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Session Proposal: Practical AI Solutions for Hydrological Forecasting (AGU25)

H115
Practical AI Solutions for Hydrological Forecasting

Session ID#: 248221

Session Description:
As climate extremes intensify, the need for accurate, actionable hydrological forecasts has never been more urgent. This session invites contributions that focus on real-world applications of AI and machine learning to hydrology forecasting challenges—including snow melting, streamflow, flood risk, drought prediction, and water management. We seek practical solutions that go beyond theory: models deployed in the field, workflows integrated into operations, and systems that are robust to data gaps, uncertainty, and changing climate conditions. We welcome submissions including but not limited to: End-to-end forecasting systems using AI/MLHybrid physics-ML models in hydrology; Forecast post-processing and uncertainty quantification; Scalable workflows for regional and global hydrologic forecasting; Tools and platforms for decision-making support; Open-source contributions and reproducible methods; Case studies and partnerships with water managers or emergency services. This session aims to foster collaboration between researchers, practitioners, and public agencies working to turn AI advances into reliable, equitable, and operational hydrological tools.
Co-Sponsor(s):
  • C - Cryosphere
  • GC - Global Environmental Change
  • IN - Informatics
  • NH - Natural Hazards
Index Terms:

0736 Snow [CRYOSPHERE]
1863 Snow and ice [HYDROLOGY]
1872 Time series analysis [HYDROLOGY]
1895 Instruments and techniques: monitoring [HYDROLOGY]
Primary Convener:  Ziheng Sun, George Mason University Fairfax, Fairfax, VA, United States
Conveners:  Nicoleta C Cristea, University of Washington, Department of Civil and Environmental Engineering, Seattle, United States, Annie Burgess, Earth Science Information Partners, Severna Park, MD, United States; Montana State University, Bozeman, United States and Justin M. Pflug, University of Colorado, Boulder, United States
 
Generative postprocessing of HRRR QPF for improved flash-flood forecasting (1857190)
Nischal Karki, Nibedita Samal and Hamid Moradkhani, The University of Alabama, Center for Complex Hydrosystems Research, Tuscaloosa, United States
 
Machine Learning Generated Streamflow Drought Forecasts for the Conterminous United States (CONUS): Developing and Evaluating an Operational Tool to Enhance Sub-seasonal to Seasonal Streamflow Drought Early Warning for Gaged Locations (1867574)
Phillip Goodling1, John Christopher Hammond2, Jeremy Diaz3, Hayley Corson-Dosch4, Aaron Heldmyer5, Scott D Hamshaw6, Ryan McShane7, Jesse Ross8, Roy R Sando9, Caelan Simeone10, Leah Staub2, William D Watkins11, Michael Wieczorek2, Kendall Wnuk2 and Jacob A Zwart12, (1)U.S. Geological Survey, Earth Surface Processes Division, Baltimore, MD, United States, (2)U.S. Geological Survey, Maryland-Delaware-DC Water Science Center, Catonsville, United States, (3)U.S. Geological Survey, Water Mission Area, Reston, United States, (4)U.S. Geological Survey, Water Resources Mission Area, Madison, United States, (5)USGS Geological Survey, WY-MT WSC, Cheyenne, United States, (6)U.S. Geological Survey, Water Mission Area-Integrated Modeling and Prediction Division, Bristol, United States, (7)USGS Wyoming-Montana Water Science Center, Cheyenne, WY, United States, (8)US Geological Survey, Water Mission Area, Los Angeles, United States, (9)USGS Wyoming-Montana Water Science Center, Helena, United States, (10)U.S. Geological Survey, Oregon Water Science Center, Portland, United States, (11)USGS Integrated Information Dissemination Division, Data Science Branch, Middleton, United States, (12)USGS Integrated Information Dissemination Division, Data Science Branch, San Francisco, United States
 
