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Uncertainties in Measuring Populations Potentially Impacted by Sea Level Rise and Coastal Flooding

机译:在测量的不确定性群体可能受到海平面上升和沿海洪灾受影响

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摘要

A better understanding of the impact of global climate change requires information on the locations and characteristics of populations affected. For instance, with global sea level predicted to rise and coastal flooding set to become more frequent and intense, high-resolution spatial population datasets are increasingly being used to estimate the size of vulnerable coastal populations. Many previous studies have undertaken this by quantifying the size of populations residing in low elevation coastal zones using one of two global spatial population datasets available – LandScan and the Global Rural Urban Mapping Project (GRUMP). This has been undertaken without consideration of the effects of this choice, which are a function of the quality of input datasets and differences in methods used to construct each spatial population dataset. Here we calculate estimated low elevation coastal zone resident population sizes from LandScan and GRUMP using previously adopted approaches, and quantify the absolute and relative differences achieved through switching datasets. Our findings suggest that the choice of one particular dataset over another can translate to a difference of more than 7.5 million vulnerable people for countries with extensive coastal populations, such as Indonesia and Japan. Our findings also show variations in estimates of proportions of national populations at risk range from <0.1% to 45% differences when switching between datasets, with large differences predominantly for countries where coarse and outdated input data were used in the construction of the spatial population datasets. The results highlight the need for the construction of spatial population datasets built on accurate, contemporary and detailed census data for use in climate change impact studies and the importance of acknowledging uncertainties inherent in existing spatial population datasets when estimating the demographic impacts of climate change.
机译:要更好地了解全球气候变化的影响,就需要有关受影响人口的位置和特征的信息。例如,由于预计全球海平面将上升,而沿海洪灾将变得更加频繁和激烈,因此越来越多地使用高分辨率空间人口数据集来估算脆弱的沿海人口的数量。之前的许多研究都是通过使用两个可用的全球空间人口数据集之一(LandScan和全球农村城市制图项目(GRUMP))对居住在低海拔沿海地区的人口规模进行量化来进行的。进行此操作时并未考虑此选择的影响,该影响取决于输入数据集的质量以及用于构建每个空间总体数据集的方法的差异。在这里,我们使用先前采用的方法通过LandScan和GRUMP计算估计的低海拔沿海地区居民人口规模,并量化通过切换数据集获得的绝对和相对差异。我们的发现表明,对于印度尼西亚和日本等沿海人口众多的国家而言,选择一个特定的数据集可能会导致750万脆弱人群的差异。我们的发现还显示,在数据集之间进行切换时,处于风险范围内的全国人口比例估计值的差异在<0.1%至45%之间,差异很大,主要是因为在构建空间人口数据集时使用了粗略和过时的输入数据的国家。结果突出表明,需要在准确,现代和详细的普查数据的基础上构建空间人口数据集,以用于气候变化影响研究;在评估气候变化的人口影响时,必须认识到现有空间人口数据集固有的不确定性。

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    Pinki Mondal; Andrew J. Tatem;

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  • 年(卷),期 -1(7),10
  • 年度 -1
  • 页码 e48191
  • 总页数 7
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