Impact-based temporal clustering of multiple meteorological hazard types in southwestern Germany

Abstract. A series of multiple meteorological extreme events in close succession can lead to a substantial increase in total losses compared to randomly distributed events. In this study, different temporal clustering methods are applied to insurance loss data on southwestern Germany from 1986 to 2023 for the following hazards: windstorms, convective gusts, hail, as well as pluvial, fluvial and mixed flood events. We assess the timing and significance of seasonal clustering of single hazard types as well as their serial combination by use of both a simple counting algorithm and the clustering metric Ripley's K. Results show that clusters of damaging hazards occur mainly during May–August. Although clustering is significant only for certain hazard types compared to a random process, clustering is robust for a combination of multiple hazard types, namely hail, mixed or pluvial floods and storms. This particular combination of hazard types is also associated with higher losses compared to their isolated occurrence. Clustering results also depend on the method of defining independent events (Peaks-over-Threshold with flexible lengths vs. Hours Clause with fixed lengths) and their resulting duration. This study demonstrates the relevance of considering multiple hazard types when evaluating clustering of meteorological hazards.