| NSE {hydroGOF} | R Documentation |
Nash-Sutcliffe Efficiency
Description
Nash-Sutcliffe efficiency between sim and obs, with treatment of missing values.
Usage
NSE(sim, obs, ...)
## Default S3 method:
NSE(sim, obs, na.rm=TRUE, fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA)
## S3 method for class 'data.frame'
NSE(sim, obs, na.rm=TRUE, fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA)
## S3 method for class 'matrix'
NSE(sim, obs, na.rm=TRUE, fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA)
## S3 method for class 'zoo'
NSE(sim, obs, na.rm=TRUE, fun=NULL, ...,
epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"),
epsilon.value=NA)
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. |
fun |
function to be applied to The first argument MUST BE a numeric vector with any name (e.g., |
... |
arguments passed to |
epsilon.type |
argument used to define a numeric value to be added to both It is was designed to allow the use of logarithm and other similar functions that do not work with zero values. Valid values of 1) "none": 2) "Pushpalatha2012": one hundredth (1/100) of the mean observed values is added to both 3) "otherFactor": the numeric value defined in the 4) "otherValue": the numeric value defined in the |
epsilon.value |
-) when |
Details
NSE = 1 -\frac { \sum_{i=1}^N { \left( S_i - O_i \right)^2 } } { \sum_{i=1}^N { \left( O_i - \bar{O} \right)^2 } }
The Nash-Sutcliffe efficiency (NSE) is a normalized statistic that determines the relative magnitude of the residual variance ("noise") compared to the measured data variance ("information") (Nash and Sutcliffe, 1970).
NSE indicates how well the plot of observed versus simulated data fits the 1:1 line.
Nash-Sutcliffe efficiencies range from -Inf to 1. Essentially, the closer to 1, the more accurate the model is.
-) NSE = 1, corresponds to a perfect match of modelled to the observed data.
-) NSE = 0, indicates that the model predictions are as accurate as the mean of the observed data,
-) -Inf < NSE < 0, indicates that the observed mean is better predictor than the model.
Value
Nash-Sutcliffe efficiency between sim and obs.
If sim and obs are matrixes, the returned value is a vector, with the Nash-Sutcliffe efficiency between each column of sim and obs.
Note
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
Author(s)
Mauricio Zambrano Bigiarini <mzb.devel@gmail.com>
References
https://en.wikipedia.org/wiki/Nash%E2%80%93Sutcliffe_model_efficiency_coefficient
Nash, J.E. and Sutcliffe, J.V. (1970). River flow forecasting through conceptual models. Part 1: a discussion of principles, Journal of Hydrology 10, pp. 282-290. doi:10.1016/0022-1694(70)90255-6.
Garrick, M.; Cunnane, C.; Nash, J.E. (1978). A criterion of efficiency for rainfall-runoff models. Journal of Hydrology 36, 375-381. doi:10.1016/0022-1694(78)90155-5.
Schaefli, B., Gupta, H. (2007). Do Nash values have value?. Hydrological Processes 21, 2075-2080. doi:10.1002/hyp.6825.
Criss, R. E.; Winston, W. E. (2008), Do Nash values have value? Discussion and alternate proposals. Hydrological Processes, 22: 2723-2725. doi:10.1002/hyp.7072.
Gupta, H.V.; Kling, H. (2011). On typical range, sensitivity, and normalization of Mean Squared Error and Nash-Sutcliffe Efficiency type metrics. Water Resources Research, 47(10). doi:10.1029/2011WR010962.
Pushpalatha, R.; Perrin, C.; Le Moine, N.; Andreassian, V. (2012). A review of efficiency criteria suitable for evaluating low-flow simulations. Journal of Hydrology, 420, 171-182. doi:10.1016/j.jhydrol.2011.11.055.
Knoben, W. J.; Freer, J. E.; Woods, R. A. (2019). Inherent benchmark or not? Comparing Nash-Sutcliffe and Kling-Gupta efficiency scores. Hydrology and Earth System Sciences, 23(10), 4323-4331. doi:10.5194/hess-23-4323-2019.
See Also
mNSE, rNSE, wNSE, KGE, gof, ggof
Examples
##################
# Example 1: basic ideal case
obs <- 1:10
sim <- 1:10
NSE(sim, obs)
obs <- 1:10
sim <- 2:11
NSE(sim, obs)
##################
# Example 2:
# 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 'NSE' for the "best" (unattainable) case
NSE(sim=sim, obs=obs)
##################
# Example 3: NSE for simulated values equal to observations plus random noise
# on the first half of the observed values.
# This random noise has more relative importance for ow flows than
# for medium and high flows.
# Randomly changing the first 1826 elements of 'sim', by using a normal distribution
# with mean 10 and standard deviation equal to 1 (default of 'rnorm').
sim[1:1826] <- obs[1:1826] + rnorm(1826, mean=10)
ggof(sim, obs)
NSE(sim=sim, obs=obs)
##################
# Example 4: NSE for simulated values equal to observations plus random noise
# on the first half of the observed values and applying (natural)
# logarithm to 'sim' and 'obs' during computations.
NSE(sim=sim, obs=obs, fun=log)
# Verifying the previous value:
lsim <- log(sim)
lobs <- log(obs)
NSE(sim=lsim, obs=lobs)
##################
# Example 5: NSE for simulated values equal to observations plus random noise
# on the first half of the observed values and applying (natural)
# logarithm to 'sim' and 'obs' and adding the Pushpalatha2012 constant
# during computations
NSE(sim=sim, obs=obs, fun=log, epsilon.type="Pushpalatha2012")
# Verifying the previous value, with the epsilon value following Pushpalatha2012
eps <- mean(obs, na.rm=TRUE)/100
lsim <- log(sim+eps)
lobs <- log(obs+eps)
NSE(sim=lsim, obs=lobs)
##################
# Example 6: NSE for simulated values equal to observations plus random noise
# on the first half of the observed values and applying (natural)
# logarithm to 'sim' and 'obs' and adding a user-defined constant
# during computations
eps <- 0.01
NSE(sim=sim, obs=obs, fun=log, epsilon.type="otherValue", epsilon.value=eps)
# Verifying the previous value:
lsim <- log(sim+eps)
lobs <- log(obs+eps)
NSE(sim=lsim, obs=lobs)
##################
# Example 7: NSE for simulated values equal to observations plus random noise
# on the first half of the observed values and applying (natural)
# logarithm to 'sim' and 'obs' and using a user-defined factor
# to multiply the mean of the observed values to obtain the constant
# to be added to 'sim' and 'obs' during computations
fact <- 1/50
NSE(sim=sim, obs=obs, fun=log, epsilon.type="otherFactor", epsilon.value=fact)
# Verifying the previous value:
eps <- fact*mean(obs, na.rm=TRUE)
lsim <- log(sim+eps)
lobs <- log(obs+eps)
NSE(sim=lsim, obs=lobs)
##################
# Example 8: NSE for simulated values equal to observations plus random noise
# on the first half of the observed values and applying a
# user-defined function to 'sim' and 'obs' during computations
fun1 <- function(x) {sqrt(x+1)}
NSE(sim=sim, obs=obs, fun=fun1)
# Verifying the previous value, with the epsilon value following Pushpalatha2012
sim1 <- sqrt(sim+1)
obs1 <- sqrt(obs+1)
NSE(sim=sim1, obs=obs1)