Fred F. Hattermann

Fred F. Hattermann, Shaochun Huang & Valentina Krysanova

Potsdam Institute for Climate Impact Research

Modeling hydro-climatic extremes and flood

damages under climate change conditions

Fred F. Hattermann

• Introduction – hydro-meteorological extremes under climate change

• Methodology - Model system - Data - Calibration and validation - Bias correction

• Results

• Conclusions

Outline

Fred F. Hattermann

Geophysical event

Meteorological event

Hydrological event

Climatological event Significant events

Natural catastrophe

Significant floods 2010

© 2010 Munich Re, Geo Risks Research, NatCatSERVICE

Fred F. Hattermann

Clausius-Clapeyron: saturated moisture content in the

atmosphere is a non-linear function of temperature

Fred F. Hattermann

Number of major floods 1980 - 2010

50

100

150

200

250

300

350

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

Number

Source: MunichRe

Fred F. Hattermann 6.

1990 1995 2000 2005 2010 2015

Fo

ssil

Fu

el E

mis

sio

n (

GtC

y-1)

5

6

7

8

9

10

A1B

A1FI

A1T

A2

B1

B2

Carbon Dioxide Information Analysis Center

International Energy Agency

Averages

Full range of IPCC individual scenarios

Raupach et al. 2007, PNAS, updated; Le Quéré et al. 2009, Nature Geoscience; International Monetary Fund 2009

Global CO2-Emissions in Gt/year

Fred F. Hattermann

Storms observed and projected

The strongest strom oberved:

Kyrill

Possible storm in future:

EH5_1: 2079-01

Storm intensity (wind speed)

Fred F. Hattermann

Co-operation with the leading Insurance

Companies (German Insurance Association)

Leading questions:

• How will climate change impact on flood generation in Germany?

• Do we have more or less intense floods under climate change?

• What is the approximated magnitude of the projected losses?

Group meetings with the insurance experts every 2-3 month

Fred F. Hattermann

• Introduction – hydro-meteorological extremes under climate change

• Methodology - Model system - Data - Calibration and validation - Bias correction

• Results

• Conclusions

Outline

Fred F. Hattermann

Model system

Reg

ion

al

Glo

bal

ECHAM5 Global climate

Lan

dscap

e

CCLM / REMO

Regional climate models

SWIM

1) Water resources and

extremes

2) Change in vegetation

growth

Land use change Climate change

xx

realisations

regional partners

Water resources

management

Agriculture and

forestry

1

2

Fred F. Hattermann

Downscaling and hydrological

modelling (~5000 river sections)

Step 1: regional climate by CCLM and REMO (multiple

scenario and realisations)

Step 2: modelling hydrological processes for each

realisation using SWIM (for ~5000 river reaches/

sections)

Step 3: calculation of extreme value statistics (reference

period) for each river reach (General Pareto

Distribution GPD)

Step 4: linking runoff and damages using GPD, calculate

flood damages for each river reach and scenario

day

/1

~)(

11),,~;()(

uxuxGxFu

0.1 1 10 100 1000Return period (Year)

400

800

1200

1600

2000

2400

Dis

ch

arg

e (

m3

/s)

Simulated with CCLM reference

95% confidence intervals

Annual maxima return level plot

30

Calculation of extreme value

distributions

Jan-81 Jan-82 Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 Jan-89 Jan-900

500

1000

1500

2000

2500

Q (

m3/s

)

Q observed

Q simulated

Intschede (Weser)

General Pareto Distribution:

Developed for return interval T:

)Pr(

1~

uxn

uq

Tu

Methodology: From climate

extremes to financial losses

parameters

thresholdu

runoffq

Fred F. Hattermann

The regional climate models and the

hydrological model

(German) Regional Climate Models:

REMO – model domain Central Europe, grid size 10 km, szenarios A1B, A2 and B1 with one realization each

CCLM – model domain Europe, grid size ~18 km, szenarios A1B and B1 with two realizations each

(Eco)-hydrological Model:

SWIM – Soil and Water Integrated Model – offspring of SWAT, soil processes

and routing same as in SWAT, improved snow module (Huang et al. 2010)

Fred F. Hattermann

Data: Main German river basins 18°0'0"E

16°0'0"E

16°0'0"E

14°0'0"E

14°0'0"E

12°0'0"E

12°0'0"E

10°0'0"E

10°0'0"E

8°0'0"E

8°0'0"E

6°0'0"E

6°0'0"E4°0'0"E

54

°0'0

"N

54

°0'0

"N

52

°0'0

"N

52

°0'0

"N

50

°0'0

"N

50

°0'0

"N

48

°0'0

"N

48

°0'0

"N

46

°0'0

"N

46

°0'0

"N

Graph made by Shaochun, Huang, PIK

Subbasins in Germany

River nets

Other countries

Germany

Rhein

Donau

Elbe

EMS

Weser

Other basins in Germany

®0 100 200

Kilometers

5473 subbasins/ river reaches, thereof 3766 in Germany

Fred F. Hattermann

Data – Climate

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18°0'0"E

16°0'0"E

16°0'0"E

14°0'0"E

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12°0'0"E

12°0'0"E

10°0'0"E

10°0'0"E

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8°0'0"E

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6°0'0"E

54

°0'0

"N

54

°0'0

"N

52

°0'0

"N

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°0'0

"N

50

°0'0

"N

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"N

48

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"N

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"N

46

°0'0

"N

46

°0'0

"N

Graph made by Shaochun, Huang, PIK

Climate stations:(Min. T, Mean T, Max. T,Precipitation, Radiation, Humidity)

