Simulating flood-peak probability in the Rhine basin and the effect of climate change
-
Upload
aline-te-linde -
Category
Technology
-
view
385 -
download
0
description
Transcript of 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
2
Outline
Introduction
Method- GRADE
Results- Climate change scenarios
- Extreme value analysis
Conclusions
3
Introduction ACER
• Recent floods / droughts major damage
• Climate change
• Need for cross-boundary cooperation
GOAL: • test robustness of
new cross-boundary adaptation strategies
4
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
5
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
6
Method
7
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
8
GRADE – Generator of rainfall and discharge extremes
Developed by Deltares, Waterdienst, KNMI
Implement• Climate change
scenarios• Measures
X Locations
9
Climate change impact
Lobith – mean monthly change Transient run
10
Detention area – flooding
11
Extreme value analysis yearly max. Q – Gumbel fit
100 yrs observed 1000 yrs resampled
12
GEV distribution fit
13
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)
14
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
15
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