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Article(id=1224796865573573074, tenantId=1146029695717560320, journalId=1149651085930835976, issueId=1224796862020997568, articleNumber=null, orderNo=null, doi=10.12284/hyxb2022037, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1620576000000, receivedDateStr=2021-05-10, revisedDate=1632758400000, revisedDateStr=2021-09-28, acceptedDate=1644508800000, acceptedDateStr=2022-02-11, onlineDate=1769943928378, onlineDateStr=2026-02-01, pubDate=1653408000000, pubDateStr=2022-05-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769943928378, onlineIssueDateStr=2026-02-01, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769943928378, creator=13701087609, updateTime=1769943928378, updator=13701087609, issue=Issue{id=1224796862020997568, tenantId=1146029695717560320, journalId=1149651085930835976, year='2022', volume='44', issue='6', pageStart='1', pageEnd='163', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769943927531, creator=13701087609, updateTime=1769995987693, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1225015218229624878, tenantId=1146029695717560320, journalId=1149651085930835976, issueId=1224796862020997568, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1225015218229624879, tenantId=1146029695717560320, journalId=1149651085930835976, issueId=1224796862020997568, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=140, endPage=149, ext={EN=ArticleExt(id=1224796865888145880, articleId=1224796865573573074, tenantId=1146029695717560320, journalId=1149651085930835976, language=EN, title=Types and statistical analysis of rip currents at 14 beaches in the Guangdong Province, columnId=1194652705852465724, journalTitle=Haiyang Xuebao, columnName=Article, runingTitle=null, highlight=null, articleAbstract=

Rip current is a kind of high speed offshore current which occurs frequently in coastal. Based on Castelle’s rip current classification model, Google Earth images of 14 beaches in Guangdong Province were interpreted and classified, and the application of rip current classification model in remote sensing images was discussed. The results show that in high risk months, the composition of rip currents is relatively simple, the mixed rips are few, and the bathymetrically-controlled rip currents play a dominant role, and the number of rip currents remains at a high level. In the middle risk months, the proportion of hydrodynamically-controlled rip currents and mixed rips increases, while the proportion of bathymetrically-controlled rip currents still has a certain proportion. There is little or no rip currents in low-risk months. The structure of rip current is closely related to the state of beach. For the beach in the bay, the length of the bay affects the number of rip currents, and the concave degree of the bay affects the composition ratio of different types of rip currents. Considering the limitations of experimental conditions, this law needs to be further studied and verified in combination with the actual terrain and landform. The classification results obtained according to the interpretation criteria presented in this paper are in good agreement with the risk evaluation model proposed by previous authors, which further demonstrates the effectiveness of the interpretation criteria and provides some reference value for the future research and classification of rip currents.

, correspAuthors=Zhiqiang Li, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright © 2022 Pratacultural Science. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Pengpeng Hu, Zhiqiang Li, Daoheng Zhu, Qianxin Su, Gaocong Li), CN=ArticleExt(id=1224796869654630966, articleId=1224796865573573074, tenantId=1146029695717560320, journalId=1149651085930835976, language=CN, title=广东省14个海滩裂流类型及统计特征分析, columnId=1149698756456657529, journalTitle=海洋学报, columnName=论文, runingTitle=null, highlight=null, articleAbstract=

裂流是沿海地区频发的一种高速离岸流。根据Castelle的裂流分类模型对广东省14个海滩的Google Earth影像进行了解译分类，对裂流分类模型在遥感影像中的应用进行了探讨。结果表明：在高风险月份，海滩裂流的组成较为单一，混合型裂流少，水深控制的裂流占据主导地位，裂流数量总体维持在较高水平；在中风险月份，水动力控制的裂流和混合型裂流的比例增加，水深控制的裂流仍然占有一定比例；在低风险月份，裂流很少出现，甚至无裂流发生。裂流的类型与海滩的状态密切相关。对于海湾中的海滩，海湾长度影响裂流的数量，海湾凹入度影响不同类型裂流的组成比例。考虑到实验条件的限制，这一规律需要后续结合实际地形地貌进行更细致和深入的研究和验证。按照本文解译标准得出的分类结果，与前人提出的裂流风险性评价模型吻合度较高，进一步说明了该解译标准的有效性，为今后对海滩裂流的研究和分类工作提供了一定的参考。

, correspAuthors=李志强, authorNote=null, correspAuthorsNote=
李志强（1974-），男，湖南省郴州市人，博士，教授，主要从事海岸资源与环境研究。E-mail：
, copyrightStatement=版权所有©《海洋学报》编辑部 2022
胡鹏鹏，李志强，朱道恒，等. 广东省14个海滩裂流类型及统计特征分析[J]. 海洋学报，2022，44(6)：140–149Hu Pengpeng，Li Zhiqiang，Zhu Daoheng, et al. Types and statistical analysis of rip currents at 14 beaches in the Guangdong Province[J]. Haiyang Xuebao，2022, 44(6)：140–149
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胡鹏鹏（1998-），男，江西省南昌市人，主要从事海岸水动力及裂流研究。E-mail：

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胡鹏鹏（1998-），男，江西省南昌市人，主要从事海岸水动力及裂流研究。E-mail：

