Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 23
Articles | Volume 18, issue 23
https://doi.org/10.5194/gmd-18-9257-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-9257-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 23
Model description paper
 | 
01 Dec 2025
Model description paper |  | 01 Dec 2025

Hybrid Lake Model (HyLake) v1.0: unifying deep learning and physical principles for simulating lake-atmosphere interactions

Yuan He and Xiaofan Yang

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Short summary
This study introduces HyLake, a hybrid lake model that embeds a deep-learning surrogate for the water temperature module within a process-based backbone. HyLake simulates lake surface temperature and the latent and sensible heat fluxes in Lake Taihu more accurately than traditional process-based models and other hybrid experiments across different forcing datasets. The proposed coupling strategy provides a reliable tool for quantifying the impacts of climate change on aquatic ecosystems.
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