RII Track-1 Lake Champlain Basin Resilience to Extreme Events

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Non-technical DescriptionLake Champlain and its surrounding watershed are important to Vermont for tourism, agriculture, natural resources and drinking water. This region has recently suffered severe economic and environmental damage due to flooding, snowstorms, and other extreme weather events. In addition, storm runoff has impeded ongoing efforts to improve lake and drinking-water quality. Seven academic institutions within Vermont will leverage existing and new investments in technology, computational resources and human resources toward developing predictive and decision-making tools for improving drinking-water quality and protecting natural and human infrastructure in the face of increasing extreme weather events. Physical, biological and social scientists and engineers will collaborate in interdisciplinary teams to understand and model the Lake Champlain basin as a complex hydro-ecological-social system. Highly sophisticated physical and social models will be employed to better understand interactions between the natural and human components of Lake Champlain and the surrounding watershed. Team members will develop and provide hands-on educational opportunities and STEM training for middle-school, high-school, and undergraduate students and support research training for undergraduate, and graduate students and post-doctoral researchers. Early career professional development will target graduate students, post-doctoral researchers and early career faculty to further improve the STEM workforce within Vermont.Technical Description Research teams will apply a systems-based, highly integrated approach to determine when and where impacts of extreme events cascade through the combined social-ecological system. Extensive water- and soil-sensor networks will provide data to develop and validate physical models of the watershed. Surveys, workshops and interviews of stakeholders and governmental agents will be used to provide data for the social-science models. The social-science models will employ Deep Knowledge Neural Networks with dynamic algorithms to incorporate land-owner, land-user and policy-maker opinions and responses. An integrated model of the watershed will be used to test management scenarios and identify strategies for maintaining infrastructure, environmental health and drinking water quality in the face of extreme weather events.