197 citations as recorded by crossref.

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8. A copula framework for depth-stratified water quality monitoring in reservoirs S. Ghasemnezhad et al. https://doi.org/10.1016/j.jwpe.2025.107057
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13. Modeling Correlations in Rail Line Construction Y. Moret & H. Einstein https://doi.org/10.1061/(ASCE)CO.1943-7862.0000507
14. Gaussian anamorphosis in the analysis step of the EnKF: a joint state-variable/observation approach J. Amezcua & P. Van Leeuwen https://doi.org/10.3402/tellusa.v66.23493
15. Modeling and application of industrial process fault detection based on pruning vine copula J. Wan & S. Li https://doi.org/10.1016/j.chemolab.2018.11.005
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24. Monitoring Test for Stability of Dependence Structure in Multivariate Data Based on Copula J. Lee & B. Kim https://doi.org/10.3390/e18120457
25. Interactive Visualization and Computation of 2D and 3D Probability Distributions M. Bobrovnikov et al. https://doi.org/10.1007/s42979-022-01206-w
26. Strategic decision-support modeling for robust management of the food–energy–water nexus under uncertainty J. Gao et al. https://doi.org/10.1016/j.jclepro.2021.125995
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29. Probabilistic assessment of tropical cyclone-induced storm surge hazards along the Bay of Bengal coast V. Adithyan et al. https://doi.org/10.1007/s11069-026-08196-5
30. Cross-Sectional Learning of Extremal Dependence Among Financial Assets X. Yan et al. https://doi.org/10.2139/ssrn.3396238
31. Large scale climate oscillations and mesoscale surface meteorological variability in the Apalachicola-Chattahoochee-Flint River Basin K. Stevens & P. Ruscher https://doi.org/10.1016/j.jhydrol.2014.06.002
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34. A mutual information based R-vine copula strategy to estimate VaR in high frequency stock market data C. Sharma et al. https://doi.org/10.1371/journal.pone.0253307
35. Compound droughts and hot extremes: Characteristics, drivers, changes, and impacts Z. Hao et al. https://doi.org/10.1016/j.earscirev.2022.104241
36. Holocene climate variability in the Levant from the Dead Sea pollen record T. Litt et al. https://doi.org/10.1016/j.quascirev.2012.06.012
37. Idealized models of the joint probability distribution of wind speeds A. Monahan https://doi.org/10.5194/npg-25-335-2018
38. An Upper Limit to Youth Psychotherapy Benefit? A Meta-Analytic Copula Approach to Psychotherapy Outcomes P. Jones et al. https://doi.org/10.1177/2167702619858424
39. Is southwestern China experiencing more frequent precipitation extremes? M. Liu et al. https://doi.org/10.1088/1748-9326/9/6/064002
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41. Predictive modelling of rainfall-induced rockfall: a copula-based approach F. Bahootoroody et al. https://doi.org/10.1080/17499518.2025.2536279
42. Copula-based approximation of particle breakage as link between DEM and PBM A. Spettl et al. https://doi.org/10.1016/j.compchemeng.2017.01.023
43. Vegetation and climate history in the Westeifel Volcanic Field (Germany) during the past 11 000 years based on annually laminated lacustrine maar sediments T. LITT et al. https://doi.org/10.1111/j.1502-3885.2009.00096.x
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45. Yet Another Discriminant Analysis (YADA): A Probabilistic Model for Machine Learning Applications R. Field et al. https://doi.org/10.3390/math12213392
46. Power outages and firm performance in Sub-Saharan Africa M. Cole et al. https://doi.org/10.1016/j.jdeveco.2018.05.003
47. A pathway for multivariate analysis of ecological communities using copulas M. Anderson et al. https://doi.org/10.1002/ece3.4948
48. Evaluation of the ability of regional climate models and a statistical model to represent the spatial characteristics of extreme precipitation L. Yang et al. https://doi.org/10.1002/joc.6602
49. Seasonal copula models for the analysis of glacier discharge at King George Island, Antarctica M. Gómez et al. https://doi.org/10.1007/s00477-016-1217-7
50. Probabilistic spatial prediction of categorical data using elliptical copulas X. Huang & Z. Wang https://doi.org/10.1007/s00477-017-1485-x
