Extremes on river networks

TL;DR

This paper extends max-stable processes to model extreme river discharges on networks, accounting for flow-connected and meteorological dependencies, with inference validated through simulations and applied to the Danube basin.

Contribution

It introduces a novel approach to model dependencies of extreme river discharges on networks considering flow and meteorological factors, expanding beyond Euclidean distance dependence.

Findings

Multivariate threshold likelihood effectively estimates model parameters.

Dependence structures vary with river and hydrological distances.

Application to Danube basin demonstrates practical utility.

Abstract

Max-stable processes are the natural extension of the classical extreme-value distributions to the functional setting, and they are increasingly widely used to estimate probabilities of complex extreme events. In this paper we broaden them from the usual situation in which dependence varies according to functions of Euclidean distance to situations in which extreme river discharges at two locations on a river network may be dependent because the locations are flow-connected or because of common meteorological events. In the former case dependence depends on river distance, and in the second it depends on the hydrological distance between the locations, either of which may be very different from their Euclidean distance. Inference for the model parameters is performed using a multivariate threshold likelihood, which is shown by simulation to work well. The ideas are illustrated with data from the upper Danube basin.