102 citations as recorded by crossref.

1. Reply to Comment by Jie Qin and Teng Wu on “A Modified Particle Filter‐Based Data Assimilation Method for a High‐Precision 2‐D Hydrodynamic Model Considering Spatial‐Temporal Variability of Roughness: Simulation of Dam‐Break Flood Inundation” Y. Cao et al. 10.1029/2020WR027315
2. 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
3. A Modified Particle Filter‐Based Data Assimilation Method for a High‐Precision 2‐D Hydrodynamic Model Considering Spatial‐temporal Variability of Roughness: Simulation of Dam‐Break Flood Inundation Y. Cao et al. 10.1029/2018WR023568
4. Evolution of ensemble data assimilation for uncertainty quantification using the particle filter‐Markov chain Monte Carlo method H. Moradkhani et al. 10.1029/2012WR012144
5. The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter D. Plaza et al. 10.5194/hess-16-375-2012
6. Analyzing the uncertainty of suspended sediment load prediction using sequential data assimilation M. Leisenring & H. Moradkhani 10.1016/j.jhydrol.2012.08.049
7. A Method for Automatic and Rapid Mapping of Water Surfaces from Sentinel-1 Imagery F. Bioresita et al. 10.3390/rs10020217
8. Closing the Gaps in Our Knowledge of the Hydrological Cycle over Land: Conceptual Problems W. Lahoz & G. De Lannoy 10.1007/s10712-013-9221-7
9. A review of low‐cost space‐borne data for flood modelling: topography, flood extent and water level K. Yan et al. 10.1002/hyp.10449
10. Data assimilation of satellite-based actual evapotranspiration in a distributed hydrological model of a controlled water system I. Hartanto et al. 10.1016/j.jag.2016.12.015
11. 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
12. Assimilation of probabilistic flood maps from SAR data into a coupled hydrologic–hydraulic forecasting model: a proof of concept C. Di Mauro​​​​​​​ et al. 10.5194/hess-25-4081-2021
13. Simultaneous estimation of inflow and channel roughness using 2D hydraulic model and particle filters Y. Kim et al. 10.1111/j.1753-318X.2012.01164.x
14. Sequential data assimilation for real-time probabilistic flood inundation mapping K. Jafarzadegan et al. 10.5194/hess-25-4995-2021
15. Development of an Automated Tool for Delineation of Flood Footprints from SAR Imagery for Rapid Disaster Response: A Case Study S. Kuntla & P. Manjusree 10.1007/s12524-020-01125-4
16. Daily discharge estimation at ungauged river sites using remote sensing S. Birkinshaw et al. 10.1002/hyp.9647
17. Toward reduction of model uncertainty: Integration of Bayesian model averaging and data assimilation M. Parrish et al. 10.1029/2011WR011116
18. Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. 10.1029/2022RG000788
19. Real-time assimilation of streamflow observations into a hydrological routing model: effects of model structures and updating methods M. Mazzoleni et al. 10.1080/02626667.2018.1430898
20. Flood Modeling and Prediction Using Earth Observation Data G. Schumann et al. 10.1007/s10712-022-09751-y
21. Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities Y. Liu et al. 10.5194/hess-16-3863-2012
22. 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
23. A Mutual Information‐Based Likelihood Function for Particle Filter Flood Extent Assimilation A. Dasgupta et al. 10.1029/2020WR027859
24. High-Resolution Flood Monitoring Based on Advanced Statistical Modeling of Sentinel-1 Multi-Temporal Stacks R. Colacicco et al. 10.3390/rs16020294
25. An Approach Using a 1D Hydraulic Model, Landsat Imaging and Generalized Likelihood Uncertainty Estimation for an Approximation of Flood Discharge Y. Jung et al. 10.3390/w5041598
26. Fusion of Sentinel-1 and Sentinel-2 image time series for permanent and temporary surface water mapping F. Bioresita et al. 10.1080/01431161.2019.1624869
27. A Tempered Particle Filter to Enhance the Assimilation of SAR‐Derived Flood Extent Maps Into Flood Forecasting Models C. Di Mauro et al. 10.1029/2022WR031940
28. A Novel Approach to Extract Water Body from ASAR Dual-Polarized Data J. Ma et al. 10.1088/1755-1315/17/1/012099
29. Improving the particle filter for data assimilation in hydraulic modeling by using a Cauchy likelihood function C. Jiang et al. 10.1016/j.jhydrol.2022.129050
30. Improving the real-time probabilistic channel flood forecasting by incorporating the uncertainty of inflow using the particle filter X. Xu et al. 10.1007/s42241-018-0110-x
31. Uncertainty propagation within a water level ensemble prediction system M. Bessar et al. 10.1016/j.jhydrol.2021.127193
32. Integrating VGI and 2D hydraulic models into a data assimilation framework for real time flood forecasting and mapping A. Annis & F. Nardi 10.1080/10095020.2019.1626135
33. Satellite-supported flood forecasting in river networks: A real case study J. García-Pintado et al. 10.1016/j.jhydrol.2015.01.084
