44 citations as recorded by crossref.

1. Unlocking the ecohydrological dynamics of vegetation growth's impact on Terrestrial Water Storage trends across the China-Pakistan Economic Corridor T. Javed et al. https://doi.org/10.1016/j.scitotenv.2024.176977
2. Enhancing water science in Earth’s second lung: AI-generated centenary hydrological insights from two decades of satellite data in the Congo Basin J. Awange & J. Wang https://doi.org/10.1016/j.srs.2026.100402
3. Revealing temporal variation of baseflow and its underlying causes in the source region of the Yangtze River (China) G. Wu et al. https://doi.org/10.2166/nh.2024.185
4. Modeling decadal snow and ice dynamics and their hydrological impacts in the Balkhash Lake Basin, Central Asia G. Guojing et al. https://doi.org/10.1016/j.jaridl.2026.04.001
5. Reconstruction of climate-driven global terrestrial water storage variations (2002–2021) using a four-parameter linear recursive model P. Xie & S. Yi https://doi.org/10.5194/hess-30-3221-2026
6. Reevaluating hydrological dynamics in the Yangtze River basin and the Pearl River basin using GRACE TWSA series reconstructed with gap-filling method W. Rao et al. https://doi.org/10.1016/j.ejrh.2026.103226
7. Investigating long-term terrestrial water storage change in the Volga River basin based on the extended triple collocation weighted fusion model L. Li et al. https://doi.org/10.1016/j.srs.2026.100422
8. Response time and lag time of vegetation drought to meteorological, soil, and groundwater droughts: Non-stationary, linear and nonlinear perspectives C. Xie et al. https://doi.org/10.1016/j.ejrh.2026.103493
9. Drivers of the pre-season drought thresholds triggering earlier autumn foliar senescence in the Northern Hemisphere W. Yan et al. https://doi.org/10.1038/s41467-025-62847-y
10. A Multidimensional Hotspot Assessment of Long-Term Terrestrial Water Storage Anomaly Change Across China X. Huang et al. https://doi.org/10.3390/rs18152478
11. Similarities and divergent patterns in hydrologic fluxes and storages simulated by global water models A. Tiwari et al. https://doi.org/10.1038/s44221-025-00435-6
12. Excess water availability in northern mid-high latitudes contiguously migrated from ocean under climate change Y. Guan et al. https://doi.org/10.1126/sciadv.adv0282
13. Impact of atmospheric circulations on droughts and drought propagation over China Z. Gu et al. https://doi.org/10.1007/s11430-023-1329-x
14. Global terrestrial drought and its projected socioeconomic implications under different warming targets N. He et al. https://doi.org/10.1016/j.scitotenv.2024.174292
15. A combination of time-variable gravity field solutions from multi-satellite datasets (1993–2024) via constrained collocation model L. Zhang et al. https://doi.org/10.5194/essd-18-5167-2026
16. Improved grid-based semi-centennial reconstruction and diagnostic assessment of global water storage leveraging ensemble machine learning R. Anjaneyulu et al. https://doi.org/10.1007/s00190-026-02098-x
17. Interpreting Global Terrestrial Water Storage Dynamics and Drivers with Explainable Deep Learning H. Huang et al. https://doi.org/10.3390/rs17132118
18. Reconstruction and evolution analysis of long-term terrestrial water storage anomalies in Xinjiang based on time series decomposition and multi-model coupling Z. Li et al. https://doi.org/10.1016/j.jhydrol.2026.136007
19. On the evaluation of global terrestrial water storage and divergent governing factors centring policymaking R. Anjaneyulu & . Abhishek https://doi.org/10.1016/j.jenvman.2025.128455
20. GRAiCE: reconstructing terrestrial water storage anomalies with recurrent neural networks I. Palazzoli et al. https://doi.org/10.1038/s41597-025-04403-3
21. Spatiotemporal correlations and driving factors of multiple drought in Central Asia K. Sun et al. https://doi.org/10.1016/j.atmosres.2025.108199
