Expanding groundwater drawdown funnels in North China Plain: A new insight from satellite gravimetry

Jiaqi Yao; Huiqing Hao; Mengran Wang; Walter A. Illman; Guoqiang Wang; Tian-Chyi Jim Yeh; Peng Gong; Qingquan Li; Nan Xu; Yonghong Hao; Hui Lu

doi:10.59717/j.tiw.2026.100010

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Expanding groundwater drawdown funnels in North China Plain: A new insight from satellite gravimetry

1.
Academy of Ecological Civilization Development for JING-JIN-JI Megalopolis, Tianjin Normal University, Tianjin 300387, China
2.
Faculty of Geography, Tianjin Normal University, Tianjin 300387, China
3.
Department of Earth and Environmental Sciences, University of Waterloo, Waterloo ON N2L 3G1, Canada
4.
Tianjin Key Lab of Water Resources and Environment, Tianjin Normal University, Tianjin 300387, China
5.
Advanced Interdisciplinary Institute of Satellite Applications, Beijing Normal University, Beijing 100875, China
6.
Department of Hydrology and Atmospheric Sciences, The University of Arizona, John W. Harshbarger Building, 1133 E. North Campus Drive, Tucson AZ 85721-0011, USA
7.
Department of Geography, Department of Earth Science, The University of Hong Kong, Pokfulam, Hong Kong SAR 999077, China
8.
MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
9.
School of Architecture & Urban Planning, Shenzhen University, Shenzhen 518060, China
10.
Department of Earth System Science, Institute for Global Change Studies, Tsinghua University, Beijing 100084, China
11.
These authors contributed equally

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Corresponding authors: hhuxunan@gmail.com (N.X.); haoyh@sxu.edu.cn (Y.H.); luhui@tsinghua.edu.cn (H.L.)
Received Date: February 01, 2026

Accepted Date: July 18, 2026

Published Online: July 23, 2026

The Innovation Water https://doi.org/10.59717/j.tiw.2026.100010 | Cite this article

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PUBLIC SUMMARY

PUBLIC SUMMARY
1. Blind downscaling reveals 5 km funnel expansion trends in North China Plain.
  
  Funnel morphology changed with > 30 m deepening and horizontal broadening.
  
  Response time rose from 20 to 60 months, indicating aquifer buffering decline.
  
  Causal analysis shows early climate control, later pumping, then partial rebound.

ABSTRACT

ABSTRACT

Quantifying the evolution and recovery of groundwater drawdown funnels in the North China Plain (NCP) remains a grand challenge due to limited spatial resolution, vertical ambiguity, and attribution complexity in current satellite-based monitoring. Here, we present an integrative framework that addresses these bottlenecks by combining data-driven downscaling of GRACE gravimetric data, stratified signal separation using multi-depth well observations, and causality analysis driven by climate and groundwater abstraction indicators. This framework enables ~5 km resolution groundwater storage reconstruction, delineates 3D funnel morphology via Gaussian fitting, and attributes dynamic changes through wavelet-informed causality inference. Our key findings include: (1) The NCP groundwater system experienced a three-stage trajectory—initial slow decline (–0.05 cm/year), accelerated depletion (-0.19 cm/year), and partial recovery (+0.16 cm/year)—with funnel radius expanding from 209 km in 2002 to 340 km in 2022. (2) Funnel morphology transformed substantially, with the lowest groundwater level deepening from 20.93 m to 52.95 m and lateral half-width expanding from 0.56 m to 4.01 m over two decades. (3) The combined analysis of the transfer function noise (TFN) model and causality analysis based on information flow further revealed that early funnel changes were dominated by climate (~75% contribution), later shifts by intensive pumping (~90%), and recent recovery partially driven by precipitation (~16%), with aquifer response time increasing from 20 to 60 months, indicating a weakened response and delayed recovery. The lagged responses revealed by the causality analysis redefines our understanding of anthropogenic impacts on aquifer systems and offers a transferable path toward sustainable groundwater governance under changing climate and socioeconomic pressures.
- Groundwater funnel /
- Blind downscaling /
- Transfer function noise /
- Causal information flow /
- Gravity satellite /
- Deep learning.

