76 citations as recorded by crossref.

1. A bilevel modeling framework to analyze the institutional gap between research and operation practices – Case on the Three Gorges Reservoir pre-impoundment problem X. Wang et al. https://doi.org/10.1016/j.jhydrol.2020.124742
2. Investigating the spatial–temporal changes of flood events across the Yangtze River Basin, China: Identification, spatial heterogeneity, and dominant impact factors J. Yu et al. https://doi.org/10.1016/j.jhydrol.2023.129503
3. Flood Seasonality Analysis in Iran: A Circular Statistics Approach M. Bagheri-Gavkosh & S. Hosseini https://doi.org/10.1061/JHYEFF.HEENG-5786
4. Flood Inundation Generation Mechanisms and Their Changes in 1953–2004 in Global Major River Basins Y. Mao et al. https://doi.org/10.1029/2019JD031381
5. Features of the Earth’s seasonal hydroclimate: characterizations and comparisons across the Köppen–Geiger climates and across continents G. Papacharalampous et al. https://doi.org/10.1186/s40645-023-00574-y
6. Climate and rivers G. McGregor https://doi.org/10.1002/rra.3508
7. A Flood Forecasting Method in the Francolí River Basin (Spain) Using a Distributed Hydrological Model and an Analog-Based Precipitation Forecast D. Carril-Rojas et al. https://doi.org/10.3390/hydrology12080220
8. Unveiling hidden risks in healthcare from flood-induced transportation disruption in Germany J. Wassmer et al. https://doi.org/10.1038/s43247-025-02645-y
9. Flood-generating mechanism attribution across U.S. catchments: the role of soil-layer depth and framework sensitivity B. Zhang et al. https://doi.org/10.1016/j.jhydrol.2026.136229
10. Historical and future changes in global flood magnitude – evidence from a model–observation investigation H. Do et al. https://doi.org/10.5194/hess-24-1543-2020
11. Application of remote sensing and GIS for the analysis of spatial differentiation and development of the erosion network in the mountainous part of the Bystrytsia Tysmenytska river basin G. Bayrak https://doi.org/10.30970/gpc.2026.1.5200
12. Extreme rainfall and temporal loading in Great Britain: Analysis of present and future trends using a convection-permitting climate model M. Asher et al. https://doi.org/10.1016/j.ejrh.2025.102750
13. Identifying Climate Change Impacts On Hydrological Behavior On Large-Scale With Machine Learning Algorithms A. Ivanov et al. https://doi.org/10.24057/2071-9388-2022-087
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15. Changing climate both increases and decreases European river floods G. Blöschl et al. https://doi.org/10.1038/s41586-019-1495-6
16. 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
17. Unveiling Changes in the Seasonality of Extreme Precipitation Shows an Anticipation of Short-Duration Extremes D. Treppiedi et al. https://doi.org/10.1007/s41748-026-01178-4
18. A Novel Method of Design Flood Hydrographs Estimation for Flood Hazard Mapping W. Gądek et al. https://doi.org/10.3390/w14121856
19. Reliance on daily mean streamflow data biases inferred flood seasonality B. Anderson et al. https://doi.org/10.1088/1748-9326/ae8e90
20. The great 1983 floods in South American large rivers: a continental hydrological modelling approach A. Fleischmann et al. https://doi.org/10.1080/02626667.2020.1747622
21. Changes in Magnitude and Shifts in Timing of the Latvian River Annual Flood Peaks E. Apsīte et al. https://doi.org/10.3390/atmos15091139
22. Performance comparison of GPM IMERG V07 with its predecessor V06 and its application in extreme precipitation clustering over Türkiye H. Aksu & S. Yaldiz https://doi.org/10.1016/j.atmosres.2024.107840
23. The Timing of Global Floods and Its Association With Climate and Topography P. Torre Zaffaroni et al. https://doi.org/10.1029/2022WR032968
24. Characterising the coincidence of soil moisture – precipitation extremes as a possible precursor to European floods A. Manoj J et al. https://doi.org/10.1016/j.jhydrol.2023.129445
