48 citations as recorded by crossref.

1. Causes, impacts and patterns of disastrous river floods B. Merz et al. https://doi.org/10.1038/s43017-021-00195-3
2. River channel conveyance capacity adjusts to modes of climate variability L. Slater et al. https://doi.org/10.1038/s41598-019-48782-1
3. Potential for seasonal flood forecasting in West Africa using climate indexes J. Hounkpè et al. https://doi.org/10.1111/jfr3.12833
4. An investigation on the non-stationarity of flood frequency across the UK M. Chen et al. https://doi.org/10.1016/j.jhydrol.2021.126309
5. Towards seasonal forecasting of flood probabilities in Europe using climate and catchment information E. Steirou et al. https://doi.org/10.1038/s41598-022-16633-1
6. Comparing Flood Projection Approaches Across Hydro‐Climatologically Diverse United States River Basins K. Schlef et al. https://doi.org/10.1029/2019WR025861
7. Climate impact on flood changes – an Austrian-Ukrainian comparison S. Snizhko et al. https://doi.org/10.2478/johh-2023-0017
8. Individual Influence of Climate Variability Indices on Annual Maximum Precipitation Across the Global Scale L. Belkhiri & T. Kim https://doi.org/10.1007/s11269-021-02882-8
9. Intra-annual dendrogeomorphic dating and climate linkages of flood events in headwaters of central Europe R. Tichavský et al. https://doi.org/10.1016/j.scitotenv.2020.142953
10. Nonstationary stochastic simulation method for the risk assessment of water allocation S. Chen et al. https://doi.org/10.1039/D0EW00695E
11. Bayesian hierarchical modelling of intensity-duration-frequency curves using a climate model large ensemble A. Rischmuller et al. https://doi.org/10.5194/ascmo-12-1-2026
12. Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability G. Papacharalampous & H. Tyralis https://doi.org/10.1016/j.jhydrol.2020.125205
13. Global-scale massive feature extraction from monthly hydroclimatic time series: Statistical characterizations, spatial patterns and hydrological similarity G. Papacharalampous et al. https://doi.org/10.1016/j.scitotenv.2020.144612
14. An updated national-scale assessment of trends in UK peak river flow data: how robust are observed increases in flooding? J. Hannaford et al. https://doi.org/10.2166/nh.2021.156
15. Climate and rivers G. McGregor https://doi.org/10.1002/rra.3508
16. Quantifying the effect of climate variability on seasonal precipitation using Bayesian clustering approach in Kebir Rhumel Basin, Algeria L. Belkhiri & N. Krakauer https://doi.org/10.1007/s00477-023-02488-z
17. Global Changes in 20‐Year, 50‐Year, and 100‐Year River Floods L. Slater et al. https://doi.org/10.1029/2020GL091824
18. A Hidden Climate Indices Modeling Framework for Multivariable Space‐Time Data B. Renard et al. https://doi.org/10.1029/2021WR030007
19. Flood trends in Europe: are changes in small and big floods different? M. Bertola et al. https://doi.org/10.5194/hess-24-1805-2020
20. Forecasting Monthly River Flows in Ukraine under Different Climatic Conditions R. Graf & V. Vyshnevskyi https://doi.org/10.3390/resources11120111
21. What Will the Weather Do? Forecasting Flood Losses Based on Oscillation Indices G. Guimarães Nobre et al. https://doi.org/10.1029/2019EF001450
22. An Overview of Flood Concepts, Challenges, and Future Directions A. Mishra et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002164
23. Forecasting Magnitude and Frequency of Seasonal Streamflow Extremes Using a Bayesian Hierarchical Framework Á. Ossandón et al. https://doi.org/10.1029/2022WR033194
24. Hydroclimatic time series features at multiple time scales G. Papacharalampous et al. https://doi.org/10.1016/j.jhydrol.2023.129160
