71 citations as recorded by crossref.

1. Flood risk analysis for flood control and sediment transportation in sandy regions: A case study in the Loess Plateau, China A. Guo et al. https://doi.org/10.1016/j.jhydrol.2018.02.076
2. A Framework for Exploring Joint Effects of Conditional Factors on Compound Floods Z. Liu et al. https://doi.org/10.1002/2017WR021662
3. Development of Dependence Indexes for Multi-Unit Risk Assessment and its Estimation Using Copula K. Jin et al. https://doi.org/10.1016/j.ress.2021.107652
4. Predictive Uncertainty Estimation of Hydrological Multi-Model Ensembles Using Pair-Copula Construction B. Klein et al. https://doi.org/10.3390/w8040125
5. A storm-centered multivariate modeling of extreme precipitation frequency based on atmospheric water balance Y. Liu & D. Wright https://doi.org/10.5194/hess-26-5241-2022
6. Probability Analysis and Control of River Runoff–sediment Characteristics based on Pair-Copula Functions: The Case of the Weihe River and Jinghe River Q. You et al. https://doi.org/10.3390/w11030510
7. 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
8. Copula–entropy theory for multivariate stochastic modeling in water engineering V. Singh & L. Zhang https://doi.org/10.1186/s40562-018-0105-z
9. A Hierarchical Archimedean Copula (HAC) model for climatic variables: an application to Kenyan data K. Otieno et al. https://doi.org/10.3389/fams.2025.1585707
10. Copula-based joint distribution modelling of precipitation, temperature and humidity events in the assessments of agricultural risks, with a case study in Morocco S. Latif et al. https://doi.org/10.1007/s00477-025-03047-4
11. Hybrid high-dimensional vine copula–Bayesian network framework for flood risk analysis in reservoir–lake systems: Addressing multisource uncertainties X. Yang et al. https://doi.org/10.1016/j.envsoft.2025.106818
12. Toward analyzing meteorological droughts in western Iran: a multivariate approach based on vine copulas N. Jahannemaei et al. https://doi.org/10.1007/s11069-022-05747-4
13. R-statistic based predictor variables selection and vine structure determination approach for stochastic streamflow generation considering temporal and spatial dependence X. Wang & Y. Shen https://doi.org/10.1016/j.jhydrol.2023.129093
14. Stability of spatial dependence structure of extreme precipitation and the concurrent risk over a nested basin Z. Liu et al. https://doi.org/10.1016/j.jhydrol.2021.126766
15. Multivariate analysis of flood characteristics in Armand Watershed, Iran using vine copulas S. Amini et al. https://doi.org/10.1007/s12517-022-11102-5
16. Copulas and hydro-economic models for assessing the impacts of climate change in irrigated agriculture I. El ouadi et al. https://doi.org/10.1051/e3sconf/202449204002
17. Parametric Vine Copula Framework in the Trivariate Probability Analysis of Compound Flooding Events S. Latif & S. Simonovic https://doi.org/10.3390/w14142214
18. Copula-based multivariate flood probability construction: a review S. Latif & F. Mustafa https://doi.org/10.1007/s12517-020-5077-6
19. Comparison of rainfall generators with regionalisation for the estimation of rainfall erosivity at ungauged sites R. Pidoto et al. https://doi.org/10.5194/esurf-10-851-2022
20. Trivariate Joint Distribution Modelling of Compound Events Using the Nonparametric D-Vine Copula Developed Based on a Bernstein and Beta Kernel Copula Density Framework S. Latif & S. Simonovic https://doi.org/10.3390/hydrology9120221
21. Development of Rainfall-Runoff Models for Sustainable Stormwater Management in Urbanized Catchments B. Szeląg et al. https://doi.org/10.3390/w14131997
22. Copula Theory as a Generalized Framework for Flow‐Duration Curve Based Streamflow Estimates in Ungaged and Partially Gaged Catchments S. Worland et al. https://doi.org/10.1029/2019WR025138
23. Copula‐based downscaling of daily precipitation fields M. Lorenz et al. https://doi.org/10.1002/hyp.13271
24. Vine Copula-Based Multivariate Distribution of Rainfall Intensity, Wind Speed, and Wind Direction for Optimizing Qatari Meteorological Stations H. Qasem et al. https://doi.org/10.3390/w16091257