51. Evolution of precipitation-runoff-sediment dependence under environmental change: A Vine Copula analysis of two Loess Plateau basins J. Li et al. https://doi.org/10.1016/j.ejrh.2026.103505
52. The value of visualization in improving compound flood hazard communication: a complementary perspective through a Euclidean geometry lens S. Radfar et al. https://doi.org/10.5194/gc-8-237-2025
53. AnaWEGE: a weather generator based on analogues of atmospheric circulation P. Yiou https://doi.org/10.5194/gmd-7-531-2014
54. Projecting changes in regional temperature and precipitation extremes in the United States J. Schoof & S. Robeson https://doi.org/10.1016/j.wace.2015.09.004
55. Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R2D2) bias correction M. Vrac https://doi.org/10.5194/hess-22-3175-2018
56. Joint probability risk modelling of storm surge and cyclone wind along the coast of Bay of Bengal using a statistical copula N. Bushra et al. https://doi.org/10.1002/joc.6068
57. An Examination of Tail Dependence in Bordeaux Futures Prices and Parker Ratings D. Cyr et al. https://doi.org/10.1017/jwe.2017.18
58. A guide to optimised spatiotemporal data co-location by mutual information maximisation A. Martin et al. https://doi.org/10.5194/amt-19-3511-2026
59. Joint probability-based classifier based on vine copula method for land use classification of multispectral remote sensing data Y. Zhang et al. https://doi.org/10.1007/s12145-020-00487-0
60. Evaluating the effects of climate extremes on crop yield, production and price using multivariate distributions: A new copula application F. Alidoost et al. https://doi.org/10.1016/j.wace.2019.100227
61. Minimum temperature mapping with spatial copula interpolation P. Bostan et al. https://doi.org/10.1016/j.spasta.2020.100464
62. Stochastic Response Assessment of Cross-Sea Bridges under Correlated Wind and Waves via Machine Learning C. Fang et al. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001554
63. Measured and modeled spatial rain rate correlation to improve the prediction method for satellite site diversity T. Tjelta et al. https://doi.org/10.1002/2014RS005476
64. Vulnerability of hop‐yields due to compound drought and heat events over European key‐hop regions V. Potopová et al. https://doi.org/10.1002/joc.6836
65. Copula-based stochastic uncertainty analysis of satellite precipitation products E. Sharifi et al. https://doi.org/10.1016/j.jhydrol.2019.01.035
66. Climate derivatives strategies as an alternative to set up guaranteed prices for agricultural producers in México S. Cruz-Aké et al. https://doi.org/10.1080/13504851.2021.1985061
67. A Similarity-Based Implementation of the Schaake Shuffle R. Schefzik https://doi.org/10.1175/MWR-D-15-0227.1
68. Design Flood Volume of the Three Gorges Dam Project D. Liu et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000287
69. Bayesian sequential design for Copula models S. Senarathne et al. https://doi.org/10.1007/s11749-019-00661-7
70. Evaluation and simulation of reproduction and survival in a reintroduced population of the endangered Nipponia nippon (Asian Crested Ibis) X. Xi et al. https://doi.org/10.1093/ornithapp/duag054
71. Assessing Hydrologic Uncertainty Processor Performance for Flood Forecasting in a Semiurban Watershed S. Han et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001828
72. Investigation of multivariate pairs trading under copula approach with mixture distribution F. He et al. https://doi.org/10.1016/j.amc.2024.128635
73. The distribution of Kendall's tau for testing the significance of cross-correlation in persistent data K. Hamed https://doi.org/10.1080/02626667.2011.586948
74. Ensemble Model Output Statistics for Wind Vectors N. Schuhen et al. https://doi.org/10.1175/MWR-D-12-00028.1
75. Analysis of extreme precipitation characteristics in low mountain areas based on three-dimensional copulas—taking Kuandian County as an example C. Wang et al. https://doi.org/10.1007/s00704-015-1692-7
76. Interdependence between temperature and precipitation: modeling using copula method toward climate protection B. Hussain et al. https://doi.org/10.1007/s40808-021-01256-8
77. Joint Modeling of Precipitation and Temperature Using Copula Theory for Current and Future Prediction under Climate Change Scenarios in Arid Lands (Case Study, Kerman Province, Iran) T. Mesbahzadeh et al. https://doi.org/10.1155/2019/6848049
