Characterization and prediction of runoff dynamics: a nonlinear dynamical view

By M. Islam, Bellie Sivakumar. 2002

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Testing for nonlinearity of streamflow processes at different timescales (2006, background): “For instance, for daily flow series, some authors take the delay time as 1 day (Porporato andRidolfi, 1997), 2 days (Jayawardena and Lai, 1994), 7 days (Islam and Sivakumar, 2002), 10 days (Elshorbagy et al.”
Nonlinear ensemble prediction of chaotic daily rainfall (2010, background): “A few studies have also tried the nonlinear prediction of rainfall and runoff series by treating them as univariate series [10,12,21,22,38] and also as multivariate series [23] taking into consideration information from other time series.”
The Research of Monthly Discharge Predictor-corrector Model Based on Wavelet Decomposition (2008, background): “In general, the hydrology system is predicted with stochastic models (Yevjevich 1972; Salas et al. 1980), engineering fuzzy set theory (Chen and Li 2004), artificial neural network (ANN) models (Raman and Sunilkumar 1995; Huang et al. 2004), chaotic theory ( Islam and Sivakumar 2002; Jayawardena et”
Is correlation dimension a reliable indicator of low‐dimensional chaos in short hydrological time series? (2002, background): “…Porporato and Ridolfi, 1997; Krasovskaia et al., 1999; Sivakumar et al., 1999, 2001; B. Sivakumar et al. (A multivariable time series phase-space reconstruction approach to investigation of chaos in hydrological processes, submitted to Water Resources Research), 2001; Islam and Sivakumar, 2002].”