CEE faculty and student research in applied water data science and sensing addresses key needs for data visualization, analysis products and tools for management, low-cost and real-time sensing, and aquatic remote sensing.
Highlighted Research Projects & Initiatives
Using Big Data approaches to assess ecohydrological resilience across scales
Land-cover transformation, amplification of biogeochemical flows, and climate disruption are triggering transitions in the Earth system that are unprecedented on human timescales. To ensure biosphere integrity and continued human flourishing, we need to understand the factors that determine ecosystem resilience to these diverse disturbances. This project brings together researchers from across the country in a Critical Zone Collaborative Network, combining data science, ecology, hydrology, and biogeochemistry. Students, researchers, and outreach partners will work in dynamic teams to create new knowledge through field and lab work, and improve education, policy, and participation in STEM fields. CEE researchers are developing and applying complex systems tools to study patterns in watershed data that will be used to inform understanding of ecohydrological resilience. This project is supported by the National Science Foundation.
UVM Collaborators: Julia Perdrial, Regina Toolin, Leon Walls, Michael Blouin, Byung Lee
Collaborating Institutions & Organizations: CUAHSI, U.S. Geological Survey, University of Nevada - Reno, Penn State, University of Kansas, University of Arizona, Desert Research Institute
An Ecologically Inspired Human-Machine Intelligence Approach to Recognizing Similitude in Multi-Scale Watershed Research
Identifying similarities in watershed characteristics and water quality conditions across the continental U.S. can help identify the causes of environmental change and enables the translation of research from individual studies to larger regions. Because the sheer size and diversity of these long-term monitoring data present challenges for traditional scientific and statistical methods, we will employ artificial intelligence methods along with domain experts in hydrology and ecology in an integrated human-machine learning framework. In so doing, we aim to identify the common environmental variables and parameters that are linked to similarity across scales and investigate trends in short-term and long-term water quality and streamflow data. Organizational frameworks from ecological and biological disciplines will be applied to create a new approach to organizing and summarizing similarities in watershed signals, where watersheds are grouped into guilds based on their similar functional traits. By identifying patterns of similitude in these large data sets and extracting watershed attributes with linkages to these patterns, findings of research on ecosystem processes conducted in individual watershed studies can be more readily translated to larger regions.
UVM Collaborators: Melissa Pespeni, Byung Lee
Collaborating Institutions & Organizations: Lawrence Berkley National Laboratory