Modelling the Evolution of the Financial Impacts of Flood and Storm Surge Between 2015 and 2050 in France

Modelling the Evolution of the Financial Impacts of Flood and Storm Surge Between 2015 and 2050 in France

David Moncoulon Martine Veysseire Jean-philippe Naulin Zixiang Wang Pierre Tinard JÉrÉmy Desarthe Chadi Hajji Thomas Onfroy Fabienne Regimbeau Michel DÉquÉ 

Caisse Centrale de Réassurance, PARIS, France

Météo France, TOULOUSE CEDEX 1

Page: 
141-149
|
DOI: 
https://doi.org/10.2495/SAFE-V6-N2-141-149
Received: 
N/A
|
Accepted: 
N/A
|
Published: 
30 June 2016
| Citation

OPEN ACCESS

Abstract: 

CCR (Caisse Centrale de Réassurance) is a French reinsurance company playing a major role in the natural catastrophe coverage in France. Since 2003, CCR has been developing tools for the estimation of its exposure to climatic risks. These tools cover three main perils: flood, storm surge and drought. Models are used to estimate the insurance losses and are systematically used for all major climatic events. Both modelling calibration and validation are based on an important policy and claim database. It was created in 2003 and supplied every year with insurer’s data. In order to evaluate the financial exposure for insurance of extreme events, a stochastic approach has been developed since 2011, for flood, storm surge and drought. The simulation of the stochastic event set allows us to estimate the mean annual losses and losses associated with different return periods. The objective of this approach is to connect the impact models for all perils with a large set of climate simulations. ARPEGE-Climate (Météo-France) is a model that is used to generate two sets of 200 years of hourly atmospheric time series: at current conditions and at year 2050 conditions according to RCP (Representative Concentration Pathways) 4.5. The main climate data used are: hourly rainfall, wind speed and atmospheric pressure and the Soil Wetness Index that is issued from a complementary surface model. The hazard and vulnerability models developed are based on the climatic data to compute continuous loss estimations. The method proposed will take into consideration development scenarios to evaluate the consequences of demographic growth and insured values evolution. The simulations show a global loss increase in 2050 which can be attributed to climatic factors such as extreme rainfall increase or sea level rise as well as, for a major part, the population and insured value growth in areas at risk.

Keywords: 

climate change, flood, insurance losses, storm surge, vulnerability.

  References

[1] Munich Re, El Niño curbs losses from natural catastrophes in 2015, 2016, available at http://www.munichre.com/site/corporate/get/params_E-1114418351_Dattachment/ 1130649/2016-01-04-natcat-2015-en.pdf

[2] SIGMA, Catastrophes naturelles et techniques en 2015, Swiss Re: Zurich, p. 44, 2016.

[3] IPCC, Summary for policymakers. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, eds. T.F.Stocker, D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex & P.M. Midgley, Cambridge University Press: Cambridge, United Kingdom and New York, NY, USA, 2013.

[4] Christensen, J.H. & Christensen, O.B., Climate modelling: Severe summertime flooding in Europe. Nature, 421(6925), pp. 805–806, 2003. http://dx.doi.org/10.1038/421805a

[5] Bates, B.C., Kundzewics, Z.W., Wu, S. & Palutikof, J.-P., Climate change and water. Technical paper of the Intergovernmental Panel on Climate Change, IPCC secretariat: Geneva, 210, 2008.

[6] Hallegatte, S., Ranger, N., Mestre, O., Dumas, P., Corfee-Morlot, J., Herweijer, C. & Wood, R.M., Assessing climate change impacts, sea level rise and storm surge risk in port cities: a case study on Copenhagen. Climatic Change, 104, pp. 113–137, 2011. http://dx.doi.org/10.1007/s10584-010-9978-3

[7] Moncoulon, D., Labat, D., Ardon, J., Leblois, E., Onfroy, T., Poulard, C., Aji, S., Rémy, A. & Quantin, A., Analysis of the French insurance market exposure to floods: a stochastic model combining river overflow and surface runoff. Natural Hazards and Earth System Science, 14, pp. 2469–2485, 2014. http://dx.doi.org/10.5194/nhess-14-2469-2014

[8] Naulin, J.P., Moncoulon, D., Le Roy, S., Pedreros, R., Idier, D. & Oliveros, C., Estimation of insurance-related losses resulting from coastal flooding in France. Natural Hazards and Earth System Science, 16, p. 13, 2016. http://dx.doi.org/10.5194/nhess-16-195-2016

[9] Déqué, M., Dreveton, C., Braun, A. & Cariolle, D., The ARPEGE-IFS atmosphere model: A contribution to the French community climate modelling. Climate Dynamics, 10, pp. 249–266, 1994. http://dx.doi.org/10.1007/BF00208992

[10] Voldoire, A., Sanchez-Gomez, E., Salas y Malia, D., Decharme, B., Cassou, C., Senesi, S., Valcke, S., Beau, I., Alias, M., Deque, M., Deshayes, J., Douville, H., Fernandez, E., Madec, G., Maisonnave, E., Moine, M.-P., Planton, S., Saint-Martin, D., Szopa, S., Tyteca, S., Alkama, R., Belamari, S., Braun, A., Coquart, L. & Chauvin, F., The CNRMCM5.1 global climate model: description and basic evaluation. Climate Dynamics, 40(9), pp. 2091–2121, 2012, http://dx.doi.org/10.1007/s00382-011-1259-y

[11] Le Moigne, P., Boone, A., Belamari, S., Brun, E., Calvet, J.-C, Decharme, B., Faroux, S., Gibelin, A.-L., Giordani, H., Lafont, S., Lebeaupin, C., Le Moigne, P., Mahfouf, J.-F., Martin, E., Masson, V., Mironov, D., Morin, S., Noilhan, J., Tulet, P., Van Den Hurk, B. & Vionnet, V., SURFEX scientific documentation, available at http://www. cnrm.meteo.fr/surfex/IMG/pdf/surfex_scidoc_v2.pdf, 2012.

[12] Hervouet, J.-M. & Van Haren, L., TELEMAC2D Version 3.0 Principle Note (No. Rapport EDF HE-4394052B), Electricité de France. Chatou Cedex: Département Laboratoire National d’Hydraulique, 1996.

[13] Guillou, N. & Chapalain, G., Modeling the tide-induced modulation of wave height in the outer Seine estuary. Journal of Coastal Research, 28(3), pp. 613–623, 2012. http://dx.doi.org/10.2112/JCOASTRES-D-11-00075.1

[14] Horritt, M.S. & Bates, P.D., Predicting floodplain inundation: raster-based modelling versus the finite-element approach. Hydrological Processes, 15, pp. 825–842, 2001. http://dx.doi.org/10.1002/hyp.188

[15] Egbert, G.D., Bennett, A.F. & Foreman, M.G.G., TOPEX/POSEIDON tides estimated using a global inverse model. Journal of Geophysical Research, 99, 1994. http://dx.doi.org/10.1029/94jc01894