CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
Extreme Scenarios and Flood Risk Management
Bruno Merz & Annegret Thieken Sektion Ingenieurhydrologie, GeoForschungsZentrum Potsdam Telegrafenberg, 14473 Potsdam,
[email protected]
Contents
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
¾ Which extreme scenarios are of interest ? ¾ How should extreme scenarios be considered in flood risk management ? ¾ How can extreme scenarios be quantified ? ¾ How can extreme scenarios be validated ?
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
Who needs which (extreme) scenarios ? Use
Required Information
Flood design for dams
Site-specific statements about extreme discharges / hydrographs, e.g. 10000year flood
Building insurance
Building-specific statements about flood hazard, e.g. 10 – 200-year floods
Re-insurance
Probable Maximum Flood, large-scale events
Local disaster management
Local scenarios including extraordinary situations and implications for disaster management, e.g. disruption of infrastructure
Federal disaster management
Large-scale, extraordinary scenarios that cannot be handled by regional agencies
Large-scale flood scenarios Example ICPR Rhine Atlas
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
T = 10 a
T = 100 a
extreme flood event
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
Spatial heterogeneity of flood events Example August flood 2002
Example Rhine basin Re e s
Lippe
R uh r D sseldorf
Abflusspegel
Köln
R
La h
n
Sieg
h e N
e ah
G ro lsh e im
n
Trier
S ch we in fu rt
ai
M
e os
M
Worm s
Ma x a u
N ec k ar
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
in
Ande rna c h K a lko fe n K o b le n z Co ch e m Ka ub Ma inz l
Strabburg
Rheinfelden
Aa re
Stuttgart
P lo ch in g e n
W rzburg
Januar 1948
November 1944 35 M axau
25
W o rms
20
M ainz Kaub
15
A nd ernach 10
Köln
5
R ees
Jährlichkeit [a]
Jährlichkeit [a]
35 30
0
30
M axau
25
W orms
20
M ainz
15
Kaub
W o rms M ainz Kaub A nd ernach
20
Köln
10
R ees
Jährlichkeit [a]
Jährlichkeit [a]
M axau
50
0
20 18 16 14 12 10 8 6 4 2 0
Mai 1983
M axau
50
W o r ms M ainz
40
Kaub
30
A nd er nach
20
Köln
10
R ees
Jährlichkeit [a]
Jährlichkeit [a]
W o r ms M ainz Kaub A nd er nach Köln R ees
300
60
0
2 50
M axau
200
W o r ms M ainz
150
Kaub
10 0
A nd er nach
50
R ees
0
Januar 1995 14 0 M axau
50
W o r ms
40
M ainz Kaub A nd er nach
20
Köln
10
R ees
Jährlichkeit [a]
70 60
Fluss
Pegel
Zeitreihe
Rhein Rhein Rhein Rhein Rhein Rhein Rhein Neckar Main Nahe Lahn Mosel
Maxau Worms Mainz Kaub Andernach Köln Rees Plochingen Schweinfurt Grolsheim Kalkofen Cochem
1922-1999 1937-1999 1931-1999 1931-1999 1931-1999 1880-1999 1931-1999 1919-1999 1845-1999 1845-1999 1936-1999 1901-1999
Köln
Dezember 1993
0
M axau
März 1988
70
Jährlichkeit [a]
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
70
30
Rees
Februar 1980
60
30
Köln
5 0
Januar 1955
40
Andernach
10
Variation of return period
12 0
M axau
10 0
W o r ms
80
M ainz
60
Kaub A nd er nach
40
Köln
20
R ees
0
Variation of return period and „severity“ of floods
Coefficient of Variation [%]
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
Data: 29 floods in the Rhine basin (1940-1999, 12 gauges)
Mean Return Period [a]
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
Unusual events, failure situations
Failure Modes and Effects Analysis (FMEA)
Component
Damage Cause
Consequences
Possible Countermeasures
Flood retention basin
Volume of flood > available retention volume
Spillway discharges; moderate inundations downstream (industrial site, urban area)
Monitoring and early warning; damage-reducing measures downstream, e.g. mobile flood walls for particularly vulnerable objects
Flood retention basin
Volume of flood > available retention volume Flood peak > spillway capacity
Overtopping of dam; possibly dam break; severe inundation downstream
Monitoring and early warning; damage-reducing measures downstream, e.g. evacuation
Flood retention basin
Obstruction of the outlet due to sediment, driftwood etc.
