Package: airGR 1.7.6

Olivier Delaigue

airGR: Suite of GR Hydrological Models for Precipitation-Runoff Modelling

Hydrological modelling tools developed at INRAE-Antony (HYCAR Research Unit, France). The package includes several conceptual rainfall-runoff models (GR4H, GR5H, GR4J, GR5J, GR6J, GR2M, GR1A) that can be applied either on a lumped or semi-distributed way. A snow accumulation and melt model (CemaNeige) and the associated functions for the calibration and evaluation of models are also included. Use help(airGR) for package description and references.

Authors:Laurent Coron [aut, trl], Olivier Delaigue [aut, cre], Guillaume Thirel [aut, ths], David Dorchies [aut], Charles Perrin [aut, ths], Claude Michel [aut, ths], Vazken Andréassian [ctb, ths], François Bourgin [ctb], Pierre Brigode [ctb], Nicolas Le Moine [ctb], Thibaut Mathevet [ctb], Safouane Mouelhi [ctb], Ludovic Oudin [ctb], Raji Pushpalatha [ctb], Audrey Valéry [ctb]

airGR_1.7.6.tar.gz
airGR_1.7.6.tar.gz(r-4.5-noble)airGR_1.7.6.tar.gz(r-4.4-noble)
airGR_1.7.6.tgz(r-4.4-emscripten)airGR_1.7.6.tgz(r-4.3-emscripten)
airGR.pdf |airGR.html✨
airGR/json (API)
NEWS

# Install 'airGR' in R:
install.packages('airGR', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/hydrogr/airgr/issues

Datasets:
  • BasinInfo - Data sample: characteristics of a different catchments
  • BasinInfo - Data sample: characteristics of a different catchments
  • BasinInfo - Data sample: characteristics of a different catchments
  • BasinInfo - Data sample: characteristics of a different catchments
  • BasinObs - Data sample: time series of observations of different catchments
  • BasinObs - Data sample: time series of observations of different catchments
  • BasinObs - Data sample: time series of observations of different catchments
  • BasinObs - Data sample: time series of observations of different catchments
  • Param_Sets_GR4J - Generalist parameter sets for the GR4J model
  • simCNGR4J - Default preview of model outputs
  • simGR4J - Default preview of model outputs

fortran

6.69 score 4 stars 4 packages 184 scripts 1.9k downloads 1 mentions 45 exports 0 dependencies

Last updated 1 years agofrom:d98088ab72. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 26 2024
R-4.5-linux-x86_64OKNov 26 2024

Exports:CalibrationCalibration_MichelCreateCalibOptionsCreateErrorCrit_GAPXCreateIniStatesCreateInputsCritCreateInputsCrit_LavenneCreateInputsModelCreateRunOptionsDataAltiExtrapolation_ValeryErrorCritErrorCrit_KGEErrorCrit_KGE2ErrorCrit_NSEErrorCrit_RMSEImaxPE_Oudinplot.OutputsModelRunModelRunModel_CemaNeigeRunModel_CemaNeigeGR4HRunModel_CemaNeigeGR4JRunModel_CemaNeigeGR5HRunModel_CemaNeigeGR5JRunModel_CemaNeigeGR6JRunModel_GR1ARunModel_GR2MRunModel_GR4HRunModel_GR4JRunModel_GR5HRunModel_GR5JRunModel_GR6JRunModel_LagSeriesAggregTransfoParamTransfoParam_CemaNeigeTransfoParam_CemaNeigeHystTransfoParam_GR1ATransfoParam_GR2MTransfoParam_GR4HTransfoParam_GR4JTransfoParam_GR5HTransfoParam_GR5JTransfoParam_GR6JTransfoParam_Lag

Dependencies:

Get Started with airGR

Guillaume Thirel, Olivier Delaigue, Laurent Coron

Rendered fromV01_get_started.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2023-10-26
Started: 2017-11-10

Generalist parameter sets for the GR4J model

Olivier Delaigue, Guillaume Thirel

Rendered fromV03_param_sets_GR4J.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2023-10-26
Started: 2017-11-10

Parameter estimation within a Bayesian MCMC framework

François Bourgin

Rendered fromV02.2_param_mcmc.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2022-02-22
Started: 2017-11-10

