In inrae/airGRiwrm: 'airGR' Integrated Water Resource Management

knitr::opts_chunk$set(echo = TRUE)

The aim of this vignette is to run the simulation of the semi-distributed model of the Seine River with naturalized flows.

Load libraries

library(airGRiwrm)

Load parameters of the GR4J runoff model

Loading network and time series data

It is necessary to run the vignette("01_First_network", package = "airGRiwrm") before this one in order to create the Rdata file loaded below:

load("_cache/V01.RData")

Loading

GR4J parameters come from a calibration realized during the ClimAware project using naturalized flows.

library(seinebasin)
data(ClimAwareParams)
names(ClimAwareParams) <- c("id_sgl", "Tau0", "K0", "X1", "X2", "X3", "X4", "NashId")
ClimAwareParams

The lag $\tau_0$ and routing $K_0$ parameters of TGR are expressed as time delay in hours corresponding to the delay time between the farthest upstream inlet and the outlet of the sub-basin. Almost all sub basins have a routing parameter equal to 0. The only exception is for La Marne à Noisiel (NOISI_17) that has a routing parameter that can be approximated to a single lag parameter equal to $\tau_0 + K_0$.

This lag parameter has to be converted in a speed in m/s used in the airGR lag model:

# Convert TGR routing parameter into speed
params <- merge(griwrm, ClimAwareParams, by.x = "id", by.y = "id_sgl")

ParamClimAware <- sapply(griwrm$id, function(id) {
  nodeParam <- ClimAwareParams[ClimAwareParams$id_sgl == id,]
  # Record hydrological model parameters
  Param <- unlist(nodeParam[c("X1", "X2", "X3", "X4")])
  # Add lag model parameter if upstream nodes exist
  UpstrNodes <- which(griwrm$down == id & !is.na(griwrm$down))
  if (length(UpstrNodes) > 0) {
    maxLength <- max(griwrm$length[UpstrNodes])
    Param <- c(
      maxLength * 1000 / ((nodeParam$Tau0 + nodeParam$K0) * 3600),
      Param
    )
  }
  return(Param)
})

GriwmRunOptions object

The CreateRunOptions() function allows to prepare the options required by the RunModel() function.

The user must at least define the following arguments:

• InputsModel: the associated input data
• IndPeriod_Run: the period on which the model is run

IndPeriod_Run <- seq(
  which(InputsModel[[1]]$DatesR == (InputsModel[[1]]$DatesR[1] + 365*24*60*60)), # Set aside a one-year period for warm up
  length(InputsModel[[1]]$DatesR) # 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(1, IndPeriod_Run[1] - 1)

RunOptions <- CreateRunOptions(
  InputsModel,
  IndPeriod_WarmUp = IndPeriod_WarmUp,
  IndPeriod_Run = IndPeriod_Run
)

Run the SD model for the whole basin

OutputsModelsClimAware <- RunModel(
  InputsModel,
  RunOptions = RunOptions,
  Param = ParamClimAware
)

Plot the result for each basin

We plot the simulated discharges against the naturalized ones.

data(QNAT)
plot(OutputsModelsClimAware, Qobs = Qnat[IndPeriod_Run,])

Save data for next vignettes

save(RunOptions, ParamClimAware, IndPeriod_Run, file = "_cache/V02.RData")

inrae/airGRiwrm documentation built on Sept. 27, 2024, 6:08 p.m.