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| Titel |
Trend assessment: applications for hydrology and climate research |
| VerfasserIn |
M. Kallache, H. W. Rust, J. Kropp |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 12, no. 2 ; Nr. 12, no. 2 (2005-02-08), S.201-210 |
| Datensatznummer |
250010483
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| Publikation (Nr.) |
 copernicus.org/npg-12-201-2005.pdf |
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| Zusammenfassung |
| The assessment of trends in climatology and hydrology still is a
matter of debate. Capturing typical properties of time series, like
trends, is highly relevant for the discussion of potential impacts of
global warming or flood occurrences.
It provides indicators for the
separation of anthropogenic signals and natural forcing factors by distinguishing
between deterministic trends and stochastic variability. In this contribution river run-off
data from gauges in Southern Germany are analysed
regarding their trend behaviour by combining a deterministic trend component
and a stochastic model part in a semi-parametric approach. In this way
the trade-off between trend and autocorrelation structure
can be considered explicitly. A test for a
significant trend is introduced via three steps: First, a
stochastic fractional ARIMA model, which is able to reproduce short-term as well
as long-term correlations, is fitted to the
empirical data. In a second step, wavelet analysis is used to
separate the variability of small and large time-scales assuming
that the trend component is part of the latter. Finally, a comparison of the
overall variability to that restricted to small scales results in a
test for a trend. The extraction of the large-scale behaviour by
wavelet analysis provides a clue concerning the shape of the
trend. |
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