25. Multidimensional risk in a nonstationary climate: Joint probability of increasingly severe warm and dry conditions A. Sarhadi et al. https://doi.org/10.1126/sciadv.aau3487
26. Variability and extremes: statistical validation of the Alfred Wegener Institute Earth System Model (AWI-ESM) J. Contzen et al. https://doi.org/10.5194/gmd-15-1803-2022
27. Modeling impacts of climate change on return period of landslide triggering D. Peres & A. Cancelliere https://doi.org/10.1016/j.jhydrol.2018.10.036
28. Influence of urban catchment characteristics and rainfall origins on the phenomenon of stormwater flooding: Case study B. Szeląg et al. https://doi.org/10.1016/j.envsoft.2022.105335
29. Snow-melt flood frequency analysis by means of copula based 2D probability distributions for the Narew River in Poland B. Ozga-Zielinski et al. https://doi.org/10.1016/j.ejrh.2016.02.001
30. Stormwater Reservoir Sizing in Respect of Uncertainty B. Szeląg et al. https://doi.org/10.3390/w11020321
31. Third-Order Polynomial Normal Transform Applied to Multivariate Hydrologic Extremes Y. Tung et al. https://doi.org/10.3390/w11030490
32. Multi-hazard joint probability distribution model for wind speed, wind direction and rain intensity W. Bi et al. https://doi.org/10.1007/s11431-022-2210-3
33. Towards new design rainfall profiles for the United Kingdom R. Villalobos Herrera et al. https://doi.org/10.1111/jfr3.12958
34. Flow duration curve prediction: A framework integrating regionalization and copula model T. Lan et al. https://doi.org/10.1016/j.jhydrol.2024.132364
35. A regional comparative analysis of empirical and theoretical flood peak-volume relationships J. Szolgay et al. https://doi.org/10.1515/johh-2016-0042
36. Stochastic simulation of precipitation-consistent daily reference evapotranspiration using vine copulas M. Pham et al. https://doi.org/10.1007/s00477-015-1181-7
37. Storm event-based frequency analysis method C. Jun et al. https://doi.org/10.2166/nh.2017.175
38. An entropy and copula-based framework for streamflow prediction and spatio-temporal identification of drought X. Ju et al. https://doi.org/10.1007/s00477-023-02388-2
39. Multivariate Copula Analysis Toolbox (MvCAT): Describing dependence and underlying uncertainty using a Bayesian framework M. Sadegh et al. https://doi.org/10.1002/2016WR020242
40. Physics-informed machine learning algorithms for forecasting sediment yield: an analysis of physical consistency, sensitivity, and interpretability A. El Bilali et al. https://doi.org/10.1007/s11356-024-34245-2
41. The negative-positive feedback transition thresholds of meteorological drought in response to agricultural drought and their dynamics X. Wei et al. https://doi.org/10.1016/j.scitotenv.2023.167817
42. Bayesian classifier with multivariate distribution based on D-vine copula model for awake/drowsiness interpretation during power nap B. Wang et al. https://doi.org/10.1016/j.bspc.2019.101686
43. A Framework of Dependence Modeling and Evaluation System for Compound Flood Events X. Wang & Y. Shen https://doi.org/10.1029/2023WR034718
44. Spatio-Temporal Synthesis of Continuous Precipitation Series Using Vine Copulas A. Callau Poduje & U. Haberlandt https://doi.org/10.3390/w10070862
45. Semiparametric Trivariate D-Vine Copula Modelling of River Temperature, Dissolved Oxygen, and Flow for Compound Water-Quality Risk in the Yamuna and Tungabhadra Rivers, India S. Latif et al. https://doi.org/10.3390/w18172063
46. Generating Continuous Rainfall Time Series with High Temporal Resolution by Using a Stochastic Rainfall Generator with a Copula and Modified Huff Rainfall Curves D. Nguyen & S. Chen https://doi.org/10.3390/w14132123
47. An intelligent and uncertain optimization framework for water-nitrogen synergistic management under extreme supply and demand water risks X. Xu et al. https://doi.org/10.1016/j.eswa.2025.127829
48. Vine-Copula-Based Quantile Regression for Cascade Reservoirs Management W. El Hannoun et al. https://doi.org/10.3390/w13070964
