simulations_multi_sites {PRSim}

Simulated runoff for four catchments

Description

The dataset is generated with the package own routines and represent 5 series of 38 years of runoff for four catchments

Usage

data("simulations_multi_sites")

Format

A list of four elements (one per catchment), containing a data frame each holding information about the observed time series and the stochastic simulations

YYYY

a numeric vector, year

MM

a numeric vector, month

DD

a numeric vector, day

timestamp

POSIXct vector of the daily runoff

Qobs

observed runoff

r1,...,r5

5 simulated runoff series

Details

The data is included to illustrate the validation and visualization routines in demo("PRSim_wave-validate").

Source

The data has been generated with

prsim.wave(data=runoff_multi_sites, number_sim=5, marginal="kappa", GoFtest = NULL,pars=NULL, p_val=NULL)

(default values for all other arguments).

References

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.

Examples

oldpar <- par(mfrow = c(2, 1), mar = c(3, 3, 2, 1))
### greys
col_vect_obs <- c('#cccccc','#969696','#636363','#252525')
### oranges
col_vect_sim <- c('#fdbe85','#fd8d3c','#e6550d','#a63603')
data(simulations_multi_sites)
sim <- simulations_multi_sites
dim(sim[[1]])
### plot time series for multiple sites
par(mfrow=c(2,1),mar=c(3,3,2,1))
### determine ylim
ylim_max <- max(sim[[1]]$Qobs)*1.5
### observed
plot(sim[[1]]$Qobs[1:1000],
    ylab=expression(bold(
        paste("Specific discharge [mm/d]"))),
    xlab="Time [d]",type="l",col=col_vect_obs[1],
    ylim=c(0,ylim_max),main='Observations')
for(l in 2:4){
  lines(sim[[l]]$Qobs[1:1000],col=col_vect_obs[l])
}
legend('topleft',legend=c('Station 1','Station 2',
        'Station 3','Station 4'),
    lty=1,col=col_vect_obs[1:4])
### simulated (one run)
plot(sim[[1]]$r1[1:1000],
    ylab=expression(bold(paste("Specific discharge [mm/d]"))),
    xlab="Time [d]",type="l",col=col_vect_sim[1],
    ylim=c(0,ylim_max),
    main='Stochastic simulations')
for(l in 2:4){
  lines(sim[[l]]$r1[1:1000],col=col_vect_sim[l])
}
par(oldpar)