128 citations as recorded by crossref.

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2. Accounting for spatial correlation errors in the assimilation of GRACE into hydrological models through localization M. Khaki et al. https://doi.org/10.1016/j.advwatres.2017.07.024
3. A study of Bangladesh's sub-surface water storages using satellite products and data assimilation scheme M. Khaki et al. https://doi.org/10.1016/j.scitotenv.2017.12.289
4. Sodankylä manual snow survey program L. Leppänen et al. https://doi.org/10.5194/gi-5-163-2016
5. A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset J. Schellekens et al. https://doi.org/10.5194/essd-9-389-2017
6. A Framework for Characterizing Component-Wise Nonstationarity in GRACE/GRACE-FO-Derived Terrestrial Water Storage Anomalies H. Pham et al. https://doi.org/10.1007/s11053-026-10755-3
7. Assimilating GRACE observations into a hydrological model in the Yangtze River Basin: The role of the Three Gorges Reservoir J. Huang et al. https://doi.org/10.1016/j.jag.2026.105577
8. Progress in joint application of crop models and hydrological models Y. You et al. https://doi.org/10.1016/j.agwat.2024.108746
9. Comparison and Assessment of Three Advanced Land Surface Models in Simulating Terrestrial Water Storage Components over the United States Y. Xia et al. https://doi.org/10.1175/JHM-D-16-0112.1
10. Global-Scale Evaluation of XCO2 Products from GOSAT, OCO-2 and CarbonTracker Using Direct Comparison and Triple Collocation Method Y. Chen et al. https://doi.org/10.3390/rs14225635
11. Hydrological trends captured by assimilating GRACE total water storage data into the CLM5-BGC model H. Chi et al. https://doi.org/10.1016/j.jhydrol.2023.130527
12. Contributions of GRACE to understanding climate change B. Tapley et al. https://doi.org/10.1038/s41558-019-0456-2
13. Exploring the Potential of Satellite Solar-Induced Fluorescence to Constrain Global Transpiration Estimates B. Pagán et al. https://doi.org/10.3390/rs11040413
14. Unveiling disparities: a surface deformation analysis influenced by the human activities and natural conditions in the middle Yarlung Zangbo River Valley L. He et al. https://doi.org/10.1016/j.indic.2025.100959
15. Deriving scaling factors using a global hydrological model to restore GRACE total water storage changes for China's Yangtze River Basin D. Long et al. https://doi.org/10.1016/j.rse.2015.07.003
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17. Terrestrial Water Storage Change Retrieved by GRACE and Its Implication in the Tibetan Plateau: Estimating Areal Precipitation in Ungauged Region Y. Jia et al. https://doi.org/10.3390/rs12193129
18. Comparing global hydrological models and combining them with GRACE by dynamic model data averaging (DMDA) N. Mehrnegar et al. https://doi.org/10.1016/j.advwatres.2020.103528
19. Data assimilation of satellite-based terrestrial water storage changes into a hydrology land-surface model A. Bahrami et al. https://doi.org/10.1016/j.jhydrol.2020.125744
20. Terrestrial water storage changes in the Bug river transboundary catchment observed by GRACE and water balance analysis T. Solovey et al. https://doi.org/10.15407/Meteorology2024.06.004
21. Understanding terrestrial water storage variations in northern latitudes across scales T. Trautmann et al. https://doi.org/10.5194/hess-22-4061-2018
22. Modelling Freshwater Resources at the Global Scale: Challenges and Prospects P. Döll et al. https://doi.org/10.1007/s10712-015-9343-1
23. Cross-evaluation of ground-based, multi-satellite and reanalysis precipitation products: Applicability of the Triple Collocation method across Mainland China C. Li et al. https://doi.org/10.1016/j.jhydrol.2018.04.039
24. Evaluation of Drydown Processes in Global Land Surface and Hydrological Models Using Flux Tower Evapotranspiration A. Martínez-de la Torre et al. https://doi.org/10.3390/w11020356
25. Climate Change and Hydrological Extremes J. Xiong & Y. Yang https://doi.org/10.1007/s40641-024-00198-4
26. Triple collocation-based multi-source evaporation and transpiration merging J. Park et al. https://doi.org/10.1016/j.agrformet.2023.109353
27. Rivers and Floodplains as Key Components of Global Terrestrial Water Storage Variability A. Getirana et al. https://doi.org/10.1002/2017GL074684
28. Estimating Terrestrial Snow Mass via Multi‐Sensor Assimilation of Synthetic AMSR‐E Brightness Temperature Spectral Differences and Synthetic GRACE Terrestrial Water Storage Retrievals J. Wang et al. https://doi.org/10.1029/2021WR029880
