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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80

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

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t o

f lo

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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%

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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

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-200

-100

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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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Austria fiscal space (bn Euro 2009)

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ion

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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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-100

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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

*

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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Atlantic Ocean

Black Sea

Mediterranean Sea

North Sea

Arctic Ocean

Arctic Circle

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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

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f E

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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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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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ba

bil

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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