kge: Model efficiency evaluation

In metno/esd: Climate analysis and empirical-statistical downscaling (ESD) package for monthly and daily data

Model efficiency evaluation

Description

The KGE function returns the 2012 Kling-Gupta model efficiency using CV for the variability of x in predicting y. The NSE function returns the dimensionless Nash Sutcliffe model efficiency, evalutaing x as a prediction of y. An efficiency of one corresponds to a perfect match, while the lowest score is -infinity.

Usage

kge(x, y, return_all = FALSE, scc = 1, sv = 1, sm = 1)

Arguments

x	a 1-D zoo object corresponding to the prediction
y	a 1-D zoo object corresponding to the reference
return_all	Directional resolution in degrees
scc	weight of (0-1)
sv	weight of (0-1)
sm	weight of (0-1)

Details

The NSE function returns the multi-objective Nash-Sutcliffe efficiency metric, which corresponds to the unbiased R^2. The metric is scaled by the observed variance. The metric is given as nse = 1 - mean_square_error/observed_variance. It is calculated for pairwise complete observations

Warning: Keep in mind that in regions with higher variance (e.g. seasonality) equal absolute deviations will be penalized less than for regions with a lower variance.

The KGE function returns the dimensionless, multi-objective Kling-Gupta efficiency metric, which is based on the correlation, variability ratio, and mean ratio between the prediction and reference objects and is a modified version of the Nash-Sutcliffe efficiency. The weight of the sub-metrics (scc, sv, sm) may be adjusted from 1 in the arguments kge = 1 - sqrt( (scc(cc-1)^2 + sv(CV_m/CV_o - 1)^2 + sm(mean(mod)/mean(obs)-1)^2 )).

Warning: The mean ratio may not be approriate for Celcius (can blow up around 0) or other variables that may have a mean within (-1,1). Though the KGE etric is dimensionless, keep in mind that variables with higher averages (e.g. temperature in Kelvin, longwave radiation) will be less penalized for mean deviations than variables with lower averages (temp. in Celsius, winter SW radiation).

Author(s)

H.B. Erlandsen

References

Gupta HV, Kling H, Yilmaz KK and Martinez GF (2009)., "Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling.", Journal of Hydrology, 377(1), 80-91. Nash JE and Sutcliffe JV (1970), River flow forecasting through conceptual models part I-A discussion of principles.???, Journal of hydrology, 10(3), pp. 282-290. Gupta HV, Kling H, Yilmaz KK and Martinez GF (2009)., "Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling.", Journal of Hydrology, 377(1), pp. 80-91.

Examples

  ## Not run: 
    data('Oslo')
    x <- subset(Oslo,it=!is.na(Oslo))
    mydata <- data.frame(x=x,t=index(x))
    fit <- lm(x ~ t, data=mydata)
    y <- zoo(fitted(fit,index(x)),order.by=index(x))
    KGE(x,y)
  
## End(Not run)

metno/esd documentation built on April 24, 2024, 9:19 p.m.