This Cooperative Agreement award from the U.S. Geological Survey (USGS) under the USGS Research and Data Collection program (CFDA 15.808) provides $249,965 to support the "HYDROCAMS: Large-Scale Automated Image-Based Flood Stage Monitoring, Phase 2 Development" research project. The project aims to experimentally evaluate and characterize the real-world potential of image-based technologies for low-cost, highly dense surface water monitoring, which could significantly expand the USGS's...
The U.S. Geological Survey (USGS) awarded a $300,024 Cooperative Agreement grant under the USGS Research and Data Collection program (CFDA 15.808) to The Pennsylvania State University (Penn State) for the project "Harnessing Physics-Informed Machine Learning to Improve Image-Based Streamflow Measurements". The 3-year project aims to leverage machine learning and artificial intelligence to enhance two image-based flow measurement techniques - space-time image velocimetry (STIV) and...
The U.S. Geological Survey (USGS) awarded a $249,990 Cooperative Agreement under the USGS Research and Data Collection program (CFDA 15.808) to the University of Oklahoma's Office of Research Administration. This 3-year project will operationalize two fixed-mount, laser scanning (LIDAR) instruments to measure cross-sectional channel geometry in shallow rivers with frequently changing geomorphologies. The data collected will be integrated into USGS workflows to produce real-time, stage-area...
This Cooperative Agreement award from the U.S. Geological Survey (USGS), under the USGS Research and Data Collection program (CFDA 15.808), provides $319,547 to the University of Virginia to develop an unmanned aerial vehicle (UAV)-based L-band passive radiometer system for high-resolution (5m) soil moisture observations. The project aims to overcome limitations of ground-based monitoring and modeling by conducting repeat surveys at soil moisture testbeds established by the Next Generation Water...
The U.S. Geological Survey (USGS) has awarded a $250,000 cooperative agreement to Deltares USA Inc. under the USGS Research and Data Collection program (CFDA 15.808). The purpose of this 1-year award is to support the development of an enterprise ecosystem of integrated and interoperable water models and model codes that can flexibly represent water availability to address USGS program needs and external stakeholder requirements. Key activities include: Providing a senior software architect to...
The U.S. Geological Survey awarded a $499,999 Cooperative Agreement under the Assistance to State Water Resources Research Institutes program (CFDA 15.805) to Cornell University to develop a distributed sensor network that integrates edge-AI and photogrammetric techniques to automate real-time urban flood monitoring and mapping. Key deliverables include: A minimum viable product (MVP) sensor network to map urban pluvial flood extents and depths in real-time, including storm sewer stage...
The University of Pittsburgh was awarded a $116,000 cooperative agreement from the U.S. Geological Survey under the USGS Research and Data Collection program (CFDA 15.808) to support process-guided machine learning for modeling stream and reservoir networks from March 22, 2021 through March 21, 2023. As part of this cooperative agreement, the University of Pittsburgh will leverage machine learning techniques to model and analyze stream and reservoir systems, in support of USGS efforts to examine...
The U.S. Geological Survey (USGS) awarded a $370,820.05 Cooperative Agreement to the University of Illinois, College of Engineering (UICOE) on December 1, 2024 under the USGS Research and Data Collection program (CFDA 15.808). This long-standing partnership provides the USGS with expanded collaborative scientific opportunities, access to university facilities and equipment, and a pipeline for recruiting qualified employees. The agreement supports joint research and educational initiatives...
This is a $337,987 Cooperative Agreement awarded by the U.S. Geological Survey (USGS) under the USGS Research and Data Collection program (CFDA 15.808). The project aims to provide remote sensing-derived estimates of dissolved organic matter (DOM) concentration for selected lakes and rivers across the continental U.S. from 1984-2024. Key deliverables include: (1) a national-scale DOM dataset, (2) statistical and machine learning models predicting DOM concentrations, and (3) at least one...
This $550,000 National Science Foundation Project Grant under the Geosciences program (CFDA 47.050) will support the development of data-driven physics-informed machine learning models to predict flood-induced flow and sediment dynamics. Over a four-year period ending June 2027, the grantee will improve existing high-fidelity numerical modeling tools and apply them to evaluate flood impacts on infrastructure stability in large waterways. They will then use simulation results to inform and...