A New Unsupervised Learning Method to Assess Clusters of Temporal Distribution of Rainfall and Their Coherence with Flood Types

Abstract

Several factors have an impact on the generation of floods, for example, antecedent moisture conditions and the shape of the catchment. A very important factor is the event rainfall, especially its temporal distribution. However, the categorization of temporal distributions is riddled with uncertainty, due to a priori assumptions on distribution types. Here, we propose a new clustering approach based on unsupervised learning, using dimensionless mass curves to describe the temporal distributions. The purpose of the proposed method is the identification of reoccurring temporal distributions of precipitation events. Additionally, the correlation of the resulting clusters of temporal distribution with rainfall-induced flood types is investigated. The application to several catchments in Germany showed the existence of spatial patterns of six different clusters for the temporal distribution and a significant coherence with the flood types. It was found that the temporal distribution of rainfall intensities shifts from early peaked to more uniformly distributed when shifting the flood type from short floods with high peaks to long-duration floods, often with several peaks.