127 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
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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
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9. 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
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12. Exploring the Potential of Satellite Solar-Induced Fluorescence to Constrain Global Transpiration Estimates B. Pagán et al. https://doi.org/10.3390/rs11040413
13. 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
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18. 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
19. 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
20. Understanding terrestrial water storage variations in northern latitudes across scales T. Trautmann et al. https://doi.org/10.5194/hess-22-4061-2018
21. Modelling Freshwater Resources at the Global Scale: Challenges and Prospects P. Döll et al. https://doi.org/10.1007/s10712-015-9343-1
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23. 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
24. Climate Change and Hydrological Extremes J. Xiong & Y. Yang https://doi.org/10.1007/s40641-024-00198-4
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26. Rivers and Floodplains as Key Components of Global Terrestrial Water Storage Variability A. Getirana et al. https://doi.org/10.1002/2017GL074684
27. 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
28. 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
29. Assimilation of gridded terrestrial water storage observations from GRACE into a land surface model M. Girotto et al. https://doi.org/10.1002/2015WR018417
30. 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
31. 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
32. 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
33. 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
34. Challenges of the Blue Economy: evidence and research trends R. Martínez-Vázquez et al. https://doi.org/10.1186/s12302-021-00502-1
35. 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
36. Surface Micro-Relief Evolution in Southeast Tibet Based on InSAR Technology . Gesangzhuoma et al. https://doi.org/10.3390/land14030503
37. 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
38. 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
39. 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
40. 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
41. 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
42. Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate V. Levizzani & E. Cattani https://doi.org/10.3390/rs11192301
43. 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
44. 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
45. Nonparametric Data Assimilation Scheme for Land Hydrological Applications M. Khaki et al. https://doi.org/10.1029/2018WR022854
46. 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
47. 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
48. Assessing modern river sediment discharge to the ocean using satellite gravimetry M. Mouyen et al. https://doi.org/10.1038/s41467-018-05921-y
49. 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
50. Modeling Groundwater Depletion at Regional and Global Scales: Present State and Future Prospects Y. Wada https://doi.org/10.1007/s10712-015-9347-x
51. 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
52. 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
53. A global streamflow reanalysis for 1980–2018 L. Alfieri et al. https://doi.org/10.1016/j.hydroa.2019.100049
54. 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
55. 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
56. 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
57. Global GRACE Data Assimilation for Groundwater and Drought Monitoring: Advances and Challenges B. Li et al. https://doi.org/10.1029/2018WR024618
58. 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
59. 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
60. 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
61. 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
62. Deduction of reservoir operating rules for application in global hydrological models H. Coerver et al. https://doi.org/10.5194/hess-22-831-2018
63. 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
64. 100 Years of Progress in Hydrology C. Peters-Lidard et al. https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0019.1
65. 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
66. 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
67. 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
68. 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
69. 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
70. Land Surface Model Calibration Using Satellite Remote Sensing Data M. Khaki https://doi.org/10.3390/s23041848
71. 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
72. Global peak water limit of future groundwater withdrawals H. Niazi et al. https://doi.org/10.1038/s41893-024-01306-w
73. 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
74. 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
75. Human–water interface in hydrological modelling: current status and future directions Y. Wada et al. https://doi.org/10.5194/hess-21-4169-2017
76. Acceleration in the Global Mean Sea Level Rise: 2005–2015 S. Yi et al. https://doi.org/10.1002/2017GL076129
77. 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
78. Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis R. Baatz et al. https://doi.org/10.1029/2020RG000715
79. 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
80. Evaluating Precipitation Datasets Using Surface Water and Energy Budget Closure S. Hobeichi et al. https://doi.org/10.1175/JHM-D-19-0255.1
81. 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
82. 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
83. Towards hybrid modeling of the global hydrological cycle B. Kraft et al. https://doi.org/10.5194/hess-26-1579-2022
84. Groundwater Pumping Is a Significant Unrecognized Contributor to Global Anthropogenic Element Cycles M. Stahl https://doi.org/10.1111/gwat.12817
85. 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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88. 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
89. 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
90. 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
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95. 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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101. 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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108. Using hydrological modeling and satellite observations to elucidate subsurface and surface hydrological responses to the extreme drought Z. Tang et al. https://doi.org/10.1016/j.jhydrol.2024.132174
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111. 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
112. 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
113. 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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115. 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
116. 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
117. Causes of changes in actual evapotranspiration and terrestrial water storage over the Eurasian inland basins Z. Liu https://doi.org/10.1002/hyp.14482
118. 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
119. 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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