Spatiotemporal classification of heavy rainfall patterns to characterize hydrographs in a high-resolution ensemble climate dataset

Peak discharge in rivers mostly varies depending on spatiotemporal characteristics of the rainfall, even with the same total rainfall amount. However, the spatiotemporal patterns of heavy rainfall such as rainfall levels used for river planning are difficult to determine due to the limited number of rainfall observations. We propose a spatiotemporal classification method for massive-ensemble rainfall datasets produced through dynamical downscaling with a regional climate model to clarify the possible rainfall patterns. This classification method was applied to several hundred heavy rainfall events from the massive-ensemble climate dataset, corresponding to identical exceedance probabilities within a given statistical confidence interval (i.e., 95% confidence interval of the 150-year return period in the probability limit test). The new classification method detected spatiotemporal rainfall patterns affecting peak discharges in the main river and tributaries within the target basin. These classified rainfall patterns can be used to investigate damage scenarios in the target basin, which are difficult to determine from the observed rainfall patterns. As a result, the proposed method with the massive-ensemble climate dataset will contribute to flood control planning.