Hybrid Lake Model (HyLake) v1.0: unifying deep learning and physical principles for simulating lake-atmosphere interactions
Year: 2025
Type: article
Abstract: . Lake surface temperature (LST) serves as a crucial indicator of climate change in Earth systems. However, the challenge of improving LST and heat fluxes predictions remains due to the simplified physical principles inherent in traditional process-based models and the "black-box" structure of purely data-driven models. Accurate lake-atmosphere interaction modeling, which is essential for predicting LST and associated changes in latent heat (LE) and sensible heat (HE) fluxes, has yet to fully benefit from the integration of process-based and deep learning-based models. This study proposed Hybrid Lake Model v1.0 (HyLake v1.0), which integrates a Bayesian Optimized Bidirectional Long Short-Term Memory-based (BO-BLSTM-based) surrogate trained from Meiliangwan (MLW) site in Lake Taihu to approximate LST changes with surface energy balance equations. The performance of HyLake v1.0 was intercompared with FLake and hybrid lake models with different surrogates. Results demonstrated that HyLake v1.0 outperformed the others, with a R and RMSE of 0.99 and 1.08 °C in LST, a R and RMSE of 0.94 and 24.65 W/m2 in LE and a R and RMSE of 0.93 and 7.15 W/m2 in HE. To assess model generalization and transferability in ungauged lake sites, HyLake v1.0 exhibited superior performance, with a MAE of 0.85 °C, 21.56 W/m2 and 6.63 W/m2 in LST, LE and HE respectively, across all lake sites compared to FLake. Under ERA5 reanalysis datasets, HyLake v1.0 performed better for 14 of 15 variables (including LST, LE, and HE across 5 lake sites), with a MAE of 0.90 °C, 35.02 W/m2 and 7.97 W/m2 in LST, LE and HE respectively, indicating strong generalization and transferability. The results supported HyLake v1.0 exhibited an excellent capacity in estimating lake-atmosphere interactions for untrained lake sites, indicating a reasonable performance for extending the application in other ungauged lakes. Furthermore, the proposed model shows promising potential for predicting lake-atmosphere interactions, laying the solid basis for future improvements.
Source: Geoscientific model development
Authors: Yuan He, Xiaofan Yang
Institutions: Beijing Normal University, Beijing Normal University, Zhuhai
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,725
Award: G890424421
OpenAlex ID: W4416840190