46 citations as recorded by crossref.

1. A tropical cyclone risk prediction framework using flood susceptibility and tree-based machine learning models: County-level direct economic loss prediction in Guangdong Province J. Yang et al. https://doi.org/10.1016/j.ijdrr.2024.104955
2. Intercomparison of global reanalysis precipitation for flood risk modelling F. McClean et al. https://doi.org/10.5194/hess-27-331-2023
3. Quantifying the importance of spatial resolution and other factors through global sensitivity analysis of a flood inundation model J. Thomas Steven Savage et al. https://doi.org/10.1002/2015WR018198
4. Comprehensive climatic variability analysis and its significance on flood occurrences in the Middle Ganga Plain, India A. Arora et al. https://doi.org/10.1007/s44288-024-00050-0
5. Development of Flood Damage Regression Models by Rainfall Identification Reflecting Landscape Features in Gangwon Province, the Republic of Korea H. Choi https://doi.org/10.3390/land10020123
6. Toward Global Stochastic River Flood Modeling O. Wing et al. https://doi.org/10.1029/2020WR027692
7. Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis E. Moges et al. https://doi.org/10.3390/w13010028
8. The impact of intense rainfall on insurance losses in two Swedish cities B. Blumenthal & L. Nyberg https://doi.org/10.1111/jfr3.12504
9. On s-convex bounds for Beta-unimodal distributions with applications to basis risk assessment C. Lefèvre et al. https://doi.org/10.1080/03461238.2020.1852596
10. Challenges, Opportunities, and Pitfalls for Global Coupled Hydrologic‐Hydraulic Modeling of Floods S. Grimaldi et al. https://doi.org/10.1029/2018WR024289
11. Application of re/insurance models to estimate increases in flood risk due to climate change J. Thistlethwaite et al. https://doi.org/10.1186/s40677-018-0101-9
12. Satellite-driven downscaling of global reanalysis precipitation products for hydrological applications H. Seyyedi et al. https://doi.org/10.5194/hess-18-5077-2014
13. What are the hydro-meteorological controls on flood characteristics? M. Nied et al. https://doi.org/10.1016/j.jhydrol.2016.12.003
14. The relationship between precipitation and insurance data for floods in a Mediterranean region (northeast Spain) M. Cortès et al. https://doi.org/10.5194/nhess-18-857-2018
15. Predictability of state-level flood damage in the conterminous United States: the role of hazard, exposure and vulnerability Q. Zhou et al. https://doi.org/10.1038/s41598-017-05773-4
16. Large‐Sample Evaluation of Radar Rainfall Nowcasting for Flood Early Warning R. Imhoff et al. https://doi.org/10.1029/2021WR031591
17. Analysis of large flood events: Based on flood data during 1985–2016 in Australia and India M. Halgamuge & A. Nirmalathas https://doi.org/10.1016/j.ijdrr.2017.05.011
18. Extending coupled hydrological-hydraulic model chains with a surrogate model for the estimation of flood losses A. Zischg et al. https://doi.org/10.1016/j.envsoft.2018.08.009
19. Spatiotemporal characteristics and hydrological implications of downscaled hourly precipitation climate scenarios for South Korea T. Lee et al. https://doi.org/10.1002/joc.7300
20. Regional flood frequency analysis at the global scale A. Smith et al. https://doi.org/10.1002/2014WR015814
21. Economic indicators of hydrologic drought insurance under water demand and climate change scenarios in a Brazilian context G. Mohor & E. Mendiondo https://doi.org/10.1016/j.ecolecon.2017.04.014
22. Basis risk management and randomly scaled uncertainty M. Mercè Claramunt et al. https://doi.org/10.1016/j.insmatheco.2022.08.005
23. Modulation of Economic Losses From European Floods by the North Atlantic Oscillation S. Zanardo et al. https://doi.org/10.1029/2019GL081956
