hydroGOF-package {hydroGOF} | R Documentation |

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>

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Krause, P., Boyle, D. P., and Base, F.: Comparison of different efficiency criteria for hydrological model assessment, Adv. Geosci., 5, 89–97, 2005

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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

https://CRAN.R-project.org/package=hydroPSO

https://CRAN.R-project.org/package=hydroTSM

obs <- 1:100 sim <- obs # Numerical goodness of fit gof(sim,obs) # Reverting the order of simulated values sim <- 100:1 gof(sim,obs) ## Not run: ggof(sim, obs) ## End(Not run) ################## # 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 ## Not run: ggof(sim=sim, obs=obs) ## End(Not run)

[Package *hydroGOF* version 0.3-10 Index]