PRSim: Stochastic Simulation of Streamflow Time Series using Phase
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. The function prsim.weather()
extends the approach to multiple variables for weather generation.
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 <doi:10.5194/hess-23-3175-2019>.
A detailed description and evaluation of the wavelet-based multi-site approach
can be found in <doi:10.5194/hess-24-3967-2020>.
A detailed description and evaluation of the multi-variable and multi-site
weather generator can be found in <doi:10.5194/esd-12-621-2021>.
||R (≥ 3.5.0)
||stats, methods, lmomco, mev, goftest, wavScalogram, splus2R
||lattice, ismev, evd, GB2, boot, MASS
||Manuela Brunner <manuela.i.brunner at gmail.com>
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