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

Abstract

Abstract. 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 ρ=0.72. The higher-resolution 5 km WRF setup is found to improve the performance in terms of bias (+4.7 %) and spatial correlation (ρ=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 (ρ=0.82) but a small improvement in the bias (2.7 %) compared to the 5 km resolution setup. Uncertainties due to extreme value theory are explored by employing three further approaches. Applied to the WRF-ERA5 data, the GEV distributions with a fixed shape parameter (bias is +2.5 %; ρ=0.79) and the generalized Pareto (GP) distributions (bias is +2.9 %; ρ=0.81) show almost equivalent results for the 10-year return period, whereas the metastatistical extreme value (MEV) distribution leads to a slight underestimation (bias is −7.8 %; ρ=0.84). For the 100-year return level, however, the MEV distribution (bias is +2.7 %; ρ=0.73) outperforms the GEV distribution (bias is +13.3 %; ρ=0.66), the GEV distribution with fixed shape parameter (bias is +12.9 %; ρ=0.70), and the GP distribution (bias is +11.9 %; ρ=0.63). Hence, for applications where the return period is extrapolated, the MEV framework is recommended. From these results, it follows that high-resolution regional climate models are suitable for generating spatially homogeneous rainfall return level products. In regions with a sparse rain gauge density or low spatial representativeness of the stations due to complex topography, RCMs can support the observational data. Further, RCMs driven by global climate models with emission scenarios can project climate-change-induced alterations in rainfall return levels at regional to local scales. This can allow adjustment of structural design and, therefore, adaption to future precipitation conditions.

Role of mean and variability change for changes in European annual and seasonal extreme precipitation events

“…These single decadal climate realizations will however be strongly influenced by natural climate variability (Lehner et al, 2020;Leduc et al, 2019;Deser et al, 2012;Hawkins and Sutton, 2009). Poschlod (2021) has shown the suitability of the CRCM5-LE and highlights the added value of using a regional SMILE for the analysis of precipitation extremes even on non-convection permitting resolutions. Other studies have shown that the CRCM5-LE, even though convection is parameterized, can show a good representation of the timing of maximum annual precipitation (Wood and Ludwig, 2020), as well as good agreement for ten-year return levels of 3h-24h annual maxima with observations (Poschlod et al, 2021) over Europe.…”

The Teddy-Tool v1.0: temporal disaggregation of daily climate model data for climate impact analysis

“…When distributing on too few hours, extreme hourly intensities evolve, which may have never occurred or may even be physically implausible. For temporal disaggregation of extreme precipitation, we recommend dynamical downscaling via high-resolution climate models (Poschlod, 2021;Poschlod et al, 2021;Zabel et al, 2012;Zabel and Mauser, 2013).…”

Uncertainty estimation of regionalised depth–duration–frequency curves in Germany

“…So far for DDF curves in Germany, there is not objective quantification of the uncertainty, but only approximative guessed tolerance ranges between 10-20% (depending on the return period) that should account for the measurement errors, uncertainties in the extreme value estimation and regionalisation, and for the climate variability (Junghänel et al, 2017). So far in Germany, the tolerance ranges are kept constant throughout duration levels and locations, nevertheless such tolerance ranges are expected to be higher for very short observations and high return periods (Poschlod, 2021) especially for short durations and drier climate (Marra et al, 2017). Therefore, there is a need to perform different simulations in order to quantify the tolerance ranges (uncertainty) dependent on duration, location and return period.…”

“…In this paper, the focus is on developing a framework that accounts for uncertainties due to short observation lengths and non-representativeness of point measurements. Once a framework is developed, it can be used to investigate the role of distribution choice as in Miniussi and Marra (2021) or the role of future climate as in Poschlod (2021).…”