1. Challenges in Assessing Socio-Economic Impacts of SLR
Nassos Vafeidis
with contributions from G. Kaiser, B. Neumann and J. Hinkel

2. Outline SLR and Impacts Metrics/methods for assessing SLR s-e impacts
Methodological, scale- and data-related issues: Population, Elevation, GDP
Ideas & future needs
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3. (Source: Nicholls and Cazenave, 2010)
Global Mean SLR
Sea Level Rise
SRES Scenarios 2000 to 2100
Vermeer and Rahmstorf (2009)
Rahmstorf (2007)
Source: IPCC Climate Change 2001: Synthesis Report. Contribution of Working Groups I, II and III to the Third Assessment Report of the Intergovernmental Panel on Climate Change [Watson,R.T et al. (eds.)]. Cambridge University Press, Cambridge, UK. p 74.
Grinsted et al. (2009)
(Source: Nicholls and Cazenave, 2010)
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4. Main Biophysical Effects
Coastal Impacts
The main biophysical effects of relative sea level rise
Displacement of costal lowlands and wetlands
Coastal erosion at
Happisburgh, UK in 2009 (2)
Mangroves, Thailand
Increased coastal erosion
Increased flooding (frequency and depth)
SLR
Coastal flooding, New York (1)
Saltwater intrusion (in surface- and groundwaters)
Others
Rio de la Plata, Argentina (3)
(1)
(2)
(3)
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5. Area Exposure – Global Scale
Areas below 10m of elevation
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6. Assessing Socio-Economic Impacts
Population and Area Exposure: Global to Regional Scales
Area: First-order assessment using elevation data
No protection/adaptation is considered: Worst-case impacts
Population density / counts (global datasets), combined with information on elevation
Nightlights, ORL
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7. Assessing socio-economic Impacts
DIVA Results
People flooded under the A2 scenario without adaptation in 2100
People per country flooded & forced to migrate due to erosion under the A2 scenario, w/o adaptation, in 2100
Hinkel et al., 2010
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8. Areas below 10m of elevation
Area Exposure
Areas below 10m of elevation
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9. Hydrological Connectivity
Hydrological connectivity not considered
Hydrologically connected areas
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10. Input Data
Estimates of area and population exposure depend heavily on the datasets that are employed for the analyses
Differences up to 150% in area estimates, around 10% for population for low elevations
Differences become smaller with higher elevations
Lichter et al., In Press
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11. Resolution of DEMs
Aster orig (30m), MFC orig/ corrected (1m), SRTM orig, corr (90m)
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12. Surface Models vs Corrected Models
DSM – Digital Surface Model
DEM – Digital Elevation Model (corrected)
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13. Elevation Data Tendency to use high-resolution datasets
These, do not always provide the best information
Combine datasets, depending on the case study?
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14. Inundation Modelling
Local Scale
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15. Inundation Modelling
Local Scale
Changes in water flux and inundated area. Large differences, depending on parameterisation
Detailed inundation models offer a high degree of precision but are subject to severe limitations regarding their use at regional and global scales
Flux
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16. Population Data Ambient population Time is important
at night...
...and by day
Ambient population
Time is important
Distribution of population (
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17. Scenarios: night, day, season
People exposure
Improved population distribution using
Land use classification
Census data
Information schools, hotels, hospitals, etc., survey
Methods for distributing people:
Dasymetric mapping
Distribution of the people according to land use classes
Scenarios: night, day, season
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18. Economic Impacts Damage costs under A2 in 2100, w/o adaptation
DIVA Results
Damage costs under A2 in 2100, w/o adaptation
Adaptation cost in 2100, under A2
Hinkel et al., 2010
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19. Economic Impacts DIVA Results
Annual damage cost per country per year in 2100 w/o adaptation (left) and with adaptation: Rahmstorf BAU scenario
Absolute (left) and annual adaptation cost in 2100
Countries are ranked as to their values under the Rahmstorf BAU scenario
Hinkel et al., accepted
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20. GDP Data GDP density: Difficult to process globally
Resolution still coarse compared to global population& elevation data (
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21. Conclusions
Scale and methods of analysis and use of data are inter- related issues
A great deal of effort and resources are required for improving global and regional datasets
Data should be employed with caution
Methods exist for improving existing data and should be applied whenever possible
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22. Thank you for your attention
Nassos Vafeidis
“Coastal Risks and Sea-Level Rise” Research Group, Institute of Geography, Future Ocean Excellence Cluster, Christian-Albrechts University Kiel, Germany
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