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

S. Muis, Martin Verlaan, Robert Nicholls, Sally Brown, Jochen Hinkel, Daniël Lincke, Athanasios Vafeidis , P. Scussolini, H.C. Winsemius, P.J. Ward

Research output: Scientific - peer-reviewArticle

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 Dynamic Interactive Vulnerability Assessment 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 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.
Original languageEnglish
JournalEarth's Future
DOIs
StatePublished - Mar 2017

Cite this

Muis, S.; Verlaan, Martin; Nicholls, Robert; Brown, Sally; Hinkel, Jochen; Lincke, Daniël; Vafeidis , Athanasios ; Scussolini, P.; Winsemius, H.C.; Ward, P.J. / A comparison of two global datasets of extreme sea levels and resulting flood exposure.

In: Earth's Future, 03.2017.

Research output: Scientific - peer-reviewArticle

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title = "A comparison of two global datasets of extreme sea levels and resulting flood exposure",
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 Dynamic Interactive Vulnerability Assessment 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 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.",
author = "S. Muis and Martin Verlaan and Robert Nicholls and Sally Brown and Jochen Hinkel and Daniël Lincke and Athanasios Vafeidis and P. Scussolini and H.C. Winsemius and P.J. Ward",
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A comparison of two global datasets of extreme sea levels and resulting flood exposure. / Muis, S.; Verlaan, Martin; Nicholls, Robert; Brown, Sally; Hinkel, Jochen; Lincke, Daniël; Vafeidis , Athanasios ; Scussolini, P.; Winsemius, H.C.; Ward, P.J.

In: Earth's Future, 03.2017.

Research output: Scientific - peer-reviewArticle

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AU - Muis,S.

AU - Verlaan,Martin

AU - Nicholls,Robert

AU - Brown,Sally

AU - Hinkel,Jochen

AU - Lincke,Daniël

AU - Vafeidis ,Athanasios

AU - Scussolini,P.

AU - Winsemius,H.C.

AU - Ward,P.J.

PY - 2017/3

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N2 - 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 Dynamic Interactive Vulnerability Assessment 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 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.

AB - 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 Dynamic Interactive Vulnerability Assessment 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 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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DO - 10.1002/2016EF000430

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JO - Earth's Future

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JF - Earth's Future

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Muis S, Verlaan M, Nicholls R, Brown S, Hinkel J, Lincke D et al. A comparison of two global datasets of extreme sea levels and resulting flood exposure. Earth's Future. 2017 Mar. Available from, DOI: 10.1002/2016EF000430