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| Titel |
Detection and predictive modeling of chaos in finite hydrological time series |
| VerfasserIn |
S. Khan, A. R. Ganguly, S. Saigal |
| 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. 1 ; Nr. 12, no. 1 (2005-01-17), S.41-53 |
| Datensatznummer |
250010378
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| Publikation (Nr.) |
 copernicus.org/npg-12-41-2005.pdf |
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| Zusammenfassung |
| The ability to detect the chaotic signal from a finite time series
observation of hydrologic systems is addressed in this paper. The
presence of random and seasonal components in hydrological time
series, like rainfall or runoff, makes the detection process
challenging. Tests with simulated data demonstrate the presence of
thresholds, in terms of noise to chaotic-signal and seasonality to
chaotic-signal ratios, beyond which the set of currently available
tools is not able to detect the chaotic component. The
investigations also indicate that the decomposition of a simulated
time series into the corresponding random, seasonal and chaotic
components is possible from finite data. Real streamflow data from
the Arkansas and Colorado rivers are used to validate these results.
Neither of the raw time series exhibits chaos. While a chaotic
component can be extracted from the Arkansas data, such a component
is either not present or can not be extracted from the Colorado
data. This indicates that real hydrologic data may or may not have a
detectable chaotic component. The strengths and limitations of the
existing set of tools for the detection and modeling of chaos are
also studied. |
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