120 citations as recorded by crossref.

1. Unfolding the relationship between seasonal forecast skill and value in hydropower production: a global analysis D. Lee et al. https://doi.org/10.5194/hess-26-2431-2022
2. On some aspects of peaks-over-threshold modeling of floods under nonstationarity using climate covariates A. Silva et al. https://doi.org/10.1007/s00477-015-1072-y
3. Climate variability and floods in China - A review Z. Kundzewicz et al. https://doi.org/10.1016/j.earscirev.2020.103434
4. Climate variability and catastrophe bond premiums C. Feng & X. Zeng https://doi.org/10.1016/j.frl.2025.108624
5. What are the hydro-meteorological controls on flood characteristics? M. Nied et al. https://doi.org/10.1016/j.jhydrol.2016.12.003
6. Should seasonal rainfall forecasts be used for flood preparedness? E. Coughlan de Perez et al. https://doi.org/10.5194/hess-21-4517-2017
7. Non-stationary rainfall and natural flows modeling at the watershed scale M. Egüen et al. https://doi.org/10.1016/j.jhydrol.2016.04.061
8. Potential for seasonal flood forecasting in West Africa using climate indexes J. Hounkpè et al. https://doi.org/10.1111/jfr3.12833
9. Projecting flood hazard under climate change: an alternative approach to model chains J. Delgado et al. https://doi.org/10.5194/nhess-14-1579-2014
10. Low frequency global‐scale modes and its influence on rainfall extremes over India: Nonstationary and uncertainty analysis S. Jha et al. https://doi.org/10.1002/joc.6935
11. Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling K. Jafarzadegan et al. https://doi.org/10.1029/2022RG000788
12. Effect of ENSO on annual maximum floods and volume over threshold in the southwestern region of Iran B. Saghafian et al. https://doi.org/10.1080/02626667.2017.1296229
13. Ensemble flood forecasting: Current status and future opportunities W. Wu et al. https://doi.org/10.1002/wat2.1432
14. Annual flood damage influenced by El Niño in the Kan River basin, Iran F. Hooshyaripor et al. https://doi.org/10.5194/nhess-20-2739-2020
15. Shifting signals: Correlations among freshwater, marine and climatic indices often investigated in Pacific salmon studies J. Gosselin et al. https://doi.org/10.1016/j.ecolind.2020.107167
16. Environmental and climatic drivers of flood susceptibility and their interactions at the global contexts T. Khan et al. https://doi.org/10.1007/s44288-026-00610-6
17. Towards Probabilistic Multivariate ENSO Monitoring T. Zhang et al. https://doi.org/10.1029/2019GL083946
18. Fluvial sediment supply to a mega-delta reduced by shifting tropical-cyclone activity S. Darby et al. https://doi.org/10.1038/nature19809
19. Revealing Hidden Climate Indices from the Occurrence of Hydrologic Extremes B. Renard & M. Thyer https://doi.org/10.1029/2019WR024951
20. A global analysis of the asymmetric effect of ENSO on extreme precipitation X. Sun et al. https://doi.org/10.1016/j.jhydrol.2015.09.016
21. Forecast-based financing: an approach for catalyzing humanitarian action based on extreme weather and climate forecasts E. Coughlan de Perez et al. https://doi.org/10.5194/nhess-15-895-2015
22. Projection of drought-flood abrupt alternation in a humid subtropical region under changing climate R. Wang et al. https://doi.org/10.1016/j.jhydrol.2023.129875
23. 50-year seasonal variability in East African droughts and floods recorded in central Afar lake sediments (Ethiopia) and their connections with the El Niño–Southern Oscillation C. Mologni et al. https://doi.org/10.5194/cp-20-1837-2024
24. Climate signal transformation in regulated river systems: Decoupling of ENSO–streamflow relationships in the Mekong basin N. Chung et al. https://doi.org/10.1016/j.htopen.2026.100060
25. Temporal variability of streamflow and plant species abundance on islets as they relate to ENSO events downstream from an inversion‐type reservoir S. Dubeau et al. https://doi.org/10.1002/rra.3218
26. Declining vulnerability to river floods and the global benefits of adaptation B. Jongman et al. https://doi.org/10.1073/pnas.1414439112
27. Atmospheric River Sequences as Indicators of Hydrologic Hazard in Historical Reanalysis and GFDL SPEAR Future Climate Projections C. Bowers et al. https://doi.org/10.1029/2023EF003536
28. Temperature response functions for climate impact assessments: The case of ENSO and electricity consumption J. Miller & F. Zhong https://doi.org/10.1016/j.eneco.2026.109301
29. Increasing stress on disaster-risk finance due to large floods B. Jongman et al. https://doi.org/10.1038/nclimate2124
