On the tail dependence in bivariate hydrological frequency analysis

Alexandre Lekina; Fateh Chebana; Taha B. M. J. Ouarda

Dependence Modeling (2015)

  • Volume: 3, Issue: 1, page 203-227, electronic only
  • ISSN: 2300-2298

Abstract

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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.

How to cite

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Alexandre Lekina, Fateh Chebana, and Taha B. M. J. Ouarda. "On the tail dependence in bivariate hydrological frequency analysis." Dependence Modeling 3.1 (2015): 203-227, electronic only. <http://eudml.org/doc/275981>.

@article{AlexandreLekina2015,
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.},
author = {Alexandre Lekina, Fateh Chebana, Taha B. M. J. Ouarda},
journal = {Dependence Modeling},
keywords = {Asymptotic; Extreme; Copula; Bivariate distribution; Non-parametric estimation; asymptotic; extreme; copula; bivariate distribution; nonparametric estimation},
language = {eng},
number = {1},
pages = {203-227, electronic only},
title = {On the tail dependence in bivariate hydrological frequency analysis},
url = {http://eudml.org/doc/275981},
volume = {3},
year = {2015},
}

TY - JOUR
AU - Alexandre Lekina
AU - Fateh Chebana
AU - Taha B. M. J. Ouarda
TI - On the tail dependence in bivariate hydrological frequency analysis
JO - Dependence Modeling
PY - 2015
VL - 3
IS - 1
SP - 203
EP - 227, electronic only
AB - 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.
LA - eng
KW - Asymptotic; Extreme; Copula; Bivariate distribution; Non-parametric estimation; asymptotic; extreme; copula; bivariate distribution; nonparametric estimation
UR - http://eudml.org/doc/275981
ER -

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