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Abstract: ML-Enabled Physically Interpretable Catchment-Scale Rainfall-Runoff Modeling: Toward Hydro-Mass-Conserving-Perceptron (Hydro-MCP) Approaches for the CONUS-Wide Large-Sample Investigation (AGU25)

ML-Enabled Physically Interpretable Catchment-Scale Rainfall-Runoff Modeling: Toward Hydro-Mass-Conserving-Perceptron (Hydro-MCP) Approaches for the CONUS-Wide Large-Sample Investigation

Yuan-Heng Wang1, Yang Yang2, Fabio Ciulla3, Hoshin Vijai Gupta4 and Charuleka Varadharajan1, (1)Lawrence Berkeley National Laboratory, Earth and Environmental Sciences Area, Berkeley, United States, (2)University of Massachusetts Boston, School for the Environment, Boston, United States, (3)Lawrence Berkeley National Laboratory, Earth and Environmental Sciences Area, Berkeley, CA, United States, (4)University of Arizona, Department of Hydrology and Atmospheric Sciences, Tucson, AZ, United States
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