Challenges in Assessing Socio-Economic Impacts of SLR

Nassos Vafeidiswith contributions from G. Kaiser,

B. Neumann and J. Hinkel

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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Global Mean SLR

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Sea Level Rise

SRES Scenarios 2000 to 2100

(Source: Nicholls and Cazenave, 2010)

Vermeer and Rahmstorf (2009)

Rahmstorf (2007)

Grinsted et al. (2009)

Main Biophysical Effects

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Coastal Impacts

The main biophysical effects of relative sea level rise

SLRIncreased flooding

(frequency and depth)

Displacement of costal lowlands and wetlands

OthersSaltwater intrusion

(in surface- and groundwaters)

Increased coastal erosion

Coastal erosion at Happisburgh, UK in 2009 (2)

Coastal flooding, New York (1)

Mangroves, Thailand

Rio de la Plata, Argentina (3)

(1) http://www.erh.noaa.gov/okx/images/coastal.jpg(2) www.happisburgh.org.uk/press/edp060209.html

(3) http://veimages.gsfc.nasa.gov/4874/Argentina.A2003026.1730.250m.jpg

Area Exposure – Global Scale

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Areas below 10m of elevation

Assessing Socio-Economic Impacts

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Population and Area Exposure: Global to Regional Scales

Nightlights, ORL

•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

Assessing socio-economic Impacts

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DIVA Results

Hinkel et al., 2010

•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

Area Exposure

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Areas below 10m of elevation

Hydrological Connectivity

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Hydrologically connected areas Hydrological connectivity not considered

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

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Lichter et al., In Press

Resolution of DEMs

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Aster orig (30m), MFC orig/ corrected (1m), SRTM orig, corr (90m)

Surface Models vs Corrected Models

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DSM – Digital Surface Model DEM – Digital Elevation Model (corrected)

Elevation Data

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•Tendency to use high-resolution datasets

•These, do not always provide the best information

Combine datasets, depending on the case study?

Inundation Modelling

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Local Scale

Inundation Modelling

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Local Scale

Flux

•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

Population Data

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(http://www.joelertola.com/grfx/index.html)

•Distribution of population•Ambient population•Time is important

at night... ...and by day

People exposure

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•Improved population distribution usingo Land use classificationo Census datao Information schools, hotels, hospitals,

etc., survey

•Methods for distributing people:o Dasymetric mappingo Distribution of the people according to

land use classes

Scenarios: night, day, season

Economic Impacts

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DIVA Results

Hinkel et al., 2010

•Adaptation cost in 2100, under A2•Damage costs under A2 in 2100, w/o adaptation

Economic Impacts

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DIVA Results

Hinkel et al., accepted

•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

GDP Data

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(http://gecon.yale.edu/large-pixeled-contour-globe)

GDP density:

•Difficult to process globally•Resolution still coarse

compared to global population& elevation data

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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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

vafeidis@geographie.uni-kiel.de

http://www.crslr.uni-kiel.de

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