E3S Web Conf.
Volume 7, 20163rd European Conference on Flood Risk Management (FLOODrisk 2016)
|Number of page(s)||10|
|Section||Residual risk and insurance|
|Published online||20 October 2016|
Surface water flood risk and management strategies for London: An Agent-Based Model approach
1 Environmental Change Institute (ECI), University of Oxford, South Parks Road, Oxford, OX1 3QY, UK
2 Grantham Research Institute on Climate Change and the Environment, The London School of Economics and Political Science, Floor 11, Tower 3, Clement’s Inn, London, WC2A 2AZ, UK
a Corresponding author: email@example.com
Flooding is recognised as one of the most common and costliest natural disasters in England. Flooding in urban areas during heavy rainfall is known as ‘surface water flooding’, considered to be the most likely cause of flood events and one of the greatest short-term climate risks for London. In this paper we present results from a novel Agent-Based Model designed to assess the interplay between different adaptation options, different agents, and the role of flood insurance and the flood insurance pool, Flood Re, in the context of climate change. The model illustrates how investment in adaptation options could reduce London’s surface water flood risk, today and in the future. However, benefits can be outweighed by continued development in high risk areas and the effects of climate change. Flood Re is beneficial in its function to provide affordable insurance, even under climate change. However, it offers no additional benefits in terms of overall risk reduction, and will face increasing pressure due to rising surface water flood risk in the future. The modelling approach and findings are highly relevant for reviewing the proposed Flood Re scheme, as well as for wider discussions on the potential of insurance schemes, and broader multi-sectoral partnerships, to incentivise flood risk management in the UK and internationally.
© The Authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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