Assessing and Managing Europe's current and future flood and drought risks Vulnerability and disaster risk mapping workshop EEA, Copenhagen, 2 July 2009.
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Assessing and Managing Europe's current and future flood and drought risks
Vulnerability and disaster risk mapping workshop
EEA, Copenhagen, 2 July 2009
Hochrainer StefanInternational Institute for Applied System Analysis
Laxenburg, Austria
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60
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90
100
Years
Nu
mb
er
of
even
ts
Drought
Earthquake
Extreme Temperature
Flood
Volcano
Wild Fires
Wind Storm
Total
Odra Flood, 1997: 5.0 billion Euro losses, 0.8 billion Euro insured losses 300.000 people evacuated
Flooding, 2002: 14.4 billion Euro losses, 3.4 billion Euro insured losses 400.000 people evacuated
Large Scale Events:
*
* Source: CRED, 2008
Point of Departure
• Losses from weather extremes such as floods, droughts, and other climate-related events in Europe (and elsewhere) have escalated in recent decades
• The increase has been more rapid than population or economic growth can fully account for
• According to the IPCC Fourth Assessment Report, anthropogenic climate change is expected to lead to increases in intensity and frequency of weather extremes
Point of Departure
• Europe vulnerable to disasters already today, key focus for EU adaptation strategy (White paper) is on managing climate variability
• Large events: – 2002 large-scale flooding over central Europe: losses > 15 billion Euro– 2003 summer heat wave of unprecedented magnitude resulting in 80,000
additional deaths– led to agricultural losses exceeding 13 € billion and a 30 per cent reduction
in gross primary production of terrestrial ecosystems.
• Risk information (maps) exist in some EU member states, but ADAM provides the first comprehensive probabilistic maps of riverine flood and drought/heatwave risks across the EU on various scales, e.g. from the regional to the national level
• Potential applications– Risk based planning: e.g., flood protection– Identification and comparison of vulnerable and “at risk” countries– Financial and economic planning: Risk sharing
Point of Departure
Methodological Approach
• Extremes are low probability -high consequence events.
• To assess and manage extreme risks probabilistic approaches have to be used to incorporate all possible future scenarios.
• Traditional risk measures are inadequate for decision making, e.g. averages will not give adequate representation of the risk.
• The „fat-tails“ and the thickness (of distributions) are important instead.
• Traditional coping mechanisms do not work in the case of extremes, e.g. Law of large numbers not applicable; hence different risk management/adaptation strategies have to be considered for catastrophes.
• Distinctions between stock and flow effects are important.
Efficiency of risk management instruments dependent on the occurrence probability
EU solidarity fund
Market based insurance
Flood protection
Advantages of Risk based approaches P
rob
abil
ity
High probability
Low probability
Flood
Risk based approach: Assessing direct risk
Risk is a function of the Hazard, the Exposure and the Vulnerability.
Approach used in ADAM:
Maximum annual average flood damage for European
provinces and regions (NUTS 2 level) as a
percentage of GDP for today’s climate regime
Risk based approach: Assessing direct flood risk
Averages are based on loss distributions on the GRID level.
New method developed toupscale losses to the nationallevel, „hybrid-convolution“Method (Hochrainer, Lugeri)
Minimum and maximum average annual flood risk across European countries measured in percent of GDP
Regions and countries in Eastern Europe are particularly flood risk hotspots
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BE
BG
CZ
DE
DK
EE
ES
FI
FR
GR
HU
IE IT LT
LU
LV
NL
PL
PT
RO
SE
SI
SK
UK
Countries
Perc
en
t o
f lo
ss t
o G
DP
Minimum Average Annual Damage in %GDP
Maximum Average Annual Damage in %GDP
Assessing direct flood risk: Results
Country A
Country Z
… losses
losses
Input: Loss distribution
Sampling
Threshold criteria EUSF
paymentsSampling
Criteria forfunding met
Extreme value distributionestimated
Managing flood risk on the European Scale: EUSF
0 500 1000 1500 2000 25000
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Million Euro
F
Payment distribution for the EUSF
upper scenariobaselinelower scenario
On average, every 7 years one can expect that the EUSF can not meet its obligations.
