1riskassessment theory

SummerSchool 2005

Herceg Novi, 3th- 11th Sep 2005

Flood Damage Assessment and Risk Mapping

Theory Script Student: _________________________ Prof. Dr.-Ing. Erik Pasche Dipl.-Ing. Stephan Krig, [email protected] Dipl.-Ing. Monika Donner, [email protected]


Herceg Novi, September 2005, TUHH - River and Coastal Engineering I

Contents

1 Introduction ....................................................................................................... 1

2 Flood Risk .......................................................................................................... 3

2.1 Risk .............................................................................................................. 3

2.2 Risk Assessment.......................................................................................... 4

2.3 Risk Management ........................................................................................ 4

2.4 Hazard.......................................................................................................... 4

2.5 Vulnerability.................................................................................................. 4

2.6 Source-Pathway-Receptor-Consequence .................................................... 5

2.7 Risk units...................................................................................................... 6

2.7.1 Probability ............................................................................................. 6

2.7.2 Frequency ............................................................................................. 7

2.7.3 Consequence........................................................................................ 7

2.8 Units of consequence................................................................................... 7

2.9 Problem of uncertainty ................................................................................. 8

2.10 Parameters for risk mapping ........................................................................ 8

3 Scale for analyse ............................................................................................... 9

3.1 Microscale approach .................................................................................... 9

3.2 Mesoscale und macroscale approach .......................................................... 9

3.3 Evaluation .................................................................................................. 10

4 Project Area ..................................................................................................... 11

4.1 Rivers and streams .................................................................................... 11

4.2 Groundwater flooding ................................................................................. 12

4.3 Flooding from overland flow ....................................................................... 12

4.4 Blocked or overloaded drainage systems................................................... 12

4.5 Kellinghusen, flood situation....................................................................... 12


Herceg Novi, September 2005, TUHH - River and Coastal Engineering II

5 Methodology for damage potential assessment .......................................... 14

5.1 Statistical-economical assessment of asset value...................................... 14

5.1.1 Net Asset Value at purchase price ...................................................... 14

5.1.2 Net Asset Value at actual price ........................................................... 14

5.2 The method of Regionalisation................................................................... 15

5.2.1 Population ........................................................................................... 15

5.2.2 Asset value: Settlement ...................................................................... 16

5.2.2.1 Property asset.............................................................................. 16

5.2.2.2 Inventory (residence contents)..................................................... 16

5.2.2.3 Motor vehicles asset .................................................................... 16

5.2.2.4 Calculation of specific asset value ............................................... 17

5.2.3 Stock value for economic landuse types ............................................. 19

5.2.3.1 Asset values: Economy................................................................ 19

5.2.3.2 Calculating of specific asset values ............................................. 21

5.2.4 Agriculture ........................................................................................... 21

5.2.4.1 Arable........................................................................................... 22

5.2.4.2 Meadows...................................................................................... 22

5.2.4.3 Forest........................................................................................... 22

6 Damage Functions .......................................................................................... 24

6.1.1 Dutch experience in stage-damage curves ......................................... 25

6.1.2 German experience in stage-damage curves...................................... 25

6.1.3 IKSE, Elbe........................................................................................... 26

6.1.4 IKSR, Rhine ........................................................................................ 26

6.1.5 Evaluation ........................................................................................... 27

6.1.5.1 Arable........................................................................................... 27

6.1.5.2 Meadows...................................................................................... 27

6.1.5.3 Forest........................................................................................... 27

7 Annual damage potential................................................................................ 29

8 Method for damage assessment.................................................................... 32

8.1 Survey Data ............................................................................................... 32

8.2 Program Run.............................................................................................. 35

8.3 Microscale damage assessment for Kellinghusen ..................................... 37

8.4 Mesoscale damage assessment for Kellinghusen ..................................... 38


Herceg Novi, September 2005, TUHH - River and Coastal Engineering III

9 Risk Assessment and Mapping...................................................................... 47

9.1 Risk classification ....................................................................................... 47

9.1.1 Thresholds, urban areas ..................................................................... 48

9.1.2 Thresholds, rural areas ....................................................................... 49

9.2 Risk mapping ............................................................................................. 51

Figures Figure 1-1: Dresden Flood, August 2002.................................................................... 1

Figure 1-2: Broken dike (Dresden) during flood event in August 2002 ....................... 2

Figure 2-1: Hazard-Risk-Vulnerability......................................................................... 3

Figure 2-2: Multi-dimensions of risk............................................................................ 4

Figure 2-3: Source-Pathway-Receptor-Consequence ................................................ 6

Figure 4-1: Catchment Area of River Str ................................................................ 11

Figure 4-2: City of Kellinghusen, flood event 2002 ................................................... 12

Figure 6-1: Damage functions of the Dutch Ministry of Transport, Public Works and

Water Management ........................................................................................... 25

Figure 6-2: Damage function of IKSE ....................................................................... 26

Figure 6-3: Damage function of IKSR....................................................................... 26

Figure 7-1: Annual damage potential, withP0=0,2..................................................... 29

Figure 8-1: Survey Data, ALK and ATKIS ................................................................ 32

Figure 8-2: Landuse types ALK, Kellinghusen.......................................................... 33

Figure 8-3: Merged landuse types, Kellinghusen...................................................... 33

Figure 8-4: Program run ........................................................................................... 35

Figure 8-5: Calculation of specific damage potential using raster data..................... 36

Figure 8-6: Boundary conditions, 2d-hydraulic Kellinghusen.................................... 38

Figure 8-7: Landuse types: damage functions.......................................................... 39

Figure 8-8: Specific annual damage, Kellingusen..................................................... 40

Figure 9-1: Hazard zones of German insurance companies .................................... 48

Figure 9-2: Risk map Kellinghusen........................................................................... 51


Herceg Novi, September 2005, TUHH - River and Coastal Engineering IV

Tables Table 3-1: Scale ......................................................................................................... 9

Table 5-1: Area and population in Schleswig-Holstein according to districts,

STATISTIC AGENCY FOR HAMBURG AND SCHLESWIG-HOLSTEIN............................... 15

Table 5-2: Private households in Schleswig-Holstein,

Statistic Agency fr Hamburg und Schleswig-Holstein...................................... 16

Table 5-3: Motor vehicles- Schleswig-Holstein,

STATISTIC AGENCY FR HAMBURG UND SCHLESWIG-HOLSTEIN............................... 17

Table 5-4: Specific asset value: settlement .............................................................. 17

Table 5-5: Stock value for economic landuses, GERMAN STATISTIC AGENCY.............. 19

Table 5-6: Net asset value at actual price, economic, Schleswig-Holstein,

STATISTIC AGENCY FR HAMBURG UND SCHLESWIG-HOLSTEIN............................... 19

Table 5-7: Areas of Real estate in Schleswig-Holstein ............................................. 20

Table 5-8: Landuse types: Areas for calculating specific asset values ..................... 20

Table 5-9: Specific asset values for main landuse types .......................................... 21

Table 5-10: Main arable products and the output in Schleswig-Holstein .................. 22

Table 5-11: asset value, forest ................................................................................. 22

Table 6-1: Comparison of damage function IKSE and IKSR .................................... 27

Table 8-1: Merged landuse groups........................................................................... 34

Table 8-2: Annual damage, Kellinghusen................................................................. 37

Table 8-3: Boundary Conditions ............................................................................... 38

Table 8-4: Landuse types: Specific asset ................................................................. 39

Table 8-5: Damage of flood event HQ10 and HQ100 for Kellinghusen...................... 39

Table 9-1: Zoning system of insurance companies in Germany............................... 47

Table 9-2: Thresholds, calculated in the river Str catchment .................................. 48


Herceg Novi, September 2005, TUHH - River and Coastal Engineering 1

1 Introduction

In the past three years significant flooding has affected a majority of states in West

and Central Europe. Floods in Germany, Chech Republic, Slovakia, Austria and Italy,

have caused a number of fatalities, destroyed businesses, homes and public

infrastructure. These flood events produced an increased public interest in flood risk

management issues, and also a greater awareness of the need for improving the

knowledge supporting flood risk management.

