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 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.
Sci Total Environ. 2017 Nov 15;598:593-603. doi: 10.1016/j.scitotenv.2017.03.216. Epub 2017 Apr 25.
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 km) 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.
对洪水风险驱动因素（危险、暴露和脆弱性）有一个清晰的理解，对于有效和高效地实施降低风险策略至关重要。在本文中，我们重点关注“暴露”，并研究了瑞士洪水暴露分析中不同方法和参数的影响。我们考虑了两种类型的暴露指标和两种不同的空间聚合方案：暴露资产密度（每公里暴露的数量）和暴露资产比例（与特定区域内的总资产相比，暴露资产的份额），分别按市和与市大小相似的网格单元进行计算。虽然确定暴露资产的高密度突出了具有成本效益的策略的重点领域，但高暴露比例可以表明对最脆弱地区（即应对灾害能力较低的地区）的策略感兴趣的领域。在瑞士，高暴露密度和暴露比例的空间分布往往是互补的。关于方法，我们发现空间聚类分析比以分位数表示的空间聚合数据的“简单”地图为洪水保护措施的优先级排序提供了更多信息。此外，我们的研究表明数据聚合方案会影响结果。它表明，基于网格单元的聚合比基于市的聚合更能支持不同地区之间的可比性，因此更适合全国性分析。
