Articles | Volume 29, issue 20
https://doi.org/10.5194/hess-29-5251-2025
© Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License.
Research article
| 
17 Oct 2025
Research article |
| 17 Oct 2025
| 17 Oct 2025
Improving heat transfer predictions in heterogeneous riparian zones using transfer learning techniques
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Cited articles
Arcomano, T., Szunyogh, I., Wikner, A., Pathak, J., Hunt, B. R., and Ott, E.: A hybrid approach to atmospheric modeling that combines machine learning with a physics-based numerical model, J. Adv. Model. Earth Sy., 14, e2021MS002712, https://doi.org/10.1029/2021MS002712, 2022.
Bandai, T. and Ghezzehei, T. A.: Physics-informed neural networks with monotonicity constraints for Richardson-Richards equation: Estimation of constitutive relationships and soil water flux density from volumetric water content measurements, Water Resour. Res., 57, e2020WR027642, https://doi.org/10.1029/2020WR027642, 2021.
Barclay, J. R., Topp, S. N., Koenig, L. E., Sleckman, M. J., and Appling, A. P.: Train, inform, borrow, or combine? Approaches to process-guided deep learning for groundwater-influenced stream temperature prediction, Water Resour. Res., 59, e2023WR035327, https://doi.org/10.1029/2023WR035327, 2023.
Brunner, P., Therrien, R., Renard, P., Simmons, C. T., and Franssen, H.-J. H.: Advances in understanding river-groundwater interactions, Rev. Geophys., 55, 818–854, https://doi.org/10.1002/2017RG000556, 2017.
Bukaveckas, P. A.: Effects of channel restoration on water velocity, transient storage, and nutrient uptake in a channelized stream, Environ. Sci. Technol., 41, 1570–1576, https://doi.org/10.1021/es061618x, 2007.
