The uncertainty associated with the use of copulas in multivariate analysis

Journal Article (2023)
Authors

Changrang Zhou (TU Delft - Water Resources)

RRP van Nooijen (TU Delft - Water Resources)

A. G. Kolechkina (TU Delft - Team Bart De Schutter)

E.F.G. Gargouri (University of Tunis El Manar)

Fairouz Slama (University of Tunis El Manar)

Nick van de van de Giesen (TU Delft - Water Resources)

Research Group
Water Resources
Copyright
© 2023 C. Zhou, R.R.P. van Nooijen, A.G. Kolechkina, E.F.G. Gargouri, Fairouz Slama, N.C. van de Giesen
To reference this document use:
https://doi.org/10.1080/02626667.2023.2249459
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 C. Zhou, R.R.P. van Nooijen, A.G. Kolechkina, E.F.G. Gargouri, Fairouz Slama, N.C. van de Giesen
Research Group
Water Resources
Issue number
15
Volume number
68
Pages (from-to)
2169-2188
DOI:
https://doi.org/10.1080/02626667.2023.2249459
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Abstract

The dependency structure between hydrological variables is of critical importance to hydrological modelling and forecasting. When a copula capturing that dependence is fitted to a sample, information on the uncertainty of the fit is needed for subsequent hydrological calculations and reasoning. A new method is proposed to report inferential uncertainty in a copula parameter. The method is based on confidence curves constructed with the use of a pseudo maximum likelihood estimator for the copula parameter. The method was tested on synthetic data and then used as a tool in two hydrological examples. The first examines the probability of major floods in two locations on the Rhine River and its tributaries in the same calendar year. In the second example, rainfall–runoff from a karst region in Tunisia was analysed to determine a confidence interval for the delay between precipitation and runoff.