hydroGOF (version 0.6-0)

wsNSE: Weighted seasonal Nash-Sutcliffe Efficiency

Description

Weighted seasonal Nash-Sutcliffe Efficiency between sim and obs, with treatment of missing values.

This function is designed to identify differences in high or low values, depending on the user-defined value given to the lambda argument. See Usage and Details.

Usage

wsNSE(sim, obs, ...)
# S3 method for default
wsNSE(sim, obs, na.rm=TRUE, 
              j=2, lambda=0.95, lQ.thr=0.6, hQ.thr=0.1, fun=NULL, ..., 
              epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"), 
              epsilon.value=NA)
# S3 method for data.frame
wsNSE(sim, obs, na.rm=TRUE, 
              j=2, lambda=0.95, lQ.thr=0.6, hQ.thr=0.1, fun=NULL, ..., 
              epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"), 
              epsilon.value=NA)
# S3 method for matrix
wsNSE(sim, obs, na.rm=TRUE, 
              j=2, lambda=0.95, lQ.thr=0.6, hQ.thr=0.1, fun=NULL, ..., 
              epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"), 
              epsilon.value=NA)
             
# S3 method for zoo
wsNSE(sim, obs, na.rm=TRUE, 
              j=2, lambda=0.95, lQ.thr=0.6, hQ.thr=0.1, fun=NULL, ..., 
              epsilon.type=c("none", "Pushpalatha2012", "otherFactor", "otherValue"), 
              epsilon.value=NA)

Value

numeric with the the weighted seasonal Nash-Sutcliffe Efficiency (wsNSE) between sim and obs. If sim and obs are matrices, the output value is a vector, with the the weighted seasonal Nash-Sutcliffe Efficiency (wsNSE) between each column of sim and obs.

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 are removed before the computation.

j

numeric, representing an arbitrary value used to power the differences between observations and simulations. By default j=2, which mimics the traditional Nash-Sutcliffe function, mainly focused on thr representation of high values. For low flows, suggested values for j are 1, 1/2 or 1/3. See Legates and McCabe, (1999) and Krausse et al. (2005) for a discussion of suggested values of j.

lambda

numeric in [0, 1] representing the weight given to the high observed values. The closer the lambda=1 value is to 1, the higher the weight given to high values. On the contrary, the closer the lambda=1 value is to 0, the higher the weight given to low values.

Low values get a weight equal to 1-lambda. Between high and low values there is a linear transition from lambda to 1-lambda, respectively.

Suggested values for lambda are lambda=0.95 when focusing in high (streamflow) values and lambda=0.05 when focusing in low (streamflow) values.

lQ.thr

numeric, representing the non-exceedence probabiliy used to identify low flows in obs. All values in obs that are equal or lower than quantile(obs, probs=(1-lQ.thr)) are considered as low values. By default lQ.thr=0.6.

On the other hand, the low values in sim are those located at the same i-th position than the i-th value of the obs deemed as low flows.

hQ.thr

numeric, representing the non-exceedence probabiliy used to identify high flows in obs. All values in obs that are equal or higher than quantile(obs, probs=(1-hQ.thr)) are considered as high flows. By default hQ.thr=0.1.

On the other hand, the high values in sim are those located at the same i-th position than the i-th value of the obs deemed as high flows.

fun

function to be applied to sim and obs in order to obtain transformed values thereof before computing this goodness-of-fit index.

The first argument MUST BE a numeric vector with any name (e.g., x), and additional arguments are passed using ....

...

arguments passed to fun, in addition to the mandatory first numeric vector.

epsilon.type

argument used to define a numeric value to be added to both sim and obs before applying fun.

It is was designed to allow the use of logarithm and other similar functions that do not work with zero values.

Valid values of epsilon.type are:

1) "none": sim and obs are used by fun without the addition of any numeric value. This is the default option.

2) "Pushpalatha2012": one hundredth (1/100) of the mean observed values is added to both sim and obs before applying fun, as described in Pushpalatha et al. (2012).

3) "otherFactor": the numeric value defined in the epsilon.value argument is used to multiply the the mean observed values, instead of the one hundredth (1/100) described in Pushpalatha et al. (2012). The resulting value is then added to both sim and obs, before applying fun.

4) "otherValue": the numeric value defined in the epsilon.value argument is directly added to both sim and obs, before applying fun.

epsilon.value

-) when epsilon.type="otherValue" it represents the numeric value to be added to both sim and obs before applying fun.
-) when epsilon.type="otherFactor" it represents the numeric factor used to multiply the mean of the observed values, instead of the one hundredth (1/100) described in Pushpalatha et al. (2012). The resulting value is then added to both sim and obs before applying fun.

Author

Mauricio Zambrano-Bigiarini <[e-mail]>

Details

The weighted seasonal Nash-Sutcliffe Efficiency was proposed by Zambrano-Bigiarini and Bellin (2012), inspired by the well-known Nash-Sutcliffe efficiency (NSE, Nash and Sutcliffe, 1970), and the commentaries made by Schaefli and Gupta (2007) and Criss and Winston (2008).

This function gives different weights to the high/low values in the (obs_i - sim_i) terms used in the Nash-Sutcliffe formula, using high weights for high or low flows, depending on how close the user-defined 'lambda' value is to 1 or zero, respectively. Between high and low values there is a linear transition from lambda to 1-lambda, respectively.

