hydroGOF (version 0.2-2)

mNSeff: Modified Nash-Sutcliffe efficiency

Description

Modified Nash-Sutcliffe efficiency between sim and obs, with treatment of missing values.

Usage

mNSeff(sim, obs, ...)
## S3 method for class 'default':
mNSeff(sim, obs, j=1, na.rm=TRUE, ...)
## S3 method for class 'data.frame':
mNSeff(sim, obs, j=1, na.rm=TRUE, ...)
## S3 method for class 'matrix':
mNSeff(sim, obs, j=1, na.rm=TRUE, ...)

Arguments

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 j=1.

na.rm

a logical value indicating whether 'NA' should be stripped before the computation proceeds. When an 'NA' value is found at the i-th position in obs OR sim, the i-th value of obs AND sim ar

...

further arguments passed to or from other methods.

Value

• 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.

Details

$$mNSeff = 1 -\frac { \sum_{i=1}^N { \left| S_i - O_i \right|^j } } { \sum_{i=1}^N { \left| O_i - \bar{O} \right|^j } }$$

When j=1, the modified NSeff is not inflated by the squared values of the differences, because the squares are replaced by absolute values.

References

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

See Also

NSeff, rNSeff

Examples

sim <- 1:10
obs <- 1:10
mNSeff(sim, obs)

sim <- 2:11
obs <- 1:10
mNSeff(sim, obs)

##################
# Loading daily streamflows of the Ega River (Spain), from 1961 to 1970
require(zoo)
data(EgaEnEstellaQts)
obs <- EgaEnEstellaQts

# Generating a simulated daily time series, initially equal to the observed series
sim <- obs 

# Computing the 'mNSeff' for the "best" (unattainable) case
mNSeff(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 'mNSeff'
mNSeff(sim=sim, obs=obs)