Spillway discharges; moderate inundations downstream
Monitoring and clearance operations during floods
Industrial site
Inundation causes release of chemicals
Pollution downstream of chemical release
Flood proofing of chemical storages
Bridge (urban area)
Obstruction of the profile due to driftwood etc.
Backwater effects; local inundation in urban area
Monitoring and clearance operations during floods
Flood wall (urban area)
River water level > height of wall
Inundation in urban area
Damage-reducing measures in the urban area
Private households (urban area)
Leakage of flooded oil fuel storages due to buoyancy
Contamination in the respective household and in its neighbourhood
Flood proofing of oil fuel storages
River segments with high velocity in case of flood (urban area)
Erosion of river bed
Damage to foundation of buildings; loss of structural stability
Protection of erosion-prone river segments
Contents
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
¾ Which extreme events are of interest ? ¾ How should extreme scenarios be considered in flood risk management ? ¾ How can extreme scenarios be quantified ? ¾ How can extreme scenarios be validated ?
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
Expected annual flood damage [€/a] as risk indicator
Contents
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
¾ Which extreme events are of interest ? ¾ How should extreme scenarios be considered in flood risk management ? ¾ How can extreme scenarios be quantified ? ¾ How can extreme scenarios be validated ?
Flood frequency analysis and uncertainty Annual maximum flood, 1880-1999, gauge Köln/Rhine 11000 10000
8000 7000
6800
6000
6600
5000 4000 3000 2000 1880
1900
1920
MHQ (für 30-Jahre s -S e g me nte ) [m 3 /s ]
Abflus s [m3 /s ]
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
9000
6400
6200
6000
1940
1960
1980
2000
Hydrologis c he s Jahr
Mann-Kendall-Test: Hypothesis „no trend“ is rejected (α=0.05)
5800
5600
5400 1900
1910
1920
1930
1940
1950
1960
Hy dro lo g is c he s Jahr
1970
1980
1990
2000
Consideration of uncertainty
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
• 2 time periods: 1880-1999, 1960-1999 • 7 distribution functions: Generalised Extreme Value, Gumbel, Log Pearson Typ 3, 3-parametric Lognormal, General Logistic, Exponential, General Pareto distribution • goodness-of-fit test: Kolmogorow-Smirnow (α=0.05) • Probability bounds as uncertainty estimation: Envelope, including all cdf that are not rejected • Best estimate: weighted flood frequency curve (weights based on agreement between empirical and theoretical values)
Uncertainty of extrapolation 1000
200
Return period [a]
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
500
100 50
20 10
5 0.6
0.8
1
1.2
1.4
1.6
Discharge [m3/s]
1.8
2
2.2 x 10
4
Additional information
E xtre me H ochw asse r in E urop a (Sta ne sc u, 20 02 ) Ho ch wa sser Od ra S om m e r 1 99 7
100000
Ho ch wa sser Do na u/Elb e So m m er 20 02 E xtre me H ochw asse r in K öln - histo ris ch vor 18 80 (n a ch K ra h e, 19 97 ) E xtre me H ochw asse r in K öln - ge m es se n (1 88 0-19 99 )
Abflussspende [l/(s*km²)]
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
10000
1000
100
10
1 1
10
100 1000 10000 Einzugsgebietsgröße [km²]
100000
1000000
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
Flood frequency analysis and additional information
Linking process simulation and probabilistic methods (Example Lower Rhine) Gauge Rees
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
Tributary Lippe Levee breach location Polder Mehrum Tributary Ruhr
Levee breach location Krefeld
Gauge Cologne © ICPR Atlas
Flood Frequency Curve Gauge Rees Discharge [m³/s] 4000 10000
6000
8000
observed discharges 1961 - 1995
1000
Return interval [a]
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
extreme value statistics (Pearson III)
100
10
1
extreme value statistics (Gumbel) probabilistic model
10000
12000
14000
16000
Contents
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
¾ Which extreme events are of interest ? ¾ How should extreme scenarios be considered in flood risk management ? ¾ How can extreme scenarios be quantified ? ¾ How can extreme scenarios be validated ?
