# Dealing with seasonal variability and inter-site dependence in regional flood frequency analysis

## TL;DR

This paper introduces two novel regional estimators for high river flow quantiles that incorporate seasonal variability and spatial dependence, improving flood risk assessment accuracy.

## Contribution

It proposes parametric and semi-parametric estimators that handle seasonal and spatial features, with proven asymptotic normality and no reliance on complex dependence models.

## Key findings

- Estimators effectively incorporate seasonal variability.
- Asymptotic normality enables uncertainty quantification.
- Application to German river data demonstrates practical utility.

## Abstract

This paper considers the regional estimation of high quantiles of annual maximal river flow distributions $F$, an important problem from flood frequency analysis. Even though this particular problem has been addressed by many papers, less attention has been payed to incorporating seasonal variability and spatial dependence into the methods. We are going to discuss two regional estimators of high quantiles of local distributions $F$ that are able to deal with these important features, namely, a parametric approach based on so-called two-component extreme value distributions and a semi-parametric approach based on regional estimation of a tail index. The asymptotic normality of the estimators is derived for both procedures, which for instance enables us to account for estimation uncertainty without the need of parametric dependence models or bootstrap procedures. A comprehensive simulation study is conducted and our main findings are illustrated on river flow series from the Mulde basin in Germany, where people have suffered several times from severe floods over the last 100 years.