Advancing disaster policies by integrating dynamic adaptive behaviour in risk assessments using an agent-based modelling approach

Submitted by Toon Haer | published 11th Apr 2019 | last updated 10th Feb 2020
Integrating dynamic adaptation in risk assessments

Overview of the integrated flood risk-assessment approach. Flood risk is a function of the hazard, the exposure and the vulnerability. Governments can raise protection standards to reduce the hazard, and households can reduce their vulnerability by elevating or flood-proofing their houses. These decisions can be influenced by the occurrence of a flood event. Additionally, different insurance schemes can influence the adaptive behaviour of households by offering premium discounts for risk reduction. Click to enlarge.

Summary

Flooding continues to be one of the most costliest natural hazard around the world. Without global investments in adaptation supported by scientific projections of risk, the future impact of floods will continue to increase in many regions due to climate change and socio-economic growth. Key to making accurate risk projections are assessments of how disaster-risk reduction (DRR) measures reduce risk over time, the potential of policies and regulations to steer DRR, and estimations of the risk that remains after DRR. Current large-scale flood-risk-assessment models are often ill-equipped to address these issues, as they assume a static adaptation path, thereby implying that vulnerability remains constant over time, as if the main agents in risk management, such as governments, neither adapt to, nor learn from, flood events and do not anticipate increased risk over time. In reality, there is an interplay between the adaptive behaviour of governments, the adaptive behaviour of individuals, and the flood risk environment, as changes in one influences the other. We present a multidisciplinary modelling approach, combining methods from the natural and social sciences that integrate (individual) adaptive behaviour dynamics from both the government and households in a continental-scale risk-assessment framework for river flooding in the European Union (EU). By applying a multi-agent model, we (1) quantitatively demonstrate how flood risk and adaptation might develop, (2) demonstrate how risk changes if adaptation of governments and households is steered towards economical optimal behaviour, for instance through DRR policies, and (3) estimate the residual risk after adaptation that has to be covered by insurance or other risk-transfer mechanisms for Loss and Damage (L&D) policies.

This article* shows how flood risk might develop at continental scale for europe due to climate change, and compares the effect of different possible adaptation mechanisms.

*Donwload the full text from the right-hand column.

Methods and application

Economic flood risk is typically modelled as a function of the hazard, the exposure of assets, and the vulnerability of assets to flood events, but with static assumptions about adaptive behaviour. Here, we apply this flood-risk framework in a modelling study integrating the dynamic adaptive behaviour of governments and EU households using an agent-based model. We focus on risk to both urban and rural residential buildings to illustrate the effects of household (micro-) adaptation on large-scale risk and government (macro-) adaptation.

The current fluvial flood risk is calculated by using current climate and socio-economic conditions to represent the hazard and the exposure. Current protection standards are based on the global database of FLOod PROtection Standards (FLOPROS). To simulate future risk, we use the flood hazard data for two representative concentration pathways (RCPs), and the data  of two shared socio-economic pathways (SSPs) to project exposure. RCP’s provide time-dependent projections of atmospheric greenhouse gases which are used in flood hazard modelling, and SSP provide among other quantitative projections of change in population growth and GDP which are used here as exposure data. To represent a change in residential building surface relevant for elevating and dry-proofing, we developed a method to represent how change in SSPs affects the spatial-temporally explicit change in residential building surface, and hence, the exposure of urban and rural residential areas to floods. Although in principle all RCPs can be linked to all SSPs, we run the model for two scenario combinations that represent a lower and upper boundary to climate change: RCP2.6-SSP1 and RCP8.5-SSP5.

On the basis of risk information, (future) stochastic flood events to mimic the influence of extreme events, and the cost of adaptation, households and governments take adaptation decisions that influence flood risk to residential buildings in both urban and rural areas. The adaptive behaviour of households follows a model of subjective, discounted expected utility (DEU), which is the mainstream theory of economic decision-making under risk. Based on the DEU, residential agents – who either have rational or boundedly rational risk perceptions – decide for each time step either to flood-proof existing buildings (that is, by dry-proofing, which reduces damage by preventing water from entering the building) or to elevate newly developed buildings (that is, by raising the structure above potential flood levels). Both elevation and dry-proofing are adaptive behaviours by households that reduce the risk to the residential building surface. In addition, we assess the effect of incentives from different insurance schemes on residential behaviour and DRR, namely, voluntary or mandatory insurance, with or without risk-based premium discounts to incentive DRR. The discount on the premium can be offered to those households that have insurance, to motivate them to reduce their risk by implementing loss-reducing measures. Finally, government agents, representing EU member states, dynamically decide to increase protection standards by raising dikes based on a cost-benefit analysis (CBA) of the total fluvial flood risk and the costs of increasing dyke heights. Governments can be proactive or reactive.

Lessons Learnt

Our multi-disciplinary modelling approach, which includes behavioural adaptation, offers a tool to significantly improve quantitative assessments of risk and adaptation. Scientific advances in modelling complexity and human behaviour cover decades of work, and although there is no real consensus about what method fits a certain application best, it is commonly agreed that human behaviour is often neglected in quantitative risk assessment approaches in the environmental sciences. Uncertainty regarding modelling projections remains due to a lack of empirical research into the influence of human decision-making on vulnerability over time, especially in face of low-probability / high-impact events. While we base our modelling on established economic models of behaviour and empirical data from surveys, additional empirical research is required to calibrate and validate the complex adaptive behaviour and the influences of for instance different risk perceptions. Nonetheless, by focusing on established flood-risk-assessment models and integrating established behavioural theories, our study indicates that individual behaviour indeed plays an important role in risk trends.

Our methods – which are transferable to other regions and other natural hazards such as storm surges, extreme winds, and earthquakes – provide a means to quantitatively analyse the potential manoeuvre space for DRR policies, taking into account dynamic decision-making processes. Moreover, our study provides a method to project the residual risk that needs to be covered by L&D policies, for instance through flood insurance mechanisms. Not only can flood insurance cover risk, but as demonstrated here, households can be stimulated via premium discounts to implement DRR measures at the building level. This could also aid in alleviating the increased stress on existing compensation mechanisms, such as the EU Solidarity Fund (EUSF).

Although this study captures some key processes and agents in dynamics adaptation, future research may explore dynamic behaviour in more detail. For instance, emerging cross-basin, cross-country cooperation’s, such as the International Commission for the Protection of the Rhine (ICPR) can have a positive influence on adaptation strategies. Cities, which are increasingly developing their own adaptation strategies (e.g. C40, National League of Cities), could prove to be an important agent to include. For these future efforts, we stress the importance of integrating the enormous aggregate potential of individual adaptive behaviour that DRR policies could tap into. Thus, it is imperative for DRR research to shift its focus toward integrating individual adaptive behaviour and interactions with the main stakeholders involved in DRR.

Further resources

Further resources