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| Titel |
Detecting nonlinearity in time series driven by non-Gaussian noise: the case of river flows |
| VerfasserIn |
F. Laio, A. Porporato, L. Ridolfi, S. Tamea |
| 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 ; 11, no. 4 ; Nr. 11, no. 4 (2004-10-26), S.463-470 |
| Datensatznummer |
250009327
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| Publikation (Nr.) |
 copernicus.org/npg-11-463-2004.pdf |
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| Zusammenfassung |
| Several methods exist for the detection of nonlinearity in
univariate time series. In the present work we consider riverflow
time series to infer the dynamical characteristics of the
rainfall-runoff transformation. It is shown that the non-Gaussian
nature of the driving force (rainfall) can distort the results of
such methods, in particular when surrogate data techniques are
used. Deterministic versus stochastic (DVS) plots, conditionally
applied to the decay phases of the time series, are instead
proved to be a suitable tool to detect nonlinearity in processes
driven by non-Gaussian (Poissonian) noise. An application to
daily discharges from three Italian rivers provides important
clues to the presence of nonlinearity in the rainfall-runoff
transformation. |
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