Asymmetric dependence in hydrological extremes

TL;DR

This paper introduces an asymmetric tail Kendall's tau to measure directional extremal dependence, revealing structural asymmetries in hydrological data that symmetric measures miss.

Contribution

It proposes a new asymmetric dependence measure, derives its properties, and demonstrates its application in hydrology to uncover directional extremal relationships.

Findings

Asymmetric tail Kendall's tau effectively captures directional extremal dependence.

Application to UK river data reveals significant asymmetries missed by symmetric measures.

The measure provides insights into the causal structure of hydrological extremes.

Abstract

Extremal dependence describes the strength of correlation between the largest observations of two variables. It is usually measured with symmetric dependence coefficients that do not depend on the order of the variables. In many cases, there is a natural asymmetry between extreme observations that can not be captured by such coefficients. An example for such asymmetry are large discharges at an upstream and a downstream stations on a river network: an extreme discharge at the upstream station will directly influence the discharge at the downstream station, but not vice versa. Simple measures for asymmetric dependence in extreme events have not yet been investigated. We propose the asymmetric tail Kendall's $\tau$ as a measure for extremal dependence that is sensitive to asymmetric behaviour in the largest observations. It essentially computes the classical Kendall's $\tau$ but conditioned on the extreme observations of one of the two variables. We show theoretical properties of this new coefficient and derive a formula to compute it for existing copula models. We further study its effectiveness and connections to causality in simulation experiments. We apply our methodology to a case study on river networks in the United Kingdom to illustrate the importance of measuring asymmetric extremal dependence in hydrology. Our results show that there is important structural information in the asymmetry that would have been missed by a symmetric measure. Our methodology is an easy but effective tool that can be applied in exploratory analysis for understanding the connections among variables and to detect possible asymmetric dependencies.