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Article(id=1200450369563259478, tenantId=1146029695717560320, journalId=1149651085930835976, issueId=1200450365842903349, articleNumber=null, orderNo=null, doi=10.12284/hyxb2024061, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1702569600000, receivedDateStr=2023-12-15, revisedDate=1715616000000, revisedDateStr=2024-05-14, acceptedDate=1717516800000, acceptedDateStr=2024-06-05, onlineDate=1764139271392, onlineDateStr=2025-11-26, pubDate=1719676800000, pubDateStr=2024-06-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1764139271392, onlineIssueDateStr=2025-11-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1764139271392, creator=13701087609, updateTime=1764139271392, updator=13701087609, issue=Issue{id=1200450365842903349, tenantId=1146029695717560320, journalId=1149651085930835976, year='2024', volume='46', issue='6', pageStart='1', pageEnd='140', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=0, articleOrder=1, issueType=-1, specialIssue=null, createTime=1764139270505, creator=13701087609, updateTime=1764139468823, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1200451197711806771, tenantId=1146029695717560320, journalId=1149651085930835976, issueId=1200450365842903349, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1200451197711806772, tenantId=1146029695717560320, journalId=1149651085930835976, issueId=1200450365842903349, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=98, endPage=113, ext={EN=ArticleExt(id=1200450369827500647, articleId=1200450369563259478, tenantId=1146029695717560320, journalId=1149651085930835976, language=EN, title=Spatial and temporal variations of sediment flux entering into the South China Sea from 2001 to 2020, columnId=1194652705852465724, journalTitle=Haiyang Xuebao, columnName=Article, runingTitle=null, highlight=null, articleAbstract=

Under the influence of human activities and rapid climate change, the fluvial sediments flux entering into the South China Sea (SCS) has changed greatly. Based on the hydrological data of rivers around the SCS and sea surface Suspended Sediment Concentration data from 2001 to 2020, this study investigated spatial and temporal variation of sediment flux entering into the SCS. The results show that the sediment flux entering into the SCS exceeds 345 Mt/a during 2001−2020. Human activities result in a reduction of 300 Mt/a in sediment flux from the Zhujiang River, Red River and Mekong River. The sediment flux is also affected by typhoons and climate change: typhoons are the most important factor affecting the sediment flux of small rivers, and the sediment flux of the Gaoping River during the typhoon can reach 89% of total. Under the influence of the East Asian monsoon system, the sediment flux entering into the SCS characterized by significant seasonal variations, the sediment flux is high in wet season and low in dry season. During the wet season, the sediment flux entering into the SCS accounted for more than 80% of total, accordingly, the river plume has the typical characteristics of high concentration and large diffusion range in the wet season. Under the influence of El Niño-Southern Oscillation, the discharge and sediment flux into the sea around the South China Sea also have different periodic changes. The river discharge and sediment flux of large rivers around the South China Sea show a 2.5−3.0 a period, and are correlated with the NIÑO3.4 index, while the sediment flux Taiwan rivers has no obvious period on the interannual scale. Based on data of the 20 a, this study systematically demonstrates the influences of extreme weather, climate change and dam construction on the sediment flux entering into SCS since the 21st century, which is of great significance in the study of source-to-sink processes and watershed management.

, correspAuthors=Yanwei Zhang, authorNote=null, correspAuthorsNote=null, copyrightStatement=Haiyang Xuebao, 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=Yafei Sun, Yanwei Zhang, Danni Lü, Weihan Ruan, Zhiyuan Zhuang, Liuzhenyi Zhang), CN=ArticleExt(id=1200450375192015747, articleId=1200450369563259478, tenantId=1146029695717560320, journalId=1149651085930835976, language=CN, title=2001−2020年南海周边河流入海沉积物通量时空变化特征研究, columnId=1149698756456657529, journalTitle=海洋学报, columnName=论文, runingTitle=null, highlight=null, articleAbstract=

在人类活动和快速气候变化的影响下，南海周边河流入海沉积物通量发生巨大变化。本文基于2001−2020年间南海周边河流数据及海表悬浮沉积物浓度数据，探究南海周边河流入海沉积物通量的时空变化特征。研究结果表明：2001−2020年南海周边河流入海沉积物通量超过345 Mt/a，人类活动导致珠江、红河、湄公河入海沉积物通量减少约300 Mt/a。南海周边河流入海沉积物通量还受台风和气候变化的影响：台风是影响南海周边河流入海沉积物通量的重要因素，尤其是台风期间台湾地区的高屏溪入海沉积物通量可达全年总量的89%。在东亚季风系统影响下，南海周边河流入海沉积物通量表现出雨季高、旱季低的变化特征，雨季期间入海沉积物通量占全年总量的80%以上，相应地，河流表层羽流在雨季具有浓度高和扩散范围大的典型特征。受厄尔尼诺−南方涛动的影响，南海周边河流流量和入海沉积物通量也存在不同周期变化，南海周边大型河流入海沉积物通量表现出2.5～3.0 a的变化周期，并与NIÑO3.4指数存在相关性。本文利用20 年的河流数据系统论证了台风、气候变化和大坝建设对21世纪以来南海周边河流入海沉积物通量的影响，在源汇过程研究及流域治理方面具有重要意义。

, correspAuthors=张艳伟, authorNote=null, correspAuthorsNote=
*张艳伟（1984—），女，山东省菏泽市人，教授，研究方向为深海沉积过程的观测和海洋多尺度动力过程。E-mail：
, copyrightStatement=版权所有©《海洋学报》编辑部 2024
孙亚非，张艳伟，吕丹妮，等. 2001−2020年南海周边河流入海沉积物通量时空变化特征研究[J]. 海洋学报，2024，46(6)：98–113Sun Yafei，Zhang Yanwei，Lü Danni, et al. Spatial and temporal variations of sediment flux entering into the South China Sea from 2001 to 2020[J]. Haiyang Xuebao，2024, 46(6)：98–113
, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=0k3wxHwqMJzE/GwUZVllTg==, magXml=kWhDbIuPA9Wt9NIixh0ppg==, pdfUrl=null, pdf=mfVlS/DUGTQTc8t6Jn/WTg==, pdfFileSize=2764587, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=i0+QC3A1P/sReXQSA+eNAg==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=WQUiMyAvBC1fAPRKjbHNag==, mapNumber=null, authorCompany=null, fund=null, authors=

孙亚非（1998—），女，山东省济南市人，研究方向为海洋沉积输运过程。E-mail：

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孙亚非（1998—），女，山东省济南市人，研究方向为海洋沉积输运过程。E-mail：

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孙亚非（1998—），女，山东省济南市人，研究方向为海洋沉积输运过程。E-mail：