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胡鹏鹏（1998-），男，江西省南昌市人，主要从事海岸水动力及裂流研究。E-mail：

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Natural Hazards, 2010, 54(1): 159−175., articleTitle=null, refAbstract=null), Reference(id=1225368187278115730, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1224796865573573074, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=38, rfOrder=45, authorNames=null, journalName=null, refType=null, unstructuredReference=Gallop S L, Woodward E, Brander R W, et al. Perceptions of rip current myths from the central south coast of England[J]. Ocean & Coastal Management, 2016, 119: 14−20., articleTitle=null, refAbstract=null)], funds=[Fund(id=1225368178864341547, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1224796865573573074, awardId=42176167，41676079, language=CN, fundingSource=国家自然科学基金(42176167，41676079), fundOrder=null, country=null), Fund(id=1225368178998559287, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1224796865573573074, awardId=Q18307, language=CN, fundingSource=广东海洋大学创新强校工程项目(Q18307), fundOrder=null, country=null), Fund(id=1225368179107611198, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1224796865573573074, awardId=060302112010, language=CN, fundingSource=广东海洋大学科研启动经费项目(060302112010), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1225368169741730725, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1224796865573573074, xref=null, ext=[AuthorCompanyExt(id=1225368169758507944, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1224796865573573074, companyId=1225368169741730725, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1. School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China), AuthorCompanyExt(id=1225368169783673771, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1224796865573573074, companyId=1225368169741730725, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.广东海洋大学 电子与信息工程学院，广东 湛江 524088)])], figs=[ArticleFig(id=1225368173726318877, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1224796865573573074, language=EN, label=Fig. 1, caption=Rip current classification framework (modified from the reference [25]), figureFileSmall=iICtuwtPizX+lkVi4zhO8Q==, figureFileBig=/4NJ+zaM0mryyQr1f+fV9g==, tableContent=null), ArticleFig(id=1225368173860536613, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1224796865573573074, language=CN, label=图1, caption=裂流分类框架图（修改自文献[25]）, figureFileSmall=iICtuwtPizX+lkVi4zhO8Q==, figureFileBig=/4NJ+zaM0mryyQr1f+fV9g==, tableContent=null), ArticleFig(id=1225368173994754357, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1224796865573573074, language=EN, label=Fig. 2, caption=Interpreting signs of various types of rip currents

a. Shear instability rips; b. flash rips; c. channel rip currents; d. focused rips; e. deflection rips; f. shadow rips; g. channel-flash rips; h. focus-channel rips; i. boundary-channel rips; j. embayed-cellular rips; k. swash rips

, figureFileSmall=34HN2UhgdZVBTeTtWXbIRQ==, figureFileBig=nfKnbKsskexK622wkjwxkQ==, tableContent=null), ArticleFig(id=1225368174124777790, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1224796865573573074, language=CN, label=图2, caption=各类型裂流的特征影像

a.剪切不稳定裂流；b.瞬变裂流；c.沟槽裂流；d.聚焦型裂流；e.偏斜裂流；f.阴影区裂流；g.沟槽–瞬变裂流；h.聚焦型–沟槽裂流；i.边界–沟槽裂流；j.岬湾单元裂流；k.冲流带裂流

, figureFileSmall=34HN2UhgdZVBTeTtWXbIRQ==, figureFileBig=nfKnbKsskexK622wkjwxkQ==, tableContent=null), ArticleFig(id=1225368174238024011, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1224796865573573074, language=EN, label=Fig. 3, caption=Location diagram of the 14 beaches

Pinghai East and Pinghai West refer to the bay on the east and west of Pinghai Town, Huizhou City, Guangdong Province respectively

, figureFileSmall=hGaaOkMLAZuWo9X0QcbOCw==, figureFileBig=/BM0wxXfYZkNqewFm/6iyw==, tableContent=null), ArticleFig(id=1225368174347075922, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1224796865573573074, language=CN, label=图3, caption=14个海滩的位置示意图

平海东、平海西分别是指广东省惠州市平海镇东侧和西侧海湾

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Castelle’s rip current classification types

, figureFileSmall=null, figureFileBig=null, tableContent=
类型种类
水动力学控制的裂流剪切不稳定裂流
瞬变裂流
水深控制的裂流沟槽裂流
聚焦型裂流
边界控制的裂流偏斜裂流
阴影区裂流
混合型裂流沟槽−瞬变裂流
聚焦型−沟槽裂流
边界−沟槽裂流
岬湾单元裂流
), ArticleFig(id=1225368177740268002, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1224796865573573074, language=CN, label=表1, caption=

Castelle裂流分类类型表

, figureFileSmall=null, figureFileBig=null, tableContent=
类型种类
水动力学控制的裂流剪切不稳定裂流
瞬变裂流
水深控制的裂流沟槽裂流
聚焦型裂流
边界控制的裂流偏斜裂流
阴影区裂流
混合型裂流沟槽−瞬变裂流
聚焦型−沟槽裂流
边界−沟槽裂流
岬湾单元裂流
), ArticleFig(id=1225368177861902829, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1224796865573573074, language=EN, label=Table 2, caption=

