A Novel Deep Learning Approach for Data Assimilation of Complex Hydrological Systems
Abstract In hydrological research, data assimilation (DA) is widely used to fuse the information contained in process‐based models and observational data to reduce simulation uncertainty. However, many popular DA methods are limited by low computational efficiency or their reliance on the Gaussian a...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-02-01
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| Series: | Water Resources Research |
| Online Access: | https://doi.org/10.1029/2023WR035389 |
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| _version_ | 1849687163251195904 |
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| author | Jiangjiang Zhang Chenglong Cao Tongchao Nan Lei Ju Hongxiang Zhou Lingzao Zeng |
| author_facet | Jiangjiang Zhang Chenglong Cao Tongchao Nan Lei Ju Hongxiang Zhou Lingzao Zeng |
| author_sort | Jiangjiang Zhang |
| collection | DOAJ |
| description | Abstract In hydrological research, data assimilation (DA) is widely used to fuse the information contained in process‐based models and observational data to reduce simulation uncertainty. However, many popular DA methods are limited by low computational efficiency or their reliance on the Gaussian assumption. To address these limitations, we propose a novel DA method called DA(DL), which leverages the capabilities of DL to model non‐linear relationships and recognize complex patterns. DA(DL) first generates a large volume of training data from the prior ensemble, and then trains a DL model to update the system knowledge (e.g., model parameters in this study) from multiple predictors. For highly non‐linear models, an iterative form of DA(DL) can be implemented. Additionally, strategies of data augmentation and local updating are proposed to enhance DA(DL) for problems involving small ensemble size and the equifinality issue, respectively. In two hydrological DA cases involving Gaussian and non‐Gaussian distributions, DA(DL) shows promising performance compared to two ensemble smoother methods, that is, ES(K) and ES(DL), which respectively apply the Kalman‐ and DL‐based updates. Potential improvements to DA(DL) can be made by designing better DL model architectures, imposing physical constraints to the training of the DL model, and further updating other important variables like model states, forcings and error terms. |
| format | Article |
| id | doaj-art-05ee1b01b03b4b939085fe82ea548aa0 |
| institution | DOAJ |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-05ee1b01b03b4b939085fe82ea548aa02025-08-20T03:22:26ZengWileyWater Resources Research0043-13971944-79732024-02-01602n/an/a10.1029/2023WR035389A Novel Deep Learning Approach for Data Assimilation of Complex Hydrological SystemsJiangjiang Zhang0Chenglong Cao1Tongchao Nan2Lei Ju3Hongxiang Zhou4Lingzao Zeng5Yangtze Institute for Conservation and Development, Hohai University Nanjing ChinaYangtze Institute for Conservation and Development, Hohai University Nanjing ChinaYangtze Institute for Conservation and Development, Hohai University Nanjing ChinaNational Demonstration Center for Environment and Planning, College of Geography and Environmental Science, Henan University Kaifeng ChinaCollege of Metrology and Measurement Engineering, China Jiliang University Hangzhou ChinaZhejiang Provincial Key Laboratory of Agricultural Resources and Environment College of Environmental and Resource Sciences, Zhejiang University Hangzhou ChinaAbstract In hydrological research, data assimilation (DA) is widely used to fuse the information contained in process‐based models and observational data to reduce simulation uncertainty. However, many popular DA methods are limited by low computational efficiency or their reliance on the Gaussian assumption. To address these limitations, we propose a novel DA method called DA(DL), which leverages the capabilities of DL to model non‐linear relationships and recognize complex patterns. DA(DL) first generates a large volume of training data from the prior ensemble, and then trains a DL model to update the system knowledge (e.g., model parameters in this study) from multiple predictors. For highly non‐linear models, an iterative form of DA(DL) can be implemented. Additionally, strategies of data augmentation and local updating are proposed to enhance DA(DL) for problems involving small ensemble size and the equifinality issue, respectively. In two hydrological DA cases involving Gaussian and non‐Gaussian distributions, DA(DL) shows promising performance compared to two ensemble smoother methods, that is, ES(K) and ES(DL), which respectively apply the Kalman‐ and DL‐based updates. Potential improvements to DA(DL) can be made by designing better DL model architectures, imposing physical constraints to the training of the DL model, and further updating other important variables like model states, forcings and error terms.https://doi.org/10.1029/2023WR035389 |
| spellingShingle | Jiangjiang Zhang Chenglong Cao Tongchao Nan Lei Ju Hongxiang Zhou Lingzao Zeng A Novel Deep Learning Approach for Data Assimilation of Complex Hydrological Systems Water Resources Research |
| title | A Novel Deep Learning Approach for Data Assimilation of Complex Hydrological Systems |
| title_full | A Novel Deep Learning Approach for Data Assimilation of Complex Hydrological Systems |
| title_fullStr | A Novel Deep Learning Approach for Data Assimilation of Complex Hydrological Systems |
| title_full_unstemmed | A Novel Deep Learning Approach for Data Assimilation of Complex Hydrological Systems |
| title_short | A Novel Deep Learning Approach for Data Assimilation of Complex Hydrological Systems |
| title_sort | novel deep learning approach for data assimilation of complex hydrological systems |
| url | https://doi.org/10.1029/2023WR035389 |
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