Liabilities Direct: obligation in any event Contingent: obligation if a particular event occurs
Explicit Government liability recognized by law or contract
Foreign and domestic sovereign borrowing, Expenditures by budget law and budget expenditures
State guarantees for nonsovereign borrowing and public and private sector entities, reconstruction of public infrastructure
Implicit A "moral" obligation of the government
Future recurrent costs of public investment projects, pension and health care expenditure
Default of subnational government and public or private entities, disaster relief
Source: Modified after Schick and Polackova Brixi, 2004
Managing current flood risk on the Country Scale
Government is a key actor:
8%20%
10%
41%
17%
58%44% 48%
62%
79%
21%
1%
11%
32%48%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Umbria EQ 1997 Poland Floods 1997 Austria Flooding2002
Spain drought 2005 Portugal drought2005
Private sector and net loss
Governm ent
Insurance
Cross-country sample of financing modalities of disaster losses by insurance, government assistance, and private sector and net loss (as a percentage of direct losses)
Managing current flood risk on the Country Scale
Modeling Impacts and Adaptation: Country scale
Indirect riskProbabilistic fiscal and
Macroeconomic impacts
Risk Management/Adaptation
Development of risk management strategies
HazardFloods, Droughts
ExposureCapital stock, population
Physical VulnerabilitySusceptibility to physical damage
Direct RiskProbabilistic asset losses
Economic resilience•Financial resilience
•Economic redundancy
Economic vulnerabilityAbility to recover and refinance
from disaster events
Dynamic Indirect risk management
Dynamic direct
risk assessment
Climate Change Global Change
HazardSudden onset
Exposure PhysicalVulnerability
Direct RiskEconomic Resilience
Economic Vulnerability
Economic Risk
Adaptationand Risk Management
Ex-post
Ex-ante
Dynamic Modeling of Impacts and Adaptation: Country scale
Projected change in flood damages in 2071-2100 (% change with respect to
1961-1990 baseline)
Climate models remain limited in their reproduction of local weather extremes due, inter alia, to inadequate (coarse) resolution
Projections of changes in future extreme weather events remain highly uncertain and hinder us from robustly projecting future flood risk
Land use changes very difficult to project
Future Floods
Government fiscal deficits and hidden liabilities due to flood risk in flood prone European countries
0%
1%
2%
3%
4%
5%
6%Au
stria
Cze
ch R
epub
lic
Hun
gary
Latv
ia
Pola
nd
Rom
ania
Bulg
aria
Slov
akia
Lith
uani
a
Per
cen
t of G
DP
Government flood risk liability Projected fiscal deficit 2009
Managing current flood risk on the Country Scale
Need for managing risk on the country level: Hidden government disaster liabilities
Austria case: Reserve Fund as a Risk instrument
• The Austrian “Katastrophenfond” (National Disaster Fund):
Reserve Fund Accumulation
-300
-200
-100
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400
500
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Year
Mill
ion
Eu
ros
The Fund became negative in 2002 and 2003 (flooding). To adjust the fund, investments more than 137 million in 2002 and more than 207 million Euros in 2003 were needed.
* Source:Hyll et al., 2004
*
Stochastic trajectories of discretionary spending including disaster risk
Fiscal space and disaster related reduction in Austria Time period of 10 years, 2009-2018
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-5.0
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5.0
10.0
15.0
20.0
25.0
Austria fiscal space (bn Euro 2009)
Bill
ion
Eu
ro 2
009
Absolute w/o disaster riskreduction (expectation)reduction (standard deviationreduction (95% quantile)reduction( 99% quantile)
Fiscal space concept: Fiscal flexibility is decreased due to disaster events
Austria case: Changes in fiscal space
Drought
PPootteennttiiaall yyiieelldd
CCRROOPP MMOODDEELLLLIINNGG ((CCrrooppssyysstt))
Hazard
Vulnerability
RRCCMM ((SSCCEENNAARRIIOO aa--AA22))
YYiieelldd lloossss rreettuurrnn ppeerriioodd
LLAANNDD UUSSEE ((CCoorriinnee))
Exposure
PPhheennoollooggyy
EExxttrreemmee eevveennttss ffrreeqquueennccyy
AAccttuuaall yyiieelldd YYiieelldd lloossss
A
B
CD
Approach used:
Annualised monetary risk due to combined heatwave and drought stress for spring wheat calculated for the present period (1975-2005)
Similar analyses for winter wheat, soybean and sunflower
Losses in 2009 € millions
Current drought and heatwave risk
Changes in annualised drought and heatwave risks to spring wheat over a future period in 2030-2060 based on SRES A2 (=2 degree) compared to today (in € millions)
without adaptation
with adaptation: advanced sowing
with adaptation: longer cycle variety
Change in 2009 € millions
Future drought and heatwave risk
• With regard to drought and heat stress to agriculture, we find Southern Europe to be particularly vulnerable
• In a future climate with a north-south precipitation change gradient, and assuming adaptation, many agricultural regions in Europe would actually benefit from a warming climate
• However, some regions in Italy and Spain would not be able to benefit and adapt, and face continued stress and substantial associated risks
• Drought and heatwave stress operate as slower onset phenomena and are more strongly characterised by mean climate conditions: greater confidence in model projections
Drought and heatwave:
• Our study suggests that regions in Eastern Europe represent disaster hotspots for flood risk, and areas in Southern Europe for drought and heat stress to agriculture: case for increased cohesion funding?