Interpretation of various types of rip currents in images

, figureFileSmall=null, figureFileBig=null, tableContent=
种类解译标准
剪切不稳定裂流位于沿岸均匀的开阔海滩，波浪高度倾斜入射，形成离岸的裂流头和狭窄的裂流颈，或物质离岸输移使颜色变深
瞬变裂流位于沿岸均匀的开阔海滩，波浪近垂直入射，形成离岸的裂流头和狭窄的裂流颈，或物质离岸输移使颜色变深
沟槽裂流沟槽切割沙坝或阶地并在开阔海滩上规律性地呈现；沟槽两侧波浪破碎形成白沫，影像上表现为白色
聚焦型裂流入射波峰线由海向岸逐渐弯曲成韵律状，沿岸有白沫规律性凸起，在凸起附近有物质离岸输移使颜色变深
偏斜裂流波浪从刚性边界迎风面入射，物质离岸输移使颜色变深
阴影区裂流波浪从刚性边界背风面入射，在边界处白沫断裂
沟槽–瞬变裂流波浪近垂直入射，沟槽宽度很小，在沟槽附近有细小的离岸白沫
聚焦型–沟槽裂流有两层白沫(双沙坝)，外侧白沫相对不明显，内沙坝沟槽切割程度较深，沟槽宽度很大且呈现规律性分布
边界–沟槽裂流位于刚性边界附近，有沟槽，沟槽两侧有白沫
岬湾单元裂流海湾凹入度小于冲浪区宽度，海湾内白沫非常明显，在海岬两侧或中心有离岸白沫
冲流带裂流有规律地形成在滩角处，呈细条状的白沫
), ArticleFig(id=1225368177979343351, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1224796865573573074, language=CN, label=表2, caption=

影像各类型裂流解译判释表

, figureFileSmall=null, figureFileBig=null, tableContent=
种类解译标准
剪切不稳定裂流位于沿岸均匀的开阔海滩，波浪高度倾斜入射，形成离岸的裂流头和狭窄的裂流颈，或物质离岸输移使颜色变深
瞬变裂流位于沿岸均匀的开阔海滩，波浪近垂直入射，形成离岸的裂流头和狭窄的裂流颈，或物质离岸输移使颜色变深
沟槽裂流沟槽切割沙坝或阶地并在开阔海滩上规律性地呈现；沟槽两侧波浪破碎形成白沫，影像上表现为白色
聚焦型裂流入射波峰线由海向岸逐渐弯曲成韵律状，沿岸有白沫规律性凸起，在凸起附近有物质离岸输移使颜色变深
偏斜裂流波浪从刚性边界迎风面入射，物质离岸输移使颜色变深
阴影区裂流波浪从刚性边界背风面入射，在边界处白沫断裂
沟槽–瞬变裂流波浪近垂直入射，沟槽宽度很小，在沟槽附近有细小的离岸白沫
聚焦型–沟槽裂流有两层白沫(双沙坝)，外侧白沫相对不明显，内沙坝沟槽切割程度较深，沟槽宽度很大且呈现规律性分布
边界–沟槽裂流位于刚性边界附近，有沟槽，沟槽两侧有白沫
岬湾单元裂流海湾凹入度小于冲浪区宽度，海湾内白沫非常明显，在海岬两侧或中心有离岸白沫
冲流带裂流有规律地形成在滩角处，呈细条状的白沫
), ArticleFig(id=1225368178105172478, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1224796865573573074, language=EN, label=Table 3, caption=

Beach characteristics and rip risk level based on morphodynamic values

, figureFileSmall=null, figureFileBig=null, tableContent=
海滩组别泥沙沉降参数相对潮差海滩类型裂流风险
反射组Ω<2RTR<3完全反射型(R)低
RTR >7低潮台地型(LTT)低
3LRTR≤7低潮台地裂流型(LTTR)中
中间状态型2≤Ω≤5RTR <3沿岸沙坝型(B)高
3BRTR≤7低潮冲流沙坝裂流型(LTBR)高
消散型Ω>5RTR <3沙坝消散型(BD)中
3BRTR≤7无沙坝消散型(NBD)低
超消散型Ω>2RTR >7平缓超消散型(UD)低
), ArticleFig(id=1225368178222612997, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1224796865573573074, language=CN, label=表3, caption=

基于地形动力指标的滨海旅游海滩裂流风险等级表

, figureFileSmall=null, figureFileBig=null, tableContent=
海滩组别泥沙沉降参数相对潮差海滩类型裂流风险
反射组Ω<2RTR<3完全反射型(R)低
RTR >7低潮台地型(LTT)低
3LRTR≤7低潮台地裂流型(LTTR)中
中间状态型2≤Ω≤5RTR <3沿岸沙坝型(B)高
3BRTR≤7低潮冲流沙坝裂流型(LTBR)高
消散型Ω>5RTR <3沙坝消散型(BD)中
3BRTR≤7无沙坝消散型(NBD)低
超消散型Ω>2RTR >7平缓超消散型(UD)低
), ArticleFig(id=1225368178373607946, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1224796865573573074, language=EN, label=Table 4, caption=