29. Assessing daily GRACE Data Assimilation during flood events of the Brahmaputra River Basin L. Retegui-Schiettekatte et al. https://doi.org/10.1016/j.scitotenv.2025.179181
30. Assimilation of gridded terrestrial water storage observations from GRACE into a land surface model M. Girotto et al. https://doi.org/10.1002/2015WR018417
31. Combining Physically Based Modeling and Deep Learning for Fusing GRACE Satellite Data: Can We Learn From Mismatch? A. Sun et al. https://doi.org/10.1029/2018WR023333
32. Multi-mission satellite remote sensing data for improving land hydrological models via data assimilation M. Khaki et al. https://doi.org/10.1038/s41598-020-75710-5
33. The potential of global reanalysis datasets in identifying flood events in Southern Africa G. Gründemann et al. https://doi.org/10.5194/hess-22-4667-2018
34. The 2019–2020 Rise in Lake Victoria Monitored from Space: Exploiting the State-of-the-Art GRACE-FO and the Newly Released ERA-5 Reanalysis Products M. Khaki & J. Awange https://doi.org/10.3390/s21134304
35. Challenges of the Blue Economy: evidence and research trends R. Martínez-Vázquez et al. https://doi.org/10.1186/s12302-021-00502-1
36. Long-term, non-anthropogenic groundwater storage changes simulated by three global-scale hydrological models B. Li et al. https://doi.org/10.1038/s41598-019-47219-z
37. Surface Micro-Relief Evolution in Southeast Tibet Based on InSAR Technology . Gesangzhuoma et al. https://doi.org/10.3390/land14030503
38. Improving drought simulations within the Murray-Darling Basin by combined calibration/assimilation of GRACE data into the WaterGAP Global Hydrology Model M. Schumacher et al. https://doi.org/10.1016/j.rse.2017.10.029
39. Performance of Different Ensemble Kalman Filter Structures to Assimilate GRACE Terrestrial Water Storage Estimates Into a High‐Resolution Hydrological Model: A Synthetic Study A. Shokri et al. https://doi.org/10.1029/2018WR022785
40. The effect of surge on riverine flood hazard and impact in deltas globally D. Eilander et al. https://doi.org/10.1088/1748-9326/ab8ca6
41. Assessing sequential data assimilation techniques for integrating GRACE data into a hydrological model M. Khaki et al. https://doi.org/10.1016/j.advwatres.2017.07.001
42. Making the Best Use of GRACE, GRACE‐FO and SMAP Data Through a Constrained Bayesian Data‐Model Integration N. Mehrnegar et al. https://doi.org/10.1029/2023WR034544
43. Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate V. Levizzani & E. Cattani https://doi.org/10.3390/rs11192301
44. Global Estimation and Assessment of Monthly Lake/Reservoir Water Level Changes Using ICESat-2 ATL13 Products N. Xu et al. https://doi.org/10.3390/rs13142744
45. Benchmarking global hydrological and land surface models against GRACE in a medium-sized tropical basin S. Bolaños Chavarría et al. https://doi.org/10.5194/hess-26-4323-2022
46. Nonparametric Data Assimilation Scheme for Land Hydrological Applications M. Khaki et al. https://doi.org/10.1029/2018WR022854
47. Global joint assimilation of GRACE and SMOS for improved estimation of root-zone soil moisture and vegetation response S. Tian et al. https://doi.org/10.5194/hess-23-1067-2019
48. Global Calibration and Error Estimation of Altimeter, Scatterometer, and Radiometer Wind Speed Using Triple Collocation A. Ribal & I. Young https://doi.org/10.3390/rs12121997
49. Assessing modern river sediment discharge to the ocean using satellite gravimetry M. Mouyen et al. https://doi.org/10.1038/s41467-018-05921-y
50. How long is the memory of forest growth to rainfall in asynchronous climates? R. Joshi et al. https://doi.org/10.1016/j.ecolind.2022.109057
51. Modeling Groundwater Depletion at Regional and Global Scales: Present State and Future Prospects Y. Wada https://doi.org/10.1007/s10712-015-9347-x
52. Unsupervised ensemble Kalman filtering with an uncertain constraint for land hydrological data assimilation M. Khaki et al. https://doi.org/10.1016/j.jhydrol.2018.06.080
53. 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
54. A global streamflow reanalysis for 1980–2018 L. Alfieri et al. https://doi.org/10.1016/j.hydroa.2019.100049
55. Impacts of climate change on the streamflow of a large river basin in the Australian tropics using optimally selected climate model outputs M. Usman et al. https://doi.org/10.1016/j.jclepro.2021.128091
