S3 functions implementing both statistical and graphical goodness-of-fit measures between observed and simulated values, to be used during the calibration, validation, and application of hydrological models.
Missing values in observed and/or simulated values can be removed before computations.
Mauricio Zambrano Bigiarini <mzb.devel@gmail.com>
Maintainer: Mauricio Zambrano Bigiarini <mzb.devel@gmail.com>
| Package: | hydroGOF |
| Type: | Package |
| Version: | 0.5-3 |
| Date: | 2024-01-21 |
| License: | GPL >= 2 |
| LazyLoad: | yes |
| Packaged: | Sun Jan 21 17:34:26 -03 2024 ; MZB |
| BuiltUnder: | R version 4.3.2 (2023-10-31) ;x86_64-pc-linux-gnu (64-bit) |
Quantitative statistics included in this package are:
me Mean Error | mae Mean Absolute Error |
mse Mean Squared Error | rmse Root Mean Square Error |
ubRMSE Unbiased Root Mean Square Error | nrmse Normalized Root Mean Square Error |
pbias Percent Bias | rsr Ratio of RMSE to the Standard Deviation of the Observations |
rSD Ratio of Standard Deviations | NSE Nash-Sutcliffe Efficiency |
mNSE Modified Nash-Sutcliffe Efficiency | rNSE Relative Nash-Sutcliffe Efficiency |
wNSE Weighted Nash-Sutcliffe Efficiency | d Index of Agreement |
dr Refined Index of Agreement | md Modified Index of Agreement |
rd Relative Index of Agreement | cp Persistence Index |
rPearson Pearson correlation coefficient | R2 Coefficient of determination |
br2 R2 multiplied by the coefficient of the regression line between sim and obs | KGE Kling-Gupta efficiency |
KGElf Kling-Gupta Efficiency for low values | KGEnp Non-parametric version of the Kling-Gupta Efficiency |
sKGE Split Kling-Gupta Efficiency | VE Volumetric efficiency |
rSpearman Spearman's rank correlation coefficient | pbiasfdc PBIAS in the slope of the midsegment of the flow duration curve |
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obs <- 1:100
sim <- obs
# Numerical goodness of fit
gof(sim,obs)
# Reverting the order of simulated values
sim <- 100:1
gof(sim,obs)
if (FALSE) {
ggof(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 observations
sim <- obs
# Getting the numeric goodness-of-fit measures for the "best" (unattainable) case
gof(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)
# Getting the new numeric goodness of fit
gof(sim=sim, obs=obs)
# Graphical representation of 'obs' vs 'sim', along with the numeric
# goodness-of-fit measures
if (FALSE) {
ggof(sim=sim, obs=obs)
}
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