Spatiotemporal monitoring of water storage in the North China Plain from 2002 to 2022 based on an improved GRACE downscaling method
Study region: North China Plain (NCP), a major agricultural region in China. Study focus: The coupling effects of key drivers on water storage dynamics were quantitatively analyzed, integrating frequency-domain correlation analysis to identify lag effects, which were incorporated into a Random Fores...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | Journal of Hydrology: Regional Studies |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214581825001958 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849716266543087616 |
|---|---|
| author | Jinze Tian Yu Chen Shuai Wang Xinlong Chen Huibin Cheng Xiaolong Tian Xue Wang Kun Tan |
| author_facet | Jinze Tian Yu Chen Shuai Wang Xinlong Chen Huibin Cheng Xiaolong Tian Xue Wang Kun Tan |
| author_sort | Jinze Tian |
| collection | DOAJ |
| description | Study region: North China Plain (NCP), a major agricultural region in China. Study focus: The coupling effects of key drivers on water storage dynamics were quantitatively analyzed, integrating frequency-domain correlation analysis to identify lag effects, which were incorporated into a Random Forest (RF) downscaling method. GRACE data were refined through this approach, enhancing spatial resolution while maintaining accuracy, with the aim of precisely characterizing water storage dynamics and examining its interactions with climatic and anthropogenic factors, particularly the long-term impact of groundwater fluctuations on surface deformation. New hydrological insight for the region: Terrestrial Water Storage Anomaly (TWSA), Shallow Water Storage Anomaly (SWSA), and Groundwater Storage Anomaly (GWSA) were derived for the NCP over the past two decades at a 0.25° resolution. The enhanced downscaling model demonstrated improved performance, with a higher Nash-Sutcliffe efficiency (+0.05), an increased correlation coefficient (+0.03), and a reduced root-mean-square error (-0.32 cm). From 2014–2022, interannual water storage fluctuations intensified, with divergent trends for TWSA (0.30 cm/year), SWSA (1.47 cm/year), and GWSA (-0.97 cm/year). Major influencing factors include water diversion projects, increased precipitation, and reduced societal water consumption. Surface deformation lags behind GWSA by 5–6 months, with a long-term lag of 10 months and a correlation of 0.81. These findings deepen the understanding of water storage dynamics and their impact on surface deformation in the NCP. |
| format | Article |
| id | doaj-art-ae3228c71e3e42b0afe7d2620ac35b3c |
| institution | DOAJ |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-ae3228c71e3e42b0afe7d2620ac35b3c2025-08-20T03:13:04ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-06-015910237010.1016/j.ejrh.2025.102370Spatiotemporal monitoring of water storage in the North China Plain from 2002 to 2022 based on an improved GRACE downscaling methodJinze Tian0Yu Chen1Shuai Wang2Xinlong Chen3Huibin Cheng4Xiaolong Tian5Xue Wang6Kun Tan7School of Environment Science and Spatial Informatics, China University of Mining and Technology (CUMT), Xuzhou 221116, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology (CUMT), Xuzhou 221116, China; Key Laboratory for Land Environment and Disaster Monitoring (Ministry of Natural Resource), China University of Mining and Technology, Xuzhou 221116, China; School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China; Corresponding author at: School of Environment Science and Spatial Informatics, China University of Mining and Technology (CUMT), Xuzhou 221116, China.School of Environment Science and Spatial Informatics, China University of Mining and Technology (CUMT), Xuzhou 221116, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology (CUMT), Xuzhou 221116, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology (CUMT), Xuzhou 221116, ChinaSchool of Environment Science and Spatial Informatics, China University of Mining and Technology (CUMT), Xuzhou 221116, ChinaKey Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, ChinaKey Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, ChinaStudy region: North China