34. Grey Forecast Rainfall with Flow Updating Algorithm for Real-Time Flood Forecasting J. Ho & K. Lee 10.3390/w7051840
35. Toward Improved Regional Hydrological Model Performance Using State‐Of‐The‐Science Data‐Informed Soil Parameters C. Li et al. 10.1029/2023WR034431
36. Scheduling satellite-based SAR acquisition for sequential assimilation of water level observations into flood modelling J. García-Pintado et al. 10.1016/j.jhydrol.2013.03.050
37. Estimating the impact of satellite observations on the predictability of large-scale hydraulic models K. Andreadis & G. Schumann 10.1016/j.advwatres.2014.06.006
38. 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
39. Enhanced biomass prediction by assimilating satellite data into a crop growth model M. Machwitz et al. 10.1016/j.envsoft.2014.08.010
40. The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework P. Abbaszadeh et al. 10.1029/2018WR023629
41. Automatic near real-time selection of flood water levels from high resolution Synthetic Aperture Radar images for assimilation into hydraulic models: A case study D. Mason et al. 10.1016/j.rse.2012.06.017
42. Multivariate Assimilation of Remotely Sensed Soil Moisture and Evapotranspiration for Drought Monitoring K. Gavahi et al. 10.1175/JHM-D-20-0057.1
43. Joint assimilation of satellite soil moisture and streamflow data for the hydrological application of a two-dimensional shallow water model G. García-Alén et al. 10.1016/j.jhydrol.2023.129667
44. Accounting for uncertainties in compound flood hazard assessment: The value of data assimilation D. Muñoz et al. 10.1016/j.coastaleng.2021.104057
45. Probabilistic mapping of flood-induced backscatter changes in SAR time series S. Schlaffer et al. 10.1016/j.jag.2016.12.003
46. Assisting Flood Disaster Response with Earth Observation Data and Products: A Critical Assessment G. Schumann et al. 10.3390/rs10081230
47. 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
48. Improving hydrodynamic modeling of river networks by incorporating data assimilation using a particle filter C. Jiang et al. 10.1016/j.ijsrc.2023.06.001
49. The use of crowdsourced social media data to improve flood forecasting C. Songchon et al. 10.1016/j.jhydrol.2023.129703
50. Improving Soil Moisture Profile Prediction With the Particle Filter-Markov Chain Monte Carlo Method H. Yan et al. 10.1109/TGRS.2015.2432067
51. Impact of the timing of a SAR image acquisition on the calibration of a flood inundation model S. Gobeyn et al. 10.1016/j.advwatres.2016.12.005
52. Observation operators for assimilation of satellite observations in fluvial inundation forecasting E. Cooper et al. 10.5194/hess-23-2541-2019
53. Data Assimilation in Hydrologic Routing: Impact of Model Error and Sensor Placement on Flood Forecasting M. Mazzoleni et al. 10.1061/(ASCE)HE.1943-5584.0001656
54. A Change Detection Approach to Flood Mapping in Urban Areas Using TerraSAR-X L. Giustarini et al. 10.1109/TGRS.2012.2210901
55. Effective Representation of River Geometry in Hydraulic Flood Forecast Models S. Grimaldi et al. 10.1002/2017WR021765
56. Underlying Fundamentals of Kalman Filtering for River Network Modeling C. Emery et al. 10.1175/JHM-D-19-0084.1
57. Ensemble-based data assimilation for operational flood forecasting – On the merits of state estimation for 1D hydrodynamic forecasting through the example of the “Adour Maritime” river S. Barthélémy et al. 10.1016/j.jhydrol.2017.06.017
58. A drifting GPS buoy for retrieving effective riverbed bathymetry R. Hostache et al. 10.1016/j.jhydrol.2014.11.018
59. Flood Routing Model with Particle Filter-Based Data Assimilation for Flash Flood Forecasting in the Micro-Model of Lower Yellow River, China M. Chen et al. 10.3390/w10111612
60. Integrating remotely sensed surface water extent into continental scale hydrology B. Revilla-Romero et al. 10.1016/j.jhydrol.2016.10.041
61. Assimilation of Typhoon Wind Field Retrieved from Scatterometer and SAR Based on the Huber Norm Quality Control B. Duan et al. 10.3390/rs9100987
62. Assimilation of remote sensing into crop growth models: Current status and perspectives J. Huang et al. 10.1016/j.agrformet.2019.06.008
63. Improving flood inundation forecasts through the assimilation of in situ floodplain water level measurements based on alternative observation network configurations A. Van Wesemael et al. 10.1016/j.advwatres.2019.05.025
64. Observation impact, domain length and parameter estimation in data assimilation for flood forecasting E. Cooper et al. 10.1016/j.envsoft.2018.03.013
65. State estimation in large-scale open channel networks using sequential Monte Carlo methods: Optimal sampling importance resampling and implicit particle filters M. Rafiee et al. 10.1029/2011WR011608
66. Correction of pumping station parameters in a one-dimensional hydrodynamic model using the Ensemble Kalman filter X. Lei et al. 10.1016/j.jhydrol.2018.10.062
