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| Titel |
Non-stationarity in annual and seasonal series of peak flow and precipitation in the UK |
| VerfasserIn |
I. Prosdocimi, T. R. Kjeldsen, C. Svensson |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Sciences ; 14, no. 5 ; Nr. 14, no. 5 (2014-05-16), S.1125-1144 |
| Datensatznummer |
250118437
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| Publikation (Nr.) |
 copernicus.org/nhess-14-1125-2014.pdf |
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| Zusammenfassung |
| When designing or maintaining an hydraulic structure, an estimate of the
frequency and magnitude of extreme events is required. The most common
methods to obtain such estimates rely on the assumption of stationarity,
i.e. the assumption that the stochastic process under study is not changing.
The public perception and worry of a changing climate have led to a wide
debate on the validity of this assumption. In this work trends for annual and
seasonal maxima in peak river flow and catchment-average daily rainfall are
explored. Assuming a two-parameter log-normal distribution, a linear
regression model is applied, allowing the mean of the distribution to vary
with time. For the river flow data, the linear model is extended to include
an additional variable, the 99th percentile of the daily rainfall for a year.
From the fitted models, dimensionless magnification factors are estimated and
plotted on a map, shedding light on whether or not geographical coherence can
be found in the significant changes. The implications of the identified
trends from a decision-making perspective are then discussed, in particular
with regard to the Type I and Type II error probabilities. One striking
feature of the estimated trends is that the high variability found in the
data leads to very inconclusive test results. Indeed, for most stations it is
impossible to make a statement regarding whether or not the current design
standards for the 2085 horizon can be considered precautionary. The power of
tests on trends is further discussed in the light of statistical power
analysis and sample size calculations. Given the observed variability in the
data, sample sizes of some hundreds of years would be needed to confirm or
negate the current safety margins when using at-site analysis. |
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