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A Probabilistic Analysis of Surface Water Flood Risk in London

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  • Katie Jenkins
  • Jim Hall
  • Vassilis Glenis
  • Chris Kilsby

Abstract

Flooding in urban areas during heavy rainfall, often characterized by short duration and high‐intensity events, is known as “surface water flooding.” Analyzing surface water flood risk is complex as it requires understanding of biophysical and human factors, such as the localized scale and nature of heavy precipitation events, characteristics of the urban area affected (including detailed topography and drainage networks), and the spatial distribution of economic and social vulnerability. Climate change is recognized as having the potential to enhance the intensity and frequency of heavy rainfall events. This study develops a methodology to link high spatial resolution probabilistic projections of hourly precipitation with detailed surface water flood depth maps and characterization of urban vulnerability to estimate surface water flood risk. It incorporates probabilistic information on the range of uncertainties in future precipitation in a changing climate. The method is applied to a case study of Greater London and highlights that both the frequency and spatial extent of surface water flood events are set to increase under future climate change. The expected annual damage from surface water flooding is estimated to be to be £171 million, £343 million, and £390 million/year under the baseline, 2030 high, and 2050 high climate change scenarios, respectively.

Suggested Citation

  • Katie Jenkins & Jim Hall & Vassilis Glenis & Chris Kilsby, 2018. "A Probabilistic Analysis of Surface Water Flood Risk in London," Risk Analysis, John Wiley & Sons, vol. 38(6), pages 1169-1182, June.
  • Handle: RePEc:wly:riskan:v:38:y:2018:i:6:p:1169-1182
    DOI: 10.1111/risa.12930
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    References listed on IDEAS

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    1. Jim Hall & Paul Sayers & Richard Dawson, 2005. "National-scale Assessment of Current and Future Flood Risk in England and Wales," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 36(1), pages 147-164, September.
    2. H. Moel & B. Jongman & H. Kreibich & B. Merz & E. Penning-Rowsell & P. Ward, 2015. "Flood risk assessments at different spatial scales," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 20(6), pages 865-890, August.
    3. Harvey Rodda, 2005. "The Development and Application of a Flood Risk Model for the Czech Republic," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 36(1), pages 207-220, September.
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    1. Chiranjit Singha & Neha Chakraborty & Satiprasad Sahoo & Quoc Bao Pham & Yunqing Xuan, 2025. "A novel framework for flood susceptibility assessment using hybrid analytic hierarchy process-based machine learning methods," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(11), pages 13765-13810, June.
    2. Kriti Mukherjee & Mónica Rivas Casado & Rakhee Ramachandran & Paul Leinster, 2024. "Harnessing long-term gridded rainfall data and microtopographic insights to characterise risk from surface water flooding," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-21, September.
    3. Wenjing Wang & Tong Wu & Yuanzheng Li & Hua Zheng & Zhiyun Ouyang, 2021. "Matching Ecosystem Services Supply and Demand through Land Use Optimization: A Study of the Guangdong-Hong Kong-Macao Megacity," IJERPH, MDPI, vol. 18(5), pages 1-15, February.

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