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The use of the annual maximum series for flood frequency analyses limits theconsidered information to one event per year and one sample that is assumed tobe homogeneous. However, flood may have different generating processes, such assnowmelt, heavy rainfall or long-duration rainfall, which makes the assumptionof homogeneity questionable. Flood types together with statisticalflood-type-specific mixture models offer the possibility to consider thedifferent flood-generating processes separately and therefore obtainhomogeneous sub-samples. The combination of flood types in a mixture model thengives classical flood quantiles for given return periods. This higherflexibility comes to the cost of more distribution parameters, which may leadto a higher uncertainty in the estimation. This study compares the classicalflood frequency models such as the annual maximum series with the type-specificmixture model for different scenarios relevant for design flood estimation interms of Bias and variance. Thee results show that despite the higher number ofparameters, the mixture model is preferable compared to the classical models,if a high number of flood events per year occurs and/or the flood types differsignificantly in their distribution parameters.

Comparison of annual maximum series and flood-type-differentiated mixture models of partial duration series