30. Spatio-Temporal Variability of Rainfall Indices and their Teleconnections with El Niño-Southern Oscillation for Tapi Basin, India L. Gehlot et al. https://doi.org/10.1007/s13143-020-00179-1
31. Climatic aridity modulates the spatial variations in the responses of terrestrial water budget terms to El Niño–Southern Oscillation W. Fei et al. https://doi.org/10.1186/s40562-026-00459-0
32. Impacts of El Niño-Southern Oscillation on surface dust levels across the world during 1982–2019 J. Li et al. https://doi.org/10.1016/j.scitotenv.2020.144566
33. Exploring the ENSO Impact on Basin‐Scale Floods Using Hydrological Simulations and TRMM Precipitation Y. Yan et al. https://doi.org/10.1029/2020GL089476
34. Influence analysis of central and Eastern Pacific El Niños to seasonal rainfall patterns over China using the intentional statistical simulations C. Heng et al. https://doi.org/10.1016/j.atmosres.2019.104706
35. Investigating Relationships Between Australian Flooding and Large‐Scale Climate Indices and Possible Mechanism J. Liu et al. https://doi.org/10.1029/2017JD028197
36. Climate-driven interannual variability of water scarcity in food production potential: a global analysis M. Kummu et al. https://doi.org/10.5194/hess-18-447-2014
37. A hybrid approach for El Niño prediction based on Empirical Mode Decomposition and convolutional LSTM Encoder-Decoder S. Wang et al. https://doi.org/10.1016/j.cageo.2021.104695
38. Causes and trends of water scarcity in food production M. Porkka et al. https://doi.org/10.1088/1748-9326/11/1/015001
39. Changes of precipitation extremes in arid Central Asia M. Zhang et al. https://doi.org/10.1016/j.quaint.2016.12.024
40. High predictability potential of highly synchronized widespread floods in monsoon regions J. Zhang et al. https://doi.org/10.1016/j.jhydrol.2026.135006
41. Attribution of Large‐Scale Climate Patterns to Seasonal Peak‐Flow and Prospects for Prediction Globally D. Lee et al. https://doi.org/10.1002/2017WR021205
42. Effects of climate change on peak runoff and flood levels in Qu River Basin, East China C. Gao et al. https://doi.org/10.1016/j.jher.2018.02.005
43. The Effect of El Niño on Flood Damages in the Western United States T. Corringham & D. Cayan https://doi.org/10.1175/WCAS-D-18-0071.1
44. Flood frequencies and durations and their response to El Niño Southern Oscillation: Global analysis P. Ward et al. https://doi.org/10.1016/j.jhydrol.2016.05.045
45. Impacts of El Niño–southern oscillation on global runoff: Characteristic signatures and potential mechanisms Y. You et al. https://doi.org/10.1002/hyp.14367
46. 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
47. Evaluation of Nonstationarity in Annual Maximum Flood Series and the Associations with Large-scale Climate Patterns and Human Activities J. Li et al. https://doi.org/10.1007/s11269-014-0900-z
48. Dependence of model-based extreme flood estimation on the calibration period: case study of the Kamp River (Austria) P. Brigode et al. https://doi.org/10.1080/02626667.2015.1006632
49. Informing Stochastic Streamflow Generation by Large-Scale Climate Indices at Single and Multiple Sites M. Zaerpour et al. https://doi.org/10.1016/j.advwatres.2021.104037
50. The Global Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of a daily streamflow archive and metadata H. Do et al. https://doi.org/10.5194/essd-10-765-2018
51. Strong influence of El Niño Southern Oscillation on flood risk around the world P. Ward et al. https://doi.org/10.1073/pnas.1409822111
52. Anthropogenic climate change exacerbates the risk of successive flood-heat extremes: Multi-model global projections based on the Inter-Sectoral Impact Model Intercomparison Project J. Zhou et al. https://doi.org/10.1016/j.scitotenv.2023.164274
53. Study of rainfall variabilities in Southeast Asia using long-term gridded rainfall and its substantiation through global climate indices V. Singh & X. Qin https://doi.org/10.1016/j.jhydrol.2019.124320
54. Assessing ENSO as a Driver of Climate Variability in Cameroon from 1981 To 2023: A Zonal Disaggregation and Composite Based Analysis across Agroecological Zones B. Benoir et al. https://doi.org/10.1007/s41748-025-00848-z
55. Machine Learning Modeling of Climate Variability Impact on River Runoff M. Norel et al. https://doi.org/10.3390/w13091177
56. Location of capture sufficiently characterises lifetime growth trajectories in a highly mobile fish J. Barrow et al. https://doi.org/10.1186/s40462-025-00541-w
