V05_Open-loop_influenced_flow_calibration.Rmd
library(airGRiwrm)
#> Loading required package: airGR
#>
#> Attaching package: 'airGRiwrm'
#> The following objects are masked from 'package:airGR':
#>
#> Calibration, CreateCalibOptions, CreateInputsCrit,
#> CreateInputsModel, CreateRunOptions, RunModel
This vignette aims at showing an example of calibrating the SD model on influenced flows while injecting observed uptakes and releases of the lakes. It will use influenced observation flows directly measured at gauging stations and flows recorded at reservoir inlets and outlets.
Loading naturalized data and influenced flows configuration:
load("_cache/V04.RData")
We remove extra items from a complete configuration to keep only the Marne system:
selectedNodes <- c("MARNE_P23", "STDIZ_04", "LOUVE_19", "VITRY_25", "MARNE_P28", "MARNE_R25", "CHALO_21", "MONTR_18", "NOISI_17")
griwrm3 <- griwrm2[griwrm2$id %in% selectedNodes,]
griwrm3[griwrm3$id == "NOISI_17", c("down", "length")] = NA # Downstream station instead of PARIS_05
plot(griwrm3)
We can now generate the new GRiwrmInputsModel
object:
library(seinebasin)
data(QOBS)
iEnd <- which(DatesR == as.POSIXct("2008-07-31", tz = "UTC"))
data(Qreservoirs)
QresMarne <- Qreservoirs[1:iEnd, grep("MARNE", colnames(Qreservoirs))]
id_GR_nodes <- griwrm3$id[!is.na(griwrm3$model)]
InputsModel3 <- CreateInputsModel(griwrm3,
DatesR[1:iEnd],
Precip[1:iEnd, id_GR_nodes],
PotEvap[1:iEnd, id_GR_nodes],
QresMarne)
#> CreateInputsModel.GRiwrm: Treating sub-basin STDIZ_04...
#> CreateInputsModel.GRiwrm: Treating sub-basin MONTR_18...
#> CreateInputsModel.GRiwrm: Treating sub-basin LOUVE_19...
#> CreateInputsModel.GRiwrm: Treating sub-basin VITRY_25...
#> CreateInputsModel.GRiwrm: Treating sub-basin CHALO_21...
#> CreateInputsModel.GRiwrm: Treating sub-basin NOISI_17...
We first define the run period:
IndPeriod_Run <- seq.int(
which(DatesR == (DatesR[1] + 365 * 24 * 60 * 60)), # Set aside warm-up period
iEnd # Until the end of the time series
)
We define the (optional but recommended) warm up period as a one-year period before the run period:
IndPeriod_WarmUp <- seq.int(1, IndPeriod_Run[1] - 1)
RunOptions <- CreateRunOptions(
InputsModel3,
IndPeriod_WarmUp = IndPeriod_WarmUp,
IndPeriod_Run = IndPeriod_Run
)
We define the objective function for the calibration:
InputsCrit <- CreateInputsCrit(
InputsModel = InputsModel3,
FUN_CRIT = ErrorCrit_KGE2,
RunOptions = RunOptions, Obs = Qobs[IndPeriod_Run,]
)
CalibOptions <- CreateCalibOptions(InputsModel3)
str(CalibOptions)
#> List of 6
#> $ STDIZ_04:List of 4
#> ..$ FixedParam : logi [1:5] NA NA NA NA NA
#> ..$ SearchRanges : num [1:2, 1:5] 1.00e-02 2.00e+01 4.59e-05 2.18e+04 -1.09e+04 ...
#> ..$ FUN_TRANSFO :function (ParamIn, Direction)
#> ..$ StartParamDistrib: num [1:3, 1:5] 11.2 12.5 15 169 247.2 ...
#> ..- attr(*, "class")= chr [1:5] "CalibOptions" "daily" "GR" "SD" ...
#> $ MONTR_18:List of 4
#> ..$ FixedParam : logi [1:4] NA NA NA NA
#> ..$ SearchRanges : num [1:2, 1:4] 4.59e-05 2.18e+04 -1.09e+04 1.09e+04 4.59e-05 ...