Figure 1-1: Dresden Flood, August 2002

Like is to be seen in Figure 1-1, different areas are endangered with floods. The most

vulnerable areas and areas where flood cause high damage are city areas. Flood,

like natural disaster is actually, only just after a flood event an interesting common

topic. Soon after a flood, public forgets this problems and only direct victims are ones

that will be aware in the future. On the other hand, migration of people is nowadays

normal and a fast process, As flood usually does not occur very often, that leads to

the situation that residents are totally unprepared for the flood. This unde-sirable

situation must be avoided by risk assessment and mapping.

Beside the loss of lives, flood can cause damage to tangible assets and causes

economic damage. At present, the following trends are noticed, pointing up the

importance of flood protection in flood affected areas:

Increase in potential damage: Increase in economic value in flood affected areas through changes in landuse

Increase in number of extreme flood events: Increase in discharge because of river training or of climate changes



Flood damage does not exist per se. It is created only if a human and his assets are

affected. However, if flood events do not occur for a long period of time, people

potentially affected by flood (stakeholders) forget about the risk to flooding and

damage the flood event can cause.

Therefore, risk maps are an important means of communication in sustainable flood

management, as they provide and map the information to the public.

Further, it is necessary to determine the risk as a function of discharge for the flood

events with high values of return period. The relation between the probability of a

flood event and corresponding damage potential, gives the damage probability, that

can be calculated by integration of the annual damage potential.

Figure 1-2: Broken dike (Dresden) during flood event in August 2002

Damage potential assessment is obtained as a result of a risk analysis, which is

composed of the following steps:

1. Determination of design discharge using Hydrological Models

2. 1d or 2d-Hydraulik Modelling to calculate design waterstages

3. Outline of innundation areas

4. Determination of damage potential and expected annual damage value

5. Risk Assessment

The extent of potential damage is considerably affected by the flood plains as well as

the water depth and the duration of a flood event and by the type of landuse.



2 Flood Risk

Flooding cause direct damage to property and infrastructure as well as human

anxiety and disruption to normal life. Flooding can also threaten sites of valuable

conservation and archaeological interest. However, the main focus is the risk to

people and property. It is neither practically nor economically feasible to eliminate all

flood risk. The most suitable approach for dealing with flooding will be to manage the

risk in a best way. To be able to analyse manage flood risk, it is necessary to

understand what is meant with terms Risk and Hazard and for that purpose it is

important that appropriate terminology is used.

2.1 Risk

While the term hazard means to be threatened by a flood event, the term risk

connects the potential damage extent (vulnerability) with the corresponding

probability. The damage that occurs is created as a result of a conflict between the

impact (flood) and landuse. Vulnerability, in technical sense, is the resistance of the

buildings and infrastructure to flood and in sociological sense, is related to the ability

of the humans to regenerate after flood events.

Hazard VulnerabilityRisk

Figure 2-1: Hazard-Risk-Vulnerability

It is important to notice that the technical vulnerability is possible to quantify in

monetary values while one can only qualitatively assess the impact of flood event on

humans. Risk therefore, is a combination of the chance of a particular event, with the

impact that the event would cause if it occurred. Risk has two components: the

chance or probability of an event occurring and the impact or consequence associa-

ted with that event. The consequence of an event may be either desirable or



undesirable. Generally, however, the flood and coastal defence community is

concerned with protecting society and hence a risk is typically concerned with the

likelihood of an undesirable consequence and our ability to manage or prevent it.

2.2 Risk Assessment

The process of identifying hazards and consequences, estimating the magnitude and

probability of consequences and assessing the significance of risk.

Figure 2-2: Multi-dimensions of risk

2.3 Risk Management

According to context, either action taken to mitigate risk, or the complete process of

risk assessment, options appraisal and risk mitigation.

2.4 Hazard

Hazard is the potential of an event to cause harm and risk is the likelihood of harm.

2.5 Vulnerability

Refers to the resilience of a particular group, people, property and the environment,

and their ability to respond to a hazardous condition. For example, elderly people

may be less able to evacuate in the event of a rapid flood than young people.



2.6 Source-Pathway-Receptor-Consequence

Source is synonymous with hazard and refers to a situation with the potential for

harm (heavy rainfall, strong winds). To implement flood defence systems that can

manage these undesirable outcomes it is necessary to understood them. The term

risk has a range of meanings and multiple dimensions relating to safety, economic,

environmental and social issues (Figure 2-2). These different meanings often reflect

the needs of particular decision-makers and as a result there is no unique specific

definition for risk and any attempt to develop one would inevitably satisfy only a

proportion of risk managers.

The pathway provides the connection between a particular hazard being realised and

the receptor that may be harmed. For example, the pathway may consist of the flood

defences and flood plain between a flow in the river channel (the source) and a

housing development (the receptor).

Receptor refers to the asset that may be harmed. For example, in the event of the

heavy rainfall (the source) flood water may propagate across the flood plain (the

pathway) and inundate housing (the receptor) that may suffer material damage (the

harm or consequence)

To understand the linkage between hazard and consequence it is useful to consider

the common adopted Source-Pathway-Receptor-Consequence (Figure 2-3) model.

This is, essentially, a simple conceptual tool for representing systems and processes

that lead to a particular consequence. For a risk to arise there must be hazard that

consists of a source or initiator event (high rainfall); a receptor (cliff top or flood plain

properties); and a pathway between the source and the receptor (flood routes

including defences, overland flow or landslide). A hazard does not automatically lead

to a harmful outcome, but identification of a hazard does mean that there is a

possibility of harm occurring. Within such an analysis it must be recognised that there

are likely to be multiple sources, pathways and receptors. Therefore, the

methodology to determine the likelihood of a defined consequence occurring

(material damage to property) must be capable of integrating several (possibly

interacting) mechanisms and the linkage between the various sources, pathways and

receptors.



Figure 2-3: Source-Pathway-Receptor-Consequence

One of the key aims of flood risk assessment is to understand and be aware of the

complexities of the situation as best as possible, then to simplify the situation down to

an acceptable level to allow practical measures to be put on place. It is also important

to notice that people have the greatest control over the receptor.

2.7 Risk units

Risk always has units. However, the units of risk depends on how the likelihood and

consequence are defined. For example, both the likelihood and consequence may be

expressed in a number of equally valid ways. Likelihood can be considered as a

general concept that describes how likely a particular event is to occur. Frequency

and probability can also be used to express likelihood. However, these terms have

different meanings and are often confused. It is important to understand the

difference between them.

2.7.1 Probability

May be defined as the chance of occurrence of one event compared to the

population of all events. It can be expressed in decimal or percentage and is often

reference to a specific time frame, for example as an annual exceedance probability

of lifetime exceedance probability.



2.7.2 Frequency

Defines the expected number of occurrences of a particular extreme event within a

specific timeframe. In the special case of Return Period this is usually expressed in

years.

2.7.3 Consequence

Represents an impact such as economic, social or environmental damage and may

be expressed quantitatively (monetary value), by category (High, Medium, Low) or

descriptively.

2.8 Units of consequence

Flooding and erosion can have many consequences, only some can be expressed in

monetary terms. Consequences can include fatalities, injuries, damage to property or

the environment. Consequences of a defence scheme can include environmental

harm or benefit, improved public access and many others including reduced risks.

