A Physics‐Informed Deep Learning Framework for Estimating Thermal Stratification in a Large Deep Reservoir
Abstract Lake water temperature (LWT) is an important indicator of physical processes within a lake, but traditional process‐based and data‐driven models are limited in their ability to estimate long‐term changes in LWT because of simplified physical laws, insufficient onsite measurements and high c...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-07-01
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| Series: | Water Resources Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2025WR040592 |
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| Summary: | Abstract Lake water temperature (LWT) is an important indicator of physical processes within a lake, but traditional process‐based and data‐driven models are limited in their ability to estimate long‐term changes in LWT because of simplified physical laws, insufficient onsite measurements and high computational demands. To overcome these limitations, this study proposes a hybrid multi‐parameter scientific knowledge‐guided neural network (MP‐KgNN) for solving 1‐D lake temperature governing equation trained using both simulations of the WRF‐Lake model and onsite LWT measurements based on a novel training framework called physics‐informed deep learning (PIDL) framework and simulates the thermodynamics in a large deep reservoir located in eastern China from 1960 to 2021. The results revealed that the MP‐KgNN can estimate the dynamic changes in LWT with satisfactory accuracy (mean absolute error [MAE] = 1.14 K, root mean square error [RMSE] = 1.49 K). Moreover, it outperformed the pre‐trained MP‐KgNN trained with only the WRF‐Lake model (MAE = 2.43 K, RMSE = 2.77 K), which indicates its successful prediction of the thermal structure of the lake. The prediction derived by MP‐KgNN showed an increasing trend (0.04 K decade−1) of LWT in the Lake Qiandaohu. Specifically, the LWT was experienced to increase at a rate of 0.10 K decade−1 near the lake surface. These changes resulted in an extension and deepening of lake thermal stratification, as indicated by a 0.58 m increase in metalimnion thickness and a 20.46 kJ increase in Schmidt stability. The proposed MP‐KgNN is expected to become a powerful tool for estimating long‐term variations in the thermodynamics of lake ecosystems. |
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| ISSN: | 0043-1397 1944-7973 |