Project Grant NA15NOS4690029

This Cooperative Agreement award, funded by the U.S. Geological Survey under the Assistance to State Water Resources Research Institutes (CFDA 15.805) program, aims to improve flood prediction and risk assessment capabilities across the United States. The $165,000 project, running from January 16, 2025 to January 15, 2026, will be carried out by Purdue University. The research team will develop a new computer model that integrates river data, rainfall patterns, city drainage systems, and land...

This Project Grant award from the U.S. Geological Survey (USGS) under the Water Resources Research Act (WRRA) Program (CFDA 15.805) provides $416,267 to Purdue University to conduct research on the impacts of hydrologic intensification on long-term water budgets across the Continental United States. The key project activities include evaluating hourly precipitation data, investigating the historical relationship between rainfall and runoff, and simulating future rainfall distribution and...

The University of Massachusetts, through its Office of Grant & Contract Administration, was awarded a $498,999.38 Cooperative Agreement from the U.S. Geological Survey under the Assistance to State Water Resources Research Institutes program (CFDA 15.805). This 3-year project aims to improve understanding of how climate variability is impacting subsurface water storage and hydrological processes in the Northeastern United States. Key deliverables include: 1) analysis of historical water...

This Project Grant award of $700,000 from the National Science Foundation's Geosciences Program (CFDA 47.050) is focused on improving computational models that describe the relationship between climate and streamflow. The project aims to address uncertainty in these models caused by the influence of human actions on the water cycle, with a specific focus on representing the impacts of dams, which control about 75% of annual runoff in the continental United States. The project will develop a...

This $599,998 National Science Foundation award under the Geosciences program (CFDA 47.050) will fund research to estimate the emergence of anthropogenic climate change signals in snow water resource metrics in mountainous regions. Led by researchers at the University of Colorado Boulder, the project will use an innovative modeling framework including a large ensemble climate model and process-based hydrological modeling. It aims to strengthen understanding of when and how much climate change...

This Project Grant award, totaling $499,681.45, was provided by the U.S. Geological Survey under the Assistance to State Water Resources Research Institutes program (CFDA 15.805). The project aims to quantify and project the water budget across the Contiguous United States (CONUS) using a hierarchical modeling framework. Key objectives include: Quantifying water budget components by leveraging hydrological experiments, applying machine learning algorithms to inform soil properties, and employing...

The University of Arizona, through a $996,526.93 Cooperative Agreement award from the Bureau of Reclamation under the Snow Water Supply Forecasting Program (CFDA 15.078), will use a 40-year snowpack dataset and newly developed regression modeling and machine learning techniques to enhance water supply forecasts across the Colorado River Basin. Specifically, the project will incorporate improved snowpack data and model optimizations into the National Resources Conservation Networks'...

This $619,990 federal Project Grant award from the U.S. Geological Survey (USGS) under the Assistance to State Water Resources Research Institutes program (CFDA 15.805) seeks to develop a comprehensive hydrologic modeling framework to enhance flood resilience and water resource management within the Willamette River Basin in Oregon. The project aims to leverage advanced technologies like artificial intelligence and machine learning to integrate streamflow observations from the USGS Next...

This $1,228,743.28 Cooperative Agreement award from the Bureau of Reclamation under the Snow Water Supply Forecasting (CFDA 15.078) program aims to improve water supply forecasts by enhancing snow monitoring through emerging technologies like aerial LiDAR surveys. The project will identify locations with unique snowpack dynamics not captured by existing monitoring networks and evaluate how incorporating LiDAR-derived snow data can improve forecasting accuracy compared to relying solely on...

This Cooperative Agreement award from the National Oceanic and Atmospheric Administration (NOAA) under the Weather and Air Quality Research program (CFDA 11.459) will enhance a machine learning-based excessive rainfall forecast system. The primary activities include: Improving and expanding historical datasets used to train and verify the model forecasts, as well as incorporating new geospatial predictors to enhance the characterization of atmospheric conditions and antecedent factors...