Param_Sets_GR4J | R Documentation |
These parameter sets can be used as an alternative for the grid-screening calibration procedure (i.e. first step in 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 (please note that the formula is erroneous in the publication). Please, see the example below.
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).
Data frame of parameters containing four numeric vectors
GR4J X1 production store capacity [mm]
GR4J X2 intercatchment exchange coefficient [mm/d]
GR4J X3 routing store capacity [mm]
GR4J X4u unajusted unit hydrograph time constant [d]
Andréassian, V., Bourgin, F., Oudin, L., Mathevet, T., Perrin, C., Lerat, J., Coron, L. and Berthet, L. (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.
RunModel_GR4J
, Calibration_Michel
, CreateCalibOptions
.
library(airGR) ## loading catchment data data(L0123001) ## loading generalist parameter sets data(Param_Sets_GR4J) str(Param_Sets_GR4J) ## computation of the real GR4J X4 Param_Sets_GR4J$X4 <- Param_Sets_GR4J$X4u / 5.995 * BasinInfo$BasinArea^0.3 Param_Sets_GR4J$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$E) ## --- calibration step ## short calibration period selection (< 6 months) Ind_Cal <- seq(which(format(BasinObs$DatesR, format = "%Y-%m-%d")=="1990-01-01"), which(format(BasinObs$DatesR, format = "%Y-%m-%d")=="1990-02-28")) ## preparation of the RunOptions object for the calibration period RunOptions_Cal <- CreateRunOptions(FUN_MOD = RunModel_GR4J, InputsModel = InputsModel, IndPeriod_Run = Ind_Cal) ## simulation and efficiency criterion (Nash-Sutcliffe Efficiency) ## with all generalist parameter sets on the calibration period OutputsCrit_Loop <- apply(Param_Sets_GR4J, 1, function(Param) { OutputsModel_Cal <- RunModel_GR4J(InputsModel = InputsModel, RunOptions = RunOptions_Cal, Param = Param) InputsCrit <- CreateInputsCrit(FUN_CRIT = ErrorCrit_NSE, InputsModel = InputsModel, RunOptions = RunOptions_Cal, Obs = BasinObs$Qmm[Ind_Cal]) OutputsCrit <- ErrorCrit_NSE(InputsCrit = InputsCrit, OutputsModel = OutputsModel_Cal) return(OutputsCrit$CritValue) }) ## best parameter set Param_Best <- unlist(Param_Sets_GR4J[which.max(OutputsCrit_Loop), ]) ## --- validation step ## validation period selection Ind_Val <- seq(which(format(BasinObs$DatesR, format = "%Y-%m-%d")=="1990-03-01"), which(format(BasinObs$DatesR, format = "%Y-%m-%d")=="1999-12-31")) ## preparation of the RunOptions object for the validation period RunOptions_Val <- CreateRunOptions(FUN_MOD = RunModel_GR4J, InputsModel = InputsModel, IndPeriod_Run = Ind_Val) ## simulation with the best parameter set on the validation period OutputsModel_Val <- RunModel_GR4J(InputsModel = InputsModel, RunOptions = RunOptions_Val, Param = Param_Best) ## results preview of the simulation with the best parameter set on the validation period plot(OutputsModel_Val, Qobs = BasinObs$Qmm[Ind_Val]) ## efficiency criterion (Nash-Sutcliffe Efficiency) on the validation period InputsCrit_Val <- CreateInputsCrit(FUN_CRIT = ErrorCrit_NSE, InputsModel = InputsModel, RunOptions = RunOptions_Val, Obs = BasinObs$Qmm[Ind_Val]) OutputsCrit_Val <- ErrorCrit_NSE(InputsCrit = InputsCrit_Val, OutputsModel = OutputsModel_Val)
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