Articles | Volume 29, issue 20
https://doi.org/10.5194/hess-29-5251-2025
https://doi.org/10.5194/hess-29-5251-2025
Research article
 | 
17 Oct 2025
Research article |  | 17 Oct 2025

Improving heat transfer predictions in heterogeneous riparian zones using transfer learning techniques

Aohan Jin, Wenguang Shi, Jun Du, Renjie Zhou, Hongbin Zhan, Yao Huang, Quanrong Wang, and Xuan Gu

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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. 
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Short summary
This study developed a novel physics-informed deep transfer learning (PDTL) approach, which integrates the physical mechanisms from an analytical model using a transfer learning technique. Results indicate that the DTL model maintains satisfactory performance even in heterogeneous conditions, with uncertainties in observations and sparse training data compared to the deep neural network (DNN) model.
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