25. Iso-severity flood hazard patterns in diverse relief and climatic regions to support flood management R. Mostafazadeh et al. https://doi.org/10.1007/s11069-025-07492-w
26. Economic consequences of cascading drought-flood events: evidence from central Europe S. Deng et al. https://doi.org/10.1088/1748-9326/ae0f43
27. Mapping dynamic changes in hydrological time series using the average directional index M. Shamseddin & H. Elmeski https://doi.org/10.1080/15715124.2020.1776302
28. Hydro Explorer: An interactive web app to investigate changes in runoff timing and runoff seasonality all over the world E. Rottler et al. https://doi.org/10.1002/rra.3772
29. Hydroclimatic time series features at multiple time scales G. Papacharalampous et al. https://doi.org/10.1016/j.jhydrol.2023.129160
30. Regional flood risk analysis for agricultural crops: Insights from the implementation of AGRIDE-c in central Italy A. Scorzini et al. https://doi.org/10.1016/j.ijdrr.2020.101999
31. Uncovering Flooding Mechanisms Across the Contiguous United States Through Interpretive Deep Learning on Representative Catchments S. Jiang et al. https://doi.org/10.1029/2021WR030185
32. Modeling riverine flood seasonality with mixtures of circular probability density functions W. Veatch & G. Villarini https://doi.org/10.1016/j.jhydrol.2022.128330
33. Clustering Simultaneous Occurrences of the Extreme Floods in the Neckar Catchment E. Modiri & A. Bárdossy https://doi.org/10.3390/w13040399
34. Diverging trends in large floods across Europe in a warming climate B. Fang et al. https://doi.org/10.1038/s43247-025-02734-y
35. Timescale-based flood typing to estimate temporal changes in flood frequencies S. Fischer et al. https://doi.org/10.1080/02626667.2019.1679376
36. Global‐Scale Prediction of Flood Timing Using Atmospheric Reanalysis H. Do et al. https://doi.org/10.1029/2019WR024945
37. Model-based assessment of flood generation mechanisms over Poland: The roles of precipitation, snowmelt, and soil moisture excess N. Venegas-Cordero et al. https://doi.org/10.1016/j.scitotenv.2023.164626
38. 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
39. Understanding meteorological and physio-geographical controls of variability of flood event classes in headstream catchments of China Y. Zhang et al. https://doi.org/10.5194/hess-29-3257-2025
40. Identification of homogeneous regions in terms of flood seasonality using a complex network approach W. Yang et al. https://doi.org/10.1016/j.jhydrol.2019.06.082
41. Nature-based solutions for hydro-meteorological hazards: Revised concepts, classification schemes and databases S. Debele et al. https://doi.org/10.1016/j.envres.2019.108799
42. GRUN: an observation-based global gridded runoff dataset from 1902 to 2014 G. Ghiggi et al. https://doi.org/10.5194/essd-11-1655-2019
43. On the Uncertainty and Changeability of the Estimates of Seasonal Maximum Flows I. Markiewicz et al. https://doi.org/10.3390/w12030704
44. Study on the Staged Operation of a Multi-Purpose Reservoir in Flood Season and Its Effect Evaluation C. Mo et al. https://doi.org/10.3390/w13182563
45. Improving Estuarine Flood Risk Knowledge through Documentary Data Using Multiple Correspondence Analysis A. Rilo et al. https://doi.org/10.3390/w14193161
46. Observation‐Constrained Projection of Global Flood Magnitudes With Anthropogenic Warming W. Liu et al. https://doi.org/10.1029/2020WR028830
47. Detecting Flood‐Rich and Flood‐Poor Periods in Annual Peak Discharges Across Europe D. Lun et al. https://doi.org/10.1029/2019WR026575
48. Statistical spatiotemporal analysis of hydro-morphological processes in China during 1950–2015 N. Wang et al. https://doi.org/10.1007/s00477-021-02007-y
49. Understanding flood seasonality and flood regime shift in the Congo River Basin G. Bola et al. https://doi.org/10.1080/02626667.2022.2083966
50. Warming-driven runoff increase and shifted seasonality in the glacierized Hotan Basin in Central Asia X. Xu et al. https://doi.org/10.1016/j.gloplacha.2026.105579
