87 citations as recorded by crossref.

1. Assessing model state and forecasts variation in hydrologic data assimilation J. Samuel et al. 10.1016/j.jhydrol.2014.03.048
2. Comparison of polynomial chaos and Gaussian process surrogates for uncertainty quantification and correlation estimation of spatially distributed open-channel steady flows P. Roy et al. 10.1007/s00477-017-1470-4
3. Ensemble prediction and data assimilation for operational hydrology D. Seo et al. 10.1016/j.jhydrol.2014.11.035
4. Data assimilation of soil water flow by considering multiple uncertainty sources and spatial–temporal features: a field-scale real case study X. Li et al. 10.1007/s00477-018-1541-1
5. Correcting satellite-based precipitation products through SMOS soil moisture data assimilation in two land-surface models of different complexity: API and SURFEX C. Román-Cascón et al. 10.1016/j.rse.2017.08.022
6. Evaluating post-processing approaches for monthly and seasonal streamflow forecasts F. Woldemeskel et al. 10.5194/hess-22-6257-2018
7. Reliable probabilistic forecasts from an ensemble reservoir inflow forecasting system D. Bourdin et al. 10.1002/2014WR015462
8. Combined assimilation of streamflow and satellite soil moisture with the particle filter and geostatistical modeling H. Yan & H. Moradkhani 10.1016/j.advwatres.2016.06.002
9. Improving the probabilistic drought prediction with soil moisture information under the ensemble streamflow prediction framework G. Kim et al. 10.1007/s00477-024-02710-6
10. The role of probabilistic precipitation forecasts in hydrologic predictability S. Seo & J. Sung 10.1007/s00704-020-03273-6
11. Soil and Water Assessment Tool (SWAT)-Informed Deep Learning for Streamflow Forecasting with Remote Sensing and In Situ Precipitation and Discharge Observations C. Huang et al. 10.3390/rs16213999
12. Revolutionizing the future of hydrological science: Impact of machine learning and deep learning amidst emerging explainable AI and transfer learning R. Maity et al. 10.1016/j.acags.2024.100206
13. Bias-corrected short-range Member-to-Member ensemble forecasts of reservoir inflow D. Bourdin & R. Stull 10.1016/j.jhydrol.2013.08.028
14. Statistical downscaling of precipitation on a spatially dependent network using a regional climate model R. Erhardt et al. 10.1007/s00477-014-0988-y
15. 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
16. Real-time hydrograph modelling in the upper Nysa Kłodzka river basin (SW Poland): a two-model hydrologic ensemble prediction approach T. Niedzielski & B. Miziński 10.1007/s00477-016-1251-5
17. Variational assimilation of streamflow data in distributed flood forecasting G. Ercolani & F. Castelli 10.1002/2016WR019208
18. Ensemble Kalman filter versus particle filter for a physically-based coupled surface–subsurface model D. Pasetto et al. 10.1016/j.advwatres.2012.06.009
19. Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble Forecasting R. Valdés-Pineda et al. 10.3390/hydrology8040188
20. An overview of approaches for reducing uncertainties in hydrological forecasting: Progress and challenges A. Panchanathan et al. 10.1016/j.earscirev.2024.104956
21. Identifying the Sensitivity of Ensemble Streamflow Prediction by Artificial Intelligence Y. Chiang et al. 10.3390/w10101341
22. 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
23. 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
24. Evaluation of Earth Observations and In Situ Data Assimilation for Seasonal Hydrological Forecasting J. Musuuza et al. 10.1029/2022WR033655
25. Assessment of a multimodel ensemble against an operational hydrological forecasting system A. Thiboult & F. Anctil 10.1080/07011784.2015.1026402
26. Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting P. Abbaszadeh et al. 10.1016/j.isci.2022.105201
27. Improving Soil Moisture Profile Prediction With the Particle Filter-Markov Chain Monte Carlo Method H. Yan et al. 10.1109/TGRS.2015.2432067
28. On the uncertainty of initial condition and initialization approaches in variably saturated flow modeling D. Yu et al. 10.5194/hess-23-2897-2019
29. Benchmarking an operational hydrological model for providing seasonal forecasts in Sweden M. Girons Lopez et al. 10.5194/hess-25-1189-2021
