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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 12, issue 4
Nonlin. Processes Geophys., 12, 461-469, 2005
https://doi.org/10.5194/npg-12-461-2005
© Author(s) 2005. 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., 12, 461-469, 2005
https://doi.org/10.5194/npg-12-461-2005
© Author(s) 2005. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  03 May 2005

03 May 2005

Streamflow dynamics at the Puget Sound, Washington: application of a surrogate data method

N. She and D. Basketfield N. She and D. Basketfield
  • Seattle Public Utilities, City of Seattle, 700 5th Ave., Suite 4900, PO Box 34018, Seattle, WA 98124-4018, USA

Abstract. Recent progress in nonlinear dynamic theory has inspired hydrologists to apply innovative nonlinear time series techniques to the analysis of streamflow data. However, regardless of the method employed to analyze streamflow data, the first step should be the identification of underlying dynamics using one or more methods that could distinguish between linear and nonlinear, deterministic and stochastic processes from data itself. In recent years a statistically rigorous framework to test whether or not the examined time series is generated by a Gaussian (linear) process undergoing a possibly nonlinear static transform is provided by the method of surrogate data. The surrogate data, generated to represent the null hypothesis, are compared to the original data under a nonlinear discriminating statistic in order to reject or approve the null hypothesis. In recognition of this tendency, the method of "surrogate data" is applied herein to determine the underlying linear stochastic or nonlinear deterministic nature of daily streamflow data observed from the central basin of Puget Sound, and as applicable, distinguish between the static or dynamic nonlinearity of the data in question.

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