Transcript Climate Impact Research in the BSR: State of the Art

J. Kropp, M. Kallache, H. Rust, K. Eisenack
Potsdam Institute for Climate Impact Research
Regional assessments of sea level rise
and river floods by computer based expert
systems: Dealing with uncertainty
Structure
1.
2.
3.
4.
5.
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
Conclusion
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1
Where are our „Achilles heels“: in the economic, natural,
and social sense?
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Coast Lines Lower Saxony
0-2000AD
Source: Behre 1999
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„Miserable Waterflood in Lower-Germany 1717“
At the North sea coast
dyke construction
since 1100AD
Reasons:
Maladaptation!
mainly landuse
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Back to Reality:
River Elbe Flood 2002/Pärnu Storm Surge 2005
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How to assess “protection level”
Wave overflow: Return level right Weser bank for current dyke heights (3900yr)
Climate change scenario: average high tide + 70cm +3.8% increase of wind speed
(return level 1000yrs).
(after Liedermann & Zimmermann 2003)
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COSTS? Secondary effects?…..
The DIVA Expert
System
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Initial Settings for DIVA Runs
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)
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Regional Sealevel Rise: 1995-2100
SRES-A1FI/B2
Worst case: A1FI
Best case: B2
How large the adaptation
costs will be?
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Adaptation Costs: Sea Level Rise
(dike construction & preservation, beach nourishment, etc.)
Year 2000
Year: 2100
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Total Adaptation Costs (Mio US$)
A1FI: Most relevant for Estonia due to sandy beaches and no dikes
Start-up investments
to guarantee 1000yr
protection level
needed....
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River impact length (1000yr flood)
A1FI
B2
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Other Possible Things.....
Salinity intrusion costs
Sea dike costs
River dike costs
People actually flooded per storm surge
Sand loss
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
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Improved Flood Risk Assessment
Retrospective on river run-offs: assumptions needed, e.g.
climate change signal can be found in run-off data (trend =
nonstationarity)
Main results:
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?
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Trends and Extremes in
Time Series
Extremes?
2
1
0
-1
-2
0
1
2
3
4
5
t
Definition:
A trend is a long-term movement which can be
distinguished from oscillation and noise.
x(t) = Trend(t) + Oscillations(t) + Noise(t)
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PRUDENCE Comparison
1961/90 – 2075/2100, A2 Hadley Boundary
DT (°C)
BSR Countries*
(model mean)
Ta
DP (%)
T (DJF)
T(JJA)
Pa
P(DJF)
P(JJA)
Denmark
1.1
1.0
1.1
2.4
9.8
-6.4
Estonia
1.4
1.6
1.2
4.2
10.2
4.5
Finland
1.4
1.7
1.0
5.8
10.9
4.6
Germany North
1.2
1.1
1.3
0.1
6.0
-7.3
Latvia
1.4
1.6
1.2
3.2
9.7
1.8
Lithuania
1.4
1.5
1.2
2.0
8.8
-1.1
Poland
1.3
1.3
1.3
0.7
6.0
-4.0
Sweden North
1.3
1.5
1.0
5.3
9.0
2.7
Sweden South
1.2
1.3
1.1
3.5
11.4
-2.1
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Gauge: Daugava/Daugavapils (JJA)
Linear trend in mean
and variance (1,1,0 obtained via model fit
routines)
Design flood values
differ significantly!
More torrential rain
in summer: regime
shift!
Kallache/Rust/Kropp 2005: Nonlinear Processes in
Geophysics
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Bootstrapping for Confining Uncertainty
Gauge: Vistula/Tczew:
Catchment: ~200.000 km2
Length: 1900-1994
Problem: data series too short!
1.
2.
3.
4.
5.
Huge model library (more than 50)
Define model selection criteria
Select best fitting model
Generate bootstrap ensemble
Perform statistics
Results:
Red: theory, asymptotic fit
100yr return level9
Grey: bootstrap ensemble
Estimates for „design flood values“ are too small (6-15% difference!)
Rust/Kallache/Kropp 2006: Advances in Water
Resources Res., under review
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My demand:
Inclusion of these procedure into the daily
practice
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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
of scientists
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
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20
We need a new science
and planning for disasters....
Climate Disruptions,
Heart Attacks and Market Crashes
Bunde/Kropp/Schellnhuber,
Springer 2002
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