modelling flood insured losses: an uncertainty …...models terrorism nuclear remote sensing...
TRANSCRIPT
Modelling flood insured losses: an
uncertainty propagation from hazard
to damages
David Moncoulon
BRGM
16 January 2018
16 January 2018 Symposium on uncertainties - BRGM2
What is CCR ?
o CCR is a French public reinsurance company
– Owned by the French Ministry of Finances
– Proposing a re-insurance coverage for Natural Disasters on the
French territory (metropolitan and overseas)
– Perils covered :
• Flood (river overflow, storm surge and surface runoff)
• Drought
• Earthquake
• Cyclonic wind
– CCR develops catastrophe models and expertise on the
financial impacts of natural events
16 January 2018 Symposium on uncertainties - BRGM 3
Natural Catastrophe Modelling in CCR
o Objectives
– Post-event simulations
• To estimate the potential losses of a catastrophic event :
• And communicate it to our clients and the French State
– Exposure mapping
• Develop stochastic event set
• estimate the exposure of the French territory to potential losses
– Modelling the financial impact of climate change by working with
IPCC models and scenario (with Meteo-France)
16 January 2018 Symposium on uncertainties - BRGM 4
Natural Catastrophe Modelling in CCR
Hydrological
models
Floods
Storm surge
Meteo models
Cyclonic winds
Agricultural
Storms
Geological
models
Droughts
Earthquakes
Anthropic
models
Terrorism
Nuclear
Remote sensing – Historical approach – Climate change – Vulnerability scenarios
Major scientific partners
16 January 2018 Symposium on uncertainties - BRGM 5
Structure of the flood impact model
o An automated chain of 3 models :
– Hazard : from rainfall data to runoff and river flood
– Vulnerability : individual and professional risk location
– Damages : applying damage curves to every single risk
Hazard modellingRainfall
Runoff
Riverflow
Hazard zone
Damage modellingBuilding level
Commune level
VulnerabilityInsurance database
Geolocalisation
Insured values
Input hazard data
Measurements
Radar
Outputs
Loss estimate
Confident interval
16 January 2018 Symposium on uncertainties - BRGM 6
An example of flood impact modelling
o The Seine-Loire watersheds floods in June 2016
– 6 different events
– Period : from the 28th to the 6th of June 2016
Event scale definition
16 January 2018 Symposium on uncertainties - BRGM 7
Hazard simulation : urban runoff
o Surface runoff simulations
16 January 2018 Symposium on uncertainties - BRGM 8
Hazard simulation : river flood
o Example of riverine flood map : the Loing river
16 January 2018 Symposium on uncertainties - BRGM 9
Post-event simulation context
o Available input data:
– Meteo-France hourly and daily rainfall and radar quantitative
precipitation estimates
– SPC river discharges on the major river network
o Important data missing :
– Extension of the flood
– River discharges on ungauged catchments
– Surface runoff measurements on the urban area
– Underground water knowledge
o Important uncertainties on the hazard maps…
16 January 2018 Symposium on uncertainties - BRGM 10
Vulnerability database
o The insurance data:
– Simplified line of business
• Individual
• Commercial
• Industrial
• Agricultural
– Estimated insured values
– Estimated location (based on automatic geocoding)
• Adress precision
• Street center
• Commune center
o Important uncertainties in the vulnerability data…
16 January 2018 Symposium on uncertainties - BRGM 11
A multi-model integration issue
o Hazard and vulnerability computed at different scales :
– 1st uncertainty: hazard
– 2nd uncertainty: address location
– 3rd uncertainty: address <> building
– 4th uncertainty: building covers more than 1 grid cell
Hazard grid : 25m resolution
Adress locationBuilding
Hazard uncertainties
16 January 2018 Symposium on uncertainties - BRGM 12
Taking into account these
uncertainties
o In the results:
– Not a single loss but a distribution of losses per events
– Expecting a relatively narrow confidence interval for application
in provisioning financial results for insurance companies
o Actual methods:
– Calibration of the damage model using historical event re-
simulation
– Estimating the confidence intervals with the historical errors at
the commune level
16 January 2018 Symposium on uncertainties - BRGM 13
Damage model calibration
o Calibration of the damage curves on a database of:
– > 2.500.000 policies
– > 25.000 claims
– > 15 calibration events (1998-2015)
o Evaluation is conduced by simulating the global event
damages and comparing it with real losses
– At the event scale
– At the commune scale
16 January 2018 Symposium on uncertainties - BRGM 14
Damage model calibration
o 15 historical events: simulated in post-event conditions
Superposition of policy and claim
database with the simulated
hazard
16 January 2018 Symposium on uncertainties - BRGM 15
Damage model calibration
o Mixing 15 events for calibration of damage curves
Water level
Probability of
claim
10%
h
Sinistrality
Water level
Destruction
rate
25%
h
Destruction rate
For each single building:
Damage = [Probability of claim] x [Destruction rate] x [Insured value]
16 January 2018 Symposium on uncertainties - BRGM 16
How to simulate damages using an
uncertain flood extent ?
