simulation of resilience of an insurance system to flood risk

Simulation of Resilience of anInsurance System to Flood Risk

Olivier Barreteau, Frédéric Grelot IRSTEA, UMR G-EAU, Montpellier

Exploration throughAgent Based Social Simulation

French « CatNat » system

• Natural risk management in France– Land use regulation: zoning– Insurance: financial solidarity

• Cat-Nat: an insurance system based on national solidarity– Every household insured against natural disasters– Insurance companies have to provide it– Unlimited warranty from state (reinsurance)– Cat-Nat fund

Viewpoints on CatNat

• Efficient to cover damages• But:– Encourage risk taking behavioural patterns– Lack of responsibility for local bodies

• Resilience to global changes?– Climate– demography

A political choice• Risk averse regime

– Zoning with large interdiction area– Few damages, low fees– Tensions over land access

• Shared risks– Limited interdiction area– Higher damages, higher fee levels– Tensions on fee collection

• Issue of acceptability– Setting new zoning constraints for new population– Maximum fee level for households out of flood prone area

Exploration through simulation

• General objective– Test ABM as a tool to simulate CatNat system– Feedbacks between HH settlements and fund’s viability– Consequences of changes in context

• Scope of this presentation– Strategies for adaptation

• No possibility for adaptation with land use regulation• Late implementation of zoning generate dangerous

irreversibilities– Impact of insurance system

Virtual space: 5 independent territories with a common fundMaximum flooding areaRiver

Rationale for 5 territories

• Represent current situation at national level

• Technical issue to compute composition of risks

• Opening towards evolution of models– Test heterogeneity of local policies– Evolution of heterogeneity of risk

exposition– Changes regarding correlation

between events

CatNatABM: Class diagram

Dynamics

Generate new HouseholdsNew HH asks for a place for setting according to current land use patterns and preferencesCheck for availability and legal accessSettle or seek closest possible place

Random generation for each territory in the same distribution

Update fee and/or zoningPurpose of budget balance

Random drawing in an homogeneous distributionreparation of houses

Model’s assumptions

• Uniformity of houses characteristics regarding damages due to a flood

• Constant demography• Perfect knowledge from policy maker on

climate and demography• No direct feedback from households to policy

makers• No default of payment

Households characteristics

• Preferences for settling– Attractivity for the river– Attractivity for already populated areas– Repulsion for ruined houses

• Flood tolerance– Reaction to flood

• Reparation of houses– Houses repaired with insurance– If no insurance, houses repaired when HH decide to stay

Indicators

• Exposure to the alea– Flood prone area occupation (population, houses)

• Damages– Number of flooded houses– Number of ruins

• Resilience of the system– Budget level of CatNat fund– Fee level– Settlement possibilities for newcomers

Scenarios: climate

Initialization• Initial conditions:– land use: centers {25; 75}– Fee level = 0.01

• Parameters– Flood tolerance {0; 0.5; 1}– River attractivity {0; 0.1; 0,2}– Repulsion factor {0; 0.5; 1}– Population increase rate {0.005; 0.01}

Evolution of populationBenchmark Insurance

floodTolerance = 0.5Pop increase rate = 0.1

Path dependence to flood sequence Stronger with centers close to the river No clear impact of insurance

Evolution of location of collectivities’ centersBenchmark Insurance

• Path dependence with non tolerant inhabitants• Impact of insurance with fully tolerant inhabitants• Biases: centers moving to the river• Should represent shifts in attitude

Evolution of fee levels• Two regimes of convergence dependingon flood tolerance•No clear influence of initial conditions

Perspectives

• Land use regulation– Introduction of local bodies– Resistance to land use regulation

• Exploration in the parameter space– Heterogeneity among subareas

• Impacts of changes in environment