hydroGOF (version 0.2-2)

hydroGOF-package: Goodness-of-fit (GOF) functions for numerical and graphical comparison of simulated and observed time series, focused on hydrological modelling.

Description

S3 functions implementing both statistical and graphical goodness-of-fit measures between observed and simulated values, for being used during the calibration, validation, and application of hydrological models. Missing values in observed and/or simulated values can removed before the computations. Quantitative statistics included are: Mean Error (me), Mean Absolute Error (mae), Root Mean Square Error (rms), Normalized Root Mean Square Error (nrms), Pearson Correlation coefficient (r), Spearman Correlation coefficient (r.Spearman), Coefficient of Determination (R2), Ratio of Standard Deviations (rSD), Nash-Sutcliffe efficiency (NSeff), Modified Nash-Sutcliffe efficiency (mNSeff), Index of Agreement (d), Modified Index of Agreement (md), Coefficient of Persistence (cp), Percent Bias (pbias), Kling-Gupta efficiency (KGE), and the coef. of determination multiplied by the slope of the linear regression between 'sim' and 'obs' (bR2)

Details

ll{ Package: hydroGOF Type: Package Version: 0.2-2 Date: 2011-04-14 License: GPL >= 2 LazyLoad: yes Packaged: Thu Apr 14 14:35:24 CEST 2011; MZB BuiltUnder: 2.12.2 (2011-02-25); i486-pc-linux-gnu }

References

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

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

Hoshin V. Gupta, 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

See Also

http://cran.r-project.org/web/packages/hydroTSM/. http://rwiki.sciviews.org/doku.php?id=guides:tutorials:hydrological_data_analysis

Examples

obs <- 1:100
sim <- obs

# Numerical goodness of fit
gof(sim,obs)

# Reverting the order of simulated values
sim <- 100:1
gof(sim,obs)

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 the observed series
sim <- obs 

# Getting the numeric goodness of fit 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 
require(hydroTSM)
ggof(sim=sim, obs=obs)