CIROH Research

Content for this page was provide by the University of Alabama Cooperative Institute for Research to Operations in Hydrology.

Improvement of Water Resources Prediction Systems. CIROH will improve geospatial intelligence, inputs, probabilistic forcings, data assimilation, operational workflows and tools, and uncertainty quantification extending water resources predictions capabilities and applications. CIROH’s major community contribution is a water prediction system testbed and model evaluation system.
Advancement and Acceleration of Community Water Resources Modeling. CIROH will advance a state-of-the-art, community driven mechanistic hydrological model with hybrid integration of artificial intelligence and data-driven approaches with biophysical-hydrological-social processes coupled to advance the speed, accuracy, and resolution of prediction. CIROH’s major community contribution is the integration of research advances into the developmental version of the Next Generation Water Resources Modeling Framework.
Advancement and Augmentation of Hydroinformatics. CIROH will promote FAIR data principles in its innovations in extreme data management, development of new informatics tools and community development platforms, creation of advanced user interfaces and experiences, and advancement of flood inundation mapping techniques.
Application of Social, Economic, and Behavioral Science in Water Resources. CIROH will serve as an integrator of research-to-operations-to-research (R2O2R) interactions connecting researchers, operators, modelers, data scientists, social scientists, decision makers, and communication and policy experts in a cooperative hydrologic research and prediction community. National-scale coordination of social and behavioral science will uncover risk perceptions, user preferences, and forecast design needs for all types of communities in the United States.

flow chart of CIROH research project process

CIROH Funded Projects

FY25

Title: Deep-learning-based Rainfall Forecasting for Prediction of Pluvial Flooding

PI(s): Witold Krajewski

Funded by: NOAA

Research Theme (RT): 1- Expansion and improvement of Water Prediction Capabilities

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Title: How Can We Improve the NEXRAD Network to Benefit Water Prediction?

PI(s): Witold Krajewski

Funded by: NOAA

Research Theme (RT): 1- Expansion and improvement of Water Prediction Capabilities

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FY24

Title: Adapting the Iowa Flood Center’s Hillslope Link Model for compatibility with the NextGen Framework

PI(s): Felipe Quintero Duque

Funded by: NOAA

Research Theme (RT): 1- Expansion and improvement of Water Prediction Capabilities

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Funding period: June 1, 2024 - May 31, 2025

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FY23

Title: A Collaborative Approach to Develop Comprehensive FIM Data, Models, and Methods with Emphasis on Transportation Infrastructure

PI(s): Jim Nelson

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Partners: Brigham Young University, University of Texas, University of Alabama, NOAA/NWS Office of Water Prediction at NWC, Aquaveo

Funding period: June 1, 2023 - May 31, 2024

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PI(s): Ibrahim Demir

Funded by: NOAA

Research Theme (RT): 3- Advancement and Augmentation of Hydroinformatics

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Funding period: June 1, 2023 - May 31, 2024

Abstract:

PI(s): Ibrahim Demir

Funded by: NOAA

Research Theme (RT): 1- Expansion and improvement of Water Prediction Capabilities

Partners: Missouri University of Science and Technology