24. Quantification of Uncertainty in Spatial Return Levels of Urban Precipitation Extremes C. Rupa & P. Mujumdar https://doi.org/10.1061/(ASCE)HE.1943-5584.0001583
25. South Asia river-flow projections and their implications for water resources C. Mathison et al. https://doi.org/10.5194/hess-19-4783-2015
26. A comprehensive assessment of precipitation products: Temporal and spatial analyses over terrestrial biomes in Northeastern Brazil J. de Andrade et al. https://doi.org/10.1016/j.rsase.2022.100842
27. Can Atmospheric Reanalysis Data Sets Be Used to Reproduce Flooding Over Large Scales? K. Andreadis et al. https://doi.org/10.1002/2017GL075502
28. A deep-learning-technique-based data-driven model for accurate and rapid flood predictions in temporal and spatial dimensions Q. Zhou et al. https://doi.org/10.5194/hess-27-1791-2023
29. Quantifying compound flood event uncertainties in a wave and tidally dominated coastal region: The impacts of copula selection, sampling, record length, and precipitation gauge selection J. Lucey & T. Gallien https://doi.org/10.1111/jfr3.12984
30. A hybrid approach to improvement of watershed water quality modeling by coupling process–based and deep learning models D. Jeong et al. https://doi.org/10.1002/wer.11079
31. Examining the utility of river restoration approaches for flood mitigation and channel stability enhancement: a recent review S. Mondal & P. Patel https://doi.org/10.1007/s12665-018-7381-y
32. Calibration of the Global Flood Awareness System (GloFAS) using daily streamflow data F. Hirpa et al. https://doi.org/10.1016/j.jhydrol.2018.09.052
33. Guidance on field survey programme design for basement flooding assessment A. Jiang et al. https://doi.org/10.1080/02626667.2020.1782412
34. Sensitivity of flood loss estimates to building representation and flow depth attribution methods in micro-scale flood modelling M. Bermúdez & A. Zischg https://doi.org/10.1007/s11069-018-3270-7
35. Extreme multi-basin flooding linked with extra-tropical cyclones P. De Luca et al. https://doi.org/10.1088/1748-9326/aa868e
36. Flood Indices for Java Island Using Global Satellite Mapping of Precipitation A. Rojali et al. https://doi.org/10.1088/1757-899X/453/1/012072
37. The monetary benefit of early flood warnings in Europe F. Pappenberger et al. https://doi.org/10.1016/j.envsci.2015.04.016
38. Trace selection method for a best representative in stochastic downscaling of precipitation T. Lee et al. https://doi.org/10.1007/s00704-019-03021-5
39. Data quality and uncertainty issues in flood prediction: a systematic review J. Yu et al. https://doi.org/10.1080/17538947.2025.2495738
40. Are 2D shallow-water solvers fast enough for early flood warning? A comparative assessment on the 2021 Ahr valley flood event S. Khosh Bin Ghomash et al. https://doi.org/10.5194/nhess-24-2857-2024
41. Assessing water options trading willingness in irrigation areas with heterogeneous resource endowments X. Cai et al. https://doi.org/10.1016/j.jhydrol.2022.128471
42. Optimizing flood resilience in China’s mountainous areas: Design flood estimation using advanced machine learning techniques X. Wang et al. https://doi.org/10.1016/j.ejrh.2025.102345
43. Uncertainty analysis of 100-year flood maps under climate change scenarios S. Alipour et al. https://doi.org/10.1016/j.jhydrol.2023.130502
44. Subseasonal variability of mesoscale convective systems over the tropical northeastern Pacific S. Berthet et al. https://doi.org/10.1002/qj.2992
45. Effects of variability in probable maximum precipitation patterns on flood losses A. Zischg et al. https://doi.org/10.5194/hess-22-2759-2018
46. Reading in the dark: Shifting governmentalities and the spatial dimensions of legible U.S. flood risk T. Brundidge https://doi.org/10.1177/0308518X241232528