Towards modelling flood protection investment as a coupled human and natural system

Abstract. Due to a number of recent high-profile flood events and the apparent threat from global warming, governments and their agencies are under pressure to make proactive investments to protect people living in floodplains. However, adopting a proactive approach as a universal strategy is not affordable. It has been argued that delaying expensive and essentially irreversible capital decisions could be a prudent strategy in situations with high future uncertainty. This paper firstly uses Monte Carlo simulation to explore the performance of proactive and reactive investment strategies using a rational cost–benefit approach in a natural system with varying levels of persistence/interannual variability in annual maximum floods. It is found that, as persistence increases, there is a change in investment strategy optimality from proactive to reactive. This could have implications for investment strategies under the increasingly variable climate that is expected with global warming.

As part of the emerging holistic approaches to flood risk management, there is increasing emphasis on stakeholder participation in determining where and when flood protection investments are made, and so flood risk management is becoming more people-centred. As a consequence, multiple actors are involved in the decision-making process, and the social sciences are assuming an increasingly important role in flood risk management. There is a need for modelling approaches which can couple the natural and human system elements. It is proposed that coupled human and natural system (CHANS) modelling could play an important role in understanding the motivations, actions and influence of citizens and institutions and how these impact on the effective delivery of flood protection investment. A framework for using agent-based modelling of human activities leading to flood investments is outlined, and some of the challenges associated with implementation are discussed.