composite calibration/evaluation criterion air · 5 20 flow [mm/d] observed simulated log scale 0.2...

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 Delaigue1, Guillaume Thirel1, Philippe Riboust1,2

1 IRSTEA – Hydrology Research Group (HYCAR) – Antony, France2 Conseil General des Hauts-de-Seine – Nanterre, 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., 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 timesteps (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 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

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

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 modelIt 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 SCAfor 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

● ● ●● ●● ● ●●● ●● ●● ●●● ●● ● ●●●

● ● ●●● ●●●●● ●● ●● ● ● ●●

●● ● ●● ●● ● ●● ● ●●

●● ●●● ●●● ●● ● ● ●● ●●●

QS

CA

0.0 0.5 1.0

Hyst

noHyst

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


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")],


Weights = list(0.75, 0.05, 0.05, 0.05, 0.05, 0.05))








Param = 432.681, -0.020, 83.096, 2.384, 0.002, 3.787, 15.000, 0.850




Param = 419.893, 0.517, 275.687, 1.345, 0.632, 3.864, 16.911, 0.472




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")],


Weights = list(0.75, 0.05, 0.05, 0.05, 0.05, 0.05))








Param = 432.681, -0.020, 83.096, 2.384, 0.002, 3.787, 15.000, 0.850




Param = 419.893, 0.517, 275.687, 1.345, 0.632, 3.864, 16.911, 0.472




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")],


Weights = list(0.75, 0.05, 0.05, 0.05, 0.05, 0.05))








Param = 432.681, -0.020, 83.096, 2.384, 0.002, 3.787, 15.000, 0.850




Param = 419.893, 0.517, 275.687, 1.345, 0.632, 3.864, 16.911, 0.472




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")],


Weights = list(0.75, 0.05, 0.05, 0.05, 0.05, 0.05))








Param = 432.681, -0.020, 83.096, 2.384, 0.002, 3.787, 15.000, 0.850




Param = 419.893, 0.517, 275.687, 1.345, 0.632, 3.864, 16.911, 0.472




1airGR 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 (Genot & 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 ungaugedbassins (Poncelet et al., submitted) available soon through a Shiny interface

I airGRtools: different useful tools like event detection, statistics computations (Base FlowIndex, 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 forPrecipitation-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. & 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 Delaigue, O., Coron, L. & Brigode, P. (2019). airGRteaching: Teaching Hydrological Modelling with the GRRainfall-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 forRainfall-Runoff Modeling and Teaching Hydrology. In: HIC 2018. 13th International Conference onHydroinformatics. EPiC Series in Engineering, 541–548. EasyChair. DOI: 10.29007/qsqj.

I Poncelet, C., Andreassian, V., & Oudin, L. (submitted). Regionalization of Hydrological Models by GroupCalibration. Water Resources Research.

I Riboust, P., Thirel, G., Le Moine, N. & Ribstein, P. (2019). Revisiting a simple degree-day model forintegrating satellite data: implementation of SWE-SCA hystereses. Journal of Hydrology andHydrodynamics, 1, 67, 70–81. DOI: 10.2478/johh-2018-0004.

National Research Instituteof Science and Technology

for Environment and Agriculture HYDRO

Olivier Delaigue <[email protected]> EGU General Assembly 2019 - 7-12 April 2019 - Vienna (Austria) - EGU2019-4219 | HS2.2.4 airGR Development Team <[email protected]>

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

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

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

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

composite calibration/evaluation criterion air · 5 20 flow [mm/d] observed simulated log scale 0.2...

Documents