Clustering model responses in the frequency space for improved simulation‐based flood risk studies: The role of a cluster number

Abstract

Hydrologic models are often employed in flood risk studies to simulate possible hydrologic responses. They are, however, linked with uncertainty that is commonly represented with uncertainty intervals constructed based on a simulation ensemble. This work adapts an alternative clustering-based approach to first, learn about hydrological responses in the frequency space, and second, select an optimal number of clusters and corresponding representative parameters sets for a hydrologic model. Each cluster is described with three parameter sets, which enable percentile and prediction intervals to be constructed. Based on a small Swiss catchment with 10,000 years of daily pseudo-discharge simulations, it was found that clustering the ensemble of 1000 members into 5–7 groups is optimal to derive reliable flood prediction intervals in the frequency space. This lowers the computational costs of using a hydrological model by 98%. The developed approach is suitable for probabilistic flood risk analysis with current or future climate conditions to assess hydrologic changes.