30 citations as recorded by crossref.

1. Revealing the spatiotemporal evolution of the 2024 extreme flood in Guangdong Province: Insights from GRACE-FO and in situ measurements S. Liu et al. https://doi.org/10.1016/j.ejrh.2025.102451
2. A novel spatial downscaling algorithm based on deep learning considering geographical spatial heterogeneity and nonlinear changes: a case study of the Yangtze River Basin C. Luo et al. https://doi.org/10.1016/j.engappai.2025.113328
3. Deep learning-aided temporal downscaling of GRACE-derived terrestrial water storage anomalies across the Contiguous United States M. Uz et al. https://doi.org/10.1016/j.jhydrol.2024.132194
4. Multi-reservoirs joint flood control scheduling using a two-layer hedging robust optimization method under uncertain inflows X. Yu et al. https://doi.org/10.1016/j.ejrh.2025.102244
5. Decomposition-based reconstruction scheme for GRACE data with irregular temporal intervals Z. Yuan & X. Chen https://doi.org/10.1016/j.jhydrol.2025.134011
6. A Framework for Characterization of Drought Events at the Basin Scale Based on High-Resolution Terrestrial Water Storage Reconstruction Z. Yuan et al. https://doi.org/10.1109/JSTARS.2025.3600026
7. Long short-term memory exploitation of satellite gravimetry to infer floods O. Sorkhabi & J. Awange https://doi.org/10.1016/j.jag.2025.104562
8. A semi-supervised LSTM framework for spatiotemporal downscaling of GRACE-derived terrestrial water storage anomalies to improve flood monitoring in the Yarlung Tsangpo River basin Y. Hu et al. https://doi.org/10.1016/j.jhydrol.2026.135275
9. Evaluating Terrestrial Water Storage, Fluxes, and Drivers in the Pearl River Basin from Downscaled GRACE/GFO and Hydrometeorological Data Y. Xiong et al. https://doi.org/10.3390/rs17233816
10. Significant variations in terrestrial water flux in mainland China during 2024 using GRACE-FO: impacts of extreme climate events Y. Zhong et al. https://doi.org/10.1016/j.jag.2025.104875
11. Applying Reconstructed Daily Water Storage and Modified Wetness Index to Flood Monitoring: A Case Study in the Yangtze River Basin C. Xiao et al. https://doi.org/10.3390/rs15123192
12. Benchmarking multi-model evapotranspiration in the Yangtze River Basin using GNSS/GRACE fusion and anthropogenic disturbance quantification C. Chen et al. https://doi.org/10.1016/j.ejrh.2026.103279
13. Spatiotemporal evolution of 1998 extreme flood event in the Yangtze River basin from the perspective of the reconstructed GRACE/GRACE-FO data L. Cui et al. https://doi.org/10.1016/j.ejrh.2025.102551
14. Evaluating flood potential in the Mahanadi River Basin, India, using Gravity Recovery and Climate Experiment (GRACE) data and topographic flood susceptibility index under non-stationary framework S. Bhere & M. Reddy https://doi.org/10.1007/s11356-024-32105-7
15. Assessing and attributing flood potential in Brazil using GPS 3D deformation X. Yang et al. https://doi.org/10.1016/j.rse.2024.114535
16. Monitoring Significant Terrestrial Water Storage Increase From the “237” Extreme Flood Around the Haihe River Basin Using GRACE-FO and Precipitation Reconstruction J. Zhou et al. https://doi.org/10.1109/JSTARS.2025.3568893
17. Spatiotemporal Variability of Current and Future Sub‐Daily Rainfall in Japan Using State‐Of‐The‐Art High‐Quality Data Sets W. Zhao et al. https://doi.org/10.1029/2022WR034305
18. Global daily mascon solutions at 330 kilometers from line-of-sight gravity differences for rapid variation analysis J. Ding et al. https://doi.org/10.1038/s43247-026-03424-z
19. Spatial downscaling of GRACE terrestrial water storage anomalies for drought and flood potential assessment G. Yin et al. https://doi.org/10.1016/j.jhydrol.2025.133144
20. Seasonal catchment memory of high mountain rivers in the Tibetan Plateau H. Gu et al. https://doi.org/10.1038/s41467-023-38966-9
21. Fusion and Analysis of Multi-Source Precipitation Data (2003–2021) in the Yangtze River Basin R. Sun et al. https://doi.org/10.3390/rs18081191
22. A three-dimensional Copula-based standardized temperature precipitation evapotranspiration water storage index for comprehensive drought monitoring X. Huang et al. https://doi.org/10.1016/j.jhydrol.2025.133898
23. Approaching the 1.5 °C warming threshold and escalating seasonal warm extremes in the Yangtze River Basin A. Dilawar et al. https://doi.org/10.1016/j.ejrh.2025.102744
24. Identification of Driving Factors of Long-Term Terrestrial Water Storage Anomaly Trend Changes in the Yangtze River Basin Based on Multisource Data and Geographical Detector Method Q. Li et al. https://doi.org/10.3390/w17192914
25. Exploring the utility of GRACE measurements for characterizing karst systems at a continental scale C. Orazulike et al. https://doi.org/10.1016/j.jhydrol.2024.132578
26. Downscaling and Gap-Filling GRACE-Based Terrestrial Water Storage Anomalies in the Qinghai–Tibet Plateau Using Deep Learning and Multi-Source Data J. Chen et al. https://doi.org/10.3390/rs17081333
27. The Flow of the Yangtze River Inverted From a Continuous Global Navigation Satellite System Station R. Zou et al. https://doi.org/10.1029/2023GL104481
28. Identification of drought-flood abrupt alternation thresholds triggering vegetation loss L. Wang et al. https://doi.org/10.1016/j.jclepro.2025.147392
29. High-resolution groundwater storage anomalies in the Middle and Lower Yangtze River Basin of China using machine learning fusion of in-situ wells, satellite gravity and hydrological model L. Hu et al. https://doi.org/10.1016/j.jenvman.2025.124322
30. Exploring potential drivers of terrestrial water storage anomaly trends in the Yangtze River Basin (2002–2019) J. Wang et al. https://doi.org/10.1016/j.ejrh.2025.102264