56. Water cycle science enabled by the GRACE and GRACE-FO satellite missions M. Rodell & J. Reager https://doi.org/10.1038/s44221-022-00005-0
57. Recent Changes in Land Water Storage and its Contribution to Sea Level Variations Y. Wada et al. https://doi.org/10.1007/s10712-016-9399-6
58. Global GRACE Data Assimilation for Groundwater and Drought Monitoring: Advances and Challenges B. Li et al. https://doi.org/10.1029/2018WR024618
59. Land–atmospheric feedbacks during droughts and heatwaves: state of the science and current challenges D. Miralles et al. https://doi.org/10.1111/nyas.13912
60. Comparison of Data‐Driven Techniques to Reconstruct (1992–2002) and Predict (2017–2018) GRACE‐Like Gridded Total Water Storage Changes Using Climate Inputs F. Li et al. https://doi.org/10.1029/2019WR026551
61. Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations P. López López et al. https://doi.org/10.5194/hess-20-3059-2016
62. A two-update ensemble Kalman filter for land hydrological data assimilation with an uncertain constraint M. Khaki et al. https://doi.org/10.1016/j.jhydrol.2017.10.032
63. Deduction of reservoir operating rules for application in global hydrological models H. Coerver et al. https://doi.org/10.5194/hess-22-831-2018
64. Error covariance calculation for forecast bias estimation in hydrologic data assimilation V. Pauwels & G. De Lannoy https://doi.org/10.1016/j.advwatres.2015.05.013
65. 100 Years of Progress in Hydrology C. Peters-Lidard et al. https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0019.1
66. The effect of climate type on timescales of drought propagation in an ensemble of global hydrological models A. Gevaert et al. https://doi.org/10.5194/hess-22-4649-2018
67. Hydrological reanalysis across the 20th century: A case study of the Amazon Basin S. Wongchuig et al. https://doi.org/10.1016/j.jhydrol.2019.01.025
68. Joint estimation of global daily 1 km surface radiation budget components from MODIS observations (2000−2023) using conservation-constrained deep neural networks J. Xu et al. https://doi.org/10.1016/j.rse.2025.115135
69. Triple Collocation-Based Uncertainty Analysis and Data Fusion of Multi-Source Evapotranspiration Data Across China D. Wang et al. https://doi.org/10.3390/atmos15121410
70. Understanding the intra-annual variability of streamflow by incorporating terrestrial water storage from GRACE into the Budyko framework in the Qinba Mountains P. Huang et al. https://doi.org/10.1016/j.jhydrol.2021.126988
71. Land Surface Model Calibration Using Satellite Remote Sensing Data M. Khaki https://doi.org/10.3390/s23041848
72. A Comparative Study of GRACE with Continental Evapotranspiration Estimates in Australian Semi-Arid and Arid Basins: Sensitivity to Climate Variability and Extremes H. Shen et al. https://doi.org/10.3390/w9090614
73. Global peak water limit of future groundwater withdrawals H. Niazi et al. https://doi.org/10.1038/s41893-024-01306-w
74. On the Use of Adaptive Ensemble Kalman Filtering to Mitigate Error Misspecifications in GRACE Data Assimilation A. Shokri et al. https://doi.org/10.1029/2018WR024670
75. A systematic impact assessment of GRACE error correlation on data assimilation in hydrological models M. Schumacher et al. https://doi.org/10.1007/s00190-016-0892-y
76. Human–water interface in hydrological modelling: current status and future directions Y. Wada et al. https://doi.org/10.5194/hess-21-4169-2017
77. Acceleration in the Global Mean Sea Level Rise: 2005–2015 S. Yi et al. https://doi.org/10.1002/2017GL076129
78. Exploring groundwater and soil water storage changes across the CONUS at 12.5 km resolution by a Bayesian integration of GRACE data into W3RA N. Mehrnegar et al. https://doi.org/10.1016/j.scitotenv.2020.143579
79. Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis R. Baatz et al. https://doi.org/10.1029/2020RG000715
80. Coupling a global glacier model to a global hydrological model prevents underestimation of glacier runoff P. Wiersma et al. https://doi.org/10.5194/hess-26-5971-2022
81. Evaluating Precipitation Datasets Using Surface Water and Energy Budget Closure S. Hobeichi et al. https://doi.org/10.1175/JHM-D-19-0255.1
82. Evaluating the accuracy of gridded water resources reanalysis and evapotranspiration products for assessing water security in poorly gauged basins E. Nkiaka et al. https://doi.org/10.5194/hess-26-5899-2022