Image statistics of 14 beaches

, figureFileSmall=null, figureFileBig=null, tableContent=
沙滩名称图像数/景无法识别/景无裂流/景有裂流/景
　　注：由于云量或光线等原因会造成某些影像无法识别。
青澳湾11137
广澳湾282026
海门湾261124
红海湾东120111
平海东264220
平海西232318
西涌海滩212415
小梅沙221147
大梅沙271147
十里银滩6024
大角湾7115
月亮湾4004
水东湾131012
东海岛231616
合计2491751176
), ArticleFig(id=1225368178440716816, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1224796865573573074, language=CN, label=表4, caption=

14个海滩的影像统计

, figureFileSmall=null, figureFileBig=null, tableContent=
沙滩名称图像数/景无法识别/景无裂流/景有裂流/景
　　注：由于云量或光线等原因会造成某些影像无法识别。
青澳湾11137
广澳湾282026
海门湾261124
红海湾东120111
平海东264220
平海西232318
西涌海滩212415
小梅沙221147
大梅沙271147
十里银滩6024
大角湾7115
月亮湾4004
水东湾131012
东海岛231616
合计2491751176
), ArticleFig(id=1225368178537185815, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1224796865573573074, language=EN, label=Table 5, caption=

Actual image number and effective image number

, figureFileSmall=null, figureFileBig=null, tableContent=
海滩名称实际影像数量/景有效影像数量/景
青澳湾109.47
广澳湾2621.18
海门湾2618.56
红海湾东1212
平海东2214.07
平海西2116.7
西涌海滩1918.43
小梅沙2222
大梅沙2625
十里银滩64.59
大角湾65.11
月亮湾43.33
水东湾129.9
东海岛2210.47
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实际影像数量和有效影像数量

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海滩名称实际影像数量/景有效影像数量/景
青澳湾109.47
广澳湾2621.18
海门湾2618.56
红海湾东1212
平海东2214.07
平海西2116.7
西涌海滩1918.43
小梅沙2222
大梅沙2625
十里银滩64.59
大角湾65.11
月亮湾43.33
水东湾129.9
东海岛2210.47
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广东省14个海滩裂流类型及统计特征分析
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胡鹏鹏 1 , 李志强 1, * , 朱道恒 1 , 苏倩欣 1 , 李高聪 1
海洋学报 | 论文 2022,44(6): 140-149
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海洋学报 | 论文 2022, 44(6): 140-149
广东省14个海滩裂流类型及统计特征分析
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胡鹏鹏1 , 李志强1, * , 朱道恒1, 苏倩欣1, 李高聪1
作者信息
  • 1.广东海洋大学 电子与信息工程学院，广东 湛江 524088

    • 胡鹏鹏（1998-），男，江西省南昌市人，主要从事海岸水动力及裂流研究。E-mail：

通讯作者:

李志强（1974-），男，湖南省郴州市人，博士，教授，主要从事海岸资源与环境研究。E-mail：
Types and statistical analysis of rip currents at 14 beaches in the Guangdong Province
Pengpeng Hu1 , Zhiqiang Li1, * , Daoheng Zhu1, Qianxin Su1, Gaocong Li1
Affiliations
  • 1. School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China
出版时间: 2022-05-25 doi: 10.12284/hyxb2022037
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摘要
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裂流是沿海地区频发的一种高速离岸流。根据Castelle的裂流分类模型对广东省14个海滩的Google Earth影像进行了解译分类，对裂流分类模型在遥感影像中的应用进行了探讨。结果表明：在高风险月份，海滩裂流的组成较为单一，混合型裂流少，水深控制的裂流占据主导地位，裂流数量总体维持在较高水平；在中风险月份，水动力控制的裂流和混合型裂流的比例增加，水深控制的裂流仍然占有一定比例；在低风险月份，裂流很少出现，甚至无裂流发生。裂流的类型与海滩的状态密切相关。对于海湾中的海滩，海湾长度影响裂流的数量，海湾凹入度影响不同类型裂流的组成比例。考虑到实验条件的限制，这一规律需要后续结合实际地形地貌进行更细致和深入的研究和验证。按照本文解译标准得出的分类结果，与前人提出的裂流风险性评价模型吻合度较高，进一步说明了该解译标准的有效性，为今后对海滩裂流的研究和分类工作提供了一定的参考。

裂流分类  /  目视解译  /  风险评价  /  海滩状态

Rip current is a kind of high speed offshore current which occurs frequently in coastal. Based on Castelle’s rip current classification model, Google Earth images of 14 beaches in Guangdong Province were interpreted and classified, and the application of rip current classification model in remote sensing images was discussed. The results show that in high risk months, the composition of rip currents is relatively simple, the mixed rips are few, and the bathymetrically-controlled rip currents play a dominant role, and the number of rip currents remains at a high level. In the middle risk months, the proportion of hydrodynamically-controlled rip currents and mixed rips increases, while the proportion of bathymetrically-controlled rip currents still has a certain proportion. There is little or no rip currents in low-risk months. The structure of rip current is closely related to the state of beach. For the beach in the bay, the length of the bay affects the number of rip currents, and the concave degree of the bay affects the composition ratio of different types of rip currents. Considering the limitations of experimental conditions, this law needs to be further studied and verified in combination with the actual terrain and landform. The classification results obtained according to the interpretation criteria presented in this paper are in good agreement with the risk evaluation model proposed by previous authors, which further demonstrates the effectiveness of the interpretation criteria and provides some reference value for the future research and classification of rip currents.