57. Complex picture for likelihood of ENSO-driven flood hazard R. Emerton et al. https://doi.org/10.1038/ncomms14796
58. Modelling Water Balance Components of River Basins Located in Different Regions of the Globe E. Gusev et al. https://doi.org/10.1134/S0097807818060246
59. Extreme flood modeling and mechanism over Crisul Alb basin in Romania M. Caian et al. https://doi.org/10.1016/j.catena.2020.104923
60. Global hydrological reanalyses: The value of river discharge information for world‐wide downstream applications – The example of the Global Flood Awareness System GloFAS C. Prudhomme et al. https://doi.org/10.1002/met.2192
61. Defining high-flow seasons using temporal streamflow patterns from a global model D. Lee et al. https://doi.org/10.5194/hess-19-4689-2015
62. Preparedness for slow‐onset environmental disasters: Drawing lessons from three decades of El Niño impacts R. Staupe‐Delgado et al. https://doi.org/10.1002/sd.1719
63. Single porosity model: Exploring the spatial resolution limits in complex urban patterns S. Nash et al. https://doi.org/10.1016/j.jhydrol.2024.131239
64. Beyond El Niño: Unsung climate modes drive African floods A. Ficchì et al. https://doi.org/10.1016/j.wace.2021.100345
65. The role of climate variability in extreme floods in Europe G. Nobre et al. https://doi.org/10.1088/1748-9326/aa7c22
66. Multiscale variability of China’s historical flood/drought index and precipitation teleconnections with ENSO using wavelet analyses D. Wang et al. https://doi.org/10.1007/s00704-022-04125-1
67. Imprints of large‐scale climate oscillations on river flow in selected Canadian river catchments A. Olusola et al. https://doi.org/10.1002/rvr2.89
68. On the spatial and temporal variability of ENSO precipitation and drought teleconnection in mainland Southeast Asia T. Räsänen et al. https://doi.org/10.5194/cp-12-1889-2016
69. Design considerations for riverine floods in a changing climate – A review B. François et al. https://doi.org/10.1016/j.jhydrol.2019.04.068
70. Using Climate-Flood Links and CMIP5 Projections to Assess Flood Design Levels Under Climate Change Scenarios: A Case Study in Southern Brazil A. Silva & M. Portela https://doi.org/10.1007/s11269-018-2058-6
71. Identifying El Niño–Southern Oscillation influences on rainfall with classification models: implications for water resource management of Sri Lanka T. De Silva M. & G. Hornberger https://doi.org/10.5194/hess-23-1905-2019
72. Climatology and Interannual Variability of Floods during the TRMM Era (1998–2013) Y. Yan et al. https://doi.org/10.1175/JCLI-D-19-0415.1
73. Combining information from multiple flood projections in a hierarchical Bayesian framework N. Le Vine https://doi.org/10.1002/2015WR018143
74. Copernicus Marine Service Ocean State Report K. von Schuckmann et al. https://doi.org/10.1080/1755876X.2018.1489208
75. Effect of Upper Tropospheric Vertical Thermal Contrast Over the Mediterranean Region on Convection over the Western Tibetan Plateau during ENSO Years B. Vaid & X. Liang https://doi.org/10.1080/07055900.2020.1751048
76. Global Asymmetries in the Influence of ENSO on Flood Risk Based on 1,600 Years of Hybrid Simulations L. Del Rio Amador et al. https://doi.org/10.1029/2022GL102027
77. Hydrological response to large-scale climate variability across the Pearl River basin, China: Spatiotemporal patterns and sensitivity X. Gu et al. https://doi.org/10.1016/j.gloplacha.2016.12.016
78. At-Site Flood Frequency Analysis Coupled with Multiparameter Probability Distributions F. Cassalho et al. https://doi.org/10.1007/s11269-017-1810-7
79. An innovative hydrological model for the sparsely-gauged Essequibo River basin, northern Amazonia D. Hughes et al. https://doi.org/10.1080/15715124.2023.2278678
80. Nordic contributions to stochastic methods in hydrology D. Rosbjerg et al. https://doi.org/10.2166/nh.2022.137
81. Exploring the use of seasonal forecasts to adapt flood insurance premiums V. Nguyen et al. https://doi.org/10.5194/nhess-24-2923-2024
82. Relative influence of ENSO, IOD, and AMO over spatiotemporal variability of hydroclimatic extremes in Narmada basin, India S. Singh et al. https://doi.org/10.2166/aqua.2023.219
83. Developing a global operational seasonal hydro-meteorological forecasting system: GloFAS-Seasonal v1.0 R. Emerton et al. https://doi.org/10.5194/gmd-11-3327-2018
84. Assessment of the effects of climate and land cover changes on river discharge and sediment yield, and an adaptive spatial planning in the Jakarta region . Poerbandono et al. https://doi.org/10.1007/s11069-014-1083-x
