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.
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), ...)
Mean Error
Mean Absolute Error
Root Mean Square Error
Normalized Root Mean Square Error
Percent Bias
PBIAS in the slope of the midsegment of the Flow Duration Curve
Ratio of RMSE to the Standard Deviation of the Observations, RSR = rms / sd(obs). ( 0 <= RSR <= +Inf )
Ratio of Standard Deviations, rSD = sd(sim) / sd(obs)
Nash-Sutcliffe Efficiency ( -Inf <= NSE <= 1 )
Modified Nash-Sutcliffe Efficiency
Relative Nash-Sutcliffe Efficiency
Index of Agreement ( 0 <= d <= 1 )
Modified Index of Agreement
Relative Index of Agreement
Persistence Index ( 0 <= PI <= 1 )
Pearson product-moment correlation coefficient ( -1 <= r <= 1 )
Spearman Correlation coefficient ( -1 <= r.Spearman <= 1 )
Coefficient of Determination ( 0 <= R2 <= 1 ).
Gives the proportion of the variance of one variable that is predictable from the other variable
R2 multiplied by the coefficient of the regression line between sim
and obs
( 0 <= bR2 <= 1 )
Kling-Gupta efficiency between sim
and obs
( 0 <= KGE <= 1 )
Volumetric efficiency between sim
and obs
( -Inf <= VE <= 1)
numeric or zoo object with with simulated values
numeric or zoo object with observed values
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.
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
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.
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.
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
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
)
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
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
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).
OPTIONAL, only used when leg.gof=TRUE
. Numeric, representing the decimal places used for rounding the goodness-of-fit indexes.
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")
character of length 2 to appear in the legend.
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
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.
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.
Character indicating the format to be used for the label of the axis. See lab.fmt
in drawTimeAxis
.
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.
OPTIONAL. Character, the date in which the validation period started.
ONLY used for drawing a vertical red line at this date.
character representing the main title of the plot.
label for the 'x' axis.
label for the 'y' axis.
character, representing the colors of sim
and obs
numeric, representing the values controlling the size of text and symbols of 'x' and 'y' with respect to the default
numeric, representing the magnification to be used for the axis annotation relative to 'cex'. See par
.
numeric, representing the magnification to be used for x and y labels relative to the current setting of 'cex'. See par
.
vector with the line width of sim
and obs
numeric with the line type of sim
and obs
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.
Mauricio Zambrano Bigiarini <mzb.devel@gmail.com>
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'
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 Base 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. doi:10.1016/0022-1694(95)02918-4
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
gof
, plot2
,
ggof
, me
, mae
, mse
, rmse
, ubRMSE
,
nrmse
, pbias
, rsr
, rSD
, NSE
, mNSE
,
rNSE
, wNSE
, d
, dr
, md
, rd
,
cp
, rPearson
, R2
, br2
, KGE
, KGElf
,
KGEnp
, sKGE
, VE
, rSpearman
, pbiasfdc
obs <- 1:10
sim <- 2:11
if (FALSE) {
ggof(sim, obs)
}
##################
# 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
if (FALSE) {
ggof(sim=sim, obs=obs, ftype="dm", FUN=mean)
}
# Getting the graphical representation of 'obs' and 'sim' along with some numeric
# goodness-of-fit measures for the seasonal time series
if (FALSE) {
ggof(sim=sim, obs=obs, ftype="seasonal", FUN=mean)
}
# 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
if (FALSE) {
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")
}
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