rip current classification  /  visual interpretation  /  risk estimate  /  beach state
胡鹏鹏, 李志强, 朱道恒, 苏倩欣, 李高聪. 广东省14个海滩裂流类型及统计特征分析. 海洋学报, 2022 , 44 (6) : 140 -149 . DOI: 10.12284/hyxb2022037
Pengpeng Hu, Zhiqiang Li, Daoheng Zhu, Qianxin Su, Gaocong Li. Types and statistical analysis of rip currents at 14 beaches in the Guangdong Province[J]. Haiyang Xuebao, 2022 , 44 (6) : 140 -149 . DOI: 10.12284/hyxb2022037
正文
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1 引言
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裂流是一种起源于破波带的快速离岸水流[1]，是近岸环流重要的组成部分。它可以将泥沙等沉积物带离近岸，是泥沙向海输移的重要途径[2-3]。裂流不仅会加剧局部海岸线和沙丘的侵蚀，对沿海基础设施造成破坏，而且会严重威胁海滩上游客的生命安全[4]。陆旭等[5]建立了我国海滩事故数据库，发现除海浪和潮汐外，裂流很可能是造成溺水事故的重要因素。由于裂流没有引起海滩游客的广泛关注，甚至有游客将其当成是快速进入深水区的通道，以致海滩安全事故频繁发生，因此裂流逐渐引起许多学者和专家的重视[6-7]。国际上已经有许多国家开展了对裂流的观测与研究，如美国国家气象局（NWS）已经开始预报美国所有海滩的裂流情况[8]，韩国气象局建立了一套预报系统以实现对裂流的监测[9]，我国原国家海洋局海洋减灾中心也在2018年联合各部门对全国重点滨海旅游海滩的裂流灾害风险进行了排查和警示工作[10]。
破波带受到波浪、潮汐、地形等影响随时都在变化，产生于破波带的裂流往往也具有一定的随机性，另外在破波带进行现场观测是困难和昂贵的，而且也具有相当的危险性。因此，使用高空视频或者遥感影像对裂流进行研究有着巨大的优势并受到一些学者的青睐。Lin等[11-12]使用航空影像和架设在高处的摄像机获得了台湾福隆海滩的图像，目视解译了影像中海滩的沙坝形态和裂流位置，并研究了福隆海滩类型和裂流分布的短期和长期变动。Maryan等[13]比较了Viola-Jones算法、卷积神经网络算法和元学习器对裂流槽图像数据集的识别效果。Silva等[14]在Google Earth上分别提取了1 740张含裂流和700张不含裂流的图像，使用Faster R-CNN和自定义时间聚合算法对裂流图像进行了识别检测，识别精度高于人类和其他裂流检测算法。本研究利用前人提出的裂流分类理论，基于Google Earth的历史影像对广东省沿岸14个主要旅游海滩的裂流类型进行了分类，并开展了海滩形态及海湾形状与裂流关系的初步分析。
2 裂流的分类
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Shepard[15]在1936年提出“裂流”这一概念，并将其描述为快速流向大海的水流。Long和Özkan-Haller[16]通过总结前人的研究[17-19]指出裂流是由于岸滩入射波高的变化和波浪增减水在沿岸方向分布不均匀，导致水位分布不均匀，从而产生辐射应力驱动生成的。
根据裂流发生的原因，Short和Brander[3,20-21]提出4种裂流类型：淤积型海滩裂流、侵蚀型海滩裂流、地形裂流和巨型裂流。淤积型海滩裂流通常在某一位置固定出现数日至数周，侵蚀型海滩裂流则倾向于沿海岸迁移，两者统称为开阔海岸裂流。地形裂流是指由地形结构控制的裂流，经常发生在丁坝、海岬等地形附近[22-23]。巨型裂流是一种大型地形裂流，与开阔海岸裂流相比可以延伸3～4个破波带的宽度。
Dalrymple等[24]把裂流分为两类: 水深控制的裂流和水动力控制的裂流，并将常见的海滩形态具体描述为具有下切沟槽的沿岸沙坝−沟槽型海滩和具有下切沟槽的阶地型海滩。另外，在陡峭的海滩滩角处发现了一种常见的裂流类型，被称为冲流带裂流。它是波浪上升过程中由冲流过程驱动的裂流，对海滩游客和海岸的危害有限。水动力控制的裂流仅仅是由水动力机制驱动的，有着更小的离岸强度和范围。
Castelle等[25]认为以是否出现在开阔海岸或者海湾海滩来区分裂流是具有误导性的。实际上许多海湾海滩上发生的裂流并不都靠近岬角或海岸结构，它们表现出与开阔海岸裂流相同的特征。所有的裂流都是由水动力驱动的，但是控制机制的主导程度不同。Castelle等[25]根据控制机制所占的主导地位，将裂流分为三大类。由于物理驱动机制不同，又进一步将每个类别分为两种类型，总共有6种基本类型。在现实场景中，各种驱动机制往往结合在一起，因此 Castelle等[25]又定义了4种混合型裂流类型（表1），冲流带裂流因为危害程度有限没有被列入分类中。Castelle等[25]结合了其他裂流分类的优点，侧重于从控制机制的主导程度对裂流进行区分，考虑到海湾海滩也会产生类似开阔海岸裂流的特征并针对现实情况提出了4种混合型裂流类型，为理解和预测裂流提供了进一步的框架。
