hydroGOF: ggof – R documentation

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ggof

Graphical Goodness of Fit

Description

Graphical comparison between two vectors (numeric, ts or zoo), with several numerical goodness of fit printed as a legend.
Missing values in observed and/or simulated values can removed before the computations.

Usage

ggof(sim, obs, na.rm = TRUE, dates, date.fmt = "%Y-%m-%d", 
     pt.style = "ts", ftype = "o",  FUN, 
     stype="default", season.names=c("Winter", "Spring", "Summer", "Autumn"),
     gof.leg = TRUE,  digits=2, 
     gofs=c("ME", "MAE", "RMSE", "NRMSE", "PBIAS", "RSR", "rSD", "NSE", "mNSE", 
             "rNSE", "d", "md", "rd", "r", "R2", "bR2", "KGE", "VE"),
     legend, leg.cex=1,
     tick.tstep = "auto", lab.tstep = "auto", lab.fmt=NULL,
     cal.ini=NA, val.ini=NA,
     main, xlab = "Time", ylab=c("Q, [m3/s]"),  
     col = c("blue", "black"), 
     cex = c(0.5, 0.5), cex.axis=1.2, cex.lab=1.2,
     lwd = c(1, 1), lty = c(1, 3), pch = c(1, 9), ...)

Arguments

`sim`	numeric or zoo object with with simulated values
`obs`	numeric or zoo object with observed values
`na.rm`	a logical value indicating whether 'NA' should be stripped before the computation proceeds. When an 'NA' value is found at the i-th position in `obs` OR `sim`, the i-th value of `obs` AND `sim` are removed before the computation.
`dates`	character, factor, Date or POSIXct object indicating how to obtain the dates for the corresponding values in the `sim` and `obs` time series If `dates` is a character or factor, it is converted into Date/POSIXct class, using the date format specified by `date.fmt`
`date.fmt`	OPTIONAL. character indicating the format in which the dates are stored in `dates`, `cal.ini` and `val.ini`. See `format` in `as.Date`. Default value is `%Y-%m-%d` ONLY required when `class(dates)=="character"` or `class(dates)=="factor"` or when `cal.ini` and/or `val.ini` is provided.
`pt.style`	Character indicating if the 2 ts have to be plotted as lines or bars. When `ftype` is NOT `o`, it only applies to the annual values. Valid values are: -) `ts` : (default) each ts is plotted as a lines along the 'x' axis -) `bar`: both series are plotted as barplots.
`ftype`	Character indicating how many plots are desired by the user. Valid values are: -) `o` : only the original `sim` and `obs` time series are plotted -) `dm` : it assumes that `sim` and `obs` are daily time series and Daily and Monthly values are plotted -) `ma` : it assumes that `sim` and `obs` are daily or monthly time series and Monthly and Annual values are plotted -) `dma` : it assumes that `sim` and `obs` are daily time series and Daily, Monthly and Annual values are plotted -) `seasonal`: seasonal values are plotted. See `stype` and `season.names`
`FUN`	OPTIONAL, ONLY required when `ftype` is in `c('dm', 'ma', 'dma', 'seasonal')`. Function that have to be applied for transforming teh original ts into monthly, annual or seasonal time step (e.g., for precipitation FUN MUST be `sum`, for temperature and flow time series, FUN MUST be `mean`)
`stype`	OPTIONAL, only used when `ftype=seasonal`. character, indicating whath weather seasons will be used for computing the output. Possible values are: -) `default` => "winter"= DJF = Dec, Jan, Feb; "spring"= MAM = Mar, Apr, May; "summer"= JJA = Jun, Jul, Aug; "autumn"= SON = Sep, Oct, Nov -) `FrenchPolynesia` => "winter"= DJFM = Dec, Jan, Feb, Mar; "spring"= AM = Apr, May; "summer"= JJAS = Jun, Jul, Aug, Sep; "autumn"= ON = Oct, Nov
`season.names`	OPTIONAL, only used when `ftype=seasonal`. character of length 4 indicating the names of each one of the weather seasons defined by `stype`.These names are only used for plotting purposes
`gof.leg`	logical, indicating if several numerical goodness of fit have to be computed between `sim` and `obs`, and plotted as a legend on the graph. If `leg.gof=TRUE`, then `x` is considered as observed and `y` as simulated values (for some gof functions this is important).
`digits`	OPTIONAL, only used when `leg.gof=TRUE`. Numeric, representing the decimal places used for rounding the goodness-of-fit indexes.
`gofs`	character, with one or more strings indicating the goodness-of-fit measures to be shown in the legend of the plot when `gof.leg=TRUE`. Possible values when `ftype!='seasonal'` are in `c("ME", "MAE", "MSE", "RMSE", "NRMSE", "PBIAS", "RSR", "rSD", "NSE", "mNSE", "rNSE", "d", "md", "rd", "cp", "r", "R2", "bR2", "KGE", "VE")` Possible values when `ftype='seasonal'` are in c("ME", "RMSE", "PBIAS", "RSR", "NSE", "d", "R2", "KGE", "VE")
`legend`	character of length 2 to appear in the legend.
`leg.cex`	OPTIONAL. ONLY used when `leg.gof=TRUE`. Character expansion factor for drawing the legend, relative to current 'par("cex")'. Used for text, and provides the default for 'pt.cex' and 'title.cex'. Default value = 1
`tick.tstep`	character, indicating the time step that have to be used for putting the ticks on the time axis. Valid values are: `auto`, `years`, `months`,`weeks`, `days`, `hours`, `minutes`, `seconds`.
`lab.tstep`	character, indicating the time step that have to be used for putting the labels on the time axis. Valid values are: `auto`, `years`, `months`,`weeks`, `days`, `hours`, `minutes`, `seconds`.
`lab.fmt`	Character indicating the format to be used for the label of the axis. See `lab.fmt` in `drawTimeAxis`.
`cal.ini`	OPTIONAL. Character, indicating the date in which the calibration period started. When `cal.ini` is provided, all the values in `obs` and `sim` with dates previous to `cal.ini` are SKIPPED from the computation of the goodness-of-fit measures (when `gof.leg=TRUE`), but their values are still plotted, in order to examine if the warming up period was too short, acceptable or too long for the chosen calibration period. In addition, a vertical red line in drawn at this date.
`val.ini`	OPTIONAL. Character, the date in which the validation period started. ONLY used for drawing a vertical red line at this date.
`main`	character representing the main title of the plot.
`xlab`	label for the 'x' axis.
`ylab`	label for the 'y' axis.
`col`	character, representing the colors of `sim` and `obs`
`cex`	numeric, representing the values controlling the size of text and symbols of 'x' and 'y' with respect to the default
`cex.axis`	numeric, representing the magnification to be used for the axis annotation relative to 'cex'. See `par`.
`cex.lab`	numeric, representing the magnification to be used for x and y labels relative to the current setting of 'cex'. See `par`.
`lwd`	vector with the line width of `sim` and `obs`
`lty`	numeric with the line type of `sim` and `obs`
`pch`	numeric with the type of symbol for `x` and `y`. (e.g., 1: white circle; 9: white rhombus with a cross inside)
`...`	further arguments passed to or from other methods.

