State updating of the Xin'anjiang model: joint assimilating streamflow and multi-source soil moisture data via the asynchronous ensemble Kalman filter with enhanced error models
<p>Assimilating either soil moisture or streamflow individually has been well demonstrated to enhance the simulation performance of hydrological models. However, the runoff routing process may introduce a lag between soil moisture and outlet discharge, presenting challenges in simultaneously a...
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Copernicus Publications
2025-01-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://hess.copernicus.org/articles/29/335/2025/hess-29-335-2025.pdf |
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| author | J. Gong J. Gong X. Liu C. Yao C. Yao Z. Li A. H. Weerts A. H. Weerts Q. Li S. Bastola Y. Huang J. Xu |
| author_facet | J. Gong J. Gong X. Liu C. Yao C. Yao Z. Li A. H. Weerts A. H. Weerts Q. Li S. Bastola Y. Huang J. Xu |
| author_sort | J. Gong |
| collection | DOAJ |
| description | <p>Assimilating either soil moisture or streamflow individually has been well demonstrated to enhance the simulation performance of hydrological models. However, the runoff routing process may introduce a lag between soil moisture and outlet discharge, presenting challenges in simultaneously assimilating the two types of observations into a hydrological model. The asynchronous ensemble Kalman filter (AEnKF), an adaptation of the ensemble Kalman filter (EnKF), is capable of utilizing observations from both the assimilation moment and the preceding periods, thus holding potential to address this challenge. Our study first merges soil moisture data collected from field soil moisture monitoring sites with China Meteorological Administration Land Data Assimilation System (CLDAS) soil moisture data. We then employ the AEnKF, equipped with improved error models, to assimilate both the observed outlet discharge and the merged soil moisture data into the Xin'anjiang model. This process updates the state variables of the model, aiming to enhance real-time flood forecasting performance. Tests involving both synthetic and real-world cases demonstrates that assimilation of these two types of observations simultaneously substantially reduces the accumulation of past errors in the initial conditions at the start of the forecast, thereby aiding in elevating the accuracy of flood forecasting. Moreover, the AEnKF with the enhanced error model consistently yields greater forecasting accuracy across various lead times compared to the standard EnKF.</p> |
| format | Article |
| id | doaj-art-660acdb8277f4c468db158eaf4dd90c0 |
| institution | Kabale University |
| issn | 1027-5606 1607-7938 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj-art-660acdb8277f4c468db158eaf4dd90c02025-01-20T10:35:11ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382025-01-012933536010.5194/hess-29-335-2025State updating of the Xin'anjiang model: joint assimilating streamflow and multi-source soil moisture data via the asynchronous ensemble Kalman filter with enhanced error modelsJ. Gong0J. Gong1X. Liu2C. Yao3C. Yao4Z. Li5A. H. Weerts6A. H. Weerts7Q. Li8S. Bastola9Y. Huang10J. Xu11College of Agricultural Science and Engineering, Hohai University, Nanjing 210024, ChinaCollege of Hydrology and Water Resources, Hohai University, Nanjing 210024, ChinaXiaolangdi Multipurpose Dam Project Management Center, Ministry of Water Resources, Zhengzhou 450003, ChinaCollege of Hydrology and Water Resources, Hohai University, Nanjing 210024, ChinaChina Meteorological Administration Hydro-Meteorology Key Laboratory, Nanjing 210024, ChinaCollege of Hydrology and Water Resources, Hohai University, Nanjing 210024, ChinaDeltares, 2600 MH Delft, the NetherlandsHydrology and Environmental Hydraulics Group, Wageningen University, 6700 HB Wageningen, the NetherlandsCollege of Hydrology and Water Resources, Hohai University, Nanjing 210024, ChinaDepartment of Civil and Environmental Engineering, University of New Orleans, New Orleans 70148, USACollege of Civil Engineering, Fuzhou University, Fuzhou 350108, ChinaCollege of Agricultural Science and Engineering, Hohai University, Nanjing 210024, China<p>Assimilating either soil moisture or streamflow individually has been well demonstrated to enhance the simulation performance of hydrological models. However, the runoff routing process may introduce a lag between soil moisture and outlet discharge, presenting challenges in simultaneously assimilating the two types of observations into a hydrological model. The asynchronous ensemble Kalman filter (AEnKF), an adaptation of the ensemble Kalman filter (EnKF), is capable of utilizing observations from both the assimilation moment and the preceding periods, thus holding potential to address this challenge. Our study first merges soil moisture data collected from field soil moisture monitoring sites with China Meteorological Administration Land Data Assimilation System (CLDAS) soil moisture data. We then employ the AEnKF, equipped with improved error models, to assimilate both the observed outlet discharge and the merged soil moisture data into the Xin'anjiang model. This process updates the state variables of the model, aiming to enhance real-time flood forecasting performance. Tests involving both synthetic and real-world cases demonstrates that assimilation of these two types of observations simultaneously substantially reduces the accumulation of past errors in the initial conditions at the start of the forecast, thereby aiding in elevating the accuracy of flood forecasting. Moreover, the AEnKF with the enhanced error model consistently yields greater forecasting accuracy across various lead times compared to the standard EnKF.</p>https://hess.copernicus.org/articles/29/335/2025/hess-29-335-2025.pdf |
| spellingShingle | J. Gong J. Gong X. Liu C. Yao C. Yao Z. Li A. H. Weerts A. H. Weerts Q. Li S. Bastola Y. Huang J. Xu State updating of the Xin'anjiang model: joint assimilating streamflow and multi-source soil moisture data via the asynchronous ensemble Kalman filter with enhanced error models Hydrology and Earth System Sciences |
| title | State updating of the Xin'anjiang model: joint assimilating streamflow and multi-source soil moisture data via the asynchronous ensemble Kalman filter with enhanced error models |
| title_full | State updating of the Xin'anjiang model: joint assimilating streamflow and multi-source soil moisture data via the asynchronous ensemble Kalman filter with enhanced error models |
| title_fullStr | State updating of the Xin'anjiang model: joint assimilating streamflow and multi-source soil moisture data via the asynchronous ensemble Kalman filter with enhanced error models |
| title_full_unstemmed | State updating of the Xin'anjiang model: joint assimilating streamflow and multi-source soil moisture data via the asynchronous ensemble Kalman filter with enhanced error models |
| title_short | State updating of the Xin'anjiang model: joint assimilating streamflow and multi-source soil moisture data via the asynchronous ensemble Kalman filter with enhanced error models |
| title_sort | state updating of the xin anjiang model joint assimilating streamflow and multi source soil moisture data via the asynchronous ensemble kalman filter with enhanced error models |
| url | https://hess.copernicus.org/articles/29/335/2025/hess-29-335-2025.pdf |
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