74 citations as recorded by crossref.

1. 420 ans d’inondations en milieu montagnard : l’exemple de la commune de Vallouise-Pelvoux (Écrins, France) L. Thanneur et al. https://doi.org/10.4000/15f3f
2. Potenzial von Teichen als Retentionsflächen im Hochwassermanagement E. Peham et al. https://doi.org/10.1007/s00506-026-01231-6
3. Increasing hourly heavy rainfall in Austria reflected in flood changes K. Haslinger et al. https://doi.org/10.1038/s41586-025-08647-2
4. Soil moisture dynamics and associated rainfall-runoff processes under different land uses and land covers in a humid mountainous watershed Z. Lin et al. https://doi.org/10.1016/j.jhydrol.2024.131249
5. Long-term analysis of changes in seasonal and maximum discharges of Slovak rivers in the period 1931–2020 V. Mitková et al. https://doi.org/10.2478/johh-2024-0030
6. Design and Performance Verification of Deep Learning-Based River Flood Prediction System Design and Digital Twin-Based Its Application H. Eom et al. https://doi.org/10.3390/math13111696
7. Storm severity assessment: Application of severity diagrams to events in Calabria (southern, Italy) D. Biondi et al. https://doi.org/10.1016/j.ejrh.2025.103000
8. Diagnosing rainy-season heterogeneity in Huaihe Basin: a generalizable clustering and teleconnection-based analytical approach K. Liu et al. https://doi.org/10.1007/s11069-026-08200-y
9. HESS Opinions: The sword of Damocles of the impossible flood A. Montanari et al. https://doi.org/10.5194/hess-28-2603-2024
10. Identifying changes in flood characteristics and their causes from an event-based perspective in the Central Taihu Basin S. Luo et al. https://doi.org/10.1016/j.scitotenv.2023.167131
11. Abflusstrends in Österreichs Fließgewässern 1977–2020 G. Laaha et al. https://doi.org/10.1007/s00506-024-01106-8
12. Transboundary deforestation and flood risk: Assessing the need for collaborative policy between India and Nepal’s Siwalik regions I. Gujree et al. https://doi.org/10.1016/j.tfp.2026.101162
13. Bedrock geology controls on new water fractions and catchment functioning in contrasted nested catchments G. Türk et al. https://doi.org/10.5194/hess-30-343-2026
14. A tracer‐aided 2D numerical framework to define fluvial and pluvial hazard mapping P. Perrini et al. https://doi.org/10.1111/jfr3.13039
15. Accelerating compound flood risk assessments through active learning: A case study of Charleston County (USA) L. Terlinden-Ruhl et al. https://doi.org/10.5194/nhess-25-1353-2025
16. Analysis of Annual Maximum Ice-Influenced and Open-Water Levels at Select Hydrometric Stations on Canadian Rivers Y. Dibike et al. https://doi.org/10.3390/w17202930
17. Deadlier natural disasters—a warning from Brazil’s 2024 floods R. Trancoso et al. https://doi.org/10.1088/1748-9326/adb7f1
18. Assessing farmers’ vulnerability to floods and landslides in Kerala using a multidimensional Societal Vulnerability Index (SVIFL) D. Balakrishnan et al. https://doi.org/10.1007/s44274-025-00444-y
19. Effects of forest disturbance on water yield and peak flow in low‐relief glaciated catchments assessed with Bayesian parameter estimation Z. McEachran et al. https://doi.org/10.1002/hyp.14956
20. Study of flooding hazards in highly urbanized area based on a coupled one- and two-dimensional hydrological-hydrodynamic model W. Yin et al. https://doi.org/10.1016/j.dwt.2024.100933
21. Werden die Hochwasser größer? Ursachen, Trends und Handlungsoptionen G. Blöschl https://doi.org/10.1007/s00506-025-01165-5
22. Normalization of Suak Ujong Kalak estuary as a flood reduction effort for Meulaboh city M. Safriani et al. https://doi.org/10.1088/1742-6596/2916/1/012007
23. Use of Soil Infiltration Capacity and Stream Flow Velocity to Estimate Physical Flood Vulnerability under Land-Use Change Scenarios Y. Hernández-Atencia et al. https://doi.org/10.3390/w15061214
24. Impact of climate change on the development of the islands of the Neris River (eastern part of the Baltic Sea basin) A. Baubinienė https://doi.org/10.3176/earth.2025.09
