Abstract
Aquatic environmental time series often display large fluctuations at many time scales, possessing stochastic properties, as well as deterministic forcing coming from seasonal or annual meteorological and climatic cycles. In this work we are interested in the characterization of these properties, using different statistical tools, borrowed from the field of turbulence, or of nonlinear time series analysis. We first present the analysis of a long (30 years) time series of daily river flow data, recorded in the Seine River (France). We consider the scale dependence and scale invariance of river flow data, using structure function analysis; we also apply a decomposition method called Empirical Mode Decomposition (EMD). We then consider the statistical properties, and the nonlinear dynamics behaviour of a long-term copepod (small crustaceans) time series sampled every week in the Meditarranean sea from 1967 to 1992. We first consider its high variability and characterize its properties, including extreme evens obeying power law tail pdf. We then consider their scale dependence, using Fourier power spectra together with an EMD approach.
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Schmitt, F.G., Huang, Y., Lu, Z., Brizard, S.Z., Molinero, J.C., Liu, Y. (2007). Analysis of Nonlinear Biophysical Time Series in Aquatic Environments: Scaling Properties and Empirical Mode Decomposition. In: Nonlinear Dynamics in Geosciences. Springer, New York, NY. https://doi.org/10.1007/978-0-387-34918-3_15
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DOI: https://doi.org/10.1007/978-0-387-34918-3_15
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