hydroGOF

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hydroGOF is an R package that provides S3 functions implementing both statistical and graphical goodness-of-fit measures between observed and simulated values, mainly oriented to be used during the calibration, validation, and application of hydrological models.

Missing values in observed and/or simulated values can be automatically removed before the computations.

Bugs / comments / questions / collaboration of any kind are very welcomed.

Installation

Installing the latest stable version from CRAN: {r} install.packages("hydroGOF")

Alternatively, you can also try the under-development version from Github: {r} if (!require(devtools)) install.packages("devtools") library(devtools) install_github("hzambran/hydroGOF")

Reporting bugs, requesting new features

If you find an error in some function, or want to report a typo in the documentation, or to request a new feature (and wish it be implemented :) you can do it here

Citation

{r} citation("hydroGOF")

To cite hydroGOF in publications use:

Zambrano-Bigiarini, Mauricio (2024). hydroGOF: Goodness-of-fit functions for comparison of simulated and observed hydrological time series. R package version 0.6-0. URL:https://cran.r-project.org/package=hydroGOF. doi:10.5281/zenodo.839854.

A BibTeX entry for LaTeX users is

@Manual{hydroGOF,
title = {hydroGOF: Goodness-of-fit functions for comparison of simulated and observed hydrological time series},
author = {Zambrano-Bigiarini, Mauricio},
note = {R package version 0.6-0},
year = {2024}, url = {https://cran.r-project.org/package=hydroGOF},
doi = {10.5281/zenodo.839854},
}

Goodness-of-fit measures

Quantitative statistics included in this package are:

• me: Mean Error (Hill et al., 2006)
• mae: Mean Absolute Error (Hodson, 2022)
  
• mse: Mean Squared Error (Yapo et al., 1996)
• rmse: Root Mean Square Error (Willmott and Matsuura, 2005)
• ubRMSE: Unbiased Root Mean Square Error (Entekhabi et al., 2010)
• nrmse: Normalized Root Mean Square Error
• pbias: Percent Bias (Yapo et al., 1996)
• rsr: Ratio of RMSE to the Standard Deviation of the Observations (Moriasi et al., 2007)
• rSD: Ratio of Standard Deviations
• NSE: Nash-Sutcliffe Efficiency (Nash and Sutcliffe, 1970)
• mNSE: Modified Nash-Sutcliffe Efficiency (Krause et al., 2005)
• rNSE: Relative Nash-Sutcliffe Efficiency (Legates and McCabe, 1999)
• wNSE: Weighted Nash-Sutcliffe Efficiency (Hundecha and Bardossy, 2004)
• wsNSE: Weighted Seasonal Nash-Sutcliffe Efficiency (Zambrano-Bigiarini and Bellin, A., 2012)
• d: Index of Agreement (Willmott, C.J., 1981)
• dr: Refined Index of Agreement (Willmott et al., 2012)
• md: Modified Index of Agreement (Krause et al., 2005)
• rd: Relative Index of Agreement (Krause et al., 2005)
• cp: Persistence Index (Kitanidis and Bras, 1980)
• rPearson: Pearson correlation coefficient (Pearson, 1920)
• R2: Coefficient of determination (Box, 1966)
• br2: R2 multiplied by the coefficient of the regression line between \code{sim} and \code{obs} (Krause et al., 2005)
• VE: Volumetric efficiency (Criss and Winston, 2008)
• KGE: Kling-Gupta efficiency (Gupta et al., 2009)
• KGElf: Kling-Gupta Efficiency for low values (Garcia et al., 2017)
• KGEnp: Non-parametric version of the Kling-Gupta Efficiency (Pool et al., 2018)
• KGEkm: Knowable Moments Kling-Gupta Efficiency (Pizarro and Jorquera, 2024)
• sKGE: Split Kling-Gupta Efficiency (Fowler et al., 2018)
• APFB: Annual Peak Flow Bias (Mizukami et al., 2019)
• HFB: High Flow Bias
• rSpearman: Spearman's rank correlation coefficient (Spearman, 1961)
• ssq: Sum of the Squared Residuals (Willmott et al., 2009)
• pbiasfdc: PBIAS in the slope of the midsegment of the flow duration curve (Yilmaz et al., 2008)
• pfactor: P-factor (Abbaspour et al., 2009)
• rfactor: R-factor (Abbaspour et al., 2009)

References

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• 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.

