  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.
