Param_Sets_GR4J.Rd 4.39 KB
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\docType{data}
\encoding{UTF-8}


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\name{Param_Sets_GR4J}
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\alias{Param_Sets_GR4J}


\title{Generalist parameter sets for the GR4J model}

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\format{Data frame of parameters containing four numeric vectors
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\itemize{
  \item {GR4J X1} {production store capacity [mm]}
  \item {GR4J X2} {intercatchment exchange coefficient [mm/d]}
  \item {GR4J X3} {routing store capacity [mm]}
  \item {GR4J X4u} {unajusted unit hydrograph time constant [d]}
}}

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\description{
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These parameter sets can be used as an alternative for the grid-screening calibration procedure (i.e. first step in \code{\link{Calibration_Michel}}).
Please note that the given GR4J X4u variable does not correspond to the actual GR4J X4 parameter. As explained in Andréassian et al. (2014; section 2.1), the given GR4J X4u value has to be adjusted (rescaled) using catchment area (S) [km2] as follows: {X4 = X4u / 5.995 * S^0.3}.
3 (please note that the formula is erroneous in the publication). Please, see the example below. \cr
As shown in Andréassian et al. (2014; figure 4), only using these parameters sets as the tested values for calibration is more efficient than a classical calibration when the amount of data is low (6 months or less).
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}

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\seealso{
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  \code{\link{RunModel_GR4J}}, \code{\link{Calibration_Michel}}, \code{\link{CreateCalibOptions}}.
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}

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\references{
Andréassian, V., F. Bourgin, L. Oudin, T. Mathevet, C. Perrin, J. Lerat, L. Coron, L. Berthet (2014).
      Seeking genericity in the selection of parameter sets: impact on hydrological model efficiency.
      Water Resources Research, 50(10), 8356-8366, doi: 10.1002/2013WR014761.
}


\examples{
library(airGR)

## loading catchment data
data(L0123001)

## loading generalist parameter sets
data(Param_Sets_GR4J)
str(Param_Sets_GR4J)

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## computation of the real GR4J X4
Param_Sets_GR4J$X4 <- Param_Sets_GR4J$X4u / 5.995 * BasinInfo$BasinArea^0.3
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Param_Sets_GR4J$X4u <- NULL
Param_Sets_GR4J <- as.matrix(Param_Sets_GR4J)

## preparation of the InputsModel object
InputsModel <- CreateInputsModel(FUN_MOD = RunModel_GR4J, DatesR = BasinObs$DatesR, 
                                 Precip = BasinObs$P, PotEvap = BasinObs$E)

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## ---- calibration step

## short calibration period selection (< 6 months)
Ind_Cal <- seq(which(format(BasinObs$DatesR, format = "\%d/\%m/\%Y \%H:\%M")=="01/01/1990 00:00"), 
               which(format(BasinObs$DatesR, format = "\%d/\%m/\%Y \%H:\%M")=="01/03/1990 00:00"))

## preparation of the RunOptions object for the calibration period
RunOptions_Cal <- CreateRunOptions(FUN_MOD = RunModel_GR4J,
                               InputsModel = InputsModel, IndPeriod_Run = Ind_Cal)

## simulation and efficiency criterion (Nash-Sutcliffe Efficiency) with all generalist parameter sets on the calibration period
OutputsCrit_Loop <- apply(Param_Sets_GR4J, 1, function(Param) {
  OutputsModel_Cal <- RunModel_GR4J(InputsModel = InputsModel, RunOptions = RunOptions_Cal, Param = Param)
  InputsCrit  <- CreateInputsCrit(FUN_CRIT = ErrorCrit_NSE, InputsModel = InputsModel, 
                                  RunOptions = RunOptions_Cal, Qobs = BasinObs$Qmm[Ind_Cal])
  OutputsCrit <- ErrorCrit_NSE(InputsCrit = InputsCrit, OutputsModel = OutputsModel_Cal)
  return(OutputsCrit$CritValue)
})
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## best parameter set
Param_Best <- unlist(Param_Sets_GR4J[which.max(OutputsCrit_Loop), ])
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## ---- validation step

## validation period selection
Ind_Val <- seq(which(format(BasinObs$DatesR, format = "\%d/\%m/\%Y \%H:\%M")=="01/01/1991 00:00"), 
               which(format(BasinObs$DatesR, format = "\%d/\%m/\%Y \%H:\%M")=="31/12/1999 00:00"))

## preparation of the RunOptions object for the validation period
RunOptions_Val <- CreateRunOptions(FUN_MOD = RunModel_GR4J,
                                   InputsModel = InputsModel, IndPeriod_Run = Ind_Val)
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## simulation with the best parameter set on the validation period
OutputsModel_Val <- RunModel_GR4J(InputsModel = InputsModel, RunOptions = RunOptions_Val, Param = Param_Best)
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## results preview of the simulation with the best parameter set on the validation period
plot(OutputsModel_Val, Qobs = BasinObs$Qmm[Ind_Val])
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## efficiency criterion (Nash-Sutcliffe Efficiency) on the validation period
InputsCrit_Val  <- CreateInputsCrit(FUN_CRIT = ErrorCrit_NSE, InputsModel = InputsModel, 
                                RunOptions = RunOptions_Val, Qobs = BasinObs$Qmm[Ind_Val])
OutputsCrit_Val <- ErrorCrit_NSE(InputsCrit = InputsCrit_Val, OutputsModel = OutputsModel_Val)
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}