Following the traditional Nash-Sutcliffe efficiency, the weighted seasonal Nash-Sutcliffe Efficiency (wsNSE) ranges from -Inf to 1, with an optimal value of 1. Higher values of wsNSE indicate lower differences between sim and obs. Essentially, the closer to 1, the more similarsim and obs are.

References

Zambrano-Bigiarini, M.; Bellin, A. (2012). Comparing goodness-of-fit measures for calibration of models focused on extreme events. EGU General Assembly 2012, Vienna, Austria, 22-27 Apr 2012, EGU2012-11549-1.

Nash, J.E.; J.V. Sutcliffe. (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.

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.

Yilmaz, K. K.; Gupta, H. V.; Wagener, T. (2008), A process-based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model, Water Resources Research, 44, W09417, doi:10.1029/2007WR006716.

Krause, P.; Boyle, D.P.; Base, F. (2005). Comparison of different efficiency criteria for hydrological model assessment, Advances in Geosciences, 5, 89-97. doi:10.5194/adgeo-5-89-2005.

Legates, D.R.; McCabe, G. J. Jr. (1999), Evaluating the Use of "Goodness-of-Fit" Measures in Hydrologic and Hydroclimatic Model Validation, Water Resour. Res., 35(1), 233-241. doi:10.1029/1998WR900018.

See Also

NSE, wNSE, wsNSE, APFB, KGElf, gof, ggof

Examples

##################
# Example 1: Looking at the difference between 'KGE', 'NSE', 'wNSE', 'wsNSE',
# 'APFB' and 'KGElf' for detecting differences in high flows

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

# Simulated daily time series, created equal to the observed values and then 
# random noise is added only to high flows, i.e., those equal or higher than 
# the quantile 0.9 of the observed values.
sim      <- obs
hQ.thr   <- quantile(obs, probs=0.9, na.rm=TRUE)
hQ.index <- which(obs >= hQ.thr)
hQ.n     <- length(hQ.index)
sim[hQ.index] <- sim[hQ.index] + rnorm(hQ.n, mean=mean(sim[hQ.index], na.rm=TRUE))

# Traditional Kling-Gupta eficiency (Gupta and Kling, 2009)
KGE(sim=sim, obs=obs)

# Traditional Nash-Sutcliffe eficiency (Nash and Sutcliffe, 1970)
NSE(sim=sim, obs=obs)

# Weighted Nash-Sutcliffe efficiency (Hundecha and Bardossy, 2004)
wNSE(sim=sim, obs=obs)

# wsNSE (Zambrano-Bigiarini and Bellin, 2012):
wsNSE(sim=sim, obs=obs)

# APFB (Mizukami et al., 2019):
APFB(sim=sim, obs=obs)


##################
# Example 2: Looking at the difference between 'KGE', 'NSE', 'wsNSE',
# 'dr', 'rd', 'md', 'APFB' and 'KGElf' for detecting differences in low flows

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

# Simulated daily time series, created equal to the observed values and then 
# random noise is added only to low flows, i.e., those equal or lower than 
# the quantile 0.4 of the observed values.
sim      <- obs
lQ.thr   <- quantile(obs, probs=0.4, na.rm=TRUE)
lQ.index <- which(obs <= lQ.thr)
lQ.n     <- length(lQ.index)
sim[lQ.index] <- sim[lQ.index] + rnorm(lQ.n, mean=mean(sim[lQ.index], na.rm=TRUE))

# Traditional Kling-Gupta eficiency (Gupta and Kling, 2009)
KGE(sim=sim, obs=obs)

# Traditional Nash-Sutcliffe eficiency (Nash and Sutcliffe, 1970)
NSE(sim=sim, obs=obs)

# Weighted seasonal Nash-Sutcliffe efficiency (Zambrano-Bigiarini and Bellin, 2012):
wsNSE(sim=sim, obs=obs, lambda=0.05, j=1/2)

# Refined Index of Agreement (Willmott et al., 2012):
dr(sim=sim, obs=obs)

# Relative Index of Agreement (Krause et al., 2005):
rd(sim=sim, obs=obs)

# Modified Index of Agreement (Krause et al., 2005):
md(sim=sim, obs=obs)

# KGElf (Garcia et al., 2017):
KGElf(sim=sim, obs=obs)


##################
# Example 3: 
# 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 'wsNSE' for the "best" (unattainable) case
wsNSE(sim=sim, obs=obs)


##################
# Example 4: wsNSE for simulated values created equal to the observed values and then 
#            random noise is added only to high flows, i.e., those equal or higher than 
#            the quantile 0.9 of the observed values and applying (natural) 
#            logarithm to 'sim' and 'obs' during computations.

wsNSE(sim=sim, obs=obs, fun=log)

# Verifying the previous value:
lsim <- log(sim)
lobs <- log(obs)
wsNSE(sim=lsim, obs=lobs)


##################
# Example 5: wsNSE for simulated values created equal to the observed values and then 
#            random noise is added only to high flows, i.e., those equal or higher than 
#            the quantile 0.9 of the observed values and applying a 
#            user-defined function to 'sim' and 'obs' during computations

fun1 <- function(x) {sqrt(x+1)}

wsNSE(sim=sim, obs=obs, fun=fun1)

# Verifying the previous value, with the epsilon value following Pushpalatha2012
sim1 <- sqrt(sim+1)
obs1 <- sqrt(obs+1)
wsNSE(sim=sim1, obs=obs1)