Screening Methodology of Natural Hazards for Buildings Presentation at IDRC Davos 2010 Content: Background Goals  Overview on the model - Application to natural hazards Conclusions
Background In Switzerland, insurance of buildings against fire and natural disasters (flooding, hailstorm, wind, avalanche and few more) is compulsory Owners of buildings with above average risks are encouraged by insurance companies to reduce their risks by taking adequate measures Option for the future: Premium depends more strongly on the effective risks, especially for costly, complex buildings   risks must be known to insurance company Building insurance of canton of Berne (GVB) decided to develop a model allowing risk estimates for each individual building
Goals Methodology should be applicable within reasonable time to several 1.000 buildings Risk due to fire and the most important types of natural hazards should be evaluated separately for each building Most important parameters influencing the risk should be taken into account: characteristics of building and – in case of natural hazards – its location Methodology should be based on literature and generally accepted risk information (e.g. intensity maps for natural hazards) Model parameters should be validated based on event data Simple software tool should be developed (Excel) No aggregation of risks per event necessary
Overview: Most relevant risks for GVB
Natural hazards: general features of model > 90% of damage due to natural hazards insured by GVB are due to flooding, hailstorm and windstorm Risks due to each of the above natural hazards depend on site related factors: frequency and intensity of events exposition of building characteristics of the building considered: type of building (e.g. sensitivity of roof to windstorm) materials used protective effects of existing safety measures Considered risk value: ratio between expectation value for damage (CHF / year) and insurance value
Methodology to estimate risk values: flooding r: risk value f j : frequency of scenario j (per year) D j : damage (CHF) V:  insurance value (CHF) d j : relative damage j: scenario (characterised by frequency class)
Intensity maps – intensity of flooding depending on frequency Hazard matrix high middle low Intensity Probability of occurrence high middle low Danger level red: high blue: middle yellow: low white: no danger j 300 years 100 years 30 years return period high middle low probability of occurrence
Methodology to estimate risk values: flooding d j : relative damage  : maximal probable loss in relation to the insurance value s: damage sensitivity (vulnerability): average damage without protection measures as a percentage of the maximal probable loss f: effect of safety measures reducing the damage i: index of building j: scenario (characterised by 1 out of 3 frequency classes)
Methodology to estimate risk values: flooding How to estimate the different variables :  : maximal probable loss per insurance value - local estimate (upon inspection, e.g. maximum 2 of 4 similar   storeys affected due to static high water    0.5 ) s: damage sensitivity: - fixed basic values depending on event intensity - factors depending on sensitivity of materials used on floors   + walls towards moisture, existence of   critical installations in ground floor (e.g. electric equipment) f: effect of safety measures reducing the damage - standard values for measures such as protection dam or   height of lowest opening
Calibration of parameters Total and average risk values should be consistent with experience 3.050 176 9.000.000 Windstorm 6.199 176 9.000.000 Hailstorm 16.370 586 30.000.000 Flooding Average damage per event to a building [CHF / year] Risk value for an average building [CHF / year] Average overall risk value [CHF / year] Hazard type
Conclusions Methodology to screen the risks of individual buildings due to flooding, hailstorm and windstorm (+fire) has been developed Calibration of parameters such that sum of risks should approximate average statistical values Screening for several 1’000 buildings is possible as intensity maps for natural hazards are generally available. Main problem is to collect all parameters characterising the individual building.  Model parameters can and should be improved after application to many buildings  Methodology can be transferred to other regions and types of hazards Basis to identify buildings with highest risks relative to their insurance value and to make a step towards insurance premiums depending on risk is given

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Screening Methodology of Natural Hazards for Buildings

  • 1. Screening Methodology of Natural Hazards for Buildings Presentation at IDRC Davos 2010 Content: Background Goals Overview on the model - Application to natural hazards Conclusions
  • 2. Background In Switzerland, insurance of buildings against fire and natural disasters (flooding, hailstorm, wind, avalanche and few more) is compulsory Owners of buildings with above average risks are encouraged by insurance companies to reduce their risks by taking adequate measures Option for the future: Premium depends more strongly on the effective risks, especially for costly, complex buildings  risks must be known to insurance company Building insurance of canton of Berne (GVB) decided to develop a model allowing risk estimates for each individual building
  • 3. Goals Methodology should be applicable within reasonable time to several 1.000 buildings Risk due to fire and the most important types of natural hazards should be evaluated separately for each building Most important parameters influencing the risk should be taken into account: characteristics of building and – in case of natural hazards – its location Methodology should be based on literature and generally accepted risk information (e.g. intensity maps for natural hazards) Model parameters should be validated based on event data Simple software tool should be developed (Excel) No aggregation of risks per event necessary
  • 4. Overview: Most relevant risks for GVB
  • 5. Natural hazards: general features of model > 90% of damage due to natural hazards insured by GVB are due to flooding, hailstorm and windstorm Risks due to each of the above natural hazards depend on site related factors: frequency and intensity of events exposition of building characteristics of the building considered: type of building (e.g. sensitivity of roof to windstorm) materials used protective effects of existing safety measures Considered risk value: ratio between expectation value for damage (CHF / year) and insurance value
  • 6. Methodology to estimate risk values: flooding r: risk value f j : frequency of scenario j (per year) D j : damage (CHF) V: insurance value (CHF) d j : relative damage j: scenario (characterised by frequency class)
  • 7. Intensity maps – intensity of flooding depending on frequency Hazard matrix high middle low Intensity Probability of occurrence high middle low Danger level red: high blue: middle yellow: low white: no danger j 300 years 100 years 30 years return period high middle low probability of occurrence
  • 8. Methodology to estimate risk values: flooding d j : relative damage  : maximal probable loss in relation to the insurance value s: damage sensitivity (vulnerability): average damage without protection measures as a percentage of the maximal probable loss f: effect of safety measures reducing the damage i: index of building j: scenario (characterised by 1 out of 3 frequency classes)
  • 9. Methodology to estimate risk values: flooding How to estimate the different variables :  : maximal probable loss per insurance value - local estimate (upon inspection, e.g. maximum 2 of 4 similar storeys affected due to static high water  0.5 ) s: damage sensitivity: - fixed basic values depending on event intensity - factors depending on sensitivity of materials used on floors + walls towards moisture, existence of critical installations in ground floor (e.g. electric equipment) f: effect of safety measures reducing the damage - standard values for measures such as protection dam or height of lowest opening
  • 10. Calibration of parameters Total and average risk values should be consistent with experience 3.050 176 9.000.000 Windstorm 6.199 176 9.000.000 Hailstorm 16.370 586 30.000.000 Flooding Average damage per event to a building [CHF / year] Risk value for an average building [CHF / year] Average overall risk value [CHF / year] Hazard type
  • 11. Conclusions Methodology to screen the risks of individual buildings due to flooding, hailstorm and windstorm (+fire) has been developed Calibration of parameters such that sum of risks should approximate average statistical values Screening for several 1’000 buildings is possible as intensity maps for natural hazards are generally available. Main problem is to collect all parameters characterising the individual building. Model parameters can and should be improved after application to many buildings Methodology can be transferred to other regions and types of hazards Basis to identify buildings with highest risks relative to their insurance value and to make a step towards insurance premiums depending on risk is given