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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Environ. Sci. | doi: 10.3389/fenvs.2019.00143

A rigorous statistical assessment of recent trends in intensity of heavy precipitation over Germany

  • 1Institute of Meteorology, Freie Universität Berlin, Germany
  • 2Research Domain IV - Transdisciplinary Concepts & Methods, Potsdam-Institut für Klimafolgenforschung (PIK), Germany
  • 3Department of Water, Environment, Construction and Safety, Hochschule Magdeburg-Stendal, Germany

Comprehensive and robust statistical estimates of trends in heavy precipitation events are essential for understanding the impact of past and future climate change on the hydrological cycle. However, methods commonly used in extreme value statistics (EVS) are often unable to detect significant trends, because of their methodologically motivated reduction of the sample size and strong assumptions regarding the underlying distribution. Here, we propose linear quantile regression (QR) as a complementary and robust alternative to estimating trends in heavy precipitation events. QR does not require any assumptions on the underlying distribution and is also able to estimate trends for the full span of the distribution without any reduction of the available data. As an example, we study here a very dense and homogenized data set of daily precipitation amounts over Germany for the period 1951--2006 to compare the results of QR and the so-called block maxima approach, a classical method in EVS. Both methods indicate an overall increase in the intensity of heavy precipitation events. The strongest trends can be found in regions with an elevation of about 500~m above sea level. In turn, larger spatial clusters of moderate or even decreasing trends can only be found in Northeastern Germany. In conclusion, both methods show comparable results. QR, however, allows for a more flexible and comprehensive study of precipitation events.

Keywords: quantile regression, Heavy precipitation, Extreme value statistics (EVS), time series analysis, Climate Change

Received: 04 Feb 2019; Accepted: 10 Sep 2019.

Copyright: © 2019 Passow and Donner. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mr. Christian Passow, Freie Universität Berlin, Institute of Meteorology, Berlin, Germany,