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Abstract: Scalable, Uncertainty-Aware Streamflow Prediction in Snow-Dominated Catchments Using Evidential Deep Learning and CLM5 Ensembles (AGU25)

Scalable, Uncertainty-Aware Streamflow Prediction in Snow-Dominated Catchments Using Evidential Deep Learning and CLM5 Ensembles

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