simulating flood-peak probability in the rhine basin and the effect of climate change

Simulating flood-peak probability in the Rhine basin and the effect of climate change

Institute for Environmental Studies (IVM)

Aline te Linde (IVM - VU / Deltares)

Jeroen Aerts (IVM - VU)

Oxford – October 2, 2008

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Outline

Introduction

Method- GRADE

Results- Climate change scenarios

- Extreme value analysis

Conclusions

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Introduction ACER

• Recent floods / droughts major damage

• Climate change

• Need for cross-boundary cooperation

GOAL: • test robustness of

new cross-boundary adaptation strategies

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Rhine basin

• Length: 1,320 km• Area 160,800 km2

• Mean Q 2,206 m3/s• Maximum observed 12,60012,600

m3/s • Safety levels vary from

1/200 to 1/12501/1250

• 58 million inhabitants (10 million flood plain)

• High economic relevance• Flood management

strategies since beginning 19th century

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Introduction

• IKSR – Flood Action Plan• D – NL Working Group on

Floods• EU Floods Directive

• Do not take into account climate change

• Research available*• Assumption – infinite dike

height• Large uncertainty

probability extreme events• Do not take into account

effect of measures

Flood management Climate change

* (Kwadijk 1993, 1998; Middelkoop, 2001; Kleinn, 2003, 2005; Te Linde, 2007)

Simulate low probability floods, combineimpact of climate change

impact of dike height

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Method

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Method - Hydrological modelling

Rainfall - runoff (HBV / VIC)• Implementing climate change

scenario• Landuse change

1D Hydrodynamic model (SOBEK)Measures• Dike heightening• Dike relocation• Landuse change flood plain

(friction)• Bypass• Detention area• Flooding (calibrated on 2D

model)

Field capacityWilting point

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GRADE – Generator of rainfall and discharge extremes

Developed by Deltares, Waterdienst, KNMI

Implement• Climate change

scenarios• Measures

X Locations

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Climate change impact

Lobith – mean monthly change Transient run

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Detention area – flooding

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Extreme value analysis yearly max. Q – Gumbel fit

100 yrs observed 1000 yrs resampled

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GEV distribution fit

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Results

Returnperiod Without With Without With

flooding flooding flooding flooding

1000 15,700 14,000 18,200 15,400500 15,000 13,700 17,700 14,800200 14,300 13,100 16,700 14,500100 12,900 12,600 15,200 13,600

Reference Climate change (Wp)

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Conclusion

• Method GRADE + extreme value analysis– possibility to analyse ensemble of events / bandwidth– narrows confidence interval extreme value distribution fit

• Impact of– Detention area effect strongly depends on event size– Climate change peak events (flooding) expected to

occur more frequently– Flooding Q > 12,000 m3/s - upstream flooding – lowers max.

Q up to 20%

(Dike heightening will increase extreme peak discharge downstream)

• Simulate combined effect

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This reseach is part of a ‘Climate Changes Spatial Planning’ project

Thank you

Adaptive Capacity to Extreme events in the Rhine basin (ACER)

More information on: www.klimaatvoorruimte.nl (english version) and www.adaptation.nl\acer

aline.te.linde@ivm.vu.nl