hydroGOF (version 0.2-2)

rNSeff: Relative Nash-Sutcliffe efficiency

Description

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

Usage

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

Arguments

sim

numeric, zoo, matrix or data.frame with simulated values

obs

numeric, zoo, matrix or data.frame with observed values

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

• Relative Nash-Sutcliffe efficiency between sim and obs. If sim and obs are matrixes, the returned value is a vector, with the relative Nash-Sutcliffe efficiency between each column of sim and obs.

Details

$$rNSeff = 1 -\frac { \sum_{i=1}^N { ( \frac{ S_i - O_i }{\bar{O}} )^2 } } { \sum_{i=1}^N { ( \frac{ O_i - \bar{O} }{\bar{O}} )^2 } }$$

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, mNSeff

Examples

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

sim <- 2:11
obs <- 1:10
rNSeff(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 'rNSeff' for the "best" (unattainable) case
rNSeff(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 'rNSeff'
rNSeff(sim=sim, obs=obs)