Impact-based classification of extreme rainfall events using a simplified overland flow model

Abstract

ABSTRACT In this study, we present a new rainfall classification method based on the potential of individual rainstorm events to generate and sustain overland flow on a simplified urban model surface. Conventional rainfall classification approaches using peak intensities or rainfall sums can lead to misjudgements of the impact of rainfall events, as they do not explicitly consider the potential of rain to generate and sustain substantial amounts of overland flow on typical sealed urban surfaces. The new method of impact-based rainfall classification proposed here comprises three steps: Identification of rainfall events from high-resolution time series, computation of overland flow depths for each event, and impact-based classification of rainfall events as a function of the magnitude of overland flow. The accuracy of this approach was evaluated by its ability to describe seasonal and annual occurrences of and variations in rainstorm impacts for high-resolution series at three different urban stations in Germany.