AbstractIn 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.
Quantile correlation coefficient: a new tail dependence measure