85. Trend Analysis of Rainstorm-Induced Flood Disasters in Hainan Island Based on Historical Data from 1368 to Present 静. 徐 https://doi.org/10.12677/ccrl.2025.144069
86. Drought Characteristics and Causes during Winter Wheat Growth Stages in North China C. Xu et al. https://doi.org/10.3390/su16145958
87. What is the most useful approach for forecasting hydrological extremes during El Niño? R. Emerton et al. https://doi.org/10.1088/2515-7620/ab114e
88. Climate Change Impacts on Water and Agriculture Sectors in Southern Africa: Threats and Opportunities for Sustainable Development C. Nhemachena et al. https://doi.org/10.3390/w12102673
89. Observed streamflow data shows El Niño–Southern Oscillation increases likelihood of extreme events in South America I. Petry et al. https://doi.org/10.1038/s43247-025-02714-2
90. Flood impact on Mainland Southeast Asia between 1985 and 2018—The role of tropical cyclones A. Chen et al. https://doi.org/10.1111/jfr3.12598
91. 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
92. Impacts and effects of a historical high and ENSO linked freshwater inflow in the tropical estuary Nha Phu, southeast Vietnam L. Lund-Hansen et al. https://doi.org/10.1016/j.rsma.2017.11.012
93. 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
94. Recent changes in climate, hydrology and sediment load in the Wadi Abd, Algeria (1970–2010) M. Achite & S. Ouillon https://doi.org/10.5194/hess-20-1355-2016
95. Identification of symmetric and asymmetric responses in seasonal streamflow globally to ENSO phase D. Lee et al. https://doi.org/10.1088/1748-9326/aab4ca
96. Response of lower Yellow River bank breachings to La Niña events since 924 CE J. Xu & F. Li https://doi.org/10.1016/j.catena.2019.01.012
97. Temporal clustering of floods in Germany: Do flood-rich and flood-poor periods exist? B. Merz et al. https://doi.org/10.1016/j.jhydrol.2016.07.041
98. Atmosphere-Land Bridge between the Pacific and Tropical North Atlantic SST’s through the Amazon River basin during the 2005 and 2010 droughts A. Builes-Jaramillo et al. https://doi.org/10.1063/1.5020502
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103. Streamflow variability and its linkage to ENSO events in the Ethiopian Rift Valley Lakes Basin A. Worako et al. https://doi.org/10.1016/j.ejrh.2021.100817
104. 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
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107. Inconsistencies in global soil moisture products and discrepancies in their relationship with vegetation productivity X. Xu et al. https://doi.org/10.1016/j.jhydrol.2025.133298
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109. The monetary benefit of early flood warnings in Europe F. Pappenberger et al. https://doi.org/10.1016/j.envsci.2015.04.016
110. Phosphorus Loadings to the World's Largest Lakes: Sources and Trends G. Fink et al. https://doi.org/10.1002/2017GB005858
111. Influence of three phases of El Niño–Southern Oscillation on daily precipitation regimes in China A. Lv et al. https://doi.org/10.5194/hess-23-883-2019
112. A 2000-year documentary record of levee breaches on the lower Yellow River and their relationship with climate changes and human activities W. Li et al. https://doi.org/10.1177/0959683620972764
113. Spatial coherence of flood-rich and flood-poor periods across Germany B. Merz et al. https://doi.org/10.1016/j.jhydrol.2018.02.082
114. Can climate knowledge enable Warragamba Dam, Sydney, Australia to be used to manage flood risk? A. Devanand et al. https://doi.org/10.1088/1748-9326/ad0afb
115. Correlations Between Extreme Atmospheric Hazards and Global Teleconnections: Implications for Multihazard Resilience H. Steptoe et al. https://doi.org/10.1002/2017RG000567
116. From extreme rainfall to drought: 250 years of annually resolved sediment deposition in Santa Barbara Basin, California I. Hendy et al. https://doi.org/10.1016/j.quaint.2015.01.026
117. Sensitivity of water scarcity events to ENSO-driven climate variability at the global scale T. Veldkamp et al. https://doi.org/10.5194/hess-19-4081-2015
118. 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
119. Forecast Based Financing for Precision Agriculture in Indonesia J. Kilmanun et al. https://doi.org/10.1051/bioconf/20236904021
120. Atmospheric Circulation Patterns Associated with Extreme United States Floods Identified via Machine Learning K. Schlef et al. https://doi.org/10.1038/s41598-019-43496-w