A statistics-based automated flood event separation

Abstract

The classification of characteristics of flood events, like peak, volume, duration and baseflow components is essential for many hydrological applications such as multivariate flood statistics, the validation of rainfall-runoff models and comparative hydrology in general. The basis for estimations of these characteristics is formed by flood event separation. It requires an indicator for the time when a flood peak occurs as well as the definition of the beginning and end of a flood event and a subdivision of the total volume into direct and baseflow components. However, the variable nature of runoff and the multiple processes and impacts that determine rainfall-runoff relationships make a separation difficult, especially an automation of it. We propose a new statistics-based flood event separation that was developed to analyse long series of daily discharges automatically to obtain flood events for flood statistics. Moreover, the related flood-inducing precipitation is identified, allowing the estimation of the flood-inducing rainfall and the runoff coefficient. With an additional tool to manually check the separation results easily and quickly, expert knowledge can be included without much effort. The algorithm was applied to seven basins in Germany, covering alpine, mountainous and flatland catchments with different runoff processes. In a sensitivity analysis, the impact of chosen parameters was evaluated. The results show that the algorithm delivers reasonable results for all catchments and only needs manual adjustment for long timeslots with increasing or high baseflow. It reliably separates flood events only instead of all runoff events and the estimated beginning and end of an event was shifted in mean by less than one day compared to manual separation.