51. Seasonality, persistency, regionalization, and control mechanism of extreme rainfall over complex terrain A. Akbas https://doi.org/10.1007/s00704-023-04440-1
52. Machine learning models for river flow forecasting in small catchments M. Luppichini et al. https://doi.org/10.1038/s41598-024-78012-2
53. Climate impact on flood changes – an Austrian-Ukrainian comparison S. Snizhko et al. https://doi.org/10.2478/johh-2023-0017
54. Detection of trends in observed river floods in Poland N. Venegas-Cordero et al. https://doi.org/10.1016/j.ejrh.2022.101098
55. Shifting in the global flood timing G. Fang et al. https://doi.org/10.1038/s41598-022-23748-y
56. Exploring multidecadal changes in climate and reservoir storage for assessing nonstationarity in flood peaks and risks worldwide by an integrated frequency analysis approach Y. Zhou https://doi.org/10.1016/j.watres.2020.116265
57. How extreme are flood peak distributions? A quasi-global analysis of daily discharge records M. Zaerpour et al. https://doi.org/10.1016/j.jhydrol.2024.130849
58. Simulating flash flood hydrographs and behavior metrics across China: Implications for flash flood management X. Zhai et al. https://doi.org/10.1016/j.scitotenv.2020.142977
59. Türkiye’de Taşkınların FlooDOT Tabanlı İncelenmesi ve Farklı Veri Kaynaklarının Mekânsal Karşılaştırılması A. Akbaş et al. https://doi.org/10.18400/tjce.1618212
60. The dynamics of peak head responses at Dutch canal dikes and the impact of climate change B. Strijker & M. Kok https://doi.org/10.5194/nhess-25-3355-2025
61. River flooding mechanisms and their changes in Europe revealed by explainable machine learning S. Jiang et al. https://doi.org/10.5194/hess-26-6339-2022
62. Changing Water Cycle under a Warming Climate: Tendencies in the Carpathian Basin I. Jánosi et al. https://doi.org/10.3390/cli11060118
63. Gauging ungauged catchments – Active learning for the timing of point discharge observations in combination with continuous water level measurements S. Pool & J. Seibert https://doi.org/10.1016/j.jhydrol.2021.126448
64. Current European flood-rich period exceptional compared with past 500 years G. Blöschl et al. https://doi.org/10.1038/s41586-020-2478-3
65. Shifted dominant flood drivers of an alpine glacierized catchment in the Tianshan region revealed through interpretable deep learning W. Liang et al. https://doi.org/10.1038/s41612-025-00918-z
66. Comparing Satellite, Reanalysis, Fused and Gridded (In Situ) Precipitation Products Over Türkiye A. Akbas & H. Ozdemir https://doi.org/10.1002/joc.8671
67. Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management L. Slater et al. https://doi.org/10.5194/hess-25-3897-2021
68. Sustainability of mining activities in the European Mediterranean region in terms of a spatial groundwater stress index E. Varouchakis et al. https://doi.org/10.1016/j.spasta.2022.100625
69. Flood frequency analysis of the Rasina River in Serbia L. Stričević et al. https://doi.org/10.37040/geografie.2026.002
70. An increase in the spatial extent of European floods over the last 70 years B. Fang et al. https://doi.org/10.5194/hess-28-3755-2024
71. Causes, seasonality and climate variability of floods in southern Brazil V. Kuchinski & R. Cauduro Dias de Paiva https://doi.org/10.1080/02626667.2026.2651371
72. Occasional Floods On The Rivers Of Russian Plain In The 20^Th –21^St Centuries M. Kireeva et al. https://doi.org/10.24057/2071-9388-2020-29
73. Trends in Global Flood and Streamflow Timing Based on Local Water Year C. Wasko et al. https://doi.org/10.1029/2020WR027233
74. Deoxynivalenol Occurrence in Triticale Crops in Romania during the 2012–2014 Period with Extreme Weather Events V. Gagiu et al. https://doi.org/10.3390/toxins13070456
75. The effects of rising temperatures on flood quantiles in the Italian Alps can be amplified by different catchment morphologies G. Evangelista et al. https://doi.org/10.1016/j.jhydrol.2025.134014
76. A Probabilistic Model of the Economic Risk to Britain's Railway Network from Bridge Scour During Floods R. Lamb et al. https://doi.org/10.1111/risa.13370