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

Allen, R. G., Pereira, L. S., and Smith, M.: Crop evapotranspiration – Guidelines for computing crop water requirements, Rome, Italy, http://www.fao.org/3/X0490E/X0490E00.htm (last access: 27 September 2022), 1998. 
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Brown, A. E., Zhang, L., McMahon, T. A., Western, A. W., and Vertessy, R. A.: A review of paired catchment studies for determining changes in water yield resulting from alterations in vegetation, J. Hydrol., 310, 28–61, https://doi.org/10.1016/j.jhydrol.2004.12.010, 2005. 
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.