hydroGOF-package {hydroGOF}R Documentation

Goodness-of-fit (GoF) functions for numerical and graphical comparison of simulated and observed time series, mainly focused on hydrological modelling.


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.

Quantitative statistics included are: Mean Error (me), Mean Absolute Error (mae), Root Mean Square Error (rms), Normalized Root Mean Square Error (nrms), Pearson product-moment correlation coefficient (r), Spearman Correlation coefficient (r.Spearman), Coefficient of Determination (R2), Ratio of Standard Deviations (rSD), Nash-Sutcliffe efficiency (NSE), Modified Nash-Sutcliffe efficiency (mNSE), Relative Nash-Sutcliffe efficiency (rNSE), Index of Agreement (d), Modified Index of Agreement (md), Relative Index of Agreement (rd), Coefficient of Persistence (cp), Percent Bias (pbias), Kling-Gupta efficiency (KGE), the coef. of determination multiplied by the slope of the linear regression between 'sim' and 'obs' (bR2), and volumetric efficiency (VE).


Package: hydroGOF
Type: Package
Version: 0.3-10
Date: 2017-08-08
License: GPL >= 2
LazyLoad: yes
Packaged: Tue Aug 8 09:11:25 CLT 2017; MZB
BuiltUnder: R version 3.4.1 (2017-06-30) -- "Single Candle"; x86_64-pc-linux-gnu (64-bit))


Mauricio Zambrano Bigiarini <mauricio.zambrano@ing.unitn.it>

Maintainer: Mauricio Zambrano Bigiarini <mauricio.zambrano@ing.unitn.it>


Boyle, D. P., H. V. Gupta, and S. Sorooshian (2000), Toward Improved Calibration of Hydrologic Models: Combining the Strengths of Manual and Automatic Methods, Water Resour. Res., 36(12), 3663–3674

Criss, R. E. and Winston, W. E. (2008), Do Nash values have value? Discussion and alternate proposals. Hydrological Processes, 22: 2723-2725. doi: 10.1002/hyp.7072

Fenicia, F., D. P. Solomatine, H. H. G. Savenije, and P. Matgen, Soft combination of local models in a multi-objective framework. Hydrological and Earth Systems Science, Vol. 4, pp. 91-123, 2007. DOI:10.5194/hessd-4-91-2007

Gupta, Hoshin V., Harald Kling, Koray K. Yilmaz, Guillermo F. Martinez. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology, Volume 377, Issues 1-2, 20 October 2009, Pages 80-91. DOI: 10.1016/j.jhydrol.2009.08.003. ISSN 0022-1694

Kitanidis, P. K., and R. L. Bras (1980), Real-Time Forecasting With a Conceptual Hydrologic Model 2. Applications and Results, Water Resour. Res., 16(6), 1034–1044

Kling, H., M. Fuchs, and M. Paulin (2012), Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios. Journal of Hydrology, Volumes 424-425, 6 March 2012, Pages 264-277, DOI:10.1016/j.jhydrol.2012.01.011

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

Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D., Veith, T.L. 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations
Transactions of the ASABE. 50(3):885-900

Nash, J.E. and J.V. Sutcliffe, River flow forecasting through conceptual models. Part 1: a discussion of principles, J. Hydrol. 10 (1970), pp. 282–290

Pushpalatha, R., Perrin, C., Le Moine, N. and 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

Yapo P. O., Gupta H. V., Sorooshian S., 1996. Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data. Journal of Hydrology. v181 i1-4. 23–48

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

See Also



obs <- 1:100
sim <- obs

# Numerical goodness of fit

# Reverting the order of simulated values
sim <- 100:1

## Not run: 
ggof(sim, obs)

## End(Not run)

# Loading daily streamflows of the Ega River (Spain), from 1961 to 1970
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
## Not run: 
ggof(sim=sim, obs=obs)

## End(Not run)

[Package hydroGOF version 0.3-10 Index]