本文选取了广东省14个主要旅游海滩，在Google Earth上对每个海滩的历史影像中的裂流进行目视解译，采用Castelle的裂流分类模型对裂流进行了分类。冲流带裂流是这14个海滩明显的特征，因此也被考虑在本文的分类之中。
3 方法和数据
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3.1 裂流解译标准
不同的裂流种类有着不同的驱动机制，研究海滩上裂流种类的组成对研究海滩的状态有重要的参考价值。目前，对于裂流分类的理论研究非常成熟，裂流的分类模型也在不断完善。本文以Castelle提出的裂流分类框架为基础（图1），根据其对每种裂流形态的描述作为解译的依据，在Google Earth上查看各个海滩的历史影像，根据分类理论选取每种裂流的特征影像（图2），提出针对影像的裂流解译标准（表2）。
3.2 基于地形动力指标的裂流风险评价模型
Wright等[26]对澳大利亚一系列的海滩进行现场调查后提出了无量纲沉降速率参数（Ω）模型。后来，Masselink和Short[27]在Ω模型的基础上引入了相对潮差（$ \mathrm{R}\mathrm{T}\mathrm{R} $），建立了更系统和全面的分类模型。李志强等[28-30]结合Ω模型和$ \mathrm{R}\mathrm{T}\mathrm{R} $模型提出了一种操作简单，能定量区分裂流出现的可能性的裂流风险性评价方法。该方法受到人们广泛的认可，现在已经应用在浴场选址、裂流风险排查等项目中。这两个参数的定义为
$ Q ={H}_{b}/{T}{{W}}_{s},$
$ \mathrm{R}\mathrm{T}\mathrm{R}=\mathrm{T}\mathrm{R}/{H}_{b},$
式中，$ {H}_{b} $是局地平均破波波高；$ T $是平均波周期；$ {W}_{s} $表示泥沙沉降速率，$ \mathrm{T}\mathrm{R} $是大潮平均潮差。根据参数Ω和$ \mathrm{R}\mathrm{T}\mathrm{R} $的阈值范围可将海滩划分为4组8类，并且各自对应不同的裂流风险等级（表3）[31]。基于地形动力的海滩状态分类模型基本能够反映海滩对波浪、潮汐、泥沙输移等因素的响应[31]，已经被地质地貌领域专家广泛接受。
3.3 数据获取
本文选取广东省沿岸14个主要旅游海滩，如图3所示，其中有9个是原国家海洋局2018年进行裂流灾害风险排查的广东省重点滨海旅游海滩。Google Earth的影像时间跨度为2002–2021年。使用Google Earth测量工具，测量每个海滩的长度，测量时以海滩向陆边界为界限来减少因潮汐导致的海滩长度的变化。采用表2中的解译标准通过目视解译法将各海滩出现过的裂流从历史影像中判别出来，再进行分类并统计，共获得7334个不同类型的裂流。需要注意的是在裂流发生时，一些狭长的裂流会参差交错，因此本文认为当多个裂流交叉最后一起向海流出时，视为一个裂流。表4是对14个海滩的Google Earth影像进行统计的结果。
某些海滩的Google Earth影像不能完全覆盖，往往是由多年的历史影像拼接而成，所以影像的数量不能表示完整海滩的数量。针对不完整的海滩影像，按照影像中海滩的长度占原始海滩长度的比例计算每景影像的有效影像数量（表5），平均裂流数等于当月的海滩裂流总数与有效影像数量的比值。
4 裂流的统计特征
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4.1 逐月分类结果与裂流风险评价模型对比
本研究收集到青澳湾、广澳湾、红海湾东、西涌海滩、十里银滩、大角湾、月亮湾以及东海岛8个海滩的裂流风险等级，数据来自广东省裂流风险排查报告[32]以及李志强和陈杏文[30]的结果。选取广澳湾、月亮湾和东海岛为例进行讨论，它们分别是这8个海滩中有效影像数量最多、最少和位于中间值的海滩。
广澳湾的有效影像数量最多，影像多分布在1月、9月、10月、11月和12月份，涵盖了风险评价模型对该海滩分类的全部范围，4月和7月缺少影像数据。如图4所示，广澳湾在1–9月裂流风险等级为高，10–12月为中，整体上看属于裂流风险危害性较大的海滩。从图中可以看出，裂流风险等级为高的月份，其裂流的组成较为单一，混合型裂流较少，水深控制的裂流组成比例最大，而且其平均数量明显大于裂流风险等级为中的月份，符合中间状态海滩的特征。查看排查报告中的Ω值和RTR值发现10–12月的海滩为沙坝消散型海滩，虽然该海滩风险等级为中，但在RTR值与其他各月在相同范围内的情况下，其Ω值的计算结果接近分界值，裂流风险也相对较高，10–12月的平均裂流数量也反映出这一点，这可能是平均裂流数没有与1月表现出明显差距的一个原因。本研究针对广澳湾的裂流分类在一定程度上符合了其裂流风险型性评价方法。