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REFERENCES

[1]	Jasechko S., Seybold H., Perrone D., et al. (2024). Rapid groundwater decline and some cases of recovery in aquifers globally. Nature 625:715−721. DOI:10.1038/s41586-023-06879-8 View in Article CrossRef Google Scholar
[2]	Liu P.-W., Famiglietti J. S., Purdy A. J., et al. (2022). Groundwater depletion in California’s Central Valley accelerates during megadrought. Nat. Commun. 13:7825. DOI:10.1038/s41467-022-35582-x View in Article CrossRef Google Scholar
[3]	Cuthbert M., Gleeson T., Bierkens M., et al. (2024). Concerns regarding proposed groundwater Earth system boundary. Nature 635:E4−E5. DOI:10.1038/s41586-023-06083-8 View in Article CrossRef Google Scholar
[4]	Hasan M. F., Smith R., Vajedian S., et al. (2023). Global land subsidence mapping reveals widespread loss of aquifer storage capacity. Nat. Commun. 14:6180. DOI:10.1038/s41467-023-41933-z View in Article CrossRef Google Scholar
[5]	Zhong Y., Zhong M., Feng W., et al. (2018). Groundwater depletion in the West Liaohe River Basin, China and its implications revealed by GRACE and in situ measurements. Remote Sens. 10:493. DOI:10.3390/rs10040493 View in Article CrossRef Google Scholar
[6]	Feng T., Shen Y., Chen Q., et al. (2022). Groundwater storage change and driving factor analysis in north china using independent component decomposition. J. Hydrol. 609:127708. DOI:10.1016/j.jhydrol.2022.127708 View in Article CrossRef Google Scholar
[7]	Yang W., Long D., Scanlon B. R., et al. (2022). Human intervention will stabilize groundwater storage across the North China Plain. Water Resour. Res. 58:e2021WR030884. DOI:10.1029/2021WR030884 View in Article CrossRef Google Scholar
[8]	Long D., Scanlon B. R., Longuevergne L., et al. (2013). GRACE satellite monitoring of large depletion in water storage in response to the 2011 drought in Texas. Geophys. Res. Lett. 40:3395−3401. DOI:10.1002/grl.50655 View in Article CrossRef Google Scholar
[9]	Rodell M., Famiglietti J. S., Wiese D. N., et al. (2018). Emerging trends in global freshwater availability. Nature 557:651−659. DOI:10.1038/s41586-018-0123-1 View in Article CrossRef Google Scholar
[10]	Gou J. and Soja B. (2024). Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms. Nat. Water 2:139−150. DOI:10.1038/s44221-024-00194-w View in Article CrossRef Google Scholar
[11]	Chen Y., Wang Y., Li L., et al. (2024). Monthly monitoring of inundated areas and water storage dynamics in China's large reservoirs using multisource remote sensing. Water Resour. Res. 60:e2023WR036450. DOI:10.1029/2023WR036450 View in Article CrossRef Google Scholar
[12]	Zhao J., Li G., Zhu Z., et al. (2024). Analysis of the spatiotemporal variation of groundwater storage in Ordos Basin based on GRACE gravity satellite data. J. Hydrol. 632:130931. DOI:10.1016/j.jhydrol.2024.130931 View in Article CrossRef Google Scholar
[13]	Rateb A., Scanlon B. R., Pool D. R., et al. (2020). Comparison of groundwater storage changes from GRACE satellites with monitoring and modeling of major US aquifers. Water Resour. Res. 56:e2020WR027556. DOI:10.1029/2020WR027556 View in Article CrossRef Google Scholar
[14]	Bhanja S. N., Mukherjee A., Saha D., et al. (2016). Validation of GRACE based groundwater storage anomaly using in-situ groundwater level measurements in India. J. Hydrol. 543:729−738. DOI:10.1016/j.jhydrol.2016.10.042 View in Article CrossRef Google Scholar