Details

Plots observed and simulated values in the same graph.

If gof.leg=TRUE, it computes the numerical values of:
'me', 'mae', 'rmse', 'nrmse', 'PBIAS', 'RSR, 'rSD', 'NSE', 'mNSE', 'rNSE', 'd', 'md, 'rd', 'cp', 'r', 'r.Spearman', 'R2', 'bR2', 'KGE', 'VE'

Value

`me`	Mean Error
`mae`	Mean Absolute Error

`rmse`	Root Mean Square Error
`nrmse`	Normalized Root Mean Square Error
`PBIAS`	Percent Bias
`pbiasfdc`	PBIAS in the slope of the midsegment of the Flow Duration Curve
`RSR`	Ratio of RMSE to the Standard Deviation of the Observations, RSR = rms / sd(obs). ( 0 <= RSR <= +Inf )
`rSD`	Ratio of Standard Deviations, rSD = sd(sim) / sd(obs)
`NSE`	Nash-Sutcliffe Efficiency ( -Inf <= NSE <= 1 )
`mNSE`	Modified Nash-Sutcliffe Efficiency
`rNSE`	Relative Nash-Sutcliffe Efficiency
`d`	Index of Agreement ( 0 <= d <= 1 )
`md`	Modified Index of Agreement
`rd`	Relative Index of Agreement
`cp`	Persistence Index ( 0 <= PI <= 1 )
`r`	Pearson product-moment correlation coefficient ( -1 <= r <= 1 )
`r.Spearman`	Spearman Correlation coefficient ( -1 <= r.Spearman <= 1 )
`R2`	Coefficient of Determination ( 0 <= R2 <= 1 ). Gives the proportion of the variance of one variable that is predictable from the other variable
`bR2`	R2 multiplied by the coefficient of the regression line between `sim` and `obs` ( 0 <= bR2 <= 1 )
`KGE`	Kling-Gupta efficiency between `sim` and `obs` ( 0 <= KGE <= 1 )
`VE`	Volumetric efficiency between `sim` and `obs` ( -Inf <= VE <= 1)

Author(s)

Mauricio Zambrano Bigiarini <mzb.devel@gmail.com>

References

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

Krause P., Boyle D.P., and B\"ase F., Comparison of different efficiency criteria for hydrological model assessment, Advances in Geosciences 5 (2005), pp. 89–97

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

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

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

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

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

Examples

obs <- 1:10
sim <- 2:11

## Not run: 
ggof(sim, obs)

## End(Not run)

##################
# Loading daily streamflows of the Ega River (Spain), from 1961 to 1970
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 measures
gof(sim=sim, obs=obs)

# Getting the graphical representation of 'obs' and 'sim' along with the numeric 
# goodness-of-fit measures for the daily and monthly time series 
## Not run: 
ggof(sim=sim, obs=obs, ftype="dm", FUN=mean)

## End(Not run)

# Getting the graphical representation of 'obs' and 'sim' along with some numeric 
# goodness-of-fit measures for the seasonal time series 
## Not run: 
ggof(sim=sim, obs=obs, ftype="seasonal", FUN=mean)

## End(Not run)

# Computing the daily residuals 
# even if this is a dummy example, it is enough for illustrating the capability
r <- sim-obs

# Summarizing and plotting the residuals
## Not run: 
library(hydroTSM)

# summary
smry(r) 

# daily, monthly and annual plots, boxplots and histograms
hydroplot(r, FUN=mean)

# seasonal plots and boxplots
hydroplot(r, FUN=mean, pfreq="seasonal")

## End(Not run)

hydroGOF

Goodness-of-Fit Functions for Comparison of Simulated and Observed Hydrological Time Series

v0.4-0

GPL (>= 2)

Authors

Mauricio Zambrano-Bigiarini [aut, cre, cph] (<https://orcid.org/0000-0002-9536-643X>)

Initial release