High-resolution global groundwater storage anomalies:  a 1&thinsp;km downscaled dataset

Zhang, Chen; Fan, Yifei; Han, Wenxia; Ge, Yingchun; Zhu, Jiabei; Che, Tao

doi:10.5194/essd-18-7593-2026

Articles | Volume 18, issue 10

https://doi.org/10.5194/essd-18-7593-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-18-7593-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 10

Data description article

|

19 Oct 2026

Data description article |

| 19 Oct 2026

High-resolution global groundwater storage anomalies: a 1 km downscaled dataset

Chen Zhang, Yifei Fan, Wenxia Han, Yingchun Ge, Jiabei Zhu, and Tao Che

Data sets

High-resolution Global Groundwater Storage Anomalies Dataset Y. Fan et al. https://doi.org/10.12072/ncdc.hydrology.db7323.2026

Downscaling GWSA model and datasets J. C. Zhang https://doi.org/10.6084/m9.figshare.22819976.v1

Central_Valley_Groundwater_Datasets P.-W. Liu https://doi.org/10.5281/zenodo.7392554

Short summary

We created a global monthly groundwater dataset at 1 km resolution for 2002–2020 by combining satellite observations, environmental data, and machine learning. The dataset reveals spatial groundwater variations that are difficult to see in coarse satellite observations. Evaluation with monitoring wells and regional studies supports its use for broad groundwater assessment, while local and seasonal interpretation should be checked against additional observations.