simulations: Simulated runoff
In PRSim: Stochastic Simulation of Streamflow Time Series using Phase Randomization

simulations

R Documentation

Simulated runoff

Description

The dataset is generated with the package own routines and represent 5 series of 18 years of runoff

Usage

data("simulations")

Format

A list of three elements, containing (i) a data frame with 6570 observations of the following variables

YYYY: a numeric vector, year
MM: a numeric vector, month
DD: a numeric vector, day
timestamp: POSIXct vector of the daily runoff
deseasonalized: deseasonalized time series
Qobs: observed runoff
r1,...,r5: 5 simulated runoff series

(ii) a data frame with the daily fitted kappa parameters and (iii) p-values of the daily ks.test.

Details

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

Source

The data has been generated with

set.seed(14); prsim( runoff[ runoff$YYYY>1999,], number_sim=5, KStest=TRUE)

(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

data(simulations)
names(simulations) 
sim <- simulations$simulation
dim(sim)
sim$day_id <- rep(seq(1:365), times=length(unique(sim$YYYY)))
mean_obs <- aggregate(sim$Qobs, by=list(sim$day_id), FUN=mean, simplify=FALSE)
plot(unlist(mean_obs[,2]),lty=1,lwd=1,col="black", ylab="Discharge [m3/s]",
     xlab="Time [d]", main="Mean hydrographs", ylim=c(0,22), type="l")

for(r in 7:(length(names(sim))-1)){
  mean_hydrograph <- aggregate(sim[,r], by=list(sim$day_id), FUN=mean, simplify=FALSE)
  lines(mean_hydrograph, lty=1, lwd=1, col="gray")
}
lines( mean_obs, lty=1, lwd=1, col="black")

PRSim documentation built on Sept. 19, 2023, 5:07 p.m.