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

在过去的50年里，生态系统的变化比人类历史上任何时候都要快。湖泊是变化的典范，各种社会、景观和气候因素推动了全球湖泊水质和水量的普遍退化。国家和国际生态网络在监测世界方面发挥着宝贵的作用。通过对关键环境变量的连续自动感知来实现美国生态系统。全球湖泊生态观测网（GLEON）一直在收集来自世界各地湖泊传感器网络的数据。在这个项目中，GLEON科学家通过整合人、数据和湖泊传感器网络的网络基础设施，将生态传感器网络从数据收集器转变为知识生成器。科学家们使用三维湖泊模拟和先进的信号处理算法来挖掘嵌入传感器网络数据中的信息，以及来自非传统来源的数据，如记录监测湖泊的城市和州雇员观察结果的网站。各种数据源与模拟模型的自动耦合将提供近乎实时的湖泊状况预测。随着新数据和模型的整合，科学家将对与社会相关的环境问题有新的认识，例如有害藻华的发展和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共同努力将为科学学科带来变革，并为更广泛的社区带来利益。使用来自互联网的非传统数据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使用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见解将有益于其他学科，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，新开发的先进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，从而促进思想交流，并出现一种研究自然系统的新方法。

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.