83. Changes of Hydrological Components in Arctic Rivers Based on Multi-Source Data during 2003–2016 H. Wu et al. https://doi.org/10.3390/w13243494
84. Towards hybrid modeling of the global hydrological cycle B. Kraft et al. https://doi.org/10.5194/hess-26-1579-2022
85. Groundwater Pumping Is a Significant Unrecognized Contributor to Global Anthropogenic Element Cycles M. Stahl https://doi.org/10.1111/gwat.12817
86. Evaluating dynamics of GRACE groundwater and its drought potential in Taihang Mountain Region, China M. Liu et al. https://doi.org/10.1016/j.jhydrol.2022.128156
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89. Determining water storage depletion within Iran by assimilating GRACE data into the W3RA hydrological model M. Khaki et al. https://doi.org/10.1016/j.advwatres.2018.02.008
90. Characterization of precipitation product errors across the United States using multiplicative triple collocation S. Alemohammad et al. https://doi.org/10.5194/hess-19-3489-2015
91. PyGLDA: a fine-scale python-based global land data assimilation system for integrating satellite gravity data into hydrological models F. Yang et al. https://doi.org/10.5194/gmd-18-6195-2025
92. An ensemble Kalman filter with rescaling disaggregation for assimilating terrestrial water storage into hydrological models L. Retegui-Schiettekatte et al. https://doi.org/10.1038/s41598-025-13602-2
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95. Linear Optimal Runoff Aggregate (LORA): a global gridded synthesis runoff product S. Hobeichi et al. https://doi.org/10.5194/hess-23-851-2019
96. Direct Comparison and Triple Collocation: Which Is More Reliable in the Validation of Coarse‐Scale Satellite Surface Albedo Products X. Wu et al. https://doi.org/10.1029/2018JD029937
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102. Global re-analysis datasets to improve hydrological assessment and snow water equivalent estimation in a sub-Arctic watershed D. Casson et al. https://doi.org/10.5194/hess-22-4685-2018
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106. Optimizing Parameters in the Common Land Model by Using Gravity Recovery and Climate Experiment Satellite Observations Y. Su & S. Zhang https://doi.org/10.3390/land13040508
107. The potential and uncertainty of triple collocation in assessing satellite precipitation products in Central Asia X. Lu et al. https://doi.org/10.1016/j.atmosres.2021.105452
108. Global groundwater droughts are more severe than they appear in hydrological models: An investigation through a Bayesian merging of GRACE and GRACE-FO data with a water balance model E. Forootan et al. https://doi.org/10.1016/j.scitotenv.2023.169476
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112. A review of current best practices and future directions in assimilating GRACE/-FO terrestrial water storage data into numerical models A. Springer et al. https://doi.org/10.5194/hess-30-985-2026
113. Sustainable agriculture in the face of water scarcity: Opportunities, challenges, and global perspectives O. Obayomi et al. https://doi.org/10.1016/j.nexres.2025.101293
114. A novel dynamic scale factor designed for recovering global TWS changes W. Chen et al. https://doi.org/10.1016/j.jhydrol.2024.131364
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116. Assessing global water mass transfers from continents to oceans over the period 1948–2016 D. Cáceres et al. https://doi.org/10.5194/hess-24-4831-2020
117. Comparison of traditional method and triple collocation analysis for evaluation of multiple gridded precipitation products across Germany Z. Duan et al. https://doi.org/10.1175/JHM-D-21-0049.1
118. Causes of changes in actual evapotranspiration and terrestrial water storage over the Eurasian inland basins Z. Liu https://doi.org/10.1002/hyp.14482
119. The application of multi-mission satellite data assimilation for studying water storage changes over South America M. Khaki & J. Awange https://doi.org/10.1016/j.scitotenv.2018.08.079
120. Variations in the lake area, water level, and water volume of Hongjiannao Lake during 1986–2018 based on Landsat and ASTER GDEM data H. Yue & Y. Liu https://doi.org/10.1007/s10661-019-7715-6
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128. Developing explainable AI models for predicting snowpack variability in Polar regions S. Sidra & S. Wagan https://doi.org/10.5817/CPR2025-1-8