Wrapper to airGR::Calibration for one sub-basin.

Calibration algorithm that optimises the error criterion selected as objective function using the provided functions.

# S3 method for InputsModel
Calibration(InputsModel, ...)

Arguments

InputsModel	[object of class InputsModel] see CreateInputsModel for details
...	Further arguments for compatibility with S3 method

Value

[list] see Calibration_Michel

See also

Calibration_Michel, ErrorCrit, TransfoParam, CreateInputsModel, CreateRunOptions, CreateInputsCrit, CreateCalibOptions.

Author

Laurent Coron, Olivier Delaigue

Examples

library(airGR)
#> 
#> Attaching package: 'airGR'
#> The following objects are masked from 'package:airGRiwrm':
#> 
#>     Calibration, CreateCalibOptions, CreateInputsCrit,
#>     CreateInputsModel, CreateRunOptions, RunModel

## loading catchment data
data(L0123001)

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

## calibration period selection
Ind_Run <- seq(which(format(BasinObs$DatesR, format = "%Y-%m-%d")=="1990-01-01"),
               which(format(BasinObs$DatesR, format = "%Y-%m-%d")=="1999-12-31"))

## preparation of RunOptions object
RunOptions <- CreateRunOptions(FUN_MOD = RunModel_GR4J,
                               InputsModel = InputsModel, IndPeriod_Run = Ind_Run)
#> Warning: model warm up period not defined: default configuration used
#>   the year preceding the run period is used 

## calibration criterion: preparation of the InputsCrit object
InputsCrit <- CreateInputsCrit(FUN_CRIT = ErrorCrit_NSE, InputsModel = InputsModel,
                               RunOptions = RunOptions, Obs = BasinObs$Qmm[Ind_Run])

## preparation of CalibOptions object
CalibOptions <- CreateCalibOptions(FUN_MOD = RunModel_GR4J, FUN_CALIB = Calibration_Michel)

## calibration
OutputsCalib <- Calibration(InputsModel = InputsModel, RunOptions = RunOptions,
                            InputsCrit = InputsCrit, CalibOptions = CalibOptions,
                            FUN_MOD = RunModel_GR4J,
                            FUN_CALIB = Calibration_Michel)
#> Grid-Screening in progress (
#> 0%
#>  20%
#>  40%
#>  60%
#>  80%
#>  100%)
#> 	 Screening completed (81 runs)
#> 	     Param =  247.151,   -0.020,   83.096,    2.384
#> 	     Crit. NSE[Q]       = 0.7688
#> Steepest-descent local search in progress
#> 	 Calibration completed (21 iterations, 234 runs)
#> 	     Param =  257.238,    1.012,   88.235,    2.208
#> 	     Crit. NSE[Q]       = 0.7988

## simulation
Param <- OutputsCalib$ParamFinalR
OutputsModel <- RunModel(InputsModel = InputsModel, RunOptions = RunOptions,
                         Param = Param, FUN = RunModel_GR4J)

## results preview
plot(OutputsModel, Qobs = BasinObs$Qmm[Ind_Run])

## efficiency criterion: Nash-Sutcliffe Efficiency
InputsCrit  <- CreateInputsCrit(FUN_CRIT = ErrorCrit_NSE, InputsModel = InputsModel,
                                RunOptions = RunOptions, Obs = BasinObs$Qmm[Ind_Run])
OutputsCrit <- ErrorCrit_NSE(InputsCrit = InputsCrit, OutputsModel = OutputsModel)
#> Crit. NSE[Q] = 0.7988

## efficiency criterion: Kling-Gupta Efficiency
InputsCrit  <- CreateInputsCrit(FUN_CRIT = ErrorCrit_KGE, InputsModel = InputsModel,
                                RunOptions = RunOptions, Obs = BasinObs$Qmm[Ind_Run])
OutputsCrit <- ErrorCrit_KGE(InputsCrit = InputsCrit, OutputsModel = OutputsModel)
#> Crit. KGE[Q] = 0.7854
#> 	SubCrit. KGE[Q] cor(sim, obs, "pearson") = 0.8985 
#> 	SubCrit. KGE[Q] sd(sim)/sd(obs)          = 0.8161 
#> 	SubCrit. KGE[Q] mean(sim)/mean(obs)      = 1.0437