4678_presentacion_v_andreassian.pdf
TRANSCRIPT
Flood hydrology for bridge
Vazken ANDRÉASSIAN
y gy gengineering
1
Vazken ANDRÉASSIANDeputy Scientific Director for Hydrology & Hydraulics
Irstea, France
What can we do against floods?
• Not much, except for :Not much, except for :– build dikes
which may increase flood risks downstream andwhich may increase flood risks downstream and can be overtopped
– build storage damswhich necessarily have a limited storage capacity
and will be transparent once they are fulldesign adequately our civil works (bridges and– design adequately our civil works (bridges and dams…) to avoid damage
– forecast floods in advance to reduce damages
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g
Flood facts
• Of course they are caused by heavyOf course they are caused by heavy precipitation (or sudden snowmelt)
H d l i l h t• Hydrological processes happen at a catchment (watershed) scale: what
t i t i t i it ti b tcounts is not point precipitation but catchment-scale precipitation (not easy t )to measure)
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catchment PEAE
areal precipitation
Can I build a bridge here ?
10Outils pour l’évaluationdes bilans simulés
Sélection de modèlespar une approche empirique
Conclusion &Perspectives
Flood facts
• The floods we are interested in have (forThe floods we are interested in have (for most of them) never been seen: their characteristics result from ancharacteristics result from an extrapolation process
E t l ti i ith• Extrapolating is neither easy nor univocal: it requires models and h thhypotheses
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On extrapolating peak discharges
40
s)
20
30
scha
rge
(m3/
s
?0
10
Peak
dis ?
0 100 200 300 400 500 600 700 800 900 1000Return period (years)
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On extrapolating peak discharges
40
s)
20
30
scha
rge
(m3/
s
0
10
Peak
dis
return period =55 yrs 400 yrs150 yrs 1100 yrs
-2.0 -1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0Gumbel reduced variate = -ln[-ln(cumulative Frequency)]
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On extrapolating peak discharges
40
s)
20
30
scha
rge
(m3/
s
0
10
Peak
dis
return period =55 yrs 400 yrs150 yrs 1100 yrs
-2.0 -1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0Gumbel reduced variate = -ln[-ln(cumulative Frequency)]
14
On extrapolating peak discharges
40
s)
20
30
scha
rge
(m3/
s
0
10
Peak
dis
400 yrs
-2.0 -1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0Gumbel reduced variate = -ln[-ln(cumulative Frequency)]
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Flood facts
• Statistical laws that we use forStatistical laws that we use for extrapolation are hypotheses
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Flood facts
• Statistical laws that we use forStatistical laws that we use for extrapolation assume a stationary climate
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What progress has been made over the last decades to account for climatelast decades to account for climate
change in flood design?
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Accounting for climate change when extrapolating floodsp g
Two main approachesTwo main approaches
• Approaches based on hydrological d llimodelling
• Approaches based on past flow record pp oac es based o past o eco danalysis (“elasticity-approaches”)
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Hydrological models
températureprécipitations
catchment
alt. (m)
ydrological
mod
el
0 10 km
hy
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simulated discharge…
An example for France (Explore 2070 project)( p p j )
1 scenario : A1B 7 GCM 1 method 2 modelsX X X1 scenario : A1B 7 GCM 1 method 2 models
- Isba-Modcou
- GR4J
14 projectionsChoice of emission scenario
for G.G.Climatic modelling
(GCM) Downscaling
X X X
Hydrological modelling
Statistical analysis of
results
…
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Empirical study of catchment elasticity
• For some rivers, we have observations of ,climate and streamflow over long (sometimes contrasted) periods
• Long periods can be divided into sub-periods of reasonable length (~10 years) pe ods o easo ab e e gt ( 0 yea s)where we can assess the variations of
– hydrological response iQy g p
– climatic characteristics
iQ
iii TEP ,,
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Potential of elasticity studies
• Where we have long streamflow records,Where we have long streamflow records, a possibility to predict the impact of climate change “without using a model”climate change without using a model
• But for extreme floods… we still need some form of extrapolationso e o o e apo a o
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A few conclusions
• Always keep in mind that there are noAlways keep in mind that there are no perfect models…
• … but that you need anyhow to make a but t at you eed a y o to a e achoice
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A few conclusions
• For projections into a changing climate,For projections into a changing climate, only use:
– widely tested models (in varied climatic settings)– widely tested models (in varied climatic settings)– parsimonious models– structures which have shown to be robust (drought ( g
/ wet years)
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My own view of robust model structures
Coron L V Andréassian C Perrin J Lerat JCoron, L., V. Andréassian, C. Perrin, J. Lerat, J. Vaze, M. Bourqui, and F. Hendrickx, 2012. Crash testing hydrological models in contrasted climate conditions: An experiment on 216 Australian catchments, Water Resources Research, 48.
Andréassian, V., Perrin, C., Berthet, L., Le Moine, N., Lerat, J., Loumagne, C., Oudin, L., Mathevet, T., Ramos, M.H., Valéry, A., 2009. Crash tests for a standardized evaluation of hydrological models. Hydrol. Earth Syst Sci 13: 1757-1764Earth. Syst. Sci. 13: 1757-1764.
Perrin, C., Michel, C. and Andréassian, V., 2003. Improvement of a parsimonious model for streamflow simulation. Journal of Hydrology, 279: 275-289.
Perrin, C., C. Michel et V. Andréassian, 2001. Does a large number of parameters enhance model performance? Comparative assessment of common catchment model structures on 429 catchments. Journal of Hydrology, 242 (3-4): 275-301.
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Hydrology, 242 (3 4): 275 301.
A few conclusions
• Keep in mind that the climatic predictionsKeep in mind that the climatic predictions have their own uncertainties…
• … and that even the good hydrologic d l d t b d t l lib t dmodels need to be adequately calibrated
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Robust calibration in essential for climate change studies
hydrologicalETP
catchment characteristics
6
9
12 débit observé
g
hydrological model
climatic conditions A
ETP
TPflow simulation A
0
3
6
01-oct 21-oct 10-nov 30-nov 20-dic
parameter f
parameters
climatic conditions Acalibration
transfer
ETP
catchment characteristics
3
4 débit …débit simulé
hydrological model
ETP
TPflow simulation B
0
1
2
18-sep 08-oct 28-oct 17-nov 07-dic
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climatic conditions Bflow simulation B
Andréassian, V., Le Moine, N., Perrin, C., Ramos, M.H., Oudin, L., Mathevet, T., Lerat, J., Berthet, L. 2012. All that glitters is not gold: the case of calibrating hydrological models. Hydrological Processes, vol. 26, p. 2206 – 2210.
Well-designed bridge (engineers of the 1st century AD)( g y )
46The Roman bridge in Vaison la Romaine, France
Well-designed bridge (engineers of the 1st century AD)( g y )
47The Roman bridge in Vaison la Romaine, France (Sept. 1992)