o Damage model calibration
Impossibility to predict the loss
for each building
Modelled hazard map Damaged building
No damage
16 January 2018 Symposium on uncertainties - BRGM 17
How to simulate damages using an
uncertain flood extent ?
o Damage model calibration
Modelled hazard map Damaged building
No damage
The model predicts 55% of
damaged building on a given area
16 January 2018 Symposium on uncertainties - BRGM 18
How to simulate damages on an
uncertain flood extent ?
63 19 19 12 83 65 12 33 65 7 11 135 36
298
413
746
166,9 31 44 23 60,2 68,5 14,9 53,8 93,1 11 16,1
188
64,6
314
707,9
772
52 55 58 58 60 67 67 76 76 91 96 135 144
321
595
730
Evénements pour les PREVS
Coût simulé reco simulée Cout simulé reco réelle Coût réel
Simulated results and validation
Simulated
D+3
Simulated
D+120Real loss
16 January 2018 Symposium on uncertainties - BRGM 19
How to simulate damages on an
uncertain flood extent ?
Error FLOOD MODEL 2018
Mean relative error 7,79%
Average event error 17,98%
Inside 10-90% range 71,43%
Mear relative error inside 10-90% range -0,16%
Median error 3,92%
Mean absolute error (€) 12 960 860
16 January 2018 Symposium on uncertainties - BRGM 20
Integration of the uncertainties in the
loss estimate
o Computing the confidence intervals:
– Ratio [real loss / simulated loss] per commune for the entire
calibration event set
– Use a bootstrap method to randomly allocate an error to a
given commune: 5000 repetitions
– Use the 5000 distributions of commune losses to estimate the
event confidence interval
Loss Mean 10% 25% 75% 90%
< 10 k€ 0,96 0,042 0,13 1,36 2,67
10 - 500
k€
1,04 0,038 0,14 1,48 2,95
> 500 k€ 0,92 0,04 0,11 1,29 2,79
Total 1,0005 0,03 0,13 1,44 2,8
16 January 2018 Symposium on uncertainties - BRGM 21
Day+3 damage estimation
o Results, communication and Day+120 first validation
Model M+4 claim informations
Seine 06.2016 Estimation (M€) Market part Estimation (M€)
Date 09/06/2016 (D+3) 30/09/2016 (J+120)
Damages (M€) [800 – 1265] 53,7% [950 – 1000]
-
200
400
600
800
1 000
1 200
1 400
1 600
MIL
LIO
NS
Total event loss distribution
16 January 2018 Symposium on uncertainties - BRGM 22
Conclusion
o Numerous uncertainties:
– Hazard data and simulation
– Vulnerability data and spatial location
– Multi-model integration issue
o But the need to produce an accurate estimation with a
narrow confidence interval
– Simulation of a loss distribution for a single event
– Use of a calibration and validation database
o Need to determine the parameters with the most
important effect on the results to reduce the
uncertainties: Phd thesis of Elodie Perrin (Mines St
Etiennes – BRGM – CCR) since October 2017
