46 citations as recorded by crossref.

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3. Filling the gaps: Calibrating a rainfall-runoff model using satellite-derived surface water extent B. Revilla-Romero et al. https://doi.org/10.1016/j.rse.2015.10.022
4. Challenges, Opportunities, and Pitfalls for Global Coupled Hydrologic‐Hydraulic Modeling of Floods S. Grimaldi et al. https://doi.org/10.1029/2018WR024289
5. A new cost-performance grid to compare different flood modelling approaches R. Hdeib et al. https://doi.org/10.1080/02626667.2021.1873346
6. Towards improving river discharge estimation in ungauged basins: calibration of rainfall-runoff models based on satellite observations of river flow width at basin outlet W. Sun et al. https://doi.org/10.5194/hess-14-2011-2010
7. The potential for remote sensing and hydrologic modelling to assess the spatio-temporal dynamics of ponds in the Ferlo Region (Senegal) V. Soti et al. https://doi.org/10.5194/hess-14-1449-2010
8. Bayesian calibration of a 2D hydraulic model using a convolutional neural network emulator J. Zevallos et al. https://doi.org/10.1016/j.envsoft.2025.106621
9. Simultaneous estimation of inflow and channel roughness using 2D hydraulic model and particle filters Y. Kim et al. https://doi.org/10.1111/j.1753-318X.2012.01164.x
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11. Assessing flood hazard changes using climate model forcing D. Callaghan & M. Hughes https://doi.org/10.5194/nhess-22-2459-2022
12. A dynamic bidirectional coupled surface flow model for flood inundation simulation C. Jiang et al. https://doi.org/10.5194/nhess-21-497-2021
13. Applying sequential Monte Carlo methods into a distributed hydrologic model: lagged particle filtering approach with regularization S. Noh et al. https://doi.org/10.5194/hess-15-3237-2011
14. An Approach Using a 1D Hydraulic Model, Landsat Imaging and Generalized Likelihood Uncertainty Estimation for an Approximation of Flood Discharge Y. Jung et al. https://doi.org/10.3390/w5041598
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20. Inter-comparison study of water level estimates derived from hydrodynamic–hydrologic model and satellite altimetry for a complex deltaic environment A. Siddique-E-Akbor et al. https://doi.org/10.1016/j.rse.2011.02.011
21. Closing the Gaps in Our Knowledge of the Hydrological Cycle over Land: Conceptual Problems W. Lahoz & G. De Lannoy https://doi.org/10.1007/s10712-013-9221-7
22. Reproducing an extreme flood with uncertain post-event information D. Fuentes-Andino et al. https://doi.org/10.5194/hess-21-3597-2017
23. Calibrating a hydrological model in ungauged small river basins of the northeastern Tibetan Plateau based on near-infrared images Z. Yu et al. https://doi.org/10.1016/j.jhydrol.2023.129158
24. Towards the sequential assimilation of SAR-derived water stages into hydraulic models using the Particle Filter: proof of concept P. Matgen et al. https://doi.org/10.5194/hess-14-1773-2010
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26. Assimilating SAR-derived water level data into a hydraulic model: a case study L. Giustarini et al. https://doi.org/10.5194/hess-15-2349-2011
27. A Novel Approach for the Integral Management of Water Extremes in Plain Areas C. Guevara Ochoa et al. https://doi.org/10.3390/hydrology6030070
28. Flood Hazard Assessment of Bago River Basin, Myanmar W. Zin et al. https://doi.org/10.20965/jdr.2018.p0014
29. Use of ENVISAT ASAR Global Monitoring Mode to complement optical data in the mapping of rapid broad-scale flooding in Pakistan D. O'Grady et al. https://doi.org/10.5194/hess-15-3475-2011
30. A drifting GPS buoy for retrieving effective riverbed bathymetry R. Hostache et al. https://doi.org/10.1016/j.jhydrol.2014.11.018
31. A multi‐temporal analysis of AMSR‐E data for flood and discharge monitoring during the 2008 flood in Iowa M. Temimi et al. https://doi.org/10.1002/hyp.8020
32. 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. https://doi.org/10.5194/hess-20-4983-2016
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34. Calibrating a hydrological model in a regional river of the Qinghai–Tibet plateau using river water width determined from high spatial resolution satellite images W. Sun et al. https://doi.org/10.1016/j.rse.2018.05.020
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36. Invited perspectives: Challenges and future directions in improving bridge flood resilience E. Tubaldi et al. https://doi.org/10.5194/nhess-22-795-2022
37. A spatial–temporal graph deep learning model for urban flood nowcasting leveraging heterogeneous community features H. Farahmand et al. https://doi.org/10.1038/s41598-023-32548-x
38. Multivariable Integrated Evaluation of Hydrodynamic Modeling: A Comparison of Performance Considering Different Baseline Topography Data P. Modi et al. https://doi.org/10.1029/2021WR031819
39. Hydraulic modelling calibration in small rivers by using coarse resolution synthetic aperture radar imagery A. Tarpanelli et al. https://doi.org/10.1002/hyp.9550
40. Impact of rainstorm patterns on the urban flood process superimposed by flash floods and urban waterlogging based on a coupled hydrologic–hydraulic model: a case study in a coastal mountainous river basin within southeastern China W. Jiang & J. Yu https://doi.org/10.1007/s11069-021-05182-x
41. Bare soil hydrological balance model “MHYSAN”: Calibration and validation using SAR moisture products and continuous thetaprobe network measurements over bare agricultural soils (Tunisia) A. Gorrab et al. https://doi.org/10.1016/j.jaridenv.2016.12.005
42. CREST-iMAP v1.0: A fully coupled hydrologic-hydraulic modeling framework dedicated to flood inundation mapping and prediction Z. Li et al. https://doi.org/10.1016/j.envsoft.2021.105051
43. Impact of the timing of a SAR image acquisition on the calibration of a flood inundation model S. Gobeyn et al. https://doi.org/10.1016/j.advwatres.2016.12.005
44. Near‐Real‐Time Assimilation of SAR‐Derived Flood Maps for Improving Flood Forecasts R. Hostache et al. https://doi.org/10.1029/2017WR022205
45. Using altimetry observations combined with GRACE to select parameter sets of a hydrological model in a data-scarce region P. Hulsman et al. https://doi.org/10.5194/hess-24-3331-2020
46. Evaluating the effects of preprocessing, method selection, and hyperparameter tuning on SAR-based flood mapping and water depth estimation J. Travert et al. https://doi.org/10.5194/nhess-26-2387-2026