25. Why structural solutions for flood control should be adapted to climate change? M. Hosseinipoor et al. https://doi.org/10.1007/s11069-024-06969-4
26. Spatially coherent changes in Chinese annual flood peaks revealed by a consensus-based machine learning framework for regionalization J. Weng et al. https://doi.org/10.1016/j.jhydrol.2025.134665
27. High and dry: A ∼300-year record of hydrologic extremes from the French Broad River in the southeastern U.S. M. Davis et al. https://doi.org/10.1016/j.ejrh.2025.102673
28. Land cover change and its effects on catchment hydrology: A quantitative analysis using SWAT in Horní Úpa N. Zelíková et al. https://doi.org/10.14712/23361980.2025.18
29. Toward Brague river flood modelling 1: validity of a simplified 1D-2D hydraulic model to flash flood understanding E. Ah-Woane et al. https://doi.org/10.1080/28375807.2025.2508436
30. Climate impact on flood changes – an Austrian-Ukrainian comparison S. Snizhko et al. https://doi.org/10.2478/johh-2023-0017
31. Geomorphic flood index 2.0: enhanced tools for delineating flood-prone areas in data-scarce regions S. Manfreda et al. https://doi.org/10.1016/j.catena.2026.110242
32. Five principles for hydrology in the era of managed waters G. Blöschl https://doi.org/10.1038/s44221-026-00657-2
33. Evolution of flood generating processes under climate change in France Y. Tramblay et al. https://doi.org/10.5194/hess-29-7023-2025
34. Quantifying representative runoff coefficients for small basins: a practical recipe for data-limited conditions G. Evangelista et al. https://doi.org/10.1080/02626667.2026.2665734
35. A hydrologic similarity-based parameters dynamic matching framework: Application to enhance the real-time flood forecasting H. Wu et al. https://doi.org/10.1016/j.scitotenv.2023.167767
36. Advancing real-time error correction of flood forecasting based on the hydrologic similarity theory and machine learning techniques P. Shi et al. https://doi.org/10.1016/j.envres.2024.118533
37. Flood vulnerability in township informal settlements S. Gcabashe & S. Pillay https://doi.org/10.4102/JAMBA.v18i1.1833
38. Integrating Earth Observations and Multi-Criteria Decision Making for Flood Risk Assessment K. Jodhani et al. https://doi.org/10.1007/s41101-025-00378-9
39. Artificial neural networks for flood susceptibility analysis in Gangarampur sub-division of Dakshin Dinajpur, West Bengal, India A. Paul https://doi.org/10.1108/FEBE-09-2024-0061
40. Projected increase in widespread riverine floods in India under a warming climate J. Nanditha & V. Mishra https://doi.org/10.1016/j.jhydrol.2024.130734
41. How Forests May Reduce the Incidence of Destructive Tropical Cyclones, Hurricanes and Typhoons D. Sheil https://doi.org/10.3390/f17030359
42. The impacts of rainfall and soil moisture to flood hazards in a humid mountainous catchment: a modeling investigation T. Yu et al. https://doi.org/10.3389/feart.2023.1285766
43. Age-Based Community Resilience Assessment Using Flood Resilience Index Approach: Inference from the Gyor City, Hungary I. Ullah et al. https://doi.org/10.3390/geographies5020016
44. Dominant flood types in Europe and their role in flood statistics S. Fischer & A. Schumann https://doi.org/10.1080/02626667.2025.2450369
45. Integrating 10Be analyses and an empirical erosion model to unveil catchment-scale landscape and sediment dynamics in a tectonically active Mediterranean area (Calabria, southern Italy) P. De Cesare et al. https://doi.org/10.1016/j.geomorph.2026.110329
46. Rain-on-wet-soil compound floods in lowlands: the combined effect of large rain events and shallow groundwater on discharge peaks in a changing climate C. Brauer et al. https://doi.org/10.5194/hess-30-249-2026
47. Mechanisms and scenarios of the unprecedent flooding event in South Brazil 2024 L. Laipelt et al. https://doi.org/10.5194/hess-30-573-2026
48. Integrated hydro-morphological assessment of the flood events and in-channel vegetation impacts on river channel morphology (Polish Western Carpathians) A. Bucała-Hrabia et al. https://doi.org/10.1016/j.jhydrol.2025.133847