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label=图1, caption=南海周边主要河流系统及其入海沉积物通量（单位：Mt/a）（a）和本文统计结果与Liu等[8]历史沉积物通量差值（单位：Mt/a）（b）, figureFileSmall=MZbGAwWmUkk7n9u1BQWo7g==, figureFileBig=K7Bbg3K3zYvIxnSXOWcPjQ==, tableContent=null), ArticleFig(id=1200860898630955717, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=EN, label=Fig. 2, caption=Annual average sediment flux of Mekong River (a) and Red River (b) from 2001 to 2020, figureFileSmall=ra71hj3PyzLog6R6DIqXLA==, figureFileBig=48XcWc6bY7Ad+TSe7t3Pjg==, tableContent=null), ArticleFig(id=1200860898727424713, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=CN, label=图2, caption=2001−2020年湄公河（a）和红河（b）年平均入海沉积物通量序列, figureFileSmall=ra71hj3PyzLog6R6DIqXLA==, figureFileBig=48XcWc6bY7Ad+TSe7t3Pjg==, tableContent=null), ArticleFig(id=1200860898828088013, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=EN, label=Fig. 3, 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figureFileSmall=iuGXxeEbVUUmihz776VTlg==, figureFileBig=ANDjroKw3nygoY3iDd+dAQ==, tableContent=null), ArticleFig(id=1200860899360764641, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=EN, label=Fig. 5, caption=Proportion of sediment flux of Zhuoshui River, Gaoping River and other rivers in southwest Taiwan, figureFileSmall=o9Q+IsvPTC8Qwga4IGssew==, figureFileBig=8S5R+sTxphKp2tD7gWLHhg==, tableContent=null), ArticleFig(id=1200860899478205158, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=CN, label=图5, caption=台湾西南地区浊水溪、高屏溪和该区域其他河流入海沉积物通量, figureFileSmall=o9Q+IsvPTC8Qwga4IGssew==, figureFileBig=8S5R+sTxphKp2tD7gWLHhg==, tableContent=null), ArticleFig(id=1200860899612422888, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=EN, label=Fig. 6, caption=Time series for daily average pressure (a), precipitation (b), discharge and sediment flux (c) at Liling-Bridge Station for Gaoping River from 2001 to 2020, figureFileSmall=GMFRY1B0aAPEYeo3gcw78w==, figureFileBig=8Tqg92nydXM2D2hV6pn2qw==, tableContent=null), ArticleFig(id=1200860899708891882, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=CN, label=图6, caption=2001−2020年高屏溪里岭大桥测站处日平均气压（a），降水量（b），流量和入海沉积物通量（c）时间序列, figureFileSmall=GMFRY1B0aAPEYeo3gcw78w==, figureFileBig=8Tqg92nydXM2D2hV6pn2qw==, tableContent=null), ArticleFig(id=1200860899813749487, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=EN, label=Fig. 7, caption=The wavelet power spectra of the daily average discharge (a) and sediment flux (b) of Gaoping River, figureFileSmall=s30stCKYDgPEvrFl3UMIqQ==, figureFileBig=gs0rujyRs/0szKFRoHoFeA==, tableContent=null), ArticleFig(id=1200860899918607090, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=CN, label=图7, caption=高屏溪日平均流量（a）和入海沉积物通量（b）小波功率谱, figureFileSmall=s30stCKYDgPEvrFl3UMIqQ==, figureFileBig=gs0rujyRs/0szKFRoHoFeA==, tableContent=null), ArticleFig(id=1200860900061213430, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=EN, label=Fig. 8, caption=Sediment flux in different periods of Gaoping River, figureFileSmall=TtlkjBzOzGCsCubKFwPAKg==, figureFileBig=qz2AY+4lsXRIpYyLb28EhQ==, tableContent=null), ArticleFig(id=1200860900187042553, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=CN, label=图8, caption=高屏溪不同时期入海沉积物通量对比, figureFileSmall=TtlkjBzOzGCsCubKFwPAKg==, figureFileBig=qz2AY+4lsXRIpYyLb28EhQ==, tableContent=null), ArticleFig(id=1200860900245762812, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=EN, label=Fig. 9, caption=Time series of daily average pressure (a), precipitation (b), discharge and sediment flux (c) at Liling-Bridge Station for Gaoping River before and after Typhoon “Soudelor” in 2015, figureFileSmall=3N1q3g3VQldrMy13PJnsTA==, figureFileBig=Wdi64mUAhRlG6ugb6Mg8vQ==, tableContent=null), ArticleFig(id=1200860900338037504, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=CN, label=图9, caption=2015年台风“苏迪罗”前后高屏溪−里岭大桥站日平均气压（a）、降水（b）、流量和入海沉积物通量（c）序列, figureFileSmall=3N1q3g3VQldrMy13PJnsTA==, figureFileBig=Wdi64mUAhRlG6ugb6Mg8vQ==, tableContent=null), ArticleFig(id=1200860900438700802, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=EN, label=Fig. 10, caption=Variations of Sea surface SSC during the pre-typhoon period (a), typhoon-active period (b), typhoon-influence period (c) and post-typhoon period (d), figureFileSmall=RJlhKP9KLtYodd/jOdM/6A==, figureFileBig=jmG6RGYAmL32LrT40KjoPg==, tableContent=null), ArticleFig(id=1200860900530975492, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=CN, label=图10, caption=台风影响前(a)，活动期(b)，影响期(c)和台风后(d)的海表悬浮物浓度, figureFileSmall=RJlhKP9KLtYodd/jOdM/6A==, figureFileBig=jmG6RGYAmL32LrT40KjoPg==, tableContent=null), ArticleFig(id=1200860900627444487, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=EN, label=Fig. 11, caption=Time series of daily average discharge and sediment flux for Zhujiang River (a) and Red River (b) from 2001 to 2020, figureFileSmall=k0HdwDjfUOB/bkKBLAn8yQ==, figureFileBig=TUwgcAAxTHR4cpEf7QUMfA==, tableContent=null), ArticleFig(id=1200860900740690698, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=CN, label=图11, caption=2001−2020年珠江（a）、红河（b）日平均流量和入海沉积物通量时间序列, figureFileSmall=k0HdwDjfUOB/bkKBLAn8yQ==, figureFileBig=TUwgcAAxTHR4cpEf7QUMfA==, tableContent=null), ArticleFig(id=1200860901898318604, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=EN, label=Fig. 12, caption=Sediment flux in different periods of Zhujiang River (a) and Red River (b), figureFileSmall=/JqaFS3cSsM2U/PmYUoWJg==, figureFileBig=9qYK8suPR9l6qAnYm1E7vg==, tableContent=null), ArticleFig(id=1200860902053507855, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=CN, label=图12, caption=珠江(a)和红河(b)不同时期入海沉积物输运量占比, figureFileSmall=/JqaFS3cSsM2U/PmYUoWJg==, figureFileBig=9qYK8suPR9l6qAnYm1E7vg==, tableContent=null), ArticleFig(id=1200860902191919891, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=EN, label=Fig. 13, caption=Proportion of sediment flux of Zhujiang River (a), Gaoping River (b), Red River (c) and Mekong River (d) in dry and wet season, figureFileSmall=TmVtlaGKBPknVG6L/EUwCg==, figureFileBig=MHyt3ZbbndzQc/5kCIjoEA==, tableContent=null), ArticleFig(id=1200860902460355351, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=CN, label=图13, caption=珠江（a），高屏溪（b），红河（c）和湄公河（d）旱、雨季入海沉积物通量占比, figureFileSmall=TmVtlaGKBPknVG6L/EUwCg==, figureFileBig=MHyt3ZbbndzQc/5kCIjoEA==, tableContent=null), ArticleFig(id=1200860902569407257, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=EN, label=Fig. 14, caption=Distribution of seasurface SSC in wet season (a), dry season (b) and difference between wet season and dry season of Gulf of Thailand (c), figureFileSmall=DbKxkji7qJUn3ZF7jPGLpQ==, figureFileBig=g73OmXZI+VYwcc1LHgSJKA==, tableContent=null), ArticleFig(id=1200860902712013596, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=CN, label=图14, caption=泰国湾雨季（a）、旱季（b）海表悬浮体浓度以及雨季和旱季的差值（c）, figureFileSmall=DbKxkji7qJUn3ZF7jPGLpQ==, figureFileBig=g73OmXZI+VYwcc1LHgSJKA==, tableContent=null), ArticleFig(id=1200860902858814238, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=EN, label=Fig. 15, caption=The wavelet power spectra for monthly average discharge (a) and sediment flux (b); the low pass filtered seriesfor discharge (c) and sediment flux (d); cross wavelet spectra for discharge (e) and sediment flux (f) between the NIÑO3.4 Index hydrological series of the Zhujiang River, figureFileSmall=ImfjmD9P5CtYKgWiOuQr+Q==, figureFileBig=7A51CEtwp4Ugkfogw/2BrQ==, tableContent=null), ArticleFig(id=1200860903039169314, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=CN, label=图15, caption=2001−2020年珠江月平均流量（a）和入海沉积物通量（b）序列小波功率谱；流量（c）和入海沉积物通量（d）低通滤波序列小波功率谱；流量（e）和入海沉积物通量（f）与NIÑO3.4指数的交叉小波谱, figureFileSmall=ImfjmD9P5CtYKgWiOuQr+Q==, figureFileBig=7A51CEtwp4Ugkfogw/2BrQ==, tableContent=null), ArticleFig(id=1200860903139832613, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=EN, label=Fig. 16, caption=Cross wavelet spectra for Red River (a) and Mekong River (b) between sediment flux and the NIÑO3.4 Index, figureFileSmall=cZOPt2V3ZKhxrPh0s7CDaw==, figureFileBig=oNNZqC40OVeVH9e4MbmUqA==, tableContent=null), ArticleFig(id=1200860903232107304, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=CN, label=图16, caption=2001−2020年红河（a）和湄公河（b）入海沉积物通量与NIÑO3.4指数的交叉小波谱, figureFileSmall=cZOPt2V3ZKhxrPh0s7CDaw==, figureFileBig=oNNZqC40OVeVH9e4MbmUqA==, tableContent=null), ArticleFig(id=1200860903328576299, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=EN, label=Fig. 17, caption=The cross wavelet spectra for Chao Phraya River (a) and Rajang River (b) between river dischargeand the NIÑO3.4 Index, figureFileSmall=PtVIoi8O+wq5mXdlD7Y1qQ==, figureFileBig=I/Ten0ddEcu2rmd7z6o44g==, tableContent=null), ArticleFig(id=1200860903420850989, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=CN, label=图17, caption=2001−2020年湄南河（a）和拉让江（b）流量与NIÑO3.4指数的交叉小波谱, figureFileSmall=PtVIoi8O+wq5mXdlD7Y1qQ==, figureFileBig=I/Ten0ddEcu2rmd7z6o44g==, tableContent=null), ArticleFig(id=1200860903550874418, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=EN, label=Table 1, caption=

