RunModel_CemaNeigeGR5H: Run with the CemaNeigeGR5H hydrological model

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/RunModel_CemaNeigeGR5H.R

Description

Function which performs a single run for the CemaNeige-GR5H hourly lumped model over the test period.

Usage

1
RunModel_CemaNeigeGR5H(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 7 (or 9 parameters if IsHyst = TRUE, see CreateRunOptions for details)

GR5H X1 production store capacity [mm]
GR5H X2 intercatchment exchange coefficient [mm/h]
GR5H X3 routing store capacity [mm]
GR5H X4 unit hydrograph time constant [h]
GR5H X5 intercatchment exchange threshold [-]
CemaNeige X1 weighting coefficient for snow pack thermal state [-]
CemaNeige X2 degree-hour melt coefficient [mm/°C/h]
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)

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_GR5H to look at the diagram of the hydrological model or RunModel_GR5J when no interception store is used.

Value

[list] containing the function outputs organised as follows:

$DatesR [POSIXlt] series of dates
$PotEvap [numeric] series of input potential evapotranspiration (E) [mm/h]
$Precip [numeric] series of input total precipitation (P) [mm/h]
$Interc [numeric] series of interception store level (I) [mm]
$Prod [numeric] series of production store level (S) [mm]
$Pn [numeric] series of net rainfall (Pn) [mm/h]
$Ps [numeric] series of the part of Pn filling the production store (Ps) [mm/h]
$AE [numeric] series of actual evapotranspiration (Ei+Es) [mm/h]
$EI [numeric] series of evapotranspiration from rainfall neutralisation or interception store (Ei) [mm/h]
$ES [numeric] series of evapotranspiration from production store (Es) [mm/h]
$Perc [numeric] series of percolation (Perc) [mm/h]
$PR [numeric] series of Pr=Pn-Ps+Perc (Pr) [mm/h]
$Q9 [numeric] series of UH outflow going into branch 9 (Q9) [mm/h]
$Q1 [numeric] series of UH outflow going into branch 1 (Q1) [mm/h]
$Rout [numeric] series of routing store level (R1) [mm]
$Exch [numeric] series of potential semi-exchange between catchments [mm/h]
$AExch1 [numeric] series of actual exchange between catchments for branch 1 [mm/h]
$AExch2 [numeric] series of actual exchange between catchments for branch 2 [mm/h]
$AExch [numeric] series of actual exchange between catchments (AExch1+AExch2) [mm/h]
$QR [numeric] series of routing store outflow (Qr) [mm/h]
$QD [numeric] series of direct flow from UH after exchange (Qd) [mm/h]
$Qsim [numeric] series of simulated discharge (Q) [mm/h]
$CemaNeigeLayers[[iLayer]]$Pliq [numeric] series of liquid precip. [mm/h]
$CemaNeigeLayers[[iLayer]]$Psol [numeric] series of solid precip. [mm/h]
$CemaNeigeLayers[[iLayer]]$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/h]
$CemaNeigeLayers[[iLayer]]$Melt [numeric] series of actual snow melt [mm/h]
$CemaNeigeLayers[[iLayer]]$PliqAndMelt [numeric] series of liquid precip. + actual snow melt [mm/h]
$CemaNeigeLayers[[iLayer]]$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]
$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.

Author(s)

Laurent Coron, Guillaume Thirel, Olivier Delaigue, Audrey Valéry, Vazken Andréassian

References

Ficchi, A. (2017). An adaptive hydrological model for multiple time-steps: Diagnostics and improvements based on fluxes consistency. PhD thesis, UPMC - Irstea Antony, Paris, France.

Ficchi, A., Perrin, C. and Andréassian, V. (2019). Hydrological modelling at multiple sub-daily time steps: model improvement via flux-matching. Journal of Hydrology, 575, 1308-1327, doi: 10.1016/j.jhydrol.2019.05.084.

Perrin, C., Michel, C. and Andréassian, V. (2003). Improvement of a parsimonious model for streamflow simulation. Journal of Hydrology, 279(1-4), 275-289, doi: 10.1016/S0022-1694(03)00225-7.

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, 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, 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, doi: 10.1016/j.jhydrol.2014.04.058.

See Also

RunModel_CemaNeige, RunModel_CemaNeigeGR4H, RunModel_GR5H, Imax, CreateInputsModel, CreateRunOptions, CreateIniStates.

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
## Not run: 
library(airGR)

## loading catchment data
data(U2345030)


## preparation of the InputsModel object
InputsModel <- CreateInputsModel(FUN_MOD = RunModel_CemaNeigeGR5H, DatesR = BasinObs$DatesR,
                                 Precip = BasinObs$P, PotEvap = BasinObs$E, TempMean = BasinObs$T,
                                 ZInputs = BasinInfo$ZInputs,
                                 HypsoData = BasinInfo$HypsoData, NLayers = 5)

## run period selection
Ind_Run <- seq(which(format(BasinObs$DatesR, format = "%Y-%m-%d %H:%M")=="2004-03-01 00:00"),
               which(format(BasinObs$DatesR, format = "%Y-%m-%d %H:%M")=="2008-12-31 23:00"))


## --- original version of CemaNeige

## Imax computation
Imax <- Imax(InputsModel = InputsModel, IndPeriod_Run = Ind_Run,
             TestedValues = seq(from = 0, to = 3, by = 0.2))

## preparation of the RunOptions object
RunOptions <- CreateRunOptions(FUN_MOD = RunModel_CemaNeigeGR5H, InputsModel = InputsModel,
                               Imax = Imax, IndPeriod_Run = Ind_Run)

## simulation
Param <- c(X1 = 218.537, X2 = -0.009, X3 = 174.862, X4 = 6.674, X5 = 0.000,
           CNX1 = 0.002, CNX2 = 3.787)
OutputsModel <- RunModel_CemaNeigeGR5H(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)

## End(Not run)

airGR documentation built on April 27, 2021, 9:07 a.m.