Stochastic Simulation of Streamflow Time Series using Phase Randomization

Provides a simulation framework to simulate streamflow time series with similar main characteristics as observed data. These characteristics include the distribution of daily streamflow values and their temporal correlation as expressed by short- and long-range dependence. The approach is based on the randomization of the phases of the Fourier transform or the phases of the wavelet transform. The function prsim() is applicable to single site simulation and uses the Fourier transform. The function prsim.wave() extends the approach to multiple sites and is based on the complex wavelet transform. We further use the flexible four-parameter Kappa distribution, which allows for the extrapolation to yet unobserved low and high flows. Alternatively, the empirical or any other distribution can be used. A detailed description of the simulation approach for single sites and an application example can be found in <>. A detailed description and evaluation of the wavelet-based multi-site approach can be found in <>.


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1.3-1 by Manuela Brunner, a year ago

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Authors: Manuela Brunner [aut, cre] , Reinhard Furrer [aut]

Documentation:   PDF Manual  

GPL-3 license

Imports stats, methods, lmomco, mev

Depends on homtest, goftest, wavScalogram, splus2R

Suggests lattice, ismev, evd, GB2, boot, MASS

See at CRAN