Sediment flux of rivers around the SCS

, figureFileSmall=null, figureFileBig=null, tableContent=
地区河流流域面积/
（103 km2）		Liu等[8]发布
入海通量/（Mt·a−1）		2001−2020平均
入海通量/（Mt·a−1）		数据源时间分辨率
台湾地区浊水溪354.172.69台湾水文年报每月30 ± 2个
北港溪0.62.22.91台湾水文年报
朴子溪0.32.10.80台湾水文年报
八掌溪0.46.33.10台湾水文年报
急水溪0.21.81.44台湾水文年报
曾文溪1.225.15.24台湾水文年报
盐水溪0.11.10.24台湾水文年报
二仁溪0.230.24.61台湾水文年报
高屏溪34930.37台湾水文年报
东港溪0.20.40.34台湾水文年报
林边溪0.33.31.76台湾水文年报
华南地区九龙江153.12.46Alain Isabwe[18]公报数据为每月平均
文献数据为每年平均
韩江30101.70王宇飞[19]
榕江4.40.28敖亮挺[20]
珠江45084.328.98中国河流泥沙公报
漠阳江6.10.81.26蔡绪军[21]
鉴江9.51.51.20张义宇[22]
南流江6.61.10.66珠江片河流泥沙公报
大风江1.90.36罗亚飞等[23]
钦江2.50.26欧芳兰等[24]
茅岭江2.90.32亢振军等[25]
南渡江6.61.10.29珠江片河流泥沙公报
昌化江5.10.080.59珠江片河流泥沙公报
万泉河3.70.16珠江片河流泥沙公报
中南半岛Thai Binh（太平江）151Milliman和Farnsworth[1]
Red River（红河）12013023.39Dethier等[6]每年平均
Ma（马江）283Milliman和Farnsworth[1]
Ca（蓝江）2743.70Phuong等[26]多年平均
Thu-bon（秋盆河）102Milliman和Farnsworth[1]
SaiGon（西贡河）4430.43Dethier等[6]每年平均
Mekong（湄公河）79016034.40Chua和Lu[29]每年平均
泰国中部Petch（碧武里河）6Milliman和Farnsworth[1]
Mae Klong（美功河）318.10.80Dethier等[6]每年平均
Chao Phraya（湄南河）160116.96Dethier等[6]每年平均
Tha Chin（塔他河）30.12Milliman和Farnsworth[1]
Bangpakhlong（邦巴功河）10Milliman和Farnsworth[1]
马来半岛Pattani（北大年河）40.35Milliman和Farnsworth[1]
Kelantan（吉兰丹河）1213.9Milliman和Farnsworth[1]
Terengganu（登嘉楼河）3.30.8Milliman和Farnsworth[1]
Pahang（彭亨河）1920.412.11Dethier等[6]每年平均
Johor（柔佛河）2.60.07Latif等[27]多年平均
苏门答腊岛Rokan（罗坎河）190.98Dethier等[6]每年平均
Siak（夏克河）16Milliman和Farnsworth[1]
Kampar（甘巴河）360.65Dethier等[6]每年平均
Inderagiri（因德拉吉里河）22Milliman和Farnsworth[1]
Hari（哈里河）505.90Dethier等[6]每年平均
Musi（穆西河）61Milliman和Farnsworth[1]
加里曼丹岛Segama（昔加末河）6Milliman和Farnsworth[1]
Padas（巴达斯河）5Milliman和Farnsworth[1]
Baram（巴拉姆河）22.81224.00Prabakaran等[28]多年平均
Kidurong（都东河）5.4Milliman和Farnsworth[1]
Rajang（拉让江）513053.86Dethier等[6]
Lupar（卢帕河）7Milliman和Farnsworth[1]每年平均
吕宋岛Cagayan（卡加延河）30.41.53Dethier等[6]每年平均
Agno（阿格诺河）6.34.7Liu等[8]
Pampanga（邦板牙河）8.63.6Liu等[8]
Angat（安加特河）0.64.6Liu等[8]
), ArticleFig(id=1200860903680897843, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=CN, label=表1, caption=

