1. www.guycarp.com Probabilistic Flood Modelling in Eastern Europe ICAR Forum, 1 st -2 nd October 2007 Silke Huebner, Munich Instrat ® Cat Modelling CEE

2. 1 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

3. 2 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- CAT TM 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

4. 3 Guy Carpenter Flood Hazard in Central and Eastern Europe Source: UNEP/GRID-Geneva Flood is a big topic!

5. 4 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

6. 5 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

7. 6 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 I Zone 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

8. 7 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

9. 8 Guy Carpenter Structure of a Probabilistic Flood Model Analysis Module Hazard Module Probabilistic event set Import Module Client data with location information Built environment Module horizontal and vertical distribution of the risks Loss Module Loss in relation to flood intensity Results Losses per return period, event sets Financial Module Consideration of deductibles, limits and reinsurance structures

10. 9 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

11. 10 Guy Carpenter Structure of a Probabilistic Flood Model Analysis Module Hazard Module Probabilistic event set Import Module Client data with location information Built Environment Module horizontal and vertical distribution of the risks Loss Module Loss in relation to flood intensity Results Losses per return period, event sets Financial Module Consideration of deductibles, limits and reinsurance structures

12. 11 Guy Carpenter Built Environment Module Building census data Land use / cover Flooded area Client portfolio Modelling units  The built environment represents the interface between flood maps and client data and has two functions: – To allow the spatial redistribution of client data into the modelling units – To complete unknown client data characteristics based on building census 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 Built environment

13. 12 Guy Carpenter Structure of a Probabilistic Flood Model Analysis Module Hazard Module Probabilistic event set Import Module Client data with location information Built environment Module horizontal and vertical distribution of the risks Loss Module Loss in relation to flood intensity Results Losses per return period, event sets Financial Module Consideration of deductibles, limits and reinsurance structures

14. 13 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

15. 14 Guy Carpenter Hazard Module Simple GIS approach Hydraulic 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 accurate digital elevation model is used for modelling

16. 15 Guy Carpenter Hazard Module Hydraulic Modelling of Flood Extents Source: wxmaps.com Rainfall Rainfall drainage Routing and water heights Flood defence systems Defense failure and lateral propagation 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

17. 16 Guy Carpenter Hazard Module  Example of hydraulic flood propagation modelling used in a GC proprietary model:

18. 17 Guy Carpenter Structure of a Probabilistic Flood Model Analysis Module Hazard Module Probabilistic event set Import Module Client data with location information Built environment Module horizontal and vertical distribution of the risks Loss Module Loss in relation to flood intensity Results Losses per return period, event sets Financial Module Consideration of deductibles, limits and reinsurance structures

19. 18 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)

20. 19 Guy Carpenter Structure of a Probabilistic Flood Model Analysis Module Hazard Module Probabilistic event set Import Module Client data with location information Built environment Module horizontal and vertical distribution of the risks Loss Module Loss in relation to flood intensity Results Losses per return period, event sets Financial Module Consideration of deductibles, limits and reinsurance structures

21. 20 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

22. 21 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

23. 22 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

24. 23 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 EventAffected Countries July 1997Germany, Poland, Czech Republic June 1999Poland, Czech Republic, Slovakia, Romania August 2002Germany, Austria, Czech Republic, Slovakia, Hungary July 2004Poland, Slovakia, Hungary March 2006Germany, Austria, Czech Republic, Slovakia, Hungary

25. 24 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 information 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!

26. With probabilistic models into the flood insurance future!

27. www.guycarp.com