30. An integrated error parameter estimation and lag-aware data assimilation scheme for real-time flood forecasting Y. Li et al. 10.1016/j.jhydrol.2014.08.009
31. Modular optimized data assimilation and support vector machine for hydrologic modeling M. Mehrparvar & K. Asghari 10.2166/hydro.2018.009
32. Exploration of sequential streamflow assimilation in snow dominated watersheds M. Abaza et al. 10.1016/j.advwatres.2015.10.008
33. Data‐Driven Model Uncertainty Estimation in Hydrologic Data Assimilation S. Pathiraja et al. 10.1002/2018WR022627
34. Reduction of the uncertainties in the water level-discharge relation of a 1D hydraulic model in the context of operational flood forecasting J. Habert et al. 10.1016/j.jhydrol.2015.11.023
35. Reanalyzing the spatial representativeness of snow depth at automated monitoring stations using airborne lidar data J. Herbert et al. 10.5194/tc-18-3495-2024
36. Improving long-term prediction of terrestrial water storage through integration with CMIP6 decadal prediction E. Zhu et al. 10.1016/j.atmosres.2024.107776
37. Prediction of Reservoir Storage Anomalies in India A. Tiwari & V. Mishra 10.1029/2019JD030525
38. On the difficulty to optimally implement the Ensemble Kalman filter: An experiment based on many hydrological models and catchments A. Thiboult & F. Anctil 10.1016/j.jhydrol.2015.09.036
39. Spatio-temporal drought forecasting within Bayesian networks S. Madadgar & H. Moradkhani 10.1016/j.jhydrol.2014.02.039
40. An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space S. Kumar et al. 10.1029/2022MS003259
41. Assimilation of Backscatter Observations into a Hydrological Model: A Case Study in Belgium Using ASCAT Data P. Baguis et al. 10.3390/rs14225740
42. Precipitation and evaporation affecting landfill gas migration into passive methane oxidation biosystems: Models development and verification M. Sun & Y. Yu 10.1016/j.wasman.2024.06.018
43. Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years M. Troin et al. 10.1029/2020WR028392
44. A Bayesian Framework for Probabilistic Seasonal Drought Forecasting S. Madadgar & H. Moradkhani 10.1175/JHM-D-13-010.1
45. Assessment of SWE data assimilation for ensemble streamflow predictions K. Franz et al. 10.1016/j.jhydrol.2014.07.008
46. Estimating snow water equivalent in a Sierra Nevada watershed via spaceborne radiance data assimilation D. Li et al. 10.1002/2016WR018878
47. Enhancing streamflow predictions with machine learning and Copula-Embedded Bayesian model averaging A. Sattari et al. 10.1016/j.jhydrol.2024.131986
48. Sequential streamflow assimilation for short-term hydrological ensemble forecasting M. Abaza et al. 10.1016/j.jhydrol.2014.08.038
49. Choosing between post-processing precipitation forecasts or chaining several uncertainty quantification tools in hydrological forecasting systems E. Valdez et al. 10.5194/hess-26-197-2022
50. Toward multi-day-ahead forecasting of suspended sediment concentration using ensemble models M. Alizadeh et al. 10.1007/s11356-017-0405-4
51. Leveraging a novel hybrid ensemble and optimal interpolation approach for enhanced streamflow and flood prediction M. El Gharamti et al. 10.5194/hess-28-3133-2024
52. Review of Snow Data Assimilation Methods for Hydrological, Land Surface, Meteorological and Climate Models: Results from a COST HarmoSnow Survey J. Helmert et al. 10.3390/geosciences8120489
53. Analyzing the sensitivity of drought recovery forecasts to land surface initial conditions C. DeChant & H. Moradkhani 10.1016/j.jhydrol.2014.10.021
54. Development and evaluation of a hydrologic data-assimilation scheme for short-range flow and inflow forecasts in a data-sparse high-latitude region using a distributed model and ensemble Kalman filtering J. Samuel et al. 10.1016/j.advwatres.2019.06.004
55. Insights on the impact of systematic model errors on data assimilation performance in changing catchments S. Pathiraja et al. 10.1016/j.advwatres.2017.12.006
56. The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework P. Abbaszadeh et al. 10.1029/2018WR023629
57. Analyzing the uncertainty of suspended sediment load prediction using sequential data assimilation M. Leisenring & H. Moradkhani 10.1016/j.jhydrol.2012.08.049