南海周边河流沉积物通量数据

, figureFileSmall=null, figureFileBig=null, tableContent=
地区河流流域面积/
（103 km2）		Liu等[8]发布
入海通量/（Mt·a−1）		2001−2020平均
入海通量/（Mt·a−1）		数据源时间分辨率
台湾地区浊水溪354.172.69台湾水文年报每月30 ± 2个
北港溪0.62.22.91台湾水文年报
朴子溪0.32.10.80台湾水文年报
八掌溪0.46.33.10台湾水文年报
急水溪0.21.81.44台湾水文年报
曾文溪1.225.15.24台湾水文年报
盐水溪0.11.10.24台湾水文年报
二仁溪0.230.24.61台湾水文年报
高屏溪34930.37台湾水文年报
东港溪0.20.40.34台湾水文年报
林边溪0.33.31.76台湾水文年报
华南地区九龙江153.12.46Alain Isabwe[18]公报数据为每月平均
文献数据为每年平均
韩江30101.70王宇飞[19]
榕江4.40.28敖亮挺[20]
珠江45084.328.98中国河流泥沙公报
漠阳江6.10.81.26蔡绪军[21]
鉴江9.51.51.20张义宇[22]
南流江6.61.10.66珠江片河流泥沙公报
大风江1.90.36罗亚飞等[23]
钦江2.50.26欧芳兰等[24]
茅岭江2.90.32亢振军等[25]
南渡江6.61.10.29珠江片河流泥沙公报
昌化江5.10.080.59珠江片河流泥沙公报
万泉河3.70.16珠江片河流泥沙公报
中南半岛Thai Binh（太平江）151Milliman和Farnsworth[1]
Red River（红河）12013023.39Dethier等[6]每年平均
Ma（马江）283Milliman和Farnsworth[1]
Ca（蓝江）2743.70Phuong等[26]多年平均
Thu-bon（秋盆河）102Milliman和Farnsworth[1]
SaiGon（西贡河）4430.43Dethier等[6]每年平均
Mekong（湄公河）79016034.40Chua和Lu[29]每年平均
泰国中部Petch（碧武里河）6Milliman和Farnsworth[1]
Mae Klong（美功河）318.10.80Dethier等[6]每年平均
Chao Phraya（湄南河）160116.96Dethier等[6]每年平均
Tha Chin（塔他河）30.12Milliman和Farnsworth[1]
Bangpakhlong（邦巴功河）10Milliman和Farnsworth[1]
马来半岛Pattani（北大年河）40.35Milliman和Farnsworth[1]
Kelantan（吉兰丹河）1213.9Milliman和Farnsworth[1]
Terengganu（登嘉楼河）3.30.8Milliman和Farnsworth[1]
Pahang（彭亨河）1920.412.11Dethier等[6]每年平均
Johor（柔佛河）2.60.07Latif等[27]多年平均
苏门答腊岛Rokan（罗坎河）190.98Dethier等[6]每年平均
Siak（夏克河）16Milliman和Farnsworth[1]
Kampar（甘巴河）360.65Dethier等[6]每年平均
Inderagiri（因德拉吉里河）22Milliman和Farnsworth[1]
Hari（哈里河）505.90Dethier等[6]每年平均
Musi（穆西河）61Milliman和Farnsworth[1]
加里曼丹岛Segama（昔加末河）6Milliman和Farnsworth[1]
Padas（巴达斯河）5Milliman和Farnsworth[1]
Baram（巴拉姆河）22.81224.00Prabakaran等[28]多年平均
Kidurong（都东河）5.4Milliman和Farnsworth[1]
Rajang（拉让江）513053.86Dethier等[6]
Lupar（卢帕河）7Milliman和Farnsworth[1]每年平均
吕宋岛Cagayan（卡加延河）30.41.53Dethier等[6]每年平均
Agno（阿格诺河）6.34.7Liu等[8]
Pampanga（邦板牙河）8.63.6Liu等[8]
Angat（安加特河）0.64.6Liu等[8]
), ArticleFig(id=1200860903781561144, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=EN, label=Table 2, caption=

Standard error of satellite observation data (calculated from discharge and sediment concentration data, data from Dethier[6])

, figureFileSmall=null, figureFileBig=null, tableContent=
中南半岛泰国中部马来半岛苏门答腊岛加里曼丹岛吕宋岛
红河西贡河美功河湄南河彭亨河罗坎河甘巴河哈里河拉让江卡加延河
误差0.150.200.170.230.150.180.170.130.140.18
), ArticleFig(id=1200860903903195962, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=CN, label=表2, caption=

卫星观测数据标准误差（由流量和沉积物浓度数据误差计算得出，Dethier等[6]研究）

, figureFileSmall=null, figureFileBig=null, tableContent=
中南半岛泰国中部马来半岛苏门答腊岛加里曼丹岛吕宋岛
红河西贡河美功河湄南河彭亨河罗坎河甘巴河哈里河拉让江卡加延河
误差0.150.200.170.230.150.180.170.130.140.18
), ArticleFig(id=1200860904058385212, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=EN, label=Table 3, caption=

Sediment rating parameters (log10(a) and b) and determination (r2) in part of rivers around the SCS

, figureFileSmall=null, figureFileBig=null, tableContent=
河流地区log10(a)br2
高屏溪台湾地区1.901.340.75
珠江[36]华南地区−7.402.570.88
红河（1988—2008年）[38]中南半岛−7.522.000.73
红河（2009—2020年）[38]中南半岛−6.401.610.80
湄公河[29]中南半岛−1.151.870.77
), ArticleFig(id=1200860904200991550, tenantId=1146029695717560320, journalId=1149651085930835976, articleId=1200450369563259478, language=CN, label=表3, caption=

南海周边部分河流水沙关系曲线参数(log10(a)、b)及决定系数(r2)

, figureFileSmall=null, figureFileBig=null, tableContent=
河流地区log10(a)br2
高屏溪台湾地区1.901.340.75
珠江[36]华南地区−7.402.570.88
红河（1988—2008年）[38]中南半岛−7.522.000.73
红河（2009—2020年）[38]中南半岛−6.401.610.80
湄公河[29]中南半岛−1.151.870.77
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2001−2020年南海周边河流入海沉积物通量时空变化特征研究
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孙亚非 1 , 张艳伟 1, * , 吕丹妮 1 , 阮威涵 1 , 庄志远 1 , 张刘祯怡 1
海洋学报 | 论文 2024,46(6): 98-113
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海洋学报 | 论文 2024, 46(6): 98-113
2001−2020年南海周边河流入海沉积物通量时空变化特征研究
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孙亚非1 , 张艳伟1, * , 吕丹妮1, 阮威涵1, 庄志远1, 张刘祯怡1
作者信息
  • 1.同济大学 海洋地质国家重点实验室，上海 200092

    • 孙亚非（1998—），女，山东省济南市人，研究方向为海洋沉积输运过程。E-mail：

通讯作者:

*张艳伟（1984—），女，山东省菏泽市人，教授，研究方向为深海沉积过程的观测和海洋多尺度动力过程。E-mail：
Spatial and temporal variations of sediment flux entering into the South China Sea from 2001 to 2020
Yafei Sun1 , Yanwei Zhang1, * , Danni Lü1, Weihan Ruan1, Zhiyuan Zhuang1, Liuzhenyi Zhang1
Affiliations
  • 1. State Key Laboratory of Marine Geology, Tongji University, Shanghai 200092, China
出版时间: 2024-06-30 doi: 10.12284/hyxb2024061
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摘要
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在人类活动和快速气候变化的影响下，南海周边河流入海沉积物通量发生巨大变化。本文基于2001−2020年间南海周边河流数据及海表悬浮沉积物浓度数据，探究南海周边河流入海沉积物通量的时空变化特征。研究结果表明：2001−2020年南海周边河流入海沉积物通量超过345 Mt/a，人类活动导致珠江、红河、湄公河入海沉积物通量减少约300 Mt/a。南海周边河流入海沉积物通量还受台风和气候变化的影响：台风是影响南海周边河流入海沉积物通量的重要因素，尤其是台风期间台湾地区的高屏溪入海沉积物通量可达全年总量的89%。在东亚季风系统影响下，南海周边河流入海沉积物通量表现出雨季高、旱季低的变化特征，雨季期间入海沉积物通量占全年总量的80%以上，相应地，河流表层羽流在雨季具有浓度高和扩散范围大的典型特征。受厄尔尼诺−南方涛动的影响，南海周边河流流量和入海沉积物通量也存在不同周期变化，南海周边大型河流入海沉积物通量表现出2.5～3.0 a的变化周期，并与NIÑO3.4指数存在相关性。本文利用20 年的河流数据系统论证了台风、气候变化和大坝建设对21世纪以来南海周边河流入海沉积物通量的影响，在源汇过程研究及流域治理方面具有重要意义。

