319 citations as recorded by crossref.

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2. Improving the realism of hydrologic model functioning through multivariate parameter estimation O. Rakovec et al. 10.1002/2016WR019430
3. Remote Sensing-Derived Water Extent and Level to Constrain Hydraulic Flood Forecasting Models: Opportunities and Challenges S. Grimaldi et al. 10.1007/s10712-016-9378-y
4. Remote sensing and modeling fusion for investigating the ecosystem water-carbon coupling processes P. Sun et al. 10.1016/j.scitotenv.2019.134064
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6. Improving Lake Level Prediction by Embedding Support Vector Regression in a Data Assimilation Framework K. Wang et al. 10.3390/w14223718
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9. The Science of NOAA's Operational Hydrologic Ensemble Forecast Service J. Demargne et al. 10.1175/BAMS-D-12-00081.1
10. Data assimilation framework: Linking an open data assimilation library (OpenDA) to a widely adopted model interface (OpenMI) M. Ridler et al. 10.1016/j.envsoft.2014.02.008
11. Improving the Informational Value of MODIS Fractional Snow Cover Area Using Fuzzy Logic Based Ensemble Smoother Data Assimilation Frameworks A. Teweldebrhan et al. 10.3390/rs11010028
12. Towards a Transferable Antecedent Rainfall—Susceptibility Threshold Approach for Landsliding E. Monsieurs et al. 10.3390/w11112202
13. Assessing the Skill of Medium-Range Ensemble Precipitation and Streamflow Forecasts from the Hydrologic Ensemble Forecast Service (HEFS) for the Upper Trinity River Basin in North Texas S. Kim et al. 10.1175/JHM-D-18-0027.1
14. OpenForecast: The First Open-Source Operational Runoff Forecasting System in Russia G. Ayzel et al. 10.3390/w11081546
15. 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
16. Assimilation of Discharge Data into Semidistributed Catchment Models for Short-Term Flow Forecasting: Case Study of the Seine River Basin S. Munier et al. 10.1061/(ASCE)HE.1943-5584.0001054
17. Impact of streamflow data assimilation and length of the verification period on the quality of short-term ensemble hydrologic forecasts A. Randrianasolo et al. 10.1016/j.jhydrol.2014.09.032
18. System response curve correction method of runoff error for real-time flood forecast Q. Li et al. 10.2166/nh.2020.048
19. Integrated real-time flood risk identification, analysis, and diagnosis model framework for a multireservoir system considering temporally and spatially dependent forecast uncertainties B. Xu et al. 10.1016/j.jhydrol.2021.126679
20. The role of rating curve uncertainty in real‐time flood forecasting D. Ocio et al. 10.1002/2016WR020225
21. Global re-analysis datasets to improve hydrological assessment and snow water equivalent estimation in a sub-Arctic watershed D. Casson et al. 10.5194/hess-22-4685-2018
22. Streamflow simulation in poorly gauged basins with regionalised assimilation using Kalman filter I. Georgievová et al. 10.1016/j.jhydrol.2023.129373
23. Development of a Regularized Dynamic System Response Curve for Real-Time Flood Forecasting Correction Y. Sun et al. 10.3390/w10040450
24. How does the quantification of uncertainties affect the quality and value of flood early warning systems? A. Thiboult et al. 10.1016/j.jhydrol.2017.05.014
25. Regional assimilation of in situ observed soil moisture into the VIC model considering spatial variability J. Zhou et al. 10.1080/02626667.2019.1662024
26. Joint identification of contaminant source characteristics and hydraulic conductivity in a tide-influenced coastal aquifer A. Dodangeh et al. 10.1016/j.jconhyd.2022.103980
27. 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
28. Temporal Variability and Trends of Rainfall and Streamflow in Tana River Basin, Kenya P. Langat et al. 10.3390/su9111963
29. Hydrological data uncertainty and its implications H. McMillan et al. 10.1002/wat2.1319
30. Impact of high-resolution sea surface temperature representation on the forecast of small Mediterranean catchments' hydrological responses to heavy precipitation A. Senatore et al. 10.5194/hess-24-269-2020
31. Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models K. Nikhil Teja et al. 10.1016/j.jhydrol.2023.130176
32. Influence of spatial distribution of sensors and observation accuracy on the assimilation of distributed streamflow data in hydrological modelling M. Mazzoleni et al. 10.1080/02626667.2016.1247211
33. 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
