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Session Proposal: Early Career Researchers Advancing Hydrology with AI and Big Data (AGU25)

H059
Early Career Researchers Advancing Hydrology with AI and Big Data

Session ID#: 248932

Session Description:
Early-career scientists are revolutionizing hydrological sciences by pioneering innovative applications of big data and machine learning (ML). This session highlights their transformative contributions in advancing the field while fostering collaborations, showcasing cutting-edge research, and discussing the challenges and opportunities of integrating ML and big data in hydrology from the perspectives of emerging scientists. We welcome contributions from early-career researchers at all stages, focusing on: (1) Development and harmonization of large-scale datasets; (2) Large-scale hydrologic modeling; (3) Integration of ML with process-based models and remote sensing; (4) Data-driven insights on fundamental water science and management.
Index Terms:

1829 Groundwater hydrology [HYDROLOGY]
1847 Modeling [HYDROLOGY]
1860 Streamflow [HYDROLOGY]
1871 Surface water quality [HYDROLOGY]
Primary Convener:  Yalan Song, Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, PA, United States
Conveners:  Frederick Y Cheng, University of Virginia, Department of Environmental Sciences, Charlottesville, United States, Wei Zhi, Hohai University, Nanjing, China and Zach Perzan, University of Nevada, Las Vegas, Geoscience, Las Vegas, United States
Student/Early Career Convener:  Shuyu Y Chang, The Pennsylvania State University, Department of Geography, University Park, United States
 
Predicting headwater streamflow: challenges from local to global scales (Invited) (1851370)
Lijing Wang, University of Connecticut, Department of Earth Sciences, Storrs, United States
 
Explainable Deep Learning Reveals Climate Change Impacts on River Discharge across Gulf Coast Watersheds (1855917)
Ajeeta Shrestha and Di Tian, Auburn University, Department of Crop, Soil & Environmental Sciences, Auburn, United States
 
Flood Forecasting in Data-Scarce Basins Using Event-Based Records and Multi-Paradigm Modeling with Similarity-Based Transfer (1873231)
Yikai Chai1, Wenyu Ouyang1, Lei Ye1, Shuolong Xu2, Xiaolan Chen3 and Yong Peng1, (1)Dalian University of Technology, School of Infrastructure Engineering, Dalian, China, (2)Dalian University of Technology, School of Computer Science and Technology, Dalian, China, (3)Hohai University, College of Hydrology and Water Resources, Nanjing, China
 
Applying Backcasting with Machine Learning at Lake Mead: What are the drivers of high water temperatures at Hoover Dam? (1874152)
Eunice Ledres1, Brooke Stemple Dr.2, Hannes H Bauser1 and Deena Hannoun Dr.2, (1)University of Nevada Las Vegas, Department of Geoscience, Las Vegas, United States, (2)Southern Nevada Water Authority, Las Vegas, United States
 
A data derived workflow for simulating reservoir operations in global hydrologic models (Invited) (1879474)
Jennie Steyaert1, Edwin Sutanudjaja1, Marc FP Bierkens1,2 and Niko Wanders1, (1)Utrecht University, Physical Geography, Utrecht, Netherlands, (2)Deltares, Delft, Netherlands
 
Scalable, Uncertainty-Aware Streamflow Prediction in Snow-Dominated Catchments Using Evidential Deep Learning and CLM5 Ensembles (1897256)
Kachinga Silwimba1, Alejandro N Flores2, Linnia R Hawkins3, Charlie Becker4, Katherine Dagon5, David John Gagne II6, Irene Cionni7, Jodi Mead8, Pamela L Sullivan9, Daniel Hirmas10, Sharon A Billings11, Hoori Ajami12, Li Li13, Jesse B Nippert14, Andy Wood5, Guoqiang Tang15 and Stanley Akor16, (1)Boise State University, Geosciences Department, Boise, United States, (2)Boise State University, Department of Geosciences, Boise, ID, United States, (3)Montana State University Bozeman, Bozeman, MT, United States, (4)NSF National Center for Atmospheric Research, Computational & Information Systems Lab, Boulder, United States, (5)NSF National Center for Atmospheric Research, Boulder, United States, (6)NCAR/MMM, Boulder, United States, (7)Boise State University, Geosciences Department, Boise, ID, United States, (8)Boise State University, Boise, ID, United States, (9)Oregon State University, College of Earth, Ocean, and Atmospheric Sciences, Corvallis, United States, (10)Texas Tech University, Department of Plant and Soil Science, Lubbock, TX, United States, (11)University of Kansas, Department of Ecology and Evolutionary Biology and Kansas Biological Survey and Center for Ecological Research, Lawrence, KS, United States, (12)University of California Riverside, Environmental Sciences, Riverside, CA, United States, (13)Pennsylvania State University Main Campus, Department of Civil and Environmental Engineering, University Park, United States, (14)Kansas State University, Biology, Manhattan, United States, (15)Wuhan University, School of Water Resources and Hydropower Engineering, Wuhan, China, (16)Boise State University, Department of Geosciences, Boise, United States
 
