Accurate GRACE terrestrial water storage estimations via a new fusion method
The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On missions hold a pivotal role in exploring global mass change and migration. However, inconsistencies in terrestrial water storage (TWS), arising from variations in geophysical background models and processing techniques among di...
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Taylor & Francis Group
2025-08-01
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| Series: | Geo-spatial Information Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2025.2543967 |
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| author | Min Dai Hao Zhou Wenjing Ma Shuyun Zheng Mingyang Xia Yaozong Li Zhicai Luo |
| author_facet | Min Dai Hao Zhou Wenjing Ma Shuyun Zheng Mingyang Xia Yaozong Li Zhicai Luo |
| author_sort | Min Dai |
| collection | DOAJ |
| description | The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On missions hold a pivotal role in exploring global mass change and migration. However, inconsistencies in terrestrial water storage (TWS), arising from variations in geophysical background models and processing techniques among different products, hinder the global and regional applicability of GRACE. The absence of independent observations at the commensurate scale compromises detection accuracy and reliability when relying on a single-institution solution. Urgently addressing this issue by optimizing different products with rigorous algorithms is crucial to enhance the comprehension of global and regional TWS variability. We employ the generalized three-cornered hat (GTCH) method to quantify uncertainties of different GRACE products and integrate it with the Bayesian model averaging (BMA) method to construct a new fusion algorithm, GTCH+BMA, which generates integrated TWS products. At the basin scale, fused TWS products exhibit excellent reliability in capturing annual amplitude and trend variations. In comparison with the single-institution solution, the fusion products effectively attenuate noise and enhance the SNR, enabling a 59.7% reduction in the average uncertainty and an 8.05-fold improvement in the SNR, especially in regions with weak hydrological signals. A 62.8% reduction in average uncertainty and a 1.54-fold improvement in the SNR are similarly achieved globally. The fusion methodology significantly improves the effectiveness of probing TWS changes globally and regionally, helping to provide a valuable basic dataset for future exploration of related environmental changes and water resources management based on reliable data. |
| format | Article |
| id | doaj-art-c62f87c1c0e54ca1b84b039a22d2846d |
| institution | Kabale University |
| issn | 1009-5020 1993-5153 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
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| series | Geo-spatial Information Science |
| spelling | doaj-art-c62f87c1c0e54ca1b84b039a22d2846d2025-08-20T03:47:02ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532025-08-0111410.1080/10095020.2025.2543967Accurate GRACE terrestrial water storage estimations via a new fusion methodMin Dai0Hao Zhou1Wenjing Ma2Shuyun Zheng3Mingyang Xia4Yaozong Li5Zhicai Luo6National Gravitation Laboratory, Institute of Geophysics, MOE Key Laboratory of Fundamental Physical Quantities Measurement, and School of Physics, Huazhong University of Science and Technology, Wuhan, ChinaNational Gravitation Laboratory, Institute of Geophysics, MOE Key Laboratory of Fundamental Physical Quantities Measurement, and School of Physics, Huazhong University of Science and Technology, Wuhan, ChinaNational Gravitation Laboratory, Institute of Geophysics, MOE Key Laboratory of Fundamental Physical Quantities Measurement, and School of Physics, Huazhong University of Science and Technology, Wuhan, ChinaNational Gravitation Laboratory, Institute of Geophysics, MOE Key Laboratory of Fundamental Physical Quantities Measurement, and School of Physics, Huazhong University of Science and Technology, Wuhan, ChinaNational Gravitation Laboratory, Institute of Geophysics, MOE Key Laboratory of Fundamental Physical Quantities Measurement, and School of Physics, Huazhong University of Science and Technology, Wuhan, ChinaNational Gravitation Laboratory, Institute of Geophysics, MOE Key Laboratory of Fundamental Physical Quantities Measurement, and School of Physics, Huazhong University of Science and Technology, Wuhan, ChinaNational Gravitation Laboratory, Institute of Geophysics, MOE Key Laboratory of Fundamental Physical Quantities Measurement, and School of Physics, Huazhong University of Science and Technology, Wuhan, ChinaThe Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On missions hold a pivotal role in exploring global mass change and migration. However, inconsistencies in terrestrial water storage (TWS), arising from variations in geophysical background models and processing techniques among different products, hinder the global and regional applicability of GRACE. The absence of independent observations at the commensurate scale compromises detection accuracy and reliability when relying on a single-institution solution. Urgently addressing this issue by optimizing different products with rigorous algorithms is crucial to enhance the comprehension of global and regional TWS variability. We employ the generalized three-cornered hat (GTCH) method to quantify uncertainties of different GRACE products and integrate it with the Bayesian model averaging (BMA) method to construct a new fusion algorithm, GTCH+BMA, which generates integrated TWS products. At the basin scale, fused TWS products exhibit excellent reliability in capturing annual amplitude and trend variations. In comparison with the single-institution solution, the fusion products effectively attenuate noise and enhance the SNR, enabling a 59.7% reduction in the average uncertainty and an 8.05-fold improvement in the SNR, especially in regions with weak hydrological signals. A 62.8% reduction in average uncertainty and a 1.54-fold improvement in the SNR are similarly achieved globally. The fusion methodology significantly improves the effectiveness of probing TWS changes globally and regionally, helping to provide a valuable basic dataset for future exploration of related environmental changes and water resources management based on reliable data.https://www.tandfonline.com/doi/10.1080/10095020.2025.2543967Multi-source GRACE datasetsgeneralized three-cornered hat methodBayesian model averaging methoddata fusionaccurate TWS products |
| spellingShingle | Min Dai Hao Zhou Wenjing Ma Shuyun Zheng Mingyang Xia Yaozong Li Zhicai Luo Accurate GRACE terrestrial water storage estimations via a new fusion method Geo-spatial Information Science Multi-source GRACE datasets generalized three-cornered hat method Bayesian model averaging method data fusion accurate TWS products |
| title | Accurate GRACE terrestrial water storage estimations via a new fusion method |
| title_full | Accurate GRACE terrestrial water storage estimations via a new fusion method |
| title_fullStr | Accurate GRACE terrestrial water storage estimations via a new fusion method |
| title_full_unstemmed | Accurate GRACE terrestrial water storage estimations via a new fusion method |
| title_short | Accurate GRACE terrestrial water storage estimations via a new fusion method |
| title_sort | accurate grace terrestrial water storage estimations via a new fusion method |
| topic | Multi-source GRACE datasets generalized three-cornered hat method Bayesian model averaging method data fusion accurate TWS products |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2025.2543967 |
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