Chaos theory in hydrology: important issues and interpretations

By Bellie Sivakumar. 2000

271 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Environmental Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/a3a5a2d891f89713e06e5101246a15268c26cb31

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Citations in the corpus

Modeling rainfall–runoff relationship using multivariate GARCH model (2013, background): “The concept of nonlinear dynamics associated with modeling of deterministic chaos in hydrology has been gaining considerable interest in the last decades (Rodriguez-Iturbe et al., 1989; Koutsoyiannis and Pachakis, 1996; Sivakumar, 2000; among others).”
Conditional resampling of hydrologic time series using multiple predictor variables: A K-nearest neighbour approach (2006, unknown): “[1,29]).”
Is correlation dimension a reliable indicator of low‐dimensional chaos in short hydrological time series? (2002, methodology): “Important issues, including that of data size, in the application of the Grassberger-Procaccia algorithm to hydrological time series have been discussed in detail by Sivakumar [2000] and therefore are not reported herein.”
Correlation dimension estimation of hydrological series and data size requirement: myth and reality/Estimation de la dimension de corrélation de séries hydrologiques et taille nécessaire du jeu de données: mythe et réalité (2005, methodology): “…addressed, in one way or another, in the hydrological literature (e.g. Ghilardi & Rosso, 1990; Tsonis et al., 1993; Koutsoyiannis & Pachakis, 1996; Porporato & Ridolfi, 1997, 2003; Wang & Gan, 1998; Sivakumar et al., 1999, 2002a,b; Sivakumar, 2000, 2001; Regonda et al., 2004; Khan et al., 2005).”
Rainfall dynamics at different temporal scales: A chaotic perspective (2001, background): “Also, recent studies (e.g. Sivakumar et al., 1999b; Sivakumar, 2000) reveal that, while small levels of noise influence significantly the accuracy of prediction estimates, the correlation dimension estimates are not influenced significantly (i.e. overestimated) even when noise levels are high.”
Hydrologic complexity and classification: a simple data reconstruction approach (2007, background): “…1994; Hsu et al., 1995; Jinno et al., 1995; Puente and Obregon, 1996; Young et al., 1996; Porporato and Ridolfi, 1997; Grayson and Blöschl, 2000a; Jayawardena and Gurung, 2000; Sivakumar, 2000a, 2004a; Sivakumar et al., 2001a,b, 2002b; Young and Parkinson, 2002; Wainwright and Mulligan, 2004).”