All the RR models included in
airGR are set up to work
in a simulation mode. However, provided that users have meteorological
forecast data and arrange their code to do so, hydrological forecasting
can be performed with the
airGR RR models
(especially the hourly and daily models).
What about inundation modelling? Can airGR do that?
No. We have some research ongoing work on it but nothing in
Is GRP in airGR?
is the operational flood forecasting model used by many flood
forecasting centres in France. It is not included in
airGR (see previous
question) but is available upon request and after signing an agreement,
both for research and real-time use, when contacting the responsible
Is OTAMIN in airGR?
OTAMIN is a
forecast uncertainty tool that is not included in
airGR. See the webpage for
Can we perform flow forecasting with airGR?
Yes, with some data manipulation, such a work has already been done
in the past. However we did not include a specific function for this in
Can we perform data assimilation of any kind in airGR?
A data assimilation scheme or framework is implemented in airGRdatassim
package. In addition, the
airGR RR models can be
plugged with data assimilation algorithms present in other R packages
(see for instance the R packages FKF, KFAS, miscFuncs, KFKSDS, nimble, DatAssim, which
we never tested but seem to be plausible solutions).
Can we perform automatic calibration of the GR models
Yes, in airGR you can
use the Calibration_Michel() function, which implements the
in-house algorithm of the team. Our algorithm is a mix between a global
method and a local method. The first step of the algorithm is to screen
the parameters space. The second step is a local steepest descent
gradient method. The calibration can be tuned (which parameter not to
calibrate, definition of complex calibration periods, etc.).
How to assess that my calibrated parameters are robust?
The robustness (i.e. ability of simulating good discharge on diverse
conditions) can be done by simply running the model on a different
period, using the optimised parameters.
Can we perform climate change impact studies with airGR?
Yes of course! What you are trying to do is very similar to a
calibration/control exercise as described in the Get Started article. You calibrate on
a historical period and you simulate the future conditions using the
parameters obtained from the previously mentioned calibration. Please
note that when analysing your future conditions, you must compare them
to simulations made by the model forced by present time GCM/RCM output,
not directly to discharge observations, as otherwise you would mix in
your analysis the bias of the hydrological model, the bias of the
GCM/RCM data and the actual impact of climate change. But this is very
common in CC studies so I guess you know all of this.
I prefer to use a MCMC-based or a particle swarm optimization
algorithm. Are these methods implemented in airGR?
I don’t have enough discharge data to calibrate GR4J. Do you have
any solution for me?
A set of generalist parameters for GR4J is provided in the
Param_Sets_GR4J object. This set of parameters can be
loaded with the following R command: data(Param_Sets_GR4J).
The Generalist parameter sets
article explains how to use this list. This set of parameters is quite
general and showed to perform better than automatic calibration when
very few data are available. Good performance is not warranted, but it
is better than nothing. We do not have such a list for the other RR
The streamflows simulated by the model are very low/high for the
first time steps. What am I doing wrong?
If not the case already, please define a long enough warm-up period.
It allows the model reservoirs to initialise to more plausible values
than the default ones.