Long-term persistence and multifractality of river runoff records: Detrended fluctuation studies

By Eva Koscielny-Bunde, Jan W. Kantelhardt, P. Braun, Armin Bunde, Shlomo Havlin. 2003

278 citations recorded by the source corpus.

We study temporal correlations and multifractal properties of long river discharge records from 41 hydrological stations around the globe. To detect long-term correlations and multifractal behaviour in the presence of trends, we apply several recently developed methods [detrended fluctuation analysis (DFA), wavelet analysis, and multifractal DFA] that can systematically detect and overcome non-stationarities in the data at all time scales. We find that above some crossover time that usually is several weeks, the daily runoffs are long-term correlated, being characterized by a correlation function C(s) that decays as C(s)ws Kg . The exponent g varies from river to river in a wide range between 0.1 and 0.9. The power‐law decay of C(s) corresponds to a power‐law increase of the related fluctuation function F2(s)ws H where HZ1Kg/2. We also find that in most records, for large times, weak multifractality occurs. The Renyi exponent t(q) for q between K10 and C10 can be fitted to the remarkably simple form tðqÞZKlnða q C b q Þ=ln2, with solely two parameters a and b between 0 and 1 with aCbR1. This type of multifractality is obtained from a generalization of the multiplicative cascade model. q 2005 Elsevier B.V. All rights reserved.

Subfields: Condensed Matter Physics, Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/0726f898241ab2fc34ae0c0517eae425186a879c

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Citations in the corpus

Statistical analysis of hydroclimatic time series: Uncertainty and insights (2007, background): “For example Koutsoyiannis [2002] showed that the sum of three Markovian processes (whose behavior, rigorously speaking is STP) is virtually indistinguishable from a process with LTP for lags as high as of the order of 1000.”
Factors influencing long range dependence in streamflow of European rivers (2014, methodology): “Koscielny-Bunde et al. (2006) conducted a study of 41 series of daily river runoff worldwide using wavelet-based techniques, again having detected long range dependence in the data.”
Multifractal Detrended Fluctuation Analysis of Streamflow in the Yellow River Basin, China (2015, background): “[9] examined the temporal correlations and multifractal properties of long-term”
Analysis of daily river flow fluctuations using Empirical Mode Decomposition and arbitrary order Hilbert spectral analysis (2009, methodology): “…detrended fluctua-45 tion analysis (DFA) and its multifractal version to describe the scaling and46 multiscaling properties of river flows (Kantelhardt et al., 2003; Livina et al.,47 2003a,a; Kantelhardt et al., 2006; Koscielny-Bunde et al., 2006; Livina et al.,48 2007; Zhang et al., 2008a,b).”
Complexity–entropy analysis of daily stream flow time series in the continental United States (2014, methodology): “…is a common property of daily stream flow records and the deseasonalization is a common preprocessing procedure (e.g., Montanari et al. 1997, 2000; Kantelhardt et al. 2003; Montanari 2005; Koscielny-Bunde et al. 2006) it is worth studying its effect on the complexity and entropy quantifiers.”
Long memory of rivers from spatial aggregation (2007, unknown): “This is supported by a recent paper [Koscielny-Bunde et al., 2006], which finds considerable d variation among 41 river runoff records.”
Generalized binomial multiplicative cascade processes and asymmetrical multifractal distributions (2014, methodology): “Koscielny-Bunde’s two-parameter model (Koscielny-Bunde et al., 2006) corresponds to a special case of the five-parameter model if letting d1 = a and d2 = b, d1 + d2 ≥ 1 andm1/h = m2/h = 1/2.”