Possibilities for validation Water balance sim ulation
Flood risk analysis
Decreasing repeatability of system states
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
System state m ay be repeated m any tim es
System state cannot be repeated
Decreasing precision with which the system state can be identified System state is precisely m easureable
System state is only m easureable in vague term s
Decreasing relevance of available measurements to the phenomenon of interest M easured system state is the variable to be predicted
(Hall & Anderson, 2002, Blockley, 1980, modified)
M easured system state is not relevant to prediction
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
Some aspects concerning model validation for extreme flood scenarios
¾ Possibilities for “validation”: ¾ Sensitivity analysis to identify important input / processes ¾ Probabilistic analysis to identify the effects of uncertainty on model results ¾ Comparison of alternative models ¾ Reporting on model assumptions and uncertainty ¾ Optimistic models may be dangerous ¾ Be aware: we tend to overestimate our knowledge
Conclusions
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
¾ Lack of certain types of extreme scenarios: ¾ Large-scale flood events ¾ Unusual events, failure situations (blockage of weirs, human error in emergency management, etc.) ¾ Use of extreme scenarios in flood risk management: ¾ Risk awareness, ‘low frequency – high damage’ events ¾ Quantification of extreme scenarios: ¾ Integration of different sources of information (historical events, formal expert knowledge, etc.) ¾ Linking of process understanding and probabilistic methods ¾ Lack of methods for validation in data-sparse situations
Definition of ‘extreme events‘ 12
Community damage (household inventory) Meuse floods Wind et al., 1999
B
Schaden 1995 [kf ]
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
10 8 6 4 2 0
0
2
4
6
8
Schaden 1993 [kf ]
10
12
¾ Extreme events are inherently contextual ¾ Extreme means ‘something rare, big, different’ ¾ ‘Extremeness’ implies an event’s behavior to cause change (Sarewitz & Pielke, 2000)
Choice / development of scenarios Schweregrad
CHR Workshop on Extreme Discharges April, 18-19, 2005, Bregenz
Eintretenswahrscheinlichkeit Häufig Schlimmstes erlebtes Ereignis (einmal innerhalb von 10 bis 50 Jahren: Wahrscheinlichkeit für die nächsten 25 Jahre ca. 100%)
Selten Schlimmstes Ereignis, an das man sich erinnern kann (einmal innerhalb von 50 bis 200 Jahren: Wahrscheinlichkeit für die nächsten 25 Jahre ca. 25%)
Sehr selten Schlimmstes vorstellbares Ereignis (einmal innerhalb von einigen 100 Jahren: Wahrscheinlichkeit für die nächsten 25 Jahre ca. 2%)
Bähler et al. (2001)
Normaler Verlauf (95%) Prozess verläuft, so wie man es auf Grund von Erfahrungen kennt. Alle Schutzmaßnahmen greifen. Die Einsatzkräfte können optimal eingesetzt werden. Keine Personen im Wirkungsraum.
Schwererr Verlauf (4%) Ausbreitung des Schadenprozesses weicht von den Erwartungen ab. Einzelne Schutzmaßnahmen funktionieren nicht. Erschwerte Einsatzverhältnisse. Unglücklich exponierte Personen werden erfasst.
Katastrophaler Verlauf (1%) Prozess verläuft sehr unüblich. Schutzmaßnahmen funktionieren nicht oder kommen zu spät. Schwierige Einsatzverhältnisse. Viele Personen exponiert und direkt betroffen (z.B. Direkttreffer)
