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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 4
Nonlin. Processes Geophys., 11, 463-470, 2004
https://doi.org/10.5194/npg-11-463-2004
© Author(s) 2004. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

Special issue: Nonlinear deterministic dynamics in hydrologic systems: present...

Nonlin. Processes Geophys., 11, 463-470, 2004
https://doi.org/10.5194/npg-11-463-2004
© Author(s) 2004. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  26 Oct 2004

26 Oct 2004

Detecting nonlinearity in time series driven by non-Gaussian noise: the case of river flows

F. Laio1, A. Porporato2, L. Ridolfi1, and S. Tamea1 F. Laio et al.
  • 1Dip. Idraulica, Trasporti ed Infrastrutture Civili, Politecnico di Torino, Torino, Italy
  • 2Dept. of Civil and Environmental Engineering, Duke University, Durham, NC, USA

Abstract. 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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