Latest developments of the airGR rainfall-runoff modelling R package: new calibration procedures and other features

HAL Id: hal-02607857

https://hal.inrae.fr/hal-02607857

Submitted on 16 May 2020

HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether they are pub- lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Latest developments of the airGR rainfall-runoff modelling R package: new calibration procedures and

other features

Olivier Delaigue, Guillaume Thirel, François Bourgin, L. Coron

To cite this version:

Olivier Delaigue, Guillaume Thirel, François Bourgin, L. Coron. Latest developments of the airGR rainfall-runoff modelling R package: new calibration procedures and other features. EGU General Assembly 2018, Apr 2018, Vienna, Austria. pp.1, 2018. �hal-02607857�

Latest developments of the airGR rainfall-runoff modelling R package:

new calibration procedures and other features

Olivier Delaigue ¹ , Guillaume Thirel ¹ , Fran¸cois Bourgin ² , Laurent Coron ³

1 IRSTEA – Hydrology Research Group (HYCAR) – Antony, France

2 IFSTTAR – GERS, EE – Nantes, France

3 EDF – PMC Hydrometeorological Center – Toulouse, France

air

GR is a family of lumped hydrological models designed for flow simulation at various time steps. The models are freely available in an R package called airGR (Coron et al., 2017a, 2017b). The models can easily be implemented on a set of catchments with limited data requirements.

GR hydrological models

I Designed with the objective to be as efficient as possible for flow simulation at various time steps (from hourly to interannual)

I Warranted complexity structures and limited data requirements

I Can be applied on a wide range of conditions, including snowy catchments (CemaNeige snow routine included)

Main components of the airGR package Plot diagnostics example (GR4J + CemaNeige)

Pot. evaporation computation (from temp.-based formula)

Optimization algorithm Calibration/Validation

testing procedure Criteria for model calibration and evaluation

Outputs

• Time series of simulated flows and internal state variables

• Efficiency criteria

• Plot diagnostics for simulation

Inputs

• Precipitation and temperature time series

• Streamflow time series

• Catchment size and latitude

• Hypsometric curve (for snow module)

Hydrological models

• GR4H (hourly)

• GR4J, GR5J, GR6J (daily)

• GR2M (monthly)

• GR1A (annual)

Snow model

• CemaNeige

7020

precip. [mm/d]

solid liquid 01/1990 01/1991 01/1992 01/1993 01/1994 01/1995 01/1996 01/1997 01/1998 01/1999

−1001020

temp. [°C]

mean layers

01/1990 01/1991 01/1992 01/1993 01/1994 01/1995 01/1996 01/1997 01/1998 01/1999

04008001200

snow pack [mm]

mean layers

01/1990 01/1991 01/1992 01/1993 01/1994 01/1995 01/1996 01/1997 01/1998 01/1999

0510152025

flow [mm/d]

01/1990 01/1991 01/1992 01/1993 01/1994 01/1995 01/1996 01/1997 01/1998 01/1999 observed simulated

1500

Jan May Sep

0150

30−days rolling mean

precip. & flow regime [mm/month]

0 0.4 0.8

non−exceedance prob. [−]

0.21520

flow [mm/d]

observed simulated

log scale

0.2 1 5 20

0.21520

observed flow [mm/d]

simulated flow [mm/d]

log scale

News since EGU 2017 – airGR 1.0.9.64 vs airGR 1.0.5.12

I The Param Sets GR4J dataset was added. It contains generalist parameter sets for the GR4J model

. If the calibration period is too short (< 6 months) and by consequence non

representative of the catchment behaviour, a local calibration algorithm can give poor results and we recommend to use the generalist parameter sets instead

I Vignettes were added. They explain how to perform parameters estimation with:

. Differential Evolution calibration algorithm . Particle Swarm calibration algorithm

. MA-LS-Chains calibration algorithm . Bayesian MCMC framework

I A new airGRteaching package (Delaigue et al., 2018) provides tools to simplify the use of the airGR hydrological package for education, including a ’Shiny’ interface

Future developments

I New version of CemaNeige that allows to use satellite snow cover area for calibration (Riboust et al., accepted)

I Parameters maps on France for GR4J, GR5J & GR6J models for ungauged bassins (Poncelet et al., submitted)

How to use other R packages to perform parameters estimation

I Definition of the necessary function:

. transformation of parameters to real space (available in airGR)

. computation of the value of the performance criterion (e.g. RMSE)

