Latest developments of the airGR rainfall-runoff modelling R-package: composite calibration/evaluation criterion and improved snow model to take into account satellite products

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Latest developments of the airGR rainfall-runoff modelling R-package: composite calibration/evaluation criterion and improved snow model to take into account

satellite products

Olivier Delaigue, Guillaume Thirel, P. Riboust

To cite this version:

Olivier Delaigue, Guillaume Thirel, P. Riboust. Latest developments of the airGR rainfall-runoff modelling R-package: composite calibration/evaluation criterion and improved snow model to take into account satellite products. EGU General Assembly 2019, Apr 2019, Vienna, Austria. pp.1, 2019.

�hal-02609368�

(2)

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

composite calibration/evaluation criterion and improved snow model to take into account satellite products

Olivier Delaigue ¹ , Guillaume Thirel ¹ , Philippe Riboust ^1,2

1

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

2

Conseil G´en´eral des Hauts-de-Seine – Nanterre, 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., 2017, 2019). 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 with 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

New features since EGU 2018 – airGR 1.0.5.12 vs airGR 1.2.13.16

I It is now possible to use a composite criterion to calibrate a GR model It can combine different:

. error criteria (NSE, KGE, KGE’)

. variables (flow, snow cover area [SCA], snow water equivalent [SWE]) . variable transformations (raw, square root, logarithm, inverse, sorted) . weights for the different variables

I A version of CemaNeige including a SWE-SCA Linear Hysteresis allows to use satellite SCA for calibration (Riboust et al., 2019)

. A new vignette explains how to use it

Validation performance criteria distributions on SCA and flow using CemaNeige with or without the Linear Hysteresis

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QSCA

0.0 0.5 1.0

Hyst noHyst

Criterion

Using a composite criterion for calibration of the Linear Hysteresis CemaNeige

I Variables needed (note the need for the SCA data)

## DatesR P T E Qls Qmm SCA1 SCA2 SCA3 SCA4 SCA5

## 1 2000-02-25 0 0.7 0.5 19046 0.721 0.228 0.678 0.865 0.935 NA

## 2 2000-02-27 0 0.1 0.4 18218 0.690 0.127 0.562 0.806 0.913 0.959

## 3 2000-02-28 0 -1.0 0.3 18855 0.714 0.158 0.604 0.844 0.932 0.946

## preparation of the InputsModel object

inMod <- CreateInputsModel(FUN_MOD = "RunModel_CemaNeigeGR4J",

DatesR = basinObs$DatesR, Precip = basinObs$P, PotEvap = basinObs$E, TempMean = basinObs$T, ZInputs = median(basinInfo$HypsoData),

HypsoData = basinInfo$HypsoData, NLayers = 5)

## calibration period selection

IndCal <- seq(which(format(basinObs$DatesR, format = "%Y-%m-%d") == "2000-09-01"), which(format(basinObs$DatesR, format = "%Y-%m-%d") == "2005-08-31"))

## preparation of the CalibOptions object

optCal <- CreateCalibOptions(FUN_MOD = "RunModel_CemaNeigeGR4J",

FUN_CALIB = Calibration_Michel, IsHyst = TRUE)

## preparation of the RunOptions object for the calibration period

optRun <- CreateRunOptions(FUN_MOD = "RunModel_CemaNeigeGR4J", InputsModel = inMod, IndPeriod_Run = IndCal, IsHyst = TRUE)

## efficiency criteria: 75 % KGE'(Q) + 5 % KGE'(SCA) on each of the 5 layers inCrit <- CreateInputsCrit(FUN_CRIT = rep("ErrorCrit_KGE2", 6),

InputsModel = inMod, RunOptions = optRun,

Obs = basinObs[IndCal, c("Qmm", "SCA1", "SCA2",

"SCA3", "SCA4", "SCA5")],

VarObs = list("Q", "SCA", "SCA", "SCA", "SCA", "SCA"), Weights = list(0.75, 0.05, 0.05, 0.05, 0.05, 0.05))

## calibration (GR4J with CemaNeige)

outCal <- Calibration(InputsModel = inMod, RunOptions = optRun, InputsCrit = inCrit, CalibOptions = optCal, FUN_MOD = "RunModel_CemaNeigeGR4J",