34. Assimilating snow observations to snow interception process simulations Z. Lv & J. Pomeroy 10.1002/hyp.13720
35. Toward improving drought monitoring using the remotely sensed soil moisture assimilation: A parallel particle filtering framework H. Yan et al. 10.1016/j.rse.2018.07.017
36. Assimilation of wide-swath altimetry water elevation anomalies to correct large-scale river routing model parameters C. Emery et al. 10.5194/hess-24-2207-2020
37. Spatio‐Temporal Model Variance‐Covariance Approach to Assimilating Streamflow Observations Into a Distributed Landscape Water Balance Model S. Tian et al. 10.1029/2021WR031649
38. State updating in a distributed hydrological model by ensemble Kalman filtering with error estimation J. Gong et al. 10.1016/j.jhydrol.2023.129450
39. Data assimilation in hydrodynamic models for system-wide soft sensing and sensor validation for urban drainage tunnels R. Palmitessa et al. 10.2166/hydro.2020.074
40. Development of dynamic system response curve method for estimating initial conditions of conceptual hydrological models Y. Sun et al. 10.2166/hydro.2018.022
41. Technical Note: Evaluation of the skill in monthly-to-seasonal soil moisture forecasting based on SMAP satellite observations over the southeastern US A. Mazrooei et al. 10.5194/hess-24-1073-2020
42. Ecohydrologic model with satellite-based data for predicting streamflow in ungauged basins J. Choi et al. 10.1016/j.scitotenv.2023.166617
43. Probabilistic predictions for meteorological droughts based on multi-initial conditions M. Torres-Vázquez et al. 10.1016/j.jhydrol.2024.131662
44. Editorial to the Special Issue “Recent Advances in Hydrological Modeling” M. He et al. 10.3390/hydrology11070108
45. Real-Time Flood Forecasting and Information System for the State of Iowa W. Krajewski et al. 10.1175/BAMS-D-15-00243.1
46. Data Assimilation for Streamflow Forecasting Using Extreme Learning Machines and Multilayer Perceptrons M. Boucher et al. 10.1029/2019WR026226
47. Regional and Global Land Data Assimilation Systems: Innovations, Challenges, and Prospects Y. Xia et al. 10.1007/s13351-019-8172-4
48. A probabilistic machine learning framework for daily extreme events forecasting A. Sattari et al. 10.1016/j.eswa.2024.126004
49. Data-worth analysis through probabilistic collocation-based Ensemble Kalman Filter C. Dai et al. 10.1016/j.jhydrol.2016.06.037
50. 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
51. Variational assimilation of streamflow data in distributed flood forecasting G. Ercolani & F. Castelli 10.1002/2016WR019208
52. From ad-hoc modelling to strategic infrastructure: A manifesto for model management T. Arnold et al. 10.1016/j.envsoft.2019.104563
53. Spatiotemporal interpolation of discharge across a river network by using synthetic SWOT satellite data R. Paiva et al. 10.1002/2014WR015618
54. A New Embedded Estimation Model for Soil Temperature Prediction X. Wang et al. 10.1155/2021/5881018
55. Comparison of ensemble-based state and parameter estimation methods for soil moisture data assimilation W. Chen et al. 10.1016/j.advwatres.2015.08.003
56. Assimilation of MODIS Snow Cover Area Data in a Distributed Hydrological Model Using the Particle Filter G. Thirel et al. 10.3390/rs5115825
57. Remote sensing and hydrogeophysics give a new impetus to integrated hydrological models: A review M. Lubczynski et al. 10.1016/j.jhydrol.2024.130901
58. What Are the Key Drivers Controlling the Quality of Seasonal Streamflow Forecasts? I. Pechlivanidis et al. 10.1029/2019WR026987
59. Review of the Kalman-type hydrological data assimilation L. Sun et al. 10.1080/02626667.2015.1127376
60. Predicting the resilience and recovery of aquatic systems: A framework for model evolution within environmental observatories M. Hipsey et al. 10.1002/2015WR017175
61. Potential and Challenges of Investigating Intrinsic Uncertainty of Hydrological Models With Stochastic, Time‐Dependent Parameters P. Reichert et al. 10.1029/2020WR028400
62. Quantifying the Uncertainty of a Conceptual Herbicide Transport Model With Time‐Dependent, Stochastic Parameters L. Ammann et al. 10.1029/2020WR028311
63. Simultaneous assimilation of water levels from river gauges and satellite flood maps for near-real-time flood mapping A. Annis et al. 10.5194/hess-26-1019-2022
64. Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review Y. Li et al. 10.3390/rs8060456
65. Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4 T. Baracchini et al. 10.5194/gmd-13-1267-2020
66. Remote sensing data assimilation A. Nair et al. 10.1080/02626667.2020.1761021
