Description Usage Arguments Details Value Note Author(s) References See Also Examples
Modified Nash-Sutcliffe efficiency between sim
and obs
, with treatment of missing values.
1 2 3 4 5 6 7 8 9 10 11 12 13 |
sim |
numeric, zoo, matrix or data.frame with simulated values |
obs |
numeric, zoo, matrix or data.frame with observed values |
j |
numeric, with the exponent to be used in the computation of the modified Nash-Sutcliffe efficiency. The default value is |
na.rm |
a logical value indicating whether 'NA' should be stripped before the computation proceeds. |
... |
further arguments passed to or from other methods. |
mNSE = 1 - ( sum( abs(obs - sim)^j ) / sum( abs(obs - mean(obs))^j )
When j=1
, the modified NSeff is not inflated by the squared values of the differences, because the squares are replaced by absolute values.
Modified Nash-Sutcliffe efficiency between sim
and obs
.
If sim
and obs
are matrixes, the returned value is a vector, with the modified Nash-Sutcliffe efficiency between each column of sim
and obs
.
obs
and sim
has to have the same length/dimension
The missing values in obs
and sim
are removed before the computation proceeds, and only those positions with non-missing values in obs
and sim
are considered in the computation
Mauricio Zambrano Bigiarini <mzb.devel@gmail.com>
Krause, P., Boyle, D. P., and Base, F.: Comparison of different efficiency criteria for hydrological model assessment, Adv. Geosci., 5, 89-97, 2005
Legates, D. R., and G. J. McCabe Jr. (1999), Evaluating the Use of "Goodness-of-Fit" Measures in Hydrologic and Hydroclimatic Model Validation, Water Resour. Res., 35(1), 233-241
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | sim <- 1:10
obs <- 1:10
mNSE(sim, obs)
sim <- 2:11
obs <- 1:10
mNSE(sim, obs)
##################
# Loading daily streamflows of the Ega River (Spain), from 1961 to 1970
data(EgaEnEstellaQts)
obs <- EgaEnEstellaQts
# Generating a simulated daily time series, initially equal to the observed series
sim <- obs
# Computing the 'mNSE' for the "best" (unattainable) case
mNSE(sim=sim, obs=obs)
# Randomly changing the first 2000 elements of 'sim', by using a normal distribution
# with mean 10 and standard deviation equal to 1 (default of 'rnorm').
sim[1:2000] <- obs[1:2000] + rnorm(2000, mean=10)
# Computing the new 'mNSE'
mNSE(sim=sim, obs=obs)
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