南海  /  河流入海沉积物通量  /  大坝建设  /  气候变化

Under the influence of human activities and rapid climate change, the fluvial sediments flux entering into the South China Sea (SCS) has changed greatly. Based on the hydrological data of rivers around the SCS and sea surface Suspended Sediment Concentration data from 2001 to 2020, this study investigated spatial and temporal variation of sediment flux entering into the SCS. The results show that the sediment flux entering into the SCS exceeds 345 Mt/a during 2001−2020. Human activities result in a reduction of 300 Mt/a in sediment flux from the Zhujiang River, Red River and Mekong River. The sediment flux is also affected by typhoons and climate change: typhoons are the most important factor affecting the sediment flux of small rivers, and the sediment flux of the Gaoping River during the typhoon can reach 89% of total. Under the influence of the East Asian monsoon system, the sediment flux entering into the SCS characterized by significant seasonal variations, the sediment flux is high in wet season and low in dry season. During the wet season, the sediment flux entering into the SCS accounted for more than 80% of total, accordingly, the river plume has the typical characteristics of high concentration and large diffusion range in the wet season. Under the influence of El Niño-Southern Oscillation, the discharge and sediment flux into the sea around the South China Sea also have different periodic changes. The river discharge and sediment flux of large rivers around the South China Sea show a 2.5−3.0 a period, and are correlated with the NIÑO3.4 index, while the sediment flux Taiwan rivers has no obvious period on the interannual scale. Based on data of the 20 a, this study systematically demonstrates the influences of extreme weather, climate change and dam construction on the sediment flux entering into SCS since the 21st century, which is of great significance in the study of source-to-sink processes and watershed management.

