Articles | Volume 28, issue 4
https://doi.org/10.5194/hess-28-917-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-917-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Feb 2024
Research article |
| 27 Feb 2024
| 27 Feb 2024
A comprehensive study of deep learning for soil moisture prediction
Yanling Wang
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
Liangsheng Shi
CORRESPONDING AUTHOR
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
Yaan Hu
State Key Laboratory of Hydrology, Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing, China
Xiaolong Hu
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
Wenxiang Song
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
Lijun Wang
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, China
Related authors
Yanling Wang, Xiaolong Hu, Yaan Hu, Leilei He, Lijun Wang, Wenxiang Song, and Liangsheng Shi
EGUsphere, https://doi.org/10.5194/egusphere-2025-4440, https://doi.org/10.5194/egusphere-2025-4440, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
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This study introduces a new interpretable deep learning method that accurately predicts multi-depth soil moisture simultaneously without physical assumptions. The model provides insights into soil properties, while delivering precise predictions across diverse scenarios. Tested under various conditions, it outperforms traditional approaches, particularly when enhanced with basic physics. This tool can help improve water management by offering reliable and efficient soil moisture forecasts.
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EGUsphere, https://doi.org/10.5194/egusphere-2025-4440, https://doi.org/10.5194/egusphere-2025-4440, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
This study introduces a new interpretable deep learning method that accurately predicts multi-depth soil moisture simultaneously without physical assumptions. The model provides insights into soil properties, while delivering precise predictions across diverse scenarios. Tested under various conditions, it outperforms traditional approaches, particularly when enhanced with basic physics. This tool can help improve water management by offering reliable and efficient soil moisture forecasts.
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To avoid overloaded monitoring cost from redundant measurements, this study proposed a non-parametric data worth analysis framework to assess the worth of future soil moisture data regarding the model-free unsaturated flow models before data gathering. Results indicated that (1) the method can quantify the data worth of alternative monitoring schemes to obtain the optimal one, and (2) high-quality and representative small data could be a better choice than unfiltered big data.
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We revise the roughness of the land surface in the CESM climate model. Guided by observational data, we increase the surface roughness of forests and decrease that of bare soil, snow, ice, and crops. These modifications alter simulated temperatures and wind speeds at and above the land surface considerably, in particular over desert regions. The revised model represents the diurnal variability of the land surface temperature better compared to satellite observations over most regions.
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Short summary
LSTM temporal modeling suits soil moisture prediction; attention mechanisms enhance feature learning efficiently, as their feature selection capabilities are proven through Transformer and attention–LSTM hybrids. Adversarial training strategies help extract additional information from time series’ data. SHAP analysis and t-SNE visualization reveal differences in encoded features across models. This work serves as a reference for time series’ data processing in hydrology problems.
LSTM temporal modeling suits soil moisture prediction; attention mechanisms enhance feature...