OptimGR4J <- function(Param_Optim) {

Param_Optim_Vre <- airGR::TransfoParam_GR4J(ParamIn = Param_Optim, Direction = "TR")

OutputsModel <- airGR::RunModel_GR4J(InputsModel = InputsModel, RunOptions = RunOptions, Param = Param_Optim_Vre) OutputsCrit <- airGR::ErrorCrit_RMSE(InputsCrit = InputsCrit,

OutputsModel = OutputsModel) return(OutputsCrit$CritValue)

}

lowerGR4J <- rep(-9.99, times = 4) upperGR4J <- rep(+9.99, times = 4)

Local optimization

startGR4J <- c(4.1, 3.9, -0.9, -8.7)

optPORT <- stats::nlminb(start = startGR4J, objective = OptimGR4J, lower = lowerGR4J, upper = upperGR4J,

control = list(trace = 1))

startGR4J <- expand.grid(data.frame(CalibOptions$StartParamDistrib)) optPORT_ <- function(x) {

opt <- stats::nlminb(start = x, objective = OptimGR4J,

lower = lowerGR4J, upper = upperGR4J, control = list(trace = 1))

}

list_opt <- apply(startGR4J, 1, optPORT_)

summary(df_port)

Global optimization Differential Evolution

optDE <- DEoptim::DEoptim(fn = OptimGR4J,

lower = lowerGR4J, upper = upperGR4J,

control = DEoptim::DEoptim.control(NP = 40, trace = 10))

Particle Swarm

optPSO <- hydroPSO::hydroPSO(fn = OptimGR4J,

lower = lowerGR4J, upper = upperGR4J, control = list(write2disk = FALSE)

MA-LS-Chains

optMALS <- Rmalschains::malschains(fn = OptimGR4J, maxEvals = 2000,

lower = lowerGR4J, upper = upperGR4J)

Results

## % latex table generated in R 3.4.2 by xtable 1.8-2 package

## % Tue Mar 27 17:21:31 2018

## \begin{table}[ht]

## \centering

## \begin{tabular}{rlrrrr}

## \hline

## & Algo & X1 & X2 & X3 & X4 \\

## \hline

## 1 & Michel & 257.00 & 1.01 & 88.20 & 2.21 \\

## 2 & PORT & 257.00 & 1.00 & 88.20 & 2.21 \\

## 3 & DE & 257.00 & 1.00 & 88.20 & 2.21 \\

1

I Definition of the lower and upper bounds of the four GR4J parameters in the transformed parameter space

OptimGR4J <- function(Param_Optim) {

Param_Optim_Vre <- airGR::TransfoParam_GR4J(ParamIn = Param_Optim, Direction = "TR")

OutputsModel <- airGR::RunModel_GR4J(InputsModel = InputsModel, RunOptions = RunOptions, Param = Param_Optim_Vre) OutputsCrit <- airGR::ErrorCrit_RMSE(InputsCrit = InputsCrit,

OutputsModel = OutputsModel) return(OutputsCrit$CritValue)

}

lowerGR4J <- rep(-9.99, times = 4) upperGR4J <- rep(+9.99, times = 4)

Local optimization

startGR4J <- c(4.1, 3.9, -0.9, -8.7)

optPORT <- stats::nlminb(start = startGR4J, objective = OptimGR4J, lower = lowerGR4J, upper = upperGR4J,

control = list(trace = 1))

startGR4J <- expand.grid(data.frame(CalibOptions$StartParamDistrib)) optPORT_ <- function(x) {

opt <- stats::nlminb(start = x, objective = OptimGR4J,

lower = lowerGR4J, upper = upperGR4J, control = list(trace = 1))

}

list_opt <- apply(startGR4J, 1, optPORT_)

summary(df_port)

Global optimization Differential Evolution

optDE <- DEoptim::DEoptim(fn = OptimGR4J,

lower = lowerGR4J, upper = upperGR4J,

control = DEoptim::DEoptim.control(NP = 40, trace = 10))

Particle Swarm

optPSO <- hydroPSO::hydroPSO(fn = OptimGR4J,

lower = lowerGR4J, upper = upperGR4J, control = list(write2disk = FALSE)

MA-LS-Chains

optMALS <- Rmalschains::malschains(fn = OptimGR4J, maxEvals = 2000,

lower = lowerGR4J, upper = upperGR4J)

