83 citations as recorded by crossref.

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24. Data Assimilation for Streamflow Forecasting: State–Parameter Assimilation versus Output Assimilation L. Sun et al. 10.1061/(ASCE)HE.1943-5584.0001475
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29. Nonparametric Data Assimilation Scheme for Land Hydrological Applications M. Khaki et al. 10.1029/2018WR022854
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31. Improving streamflow predictions at ungauged locations with real-time updating: application of an EnKF-based state-parameter estimation strategy X. Xie et al. 10.5194/hess-18-3923-2014
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33. Constraining the ensemble Kalman filter for improved streamflow forecasting D. Maxwell et al. 10.1016/j.jhydrol.2018.03.015
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36. A regional coupled approach to water cycle prediction during winter 2013/14 in the United Kingdom H. Lewis & S. Dadson 10.1002/hyp.14438
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38. Sensitivity analysis of the hyperparameters of an ensemble Kalman filter application on a semi-distributed hydrological model for streamflow forecasting B. Sabzipour et al. 10.1016/j.jhydrol.2023.130251
39. Accounting for three sources of uncertainty in ensemble hydrological forecasting A. Thiboult et al. 10.5194/hess-20-1809-2016
40. A Bayesian consistent dual ensemble Kalman filter for state-parameter estimation in subsurface hydrology B. Ait-El-Fquih et al. 10.5194/hess-20-3289-2016
41. The role of rating curve uncertainty in real‐time flood forecasting D. Ocio et al. 10.1002/2016WR020225
42. Sequential Data Assimilation for Streamflow Forecasting: Assessing the Sensitivity to Uncertainties and Updated Variables of a Conceptual Hydrological Model at Basin Scale G. Piazzi et al. 10.1029/2020WR028390
43. Recursively updating the error forecasting scheme of a complementary modelling framework for improved reservoir inflow forecasts A. Gragne et al. 10.1016/j.jhydrol.2015.05.039
44. Assessment of SWE data assimilation for ensemble streamflow predictions K. Franz et al. 10.1016/j.jhydrol.2014.07.008
45. A physically based and machine learning hybrid approach for accurate rainfall-runoff modeling during extreme typhoon events C. Young et al. 10.1016/j.asoc.2016.12.052
46. A coupled ensemble filtering and probabilistic collocation approach for uncertainty quantification of hydrological models Y. Fan et al. 10.1016/j.jhydrol.2015.09.035
47. Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions J. Bergeron et al. 10.5194/hess-20-4375-2016
48. Variational assimilation of streamflow data in distributed flood forecasting G. Ercolani & F. Castelli 10.1002/2016WR019208
49. Improving flood forecasting using an input correction method in urban models in poorly gauged areas M. Fava et al. 10.1080/02626667.2020.1729984
50. Analysis of the hydrological response of a distributed physically-based model using post-assimilation (EnKF) diagnostics of streamflow and in situ soil moisture observations M. Trudel et al. 10.1016/j.jhydrol.2014.03.072
51. A review of applications of satellite SAR, optical, altimetry and DEM data for surface water modelling, mapping and parameter estimation Z. Musa et al. 10.5194/hess-19-3755-2015
52. Nonparametric catchment clustering using the data depth function S. Singh et al. 10.1080/02626667.2016.1168927
53. Validation of a national hydrological model H. McMillan et al. 10.1016/j.jhydrol.2016.07.043
54. Review of the Kalman-type hydrological data assimilation L. Sun et al. 10.1080/02626667.2015.1127376
55. Examining dynamic interactions among experimental factors influencing hydrologic data assimilation with the ensemble Kalman filter S. Wang et al. 10.1016/j.jhydrol.2017.09.052
56. RBFNN Versus Empirical Models for Lag Time Prediction in Tropical Humid Rivers M. Seyam et al. 10.1007/s11269-016-1518-0
57. The Met Office Operational Soil Moisture Analysis System B. Gómez et al. 10.3390/rs12223691
58. Spatio‐Temporal Model Variance‐Covariance Approach to Assimilating Streamflow Observations Into a Distributed Landscape Water Balance Model S. Tian et al. 10.1029/2021WR031649
59. Discharge Estimation via Assimilation of Multisatellite-Based Discharge Products: Case Study Over the Amazon Basin C. Emery et al. 10.1109/LGRS.2020.3020285
60. Assimilating uncertain, dynamic and intermittent streamflow observations in hydrological models M. Mazzoleni et al. 10.1016/j.advwatres.2015.07.004
61. 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
62. Underlying Fundamentals of Kalman Filtering for River Network Modeling C. Emery et al. 10.1175/JHM-D-19-0084.1
63. State updating in a distributed hydrological model by ensemble Kalman filtering with error estimation J. Gong et al. 10.1016/j.jhydrol.2023.129450
64. On noise specification in data assimilation schemes for improved flood forecasting using distributed hydrological models S. Noh et al. 10.1016/j.jhydrol.2014.07.049
65. Review of assimilating GRACE terrestrial water storage data into hydrological models: Advances, challenges and opportunities S. Soltani et al. 10.1016/j.earscirev.2020.103487
66. Assessing model state and forecasts variation in hydrologic data assimilation J. Samuel et al. 10.1016/j.jhydrol.2014.03.048
67. Ensemble streamflow data assimilation using WRF-Hydro and DART: novel localization and inflation techniques applied to Hurricane Florence flooding M. El Gharamti et al. 10.5194/hess-25-5315-2021
68. Development of the consider cubature Kalman filter for state estimation of hydrological models with parameter uncertainty Y. Sun et al. 10.1016/j.jhydrol.2023.130080
69. Remote sensing data assimilation A. Nair et al. 10.1080/02626667.2020.1761021
70. Towards reliable uncertainty quantification for hydrologic predictions, part II: Characterizing impacts of uncertain factors through an iterative factorial data assimilation framework Y. Fan et al. 10.1016/j.jhydrol.2022.128136
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73. Assimilating flow and level data into an urban drainage surrogate model for forecasting flows and overflows N. S.V. Lund et al. 10.1016/j.jenvman.2019.05.110
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75. Probabilistic Conditioning and Recalibration of an Event-Based Flood Forecasting Model Using Real-Time Streamflow Observations K. Bahramian et al. 10.1061/(ASCE)HE.1943-5584.0002236
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77. Comparison of Sequential and Variational Streamflow Assimilation Techniques for Short-Term Hydrological Forecasting M. Abaza et al. 10.1061/(ASCE)HE.1943-5584.0001013
78. Assimilation of citizen science data in snowpack modeling using a new snow data set: Community Snow Observations R. Crumley et al. 10.5194/hess-25-4651-2021
79. Analysis of rainfall intensity impact on the lag time estimation in tropical humid rivers e. Seyam 10.21833/ijaas.2017.010.003
80. Improving Treatment of Noise Specification of Kalman Filtering for State Updating of Hydrological Models: Combining the Strengths of the Interacting Multiple Model Method and Cubature Kalman Filter Y. Sun et al. 10.1029/2022WR033635
81. The streamflow estimation using the Xinanjiang rainfall runoff model and dual state-parameter estimation method H. Lü et al. 10.1016/j.jhydrol.2012.12.011
82. Impact of improved meteorological forcing, profile of soil hydraulic conductivity and data assimilation on an operational Hydrological Ensemble Forecast System over France M. Coustau et al. 10.1016/j.jhydrol.2015.04.022
83. 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