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31.
Complexity–entropy causality plane (CECP) is a diagnostic diagram plotting normalized Shannon entropy \({\cal H}_S\) versus Jensen–Shannon complexity \({\cal C}_{JS}\) that has been introduced in nonlinear dynamics analysis to classify signals according to their degrees of randomness and complexity. In this study, we explore the applicability of CECP in hydrological studies by analyzing 80 daily stream flow time series recorded in the continental United States during a period of 75 years, surrogate sequences simulated by autoregressive models (with independent or long-range memory innovations), Theiler amplitude adjusted Fourier transform and Theiler phase randomization, and a set of signals drawn from nonlinear dynamic systems. The effect of seasonality, and the relationships between the CECP quantifiers and several physical and statistical properties of the observed time series are also studied. The results point out that: (1) the CECP can discriminate chaotic and stochastic signals in presence of moderate observational noise; (2) the signal classification depends on the sampling frequency and aggregation time scales; (3) both chaotic and stochastic systems can be compatible with the daily stream flow dynamics, when the focus is on the information content, thus setting these results in the context of the debate on observational equivalence; (4) the empirical relationships between \({\mathcal H}_S\) and \({\mathcal C}_{JS}\) and Hurst parameter H, base flow index, basin drainage area and stream flow quantiles highlight that the CECP quantifiers can be considered as proxies of the long-term low-frequency groundwater processes rather than proxies of the short-term high-frequency surface processes; (6) the joint application of linear and nonlinear diagnostics allows for a more comprehensive characterization of the stream flow time series.  相似文献   
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A 1-year set of measurements of CO2 and energy turbulent fluxes above and within a 25-m pine forest in southern Brazil is analyzed. The study focuses on the coupling state between two levels and its impact on flux determination by the eddy-covariance method. The turbulent series are split in their typical temporal scales using the multiresolution decomposition, a method that allows proper identification of the time scales of the turbulent events. Initially, four case studies are presented: a continually turbulent, a continually calm, a calm then turbulent, and an intermittent night. During transitions from calm to turbulent, large scalar fluxes of opposing signs occur at both levels, suggesting the transference of air accumulated in the canopy during the stagnant period both upwards and downwards. Average fluxes are shown for the entire period as a function of turbulence intensity and a canopy Richardson number, used as an indicator of the canopy coupling state. Above the canopy, CO2 and sensible heat fluxes decrease in magnitude both at the neutral and at the very stable limit, while below the canopy they increase monotonically with the canopy Richardson number. Latent heat fluxes decrease at both levels as the canopy air becomes more stable. The average temporal scales of the turbulent fluxes at both levels approach each other in neutral conditions, indicating that the levels are coupled in that case. Average CO2 fluxes during turbulent periods that succeed very calm ones are appreciably larger than the overall average above the canopy and smaller than the average or negative within the canopy, indicating that the transfer of air accumulated during calm portions at later turbulent intervals affects the flux average. The implications of this process for mean flux determination are discussed.  相似文献   
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Ocean Dynamics - Marine resources are outstanding among renewable energy for their wide occurrence and diverse possibilities of exploitation. The tidal resource can be harnessed by the use of both...  相似文献   
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