A new global dataset with extreme sea-levels and its
application for assessing flood risk
Philip Ward , Sanne Muis, Martin Verlaan, Hessel Winsemius, Jeroen Aerts
A new global dataset with extreme sea-levels
GTSR dataset: a global reanalysis of tides and storm
surges that can be applied for flood risk assessments
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A new global dataset with extreme sea-levels
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Why develop a new global dataset with sea-level extremes?
 Increasing trend in coastal flood risk
• Need for an accurate understanding of drivers of global risk
 Past datasets are limited in their application to risk
Observations from tide gauge stations
Extreme surge level in DIVA
Vafeidis et al.
(2008); Hinkel
and Klein (2009)
Tamisiea et al.
(2014)
 GTSR is the first dataset that:
• is physically-based
• has been extensively validated against observations
• provides full timeseries over the period 1979-2014
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Method: Modelling storm surge surges
GTSM uses unstructured
grids, which allows the
locally refinement of the
computational grid
Resolution ranges from ~1/20° (5 km) in
coastal areas to ~1/2° (50 km) resolution in
the deeper parts of the ocean
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Method: Modelling storm surge surges
 Global Tide and Surge Model (GTSM)
• Global hydrodynamic model developed by Deltares and based on Delft3D-FM
• Solves the vertical integrated 1-D-2-D shallow water equations
 Model setup
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•
•
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Computational time-step is 10 minutes
Model spin-up time is ~10 days
1 yr of simulation ~ 2 days on 16 cores
GEBCO bathymetry interpolated to the model grid
 Meteorological forcing
• Wind fields and mean sea-level pressure from ERA-Interim
(1979-2013) with a 6 h time step
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Method: Modelling tides
 Tides are not adequately represented in GTSM, mainly because selfattraction and loading and internal tidal waves are not (yet) included
 FES2012 is used to model the tides (Carrere et al., 2013)
• A hydrodynamic model which assimilates altimeter and in situ data
 Total water levels are calculated as surge + tide
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Results: Validation of annual maxima
• Generally also extreme are adequately represented
 ρ >0.75 for 75% of the stations
• Lower correlations in areas prone to tropical cyclones
Correlation
coeff. (ρ)
Tropical
0.77 (0.15)
Extra-tropical
0.87 (0.11)
Global
0.83 (0.14)
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Results: 1-in-10 year extremes
• Extreme values are generally underestimated,
particularly in the tropics
• This is caused by the resolution of the
meteorological forcing, bathymetry and model
10-yr
return period
Absolute error
(m)
Tropical
-0.23 ± 0.58
Extra-tropical
-0.05 ± 0.33
Global
-0.21 ± 0.36
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Results: Global flood hazard
 Planar flood levels
 No flood protection
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Results: Global flood exposure
Country
 Planar flood levels
 No flood protection
Absolute
Relative exposure
exposure
(% of population)
(in millions)
1 China
36
2.9
2 Netherlands
8.4
53
3 Vietnam
4.7
6.0
4 Egypt
4.3
6.4
5 Germany
2.9
3.5
6 India
2.5
0.3
7 UK
2.4
4.1
8 Japan
2.0
1.6
9 Bangladesh
1.4
1.1
10 Belgium
1.1
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World
76
1.3
How does GTSR compare to previous datasets?
GTSR
DIVA
Large differences between the two datasets
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Comparison of GTSR and DIVA relative to observed extremes
Return
period (yr)
10
100
1000
Abs. difference (m)
GTSR
-0.16
-0.20
-0.22
DIVA
0.53
0.63
0.77
Overestimation of extremes in DIVA, whereas
extremes in GSTR are underestimated
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Differences in flood hazard and exposure
DIVA
GTSR
Relative difference (%)
Total inundated
areas (103 km2)
648
448
-31
Total exposed population
(millions of people)
157
99
-37
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Building a global database
of flood protection standards
Scussolini et al., 2016. NHESS
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FLOPROS
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FLOPROS Merged layer
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Outlook
• GTSR: 36-year reanalysis of tides and surge levels
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providing extreme sea-levels for the entire world’s coastlines
Validation shows very good performance
Extreme values generally underestimated
Large differences with DIVA dataset
• Application for risk assessments
• Requires global database of coastal flood protection standards
• Coupling with GLOFRIS global river flood risk model
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