Plain (NCP), a major agricultural region in China. Study focus: The coupling effects of key drivers on water storage dynamics were quantitatively analyzed, integrating frequency-domain correlation analysis to identify lag effects, which were incorporated into a Random Forest (RF) downscaling method. GRACE data were refined through this approach, enhancing spatial resolution while maintaining accuracy, with the aim of precisely characterizing water storage dynamics and examining its interactions with climatic and anthropogenic factors, particularly the long-term impact of groundwater fluctuations on surface deformation. New hydrological insight for the region: Terrestrial Water Storage Anomaly (TWSA), Shallow Water Storage Anomaly (SWSA), and Groundwater Storage Anomaly (GWSA) were derived for the NCP over the past two decades at a 0.25° resolution. The enhanced downscaling model demonstrated improved performance, with a higher Nash-Sutcliffe efficiency (+0.05), an increased correlation coefficient (+0.03), and a reduced root-mean-square error (-0.32 cm). From 2014–2022, interannual water storage fluctuations intensified, with divergent trends for TWSA (0.30 cm/year), SWSA (1.47 cm/year), and GWSA (-0.97 cm/year). Major influencing factors include water diversion projects, increased precipitation, and reduced societal water consumption. Surface deformation lags behind GWSA by 5–6 months, with a long-term lag of 10 months and a correlation of 0.81. These findings deepen the understanding of water storage dynamics and their impact on surface deformation in the NCP.http://www.sciencedirect.com/science/article/pii/S2214581825001958North China PlainGRACE downscalingWater storage dynamicsDriving factorsSurface deformation |
| spellingShingle | Jinze Tian Yu Chen Shuai Wang Xinlong Chen Huibin Cheng Xiaolong Tian Xue Wang Kun Tan Spatiotemporal monitoring of water storage in the North China Plain from 2002 to 2022 based on an improved GRACE downscaling method Journal of Hydrology: Regional Studies North China Plain GRACE downscaling Water storage dynamics Driving factors Surface deformation |
| title | Spatiotemporal monitoring of water storage in the North China Plain from 2002 to 2022 based on an improved GRACE downscaling method |
| title_full | Spatiotemporal monitoring of water storage in the North China Plain from 2002 to 2022 based on an improved GRACE downscaling method |
| title_fullStr | Spatiotemporal monitoring of water storage in the North China Plain from 2002 to 2022 based on an improved GRACE downscaling method |
| title_full_unstemmed | Spatiotemporal monitoring of water storage in the North China Plain from 2002 to 2022 based on an improved GRACE downscaling method |
| title_short | Spatiotemporal monitoring of water storage in the North China Plain from 2002 to 2022 based on an improved GRACE downscaling method |
| title_sort | spatiotemporal monitoring of water storage in the north china plain from 2002 to 2022 based on an improved grace downscaling method |
| topic | North China Plain GRACE downscaling Water storage dynamics Driving factors Surface deformation |
| url | http://www.sciencedirect.com/science/article/pii/S2214581825001958 |
| work_keys_str_mv | AT jinzetian spatiotemporalmonitoringofwaterstorageinthenorthchinaplainfrom2002to2022basedonanimprovedgracedownscalingmethod AT yuchen spatiotemporalmonitoringofwaterstorageinthenorthchinaplainfrom2002to2022basedonanimprovedgracedownscalingmethod AT shuaiwang spatiotemporalmonitoringofwaterstorageinthenorthchinaplainfrom2002to2022basedonanimprovedgracedownscalingmethod AT xinlongchen spatiotemporalmonitoringofwaterstorageinthenorthchinaplainfrom2002to2022basedonanimprovedgracedownscalingmethod AT huibincheng spatiotemporalmonitoringofwaterstorageinthenorthchinaplainfrom2002to2022basedonanimprovedgracedownscalingmethod AT xiaolongtian spatiotemporalmonitoringofwaterstorageinthenorthchinaplainfrom2002to2022basedonanimprovedgracedownscalingmethod AT xuewang spatiotemporalmonitoringofwaterstorageinthenorthchinaplainfrom2002to2022basedonanimprovedgracedownscalingmethod AT kuntan spatiotemporalmonitoringofwaterstorageinthenorthchinaplainfrom2002to2022basedonanimprovedgracedownscalingmethod |