Helmholtz Centre for Environmental Research – UFZ, Department
Computational of Hydrosystems, Leipzig, Germany
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research – Atmospheric Trace Gases and Remote Sensing, Karlsruhe, Germany
Friedrich Schiller University Jena, Institute of Geoscience, Jena,
Germany
Karlsruhe Institute of Technology (KIT), Institute of Water and River Basin Management – Hydrology, Karlsruhe, Germany
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research – Atmospheric Trace Gases and Remote Sensing, Karlsruhe, Germany
Erwin Zehe
Karlsruhe Institute of Technology (KIT), Institute of Water and River Basin Management – Hydrology, Karlsruhe, Germany
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Total article views: 1,952 (including HTML, PDF, and XML)
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In this study, we combine a deep-learning approach that predicts sap flow with a hydrological model to improve soil moisture and transpiration estimates at the catchment scale. Our results highlight that hybrid-model approaches, combining machine learning with physically based models, are a promising way to improve our ability to make hydrological predictions.
In this study, we combine a deep-learning approach that predicts sap flow with a hydrological...
