使更广泛的社区受益。来自互联网的非传统数据的使用将为研究生态系统和社会如何相互作用的多学科合作揭示新的途径。快速扩展的环境传感器网络领域将受益于这里开发的数据模型集成技术。计算机科学家对数据-模型耦合的洞察将有益于其他学科，如社会科学和生物流行病学，在这些学科中，不同的数据源和复杂的模型被应用于复杂的问题。最后，开发出的新颖和先进的技术将使下一代科学家能够以以前不可能的方式研究湖泊。来自多个学科的学生团队将参与这些新技术的创造，促进合作，从而导致思想交流和研究我们自然系统的新方法的出现。