tools:::Rd_package_description("PRSim")
tools:::Rd_package_author("PRSim")
Maintainer: tools:::Rd_package_maintainer("PRSim")
The DESCRIPTION file: tools:::Rd_package_DESCRIPTION("PRSim") tools:::Rd_package_indices("PRSim") Contains two functions for the stochastic simulation of continuous discharge time series: prsim and prsim.wave both using phase randomization. prsim is based on the Fourier transform while prsim.wave uses the wavelet transform.
prsim: Simulation in the frequency domain is based on the randomization of the phases of the Fourier transform. We here combine phase randomization simulation with the flexible, four-parameter kappa distribution, which allows for the extrapolation to yet unobserved low and high flows. Alternative distributions or the empirical distribution can be used instead. The simulation approach consists of eight steps: (1) fitting of theoretical Kappa distribution, (2) normalization and deseasonalization, (3) Fourier transformation, (4) Fourier phases computation, (5) random phase generation, (6) inverse Fourier transformation, (7) back transformation, and (8) simulation.
prsim.wave: Simulation for multiple sites in the frequency domain based on the randomization of the phases of the continuous wavelet transform. We combine phase randomization with the flexible, four-parameter kappa distribution. Alternative theoretical distributions or the empirical distribution can be used instead. The simulation procedure consists of five steps: (1) Derivation of random phases from a white noise time series, (2) Fitting of kappa distribution, (3) Wavelet transform, (4) Inverse wavelet transform, and (5) Transformation to the kappa distribution (or the distribution of choice).
prsim.weather: Simulation of two variables (temperature and precipitation) for multiple sites in the frequency domain based on the randomization of the phases of the continuous wavelet transform. We combine phase randomization with the flexible, skewed exponential power (sep) and extended generalized pareto distributions (egpd). Alternative theoretical distributions can be used instead. The simulation procedure consists of five steps: (1) Derivation of random phases from a randomly sampled time series, (2) Fitting of temperature and precipitation disstributions, (3) Wavelet transform, (4) Inverse wavelet transform, and (5) Transformation to the desired distributions.
Brunner, M. I., A. Bárdossy, and R. Furrer (2019). Technical note: Stochastic simulation of streamflow time series using phase randomization. Hydrology and Earth System Sciences, 23, 3175-3187, https://doi.org/10.5194/hess-23-3175-2019.
Brunner, M. I., and E. Gilleland (2020). Stochastic simulation of streamflow and spatial extremes: a continuous, wavelet-based approach, Hydrology and Earth System Sciences, https://doi.org/10.5194/hess-24-3967-2020.
Brunner, M. I., and E. Gilleland (2021). Spatial compound hot-dry events in the United States: assessment using a multi-site multi-variable weather generator, in preparation.
# \donttest{
demo("PRSim")
demo("PRSim-validate")
demo("PRSim_wave")
demo("PRSim_wave-validate")
demo("PRSim_weather")
demo("PRSim_weather-validate")
# }
Run the code above in your browser using DataLab