Back
Identifying spatial clusters of flood exposure to support decision making in risk management.
Journal article

Identifying spatial clusters of flood exposure to support decision making in risk management.

  • Röthlisberger V University of Bern, Institute of Geography, Hallerstrasse 12, CH-3012 Bern, Switzerland; University of Bern, Oeschger Centre for Climate Change Research, Mobiliar Lab for Natural Risks, Falkenplatz 16, CH-3012 Bern, Switzerland. Electronic address: veronika.roethlisberger@giub.unibe.ch.
  • Zischg AP University of Bern, Institute of Geography, Hallerstrasse 12, CH-3012 Bern, Switzerland; University of Bern, Oeschger Centre for Climate Change Research, Mobiliar Lab for Natural Risks, Falkenplatz 16, CH-3012 Bern, Switzerland; University of Bristol, School of Geographical Sciences, University Road, BS8 1SS Bristol, United Kingdom. Electronic address: andreas.zischg@giub.unibe.ch.
  • Keiler M University of Bern, Institute of Geography, Hallerstrasse 12, CH-3012 Bern, Switzerland. Electronic address: margreth.keiler@giub.unibe.ch.
  • 2017-04-29
Published in:
  • The Science of the total environment. - 2017
Flood exposure
Flood risk management
MAUP
Prioritization strategies
Spatial cluster analysis
Switzerland
English A sound understanding of flood risk drivers (hazard, exposure and vulnerability) is essential for the effective and efficient implementation of risk-reduction strategies. In this paper, we focus on 'exposure' and study the influence of different methods and parameters of flood exposure analyses in Switzerland. We consider two types of exposure indicators and two different spatial aggregation schemes: the density of exposed assets (exposed numbers per km2) and the ratios of exposed assets (share of exposed assets compared to total amount of assets in a specific region) per municipality and per grid cells of similar size as the municipalities. While identifying high densities of exposed assets highlights priority areas for cost-efficient strategies, high exposure ratios can suggest areas of interest for strategies focused on the most vulnerable regions, i.e. regions with a low capacity to cope with a disaster. In Switzerland, the spatial distribution of high exposure densities and exposure ratios tend to be complementary. With regards to the methods, we find that the spatial cluster analysis provides more information for the prioritization of flood protection measures than 'simple' maps of spatially aggregated data represented in quantiles. In addition, our study shows that the data aggregation scheme influences the results. It suggests that the aggregation based on grid cells supports the comparability of different regions better than aggregation based on municipalities and is, thus, more appropriate for nationwide analyses.
Language
  • English
Open access status
hybrid
Identifiers
Persistent URL
https://sonar.ch/global/documents/140333
Statistics
Document views: 50 File downloads: