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

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

doi:https://doi.org/10.5194/hess-26-4757-2022

Articles | Volume 26, issue 18

https://doi.org/10.5194/hess-26-4757-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-26-4757-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 18

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

Data sets

The Weierbach Experimental Catchment (WEC) hydrological and isotopic database C. Hissler, N. Martínez-Carreras, F. Barnich, L. Gourdol, J. F. Iffly, J. Juilleret, J. Klaus, and L. Pfister https://doi.org/10.5281/zenodo.4537700

Model code and software

Leveraging sap flow data in a catchment-scale hybrid model to improve soil moisture and transpiration estimates R. Loritz and M. Bassiouni https://doi.org/10.5281/zenodo.6821189

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.