Temporal Super-Resolution for Flood Forecasting using Deep Learning in Norfolk, Virginia (1872653)
Chetan Kumar1, Diana McSpadden2, Malachi Schram2,3, Heather Richter4,5, Yidi Wang6, Binata Roy7 and Jonathan L Goodall6, (1)Old Dominion University - Thomas Jefferson National Accelerator Facility Joint Institute on Advanced Computing for Environmental Studies, Portsmouth, VA, United States, (2)Thomas Jefferson National Accelerator Facility, Data Science, Newport News, VA, United States, (3)Old Dominion University - Thomas Jefferson National Accelerator Facility Joint Institute on Advanced Computing for Environmental Studies, Norfolk, United States, (4)Old Dominion University, Thomas Jefferson National Accelerator Facility Joint Institute on Advanced Computing for Environmental Studies, Norfolk, VA, United States, (5)Old Dominion University, Norfolk, VA, United States, (6)University of Virginia, Department of Civil and Environmental Engineering, Charlottesville, VA, United States, (7)University of Virginia, Department of Civil and Environmental Engineering, Charlottesville, United States
 
Advancing Seasonal-to-Subseasonal Predictions of Net Basin Supply Components in the North American Great Lakes Using Machine Learning (1891421)
Lindsay Fitzpatrick1, Dani Jones1, Matthew McAnear2, Yi Hong1 and Lauren M Fry3, (1)Cooperative Institute for Great Lakes Research, University of Michigan, Ann Arbor, United States, (2)University of Michigan, Ann Arbor, United States, (3)NOAA Great Lakes Environmental Research Laboratory, Ann Arbor, United States
 
Incremental Learning for Probabilistic Forecasts of Water Levels at Virginia Beach, Virginia (1922105)
Diana McSpadden1, Chetan Kumar2, Ahmed Hossam Mohammed3, Malachi Schram1, Alisa Udomvisawakul4, Sridhar Katragadda5, Armen Kasparian3, Kishansingh Rajput3, Mahmudul Hasan3, Binata Roy6, Yidi Wang7, Jonathan L Goodall7 and Heather Richter8, (1)Thomas Jefferson National Accelerator Facility, Data Science, Newport News, VA, United States, (2)Old Dominion University, Norfolk, United States, (3)Thomas Jefferson National Accelerator Facility, Newport News, United States, (4)City of Virginia Beach, Department of Communications and Information Technology, Virginia Beach, United States, (5)City of Virginia Beach, Virginia Beach, United States, (6)University of Virginia, Department of Civil and Environmental Engineering, Charlottesville, United States, (7)University of Virginia, Department of Civil and Environmental Engineering, Charlottesville, VA, United States, (8)Old Dominion University, Thomas Jefferson National Accelerator Facility Joint Institute on Advanced Computing for Environmental Studies, Norfolk, VA, United States
 
Democratizing Weather Prediction: A Blueprint for Operational AI-based Forecasting to Address Real World Challenges (1928605)
Adam Marchakitus1, Katie Kowal2, Colin Aitken3, Mayank Gupta4, Rajat Masiwal1, William R Boos5, Amir Jina6 and Pedram Hassanzadeh1, (1)University of Chicago, Department of the Geophysical Sciences, Chicago, United States, (2)University of Chicago, Data Science Institute, Chicago, United States, (3)University of Chicago, Development Innovation Lab, Chicago, United States, (4)University of Chicago Trust in India, Center for Climate Studies, Mumbai, India, (5)University of California Berkeley, Department of Earth and Planetary Science, Berkeley, CA, United States, (6)University of Chicago, Harris School of Public Policy, Chicago, United States
 
Improving Deep Learning Streamflow Forecasts by Explicitly Training with Real-world Precipitation Forecasting (1932084)
Wenyu Ouyang1, Yuan Luo1, Lei Ye1, Shuolong Xu2 and Yikai Chai1, (1)Dalian University of Technology, School of Infrastructure Engineering, Dalian, China, (2)Dalian University of Technology, School of Computer Science and Technology, Dalian, China
 