[15]	Feng W., Zhong M., Lemoine J. M., et al. (2013). Evaluation of groundwater depletion in North China using the gravity recovery and climate experiment (GRACE) data and ground‐based measurements. Water Resour. Res. 49:2110−2118. DOI:10.1002/wrcr.20192 View in Article CrossRef Google Scholar
[16]	Frappart F. and Ramillien G. (2018). Monitoring groundwater storage changes using the Gravity Recovery and Climate Experiment (GRACE) satellite mission: A review. Remote Sens. 10:829. DOI:10.3390/rs10060829 View in Article CrossRef Google Scholar
[17]	Li H., Pan Y., Huang Z., et al. (2023). A new GRACE downscaling approach for deriving high-resolution groundwater storage changes using ground-based scaling factors. Water Resour. Res. 60:e2023WR035210. DOI:10.1029/2023WR035210 View in Article CrossRef Google Scholar
[18]	Wang Y., Li C., Cui Y., et al. (2024). Spatial downscaling of GRACE-derived groundwater storage changes across diverse climates and human interventions with Random Forests. J. Hydrol. 640:131708. DOI:10.1016/j.jhydrol.2024.131708 View in Article CrossRef Google Scholar
[19]	Chen L., He Q., Liu K., et al. (2019). Downscaling of GRACE-derived groundwater storage based on the random forest model. Remote Sens. 11:2979. DOI:10.3390/rs11242979 View in Article CrossRef Google Scholar
[20]	Yin W., Hu L., Zhang M., et al. (2018). Statistical downscaling of GRACE‐derived groundwater storage using ET data in the North China plain. J. Geophys. Res. Atmos. 123:5973−5987. DOI:10.1029/2017JD027468 View in Article CrossRef Google Scholar
[21]	Long D., Pan Y., Zhou J., et al. (2017). Global analysis of spatiotemporal variability in merged total water storage changes using multiple GRACE products and global hydrological models. Remote Sens. Environ. 192:198−216. DOI:10.1016/j.rse.2017.02.011 View in Article CrossRef Google Scholar
[22]	Reichstein M., Camps-Valls G., Stevens B., et al. (2019). Deep learning and process understanding for data-driven Earth system science. Nature 566:195−204. DOI:10.1038/s41586-019-0912-1 View in Article CrossRef Google Scholar
[23]	Humphrey V. and Gudmundsson L. (2019). GRACE-REC: A reconstruction of climate-driven water storage changes over the last century. Earth Syst. Sci. Data 11:1153−1170. DOI:10.5194/essd-11-1153-2019 View in Article CrossRef Google Scholar
[24]	Kalu I., Ndehedehe C. E., Ferreira V. G., et al. (2024). Statistical downscaling of GRACE terrestrial water storage changes based on the Australian Water Outlook model. Sci. Rep. 14:10113. DOI:10.1038/s41598-024-60366-2 View in Article CrossRef Google Scholar
[25]	Liang X. S. (2014). Unraveling the cause-effect relation between time series. Phys. Rev. E 90:052150. DOI:10.1103/PhysRevE.90.052150 View in Article CrossRef Google Scholar
[26]	Thomas B. F. and Famiglietti J. S. (2019). Identifying climate-induced groundwater depletion in GRACE observations. Sci. Rep. 9:4124. DOI:10.1038/s41598-019-40155-y View in Article CrossRef Google Scholar
[27]	Li P., Zha Y., Shi L., et al. (2021). Identification of the terrestrial water storage change features in the North China Plain via independent component analysis. J. Hydrol. Reg. Stud. 38:100955. DOI:10.1016/j.ejrh.2021.100955 View in Article CrossRef Google Scholar
[28]	Alam S., Gebremichael M., Ban Z., et al. (2021). Post‐drought groundwater storage recovery in California's Central Valley. Water Resour. Res. 57:e2021WR030352. DOI:10.1029/2021WR030352 View in Article CrossRef Google Scholar