FUN_CALIB = Calibration_Michel)

Grid-Screening in progress (0% 20% 40% 60% 80% 100%) Screening completed (6561 runs)

Param = 432.681, -0.020, 83.096, 2.384, 0.002, 3.787, 15.000, 0.850 Crit. Composite = 0.8139

Steepest-descent local search in progress

Calibration completed (107 iterations, 8248 runs)

Param = 419.893, 0.517, 275.687, 1.345, 0.632, 3.864, 16.911, 0.472 Crit. Composite = 0.8995

Formula: sum(0.75 * KGE’[Q], 0.05 * KGE’[SCA], 0.05 * KGE’[SCA], 0.05 * KGE’[SCA], 0.05 * KGE’[SCA], 0.05 * KGE’[SCA])

1

I Data preparation

## 1 2000-02-25 0 0.7 0.5 19046 0.721 0.228 0.678 0.865 0.935 NA

## 2 2000-02-27 0 0.1 0.4 18218 0.690 0.127 0.562 0.806 0.913 0.959

## 3 2000-02-28 0 -1.0 0.3 18855 0.714 0.158 0.604 0.844 0.932 0.946

"SCA3", "SCA4", "SCA5")],

1

I Calibration options preparation (note the need for the new IsHyst argument)

## 1 2000-02-25 0 0.7 0.5 19046 0.721 0.228 0.678 0.865 0.935 NA

## 2 2000-02-27 0 0.1 0.4 18218 0.690 0.127 0.562 0.806 0.913 0.959

## 3 2000-02-28 0 -1.0 0.3 18855 0.714 0.158 0.604 0.844 0.932 0.946

"SCA3", "SCA4", "SCA5")],

1

I Composite criterion preparation

## 1 2000-02-25 0 0.7 0.5 19046 0.721 0.228 0.678 0.865 0.935 NA

## 2 2000-02-27 0 0.1 0.4 18218 0.690 0.127 0.562 0.806 0.913 0.959

## 3 2000-02-28 0 -1.0 0.3 18855 0.714 0.158 0.604 0.844 0.932 0.946

"SCA3", "SCA4", "SCA5")],

1

I Model calibration

## 1 2000-02-25 0 0.7 0.5 19046 0.721 0.228 0.678 0.865 0.935 NA

## 2 2000-02-27 0 0.1 0.4 18218 0.690 0.127 0.562 0.806 0.913 0.959

## 3 2000-02-28 0 -1.0 0.3 18855 0.714 0.158 0.604 0.844 0.932 0.946

"SCA3", "SCA4", "SCA5")],

1

airGR websites: get started with the packages or discover advanced uses

I High degree of customization with airGR:

. https://hydrogr.github.io/airGR/

I Simple features to learn hydrology with airGRteaching (Delaigue et al., 2018, 2019):

. https://hydrogr.github.io/airGRteaching/

airGRmaps interface (G´enot & Delaigue, 2019) to get parameter values of GR4J, GR5J or GR6J all over France

Future developments

I airGRmaps: parameter maps on France for GR4J, GR5J & GR6J models for ungauged bassins (Poncelet et al., submitted) available soon through a Shiny interface

I airGRtools: different useful tools like event detection, statistics computations (Base Flow Index, Standardized Streamflow Index), etc.

Download the airGR packages on the Comprehensive R Archive Network

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

I airGRteaching: https://CRAN.R-project.org/package=airGRteaching/

References

I Coron L., Delaigue, O., Thirel, G., Perrin C. & Michel C. (2019). airGR: Suite of GR Hydrological Models for Precipitation-Runoff Modelling. R package version 1.2.13.16.

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

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 Delaigue, O., Coron, L. & Brigode, P. (2019). airGRteaching: Teaching Hydrological Modelling with the GR Rainfall-Runoff Models (‘Shiny’ Interface Included). R package version 0.2.6.14.

URL: https://CRAN.R-project.org/package=airGRteaching.

I Delaigue O., Thirel G., Coron L. & Brigode P. (2018). airGR and airGRteaching: Two Open-Source Tools for Rainfall-Runoff Modeling and Teaching Hydrology. In: HIC 2018. 13th International Conference on

Hydroinformatics. EPiC Series in Engineering, 541–548. EasyChair. DOI: 10.29007/qsqj.

