Climate change impact assessment on pluvial flooding using a distribution-based bias correction of regional climate model simulations

The expected intensification of extreme precipitation events under climate change likely results in more frequent and intense pluvial floods worldwide. To study the climate change impact on urban pluvial flooding, fine-scale climate model simulations are needed. The number of such simulations is, however, still limited and not widely available, thus entailing the downscaling of the model outputs. One of the commonly used methods to meet this demand is statistical downscaling, where the statistical properties of large(r)-scale climate simulations are used to derive local climate variables. This study focuses on applying a distribution-based bias correction method on regional climate model (RCM) simulations to explore how climate change affects extreme precipitation and urban flood events at the end of this century (2071-2100). A 1D-2D hydrodynamic model, implemented in Infoworks ICM, is used to simulate pluvial flood events for several return periods for a case study in the city of Antwerp in Belgium. The results show that the statistical downscaling approach can effectively decrease the bias in the model simulations and offer strong scaling relations to derive high-resolution extreme precipitation time series. The analyses also reveal that climate change may cause an increase of 16%, 31%, 47%, 63%, 73% and 84% in the flood volume for 2-, 5-, 10-, 20-, 30- and 50-year return periods, respectively. This projected increase in the flood volume enlarges the inundated area by 32%, 49%, 56%, 58%, 58% and 59% for the respective return periods. The flood frequency is also projected to almost double in the future, so that a 5-year flood event in the historical period will most likely be a 2-year event in the future period.