东海岛的有效影像数量相对广澳湾少了近一半，而且主要分布在风险评价等级为中的月份，在低等级月份无影像，在高等级月份的有效影像数量仅为0.5个。从图5可以发现，3月水深控制的裂流比例最大，其他月份水动力控制的裂流或混合型裂流所占比例较大。除去1月和9月，剩下中等级月份的平均裂流数量接近持平。4月、10–12月的平均裂流数量差距不大，并且水深控制的裂流比例较低，符合中风险等级的海滩评价, 但由于缺乏其他风险等级的海滩影像，所以导致对东海岛的裂流分类与评价模型无法完全对应。
月亮湾仅有4景影像（图6）。在10月，月亮湾冲流带裂流的平均裂流数量很多，与其他海滩的冲流带裂流不同的是，其波浪的冲流过程更强，对海滩的侵蚀程度更大。不过由于该海滩的有效影像数量少，能解译出的裂流数量非常有限，因此对比效果最差。
通过上述研究发现，裂流分类结果与裂流评价模型的对比效果与有效影像数量有直接的关系，对比效果越好，海滩的有效影像数量越大。不过在图4中5月、8月和图5中1月、9月的平均裂流数量与同裂流风险等级的月份差距很大，另外也出现图4中8月份裂流全部由混合裂流组成的情况，这与该月有效影像数量较少有关。
4.2 海滩裂流的类型结构
剪切不稳定裂流和瞬变裂流在影像上主要是通过波浪的入射角度来区分。Google Earth的一些影像由于图像质量等原因无法分辨入射波方向。另外水动力控制的裂流在空间和时间上都是短暂的，因此不可预测。受条件的限制，解译上述裂流时不作区分，统一解译为大类裂流。根据各种裂流占总裂流的比例计算百分比，图7为绘制的14个海滩的裂流类型结构图，下面对结构图进行分析。
冲流带裂流被认为是危害程度最低的裂流类型。从结构图可以看出，冲流带裂流频率最高的前3个海滩分别是：小梅沙、月亮湾和大梅沙，均超过了80%，其中小梅沙海滩完全由冲流带裂流组成，大梅沙海滩有少部分由水动力控制的裂流，月亮湾有比例很低的沟槽裂流。大梅沙、小梅沙海滩的裂流主要由冲流带裂流组成，从裂流结构上很容易判断其裂流危险性较低。东海岛无冲流带裂流。
水动力控制的裂流具有不可预测性，而且持续时间相对较短，一般为3～5 min，裂流的离岸强度相比于沟槽裂流较弱，但危险性依然很高。青澳湾、平海西和东海岛的水动力控制的裂流的组成比例最高，其他海滩除了小梅沙、大角湾和月亮湾均有这种裂流类型，而且比例低于沟槽裂流。
沟槽裂流与中等海滩状态相关联[33]，是记录最多、理解最透彻的裂流类型[34]，结构图也反映出这一点。除大梅沙、小梅沙外，其他海滩均出现了沟槽裂流，并且是主要的结构类型。沟槽−瞬变裂流、聚焦型−沟槽裂流和边界−沟槽裂流形成时也受到沟槽的影响，加上这些裂流后由沟槽影响的裂流比重将进一步扩大。
聚焦型−沟槽裂流一般出现在双沙坝系统的海滩上，波浪折射并且穿过外沙坝导致波浪破碎后，内沙坝深度的沿岸变化进一步引起破波高沿岸变化，驱动了这种裂流的产生。青澳湾和东海岛的聚焦型−沟槽裂流的组成比例较高，说明相对来说这两个海滩容易出现双沙坝系统。
边界控制的裂流如偏斜裂流和阴影区裂流的比例很小。一方面是由于边界裂流只在刚性边界产生，产生所需条件相对苛刻；另一方面是因为人工建筑少和大多数海滩的岬角弧形过大，不易产生。平海东海滩长14.4 km，包括3个岬角距离很小的海湾海滩，因此在平海东海滩会产生边界控制的裂流和岬湾单元裂流，特别是岬湾单元裂流，只出现在海湾海滩中。另外，广澳湾、大角湾也有一定比例的偏斜裂流和阴影区裂流。
其他裂流类型由于比例过小或特征不够典型而不作具体分析。通过上述分析可知，裂流的类型结构与海滩的状态密切相关，受沟槽影响的裂流与水动力控制的裂流是海滩上最容易出现的裂流类型。
4.3 海湾形状与裂流
研究的14个海滩中有11个位于受岬角控制的海湾中，其中十里银滩、水东湾和东海岛是无岬角控制的弧形海滩。两端岬角之间的距离为海湾长度；海滩最凹处至海湾长度线上的垂直距离为海湾凹入度，使用Google Earth测量工具测量海滩长度以及海湾长度和海湾凹入度。
从图8的变化趋势看，年平均裂流数随着海滩长度而变化，但在水东湾和东海岛处有较大偏差。广澳湾于2013年开始修建防坡堤，改变了该海湾自然岬角对海滩的控制，因此对广澳湾的海湾长度修正为防坡堤两端的距离，海湾长度也相应改变。排除非岬角海湾后发现，年平均裂流数与海湾长度变化趋势的符合程度更高（图9）。
图10显示，海滩出现冲流带裂流的比例与海湾凹入度存在一定的负相关性，而沟槽裂流则刚好相反。不过也有例外，如海湾凹入度小的青澳湾的冲流带裂流比例也很低。水动力控制的裂流和凹入度在青澳湾、广澳湾、海门湾、红海湾东、平海东、平海西、西涌海滩的变化趋势相同，在大小梅沙、大角湾和月亮湾无明显关系。
上述统计规律显示，海湾长度越长的海滩出现的裂流数量越多，海湾凹入度可以影响裂流的组成比例，海湾凹入度越长的海湾，沟槽裂流所占的比例越高。不过考虑研究条件的限制，这种裂流与海湾长度和凹入度的关系还有待进一步的研究。
