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Cooperative Institute for Research to Operations in Hydrology

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Portrait of Andrés Ramírez Molina outdoors, with mountains and trees in the background.

Machine Learning Model Improves River Flow Estimates in Ungauged Basins

May 15, 2025

By: Ryan Ruiz

TUSCALOOSA, Ala. – A new study published in the Journal of Geophysical Research: Machine Learning and Computation has found that observed water flow in downstream rivers can be used to accurately estimate upstream flows, even in places without physical sensors. This breakthrough could help address one of hydrology’s most persistent challenges: estimating streamflow in ungauged basins.

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Portrait of Savalan Naser Neisary smiling outdoors

New Machine Learning Framework Improves National Water Model Accuracy in Drought-Prone Western Watersheds

May 6, 2025

By: Ryan Ruiz

TUSCALOOSA, Ala. – A newly published study from University of Alabama Ph.D. student Savalan Naser Neisary and his advisor Steven Burian introduces a novel machine learning framework that dramatically improves the accuracy of the National Water Model (NWM) in predicting streamflow in complex, regulated basins and the drought-prone Western U.S.

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Two researchers installing hardware to enhance an existing streamgage.

Advancing Weather Monitoring in American Samoa: Bridging Critical Gaps in Forecasting

April 25, 2025

By: Kayla Roberson

PAGO PAGO, American Samoa – American Samoa faces significant challenges in weather monitoring due to its remote location and limited infrastructure. The lack of critical meteorological and hydrological monitoring assets further heightens the risk of unpreparedness for extreme weather events. In response, researchers have developed innovative solutions to enhance data collection and disaster preparedness in the region.

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