67. Sequential streamflow assimilation for short-term hydrological ensemble forecasting M. Abaza et al. 10.1016/j.jhydrol.2014.08.038
68. Real-Time River Flood Control under Historical and Future Climatic Conditions: Flanders Case Study E. Vermuyten et al. 10.1061/(ASCE)WR.1943-5452.0001144
69. 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
70. A Prior Estimation of the Spatial Distribution Parameter of Soil Moisture Storage Capacity Using Satellite-Based Root-Zone Soil Moisture Data Y. Tian et al. 10.3390/rs11212580
71. Impact of seasonal changes in vegetation on the river model prediction accuracy and real‐time flood control performance E. Vermuyten et al. 10.1111/jfr3.12651
72. Optimal level of wavelet decomposition for daily inflow forecasting P. Freire & C. Santos 10.1007/s12145-020-00496-z
73. The Use of H-SAF Soil Moisture Products for Operational Hydrology: Flood Modelling over Italy C. Massari et al. 10.3390/hydrology2010002
74. Hydrological application of radar rainfall nowcasting in the Netherlands D. Heuvelink et al. 10.1016/j.envint.2019.105431
75. Hydrological ensemble forecasting using a multi-model framework P. Dion et al. 10.1016/j.jhydrol.2021.126537
76. Stormwater digital twin with online quality control detects urban flood hazards under uncertainty Y. Kim et al. 10.1016/j.scs.2024.105982
77. Assessing model state and forecasts variation in hydrologic data assimilation J. Samuel et al. 10.1016/j.jhydrol.2014.03.048
78. Real-time correction of water stage forecast using combination of forecasted errors by time series models and Kalman filter method J. Shen et al. 10.1007/s00477-015-1074-9
79. Ensemble prediction and data assimilation for operational hydrology D. Seo et al. 10.1016/j.jhydrol.2014.11.035
80. 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
81. A triple collocation-based 2D soil moisture merging methodology considering spatial and temporal non-stationary errors J. Zhou et al. 10.1016/j.rse.2021.112509
82. Exploring the influence of citizen involvement on the assimilation of crowdsourced observations: a modelling study based on the 2013 flood event in the Bacchiglione catchment (Italy) M. Mazzoleni et al. 10.5194/hess-22-391-2018
83. Enhancing state and parameter estimations of a dynamic crop model by a recombination particle filter Y. Orlova & R. Linker 10.1016/j.compag.2023.108355
84. Operational snow mapping with simplified data assimilation using the seNorge snow model T. Saloranta 10.1016/j.jhydrol.2016.03.061
85. Operational hydrological forecasting during the IPHEx-IOP campaign – Meet the challenge J. Tao et al. 10.1016/j.jhydrol.2016.02.019
86. Catchment tomography - An approach for spatial parameter estimation D. Baatz et al. 10.1016/j.advwatres.2017.06.006
87. Efficient deep data assimilation with sparse observations and time-varying sensors S. Cheng et al. 10.1016/j.jcp.2023.112581
88. Review article: Hydrological modeling in glacierized catchments of central Asia – status and challenges Y. Chen et al. 10.5194/hess-21-669-2017
89. Environmental reporting and accounting in Australia: Progress, prospects and research priorities A. van Dijk et al. 10.1016/j.scitotenv.2013.12.053
90. Application of Parameter Screening to Derive Optimal Initial State Adjustments for Streamflow Forecasting J. Mai et al. 10.1029/2020WR027960
91. Integrating Qualitative Flow Observations in a Lumped Hydrologic Routing Model M. Mazzoleni et al. 10.1029/2018WR023768
92. Data assimilation of soil water flow via ensemble Kalman filter: Infusing soil moisture data at different scales P. Zhu et al. 10.1016/j.jhydrol.2017.10.078
93. Reduction of Used Memory Ensemble Kalman Filtering (RumEnKF): A data assimilation scheme for memory intensive, high performance computing R. Hut et al. 10.1016/j.advwatres.2015.09.007
94. Seasonal forecasting of groundwater levels in principal aquifers of the United Kingdom J. Mackay et al. 10.1016/j.jhydrol.2015.10.018
95. Improvement of Flood Extent Representation With Remote Sensing Data and Data Assimilation T. Nguyen et al. 10.1109/TGRS.2022.3147429
96. Optimization of water uptake and photosynthetic parameters in an ecosystem model using tower flux data L. He et al. 10.1016/j.ecolmodel.2014.09.019
97. An advanced error correction methodology for merging in-situ observed and model-based soil moisture Z. Wu et al. 10.1016/j.jhydrol.2018.09.018
98. Technical Note: Sequential ensemble data assimilation in convergent and divergent systems H. Bauser et al. 10.5194/hess-25-3319-2021
