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Uncertainties in tidally adjusted estimates of sea level rise flooding (bathtub model) for the Greater London

Yunus, A. P., Avtar, R., Kraines, S., Yamamuro, M., Lindberg, F. and Grimmond, C. S. B. ORCID: https://orcid.org/0000-0002-3166-9415 (2016) Uncertainties in tidally adjusted estimates of sea level rise flooding (bathtub model) for the Greater London. Remote Sensing, 8 (5). 366. ISSN 2072-4292

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To link to this item DOI: 10.3390/rs8050366

Abstract/Summary

Sea-level rise (SLR) from global warming may have severe consequences for coastal cities, particularly when combined with predicted increases in the strength of tidal surges. Predicting the regional impact of SLR flooding is strongly dependent on the modelling approach and accuracy of topographic data. Here, the areas under risk of sea water flooding for London boroughs were quantified based on the projected SLR scenarios reported in Intergovernmental Panel on Climate Change (IPCC) fifth assessment report (AR5) and UK climatic projections 2009 (UKCP09) using a tidally-adjusted bathtub modelling approach. Medium- to very high-resolution digital elevation models (DEMs) are used to evaluate inundation extents as well as uncertainties. Depending on the SLR scenario and DEMs used, it is estimated that 3%–8% of the area of Greater London could be inundated by 2100. The boroughs with the largest areas at risk of flooding are Newham, Southwark, and Greenwich. The differences in inundation areas estimated from a digital terrain model and a digital surface model are much greater than the root mean square error differences observed between the two data types, which may be attributed to processing levels. Flood models from SRTM data underestimate the inundation extent, so their results may not be reliable for constructing flood risk maps. This analysis provides a broad-scale estimate of the potential consequences of SLR and uncertainties in the DEM-based bathtub type flood inundation modelling for London boroughs.

Item Type:Article
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:65088
Publisher:Multidisciplinary Digital Publishing Institute

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