The issue of how some of these consequences can be valued continues to be the

subject of contemporary research. However, risk-based decision-making is greatly

simplified if common units of consequence can be agreed upon. It is, therefore, often

better to use 'surrogate measures' of consequence for which data are available. For

example, 'Number of Properties' may be a reasonable surrogate for the degree of

harm/significance of flooding and has the advantage of being easier to evaluate than,

for example economic damage or social impact. An important part of the design of a

risk assessment system is to decide on how the impacts are to be evaluated. Typical

descriptions of consequence are:

Economic damage Number of people /properties affected Occurrence of specified event Degree of harm to an individual (injury, stress etc)



2.9 Problem of uncertainty

In flood defence there is often considerable difficulty in determining the probability

and consequences of flood events. Most engineering failures arise from a complex

combination of events and thus statistical information on their probability and

consequence may be scarce or unavailable. Under these circumstances the engineer

has to resort to hydrological and hydraulic models and expert judgement. Models will

inevitably be an incomplete representation of reality so they will generate a

probability of failure which is inherently uncertain. Similarly, expert judgement are

subjective and inherently uncertain. Thus practically every measure of risk has

uncertainty associated with it.

2.10 Parameters for risk mapping

At present there is in Europe no unique method for risk mapping available. Some

approaches determine risk only on water depths or in combination with velocity and

the return period. The main problem of these methods is the fact that some hydraulic

parameters can cause different damages on various landuse types. Therefore to take

this in account, quantifying monetary damage of a flood event is necessary that these

values can be used in combination with the return period for risk mapping. The

method quantifies the damage potential by using only monetary assessment of direct

damage, is itemised in the following way:

Fixtures Buildings Movable assets Outside facilities (e.g yard, garden) Stock value (industry, agriculture, retail) Persons Animals Forest and farm vegetation Infrastructure

The most difficult task is to determine the monetary value of personal damage. In

practice, it is usually done by assessing the number of people affected by a flood

event and provide it as additional information to the risk map.



3 Scale for analyse

Before starting to assess the damage/risk analysis, it is necessary to define the scale

for different levels of planning. In Table 3-1 three different layers with the correspon-

ding scale are presented.

Level of planning international river

regional river course

local river course

International flood action plans

Macroscale

Regional food action plans

Mesoscale

Evaluation of local flood protection projects

Microscale

Table 3-1: Scale

3.1 Microscale approach

This object related method is based on empirically obtained data, using

questionnaires or interviews with people who are affected by flood. The damage is

assessed for each object separately which requires reliable data collection and

management. Data obtained in this way, can be used as statistical data for

mesoscale approach introduced in the following section.

3.2 Mesoscale und macroscale approach

The mesoscale, area related, approach aggregates single landuse units (settlement,

industry, infrastructure) based on detailed digital administrative geographic data and

gives the value of specific damage based on statistical economic values. The

required data for this approach are economic as well as ATKIS/ALK OR CORINE

landuse data.

Macroscale approach is similar to measoscale, only the level is higher, e.g. the scope

is international river catchments like Rhine or Elbe.



3.3 Evaluation

Data collection in case of object related damage assessment is very time-consuming

and is effective only in case of local planning and defining detailed flood protection

policy. Nevertheless, this method is applied in combination with area related when it

is necessary to assess the damage potential of objects of high importance (hospital,

landmarks).

So far, the mesoscale approach based on the asset values obtained from statistical-

economic data as well as the landuse types obtained from land registry office has

shown good results in many projects.



4 Project Area

First general step in risk assessment is the determination of the river system and

catchment area for analysing all possible influences on flood occurrence in

mesoscale analysis.

Figure 4-1: Catchment Area of River Str

Figure 4-1 shows the catchment area of the river Str. The Str is located in Northern

Germany in the Lnder Schleswig-Holstein. For practical work on risk mapping the

town of Kellinghusen is chosen. Analysing catchment area make experts able to

realise which types of flood can occur. Here is important to find out possible sources

of flooding. There are numbers of possible flooding scenarios.

4.1 Rivers and streams

Excessive rainfall, snow or hail, or a combination of high river levels and high tides

can cause river flooding. Flooding occurs when surface water run-off from the

surrounding area exceeds the flow capacity of the river or stream. Saturation of

surface soils due to wet weather can lead to greater run-off rates and higher flooding

levels. Human activity has increased the risk of flooding from rivers and streams in

many areas. Development has reduced the natural capacity of floodplains and

increased the rate of surface water run-off.

Town ofKellinghusen

Germany



4.2 Groundwater flooding

Flooding from groundwater is most likely to occur in areas of chalk, limestone or

other aquifers. This type of flooding generally affects older buildings that back at

hillsides, buildings close to winterbourne streams or houses with basements which

are particularly prone to groundwater flooding.

4.3 Flooding from overland flow

Overland flows can be caused by intensive rainfall on saturated ground, where

groundwater levels are already high, or on paved areas of tarmac or concrete with

inadequate drainage. Properties can be flooded by overland flows if they are located

in areas where floodwater can accumulate.

4.4 Blocked or overloaded drainage systems

Localised flash flooding from blocked or overloaded drainage systems can occur at

times of heavy rainfall. This type of flooding is unpredictable and often occurs in

unexpected locations depending on the location and intensity of rainfall. Such

drainage systems include open drainage ditches and culverts and buried drains and

sewers. Where flooding occurs from full sewers the floodwater will often be contaminated with sewage. In some cases, contaminated floodwater can flow back

though sewers causing flooding inside buildings.

4.5 Kellinghusen, flood situation

Kellinghusen was often affected by flood in the last years. Wide areas in the

floodplains were inundated both by the Str and from groundwater.

Figure 4-2: City of Kellinghusen, flood event 2002



Exercise 1 Create new GIS-Project with the basic topographic data of Kellinghusen!

Program Run:

1) Open ArcView and load the extension:

3d Analyst, Spatial Analyst and relative Pfade

2) Load topographic maps (scale 1:5000) in folder Exercise_1\maps and make

white colour transparent.

3) Load Exercise_1\landuse\kellinghusen.shp, for English translation of the

different landuse types import table: Exercise_1\landuse\alk_key_eng.dbf and

do a table join, based on field objart!

4) Load Exercise_1/finite-elemente-net/2d_net_kelling.shp, to visualise the finite-

elemente-net of the 2d-hydraulic model!

5) Import data Source: Exercise_1/inundation_area/wsp_hq100.asc and convert

it to a GRID! Display water depth using legend wsp_hq100.avl!

This inundation area is calculated for a 100year flood event. Would you like to

make a picnic during such an event at location: 3547401,21 / 5979681,14?

5) Create a layout!

6) Use the two scripts: GIS_exercise_engl_WS0405.pdf and GIS_theory_engl-

_WS0405.pdf for additional information, working with ArcView.

Figure 1: 100year flood event calculated for Kellinghusen



5 Methodology for damage potential assessment

Tangible monetary asset values from flood damage are determined in this approach

on the basis on two parameters:

Maximal Damage Corresponds to the calculated asset value of a landuse unit. It is to consider

that even in case of extreme flood events, this value is not reached.

Damage Factor Damage functions represent the relation between hydraulic parameters (water

depth) and damage (vulnerability). These functions give information about the

damage extent in percent for different water depths, for each landuse type.

5.1 Statistical-economical assessment of asset value

In addition to the type of landuse in the project area, it is necessary to assess the

monetary value of each landuse type. The asset value is calculated based on the net

asset value. The construction costs as well as the inventory is included in this value.

Net asset value can be calculated applying the following two methods.

5.1.1 Net Asset Value at purchase price

Net Asset Value at purchase price is calculated considering the cost price at the

moment of there purchasing. It is important to distinguish between two concepts:

Brutto concept considers the constant value during the whole lifecycle of asset Netto concept takes depreciation and amortisation of fixed

assets into account.

If the assets are changing during the time period problems arise.

5.1.2 Net Asset Value at actual price

Net Asset Value at actual price includes all prices for rebuilding flood affected units.

Also in this method two concepts exists:

Netto concept takes into account depreciation. Brutto concept evaluates without loss of actual value.

Damage assessment based on the netto concept determines realistic monetary

values.



