Articles | Volume 16, issue 5
Hydrol. Earth Syst. Sci., 16, 1379–1387, 2012

Special issue: HESS Opinions 2012

Hydrol. Earth Syst. Sci., 16, 1379–1387, 2012

Opinion article 11 May 2012

Opinion article | 11 May 2012

HESS Opinions "More efforts and scientific rigour are needed to attribute trends in flood time series"

B. Merz, S. Vorogushyn, S. Uhlemann, J. Delgado, and Y. Hundecha B. Merz et al.
  • GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany

Abstract. The question whether the magnitude and frequency of floods have changed due to climate change or other drivers of change is of high interest. The number of flood trend studies is rapidly rising. When changes are detected, many studies link the identified change to the underlying causes, i.e. they attribute the changes in flood behaviour to certain drivers of change. We propose a hypothesis testing framework for trend attribution which consists of essential ingredients for a sound attribution: evidence of consistency, evidence of inconsistency, and provision of confidence statement. Further, we evaluate the current state-of-the-art of flood trend attribution. We assess how selected recent studies approach the attribution problem, and to which extent their attribution statements seem defendable. In our opinion, the current state of flood trend attribution is poor. Attribution statements are mostly based on qualitative reasoning or even speculation. Typically, the focus of flood trend studies is the detection of change, i.e. the statistical analysis of time series, and attribution is regarded as an appendix: (1) flood time series are analysed by means of trend tests, (2) if a significant change is detected, a hypothesis on the cause of change is given, and (3) explanations or published studies are sought which support the hypothesis. We believe that we need a change in perspective and more scientific rigour: detection should be seen as an integral part of the more challenging attribution problem, and detection and attribution should be placed in a sound hypothesis testing framework.

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