1 Flood Vulnerability Analysis Session 2 Dr. Heiko Apel Risk Analysis Flood Vulnerability Analysis

1

Flood Vulnerability Analysis

Session 2

Dr. Heiko Apel

Risk AnalysisFlood Vulnerability Analysis

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Learning objectives


Get familiar:

With the principles of flood vulnerability analysis

With the elements at risk

Learn:

The exposure mapping of elements at risk

Systematic classification of flood losses

How to collect food loss data and build flood loss models in general

Specific insights in the flood loss analysis of buildings, agriculture and lives

Understand:

The importance of impact and resilience on flood losses

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Vulnerability analysis provides

Understanding of vulnerabilities in order to identify efficient measures to reduce them and to minimize impact of future floods

The foundation for cost-effective planning of flood mitigation

The inputs for realistic flood scenario modeling emergency planning

Data for risk mapping to be used to improve public flood risk awareness, which can motivate precautionary measures

Input for financial appraisals for (re-)insurance to determine insurance rates, estimate probable

maximum loss (PML) to support disaster response and governmental decisions about loss

compensation immediately after floods

Data for the quantitative comparison of different risks within community or region


Elements at Risk

Economic sectors Housing Infrastructure

(traffic, power supply, water supply, administration)

Food production Transport Trade

Cultural sectors Cultural heritage Daily life

Social sectors Population Health care Food supply Mobility

Environmental Sectors Ecosystem stability Environmental

health (Pollution) Biodiversity

4Risk AnalysisFlood Vulnerability Analysis

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Exposure Databases

Data should reflect the location and value of the assets at risk

Use analog maps or preferably GeoInformationSystems (GIS) for spatial reference, display and intersection

Explore the capabilities of remote sensing (RS) products for mapping of elements at risk

Most commonly used satellite imagery libraries: MODIS, LandSAT, ASTER, Quickbird, SPOT

Utilize statistical databases and spatial disaggregation methods to distribute aggregated values (e.g. dasymetric mapping)


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Exposure Databases (cont.)

Examples:

Micro-scale: detailed topography and building location

Meso-scale: CORINE land use data set


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Increase of flood plains and losses

Loss increase in the last decades:

Caused by: Increasing number of disasters River training Increasing use of floodplains (urbanization, population

growth) The accumulation of valuable goods Decreasing awareness of flood risk


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Flood impact classification


Flood Impact

Direct

TangiblePropert

y Damag

e, Infrastructure, Agricult

ure

IntangibleFatalities,

Evacuees,

Ecosystems,

Cultural Heritag

e

Indirect

Tangible

Disruption of Trade and

Traffic

Intangible

Psychological Stress, Migrati

on

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Factors affecting flood loss

Example: flood loss of buildings

Impact and resistance should be defined for every element at risk

Source: Thieken et al. (2005)


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The role of awareness and preparedness

Significantly reduces flood losses Example: loss in private households in the flood

of 2002 in Germany (questionnaire results, n 2150)

use

0

5

10

15

20

25

adapted not adapted

da

ma

ge

ra

tio

bu

ild

ing

[%

]

n = 78 580

furnishing

0

5

10

15

20

25

adapted not adapted

n = 67 589

heating, electr. utilities

0

5

10

15

20

25

in storeys in cellarn = 53 560

use

0

10

20

30

40

50

adapted not adapted

dam

ag

e r

ati

o c

on

ten

ts [

%]

furnishing

0

10

20

30

40

50

adapted not adapted

75%-Quantile

25%-Quantile

Median (50%-Quantile)

Mean

Source: Kreibich et al. (2005)

Mean damage reduction due to precautionary measures:

29.000 € (buildings) 31.000 € (assets) 24.000 € (utilities)


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Scales of loss estimation

Scale of analysis determines data and methods:

Micro-scale Object specific Detailed input datasets (direct surveys)

required Cities, communes, counties Results aggregated, but detailed results

available

Meso-scale Regional to national Aggregated input data (statistics, census

data, land use units) Cumulated loss estimates, no site specific

interpretation possibleRisk Analysis

Flood Vulnerability Analysis

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Flood loss data collection

Is the first step for establishment and evaluation of loss models:

Problem: data availability and compatibility Different stakeholders (e.g. insurance industry, science,

public administration) collect data on flood losses with different methods:

Methods to collect data Building Surveyors – high level of standardization, consistent data

quality, limited set of parameters, expensive method (100 € per case*)

Questionnaires – answers dependent on respondents, unknown data quality, representativeness via sampling, lower cost (25-40 € per case*)

* Prices refer to Germany


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Flood loss assessment - buildings

Region specific Damage types:

Structural damage Contents

Differentiated into Building types: construction, materials, size, stories Building uses: private, commercial, industrial

Loss estimation Absolute or relative damage Loss functions: functional

relationship between flood indicators and damage (be careful to consider all factors)

Solution: rule based loss model


Private PrecautionNone Good

Very Good

Contamination None 0.92 0.64 0.41

Moderate 1.2 0.86 0.71

Severe 1.58 --- ---

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Flood loss assessment – buildings (cont.)

Rule-based flood Loss Estimation Model FLEMO (GFZ)

Source: Büchele et al. (2006)

0

5

10

15

20

25

30

35

< 21 cm 21-60 cm 61-100 cm 101-150 cm > 150 cm

Water level (above ground surface)

Lo

ss

ra

tio

of

a b

uild

ing

[%

] one-family house

(semi-)detached

multifamily house

poor/average building quality

high building quality

Step 2: modification of loss ratio (FLEMOps+)

Step 1: damage ratio estimation by water depths and rule based model FLEMOps (step functions)


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Flood loss assessment - agriculture

Two-step process: Relative loss estimation respective to season Estimation of regional market value of crops

Loss of wheat crops in an early summer flood in East Germany


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Flood loss assessment – loss of life

Data-based empirical approach (based on flash flood and dam failure data in USA, LOL = f(PAR)*, Source: Brown & Graham 1988 )

Process-based approaches (simulates exposure of people in buildings etc., evacuation possibilities and survival rates)

*Loss of life (LOL), Population at risk (PAR)

Risk AnalysisFlood Risk Analysis

Warning time Loss of lifeLess than 15 min 0.5 PAR

15 min – 1.5 h PAR0.56

More than 1.5 h 0.0002 PAR

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Flood loss assessment– loss of life (cont.)

Risk AnalysisFlood Risk Analysis

Example of processed-based approach:Hydraulic experiments by REDSCAM (2000), Helsinki University of Technology, Finland

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1 Flood Vulnerability Analysis Session 2 Dr. Heiko Apel Risk Analysis Flood Vulnerability Analysis