Articles | Volume 26, issue 18
https://doi.org/10.5194/hess-26-4757-2022
https://doi.org/10.5194/hess-26-4757-2022
Research article
 | 
28 Sep 2022
Research article |  | 28 Sep 2022

Leveraging sap flow data in a catchment-scale hybrid model to improve soil moisture and transpiration estimates

Ralf Loritz, Maoya Bassiouni, Anke Hildebrandt, Sibylle K. Hassler, and Erwin Zehe

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Cited articles

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Bennett, A. and Nijssen, B.: Deep Learned Process Parameterizations Provide Better Representations of Turbulent Heat Fluxes in Hydrologic Models, Water Resour. Res., 57, 1–14, https://doi.org/10.1029/2020WR029328, 2021. 
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Burgess, S. S. O., Adams, M. A., Turner, N. C., Beverly, C. R., Ong, C. K., Khan, A. A. H., and Bleby, T. M.: An improved heat pulse method to measure low and reverse rates of sap flow in woody plants, Tree Physiol., 21, 589–598, https://doi.org/10.1093/treephys/21.9.589, 2001. 
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Short summary
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.
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