5.2 The method of Regionalisation

The method of Regionalisation to calculate the flood damage potential is composed

of three steps:

1. Determination of the asset value per landuse type []

2. Identification of corresponding landuse areas [m]

3. Intersection of statistical-economic data and landuse types for calculation of

specific asset value [/m]

The main advantage of this method is no use of time-consuming mircoscale object

orientated determination of asset values. This approach bases only on statistical

governmental data. The user should recognise that the calculated damages with

these statistical data sets do not represent real damage. The results should be

presented in public therefore carefully cause occurring damage can be much higher

or lower for single objects. The next chapters presents the input data for the

calculation of the net asset values for special landuse types in Schleswig-Holstein.

5.2.1 Population

In this study only the tangible assets are considered. Data about the density of

population serves only as input data for calculation of total asset value and is used to

distinguish between urban and rural districts. Basic data are obtained from

STATISTICS AGENCY FOR HAMBURG AND SCHLESWIG-HOLSTEIN and provided in form of

Table 5-1.

km

FLENSBURG 56,38 1 1 1 - - - 84.480 40.902 1.496

KIEL 118,39 1 1 1 - - - 232.242 112.698 1.962

LBECK 214,14 1 1 1 - - - 213.496 101.223 997

NEUMNSTER 71,63 1 1 1 - - - 79.646 38.463 1.113

Ditmarschen 1428,64 117 6 5 111 - 12 137.447 67.516 96

Herzogtum Lauenburg 1263,00 133 6 5 127 - 11 181.661 88.346 144

Nordfriesland 2048,59 136 11 7 125 1 16 165.026 80.829 81

Ostholstein 1391,54 39 16 6 23 - 6 203.386 98.188 146

Pinneberg 664,09 49 12 7 37 - 7 293.914 144.023 443

Pln 1082,74 86 10 3 76 - 7 133.624 67.199 123

Rendsburg-Eckernfrde 2185,38 166 10 4 156 - 19 271.643 134.199 124

Schleweig-Flensburg 2071,64 136 5 3 131 1 18 198.390 98.627 96

Segeberg 1344,35 96 9 5 87 - 9 252.758 124.108 188

Steinburg 1056,14 114 5 4 109 1 9 136.548 67.487 129

Stormann 766,25 55 11 6 44 - 5 219.988 106.818 287

Schleswig-Holstein: 15762,90 1.131 105 59 1.026 3 119 2.804.249 1.370.626 178

inhabitants per km at

31st december

2001

thereof department "free" communities

thereof department affiliated communities

cities among these

communitiestotal total

cities among these

communities

number of communities at 31st december 2002number of departments at 31st december

2002

population at 31st december 2001

total male among these

district "free" town,

in the district of

area at 31st

december 2002 total

Table 5-1: Area and population in Schleswig-Holstein according to districts,

STATISTIC AGENCY FOR HAMBURG AND SCHLESWIG-HOLSTEIN



5.2.2 Asset value: Settlement

Total asset value of settlement is composed of property (asset value), inventory

(residence contents) and private vehicles.

5.2.2.1 Property asset In 1995 private property asset was fixed to 3.300 Mrd. for 36.22 Mio. households

in Germany. The net asset value can be calculated for each household to 91.000 .

In Table 5-2 private household data for Schleswig-Holstein is given. Total property

asset is set based on these facts to 43.000 per inhabitant for the year 2002.

1000 in % 1000 in % 1000 in % 1000 in % 1000 in % 1000 in %

227 25 299 29 373 32 451 35 477 36 478 36

253 28 305 30 398 34 478 37 491 37 497 37

174 19 178 17 201 17 176 14 169 13 166 12

145 16 158 15 147 13 139 11 140 11 141 11

114 13 87 8 53 4 59 5 57 4 58 4

913 100 1.026 100 1.172 100 1.304 100 1.333 100 1.340 100

2.484 100 2.565 100 2.638 100 2.811 100 2.828 100 2.844 100

650 26 477 19 279 11 319 11 303 11 311 11

household

household with 1 person

persons in private households altogether

2 persons

3 persons

4 persons

5 and more persons

2000 2001 2002

among these households with 5 and more persons

1970 1980 1990

households (total)

Table 5-2: Private households in Schleswig-Holstein,

Statistic Agency fr Hamburg und Schleswig-Holstein

5.2.2.2 Inventory (residence contents) The values of 50.000 is taken, based on the data obtained from different insurance

companies for the year 2002.

5.2.2.3 Motor vehicles asset In this study, the heavy goods vehicles, mini buses and tractors are not considered

cause they are part of the economic assets.

The values of 10.000 per motor vehicle and 3.000 per motorcycle are adopted,

based on statistic data (Case Study FLOOD DAMAGE POTENTIAL ON THE RIVER RHEIN

AND MOSEL). The total motor vehicle asset value of Schleswig-Holstein is 15 billion

for the year 2003 (Table 5-3). Using the number of households, the total motor

vehicle asset can be divided into the administrative districts.



1996 1.652.323 79.079 1.398.533 74.036 3.046 70.692 26.937 158.3751997 1.683.873 87.370 1.419.432 76.158 2.980 70.140 27.793 167.4601998 1.710.989 96.009 1.431.868 81.740 2.914 69.921 28.537 174.5921999 1.735.239 104.878 1.443.006 85.183 2.913 69.931 29.328 181.006

2000 1.764.890 112.118 1.461.713 88.046 2.967 70.170 29.876 191.6272001 1.826.972 120.415 1.507.812 93.186 2.958 71.525 31.076 201.4942002 1.859.272 125.437 1.531.853 95.524 2.937 71.622 31.899 208.6762003 1.870.492 129.230 1.538.893 95.230 2.987 71.628 32.524 214.339

motor vehicle

trumbrils

there of...year motor vehicles total motor cycle automobiles & estate cars trucks omnibus tractors

other motor vehicles

Table 5-3: Motor vehicles- Schleswig-Holstein,

STATISTIC AGENCY FR HAMBURG UND SCHLESWIG-HOLSTEIN

5.2.2.4 Calculation of specific asset value In contrast to other landuse types, it is important to consider differences in types of

rural and urban areas for calculating the specific asset value for settlement. In Table

5-4 the proposed method is presented. First determine the asset value per inhabitant

then calculate the specific value based on of the listed administrative districts.

District Number of

Households

Number

Inhabitants

Buildings and

open space

Settlement and

Asset value

Settlement

Asset value

per

inhabitant

Asset value

settlement/m of

buildings and

open space

[ha] [Mio ] [/EW] [/m]

(1) (2) (3) (4) (5) (6)

(2) * (5) (4) / (3)

Steinburg 35.582 4.179

Pinneberg 96.121 5.253

S-H 1.340.000 2.804.249 83.134 121.940 43.000 147

District Inventory

Inventory/m of

buildings and

open space

Asset value/m

of buildings

and open

space

Asset vehicles Specific

asset

vehicles

Specific asset

settlement

[/Household] [/m] [/m] [Mio ] [/m] [/m]

(7) (8) (9) (10) (11) (12)

(1)* (7) / (3) (6) + (8) (10) / (3) (9) + (11)

Steinburg

Pinneberg

S-H 50.000 81 228 15.000 18 246

Table 5-4: Specific asset value: settlement



Exercise 2 Calculate the specific asset [/m] for the landuse type settlement in the year 2002 for

the two districts like shown in Table 1:

Steinburg Pinneberg

District Number of

Households

Number

Inhabitants

Buildings and

open space

Settlement and

Asset value

Settlement

Asset value

per

inhabitant

Asset value

settlement/m of

buildings and

open space

[ha] [Mio ] [/EW] [/m]

(1) (2) (3) (4) (5) (6)

(2) * (5) (4) / (3)

Steinburg 35.582 4.179

Pinneberg 96.121 5.253

District Inventory

Inventory/m of

buildings and

open space

Asset value/m

of buildings

and open

space

Asset vehicles Specific

asset

vehicles

Specific asset

settlement

[/Household] [/m] [/m] [Mio ] [/m] [/m]

(7) (8) (9) (10) (11) (12)

(1)* (7) / (3) (6) + (8) (10) / (3) (9) + (11)

Steinburg

Pinneberg

Table 1: Specific asset value: settlement



5.2.3 Stock value for economic landuse types

Stock value considers basic materials or operating supply units for producing goods.

