Flood hydrology for bridge

Vazken ANDRÉASSIAN

y gy gengineering

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Vazken ANDRÉASSIANDeputy Scientific Director for Hydrology & Hydraulics

Irstea, France

Who should care for floods?

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Who should care for floods?

3Paris, 1910

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High water marks on a Loire River bridge

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Bridges… are not the only preoccupation of civil engineersg

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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

What do we know for sure about floods?floods?

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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)]

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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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Illustration on a recent intercomparison of flood extrapolation methodsp

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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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Ongoing climate change

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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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Upcoming changes in summer temperatures[future (2046-2065)-reference]/reference (1961-1990)

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Upcoming changes in winter rainfall[future (2046-2065)-reference]/reference (1961-1990)

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Simulated evolution of annual discharge

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10-year return period flood evolution

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Studies based on catchment elasticity

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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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Summary

ΔQ

ΔClim

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Summary

ΔQ

ΔClim

32Each point corresponds to a 10-year long sub-period

Actual example

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Overall results over French catchments

Precipitation elasticity Temperature elasticity

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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

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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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Which model should be chosen?

m, S

MH

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BC

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C

Which model is the most similar to the Spitzfire?p

m, S

MH

IB

ergs

tröm

of S

ten

BC

ourte

sy

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C

Which model can fly?

m, S

MH

IB

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BC

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40

C

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.

A few conclusions

• Always remain modestAlways remain modest

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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)

Badly-designed bridge (engineers of the 20th century)( g y)

Loire River

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Loire River, 1983

Thank you for your attention

Loire River

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Loire River, 1983

Uncertainties assessment for the mean average flowaverage flow

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Low-flows evolution

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