Extreme floods in Europe: going beyond observations using reforecast ensemble pooling

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 severity is related to physiographic and meteorological catchment characteristics. We work with a set of 234 catchments from the Global Runoff Data Centre matched to EFAS catchments and for which the performance of simulated floods is good when compared to observed streamflow. We pool EFAS-simulated flood events for 10 perturbed ensemble members and lead times ranging from 22 to 46 d, where flood events are only weakly dependent (<0.25 average correlation across lead times). The resulting large ensemble (130 time series instead of 1) enables the 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, for a given return period, specific floods are highest in steep, cold, 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 good hydrological model performance.