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Cooperative Institute for Research to Operations in Hydrology

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.

TitlePrincipal InvestigatorInstitution
Adapt Precipitation Super-Resolution and Data Fusion Deep Learning Techniques for Operational Flood ForecastingSara GravesThe University of Alabama, Huntsville
Advance Land Surface Modeling Coupled with the NexGen NWM and Integrate with NOAA UFS to Improve Hydrologic Forecasting CapabilityTy (Paul) FerreUniversity of Arizona
Advance Riverine-Coastal Model Coupling and Forecast Verification for Total Water Quantity and Water Quality PredictionShaowu BaoCoastal Carolina University
Advance the predictive capabilities of the NextGen National Water ModelMartyn ClarkUniversity of Saskatechewan, University of Calgary
Advancing research in cold regions hydrology to support the modeling and mapping of ice-induced flood inundationMarouane TemimiStevens Institute of Technology
An Analysis and Demonstration of the National Water Model’s Applicability to Community Resilience PlanningKristin RaubCUAHSI, The Global Resilience Institute at Northeastern University
Channel Roughness, Morphology, Bankfull Discharge and Hydraulic Modeling (FIM)Sagy CohenUniversity of Alabama
CIROH: Building Knowledge to Support Equitable Climate Resilience in the Upper Mississippi River BasinMelissa A. KenneyUniversity of Minnesota
Coastal Nature Based Solutions to Mitigate Flood Impacts and Enhance ResilienceHamed MoftakhariUniversity of Alabama
Collection of Post-Event Flood Inundation Extent Data via Small Unmanned Aircraft Systems (sUAS) for Forecast Evaluation ServicesCasey CalamaioUniversity 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 BedAndrew SchrothUniversity of Vermont, USGS, Purdue University
CUAHSI HydroShare ModernizationJordan ReadConsortium of Universities for the Advancement of Hydrologic Sciences, Inc., CUAHSI
Data Assimilation and Fusion for Operational NextGen NWM: An Enhanced Hydrologic Forecasting FrameworkHamid MoradkhaniUniversity of Alabama
Development of a machine learning model for local to national river temperature modelingTerri HogueColoradon School of Mines
Development of benchmarking datasets for testing and improving NexGen NWM predictionsPrabhakar ClementThe University of Alabama
Empaneled focus groups to determine how end users respond to visual cues of forecast productsMelissa KenneyUniversity of Minnesota, NOAA Office of Water Prediction
Enhanced Forecast Design Through Experimental Gaming and Social Impact Assessment of Connected River and FloodplainsScott MerrillUniversity 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 VanDykeUniversity of Alabama
Forecasting the Incidence and Duration of Harmful Algal Blooms (HABs) at Daily, Weekly and Seasonal ScalesAsim ZiaDepartment 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 SectorsGeorge Van HoutvenRTI International
Hydrometeorological Prediction Testbed supporting the US National Water ModelAndy WoodColoradon School of Mines
Improved Representation of Floodplains and Natural Features for Channel RoutingBeverley WempleUniversity of Vermont
Improving modeling and forecast product delivery in agricultural landsJoseph QuansahTuskegee University
Improving the integration of ML with physically-based hydrologic and routing modeling via large-scale parameter and structure learning schemesChaopeng ShenPennsylvania State University
Integrate observations and field work in support of expanded evaluation data testbeds in the NortheastAsim ZiaUniversity of Vermont
Integrated Evaluation System Prototype for Testing Research and Operational AdvancementsKatie van WerkhovenRTI International
Intelligent Data Analytics and CommunicationLarry WeberIIHR 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 networksAndrew SchrothUniversity of Vermont, USGS
Modeling dam and levee breach and the impact of hydraulic structures on channel routing and flood inundationM. Hanif ChaudhryUniversity of South Carolina
Modeling dam and levee breach and the impact of hydraulic structures on channel routing and flood inundationM. Hanif ChaudhryUniversity of South Carolina
Modernized Standards and Tools for Sharing and Integrating Real time Hydrologic Observations DataJeffery S. HorsburghUtah State Univesity
National Cyberinfrastructure Framework for Engaging the Hydrologic Community (NCF)Dan AmesCivil & Construction Engineering, Brigham Young University, Provo, Utah
Physics-informed Machine Learning for Compound Flood MappingHamed MoftakhariThe University of Alabama
Post-processing NWM output with spatially distributed turbidity sensing to forecast turbidity loading and source for reservoir operation managementAndrew SchrothUniversity of Vermont, USGS