78. A physiographic approach to downscaling fractional snow cover data in mountainous regions R. Walters et al. https://doi.org/10.1016/j.rse.2014.07.001
79. A new method for wind speed forecasting based on copula theory Y. Wang et al. https://doi.org/10.1016/j.envres.2017.09.034
80. Climate change‐induced impacts on urban flood risk influenced by concurrent hazards A. Pedersen et al. https://doi.org/10.1111/j.1753-318X.2012.01139.x
81. Spatial extreme modeling using student t copula approach in Ngawi Regency M. Fauziyah et al. https://doi.org/10.1088/1742-6596/1538/1/012051
82. Developing an earthquake model based on simultaneous peak ground acceleration occurrences using the D-vine copula approach A. Ahdika et al. https://doi.org/10.1007/s40808-023-01846-8
83. The contribution of climate drivers to compound drought and extreme temperature events increased in recent decades S. Li et al. https://doi.org/10.1016/j.wace.2025.100793
84. Heterogeneous spatiotemporal copula‐based kriging for air pollution prediction J. Wang et al. https://doi.org/10.1111/tgis.12823
85. Selecting and estimating regular vine copulae and application to financial returns J. Dißmann et al. https://doi.org/10.1016/j.csda.2012.08.010
86. Research on quantitative monitoring of new meteorological-agricultural compound drought characteristics Y. Yu et al. https://doi.org/10.1007/s00704-025-05752-0
87. Application of the Gaussian anamorphosis to assimilation in a 3-D coupled physical-ecosystem model of the North Atlantic with the EnKF: a twin experiment E. Simon & L. Bertino https://doi.org/10.5194/os-5-495-2009
88. Analysis of Reserves in Multiple Life Insurance using Copula I. Lee et al. https://doi.org/10.5351/CSAM.2014.21.1.023
89. Entropy–Copula in Hydrology and Climatology A. AghaKouchak https://doi.org/10.1175/JHM-D-13-0207.1
90. A new kind of sensitivity index for multivariate output L. Li et al. https://doi.org/10.1016/j.ress.2015.11.006
91. Impedance Response Influenced by Variability in the Random Distribution of Physical Properties of Coated Materials in Two-Dimensional Space S. Cho et al. https://doi.org/10.1149/1945-7111/acf1d0
92. Time of emergence of compound events: contribution of univariate and dependence properties B. François & M. Vrac https://doi.org/10.5194/nhess-23-21-2023
93. Continuous displacement interpolation between checkerboard copulas O. Grothe & N. Luo https://doi.org/10.1515/demo-2025-0018
94. A method for preferential selection of dates in the Schaake shuffle approach to constructing spatiotemporal forecast fields of temperature and precipitation M. Scheuerer et al. https://doi.org/10.1002/2016WR020133
95. Statistical modelling of extreme storms using copulas: A comparison study F. Li et al. https://doi.org/10.1016/j.coastaleng.2018.09.007
96. Decentralized monitoring for large‐scale process using copula‐correlation analysis and Bayesian inference–based multiblock principal component analysis Y. Tian et al. https://doi.org/10.1002/cem.3158
97. Construction of combined drought index based on bivariate joint distribution Y. Li et al. https://doi.org/10.1016/j.aej.2021.01.006
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101. Improving multisite precipitation generators based on generalised linear models J. Wessel & R. Chandler https://doi.org/10.5194/ascmo-12-149-2026
102. Impact of bias nonstationarity on the performance of uni- and multivariate bias-adjusting methods: a case study on data from Uccle, Belgium J. Van de Velde et al. https://doi.org/10.5194/hess-26-2319-2022
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106. Joint statistical models for significant wave height and wave period in a changing climate E. Vanem https://doi.org/10.1016/j.marstruc.2016.06.001
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108. Nonlinear time series analysis of palaeoclimate proxy records N. Marwan et al. https://doi.org/10.1016/j.quascirev.2021.107245
109. Multivariate probabilistic forecasting using ensemble Bayesian model averaging and copulas A. Möller et al. https://doi.org/10.1002/qj.2009
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114. The spatial structure of European wind storms as characterized by bivariate extreme-value Copulas A. Bonazzi et al. https://doi.org/10.5194/nhess-12-1769-2012
