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Detecting Nonlinearity in Time Series Driven by Non-gaussian Noise: the Case of River Flows : Volume 11, Issue 4 (26/10/2004)

By Laio, F.

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Book Id: WPLBN0004019624
Format Type: PDF Article :
File Size: Pages 8
Reproduction Date: 2015

Title: Detecting Nonlinearity in Time Series Driven by Non-gaussian Noise: the Case of River Flows : Volume 11, Issue 4 (26/10/2004)  
Author: Laio, F.
Volume: Vol. 11, Issue 4
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2004
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Ridolfi, L., Porporato, A., Tamea, S., & Laio, F. (2004). Detecting Nonlinearity in Time Series Driven by Non-gaussian Noise: the Case of River Flows : Volume 11, Issue 4 (26/10/2004). Retrieved from http://hawaiilibrary.net/


Description
Description: Dip. Idraulica, Trasporti ed Infrastrutture Civili, Politecnico di Torino, Torino, Italy. 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.

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

 

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