49 citations as recorded by crossref.

1. 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
2. Numerical stability analysis of the local inertial equation with semi- and fully implicit friction term treatments: assessment of the maximum allowable time step T. Tanaka & H. Yoshioka 10.15748/jasse.4.162
3. A Physics‐Aware Machine Learning‐Based Framework for Minimizing Prediction Uncertainty of Hydrological Models A. Roy et al. 10.1029/2023WR034630
4. A new moving strategy for the sequential Monte Carlo approach in optimizing the hydrological model parameters G. Zhu et al. 10.1016/j.advwatres.2018.02.007
5. 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
6. A Data Assimilation Framework for Discrete Event Simulations X. Hu & P. Wu 10.1145/3301502
7. Improving the Forecast Performance of Hydrological Models Using the Cubature Kalman Filter and Unscented Kalman Filter Y. Sun et al. 10.1029/2022WR033580
8. Enhancing hydrologic data assimilation by evolutionary Particle Filter and Markov Chain Monte Carlo P. Abbaszadeh et al. 10.1016/j.advwatres.2017.11.011
9. Assimilation of Sentinel-2 Data into a Snowpack Model in the High Atlas of Morocco M. Baba et al. 10.3390/rs10121982
10. Simultaneous estimation of soil moisture and hydraulic parameters using residual resampling particle filter H. Bi et al. 10.1007/s11430-013-4742-y
11. Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. 10.1029/2022RG000788
12. DEVELOPMENT OF A HYDROLOGICAL MODELING FRAMEWORK FOR DATA ASSIMILATION WITH PARTICLE FILTERS S. NOH et al. 10.2208/journalofjsce.1.1_69
13. The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework P. Abbaszadeh et al. 10.1029/2018WR023629
14. Assimilation of stream discharge for flood forecasting: Updating a semidistributed model with an integrated data assimilation scheme Y. Li et al. 10.1002/2014WR016667
15. 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
16. An MADM framework for assessing the overall behaviors of confidence interval in uncertainty analysis of hydrological simulations Z. Li et al. 10.1007/s12665-024-11662-7
17. Spatio‐Temporal Model Variance‐Covariance Approach to Assimilating Streamflow Observations Into a Distributed Landscape Water Balance Model S. Tian et al. 10.1029/2021WR031649
18. Operational hydrological data assimilation with the recursive ensemble Kalman filter H. McMillan et al. 10.5194/hess-17-21-2013
19. Benefits of upstream data for downstream streamflow forecasting: data assimilation in a semi-distributed flood forecasting model P. Royer-Gaspard et al. 10.1080/27678490.2024.2374081
20. Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities Y. Liu et al. 10.5194/hess-16-3863-2012
21. Hybridizing Bayesian and variational data assimilation for high-resolution hydrologic forecasting F. Hernández & X. Liang 10.5194/hess-22-5759-2018
22. The role of rating curve uncertainty in real‐time flood forecasting D. Ocio et al. 10.1002/2016WR020225
23. Ensemble Kalman Filtering and Particle Filtering in a Lag-Time Window for Short-Term Streamflow Forecasting with a Distributed Hydrologic Model S. Noh et al. 10.1061/(ASCE)HE.1943-5584.0000751
24. Assimilating water level observations with the ensemble optimal interpolation scheme into a rainfall‐runoff‐inundation model: A repository‐based dynamic covariance matrix generation approach M. Khaniya et al. 10.1111/jfr3.13017
25. A real-time probabilistic channel flood-forecasting model based on the Bayesian particle filter approach X. Xu et al. 10.1016/j.envsoft.2016.11.010
26. STUDY ON A PARTICLE-FILTER APPLICATION METHOD FOR FLOOD FORECASTING CONSIDERING RAINFALL-RUNOFF CHARACTERISTICS D. KAKINUMA et al. 10.2208/jscejhe.78.2_I_403
27. Assimilation of water temperature and discharge data for ensemble water temperature forecasting S. Ouellet-Proulx et al. 10.1016/j.jhydrol.2017.09.027
28. Assimilation of microwave brightness temperatures for soil moisture estimation using particle filter H. Bi et al. 10.1088/1755-1315/17/1/012126
29. 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
30. Variational assimilation of streamflow data in distributed flood forecasting G. Ercolani & F. Castelli 10.1002/2016WR019208
31. DEVELOPMENT OF A REAL-TIME RUNOFF PREDICTION SYSTEMS USING PARTICLE FILTER AND ACCURACY COMPARISON OF PREDICTION SYSTEMS WITH DIFFERENT STATE VARIABLES Y. TACHIKAWA et al. 10.2208/jscejhe.74.32
32. 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
33. Data assimilation using the ensemble Kalman filter in a distributed hydrological model on the Tocantins River, Brasil K. Jiménez et al. 10.1590/2318-0331.241920180031
34. 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
35. Particle Filters to Estimate Properties of Confined Aquifers G. Field et al. 10.1007/s11269-016-1339-1
36. Operational aspects of asynchronous filtering for flood forecasting O. Rakovec et al. 10.5194/hess-19-2911-2015
37. Hydroclimatic variability and predictability: a survey of recent research R. Koster et al. 10.5194/hess-21-3777-2017
38. A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy A. Roy et al. 10.1029/2022WR033318
39. Assimilation of hydrologic and hydrometeorological data into distributed hydrologic model: Effect of adjusting mean field bias in radar-based precipitation estimates H. Lee & D. Seo 10.1016/j.advwatres.2014.09.002
40. Some statistical inferences of parameter in MCMC approach and the application in uncertainty analysis of hydrological simulation P. Shi et al. 10.1016/j.jhydrol.2022.128767
41. Improving Soil Moisture Estimation with a Dual Ensemble Kalman Smoother by Jointly Assimilating AMSR-E Brightness Temperature and MODIS LST W. Chen et al. 10.3390/rs9030273
42. Comparison of Sequential and Variational Streamflow Assimilation Techniques for Short-Term Hydrological Forecasting M. Abaza et al. 10.1061/(ASCE)HE.1943-5584.0001013
43. 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
44. Quasi‐Online Groundwater Model Optimization Under Constraints of Geological Consistency Based on Iterative Importance Sampling M. Ramgraber et al. 10.1029/2019WR026777
45. Investigating the thermal state of permafrost with Bayesian inverse modeling of heat transfer B. Groenke et al. 10.5194/tc-17-3505-2023
46. An Improved Particle Filter Algorithm Based on Ensemble Kalman Filter and Markov Chain Monte Carlo Method H. Bi et al. 10.1109/JSTARS.2014.2322096
47. Efficient hydrological model parameter optimization with Sequential Monte Carlo sampling E. Jeremiah et al. 10.1016/j.envsoft.2012.07.001
48. Dealing with Uncertainty in Water Distribution System Models: A Framework for Real-Time Modeling and Data Assimilation C. Hutton et al. 10.1061/(ASCE)WR.1943-5452.0000325
49. Using Residual Resampling and Sensitivity Analysis to Improve Particle Filter Data Assimilation Accuracy H. Zhang et al. 10.1109/LGRS.2013.2258888