Plugging in new calibration algorithms in airGR

François Bourgin, Guillaume Thirel

Rendered fromV02.1_param_optim.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2023-10-26
Started: 2017-11-10

Simulated vs observed upstream flows in calibration of semi-distributed GR4J model

David Dorchies

Rendered fromV05_sd_model.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2023-10-26
Started: 2021-01-22

Using satellite snow cover area data for calibrating and improving CemaNeige

Guillaume Thirel, Olivier Delaigue

Rendered fromV04_cemaneige_hysteresis.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2022-02-22
Started: 2019-04-03

Readme and manuals

Help Manual

Help pageTopics
Suite of GR Hydrological Models for Precipitation-Runoff ModellingairGR-package airGR
Data sample: characteristics of a different catchmentsBasinInfo
Data sample: time series of observations of different catchmentsBasinObs L0123001 L0123002 L0123003 X0310010
Calibration algorithm that optimises the error criterion selected as objective function using the provided functionsCalibration
Calibration algorithm that optimises the error criterion selected as objective function using the Irstea procedure described by C. MichelCalibration_Michel
Creation of the CalibOptions object required but the Calibration* functionsCreateCalibOptions
Creation of the ErrorCrit_GAPX functionCreateErrorCrit_GAPX
Creation of the IniStates object possibly required by the CreateRunOptions functionsCreateIniStates
Creation of the InputsCrit object required to the ErrorCrit functionsCreateInputsCrit
Creation of the InputsCrit object for Lavenne CriterionCreateInputsCrit_Lavenne
Creation of the InputsModel object required to the RunModel functionsCreateInputsModel [.InputsModel
Creation of the RunOptions object required to the RunModel functionsCreateRunOptions
Altitudinal extrapolation of precipitation and temperature series described by A. ValeryDataAltiExtrapolation_Valery
Error criterion using the provided functionErrorCrit
Error criterion based on the KGE formulaErrorCrit_KGE
Error criterion based on the KGE' formulaErrorCrit_KGE2
Error criterion based on the NSE formulaErrorCrit_NSE
Error criterion based on the RMSEErrorCrit_RMSE
Computation of the maximum capacity of the GR5H interception storeImax
Generalist parameter sets for the GR4J modelParam_Sets_GR4J
Computation of series of potential evapotranspiration at the daily or hourly time steps with Oudin's formulaPE_Oudin
Default preview of model outputsexampleSimPlot plot plot.OutputsModel simCNGR4J simGR4J
Run with the provided hydrological model functionRunModel
Run with the CemaNeige snow moduleRunModel_CemaNeige
Run with the CemaNeigeGR4H hydrological modelRunModel_CemaNeigeGR4H
Run with the CemaNeigeGR4J hydrological modelRunModel_CemaNeigeGR4J
Run with the CemaNeigeGR5H hydrological modelRunModel_CemaNeigeGR5H
Run with the CemaNeigeGR5J hydrological modelRunModel_CemaNeigeGR5J
Run with the CemaNeigeGR6J hydrological modelRunModel_CemaNeigeGR6J
Run with the GR1A hydrological modelRunModel_GR1A
Run with the GR2M hydrological modelRunModel_GR2M
Run with the GR4H hydrological modelRunModel_GR4H
Run with the GR4J hydrological modelRunModel_GR4J
Run with the GR5H hydrological modelRunModel_GR5H
Run with the GR5J hydrological modelRunModel_GR5J
Run with the GR6J hydrological modelRunModel_GR6J
Run with the Lag modelRunModel_Lag
Conversion of time series to another time step (aggregation only) and regime computationSeriesAggreg SeriesAggreg.data.frame SeriesAggreg.InputsModel SeriesAggreg.list SeriesAggreg.OutputsModel
Transformation of the parameters using the provided functionTransfoParam TransfoParam_CemaNeige TransfoParam_CemaNeigeHyst TransfoParam_GR1A TransfoParam_GR2M TransfoParam_GR4H TransfoParam_GR4J TransfoParam_GR5H TransfoParam_GR5J TransfoParam_GR6J TransfoParam_Lag