South China Sea  /  fluvial sediment flux into the sea  /  dam construction  /  climate change
孙亚非, 张艳伟, 吕丹妮, 阮威涵, 庄志远, 张刘祯怡. 2001−2020年南海周边河流入海沉积物通量时空变化特征研究. 海洋学报, 2024 , 46 (6) : 98 -113 . DOI: 10.12284/hyxb2024061
Yafei Sun, Yanwei Zhang, Danni Lü, Weihan Ruan, Zhiyuan Zhuang, Liuzhenyi Zhang. Spatial and temporal variations of sediment flux entering into the South China Sea from 2001 to 2020[J]. Haiyang Xuebao, 2024 , 46 (6) : 98 -113 . DOI: 10.12284/hyxb2024061
正文
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1 引言
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全球河流每年将约150 × 108～200 × 108 t沉积物输运至边缘海，是深海沉积物的重要来源[1]。河流入海沉积物是颗粒有机碳的主要载体[2]，全球河流每年向海输运约200 Mt颗粒有机碳，河流入海沉积物通量的变化，会导致颗粒有机碳入海通量变化，进而影响全球碳循环[3]。河流入海沉积物通量的变化也会引发许多问题，入海沉积物通量的增加会导致河道淤积，入海沉积物通量的降低会导致河口三角洲的侵蚀[4]，营养物质输入减少，进而影响河口地区生物多样性[5]。近60年，全球河流入海沉积物通量发生了显著变化，很多学者从人类活动和极端天气、气候变化等方面进行研究，解释了河流入海沉积物通量在时间尺度上变化的原因[6–7]。
南海（South China Sea, SCS）是西太平洋接收陆源碎屑沉积物最多的边缘海[8]。南海北部沉积物主要来源于亚洲大陆，主要输入河流包括珠江、红河等；南海东部和东北部沉积物主要来源于台湾岛和吕宋岛的河流；南海西、南部沉积物主要来源于中南半岛和加里曼丹岛，主要输入河流包括湄公河和拉让江等。这些沉积物为研究南海有机碳等的源汇过程提供了重要依据[9]。
南海周边河流入海沉积物通量受人类活动、台风、夏季风降水和厄尔尼诺−南方涛动（El Niño and Southern Oscillation, ENSO）影响显著。大坝建设等人类活动显著减少河流入海沉积物通量，1992年曼湾大坝建设导致湄公河入海沉积物通量由150～170 Mt/a下降至110 Mt/a[10]。南海是受台风影响最强烈的边缘海，台风引起的极端降水会导致河流入海沉积物通量显著增加，Hung等[11]估算了2009年台风“莫拉克”对台湾浊水溪、曾文溪和高屏溪入海沉积物通量的影响，台风期间3条河流向南海输运超过750 Mt沉积物，占3条河流当年总量的90%以上。南海处于亚洲东南部季风地带，东亚夏季风降水对南海沉积物源区的物质供应具有明显的控制作用[9]，河流入海沉积物通量表现出雨季高、旱季低的特征[12]。在年际尺度上，夏季风降水量、河流入海沉积物通量受ENSO影响，Hu等[13]认为ENSO通过影响亚洲夏季风持续时间进而影响降水量，Liu等[14]发现1960−2002年珠江河流径流量、入海沉积物通量在2～4.6 a尺度的周期变化与ENSO活动高度相关。
河流羽流、重力流以及近底沉积物输运是河流沉积物向海输运的主要方式[15]。相较于后二者，河流羽流在全球范围内的沿海地区均可被观测，21世纪以来，通过卫星遥感获取的大范围、长时间海洋表层悬浮沉积物浓度（Suspended Sediment Concentration, SSC）数据，也为评估沉积物通过羽流向海输运过程提供了数据支撑。Guo等[16]认为河流流量是控制黄河羽流扩散面积的主要因素。Mertes和 Warrick[17]利用SeaWiFS数据计算出1998−1999年台风期间加利福尼亚北部河流通过表层羽流的输运量，尽管通过表层羽流的输运量仅占总通量的1%～2%，但这些羽流覆盖面积可达30000 km2，扩散距离可超过30 km，携带沉积物远距离输运。
尽管已经认识到人类活动、极端天气和气候变化对南海周边河流入海沉积物通量的重要影响，但对南海周边河流入海沉积物通量时间变化的研究多局限于单个流域，南海沉周边河流入海积物通量的整体认识主要基于Milliman和Farnsworth[1]发布的代表大规模大坝建设前的沉积物通量，缺少21世纪以来南海周边河流入海沉积物通量变化的最新研究。本文整理了2001−2020年南海周边河流入海沉积物通量数据、海表悬浮沉积物浓度数据，统计分析了南海周边河流入海沉积物通量的时空变化特征，并探讨大坝建设、气候变化和台风等因素对入海沉积物通量的影响，为近20年快速气候变化背景下南海周边河流入海沉积物通量响应提供数据支撑。
2 数据和方法
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2.1 数据来源
本文使用的河流水文数据来源于中国河流泥沙公报(http://www.mwr.gov.cn/)、珠江片河流泥沙公报(http://pearlwater.gov.cn)、台湾水文年报(https://gweb.wra.gov.tw)，及南海周边河流相关研究文献[6, 18–29]（表1）。根据数据来源不同，上述数据可分为河流实测数据和卫星观测数据两种类型，除台湾地区外，其他地区河流实测数据为相关文献或公报发布的月或年通量数据，台湾地区河流入海沉积物通量的计算方法详见2.2节；卫星观测数据来源于Dethier等[6]计算的年通量数据，其数据与实地观测结果较为吻合，本文引用了其研究中10条河流沉积物通量，其误差见表2，认为该结果可用。河流流量模式数据来自于欧洲中期天气预报中心（European Centre for Medium-Range Weather Forecasts，ECMWF）发布的河流流量及其历史数据集（https://doi.org/10.24381/cds.a4fdd6b9），该数据与南海周边大型河流的实测流量数据有良好的相关性（绝对误差小于2.0 mm/d），数据时间分辨率为1 d，空间分辨率为5.5 km × 5.5 km。高屏峡谷浊流信息来源于相关文献中海底电缆断裂情况统计及观测数据[30–31]。大坝信息来源于全球大坝地理参考数据集（Global Georeferenced Database of Dams, GOODD）[32]，该数据集提供了截至2020年所有的大坝位置信息。台风数据来源于中国气象局热带气旋资料中心最佳路径数据集[33–34]，该数据集提供了自1949年以来年西北太平洋海域热带气旋中心位置、强度信息，时间分辨率为6 h。此外还参考了中国水情年报和台湾水文年报及新闻报道的台风信息。
2001−2020年南海气压、降水数据来自ECMWF发布的全球气候第五代大气再分析数据集（ECMWF Reanalysis v5，ERA5）（https://doi.org/10.24381/cds.adbb2d47），该数据时间分辨率为1 h，空间分辨率为28 km × 28 km。海表悬浮物浓度的卫星观测数据来自哥白尼海洋环境观测服务（Copernicus Marine Environment Monitoring Service，CMEMS）（https://doi.org/10.48670/moi-00280），数据时间分辨率为1 d，空间分辨率为1 km×1 km。采用国际常用的NIÑO3.4指数作为ENSO过程的指标，2001−2020年NIÑO3.4指数来自美国国家海洋大气预测研究中心（https://psl.noaa.gov）。
2.2 河流入海沉积物通量的计算
目前，河流水沙关系曲线法被广泛应用于河流入海沉积物通量的估算[35−36]，其常见表达为
$ Q_s=aQ^b, $
也可以写成：
$ \mathrm{log}_{10}(Q_s)=\mathrm{log}_{10}(a)+b\mathrm{log}_{10}(Q), $
其中，Qs（g/s）是入海沉积物通量，Q（m3/s）是河流流量，a和b是水沙关系曲线参数，通过沉积物浓度或通量数据拟合得出。水沙关系曲线是流域内沉积物供应与输运综合作用的结果，其参数a、b与流域特征有关，系数a常被用于表征流域侵蚀程度，流域内物源供应越充足，参数值越大；指数b常用于表征河流冲刷程度，其数值取决于河道形状、沉积物分布和来源等[37]。本文收集了南海周边主要河流的水沙关系曲线参数，并利用公式(2)计算了2001−2020年台湾地区河流入海沉积物通量。表3为本文主要分析河流的曲线参数。
2.3 台风影响判断方法