Results

## % latex table generated in R 3.4.2 by xtable 1.8-2 package

## % Tue Mar 27 17:21:31 2018

## \begin{table}[ht]

## \centering

## \begin{tabular}{rlrrrr}

## \hline

## & Algo & X1 & X2 & X3 & X4 \\

## \hline

## 1 & Michel & 257.00 & 1.01 & 88.20 & 2.21 \\

## 2 & PORT & 257.00 & 1.00 & 88.20 & 2.21 \\

## 3 & DE & 257.00 & 1.00 & 88.20 & 2.21 \\

1

I Local optimisation

. Single-start (here) or multi-start approach to test the consistency of the local optimisation

OptimGR4J <- function(Param_Optim) {

Param_Optim_Vre <- airGR::TransfoParam_GR4J(ParamIn = Param_Optim, Direction = "TR")

OutputsModel <- airGR::RunModel_GR4J(InputsModel = InputsModel, RunOptions = RunOptions, Param = Param_Optim_Vre) OutputsCrit <- airGR::ErrorCrit_RMSE(InputsCrit = InputsCrit,

OutputsModel = OutputsModel) return(OutputsCrit$CritValue)

}

lowerGR4J <- rep(-9.99, times = 4) upperGR4J <- rep(+9.99, times = 4)

Local optimization

startGR4J <- c(4.1, 3.9, -0.9, -8.7)

optPORT <- stats::nlminb(start = startGR4J, objective = OptimGR4J, lower = lowerGR4J, upper = upperGR4J,

control = list(trace = 1))

startGR4J <- expand.grid(data.frame(CalibOptions$StartParamDistrib)) optPORT_ <- function(x) {

opt <- stats::nlminb(start = x, objective = OptimGR4J,

lower = lowerGR4J, upper = upperGR4J, control = list(trace = 1))

}

list_opt <- apply(startGR4J, 1, optPORT_)

summary(df_port)

Global optimization Differential Evolution

optDE <- DEoptim::DEoptim(fn = OptimGR4J,

lower = lowerGR4J, upper = upperGR4J,

control = DEoptim::DEoptim.control(NP = 40, trace = 10))

Particle Swarm

optPSO <- hydroPSO::hydroPSO(fn = OptimGR4J,

lower = lowerGR4J, upper = upperGR4J, control = list(write2disk = FALSE)

MA-LS-Chains

optMALS <- Rmalschains::malschains(fn = OptimGR4J, maxEvals = 2000,

lower = lowerGR4J, upper = upperGR4J)

Results

## % latex table generated in R 3.4.2 by xtable 1.8-2 package

## % Tue Mar 27 17:21:31 2018

## \begin{table}[ht]

## \centering

## \begin{tabular}{rlrrrr}

## \hline

## & Algo & X1 & X2 & X3 & X4 \\

## \hline

## 1 & Michel & 257.00 & 1.01 & 88.20 & 2.21 \\

## 2 & PORT & 257.00 & 1.00 & 88.20 & 2.21 \\

## 3 & DE & 257.00 & 1.00 & 88.20 & 2.21 \\

1

I Global optimisation

Most often used when facing a complex response surface, with multiple local mimina . Differential Evolution

OptimGR4J <- function(Param_Optim) {

Param_Optim_Vre <- airGR::TransfoParam_GR4J(ParamIn = Param_Optim, Direction = "TR")

OutputsModel <- airGR::RunModel_GR4J(InputsModel = InputsModel, RunOptions = RunOptions, Param = Param_Optim_Vre) OutputsCrit <- airGR::ErrorCrit_RMSE(InputsCrit = InputsCrit,

OutputsModel = OutputsModel) return(OutputsCrit$CritValue)

}

lowerGR4J <- rep(-9.99, times = 4) upperGR4J <- rep(+9.99, times = 4)

Local optimization

startGR4J <- c(4.1, 3.9, -0.9, -8.7)

optPORT <- stats::nlminb(start = startGR4J, objective = OptimGR4J, lower = lowerGR4J, upper = upperGR4J,

control = list(trace = 1))

startGR4J <- expand.grid(data.frame(CalibOptions$StartParamDistrib)) optPORT_ <- function(x) {

opt <- stats::nlminb(start = x, objective = OptimGR4J,

lower = lowerGR4J, upper = upperGR4J, control = list(trace = 1))

}

list_opt <- apply(startGR4J, 1, optPORT_)

summary(df_port)

