Bayesian objective classification of extreme UK daily rainfall for flood risk applications

Abstract. In this study we describe an objective classification scheme for extreme UK daily precipitation to be used in flood risk analysis applications. We create a simplified representation of the spatial layout of extreme events based on a new digital archive of UK rainfall. This simplification allows a Bayesian clustering algorithm to compress these representations down to eight prototypical patterns of extreme falls. These patterns are then verified against a five-class, manual, subjective typing scheme, produced independently using known meteorological mechanisms, isohyetal maps and additional descriptive text from the archive. Compared against the manual scheme, the new objective scheme can reproduce the known meteorological conditions, both in terms of spatial layout and seasonal timing, and is shown to be of hydrological relevance when matched to several notable flooding events in the past century. Furthermore, it is computationally simple and straightforward to apply in classifying future extreme rainfall events. We discuss the practical use of this new typing scheme in flood simulations and climate change applications.