2022 Research Projects

CIROH launched 42 projects in 2022 in its four research themes and cross-cutting. Those 42 projects are supporting researchers in 21 states and are connecting locally to dozens of project partners.

Title	Principal Investigator	Institution
Adapt Precipitation Super-Resolution and Data Fusion Deep Learning Techniques for Operational Flood Forecasting	Sara Graves	The University of Alabama, Huntsville
Advance Land Surface Modeling Coupled with the NexGen NWM and Integrate with NOAA UFS to Improve Hydrologic Forecasting Capability	Ty (Paul) Ferre	University of Arizona
Advance Riverine-Coastal Model Coupling and Forecast Verification for Total Water Quantity and Water Quality Prediction	Shaowu Bao	Coastal Carolina University
Advance the predictive capabilities of the NextGen National Water Model	Martyn Clark	University of Saskatechewan, University of Calgary
Advancing research in cold regions hydrology to support the modeling and mapping of ice-induced flood inundation	Marouane Temimi	Stevens Institute of Technology
An Analysis and Demonstration of the National Water Model’s Applicability to Community Resilience Planning	Kristin Raub	CUAHSI, The Global Resilience Institute at Northeastern University
Channel Roughness, Morphology, Bankfull Discharge and Hydraulic Modeling (FIM)	Sagy Cohen	University of Alabama
CIROH: Building Knowledge to Support Equitable Climate Resilience in the Upper Mississippi River Basin	Melissa A. Kenney	University of Minnesota
Coastal Nature Based Solutions to Mitigate Flood Impacts and Enhance Resilience	Hamed Moftakhari	University of Alabama
Collection of Post-Event Flood Inundation Extent Data via Small Unmanned Aircraft Systems (sUAS) for Forecast Evaluation Services	Casey Calamaio	University of Alabama - Huntsville
Coupling novel low cost spatially distributed nutrient sensors and National Water Model output to forecast nutrient loading and inform state implementation of EPA mandated nutrient reduction targets—The Lake Champlain Basin Test Bed	Andrew Schroth	University of Vermont, USGS, Purdue University
CUAHSI HydroShare Modernization	Jordan Read	Consortium of Universities for the Advancement of Hydrologic Sciences, Inc., CUAHSI
Data Assimilation and Fusion for Operational NextGen NWM: An Enhanced Hydrologic Forecasting Framework	Hamid Moradkhani	University of Alabama
Development of a machine learning model for local to national river temperature modeling	Terri Hogue	Coloradon School of Mines
Development of benchmarking datasets for testing and improving NexGen NWM predictions	Prabhakar Clement	The University of Alabama
Empaneled focus groups to determine how end users respond to visual cues of forecast products	Melissa Kenney	University of Minnesota, NOAA Office of Water Prediction
Enhanced Forecast Design Through Experimental Gaming and Social Impact Assessment of Connected River and Floodplains	Scott Merrill	University of Vermont, University of Kansas
Exploring Decision-Makers’ and Public Risk Perception and Information Seeking Behaviors Related to Water Quantity in the Southeastern U.S.	Matt VanDyke	University of Alabama
Forecasting the Incidence and Duration of Harmful Algal Blooms (HABs) at Daily, Weekly and Seasonal Scales	Asim Zia	Department of Community Development and Applied Economics, University of Vermont, Department of Computer Science, University of Vermont, Department of Civil and Environmental Engineering, University of Vermont, Department of Geography and Geosciences, University of Vermont, Department of Environmental Conservation, Vermont Agency of Natural Resources, Water Quality Solutions, Inc., Vermont EPSCOR, University of Vermont, Rubsenstein School of Environment and Natural Resources, University of Vermont, Department of Electrical and Biomedical Engineering, University of Vermont
Framework for Connecting Forecast System Investments to the Economic and Societal Value of Forecast Improvements across Stakeholder Sectors	George Van Houtven	RTI International
Hydrometeorological Prediction Testbed supporting the US National Water Model	Andy Wood	Coloradon School of Mines
Improved Representation of Floodplains and Natural Features for Channel Routing	Beverley Wemple	University of Vermont
Improving modeling and forecast product delivery in agricultural lands	Joseph Quansah	Tuskegee University
Improving the integration of ML with physically-based hydrologic and routing modeling via large-scale parameter and structure learning schemes	Chaopeng Shen	Pennsylvania State University
Integrate observations and field work in support of expanded evaluation data testbeds in the Northeast	Asim Zia	University of Vermont
Integrated Evaluation System Prototype for Testing Research and Operational Advancements	Katie van Werkhoven	RTI International
Intelligent Data Analytics and Communication	Larry Weber	IIHR Hydroscience & Engineering, University of Iowa & Civil and Environmental Engineering, University of Iowa
Leveraging emerging sensing technology and machine learning to improve and expand hydrological forecasting to hyper-local scales with NWM-coupled adaptive sensor networks	Andrew Schroth	University of Vermont, USGS
Modeling dam and levee breach and the impact of hydraulic structures on channel routing and flood inundation	M. Hanif Chaudhry	University of South Carolina
Modeling dam and levee breach and the impact of hydraulic structures on channel routing and flood inundation	M. Hanif Chaudhry	University of South Carolina
Modernized Standards and Tools for Sharing and Integrating Real time Hydrologic Observations Data	Jeffery S. Horsburgh	Utah State Univesity
National Cyberinfrastructure Framework for Engaging the Hydrologic Community (NCF)	Dan Ames	Civil & Construction Engineering, Brigham Young University, Provo, Utah
Physics-informed Machine Learning for Compound Flood Mapping	Hamed Moftakhari	The University of Alabama
Post-processing NWM output with spatially distributed turbidity sensing to forecast turbidity loading and source for reservoir operation management	Andrew Schroth	University of Vermont, USGS