On the tail dependence in bivariate hydrological frequency analysis

Lekina Alexandre, Chebana Fateh and Ouarda Taha B. M. J.
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Lekina Alexandre: ITE - VEDECOM, Institut de la Transition Énergétique du Véhicule Décarboné et Communicant et de sa Mobilité. Fondation Partenariale de l’Université de Versailles Saint-Quentin-en-Yvelines, 77 rue des Chantiers, 78000- Versailles,France
Chebana Fateh: Institut National de la Recherche Scientifique, INRS-ETE, 490 rue de la Couronne, Québec (Québec) G1K 9A9, Canada
Ouarda Taha B. M. J.: Institut National de la Recherche Scientifique, INRS-ETE, 490 rue de la Couronne, Québec (Québec) G1K 9A9, Canada

Dependence Modeling, 2015, vol. 3, issue 1, 25

Abstract: In Bivariate Frequency Analysis (BFA) of hydrological events, the study and quantification of the dependence between several variables of interest is commonly carried out through Pearson’s correlation (r), Kendall’s tau (τ) or Spearman’s rho (ρ). These measures provide an overall evaluation of the dependence. However, in BFA, the focus is on the extreme events which occur on the tail of the distribution. Therefore, these measures are not appropriate to quantify the dependence in the tail distribution. To quantify such a risk, in Extreme Value Analysis (EVA), a number of concepts and methods are available but are not appropriately employed in hydrological BFA. In the present paper, we study the tail dependence measures with their nonparametric estimations. In order to cover a wide range of possible cases, an application dealing with bivariate flood characteristics (peak flow, flood volume and event duration) is carried out on three gauging sites in Canada. Results show that r, τ and ρ are inadequate to quantify the extreme risk and to reflect the dependence characteristics in the tail. In addition, the upper tail dependence measure, commonly employed in hydrology, is shown not to be always appropriate especially when considered alone: it can lead to an overestimation or underestimation of the risk. Therefore, for an effective risk assessment, it is recommended to consider more than one tail dependence measure.

Keywords: Asymptotic; Extreme; Copula; Bivariate distribution; Non-parametric estimation (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:demode:v:3:y:2015:i:1:p:25:n:15

DOI: 10.1515/demo-2015-0015

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