Global optimization Differential Evolution

optDE <- DEoptim::DEoptim(fn = OptimGR4J,

lower = lowerGR4J, upper = upperGR4J,

control = DEoptim::DEoptim.control(NP = 40, trace = 10))

Particle Swarm

optPSO <- hydroPSO::hydroPSO(fn = OptimGR4J,

lower = lowerGR4J, upper = upperGR4J, control = list(write2disk = FALSE)

MA-LS-Chains

optMALS <- Rmalschains::malschains(fn = OptimGR4J, maxEvals = 2000,

lower = lowerGR4J, upper = upperGR4J)

Results

## % latex table generated in R 3.4.2 by xtable 1.8-2 package

## % Tue Mar 27 17:21:31 2018

## \begin{table}[ht]

## \centering

## \begin{tabular}{rlrrrr}

## \hline

## & Algo & X1 & X2 & X3 & X4 \\

## \hline

## 1 & Michel & 257.00 & 1.01 & 88.20 & 2.21 \\

## 2 & PORT & 257.00 & 1.00 & 88.20 & 2.21 \\

## 3 & DE & 257.00 & 1.00 & 88.20 & 2.21 \\

1

. Particle Swarm

OptimGR4J <- function(Param_Optim) {

Param_Optim_Vre <- airGR::TransfoParam_GR4J(ParamIn = Param_Optim, Direction = "TR")

OutputsModel <- airGR::RunModel_GR4J(InputsModel = InputsModel, RunOptions = RunOptions, Param = Param_Optim_Vre) OutputsCrit <- airGR::ErrorCrit_RMSE(InputsCrit = InputsCrit,

OutputsModel = OutputsModel) return(OutputsCrit$CritValue)

}

lowerGR4J <- rep(-9.99, times = 4) upperGR4J <- rep(+9.99, times = 4)

Local optimization

startGR4J <- c(4.1, 3.9, -0.9, -8.7)

optPORT <- stats::nlminb(start = startGR4J, objective = OptimGR4J, lower = lowerGR4J, upper = upperGR4J,

control = list(trace = 1))

startGR4J <- expand.grid(data.frame(CalibOptions$StartParamDistrib)) optPORT_ <- function(x) {

opt <- stats::nlminb(start = x, objective = OptimGR4J,

lower = lowerGR4J, upper = upperGR4J, control = list(trace = 1))

}

list_opt <- apply(startGR4J, 1, optPORT_)

summary(df_port)

Global optimization Differential Evolution

optDE <- DEoptim::DEoptim(fn = OptimGR4J,

lower = lowerGR4J, upper = upperGR4J,

control = DEoptim::DEoptim.control(NP = 40, trace = 10))

Particle Swarm

optPSO <- hydroPSO::hydroPSO(fn = OptimGR4J,

lower = lowerGR4J, upper = upperGR4J, control = list(write2disk = FALSE)

MA-LS-Chains

optMALS <- Rmalschains::malschains(fn = OptimGR4J, maxEvals = 2000,

lower = lowerGR4J, upper = upperGR4J)

Results

## % latex table generated in R 3.4.2 by xtable 1.8-2 package

## % Tue Mar 27 17:21:31 2018

## \begin{table}[ht]

## \centering

## \begin{tabular}{rlrrrr}

## \hline

## & Algo & X1 & X2 & X3 & X4 \\

## \hline

## 1 & Michel & 257.00 & 1.01 & 88.20 & 2.21 \\

## 2 & PORT & 257.00 & 1.00 & 88.20 & 2.21 \\

## 3 & DE & 257.00 & 1.00 & 88.20 & 2.21 \\

1

. MA-LS-Chains

OptimGR4J <- function(Param_Optim) {

Param_Optim_Vre <- airGR::TransfoParam_GR4J(ParamIn = Param_Optim, Direction = "TR")

OutputsModel <- airGR::RunModel_GR4J(InputsModel = InputsModel, RunOptions = RunOptions, Param = Param_Optim_Vre) OutputsCrit <- airGR::ErrorCrit_RMSE(InputsCrit = InputsCrit,

OutputsModel = OutputsModel) return(OutputsCrit$CritValue)

}

lowerGR4J <- rep(-9.99, times = 4) upperGR4J <- rep(+9.99, times = 4)

