Articles | Volume 23, issue 3
https://doi.org/10.5194/nhess-23-1227-2023
https://doi.org/10.5194/nhess-23-1227-2023
Brief communication
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29 Mar 2023
Brief communication | Highlight paper |  | 29 Mar 2023

Brief communication: On the extremeness of the July 2021 precipitation event in western Germany

Katharina Lengfeld, Paul Voit, Frank Kaspar, and Maik Heistermann

Data sets

Heavy precipitation events Version 2022.01 exceeding DWD warning level 3 for severe weather based on RADKLIM-RW Version 2017.002 Katharina Lengfeld, Ewelina Walawender, Tanja Winterrath, Elmar Weigl, and Andreas Becker https://doi.org/10.5676/DWD/CatRaRE_W3_Eta_v2022.01

Gauge-adjusted one-hour precipitation sum (RW), RADKLIM Version 2017.002: Reprocessed gauge-adjusted radar data, one-hour precipitation sums (RW) Tanja Winterrath, Christoph Brendel, Mario Hafer, Thomas Junghänel, Anna Klameth, Katharina Lengfeld, Ewelina Walawender, Elmar Weigl, and Andreas Becker https://doi.org/10.5676/DWD/RADKLIM_RW_V2017.002

Model code and software

xWEI-Quantifying-the-extremeness-of-precipitation-across-scales: xWEI Paul Voit https://doi.org/10.5281/zenodo.6556463

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Executive editor
Here approaches to rank precipitation events according to their hazard characteristics are presented, while their impacts depend on many other factors as well like the orography, hydrological situation, exposure and vulnerability. Determining the severity of a precipitation event is not straight forward, as it depends on both the intensity on different time scales and the spatial extent. The weather extremity index (WEI) and the cross-scale WEI (xWEI) are used to determine the extremeness of precipitation events. The devastating event in the Ahr valley in Germany in July 2021 is shown to rank No 1 or 4 for Germany, dependent on the measure used. This emphasizes that it was extreme across multiple spatial and temporal scales, and the importance of considering different scales to determine the extremeness of rainfall events.
Short summary
Estimating the severity of a rainfall event based on the damage caused is easy but highly depends on the affected region. A less biased measure for the extremeness of an event is its rarity combined with its spatial extent. In this brief communication, we investigate the sensitivity of such measures to the underlying dataset and highlight the importance of considering multiple spatial and temporal scales using the devastating rainfall event in July 2021 in central Europe as an example.
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