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16 年的槽垄型高能中-大潮差沙滩地形勘测。

16 years of topographic surveys of rip-channelled high-energy meso-macrotidal sandy beach.

机构信息

CNRS/Univ. de Bordeaux, UMR EPOC, Talence, France.

CNRS/Univ. de Bordeaux, UMS POREA, Talence, France.

出版信息

Sci Data. 2020 Nov 20;7(1):410. doi: 10.1038/s41597-020-00750-5.

DOI:10.1038/s41597-020-00750-5
PMID:33219249
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7679394/
Abstract

Sandy beaches are highly dynamic environments buffering shores from storm waves and providing outstanding recreational services. Long-term beach monitoring programs are critical to test and improve shoreline, beach morphodynamics and storm impact models. However, these programs are relatively rare and mostly restricted to microtidal alongshore-uniform beaches. The present 16-year dataset contains 326 digital elevation models and their over 1.635 × 10 individual sand level measurements at the high-energy meso-macrotidal rip-channelled Truc Vert beach, southwest France. Monthly to bimonthly topographic surveys, which coverage progressively extended from 300 m to over 2000 m to describe the alongshore-variable changes, are completed by daily topographic surveys acquired during a 5-week field campaign. The dataset captures daily beach response at the scale of a storm to three large cycles of interannual variability, through the impact of the most energetic winter since at least 75 years and prominent seasonal erosion/recovery cycles. The data set is supplemented with high-frequency time series of offshore wave and astronomical tide data to facilitate its future use in beach research.

摘要

沙滩是极具动态的环境,可以缓冲海浪对海岸的冲击,并提供极好的娱乐服务。长期的海滩监测计划对于测试和改进海岸线、海滩形态动力学和风暴影响模型至关重要。然而,这些计划相对较少,且主要限于小潮沿海岸均匀分布的海滩。本研究提供了一个包含 16 年数据的数据集,该数据集包含法国西南部高能中潮到大潮、有裂流槽的特鲁夫特角海滩的 326 个数字高程模型及其超过 1635×10 的单个沙层测量值。每月至每两个月进行一次地形测量,覆盖范围从 300 米逐渐扩展到 2000 米以上,以描述沿滩的变化情况,同时在为期 5 周的野外考察中每天进行地形测量。该数据集通过记录至少 75 年来最强烈的冬季和显著的季节性侵蚀/恢复周期对风暴的影响,捕捉到了每天的海滩响应规模的情况。该数据集还补充了近海波浪和天文潮汐的高频时间序列数据,以方便未来在海滩研究中的使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60ae/7679394/dc1e58493522/41597_2020_750_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60ae/7679394/c75c6387f65c/41597_2020_750_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60ae/7679394/4f1bf667757b/41597_2020_750_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60ae/7679394/dc1e58493522/41597_2020_750_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60ae/7679394/c75c6387f65c/41597_2020_750_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60ae/7679394/4f1bf667757b/41597_2020_750_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/60ae/7679394/dc1e58493522/41597_2020_750_Fig3_HTML.jpg

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厄尔尼诺对全球海岸线位置变化的影响。
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A 1.2 Billion Pixel Human-Labeled Dataset for Data-Driven Classification of Coastal Environments.一个用于基于数据驱动的海岸环境分类的 12 亿像素人类标注数据集。
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