142 citations as recorded by crossref.

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38. Probabilistic assessment of concurrent tornado and storm‐related flash flood events Y. Ngui et al. https://doi.org/10.1002/joc.8084
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43. Copula‐Based Modeling of Flood Control Reservoirs M. Balistrocchi et al. https://doi.org/10.1002/2017WR021345
44. The effect of copulas on time-variant reliability involving time-continuous stochastic processes Á. Rózsás & Z. Mogyorósi https://doi.org/10.1016/j.strusafe.2017.02.004
45. Safety design for water-carrying Lake flood control based on copula function: A Case study of the Hongze Lake, China Y. Luo et al. https://doi.org/10.1016/j.jhydrol.2021.126188
46. Impact of Climate Change on Wind Power Generation Studied Using Multivariate Copula Downscaling: A Case Study in Northwestern China S. Wang et al. https://doi.org/10.3390/en18081963
47. An improved nonstationary model for flood frequency analysis and its implication for the Three Gorges Dam, China Q. Dong et al. https://doi.org/10.1080/02626667.2019.1596274
48. Mixture copula parameter estimation with metaheuristic algorithms, comparative study under hydrological context E. Gontara & F. Chebana https://doi.org/10.1007/s00477-025-02914-4
49. An update on multivariate return periods in hydrology B. Gräler et al. https://doi.org/10.5194/piahs-373-175-2016
50. Bivariate Seasonal Design Flood Estimation Based on Copulas J. Yin et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001594
51. Uncertainty quantification and partition for multivariate risk inferences through a factorial multimodel Bayesian copula (FMBC) system Y. Fan et al. https://doi.org/10.1016/j.jhydrol.2021.126406
52. Uncertainty Analysis of Bivariate Design Flood Estimation and its Impacts on Reservoir Routing J. Yin et al. https://doi.org/10.1007/s11269-018-1904-x
53. Nonstationary flood coincidence risk analysis using time-varying copula functions Y. Feng et al. https://doi.org/10.1038/s41598-020-60264-3
54. Bivariate Nonstationary Extreme Flood Risk Estimation Using Mixture Distribution and Copula Function for the Longmen Reservoir, North China Q. Li et al. https://doi.org/10.3390/w14040604
55. Multivariate hydrologic design methods under nonstationary conditions and application to engineering practice C. Jiang et al. https://doi.org/10.5194/hess-23-1683-2019
56. A data-driven spatial approach to characterize the flood hazard R. Mostafiz et al. https://doi.org/10.3389/fdata.2022.1022900
57. Probable maximum precipitation in a warming climate over North America in CanRCM4 and CRCM5 M. Ben Alaya et al. https://doi.org/10.1007/s10584-019-02591-7
58. Analysis of the Effects of Reservoir Operating Scenarios on Downstream Flood Damage Risk Using an Integrated Monte Carlo Modelling Approach G. Brigandì et al. https://doi.org/10.3390/w15030550
59. A copula‐based nonstationary frequency analysis for the 2012–2015 drought in California H. Kwon & U. Lall https://doi.org/10.1002/2016WR018959
60. Multivariate Modeling of Annual Instantaneous Maximum Flows Using Copulas F. Tosunoglu & V. Singh https://doi.org/10.1061/(ASCE)HE.1943-5584.0001644
61. Archimedean copula-based bivariate flood-frequency analysis on Sukkur, Pakistan S. Naz et al. https://doi.org/10.1007/s12517-020-5223-1
62. Safety design for rivers-connected lake flood control based on Copula function: A case study of Lake Hongze L. Yun et al. https://doi.org/10.18307/2021.0320
63. Extreme Flood Flow Routing for Panchet and Maithan Reservoirs of India Using Modified Puls Technique M. Dasgupta & S. Das https://doi.org/10.3390/w16050663
64. Análisis de frecuencias de crecientes trivariados con dependencia regional y funciones Cópula D. Campos-Aranda https://doi.org/10.24850/j-tyca-2025-01-08
65. Trivariate flood frequency analysis using copula functions: assessing non-stationary hydrological risks under climate change S. Soltaninia & M. Eskandaripour https://doi.org/10.2166/wcc.2025.068
66. Multivariate hydrological risk under heterogeneous conditions E. Gontara & F. Chebana https://doi.org/10.1016/j.jhydrol.2025.134588
67. Estimation method for mixture copula models in hydrological context I. Ben Nasr & F. Chebana https://doi.org/10.1016/j.jhydrol.2022.128603
68. Bivariate Risk Analysis of Droughts Using a Nonparametric Multivariate Standardized Drought Index and Copulas S. Vazifehkhah et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001775
69. Criterios gasto prioridad y volumen prioridad para la estimación de hidrogramas de avenidas mediante cópulas E. Copca Maya et al. https://doi.org/10.4995/ia.23895
70. WITHDRAWN: Modelling the long-term suspended sedimentological effects on stormwater pond performance in an urban catchment S. Ahilan et al. https://doi.org/10.1016/j.hydroa.2019.100022
71. Spatio‐temporal drought patterns of multiple drought indices based on precipitation and soil moisture: A case study in South Korea M. Kwon et al. https://doi.org/10.1002/joc.6094
72. Multivariate L-moment based tests for copula selection, with hydrometeorological applications I. Ben Nasr & F. Chebana https://doi.org/10.1016/j.jhydrol.2019.124151
73. A process-based analysis of the suitability of copula types for peak-volume flood relationships J. Szolgay et al. https://doi.org/10.5194/piahs-370-183-2015
74. Hydrological risk analysis of dam overtopping using bivariate statistical approach: a case study from Geheyan Reservoir, China Z. Liu et al. https://doi.org/10.1007/s00477-018-1550-0
75. Univariate and Bivariate Hydrological Drought Frequency Analysis by Copula Functions I. Deger et al. https://doi.org/10.1007/s11269-023-03586-x
76. Inconsistent variation of return periods of temperature extremum in China and its projection based on CMIP6 results X. Kuang et al. https://doi.org/10.1007/s42452-021-04863-3
77. Probabilistic dependence between streamflow and hydroclimatic variables and the possible linkages to large-scale atmospheric circulation: A case study in Baden-Württemberg, Southwest Germany Z. Liu & L. Menzel https://doi.org/10.1016/j.jhydrol.2018.08.054
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79. Comprehensive groundwater safety assessment under potential shale gas contamination based on integrated analysis of reliability–resilience–vulnerability and gas migration index H. Lu et al. https://doi.org/10.1016/j.jhydrol.2019.124072
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