The value is only aggregated for throughout Germany for the time period of one year.

Therefore to take it in account for Schleswig-Holstein the percentage between

inventory and net asset for each landuse type for whole Germany is calculated. By

multiplying the net asset value of each landuse with this factor the inventory then can

be taken into account for the calculation of the specific asset value of economic

landuses.

Landuse types Total inventory Germany Net asset Germany Percentage

1994 1995 of inventory

[Mio ] [Mio ] 1995

Agriculture 12.650.000 120.065.000 10,54

Industry 150.950.000 500.275.000 30,17

Trade 95.895.000 201.570.000 47,57

Traffic --- 278.085.000 1,00

Table 5-5: Stock value for economic landuses, GERMAN STATISTIC AGENCY

5.2.3.1 Asset values: Economy For Schleswig-Holstein the net asset value at actual price is available for the sectors:

Agriculture Industry Traffic Public and private service

like shown in Table 5-6.

Sector 2001

1000

Agriculture 6.648.934

Industrie 17.965.601

Traffic 18.255.305

Public and private service 40.611.988

Table 5-6: Net asset value at actual price, economic, Schleswig-Holstein, STATISTIC AGENCY FR HAMBURG UND SCHLESWIG-HOLSTEIN



To determine the specific net asset [/m], the total area of each sector in the Lnder

Schleswig-Holstein is used. The STATISTIC AGENCY FR HAMBURG UND SCHLESWIG-

HOLSTEIN publishes the area of real estate divided in special types of landuse (Table

5-7). To achieve the same landuse types which were used for the net asset,

aggregations are necessary and the result is presented exemplary in (Table 5-8).

extract from the "Liegenschaftskataster" annual account 2002 -Liegenschaftsbuch-

area collection date: 30/12/2002

district "free" town 01001000Flensburg page 1

area M1 area M2

21-100 TO 21-299 BUILDINGS AND OPEN SPACE ................................................. 18.641.503 THEREOF21-130 BUILDINGS; OPEN SPACE AND SETTLEMENT 12.320.30021-170 BUILDINGS; OPEN SPACE AND INDUSTRY 2.402.582

21-300 TO 21-399 TRADE AND INDUSTRY ....................................................... 754.469 THEREOF21-310 TRADE AREA AND MINING 41.751

21-400 TO 21-499 RECREATION AREA ............................................................... 1.058.992 THEREOF21-420 GREEN CORRIDOR 617.974

21-500 TO 21-599 TRAFFIC .................................................................................. 6.954.046 THEREOF21-510 RAODS 4.443.34421-520 LANE 399.96721-530 PLACE 155.111

21-600 TO 21-699 AGRICULTURE ......................................................................... 16.101.968 THEREOF21-650 MOOR ANS SWAMP 34.91521-660 HEATHLAND 1.181.200

21-700 TO 21-799 FOREST ................................................................................... 3.447.258

Table 5-7: Areas of Real estate in Schleswig-Holstein

Area per district [ha], 2002

Flensburg Hansestadt Lbeck Neumnster Stormarn Schleswig-Holstein

Settlement 1.624 3.616 2.115 6.573 83.134

Industry 316 1.042 283 1.357 13.808

Traffic 695 1.709 712 3.727 27.617

Agriculture 1.610 7.354 3.240 52.337 1.180.164

Forest 345 2.973 306 9.977 149.582

Miscellaneous 1.047 4.719 507 2.654 122.004

Table 5-8: Landuse types: Areas for calculating specific asset values



5.2.3.2 Calculating of specific asset values In the following chapters the basic data sets for the calculation of the specific assets

for each landuse are explained and the specific damage can now be determined. The

asset value is the damage potential which can be destroyed by a flood event in the

worst case.

Landuse: Settlement Specific asset value = 246 /m (Table 5-4 for whole Schleswig-Holstein)

Landuse: Industry and trade Asset value = 17.965.601.000 (Bau- und Produzierendes Gewerbe)

Area of real estate = 13.808 ha

Specific asset value = 130,11 /m

Specific stock value: 30,2 % => 39,29 /m

Specific asset value = 169,40 /m

The calculation of the specific net asset of the other landuse types is part of exercise

3. To get an impression of the specific asset values for some landuse types Table 5-9

presents the values of two other mesoscale risk analysis.

[/m] IKSR (averaged), Rhine IKSE, Elbe

Settlement 279,00 225,00

Industry 333,00 27,00

Traffic 237,00 10,00

Arable 7,00 0,10

Meadows 7,00 0,10

Forest 1,00 0,025

Table 5-9: Specific asset values for main landuse types

5.2.4 Agriculture

Agriculture can be separated in the three sectors: arable land, meadows and forest,

which are very different in there vulnerability according to inundation. Cause of the

general result for the specific asset value in chapter 5.2.3.2 no detailed analysis is

possible. Furthermore includes the net asset value only buildings and inventory and

no farming products for the landuse agriculture. In contrast to this farmers get the

main part of there asset in the fruits and plants.



For damage potential analysis these facts should be take in account: As

consequence the asset value for agriculture is determined on the basis of annual

harvest.

5.2.4.1 Arable Area [ha] [%] Price [/dt] Harvest [dt/ha] Output [/ha/a]

Wheat 179.786 59,49 10,76 91 582,54

Rye 33.094 10,95 9,15 67,7 67,84

Barley 80.504 26,64 9,44 76,1 191,38

Oat 8.808 2,91 8,89 57,3 14,85

302.192 856,60

Table 5-10: Main arable products and the output in Schleswig-Holstein

Averaging the main arable products the asset value is calculated to 856 /ha/a.

5.2.4.2 Meadows With the adoption that all meadows are intensive used, there are three grass

harvests fixed. Annual asset is calculated to 675 /ha/a, according a crop of 10, 15

and 25 m/ha by an output of 15 /m.

5.2.4.3 Forest Forest areas are evaluated by statistical estimated values for wood harvest, cause no

real data is published. Averaged asset is set to: 810 /ha/a.

Average land value

deciduous forest 990 /ha/a

mixed forest 810 /ha/a

coniferous forest 630 /ha/a

Table 5-11: asset value, forest



Exercise 3 Calculate the specific assets for the main landuse types based on the method of

Regionalisation:

Traffic Agriculture

Explain the advantage of calculating the specific asset for agriculture on the basis of

the annual harvest!



6 Damage Functions

Damage functions are used to represent the relation between inundation depth and

asset value. The economic value of the landuse type must be known to calculate the

damage, this step is already done. The damage function is a function between 0 and

1, with the value of 0 if there is no damage and the value 1, if there is a total loss of

asset. But even in cases of extreme flood there will be no loss of all material assets

found on the surface.

Damage functions are characterised by the following parameters:

First occurrence of damage Maximum damage value Shape of the function

There are two types of stage-damage curves, one type is based on actual damage

costs and the other is based on synthetic costs. The synthetic cost stage-damage

curves are mostly used for the prediction of flood costs such as in benefit-cost

analyses. The development of residential synthetic cost stage-damage curves needs

the following steps:

In the area of study, representative classes of houses are selected, usually based on building size.

A sample of houses is selected in dwelling class. In each room the contents are checked and the values are noted. Information on the height above floor

level can also be noted or heights can be taken as the same in all dwellings.

Preferably, a qualified quantity surveyor or value should undertake this step.

Values are averaged across each sample for each class of house and the stage-damage curves are constructed.

The damage function constructed by the synthetic cost method are for potential

damage, not actual damage. A similar approach can be used for constructing actual

cost stage-damage curves soon after a flood.

Damage functions are very difficult comparable for different countries. Although

synthetic stage-damage curves are internationally accepted as the standard

approach to assess damage.