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• Hundecha, Y., Bardossy, A. (2004). Modeling of the effect of land use changes on the runoff generation of a river basin through parameter regionalization of a watershed model. Journal of hydrology, 292(1-4), 281-295. doi:10.1016/j.jhydrol.2004.01.002.

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• Kling, H.; Fuchs, M.; Paulin, M. (2012). Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios. Journal of Hydrology, 424, 264-277, doi:10.1016/j.jhydrol.2012.01.011.

• 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.

• 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.

• Krstic, G.; Krstic, N.S.; Zambrano-Bigiarini, M. (2016). The br2-weighting Method for Estimating the Effects of Air Pollution on Population Health. Journal of Modern Applied Statistical Methods, 15(2), 42. doi:10.22237/jmasm/1478004000.

• 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.

• Ling, X.; Huang, Y.; Guo, W.; Wang, Y.; Chen, C.; Qiu, B.; Ge, J.; Qin, K.; Xue, Y.; Peng, J. (2021). Comprehensive evaluation of satellite-based and reanalysis soil moisture products using in situ observations over China. Hydrology and Earth System Sciences, 25(7), 4209-4229. doi:10.5194/hess-25-4209-2021.

• Mizukami, N.; Rakovec, O.; Newman, A.J.; Clark, M.P.; Wood, A.W.; Gupta, H.V.; Kumar, R.: (2019). On the choice of calibration metrics for "high-flow" estimation using hydrologic models, Hydrology Earth System Sciences 23, 2601-2614, doi:10.5194/hess-23-2601-2019.

• 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 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.

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• Pfannerstill, M.; Guse, B.; Fohrer, N. (2014). Smart low flow signature metrics for an improved overall performance evaluation of hydrological models. Journal of Hydrology, 510, 447-458. doi:10.1016/j.jhydrol.2013.12.044.

• Pizarro, A.; Jorquera, J. (2024). Advancing objective functions in hydrological modelling: Integrating knowable moments for improved simulation accuracy. Journal of Hydrology, 634, 131071. doi:10.1016/j.jhydrol.2024.131071.

• Pool, S.; Vis, M.; Seibert, J. (2018). Evaluating model performance: towards a non-parametric variant of the Kling-Gupta efficiency. Hydrological Sciences Journal, 63(13-14), pp.1941-1953. doi:10.1080/02626667.2018.1552002.

• 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.

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• Schuol, J.; Abbaspour, K.C.; Srinivasan, R.; Yang, H. (2008b), Estimation of freshwater availability in the West African sub-continent using the SWAT hydrologic model, Journal of Hydrology, 352(1-2), 30, doi:10.1016/j.jhydrol.2007.12.025.

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• 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.

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Vignette

Here you can find an introductory vignette illustrating the use of several hydroGOF functions.

Related Material

• R: a statistical environment for hydrological analysis (EGU-2010) abstract, poster.

• Comparing Goodness-of-fit Measures for Calibration of Models Focused on Extreme Events (EGU-2012) abstract, poster.

• Using R for analysing spatio-temporal datasets: a satellite-based precipitation case study (EGU-2017) abstract, poster.

See Also

• hydroTSM: Time series management, analysis and interpolation for hydrological modelling.

• hydroPSO: Model-independent Particle Swarm Optimisation (PSO) for environmental/hydrological models.

• RFmerge: Merging of Satellite Datasets with Ground Observations using Random Forests.