台风路径是研究台风对河流不同影响的关键参数。本文以中国气象局热带气旋资料中心最佳路径数据集提供的台风路径数据为基础，分析不同台风对河流的影响。对于水文数据监测完备的河流，首先选定距离河流入海口最近测站的位置，计算台风距离该点位的距离，并统计日期、强度等相关信息。再分别绘制该点位处气压、降水量、河流流量和沉积物通量的时间序列图，并在图上标注出台风日期。若在标定日期前后，可以识别出气压极小值，并且降水量和河流流量显著增加，则认为该台风对该河流有影响。此外，还搜集了年报、新闻报道等信息作为辅助资料用于判断台风是否对河流产生影响。
2.4 表层羽流输运的计算方法
Mertes和 Warrick[17]基于卫星和实测数据提出了表层羽流浓度和厚度的关系：当SSC大于25 g/m3时，羽流厚5 m；当SSC为10～25 g/m3时，羽流厚4 m；当浓度为5～10 g/m3时，羽流厚3 m。根据上述关系可以大致估算出高流量事件期间，河流通过表层羽流的向海输运量，公式如下：
$ Q_{ss1}=\int (d_1\times d_t \times C_i\times V){\mathrm{d}}t， $
式中，dl为选取河口外断面宽度，dt为羽流厚度，Ci为海表SSC，V为海表海流速度。
2.5 小波变换和交叉小波变换
小波变换（wavelet transform，WT）是一种广泛应用于降水和河流序列的分析方法。选取Morlet小波作为母小波，其公式为：
$ \psi _0(t)={\text{π}}^{1/4}{\mathrm{e}}^{{\mathrm{i}} \omega_{0}\eta }{\mathrm{e}}^{\eta^2/2} $
式中，$ \omega_{0} $为无量纲频率，$ \eta $为时间。假设所考虑的时间序列为$ x_{n}(n=0,1,2, \cdots, N-1) $，其中$ N $为时间序列长度，则时间序列$ x_{n} $的连续小波变换定义为
$ W_n^x(s)=\sum_{n'=0}^{N-1}x_{n'}\psi^*\left[\frac{(n'-n\text{δ}t)}{s}\right]， $
其中$ W_{n}{ }^{x}(s) $为小波系数，$ \psi^{*} $为复共轭函数，$ {\text{δ}} t $为时间间隔，$ s $为尺度因子，反映小波周期长度，$ n $为时间因子，反映时间上的平移。由于长时间尺度的周期变化会被年时间尺度的显著周期变化掩盖，可以利用傅里叶变换获的低通滤波的时间序列，消除短时间尺度上周期变化的影响[14]。
交叉小波变换（Cross wavelet transform，XWT）可以用来研究两个时间序列的相互关系，两个时间序列的XWT定义为
$ W_n^{xy}=W^x_n{W^y_n}^* ,$
式中，${ {W}_{{n}}^{ {y}}}^{*} $为复共轭函数，交叉小波功率可定义为为$ \left|{W^{x y}}\right| $。变换系数较大则代表两信号具有较强的相关性。在交叉小波功率图中，箭头表示两者之间的位相关系，→表示两个要素之间为同相关系，←表示两个要素为反相关系。本文使交叉小波分析来研究2001—2020年河流入海沉积物通量与NIÑO3.4指数之间的关系。
3 结果与讨论
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3.1 南海周边河流及入海沉积物通量概况
为统计方便，本文按流域面积将南海周边河流划分为 3种类型[39]：小型河流（＜3000 km2），中型河流（3000～50000 km2）和大型河流（＞50000 km2）。在本文统计的河流中（表1），大型河流主要有珠江、红河、湄公河、湄南河、哈里河、穆西河和拉让江，小型河流包括台湾地区河流，华南地区的大风江、钦江、茅岭江，泰国中部的他钦河，马来半岛柔佛河以及吕宋岛安加特河。
根据表1数据计算了2001−2020年南海周边河流入海沉积物通量多年平均值（图1a，黑色数值），南海周边河流每年向南海以及台湾海峡输运超过345 Mt沉积物，其中浊水溪、高屏溪、珠江、红河、湄公河、拉让江、巴拉姆河是入海沉积物的主要来源。台湾地区河流入海沉积物通量最大，约123 Mt/a，主要来自浊水溪（73 Mt/a）和高屏溪（30 Mt/a）。华南河流入海沉积物通量为37 Mt/a，主要来自珠江（29 Mt/a）。中南半岛河流入海沉积物通量约79 Mt/a，主要来自湄公河（52 Mt/a）和红河（23 Mt/a）。泰国中部的几条河流入海沉积物通量超过8 Mt/a，主要来自湄南河（7 Mt/a）。马来半岛最大河流彭亨河入海沉积物通量约12 Mt/a。加里曼丹岛北部两条较大的河流，拉让江和巴拉姆河入海沉积物通量分别为53 Mt/a和24 Mt/a，苏门答腊岛东部较大的哈里河入海沉积物通量约6 Mt/a。在南海东部，吕宋岛最大河流卡加延河入海沉积物通量约1.5 Mt/a。
本文统计结果显著低于前人结果。Liu等[8]根据Dadson等[41]，Zhang等[36]以及Milliman和Farnsworth[1]的数据统计了南海周边河流入海沉积物通量，这些数据代表大规模大坝建设之前的入海沉积物通量（图1a紫色数据）。2001−2020年南海周边河流入海沉积物通量多年平均值较Liu等[8]统计结果下降超过390 Mt/a，其中，中南半岛河流入海沉积物通量下降量最大，湄公河和红河入海沉积物通量下降均超过100 Mt/a。珠江入海沉积物通量下降了55 Mt/a，高屏溪和曾文溪入海沉积物通量均下降了约20 Mt/a。除加里曼丹岛和苏门答腊岛外的其他地区河流入海沉积物通量也有不同程度的下降（图1b）。一方面，20世纪80年代以来亚洲地区广泛的大坝建设导致河流上游沉积物淤积，入海沉积物通量大幅度降低；另一方面，气候变化引起的降水、径流变化导致河流入海沉积物通量波动变化。
3.2 人类活动对河流入海沉积物通量的影响
根据上文计算结果，中南半岛、华南和台湾地区河流的入海沉积物通量下降量较大，占总下降量的85%以上。人类活动是导致河流入海沉积物通量大幅度降低的主要原因，大坝建设是河流入海沉积物通量降低的重要因素之一。截至2020年，南海周边河流共建有2087个大坝（图1b），多数大坝建设于20世纪80年代后，中南半岛和华南地区的是大坝建设最密集的地区，也是河流入海沉积物通量降幅最大的地区。
大坝建设也导致湄公河、红河入海沉积物通量急剧降低。Walling[42]，Lu和Siew[10]等学者以1992年底曼湾大坝运行为分界点，将湄公河入海沉积物通量变化划分为“坝前”和“坝后”两个时期。“坝前”时期，湄公河入海沉积物通量为150～170 Mt/a[1]，“坝后”1993−2000年期间，湄公河入海沉积物通量下降至110 Mt/a。与湄公河情况相似，1988年和平大坝建设导致红河入海沉积物通量由130 Mt/a下降至46 Mt/a。此后，截至2020年，湄公河流域共有超过100个大坝建成[29]，干流至少有13座大坝投入使用，红河干流至少有9座大坝投入使用（图1b），进一步降低了湄公河和红河的入海沉积物通量。图2显示了湄公河和红河入海沉积物通量序列，湄公河入海沉积物通量在2001−2015年间虽略有波动但整体变化不大，多年平均入海沉积物通量下降至52 Mt/a；红河多年平均入海沉积物通量下降至23 Mt/a，随着红河上游系列大坝建成，红河入海沉积物通量整体呈下降趋势，2008年红河干流南沙、宣光大坝建设，导致红河水沙关系曲线参数发生变化，入海沉积物通量降低。
珠江在近30年间入海沉积物通量发生巨大变化。1990年之前，珠江入海沉积物通量超过80 Mt/a，1992年岩滩大坝和1997年天胜桥大坝建设使1991−2002年珠江入海沉积物通量下降至70 Mt/a[36]。图3显示了2002−2020年珠江入海沉积物通量，其中2003年、2007年、2009年、2011年等年份入海沉积物通量均处于较低水平。2003年建设的龙滩大坝是珠江流域最大的水利工程，其库容量达273 × 108 m3，是岩滩大坝和天胜桥大坝库容量总和的两倍以上，进而使由于龙滩大坝建设导致的入海沉积物通量下降量远大于之前大坝引起的下降量。
3.3 台风对河流入海沉积物通量的影响
南海是受台风影响最显著的边缘海之一，根据2001−2020年中国气象局热带气旋资料中心最佳路径数据集提供的台风路径及强度数据，对台风强度进行插值和归一化处理(图4)。南海东北部的台湾岛和吕宋岛是受台风影响最强的地区，华南和中南半岛的沿海地区次之，南海南部的加里曼丹岛几乎不受台风影响。相应导致台风对不同地区河流入海沉积物通量影响存在差异。
在南海周边河流系统中，台湾地区是入海沉积物通量最高且受台风影响最严重的地区之一(图4)，浊水溪和高屏溪是该区域中入海沉积物通量最大的河流，占台湾西南地区河流总输运量的80%以上（图5，表1），而高屏溪是台湾地区入海沉积物通量第二大的河流，直接与海底峡谷相连，沉积物可以直接被搬运至深海。
