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

Related authors

Benefits of multi-target and self-supervised LSTM models for water quantity and quality
Jean-Paul Brede, Pia Ebeling, Jens Kiesel, and Ralf Loritz
EGUsphere, https://doi.org/10.5194/egusphere-2026-3604,https://doi.org/10.5194/egusphere-2026-3604, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
CAMELS-DE-1h: hourly hydro-meteorological time series, weather forecasts, and attributes for 1611 catchments in Germany
Alexander Dolich, Eduardo Acuña Espinoza, Uwe Ehret, Michael Kraft, Jan Bondy, Johannes Meuer, and Ralf Loritz
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-289,https://doi.org/10.5194/essd-2026-289, 2026
Preprint under review for ESSD
Short summary
Better data or better architecture? Improving deep-learning-based prediction in ungauged basins
Benedikt Heudorfer, Hoshin Gupta, Alexander Dolich, and Ralf Loritz
EGUsphere, https://doi.org/10.5194/egusphere-2026-1965,https://doi.org/10.5194/egusphere-2026-1965, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
The need for uncertainty: why probabilistic LSTMs are key to improving flood predictions and enabling learned warning rules
Sanika Baste, Sebastian Lerch, Daniel Klotz, and Ralf Loritz
EGUsphere, https://doi.org/10.5194/egusphere-2026-469,https://doi.org/10.5194/egusphere-2026-469, 2026
Short summary
Can discharge be used to inversely correct precipitation?
Ashish Manoj J, Ralf Loritz, Hoshin Gupta, and Erwin Zehe
Hydrol. Earth Syst. Sci., 29, 6115–6135, https://doi.org/10.5194/hess-29-6115-2025,https://doi.org/10.5194/hess-29-6115-2025, 2025
Short summary

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. 
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. 
Breuer, L., Eckhardt, K., and Frede, H.-G.: Plant parameter values for models in temperate climates, Ecol. Model., 169, 237–293, https://doi.org/10.1016/S0304-3800(03)00274-6, 2003. 
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. 
Download
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.
Share