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CreateInputsCrit_Lavenne.Rd 5.20 KiB
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\encoding{UTF-8}
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\name{CreateInputsCrit_Lavenne}
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\alias{CreateInputsCrit_Lavenne}
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\title{Creation of the InputsCrit object for De Lavenne Criterion}
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\description{
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Creation of the \code{InputsCrit} object required to the \code{\link{ErrorCrit}} function. This function defines a composed criterion based on the formula proposed by de Lavenne et al. (2019).
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}
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\usage{
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CreateInputsCrit_Lavenne(FUN_CRIT = ErrorCrit_KGE,
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                           InputsModel,
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                           RunOptions,
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                           Obs,
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                           VarObs = "Q",
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                           AprParamR,
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                           AprCrit = 1,
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                           k = 0.15,
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                           BoolCrit = NULL,
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                           transfo = "sqrt",
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                           epsilon = NULL)
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}
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\arguments{
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\item{FUN_CRIT}{[function] error criterion function (e.g. \code{\link{ErrorCrit_KGE}}, \code{\link{ErrorCrit_NSE}})}. Default \code{\link{ErrorCrit_KGE}}
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\item{InputsModel}{[object of class \emph{InputsModel}] see \code{\link{CreateInputsModel}} for details}
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\item{RunOptions}{[object of class \emph{RunOptions}] see \code{\link{CreateRunOptions}} for details}
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\item{Obs}{[numeric (atomic or list)] series of observed variable ([mm/time step] for discharge or SWE, [-] for SCA)}
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\item{VarObs}{(optional) [character (atomic or list)] names of the observed variable (\code{"Q"} by default, or one of \code{"SCA"}, \code{"SWE"}])}
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\item{AprParamR}{[numeric] a priori parameter set from a donor catchment}
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\item{AprCrit}{(optional) [numeric] performance criterion of the donor catchment (1 by default)}
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\item{k}{(optional) [numeric] coefficient used for the weighted average between \code{FUN_CRIT} and the gap between optimised parameter set and a priori parameter set calculated with the funciton produced by \code{\link{CreateErrorCrit_GAPX}}}
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\item{BoolCrit}{(optional) [boolean] boolean (the same length as \code{Obs}) giving the time steps to consider in the computation (all time steps are considered by default. See details)}
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\item{transfo}{(optional) [character] name of the transformation applied to the variables (e.g. \code{""}, \code{"sqrt"}, \code{"log"}, \code{"inv"}, \code{"sort"}, \code{"boxcox"} or a numeric value for power transformation for \code{FUN_CRIT}. Default value is \code{"sqrt"}. See details of \code{\link{CreateInputsCrit}}}
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\item{epsilon}{(optional) [numeric] small value to add to all observations and simulations for \code{FUN_CRIT} when \code{"log"} or \code{"inv"} transformations are used [same unit as \code{Obs}]. See details of \code{\link{CreateInputsCrit}}}
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}
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\value{
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\code{CreateInputsCrit_Lavenne} returns an object of class \emph{Compo} that is a list of lists such as the one described in \code{\link{CreateInputsCrit}}.
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Items \code{Weights} of the criteria are respectively equal to \code{k} and \code{k * max(0,AprCrit)}.
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To calculate the De Lavenne criterion, it is necessary to use the \code{ErrorCrit} function as for any other composed criterion.
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}
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\details{
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The parameters \code{FUN_CRIT}, \code{Obs}, \code{VarObs}, \code{BoolCrit}, \code{transfo}, and \code{epsilon} must be use as they would be used for \code{\link{CreateInputsCrit}} in the case of a single criterion.
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\code{\link{ErrorCrit_RMSE}} cannot be used in a composite criterion since it is not a unitless value.
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\code{CreateInputsCrit_Lavenne} creates a composed criterion in respect with Equations 1 and 2 of de Lavenne et al. (2019). } \examples{ library(airGR) ## loading catchment data data(L0123001) ## preparation of the 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$DatesR, format = "\%Y-\%m-\%d")=="1999-12-31")) ## preparation of RunOptions object RunOptions <- CreateRunOptions(FUN_MOD = RunModel_GR4J, InputsModel = InputsModel, IndPeriod_Run = Ind_Run) ## simulation Param <- c(X1 = 257.238, X2 = 1.012, X3 = 88.235, X4 = 2.208) OutputsModel <- RunModel_GR4J(InputsModel = InputsModel, RunOptions = RunOptions, Param = Param) ## The "a priori" parameters for De Lavenne formula AprParamR <- c(X1 = 157, X2 = 0.8, X3 = 100, X4 = 1.5) ## Single efficiency criterion: GAPX with a priori parameters IC_DL <- CreateInputsCrit_Lavenne(InputsModel = InputsModel, RunOptions = RunOptions, Obs = BasinObs$Qmm[Ind_Run], AprParamR = AprParamR) str(ErrorCrit(InputsCrit = IC_DL, OutputsModel = OutputsModel)) } \author{ David Dorchies } \references{ De Lavenne, A., Andréassian, V., Thirel, G., Ramos, M.-H., Perrin, C., 2019. A Regularization Approach to Improve the Sequential Calibration of a Semidistributed Hydrological Model. Water Resources Research 55, 8821–8839. \url{https://doi.org/10.1029/2018WR024266} } \seealso{ \code{\link{RunModel}}, \code{\link{CreateInputsModel}}, \code{\link{CreateRunOptions}}, \code{\link{CreateCalibOptions}}, \code{\link{ErrorCrit}} }