Using Remote Sensing Data for Catastrophe Model Development

Geospatial Data and Machine Learning for #Risk Prevention and Management - Satellite Applications Catapult Friday 25 November 2016 Prepared by Chris Ewing, Impact Forecasting

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About Impact Forecasting

Part of Aon Benfield

Catastrophe models using to price

insurance and reinsurance

IF is a catastrophe model

development team

Independent, transparent, open,

modular and bespoke catastrophe

models

Natural (flood, earthquake, wind)

and man-made perils

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

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More than 100 models in over 60 countries

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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

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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

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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

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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

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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

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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

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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 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

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Thanks for your time

Chris Ewing

chris.ewing@aonbenfield.com

www.impactforecasting.com

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