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Mapping the Sensitivity of Population Exposure to Changes in Flood Magnitude: Prospective Application From Local to Global Scale
Frontiers in Earth Science ( IF 2.9 ) Pub Date : 2020-08-17 , DOI: 10.3389/feart.2020.534735
Andreas Paul Zischg , María Bermúdez

The floodplains of rivers are relevant living spaces for population globally and provide favorable locations for economic development. However, these areas are commonly exposed to floods, and the increasing population together with the changes in storminess as a result of global warming mean that the risks from flooding are expected to rise. Most studies investigating the impact that climatic change has on flood risk are based on a cascade of global climate model simulations coupled with regional climate models, hydrologic models, inundation models, and flood impact models. However, this approach is subject to uncertainties. Model results are found to be sensitive to climate forcing, the structure of the underlying models, the choice of methods used for downscaling and bias correction, and the use of extreme value analysis for both current and future climate conditions. Moreover, uncertainties are expected to propagate through the model cascade. To overcome these problems, we propose a method for analyzing and mapping the sensitivity of population exposure in floodplains to changes in flood magnitude. The method is based on downward counterfactuals, namely perturbations of a selected flood scenario by increasing its magnitude, interpreted in this case as the worsening of a today’s design flood event as a result of climatic changes. The increase in the impact of a current design flood compared to its counterfactual illustrates the sensitivity to changes in hazard. We calculate the normalized gradients of the flood exposure curves, that is, the increase in the exposure and magnitude of the perturbed event relative to the exposure and magnitude of the current scenario. We test the applicability of the method on local, national, and global scale by using existing data sets, including flood hazard maps, flood protection standards, floodplain delineation, river network definition, and spatial population distribution. The gradients were found to vary remarkably across the globe and are overall smaller in the upper range of flood magnitudes that in the lower range. Based on these results, we compare the drivers of the sensitivity in different parts of the world and identify river reaches with the highest relative gradients. These river reaches might be the most affected by climate change and thus deserve an in-depth investigation of the underlying characteristics of the floodplains and the need for climate change adaptation.



中文翻译:

绘制人口暴露对洪水幅度变化的敏感性图:从局部到全球尺度的前瞻性应用

河流的洪泛区是全球人口的重要生活空间,为经济发展提供了有利的场所。但是,这些地区通常容易遭受洪水袭击,全球变暖导致的人口增加以及暴风雨的变化意味着洪水的风险有望增加。大多数研究气候变化对洪水风险影响的研究都是基于一系列的全球气候模型模拟,以及区域气候模型,水文模型,淹没模型和洪水影响模型。但是,这种方法存在不确定性。发现模型结果对气候强迫,基本模型的结构,缩小规模和偏差校正所用方法的选择敏感,以及在当前和未来气候条件下使用极值分析。此外,预计不确定性将通过模型级联传播。为了克服这些问题,我们提出了一种方法,用于分析和绘制洪泛区人口暴露对洪水强度变化的敏感性。该方法基于向下的反事实,即通过增加选定洪水的幅度来扰动选定的洪水情景,在这种情况下,可以将其解释为由于气候变化导致当今设计洪水事件的恶化。与反事实相比,当前设计洪水的影响有所增加,说明了对危害变化的敏感性。我们计算洪水暴露曲线的归一化梯度,即 相对于当前场景的暴露程度和严重程度,扰动事件的暴露程度和严重程度的增加。我们通过使用现有数据集(包括洪水灾害图,防洪标准,洪泛区划定,河网定义和空间人口分布)测试该方法在地方,国家和全球范围内的适用性。人们发现,全球范围内的梯度变化很大,在洪水幅度的较高范围内总体较小,而在较低范围内总体较小。根据这些结果,我们比较了世界不同地区敏感性的驱动因素,并确定了相对梯度最高的河流。

更新日期:2020-09-03
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