airGR (version 1.7.6)

CreateInputsCrit_Lavenne: Creation of the InputsCrit object for Lavenne Criterion

Description

Creation of the InputsCrit object required to the ErrorCrit function. This function defines a composite criterion based on the formula proposed by Lavenne et al. (2019).

Usage

CreateInputsCrit_Lavenne(FUN_CRIT = ErrorCrit_KGE,
                         InputsModel,
                         RunOptions,
                         Obs,
                         VarObs = "Q",
                         AprParamR,
                         AprCrit = 1,
                         k = 0.15,
                         BoolCrit = NULL,
                         transfo = "sqrt",
                         epsilon = NULL)

Value

[list] object of class InputsCrit containing the data required to evaluate the model outputs (see CreateInputsCrit for more details).

CreateInputsCrit_Lavenne returns an object of class Compo.

Items Weights of the criteria are respectively equal to k and k * max(0, AprCrit).

To calculate the Lavenne criterion, it is necessary to use the ErrorCrit function as for any other composite criterion.

Arguments

FUN_CRIT

[function] error criterion function (e.g. ErrorCrit_KGE, ErrorCrit_NSE). Default ErrorCrit_KGE

InputsModel

[object of class InputsModel] see CreateInputsModel for details

RunOptions

[object of class RunOptions] see CreateRunOptions for details

Obs

[numeric (atomic or list)] series of observed variable ([mm/time step] for discharge or SWE, [-] for SCA)

VarObs

(optional) [character (atomic or list)] names of the observed variable ("Q" by default, or one of "SCA", "SWE")

AprParamR

[numeric] a priori parameter set from a donor catchment

AprCrit

(optional) [numeric] performance criterion of the donor catchment (1 by default)

k

(optional) [numeric] coefficient used for the weighted average between FUN_CRIT and the gap between the optimised parameter set and an a priori parameter set calculated with the function produced by CreateErrorCrit_GAPX

BoolCrit

(optional) [boolean] boolean (the same length as Obs) giving the time steps to consider in the computation (all time steps are considered by default. See details)

transfo

(optional) [character] name of the transformation applied to the variables (e.g. "", "sqrt", "log", "inv", "sort", "boxcox" or a numeric value for power transformation for FUN_CRIT. Default value is "sqrt". See details of CreateInputsCrit

epsilon

(optional) [numeric] small value to add to all observations and simulations for FUN_CRIT when "log" or "inv" transformations are used [same unit as Obs]. See details of CreateInputsCrit

Author

David Dorchies

Details

The parameters FUN_CRIT, Obs, VarObs, BoolCrit, transfo, and epsilon must be used as they would be used for CreateInputsCrit in the case of a single criterion.

ErrorCrit_RMSE cannot be used in a composite criterion since it is not a unitless value.

CreateInputsCrit_Lavenne creates a composite criterion in respect with Equations 1 and 2 of de Lavenne et al. (2019).

References

de Lavenne, A., Andréassian, V., Thirel, G., Ramos, M.-H. and Perrin, C. (2019). A Regularization Approach to Improve the Sequential Calibration of a Semidistributed Hydrological Model. Water Resources Research 55, 8821–8839. tools:::Rd_expr_doi("10.1029/2018WR024266")

See Also

RunModel, CreateInputsModel, CreateRunOptions, CreateCalibOptions, ErrorCrit

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 the 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))