67. Estimating river bathymetry from data assimilation of synthetic SWOT measurements Y. Yoon et al. 10.1016/j.jhydrol.2012.07.028
68. Integrating remote sensing data with flood inundation models: how far have we got? P. Bates 10.1002/hyp.9374
69. Variational assimilation of remotely sensed flood extents using a 2-D flood model X. Lai et al. 10.5194/hess-18-4325-2014
70. Accounting for image uncertainty in SAR-based flood mapping L. Giustarini et al. 10.1016/j.jag.2014.06.017
71. Control theory-based data assimilation for hydraulic models as a decision support tool for hydropower systems: sequential, multi-metric tuning of the controllers M. Milašinović et al. 10.2166/hydro.2021.078
72. Multivariate remotely sensed and in-situ data assimilation for enhancing community WRF-Hydro model forecasting P. Abbaszadeh et al. 10.1016/j.advwatres.2020.103721
73. Towards an automated SAR-based flood monitoring system: Lessons learned from two case studies P. Matgen et al. 10.1016/j.pce.2010.12.009
74. Exploration of sequential streamflow assimilation in snow dominated watersheds M. Abaza et al. 10.1016/j.advwatres.2015.03.011
75. Enhancing hydrologic data assimilation by evolutionary Particle Filter and Markov Chain Monte Carlo P. Abbaszadeh et al. 10.1016/j.advwatres.2017.11.011
76. Estimation of inundation depth using flood extent information and hydrodynamic simulations N. Nguyen et al. 10.3178/hrl.10.39
77. Iber-PEST: AUTOMATIC CALIBRATION IN FULLY DISTRIBUTED HYDROLOGICAL MODELS BASED ON THE 2D SHALLOW WATER EQUATIONS G. García-Alén et al. 10.1016/j.envsoft.2024.106047
78. 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
79. Reducing Inconsistencies in Point Observations of Maximum Flood Inundation Level B. Parkes et al. 10.1175/2012EI000475.1
80. Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation C. DeChant & H. Moradkhani 10.5194/hess-15-3399-2011
81. Fast data assimilation for open channel hydrodynamic models using control theory approach M. Milašinović et al. 10.1016/j.jhydrol.2020.124661
82. Probabilistic Flood Mapping Using Synthetic Aperture Radar Data L. Giustarini et al. 10.1109/TGRS.2016.2592951
83. Assimilation of surface reflectance in snow simulations: Impact on bulk snow variables J. Revuelto et al. 10.1016/j.jhydrol.2021.126966
84. Towards operational SAR-based flood mapping using neuro-fuzzy texture-based approaches A. Dasgupta et al. 10.1016/j.rse.2018.06.019
85. Challenges, Opportunities, and Pitfalls for Global Coupled Hydrologic‐Hydraulic Modeling of Floods S. Grimaldi et al. 10.1029/2018WR024289
86. The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review D. Jiang & K. Wang 10.3390/w11081615
87. On the Impacts of Observation Location, Timing, and Frequency on Flood Extent Assimilation Performance A. Dasgupta et al. 10.1029/2020WR028238
88. Estimating reach-averaged discharge for the River Severn from measurements of river water surface elevation and slope M. Durand et al. 10.1016/j.jhydrol.2013.12.050
89. Technical note: Assessment of observation quality for data assimilation in flood models J. Waller et al. 10.5194/hess-22-3983-2018
90. Sentinel-1-based flood mapping: a fully automated processing chain A. Twele et al. 10.1080/01431161.2016.1192304
91. 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
92. 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
93. Exploiting the proliferation of current and future satellite observations of rivers G. Schumann & A. Domeneghetti 10.1002/hyp.10825
94. Near‐Real‐Time Assimilation of SAR‐Derived Flood Maps for Improving Flood Forecasts R. Hostache et al. 10.1029/2017WR022205
95. Assimilating SAR-derived water level data into a hydraulic model: a case study L. Giustarini et al. 10.5194/hess-15-2349-2011
96. Calibration of channel depth and friction parameters in the LISFLOOD-FP hydraulic model using medium-resolution SAR data and identifiability techniques M. Wood et al. 10.5194/hess-20-4983-2016
97. 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
98. Kalman filters for assimilating near-surface observations into the Richards equation – Part 1: Retrieving state profiles with linear and nonlinear numerical schemes G. Chirico et al. 10.5194/hess-18-2503-2014
99. The potential of flood forecasting using a variable-resolution global Digital Terrain Model and flood extents from Synthetic Aperture Radar images D. Mason et al. 10.3389/feart.2015.00043
100. Exploration of sequential streamflow assimilation in snow dominated watersheds M. Abaza et al. 10.1016/j.advwatres.2015.10.008
101. Hydraulic parameter estimation by remotely-sensed top soil moisture observations with the particle filter C. Montzka et al. 10.1016/j.jhydrol.2011.01.020
102. Estimation of Radiative Transfer Parameters from L‐Band Passive Microwave Brightness Temperatures Using Advanced Data Assimilation C. Montzka et al. 10.2136/vzj2012.0040