#> ..$ FUN_TRANSFO :function (ParamIn, Direction)
#> ..$ StartParamDistrib: num [1:3, 1:4] 169.017 247.151 432.681 -2.376 -0.649 ...
#> ..- attr(*, "class")= chr [1:4] "CalibOptions" "daily" "GR" "HBAN"
#> $ LOUVE_19:List of 4
#> ..$ FixedParam : logi [1:4] NA NA NA NA
#> ..$ SearchRanges : num [1:2, 1:4] 4.59e-05 2.18e+04 -1.09e+04 1.09e+04 4.59e-05 ...
#> ..$ FUN_TRANSFO :function (ParamIn, Direction)
#> ..$ StartParamDistrib: num [1:3, 1:4] 169.017 247.151 432.681 -2.376 -0.649 ...
#> ..- attr(*, "class")= chr [1:4] "CalibOptions" "daily" "GR" "HBAN"
#> $ VITRY_25:List of 4
#> ..$ FixedParam : logi [1:4] NA NA NA NA
#> ..$ SearchRanges : num [1:2, 1:4] 4.59e-05 2.18e+04 -1.09e+04 1.09e+04 4.59e-05 ...
#> ..$ FUN_TRANSFO :function (ParamIn, Direction)
#> ..$ StartParamDistrib: num [1:3, 1:4] 169.017 247.151 432.681 -2.376 -0.649 ...
#> ..- attr(*, "class")= chr [1:4] "CalibOptions" "daily" "GR" "HBAN"
#> $ CHALO_21:List of 4
#> ..$ FixedParam : logi [1:5] NA NA NA NA NA
#> ..$ SearchRanges : num [1:2, 1:5] 1.00e-02 2.00e+01 4.59e-05 2.18e+04 -1.09e+04 ...
#> ..$ FUN_TRANSFO :function (ParamIn, Direction)
#> ..$ StartParamDistrib: num [1:3, 1:5] 11.2 12.5 15 169 247.2 ...
#> ..- attr(*, "class")= chr [1:5] "CalibOptions" "daily" "GR" "SD" ...
#> $ NOISI_17:List of 4
#> ..$ FixedParam : logi [1:5] NA NA NA NA NA
#> ..$ SearchRanges : num [1:2, 1:5] 1.00e-02 2.00e+01 4.59e-05 2.18e+04 -1.09e+04 ...
#> ..$ FUN_TRANSFO :function (ParamIn, Direction)
#> ..$ StartParamDistrib: num [1:3, 1:5] 11.2 12.5 15 169 247.2 ...
#> ..- attr(*, "class")= chr [1:5] "CalibOptions" "daily" "GR" "SD" ...
#> - attr(*, "class")= chr [1:2] "GRiwrmCalibOptions" "list"
The optimization (i.e. calibration) of parameters can now be performed:
OutputsCalib <- Calibration(InputsModel3, RunOptions, InputsCrit, CalibOptions)
#> Calibration.GRiwrmInputsModel: Treating sub-basin STDIZ_04...
#> Grid-Screening in progress (0% 20% 40% 60% 80% 100%)
#> Screening completed (243 runs)
#> Param = 15.000, 169.017, -0.020, 83.096, 2.384
#> Crit. KGE2[Q] = 0.8620
#> Steepest-descent local search in progress
#> Calibration completed (138 iterations, 1625 runs)
#> Param = 19.990, 166.297, -0.235, 69.322, 3.730
#> Crit. KGE2[Q] = 0.9179
#> Calibration.GRiwrmInputsModel: Treating sub-basin MONTR_18...
#> Grid-Screening in progress (0% 20% 40% 60% 80% 100%)
#> Screening completed (81 runs)
#> Param = 247.151, -0.649, 42.098, 2.384
#> Crit. KGE2[Q] = 0.8117
#> Steepest-descent local search in progress
#> Calibration completed (32 iterations, 331 runs)
#> Param = 198.455, -1.070, 77.183, 2.473
#> Crit. KGE2[Q] = 0.8311
#> Calibration.GRiwrmInputsModel: Treating sub-basin LOUVE_19...