25. Investigating the Combined Influence of the North Atlantic Oscillation and the East Atlantic Pattern on Monthly Precipitation Across Mainland Spain C. Cording & H. West https://doi.org/10.3390/meteorology5030022
26. Detecting Flood‐Rich and Flood‐Poor Periods in Annual Peak Discharges Across Europe D. Lun et al. https://doi.org/10.1029/2019WR026575
27. A method for detecting the non-stationarity during high flows under global change Z. Zhang et al. https://doi.org/10.1016/j.scitotenv.2022.158341
28. The NAO Variability Prediction and Forecasting with Multiple Time Scales Driven by ENSO Using Machine Learning Approaches B. Mu et al. https://doi.org/10.1155/2022/6141966
29. Season-Ahead Flood Quantile Forecasting with Climate-Informed Models for Indian Catchments A. Ganapathy et al. https://doi.org/10.1061/JHYEFF.HEENG-6750
30. A tiered stochastic framework for assessing crop yield loss risks due to water scarcity under different uncertainty levels V. Uddameri et al. https://doi.org/10.1016/j.agwat.2020.106226
31. Unraveling the impact of large-scale climate circulations on the maximum discharges in the middle and lower Danube basin I. Leščešen et al. https://doi.org/10.2478/johh-2025-0027
32. A Global Map for Selecting Stationary and Nonstationary Methods to Estimate Extreme Floods Z. Li et al. https://doi.org/10.3390/w15213835
33. How Changes in Future Precipitation Impact Flood Frequencies: A Quantile‐Quantile Mapping Approach L. Cafiero et al. https://doi.org/10.1029/2024WR038471
34. Amplified seasonality in western Europe in a warmer world N. de Winter et al. https://doi.org/10.1126/sciadv.adl6717
35. Current European flood-rich period exceptional compared with past 500 years G. Blöschl et al. https://doi.org/10.1038/s41586-020-2478-3
36. A novel deep learning-based approach for reconstruction of historical long-term high-quality gridded meteorological dataset Y. Jeong et al. https://doi.org/10.1016/j.jhydrol.2025.132850
37. On the need of ensemble flood forecast in India J. Nanditha & V. Mishra https://doi.org/10.1016/j.wasec.2021.100086
38. Modelling non-stationary flood frequency in England and Wales using physical covariates D. Faulkner et al. https://doi.org/10.2166/nh.2024.134
39. Modifying Design Standards: The 2023 Extreme Flood’s Impact on Design Discharges in Slovenia M. Šraj & N. Bezak https://doi.org/10.3390/w17152198
40. Non-stationary multi-covariate flood frequency analysis with TRUE: A tool for time-varying return levels of univariate extremes U. Mohseni et al. https://doi.org/10.1016/j.envsoft.2026.106992
41. Trends in Compound Flooding in Northwestern Europe During 1901–2014 P. Ganguli & B. Merz https://doi.org/10.1029/2019GL084220
42. Comparison of time trend- and precipitation-informed models for assessing design discharges in variable climate M. Šraj & N. Bezak https://doi.org/10.1016/j.jhydrol.2020.125374
43. Synchronization and Delay Between Circulation Patterns and High Streamflow Events in Germany F. Conticello et al. https://doi.org/10.1029/2019WR025598
44. Climatology of long-term extreme precipitation using rain gauges data over France A. Dommange et al. https://doi.org/10.1016/j.atmosres.2026.108886
45. Do small and large floods have the same drivers of change? A regional attribution analysis in Europe M. Bertola et al. https://doi.org/10.5194/hess-25-1347-2021
46. Using climate information as covariates to improve nonstationary flood frequency analysis in Brazil G. Anzolin et al. https://doi.org/10.1080/02626667.2023.2182212
47. A systematic review of compound flooding: Drivers, models, and future directions U. Mohseni et al. https://doi.org/10.1016/j.eiar.2026.108620
48. Combined predictive and descriptive tests for extreme rainfall probability distribution selection A. Ballarin et al. https://doi.org/10.1080/02626667.2022.2063725