Using high-resolution regional climate models to estimate return levels of daily extreme precipitation over Bavaria

• Published in: Natural hazards and earth system sciences 2021-11, Vol.21 (11), p.3573-3598
• Main Author: Poschlod, Benjamin
• Format: Article
• Language: English
• Subjects: Analysis; Atmospheric precipitations; Bias; Building codes; Climate; Climate change; Climate models; Climatic changes; Convection; Convective precipitation; Correlation; Daily; Daily rainfall; Datasets; Distribution; Extreme value theory; Extreme values; Extreme weather; Flood management; Floods; Future precipitation; Gauges; Global climate; Global climate models; High resolution; Insurance industry; Landslides & mudslides; Meteorological research; Numerical weather forecasting; Parameterization; Parameters; Performance enhancement; Performance evaluation; Precipitation; Rain; Rain and rainfall; Rain gauges; Rainfall; Rainfall levels; Rainfall-climatic change relationships; Regional climate models; Regional climates; Resolution; River networks; Shape; Spatial data; Spatial discrimination; Spatial resolution; Structural design; Structural engineering; Topography; Water management; Weather; Weather forecasting
• ISSN: 1684-9981; 1561-8633; 1684-9981
• DOI: 10.5194/nhess-21-3573-2021

Extreme daily rainfall is an important trigger for floods in Bavaria. The dimensioning of water management structures as well as building codes is based on observational rainfall return levels. In this study, three high-resolution regional climate models (RCMs) are employed to produce 10- and 100-year daily rainfall return levels and their performance is evaluated by comparison to observational return levels. The study area is governed by different types of precipitation (stratiform, orographic, convectional) and a complex terrain, with convective precipitation also contributing to daily rainfall levels. The Canadian Regional Climate Model version 5 (CRCM5) at a 12 km spatial resolution and the Weather and Forecasting Research (WRF) model at a 5 km resolution both driven by ERA-Interim reanalysis data use parametrization schemes to simulate convection. WRF at a 1.5 km resolution driven by ERA5 reanalysis data explicitly resolves convectional processes. Applying the generalized extreme value (GEV) distribution, the CRCM5 setup can reproduce the observational 10-year return levels with an areal average bias of +6.6 % and a spatial Spearman rank correlation of Ï=0.72. The higher-resolution 5 km WRF setup is found to improve the performance in terms of bias (+4.7 %) and spatial correlation (Ï=0.82). However, the finer topographic details of the WRF-ERA5 return levels cannot be evaluated with the observation data because their spatial resolution is too low. Hence, this comparison shows no further improvement in the spatial correlation (Ï=0.82) but a small improvement in the bias (2.7 %) compared to the 5 km resolution setup.