根据ERA5发布的气压、降水量数据和台湾水文年报发布的水文数据得到的2001−2020年高屏溪里岭大桥测站处气压、降水量、河流流量和入海沉积物通量序列（图6）。2001−2020年高屏溪流域年平均降水量为2425 mm，年平均流量为207.9 m3/s，入海沉积物通量为30.4 Mt/a。高屏溪降水量、流量和入海沉积物通量存在显著季节变化趋势，受台风影响显著。台风影响期间，气压降低（图6a），降水量（图6b）、流量和入海沉积物通量显著增大（图6c）。此外，台风还是引起河流−峡谷体系中沉积物向深海输运的重要因素，在本文统计的高屏峡谷27次浊流事件中（图6），有17次是台风触发的。
对高屏溪（图7）流量及入海沉积物通量的小波分析也有类似结果。流量的小波序列显示出较强的年变化特征（图7a），入海沉积物通量则无明显周期变化，但可从功率谱上识别出多个上窄下宽的锥形高功率区域（图7b），这些区域与流量的高功率区域对应，并与强台风影响时间基本吻合。在功率谱上可以明显识别出几个受台风影响较小的年份，如2002，2003，2018年，及对河流影响较大的台风，如2001年台风“百合”，2009年台风“莫拉克”。台风期间高屏溪入海沉积物通量占总量的89%（图8）。这说明台风是决定以高屏溪为代表的台湾地区河流入海沉积物通量变化的主要因素。
以2015年登陆台湾的13号台风“苏迪罗”为例，该台风于2015年8月8日至10日先后影响我国台湾地区及东南沿海地区，造成了大量人员伤亡，是2015年西北太平洋最强的台风，并且在台风过后4 d内，在高屏溪河口外可以观察到清晰明显的羽流图像，选择这一台风分析台风对河流入海沉积物通量的影响。台风影响期间，在强降水的影响下高屏溪河流流量、入海沉积物通量均增加了2个数量级，且在随后一段期间内河流入海沉积物通量仍高于台风前的水平（图9c）。根据水文年报提供的实测数据，计算出台风经过后1周内（8月8日至15日），高屏溪−里岭大桥站处沉积物总运输量约1.0 Mt，占全年总量的37%。
图10显示了台风“苏迪罗”影响前、活动期、影响期及台风后，台湾附近海域海表悬浮物浓度变化情况。台风过境期间，强降水加速了沿海地区陆源物的径流供给，使得台风过后4 d内台湾海峡两侧仍存在悬浮沉积物浓度高值（图10c）。表层羽流是台风期间河流沉积物向海输运的重要途径，通过卫星图像（图10c）可以计算得出高屏溪过表层羽流向海输运约8.9 × 104 t沉积物，约占台风期间总量的10%，直至约一周后近岸海表悬浮物浓度所有数值恢复至台风前的平均水平。
华南及中南半岛东部沿海地区也是受台风影响较强的区域(图4)。根据ECMWF发布的河流流量数据以及相关文献中发表的水沙关系曲线参数，得到了2001−2020年珠江、红河的流量和入海沉积物通量序列（图11）。台风期间河流径流量和沉积物通量均有增加，珠江和红河在台风期间沉积物输运量分别占年总输运量的15%和13%（图12）。这一比例远低于台湾地区河流，一方面，台风对华南和中南半岛河流的影响多集中在下游河口地区，相对强度弱于台湾地区；另一方面，台湾地区河流流域面积较小，台风影响时河流流量相对增量较大，沉积物多集中于台风期间入海，台风对台湾地区河流的入海沉积物通量影响更显著。
3.4 气候变化对河流入海沉积物通量的影响
3.4.1 夏季风对河流入海沉积物通量的影响
南海及周边地区还受东亚夏季风的影响，东亚夏季风通过影响降水进而影响入海沉积物通量[8]。南海周边河流入海沉积物通量表现出雨季高、旱季低的特征[8, 14, 43–45]。本文统计了不同地区河流入海沉积物通量季节分配特征（图13），雨季期间河流入海沉积物输运量占全年总量的80%以上。而雨季期间如果遇到异常气候会导致入海沉积物通量偏离正常年份，如2004−2005年，2009−2010年发生在湄公河流域的干旱事件，2007年、2009年发生在珠江流域的干旱事件，导致当年入海沉积物通量低于正常年份（图2a，图3），2009年台风“莫拉克”导致高屏溪当年入海沉积物通量显著高于正常年份（图6c）。对于其他缺少每月沉积物数据、无可用于评估沉积物通量的曲线参数的河流，可通过海表悬浮沉积物浓度分析其入海沉积物通量的季节变化特征,以泰国中部河流为例，该区域既包含大型河流（湄南河），也有中型河流（美功河等）和小型河流（他钦河）（图14），雨季期间大量沉积物向海输运，导致泰国湾北部表层悬浮沉积物浓度为其旱季期间的1.5倍，河口外羽流浓度和扩散范围高于旱季。
3.4.2 ENSO对河流入海沉积物通量的影响
ENSO是热带太平洋地区海气系统年代际变化的最强信号，河流入海沉积物通量的年际变化受ENSO调控。在南海周边河流中，ENSO对不同河流的影响存在差异。对于台湾地区河流，虽然其入海沉积物通量受台风影响显著，但在年际尺度上，并未显示出明显的周期变化（图7）。而珠江月平均流量、入海沉积物通量的小波变换分析结果除了展示出显著的年变化周期之外，还具有较明显的月变化和年代际变化周期（图15a,b）。为了突出展示年代际变化特征，使用低通滤波来消除时间尺度小于2 a的周期性波动对长时间尺度周期识别的影响。月流量和沉积物通量低通滤波序列的小波变换结果显示，流量、沉积物通量具有约2.5 a的周期（图15c,d）。对珠江流量和入海沉积物通量与NIÑO3.4指数的交叉小波分析可以看出，在1 a周期上，流量与入海沉积物通量与NIÑO3.4指数有较显著的同相关系（图15e,f），此外，入海沉积物通量与NIÑO3.4指数在2.5 a周期上有较显著的同相关系（图15f），这说明ENSO对珠江入海沉积物通量年代际变化的调控作用。
对红河和湄公河的分析也有类似结论，图16为红河和湄公河的入海沉积物通量与NIÑO3.4指数交叉小波谱，除1 a周期上的显著同相关系外，二者分别在2.8 a和3.0 a周期上有较强的功率，表现出不显著的反相关系。对于其他大型河流，由于其沉积物数据为年数据且无可查的水沙关系曲线参数，仅分析了其流量与NIÑO3.4指数的关系，湄南河和拉让江的流量与NIÑO3.4指数交叉小波谱分别在3.0 a和2.5 a周期上与NIÑO3.4指数呈现不显著反相关系（图17），还需要更多沉积物监测数据来验证ENSO对河流入海沉积物通量的影响。
4 结论
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本文以河流实测数据、海洋水色卫星观测数据为基础，系统分析了2001−2020年南海周边河流入海沉积物通量的时间变化特征。南海周边河流入海沉积物通量超过345 Mt/a。人类活动导致珠江、红河、湄公河入海沉积物通量减少约300 Mt/a，是河流入海沉积物通量大幅度降低的主要原因。南海周边河流入海沉积物通量还受台风和气候变化的影响：不同地区河流入海沉积物通量受台风影响程度不同，台风期间高屏溪入海沉积物通量可达全年总量的89%，通过表层羽流向海输运量占台风期间总量的10%，预示着更多的河流向海沉积物输运需要依赖海洋深层动力过程；其他地区河流受台风影响较弱，虽然台风也会造成流量的增加和沉积物的向海输运，然而台风期间入海沉积物通量占年总通量的比例较低。在季节尺度上，南海周边河流入海沉积物通量在东亚季风系统影响下，表现出雨季高、旱季低的变化特征，雨季期间入海沉积物通量占全年总量的80%以上，相应地导致河流表层羽流在雨季具有浓度高和扩散范围大的典型特征。在年际尺度上，南海周边河流也受到ENSO的影响，不同地区河流受ENSO事件影响存在差异。在年际尺度上，台湾地区河流并未显示出明显的周期变化，珠江、红河、湄公河的入海沉积物通量分别在2.5 a，2.8 a和3.0 a的周期上受ENSO调控，湄南河和拉让江则分别在3.0 a和2.5 a周期上受ENSO调控，在讨论ENSO事件对南海周边河流入海沉积物通量的影响时，还需具体河流具体讨论。
基金
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  • 国家自然科学基金面上项目“强台风触发深海浊流垂直结构的高分辨率锚系观测研究”(41876048)
  • 同济大学学科交叉联合攻关项目
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2024年第46卷第6期
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doi: 10.12284/hyxb2024061
  • 接收时间：2023-12-15
  • 首发时间：2025-11-26
  • 出版时间：2024-06-30
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  • 收稿日期：2023-12-15
  • 修回日期：2024-05-14
  • 录用日期：2024-06-05
基金
国家自然科学基金面上项目“强台风触发深海浊流垂直结构的高分辨率锚系观测研究”(41876048)
同济大学学科交叉联合攻关项目
作者信息
    1.同济大学 海洋地质国家重点实验室，上海 200092

通讯作者:

*张艳伟（1984—），女，山东省菏泽市人，教授，研究方向为深海沉积过程的观测和海洋多尺度动力过程。E-mail：
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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