49. Global river water quality under climate change and hydroclimatic extremes M. van Vliet et al. https://doi.org/10.1038/s43017-023-00472-3
50. Probability Characteristics of High and Low Flows in Slovakia: A Comprehensive Hydrological Assessment P. Pekárová et al. https://doi.org/10.3390/hydrology12080199
51. Palynological approaches to forest restoration in Southeast Asia: Challenges and opportunities for Thailand—A systematic review T. Sattraburut et al. https://doi.org/10.1016/j.tfp.2024.100714
52. From a Topographic Gradient Perspective: Ecosystem Service Effects of Agricultural Land Transformation in Karst Mountainous Areas—A Case Study of Puding County, Guizhou Province X. Wu et al. https://doi.org/10.3390/land15040582
53. Mega Forest Fires Intensify Flood Magnitudes in Southeast Australia Z. Xu et al. https://doi.org/10.1029/2023GL103812
54. Hyper-resolution flood hazard mapping at the national scale G. Blöschl et al. https://doi.org/10.5194/nhess-24-2071-2024
55. Using artificial neural networks for predicting flood events in Artvin, Türkiye O. Aydın & N. Raja https://doi.org/10.31127/tuje.1530593
56. A Simple Model of Flood Peak Attenuation R. Paiva & S. Lima https://doi.org/10.1029/2023WR034692
57. On the Performance of Sentinel-3 Altimetry over High Mountain and Cascade Reservoirs Basins: Case of the Lancang and Nu River Basins Y. Cheng et al. https://doi.org/10.3390/rs15071769
58. Drivers, changes, and impacts of hydrological extremes in India: A review V. Mishra et al. https://doi.org/10.1002/wat2.1742
59. Hydrological analysis of the Oder droughts for the period 1950‒2022 in the context of the 2022 river disaster M. Ali & M. Grygoruk https://doi.org/10.22630/srees.10421
60. Disentangling atmospheric, hydrological, and coupling uncertainties in compound flood modeling within a coupled Earth system model D. Feng et al. https://doi.org/10.5194/nhess-25-3619-2025
61. The influence of topography on floods in southern Brazil V. Kuchinski & R. Paiva https://doi.org/10.1590/2318-0331.302520250009
62. Future Changes in Hydro-Climatic Extremes across Vietnam: Evidence from a Semi-Distributed Hydrological Model Forced by Downscaled CMIP6 Climate Data H. Do et al. https://doi.org/10.3390/w16050674
63. Quantifying the relative contributions of rainfall and antecedent soil moisture to flood generation: Analysis of 963 Iranian catchments A. Jahanshahi & M. Booij https://doi.org/10.1016/j.jaridenv.2025.105328
64. Global acceleration of compound flood risks through fluvial-tidal interactions in a warming climate W. Chen et al. https://doi.org/10.1038/s44304-026-00179-7
65. 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
66. Assessing multi-source random forest classification and robustness of predictor variables in flooded areas mapping C. Albertini et al. https://doi.org/10.1016/j.rsase.2024.101239
67. Optimally integrating a reservoirs group and a flood detention basin for improving their flood control performance Z. Mu et al. https://doi.org/10.1016/j.ejrh.2025.102735
68. 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
69. Identifying physical vulnerability drivers increasing in exposed structures to floods Y. Hernández-Atencia et al. https://doi.org/10.1016/j.pdisas.2025.100512
70. Large-scale flood mapping using Sentinel-1 and Sentinel-2 imagery: Spatio-temporal analysis of the 23·7 Haihe basin-wide extreme flood L. Lan & X. Wang https://doi.org/10.1016/j.jhydrol.2025.132777
71. Unprecedented April-May 2024 rainfall in South Brazil sets new record W. Collischonn et al. https://doi.org/10.1590/2318-0331.292420240088
72. Advancing river flood forecasting with a collaborative integrated modeling method Y. He et al. https://doi.org/10.1016/j.jenvman.2024.123677
73. Nonlinear behavior of urban flood peaks in the U.S. Mid-Atlantic region G. Hua et al. https://doi.org/10.2166/hydro.2025.005
74. Identifying Flood Events and Causes Based on Their Hydrograph Characteristics S. Wang et al. https://doi.org/10.1016/j.rcar.2026.02.003