A comparison of two global datasets of extreme sea levels and resulting flood exposure
A comparison of two global datasets of extreme sea levels and resulting flood exposure
复制标题
DOI：
10.1002/2016ef000430
复制
发表时间：
2017-04-01
期刊：
影响因子：
8.2
通讯作者：
Ward, Philip J.
中科院分区：
文献类型：
--
作者：
Muis, Sanne;Verlaan, Martin;Ward, Philip J.
估计当前沿海洪水的风险需要有关极端海平面的充分信息。十多年来，唯一可用的全球数据是 DINAS-COAST 极端海平面 (DCESL) 数据集，该数据集应用静态近似来估计极端海平面。最近，开发了一个动态导出的数据集：全球潮汐和浪涌再分析（GTSR）数据集。在这里，我们比较两个数据集。 DCESL 和 GTSR 之间的差异通常大于 GTSR 的置信区间。与观察到的极值相比，DCESL 通常高估了极值，平均偏差为 0.6m。 GTSR 的平均偏差为 -0.2m，通常会低估极端情况，特别是在热带地区。动态交互式脆弱性评估模型用于根据陆地面积和一百年一遇海平面以下的人口来计算当前的洪水风险。当基于 GTSR 而非 DCESL 时，全球暴露人口减少了 28%。考虑到当时可用的数据有限，DCESL 提供了对世界各地极端事件空间变化的良好估计。然而，GTSR 可以改进对沿海洪水影响的评估，包括置信界限。我们通过纠正海平面极端值和陆地海拔的相互冲突的垂直基准，进一步改进了对沿海影响的评估，这在之前的全球评估中没有得到考虑。将极端海平面转换为用于高程数据的相同垂直参考被证明是一个关键步骤，导致人口暴露估计值提高 39-59%。
Estimating the current risk of coastal flooding requires adequate information on extreme sea levels. For over a decade, the only global data available was the DINAS-COAST Extreme Sea Levels (DCESL) dataset, which applies a static approximation to estimate extreme sea levels. Recently, a dynamically derived dataset was developed: the Global Tide and Surge Reanalysis (GTSR) dataset. Here, we compare the two datasets. The differences between DCESL and GTSR are generally larger than the confidence intervals of GTSR. Compared to observed extremes, DCESL generally overestimates extremes with a mean bias of 0.6m. With a mean bias of -0.2m GTSR generally underestimates extremes, particularly in the tropics. The Dynamic Interactive Vulnerability Assessment model is applied to calculate the present-day flood exposure in terms of the land area and the population below the 1 in 100-year sea levels. Global exposed population is 28% lower when based on GTSR instead of DCESL. Considering the limited data available at the time, DCESL provides a good estimate of the spatial variation in extremes around the world. However, GTSR allows for an improved assessment of the impacts of coastal floods, including confidence bounds. We further improve the assessment of coastal impacts by correcting for the conflicting vertical datum of sea-level extremes and land elevation, which has not been accounted for in previous global assessments. Converting the extreme sea levels to the same vertical reference used for the elevation data is shown to be a critical step resulting in 39-59% higher estimate of population exposure.