The deadline to submit a session, town hall, or pre-conference workshop proposal for AGU25 has passed. Please search for and review proposed sessions below. Please note this site is frozen as of 4/29/25 and will not reflect any changes to proposals made after this date.


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Abstract: Learning Data-Driven Hydrological Signatures from Daily Runoff with Contrastive Learning (AGU25)

Learning Data-Driven Hydrological Signatures from Daily Runoff with Contrastive Learning

Yang Yang1,2, Yuan-Heng Wang3, Hoshin Gupta4, Shuxin Luo5,6 and Ting Fong May Chui2, (1)University of Massachusetts Boston, School for the Environment, Boston, United States, (2)The University of Hong Kong, Department of Civil Engineering, Hong Kong SAR, China, (3)Lawrence Berkeley National Laboratory, Earth and Environmental Sciences Area, Berkeley, United States, (4)Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, United States, (5)Southern University of Science and Technology, School of Environmental Science and Engineering, Shenzhen, China, (6)Eastern Institute of Technology, Zhejiang Key Laboratory of Industrial Intelligence and Digital Twin, Ningbo, China