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Pour mieux affirmer ses missions, le Cemagref devient Irstea
G. Thirel, L. Coron, V. Andréassian, C. Perrin
22 July 2013
Application of several hydrological models (and objective functions) to the complete dataset of the workshop
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Introduction
IAHS Hw15 22 July 2013
• What is the main issue when we fail on non-stationarity?
• Models?• Objective functions?• Something else?
• Application of 3 models• Application of 6 objective functions
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Outline of this presentation
• Impact of using different models
• The models
• The results
• Impact of using different objective functions
• The objective functions
• The resultsIAHS Hw15
22 July 2013
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Outline of this presentation
• Impact of using different models
• The models
• The results
• Impact of using different objective functions
• The objective functions
• The resultsIAHS Hw15
22 July 2013
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GR4J and GR5J
Lumped conceptual models, resp. 4 and 5 parameters
GR5JGR4J
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MORDOR6
Lumped conceptual model with 6 parameters (simplification of the MORDOR model).
IAHS Hw15 22 July 2013
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The snow module
• No snow module: Axe Creek, Gilbert, Flinders, Wimmera and Bani Rivers.• CemaNeige: all the other basins.
CemaNeige = degree-day model, 2 free parameters.
IAHS Hw15 22 July 2013
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The objective function
The Nash on root square of discharge is used in this part.
IAHS Hw15 22 July 2013
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Outline of this presentation
• Impact of using different models
• The models
• The results
• Impact of using different objective functions
• The objective functions
• The resultsIAHS Hw15
22 July 2013
10
Rivers with T increase
IAHS Hw15 22 July 2013
• GR4J and GR5J are the best for the Kamp, except during P2
• No big difference for the Garonne
High flows
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MORDOR6 misses the 2002
Kamp flood
Rivers with T increase
Observed peak value
MORDOR6 peak values
IAHS Hw15 22 July 2013
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Rivers with T increase
IAHS Hw15 22 July 2013
• GR5J the best for the Kamp• MORDOR6 and GR5J the best for the Garonne
Low flows
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Rivers with T increase
The model choice and calibration induce the same order of variability
VARIABILITY DUE TO MODEL AND CALIBRATION CHOICES
IAHS Hw15 22 July 2013
GR4J GR5J
MORDOR6Kamp
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Rivers with discharge change or high variability
Wimmera Best performance for
GR5J
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High flows
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Rivers with discharge change or high variability
Wimmera No model has the « solution » for handling the
Millenium Drought
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Rivers with T increase
The model choice and calibration induce the same order of variability
VARIABILITY DUE TO MODEL AND CALIBRATION CHOICES
IAHS Hw15 22 July 2013
GR4J GR5J
MORDOR6Wimmera
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Rivers with discharge change or high variability
Bani
GR4J
Severe crash from GR4J due to high reactivity
Attempts to increase the reaction time or to better initialize the parameters all failed
The structure of GR4J (&GR5J) is to revise for such a basin
IAHS Hw15 22 July 2013 MORDOR6
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Outline of this presentation
• Impact of using different models
• The models
• The results
• Impact of using different objective functions
• The objective functions
• The resultsIAHS Hw15
22 July 2013
19
The objective functionsFOR THIS PART ONLY THE GR4J MODEL IS USED
IAHS Hw15 22 July 2013
Inverse of discharge
Square root of discharge
Discharge
Nash NaIQ NaRQ NaQ
KGE KGEIQ KGERQ KGEQ
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Outline of this presentation
• Impact of using different models
• The models
• The results
• Impact of using different objective functions
• The objective functions
• The resultsIAHS Hw15
22 July 2013
21
Rivers with T increase
Calibrating on IQ gives the lowest Nash(Q) -> of course!
IAHS Hw15 22 July 2013
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Rivers with T increase
Calibrating on Q gives the lowest Nash(IQ) -> of course!
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Rivers with P decrease
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Kamp
NaQ NaRQ NaIQ
KGEQ KGERQ KGEIQ
VARIABILITY DUE TO CALIBRATION AND OBJECTIVE FUNCTION CHOICES
The objective functions impact the model bias more than the model choice
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Rivers with discharge change or high variability
Wimmera Only NaRQ does not show disastrous results on P5 when calibrated on wet period.
KGERQ performs the best on wet periods when calibrated on P5. IAHS Hw15
22 July 2013
Prod. Store: NaRQ > KGERQLoss for P5: KGERQ > NaRQ
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Rivers with P decrease
IAHS Hw15 22 July 2013
Wimmera
NaQ NaRQ NaIQ
KGEQ KGERQ KGEIQ
VARIABILITY DUE TO CALIBRATION AND OBJECTIVE FUNCTION CHOICES
The objective functions strongly impact the model bias
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Conclusions
IAHS Hw15 22 July 2013
Attempts to quantify the (un-)stability induced by : -The model choice –> low impact
-The calibration period -> low impact on variability, high impact on bias
-The objective function -> huge impact
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Thank you!