NDLI: Measuring nonlinear dependence in hydrologic time series

Content Provider	Springer Nature Link
Author	Kim, H. S. Lee, K. H. Kyoung, M. S. Sivakumar, B. Lee, E. T.
Copyright Year	2008
Abstract	It has been a common practice to employ the correlation dimension method to investigate the presence of nonlinearity and chaos in hydrologic processes. Although the method is generally reliable, potential limitations that exist in its applications to hydrologic data cannot be dismissed altogether. As for these limitations, two issues have dominated the discussions thus far: small data size and presence of noise. Another issue that is equally important, but less discussed in the literature, is the selection of delay time (τ d ) for reconstruction of the phase-space, which is an essential first step in the correlation dimension method, or any other chaos identification and prediction method for that matter. It has also been increasingly recognized that fixing the delay time window (τ w ) rather than just the delay time itself could be more appropriate, since the delay time window is the one that is of actual interest at the end to represent the dynamics. To this effect, Kim et al. (1998a) [Phys Rev E 58(5):5676–5682] developed a procedure for fixing the delay time window and demonstrated its effectiveness on three artificial chaotic series, and followed it up with the development of the C–C method to estimate both the delay time and the delay time window. The purpose of the present study is to test this procedure on real hydrologic time series and, hence, to assess their nonlinear deterministic characteristics. Three hydrologic time series are studied: (1) daily streamflow series from St. Johns near Cocoa, FL, USA; (2) biweekly volume time series from the Great Salt Lake, UT, USA; and (3) daily rainfall series from Seoul, South Korea. The results are also compared with those obtained using the conventional autocorrelation function (ACF) method.
Starting Page	907
Ending Page	916
Page Count	10
File Format	PDF
ISSN	14363240
Journal	Stochastic Environmental Research and Risk Assessment
Volume Number	23
Issue Number	7
e-ISSN	14363259
Language	English
Publisher	Springer-Verlag
Publisher Date	2008-09-23
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Hydrologic time series Nonlinearity Chaos Correlation dimension Delay time Delay time window Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution Computational Intelligence Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences Mathematical Applications in Earth Sciences Probability Theory and Stochastic Processes Math. Application in Environmental Science
Content Type	Text
Resource Type	Article
Subject	Environmental Chemistry Environmental Engineering Water Science and Technology Safety, Risk, Reliability and Quality

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