Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge

• Yeonuk Kim,

  Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Writing – original draft

  Affiliations Institute for Resources, Environment and Sustainability, University of British Columbia, Vancouver, Canada, Division of Hydrologic Sciences, Desert Research Institute, Las Vegas, Nevada, United States of America

  https://orcid.org/0000-0003-2993-8687

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• Monica Garcia,

  Roles Funding acquisition, Investigation, Supervision, Writing – review & editing

  Affiliation Estación Experimental de Zonas Áridas. Consejo Superior de Investigaciones Científicas (EEZA-CSIC), Almería, Spain

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• T. Andrew Black,

  Roles Funding acquisition, Investigation, Supervision, Writing – review & editing

  Affiliation Faculty of Land and Food Systems, University of British Columbia, Vancouver, Canada

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• Mark S. Johnson

  Roles Conceptualization, Funding acquisition, Investigation, Supervision, Writing – review & editing

  * E-mail: mark.johnson@ubc.ca

  Affiliations Institute for Resources, Environment and Sustainability, University of British Columbia, Vancouver, Canada, Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, Canada

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