• Flood hazards are likely to worsen over much of Europe, yet due to a lack of localised projections from climate models, we considered risk projections not robust
• In contrast, we feel more confident in projecting drought and heatwave risk as well as adaptation as a function of changes in broader-scale average climates
Conclusions
• Although drought and heatwaves are likely to worsen across much of Europe, effective adaptation interventions exist
• Yet regional heterogeneity in risk and response will continue, leading to climate change “winners” and “losers”.
• Irrespective of future changes, weather-related disasters today already pose substantial burdens for households, businesses, and governments
• Risk-based adaptation planning seems important: prevention better than the cure
Conclusions
End of Presentation
Partners
Introduction: Country perspective: Austria
• The Austrian “Katastrophenfond” (National Disaster Fund):
Reserve Fund Accumulation
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-200
-100
0
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400
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1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Year
Mill
ion
Eu
ros
The Fund became negative in 2002 and 2003 (flooding). To adjust the fund, investments more than 137 million in 2002 and more than 207 million Euros in 2003 were needed.
* Source:Hyll et al., 2004
*
Introduction: European perspective: Solidarity Fund
• Fund may be heavily exposed to one (large scale) event only, as experienced in 2002 with ¾ of the fund depleted due to flood events
Year 2002 2003 2004 2005
Aid granted (mill. Euro) 728 107 20 204
Successful Applications 4/4 6/10 1/11 10/12
• Nearly all (13/16) of the rejected applications were regional disaster events.
• Given the fact that a number of new member states are very hazard-prone and disaster losses are likely to rise, the Solidarity Fund is likely to be severely under-funded in the future
Question: - How one could model disaster impacts (direct and indirect) on the Country or European scale - How to incorporate adaptation strategies
Modeling Impacts and Adaptation: Introduction
Risk bearers (aggregate country level):
- Public sector: Government: Infrastructure and Relief- Private sector: business and households.
-property owners, -insurers, -reinsurers -and the capital market.
Each stakeholder may implement a wide range of risk management and adaptation strategies, including
- risk reduction: Structural or physical mitigation - risk preparedness: Loss absorption, e.g. via savings- and risk transfer: Risk spreading or financing
Furthermore, one can distinguish between ex-post or after-the-fact approaches, and proactive (ex-ante) approaches.
Standard Approach: Four basic components
• Hazard : Characterization of risk• Inventory: The elements at risk • Vulnerability: Susceptibility to damage• Loss: Direct or indirect
Structure:
Model output:
- Hazard maps- Exceedance Probability (EP) curves- Probable maximum loss (PML)- Distribution of losses.
Modeling Impacts and Adaptation: Direct Risk
Exceedance probability curve Loss frequency distribution
Direct Risk
Modeling Impacts and Adaptation: Direct Risk
Modeling Impacts and Adaptation: Indirect Risks
Possible indirect effectson the macro-level
Economic vulnerability, e.g. the ability to finance the losses, is time dependent
It is important to incorporate indirect effects within a risk management framework
Risk management/ adaptation strategies on the country level, have toincorporate indirect risks as well in their decision strategy.
