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A comparison of two global datasets of extreme sea levels and resulting flood exposure

A comparison of two global datasets of extreme sea levels and resulting flood exposure
A comparison of two global datasets of extreme sea levels and resulting flood exposure
Estimating the current risk of coastal flooding requires adequate information on extreme sea levels. For over a decade, the only global data available was the DINAS-COAST Extreme Sea Levels (DCESL) dataset, which applies a static approximation to estimate extreme sea levels. Recently, a dynamically derived dataset was developed: the Global Tide and Surge Reanalysis (GTSR) dataset. Here, we compare the two datasets. The differences between DCESL and GTSR are generally larger than the confidence intervals of GTSR. Compared to observed extremes, DCESL generally overestimates extremes with a mean bias of 0.6 m. With a mean bias of -0.2 m GTSR generally underestimates extremes, particularly in the tropics. The DIVA model is applied to calculate the present-day flood exposure in terms of the land area and the population below the 1 in 100-year sea levels. Global exposed population and is 28% lower when based on GTSR instead of DCESL. Considering the limited data available at the time, DCESL provides a good estimate of the spatial variation in extremes around the world. However, GTSR allows for an improved assessment of the impacts of coastal floods, including confidence bounds. We further improve the assessment of coastal impacts by correcting for the conflicting vertical datum of sea level extremes and land elevation, which has not been accounted for in previous global assessments. Converting the extreme sea levels to the same vertical reference used for the elevation data is shown to be a critical step resulting in 39-59% higher estimate of population exposure.
Muis, Sanne
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Verlaan, Martin
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Nicholls, Robert
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Brown, Sally
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Hinkel, Jochen
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Lincke, Daniel
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Vafeidis, Athanasios T
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Scussolini, Paolo
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Winsemius, Hessel C
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Ward, Philip J
f1bb4eea-b47f-4d33-895b-d4cd1d021bd1
Muis, Sanne
abb9cf16-d84f-4006-8c9f-66202486dd88
Verlaan, Martin
94c81bae-d88a-48dd-a825-0a568973966e
Nicholls, Robert
4ce1e355-cc5d-4702-8124-820932c57076
Brown, Sally
dd3c5852-78cc-435a-9846-4f3f540f2840
Hinkel, Jochen
9c7e8026-955c-42cd-9179-6113efbf1339
Lincke, Daniel
8b279c5b-dd6e-46f4-9c8d-adf83f6ea2cd
Vafeidis, Athanasios T
19100c07-8899-4114-8f8a-a42042201e7d
Scussolini, Paolo
38f2e04f-e7a2-4895-b241-d3af277b7e8d
Winsemius, Hessel C
0934e633-e76e-4dfa-ad4a-2839fbe60f3a
Ward, Philip J
f1bb4eea-b47f-4d33-895b-d4cd1d021bd1

Muis, Sanne, Verlaan, Martin, Nicholls, Robert, Brown, Sally, Hinkel, Jochen, Lincke, Daniel, Vafeidis, Athanasios T, Scussolini, Paolo, Winsemius, Hessel C and Ward, Philip J (2017) A comparison of two global datasets of extreme sea levels and resulting flood exposure Earth's Future (doi:10.1002/2016EF000430).

Record type: Article

Abstract

Estimating the current risk of coastal flooding requires adequate information on extreme sea levels. For over a decade, the only global data available was the DINAS-COAST Extreme Sea Levels (DCESL) dataset, which applies a static approximation to estimate extreme sea levels. Recently, a dynamically derived dataset was developed: the Global Tide and Surge Reanalysis (GTSR) dataset. Here, we compare the two datasets. The differences between DCESL and GTSR are generally larger than the confidence intervals of GTSR. Compared to observed extremes, DCESL generally overestimates extremes with a mean bias of 0.6 m. With a mean bias of -0.2 m GTSR generally underestimates extremes, particularly in the tropics. The DIVA model is applied to calculate the present-day flood exposure in terms of the land area and the population below the 1 in 100-year sea levels. Global exposed population and is 28% lower when based on GTSR instead of DCESL. Considering the limited data available at the time, DCESL provides a good estimate of the spatial variation in extremes around the world. However, GTSR allows for an improved assessment of the impacts of coastal floods, including confidence bounds. We further improve the assessment of coastal impacts by correcting for the conflicting vertical datum of sea level extremes and land elevation, which has not been accounted for in previous global assessments. Converting the extreme sea levels to the same vertical reference used for the elevation data is shown to be a critical step resulting in 39-59% higher estimate of population exposure.

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Accepted/In Press date: 12 March 2017
e-pub ahead of print date: 3 April 2017
Organisations: Energy & Climate Change Group

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Local EPrints ID: 407474
URI: http://eprints.soton.ac.uk/id/eprint/407474
PURE UUID: e5c45fe0-1b9f-449d-b82d-42ce041c6671
ORCID for Sally Brown: ORCID iD orcid.org/0000-0003-1185-1962

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Date deposited: 12 Apr 2017 01:05
Last modified: 17 Jul 2017 13:52

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Contributors

Author: Sanne Muis
Author: Martin Verlaan
Author: Robert Nicholls
Author: Sally Brown ORCID iD
Author: Jochen Hinkel
Author: Daniel Lincke
Author: Athanasios T Vafeidis
Author: Paolo Scussolini
Author: Hessel C Winsemius
Author: Philip J Ward

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