58. Integration of machine learning and particle filter approaches for forecasting soil moisture K. Tandon et al. 10.1007/s00477-022-02258-3
59. The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review D. Jiang & K. Wang 10.3390/w11081615
60. Using Deep Learning to Improve Ensemble Smoother: Applications to Subsurface Characterization J. Zhang et al. 10.1029/2020WR027399
61. Spatio‐Temporal Model Variance‐Covariance Approach to Assimilating Streamflow Observations Into a Distributed Landscape Water Balance Model S. Tian et al. 10.1029/2021WR031649
62. Snow data assimilation for seasonal streamflow supply prediction in mountainous basins S. Metref et al. 10.5194/hess-27-2283-2023
63. 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
64. Role of multimodel combination and data assimilation in improving streamflow prediction over multiple time scales W. Li et al. 10.1007/s00477-015-1158-6
65. Accounting for model structure, parameter and input forcing uncertainty in flood inundation modeling using Bayesian model averaging Z. Liu & V. Merwade 10.1016/j.jhydrol.2018.08.009
66. 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
67. Analysis of the effects of biases in ensemble streamflow prediction (ESP) forecasts on electricity production in hydropower reservoir management R. Arsenault & P. Côté 10.5194/hess-23-2735-2019
68. Ensemble Streamflow Prediction: Climate signal weighting methods vs. Climate Forecast System Reanalysis M. Najafi et al. 10.1016/j.jhydrol.2012.04.003
69. Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters S. Shamshirband et al. 10.1080/19942060.2018.1553742
70. Verification of Ensemble Water Supply Forecasts for Sierra Nevada Watersheds M. He et al. 10.3390/hydrology3040035
71. Generating Coherent Ensemble Forecasts After Hydrological Postprocessing: Adaptations of ECC‐Based Methods J. Bellier et al. 10.1029/2018WR022601
72. An intercomparison of approaches for improving operational seasonal streamflow forecasts P. Mendoza et al. 10.5194/hess-21-3915-2017
73. Estimation of Radiative Transfer Parameters from L‐Band Passive Microwave Brightness Temperatures Using Advanced Data Assimilation C. Montzka et al. 10.2136/vzj2012.0040
74. Evolution of ensemble data assimilation for uncertainty quantification using the particle filter‐Markov chain Monte Carlo method H. Moradkhani et al. 10.1029/2012WR012144
75. A probabilistic drought forecasting framework: A combined dynamical and statistical approach H. Yan et al. 10.1016/j.jhydrol.2017.03.004
76. Accounting for three sources of uncertainty in ensemble hydrological forecasting A. Thiboult et al. 10.5194/hess-20-1809-2016
77. Hydrological ensemble forecasting using a multi-model framework P. Dion et al. 10.1016/j.jhydrol.2021.126537
78. Estimation of nonfluctuating reservoir inflow from water level observations using methods based on flow continuity C. Deng et al. 10.1016/j.jhydrol.2015.09.037
79. Estimation of initial conditions for surface suspended sediment simulations with the adjoint method: A case study in Hangzhou Bay Y. Du et al. 10.1016/j.csr.2021.104526
80. Assimilation of near-real time data products into models of an urban basin J. Leach et al. 10.1016/j.jhydrol.2018.05.064
81. Exploration of sequential streamflow assimilation in snow dominated watersheds M. Abaza et al. 10.1016/j.advwatres.2015.03.011
82. Enhancing hydrologic data assimilation by evolutionary Particle Filter and Markov Chain Monte Carlo P. Abbaszadeh et al. 10.1016/j.advwatres.2017.11.011
83. An Overview of Snow Water Equivalent: Methods, Challenges, and Future Outlook M. Taheri & A. Mohammadian 10.3390/su141811395
84. Improving the Forecast Performance of Hydrological Models Using the Cubature Kalman Filter and Unscented Kalman Filter Y. Sun et al. 10.1029/2022WR033580
85. Recent advance in earth observation big data for hydrology L. Chen & L. Wang 10.1080/20964471.2018.1435072
86. Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities Y. Liu et al. 10.5194/hess-16-3863-2012
87. Toward a reliable prediction of seasonal forecast uncertainty: Addressing model and initial condition uncertainty with ensemble data assimilation and Sequential Bayesian Combination C. DeChant & H. Moradkhani 10.1016/j.jhydrol.2014.05.045