Modeling Impacts and Adaptation: Resilience
Public sector ex-post and ex-ante financing sources
Type Source Model
Ex-post sources
Decreasing government expenditures Diversion from budget yes
Raising government revenues Taxation -
Deficit financing Domestic
Central Bank credit -
Foreign reserves -
Domestic bonds and credit yes
Deficit financingExternal
International borrowing yes
Outside support, yes
Ex-ante sources
Insurance yes
Reserve fund yes
Contingent credit yes
Catastrophe Reserve Fund Reinsurance
Catastrophe Contingent Credit Mitigation
Modeling Impacts and Adaptation: Ex-ante Options
Modeling Impacts and Adaptation: Summary
Direct Risk: Probability of Asset losses (in monetary terms)
Economic Vulnerability: Ability to finance the losses Economic Vulnerability is a function of
- Economic resilience: Loss Financing Instruments- Direct Risk
Possible risk measures: Financing gap approach e.g., shortfall between losses and financing possibilities
Indirect Risk: Probabilistic impacts and economic vulnerability lead ultimately to macroeconomic effects.
Adaptation Strategies: Increase the economic resilience or will decrease the direct risk.
Possible Hotspots in the European Union: Flood
Use direct risk (annual average losses) and debt indicators as first proxies for economic vulnerability of the given country:
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AT BE BG CZ DE DK EE ES FI FR GR HR HU IE IT LT LU LV NL PL PT RO SE SI SK UK
Losses/Capital Stock AAD MIN
Losses/Capital Stock AAD MAX
This approach would lead to the following countries as possibleHotspots: Bulgaria, Romania, Croatia, Hungary, Czech Republic, Slovakia, and Poland
Possible Hotspots in the European Union: Flood
Europe0 240 480 720 960120
Miles
:Legend
country
hotspot
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1
Atlantic Ocean
Black Sea
Mediterranean Sea
North Sea
Arctic Ocean
Arctic Circle
70°
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-10°-20°-30°-40°
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Robinson ProjectionCentral Meridian: 30.00
Solidarity Fund: 0.4 billion Euros needed each year with a standard deviation of around 0.3 billion (large scale events not incorporated)
Country Perspective: Austria Flooding
Loss Exceedance Curve
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0 0.013 0.074 0.15 0.343
Losses in percent of Capital Stock
Pro
ba
bilit
y o
f E
xc
ee
da
nc
e
Direct Risk:
Economic Resilience:
• 2.5 percent of the direct losses of the public sector are financed from outside assistance, namely the European Solidarity fund for losses higher than 3 billion Euros or 0.6 percent of GDP. • The disaster fund is set to 30 million Euros each year. • The credit buffer, e.g. the maximum amount of credit from abroad the government can or may use is set to 5 billion Euro.
Country Perspective: Austria Flooding
Fiscal consequences due to flooding in the next 10 years
Risk
Time
Ability to start new projects
Standard Approach:
Modeling Impacts and Adaptation: Direct Risk
Einführung in die Problematik: Effekte auf Landesebene
Oben: Mögliche Entwicklungsliniennach einer Katastrophe
Links: Durchschnittliche Wachstumsraten nach einer Katastrophe
Source: Hochrainer, 2006.
I:
Operationalisation of economic vulnerability
ElementDescription Operationalisation in model
Financial vulnerability
Ability to share risks Financial vulnerability algorithm for determining internal and external savings for reconstruction, relief and recovery given direct disaster losses
Economic redundancy
Ability to pool risks: geographic and economic diversification
CES function specification: input factors are not perfectly substitutable (not fully implemented )
I:Public sector ex post and ex ante financing sources for relief and reconstruction
TypeSource Considered in model
Ex-post sources
Decreasing government expenditures
Diversion from budget yes
Raising government revenues
Taxation -
Deficit financing Domestic
Central Bank credit -
Foreign reserves -
Domestic bonds and credit yes
Deficit financingExternal
International borrowing yes
Outside support, e.g. from EU solidarity funds
yes
Ex-ante sources
Insurance yes
Reserve fund yes
Contingent credit yes
CatSim: Software und Algorithmusstruktur
Financial vulnerability
Financing sources: financing supply
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Amount available
Ma
rgin
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co
st
Diversion
International bonds
Domestic bonds
and credit
Borrowing from IFIs
Grants
Loss function: financing needs
200 year event100 year event
10 year event
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Pro
ba
bil
ity
financing gap
Indirect riskProbabilistic fiscal and Macroeconomic impacts
Risk Management/Adaptation
Development of risk management strategies
HazardFloods, Droughts
ExposureCapital stock, population
Physical VulnerabilitySusceptibility to physical damage
Direct RiskProbabilistic asset losses
Economic resilience•Financial resilience
•Economic redundancy
Economic vulnerabilityAbility to recover and refinance
from disaster events
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