www.guycarp.com

Probabilistic Flood Modelling in Eastern Europe

ICAR Forum, 1st -2nd October 2007

Silke Huebner, MunichInstrat® Cat Modelling CEE

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Guy Carpenter

Agenda

Introduction

Types of Natural Catastrophe Models

Structure of a Probabilistic Flood Model

Flood Modelling Issues in Eastern Europe

Probabilistic Flood Modelling in Eastern EuropeProbabilistic Flood Modelling in Eastern EuropeProbabilistic Flood Modelling in Eastern EuropeProbabilistic Flood Modelling in Eastern Europe

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Guy Carpenter

Guy Carpenter in CEE

Why is Guy Carpenter dealing with such topics?– GC is one of the leading reinsurance brokers– There’s a growing demand for natural catastrophe analyses– GC has its own analytical unit (Instrat®), with the following core services:

Actuarial analysis Reinsurance structuring Risk management consultancy Natural Catastrophe Modelling

– GC not only licenses the commercially available models (e.g. AIR, EQECAT, RMS) but also develops internal models on its proprietary G-CATTM platform

– CEE modelling support is provided by the Munich Instrat® team– The European Model Development team, with 10 members across Europe,

deals with the internal model developments

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Guy Carpenter

Flood Hazard in Central and Eastern Europe

Source: UNEP/GRID-Geneva

Flood is a big topic!

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Guy Carpenter

Agenda

Introduction

Types of Natural Catastrophe Models

Structure of a Probabilistic Flood Model

Flood Modelling Issues in Eastern Europe

Probabilistic Flood Modelling in Eastern Europe

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Guy Carpenter

Types of Natural Catastrophe Models

Should I underwrite this risk?

What may happen in a specific scenario?

What‘s the probability for a loss

≥ x?

Zonation- or Rating Models

Deterministic Models

Probabilistic Models

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Guy Carpenter

Types of Natural Catastrophe Models

All types of models have value

Zonation models are very valuable during the underwriting process of primary insurers or facultative reinsurers

Zone IZone II

Zone III

But for risk management purposes / reinsurance considerations– millions of risks have to be analysed,– losses have to be estimated for various probabilities and– it should be possible to do analyses for combined perils

Probabilistic models are the most suitable to meet those conditions

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Guy Carpenter

Agenda

Introduction

Types of Natural Catastrophe Models

Structure of a Probabilistic Flood Model

Flood Modelling Issues in Eastern Europe

Probabilistic Flood Modelling in Eastern Europe

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Guy Carpenter

Structure of a Probabilistic Flood Model

Analysis Module

HazardModule

Probabilistic event set

Import Module

Client data with location information

Built environment

Module

horizontal and vertical distribution

of the risks

LossModule

Loss in relation to flood intensity

Results

Losses per return period, event sets

FinancialModule

Consideration of deductibles, limits and reinsurance

structures

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Guy Carpenter

Import Module

Module is used to import the formatted client dataset into the model environment:Location, number of risks, total insured value, deductibles, limits

Flood hazard can vary over short distances

Knowledge of exact risk location important

But: Sufficient address level exposure data is often unavailable in CEE countries

Therefore: Models must be able to cope with coarser data

Reliable results can still be achieved by using detailed land use data for disaggregation

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Guy Carpenter

Structure of a Probabilistic Flood Model

Analysis Module

HazardModule

Probabilistic event set

Import Module

Client data with location information

Built Environment

Module

horizontal and vertical distribution

of the risks

LossModule

Loss in relation to flood intensity

Results

Losses per return period, event sets

FinancialModule

Consideration of deductibles, limits and reinsurance

structures

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Guy Carpenter

Built Environment Module

Building census data

Land use / cover

Flooded area

Client portfolio

Modelling units

The built environment represents theinterface between flood maps and clientdata and has two functions:– To allow the spatial redistribution of

client data into the modelling units– To complete unknown client data

characteristics based on buildingcensus information

It works both horizontally and vertically

It not only tells where are risk is likely to be located but also if it’s rather a multi-storey building or a single family house

Bu

ilt

en

vir

on

me

nt

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Guy Carpenter

Structure of a Probabilistic Flood Model

Analysis Module

HazardModule

Probabilistic event set

Import Module

Client data with location information

Built environment

Module

horizontal and vertical distribution

of the risks

LossModule

Loss in relation to flood intensity

Results

Losses per return period, event sets

FinancialModule

Consideration of deductibles, limits and reinsurance

structures

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Guy Carpenter

Hazard Module

The hazard module defines the characteristics of modelled flood events:

– Water heights– Flood extents– Event frequencies

The probabilistic event set attempts to quantify the entire spectrum of risk by defining a representative subset of all possible future scenarios and their relative frequency

The historic event set is used to define what is representative Water heights and flood extents and can be modelled by using two

different approaches:– Simple GIS approach

– Hydraulic modelling techniques

Flood intensity

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Guy Carpenter

Hazard Module

Simple GIS approachHydraulic modelling

techniques

Which area is flooded if the water level rises by x metres?

Disregards water flow constraints

Water levels at confluences are difficult to capture

How does the water propagate according to various parameters of main rivers and tributaries?

