GFZ German Research Centre for Geosciences, Section Hydrology, 14473, Potsdam, Germany.
Institute of Environmental Science and Geography, University of Potsdam, 14476, Potsdam, Germany.
Sci Rep. 2020 Nov 9;10(1):19387. doi: 10.1038/s41598-020-76523-2.
Recently, flood risk assessments have been extended to national and continental scales. Most of these assessments assume homogeneous scenarios, i.e. the regional risk estimate is obtained by summing up the local estimates, whereas each local damage value has the same probability of exceedance. This homogeneity assumption ignores the spatial variability in the flood generation processes. Here, we develop a multi-site, extreme value statistical model for 379 catchments across Europe, generate synthetic flood time series which consider the spatial correlation between flood peaks in all catchments, and compute corresponding economic damages. We find that the homogeneity assumption overestimates the 200-year flood damage, a benchmark indicator for the insurance industry, by 139%, 188% and 246% for the United Kingdom (UK), Germany and Europe, respectively. Our study demonstrates the importance of considering the spatial dependence patterns, particularly of extremes, in large-scale risk assessments.
最近，洪水风险评估已经扩展到国家和大陆尺度。这些评估大多假设同质情景，即通过将局部估计值相加来获得区域风险估计，而每个局部破坏值具有相同的超越概率。这种同质性假设忽略了洪水生成过程的空间变异性。在这里，我们为欧洲的 379 个流域开发了一个多站点、极值统计模型，生成了综合洪水时间序列，这些序列考虑了所有流域洪峰之间的空间相关性，并计算了相应的经济损失。我们发现，同质性假设高估了 200 年洪水灾害，即保险业的基准指标，对英国（英国）、德国和欧洲的估计值分别高出 139%、188%和 246%。我们的研究表明，在大规模风险评估中考虑空间相关性模式的重要性，特别是极端情况。