99. Application of the EnKF method for real-time forecasting of smoke movement during tunnel fires J. Ji et al. 10.1016/j.advengsoft.2017.10.007
100. Evaluating Operational Risk in Environmental Modeling: Assessment of Reliability and Sharpness for Ensemble Selection S. Pokorny et al. 10.1061/JHYEFF.HEENG-5833
101. On the potential of variational calibration for a fully distributed hydrological model: application on a Mediterranean catchment M. Jay-Allemand et al. 10.5194/hess-24-5519-2020
102. Propagating reliable estimates of hydrological forecast uncertainty to many lead times J. Bennett et al. 10.1016/j.jhydrol.2021.126798
103. Anomaly Kriging Helps to Remove Bias in Spatial Model Runoff Estimates N. Loonat et al. 10.1029/2019WR026240
104. Streamflow data assimilation in SWAT model using Extended Kalman Filter L. Sun et al. 10.1016/j.jhydrol.2015.10.060
105. Exploration of sequential streamflow assimilation in snow dominated watersheds M. Abaza et al. 10.1016/j.advwatres.2015.03.011
106. EnKF with closed-eye period – towards a consistent aggregation of information in soil hydrology H. Bauser et al. 10.5194/hess-20-4999-2016
107. Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. 10.1029/2022RG000788
108. Assimilating multi-source data into a three-dimensional hydro-ecological dynamics model using Ensemble Kalman Filter C. Chen et al. 10.1016/j.envsoft.2019.03.028
109. How Does Snow Persistence Relate to Annual Streamflow in Mountain Watersheds of the Western U.S. With Wet Maritime and Dry Continental Climates? J. Hammond et al. 10.1002/2017WR021899
110. Post‐processing of medium‐range probabilistic hydrological forecasting: impact of forcing, initial conditions and model errors E. Roulin & S. Vannitsem 10.1002/hyp.10259
111. Quasi-global machine learning-based soil moisture estimates at high spatio-temporal scales using CYGNSS and SMAP observations F. Lei et al. 10.1016/j.rse.2022.113041
112. A review of fully coupled atmosphere-hydrology simulations L. Ning et al. 10.1007/s11442-019-1610-5
113. Assimilating uncertain, dynamic and intermittent streamflow observations in hydrological models M. Mazzoleni et al. 10.1016/j.advwatres.2015.07.004
114. Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors H. Beck et al. 10.5194/hess-25-17-2021
115. An improved Ensemble Kalman Filter for optimizing parameters in a coupled phosphorus model for lowland polders in Lake Taihu Basin, China J. Huang & J. Gao 10.1016/j.ecolmodel.2017.04.019
116. 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
117. Improved Flood Forecasting in Basins With No Precipitation Stations: Constrained Runoff Correction Using Multiple Satellite Precipitation Products Y. Dou et al. 10.1029/2021WR029682
118. 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
119. An extension of data assimilation into the short-term hydrologic forecast for improved prediction reliability J. Leach & P. Coulibaly 10.1016/j.advwatres.2019.103443
120. State updating of root zone soil moisture estimates of an unsaturated zone metamodel for operational water resources management M. Pezij et al. 10.1016/j.hydroa.2019.100040
121. Use of multiple reference data sources to cross-validate gridded snow water equivalent products over North America C. Mortimer et al. 10.5194/tc-18-5619-2024
122. A Novel Multichannel Long Short-Term Memory Method With Time Series for Soil Temperature Modeling Q. Li et al. 10.1109/ACCESS.2020.3028995
123. The value of soil temperature data versus soil moisture data for state, parameter, and flux estimation in unsaturated flow model R. Kandala et al. 10.1002/vzj2.20298
124. Operational river discharge forecasting in poorly gauged basins: the Kavango River basin case study P. Bauer-Gottwein et al. 10.5194/hess-19-1469-2015
125. Multi-parametric variational data assimilation for hydrological forecasting R. Alvarado-Montero et al. 10.1016/j.advwatres.2017.09.026
126. Empirical assessment of flood wave celerity–discharge relationships at local and reach scales A. Meyer et al. 10.1080/02626667.2018.1557336
127. Accounting for spatial correlation errors in the assimilation of GRACE into hydrological models through localization M. Khaki et al. 10.1016/j.advwatres.2017.07.024
128. Assimilating in situ and radar altimetry data into a large-scale hydrologic-hydrodynamic model for streamflow forecast in the Amazon R. Paiva et al. 10.5194/hess-17-2929-2013
129. Modeling Soil Temperature for Different Days Using Novel Quadruplet Loss-Guided LSTM X. Wang et al. 10.1155/2022/9016823
