RII Track-1 Lake Champlain Basin Resilience to Extreme Events

非技术描述尚普兰湖及其周围的分水岭对佛蒙特州的旅游业、农业、自然资源和饮用水都很重要。最近，由于洪水、暴风雪和其他极端天气事件，该地区遭受了严重的经济和环境破坏。此外，暴雨径流阻碍了正在进行的改善湖泊和饮用水质量的努力。佛蒙特州的七个学术机构将利用现有的和新的技术、计算资源和人力资源投资，开发预测和决策工具，以改善饮用水质量，并保护自然和人类基础设施，以应对日益增多的极端天气事件。物理、生物和社会科学家和工程师将在跨学科团队中合作，将尚普兰湖流域理解为复杂的水生态社会系统并将其建模。将使用高度复杂的物理和社会模型来更好地了解尚普兰湖自然和人类组成部分与周围分水岭之间的相互作用。团队成员将为初中生、高中生和本科生开发和提供实践教育机会和STEM培训，并支持本科生、研究生和博士后研究人员的研究培训。早期职业专业发展将以研究生、博士后研究人员和早期职业教师为目标，以进一步改善佛蒙特州的STEM劳动力队伍。技术描述研究团队将应用基于系统的、高度集成的方法来确定极端事件的影响何时何地通过综合社会生态系统。广泛的水和土壤传感器网络将提供数据，以开发和验证流域的物理模型。将利用对利益攸关方和政府机构的调查、讲习班和访谈，为社会科学模型提供数据。社会科学模型将采用深度知识神经网络和动态算法，将土地所有者、土地使用者和政策制定者的意见和反应结合在一起。将使用分水岭的综合模型来测试管理情景，并确定在面临极端天气事件时维护基础设施、环境健康和饮用水质量的战略。

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.