Hydrology Research (Apr 2021)

Analysis of diurnal, seasonal, and annual distribution of urban sub-hourly to hourly rainfall extremes in Germany

  • Nasrin Haacke,
  • Eva Nora Paton

DOI
https://doi.org/10.2166/nh.2021.181
Journal volume & issue
Vol. 52, no. 2
pp. 478 – 491

Abstract

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The timing of short extreme rainstorm, which was usually thought to occur on midsummer afternoons, was investigated to improve future mitigation options for infrastructure and safety from localised flash flooding. Using a peak-over-threshold approach, the timing of 10- and 60-min extreme events was filtered from high-resolution rainfall series assessing diurnal, seasonal, and annual distributions and analysed for spatial variations and prevailing atmospheric circulation types (CTs). The diurnal distribution showed a clear deviation from that of the entire rainfall regime. A complex spatial pattern was identified with distinct timing signatures of storms in the northern (mostly afternoon) and southern regions (a bimodal distribution with a second peak in the early morning) of Germany and a more homogenous diurnal distribution of events across the central regions. Most storms occurred in summer, but 42% of 10-min events occurred outside the summer months (June–July–August). A distinct annual clustering of extremes was identified, which varied distinctly between the 10- and 60-min extremes, indicating that the sub-hourly and hourly events were far from running conterminously. The timing of extreme events on the investigated time scales was not dominated by the occurrence of specific CTs in most cases, suggesting that other factors control these extremes. HIGHLIGHTS Knowledge about exact timing is essential for urban water management, as these events cause damage at different spatial scales.; Information on diurnal timing may provide fire brigades and traffic wardens with a clear picture, and seasonal street cleaning could be optimised.; Timing is important for the planning of scientific monitoring.; Categorised atmospheric circulation patterns can be used as the first trend indicator for risk assessment.;

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