115. D‐vine‐copula‐based postprocessing of wind speed ensemble forecasts D. Jobst et al. https://doi.org/10.1002/qj.4521
116. Mixture of D-Vine copulas for chemical process monitoring W. Zheng et al. https://doi.org/10.1016/j.chemolab.2017.08.002
117. Generating Nonnormal Multivariate Data Using Copulas: Applications to SEM P. Mair et al. https://doi.org/10.1080/00273171.2012.692629
118. Probabilistic assessment of regional climate change in Southwest Germany by ensemble dressing C. Schölzel & A. Hense https://doi.org/10.1007/s00382-010-0815-1
119. Changes in Large Lake Water Level Dynamics in Response to Climate Change A. VanDeWeghe et al. https://doi.org/10.3389/frwa.2022.805143
120. Vine copula models for predicting water flow discharge at King George Island, Antarctica M. Gómez et al. https://doi.org/10.1007/s00477-018-1599-9
121. Copula-based assimilation of radar and gauge information to derive bias-corrected precipitation fields S. Vogl et al. https://doi.org/10.5194/hess-16-2311-2012
122. Review of dependence modeling in hydrology and water resources Z. Hao & V. Singh https://doi.org/10.1177/0309133316632460
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124. Bivariate bias correction of the regional climate model ensemble over the Adriatic region R. Sokol Jurković et al. https://doi.org/10.1002/joc.7564
125. Compound summer temperature and precipitation extremes over central Europe K. Sedlmeier et al. https://doi.org/10.1007/s00704-017-2061-5
126. Future multivariate weather generation by combining Bartlett-Lewis and vine copula models J. Van de Velde et al. https://doi.org/10.1080/02626667.2022.2144322
127. Copula‐based uncertainty modelling: application to multisensor precipitation estimates A. AghaKouchak et al. https://doi.org/10.1002/hyp.7632
128. Detecting and modeling critical dependence structures between random inputs of computer models N. Benoumechiara et al. https://doi.org/10.1515/demo-2020-0016
129. Generation of realistic virtual adult populations using a model-based copula approach Y. Guo et al. https://doi.org/10.1007/s10928-024-09929-4
130. Time-variant reliability assessment for bridge structures based on deep learning and regular vine copula models Q. Xiao et al. https://doi.org/10.1016/j.measurement.2024.115253
131. Estimation of the correlation between temperature and precipitation in Bafra Plain using Copula Ç. Sözen et al. https://doi.org/10.1007/s00704-025-05640-7
132. Copula modeling from Abe Sklar to the present day C. Genest et al. https://doi.org/10.1016/j.jmva.2023.105278
133. Multivariate density model comparison for multi-site flood-risk rainfall in the French Mediterranean area J. Carreau & C. Bouvier https://doi.org/10.1007/s00477-015-1166-6
134. Interdependency analysis between precipitation and temperature in Bangladesh: a copula-based approach M. Prodhan & M. Islam https://doi.org/10.1007/s00704-024-05297-8
135. Windblown sand saltation: A statistical approach to fluid threshold shear velocity L. Raffaele et al. https://doi.org/10.1016/j.aeolia.2016.10.002
136. Modelling of interdependence between rainfall and temperature using copula P. Pandey et al. https://doi.org/10.1007/s40808-018-0454-9
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138. How well do regional climate models simulate the spatial dependence of precipitation? An application of pair‐copula constructions I. Hobæk Haff et al. https://doi.org/10.1002/2014JD022748
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142. Analysis of the Parametric Correlation in Mathematical Modeling of In Vitro Glioblastoma Evolution Using Copulas J. Ayensa-Jiménez et al. https://doi.org/10.3390/math9010027
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158. The use of copula functions for predictive analysis of correlations between extreme storm tides K. Domino et al. https://doi.org/10.1016/j.physa.2014.07.020
159. Bivariate return periods of temperature and precipitation explain a large fraction of European crop yields J. Zscheischler et al. https://doi.org/10.5194/bg-14-3309-2017
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165. Copula-based joint distribution analysis of the ENSO effect on the drought indicators over Borneo fire-prone areas M. Najib et al. https://doi.org/10.1007/s40808-021-01267-5
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