Application of several
hydrological models (and
objective functions) to the
complete dataset of the
workshop
Pour mieux
affirmer
ses missions,
le Cemagref
devient Irstea
www.irstea.fr
G. Thirel, L. Coron, V. Andréassian, C. Perrin
22 July 2013
2
Introduction
• 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
IAHS Hw15
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Outline of this presentation
• Impact of using different models
•
The models
•
The results
• Impact of using different objective functions
IAHS Hw15
22 July 2013
•
The objective functions
•
The results
4
Outline of this presentation
• Impact of using different models
•
The models
•
The results
• Impact of using different objective functions
IAHS Hw15
22 July 2013
•
The objective functions
•
The results
5
GR4J and GR5J
Lumped conceptual models, resp. 4 and 5 parameters
GR4J
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GR5J
6
MORDOR6
Lumped conceptual model with 6 parameters (simplification of the
MORDOR model).
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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.
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The objective function
The Nash on root square of discharge is used in this part.
IAHS Hw15
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Outline of this presentation
• Impact of using different models
•
The models
•
The results
• Impact of using different objective functions
IAHS Hw15
22 July 2013
•
The objective functions
•
The results
10
Rivers with T increase
High flows
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• GR4J and GR5J are the best for the Kamp, except during P2
• No big difference for the Garonne
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Rivers with T increase
MORDOR6 misses the 2002
Kamp flood
Observed peak value
MORDOR6 peak values
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Rivers with T increase
Low flows
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• GR5J the best for the Kamp
• MORDOR6 and GR5J the best for the Garonne
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Rivers with T increase
VARIABILITY DUE TO MODEL AND CALIBRATION CHOICES
GR4J
Kamp
GR5J
MORDOR6
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The model choice and calibration induce the same order of variability
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Rivers with discharge change or high variability
Wimmera
High flows
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Best
performance for
GR5J
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Rivers with discharge change or high variability
Wimmera
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No model has
the « solution »
for handling the
Millenium
Drought
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Rivers with T increase
VARIABILITY DUE TO MODEL AND CALIBRATION CHOICES
GR4J
Wimmera
GR5J
MORDOR6
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The model choice and calibration induce the same order of variability
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Rivers with discharge change or high variability
Severe crash from GR4J due to high
reactivity
Bani
GR4J
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
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MORDOR6
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Outline of this presentation
• Impact of using different models
•
The models
•
The results
• Impact of using different objective functions
IAHS Hw15
22 July 2013
•
The objective functions
•
The results
19
The objective functions
FOR THIS PART ONLY THE GR4J MODEL IS USED
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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
IAHS Hw15
22 July 2013
•
The objective functions
•
The results
21
Rivers with T increase
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Calibrating on IQ gives the lowest Nash(Q) -> of course!
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Rivers with T increase
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Calibrating on Q gives the lowest Nash(IQ) -> of course!
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Rivers with P decrease
Kamp
VARIABILITY DUE TO CALIBRATION AND OBJECTIVE FUNCTION CHOICES
NaQ
KGEQ
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NaRQ
KGERQ
NaIQ
KGEIQ
The objective functions impact the model bias more than the model
choice
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Rivers with discharge change or high variability
Only NaRQ does
not show
disastrous
results on P5
when calibrated
on wet period.
Wimmera
KGERQ
performs the
best on wet
periods when
calibrated on P5.
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Prod. Store: NaRQ > KGERQ
Loss for P5: KGERQ > NaRQ
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Rivers with P decrease
Wimmera
VARIABILITY DUE TO CALIBRATION AND OBJECTIVE FUNCTION CHOICES
NaQ
NaRQ
KGEQ
KGERQ
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The objective functions strongly impact the model bias
NaIQ
KGEIQ
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Conclusions
Attempts to quantify the (un-)stability induced by :
- The model choice –> low impact
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-
The calibration period -> low impact on variability, high impact on bias
-
The objective function -> huge impact
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Thank you!