Downscaling inversion of GRACE-derived groundwater storage changes based on ensemble learning
Gravity Recovery and Climate Experiment (GRACE) satellite data monitors changes in terrestrial water storage, including groundwater, at a regional scale. However, the coarse spatial resolution limits its applicability to small watershed areas. This study introduces a novel ensemble learning-based mo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17538947.2023.2242316 |
| _version_ | 1797678405635801088 |
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| author | Pengao Li Haiyang Yu Peng Zhou Ping Zhang Ruili Wang |
| author_facet | Pengao Li Haiyang Yu Peng Zhou Ping Zhang Ruili Wang |
| author_sort | Pengao Li |
| collection | DOAJ |
| description | Gravity Recovery and Climate Experiment (GRACE) satellite data monitors changes in terrestrial water storage, including groundwater, at a regional scale. However, the coarse spatial resolution limits its applicability to small watershed areas. This study introduces a novel ensemble learning-based model using meteorological and topographical data to enhance spatial resolution. The effectiveness was evaluated using groundwater-level observation data from the Henan rainstorm-affected area in July 2021. The factors influencing Groundwater Storage Anomalies (GWSA) were explored using Permutation Importance (PI) and other methods. The results demonstrate that feature engineering and Blender ensemble learning improve downscaling accuracy; the Root Mean Square Error (RMSE) can be reduced by up to 18.95%. Furthermore, Blender ensemble learning decreased the RMSE by 3.58%, achieving an R-Square (R2) value of 0.7924. Restricting the downscaling inversion to June–August data greatly enhanced the accuracy, as evidenced by a holdout dataset test with an R2 value of 0.8247. The overall GWSA variation from January to August exhibited ‘slow rise, slow fall, sharp fall, and sharp rise.’ Additionally, heavy rain exhibits a lag effect on the groundwater supply. Meteorological and topographical factors drive fluctuations in GWSA values and changes in spatial distribution. Human activities also have a significant impact. |
| first_indexed | 2024-03-11T22:59:18Z |
| format | Article |
| id | doaj.art-1bd9999308e24cacb8419be2d772eb7d |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T22:59:18Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-1bd9999308e24cacb8419be2d772eb7d2023-09-21T15:09:03ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552023-12-011612998302210.1080/17538947.2023.22423162242316Downscaling inversion of GRACE-derived groundwater storage changes based on ensemble learningPengao Li0Haiyang Yu1Peng Zhou2Ping Zhang3Ruili Wang4Henan Polytechnic UniversityHenan Polytechnic UniversityHenan Polytechnic UniversityHenan Polytechnic UniversityHenan Polytechnic UniversityGravity Recovery and Climate Experiment (GRACE) satellite data monitors changes in terrestrial water storage, including groundwater, at a regional scale. However, the coarse spatial resolution limits its applicability to small watershed areas. This study introduces a novel ensemble learning-based model using meteorological and topographical data to enhance spatial resolution. The effectiveness was evaluated using groundwater-level observation data from the Henan rainstorm-affected area in July 2021. The factors influencing Groundwater Storage Anomalies (GWSA) were explored using Permutation Importance (PI) and other methods. The results demonstrate that feature engineering and Blender ensemble learning improve downscaling accuracy; the Root Mean Square Error (RMSE) can be reduced by up to 18.95%. Furthermore, Blender ensemble learning decreased the RMSE by 3.58%, achieving an R-Square (R2) value of 0.7924. Restricting the downscaling inversion to June–August data greatly enhanced the accuracy, as evidenced by a holdout dataset test with an R2 value of 0.8247. The overall GWSA variation from January to August exhibited ‘slow rise, slow fall, sharp fall, and sharp rise.’ Additionally, heavy rain exhibits a lag effect on the groundwater supply. Meteorological and topographical factors drive fluctuations in GWSA values and changes in spatial distribution. Human activities also have a significant impact.http://dx.doi.org/10.1080/17538947.2023.2242316grace gravity satellitesensemble learning modelgroundwater reserve‘7.20’ henan rainstorm |
| spellingShingle | Pengao Li Haiyang Yu Peng Zhou Ping Zhang Ruili Wang Downscaling inversion of GRACE-derived groundwater storage changes based on ensemble learning International Journal of Digital Earth grace gravity satellites ensemble learning model groundwater reserve ‘7.20’ henan rainstorm |
| title | Downscaling inversion of GRACE-derived groundwater storage changes based on ensemble learning |
| title_full | Downscaling inversion of GRACE-derived groundwater storage changes based on ensemble learning |
| title_fullStr | Downscaling inversion of GRACE-derived groundwater storage changes based on ensemble learning |
| title_full_unstemmed | Downscaling inversion of GRACE-derived groundwater storage changes based on ensemble learning |
| title_short | Downscaling inversion of GRACE-derived groundwater storage changes based on ensemble learning |
| title_sort | downscaling inversion of grace derived groundwater storage changes based on ensemble learning |
| topic | grace gravity satellites ensemble learning model groundwater reserve ‘7.20’ henan rainstorm |
| url | http://dx.doi.org/10.1080/17538947.2023.2242316 |
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