Using Remote Sensing Data to Develop Catastrophe Models
- 1. Using Remote Sensing Data to Develop Catastrophe Models Satellite remote sensing for disaster risk reduction and insurance event Hosted by RSPSoc and AGI Friday 16 September 2016 Prepared by Impact Forecasting
- 2. 1Proprietary & Confidential The Protection Gap Less than 25 percent of global economic losses covered by insurance – APAC: 10% – Americas: 12% – EMEA: 23% – United States: 45% Insurance Development Forum and data/modelling group launched. Opportunities for: – Sharing knowledge between DRR and insurance sector – New insurance products – Using RS data and catastrophe models to quantify “new” risks
- 3. 2Proprietary & Confidential About Impact Forecasting Catastrophe model development team Independent, transparent, open, modular and bespoke models Natural (flood, earthquake, wind) and man-made perils Filling the gaps and main perils – e.g. Thai flood, East Africa EQ, Chile tsunami, Indonesia flood – e.g. US flood, European windstorm Using RS data for cat model development and event response Cat model platform ELEMENTS Cat Model Developers 90 team members in 5 time zones Products licensed to nearly 50 insurers and reinsurers Active users of Remote Sensing data Natural and man- made hazards
- 4. 3Proprietary & Confidential RS Data for Model Development Digital Terrain Models – Satellite based and global • low resolution (30m) and free e.g. SRTM – Airborne based and in-country • high resolution (2 to 10m) e.g LiDAR / InSAR Population density – Proxy for spatial distribution of exposure • e.g. LandScan – Useful for data poor regions Land use / land cover – Various sources • e.g LandSat, MODIS, CORINE – To derive hydraulic roughness and identify specific lines of businesses (forestry, greenhouses, industrial) LandScan - http://web.ornl.gov/sci/landscan SRTM - http://photojournal.jpl.nasa.gov/catalog/PIA04961 NLCD – http://www.mrlc.gov/nlcd2011.php
- 5. 4Proprietary & Confidential RS Data for Event Response We use satellite data for quick flood loss estimates Approaches: – Real-time satellite flood extents • International Charter on Space and Major Disasters • Copernicus Emergency Management Service • PERILS distributed these • Case Study A – CEE flooding in 2013 – Real-time satellite precipitation • TRMM (satellite radar based precipitation estimates) • Directly usable for surface flooding • For fluvial flooding usable after coupling with rainfall/runoff and hydraulic models • Case Study B – Louisiana flooding in Aug 2016
- 6. 5Proprietary & Confidential Case Study A – CEE Flooding (2013) Strong storms, snow melt in higher altitudes and saturated soil after rainy spring caused severe flooding in CEE in June 2013 IF has probabilistic models for Czech Republic and Austria – quick implementation of events in ELEMENTS using online discharges from river gauges – Consistency with model was important For Germany we built a new scenario model – Flood extents come from satellite data (Copernicus, ZKI and PERILS) – Loss ratios applied on the affected sum insured – Model was quickly implemented in ELEMENTS and available to all users
- 7. 6Proprietary & Confidential Case Study B – Louisiana flooding (Aug 2016) Record rainfall fell across parts of Louisiana that spawned major riverine and flash flooding IF developed as-if events: – For riverine flooding • Data: River gauge observations • Method: 2D hydraulic modelling – For pluvial flooding • Data: TRMM satellite based precipitation • Method: 2D hydrodynamic rainfall- runoff model TRMM 14-day observed precipitation
- 8. 7Proprietary & Confidential Case Study B – Louisiana flooding (Aug 2016) Record rainfall fell across parts of Louisiana that spawned major riverine and flash flooding IF developed as-if events: – For riverine flooding • Data: River gauge observations • Method: 2D hydraulic modelling – For pluvial flooding • Data: TRMM satellite based precipitation • Method: 2D hydrodynamic rainfall- runoff model Modelled flood extents in Baton Rouge
- 9. 8Proprietary & Confidential Benefits and Challenges of Using RS Data Benefits Data for modelling – Many useful RS datasets – Potential for cooperation (sharing RS datasets through a common non- industry body e.g. PERILS Current event loss reporting – Observed flood maps can serve as quick first loss estimates – Wide spatial coverage – Much more cost effective than field survey Challenges Data for modelling – Cost – No middle ground for RS datasets (free low resolution or costly high resolution) – Fitness for purpose – often data needs further processing to be useful Current event loss reporting – Time sampling – for quicker events, satellites cannot capture peaks – Real time precipitation datasets do not (yet) have enough spatial coverage and resolution
- 10. 9Proprietary & Confidential Summary 1. Many opportunities for using RS data for cat modelling and event response 2. Combination of observations (from satellite data) and modelling is the ideal solution (but manual checking is important) 3. To enable easier access to satellite data and models we need: – Centralised access for multiple end users – Wider coverage and price optimisation – Information products tailored to specific DRR and insurance use cases 4. Cooperation needed to achieve economies of scale in satellite data access 5. The needs and benefits of the end user differ – there is not a one size fits all solution
- 11. 10Proprietary & Confidential Thanks for your time Chris Ewing chris.ewing@aonbenfield.com www.impactforecasting.com
- 12. 11Proprietary & Confidential Further reading Aon Benfield Impact Forecasting (2016), 2015 Annual Global Climate and Catastrophe Report, http://thoughtleadership.aonbenfield.com/Documents/20160113-ab-if-annual-climate-catastrophe-report.pdf, accessed 10th Sep 2016 Swiss Re (2015), Closing the protection gap: tools to use before they’re needed, http://media.swissre.com/documents/Closing_the_Gap_2015_FINAL.pdf, accessed 10th Sep 2016 ICMIF (2016), Formation of the Insurance Development Forum by the United Nations, the World Bank Group and the insurance industry, http://www.icmif.org/news/formation-insurance-development-forum-united-nations- world-bank-group-and-insurance-industry, accessed 10th Sep 2016 NASA JPL (2016), U.S. Releases Enhanced Shuttle Land Elevation Data, http://www2.jpl.nasa.gov/srtm/, accessed 11th Sep 2016
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