6.1.1 Dutch experience in stage-damage curves

For the purpose of project done by Natural Hazard unit, HKV consultants made

stage-damage curves for the land use types. These curves are based on experience

of HKV consultants in assessing damage of the floods of the river Meuse.

It is necessary to introduce curves for each landuse type. A flood in urban area

results in higher damage than a flood in rural area.

0

0,2

0,4

0,6

0,8

1

0 1 2 3 4 5 6

water depth (m)

dam

age Settlements

IndustryAgricultureTraffic

Figure 6-1: Damage functions of the Dutch Ministry of Transport, Public Works and Water

Management

6.1.2 German experience in stage-damage curves

Systematic collecting of flood damage data in Germany started in 1985, and until now

more of 3.200 objects and damage caused by flood are processed.

With software system HOWAS it is possible to collect flood damage data after

specific use, regional and hydrologic criteria. This data can be arranged and under

indication of certain criteria be selected, linked and finally evaluated. The program

system HOWAS is divided into four parts:

Data input and data correction Data administration Data selection and data linkage and Evaluation



The results can be plotted and stored for the subsequent treatment with other

programs. The structure and all functions of HOWAS are strongly dependant on the

kinds of buildings or surfaces.

6.1.3 IKSE, Elbe

Damage functions of ISKE

0

20

40

60

80

100

0,0 0,5 1,0 1,5 2,0 2,5 3,0 3,5 4,0 4,5 5,0

water depth [m]

dam

age

fact

or [%

] settlementindustrytrafficgreen corridor

Figure 6-2: Damage function of IKSE

6.1.4 IKSR, Rhine

Damage functions of IKSR

0

20

40

60

80

100

0,0 0,5 1,0 1,5 2,0 2,5 3,0 3,5 4,0 4,5 5,0 5,5 6,0 6,5 7,0

water depth [m]

dam

age

fact

or [%

]

settlementindustrytraff icforestmeadow s

Figure 6-3: Damage function of IKSR



6.1.5 Evaluation

The river Str is part of the Elbe catchment, therefore in the following potential

damage analysis damage functions of the IKSE are used.

Damage function

IKSR IKSE

Settlement Y=6,4x+4,9 Y=-2x+18x

Industry Y=9,6x+6 Y=-3,3x+24,4x

Traffic Y=10 Y=2x-8x+13x

Agriculture Y=50 Y=100

Meadows Y=50 Y=100

Forestry Y=1 Y=100

X : Water depth [m] and Y :damage factor [%]

Table 6-1: Comparison of damage function IKSE and IKSR

Damage functions for agriculture should reflect also the new approach for calculating

the asset values, explained in chapter 5.2.4. The damage functions in Table 6-1 are

determined for asset values based on the net asset value at actual price. In the next

chapters the damage potential for arable areas is calculated by a factor which is

independent from water stage and duration time.

6.1.5.1 Arable Inundation of arable land causes a 100percentage loss of crop.

6.1.5.2 Meadows A flood event will destroy only one harvest. Therefore an averaged damage asset of

225 /ha will occur (adoption: 15 m/ha and 15 /m), damage factor of 100percent.

6.1.5.3 Forest During an inundation of forest no damage will occur, if the water depth declines after

some days. An averaged damage factor of 5percent causes then an asset value of

40,5 /ha.



Exercise 4 Explain the damage function in Figure 1 for one building in the diagram. The function

is created in a mircoscale damage assessment. Why is there a discontinuity and

what can be the two locations inside the building!

Water stage [m]

Dam

age

[]

Location:______________Location:______________

Figure 1: Microscale approach, Damage function



7 Annual damage potential

Cause the occurrence of flood events is a stochastic process, the potential damage

can not be exactly determined. The calculation is based on the available statistical

data and is expressed as annual flood damage potential. Therefore the damage is

weighted by the frequency of returning. The flood damage potential is a function of

flooded area, landuse and water depth. It is summarised in the following formula:

= maxo

P

PdP)P(SS = i

max

1i

i1i P2

SSS +=

= Eq. 1

S Annual damage potential [/a], S(P) Damage S [] per flood event as a function of flood probability [1/a]

P Flood probability of a flood event [1/a]

Po Flood probability of the critical flood event starting from this event, damage occur [1/a]

Pmax Flood probability of the highest flood event [1/a]

i1ii PPP = + i Return period

0,2 0,1 0,02 0,01P [1/a]

100000

200000

300000

400000

500000

500000

600000

S []

Si-1+SiPi 2

Figure 7-1: Annual damage potential, withP0=0,2



For solving equation 1, it is necessary to calculate the potential damages for flood

events of different estimated probabilities. As input data, water depths for each flood

event are required. By using the damage functions, damage potential can be

estimated. Total damage potential is the result of integration of all damage potential

values, assuming linear function of damage occurrence. Applying this method a

combination of flood probability and monetary damage assessment is possible.

Finally, the flood risk can be estimated based on annual flood damage potential.



Exercise 5 During the flood event 2002 the city of Dresden was affected by a heavy inundation

and enormous damages were the consequence. Calculate the annual damage

potential based on the determined damages for several statistical return periods

shown in Table 1!

Figure 1: Calculated Inundation area and water depth for flood event 17.08.2002, Dresden

Ti [a] Pi [1/a] Pi [1/a] Discharge

[m/s]

Water stage gauging

station Dresden [mNN]

S(Pi)[Mio.] i [Mio.] i * Pi [Mio./a]

i * Pi [Mio./a]

10 2630 110,27 6,40

20 3130 110,84 51,73

25 3355 111,08 54,06

50 3820 111,51 68,45

100 4370 111,97 74,70

Table1: Specific annual damage, Dresden

Water depth [m]



8 Method for damage assessment

In the following chapter, a method for damage assessment and mapping by using a

Geographic Information System like ArcView is explained.

8.1 Survey Data

Presently, there are two types of survey data available in Germany: The Automated

Real Estate Map (ALK, scale 1:1000) and the Authoritative Topographic and

Cartographic Information System (ATKIS) with the Digital Landscape Model (DLM,

scale 1:5000). These data are available for most of the countries and represent the

basis for urban and landscape planning. Figure 8-1 shows the relationship between

the two types of survey data and their use.

For the requisite of this project from the FEDERAL AGENCY FOR CARTOGRAPHY AND

GEODESY AND THE STATE SURVEY OFFICES, ATKIS and ALK data are obtained for the

project area of Kellinghusen.

Figure 8-1: Survey Data, ALK and ATKIS

The Automated Real Estate Map includes digital data of the Real Estate Cadastre

like individual parcels with their boundaries, buildings, results of soil classification and

the current use of landscape. The map is scaled in 1:1000 and is parcel related. The

respective parcels are identified by an individual parcel identification number. It is

based on the land parcel, which location data is stored in ALK, both landuse type and

ownership are stored in ALB.



ATKIS is a project of the AdV (WORKING COMMITTEE OF THE SURVEYING AUTHORITIES

OF THE STATES OF THE FEDERAL REPUBLIC OF GERMANY) which is performed at the

federal level. This project aims at the provision of digital models of the earth 's

surface suitable for data processing. In this way ATKIS constitutes a data base for

computer-assisted digital processing and analogue output forms, but also a base of

spatial. It can therefore be described as a geobased information system.

In a first step different types of landuse like separated in the ALK are mereged in nine

groups (Table 8-1) which represent the landuse types and the calculated specific

asset values done in chapter 5.