49. Urbanization and Management of the Catchment Retention in the Aspect of Operation of Storm Overflow: A Probabilistic Approach B. Szeląg et al. https://doi.org/10.3390/su11133651
50. A novel multivariate vine-copula–based drought index with application to high-elevation catchments Z. Badar et al. https://doi.org/10.1007/s11356-026-38256-z
51. A Probabilistic Model for Maximum Rainfall Frequency Analysis M. Ciupak et al. https://doi.org/10.3390/w13192688
52. Assessment of environmental risk using trivariate joint distribution of river water temperature, river flow and dissolved oxygen with semiparametric D-vine copula framework S. Latif et al. https://doi.org/10.1016/j.wace.2026.100858
53. Streamflow estimation at partially gaged sites using multiple-dependence conditions via vine copulas K. Ahn https://doi.org/10.5194/hess-25-4319-2021
54. Evaluation of Dependency Structure for Multivariate Weather Predictors Using Copulas S. Maina et al. https://doi.org/10.1145/3616384
55. Application of logistic regression to simulate the influence of rainfall genesis on storm overflow operations: a probabilistic approach B. Szeląg et al. https://doi.org/10.5194/hess-24-595-2020
56. Generating monthly rainfall amount using multivariate skew-t copula N. Radi et al. https://doi.org/10.1088/1742-6596/890/1/012133
57. Probabilistic subseasonal precipitation forecasts using preceding atmospheric intraseasonal signals in a Bayesian perspective Y. Li et al. https://doi.org/10.5194/hess-26-4975-2022
58. Modeling Joint Relationship and Design Scenarios Between Precipitation, Surface Temperature, and Atmospheric Precipitable Water Over Mainland China O. Ayantobo et al. https://doi.org/10.1029/2020EA001513
59. Constructing multivariate distribution of rainfall characteristics: A Bayesian vine algorithm A. Sharma et al. https://doi.org/10.1016/j.jhydrol.2024.131392
60. Comparison between canonical vine copulas and a meta-Gaussian model for forecasting agricultural drought over China H. Wu et al. https://doi.org/10.5194/hess-26-3847-2022
61. Multivariate Modeling of Annual Instantaneous Maximum Flows Using Copulas F. Tosunoglu & V. Singh https://doi.org/10.1061/(ASCE)HE.1943-5584.0001644
62. Swiss Rainfall Mass Curves and their Influence on Extreme Flood Simulation F. Zeimetz et al. https://doi.org/10.1007/s11269-018-1948-y
63. A new framework for a multi-site stochastic daily rainfall model: Coupling a univariate Markov chain model with a multi-site rainfall event model C. Gao et al. https://doi.org/10.1016/j.jhydrol.2021.126478
64. Meteorological drought analysis in response to climate change conditions, based on combined four-dimensional vine copulas and data mining (VC-DM) A. Farrokhi et al. https://doi.org/10.1016/j.jhydrol.2021.127135
65. The response of agricultural drought to meteorological drought modulated by air temperature X. Wei et al. https://doi.org/10.1016/j.jhydrol.2024.131626
66. A novel risk analysis approach of casing under complex conditions using copulas L. Zhao et al. https://doi.org/10.1016/j.petrol.2020.106955
67. An Alternative for Estimating the Design Flood Interval of Agricultural Reservoirs under Climate Change Using a Non-Parametric Resampling Technique J. Park et al. https://doi.org/10.3390/w12071894
68. Modeling cold-related excess deaths via stationary vine copulas H. Li et al. https://doi.org/10.1080/03461238.2026.2650321
69. Short time step continuous rainfall modeling and simulation of extreme events A. Callau Poduje & U. Haberlandt https://doi.org/10.1016/j.jhydrol.2017.06.036
70. A new nonparametric copula framework for the joint analysis of river water temperature and low flow characteristics for aquatic habitat risk assessment S. Latif et al. https://doi.org/10.1016/j.jhydrol.2024.131079
71. Understanding copula-based multivariate standardized drought indices for characterizing meteorological, hydrological and agricultural droughts across global land areas Y. Liu et al. https://doi.org/10.1016/j.agwat.2025.109864