Source: PIK

Country

Germany

c 2399 observed climate stations

Climate data from models

®0 100 200

Kilometers

2342 climate stations

Fred F. Hattermann

Data – damage functions

Damage functions provided by the German Insurance Associatin

(including the world largest Re-Insurance companies (Munich

Re, Swiss Re, German Re))

One damage function for each of the more than 8000 zip code

zones in Germany

Linked to the 3766 subbasins/ river reaches in Germany

Considered are private estates and small enterprizes

Average losses/ damages today: ~500 Million Euro per year

Fred F. Hattermann

• Introduction – hydro-meteorological extremes under climate change

• Methodology - Model system - Data - Calibration and validation - Bias correction

• Results

• Conclusions

Outline

Fred F. Hattermann

Calibration and validation using observed

climate data

Dan

ub

e R

hin

e

Fred F. Hattermann

Flood damages for specific river reaches – Rhine at

Cologne and Main at Frankfurt

Köln (Rhein)

0

1000

2000

3000

4000

5000

6000

1961 1966 1971 1976 1981 1986 1991 1996

Jahr

Sch

ad

en

[ta

usen

d E

uro

] Kosten_beob

Kosten_sim

Frankfurt (Main)

0

20

40

60

80

100

120

1961 1966 1971 1976 1981 1986 1991 1996

Sch

ad

en

[ta

usen

d E

uro

] Konsten_beob

Kosten_sim

Losses calculated via a) observed

runoff, flood statistics and

damage funcitons and b)

simulated runoff …

Fred F. Hattermann

Bias correction

Runoff -> return interval T

Dam

age D

Damage function D(T)

Ru

no

ff

re

turn

le

ve

l q

Return interval T

Simulated climate -> SWIM

GPD reference

Events future

Observation

Fred F. Hattermann

• Introduction – hydro-meteorological extremes under climate change

• Methodology - Model system - Data - Calibration and validation - Bias correction

• Results

• Conclusions

Outline

Fred F. Hattermann

Results: Climate – temperature increase

Fred F. Hattermann

Downscaling and hydrological

modelling (~5000 river sections)

Step 1: regional climate by CCLM and REMO (multiple

scenario and realisations)

Step 2: modelling hydrological processes for each

realisation using SWIM (for ~5000 river reaches/

sections)

Step 3: calculation of extreme value statistics (reference

period) for each river reach (General Pareto

Distribution GPD)

Step 4: linking runoff and damages using GPD, calculate

flood damages for each river reach and scenario

day

/1

~)(

11),,~;()(

uxuxGxFu

0.1 1 10 100 1000Return period (Year)

400

800

1200

1600

2000

2400

Dis

ch

arg

e (

m3

/s)

Simulated with CCLM reference

95% confidence intervals

Annual maxima return level plot

30

Calculation of extreme value

distributions

Jan-81 Jan-82 Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 Jan-89 Jan-900

500

1000

1500

2000

2500

Q (

m3/s

)

Q observed

Q simulated

Intschede (Weser)

General Pareto Distribution:

Developed for return interval T:

)Pr(

1~

uxn

uq

Tu

Methodology: From climate

extremes to financial losses

parameters

thresholdu

runoffq

Fred F. Hattermann

Results: Flood damages in the Rhine basin

under climate change

Source of damage functions: German Insurance Association

(GDV), modelled at PIK

Fred F. Hattermann

Maps of the flood-related damages under

scenario conditions

2011-40 2041-70

Source of damage functions: German Insurance Association

(GDV), modelled at PIK

Fred F. Hattermann

Damages per climate model, scenario

and realization

0 200 400 600 800 1000 1200 1400 1600

Damages [million Euro per year]

CCLM A1B - 1

CCLM A1B - 2

CCLM B1 - 1

CCLM B1 - 2

REMO A1B

REMO A2

REMO B1

Average annual damages per year

2071 - 2100

2041 - 2070

2011 - 2040

1961 - 2000

Source of damage functions: German Insurance Association

(GDV), modelled at PIK

Fred F. Hattermann

0

200

400

600

800

1000

1200

1400

1600

1961-2000 2011-2040 2041-2070 2071-2100

Period

Da

ma

ge

s [

millio

n E

uro

pe

r y

ea

r]

+ ~0.5 + ~1.5 + ~3.0 T increase

Average annual damages per period

Source of damage functions: German Insurance Association

(GDV), modelled at PIK

Fred F. Hattermann

Conclusions

• All scenarios project higher damages in future

• Increase about 100 %

• Results have high uncertainty

• Understanding of climate change has to be improved

• Hydrology is very sensitive to changes in climate

• Unexpected events may happen, never observed before

Fred F. Hattermann

Thank you very much!

Especially to the German Insurance Association for providing the damage data

Fred F. Hattermann

Bias control: Comparison flood statistics with oberved

climate as input and with simulated climate as input for

the reference period 1961 - 2000

Fred F. Hattermann

Global change in temperature

IPCC 2007

Fred F. Hattermann

Global change in precipitation

IPCC 2007

Fred F. Hattermann

PRUDENCE: Signal in Precipitation

2071-2100 minus 1961-1990, A2

Jacop et al. 2008

Fred F. Hattermann

Climate Impacts in a 2 3 4 Degree World

• Regional investigations worldwide

Project ongoing

Project applied

Fred F. Hattermann

PIK Model System

Fred F. Hattermann

Fred F. Hattermann

Fred F. Hattermann

Source: http://data.giss.nasa.gov/

Fred F. Hattermann

Source: http://data.giss.nasa.gov/