This $525,899 National Science Foundation project grant supports the development of Ecology-Knowledge Guided Machine Learning (Eco-KGML) models to advance understanding of lake water quality dynamics across spatial and temporal scales in the United States. Funded under the Biological Sciences program (CFDA 47.074), the University of Wisconsin-Madison will utilize hybrid process-based and machine learning techniques to predict and analyze metrics such as water clarity, phytoplankton biomass,...
The National Science Foundation (NSF) Division of Emerging Frontiers awarded a $2,076,344 Project Grant to Virginia Polytechnic Institute & State University, doing business as Virginia Tech, for the "Collaborative Research: UROL:ASC: Applying Rules of Life to Forecast Emergent Behavior of Phytoplankton and Advance Water Quality Management" project. This project aims to develop an automated, real-time forecasting system to predict phytoplankton blooms in lakes and reservoirs,...
This $281,104 Project Grant award from the National Science Foundation (CFDA 47.074 - Biological Sciences) to Virginia Polytechnic Institute & State University (Virginia Tech) will support a Long-Term Research in Environmental Biology (LTREB) project to quantify ecosystem predictability across daily to decadal timescales. The project will establish a real-time monitoring and data-sharing program at two drinking water supply reservoirs, collecting data on water temperature, clarity,...
This $371,679 Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) will fund research at Virginia Polytechnic Institute & State University (Virginia Tech) to advance the understanding of dynamic connectivity in landscapes and its impact on water quality and sustainable management. The key products and services to be delivered include: 1) Quantifying dynamic connectivity through time and across space in the United States using high-frequency aquatic...
The National Science Foundation (NSF) awarded a $250,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of Virginia. The grant supports the development of new physics-guided graph network models to capture complex, non-stationary, and poorly observed water dynamics in freshwater ecosystems. Key innovations include new graph-based architectures, continual learning strategies, and model initialization methods that leverage...
The National Science Foundation Division of Environmental Biology awarded a $300,243 Project Grant to Virginia Polytechnic Institute & State University under the Biological Sciences federal grant program (CFDA 47.074) to conduct collaborative research from January 2023 through December 2025. The research aims to understand how stochasticity and spatial context impact the dynamics of functional diversity in freshwater ecosystems under global change. Specifically, the grantee will conduct...
This Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) will provide $500,000.00 to Virginia Polytechnic Institute & State University (Virginia Tech) to develop a real-time forecasting system that predicts drinking water quality in three Appalachian reservoirs. The project will create an integrated catchment-reservoir model to predict how environmental hazards like droughts, wildfires, and floods impact water quality, allowing water managers to...
This National Science Foundation (NSF) Project Grant under the Biological Sciences (CFDA 47.074) program will provide $463,568 in funding to the University of Virginia (UVA) from October 1, 2024 to September 30, 2027. The grant will support the training of 30 students over 3 summers (2025-2027) in ecological, evolutionary, and behavioral field research at the Mountain Lake Biological Station in Pembroke, Virginia. Students, primarily from schools with limited research opportunities or...
This five-year, $554,014 National Science Foundation project grant supports research into phytoplankton communities and ecosystem function in freshwater lakes. Funded through NSF's Biological Sciences program (CFDA 47.074), the goal is to describe a disturbance phenology framework to predict feedbacks and threshold shifts for lake ecosystem function across spatial and temporal scales. The University of Vermont will conduct experiments in laboratories, outdoor mesocosms, and five lakes across a...
The National Science Foundation (NSF) Division of Environmental Biology awarded a $399,132 project grant to the University of Virginia for a three-year collaborative research project titled "Whole Ecosystem Test of Restoring Resilience in Lakes". The goal of the project is to experimentally test methods for enhancing the resilience of lake ecosystems to algal blooms, which threaten drinking water, reduce tourism, and harm wildlife. The researchers will measure and evaluate the...
This $567,340 Project Grant from the National Science Foundation Division of Environmental Biology under the Biological Sciences federal grant program (CFDA 47.074) will fund research at Virginia Polytechnic Institute and State University to advance understanding of lake water quality dynamics across spatial scales using knowledge-guided machine learning. The research aims to develop Ecology-knowledge guided Machine Learning (Eco-KGML) models to improve predictions of water clarity, phytoplankton biomass, and hypolimnetic anoxia in lakes nationwide. By incorporating both ecological understanding and machine learning techniques, the models seek to identify dominant water quality processes, their variation across space and time, and interactions between climate, land use, and ecosystem memory that influence water quality from local to continental scales. Novel applications of machine learning are expected to yield new insights into scale-dependent relationships and parameters governing water quality processes. The award period is from November 1, 2022 through October 31, 2026.