Approach used by GC in the current CEE development projects

Both approaches can only produce reasonable results if an accuratedigital elevation model is used for modelling

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Guy Carpenter

Hazard ModuleHydraulic Modelling of Flood Extents

Source: wxmaps.com

Rainfall

Rainfall drainage

Routingand water heights

Flood defence systems

Defense failure and lateral propagation

Discharge [m³/s]

0

2000

4000

6000

8000

10000

12000

Height [cm]

0

200

400

600

800

1000

1200

Advantage of starting the modelling with rainfall data:

Correlations between rivers are easier to determine

Advantage of starting with water height and discharge data:

Difficulties with the modelling of the rainfall drainage are avoided

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Guy Carpenter

Hazard Module

Example of hydraulic flood propagation modelling used in a GC proprietary model:

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Guy Carpenter

Structure of a Probabilistic Flood Model

Analysis Module

HazardModule

Probabilistic event set

Import Module

Client data with location information

Built environment

Module

horizontal and vertical distribution

of the risks

LossModule

Loss in relation to flood intensity

Results

Losses per return period, event sets

FinancialModule

Consideration of deductibles, limits and reinsurance

structures

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Guy Carpenter

Loss Module

The loss module assigns the degree of loss if a building is affected

The loss degree depends on– Flood intensity– Type of risk (residential, commercial, industrial etc.)– Coverage (buildings, contents etc.)– Building type and occupancy

Vulnerability functions have to be calibrated for each country and preferably each client

The best calibration can be achieved if detailed loss data from recent events exists and is provided by the clients(best example: Czech Republic)

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Guy Carpenter

Structure of a Probabilistic Flood Model

Analysis Module

HazardModule

Probabilistic event set

Import Module

Client data with location information

Built environment

Module

horizontal and vertical distribution

of the risks

LossModule

Loss in relation to flood intensity

Results

Losses per return period, event sets

FinancialModule

Consideration of deductibles, limits and reinsurance

structures

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Guy Carpenter

Results of a Probabilistic Model

Probabilistic models have two main outputs:

“Event Set”:– Contains the frequency and modelled loss for each simulated event– Can be used in risk management / dfa-tools for comprehensive risk

analyses (e.g. testing of reinsurance structures, multi-peril analyses)

“EP-Curve” (EP: exceedance probability)– Gives the probability that loss x is exceeded– Either in tabular or graphical format

Analysis I Analysis II Analysis III Analysis I Analysis II Analysis III

5 14.727.104 23.713.066 9.276.871 16.262.650 31.845.940 18.631.404

10 65.854.092 58.020.271 16.281.915 70.855.336 70.983.665 27.958.899

25 201.559.370 135.084.090 37.475.027 213.446.363 156.926.396 49.715.822

50 316.440.125 225.012.203 62.673.739 328.389.772 252.025.210 75.952.504

75 368.197.040 288.982.531 82.594.442 393.486.468 318.325.227 96.462.415

100 424.264.333 334.788.550 99.819.777 457.453.067 365.847.504 114.022.482

150 507.597.027 411.915.607 129.222.490 525.654.311 441.278.402 143.833.144

200 561.134.055 476.148.538 154.163.072 577.057.361 504.888.184 168.983.025

250 602.519.918 537.789.523 175.945.806 638.314.717 563.657.726 190.921.548

500 782.828.342 692.894.465 257.498.219 836.787.271 729.387.938 272.817.736

1.000 926.986.055 907.975.913 358.247.804 988.970.599 944.627.727 373.957.146

Return Period (years)

Loss per Occurence (EUR) Annual Loss (EUR)

0

100

200

300

400

500

600

700

800

0 50 100 150 200 250 300 350 400 450 500

Mio

Return Period (years)

Lo

ss

(E

UR

)

Analysis I

Analysis II

Analysis III

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Guy Carpenter

Agenda

Introduction

Types of Natural Catastrophe Models

Structure of a Probabilistic Flood Model

Flood Modelling Issues in Eastern Europe

Probabilistic Flood Modelling in Eastern Europe

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Guy Carpenter

Special Flood Modelling Issues in CEE

Compulsory insurance schemes are discussed more and more and probabilistic (flood) modelling results represent a vital part for pricing and structuring

The CEE insurance market is growing and with it the demand for flood covers and analyses

Data availability and quality– Low insurance density and rather coarse client data recording in many

parts

Limited availability of detailed client and technical data provides challenges that can be overcome by detailed built environment modelling

Cross border correlation often one flood event affects more than one country

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Guy Carpenter

Cross Border Correlation

Flood events do not stop at borders!

Especially in Central and Eastern Europe floods tend to affect more than one country

The cross border correlation needs to be considered for detailed multi-country analyses

But: It‘s certainly more important to have some risk measurement tool in place before starting to consider correlations

And: Rather take the time to build a model step-by-step than trying to incorporate everything in one go

Event Affected Countries

July 1997 Germany, Poland, Czech Republic

June 1999 Poland, Czech Republic, Slovakia, Romania

August 2002 Germany, Austria, Czech Republic, Slovakia, Hungary

July 2004 Poland, Slovakia, Hungary

March 2006 Germany, Austria, Czech Republic, Slovakia, Hungary

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Guy Carpenter

Data essentials for the development of a good probabilsitic flood model

Digital elevation model with adequate resultion 1km is definetly too coarse for a reliable flood model

River network information

Sufficient history of rainfall or gauge station informationHeight [cm]

0

200

400

600

800

1000

1200

Loss information to calibrate the vulnerability functions- preferably from the country modelled- from other countries with similar building stock

Information on land use and building stock

GC provides the know-how to bring all this together!

With probabilistic models into the flood insurance future!

www.guycarp.com