airGR (version 1.3.2.23)

RunModel_GR5J: Run with the GR5J hydrological model

Description

Function which performs a single run for the GR5J daily lumped model over the test period.

Usage

RunModel_GR5J(InputsModel, RunOptions, Param)

Arguments

InputsModel

[object of class InputsModel] see CreateInputsModel for details

RunOptions

[object of class RunOptions] see CreateRunOptions for details

Param

[numeric] vector of 5 parameters

GR5J X1 production store capacity [mm]
GR5J X2 intercatchment exchange coefficient [mm/d]
GR5J X3 routing store capacity [mm]
GR5J X4 unit hydrograph time constant [d]
GR5J X5 intercatchment exchange threshold [-]

Value

[list] list containing the function outputs organised as follows:

$DatesR [POSIXlt] series of dates
$PotEvap [numeric] series of input potential evapotranspiration [mm/d]
$Precip [numeric] series of input total precipitation [mm/d]
$Prod [numeric] series of production store level [mm]
$Pn [numeric] series of net rainfall [mm/d]
$Ps [numeric] series of the part of Pn filling the production store [mm/d]
$AE [numeric] series of actual evapotranspiration [mm/d]
$Perc [numeric] series of percolation (PERC) [mm/d]
$PR [numeric] series of PR=Pn-Ps+Perc [mm/d]
$Q9 [numeric] series of UH1 outflow (Q9) [mm/d]
$Q1 [numeric] series of UH2 outflow (Q1) [mm/d]
$Rout [numeric] series of routing store level [mm]
$Exch [numeric] series of potential semi-exchange between catchments [mm/d]
$AExch1 [numeric] series of actual exchange between catchments for branch 1 [mm/d]
$AExch2 [numeric] series of actual exchange between catchments for branch 2 [mm/d]
$AExch [numeric] series of actual exchange between catchments (1+2) [mm/d]
$QR [numeric] series of routing store outflow (QR) [mm/d]
$QD [numeric] series of direct flow from UH2 after exchange (QD) [mm/d]
$Qsim [numeric] series of simulated discharge [mm/d]
$StateEnd [numeric] states at the end of the run (res. levels, UH1 levels, UH2 levels) [mm],
see CreateIniStates for more details

(refer to the provided references or to the package source code for further details on these model outputs)

Details

For further details on the model, see the references section. For further details on the argument structures and initialisation options, see CreateRunOptions.

Figure: diagramGR5J-EN.png

References

Le Moine, N. (2008). Le bassin versant de surface vu par le souterrain : une voie d'am<U+00E9>lioration des performances et du r<U+00E9>alisme des mod<U+00E8>les pluie-d<U+00E9>bit ? PhD thesis (french), UPMC, Paris, France.

Pushpalatha, R., C. Perrin, N. Le Moine, T. Mathevet, and V. Andr<U+00E9>assian (2011). A downward structural sensitivity analysis of hydrological models to improve low-flow simulation. Journal of Hydrology, 411(1-2), 66-76. doi:10.1016/j.jhydrol.2011.09.034.

See Also

RunModel_GR4J, RunModel_GR6J, RunModel_CemaNeigeGR5J, CreateInputsModel, CreateRunOptions, CreateIniStates.

Examples

Run this code
# NOT RUN {
library(airGR)

## loading catchment data
data(L0123001)

## preparation of the InputsModel object
InputsModel <- CreateInputsModel(FUN_MOD = RunModel_GR5J, DatesR = BasinObs$DatesR, 
                                 Precip = BasinObs$P, PotEvap = BasinObs$E)

## run 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 the RunOptions object
RunOptions <- CreateRunOptions(FUN_MOD = RunModel_GR5J,
                               InputsModel = InputsModel, IndPeriod_Run = Ind_Run)

## simulation
Param <- c(X1 = 245.918, X2 = 1.027, X3 = 90.017, X4 = 2.198, X5 = 0.434)
OutputsModel <- RunModel_GR5J(InputsModel = InputsModel,
                              RunOptions = RunOptions, Param = Param)

## 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)
# }

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