LSTM-Enhanced Streamflow Forecasting for California-Nevada Region: a Post-Processing Framework for CHPS and WRF-Hydro (1982287)
Mu Xiao1, Ming Pan2, Yuan Yang3, Qian Cao4, Taylor Dixon2, Gabriel Lewis5, Lu Su4, Robert Hartman6, Michael J Deflorio7, Julie Kalansky8, Luca Delle Monache9 and Marty Ralph10, (1)University of California San Diego, La Jolla, CA, United States, (2)Center for Western Weather and Water Extremes (CW3E), Scripps Institution of Oceanography, University of California San Diego, La Jolla, United States, (3)University of California San Diego, Scripps Institution of Oceanography, Center for Western Weather and Water Extremes, La Jolla, United States, (4)University of California San Diego, Scripps Institution of Oceanography, La Jolla, United States, (5)NOAA/NWS California-Nevada River Forecast Center, Sacramento, United States, (6)Robert K. Hartman Consulting Associates, Roseville, United States, (7)Scripps Institution of Oceanography, UC San Diego, Center for Western Weather and Water Extremes (CW3E), La Jolla, United States, (8)Center for Western Weather and Water Extremes (CW3E), Scripps Institution of Oceanography, University of California San Diego, San Diego, United States, (9)Center for Western Weather and Water Extremes (CW3E), Scripps Institution of Oceanography, UC San Diego, La Jolla, United States, (10)Scripps Institution of Oceanography, University of California San Diego, Center for Western Weather and Water Extremes (CW3E), La Jolla, United States
 
Hybrid Model for Rainfall-Runoff Prediction: A Combination of Time Series and Non-Time Series Architectures (1982324)
Ming Chen, University of Nevada, Las Vegas, Las Vegas, United States
 
Machine-Learning Approach to Predict the timing of Spring Breakup of River Ice (1988005)
Mahmoud Ayyad, Marouane Temimi and Baya Cherif, Stevens Institute of Technology, Department of Civil, Environmental, and Ocean Engineering (CEOE), Hoboken, United States
 
Physics-Informed Graph Neural Network for SWE Forecasting (1989676)
Ziheng Sun1, Keren Zhou2, Bowen Cui2, Nicoleta C Cristea3, Annie Bryant Burgess4 and Mingrui Liu5, (1)George Mason University Fairfax, Fairfax, VA, United States, (2)George Mason University Fairfax, Computer Science, Fairfax, United States, (3)University of Washington, Department of Civil and Environmental Engineering, Seattle, United States, (4)University of Southern California, Computer Science, Los Angeles, CA, United States, (5)George Mason University Fairfax, Fairfax, United States
 
A Machine learning Approach for CMIP6-based Streamflow projections in Peruvian Andean Basins (1993316)
Dr. Nicolas Velasquez1, Juan Guerrero-Gallego1 and Liliana Villaordoña2, (1)Florida Institute of Technology, Melbourne, FL, United States, (2)Orygen Peru, Lima, Peru
 
Integration and Evaluation of Operational Machine Learning Hydrological Forecasts in Reclamation Regions (1998602)
Laura K Read, Upstream Tech, Denver, United States, Chris Frans, US Army Corps of Engineers, Seattle, United States, Lindsay A Bearup, Colorado School of Mines, Golden, United States, Melanie Feen, Upstream Tech, HydroForecast, Somerville, United States and Mostafa Elkurdy, University of Guelph, Guelph, ON, Canada
 
Assessing the Accuracy of AI Models in SWE Prediction (2001256)
Bill Huang, Lake Braddock Secondary School, Burke, United States, Jiaqi Sun, John Randolph Tucker High School, Glen Allen, United States, Raymond Gao, Acton Boxborough Regional High School, Acton, United States and Ziheng Sun, George Mason University, Center for Spatial Information Science and Systems, Fairfax, VA, United States
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