US-CoastEX:美国风暴潮和海平面极端值的基于观测的概率再分析。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Joao Morim, D J Rasmussen, Thomas Wahl, Francisco M Calafat, Robert E Kopp, Michael Oppenheimer, Soenke Dangendorf
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引用次数: 0

摘要

风暴潮和海平面极端值的可靠估计以及适当的不确定性量化是成本效益高的风险/适应规划的关键。然而,由于数据缺乏,大多数海岸线上的观测估计往往无法获得或不确定。在这里,我们提供了一个完全由观测驱动的美国海岸风暴潮和海平面极端事件概率数据集(US-CoastEX)(1950-2020)。通过将贝叶斯方法应用于美国验潮仪网络,并辅以常用验潮仪数据中不可用的额外风暴数据,为测量和未测量的站点生成了非平稳极端风暴潮分布。这些分布与潮汐峰值数据相结合,以估计极端海平面的重现期和水平及其不确定性。我们的研究结果表明,基于现有模型数据的传统逐点估算,以及对标准潮汐计数据的区域汇总分析,已经将美国大部分沿海地区100年来的极端海平面(平均)低估了50%,特别是在遭受极端风暴的地区。这些数据支持海岸管理人员做出决策,特别是在现场海平面监测有限的脆弱地区。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
US-CoastEX: Observation-based probabilistic reanalysis of storm surge and sea level extremes for the United States.

Reliable estimates of storm surge and sea level extremes with proper uncertainty quantification are key for cost-effective risk/adaptation planning. However, observational estimates are often unavailable or uncertain along most coastlines owing to data scarcity. Here, we provide a fully observational-driven probabilistic dataset (US-CoastEX) of storm surge and sea level extremes for the U.S. coast (1950-2020). Non-stationary extreme storm surge distributions are generated for gauged and ungauged sites by applying Bayesian methods to the U.S. tide gauge network, complemented with additional storm data unavailable in commonly used tide gauge data. The distributions are combined with tidal peak data to estimate return periods and levels of extreme sea levels and their uncertainty. Ou results show that traditional site-by-site estimates based on existing model data, as well as regionally-aggregated analysis of standard tide gauge data, have underestimated 100-year extreme sea levels by 50% (on average) along much of the U.S. coast, especially in regions exposed to extreme storms. The data supports coastal managers to make decisions, especially in vulnerable areas where in-situ sea-level monitoring is limited.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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