This part explains how to run the airGR hydrological models in only three simple steps with airGRteaching.
A data.frame
of daily hydrometeorological observations
time series at the catchment scale is needed. The required fields
are:
POSIXt
format## DatesR P E Qmm T
## 1 1984-01-01 4.1 0.2 0.6336 0.5
## 2 1984-01-02 15.9 0.2 0.8256 0.2
## 3 1984-01-03 0.8 0.3 2.9280 0.9
## 4 1984-01-04 0.0 0.3 1.8240 0.5
## 5 1984-01-05 0.0 0.1 1.5000 -1.6
## 6 1984-01-06 0.0 0.3 1.3560 0.9
Before running a model, airGRteaching functions require data and options with specific formats.
For this step, you just have to use the PrepGR()
function. You have to define:
ObsDF
: data.frame
of hydrometeorological
observations time seriesHydroModel
: the name of the hydrological model you want
to run (GR1A, GR2M, GR4J, GR5J, GR6J, GR4H or GR5H)CemaNeige
: if you want or not to use the snowmelt and
accumulation modelIf you want to use CemaNeige, you also have to define:
ObsDF
or in
TempMean
HypsoData
: a vector of 101 reals: min, quantiles (1 %
to 99 %) and max of catchment elevation distribution [m]; if not defined
a single elevation layer is used for CemaNeigeNLayers
: the number of elevation layers requested
[-]To calibrate a model, you just have to use the CalGR()
function. By default, the objective function used is the Nash–Sutcliffe
criterion ("NSE"
), and the warm-up period is automatically
set (depends on model). You just have to define:
PrepGR
: the object returned by the
PrepGR()
functionCalPer
: a vector of 2 dates to define the calibration
periodYou can obviously define another objective function or warm-up period:
CalCrit
: name of the objective function
("NSE", "KGE", "KGE2", "RMSE"
)WupPer
: a vector of 2 dates to define the warm-up
periodThe calibration algorithm has been developed by Claude Michel
(Calibration_Michel()
function in the
airGR package) .
CAL <- CalGR(PrepGR = PREP, CalCrit = "KGE2",
WupPer = NULL, CalPer = c("1990-01-01", "1993-12-31"))
## Grid-Screening in progress (0% 20% 40% 60% 80% 100%)
## Screening completed (243 runs)
## Param = 175.915, -0.110, 83.931, 1.857, 0.467
## Crit. KGE2[Q] = 0.8300
## Steepest-descent local search in progress
## Calibration completed (18 iterations, 406 runs)
## Param = 188.670, 1.456, 83.931, 1.779, 0.493
## Crit. KGE2[Q] = 0.8787
To run a model, please use the SimGR()
function. The
PrepGR
and WupPer
arguments of
SimGR()
are similar to the ones of the CalGR()
function. Here, EffCrit
is used to calculate the
performance of the model over the simulation period SimPer
and Param
is the object returned by the
CalGR()
function.
SIM <- SimGR(PrepGR = PREP, Param = CAL, EffCrit = "KGE2",
WupPer = NULL, SimPer = c("1994-01-01", "1998-12-31"))
## Crit. KGE2[Q] = 0.8549
## SubCrit. KGE2[Q] cor(sim, obs, "pearson") = 0.9012
## SubCrit. KGE2[Q] cv(sim)/cv(obs) = 0.8974
## SubCrit. KGE2[Q] mean(sim)/mean(obs) = 0.9724
The call of the as.data.frame()
function with
PrepGR
, CalGR
or SimGR
objects
allows to coerce the outputs to a data frame.
## Dates PotEvap PrecipObs PrecipFracSolid_CemaNeige TempMeanSim_CemaNeige
## 1 1984-01-01 0.2 4.1 NA NA
## 2 1984-01-02 0.2 15.9 NA NA
## 3 1984-01-03 0.3 0.8 NA NA
## 4 1984-01-04 0.3 0.0 NA NA
## 5 1984-01-05 0.1 0.0 NA NA
## 6 1984-01-06 0.3 0.0 NA NA
## Qobs Qsim
## 1 0.6336 NA
## 2 0.8256 NA
## 3 2.9280 NA
## 4 1.8240 NA
## 5 1.5000 NA
## 6 1.3560 NA
## Dates PotEvap PrecipObs PrecipFracSolid_CemaNeige TempMeanSim_CemaNeige
## 1 1990-01-01 0.3 0.0 NA NA
## 2 1990-01-02 0.4 9.3 NA NA
## 3 1990-01-03 0.4 3.2 NA NA
## 4 1990-01-04 0.3 7.3 NA NA
## 5 1990-01-05 0.1 0.0 NA NA
## 6 1990-01-06 0.1 0.0 NA NA
## Qobs Qsim
## 1 1.992 2.523954
## 2 1.800 2.446199
## 3 2.856 2.943436
## 4 2.400 3.286214
## 5 3.312 3.512572
## 6 3.072 3.224969
## Dates PotEvap PrecipObs PrecipFracSolid_CemaNeige TempMeanSim_CemaNeige
## 1 1994-01-01 0.4 2.2 NA NA
## 2 1994-01-02 0.4 0.0 NA NA
## 3 1994-01-03 0.6 0.7 NA NA
## 4 1994-01-04 0.6 3.2 NA NA
## 5 1994-01-05 0.6 35.1 NA NA
## 6 1994-01-06 0.5 21.3 NA NA
## Qobs Qsim
## 1 2.904 3.593023
## 2 2.832 3.414026
## 3 2.364 2.988078
## 4 2.544 2.668972
## 5 2.640 3.526016
## 6 8.928 8.819935
The call of the plot()
function with a
PrepGR
object draws the observed precipitation and
discharge time series.
By default (with the argument which = "synth"
), the call
of the plot()
function with a CalGR
object
draws the classical airGR
plot diagnostics (observed and simulated time series together with
diagnostic plot)
With the CalGR
object, if the argument
which
is set to "iter"
, the
plot()
function draws the evolution of the parameters and
the values of the objective function during the second step of the
calibration (steepest descent local search algorithm):
With the CalGR
object, if the argument
which
is set to "ts"
, the plot()
function simply draws the time series of the observed precipitation, and
the observed and simulated flows:
## Warning in plot.OutputsModel(x$OutputsModel, Qobs = x$Qobs, which = which, :
## zeroes detected in 'Qsim': some plots in the log space will not be created
## using all time-steps
The call of the plot()
function with a
SimGR
object displays the classical
airGR plot
diagnostics.
Dynamic plots, using the dygraphs JavaScript charting library, can be displayed by the package.
The dyplot()
function can be applied on
PrepGR
, CalGR
and SimGR
objects
and draws the time series of the observed precipitation, and the
observed and simulated (except with PrepGR
objects)
flows.
The user can zoom on the plot device and can read the exact values.
With this function, users can easily explore the data time series and also explore and interpret the possible problems of the calibration or simulation steps.
The airGRteaching
package also provides the ShinyGR()
function, which allows
to launch a graphical user interface using the shiny
package.
The ShinyGR()
function needs at least:
ObsDF
: a (list of) data.frame
(or
independant vector instead, see ?ShinyGR
)SimPer
: a (list of) vector(s) of 2 dates to define the
simulation period(s)Only the monthly model (GR2M) and the daily models (GR4J, GR5J, GR6J
+ CemaNeige) are currently available.
If you want to use CemaNeige, you also have to define the same arguments
desribed above for the PrepGR()
function.