CIROH, a partnership between NOAA and The University of Alabama, is a national consortium committed to advancing water prediction – the forecasting of streamflow entering water systems, extreme events such as floods and droughts, and water quality – and building community resilience to water-related challenges. CIROH scientists, from 28 different institutions—academic, government, and private, work to improve the understanding of hydrologic processes, operational hydrologic forecasting techniques and workflows, community water modeling, translation of forecasts to actionable products, and use of water predictions in decision making.
Goals
Research Leader
Advance research and leadership in support of NOAA’s Office of Water Prediction mission to: “collaboratively research, develop and deliver state-of-the science national hydrologic analyses, forecast information, data, guidance, and equitable decision-support services to inform essential emergency management and water resources decisions across all time scales;”
Knowledge Mobilizer
Reinforce the NWS National Water Center’s mission to “promote collaboration across the scientific community, serving as both a catalyst to accelerate the transition of research into operations and a center of excellence for water resources science, information, and prediction services;” and
Community Catalyst
Strengthen communities of practice to synthesize a new generation of interdisciplinary and innovative research products, education, and outreach supporting NOAA’s vision of a water- and weather-ready nation.
CIROH IN THE NEWS
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.
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.
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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