22. 中国陆域干旱的大气环流机制及旱情传播规律 子. 顾 et al. https://doi.org/10.1360/SSTe-2023-0245
23. An Integrated Drought Index (Vapor Pressure Deficit–Soil Moisture–Sun-Induced Chlorophyll Fluorescence Dryness Index, VMFDI) Based on Multisource Data and Its Applications in Agricultural Drought Management C. Deng et al. https://doi.org/10.3390/rs16244666
24. Assessments of eight ET products based on long-term multi-source water balance elements in 133 catchments across China X. Fu et al. https://doi.org/10.1016/j.jhydrol.2026.135928
25. An Ensemble Machine Learning Approach for High-Resolution Estimation of Groundwater Storage Anomalies Y. Yuan et al. https://doi.org/10.3390/w17101445
26. A knowledge map-based review of GRACE satellite applications across multiple domains: challenges and future directions J. Sun et al. https://doi.org/10.1007/s11600-026-01827-2
27. Drought characteristics and propagation patterns in the Yangtze River Basin during 2002–2022 C. Wang et al. https://doi.org/10.1007/s00382-026-08206-z
28. Challenges and opportunities of ML and explainable AI in large-sample hydrology L. Slater et al. https://doi.org/10.1098/rsta.2024.0287
29. A novel XGBoost-based approach for reconstruction terrestrial water storage variations with GNSS in the Northeastern Tibetan Plateau T. Zhang et al. https://doi.org/10.1016/j.jhydrol.2025.133255
30. Towards a Global Water Use Scarcity Risk Assessment Framework: Integration of Remote Sensing and Geospatial Datasets Y. Wang et al. https://doi.org/10.3390/rs17243999
31. Warming-induced intensifying compound hot-dry events lead to disproportionate losses in vegetation productivity J. Liu et al. https://doi.org/10.1007/s00704-026-06466-7
32. Retrievals and simulations of terrestrial water storage changes and runoff over the Tibetan Plateau: Challenges and opportunities X. Li et al. https://doi.org/10.1016/j.fmre.2025.11.012
33. Decadal prediction of terrestrial water storage and sea-level change using physics-informed neural networks M. Kiani Shahvandi https://doi.org/10.1016/j.geog.2026.07.002
34. Reconstructed centennial precipitation-driven water storage anomalies in the Nile River Basin using RecNet and their suitability for studying ENSO and IOD impacts J. Wang et al. https://doi.org/10.1016/j.jhydrol.2024.132272
35. Knowledge-guided hybrid AI framework for GRACE data reconstruction and generalization in South Asia: A spatiotemporal perspective P. Paramaguru et al. https://doi.org/10.1016/j.envsoft.2026.106976
36. Annual memory in the terrestrial water cycle W. Berghuijs et al. https://doi.org/10.5194/hess-29-1319-2025
37. Integrating Hidden Markov and Multinomial models for hydrological drought prediction under nonstationarity M. Santos & L. Slater https://doi.org/10.1016/j.advwatres.2025.104974
38. Exploring the Main Driving Factors for Terrestrial Water Storage in China Using Explainable Machine Learning X. Ma et al. https://doi.org/10.3390/rs17122078
39. Advances in GRACE satellite studies on terrestrial water storage: a comprehensive review J. Karki et al. https://doi.org/10.1080/10106049.2025.2482706
40. A Machine Learning approach for Total Water storage anomaly eXtension back to 1980 (ML-TWiX) P. Saemian et al. https://doi.org/10.1038/s41597-026-06604-w
41. Improving understanding of drought using extended and downscaled GRACE data in the Pearl River Basin X. Wan et al. https://doi.org/10.1016/j.ejrh.2025.102277
42. A 6-hourly 0.1° resolution freezing rain dataset of China during 2000–2019 based on deep kernel learning J. Liu et al. https://doi.org/10.1038/s41597-025-04582-z
43. Decomposition-based reconstruction scheme for GRACE data with irregular temporal intervals Z. Yuan & X. Chen https://doi.org/10.1016/j.jhydrol.2025.134011
44. Revealing groundwater depletion and seasonal dynamics in Northwest China by integrating GRACE with physically based and data-driven modeling M. Li et al. https://doi.org/10.1016/j.jhydrol.2025.134735