49 citations as recorded by crossref.

1. Nature-based solutions as buffers against coastal compound flooding: Exploring potential framework for process-based modeling of hazard mitigation S. Radfar et al.
2. 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.
3. Iber-PEST: Automatic calibration in fully distributed hydrological models based on the 2D shallow water equations G. García-Alén et al.
4. Time-varying parameters of the hydrological simulation model under a changing environment R. Liu et al.
5. Editorial: Identifying hotspots of hydro-hazards under global change M. Pregnolato et al.
6. Advancing harmful algal bloom predictions using chlorophyll-a as an Indicator: Combining deep learning and EnKF data assimilation method I. Busari et al.
7. Rapid Probabilistic Inundation Mapping Using Local Thresholds and Sentinel-1 SAR Data on Google Earth Engine J. Liang et al.
8. Comparison of data assimilation based approach for daily streamflow simulation under multiple scenarios in Ganjiang River Basin W. Weiguang et al.
9. Enhancing streamflow predictions with machine learning and Copula-Embedded Bayesian model averaging A. Sattari et al.
10. Ensemble Kalman Inversion for upstream parameter estimation and indirect streamflow correction: A simulation study A. Pensoneault et al.
11. A comparative assessment of human entrapment and instability risks in flash floods X. Cheng et al.
12. Real-time coastal flood hazard assessment using DEM-based hydrogeomorphic classifiers K. Jafarzadegan et al.
13. Towards flood risk mapping based on multi-tiered decision making in a densely urbanized metropolitan city of Istanbul Ö. Ekmekcioğlu et al.
14. A Framework for Mechanistic Flood Inundation Forecasting at the Metropolitan Scale J. Schubert et al.
15. River network and hydro-geomorphological parameters at 1∕12° resolution for global hydrological and climate studies S. Munier & B. Decharme
16. Accounting for the uncertainty of precipitation forecasts and its impacts on probabilistic flood inundation mapping skill F. Gomez et al.
17. Improving flood inundation modeling skill: interconnection between model parameters and boundary conditions N. Oruc Baci et al.
18. Probabilistic flood inundation mapping through copula Bayesian multi-modeling of precipitation products F. Gomez et al.
19. Efficient Data Assimilation Method for Optimizing Prediction of Flow Field and Sediment Transport in Open Channel M. Ahmed & S. Li
20. Development of a Fast and Accurate Hybrid Model for Floodplain Inundation Simulations N. Fraehr et al.
21. An ensemble data assimilation approach to improve farm-scale actual evapotranspiration estimation P. Deb et al.
22. Ensemble data assimilation for operational streamflow predictions in the next generation (NextGen) framework E. Foroumandi et al.
23. Improving Evapotranspiration Estimation in SWAT-Based Hydrologic Simulation through Data Assimilation in the SEBAL Algorithm O. Mikaeili & M. Shourian
24. A surrogate machine learning model using random forests for real-time flood inundation simulations S. Sasanapuri et al.
25. Beyond a fixed number: Investigating uncertainty in popular evaluation metrics of ensemble flood modeling using bootstrapping analysis T. Huang & V. Merwade
26. Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models D. Muñoz et al.
27. Flood Water Depth Prediction with Convolutional Temporal Attention Networks P. Chaudhary et al.
28. Mapping Compound Flooding Risks for Urban Resilience in Coastal Zones: A Comprehensive Methodological Review H. Sun et al.
29. Flood hazard assessment using AHP–FR–Fuzzy ensemble of a sub-Himalayan catchment in a transboundary piedmont landscape of the Eastern Himalayas D. Chandra et al.
30. Probabilistic urban flood prediction with multivariate assimilation of sewer and surface observations B. Kim et al.
31. Assessing the impact of global change on flood dynamics and rice submergence susceptibility in the Kilombero floodplain, Tanzania M. Tuschen et al.
32. Toward improved river boundary conditioning for simulation of extreme floods K. Jafarzadegan et al.
33. The use of crowdsourced social media data to improve flood forecasting C. Songchon et al.
34. Assessment of the 2022 Floods in Lower Indus Basin Using Suite of Satellite Sensors and Hydrological Modelling P. Gupta et al.
35. Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al.
36. Constraining Flood Forecasting Uncertainties through Streamflow Data Assimilation in the Tropical Andes of Peru: Case of the Vilcanota River Basin H. Llauca et al.
37. GeoFlood Enhancement for Robust Flood Inundation Mapping in Flat Terrain Zones M. Wahba et al.
38. Fast Flood Extent Monitoring With SAR Change Detection Using Google Earth Engine E. Hamidi et al.
39. Improving Bayesian Model Averaging for Ensemble Flood Modeling Using Multiple Markov Chains Monte Carlo Sampling T. Huang & V. Merwade
40. Dual State‐Parameter Assimilation of SAR‐Derived Wet Surface Ratio for Improving Fluvial Flood Reanalysis T. Nguyen et al.
41. AI-Empowered Latent Four-dimensional Variational Data Assimilation for River Discharge Forecasting K. Wang et al.
42. Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting P. Abbaszadeh et al.
43. Bayes_Opt-SWMM: A Gaussian process-based Bayesian optimization tool for real-time flood modeling with SWMM A. Tanim et al.
44. Intercomparison of flood inundation models across land use types and hydrological flood stages P. Nikrou et al.
45. Towards a typology for hybrid compound flood modeling S. Radfar et al.
46. LISFLOOD-FP 8.1: new GPU-accelerated solvers for faster fluvial/pluvial flood simulations M. Sharifian et al.
47. SWAT_DA: Sequential Multivariate Data Assimilation‐Oriented Modification of SWAT M. Bayat et al.
48. A probabilistic machine learning framework for daily extreme events forecasting A. Sattari et al.
49. Improvement of Flood Extent Representation With Remote Sensing Data and Data Assimilation T. Nguyen et al.