Figure 8-2: Landuse types ALK, Kellinghusen

Figure 8-3: Merged landuse types, Kellinghusen



Table 8-1: Merged landuse groups

1000 to 1490

buildings and open space

other buildings for treading and serviceand2100 to 2990

buildings and open space - mixed use with settlement

other open spaces1700 to 1790

buildings and open space - trade and industry

other buildings for trade and industryand3000 to3620

factorys

closedowns

Traffic5000 to 5940

traffic

traffic area next to waterway6000 to 6140

agriculture

asparagus63206400

tree nurserywine garden

and6700 to 6800

fruit-growing

agricultural traffic area

Meadows6200 to 6310

green corridor (in general)

garden

Forest7000 to 7600

forest

forestry traffic area

Water8000 to 8900

ponds

water areas

Nature residual objects according to definitionsareas which don't include asset values and which won't be refurbished after a flooding event

Infrastructure (is similar displayed as traffic)

residual objects according to definitions

public areas which must be refurbished after a flooding event. The damge is calculated similar to the traffic areas

object specification as per ALK

Settlement

Industry

Arable

landuse object type from ALK-layer 21



Hydrology

Water stages -probability, longitudinal profiles

Inundation maps for flood event of specific return period

Specific damage for each inundation scenario

Classification of affected flood

plain

Economy

Asset in dependence of land use

Specific asset value: Regionalisation

Annual Damage Potential (total/specific)

Stage damage functions

8.2 Program Run

Figure 8-4: Program run

Applying a Geographic Information System, it is possible to combine all necessary

data for determination of damage potential in one system:

Inundation areas with water depth Landuse distribution Specific asset values Damage functions

In Figure 8-4 calculation of damage potential is visualised. The key idea of the

method is the conversion of all relevant information in raster data (Figure 8-5).



Figure 8-5: Calculation of specific damage potential using raster data

For the program run following equation can be considered:

= , ,Ln Ln Lni j i jD C V Eq. 2

jiD , Specific damage potential value i,j (/m)

)( ,, jiLnji hfC = Damage factor of damage function for each landuse type [%] jih , Water depth i,j [m] Ln

V Specific asset value [/m] for each landuse type Ln

damage Assessment results in a raster in which for each cell the specific damage in

/m is calculated. Summarising these values and multiply with the affected area the

total damage for a flood event can be determined. Annual damage [/m/a] can be

evaluated in a next step also for each cell of the raster.



8.3 Microscale damage assessment for Kellinghusen

In February 2002 Kellinghusen was affected by inundation of a 30year flood event.

Wide areas are flooded and by doing a mircoscale damage assessment a sum of

360.000 damage was determined. 88 buildings in 27 streets were inundated and

their owners were interviewed about damage. 7 trades lost money by stopping their

sale or production. 89 % of damage was located at building structure only 11 % was

destroyed stock. People were aware of flooding and prepared their buildings

therefore with flood protection measures. Furthermore basements were used only as

store or garage.

For comparing this mircocale damage assessment with the mesoscale approach in

chapter 8.4 annual damage is used. Based on research for prices to rebuild affected

building structures and new stock, damage potential for the known objects, could be

calculated for different water stages and inundation areas.

Problems occur if more objects were affected then located during mircoscale

approach, taken in account flood events with rare return periods. With these

adoptions damage potential was calculated for:

HQ1: 750.000 HQ10: 1.300.000

By weighting statistically an annual damage of 925.000 like explained in Table 8-2

was calculated.

Table 8-2: Annual damage, Kellinghusen



8.4 Mesoscale damage assessment for Kellinghusen

Like mentioned in chapter 8.2 as input data to determine damage inundation areas

with water depths must be available. Therefore first a 2d-hydraulic modelling is done

for six flood events with the return periods of: 2, 5, 10, 20, 50 and 100 years.

Hydraulic boundary conditions are upstream discharges of the rivers Str at Rensing

and Bramau and downstream water stages at the village Grnhude (Figure 8-6).

Figure 8-6: Boundary conditions, 2d-hydraulic Kellinghusen

Table 8-3: Boundary Conditions

The mesoscale damage assessemt using the specific asset values of Table 8-4 and

the damage functions of Figure 8-7 evaluates an annual damage of 430.000 . In

Figure 8-8 is the spatial distribution of specific annual damage for the city of Kelling-

husen pictured.

QRensing

QBramau

WGrnhud



Landuse types Specific asset[/m]

Settlement 246,00 total Schleswig-Holstein

Industry 169,40

Traffic 66,76

Arable 0,086

Meadows 0,023

Forerst 0,081

Table 8-4: Landuse types: Specific asset

Figure 8-7: Landuse types: damage functions

In Table 8-5 separated for each landuse type the damage potentials of flood events

HQ10 and HQ100 are summarised.

Table 8-5: Damage of flood event HQ10 and HQ100 for Kellinghusen



Figure 8-8: Specific annual damage, Kellingusen

Legend Specific annual damage [/m/a]



Exercise 6 Explain the differences between both approaches and reason the varieties on the

basis of HQ10.



Exercise 7 Calculate the annual damage potential for Kellinghusen, using the software KALYPSO:

FLOODRISKANALYSIS! Program Run:

1. Merging of landuse types At first the different types of landuse like separated in the ALK (chapter 8.1

und exercise 1) are to be merged in nine groups (Table 8-1) which represent

the landuse types and the calculated specific asset values done in chapter 5.

Be aware of case sensitive landuse names. Open existing ArcView project of

Exercise 1 and use for aggregation in ArcView the tool Field Calculator.

Create a new field in the attribute table of existing shape file Kellinghusen.shp:

landuse_eng.

2. FLOODRISKANALYSIS Start Program FLOODRISKANALYSIS by execute the file Exercise_7 \FloodRisk-

Analysis\FloodRiskAnalysis.bat and open the project folder Kelling-River-Risk.

Log View opens and the mapping between landuse types, asset values and

damage functions is visualised (Figure 1). To be able to change these

boundary conditions open the file Exercise_7\Kelling-RiverRisk\Control\

ContextModell.gml. Using XML the context information for calculating damage

potential and risk zones are stored in a XML-Shema file.

XML Introduction: XML stands for EXtensible Markup Language XML is a markup language much like HTML XML was designed to describe data XML tags are not predefined. You must define your own tags XML uses a Document Type Definition (DTD) or an

XML Schema to describe the data

XML is a W3C Recommendation



Figure 1: Log View FLOODRISKANALYSIS, Open Project

3. Inundation areas In the folder Exercise_7\Kelling-River-Risk\waterlevel inundation areas of six

design flood events with the return period of 2, 5, 10, 20, 50 and 100 years are

stored. These areas are the results of a 2d-hydraulic flow simulation with the

finite-element-net and boundary conditions like mentioned in chapter 8.4. Next

step in program run converts the inundation areas from ASCII raster format

.asc to .gml raster format.

The Geography Markup Language (GML) utilises XML to express geogra-

phical features. It can serve as a modelling language for geographic systems

as well as an open interchange format for geographic data.

Figure 1: Finite-Element-Net, Knots, Bankline and Inundation Area (Raster .asc)



Results of 2d-hydraulic modelling are: water depth as one information for each

knot of the finite-element-net and the bankline. By using also mesh of finite-

element-net as breaklines these inundation areas are rasterized to .asc format

(Figure 2).

File name for each inundation area is fixed by allocating return period;

wsp_hq100. Start converting waterlevel by menu item Covert\Waterlevel.

Figure 2: Log View, FLOODRISKANALYSIS, Convert Waterlevel

4. Landuse Next step in program run is to rasterize the landuse shape-file to a .gml raster

format equal to inundation area. Reducing disc space biggest inundation is

chosen by system to define landuse shapes, which are affected by flood. Only

these areas are necessary to be rasterized for damage assessment.

Figure 3: Rasterizing of landuse shape-file



Before starting rasterizing, copy shape file with merged landuse types (Part 1

of this exercise) to folder Exercise_7\Kelling-River-Risk\landuse and rename it

to landuse.shp. In Popup menu choose landuse_eng for property name. In

Log View process of conversation is presented. Visualize result landuse.asc

file in ArcView like explained in exercise 1, but cell values should be converted

to integer for allocating corresponding landuse types!

5. Damage assessment At first choose menu item Calculate damage to calculate specific damage for

each design flood event. After this choose menu item Calculate annual

damage to calculate specific annual damage. Results are stored in folder

Exercise_7\Kelling-River-Risk\damage in .asc and .gml format.