[29]	Li M., Sun J., Xue L., et al. (2025). Characterizing aquifer properties and groundwater storage at North China Plain using geodetic and hydrological measurements. Water Resour. Res. 61:e2024WR037425. DOI: 10.1029/2024WR037425 View in Article Google Scholar
[30]	Gong H., Pan Y., Zheng L., et al. (2018). Long-term groundwater storage changes and land subsidence development in the North China Plain (1971–2015). Hydrogeol. J. 26:1417−1427. DOI:10.1007/s10040-018-1768-4 View in Article CrossRef Google Scholar
[31]	Cai X. (2008). Water stress, water transfer and social equity in Northern China-implications for policy reforms. J. Environ. Manage. 87:14–25. DOI:10.1016/j.jenvman.2006.12.046 View in Article Google Scholar
[32]	Chen X., Wang P., Muhammad T., et al. (2020). Subsystem-level groundwater footprint assessment in North China Plain – The world’s largest groundwater depression cone. Ecol. Indicators 117:106662. DOI: 10.1016/j.ecolind.2020.106662 View in Article Google Scholar
[33]	Yang J. and Huang X. (2021). The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019. Earth Syst. Sci. Data 13:3907−3925. DOI:10.5194/essd-13-3907-2021 View in Article CrossRef Google Scholar
[34]	Zongyu C., Yuhong F., Jinxiang M., et al. (2009). Analysis on evolution of groundwater depression cones and its leading factors in North China Plain. Resour. Sci. 31:394–399. https://www.resci.cn/EN/Y2009/V31/I3/394 View in Article Google Scholar
[35]	Zhao X., Yu Y., Cheng J., et al. (2024). A novel framework for heterogeneity decomposition and mechanism inference in spatiotemporal evolution of groundwater storage: Case study in the North China Plain. Water Resour. Res. 60:e2023WR036102. DOI:10.1029/2023wr036102 View in Article Google Scholar
[36]	He H., Yang K., Wang S., et al. (2021). Deep learning approaches to spatial downscaling of GRACE terrestrial water storage products using EALCO model over Canada. Canadian J. Remote Sens. 47:657−675. DOI:10.1080/07038992.2021.1954498 View in Article CrossRef Google Scholar
[37]	von Asmuth J. R., Bierkens M. F. and Maas K. (2002). Transfer function‐noise modeling in continuous time using predefined impulse response functions. Water Resour. Res. 38:23–21–23–12. DOI: 10.1029/2001WR001136 View in Article Google Scholar
[38]	Collenteur R. A., Bakker M., Klammler G., et al. (2021). Estimation of groundwater recharge from groundwater levels using nonlinear transfer function noise models and comparison to lysimeter data. Hydrol. Earth Syst. Sci. 25:2931−2949. DOI:10.5194/hess-25-2931-2021 View in Article CrossRef Google Scholar
[39]	Liang X. S., Xu F., Rong Y., et al. (2021). El Niño Modoki can be mostly predicted more than 10 years ahead of time. Sci. Rep. 11:17860. DOI:10.1038/s41598-021-97111-y View in Article CrossRef Google Scholar

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Yao J., Hao H., Wang M., et al. (2026). Expanding groundwater drawdown funnels in North China Plain: A new insight from satellite gravimetry. The Innovation Water 1:100010. https://doi.org/10.59717/j.tiw.2026.100010

Yao J., Hao H., Wang M., et al. (2026). Expanding groundwater drawdown funnels in North China Plain: A new insight from satellite gravimetry. The Innovation Water 1:100010. https://doi.org/10.59717/j.tiw.2026.100010

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Expanding groundwater drawdown funnels in North China Plain: A new insight from satellite gravimetry

GRAPHICAL ABSTRACT

PUBLIC SUMMARY

ABSTRACT