synth.peak.error: Generate synthetic peaks and peak errors
In tiger: TIme series of Grouped ERrors

Description Usage Arguments Value Author(s) References See Also Examples

These functions allow generation of synthetic hydrologic peaks generated from a combination of exponetial functions. Also, synthetic errors for the reproduction of a reference peak can be generated in order to subsequentially test the behaviour of performance measures with respect to these errors.

synth.peak(base = 0.07, base.time = 6, rise.time = 5, rise.factor,
recession.const = 0.2, length.out = 240, rez.time = length.out -
ceiling(base.time) - ceiling(rise.time))
synth.peak.error(base = 0.07, base.time = 6, rise.time = 5,
rise.factor, rise.factor2, recession.const = 0.2, length.out = 240,
rez.time = length.out - base.time - rise.time, err1.factor = c(1.2,
1.4, 1.6), err2.factor = c(0.01, 0.02, 0.04), err3.factor = c(2, 4,
8), err4.factor = c(9, 18, 27), err5.factor = c(0.1, 0.2, 0.4),
err6.factor = c(1.5, 2, 3), 
err9.factor = c(2, 3, 4.5))
p.synth.peak.error(peaks, y.max = (max(peaks, na.rm = TRUE)),
                 peak.cluster = NULL, peak.palette = grey(c(0, 0.6,
                 0.8)), use.layout = TRUE, show.errors = 1:n.errors,
                 peak.lty = rep(1, n.errors), mfrow = c(2,
                 ceiling(length(show.errors)/2)), plot.legend = TRUE,
                 print.error.nr = TRUE)

`base`	level of the base flow component
`base.time`	number of time steps before the rise phase starts. May be negative, such that the peak starts outside the window.
`rise.time`	Number of time steps for the rise phase
`rise.factor`	The peak maximum is about `rise.factor` higher than the base flow.
`rise.factor2`	`rise.factor` for the "peaks" which only show a recession phase.
`recession.const`	Recession constant for the peak.
`length.out`	Total length of the time series to be returned.
`rez.time`	Length of the recession phase
`err1.factor`	Factors to use for the first error type: Over- and underestimation of the peak
`err2.factor`	Factors to use for the second error type: shifting of the entire time series
`err3.factor`	Factors to use for the third error type: Recession too fast/too slow
`err4.factor`	Factors to use for the fourth error type: Lag time
`err5.factor`	Factors to use for the fifth error type: Correct total volume, but peak over/underestimated
`err6.factor`	Factors to use for the sixth error type: Peak too wide/too narrow
`err9.factor`	Factors to use for the ninth error type: Shift during the recession phase
`peaks`	object returned from synth.peak.error()
`y.max`	upper limit for the y-axis
`peak.cluster`	object returned from `peaks.in.clusters` used for coloring the cluster assignment of synthetic peaks (see examples)
`peak.palette`	Colors to use if peak.cluster is NULL: first color for reference, second and third for peaks over- and underestimating the reference
`use.layout`	Boolean, indicating whether to use the predefined layout
`plot.legend`	Boolean, indicating whether to show the legend
`print.error.nr`	Boolean, indicating whether to label each subplot with a number
`mfrow`	mfrow plot parameter (only used, if use.layout=FALSE)
`show.errors`	Vector with indices indicating which errors to display
`peak.lty`	Line types for either clusters as defined in peak.cluster or as in peak.palette

synth.peak returns a vector with the synthetic peak according to the provided parameters synth.peak.error returns an array with dimension 3. The first dimension corresponds to the error type. The second dimension to the level of the corresponding error type. The third dimension corresponds to the time.

Dominik Reusser

Reusser, D. E., Blume, T., Schaefli, B., and Zehe, E.: Analysing the temporal dynamics of model performance for hydrological models, Hydrol. Earth Syst. Sci. Discuss., 5, 3169-3211, 2008.

The package vignette

     ref.peak <- synth.peak(rise.factor=2, recession.const=0.02)
peaks <- synth.peak.error(rise.factor=2, recession.const=0.02, rise.factor2=1.5)
peaks2 <- synth.peak.error(rise.factor=2, recession.const=0.02,
     rise.factor2=1.5, err1.factor=c(1.3,1.5,2.0),
     err2.factor = c(0.02,0.03,0.06), 
     err3.factor=c(2,4,10), 
     err4.factor = c(9,22,40), 
     err5.factor = c(0.2,0.3,0.5),
     err6.factor =c(2,3,5),
     err9.factor=c(1.5,3,6)
   )

    p.synth.peak.error(peaks)
    p.synth.peak.error(peaks2)

    data(tiger.example)
    peak.cluster <- peaks.in.clusters(result=tiger.single, 
 	solution=5, new.order=c(2,3,5,1,4))
    p.synth.peak.error(peaks=tiger.single$synthetic.errors, 
	peak.cluster=peak.cluster, peak.palette=rainbow(5))