Figure 4: Damage assessment

Open menu item Statistic View (Figure 5) and determine the damage potential

for flood event HQ10 and HQ100! No template GRID should be used, this

function is necessary for determination of the thresholds for risk classes.

This damage potential covers only the floodplain, cause finite-element-net is

not created for city part of Kellinghusen. But in this area high damage potential

is located. Therefore a second project Exercise_7\Kelling-Town-Risk is set up,

by determine waterlevels in city parts of Kellinghusen.



Figure 5: Statistic View

Open this project and execute damage assessment! Determine in same way

damage potential for flood event HQ10 and HQ100! Calculate total damage

potential for Kellinghusen for each landuse type and in general, like shown in

Table 1! Explain the results of the damage assessment and compare both

flood scenarios!

HQ10 HQ100

Landuse Flood Plain

Town Total Flood Plain

Town Total

Settlement

Industry

Traffic

Arable

Meadows

Forest

Table 1: Damage potential, Kellinghusen

Visualize the annual specific damage for Kellinghusen on flood plain and city

areas in the existing ArcView project! Create a legend with equal intervals and

visualize result in a new layout! Use the extension Legend tool!



9 Risk Assessment and Mapping

By mapping only water depth and inundation area, no decision support for flood

resilience related planning is prepared. Risk maps, resulting from the explained

method include the consequences of flood events in monetary values in combination

with the return period as weighting factor and offer in this way decision makers a

basis for sustainable planning.

Distinguish between two different user groups for publishing risk maps:

Water related engineers or administrative working people Inhabitants who are affected by flood

When risk maps are presented in public, a clear plain risk zoning is necessary. High

consternation will occur in areas with high damage potential, which are located near

by the river.

9.1 Risk classification

Risk is defined as the combination both the consequence and the return period of

flood events. Like explained in chapter 8.2 the specific annual damage for each cell

of the raster can be calculated. This value is used for zoning the risk in the three different parts:

LOW MEDIUM HIGH

To determine the thresholds, a zoning system of German insurance companies is

used. The zones are defined based on hazard, expressed in terms of probability.

Both components are considered when defining risk zones (Figure 9-1).

Zone Risk zone Return period

I LOW Areas will be flood rarely then HQ50

II MEDIUM Areas will be flood rarely HQ10 then and more often then HQ50

III HIGH Areas will be flood more often then HQ10

Table 9-1: Zoning system of insurance companies in Germany



Figure 9-1: Hazard zones of German insurance companies

The calculation of thresholds is different for urban and rural areas cause of the

varieties in damage potential.

9.1.1 Thresholds, urban areas

To calculate the thresholds a geographic analysis is necessary in which the highest

summands of the specific annual damage, like explained in equation 1 in chapter 7,

are determined. To fulfil the adoption in Figure 9-1 when searching for the maximum

of each summand, only inundation areas like defined in Table 9-1 can be taken in

account. Otherwise wrong thresholds would be fixed.

Risk Zones, urban areas

Threshold: low/medium risk (Gl/m) Gl/m = max{P=0,01}

Threshold: medium/high risk (Gm/h) Gm/h = Gl/m + max{P}

In Table 9-2 the results of a threshold determination for three rivers in the catchment

area of the river Str are listed.

Threshold Str Schwale, upstrem Schwale, donstrem Ohlau

Gl/m 0,05 0,07 0,03 0,16

Gm/h 0,29 0,51 0,3 0,45

Table 9-2: Thresholds, calculated in the river Str catchment

p = 0,01 p = 0,08

High Risk MediumRisk

Low Risk



For risk assessment and mapping unique thresholds for the whole catchment area

are proposed, to compare risk maps of different rivers within the total catchment:

Threshold: low/medium risk (Gl/m) Gl/m = 0,1 /m/a

Threshold: medium/high risk (Gm/h) Gm/h = 1,0 /m/a

9.1.2 Thresholds, rural areas

In contrast to urban areas in these areas no high risk should occur cause agricultural

use is the most suitable kind of anthropogenic landuse in flood affected areas.

Therefore the adoption is made, that meadows which are flooded ones in two years

will be allotted to the class low risk:

p * specific asset = 0,49 * 0,023 = 0,012 [/ma] p = 0,01 0,5 = 0,49, Pmax = 0,01



Exercise 8 What does the thresholds of 0,1 /m/a and 1,0 /m/a for urban areas mean to an

affected inhabitant with a parcel of 1000 m and what do you think about this kind of

classification!

Calculate based on chapter 9.1.2 which risk zones for different rural landuse types

are possible:

Arable Meadows Forest



9.2 Risk mapping

To present flood risk in a map the following legend is proposed:

Moderate consternation, urban area Medium consternation, urban area High consternation, urban area Moderate consternation, rural area Medium consternation, urban area

The new terminology of consternation is chosen to associate the exigency to change

the situation for affected people. In a next step authorities can connect these zones

witch options to advance flood protection for buildings or show examples to change

landuse in agriculture areas. The colour of the legend is ajar to a traffic light. But in

contrast to the known signs the risk map colour for a moderate risk is not displayed

green but yellow, cause these areas are still inside the inundation area. Rural areas

have a complete different colour to differ these landuse types clear from urban parts.

Figure 9-2: Risk map Kellinghusen



Exercise 9 Generate a risk map for Kellinghusen, using the software KALYPSO: FLOODRISK

ANALYSIS! Program Run:

1. Thresholds, Context information Open file Exercise_7\Kelling-Town-Risk\Control\RiskContextModell.gml. In this

file you are able to change risk zone thresholds.

2. Thresholds, Calculation First step: calculate threshold between moderate and medium consternation,

urban area. Create a new folder border in project folder Kelling-Town-Risk!

Open project Kelling-Town-Risk and choose Tools/ Subtract Grids. To fulfil the

adoption in Figure 9-1 of chapter 9.1 when searching for the maximum of each summand of the specific annual damage value, otherwise wrong thresholds

would be fixed. In the end only inundation areas like defined in Table 9-1 can

be taken in account.

Therefore by using the tool Subtract Grids the corresponding areas can be

calculated based on the waterlevel grids for each return period, like shown in Figure 1. The target raster should be saved in the folder border.

Figure 1: Subtract Grids

Statistic View uses this target file to border the searched area for maximal

value for each summand of the specific annual damage (Figure 2). During the

damage calculation, for each summand a separate raster is calculated named

e.g. tempGrid_deltaP0.01.asc by the corresponding p. Based on this adoptions the threshold between moderate and medium consternation, urban

areas results to: 0,0283 /m/a.



Figure 2: Calculation of thresholds



3. Calculate the thresholds for the other two risk classes! Be aware of the definition of the annual specific asset (Figure 3). Between the inundation areas

of HQ2 and HQ5 are no differences. Therefore no subtraction is necessary.

0,2 0,1 0,02P [1/a]

S []

tempGriddeltaP0.1.asc tempGriddeltaP0.08.asc

Figure 3: Annual specific damage

4. Copy the file RiskContextModell and rename it as RiskContextModell-01-1. Choose menu item Calculate/ Flood Risk to generate a risk raster! But first,

change the thresholds in the XML shema, cause there are the unique ones

fixed! Thresholds for rural areas are defined like explained in chapter 9.1.2.

Take care of renaming the calculated raster data other wise the program will overwrite the results.

Visualize the risk raster in the existing ArcView project! Create a legend with

the classes like mentioned in chapter 9.2 and present the result in a new

layout like a risk map!

5. Create a second risk map with the unique thresholds 0,1 and 1,0 /m/a and view it in the same layout as before in step 4! Compare the results!

6. Create a complete risk map for Kellinghusen on the flood plains and city areas with these general values in a new layout!

ii

ii PSSS += =

max

1

1

2



Exercise 10 Write a report of your work during this course and present your calculated results in a

presentation!

1riskassessment theory

Documents

flood risk

risk assessment

risk units

risk management

flood damage assessment

risk mapping herceg

flood situation

units of consequence