A Random Forest Model to Estimate Water Table Depth Over Brazil (1911904)
Maria Fleury1, Yueling Ma2 and Reed M Maxwell1, (1)Princeton University, Civil and Environmental Engineering, Princeton, United States, (2)Princeton University, Integrated GroundWater Modeling Center, Princeton, United States
 
Understanding Nevada’s Groundwater using Geospatial Data and Machine Learning: Developing Student Pathways into Earth Science Research (1929923)
Sayantan Majumdar1, Jenae Payne2, Brian Padilla2, Isabel de la Garza-Gibson3, Eleanor Smith2, Roger Putnam4 and Meghan Collins5, (1)Desert Research Institute, Division of Hydrologic Sciences, Reno, United States, (2)Truckee Meadows Community College, Reno, United States, (3)University of Nevada Reno, Department of Chemistry, Reno, United States, (4)Truckee Meadows Community College, Physical Sciences Department, Reno, United States, (5)Desert Research Institute, Division of Earth and Ecosystem Sciences, Reno, United States
 
How Does Water Quality Vary in Time and Space Across the United States? (1950243)
Seyed Abolfazl Ebrahimi1, Nicolas Fernandez2, Dan Dai2, Jaehyeon Lee2, Lindsey Cromwell3, Luwen Wan4, Jasmine Krause5, Linus Schauer6, Sajad Soleymani Hasani7, Andrew A. Ali8, Deandre Presswood9, Aléxia Oliveira10, Luisa Vieira Lucchese11, Kate Maher12, Adam S Ward13, Mauricio Arias14, Andreas Musolff6, Joanna Blaszczak15, Arial J. Shogren8, Harald Klammler16, John Gardner17, Matthew J Cohen18 and James W Jawitz19, (1)University of Florida, Ft Walton Beach, FL, United States, (2)University of Florida, Department of Soil, Water, and Ecosystem Sciences, Ft Walton Beach, FL, United States, (3)School of Forest Resources and Conservation, University of Florida, Gainesville, United States, (4)Michigan State University, Department of Earth and Environmental Sciences, East Lansing, MI, United States, (5)Oregon State University, Corvallis, OR, United States, (6)Helmholtz Centre for Environmental Research UFZ Leipzig, Hydrogeology, Leipzig, Germany, (7)University of South Florida, Tampa, United States, (8)University of Alabama, Department of Biological Sciences, Tuscaloosa, United States, (9)University of Nevada, Reno, Reno, United States, (10)Federal University of Bahia, Salvador, Brazil, (11)University of Pittsburgh, Geology and Environmental Science, Pittsburgh, PA, United States, (12)Stanford University, Earth System Science, Stanford, United States, (13)Oregon State University, Department of Biological and Ecological Engineering, Corvallis, OR, United States, (14)University of South Florida, Department of Civil & Environmental Engineering, Tampa, United States, (15)University of Nevada Reno, Department of Natural Resources & Environmental Science, Reno, United States, (16)Federal University of Bahia, Department of Geophysics, Salvador, Brazil, (17)University of North Carolina at Chapel Hill, Earth, Marine, and Environmental Sciences, Chapel Hill, United States, (18)University of Florida, School of Forest, Fisheries, and Geomatics Sciences, Gainesville, United States, (19)University of Florida, Department of Soil, Water, and Ecosystem Sciences, Gainesville, United States
 
Surface Quantitative Precipitation Estimates (SQUIRE) from ARM’s Scanning Precipitation Radars (1955008)
Joseph Robert O'Brien1, Robert Clyde Jackson1, Maxwell Grover1, Scott M Collis2, Adam Theisen1, Zachary Sherman3 and Bhupendra Raut4, (1)Argonne National Laboratory, Argonne, IL, United States, (2)Argonne National Laboratory, Lemont, IL, United States, (3)Argonne National Laboratory, Argonne, United States, (4)Argonne National Laboratory, Lemont, United States
 
Why and Where Physics-Machine Learning Integration Matters for Hydrologic Modeling (Invited) (1991248)
Dapeng Feng, Stanford University, Earth System Science, Stanford, CA, United States
See more of: Hydrology