144 citations as recorded by crossref.

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28. SWAT‐Based Hydrological Data Assimilation System (SWAT‐HDAS): Description and Case Application to River Basin‐Scale Hydrological Predictions Y. Zhang et al. 10.1002/2017MS001144
29. Assessment of bottom-up satellite rainfall products on estimating river discharge and hydrologic signatures in Brazilian catchments A. Almagro et al. 10.1016/j.jhydrol.2021.126897
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31. Improving Rain/No-Rain Detection Skill by Merging Precipitation Estimates from Different Sources J. Dong et al. 10.1175/JHM-D-20-0097.1
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37. Evaluating the impact of climate change on surface water resources in the upper Ganjiang River Basin, China P. Song et al. 10.2166/wcc.2022.258
38. Comparison of sequential and variational assimilation methods to improve hydrological predictions in snow dominated mountainous catchments R. Alvarado-Montero et al. 10.1016/j.jhydrol.2022.127981
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45. Evaluation and bias correction of satellite rainfall data for drought monitoring in Indonesia R. Vernimmen et al. 10.5194/hess-16-133-2012
46. Exploiting Satellite-Based Surface Soil Moisture for Flood Forecasting in the Mediterranean Area: State Update Versus Rainfall Correction C. Massari et al. 10.3390/rs10020292
47. Satellite soil moisture data assimilation for improved operational continental water balance prediction S. Tian et al. 10.5194/hess-25-4567-2021
48. Hydrologic model calibration using remotely sensed soil moisture and discharge measurements: The impact on predictions at gauged and ungauged locations Y. Li et al. 10.1016/j.jhydrol.2018.01.013
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52. Ground observation-based analysis of soil moisture spatiotemporal variability across a humid to semi-humid transitional zone in China K. Zhang et al. 10.1016/j.jhydrol.2019.04.087
53. 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. 10.1029/2018WR022785
54. Adjustment to the curve number (NRCS-CN) to account for the vegetation effect on hydrological processes A. Gonzalez et al. 10.1080/02626667.2014.898119
55. Assessment of Remotely Sensed and Modelled Soil Moisture Data Products in the U.S. Southern Great Plains B. Jiang et al. 10.3390/rs12122030
56. Assimilation of soil moisture and streamflow observations to improve flood forecasting with considering runoff routing lags S. Meng et al. 10.1016/j.jhydrol.2017.05.024
57. Predicting event response in a nested catchment with generalized linear models and a distributed watershed model T. Graeff et al. 10.1002/hyp.8463
58. The value of glacier mass balance, satellite snow cover images, and hourly discharge for improving the performance of a physically based distributed hydrological model D. Finger et al. 10.1029/2010WR009824
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60. Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. 10.1029/2022RG000788
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62. Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities Y. Liu et al. 10.5194/hess-16-3863-2012
63. Impact of different satellite soil moisture products on the predictions of a continuous distributed hydrological model P. Laiolo et al. 10.1016/j.jag.2015.06.002
64. Assimilation of Sentinel 1 and SMAP – based satellite soil moisture retrievals into SWAT hydrological model: the impact of satellite revisit time and product spatial resolution on flood simulations in small basins S. Azimi et al. 10.1016/j.jhydrol.2019.124367
65. On the Choice of Metric to Calibrate Time-Invariant Ensemble Kalman Filter Hyper-Parameters for Discharge Data Assimilation and Its Impact on Discharge Forecast Modelling J. Bergeron et al. 10.3390/hydrology8010036
66. Integrating coarse-scale uncertain soil moisture data into a fine-scale hydrological modelling scenario H. Vernieuwe et al. 10.5194/hess-15-3101-2011
67. Remotely sensed ET for streamflow modelling in catchments with contrasting flow characteristics: an attempt to improve efficiency A. Kunnath-Poovakka et al. 10.1007/s00477-018-1528-y
68. Potentials and constraints of different types of soil moisture observations for flood simulations in headwater catchments A. Bronstert et al. 10.1007/s11069-011-9874-9
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72. A past discharge assimilation system for ensemble streamflow forecasts over France – Part 2: Impact on the ensemble streamflow forecasts G. Thirel et al. 10.5194/hess-14-1639-2010
73. Assimilation of near-real time data products into models of an urban basin J. Leach et al. 10.1016/j.jhydrol.2018.05.064
74. A comparison of ASCAT and modelled soil moisture over South Africa, using TOPKAPI in land surface mode S. Sinclair & G. Pegram 10.5194/hess-14-613-2010
75. A multi-source satellite data approach for modelling Lake Turkana water level: calibration and validation using satellite altimetry data N. Velpuri et al. 10.5194/hess-16-1-2012
76. Dual Forcing and State Correction via Soil Moisture Assimilation for Improved Rainfall–Runoff Modeling F. Chen et al. 10.1175/JHM-D-14-0002.1
77. Can SMOS Data be Used Directly on the 15-km Discrete Global Grid? G. Dumedah et al. 10.1109/TGRS.2013.2262501
78. Data Assimilation of Terrestrial Water Storage Observations to Estimate Precipitation Fluxes: A Synthetic Experiment M. Girotto et al. 10.3390/rs13061223
79. A Multi‐Scale Soil Moisture Monitoring Strategy for California: Design and Validation J. Curtis et al. 10.1111/1752-1688.12744
80. Impact of soil moisture on dust outbreaks in East Asia: Using satellite and assimilation data H. Kim & M. Choi 10.1002/2015GL063325
81. Retrieving accurate soil moisture over the Tibetan Plateau using multi-source remote sensing data assimilation with simultaneous state and parameter estimations W. Chen et al. 10.1175/JHM-D-20-0298.1
82. Potential of soil moisture observations in flood modelling: Estimating initial conditions and correcting rainfall C. Massari et al. 10.1016/j.advwatres.2014.08.004
83. A Review of Satellite-Derived Soil Moisture and Its Usage for Flood Estimation S. Kim et al. 10.1007/s41976-019-00025-7
84. Investigating the impact of surface soil moisture assimilation on state and parameter estimation in SWAT model based on the ensemble Kalman filter in upper Huai River basin Y. Liu et al. 10.1515/johh-2017-0011
85. Streamflow data assimilation in SWAT model using Extended Kalman Filter L. Sun et al. 10.1016/j.jhydrol.2015.10.060
86. Improving Soil Moisture Profile Prediction With the Particle Filter-Markov Chain Monte Carlo Method H. Yan et al. 10.1109/TGRS.2015.2432067
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89. The Potential Evaluation of Multisource Remote Sensing Data for Extracting Soil Moisture Based on the Method of BP Neural Network H. Ma & S. Liu 10.1080/07038992.2016.1160773
90. Mapping high‐resolution soil moisture and properties using distributed temperature sensing data and an adaptive particle batch smoother J. Dong et al. 10.1002/2016WR019031
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