Statistical regionalization for estimation of extreme river discharges

TL;DR

This paper introduces a statistical regionalization method for estimating extreme river discharges at ungauged locations by regionalizing GEV distribution parameters, improving uncertainty estimates for long return periods.

Contribution

The paper presents a novel statistical approach for regionalizing GEV parameters that optimally selects regions of influence, enhancing estimation accuracy at ungauged sites.

Findings

Method improves estimation uncertainty for long return periods.

Application to Rhine basin demonstrates better performance than classical methods.

Optimal regionalization enhances similarity and homogeneity in estimates.

Abstract

Regionalization methods have long been used to estimate high return levels of river discharges at ungauged locations on a river network. In these methods, the recorded discharge measurements of a group of similar, gauged, stations is used to estimate high quantiles at the target catchment that has no observations. This group is called the region of influence and its similarity to the ungauged location is measured in terms of physical and meteorological catchment attributes. We develop a statistical method for estimation of high return levels based on regionalizing the parameters of a generalized extreme value distribution. The region of influence is chosen in an optimal way, ensuring similarity and in-group homogeneity. Our method is applied to discharge data from the Rhine basin in Switzerland, and its performance at ungauged locations is compared to that of classical regionalization methods. For gauged locations we show how our approach improves the estimation uncertainty for long return periods by combining local measurements with those from the region of influence.