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Regional assessments of sea level rise and river floods by computer based expert systems: Dealing with uncertainty. J. Kropp, M. Kallache, H. Rust, K. Eisenack Potsdam Institute for Climate Impact Research. Structure. How to deal with uncertainty in the adaptation discussion?
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J. Kropp, M. Kallache, H. Rust, K. EisenackPotsdam Institute for Climate Impact Research
How to deal with uncertainty in the adaptation discussion?
Adaptation to sea level rise: Regional assessments via DIVA
River floods assessment, limitations and Chances: The Vistula example
Consequences for local adaptation policies
Where are our „Achilles heels“: in the economic, natural,
and social sense?
Back to Reality:
River Elbe Flood 2002/Pärnu Storm Surge 2005
Protection level: 1000yr/return level, storm surge/river flood
Dike failure (breach) mode: wave overflow
Tidal basin, nourishment: CBA
Migration allowed due to changing env. conditions: yes
Time steps of calculation: 5 yrs
Simulation time: 2000-2100
Input SRES scenarios: A1FI („worst case“), B2 („best case“);
regionalized SLR scenarios based on PIK‘s CLIMBER model
(for each SRES family, low/medium/high-uniform/regionalized)
Salinity intrusion costs
Sea dike costs
River dike costs
People actually flooded per storm surge
Loss of flats
Beach nourishment costs
Area influenced by salinization due to slr
Tidal basin demand for sand nourishment
Typical expert system which means that usage by stakeholders
Needs involvement of experts for simulation runs and interpretation
Retrospective on river run-offs: assumptions needed, e.g. climate change signal can be found in run-off data (trend = nonstationarity)
No uniform behaviour for rivers worldwide
Standard statistics is unsuitable for assessment tasks
Adequate analytical procedures can confine uncertainty
Examples (annual – stationary, implies no trends!):
Odra/Gozdowice (109729 km2, Poland)
Vistula/Tczew (194376 km2, Poland)
Daugava/Daugavpils (64500 km2Latvia)
Nemunas/Smalininkai (81200 km2 Lithuania)
But is this the end of the story?
Catchment: ~200.000 km2
Problem: data series too short!
Huge model library (more than 50)
Define model selection criteria
Select best fitting model
Generate bootstrap ensemble
Red: theory, asymptotic fit
Grey: bootstrap ensemble
100yr return level9
Estimates for „design flood values“ are too small (6-15% difference!)
Rust/Kallache/Kropp 2006: Advances in Water
Resources Res., under review
Conclusion - Main Findings with Respect to Adaptation
1. Improved technique integrated and views can reduce adverse impacts
2. Communities can adapt autonomously only partly, they need help
4. Planned (anticipated )adaptation measures usually have immediate benefits
6. Adaptive capacity varies considerably among countries, regions and socio-economic groups
8. Enhancement of adaptive capacity is necessary to reduce vulnerability, especially for the most vulnerable (people, regions…)
9. Current knowledge of adaptation & adaptive capacity is insufficient
10. Technical progress is essential for suitable adaptation
Heart Attacks and Market Crashes
We need a new science
and planning for disasters....