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Using global tide gauge data to validate and improve the representation of extreme sea levels in flood impact studies

Using global tide gauge data to validate and improve the representation of extreme sea levels in flood impact studies
Using global tide gauge data to validate and improve the representation of extreme sea levels in flood impact studies
The largest collection of tide gauge records assembled to date, called GESLA-2, has been used to provide reliable extreme sea level parameters at 655 locations around the world. This has enabled a rigorous assessment of the European Union-funded DINAS-COAST (D-C) data set of extreme sea level information for the global coastline that has been used in many published flood impact studies. We find the D-C extreme levels to be generally both too high, compared to those from GESLA-2, and too flat, when plotted as a function of return period. This leads to an over-estimation of the probability of extreme sea levels in the present day for most locations around the world, and also to an overestimation
of the probability of extreme sea levels in the future as sea level rises. A detailed impact study is conducted for the world’s largest coastal cities following the approach of Hallegatte et al. (2013), resulting in similar conclusions for these particular locations. We suggest that most previous
studies that have relied upon D-C information should be re-assessed in the light of these findings using more recent modelling-based estimates of extreme sea level information.
Keywords: Extreme sea level parameters; GESLA-2 tide gauge data set; DINAS-COAST data set; Major
0921-8181
34-45
Hunter, J.R.
a1b48d21-f25f-43cd-8b95-c4216f3f985e
Woodworth, P.L.
87be7ad1-fdaa-479e-9750-2aef89ecdab7
Wahl, T.
192e987e-1eac-4376-84da-a4c89ef91b47
Nicholls, R.J.
4ce1e355-cc5d-4702-8124-820932c57076
Hunter, J.R.
a1b48d21-f25f-43cd-8b95-c4216f3f985e
Woodworth, P.L.
87be7ad1-fdaa-479e-9750-2aef89ecdab7
Wahl, T.
192e987e-1eac-4376-84da-a4c89ef91b47
Nicholls, R.J.
4ce1e355-cc5d-4702-8124-820932c57076

Hunter, J.R., Woodworth, P.L., Wahl, T. and Nicholls, R.J. (2017) Using global tide gauge data to validate and improve the representation of extreme sea levels in flood impact studies. Global and Planetary Change, 156, 34-45. (doi:10.1016/j.gloplacha.2017.06.007).

Record type: Article

Abstract

The largest collection of tide gauge records assembled to date, called GESLA-2, has been used to provide reliable extreme sea level parameters at 655 locations around the world. This has enabled a rigorous assessment of the European Union-funded DINAS-COAST (D-C) data set of extreme sea level information for the global coastline that has been used in many published flood impact studies. We find the D-C extreme levels to be generally both too high, compared to those from GESLA-2, and too flat, when plotted as a function of return period. This leads to an over-estimation of the probability of extreme sea levels in the present day for most locations around the world, and also to an overestimation
of the probability of extreme sea levels in the future as sea level rises. A detailed impact study is conducted for the world’s largest coastal cities following the approach of Hallegatte et al. (2013), resulting in similar conclusions for these particular locations. We suggest that most previous
studies that have relied upon D-C information should be re-assessed in the light of these findings using more recent modelling-based estimates of extreme sea level information.

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Hunter_et_al_in_press - Accepted Manuscript
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More information

Accepted/In Press date: 23 June 2017
e-pub ahead of print date: 26 June 2017
Published date: September 2017
Keywords: Keywords: Extreme sea level parameters; GESLA-2 tide gauge data set; DINAS-COAST data set; Major

Identifiers

Local EPrints ID: 415372
URI: http://eprints.soton.ac.uk/id/eprint/415372
ISSN: 0921-8181
PURE UUID: 4a6be12c-2824-465b-ace6-b75a2ae5a89d
ORCID for R.J. Nicholls: ORCID iD orcid.org/0000-0002-9715-1109

Catalogue record

Date deposited: 08 Nov 2017 17:30
Last modified: 18 Feb 2021 17:02

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