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

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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.