#> Grid-Screening in progress (0% 20% 40% 60% 80% 100%)
#> Screening completed (81 runs)
#> Param = 247.151, -2.376, 83.096, 2.384
#> Crit. KGE2[Q] = 0.9123
#> Steepest-descent local search in progress
#> Calibration completed (25 iterations, 270 runs)
#> Param = 174.509, -3.018, 96.535, 2.344
#> Crit. KGE2[Q] = 0.9306
#> Calibration.GRiwrmInputsModel: Treating sub-basin VITRY_25...
#> Grid-Screening in progress (0% 20% 40% 60% 80% 100%)
#> Screening completed (81 runs)
#> Param = 432.681, -0.649, 83.096, 2.384
#> Crit. KGE2[Q] = 0.8712
#> Steepest-descent local search in progress
#> Calibration completed (64 iterations, 612 runs)
#> Param = 299.290, -1.228, 91.986, 5.101
#> Crit. KGE2[Q] = 0.9531
#> Calibration.GRiwrmInputsModel: Treating sub-basin CHALO_21...
#> Grid-Screening in progress (0% 20% 40% 60% 80% 100%)
#> Screening completed (243 runs)
#> Param = 11.250, 432.681, -2.376, 20.697, 1.417
#> Crit. KGE2[Q] = 0.8932
#> Steepest-descent local search in progress
#> Calibration completed (127 iterations, 1522 runs)
#> Param = 0.389, 1242.784, -2.031, 4.986, 3.967
#> Crit. KGE2[Q] = 0.9569
#> Calibration.GRiwrmInputsModel: Treating sub-basin NOISI_17...
#> Grid-Screening in progress (0% 20% 40% 60% 80% 100%)
#> Screening completed (243 runs)
#> Param = 11.250, 432.681, -2.376, 83.096, 1.417
#> Crit. KGE2[Q] = 0.7979
#> Steepest-descent local search in progress
#> Calibration completed (167 iterations, 1972 runs)
#> Param = 0.778, 2364.963, -2.338, 24.380, 2.694
#> Crit. KGE2[Q] = 0.9502
Now that the model is calibrated, we can run it with the optimized parameter values:
Param5 <- sapply(griwrm3$id, function(x) {OutputsCalib[[x]]$Param})
OutputsModels3 <- RunModel(
InputsModel3,
RunOptions = RunOptions,
Param = Param5
)
#> RunModel.GRiwrmInputsModel: Treating sub-basin STDIZ_04...
#> Warning in RunModel_Lag(InputsModel, RunOptions, Param[1], OutputsModel): 136
#> time steps with negative flow, set to zero.
#> RunModel.GRiwrmInputsModel: Treating sub-basin MONTR_18...
#> RunModel.GRiwrmInputsModel: Treating sub-basin LOUVE_19...
#> RunModel.GRiwrmInputsModel: Treating sub-basin VITRY_25...
#> RunModel.GRiwrmInputsModel: Treating sub-basin CHALO_21...
#> Warning in RunModel_Lag(InputsModel, RunOptions, Param[1], OutputsModel): 10
#> time steps with negative flow, set to zero.
#> Warning in RunModel_Lag(InputsModel, RunOptions, Param[1], OutputsModel): 3 time
#> steps with NA values
#> RunModel.GRiwrmInputsModel: Treating sub-basin NOISI_17...
#> Warning in RunModel_Lag(InputsModel, RunOptions, Param[1], OutputsModel): 7 time
#> steps with NA values
We can compare these simulated flows with influenced discharge measurements:
htmltools::tagList(lapply(
griwrm3$id[!is.na(griwrm3$model)],
function(x) {
Q3 <- Qobs[RunOptions[[1]]$IndPeriod_Run, x]
iQ3 <- which(!is.na(Q3))
IndPeriod_Obs <- iQ3[1]:tail(iQ3, 1)
OutputsModels <- ReduceOutputsModel(OutputsModels3[[x]], IndPeriod_Obs)
plot(OutputsModels, Qobs = Q3[IndPeriod_Obs], main = x)
}
))
#> Warning in plot.OutputsModel(OutputsModels, Qobs = Q3[IndPeriod_Obs], main = x):
#> zeroes detected in 'Qsim': some plots in the log space will not be created using
#> all time-steps
save(Param5, file = "_cache/V05.RData")