130. Advancing Medium-Range Streamflow Forecasting for Large Hydropower Reservoirs in Brazil by Means of Continental-Scale Hydrological Modeling A. Kolling Neto et al. 10.3390/w15091693
131. Impact of uncertainty description on assimilating hydraulic head in the MIKE SHE distributed hydrological model D. Zhang et al. 10.1016/j.advwatres.2015.07.018
132. Toward Discharge Estimation for Water Resources Management with a Semidistributed Model and Local Ensemble Kalman Filter Data Assimilation S. Wongchuig et al. 10.1061/(ASCE)HE.1943-5584.0002027
133. HYBRID DEEP NEURAL NETWORK AND DISTRIBUTED RAINFALL-RUNOFF MODEL FOR THE REAL-TIME RIVER STAGE PREDICTION M. HITOKOTO & M. SAKURABA 10.2208/jscejhe.73.22
134. Evaluation of Maximum a Posteriori Estimation as Data Assimilation Method for Forecasting Infiltration-Inflow Affected Urban Runoff with Radar Rainfall Input J. Pedersen et al. 10.3390/w8090381
135. Parameter uncertainty and temporal dynamics of sensitivity for hydrologic models: A hybrid sequential data assimilation and probabilistic collocation method Y. Fan et al. 10.1016/j.envsoft.2016.09.012
136. Covariance resampling for particle filter – state and parameter estimation for soil hydrology D. Berg et al. 10.5194/hess-23-1163-2019
137. Variational Autoencoder or Generative Adversarial Networks? A Comparison of Two Deep Learning Methods for Flow and Transport Data Assimilation J. Bao et al. 10.1007/s11004-022-10003-3
138. Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting P. Abbaszadeh et al. 10.1016/j.isci.2022.105201
139. Evaluation of multiple bias correction methods with different satellite rainfall products in the Main Beles Watershed, Upper Blue Nile (Abbay) Basin, Ethiopia A. Nigussie et al. 10.2166/wcc.2022.244
140. Hyper-resolution ensemble-based snow reanalysis in mountain regions using clustering J. Fiddes et al. 10.5194/hess-23-4717-2019
141. Usefulness of Global Root Zone Soil Moisture Product for Streamflow Prediction of Ungauged Basins J. Choi et al. 10.3390/rs13040756
142. Assimilation of transformed water surface elevation to improve river discharge estimation in a continental-scale river M. Revel et al. 10.5194/hess-27-647-2023
143. Improving real-time inflow forecasting into hydropower reservoirs through a complementary modelling framework A. Gragne et al. 10.5194/hess-19-3695-2015
144. Progress in integrating remote sensing data and hydrologic modeling X. Xu et al. 10.1177/0309133314536583
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148. A first integrated modelling of a river-lagoon large-scale hydrological system for forecasting purposes V. Lopes et al. 10.1016/j.jhydrol.2018.08.011
149. Improving flood forecasting using conditional bias-penalized ensemble Kalman filter H. Lee et al. 10.1016/j.jhydrol.2019.05.072
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151. 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
152. An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture–data assimilation L. Cenci et al. 10.5194/adgeo-44-89-2017
153. Ensemble Streamflow Forecasting Using an Energy Balance Snowmelt Model Coupled to a Distributed Hydrologic Model with Assimilation of Snow and Streamflow Observations T. Gichamo & D. Tarboton 10.1029/2019WR025472
154. Improving Forecast Skill of Lowland Hydrological Models Using Ensemble Kalman Filter and Unscented Kalman Filter Y. Sun et al. 10.1029/2020WR027468
155. Improving flood forecasting using an input correction method in urban models in poorly gauged areas M. Fava et al. 10.1080/02626667.2020.1729984
156. Model uncertainty reduction for real-time flood control by means of a flexible data assimilation approach and reduced conceptual models E. Vermuyten et al. 10.1016/j.jhydrol.2018.07.033
157. Variable update strategy to improve water quality forecast accuracy in multivariate data assimilation using the ensemble Kalman filter S. Park et al. 10.1016/j.watres.2020.115711
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160. Operational hydrological data assimilation with the recursive ensemble Kalman filter H. McMillan et al. 10.5194/hess-17-21-2013
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164. Improving the Predictive Skill of a Distributed Hydrological Model by Calibration on Spatial Patterns With Multiple Satellite Data Sets M. Dembélé et al. 10.1029/2019WR026085
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170. Estimating the occurrence of slow slip events and earthquakes with an ensemble Kalman filter H. Diab-Montero et al. 10.1093/gji/ggad154
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