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| Titel |
HESS Opinions "More efforts and scientific rigour are needed to attribute trends in flood time series" |
| VerfasserIn |
B. Merz, S. Vorogushyn, S. Uhlemann, J. Delgado, Y. Hundecha |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 16, no. 5 ; Nr. 16, no. 5 (2012-05-11), S.1379-1387 |
| Datensatznummer |
250013294
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| Publikation (Nr.) |
 copernicus.org/hess-16-1379-2012.pdf |
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| Zusammenfassung |
| 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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