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Abstract:
Many observed time series of precipitation and streamflow show heavy tail behaviour. This means that the occurrence probability of extreme events is higher than for distributions with an exponentially receding tail. If we neglect heavy tail behaviour we might considerably underestimate rarely observed, high-impact events. More robust estimations of upper tail behaviour can be achieved by using long time series and by improving the understanding of the relevant process controls. Here, a conceptual rainfall-runoff model is used to analyse how precipitation and runoff generation characteristics affect the upper tail of flood peak distributions. With a stochastic weather generator long, synthetic precipitation time series with different tail behaviour are produced and subsequently used as input for a rainfall-runoff model. Between different model runs, catchment characteristics linked to a threshold process in the runoff generation are varied. The upper tail behaviour of the simulated discharge time series is characterized with the shape parameter of the generalized extreme value distribution (GEV). Our analysis shows that the rainfall distributions asymptotically govern the flood peak distributions above a certain, catchment-specific return period. Below this return period, threshold processes in the runoff generation lead to heavier tails of flood peak distributions. We conclude that, for return periods that are mostly of interest to flood risk management, runoff generation is often a more pronounced control of flood heavy tails than precipitation.
