|
To obtain help regarding the use of the airGRteaching or airGR packages, or to suggest modifications, send an email to airGR@inrae.fr
|
airGRteaching is a package developed in the
language devoted to the use of the GR rainfall-runoff models and the CemaNeige snowmelt and accumulation model by students and teachers.
airGRteaching is an add-on package of the airGR hydrological package.
It simplifies the use of the airGR functionalities as it only requires a basic level of programming.
The GR hydrological models in a few words: * lumped conceptual rainfall-runoff models (GR1A, GR2M, GR4J, GR5J, GR6J, GR4H and GR5H) * designed with the objective to be as efficient as possible for flow simulation at various time steps (from annual to hourly) * their structures were developed to have warranted complexity and limited data requirements * can be applied on a wide range of conditions, including snowy catchments (thanks to the CemaNeige snow model)
airGRteaching functionalities
- Only three simple functions for a full modelling exercise:
- data preparation
- model calibration
- model simulation
- Pre-defined graphical plots:
- static plotting functions
- mouse events and interactive graphics (using the dygraphs JavaScript charting library)
- Graphical user interface based on a Shiny interface:
- interactive flow simulation and plotting with parameters modifications
- automatic calibration button
- internal variables evolution graphs
- time period selection
- only monthly and daily models are currently available (GR2M, GR4J, GR5J, GR6J + CemaNeige)
- a demonstrator of the graphical interface is available for free online on the Sunshine website
See the “Functionalities” tab for examples including
commands.
airGR functionalities
- Easy implementation on numerous catchments
- Data requirements limited to lumped precip., temp. and streamflow time series
- One automatic calibration procedure
- A set of efficiency criteria
- Limited computation times (use of Fortran routines to run the models)
- Pre-defined graphical plots
- Outputs include simulated flow time series and internal variables
- User can implement its own models, efficiency criteria or optimization algorithms