Abstract

Assessing the rarity and magnitude of very extreme flood events occurring less than twice a century is challenging due to the lack of observations of such rare events. Here we develop a new approach, pooling reforecast ensemble members from the European Flood Awareness System (EFAS) to increase the sample size available to estimate the frequency of extreme local and regional flood events. We assess the added value of such pooling, determine where in Central Europe one might expect the most extreme events, and evaluate how event extremeness is related to physiographic and meteorological catchment characteristics. We work with a set of 234 catchments from the Global Runoff Data Center for which performance of simulated floods is satisfactory when compared to observed streamflow. We pool EFAS-simulated flood events for 10 perturbed ensemble members and lead times from 22 to 46 days, where flood events are only weakly dependent (< 0.25 average correlation across lead times). The resulting large ensemble (130 time series instead of one) enables analyses of very extreme events, which occur less than twice a century. We demonstrate that such ensemble pooling produces more robust estimates with considerably reduced uncertainty bounds (by ~80 % on average) than observation-based estimates but may equally introduce biases arising from the simulated meteorology and hydrological model. Our results show that specific flood return levels are highest in steep and wet regions and are comparably low in regions with strong flow regulation through dams. Furthermore, our pooled flood estimates indicate that the probability of regional flooding is higher in Central Europe and Great Britain than in Scandinavia. We conclude that reforecast ensemble pooling is an efficient approach to increase sample size and to derive robust local and regional flood estimates in regions with sufficient hydrological model performance.

Highlights

  • Reliable estimates of the frequency and magnitude of extreme flood events are needed to develop suitable preparedness and adaptation measures

  • We propose to pool flood events extracted from different model runs generated by the European Flood Awareness System (EFAS) for different lead times and perturbed members to create a large ensemble of extreme flood events

  • To assess the potential value of reforecast ensemble pooling in flood frequency analysis, we use reforecast simulations of streamflow generated by the European Flood Awareness System (EFAS)

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Summary

Introduction

Reliable estimates of the frequency and magnitude of extreme flood events are needed to develop suitable preparedness and adaptation measures. Estimates of flood events occurring less than twice a century are usually affected by large uncertainty and low reliability due to the shortness of observed records. To increase the sample size available for flood frequency analysis, different model-based approaches have been proposed. There are two important classes of methods to increase sample size, namely stochastic models and large ensembles, that rely on climate simulations. Stochastic models rely on statistical principles to generate large samples of flood events with similar characteristics to the observations (Rajagopalan et al, 2010; Vogel, 2017; Brunner and Gilleland, 2020). The large ensemble approach is more physically based and relies on a large ensemble of climate simulations (Deser et al, 2020) which are fed into a hydrological model to generate a streamflow time series ensemble (van der Wiel et al, 2019; Willkofer et al, 2020; Brunner et al, 2021b)

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