Local optimization

startGR4J <- c(4.1, 3.9, -0.9, -8.7)

optPORT <- stats::nlminb(start = startGR4J, objective = OptimGR4J, lower = lowerGR4J, upper = upperGR4J,

control = list(trace = 1))

startGR4J <- expand.grid(data.frame(CalibOptions$StartParamDistrib)) optPORT_ <- function(x) {

opt <- stats::nlminb(start = x, objective = OptimGR4J,

lower = lowerGR4J, upper = upperGR4J, control = list(trace = 1))

}

list_opt <- apply(startGR4J, 1, optPORT_)

summary(df_port)

Global optimization Differential Evolution

optDE <- DEoptim::DEoptim(fn = OptimGR4J,

lower = lowerGR4J, upper = upperGR4J,

control = DEoptim::DEoptim.control(NP = 40, trace = 10))

Particle Swarm

optPSO <- hydroPSO::hydroPSO(fn = OptimGR4J,

lower = lowerGR4J, upper = upperGR4J, control = list(write2disk = FALSE)

MA-LS-Chains

optMALS <- Rmalschains::malschains(fn = OptimGR4J, maxEvals = 2000,

lower = lowerGR4J, upper = upperGR4J)

Results

## % latex table generated in R 3.4.2 by xtable 1.8-2 package

## % Tue Mar 27 17:21:31 2018

## \begin{table}[ht]

## \centering

## \begin{tabular}{rlrrrr}

## \hline

## & Algo & X1 & X2 & X3 & X4 \\

## \hline

## 1 & Michel & 257.00 & 1.01 & 88.20 & 2.21 \\

## 2 & PORT & 257.00 & 1.00 & 88.20 & 2.21 \\

## 3 & DE & 257.00 & 1.00 & 88.20 & 2.21 \\

1

I Results

Algo X1 X2 X3 X4 RMSE CPU

airGR 257.238 1.012 88.235 2.208 0.7852 00.53 s PORT 256.808 1.004 88.167 2.205 0.7852 01.79 s DE 256.808 1.004 88.167 2.205 0.7852 75.10 s PSO 256.836 1.006 88.207 2.204 0.7852 29.55 s MA-LS 256.808 1.004 88.167 2.205 0.7852 18.42 s

Download the airGR package

The airGR package is available on the Comprehensive Archive Network:

https://CRAN.R-project.org/package=airGR/

Evolution of 3 Markov chains & posterior density for each parameter

X4 X3 X2 X1

0 500 1000 1500 2000

0 500 1000 1500 2000

0 500 1000 1500 2000

0 500 1000 1500 2000

200 300 400

0.8 1.0 1.2 1.4

60 80 100 120 140

1.5 2.0 2.5 3.0

Iteration

v alue

X4 X3 X2 X1

2.15 2.20 2.25 2.30

84 88 92 96

0.9 1.0 1.1

230 240 250 260 270 280

0.00 0.02 0.04 0.06

0.0 2.5 5.0 7.5

0.00 0.05 0.10 0.15 0.20

0 5 10 15

value

density

References

I Coron, L., Thirel, G., Delaigue, O., Perrin, C. & Andr´eassian, V. (2017). The suite of lumped GR hydrological models in an R package. Environmental Modelling & Software 94, 166–171. DOI:

10.1016/j.envsoft.2017.05.002.

I Coron L., Perrin C., Delaigue, O., Thirel, G. & Michel C. (2017). airGR: Suite of GR Hydrological Models for Precipitation-Runoff Modelling. R package version 1.0.9.64. URL:

https://webgr.irstea.fr/en/airGR/.

I Delaigue, O., Coron, L. & Brigode, P. (2018). airGRteaching: Teaching Hydrological Modelling with the GR Rainfall-Runoff Models (’Shiny’ Interface Included). R package version 0.2.2.2. URL:

https://webgr.irstea.fr/en/airGR/.

I Poncelet, C., Andr´eassian, V., & Oudin, L. (submitterd). Regionalization of Hydrological Models by Group Calibration. Water Resources Research.

I Riboust, P., Thirel, G., Le Moine, N. & Ribstein, P. (accepted). Revisiting a simple degree-day model for integrating satellite data: implementation of SWE-SCA hystereses. Journal of Hydrology and

Hydrodynamics, DOI: 10.2478/johh-2018-0004.

National Research Institute of Science and Technology for Environment and Agriculture

HYDR O

Olivier Delaigue <[email protected]> EGU General Assembly 2018 - 8-13 April 2018 - Vienna (Austria) - EGU2018-13049 | HS3.1 airGR Development Team <[email protected]>