An improved database of coastal flooding in the United Kingdom from 1915 to 2016

Ivan D. Haigh*, Ozgun Ozsoy, Matthew P. Wadey, Robert J. Nicholls, Shari L. Gallop, Thomas Wahl, Jennifer M. Brown

*Corresponding author for this work

Research output: Contribution to journalArticle

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Abstract

Coastal flooding caused by extreme sea levels can produce devastating and wide-ranging consequences. The 'SurgeWatch' v1.0 database systematically documents and assesses the consequences of historical coastal flood events around the UK. The original database was inevitably biased due to the inconsistent spatial and temporal coverage of sea-level observations utilised. Therefore, we present an improved version integrating a variety of 'soft' data such as journal papers, newspapers, weather reports, and social media. SurgeWatch2.0 identifies 329 coastal flooding events from 1915 to 2016, a more than fivefold increase compared to the 59 events in v1.0. Moreover, each flood event is now ranked using a multi-level categorisation based on inundation, transport disruption, costs, and fatalities: from 1 (Nuisance) to 6 (Disaster). For the 53 most severe events ranked Category 3 and above, an accompanying event description based upon the Source-Pathway-Receptor-Consequence framework was produced. Thus, SurgeWatch v2.0 provides the most comprehensive and coherent historical record of UK coastal flooding. It is designed to be a resource for research, planning, management and education.

Original languageEnglish
Article number170100
Pages (from-to)1-10
Number of pages10
JournalScientific Data
Volume4
DOIs
Publication statusPublished - 1 Aug 2017

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Copyright the Author(s) 2017. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

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Haigh, I. D., Ozsoy, O., Wadey, M. P., Nicholls, R. J., Gallop, S. L., Wahl, T., & Brown, J. M. (2017). An improved database of coastal flooding in the United Kingdom from 1915 to 2016. Scientific Data, 4, 1-10. [170100]. https://doi.org/10.1038/sdata.2017.100