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

I Riboust, P., Thirel, G., Le Moine, N. & Ribstein, P. (2019). Revisiting a simple degree-day model for integrating satellite data: implementation of SWE-SCA hystereses. Journal of Hydrology and

Hydrodynamics, 1, 67, 70–81. 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 2019 - 7-12 April 2019 - Vienna (Austria) - EGU2019-4219 | HS2.2.4 airGR Development Team <[email protected]>

Latest developments of the airGR rainfall-runoff modelling R-package: composite calibration/evaluation criterion and improved snow model to take into account satellite products

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

composite calibration/evaluation criterion and improved snow model to take into account satellite products

Olivier Delaigue 1 , Guillaume Thirel 1 , Philippe Riboust 1,2

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

Conseil G´en´eral des Hauts-de-Seine – Nanterre, 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., 2017, 2019). 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 with CemaNeige)

New features since EGU 2018 – airGR 1.0.5.12 vs airGR 1.2.13.16

I It is now possible to use a composite criterion to calibrate a GR model It can combine different:

. error criteria (NSE, KGE, KGE’)

. variables (flow, snow cover area [SCA], snow water equivalent [SWE]) . variable transformations (raw, square root, logarithm, inverse, sorted) . weights for the different variables

I A version of CemaNeige including a SWE-SCA Linear Hysteresis allows to use satellite SCA for calibration (Riboust et al., 2019)

. A new vignette explains how to use it

Validation performance criteria distributions on SCA and flow using CemaNeige with or without the Linear Hysteresis

Using a composite criterion for calibration of the Linear Hysteresis CemaNeige

I Variables needed (note the need for the SCA data)

I Data preparation

I Calibration options preparation (note the need for the new IsHyst argument)

I Composite criterion preparation

I Model calibration

airGR websites: get started with the packages or discover advanced uses

I High degree of customization with airGR:

. https://hydrogr.github.io/airGR/

I Simple features to learn hydrology with airGRteaching (Delaigue et al., 2018, 2019):

. https://hydrogr.github.io/airGRteaching/

airGRmaps interface (G´enot & Delaigue, 2019) to get parameter values of GR4J, GR5J or GR6J all over France

Future developments

I airGRmaps: parameter maps on France for GR4J, GR5J & GR6J models for ungauged bassins (Poncelet et al., submitted) available soon through a Shiny interface

I airGRtools: different useful tools like event detection, statistics computations (Base Flow Index, Standardized Streamflow Index), etc.

Download the airGR packages on the Comprehensive R Archive Network

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

I airGRteaching: https://CRAN.R-project.org/package=airGRteaching/

References

I Coron L., Delaigue, O., Thirel, G., Perrin C. & Michel C. (2019). airGR: Suite of GR Hydrological Models for Precipitation-Runoff Modelling. R package version 1.2.13.16.

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

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 Delaigue, O., Coron, L. & Brigode, P. (2019). airGRteaching: Teaching Hydrological Modelling with the GR Rainfall-Runoff Models (‘Shiny’ Interface Included). R package version 0.2.6.14.

URL: https://CRAN.R-project.org/package=airGRteaching.

I Delaigue O., Thirel G., Coron L. & Brigode P. (2018). airGR and airGRteaching: Two Open-Source Tools for Rainfall-Runoff Modeling and Teaching Hydrology. In: HIC 2018. 13th International Conference on

Hydroinformatics. EPiC Series in Engineering, 541–548. EasyChair. DOI: 10.29007/qsqj.

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

I Riboust, P., Thirel, G., Le Moine, N. & Ribstein, P. (2019). Revisiting a simple degree-day model for integrating satellite data: implementation of SWE-SCA hystereses. Journal of Hydrology and

Hydrodynamics, 1, 67, 70–81. 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 2019 - 7-12 April 2019 - Vienna (Austria) - EGU2019-4219 | HS2.2.4 airGR Development Team <[email protected]>

Olivier Delaigue ¹ , Guillaume Thirel ¹ , Philippe Riboust ^1,2