KGETrendUncertainty {greenbrown} | R Documentation |
This function samples time series for different combinations of start and end years and computes for each combination the KGE (see KGE
).
KGETrendUncertainty(x, ref, trend = TRUE, eTrend.ifsignif = FALSE, sample.method = c("sample", "all", "none"), sample.min.length = 0.75, sample.size = 30, ...)
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.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 |
... |
further arguments for the function |
...
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)
Matthias Forkel <matthias.forkel@geo.tuwien.ac.at> [aut, cre]
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.
# 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)