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• W3009505811 abstract "Extreme sea levels and sea-level rise are two hazards that can harm populations situated in low elevated coastal areas. Impact and vulnerability assessments quantify the number of people living in flood prone areas and serve as a basis for planning adaptation measures. One of the prerequisites for these assessments is a realistic spatial representation of the potentially affected coastal population. Census data provide the highest spatial detail for assessments on coarse scales (multi-national to global). Within census units, population is assumed to be distributed homogeneously. This assumption might hold true for small census units (few hectares) but does not represent the actual distribution of population in large census units (several square kilometres). Previous studies use many ancillary data to allocate population within cen-sus units which restricts their transferability to data sparse regions.This thesis in contrast uses a novel approach by deriving settlement extents from the ‘Global Urban Footprint’. It examines whether these derived settlement areas are sufficient to reproduce the actual population distribution within census units. For the German Baltic Sea region, the settlement extents capture 95.3 % of the actual population, qualifying them as a suitable parameter to model the distribution of population. In addition, this thesis compares six approaches to differentiate population density within the derived settlement extents. Compared to a homogeneous distribution of population, the error in the tested approaches is up to six times smaller. In flood prone areas, the tested approaches reduce the overestimation of population exposure by up to 29 %. These results thus show that settlement extents derived from the ‘Global Urban Footprint’ lead to a more realistic distribution of the current popula-tion within census units, which improves the assessment accuracy of population exposure to coastal flooding.The regionalisation of population growth projections in coastal areas is the second focus of this thesis. Previous studies that assess exposure to coastal flooding account for changing sea-levels or the changes in the frequency and intensity of floods due to climate change. However, these studies do not consider socioeconomic development at all, or do not differen-tiate socioeconomic development at subnational level. This thesis extends the ‘Shared Socio-economic Pathways’ for the coastal zone by providing narratives of socioeconomic develop-ment under five scenarios. Furthermore, it combines observed differences in population growth between coastal and inland areas with existing projections on population and urbanisation to develop spatially explicit population projections for the entire globe. Subsequently, this work compares these projections to projections that do not differentiate population growth within countries. Depending on the scenario, it finds the population living in the ‘low elevation coastal zone’ in the regionalised projections in 2100 to be 85 million to 229 million people larger. Furthermore, the thesis investigates, to what extent urbanisation or coastal mi-gration contribute to these differences and whether urban sprawl increases or decreases these differences by employing the ‘Dynamic Interactive Vulnerability Assessment’ Tool. Com-pared to homogeneous population growth within countries, urbanisation increases the assessed exposure by 7 % to 20 %, coastal migration increases the assessed exposure by 1 % to 20 % and urban sprawl decreases the assessed exposure by 12 % to 22 %. The results show that urbanisation, coastal migration and urban sprawl lead to heterogeneous population growth on a subnational level. Accounting for these differences in population growth contributes to improved estimates of the future population exposure to coastal floods." @default.
• W3009505811 created "2020-03-13" @default.
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• W3009505811 date "2020-02-06" @default.
• W3009505811 modified "2023-09-24" @default.
• W3009505811 title "Improving the representation of the spatial distribution of population for coastal impact and vulnerability assessments" @default.
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