KGETrendUncertainty {greenbrown} R Documentation

## Compute uncertainty of Kling-Gupta efficiency based on beginning and end of time series

### Description

This function samples time series for different combinations of start and end years and computes for each combination the KGE (see `KGE`).

### Usage

```KGETrendUncertainty(x, ref, trend = TRUE, eTrend.ifsignif = FALSE,
sample.method = c("sample", "all", "none"), sample.min.length = 0.75,
sample.size = 30, ...)```

### Arguments

 `x` time series from model result or factorial model run `ref` reference time series (observation or standard model run) `trend` Include the effect of trend in the calculation? `eTrend.ifsignif` compute effect on trend only if trend in reference series is significant, if FALSE compute always effect on trend (if trend = TRUE) `sample.method` Sampling method for combinations of start and end dates to compute uncertainties in trends. If "sample" (default), trend statistics are computed for a sample of combinations of start and end dates according to `sample.size`. If "all", trend statistics are computed for all combinations of start and end dates longer than `sample.min.length`. If "none", trend statistics will be only computed for the entire time series (i.e. no sampling of different start and end dates). `sample.min.length` Minimum length of the time series (as a fraction of total length) that should be used to compute trend statistics. Time windows between start and end that are shorter than min.length will be not used for trend computation. `sample.size` sample size (number of combinations of start and end dates) to be used if `method` is sample. `...` further arguments for the function `Trend`

...

### Value

The function returns a data.frame with the following components:

• `start` start of the time series

• `end` end of the time series

• `length` length of the time series

• `KGE` Kling-Gupta effciency = 1 - eTotal

• `eTotal` total effect, i.e. euclidean distance

• `fMean` fraction of mean to the total effect

• `fVar` fraction of variance to the total effect

• `fCor` fraction of correlation to the total effect

• `fTrend` fraction of trend to the total effect (only if trend=TRUE)

• `eMean` effect of mean

• `eVar` effect of variance

• `eCor` effect of correlation

• `eTrend` effect of trend (only if trend=TRUE)

### Author(s)

Matthias Forkel <matthias.forkel@geo.tuwien.ac.at> [aut, cre]

### References

Gupta, H. V., H. Kling, K. K. Yilmaz, G. F. Martinez (2009): Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology 377, 80-91.

`Trend`

### Examples

```# load a time series of NDVI (normalized difference vegetation index)
data(ndvi)
plot(ndvi)

# change the variance and compute effect
x <- ndvi + rnorm(length(ndvi), 0, 0.01)
plot(x, ndvi); abline(0,1)
unc <- KGETrendUncertainty(x, ndvi)
hist(unc\$KGE)

```

[Package greenbrown version 2.4.3 Index]