17 citations as recorded by crossref.

1. GRAiCE: reconstructing terrestrial water storage anomalies with recurrent neural networks I. Palazzoli et al. https://doi.org/10.1038/s41597-025-04403-3
2. Sea-level fingerprints emergent from GRACE mission data S. Adhikari et al. https://doi.org/10.5194/essd-11-629-2019
3. Long‐Term Wetting and Drying Trends in Land Water Storage Derived From GRACE and CMIP5 Models L. Jensen et al. https://doi.org/10.1029/2018JD029989
4. A multimodal machine learning fused global 0.1° daily evapotranspiration dataset from 1950-2022 Q. Xu et al. https://doi.org/10.1016/j.agrformet.2025.110645
5. GRACE-REC: a reconstruction of climate-driven water storage changes over the last century V. Humphrey & L. Gudmundsson https://doi.org/10.5194/essd-11-1153-2019
6. Polar Drift in the 1990s Explained by Terrestrial Water Storage Changes S. Deng et al. https://doi.org/10.1029/2020GL092114
7. Orographic Effects of Geomorphology on Precipitation in a Pluvial Basin of the Eastern Tibetan Plateau M. Yang & W. Zhang https://doi.org/10.3390/w11020250
8. A Comprehensive Evaluation of GRACE‐Like Terrestrial Water Storage (TWS) Reconstruction Products at an Interannual Scale During 1981–2019 S. Deng et al. https://doi.org/10.1029/2022WR034381
9. Assessment of Three Common Methods for Estimating Terrestrial Water Storage Change with Three Reanalysis Datasets S. Deng et al. https://doi.org/10.1175/JCLI-D-18-0637.1
10. Contribution of snow water equivalent to the terrestrial water storage changes in High Mountain Asia based on multiple datasets Q. Li et al. https://doi.org/10.1016/j.ejrh.2025.102401
11. GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present J. Yin et al. https://doi.org/10.5194/essd-15-5597-2023
12. A basin-scale water budget calibration method for sustainable water management: A case study in the Loess Plateau, China Z. Ma et al. https://doi.org/10.1016/j.geosus.2025.100400
13. Assessing the response of terrestrial water storage to climate warming in China by coupling CMIP6 multi-model ensembles, hydrological model, and machine learning algorithms X. Cao et al. https://doi.org/10.1007/s10584-025-04082-4
14. HRU-based Downscaling of GRACE-TWS to Quantify the Hydrogeological Fluxes and Specific Yield in the Lower Middle Ganga Basin R. Kumar et al. https://doi.org/10.1016/j.jhydrol.2024.131591
15. A database of water and heat observations over grassland in the north-east of Japan W. Ma et al. https://doi.org/10.5194/essd-10-2295-2018
16. Using Satellite-Based Terrestrial Water Storage Data: A Review V. Humphrey et al. https://doi.org/10.1007/s10712-022-09754-9
17. Changes and drivers of long-term land evapotranspiration in the Yangtze River Basin: A water balance perspective H. Bai et al. https://doi.org/10.1016/j.jhydrol.2025.132763