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2013 年冬季塔霍河羽状流的亚潮差变化。

Subtidal variability of the Tagus river plume in winter 2013.

机构信息

CESAM, Departamento de Física, Universidade de Aveiro, Campus de Santiago, 3810-193 Aveiro, Portugal.

Hidromod, Rua Rui Teles Palhinha, n° 4 1° Andar, 2740-278 Porto, Salvo, Portugal.

出版信息

Sci Total Environ. 2018 Jun 15;627:1353-1362. doi: 10.1016/j.scitotenv.2018.01.325. Epub 2018 Feb 20.

DOI:10.1016/j.scitotenv.2018.01.325
PMID:30857099
Abstract

The subtidal variability of surface plume response to local wind and river runoff is studied off central Portugal using a hindcast model for the Tagus Estuary (TE) plume. In addition to the model, the main patterns of variability of the TE plume were extracted in an automated mode using Self Organizing Maps (SOM). Three characteristic spatial patterns are extracted in a 3 × 2 SOM array: in synoptic time scales, northward and southward propagation of the plume and transitional periods. Results suggest that river runoff and wind induce strong variability in the coastal ocean, and that the TE plume has a rapid response to their changes. Under high river runoff there is a southern propagation of the plume, even during downwelling favorable winds, revealing bi-directional patterns due to the combined effect of estuarine outflow, wind direction and coastline in establishment the plume direction. The same is found during transitional periods, revealing that the TE plume has significant influence both north and south of the estuary mouth. SOM was applied for the first time to extract spatial and temporal features of the TE plume and prove to be a reliable tool to study large quantities of data in addition to the model results analysis.

摘要

本研究使用塔霍河口(TE)羽状流的回溯模型,研究了葡萄牙中部近海羽状流对局部风和河流径流的表面羽状流响应的亚潮变动。除了模型之外,还使用自组织映射(SOM)以自动化模式提取了 TE 羽状流的主要变动模式。在 3×2 SOM 数组中提取了三个特征空间模式:在天气尺度上,羽状流的向北和向南传播以及过渡时期。结果表明,河流径流量和风向会在沿海水域引起强烈的变动,并且 TE 羽状流对其变化的响应迅速。在高径流量下,即使在有利于下降流的风期间,羽状流也会向南传播,这揭示了由于河口流出、风向和海岸线的共同作用,导致羽状流方向的双向模式。在过渡时期也发现了同样的情况,这表明 TE 羽状流对河口以北和以南都有重大影响。SOM 首次被应用于提取 TE 羽状流的时空特征,并被证明是一种可靠的工具,除了模型结果分析之外,还可以用于研究大量数据。

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