5 结论和展望
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随着海滩逐渐成为许多人出游热门目的地，海滩事故的发生率也逐渐增加。作为“海滩第一杀手”[35-37]的裂流成为众多学者关注和研究的对象。裂流的产生原因和分类也一直是众多学者研究的重要课题。各种裂流的离岸强度有所区别，对裂流进行分类可以帮助理解海滩裂流的类型结构，与裂流风险性评价模型互为补充。不同的裂流类型会产生不同的视觉特征，例如沟槽裂流，与两侧的浅沙坝相比，中间波浪破碎相对较少，在视觉上呈现为一个更暗、更平静的水域，缺乏经验的游客通常会误以为这片较平静的水域是最安全的游泳场所[38]。提高公众对各类裂流的认识，有助于降低海滩事故的伤亡概率。
本文选取了广东省14个海滩作为研究对象，根据Castelle提出的裂流分类理论在Google Earth上选取特征影像，提出了针对影像的裂流解译标准，随后对各个海滩的历史影像进行解译分类，获得了裂流分类数据。裂流逐月分类结果符合裂流风险性评价模型，从裂流的逐月分布来看，在裂流风险等级为高的月份中，海滩裂流的组成结构较为单一，混合型裂流少，水深控制的裂流比例大于其他裂流类型；当裂流风险等级降为中时，水动力控制的裂流和混合型裂流的组成比例增加，水深控制的裂流的比例相应减少；在裂流风险等级为低的月份出现的裂流很少，甚至无裂流发生。裂流的类型结构与海滩的状态密切相关，受沟槽影响的裂流和水动力控制的裂流是海滩上主要的裂流类型。通过统计还发现海湾形状与裂流产生具有一定的规律：海湾长度和海湾凹入度分别会影响裂流出现的数量和不同类型裂流的组成比例。
在使用Google Earth进行研究时遇到了一些限制因素，如：（1）海滩的影像不完整，有些海滩往往是由多景影像拼接而成的；（2）影像的时间不连续，甚至某些月份的影像缺失，以致于每个海滩的历史影像数量不一致；（3）影像质量优劣不一导致识别困难。针对第一点和第三点，本文在解译和统计时提出了一些相应的解决办法：对于海滩影像的不完整，使用有效影像数计算平均裂流数量；解译剪切不稳定裂流和瞬变裂流时不作区分，归类为大类。另外，由于解译裂流受到解译者主观判断的影响较大，以后可以通过现场观测的方式训练解译者的识别能力，并提出一套基于现场观测的裂流解译标准来提高裂流解译的精确性。
对数据进行分析后发现，裂流逐月分类结果与有效影像数量有直接的关系，有效影像数最多的广澳湾的裂流逐月分类结果与裂流风险评价模型吻合相对较好。裂流在破波带产生，受破波带状态和宽度的限制，裂流的识别对影像的时间和空间分辨率要求很高。目前，常用的高分辨率卫星有美国的Worldview、法国的SPOT等。我国也在2016年发射了高分三号卫星，其空间分辨率能达到1 m。如果增加统计影像的数量，可以提高分类结果与裂流风险性评价模型的吻合程度。裂流的分类对海滩研究具有重要的意义，在后期的工作中进一步提高影像数量和解译精度，将获得更为精确的分类效果。
近年来，图像分类与目标检测技术成为计算机领域研究的热点。已经有学者将卷积神经网络技术应用在沟槽裂流的识别上，识别结果比人类识别结果精度更高[14]。不同类型的裂流具有不同特征，使用图像识别技术对各类裂流进行分类也是未来的研究方向之一。
基金
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  • 国家自然科学基金(42176167，41676079)
  • 广东海洋大学创新强校工程项目(Q18307)
  • 广东海洋大学科研启动经费项目(060302112010)
文献
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2022年第44卷第6期
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doi: 10.12284/hyxb2022037
  • 接收时间：2021-05-10
  • 首发时间：2026-02-01
  • 出版时间：2022-05-25
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  • 收稿日期：2021-05-10
  • 修回日期：2021-09-28
  • 录用日期：2022-02-11
基金
国家自然科学基金(42176167，41676079)
广东海洋大学创新强校工程项目(Q18307)
广东海洋大学科研启动经费项目(060302112010)
作者信息
    1.广东海洋大学 电子与信息工程学院，广东 湛江 524088

通讯作者:

李志强（1974-），男，湖南省郴州市人，博士，教授，主要从事海岸资源与环境研究。E-mail：
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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