54 citations as recorded by crossref.

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8. Rapid Probabilistic Inundation Mapping Using Local Thresholds and Sentinel-1 SAR Data on Google Earth Engine J. Liang et al. https://doi.org/10.3390/rs17101747
9. Application of real-time data assimilation system to improve streamflow forecasts in Japan Y. Liu et al. https://doi.org/10.1016/j.jhydrol.2026.135680
10. Comparison of data assimilation based approach for daily streamflow simulation under multiple scenarios in Ganjiang River Basin W. Weiguang et al. https://doi.org/10.18307/2023.0323
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19. Accounting for the uncertainty of precipitation forecasts and its impacts on probabilistic flood inundation mapping skill F. Gomez et al. https://doi.org/10.1016/j.jhydrol.2026.135429
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21. Probabilistic flood inundation mapping through copula Bayesian multi-modeling of precipitation products F. Gomez et al. https://doi.org/10.5194/nhess-24-2647-2024
22. Efficient Data Assimilation Method for Optimizing Prediction of Flow Field and Sediment Transport in Open Channel M. Ahmed & S. Li https://doi.org/10.1002/rra.70092
23. Development of a Fast and Accurate Hybrid Model for Floodplain Inundation Simulations N. Fraehr et al. https://doi.org/10.1029/2022WR033836
24. An ensemble data assimilation approach to improve farm-scale actual evapotranspiration estimation P. Deb et al. https://doi.org/10.1016/j.agrformet.2022.108982
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26. Improving Evapotranspiration Estimation in SWAT-Based Hydrologic Simulation through Data Assimilation in the SEBAL Algorithm O. Mikaeili & M. Shourian https://doi.org/10.1007/s11269-024-03854-4
27. A surrogate machine learning model using random forests for real-time flood inundation simulations S. Sasanapuri et al. https://doi.org/10.1016/j.envsoft.2025.106439
28. Beyond a fixed number: Investigating uncertainty in popular evaluation metrics of ensemble flood modeling using bootstrapping analysis T. Huang & V. Merwade https://doi.org/10.1111/jfr3.12982
29. Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models D. Muñoz et al. https://doi.org/10.5194/hess-28-2531-2024
30. Flood Water Depth Prediction with Convolutional Temporal Attention Networks P. Chaudhary et al. https://doi.org/10.3390/w16091286
31. Mapping Compound Flooding Risks for Urban Resilience in Coastal Zones: A Comprehensive Methodological Review H. Sun et al. https://doi.org/10.3390/rs16020350
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33. Probabilistic urban flood prediction with multivariate assimilation of sewer and surface observations B. Kim et al. https://doi.org/10.1016/j.watres.2026.126065
34. Assessing the impact of global change on flood dynamics and rice submergence susceptibility in the Kilombero floodplain, Tanzania M. Tuschen et al. https://doi.org/10.3389/feart.2025.1672749
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37. Assessment of the 2022 Floods in Lower Indus Basin Using Suite of Satellite Sensors and Hydrological Modelling P. Gupta et al. https://doi.org/10.1007/s12524-024-02105-8
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39. Constraining Flood Forecasting Uncertainties through Streamflow Data Assimilation in the Tropical Andes of Peru: Case of the Vilcanota River Basin H. Llauca et al. https://doi.org/10.3390/w15223944
40. GeoFlood Enhancement for Robust Flood Inundation Mapping in Flat Terrain Zones M. Wahba et al. https://doi.org/10.3390/geomatics6010019
41. Fast Flood Extent Monitoring With SAR Change Detection Using Google Earth Engine E. Hamidi et al. https://doi.org/10.1109/TGRS.2023.3240097
42. Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling T. Huang & V. Merwade https://doi.org/10.1029/2023WR034947
43. Dual State‐Parameter Assimilation of SAR‐Derived Wet Surface Ratio for Improving Fluvial Flood Reanalysis T. Nguyen et al. https://doi.org/10.1029/2022WR033155
44. AI-Empowered Latent Four-dimensional Variational Data Assimilation for River Discharge Forecasting K. Wang et al. https://doi.org/10.1109/JSTARS.2025.3611136
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46. Bayes_Opt-SWMM: A Gaussian process-based Bayesian optimization tool for real-time flood modeling with SWMM A. Tanim et al. https://doi.org/10.1016/j.envsoft.2024.106122
47. Intercomparison of flood inundation models across land use types and hydrological flood stages P. Nikrou et al. https://doi.org/10.1016/j.jhydrol.2026.135410
48. Optimal Forecast of Open-Channel Flow through Sequential Node Selection for Data Assimilation in a Hydrodynamic Model M. Ahmed & S. Li https://doi.org/10.1061/JHEND8.HYENG-14650
49. A unified pluvial–fluvial urban flood inundation modeling framework at the metropolitan scale A. Samadi et al. https://doi.org/10.1007/s40808-026-02912-7
50. Towards a typology for hybrid compound flood modeling S. Radfar et al. https://doi.org/10.5194/hess-30-1397-2026
51. LISFLOOD-FP 8.1: new GPU-accelerated solvers for faster fluvial/pluvial flood simulations M. Sharifian et al. https://doi.org/10.5194/gmd-16-2391-2023
52. SWAT_DA: Sequential Multivariate Data Assimilation‐Oriented Modification of SWAT M. Bayat et al. https://doi.org/10.1029/2022WR032397
53. A probabilistic machine learning framework for daily extreme events forecasting A. Sattari et al. https://doi.org/10.1016/j.eswa.2024.126004
54. Improvement of Flood Extent Representation With Remote Sensing Data and Data Assimilation T. Nguyen et al. https://doi.org/10.1109/TGRS.2022.3147429