Latest developments of the airGR rainfall-runoff modelling ...€¦ · Latest developments of the...

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

Olivier Delaigue1, Guillaume Thirel1, Francois Bourgin2, Laurent Coron3

1 IRSTEA – Hydrology Research Group (HYCAR) – Antony, France2 IFSTTAR – GERS, EE – Nantes, France

3 EDF – PMC Hydrometeorological Center – Toulouse, France

airGR 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 varioustime 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 (CemaNeigesnow 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

prec

ip. [

mm

/d]

solidliquid

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

−10

010

20

tem

p. [°

C]

meanlayers

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

040

080

012

00

snow

pac

k [m

m] mean

layers

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

05

1015

2025

flow

[mm

/d]

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

observedsimulated

150

0

Jan May Sep

015

0

30−days rolling mean

prec

ip. &

flow

reg

ime

[mm

/mon

th]

0 0.4 0.8

non−exceedance prob. [−]

0.2

15

20

flow

[m

m/d

] observedsimulated

log scale

0.2 1 5 20

0.2

15

20

observed flow [mm/d]

sim

ulat

ed fl

ow [m

m/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 theGR4J 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 poorresults 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 useof 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 optimizationDifferential 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 thetransformed 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 optimizationDifferential 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 optimizationDifferential 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 optimisationMost 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 optimizationDifferential 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 optimizationDifferential 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 optimizationDifferential 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

valu

e

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

dens

ity

References

I Coron, L., Thirel, G., Delaigue, O., Perrin, C. & Andreassian, V. (2017). The suite of lumped GRhydrological 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 Modelsfor 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 theGR Rainfall-Runoff Models (’Shiny’ Interface Included). R package version 0.2.2.2. URL:https://webgr.irstea.fr/en/airGR/.

I Poncelet, C., Andreassian, V., & Oudin, L. (submitterd). Regionalization of Hydrological Models byGroup 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 Instituteof Science and Technology

for Environment and Agriculture

HYDRO

Olivier Delaigue <olivier.delaigue@irstea.fr> EGU General Assembly 2018 - 8-13 April 2018 - Vienna (Austria) - EGU2018-13049 | HS3.1 airGR Development Team <airGR@irstea.fr>

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

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