Learn R Programming
airGR (version 1.7.6)

RunModel_CemaNeigeGR5J: Run with the CemaNeigeGR5J hydrological model

Description

Function which performs a single run for the CemaNeige-GR5J daily lumped model.

Usage

RunModel_CemaNeigeGR5J(InputsModel, RunOptions, Param)

Value

[list] containing the function outputs organised as follows:

$DatesR [POSIXlt] series of dates
$PotEvap [numeric] series of input potential evapotranspiration (E) [mm/d]
$Precip [numeric] series of input total precipitation (P) [mm/d]
$Prod [numeric] series of production store level (S) [mm]
$Pn [numeric] series of net rainfall (Pn) [mm/d]
$Ps [numeric] series of the part of Pn filling the production store (Ps) [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 (Pr) [mm/d]
$Q9 [numeric] series of UH outflow going into branch 9 (Q9) [mm/d]
$Q1 [numeric] series of UH outflow going into branch 1 (Q1) [mm/d]
$Rout [numeric] series of routing store level (R1) [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 (AExch1+AExch2) [mm/d]
$QR [numeric] series of routing store outflow (Qr) [mm/d]
$QD [numeric] series of direct flow from UH after exchange (Qd) [mm/d]
$Qsim [numeric] series of simulated discharge (Q) [mm/d]
$CemaNeigeLayers[list] CemaNeige outputs (1 element per layer)
$CemaNeigeLayers[[iLayer]]$Pliq [numeric] series of liquid precip. [mm/d]
$CemaNeigeLayers[[iLayer]]$Psol [numeric] series of solid precip. [mm/d]
$CemaNeigeLayers[[iLayer]]$SnowPack [numeric] series of snow pack (snow water equivalent) [mm]
$CemaNeigeLayers[[iLayer]]$ThermalState[numeric] series of snow pack thermal state [°C]
$CemaNeigeLayers[[iLayer]]$Gratio [numeric] series of Gratio [0-1]
$CemaNeigeLayers[[iLayer]]$PotMelt [numeric] series of potential snow melt [mm/d]
$CemaNeigeLayers[[iLayer]]$Melt [numeric] series of actual snow melt [mm/d]
$CemaNeigeLayers[[iLayer]]$PliqAndMelt [numeric] series of liquid precip. + actual snow melt [mm/d]
$CemaNeigeLayers[[iLayer]]$Temp [numeric] series of air temperature [°C]
$CemaNeigeLayers[[iLayer]]$Gthreshold [numeric] series of melt threshold [mm]
$CemaNeigeLayers[[iLayer]]$Glocalmax [numeric] series of local melt threshold for hysteresis [mm]
RunOptions$WarmUpQsim[numeric] series of simulated discharge (Q) on the warm-up period [mm/d]
RunOptions$Param [numeric] parameter set parameter set used by the model
$StateEnd[numeric] states at the end of the run: store & unit hydrographs levels [mm], CemaNeige states [mm & °C]. See CreateIniStates for more details

Refer to the provided references or to the package source code for further details on these model outputs

Arguments

InputsModel

[object of class InputsModel] see CreateInputsModel for details

RunOptions

[object of class RunOptions] see CreateRunOptions for details

Param

[numeric] vector of 7 (or 9 parameters if IsHyst = TRUE, see CreateRunOptions for details)

GR5J X1production store capacity [mm]
GR5J X2intercatchment exchange coefficient [mm/d]
GR5J X3routing store capacity [mm]
GR5J X4unit hydrograph time constant [d]
GR5J X5intercatchment exchange threshold [-]
CemaNeige X1weighting coefficient for snow pack thermal state [-]
CemaNeige X2degree-day melt coefficient [mm/°C/d]
CemaNeige X3(optional) accumulation threshold [mm] (needed if IsHyst = TRUE)
CemaNeige X4(optional) percentage (between 0 and 1) of annual snowfall defining the melt threshold [-] (needed if IsHyst = TRUE)

Author

Laurent Coron, Claude Michel, Nicolas Le Moine, Audrey Valéry, Vazken Andréassian, Olivier Delaigue, Guillaume Thirel

Details

The choice of the CemaNeige version is explained in CreateRunOptions.
For further details on the model, see the references section.
For further details on the argument structures and initialisation options, see CreateRunOptions.

See RunModel_GR5J to look at the diagram of the hydrological model.

References

Le Moine, N. (2008). Le bassin versant de surface vu par le souterrain : une voie d'amélioration des performances et du réalisme des modèles pluie-débit ? PhD thesis (in French), UPMC - Cemagref Antony, Paris, France.

Pushpalatha, R., Perrin, C., Le Moine, N., Mathevet, T. and Andréassian, V. (2011). A downward structural sensitivity analysis of hydrological models to improve low-flow simulation. Journal of Hydrology, 411(1-2), 66-76, tools:::Rd_expr_doi("10.1016/j.jhydrol.2011.09.034").

Riboust, P., Thirel, G., Le Moine, N. and Ribstein, P. (2019). Revisiting a simple degree-day model for integrating satellite data: Implementation of SWE-SCA hystereses. Journal of Hydrology and Hydromechanics, 67(1), 70–81, tools:::Rd_expr_doi("10.2478/johh-2018-0004").

Valéry, A., Andréassian, V. and Perrin, C. (2014). "As simple as possible but not simpler": What is useful in a temperature-based snow-accounting routine? Part 1 - Comparison of six snow accounting routines on 380 catchments. Journal of Hydrology, 517(0), 1166-1175, tools:::Rd_expr_doi("10.1016/j.jhydrol.2014.04.059").

Valéry, A., Andréassian, V. and Perrin, C. (2014). "As simple as possible but not simpler": What is useful in a temperature-based snow-accounting routine? Part 2 - Sensitivity analysis of the Cemaneige snow accounting routine on 380 catchments. Journal of Hydrology, 517(0), 1176-1187, tools:::Rd_expr_doi("10.1016/j.jhydrol.2014.04.058").

See Also

RunModel_CemaNeige, RunModel_CemaNeigeGR4J, RunModel_CemaNeigeGR6J, RunModel_GR5J, CreateInputsModel, CreateRunOptions, CreateIniStates.

Examples

Run this code
library(airGR)

## loading catchment data
data(L0123002)

## preparation of the InputsModel object
InputsModel <- CreateInputsModel(FUN_MOD = RunModel_CemaNeigeGR5J, DatesR = BasinObs$DatesR,
                                 Precip = BasinObs$P, PotEvap = BasinObs$E, TempMean = BasinObs$T,
                                 ZInputs = median(BasinInfo$HypsoData),
                                 HypsoData = BasinInfo$HypsoData, NLayers = 5)

## 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_CemaNeigeGR5J, InputsModel = InputsModel,
                               IndPeriod_Run = Ind_Run)

## simulation
Param <- c(X1 = 179.139, X2 = -0.100, X3 = 203.815, X4 = 1.174, X5 = 2.478,
           CNX1 = 0.977, CNX2 = 2.774)
OutputsModel <- RunModel_CemaNeigeGR5J(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)


## simulation with the Linear Hysteresis
## preparation of the RunOptions object
RunOptions <- CreateRunOptions(FUN_MOD = RunModel_CemaNeigeGR5J, InputsModel = InputsModel,
                               IndPeriod_Run = Ind_Run, IsHyst = TRUE)
Param <- c(179.139, -0.100, 203.815, 1.174, 2.478, 0.977, 2.774, 100, 0.4)
OutputsModel <- RunModel_CemaNeigeGR5J(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)

Run the code above in your browser using DataLab